Tumor microenvironment (TME) is composed of various cellular components such as tumor cells, stromal cells including fibroblasts, adipocytes, mast cell, lymphatic vascular cells and infiltrating immune cells, macrophages, dendritic cells and lymphocytes. The intricate interplay between these cells influences tumor growth, metastasis and therapy failure. Significant advancements in breast cancer therapy have resulted in a substantial decrease in mortality. However, existing cancer treatments frequently result in toxicity and nonspecific side effects. Therefore, improving targeted drug delivery and increasing the efficacy of drugs are crucial for enhancing the treatment outcome and reducing the burden of toxicity. In this review, we provided an overview of how tumor and stroma-derived osteopontin (OPN) plays a key role in regulating the oncogenic potential of various cancers including breast. Next, we have dissected the signalling network by which OPN regulates tumor progression through interaction with selective integrins and CD44 receptors. This review addresses the latest advancement in role of various splice variants of OPN in cancer progression and OPN-mediated tumor-stromal interaction, EMT, CSCs enhancement, immunomodulation, metastasis, chemoresistance, metabolic reprogramming and further suggest that OPN might be a potential therapeutic target and prognostic biomarker for the evolving landscape of cancer management.

Gut microbiota is a complex and dynamic ecosystem that influences various aspects of human health. Unfortunately, there is a lack of literature highlighting the importance of gut microbiome especially in diabetes management. This paper performs bibliometric analysis to establish the rising interest of researchers in this area and the importance of gut microbiota in diabetes management. Subsequently, the study performs a narrative review to provide an overview of the gut microbiota consisting of bacteria, archaea, fungi, and viruses, and its diversity, composition, and variation among individuals. The paper further discusses the role of the gut microbiota in different diseases, including Clostridium difficile infection, behavioral disorders, cancer, obesity, diabetes, atherosclerosis, and inflammatory bowel disease, among others. It emphasizes the link between gut dysbiosis (imbalanced gut microbiota) and the development of chronic metabolic disorders. Additionally, the paper discusses the factor negatively impacting the gut microbiota. The paper concludes by explaining the mechanistic abnormalities that link dysbiosis to type 2 diabetes, including changes in short-chain fatty acid production, amino acid metabolism, bile acid regulation, and the production of specific bacterial products. Thus, the paper highlights the importance of understanding gut microbiota and related mechanisms for developing therapeutic interventions targeting the gut microbiota.

This paper investigates an Energy Neutral System using Micro Energy Harvesters for a Health Monitoring Wireless Sensor Node (HM-WSN). We have implemented HM-WSN consisting of a solar cell energy harvester, a Power Management Unit (PMU) and the ucontroller, sensor and transmitter acting as load. A battery extender and a switching logic circuit for sensor node is designed to evaluate the efficiency of the proposed system. The HM-WSN senses body temperature with improvement in lifetime of coincell. The HM-WSN is built using commercially available off the shelf (COTS components and consumes 47.2uW for data being updated every 134s. It can be operated for approximately 1.32 years only on a coincell. Furthermore, the HM-WSN can be operated with solar energy at 200lux, achieving 96.36 years with a combination of a coincell with an additional capacitor of 2.2mF. It attains 4.07 years to 16.87 years lifetime with the load capacitor varying from 1mF to 1.5mF at 350lux lighting condition. With the same ambient condition and a 2mF load capacitor, HM-WSN attains complete energy autonomy.

The thermal degradation behaviour of 10:90 and 90:10 blends of Polyethylene Terephthalate (PET) and petroleum coke has been investigated using mechanical alloying (milling) at 300 rpm and for periods up to 1-10 hours. Milled specimens were characterised using SEM and x-ray diffraction; their thermal degradation behaviour was investigated using thermo-gravimetric analyser (TGA) for temperatures up to 1200°C. Whereas PET specimens showed a tendency towards flattening out and increased surface area during collisions in ball mills, petroleum coke particles tended to break down into smaller particles. 10:90 and 90:10 blends of PET and coke showed significant microstructural evolution with increasing speeds and time including fracture, strain hardening and re-welding. X-ray diffraction results showed clear evidence for increasing amorphous component in petroleum coke without much influence on PET crystallinity. Thermo-gravimetric results showed a significant increase in the overall degradation and much higher weight losses associated with mechanical alloying. This study has shown that mechanical alloying could be used to modify the degradation behaviour of coke/plastic blends and corresponding yield during pyrolysis with implications for plastic waste management.

Despite saltmarshes being considered as nature-based solutions (NbS) towards climate change mitigation, India’s saltmarsh ecosystems are least studied. This study quantifies the seasonal variation in carbon stocks mono-specific saltmarsh species (Porteresia coarctata) and its potential to play an important role in India’s climate change mitigation plans. Seasonal (pre-and post-monsoon) sampling of P. coarctata surface water, biomass and 30 cm sediment core was collected across four locations on the east coast of India to quantify sediment dry bulk density (DBD), organic matter (OM%), organic carbon (Corg%), Corg stocks of sediment and biomass, total carbon (C%) and nitrogen (N%) and stable isotopes of δ13C and δ15N. In general, the sediment DBD, OM and Corg of P. coarctata meadows was higher in post-monsoon and was influenced by salinity and pH changes. Isotope (δ¹³C) modelling of various sediment C sources suggested, particulate organic matter (POM) contribution was highest (0.04-0.79%) followed by P. coarctata (0.01–052%) and other macrophytes across our study locations. The seasonal variation of δ¹³C showed increased contribution of marine derived particulate and dissolved organic matter into P. coarctata meadows in post-monsoon season. Heavier δ¹⁵N values were observed in post-monsoon season suggesting anthropogenic input, that was utilized by P. coarctata to increase its above and below-ground biomass and shoot density. The combined ecosystem (30 cm sediment + biomass) Corg stocks of P. coarctata was 1.7-fold higher in post-monsoon (4021.20± 917 Mg C) compared to pre-monsoon (2297.36 ±647 Mg C) season among the four locations with a sediment Corg contribution >70%. Based on the International Panel for Climate Change Tier II assessment the P. coarctata meadows (443 ha) can help in avoiding the pre and post-monsoon emissions of 8431.34 and 14757.84 Mg CO₂ respectively. The combined price of the total CO₂ equivalent stored in P. coarctata meadows in pre- and post-monsoon is US$ 14.50 and US$ 25.38 million respectively. Further studies quantifying the NbS potential of P. coarctata mono-specific and mixed meadows of India’s coast is needed along with integration of saltmarsh ecosystems into India’s National Action Plan on Climate Change.

Epitranscriptomic RNA modifications play a crucial role in the posttranscriptional regulation of gene expression. N6-methyladenosine (m6A) is the most prevalent internal modification of eukaryotic RNA and plays a pivotal role in RNA fate. RNA m6A modification is regulated by a group of cellular proteins, methyltransferases (writers) and demethylases (erasers), which add and remove the methyl group from adenosine, respectively. m6A modification is recognized by a group of cellular RNA-binding proteins (readers) that specifically bind to m6A-modified RNA, mediating effects on RNA stability, splicing, transport, and translation. The functional significance of m6A modification of viral and cellular RNA is an active area of virology research. In this review, we summarize and analyze current literature on m6A modification of HIV-1 RNA, the multifaceted functions of m6A in regulating HIV-1 replication, and the role of viral RNA m6A modification in evading the innate immune responses to infection. Furthermore, we briefly discuss future directions and therapeutic implications of mechanistic studies of HIV-1 epitranscriptomic modifications.

In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation, the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.

Viruses are silent enemies that intrude and take control of the plant cell's machinery for their own multiplication. Infection by viruses and the resulting damage is still a major challenge in the agriculture sector. Plants have the ability to fight back, but the ability of viruses to counteract and mutate at a fast rate helps them to hide from the host response. Therefore, classical approaches for introgressing resistance genes by breeding have obtained limited success in counteracting the virus menace. Genetic modification (GM) based strategies have been successful in engineering artificial resistance in plants. Several different approaches based on pathogen derived resistance, antisense constructs, hairpin RNA, double stranded RNA etc. have been used to enhance plant resistance to the virus. Recently, genome editing (GE) strategies mainly involving the CRISPR/Cas mediated modifications are being used for virus control. In this review, we discuss the developments and advancements in the GM and GE based methods in tackling virus infection in plants.

Evaluation of market connectedness and asymmetric volatility spillover has recently seen a surge in financial risk analytics and portfolio diversification. We carried out a meta-literature review on connectedness and spillovers, providing solid insight into the research field and robust guidelines for future investigation. The review consists of a quantitative bibliometric analysis of 594 papers and a qualitative content analysis of 77 papers covering 1991 to 2021. The results of the meta-citation analysis shows that Diebold's Spillover index (2007) is the predominant method in most works as far as market connectedness and spillover is concerned. With an extensive review, we answered the following objectives (1) Analyze the most influential authors, journals, and publications, (2) Understand the research streams and most studied streams, (3) Understand the theme structure and thematic evolution, and keyword trends, (4) Examine the pattern of collaboration, and most productive affiliations, (5) Explore future research directions and untapped areas. The content analysis revealed the following important research streams in the current literature (1) Asymmetries in the market connectedness, (2) Influence of Macro Factors in the market connectedness and spillover, (3) the Role of Oil in market spillovers and hedging portfolios, (4) Dynamic cross-market connectedness and spillovers. The ongoing financial turmoil and market advancements make market connectedness a vital research topic; thus, our work would significantly contribute to macroeconomic policymakers, researchers and hedging investors.

The bioindicator potential of seagrass ecosystems in coastal trace metal monitoring is low for tropical island ecosystems. This study evaluated the bioindicator potential of six seagrass species exposed to anthropogenic trace metal pollution in the Andaman and Nicobar Islands (ANI) of India. Sediment and seagrass biomass, samples were analyzed for trace metals (Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Zn) from four locations of ANI exposed to anthropogenic contamination. Geo accumulation Index values indicated moderate trace metal contamination in seagrass sediment. Seagrass trace metal accumulation from sediment was both species-specific and location- specific for ANI. Small seagrass species such as H. ovalis, H. beccarii and H. uninervis accumulated the maximum concentration of six trace metals (Cr, Cu, Fe, Ni, Pb, Zn) in their tissues compared to big seagrass species like T. hemprichii and E. acoroides of ANI. This study indicates leaves of small seagrass and roots of big seagrass species can serve as short- and long-term bioindicators respectively, of coastal trace metal contamination in tropical islands of ANI.

The reinforcing ability of the fillers results in significant improvements in properties of polymer matrix at extremely low filler loadings compared to conventional fillers. In view of this, present review article describes the different methods used in preparation of different rubber nanocomposites reinforced with nanodimensional individual carbonaceous fillers, such as graphene, expanded graphite, single walled carbon nanotubes, multiwalled carbon nanotubes and graphite oxide, graphene oxide and hybrid fillers consisting combination of individual fillers. This is followed by review of mechanical properties (tensile strength, elongation at break, Young modulus, and fracture toughness) and dynamic mechanical properties (glass transition temperature, crystallization temperature, melting point) of these rubber nanocomposites. Finally, Payne and Mullin Effects have also been reviewed in rubber filled with different carbon based nanofillers.

Cancer is a catchall term that refers to a collection of more than one chronic disease that can adversely affect the body, including the organs of the body. These diseases can be grouped together under the umbrella term "cancer." Cancer is a catch-all term that is used to refer to a number of different diseases. During the process of looking for new chemical entities, it was found that certain compounds with a basic nucleus of benzoic acid (BA) exhibit a remarkable anticancer potential. This was discovered while the researchers were looking for new chemical entities. In the sixteenth century, the aromatic chemical known as BA, which is also known as an aromatic carboxylic acid, was discovered. BA is also referred to as an aromatic carboxylic acid. When it comes to the chemical synthesis of a wide range of different active molecules, the utilization of a sizeable quantity of BA as a raw material is absolutely necessary. In addition to being present in a variety of other naturally occurring compounds, the BA moiety is the component that is responsible for the activity of the naturally occurring compounds vanillin, gallic acid, and syringic acid. In the synthesis of a wide variety of synthetic bioactive molecules, the BA scaffold is utilized as a building block. The drugs furosemide, bumetanide, benzocaine, tetracaine, and bexarotene are all examples of molecules that fall into this category. The scope of this review article allows for the discussion of a number of noteworthy and recent achievements of BA derivatives in terms of their effectiveness against cancer.

The Voltammetric and polarographic methods are proposed to determine the short time corro-sion rates of brass in 1N HCl solution. It should be noted that it is also possible to determine the corrosion rates of each species present in the solution, simultaneously like Cu (II) and Zn (II) in brass. Samples extracted from bark of Babul (ESBB) is used as corrosion inhibitors. The corrosion inhibition efficiency is 96% at 24h after the test started. These results are compared with the av-erage corrosion rates obtained by using gravimetric (PIT) and some electrochemical methods i.e. galvanostatic and potentiodynamic polarization measurements.

