This work aims to describe the dielectric behaviours of four main tissues in the human forearm using mathematical modelling, including fat, muscle, blood, and bone. Determining the electrical response of multiple tissues can help develop physiological monitoring of an organ or a body’s section by filtering out the impedance contributions from the surrounding tissues to blood flow-induced impedance variations. Multi-frequency bioimpedance analysis (MF-BIA) was initially performed using the finite element method (FEM) with a 3D forearm model followed by a pilot study to characterise the response of actual forearm tissues from 1 kHz to 349 kHz. Both the simulation and experimental results were fitted to a single-dispersion Cole model (SDCM) and a multi-dispersion Cole model (MDCM) to determine the Cole parameters for each tissue. The correlation analysis and Bland-Altman plot indicated a good fit between raw and fitted impedance values using both SDCM and MDCM. Overall, MDCM exhibited better performance in fitting and estimation of the Cole parameters with correlation coefficient (R2) of 0.99 and 0.97, root mean squared error (RMSE) of 0.09 Ω and 0.14 Ω, and mean difference (mean±standard deviation) of 0.00±0.09 Ω and -0.03±0.14 Ω for the real part and imaginary part of impedance, respectively.

Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence with their ability to understand and generate natural language discourse. This has led to the use of LLMs in various creative fields such as music, art, and storytelling, which has sparked a debate on the impact of LLMs on human creativity. This article explores the cyclic effect of LLMs on human creativity, where LLMs can generate an ever-evolving cycle of creative output from both humans and machines. The article also highlights the importance of responsible and ethical use of LLMs, including privacy and copyright issues with the training data used by LLMs. The article emphasizes the need for transparency, data security measures, and AI governance policies to protect user data and ensure the safe use of LLMs. It also calls for the development of educational materials and resources to increase public understanding of LLMs and their ethical implications. The article concludes by highlighting the potential of LLMs as powerful tools for communication and education, but also emphasizes the need for ethical considerations to ensure the responsible and beneficial use of LLMs in society.

Specialized classes of proteins, working together in a tightly orchestrated manner, induce and maintain highly curved cellular and organelles membrane morphology. Due to the various ex- perimental constraints, including the resolution limits of imaging techniques, it is non-trivial to accurately elucidate interactions among the various components involved in membrane deformation. The spatial and temporal scales of the systems also make it formidable to investigate them using simulations with molecular details. Interestingly, mechanics-based mesoscopic models have been used with great success in recapitulating the membrane defor- mations observed in experiments. In this review, we collate together and discuss the various mechanics based mesoscopic models for protein-mediated membrane deformation studies. In particular, we provide an elaborate description of a mesoscopic model where the membrane is modeled as a triangulated sheet and proteins are represented as either nematics or fila- ments. This representation allows us to explore the various aspects of protein-protein and protein-membrane interactions as well as examine the underlying mechanistic pathways for emergent behavior such as curvature-mediated protein localization and membrane deforma- tion. We also put forward current efforts in the field towards back-mapping these mesoscopic models to finer-grained particle based models - a framework that could be used to explore how molecular interactions propagate to physical scales and vice-versa. We end the review with an integrative-modeling based road map where experimental imaging micrograph and biochemical data are combined with mesoscopic and molecular simulations methods in a theoretically consistent manner to faithfully recapitulate the multiple length and time scales in the membrane remodeling processes.

1) We shall discuss what modern interpretations say about the Heisenberg's uncertainties. These interpretations explain that a quantity begins to 'lose' meaning when a conjugate property begins to 'acquire' definite meaning. We know that a quantity losing meaning means that it has no fixed value and has an uncertainty . In this paper we look deeper into this interpretation and the outcome reveals more evidence of the quantity losing meaning. Newer insights appear. 2) We consider two extreme cases of hypothetical processes nature undergoes, without interference by a measurement: One, a system collapses to an energy eigenstate under the influence of a Hamiltonian instantaneously at a time $t$. This is thus what would happen if we would measure the system's energy. Next, when a particle becomes localised to a point at a time $t_0$ under the influence of a Hamiltonian. This is thus what would happen if we would measure the system's position. We shall prove that both these situations cannot arise under ordinary circumstances and thus measurement processes cannot be modelled by physical Hamiltonians.

We report a very frugal microscope building method from easily realizable parts. We show a highly portable field ready compound type microscope with bright field, dark field and projection microscopy capabilities. It has good resolution and magnification for multiple aspects of education and diagnosis. We demonstrate that such a system can be built from simple lenses in laser pointers and camera modules with no specific 3d printed parts or costly lenses. We show all the parts of the system like stage, coarse and fine focussing system, microscope body, even slides can be built from commonly available materials like soda bottle and DVD disks. We list alternate, safer and easily available staining methods and chemicals. The microscope is developed with a target that it can be self-developed even in rural areas of the world with only resources at one’s disposal with very little education. This features a stable focussing and panning system for comfortable viewing through eyes and also easy imaging with a smartphone. We show its utility for microorganism analysis and potential for clinical diagnosis.

Photovoltaic (PV) systems, which are renewable energy sources, are increasingly being utilized in distributed generation. To maintain a stable energy balance, energy storage systems such as batteries (BAT) play a vital role. Electric vehicles (EVs) offer a promising solution with rechargeable battery systems for operation. The BATs must maintain their state of charge within design boundaries, despite intermittent PV and load power fluctuations, and advanced power control and management techniques are essential for their effective operation. The article evaluates an intelligent controller that uses a Sliding Mode Control adaptive deep learning algorithm Convolutional Neural Networks (SMC-CNN) and a unique Strategy for managing supervisory power. (SPMS) for photovoltaic systems with battery energy storage for better regulation of DC bus voltage. SMC-CNN offers exceptional resilience and seamless functionality, minimizing fluctuations in DC bus voltage in the control strategy design requirements. The primary goals are maintaining a consistent power supply and ensuring continuous service by preventing system components from exceeding capacity. This study aims to enhance the control of DC bus voltage in the PV and battery system, with the primary significance focusing on the following aspects. The advanced SPMS has been created, incorporating control system constraints to enhance SOC balancing speed and reduce fluctuations in DC bus voltage. Power flow management involves optimizing energy flow between the PV system, battery system, and load while minimizing battery capacity requirements. The proposed SMC-CNN and SPMS are demonstrated through real-time simulation using Matlab/Simulink, as demonstrated in comprehensive case studies.

Water splitting plays an important role in electrochemical and photoelectrochemical conversion of energy devices. Electrochemical water splitting by the hydrogen evolution reaction (HER) is a straightforward route to produce hydrogen (H2), which requires an efficient electrocatalysts to minimize energy consumption. Recent advances have created a rapid rise in new electrocatalysts, particularly those based on non-precious metals. In this review, we present a comprehensive overview of the recent developments of ternary and quaternary 6d-group transition metal chalcogenides (TMCs) based electrocatalysts for water splitting, especially for HER. Detailed discussion is organized from binary to quaternary TMCs including, surface engineering, heterostructures, chalcogen substitutions, and hierarchically structural design in TMCs. Moreover, emphasis is placed on future research scope and important challenges facing these electrocatalysts for further development in their performance towards water splitting.