Polyhedral oligomeric silsesquioxane (POSS) is a suitable nanoscale reinforcement for thermosetting polymers, such as epoxy resin systems in order to modify its mechanical, thermal, and chemical properties. The inclusion of POSS in the epoxy resin at higher loading (greater than 1 wt.%), however, introduces the ductility during the fracture behavior of these nanocomposites. Consequently, the J-integral is used to quantify the fracture behavior of these materials and characterize the crack growth resistance curve against stable crack growth. A range of nanocomposites is prepared by adding 0.5, 1, 3, 5, and 8 wt.% of glycidyl POSS into DGEBF epoxy resin cured with an amine-based curing agent. From the fracture toughness experiment the load-displacement result confirms that when the POSS reinforcement is greater than 1 wt.%, the fracture behavior of the nanocomposite changes from brittle to ductile. For both brittle and ductile nanocomposites, the addition of POSS molecules improves the crack initiation toughness. The development of POSS–POSS compliant domains are reported previously, is attributed for this change in the failure behavior. The fractured images of POSS–epoxy nanocomposites, obtained using scanning electron microscopy, show that the increase in fracture resistance at higher values of POSS loading occurs due to the extensive shear yielding. Meanwhile, the increased fracture toughness at lower values of POSS loading occurs due to crack pinning and crack deflection.

This paper presents work done as part of a transformation effort towards a greener and more sustainable Aluminium manufacturing plant. The effort includes reducing the carbon footprint by minimising waste and increasing operational efficiency. The contribution of this work includes the reduction of waste through the implementation of autonomous, real-time quality measurement and classification at an Aluminium casthouse. Data is collected from the MV20/20 which uses ultrasound pulses to detect molten Aluminium inclusions, which degrade the quality of the metal and cause subsequent metal waste. The sensor measures cleanliness, inclusion counts and distributions from 20 - 160 microns. The contribution of this work is in the development of business analytics to implement condition-based monitoring through anomaly detection, and to classify inclusion types for samples that failed. For anomaly detection, multivariate K-Means and DBSCAN algorithms are compared as they have been proven to work in a wide range of datasets. For classification, a two-stage classifier is implemented. The first stage classifies the success or failure of the sample, while the second stage classifies the inclusion responsible for the failed sample. The algorithms considered include logistic regression, support vector machine, multi-layer perceptron and radial basis function network. The multi-layer perceptron offers the best performance using k-fold cross-validation, and is further tuned using grid search to explore the possibility of an even better performance. The results reveal that the model has achieved a global maximum in performance. Recommendations include the integration of additional sensor systems and the improvements in quality assurance practices.

This paper presents the use of Graph Neural Networks (GNNs) to predict the tensile strength of Fused Deposition Modeling (FDM) specimens. In the present work, there are four main input parameters i.e. Infill percentage, Layer height, Print speed and Extrusion temperature while the Tensile Strength is an output parameter were considered. This study includes use of central composite design based response surface methodology to finalize the experimental layout. 3D printed specimen were manufactured as per the ASTM E8 standard on FDM printer using Polylactic Acid (PLA) as filament. Micro-tensile test were performed on the printed specimen as per ASTM E8 standard. The GNN algorithm was trained on a dataset of FDM specimens, achieving a mean squared error (MSE) of 2.47, mean absolute error (MAE) of 1.14, and R-squared value of 0.78. An adjacency matrix, which shows the connections between nodes in a graph. The obtained plot for nodes and weights in a GNN provide valuable information about the model and its performance. The results show the potential of using GNNs in predicting the mechanical properties of additively manufactured specimens and provide a promising direction for further research in this field.

We reviewed the current status of a Vulnerable seagrass, Halophila beccarii from the coast of India using the published data from 1977-2020. We found that the seagrass, H. beccarii has a pan India distribution on both east and west coast. It is abundant in the intertidal silty-muddy region on the west coast, while on the east coast it is found on sandy habitats, with few exceptions of muddy habitat. H. beccarii was found to be associated with mangroves or smaller seagrass species within a depth limit of 1.7m. Low salinity and high nitrate levels were observed for the H. beccarii meadows of the west coast due to its association with mangroves. The nutrient levels in H. beccarii meadows of India were comparatively lower than other seagrass meadows. Most of the research on H. beccarii has focoused on its morphometrics (41%), reproductive (33%) and distribution (29%) along the coast of India. Reproductive traits such as flowering and fruiting varying according to the seasons of each coast due to the influence of monsoon and its associated temperature, salinity and nutrient influx. H. beccarii has a great potential of various bioactive compounds, which needs further investigation. Habitat disturbance, anthropogenic pollution and coastal development are the major cause of declining H. beccarii ecosystems in India. Significant loss of the seagrass was observed from the west coast of India due to increased coastal development activities. There is a significant need in quantifying H. beccarii population trends, impact of climate and anthropogenic stressors, economic values of ecosystem services and the role of ecological connectivity for better conservation and management of H. beccarii seascapes across India. There is a need for integration of research outcomes in policy framing for preventing the decline and further loss of this vulnerable seagrass ecosystem.

Natural products from plants such as, chemopreventive agents attract huge attention because of their low toxicity and high specificity. The rational drug design in combination with structure based modeling and rapid screening methods offer significant potential for identifying and developing lead anticancer molecules. Thus, the molecular docking method plays an important role in screening a large set of molecules based on their free binding energies and proposes structural hypotheses of how the molecules can inhibit the target. Several peptide based therapeutics have been developed to combat several health disorders including cancers, metabolic disorders, heart-related, and infectious diseases. Despite the discovery of hundreds of such therapeutic peptides however, only few peptide-based drugs have made it to the market. Moreover, until date the activities of cyclic peptides towards molecular targets such as protein kinases, proteases, and apoptosis related proteins have never been explored. In this study we explore the in silico kinase and protease inhibitor potentials of cyclosaplin as well as study the interactions of cyclosaplin with other cancer-related proteins. Previously, the structure of cyclosaplin was elucidated by molecular modeling associated with dynamics that was used in the current study. Docking studies showed strong affinity of cyclosaplin towards cancer-related proteins. The binding affinity closer to 10 indicated efficient binding. Cyclosaplin showed strong binding affinities towards protein kinases such as EGFR, VEGFR2, PKB and p38 indicating its potential role in protein kinase inhibition. Moreover, it displayed strong binding affinity to apoptosis related proteins and revealed the possible role of cyclosaplin in apoptotic cell death. The protein-ligand interactions using LigPlot displayed some similar interactions between cyclosaplin and peptide-based ligands especially in case of protein kinases and a few apoptosis related proteins. Thus, the in silico analyses gave an insight of cyclosaplin as a potential apoptosis inducer and protein kinase inhibitor.

Our brain continuously interacts with the body as we engage with the world. Although we are mostly unaware of internal bodily processes, such as our heartbeats, they may be influenced by and in turn influence our perception and emotional feelings. While there is a recent focus on understanding cardiac interoceptive activity and interaction with brain activity during emotion processing, the investigation of cardiac-brain interactions with more ecologically valid naturalistic emotional stimuli is still very limited. We also do not understand how an essential aspect of emotions like context familiarity influences affective feelings and is linked to cardiac-brain interactions. Hence to answer these questions, we designed an exploratory study by recording ECG and EEG signals for the emotional events while participants were watching emotional movie clips. Participants also rated their familiarity with the stimulus on the familiarity scale. Linear mixed effect modelling was performed in which the ECG power and familiarity were considered as predictors of EEG power. We focused on three brain regions, including prefrontal (PF), frontocentral (FC) and parietooccipital (PO). The analyses showed that cardiac-brain interaction is dependent on familiarity such that the interaction is stronger with high familiarity. In addition, the results indicate that arousal is predicted by cardiac-brain interaction, which also depends on familiarity. The results support emotional theories that emphasize context dependency and interoception. Multimodal studies with more realistic stimuli would further enable us to understand and predict different aspects of emotional experience.

This study aims to investigate the hydration and strength characteristics of green cementitious mortars with ultrahigh-volume limestone-calcined clay as well as two kinds of Class F fly ash. Using the ASTM C311 strength activity index test method, the effect of different pozzolan replacement levels of cement (0%, 20%, 50%, and 80%, by weight) were investigated. Compressive strength at 3, 7, 14, 28 and 90 days under standard curing was recorded, and hydration heat of the 20% and 80% replacement mixes was studied using iso-thermal calorimetry. It was observed that the effectiveness of the pozzolan in mortars depends on particle size distribution, glassy or amorphous nature, surface area and replacement level. The sum of all these effects can be captured by the strength activity test only if the standard recommended 20% pozzolan mix is substituted with the actual mix composition. The results in this study provide insights into the mix design and applications of ultrahigh-volume pozzolanic cementitious materials specifically made with limestone-calcined clay, and promote greener cement and concrete in construction industry.

A quantitative analysis of X-ray emission from an electron cyclotron resonance (ECR) plasma was performed to probe the spatial properties of electrons having energy for effective ionisation. A series of measurements were taken by INFN-LNS and ATOMKI, capturing spatially and spectrally resolved X-ray maps as well as volumetric emissions from argon plasma. Comparing the former with model generated maps (involving space-resolved phenomenological electron energy distribution function and geometrical efficiency calculated using ray-tracing MC routine) furnished information on structural aspects of the plasma. Similarly, fitting a model composed of bremsstrahlung and fluorescence to the volumetric X-ray spectrum provided valuable insight into the density and temperature of confined and lost electrons. The latter can be fed back to existing electron kinetics models for simulating more relevant energies, consequently improving theoretical X-ray maps and establishing the method as an excellent indirect diagnostic tool for warm electrons, required for both fundamental and applied research in ECR plasmas.

The synthesis of the presented benzo[d]thiazol-2-amine derivatives was performed by condensing-(4-chlorobenzylidene) benzo[d]thiazol-2-amine with a number of substituted phenols in the presence of potassium iodide and anhydrous potassium carbonate in dry acetone. IR spectroscopy, 1HNMRspectroscopy and spectroscopy methods were used to characterize the structural properties of newly synthesized derivatives. In order to observe the attributes of drug-like candidates of these derivatives, the number of their molecular properties was estimated. Benzo[d]thiazol-2-amine derivatives were molecularly docked with selective enzymes COX-1 and COX-2. Findings suggested that Compounds G3, G4, G6, G8 and G11 possess higher binding affinity than diclofenac sodium, when docking was performed with enzyme COX-1. Compounds G1, G3, G6, G8and G10 showed lower binding affinity than Indomethacin when docking performed with enzyme COX-2.In vitro evaluation of the COX-1 and COX-2 enzyme inhibitory activities were performed for synthesized compounds. Compounds 10 and 11 exhibited significant COX-1 and COX- enzyme inhibitory action with IC50 values of 5.0 and 10 µM, respectively. Using the hot plate method and the carrageenan-induced rat paw edema model, the synthesized compounds were screened for their biological activities, including analgesic and anti-inflammatory activities. The highest analgesic action was exhibited by derivative G11and the compound G10 showed the highest anti-inflammatory response.

The ABCDEFG Protocol provides an evolving tool for imposing structure on the flow of Covid infection information obtained from community testing, collective policy and individual compliance. ABCDEFG Protocol could not assume soundness, invariance, symmetry and completeness of the available information and relied on signaling game theory to design solutions that could evolve with the variable narratives, theories, individual utilities and pathogen variants. Thus, ABCDEFG Protocol suggests a novel and a very flexible pool-testing and badging protocol in the context of controlling contagious epidemics and tackling the far-reaching associated challenges, including understanding and evaluating individual and collective risks of returning prior infected individuals to normal society and other economic and social arrangements and interventions to protect against disease. AbCDEFG Proto uses both control theoretic and game theoretic mathematical models that may be centralized (an optimizing policy maker mandates behavior based on estimated models) or decentralized (a strategizing individual selects their behavior based on available asymmetric information). ABCDEFG protocol demonstrates how society can continue to carry out plausible economic activities in addition to controlling the prevalence of a contagious disease by keeping the number of infected people below a desired limit without compromising an individuals' privacy despite the presence of deception and selfishness among people, and limitations of available resources. Different types of badges would come with different restrictions. Badges would be reissued periodically by third-party testing centers via suitably frequent pool testing of samples of the participants. The size of the pools, frequency of tests, and allowable activities for people with a given type of badge would depend on the available resources, the prevalence of the disease, and the efficacy of the equipment used in the tests.

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of the wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy by energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex as it involves non-convex variables and requires combinatorial relay selection indicators optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimisation techniques would be difficult to solve in complex problem environments. The proposed algorithm significantly improves the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%,27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

The effect of transition metal substitution for Fe and the structural and magnetic properties of Gd2Fe16Ga0.5TM0.5 (TM=Cr, Mn, Co, Ni, Cu, and Zn) compounds were investigated. Rietveld analysis of X-ray diffraction indicates that all the samples crystallize in the hexagonal Th2Ni17 structure. The lattice parameters: a, c and unit cell volume show TM ionic radii dependence. Both Ga and TM atoms show preferred site occupancy for 12j and 12k sites. The saturation magnetization at maximum room temperature was observed for Co, Ni, and Cu of 69, 73, and 77 emu/g, respectively while minimum value was observed for Zn (62emu/g) doping Gd2Fe16Ga0.5TM0.5. Highest Curie temperature of 590 K was observed for Cu doping which is 15% and 5% and thesehigher than Gd2Fe17 and Gd2Fe16Ga compounds, respectively. The hyperfine parameters viz. hyperfine field and isomer shift, show systematic dependence on the TM atomic number. The observed magnetic and Curie temperature behavior in Gd2Fe16Ga0.5TM0.5 is explained on the basis of Fe(3d)-TM(3d) hybridization. The superior Curie temperature and magnetization value of Co, Ni, and Cu doped Gd2Fe16Ga0.5TM0.5 compounds as compared to pure Gd2Fe17 or Gd2Fe16Ga makes Gd2Fe16Ga0.5TM0.5 a potential candidate for high-temperature industrial magnet applications.

Introduction: To compare the results of patient-specific absolute dosimetry using homogeneous slab phantom and anthropomorphic heterogeneous female pelvic phantom in cervical cancer patients. Materials and method: Thirty RapidArc plans already planned on treatment planning system (TPS) for cervical cancer patients were exported on both the phantoms viz. RW3 slab phantom and AHFP and dose were calculated using an anisotropic analytic algorithm (AAA). All the plans were delivered by linear accelerator (LA) and the dose for each plan was measured by a 0.6cc ion chamber. The percentage (%) variation between planned and measured doses were calculated and analyzed.Results: In the case of slab phantom, the mean percentage variations between planned and measured doses of all rapid arc QA plans were as 1.4299 and standard deviation 0.768 (t=0.00508, ρ= 0.497982) The result is not significant at p < .05. For the AHFP phantom, the mean percentage variations between planned and measured doses of all rapid arc QA plans were as 6.890 and standard deviation 2.565 (t= 3.21604, ρ= 0.001063 <0.05), the outcome is significant. Discussion: In the case of homogenous slab phantom, there is less than a 3% difference in percentage between planned and measured doses with a standard deviation of 0.7682 (t=0.00508, p= .497982. The result is not significant at p< 0.05). The deviations of planned and measured value of dose in the AHFP phantom were found as 10.67% (maximum value), 2.31% (minimum value) and 6.89% (average value) with standard deviation 2.565 (t=3.21604, p=0.001063. The result is significant at p<0.05). Also, the percentage of variation between homogeneous slab phantoms with AHFP phantom, the t-value is -11.17016. The p-value is < .00001. The result is significant at p<0.05. We can see that the outcomes differ significantly due to the influence of heterogeneous media.Conclusion: AHFP phantom results showed more dose variability than slab homogenous phantom outcomes. Therefore, patient-specific absolute dosimetry should be performed using a heterogeneous phantom that closely resembles the actual human body in terms of both density and design.

Seagrass ecosystems are lost due to habitat disturbance, coastal development and human pressure. We assessed the impact of boat anchors from traditional fishing and recreational activities on the seagrass Halophila ovalis from the Andaman and Nicobar Isalnds of India. The plant density, biomass, morphometrics, canopy height and percentage cover were estimated from two sites of Govind Nagar beach of Andaman and Nicobar Islands. The shoot density of H. ovalis was reduced by physical damage caused by boat anchors. The morphometrics of H. ovalis, such as number of leaves per ramet, leaf length, width and horizontal rhizome length were significantly reduced when impacted by boat anchors. Seagrass canopy height and percentage cover were reduced by 41% and 47% respectively. Though the impact of boat anchors reported here is on small-scale, it may impact feeding grounds of locally endangered dugongs. Therefore, proper management and preventive measures should be implemented to prevent the loss of dugong grass habitats from tourism, recreational and fishing activities.

Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable and plant diseases. The mRNA isoforms of the same gene, such as the apoptosis associated CASP3 gene, can have dramatically different functions. The shorter mRNA isoform product CASP3-S inhibits apoptosis, while the longer CASP3-L mRNA isoform promotes apoptosis. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.

In the present work, doped ZnO (ZnO:Al) thin film has been grown on Silicon (Si) substrate by DC sputtering. The obtained thickness of the film is 230 ± 5 nm. The films were subjected to swift heavy ion (SHI) irradiation 120 MeV, Ag⁹⁺ with different fluences ranging from 3 × 10¹¹ to 3 × 10¹³ ions/cm². To study the impact of SHI, both pristine and irradiated samples were characterized to obtain the structural, surface morphological and electrical properties using X-ray diffractometry (XRD), atomic force microscopy (AFM) and hall effect measurement system respectively. From XRD results it is observed that there is change in crystallinity of the film with increase in irradiation fluence. The surface morphological studies through AFM shows the increase in surface roughness with increase in fluence. A significant change is also observed in electrical parameters viz conductivity, mobility and carrier concentration. Conductivity, mobility and carrier concentration decreases with increasing fluence.

The coronavirus disease 2019 (COVID-19), the pandemic is an unprecedented health emergency never seen in the recorded history of humankind due to its sheer scale, rapid spread, and subsequent shock to the global economy. The past respiratory viral pandemics of the 21st century (SARS-CoV-2 in 2003, Influenza AH1N1 in 2009) have revealed seasonality in environmental factors to play a role in the dynamics of their spread. Here, we report the observed state-level relationship between environmental factors such as temperature (T), relative humidity (RH), specific humidity (SH), and solar radiation (SR) on the COVID-19 spread over the Indian region. The results show that T and RH have a significant impact on the disease growth rate and doubling time. Every degree rise in temperature corresponds to a 0.99 % decrease in the number of cases and an increase in doubling time by ~ 1.13 days implying a slowing down of spread. A similar analysis for RH reveals that more moisture leads to a higher growth rate and reduced doubling time. Lower SH and higher surface-reaching SR are found to reduce the spread and increase the doubling time similar to that of temperature. The range of average state-level T (RH) encountered during this period was between 24 and 35^oC (30 and 87%) which implies that environmental impact is still effective at all these T (RH) and is not limited to specific T (RH) ranges. The progression of the season towards monsoon, post-monsoon, and thereafter winter with a continuous reduction in temperature will prove a major challenge for health workers and policymakers attempting to enforce mitigation and control measures.

The present study documented the presence of seagrass Halodule uninervis for the first time along with previously documented Halophila ovalis at Haripur creek. The population structure of both these seagrass species is assessed. The physico-chemical parameters were similar for both seagrass species except for the sediment grain size fractions. The sand content of H. ovalis patches was 1.2-fold higher than H. uninervis beds, whereas the silt content of H. uninervis beds was 2-fold higher than H. ovalis patches. The pH levels were lower than the standard oceanic pH of 8.2. Macroalgae like Ceramium sp. and Gracilaria verrucosa were growing on the leaves of H. uninervis due to high nitrate and phosphate levels of the creek waters. Leaf reddening was only observed in the leaves of H. ovalis. Under similar environmental conditions, H. ovalis (5004 ± 114.51 ind. m-²) had a 2-fold lower shoot density than that of the H. uninervis (11598 ± 187.52 ind. m-²). Both above- and below-ground biomass of H. ovalis (96.34 ± 10.18 and 197.5 ± 18.30 g DW m^-2) was 2-fold lower than that of H. uninervis (198 ±7.45 and 456 ± 9.59 g DW m^-2). H. uninervis leaves were 9-fold longer than that of H. ovalis, whereas H. ovalis leaves were 5-fold wider than H. uninervis. The leaf plastochrone interval is 2.3 days for H. ovalis and 9.6 days for H. uninervis. Consequently, the leaf growth rate of H. ovalis is 2-fold lower than that of H. uninervis. H. ovalis had 2.6-fold longer internodes than H. uninervis. The root length of H. uninervis was longer than H. ovalis. Consequently, the shorter root length of H. ovalis led to higher branching frequency than H. uninervis. The total C and N content were higher in the leaves of H. ovalis than H. uninervis.

Development of novel anti-cancer peptides requires a rapid screening process which can be accelerated by using appropriate in vitro tumor models. Breast carcinoma tissue is a three dimensional (3D) microenvironment, which contains a hypoxic center surrounded by dense proliferative tissue. Biochemical clues provided by such 3D cell mass cannot be recapitulated in conventional 2D culture systems. In this experiment, we evaluate the efficacy of the sandalwood peptide, cyclosaplin on established in vitro 3D silk breast cancer model using invasive MDA-MB-231 cell line. The anti-proliferative effect of the peptide on 3D silk tumor model is monitored by alamar blue assay, with conventional 2D culture as control. The proliferation rate, glucose consumed, LDH, and MMP-9 activity of Human breast cancer cells are higher in 3D constructs compared to 2D. A higher concentration of drug is required to achieve 50% cell death in 3D culture than 2D cultures. The cyclosaplin treated MDA-MB-231 cells showed significant decrease in MMP-9 activity in 3D constructs. Microscopic analysis revealed the formation of cell clusters evenly distributed in the scaffolds. The drug treated cells were less in number, smaller and showed unusual morphology. Overall, these findings indicate the role of cyclosaplin as a promising anti-cancer therapeutics.

The diagnosis of Cardio-Vascular diseases (CVD) is highly dependent on analysis of ECG signals. ECG analysis can be helpful in estimating the underlying cause and condition of heart in cardiac abnormality. The effectiveness of ECG signal analysis in detection of CVDs is widely accepted by professional healthcare service provider. Many algorithms have been proposed but almost all of them have some kind of limitations, and these limitations largely influence the effectiveness of ECG analysis. The performed research work is dedicated for design of unique self-organizing maps (SOMs) based neural network for classification of arrhythmia according to a particular ECG signal, the generation of SOMs is based on the certain unique signatures of ECG signals and have potential to classify different cardiac conditions. For extraction of unique features from ECG signals, we have proposed to use Fast Discrete Stockwell Transform (FDST). Since the proposed technique is a result of combining two different techniques hence called as hybrid technology. The purpose of using FDST is to identify unique signatures of ECG signals in a more improved manner than existing one, the term improved is used because it has several advantages over existing techniques such as wavelet and Fourier Transform based methods. Results obtained from the implementation of the technique are capable in visualizing the ECG sinus rhythm and arrhythmia conditions in form of unique SOM for each associated arrhythmia condition. This unique SOM based classification makes them ideal for being used as a diagnostic tool. This ability of arrhythmia classification using FDST and SOMs makes the technique unique and useful providing valuable information about patient condition. Using proposed technology a portable diagnosis tool for monitoring of patient at their site may be facilitated later, that will improve the quality of life of the patient by diagnosing cardiac condition.

Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. In this study, we present a comprehensive investigation into the relationship between surface roughness and structural integrity of Polyactic Acid (PLA) specimens produced through additive manufacturing. This research work focuses on the prediction of surface roughness of Additive Manufactured Polyactic Acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, Explainable AI techniques are employed to enhance the interpretability of the machine learning models. The eight algorithms used in this study are Support Vector Regression, Random Forest, XG Boost, Ada Boost, Catboost, Decision Tree, Extra Tree regressor, and Gradient Boosting regressor. The study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impact of individual input parameters through Explainable AI methods. The experimental results indicate that the XG Boost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XG Boost algorithm provides the most accurate predictions for surface roughness compared to other algorithms. The study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from Explainable AI techniques.

This paper examines the regional changes of corn production and the relationship between ethanol production and corn production. The underlying hypothesis is that the rapid growth in ethanol production causes regional expansion of corn production outside the traditional regions. This paper introduces the information approach developed by entropy theory to describe these regional changes. The results support the hypothesis that ethanol production leads to expansion of corn production outside traditional corn producing regions.

RNA interference is a highly conserved process in which non-coding small RNAs (sRNA) modulate gene expression at the post-transcriptional level influencing plant growth and development. Dicer-like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) are the core elements involved in gene silencing and their gene families have been explored in many plants. However, these genes and their response to abiotic stresses have not yet been well in wheat. In this study, 82 AGO, 31 DCL, and 31 RDR genes were identified and phylogenetic analysis of these proteins showed that clustered into ten, four and four clades respectively. RNA-seq analysis revealed constitutive expression of AGO1, AGO9, and DCL2 family expression analysis in tissues under normal and stress conditions, whereas RDR1 which is known to engage in siRNA biogenesis showed higher expression levels in wheat leaf tissues. Our findings build the foundation for comparative genomics analyses of RNA silencing elements in cereal crops, as well as new insights into the functional complexity of RNA silencing in wheat stress responses, which is critical for understanding the processes underlying wheat stress responses.