This work discusses laboratory measurements of atomic and diatomic molecular species in laser-plasma generated in gases. Noticeable self-absorption of the Balmer series hydrogen alpha line occurs for electron densities of the order of one tenth of standard ambient temperature and pressure density. Emission spectra of selected diatomic molecules in air or specific gaseous mixtures at or near atmospheric pressure reveal minimal plasma re-absorption. Abel inversion of the plasma in selected gases and gas mixtures confirm expansion dynamics that unravel regions of atomic and molecular species of different electron temperature and density. Time resolved spectroscopy diagnoses self-absorption of hydrogen alpha and hydrogen beta lines in ultra-high pure hydrogen gas. Radiation from a Nd:YAG laser device induces micro-plasma for pulse widths in the range of 6 ns to 14 ns, energies in the range of 100 mJ to 800 mJ, and for peak irradiance of the order 1 to 10 TW/cm². Atomic line profiles yield electron density and temperature from fitting of line profiles to wavelength and sensitivity corrected spectral radiance data. Analysis of measured diatomic emission data yields excitation temperature of primarily molecular recombination spectra. Applications of the laboratory experiments extend to investigations of stellar astrophysics white dwarf spectra.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Radish is an annual herbaceous root crop, fruit, and oil crop plant belonging to the Cruciferae family. The important traits for radish breeding include high yield, early maturity, late bolting, pungency, cold-hardiness, drought resistance, heat tolerance, and soil adaptability. For successful radish production, need to the understand nature and behavior of the flower, and very important to identify the S haplotypes of parental lines to produce F₁ hybrids based on self-incompatibility to get rid of laborious hand emasculation in radish. In radish some desirable genes are not present within varieties. Therefore, further breeding programmes depend on inter-specific and intra-specific hybridization, which has a vital role in genomic studies and crop improvement by introducing desirable agronomic characters. It is essential to acquire detailed genetic information on chromosomes and information on inheritance. Genomics is now at the core of crop improvement, and radish crop is exploited to study the underlying differences in genotypes. But some monogenic characters are improved by genetic engineering. A three-decade span following the first documented instance of genetic engineering has witnessed its application's unprecedented growth. Researchers have successfully produced transgenic radishes with various agronomic characteristics over the last decade.

Studies on N balance due to N inputs and outputs and soil N retention to measure cropping system performance and environmental sustainability are limited due to the complexity of measurements of some parameters. We measured N balance based on N inputs and outputs and soil N retention under dryland agroecosystem affected by cropping system and N fertilization from 2007 to 2011 in the northern Great Plains, USA. Cropping systems were conventional tillage barley (Hordeum vulgaris L.)-fallow (CTB-F), no-tillage barley-fallow (NTB-F), no-tillage barley-pea (Pisum sativum L.) (NTB-P), and no-tillage continuous barley (NTCB). Nitrogen rates to barley were 0, 40, 80, and 120 kg N ha-1. Total N input due to N fertilization, pea N fixation, soil N mineralization, atmospheric N deposition, nonsymbiotic N fixation, and crop seed N and total N output due to grain N removal, denitrification, volatilization, N leaching, gaseous N (NOx) emissions, surface runoff, and plant senescence were 28 to 37% greater with NTB-P and NTCB than CTB-F and NTB-F. Total N input and output also increased with increased N rate. Nitrogen sequestration rate at 0 to 10 cm averaged 22 kg N ha-1 yr-1 for all treatments. Nitrogen deficit ranged from 5 to 16 kg N ha-1 yr-1, with greater deficits for CTB-F and NTB-P and higher N rates. Because of increased grain N removal and reduced N loss to the environment and N fertilizer requirement, NTB-P with 40 kg N ha-1 can enhance agronomic performance and environmental sustainability while reducing N inputs compared to other management practices.

Speckle patterns are formed by random interferences of mutually coherent beams. While speckles are often considered as an unwanted noise in many areas, they also formed the foundation for the development of numerous speckle-based imaging, holography and sensing technologies. In the recent years, artificial speckle patterns have been generated with spatially incoherent sources using static and dynamic optical modulators for advanced imaging applications. In this report, a fundamental study has been carried out with Bessel distribution as the fundamental building block of the speckle pattern: speckle patterns formed by randomly interfering Bessel beams. Indirect computational imaging framework has been applied to study the imaging characteristics. In general, Bessel beams have a long focal depth, which in this scenario is counteracted by the increase in randomness enabling tunability of the axial resolution between the limits of Bessel beam and a Gaussian beam. Three-dimensional computational imaging has been synthetically demonstrated. The presented study will lead to a new generation of incoherent imaging technologies.

This work communicates the connection of measured shadowgraphs from optically induced air breakdown with emission spectroscopy in selected gas mixtures. Laser-induced optical breakdown is generated using 850 mJ and 170 mJ, 6-ns pulses at a wavelength of 1064 nm, the shadowgraphs are recorded using time-delayed 5-ns pulses at a wavelength of 532 nm and a digital camera, and emission spectra are recorded for typically a dozen of discrete time-delays from optical breakdown by employing an intensified charge-coupled device. The symmetry of the breakdown event can be viewed as close-to spherical symmetry for time-delays of several 100 ns. Spectroscopic analysis explores well-above hypersonic expansion dynamics using primarily the diatomic molecule cyanide and atomic hydrogen emission spectroscopy. Analysis of the air breakdown and selected gas breakdown events permits the use of Abel inversion for inference of the expanding species distribution. Typically, species are prevalent at higher density near the hypersonically expanding shockwave, measured by tracing cyanide and a specific carbon atomic line. Overall, recorded air breakdown shadowgraphs are indicative of laser-plasma expansion in selected gas mixtures, and optical spectroscopy delivers analytical insight into plasma expansion phenomena.

This work examines atomic and molecular signatures in laser-induced plasma in standard ambient temperature and pressure environments, including background contributions to the spectra that depend on the laser pulse-width. Investigations include solids, gases, and nano-particles. Abel inversions of measured line-of-sight data reveal insight into the radial plasma distribution. For nominal 6 nanosecond laser pulses and for pulse-energies in the range of 100 to 800 mJ, expansion dynamics and turbulence due to shock phenomena are elucidated to address local equilibrium details that are frequently assumed in spatially averaged emission spectroscopy. Chemical equilibrium computations reveal temperature dependence of selected plasma species. Specific interests include atomic hydrogen (H) and cyanide (CN). Atomic hydrogen spectra indicate axisymmetric shell structures and isentropic expansion of the plasma kernel over and above the usual shockwave. The recombination radiation of CN emanates within the first 100 nanoseconds for laser-induced breakdown in a 1:1 CO2:N2 gas mixture when using nanosecond laser pulses to create the micro-plasma. The micro-plasma is generated using 1064 nm, 150 mJ, 6 ns Q-switched Nd:YAG laser radiation. Measurements of the optical emission spectra utilize a 0.64 m Czerny-Turner type spectrometer and an intensified charge-coupled device.