Viroids are smallest pathogen that consist of non-capsidated, single-stranded non-coding RNA replicons and exploits host factors for their replication and propagation. The severe stunting disease caused by Citrus bark cracking viroid (CBCVd) is a serious threat, which spread rapidly within hop gardens. In this study, we employed comprehensive transcriptome analyses to dissect host-viroid interactions and identify gene expression changes associated with disease development in hop. Our analysis revealed that CBCVd-infection resulted in the massive modulation of activity of over 2000 genes. Expression of genes associated with plant immune responses (protein kinase and mitogen-activated protein kinase), hypersensitive responses, phytohormone signaling pathways, photosynthesis, pigment metabolism, protein metabolism, sugar metabolism and modification and others were altered, which could be attributed to systemic symptom development upon CBCVd-infection in hop. In addition, genes encoding RNA-dependent RNA polymerase, pathogenesis-related protein, chitinase as well as those related to basal defense responses were up-regulated. The expression levels of several genes identified from RNA sequencing analysis were confirmed by qRT-PCR. Our systematic comprehensive CBCVd-responsive transcriptome analysis provides a better understanding and insights into complex viroid-hop plant interaction. This information will assist further in the development of future measures for the prevention of CBCVd spread in hop fields.

ABSTRACT Underwater mines may destroy friendly submarines and marine habitats, compromising nonmilitary security. Finding credible information is vital to avoiding scams and spotting hazards. This article employs Supervised Machine literacy models to appropriately categorize subsea items like mines or gems based on Sonar sounds. Python constructs three double classifier models using Gorman and Sejnowski (1988) training data. Through the extraction of key aspects from the Sonar signals, such as spectral components and intensity, the models are able to match newly obtained data with the gemstone order or mine. Delicacy decreases the chance of misidentifications in nonmilitary activities. Machine literacy offers full validation that accommodates real-world Sonar data noise and unpredictability. Based on test results, the highest performing algorithm will deliver the required ratio of accuracy to perceptivity for the bracket task. Central keywords: vaticination model, bracket algorithms, supervised machine literacy, SONAR, and aquatic mining emphasize particular foundations. Improved sophistication in spotting undersea Mines let nonmilitary defence systems respond to proven threats only, preventing friendly submarines or sea life from unnecessary damage owing to false duplicity. This work is a promising start to real-world mine detection devices. Further developments may incorporate greater training data sets, real-time testing on subsea platforms equipped with integrated sonar, and optimization for the discovery of new mine-types. Overall, machine learning and Sonar vision decrease signal fluctuation and noise, making it easier to determine accurate boundaries for non-military applications and environmental protection. The recommended automated discovery technique must balance computational efficiency, generalizability, and delicacy. Prior a real crime, new quality control and verification procedures could boost trust even further.

The sudden onset of the Covid-19 pandemic disrupted the modern multi-national interconnected society and led the countries and societies to enforce unprecedented restrictions on movement. Among myriad containment measures, the policy of trace and quarantine found universal adoption among countries; the swift adoption of the policy was soon met with widespread criticism and opposition activists who questioned the utility and the risk associated with such a large scale collection of data and infringement on the movement of individuals. Consequently, one often tends to be either pro- or anti-trace and quarantine; the ensuing polarizing and politicized left little room for nuance. In this work, we undertake a methodology study to understand the nuances of the impact of different implementations of trace and quarantine. To this end, we design a user-friendly and intuitive tool that can be employed by experts to model the disease dynamics and societal structure. We focus on the study of the cost of policy with respect to quarantine degree, which captures the distance between the person required to quarantine after a person is detected to be infected. Our study results in a surprising conclusion: the cost is not necessarily monotone with respect to the degree of quarantine. Our analysis indicates that governments must curb the urge to adopt simplistic policy and the optimal policy of trace and quarantine for a country strongly depends on its societal structure and disease dynamics.

The SnRK gene family is a key regulator playing an important role in plant stress response by phosphorylating the target protein to regulate the signalling pathways. The function of SnRK gene family has been reported in many species but is limited to Triticum asetivum. In this study, SnRK gene family in the wheat genome was identified and its structural characteristics were described. One hundred forty-seven SnRK genes distributed across 21 chromosomes were identified in the Triticum aestivum genome and categorised into three subgroups (SnRK1/2/3) based on phylogenetic analyses and domain types. The gene intron-exon structure and protein-motif composition of SnRKs were similar within each subgroup but different amongst the groups. Gene duplication between the wheat, Arabidopsis, rice and barley genomes was also investigated in order to get insight into the evolutionary aspects of the TaSnRK family genes. SnRK genes showed differential expression patterns in leaves, roots, spike, and grains. Redundant stress-related cis-elements were also found in the promoters of 129 SnRK genes and their expression levels varied widely following drought, ABA and light regulated elements. In particular, TaSnRK2.11 had higher and increased expression under the abiotic stresses and can be a candidate gene for the abiotc stress tolerance. The findings will aid in the functional characterization of TaSnRK genes for further research.

Cucumber green mottle mosaic virus (CGMMV) is a re-emerging threat to greenhouse cucumber and other Cucurbitaceae crop productions worldwide. This seed-borne virus can easily spread from a contaminated seed to seedlings and to adjacent plants through mechanical contact of the foliage of diseased and healthy plants causing extensive yield losses. Additionally, infection may not be limited to the current crop but may also affect subsequent crops due to the long-term persistence of the virus on contaminated crop residues, greenhouse hard surfaces and soil or soil-less greenhouse substrates. In the present work, three greenhouse trials were conducted to develop an integrated pest management strategy towards controlling CGMMV in commercial cucumber greenhouses, by implementing an effective sanitization program and using resistant and grafted cucumber varieties. Results of sanitization trial highlighted that pressure washing and cleansing with an alkaline foam cleanser has eliminated CGMMV on some of the most heavily infested areas. However, three successive applications of cleanser and disinfectants were essential to completely eliminate CGMMV on porous and uneven surfaces, such as cement alleyway, tray gutter and floor mats. The varietal trial revealed that out of 15 cucumber varieties evaluated, two Mini (‘Katrina’ and ‘Khassib’) and three Long English (‘Sepire’, ‘Bomber’ and ‘LC13900’) had reduced or delayed CGMMV infection spread in the greenhouse but were intermediate in yield. The varieties ‘Sunniwell’ and ‘Bonbon’ were the most tolerant to CGMMV. They showed a high CGMMV infection level without compromising yield. These results proved the need for new productive cucumber varieties with CGMMV resistance. Grafting experiment showed only yield increase in case of grafted ‘Picowell’ over ‘Bonbon’ and not marked CGMMV resistance, which is a much desirable result when the grafting experiments are evaluated for their economic potential. In all, the current experimental trials unfold unique methodologies on CGMMV management in commercial greenhouses that are recommended to the growers to be followed for reducing crop losses and get benefitted on revenue compromise.

High prevalence of allergic disorders in Indian pediatric patients demands early diagnosis of allergens so that appropriate allergen avoidance methods, pharmacotherapy or immunotherapy can be provided in time to avoid the worsening of symptoms due to triggers. In this context, identification of the most prevalent aeroallergens in a given area is critical for diagnosis and treatment of various allergic reactions especially in case of rhino-conjunctivitis and asthma. The specific diagnosis of IgE mediated allergy is usually based on the correlation between the clinical history and signs and symptoms endorsed by the diagnostic tests. Skin prick tests are the gold standard however has some limitation. In view of this, the aim of this study was to assess the significance of ImmunoCAP assay for assessing serum specific IgE levels, available in India, against skin prick test by standardized allergens, for the diagnosis of allergic sensitization. A single-center observational study was conducted at tertiary care corporate hospitals in North India from 2017-2020. Patients with self-reported allergic symptoms or whose symptoms were suspected for allergies were tested for the type of sensitization using skin prick test (SPT) after their consents followed by ImmunoCAP based quantification of serum specific IgE levels. A total of 256 (95.9%) pediatric patients were analyzed in the study and were analysed. The patients were most commonly (30% patients) sensitized to D. farinae (173 [67.6%]), D. pteronyssinus (174 [68.0%]), Acarus siro (93 [36.3%]), Lepidoglyphus destructor (96 [37.5%]), Plantago lanceolata (Engl.). The ROC curve of Bermuda grass shows that the AUC of serum-specific IgE is larger than that of SPT (1.000 / 0.881). The receiver operating characteristic (ROC) analysis of serum-specific IgE reference method resulted in the largest AUC of all the allergens taken into consideration (0.711 / 1.000) compared to those of SPT (0.589 / 1.000). These results revealed that serum-specific IgE is more reliable method over prick test for accessing the sensitivity of patient for various allergens. This study may be helpful in performing early diagnosis of allergies as well as in taking appropriate preventive measures for managing life threatening allergic reactions

Seagrasses are considered as efficient bioindicators of coastal trace element contamination. This chapter provides an overview on the trace element accumulation, tolerance and biomonitoring capacity of the various seagrass species distributed along the coast of India. A total of 10 trace elements are reported in seagrasses, 11 in sediment and nine in the water column from India. From the 11 seagrass species studied, 60% of research have focused on Syringodium isoetifolium, Cymodocea serrulata, Cymodocea rotundata and Halophila ovalis. 78% of seagrass trace element research in India is from Palk bay and Gulf of Mannar (GOM), Tamil Nadu and 16% from Lakshadweep Islands. Out of the 10 trace elements, Cd, Cu, Pb and Zn are the most studied in seagrass, Fe, Mn, Ni and Pb in sediment and Cu, Fe, Mg, Ni and Zn in the water column. Accumulation capacity of various trace elements in seagrass were species-specific. S. isoetifolium have the highest concentration of Cd and Mg at Palk bay and Lakshadweep Islands respectively. The concentration of Cu was higher in C. serrulata at GOM. Halodule uninervis and Halophila decipens have the highest concentration of Co, and Cr, Ni, Pb and Zn from Lakshadweep Islands. The highest concentration of Fe and Mn were highest in Halophila beccarii and H. ovalis from the coast of Goa and Palk bay respectively. Threshold levels (>10 mg L^-1) of Cd, Cu, Pb and Zn were observed for C. serrulata, H. ovalis, H. uninervis and T. hemprichii, that can affect the Photo System -II of these seagrasses and exert cellular stress leading to seagrass loss and die-off. High concentration of these elements can exert negative impacts on seagrass associated trophic assemblages and ecosystem functioning. Seagrasses of India can be utilized as bioindicators of coastal trace element contamination but the associated toxicity and human health risks needs further investigation.

Transient receptor potential (TRP) channel family proteins are sensors for pain, which sense variety of thermal and noxious chemicals. Sensory neurons innervating the gut abundantly express TRPA1 and TRPV1 channels and are in close proximity of gut microbes. Emerging evidence indicates a bi-directional gut-brain cross-talk in several entero-neuronal pathologies; however, the direct evidence of TRP channels interacting with gut microbial populations is lacking. Herein, we examine whether and how the knockout (KO) of TRPA1 and TRPV1 channels individually or combined TRPA1/V1 double-knockout (dKO) impacts the gut microbiome in mice. We detect distinct microbiome clusters among the three KO mouse models versus wild-type (WT) mice. All three TRP-KO models have reduced microbial diversity, harbor higher abundance of Bacteroidetes, and reduced proportion of Firmicutes. Specifically distinct arrays in the KO models are determined mainly by S24-7, Bacteroidaceae, Clostridiales, Prevotellaceae, Helicobacteriaceae, Rikenellaceae, and Ruminococcaceae. A1KO mice have lower Prevotella, Desulfovibrio, Bacteroides, Helicobacter and higher Rikenellaceae and Tenericutes; V1KO mice demonstrate higher Ruminococcaceae, Lachnospiraceae, Ruminococcus, Desulfovibrio and Mucispirillum; while A1V1dKO mice exhibit higher Bacteroidetes, Bacteroides and S24-7 and lower Firmicutes, Ruminococcaceae, Oscillospira, Lactobacillus and Sutterella abundance. Also, the abundance of taxa involved in biosynthesis of lipids and primary and secondary bile acids is higher while that of fatty acid biosynthesis-associated taxa is lower in all KO groups. To our knowledge, this is the first study demonstrating distinct gut microbiome signatures in TRPA1, V1 and dKO models and should facilitate prospective studies exploring novel diagnostic/ therapeutic modalities regarding the pathophysiology of TRP channel proteins.