An astonishing increase in temperature is posing several harmful impacts on crop plants. Heat stress is an abiotic environmental phenomenon that causes limits, inhibits plant growth, metabolism, and productivity worldwide, resulting in losses in production yields. Heat stress is caused by human activities and global warming,s such as greenhouse gases, carbon dioxide, methane, nitrous oxide, and water vapour. There are many pieces of evidence to support that heat stress reduces the crop plants yield worldwide, and the effects of heat stress are challenging to meet nutritional security and global food security for human beings. Heat stress has negative impacts on each developmental stage, including from germination to harvesting. Prevalent approaches for heat adaption is inadequate management that is unable either to increase the crop productivity or sustain ld. Several responses to dissect the relevant knowledge about heat stress mechanism involving morphological phenomena, physiological phenomena, reproductive replies, and molecular responses such as heat shock proteins act as mRNA synthesis, mRNA control (effects of genes during heat stress), the translation process, heat response element. There are such phenomena involving disseminating the knowledge concerning heat stress. In this review, we summarise the effect of heat stress on plant mechanisms, including morphological, biochemical and molecular responses.

Micro-plasma is generated in ultra-high-pure hydrogen gas filled inside a cell at a pressure of (1.08 ± 0.033) × 10⁵ Pa (810 ± 25 Torr) by using a Q-switched Nd:YAG laser device operated at 1064 nm wavelength and 14 ns pulse duration. Micro-plasma emission spectra of the hydrogen Balmer alpha line, H_α, are recorded with a Czerny-Turner type spectrometer and an intensified charge-coupled device. The spectra are calibrated for wavelength and corrected for detector sensitivity. During the first few tens of nanoseconds after initiating optical breakdown, significantly Stark-broadened and Stark-shifted H_α lines mark the well-above hypersonic outward expansion. The vertical diameters of the spectrally resolved plasma images are measured for time delays of 10 ns to 35 ns to determine expansion speeds of the order of 100 km/s to 10 km/s. For time delays of the order of 0.5 µs to 1 µs, the expansion decreases to the speed of sound of 1.3 km/s in the near ambient temperature and pressure hydrogen gas.

This work examines atomic and molecular signatures in laser-induced plasma in standard ambient temperature and pressure environments, including background contributions to the spectra that depend on the laser pulse-width. Investigations include solids, gases, and nano-particles. Abel inversions of measured line-of-sight data reveal insight into the radial plasma distribution. For nominal 6 nanosecond laser pulses and for pulse-energies in the range of 100 to 800 milli-Joules, expansion dynamics and turbulence due to shock phenomena are elucidated to address local equilibrium details that are frequently assumed in spatially averaged emission spectroscopy. Chemical equilibrium computations reveal temperature dependence of selected plasma species. Specific interests include atomic hydrogen (H) and cyanide (CN). The atomic H spectra, collected following optical breakdown in ultra-high-pure hydrogen and 9:1 mixtures of ultra-pure hydrogen and nitrogen gases, indicate spherical shell structures and isentropic expansion of the plasma kernel over and above the usual shockwave. The recombination radiation of CN emanates within the first 100 nanoseconds for laser-induced breakdown in a 1:1 CO₂:N₂ gas mixture when using nanosecond laser pulses to create the micro-plasma. The micro-plasma is generated using 1064 nm, 150 mJ, 6 ns Q-switched Nd:YAG laser radiation. Measurements of the optical emission spectra utilize a 0.64 m Czerny-Turner type spectrometer and an intensified charge-coupled device.

Evaluation of the effectiveness of protected areas is critical for forest conservation policies and priorities. To evaluate their effectiveness, we used 30-m resolution forest cover change data between 1990 and 2010 for ~4,000 protected areas and analyzed the relationships of the effectiveness of protected areas with socio-economic variables. Our results show that protected areas in the Tropics avoided 83,500 ± 21,200 km2 of deforestation during the 2000s. Brazil’s protected areas have the largest amount of avoided deforestation of 50,000 km2. We also show the amount of international aid received by tropical countries compared to the effectiveness of protected areas. International aid had major benefits in Latin America led by Brazil while tropical Asian countries used the resource ineffectively. Our results demonstrate that protected areas have been relatively more efficient in countries where deforestation pressures were increasing, and governance and forest change monitoring capacity are important factors for enhancing the efficacy of international aid.

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.

Functional Connectivity analysis using Electroencephalography signals is common. The EEG signals are converted to networks by transforming the signals into a correlation matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks, are implemented on the correlation matrix data to classify them either on their psychometric assessment or the effect of therapy; The EEG data is trail-based/event-related. The classifications based on RNN provided higher accuracy( 74-88%) than the other three models( 50-78%). Instead of using individual graph features, a correlation matrix provides an initial test of the data. When compared with the time-resolved correlation matrix, it offered a 4-5% higher accuracy. The time-resolved correlation matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the correlation matrix, a static feature.

Spatial-structured longitudinal light beams are optical fields sculpted in three-dimensional (3D) space by diffractive optical elements. These beams have been recently suggested for use in im-proving several imaging capabilities, such as 3D imaging, enhancing image resolution, engineering the depth of field, and sectioning 3D scenes. All these imaging tasks are performed using coded aperture correlation holography systems. Each system designed for a specific application is characterized by a point spread function of a different spatial-structured longitudinal light beam. This article reviews the topic of applying certain structured light beams for optical imaging.

Conventional phenotyping breeding approaches for vegetable crops like Solanaceae, Bulb, Root crops, have made a significant contribution by developing many varieties. Despite this, conventional phenotyping approaches are not sufficient due to the longer time taken to develop a variety, low genetic gain, environmental factors and some other externalities that affect the phenotype-based selection. To address the challenges of conventional phenotype, a new recent method of high throughput phenotyping (HTP) is considered a promising tool. The development of high-throughput phenotyping technology began in the preceding decade as advancements in sensor, computer vision, automation, and advanced machine learning technologies. HTP platforms are being utilized to undertake non-destructive assessments of the complete plant system in a range of crops. HTP provides the precise measurements and suggests the collection of high-quality and accurate data which is necessary for standardizing phenotyping for the collection of genetic dissection and genomic assisted breeding such as genome-wide association studies (GWAS), linkage mapping, marker-assisted selection (MAS), genomic selection (GS). The remainder of this chapter discusses how high-throughput phenotyping technologies can be used in genomic-assisted breeding for vegetable crops

Conventional plant breeding has contributed enormously towards feeding the world and has played crucial roles in the development of modern society. The conventional method creates variation by transferring genes between or within the species. In general, these methods are more expensive and takes more time, to overcome these limitations, new technology is required. Genome editing is a powerful tool for biotechnology applications, with the capacity to alter the function of any gene. With the availability of gene information for the majority of the traits, genome editing emerged as a potential to create a new variation with the introduction of any transgene. The important genome editing tools used nowadays are ZFNs, TALEN, Pentatricopeptide repeats protein, adenine base editor, RNA interference, and CRISPR/Cas9. These tools have opened a new era for crop improvement. Due to the complex genetic architecture of most traits, it is challenging to edit genes controlling them. To overcome these challenges, genome editing provides a broader perspective. Among the above-mentioned tools, CRISPR/Cas9 is the most powerful tool for gene editing. These technologies are being used to create abiotic and biotic resistance crop varieties.

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015, the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the “as-it-is” scenario, the second scenario is based on milestones set for achieving goals of “Vision 2023” document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

: Using line × tester analysis, the current research analyses parental genotypes and their combinations in normal conditions and identifies the genes influencing yield characteristics. In the present study, 15 diverse genotypes, including 10 lines, 5 testers, and 50 F1s hybrids, were evaluated for 13 morphological and 2 biochemical traits. A suitable location was taken to study the effect of 15 characters. The results exposed that ability mean squares were significant for all studied additive and non-additive components. In this direction, the general combining ability of PBW-343, DBW-39, K-402, K-1317, KRL-210, and K-68 were higher than the remaining parents. For morphological traits like yield, the top five crosses were described based on SCA effects, namely, HD-3086 × HD-3171, K-402 × K-9107, K-1317 × K-9107, HD-2967 × K-0307 and K-402 × K-68 in F1 generation. In addition, the high value of heritability was estimated for plant height (77.32%), spike length (32.26%), biological yield/plant (59.52%), and grain yield/plant (68.76%). However, the moderate values of heritability were estimated for days to maturity (22.78%) and phenol color reaction (18.00%). The higher genetic advance was not found for recorded characters; however, a moderate genetic advance was recorded for grain yield per plant (13.15%) and harvest index (11.72%). High heritability coupled with moderate genetic advance was recorded for two characters grain yield per plant and harvest index in F1 and F2 generations.

The evaluation of robotic hands is a subjectively biased, complex process. The fields pertaining to robotic hands are human-centric in nature, making human hands a good standard for benchmark comparisons of robotic hands. To achieve this, we propose a new evaluation index, where we evaluate robotic hands on three fronts: their form, features and performance. An evaluation on how anthropomorphic robotic hands are in basic mobility, and appearance constitutes the ``Form'', while features that can be read, changed and actuated for effective control of robotic hands constitutes the ``Features''. We derived these key features from an extensive analysis of robotic hands in literature. Finally, the robotic hands carry out a series of tasks that evaluate their ``Performance''. An individual score for each category is drawn and we carry out a three-pronged analysis. We also propose an additional feature in the form of price to provide context when analysing multiple hands.

Reservoirs are recognized as one of the most efficient infrastructure components in integrated water resources management and development. At present, with the ongoing advancement of social economy and requirement of water, the water resources shortage problem has worsened, and the operation of reservoirs, in terms of consumption of flood water, has become significantly important. Reservoirs perform both regulation of flood and integrated water resources management, in which the flood limited water level is considered as the most important parameter for trade-off between regulation of flood and conservation. To achieve optimal operating policies for reservoirs, large numbers of simulation and optimization models have been developed in the course of recent decades, which vary notably in their applications and working. Since each model has their own limitations, the determination of fitting model for derivation of reservoir operating policies is challenging and most often there is always a scope for further improvement as the selection of model depends on availability of data. Subsequently, assessment and evaluation associated with the operation of reservoir stays conventional. In the present study, the Soil and Water Assessment Tool (SWAT) models and a Genetic Algorithm model has been developed and applied to two reservoirs in Ganga River basin, India to derive the optimal operational policies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. As a result, a simulation-based optimization model was recommended for optimal reservoir operation, such as allocation of water, flood regulation, hydropower generation, irrigation demands and navigation and e-flows using a definite combination of decision variables. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on simulated result, in the present case study it is concluded that GA-derived policies are promising and competitive and can be effectively used operation of the reservoir.

We studied the dynamics of stable carbon (δ13C) and nitrogen (δ15N) isotopes in litters from Norway spruce (NSL) (Picea abies) and Scots pine (SPL) (Pinus silvestris) during in situ decomposition over a period of more than 4 years. Relative to initial values, δ13CNSL showed a weak enrichment (0.33‰), whereas δ13CSPL resulted in depletion (-0.74‰) at the end of decomposition. Both litter types experienced a depletion in δ15N during decomposition; δ15NNSL decreased by -1.74‰ and δ15NSPL decreased by -1.99‰. The effect of the selective preservation of or acid unhydrolyzable residue (AUR) in lowering δ13C of the residual litter was evident only in SPL. In the NSL, only in the initial stage C/N had a large effect on the δ13C values. In the later stages, there was a non-linear decrease in δ13CNSL with a simultaneous increase in AUR concentrations , but the effect size was large, suggesting the role of lignin in driving δ13C of residues in later stages. Depletion in 15N of the residual litters concomitant with the increase in N concentration suggests bacterial transformation of the litter over fungal components. A consistent decline in δ15N values further implies that bacterial dominance prompted this by immobilizing nitrate depleted in 15N in the residual litter.

In order to support a massive number of resource-constrained Internet-of-Things (IoT) devices and machine-type devices, it is crucial to design future beyond 5G/6G wireless networks in an energy-efficient manner while incorporating suitable network coverage expansion methodologies. To this end, this invited paper proposes a novel two-hop hybrid active-and-passive relaying scheme to facilitate simultaneous wireless information and power transfer (SWIPT) considering both the time-switching (TS) and power-splitting (PS) receiver architectures, while dynamically modelling the involved dual-hop time-period (TP) metric. An optimization problem is formulated to jointly optimize the throughput, harvested energy, and transmit power of a SWIPT-enabled system with the proposed hybrid scheme. In this regard, we provide two distinct ways to obtain suitable solutions based on the Lagrange dual technique and Dinkelbach method assisted convex programming, respectively, where both the approaches yield an appreciable solution within polynomial computational-time. The experimental results are obtained by directly solving the primal problem using a non-linear optimizer. Our numerical results in terms of weighted utility function show the superior performance of proposed hybrid scheme over passive repeater-only and active relay-only schemes, while also depicting their individual performance benefits over the corresponding benchmark SWIPT systems with the fixed-TP.