Abstract: Role of gut microbiome in human health is becoming apparent. The major functional impact of gut microbiome is transmitted through the microbial metabolites that are produced in the gut and interact with host cells either in the local gut environment or get absorbed in the circulation to impact distant cells/organs. Short chain fatty acids (SCFAs) are the major microbial metabolites that are produced in the gut through fermentation of non-digestible fibers. SCFAs are known to function through various mechanism, however, their signaling through free-fatty acid receptor 2 and 3 (FFAR2/3; type of G-coupled protein receptors) is new therapeutic approach. FFAR2/3 are widely expression in diverse cell types in human and mice, and functions as sensors of SCFAs to change several physiological and cellular functions. FFAR2/3 modulates neurological signaling, energy metabolism, intestinal cellular homeostasis, immune response and hormone synthesis. FFAR2/3 functions through Gi and/or Gq signaling, that is mediated through specific structural features of SCFAs-FFAR2/3 bindings and modulating specific signaling pathway. In this review, we discussed the wide-spread expression and structural homologies between human and mice FFAR2/3, and their role in different human health conditions. This information can unlock opportunities to weigh the potential of FFAR2/3 as drug target to prevent human diseases.

The outbreak of Coronavirus, as well as its emerging potential consequences, has become a global challenge worldwide, demanding effective and controlled therapeutic strategies. Potential drug candidates could achieve that with minimal toxicity. The current investigation selected 2-Deoxy-D-glucose prescribed by Defense Research and Development Organization (DRDO), India. The derivative and modified form was tested through in silico analysis against COVID-19 main protease complex with N3 inhibitor. The derived form of 2-Deoxy-D-glucose was generated by replacing the hydroxy group with a hydrogen atom, and Cypate 2-Deoxy-D-glucose was chosen as a derivative against the COVID-19 main protease complex. A molecular docking approach was adopted to identify the stable and competent form among the modified and derivative forms of 2-Deoxy-D-glucose based on binding energy. By further promoting the stabilized complex, these compounds' toxicity was also scrutinized through ADMET analysis to predict the potential candidate. The current investigation suggested that the modified version of 2-Deoxy-D-glucose was more stable with minimal toxicity against COVID-19 main protease.

Quantum computing is an emerging field that have given a significant impact on optimization. Among the diverse quantum algorithm, the quantum gradient descent has become a prominent technique for solving unconstrained optimization problems. In this paper, we propose a quantum-spectral Polak-Ribi{\'e}re-Polyak (PRP) conjugate gradient approach. The technique is considered as a generalization of the spectral PRP method which employs a $q$-gradient that approximate the classical gradient with quadratically better dependence on the quantum variable $q$. Additionally, the proposed method reduces to the classical variant as quantum variable $q$ gets closer to $1$. The quantum search direction always satisfies the sufficient descent condition and does not depend on any line search. This approach is globally convergent with the standard Wolfe line search without any convexity assumption. Numerical experiments are conducted and compared with the existing approach to demonstrate the improvement of the proposed strategy.

In this paper, we consider a class of bilevel programming problems (abbreviated as, BLPP). Exploiting the generalized approximate convexity assumptions, we investigate the relations among the solutions of approximate Minty (respectively, Stampacchia) type variational inequalities (abbreviated as, AMTVI (respectively, ASTVI)), and the local ϵ-quasi solutions of the BLPP. Moreover, by employing the generalized Knaster–Kuratowski–Mazurkiewicz (abbreviated as, KKM)-Fan’s lemma, we derive some existence results for the solutions of approximate variational inequalities (abbreviated as, AVI), namely, AMTVI and ASTVI. A non-trivial example is given to highlight the importance of the established results. To the best of our knowledge, there is no research paper available in the literature that establishes relationships between the AVI and the BLPP under the assumptions of generalized approximate convexity in terms of limiting subdifferentials.

Call set up success rate (CSSR) in a mobile network is an important quality parameter, which directly influences overall key performance indicators (KPIs) of the network service providers. Network KPIs especially the CSSR is the fraction of the attempts to make a call that result in a successful connection to the dialled number. Nevertheless, all call attempts do not end with a connection to the dialled number. In this research, six sigma methodology is applied to improve the call quality of a legacy mobile network and subsequently to boost comprehensive CSSR of a telecom service provider. This research elucidates an empirical study of improving overall CSSR by employing DMAIC methodology consisting of five stages, e.g. Define, Measure, Analysis, Improve and Control. In addition, analytic hierarchy process (AHP) technique is used for determining the vital causes out of all the identified network parameters affecting overall CSSR. Furthermore, the identified vital parameters are being upgraded in order to improve the overall CSSR and finally the system achieved a higher CSSR.

Different water treatment regiments are revealed to have potential in enriching antibiotic resistant bacteria (ARB). Advanced oxidation processes (AOPs) based disinfection techniques have been studied widely in the recent times due to their advantages over conventional treatment methods. However, bacterial response and adaptations against the hostile environments of AOPs is not clearly understood yet. Based on the existing knowledge on the ways in which bacteria surpass the antibiotic treatment, here we propose few important aspects of bacterial adaptation which could be true for AOPs as well since both antibiotics and AOPs generate reactive oxygen species (ROS) during their modes of action. We discuss the plausible role of ROS in the selection of ARB and bacterial heterogeneity as a strategy to bypass the lethal action of AOPs. Understanding bacterial adaptation during disinfection plays a vital role in devising strategies to outclass the bacterial survival. Hence, more importance should be given to such studies in the near future for the successful implementation of AOPs.

Genetic engineering has become an essential element in developing climate-resilient crops and environmental sustainable solutions to respond to the increasing need for global food security. Genome editing using CRISPR/Cas [Clustered regulatory interspaced short palindromic repeat (CRISPR)-associated protein (Cas)] technology is being applied to a variety of organisms, including plants. This technique has become popular because of its high specificity, effectiveness, and low production cost. Therefore, this technology has the potential to revolutionize agriculture and contribute to global food security. Over the past few years, increasing efforts have been seen in its application in developing higher-yielding, nutrition rich, disease resistant, stress-tolerant “crops” fruits and vegetables. Cas proteins such as Cas9, Cas12, Cas13, and Cas14, among others, have distinct architectures and have been used to create new genetic tools that improve features that are important for agriculture. The versatility of Cas has accelerated genomic analysis and facilitated the use of CRISPR/Cas to manipulate and alter nucleic acid sequences in cells of different organisms. This review provides the evolution of CRISPR technology exploring its mechanisms and contrasting it with traditional breeding and transgenic approaches to improve different stress tolerance. We have also discussed the CRISPR/Cas system and explored three Cas proteins that are currently known to exist: Cas12, Cas13, and Cas14 and their potential to generate foreign DNA free or non-transgenic crops that could be easily regulated for commercialization in most countries.

Protein molecules show varying degrees of flexibility throughout their three-dimensional structures. This flexibility is associated with the fluctuation of the torsion angle in proteins. The protein backbone can be defined mainly by two torsion angles, phi (φ) and psi (ψ). The fluctuation of torsion angles is derived from different NMR models’ variations in backbone torsion angles. The angle fluctuations in the cartesian coordinate space are used to characterize the protein’s structural flexibility. Fluctuation of torsion angle is useful in predicting protein function and structure when the torsion angles are used as restraints. This study aims to develop a machine learning method to predict torsion angle fluctuations directly from protein sequences. It explores various useful features such as disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, monograms, bigram, position-specific estimated energy, half-sphere exposures, etc. Likewise, it explores well-known machine learning methods and proposes an optimized Light Gradient Boosting Machine Regressor (LightGBM) method, named TAFPred, to predict torsion angle fluctuations with the selected features. The proposed method achieves ten-fold cross-validated correlation coefficients of 0.746 and 0.737 and mean absolute errors of 0.114 and 0.123 for the angle fluctuation of φ and ψ, respectively, and attains an improvement of 10.08% in MAE, 24.83% in PCC in the phi angle, and 9.93% in MAE, 22.37% in PCC in psi angle compared to the state-of-the-art method proposed by Zhang et al.

Debris-covered glaciers are a notable feature in the greater Himalaya, and their ongoing mass loss under changing climate will affect the water resources of over a billion people. The current knowledge of the mass balance of Himalayan glaciers is restricted by the paucity of in-situ measurements of glaciers in both space and time, as well as the resolution of satellite remote sensing imageries. Recently, the use of Unmanned Aerial System (UAS) imagery has shown the potential to bridge this gap by enabling very detailed monitoring of inaccessible glacial areas. UAS imagery-based monitoring of Himalayan glaciers has so far been limited to a single glacier in the entire Himalaya, providing a limited understanding of spatial variability in glacier mass balance and driving factors. In the first UAS based glacial mass change estimation in the trans-Himalaya, we conducted two Unmanned Aerial System (UAS) surveys (May and November 2019) over the debris-covered Annapurna III glacier in the Himalaya. We performed Structure-from-Motion (SfM) analysis and utilized differential GPS field observations to derive geometrically accurate point clouds, ortho-mosaics and digital surface models (DSMs). The glacial volumetric loss was estimated from DSM differencing, and the magnitude and spatial variability of glacier surface change was derived from 3-D differencing of point clouds. Results revealed a heterogeneous glacial melt pattern, with an average elevation loss of 0.89 m during the monitored time period. The majority of the glacial tongue exhibited surface lowering except the area above and around the glacial snout that surprisingly exhibited significant elevation gain. Both the highest magnitude of mass loss and the highest spatial variability in mass change was observed in areas with exposed ice-cliffs and supraglacial ponds. Glacial surface velocity derived from manual feature tracking showed velocity ranging from 0-4.1 m. A detailed evaluation of specific areas allowed an improved understanding of the complex interplay of factors leading to observed surface change. Our findings expand the extent of UAS based monitoring of debris-covered glaciers in the Himalaya and conclude that UAS derived 3D topographic products will become increasingly important for monitoring of thinning debris-covered glaciers.

Accumulation of extra fats in the body results in disturbance in homeostasis causing obesity. The pathophysiology of obesity has been associated with various genetic, epigenetic factors and the disturbed lifestyles of an individual. One of the outcomes is disturbance in the sleep cycle which leads to sleep disorders. The evolutionary significance of sleep lies in the fact that it saves energy and is crucial for adjustment of an individual to ecological and environmental factors, and thus maintains homeostasis. Sleep has an anabolic function and replenishes the wakefulness-associated loss of glycogen stores. However, the modern lifestyle is well known to disrupt sleep behavior and patterns affecting human health negatively. Alteration in homeostasis leads to several acute and chronic pathophysiological conditions like obesity and related metabolic morbidities being one amongst them. Shorter sleep duration and poor sleep quality are important risk factors associated with the development of obesity and have more adverse effects on energy homeostasis. Consequently, it is crucial to comprehend the obesity and sleep relationship and its therapeutic strategies, given that strong crosstalk exists between energy balance that favors obesity and sleep apnea. It is therefore highly recommended to the people with a high risk of obesity to maintain sleep discipline and good sleep hygiene in our daily life. Interventions are required to improve both sleep quality and quantity, and associated obesity. More focused research is needed for uncovering the pieces of evidence that determine whether such interventions can improve obesity-related health consequences. The present review discusses the important findings on the therapeutic and neuroendocrine mechanisms regulating sleep and obesity.

Two histamine receptor subtypes (HR), namely H1R and H4R, as key components, are involved in the transmission of histamine-induced itch. Although exact downstream signaling mechanisms are still elusive, transient receptor potential (TRP) ion channels play important roles in the sensation of histaminergic and non-histaminergic itch. Aim of this study was to investigate the involvement of TRPV1 and TRPA1 channels in the transmission of histaminergic itch. The potential of TRPV1 and TRPA1 inhibitors to modulate H1R- and H4R-induced signal transmission was tested in a scratching assay in mice in vivo and in vitro via Ca2+ imaging of murine sensory dorsal root ganglia (DRG) neurons. The TRPV1 inhibition led to a reduction of H1R- and H4R- induced itch and reduced Ca2+ influx into the neurons. The TRPA1 inhibitor reduced H4R-induced itch and both H1R- and H4R-induced Ca2+ influx. In conclusion, these results indicate that both channels, TRPV1 and TRPA1 are involved in the transmission of histamine-induced pruritus.

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. The study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020, in addition to macroeconomic variables such as EPU. This study investigates the moderating effects of EPU on investments in fixed and intangible assets, as well as its effect on Tobin's Q and the market price per share. The outcomes demonstrate that EPU reduces the costs of both tangible and intangible assets for businesses. In addition, companies with a greater Tobin's Q and market price per share are more impacted by uncertain corporate investment policies. However, financial leverage is found to be negatively correlated with share price and positively correlated with earnings per share and earnings per unit. Tobin's Q is positively correlated with financial leverage, indicating that firms that raise capital through debt are more likely to create value for investors. The research indicates that market-dependent enterprises are more susceptible to the unpredictability of monetary policy. Eventually, the findings suggest that consistent and transparent economic policies may increase the efficiency of corporate investment.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on the plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of highly-priority transcription factors (TFs) dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We revealed distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficients nodes. Dynamic transcriptional regulatory modeling on integrated TF-target and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that can potentially be used in future genetic crop engineering programs.