Taro is an important root vegetable and has several medicinal and therapeutic properties. The two major therapeutic ingredients are flavonoids and triterpenoids, which are of prime importance. Additionally, the plant has antibacterial, antiparasitic, antiallergic, and anti-inflammatory properties. People often use it to treat a number of diseases by prescribing them as medications. Researchers from the pharmaceutical industry can thrive on C. esculenta by using whole-plant extracts. The identification of valuable terpenoids and therapeutics and their preparations are profusely dealt with in this article. Also, the new structural details of connective tissue are also studied as it helps researchers to investigate further uses of taro for humans, it is well known that xanthan can be obtained commercially as stabilizers, thickeners, binders, and more. It is essential to explore this plant for both medicinal and pharmaceutical properties.

Digital dentistry, including CAD/CAM dentistry, is perhaps the most disruptive innovation in dentistry to date. The rapid development of digital dentistry technologies over the past several decades has enabled clinicians to improve patient care by significantly reducing the time necessary for the restorative phase of treatment. Advancements in intra-oral scanning and computer-aided design and manufacturing (CAD/CAM), in conjunction with new dental CAD/CAM ceramic materials, have simplified patient care and made same-day dentistry a reality. This review aims to present the most recent advancements described in current literature as well as to document the successful implementation of digital dentistry into a predoctoral program. The overall process of CAD/CAM same-day dentistry and the accompanying advancements in software and materials were presented and discussed. Implementation of technology and personnel requirements were reviewed. CAD/CAM dentistry has been influential in shaping and improving dental practice and education, and this influence will only continue with time.

Fresnel incoherent correlation holography (FINCH) is a well-established incoherent imaging technique. In FINCH, three self-interference holograms are recorded with calculated phase differences between the two interfering, differently modulated object waves and projected into a complex hologram. The object is reconstructed without the twin image and bias terms by a numerical Fresnel back propagation of the complex hologram. A modified approach to implement FINCH by a single camera shot by pre-calibrating the system involving recording of the point spread function library and reconstruction by a non-linear cross-correlation has been introduced recently. The expression of the imaging characteristics from the modulation functions in original FINCH and the modified approach by pre-calibration in spatial and polarization multiplexing schemes are reviewed. The study reveals that a reconstructing function completely independent of the function of the phase mask is required for the faithful expression of the characteristics of the modulating function in the image reconstruction. In polarization multiplexing method by cross-correlation, a partial expression was observed, while in spatial multiplexing method by cross-correlation, the imaging characteristics converged towards a uniform behavior.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

Imaging systems mounted to ground vehicles are used to image fruit tree canopies for estimation of fruit load, but frequently need correction for fruit occluded by branches, foliage or other fruits. This can be achieved using an orchard ‘occlusion factor’, estimated from a manual count of fruit load on a sample of trees (referred to as the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruit. Five approaches to correct for occluded fruit based on canopy images were compared using data of three mango orchards in two seasons. However, no attribute correlates to the number of hidden fruit were identified. Several image features obtained through segmentation of fruit and canopy areas, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest fruit count on trees. The supervised machine learning methods for direct estimation of fruit load per tree delivered an improved prediction outcome over the reference method for data of the season/orchard from which training data was acquired. For a set of 2017 season tree images (n=98 trees), a R2 of 0.98 was achieved for the correlation of the number of fruits predicted by a Random forest model and the ground truth fruit count on the trees, compared to a R2 of 0.68 for the reference method. The best prediction of whole orchard (n = 880 trees) fruit load, in the season of the training data, was achieved by the MLP model, with an error to packhouse count of 1.6% compared to the reference method error of 13.6%. However, the performance of these models on new season data (test set images) was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model. This outcome was attributed to variability in tree architecture and foliage density between seasons and between orchards, such that the characters of the canopy visible from the interrow that relate to the proportion of hidden fruit are not consistent. Training of these models across several seasons and orchards is recommended.

Spinal cord stimulation (SCS) is a therapeutic modality for the treatment of various chronic pain conditions that has rapidly evolved over the past 50 years. Unfortunately, over time, patients implanted with SCS undergo a habituation phenomenon leading to decreased pain relief. Consequently, discovery of new stimulation waveforms and SCS applications have shown to prolong efficacy and reduce explantation rates. This article explores various SCS waveforms, their applications and proposes a graded approach to habituation mitigation. We suspect the neural habituation phenomenon parallels that seen in pharmacology. Consequently we urge further exploration of early introduction of these stimulation strategies to abate spinal cord stimulation habituation.

This work communicates a review on Balmer series hydrogen beta line measurements and applications for analysis of white dwarf stars. Laser-induced plasma investigations explore electron density and temperature ranges comparable to white dwarf star signatures such as Sirius B, the companion to the brightest star observable from the earth. Spectral line shape characteristics of the hydrogen beta line include width, peak separation, and central dip-shift, thereby providing three indicators for electron density measurements. The hydrogen alpha line shows two primary line-profile parameters for electron density determination, namely, width and shift. Both Boltzmann plot and line-to-continuum ratios yield temperature. The line-shifts recorded with temporally- and spatially- resolved optical emission spectroscopy of hydrogen plasma in laboratory settings can be larger than gravitational redshifts that occur in absorption spectra from radiating white dwarfs. Published astrophysical spectra display significantly diminished Stark or pressure broadening contributions to red-shifted atomic lines. Gravitational redshifts allow one to assess the ratio of mass and radius of these stars, and subsequently, the mass from cooling models.

Endothelial cells form a powerful interface between tissues and immune cells. In fact, one of the underappreciated roles of endothelial cells is to orchestrate immune attention to specific sites. Tumor endothelial cells have a unique ability to dampen the immune responses and thereby maintain an immunosuppressive microenvironment. Recent approaches to trigger immune responses in cancers have focused on activating nucleic acid sensors such as cGAS/STING in combination with immunotherapies. In this review, we present a case for targeting nucleic acid sensing pathways within the tumor vasculature to invigorate tumor immune responses. We introduce two specific nucleic acid sensors, the DNA sensor TREX1 and the RNA sensor RIG-I and discuss their functional roles in the vasculature. Finally, we present perspectives on how these nucleic acid sensors in the tumor endothelium can be targeted in an antiangiogenic and immune activation context. We believe understanding the role of nucleic acid sensing in the tumor vasculature can enhance our ability to design more effective therapies targeting the tumor microenvironment.

The sudden pervasive of severe acute respiratory syndrome Covid-19 has been leading the universe into a prominent crisis. It has influenced each zone, for example, industrial area, horticultural zone, Public transportation, economic zone, and so on. So as to see how Covid-19 affected the globe, we conducted an investigation characterizing the effects of the pandemic over the world using Machine Learning (ML) method. Prediction is a typical data science exercise that helps the administration with function planning, objective setting, and anomaly detection. We propose an additive regression model with interpretable parameters that can be naturally balanced by experts with domain intuition about the time series. We focus on global data beginning from 22^nd January 2020, till 26^th April 2020 and performed dynamic map visualization of Covid-19 expansion globally by date wise and predicting the spread of virus on all countries and continents. The major advantages of this work include accurate analysis of country-wise as well as province/state-wise confirmed cases, recovered cases, deaths, prediction of pandemic viral attack and how far it is expanding globally.

Without the use of medicine drones, users are responsible for transporting goods at a high cost. The goal of this patient monitoring system is to identify people's health and, if necessary, administer medicine. The transportation drone is capable of finding healthcare information in a 360-degree rotation within the transportation infrastructure. IoT: Drone doctors are critical for the transportation of small gadgets such as drug treatments, blood leakages, and vaccines in the critical conditions of a heart patient, in the event of failure of traffic location infrastructure, highway roads, or hospitals due to intense climate or site visitor congestion, and for the transportation of small gadgets such as drug treatments, blood leakages, and vaccines. When transmission is difficult, the self-maintaining drone is the format for quickly transporting drug treatments to the appropriate locations. The shipping drone equipped with an Ardupilot can drive in autopilot mode, which guides the drone to its destination. It has a 7-minute flying period and can deliver up to 2 kg of medicine. When the detected heartbeat is 94bpm and the fever range is 101 degrees, a method has been presented in which an IoT Drone Doctor (IDD) System distributes the first AID box to vulnerable patients and heart patients.

Melatonin (MT) controls various physiological functions and enhances plant drought tolerance in response to environmental stressors, including water deficit. This study aimed to evaluate the effect of exogenous MT on the morphophysiological attributes of Ranunculus asiaticus under normal and drought conditions. R. asiaticus seedlings were divided into drought-stress and control groups and subjected to foliar application of MT at various concentrations (0, 50, 100, and 200 μM) four times during the study. The drought-stress group exhibited considerably decreased shoot length, leaf number, leaf area, fresh and dry vegetative weights, total chlorophyll and carotenoid contents, and relative water content; delayed emergence of flower stalks; and increased relative electrolyte leakage compared with well-watered plants. Conversely, foliar application of MT notably increased growth parameters compared with their no-treatment counterparts. Foliar treatment with 200 µM MT resulted in the most significant growth response in R. asiaticus under normal or drought-stress conditions. Moreover, compared with no treatment, exogenously applied MT induced the appearance of flower buds and increased relative water and proline contents as well as peroxidase activity while reducing electrolyte leakage. Regarding tolerance index percentages, higher peroxidase and proline contents indicated their suitability for use as markers for drought tolerance, supporting the effective role of exogenous MT in enhancing the adaptability of Ranunculus to drought stress.

The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

A pixel-based segmentation method was demonstrated to be confounded by developmental stage in estimation of flowering of mango. Categorization of panicles into three developmental stages was undertaken with a single and a two-stage deep learning framework (YOLO and R2CNN), using either upright or rotated bounding boxes. For a validation image set and for total panicle count, the models MangoYOLO(-upright), MangoYOLO-rotated, YOLOv3-rotated, R2CNN(-rotated) and R2CNN-upright achieved: (i) RMSEs of 25.6, 16.0, 15.4, 25.8 and 32.3 panicles per tree image, (ii) Mean average precision (mAP) scores of 72.2, 69.1, 65.0, 62.5 and 70.9% and (iii) weighted F1-scores of 76.5, 76.1, 74.9, 74.0 and 82.0, respectively. For a test set of images involving a different orchard and cultivar and use of a different camera, the R2 for machine vision to human count of panicles per tree was 0.86, 0.80, 0.83, 0.81 and 0.76 for the same models, respectively. Thus, models generalised well, but with no consistent benefit from use of rotated over upright bounding boxes. While the YOLOv3-rotated model was superior in terms of total panicle count, the R2CNN-upright model was more accurate for panicle stage classification. To demonstrate practical application, panicle counts were made weekly for an orchard of 994 trees, with a peak detection routine applied to document multiple flowering events.

This systematic review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement to synthesizes the existing literature with a goal to review current conceptual frameworks of mental health of forced migrants for further practice research priorities in social work. The search was done between January 2015 to January 2021. As a result, 29 studies met inclusion criteria. Medicalizing mental health issues by relying solely on the effectiveness of medicine was a controversial risk factor that negatively affected the daily life activities of refugees and reduced their willingness for seeking professional mental health services. Empowering vulnerable minorities by giving them back their power and agency to be able to speak for themselves and raise voices of trauma and recovery was the missing protective factor for sustainable mental health practice. The benefits of group-based interventions are highlighted in which communities and individuals address mental health issues as well as isolation through building collective identities and support networks. Information and communication technologies (ICTs) can add more strength to any kind of mental health intervention. Finally, the benefits of applying ecological perspective for the mental health of refugees, and its implications for a sustainable intercultural practice are discussed. Social workers in this model are the representatives of at-risk groups, thus need more agency and creativity in reflecting client’s concrete needs.

The overall contribution of otolith receptors to eye movements, postural control and perceptual functions is the basis for clinical testing of otolith function. With such wide range of contributions, it is important to recognize that the functional outcomes of these tests may vary depending on the specific method employed to stimulate the hair cells. In this article, we review common methods used for clinical evaluation of otolith function and how different aspects of physiology may affect the functional measurements in these tests. We compare the properties and performance of various clinical tests with the emphasis on the newly-developed video ocular counter roll (vOCR), measurement of ocular torsion on fundus photography, and subjective visual vertical or horizontal (SVV/SVH) testing.

Large Language Models (LLMs) have proved significant proficiency when comes to code generation especially in Structured Query Language (SQL) for databases and recent successful Text-to-SQL method involves fine-tuning pre-trained LLMs for SQL generation tasks. Transforming natural language text into SQL queries, has been attempted to solve with various learning techniques including Few-shot learning[1], fine tuning. In this paper we propose Supervised fine-tuning (SFT) as a better alternative for learning technique for text-to-SQL generation task using Code-Llama that pushes state of art accuracy on spider test suite to 89.6% on dev set which represent first instance of surpassing the earlier best-in-class with 5.5% higher score and 86.8% of exact match accuracy on dev set. Furthermore, we demonstrate that properly prompted LLM along with SFT provides far fewer hallucinations and much more robust LLM that can be used as a general tool for any text-to-SQL generation use case.