Accurately quantifying the above-ground volume (AGV) and thus above-ground biomass (AGB) of forest stands is an important aspect in the conservation of mangrove ecosystem owing to their ecological and economic benefits. However, the number of studies focusing on quantifying mangrove forests’ biomass has been relatively low due to their marshy terrain, making exploratory studies challenging. In recent times, the use of LiDAR technologies in forest inventory studies has become increasingly popular, due to the reliability of LiDAR as a highly accurate means of 3D spatial data acquisition. In this study, we propose an end-to-end methodology for estimating AGV of mangrove forest stands from terrestrial LiDAR data. Many of the recent studies on this topic effectively employ machine learning algorithms such as multi layer perceptron, random forests, etc. for filtering foliage in the point cloud data of single trees. This study further extends that approach by incorporating the impact of class imbalance of forest point cloud data in a weighted random forest classifier. For the task of segmentation of wood/foliage points in a single tree point cloud, this approach yielded an average increase of 2.737% in the balanced accuracy score, 0.007 in the Cohen’s kappa score, 2.745% in the ROC AUC score and 0.857% in the F1 score. For the task of AGV estimation of a single tree, this approach resulted in an average coefficient of determination of 0.93 with respect to the ground truth volumes. For the task of counting pneumatophores in a plot-level point cloud, the proposed breadth-first searching method yielded an average coefficient of determination of 0.9391. Also, the machine learning classifier and geometric features used in this study were invariant to tree species and hence could be generalised for the classification of point clouds of other tree species as well. Finally, a breadth-first graph-search segmentation based approach is also proposed as part of this pipeline to estimate the contribution of pneumatophores to the AGB of mangrove forest stands. Since pneumatophores are a special adaptation of mangrove forests for gaseous exchange in marshy environments, this study aims to incorporate the detection and AGB estimation of pneumatophores in the inventory of mangrove forest stands. Studying the contribution of pneumatophores to the AGB of mangrove forest plots could also aid future mangrove forest inventory studies in modeling the underlying root network and estimating the below-ground biomass of mangrove trees.

United Nations Framework Conventions on Climate Change (UNFCC) conventions in their conference of parties (COPs) has continuously considered and agreed reducing emission level in order to minimize the impact of global climate change. Reducing emission due to deforestation and degradation (REDD) ,was considered as one of the major activities in this regard during Kyoto protocol in 2009 which laid foundation for the participating countries to be compensated financially for reduced carbon emission. Mexico convention -2012 required the countries to develop and implement a transparent and consistent monitoring, reporting and verification (MRV) process. Later in Paris agreement-2015, the parties agreed to limit the global warming to 2 degree centigrade and with further efforts to 1.5-degree centigrade furthering entailing the parties to prepare and communicate nationally determined contributions (NDCs) every five years. Nepal aimed to decrease the average annual deforestation rate by 0.05 percent from existing 0.44 percent in the terai region and 0.1 percent in the Chure. Nepal decided to develop its forest reference level (FRL) in national level for the historical period 2000-2010 considering Carbon dioxide and carbon pools above and below ground. As per the Forestry Sector Strategy, Nepal aims to increase carbon stock growth by at least 5% by 2025 as compared to 2015 and decrease mean annual deforestation rate to 0.05. After major change in administrative division in Nepal, forest management responsibility has shifted down to the Sub-national level. But forest resource studies have not been conducted yet in these levels. Despite a small country, Nepal has at least four clear physiological regions. The amount of carbon stock stored by different forest type are different depending upon species distribution, carbon volume and density for each species, and their distribution along ecological and physiological regions. Sal (shorea Robusta), for example, having one of the highest carbon densities, is a major forest types in Nepal. The purpose of this study was to generate forest map of the country, calculate carbon stock, gain and loss, and their rate in each province due to deforestation/afforestation using remote sensing data. Further Sal forest map was generated and its contribution in carbon stock was calculated using averaged national carbon density as well as using regional density method. According to the study, around 5.1 million hectares of Nepali land was forest in 2015 increasing from 4.2 million hectares in 2005. However, Sal forest has decreased during the same period. Province 1 contributed the maximum (130 Tg) and Province 2 the minimum (40Tg) of Carbon stock in 2015. Using the conventional method of calculation with national average density (108.08 t/ha), a total of 36.7T CO2 yr-1 carbon sink was observed in the Country. Whereas, with the new approach of calculation, a total of 44.7 T CO2 e of carbon sink per year was estimated during the same period. This approach holds potential for qualifying as an MRV process of Nepal. The subnational level forest and carbon statistics produced during this study can be important assets for the better forest governance. This can also pave way for policy formation and preparation of action plan for sustainable forest management and intervention strategy and obtaining better financial incentives participating in the reduction of emission due to deforestation and forest degradation (REDD) plus programs.

Nucleosome positioning plays an important role in crucial biological processes like replication, transcription, and gene regulation. It has been widely used to predict the genome’s function and chromatin organisation. So far, the studies of patterns in nucleosome positioning have been limited to transcription start sites, CTCFs binding sites, and some promoter and loci regions. The genome-wide organisational pattern remains unknown. We have developed a theoretical model to coarse-grain nucleosome positioning data in order to obtain patterns in their distribution. Using hierarchical clustering on the auto-correlation function of this coarse-grained nucleosome positioning data, a genome-wide clustering is obtained for Candida albicans. The clustering shows the existence beyond hetero- and eu-chromatin inside the chromosomes.

A bacterium known as Akkermansia muciniphila (AkkermansiaA. muciniphila) was detected in human feces in 2004. It is defined as a gram-negative, anaerobic, and mucin-degrading microbe belonging to the Verrucomicrobia species. Although the majority of A. muciniphila is found in the human intestinal tract playing a role in the gut microbiome, there are also smaller levels detected in breast milk and oral cavities as well as in the pancreas and appendix.Evidence suggests that having A. muciniphila present in the gut microbiome has positive effects on human health. As a result, A. muciniphila has been shown to have health benefits when taken as a dietary supplementation where it is considered a promising next-generation probiotic. However, the large-scale production of A. muciniphila remains a challenge due to its need for a stable environment. While culturing A. muciniphila in a mucin-based medium is one option, other strategies that use plate-based mediums have also been explored. Currently, both live and pasteurized forms of A. muciniphila are available on the market and shown to have promising outcomes in animal models. Additionally, researchers are investigating various prebiotics to increase the abundance of A. muciniphila in the human gut. This article presents a detailed summary of the phenotypic characteristics of A. muciniphila and its associations with various human health condition illnesses. Finally, the paper explores A. muciniphila's potential as an effective next-generation probiotic.

The domestic ducks (Anas platyrhynchos domesticus) are resistant to most of the highly pathogenic avian influenza virus (HPAIV) infections. In this study, we characterized the lung proteome and phosphoproteome of ducks infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala) at 12 hr, 48 hr and 5 days post infection condition. Total 2082 proteins were differentially expressed and 320 phosphorylation sites mapping to 199 phosphopeptides corresponding to 129 proteins were identified. Functional annotation of proteome data analysis reveals activation of RIG-I-like receptor and Jak-STAT signaling pathways, that lead to induction of interferon stimulated genes (ISGs) expression. The pathway analysis of phosphoproteome datasets also confirms the activation of RIG I, Jak-STAT signaling, NF-kappa B signaling and MAPK signaling pathways in the lung tissues. The induction of ISGs proteins (STAT1, STAT3, STAT5B, STAT6, IFIT5 and PKR) establishes protective antiviral immune response in duck lung tissue. Further, the protein-protein interaction network analysis identified proteins like AKT1, STAT3, JAK2, RAC1, STAT1, PTPN11, RPS27A, NFKB1 and MAPK1 as main hub proteins that might play important roles in disease progression in ducks. Together, functional annotation of proteome and phosphoproteome datasets reveals the molecular basis of the disease progression and disease resistance mechanism in ducks infected with HPAI H5N1 virus.

Along with the economic growth, the companies must contribute to social progress and promote environmental sustainability in equal harmony. Sustainable human resource management (SHRM) strategies make it possible to attain the economic, social and environmental goals of a firm. In this regard, a survey method is discussed using the literature review and online questionnaire to identify the main factors/indicators during the SHRM evaluation of manufacturing firms in India. Uncertainty is commonly occurred in the assessment of SHRM factors. As a generalized version of fuzzy sets, single-valued neutrosophic set (SVNS) has been demonstrated as a valuable tool to illustrate the indeterminate, inconsistent and uncertain data of realistic decision-making problems. Considering the idea of SVNSs, this study develops a hybrid multi-criteria group decision-making (MCGDM) approach for assessing the SHRM of manufacturing firms under uncertainty settings. For this purpose, an SVN-alternative ranking order method accounting for two-step normalization (AROMAN) is proposed based on VIFI-score function-based decision experts’ (DEs’) weighting tool and integrated criteria weight-determining model to solve the MCGDM problems with fully unknown DEs and criteria weights. In this regard, we develop new SVN-distance measure to compute the degree of difference between SVNSs. Some examples are presented to demonstrate the efficacy of developed measure over the existing ones. In addition, new criteria weight-determination model is presented with the integration of objective weights through IVIF-distance measure-based model and subjective weights through ranking comparison (RANCOM) tool on SVNSs. The proposed ranking method is applied to an empirical study of SHRM assessment for manufacturing firms in India, which shows its applicability and feasibility. In this study, the evaluation criteria are characterized into social, environmental and economic aspects with DE’s opinions. Comparative and sensitivity analyses are made to show the strength and steadiness of presented approach. This study provides an innovative MCGDM analysis framework, which makes a significant contribution to the SHRM assessment problem under indeterminate, inconsistent and uncertain setting.

Solid-state transformer (SST) is a promising technology for smart grids due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The SST is designed in tandem with three stages, i.e., the input, isolation, and output stages. As three converter stages are used to design the SST to make a more reliable device in practice, it is highly prone to instabilities. Moreover, the key objective of SST design is to implement a modern power distribution system to make it more intelligent and reliable. The stability issue can be even more complicated by the presence of distributed energy resources in the distribution system. Thus, the stability of SST must be measured prior to /during the design, and to determine, a state-space model is used, which is developed in this paper. Each stage of SST is modeled with its controller, and the system's stability is measured through the controllability and observability test. Further, the conversion from state-space to transfer function model is done, and the stability of SST is shown using frequency and time-domain diagrams. With these, the different plots, namely, the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot, are used to ascertain the relative and absolute stability. Finally, the SST Simulink model is tested and validated through hardwate-in-the-loop simulation, i.e., OPALRT, to show its effectiveness and applicability.

The members of the Liliaceae family have been regarded as an excellent source of biologically active compounds. However, the work on antimicrobial potential and characterization of the bioactive fractions of Lilium philadelphicum flower is limited and needs to be explored. The present study reports the antimicrobial potential, anti-inflammatory and anticancer potential of the bioactive fraction extracted from the flower of L. philadelphicum (Red Lily) and characterization of these bioactive compounds. The antimicrobial activity was tested against nine different Gram-positive and Gram-negative bacterial strains. The minimum inhibitory concentration (MIC) values of methanolic extract of L. philadelphicum flower against Acinetobacter bouvetii, Achromobacter xylosoxidans, Bacillus subtilis MTCC 121, Candida albicans MTCC 183, Klebsiella pneumoniae MTCC 3384, and Salmonella typhi MTCC 537 were 25, 50, 12.5, 50, 100 and 50 μg mL-1, respectively. The phytochemical analysis of the extract reveals the presence of phenols, flavonoids, tannins, terpenoids, glycosides, coumarins, and quinones. The cytotoxicity of the partially purified compound against the HepG2 cell line in MTT assay demonstrates up to 90% cell viability with a bioactive compound concentration of 50 μg/ml. However, with the increase in bioactive compound concentration up to 1000 μg/ml results into nearly 80% cell viability, just a minor decline in cell viability suggests the importance of bioactive compounds for suitable therapeutic applications. Spectroscopic studies of the bioactive compound by UV-Visible spectroscopy, FT-Infra Red spectroscopy, Gas Chromatography-Mass Spectrometry (GCMS) as well as its phytochemical analysis suggests the presence of terpenoids moiety, responsible for the antimicrobial property of L. philadelphicum flower.