The disease COVID-19 is brought on by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) First appeared in China in December 2019 and quickly spread around the world after being declared a worldwide pandemic by the World Health Organization in March 2020. COVID-19, Medical Laboratory Professionals, SARS-COV-2, Diagnostic, Importance and difficulties for testing were the selected terms to search the databases of PubMed and Google Scholar for previously published material. Many diagnostic tests are applied in corona virus detection like Droplet Digital PCR (ddPCR), Multiplex PCR, Loop-mediated Isothermal Amplification (LAMP) and more. There is a lack of availability of molecular and serologic tests that have been clinically validated or authorized by national or international regulatory bodies. Clinical laboratory experts are tackling threats to our global defense and wellness, including infectious diseases. The opportunity to express gratitude to the unsung medical laboratory heroes and COVID0-19 pandemic allies is now greater than ever. Each year, Medical Laboratory Professionals Week should honors those who contribute key diagnostic data that help save lives. This review article explore overall summary on testing methods including Important and Difficult Tasks for Medical Laboratories which will provide good message for better public health matter.

Pearl millet is an important food crop in the arid and semi-arid tropical regions of Africa and Asia. These regions are home to millions of poor smallholder’s households living in harsh agro ecology and reported higher prevalence of malnutrition. Such poor households have few options in terms of food crops, besides the limited markets. Indeed, pearl millet is one of the food crops they continue to grow for their food and nutritional security. Pearl millet is important sources of dietary carbohydrates, energy, protein, and important minerals such as calcium, iron and zinc. Considering inherent high nutritional values and climate resilient nature (drought and heat), demand for pearl millet as food, beside valued for its Stover as a source of livestock fodder, is projected to grow strongly in Asia (India) and Africa (West and Central Africa). Iron (cause anemia) and zinc (cause stunting) deficiencies are widespread and serious public health problems worldwide, including India and Africa. Biofortification is a cost-effective and sustainable agricultural strategy to address this problem. Research on pearl millet has shown that large genetic variability (30-140 mg/kg Fe and 20-90 mg/kg Zn) available in this crop can be effectively utilized to develop high-yielding cultivars with high iron and zinc densities. Both Open –pollinated varieties (Dhanshakti and Chakti) and hybrids (ICMH 1202, ICMH 1203 and ICMH 1301) of pearl millet with high grain yield (>3.5 tons/ha in hybrids) and high levels of iron (70-75 mg/kg) and zinc (35-40 mg/kg) densities have been developed and released. Currently, India growing >70,000 ha of biofortified pearl millet, besides more pipeline hybrids and varieties are under various stage of testing at the national (India) and international (west Africa) trials for possible release. Genomic tools will be an integral part of breeding program particularly for nutritional traits to use diagnostic markers and genomic selection. Clinical studies showed that 200g grains from biofortified cultivar would provide bioavailable Fe to meet full recommended daily allowance (RDA) in children, adult men and 80% of the RDA in women. Till today, no markets to promote biofortified cultivars/grains/products as no incentive price and such products aims to address food and nutritional security challenges simultaneously. The demand is likely to increase only after investment and integration into modern public distribution system, nutritional intervention schemes, private seed and food companies with strong mainstreaming nutritional policies. In the non-traditional regions, this will contribute to livestock and poultry feed industry as spill-over benefits to improve nutrition.

Photosynthesis is a pivotal process that determines the synthesis of carbohydrates required for sustaining growth under normal or stress situation. Stress exposure reduces the photosynthetic potential owing to the excess synthesis of reactive oxygen species that disturb the proper functioning of photosynthetic apparatus. This decreased photosynthesis is associated with disturbances in carbohydrate metabolism resulting in reduced growth under stress. We evaluated the importance of melatonin in reducing heat stress-induced severity in wheat plants (Triticum aestivum L.). The plants were subjected to 25 ˚C (optimum temperature) or 40 ˚C (heat stress) for 15 days at 6 hours time duration and then developed the plants for 30 days. Heat stress led to oxidative stress with increased production of TBARS and H2O2 content and reduced accrual of total soluble sugars, starch and carbohydrate metabolism enzymes which are reflected in reduced photosynthesis. Application of melatonin not only reduced oxidative stress through lowering TBARS and H2O2 content, through augmenting the activity of antioxidative enzymes but also increased the photosynthesis in plant and carbohydrate metabolism that is needed to provide energy and carbon skeleton to the developing plant under stress. However, the increase in these parameters with melatonin was mediated via hydrogen sulfide (H2S), as the inhibition of H2S by hypotaurine (HT; H2S inhibitor) reversed the ameliorative effect of melatonin. This suggests a crosstalk of melatonin and H2S in protecting heat stress-induced photosynthetic inhibition via regulation of carbohydrate metabolism.

The current study aims to fermentation parameter for producing microbial products. Microwave parameters consist on 245 MHz, and 600-watt for 60 seconds. Ethyl acetate was the best solvent used for extraction purposes. Antioxidant properties were differentiated by blocking the oxidation of the linoleic acid with an inhibition rate of 73.13% at a concentration of 200 mg/mL, in addition to increasing its effectiveness for free radical extraction and reduction strength by increasing concentrations gradually. The bond ability to irons was lower compared to the EDTA-2Na, in addition to the obtained total content corresponding to phenolic compounds in the ethyl acetate extract of fermented rice (Koji) by A. flavus was 232.11 mg, on the basis of galic acid/mg. The stability of the antioxidant compounds of the ethyl acetate extract of fermented rice (Koji) by A. flavus was also studied; showing stability under neutral conditions, as well as at high temperatures (185 °C during two hours). However, no stability was obtained under acidic and alkaline conditions.

The production and quality of Phaseolus vulgaris (snap bean) have been negatively impacted by leaf crumple disease caused by two whitefly-transmitted begomoviruses; cucurbit leaf crumple virus (CuLCrV) and sida golden mosaic Florida virus (SiGMFV), which often appear as a mixed infection in Georgia. Host resistance is the most economical management strategy against whitefly-transmitted viruses. Currently, information is not available with respect to resistance to these two viruses in commercial cultivars. In two field seasons (2018 and 2019), we screened Phaseolus sp. genotypes (n = 84 in 2018; n = 80 in 2019; most of the genotypes were common in both years with a few exceptions) for resistance against CuLCrV and/or SiGMFV. Twenty genotypes with high- to moderate-levels of resistance (disease severity ranging from 5-50%) to CuLCrV and/or SiGMFV were identified. Twenty-one genotypes were found to be highly susceptible with a disease severity of ≥66%. Adult whitefly counts differed significantly among snap bean genotypes for both years. The whole genome of these Phaseolus sp. (n=82) genotypes was sequenced and genetic variability among them was identified. Over 900 giga-base (Gb) of filtered data were generated and >88% of the resulting data were mapped to the reference genome, and SNP and Indel variants in Phaseolus genotypes were obtained. A total of 645,729 SNPs and 68,713 Indels including 30,169 insertions and 38,543 deletions were identified, which were distributed in 11 chromosomes with chromosome 02 harboring the maximum number of variants. These phenotypic and genotypic information will be helpful in genome-wide association studies that will aid in identifying genetic basis of resistance to these begomoviruses in Phaseolus sp.