The objective of the current investigation is to identify the first or first draining node or sentinel lymph node (SLN) from the primary tumor mass in a regional lymphocenter. Four different indirect lymphography (IL) methods were employed in 96 canine patients with different types of cancer between 2018 and 2021. The IL technique involved intradermal, submucosal, and peritumoral injections of 2ml contrast agent in the four-quadrant principle which were divided into equal aliquots. Lymphatic mapping with lipiodol (iodized oil) was 100% in squamous cell carcinoma of the head and neck, anal sac apocrine gland adenocarcinoma, mast cell tumor, squamous cell carcinoma of the skin, mammary carcinoma, and with methylene blue dye, 100% detection was achieved in testicular tumor and mammary carcinoma. Instead of a very short washout time of 2 minutes, Iohexol showed an excellent detection in indirect CT lymphography for histiocytic sarcoma and in indirect radiographic lymphography for lymphosarcoma. The significance of contrast and blue dyes in detecting the lymphatic spread of canine cancers is clearly emphasized in the current investigation. The nature of cancerous tissue was again analyzed through image and machine learning approach in this work. Supervised machine learning technique is applied in this work for automatic classification of cancerous and non-cancerous regions. Various statistical and texture-based features are extracted from X-Ray images and support vector machine with linear, polynomial, multilayer perceptron (MLP), and RBF kernel functions are applied for classification. Highest 95.53%, 94.64%, 93.05% sensitivity, specificity, accuracy, respectively, is achieved using RBF kernel function.

Gut microbiota and its metabolites like short chain fatty acids (SCFAs) are linked with pathology of Alzheimer’s disease (AD)- a debilitating public health problem in older adults. However, strategies to beneficially modulate gut microbiota and its sensing signaling pathways remain largely unknown. Here, we screened, validated and established the agonists of free fatty acid receptor 2 (FFAR2) signaling, which senses beneficial signals from SCFAs produced by microbiota in the gut. We demonstrated that inhibition of FFAR2 signaling increases amyloid-beta (Aβ) stimulated neuronal toxicity. Thus, we screened FFAR2 agonists, using in-silico library of more than 144,000 natural compounds, and 15 compounds were selected based on binding with FFAR2 agonist sites. Further, cell culture toxicity and FFAR2 stimulatory experiments demonstrated that Fenchol (a natural compound commonly present in basil) was potent FFAR2 agonist in neuronal cells. Interestingly, we also demonstrated that Fenchol protects Aβ-stimulated neurodegeneration in FFAR2 dependent manner. In addition, Fenchol reduced AD like phenotypes such as Aβ-accumulation and, learning and memory behaviors in Caenorhabditis (C.) elegans. Fenchol increased Aβ-clearance by increasing proteasome/lysosome activity and reduced senescence in neuronal cells. These results demonstrated that the inhibition of FFAR2 signaling promotes Aβ-induced neurodegeneration, while activating it by Fenchol as a natural agonist reverse it by promoting Aβ-clearance and reducing cellular senescence; thus stimulation of FFAR2 signaling can be a therapeutic approach to prevent/ treat AD.

Despite advancements in antiretroviral therapy (ART) that reduces the viral load to undetectable levels, viral eradication has not been achieved due to HIV-1 persistence in resting CD4+ T-cells. We, therefore, characterized the tat gene, which is essential for HIV-1 replication, from 20 viro-logically controlled HIV-infected (HIV+) older patients on long-term ART with improved CD4+ T-cell counts. PBMC genomic DNA from HIV+ patients were used to amplify tat gene by PCR fol-lowed by nucleotide sequencing and analysis. Phylogenetic analysis showed that each patient’s tat sequences were confined to their own subtrees and well discriminated from other patients’ sequences. Moreover, there was a low degree of viral heterogeneity and lower estimates of genetic diversity within these patients’ tat sequences. Most patients’ Tat deduced amino acid sequences showed intact open reading frames and maintained the important functional domains for Tat functions, including transactivation, TAR binding and nuclear localization. Furthermore, Tat deduced amino acid sequences showed variation in previously characterized cytotoxic T lym-phocytes (CTL) epitopes, suggesting escape mutants. In conclusion, a low degree of genetic varia-bility and conservation of functional domains and variations in CTL epitopes were the features of tat sequences from 20 HIV+ older patients with undetectable viral load on long-term ART.

This review paper delves into the multifaceted aspects of pineapple cultivation, focusing on morphology studies, harvesting challenges, and mechanization efforts. Pineapple, renowned for its commercial value and unique flavor, holds a prominent position in global agriculture, with India alone producing 19.64 lakh tonnes annually. However, the hilly terrains of regions like northeastern India pose significant hurdles to traditional harvesting methods, necessitating innovative solutions. Understanding the morphology of pineapple plants is crucial for optimizing cultivation practices and designing efficient harvesting equipment. Insights from various studies shed light on leaf structure, fruit maturity, and growth patterns, providing valuable information for engineering tailored solutions. Mechanization endeavors, including semi-mechanized harvesting machines and semi-manual harvesters, demonstrate promising results in improving efficiency and reducing labor-intensive practices. Despite challenges such as increased crop losses and operational costs, continued research and development in mechanization hold the key to sustainable pineapple cultivation. This paper underscores the importance of integrating morphology studies and mechanization efforts to address the unique challenges faced by pineapple farmers, particularly in hilly terrains, and highlights the need for ongoing innovation to ensure the long-term sustainability of pineapple cultivation.

Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is a challenging problem. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Identifying objects in floor plan images is also challenging due to the variety of floor plans and different objects. We faced a problem in training our network because of the lack of publicly available datasets. Currently, available public datasets do not have enough images to train deep neural networks efficiently. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10000 images to address this issue. Our proposed method conveniently surpasses the previous state-of-the-art results on the SESYD dataset and sets impressive baseline results on the proposed SFPI dataset. The dataset can be downloaded from SFPI Dataset Link. We believe that the novel dataset enables the researcher to enhance the research in this domain further.

We used population reconstruction techniques to assess for the first time the population dynamics of a seagrass, Cymodocea nodosa, exposed to long-term elevated CO₂ near three volcanic seeps and compare them with reference sites away from the seeps. Under high CO₂, the density of shoots and of individuals (apical shoots), and the vertical and horizontal elongation and production rates, were higher. Nitrogen effects on rhizome elongation and production rates and on biomass, were stronger than CO₂ as these were highest at the location where the availability of nitrogen was highest. At the seep where the availability of CO₂ was highest and nitrogen lowest, density of shoots and individuals were highest, probably due to CO₂ effects on shoot differentiation and induced reproductive output, respectively. In all three seeps there was higher short- and long-term recruitment and growth rates around zero, indicating that elevated CO₂ increases the turnover of C. nodosa shoots.

A complete halt on all anthropogenic activities and human movement due to COVID-19 lockdown has provided a great opportunity to assess the impact of human activities on coastal marine ecosystems. The current study assessed the concentration of the metals in water samples of the largest brackish water lagoon of Asia; the Chilika lagoon in the state of Odisha, India between pre-COVID-19 and post-COVID-19 lockdown scenarios. Monthly water samples (n=30 stations) from 0.3 m depth were collected from three sectors of the lagoon seasonally; pre-monsoon, monsoon, and post-monsoon. In addition to various physical parameters [pH, salinity, alkalinity, (DO) dissolved oxygen, (TDS) total dissolved solids, and (EC) electrical conductivity] the collected water samples were analysed for 18 trace metals (Al, As, Ba, Be, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sr, Th, Tl, U, V). Most of the physical parameters showed a significant variation between pre-and post-COVID-19 scenarios, except for pH and DO. The concentration of five metals (Be, Cd, Co, Ni, and Pb) remained below detection limits in all water samples. The impact of COVID-19 lockdown on the concentration of the metal in the water samples was noticed along with the three sectors of the lagoon. However, eight metals (Al, As, Cr, Fe, Mn, Th, U, and V) were significantly different between the COVID-19 scenarios and the remaining five metals were not statistically significant. The mean concentration of Al, As, Fe, Th, and V were higher in the pre-COVID-19 scenarios, whereas only Cr and Mn were higher in the post-COVID-19 scenarios. The mean concentration of U was similar among both COVID-19 scenarios, even though there were seasonal and sectoral differences. The seasonal influence of riverine influx was more evident on metal concentration during the monsoon season, whereas the difference between sectors was more prominent during the post-monsoon season. An increased number of correlations between physical parameters and metal concentration were observed in the post-monsoon season and post-COVID-19 scenario. This study provides evidence that the imposition of COVID-19 lockdown reduced metal influx in the water column and improved the water quality of the Chilika lagoon. Our results can be used as baseline for metal concentration in surface waters of the lagoon.

Water borne pathogens present a threat to human health and their disinfection from water poses a challenge, prompting search for newer methods and newer materials. Disinfection of Gram-negative bacterium Escherichia coli and Gram-positive coccal bacterium Staphylococcus aureus in aqueous matrix was achieved within 60 and 90 minutes respectively at 35⁰C using solar-photocatalysis mediated by sonochemically synthesized Ag@ZnO core-shell nanoparticles. The efficiency of the process increased with increase in temperature and at 55⁰C the disinfection could be achieved in 45 and 60 min respectively for the two bacteria. A new ultrasound assisted chemical precipitation technique was used for the synthesis of Ag@ZnO core-shell nanoparticles. The characteristics of the synthesized material were established using physical techniques. The material remained stable even at 400o C. Disinfection efficiency of the Ag@ZnO core-shell nanoparticles was confirmed in case of real world water samples from pond, river, municipal tap and was found to be better than that of pure ZnO and TiO2 (Degussa P25). When the nanoparticle based catalyst was recycled and reused for subsequent disinfection experiments, its efficiency did not change remarkably even after three cycles. The sonochemically synthesized Ag@ZnO core-shell nanoparticles have a good potential for application in solar photocatalytic disinfection of water borne pathogens.

Glioblastoma multiforme (GBM) is a highly invasive brain malignancy originating from astrocytes, accounting for approximately 30% of central nervous system malignancies. Despite advancements in therapeutic strategies including surgery, chemotherapy, and radiopharmaceutical drugs, the prognosis for GBM patients remains dismal. The aggressive nature of GBM necessitates the identification of molecular targets and the exploration of effective treatments to inhibit its proliferation. The Notch signaling pathway, which plays a critical role in cellular homeostasis, becomes deregulated in GBM, leading to increased expression of pathway target genes such as MYC, Hes1, and Hey1, thereby promoting cellular proliferation and differentiation. Recent research has highlighted the regulatory role of non-coding RNAs (ncRNAs) in modulating Notch signaling by targeting critical mRNA expression at the post-transcriptional or transcriptional levels. Specifically, various types of ncRNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), have been shown to control multiple target genes and significantly contribute to the carcinogenesis of GBM. Furthermore, these ncRNAs hold promise as prognostic and predictive markers for GBM. This review aims to summarize the latest studies investigating the regulatory effects of ncRNAs on the Notch signaling pathway in GBM.

To meet an ever global population's food demand, crop yields must be sustained and increased. Drought, which is getting harsher as a result of global warming, is largely impeding the agricultural productivity. Maize is widely used as food and animal feed in many regions of the world, but its yields are largely effected by drought and heat stress. Historical data on climate change predicts that drought and heat stress becoming major threat for maize cultivation in coming years, which will have huge impact on food security of the world especially in Africa and Asia. Thus there is an immense necessary to develop drought tolerant and climate resilient maize to feed the predicted population of the world. Availability and accessibility of crop genetic resources plays a huge role in development of drought-tolerant maize cultivars. A huge genetic resources of maize, including its landraces and crop wild relatives (CWR) have been reported naturally and many of them have stored in National and International gene banks globally. Conventional breeding methods have been tremendously increased maize yields, but these methods frequently fall short of achieving the demand for improved drought stress resistance. In this article, we have briefly discussed about impact of climate variability on crop production, maize yield losses due to drought, drought tolerance in maize landraces and CWR, and origin and evolution of Mexican landraces. This information may help in utilization of these potential resources in various pre-breeding programs.

Multiple Sclerosis (MS) is a complex disease of the central nervous system (CNS) that involves the intricate interplay of different immune cells going awry leading to inflammation, demyelination, and neurodegeneration. Its diagnosis is quite arduous because of the baffling number of symptoms it elicits and the varied clinical manifestation it presents. The simplified criteria (in form of Macdonald’s Criteria) which have got modified several times is now the single most important criteria accepted by neurology bodies for diagnosing MS. Biomarkers from time to time have been explored to simplify the diagnosis and prognosticate MS along with anecessity to monitor treatment outcome. In recent years, research on biomarkers has advanced rapidly due to their ability to be easily and rapidly measured, their specificity, safety, and their ability to yield precise results. Biomarkers are classified into various categories including predictive, diagnostic, prognostic, related to disease activity, and monitoring treatment outcome. Each representative of the disease activity category reflects a variety of pathological processes of MS such as neuroaxonal loss, gliosis, demyelination, disability progression, remyelination, etc. This review discusses several promising serum and cerebrospinal fluid biomarkers and imaging biomarkers used in clinical practice. Myelin oligodendrocyte glycoprotein antibody disease which is recently recognized as a definite disease will also be discussed. Furthermore, it sheds light on the criteria and the challenges a biomarker faces to be considered as a standard one.

Introduction: The COVID-19 severely marred the Indian poultry industry, worth approximately one trillion INR. Hence, this study was conducted to understand the COVID-19 related factors that harmed the poultry production and distribution network and explore their varied impact on its actors in Gujarat, India. Methods: An exploratory qualitative study, using semi-structured interviews, was conducted with 34 poultry stakeholders in Gujarat. The data were thematically analysed using an interpretative phenomenological approach. Results: The study revealed that COVID-19 and the associated lockdown had hugely impacted the production, distribution and consumption of poultry products. The first COVID-19 lockdown disrupted the supply of production inputs and the distribution of poultry and poultry products because of movement restrictions. Rumours also played a crucial role in decreasing the consumption of poultry products between March and June 2020. The market situation, including the prices and availability of poultry products, was found to be improved post-lockdown as there was an increase in consumption; however, the profits were not sufficient to immediately compensate for the losses incurred during the lockdown. Conclusion: The first COVID-19 lockdown restricted the production and distribution of essential goods and influenced the perception of consumers regarding poultry products. COVID-19 resulted in many short- and medium-term challenges in the poultry sectors in India, which need to be addressed to make this sector more resilient to face any such crisis in future.

Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020-21, 2021-22, and 2022-23), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K SNP Infinium iSelect SNP assay to obtain genome-wide SNP marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 MTAs located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p≤0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020-21 and 2021-22 environments. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host-pathogen interactions related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins, were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.

Antibiotic residues in aquatic environment have the possibility to induce resistance in environmental bacteria, which ultimately might get transferred to pathogens making treatment of diseases difficultand poses a serious threat to public health. If antibiotic residues in the environment can be eliminated or reduced, it has the possibility to contribute antibiotic resistance. Towards this objective, water containing ciprofloxacin was treated with sunlight assisted photocatalysis using Fe doped ZnOnanoparticles for assessing the degradation potential of this system.Parameters like pH, temperature, catalytic dosage were assessed for the optimum performance of the system. To evaluate degradation of ciprofloxacin,both spectrophotometricas well as microbiological (loss of antibiotic activity)methods were employed. 100 mg/L Fe doped ZnO nanoparticle catalyst and sunlight intensity of 120,000–135,000 lux system gave optimum performance at pH 9 at 30 °C and 40 °C. At these conditions spectrophotometric analysis showed complete degradation of ciprofloxacin (10mg/L) by 210 min. Microbiological studies showed loss of antibacterial activity of the photocatalytically treated ciprofloxacin containingwater against Staphylococcus aureus (10⁸ CFU) in 60 min and for Escherichia coli (10⁸ CFU) in 75 min. Thedeveloped system, thus possess a potential for treatment of antibiotic contaminated waters for eliminating/reducing antibiotic residues from environment.

The concept of omics-based technological approaches has promoted translational research by integrating knowledge from diverse areas to understand their dynamics by exploring the molecular mechanisms underlying various processes paving the way for further improvements in the crops quality by providing sound knowledge on controlling plant diseases. This area is not profoundly investigated a decade back and hence this knowledge has become exceedingly important in modern times as it has got potential for being utilized in crop improvement programs. In this review, the contributions of different omics technologies including genomics, transcriptomics, proteomics, and metabolomics in understanding and materializing the ways involved in the plant pathogen interaction (PPI) is discussed. Furthermore, opportunities, challenges, and perspectives of omics linked to signaling mechanisms have also been highlighted that are significantly linked to plant-microbe interactions.

Infection by the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) results in the novel coronavirus disease COVID-19, which has posed a serious threat globally. Infection of SARS-CoV-2 during pregnancy is associated with complications like preterm labor and premature rupture of membranes; a proportion of neonates born to the infected mothers are also positive for the virus. During pregnancy, the placental barrier protects the fetus from pathogens and ensures healthy development. However, whether or not SARS-CoV-2 can infect the placenta is unknown. Herein, utilizing single-cell RNA-seq data, we report that the SARS-CoV-2 binding receptor ACE2 and the S protein priming protease TMPRSS2 are co-expressed by a subset of syncytiotrophoblasts (STB) in the first trimester and extra villous trophoblasts (EVT) in the second trimester human placenta. The ACE2- and TMPRSS2-positive (ACE2+TMPRSS2+) placental subsets express mRNA for proteins involved in viral budding and replication. These cells also express mRNA for proteins that interact with SARS-CoV-2 structural and non-structural proteins in the host cells. We also discovered unique signatures of genes in ACE2+TMPRSS2+ STBs and EVTs. The ACE2+TMPRSS2+ STBs are highly differentiated cells and express genes involved mitochondrial metabolism and glucose transport. The second trimester ACE2+TMPRSS2+ EVTs are enriched for markers of endovascular trophoblasts. Further, both these subtypes abundantly expressed genes in Toll like receptor pathway, the second trimester EVTs (but not first trimester STBs) are also enriched for component of the JAK-STAT pathway that drive inflammation. To conclude, herein we uncovered the cellular targets for SARS-CoV-2 entry and show that these cells can potentially drive viremia in the developing human placenta. Our results provide a basic framework towards understanding the paraphernalia involved in SARS-CoV-2 infections in pregnancy.

The pressure on freshwater resources is leading to diminishing flows in some of the critical river systems across the globe and India is no exception and this is mainly because of water withdrawal for irrigation, which is often to the tune of 70% to 80% with some proportion for domestic and industrial use. While graduating from the concept of environmental flows and its assessment methodologies in India, the water-managers, the researchers and the conservationists are now moving towards answering the next question if the rivers are to be revived, then where will the water come from, especially in the case of over-allocated rivers, including the river Ganga. While the logical way is to look at the biggest user of water, i.e. irrigation, it remains to be seen whether the irrigation water savings will actually lead to enhancing flows in a river, complementing the efforts towards maintaining e-flows in rivers, or whether it will lead to more area under agriculture, bring changes in cropping patterns towards more water-intensive crops or result in something else. This is a growing debate across the globe, where India is no exception, and there has been a wide range of opinions in this regard. This paper discusses the process, findings and lessons from a joint initiative involving farmers, the Uttar Pradesh state Irrigation and Water Resources Department, Bijnor District Administration and a conservation organisation to enhance flows in a rivulet, called karula River, which is part of the Ganga river system.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p &lt; 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.

COX II containing a dual core CuA active site is one of the three core subunits of mitochondrial Cco, which plays a significant role in the physiological process. In this report, the full-length cDNA of COXⅡ gene was cloned from Sitophilus zeamais, which had an ORF of 684 bp encoding 227 amino acids residues. The predicted COXⅡ protein had a molecular mass of 26.2 kDa with pI value of 6.37, and multiple sequence alignment and phylogenetic analysis indicated that Sitophilus zeamais COXⅡ had high sequence identity 78.51% with the COXⅡ of other insect species, especially similarity to sitophilus oryzae. This gene was subcloned into the prokaryotic expression vector pET-32a, and induced by IPTG in E.coli Transetta (DE3) expression system. Finally the COXⅡ with 6-His tag was purified using affinity chromatography with Ni²⁺-NTA agarose. WB showed the recombinant COXⅡ was about 44 kD, and the concentration of fusion protein was 50μg/mL. UV-spectrophotometer and infrared spectrometer analysis showed that recombinant COXⅡ could catalyze the oxidation of substrate Cytc, and influenced by AITC. It was found that AITC could form a hydrophobic region with COXⅡ protein via molecular docking, besides, a sulfur atom of AITC structure could form a length of 2.9 Å hydrogen bond with Leu-31. These results will provide valuable information for elucidating the role of COXⅡ in Sitophilus zeamais responses to AITC, meanwhile, it will helpful to carry out a point mutation in AITC binding sites for the future research.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

This paper is focused on typical rural settlements in the Stara Planina Mountain area in Serbia. They are distinguished by their authenticity, identity and character, and they have still not been destroyed by inappropriate development as is the case in some other mountain areas in Serbia, where the effects of urbanization, globalization and homogenization are obvious. Consequently, there is a need to prevent these effects in mountain areas where there is a threat of disbalance between development and the inherited natural and cultural values. The paper starts from several important concepts that relate contemporary approaches to cultural heritage preservation, place morphology and planning future development. The concept definitions from recent documents from related fields are used. These concepts are then applied to the area of Stara Planina Mountain and its rural settlements. The necessity for an integrative approach to planning this area with significant natural and anthropogenic potential is pointed out, including its integration into international programs of sustainable development.

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.

Promoting green development has become an important way to optimize the ecological and economic structure and promote sustainable development. The Yangtze River Economic Belt is an important economic corridor in China, and it remains to be examined whether the implementation of environmental regulations has enhanced the green total factor productivity (GTFP) of cities. This paper calculates the green total factor productivity of 107 cities within the Yangtze River Economic Belt by using the super-efficient SBM model and the GML index with Chinese city panel data from 2007-2019 and measures the intensity of environmental regulations through textual analysis. The bidirectional fixed-effects model constructed in this paper suggests an inverted U-shaped relationship between environmental regulation and GTFP, which is confirmed by a series of robustness tests. Green technology innovation and advanced industrial structure play a mechanism role in this. In regions with lower levels of green technology and industrial structure, the inverted U-shaped relationship between environmental regulation and green total factor productivity is more significant. In addition, the phenomenon of GTFP rising and then falling because of environmental regulation is more obvious in cities in the middle and upper reaches of the Yangtze River and outside the urban agglomerations. Therefore, when formulating environmental regulation policies, it is necessary to balance the relationship between economic development and environmental protection and harmonize the environment and economic development of the entire Yangtze River Basin.

The wetlands in northeastern Bangladesh are home to some of the most prolific and valuable ecosystems on the planet, offering a wide range of benefits to both the local and wider communities. A limited number of research on this topic have been carried out in various wetlands in coastal hilsa, but none have been located in the northeastern region of Bangladesh. Thus, the goal of this study was to better understand the most commercially relevant species (anadromous hilsa) in northeastern Bangladesh as well as to assess the wetland ecosystem services more thoroughly with the contribution of their fish genetic resources. In addition to laboratory studies, fieldwork was done in the fishing settlements along the Surma River, Hakaluki Haor, and Tanguar Haor. The study classified 77 fish species into 21 subcategories of ecosystem services, of which 56% were not threatened, 17% were vulnerable, and 10% were highly endangered. The species Hilsa was found to be the most economically significant, accounting for 1% of the GDP overall. The study determined if the hilsa stock of the examined wetlands maintains a single or several subpopulations in comparison to other parts of the country when they migrate from saline water to northeastern Bangladesh (freshwater). But when compared to other groups, the FST score was between 0.000 to 0.017, indicating that the populations are identical and that there is genetic material sharing between them. Additionally, these statistics imply that the Sylhet haor and adjacent river systems contain a single stock of Hilsa fish. Nevertheless, a number of stresses and risks, including overexploitation, illicit and destructive fishing, siltation, and natural disasters like flash floods, have been linked to changes in the supply of hilsa and other ecosystem services. This study also emphasizes the necessity of policy attention and research to address the concerns.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

To secure logic circuits against the illegal copying the insertion of the so-called key bit inputs using XOR/XNOR gates has been proposed in . The inserted gates modify (lock) the behavior of the original circuit and only the correct choice of the secret key bits guarantees the equivalent behaviour of the locked circuit to the original circuit. To break this kind of security, i.e. to find the secret key bits, there was a SAT solver based attack proposed in . The aim of this work is to experimentally verify this attack on several locked circuits with various number of primary inputs and outputs and with various number of gates. Except for the random placement of the key gates we evaluate also the positioning with chaining elimination. The obtained and presented results show that we were able to unlock 10 of 11 circuits within few minutes on a common PC.

Intermetallic compounds are increasingly being expected to utilize in tribological environments, but to date the works are hindered by insufficient ductility at low and medium temperature. This paper presents a novel multiphase intermetallic alloy with the chemical composition of Mo-40Ni-13Si (at.%). Microstructure characterization reveals that a certain amount of ductile Mo phases formed during the solidification process of ternary Mo-Ni-Si alloy melt, which is undoubtedly beneficial to the ductility improvement of intermetallic alloy. Tribological properties of the designed alloy, including wear resistance, friction coefficient and metallic tribological compatibility, were evaluated under dry sliding wear test conditions at room temperature. Results suggested that the multiphase alloy possesses an excellent property for room-temperature wear applications, which is attributed to the unique microstructural features and thereby good combination in hardness and ductility. The corresponding wear mechanism is also reported by observing the worn surface, subsurface and wear debris of alloy, which to be found is soft abrasive wear.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.