Bread wheat (Triticum aestivum L.) is one of the world's most important staple food crop providing 20% global energy and dietary proteins. It is widely grown in sub-tropical and tropical areas and as such exposed to heat-stress especially at grain filling period (GFP). Global warming has further affected its production and productivity in these heat-stressed environments. We examined the effect of heat-stress on 18 morpho-physiological and yield-related traits in 96 bread wheat accessions. Heat susceptibility index (HSI <0.60) and yield stability (i <0.55) used as criteria for selecting the tolerant accessions. Heat-stress, imposed by delayed sowing, decreased crop growth and GFP, and as a result reduced morphological and yield-related traits, namely days to 50% anthesis, plant height, peduncle length, flag-leaf area, spike length, spikelets per spike, grain length and width, thousand grain weight (TGW), harvest index and yield. The reduction in the trait values was severe in susceptible accessions (48.2% yield reduction in IC277741) than the tolerant. Physiological traits like chlorophyll content, canopy temperature depression (CTD), normalized difference vegetation index (NDVI), plant waxiness and leaf rolling showed higher expression in the tolerant accessions under heat-stress. Scanning electron microscopy of matured wheat grains revealed ultrastructural changes in endosperm and aleurone cells caused due to heat-stress. The reduction of size and density of large starch granules is the major cause of yield and TGW decrease in the heat-stress susceptible lines. The most stable and high-yielding accessions namely IC566223, IC128454, IC335792, EC576707, IC535176, IC529207, IC446713 and IC416019 were identified as the climate-smart germplasm lines. Germplasm lines possessing desirable traits were selected as po-tential parents for the development of bi-parental and multi-parental populations.

The Estimation of systemic risk in India is still in its infancy stage. There are several methods which are available but none of the methods are fully compatible to forecast the systemic risk since under different circumstances the factors responsible for the risk differs. In this paper the systemic risk estimation in India being carried out based on spread in daily stock market price(Difference between the bid and ask price of a share) of the top 100 firms in India according to market capitalization for the period of July2007 to March 2016. The results were compared with the Financial Stability Report published by Reserve Bank Of India for the period of March 2010 to June 2016.The results clearly indicates that there exits relationship between market illiquidity represented by spread and risks associated with the Financial System. In most of the cases the Z score (deviation from the mean/Standard Deviation) of the spread has become negative which provides the spread which is farther from the mean, also a good indicator of volatility in market and risk to financial system. It is also seen that the Systemic Risk Survey conducted by Reserve Bank of India which started during October 2011 has supported the results.

Phosphodiesterases (PDEs) hydrolyze cyclic nucleotides to modulate multiple signaling events in cells. PDEs are recognized to actively associate with cyclic nucleotide receptors (Protein Kinases, PK) in larger macromolecular assemblies referred to as signalosomes. Complexation of PDEs with PK generates an expanded active site which enhances PDE activity. This facilitates signalosome-associated PDEs to preferentially catalyze active hydrolysis of cyclic nucleotides bound to PK, and aid in signal termination. PDEs are important drug targets and current strategies for inhibitor discovery are based entirely on targeting conserved PDE catalytic domains. This often results in inhibitors with cross-reactivity amongst closely related PDEs and attendant unwanted side effects. Here, our approach targets PDE-PK complexes as they would occur in signalosomes, thereby offering greater specificity. Our developed fluorescence polarization assay has been adapted to identify inhibitors that block cyclic nucleotide pockets in PDE-PK complexes in one mode, and disrupt protein-protein interactions between PDEs and cyclic nucleotide activating protein kinases in a second mode. We tested this approach with three different systems: cAMP-specific PDE8-PKAR, cGMP-specific PDE5-PKG and dual-specificity RegA-RD complexes and ranked inhibitors according to their inhibition potency. Targeting PDE-PK complexes offers biochemical tools for describing the exquisite specificity of cyclic nucleotide signaling networks in cells.

The proposed research study explore the decision tool for cropwise variability of NDVI using RS techniques and GIS tool as a crucial indicators for monitoring vegetation status.A small case study for Parbhani district of Maharashtra, India was conducted using Landsat-9 satellite images of the Rabi season in the year 2022-23 were processed using ArcGIS 10.7 software to derive the NDVI values. During the analysis, the maximum NDVI in the study area was recorded at 0.5857 on February 03, 2023, while the minimum was observed as -0.1018 on February 19, 2023. For specific crops, the average NDVI ranged from 0.1183 to 0.3189 for wheat, 0.1679 to 0.2891 for jowar, and 0.1163 to 0.3367 for gram. The mean NDVI values for wheat, jowar, and gram crops during the Rabi season of 2022-23 were 0.218, 0.219, and 0.217, respectively.Furthermore, the average NDVI values exhibited an increasing from the initial stage to the mid-crop growth stage, followed by a decrease during the end-season stage for wheat, jowar, and gram crops. Interestingly, the mean NDVI values of major Rabi crops, wheat, jowar, and gram for the year 2022-23, is approximately similar to the mean NDVI values observed for of year 2022-23.

There are some limitations in the null test measurement in the stitching interferometry. In order to meet the null test conditions, the moving distance between the sub-apertures often deviates from the theoretical preset distance, which leads to a position deviation of sub-apertures under measurements. To overcome this problem, an algorithm for data processing is proposed. We used iterative calculation of the deviations between the sub-apertures to adjust their positions, to ensure the local validity of the linear approximation algorithm and realize the exact stitching. A cylindrical lens as an object for experimental examination of the proposed method was taken. The obtained results demonstrate the validity, reliability and feasibility of our iterative stitching algorithm.

Bio-surfactants are surface-active molecules which are produced by the wide range of microbes including bacteria, fungi, and yeast. This study was conducted to identify bio-surfactants by Bacillus subtilis combined with use of cheap substrates and industrial wastes (Mustard cake, Whey and Soya cake) which are found locally in Nepal. Bacillus subtilis, one of the most potential bio-surfactants producer; was isolated from soil sample of hydrocarbon contaminated site. Isolates were grown in a Minimal Salt Media (MSM) with 10% (v/v) mustard oil cake, whey and soya cake separately. The presence and potential of surfactant was determined by the oil spreading technique, emulsification index (%E24) and surface tension measurement. It was revealed that the surface tensions of cell free extract were 54.41, 60.02 and 56.64 mN/m for from mustard cake, whey and soya cake respectively as compared to distilled water (72.09) at 25oC. The emulsification index values are was found to be highest in engine oil from the bio-surfactant extracted from mustard cake, soya cake and whey respectively. Similarly, mustard oil showed the lowest value of emulsification index. The highest emulsification activity was shown in mustard oil i.e. 1.13 from the cell free extract from mustard oil and lowest in engine oil i.e., 0.07, by the extract from soya cake medium, when measured in spectrophotometer at 540 nm. In conclusion, strain of Bacillus subtilis was found to be the potential surface active agent producers on the mustard oil cake, which can be useful medium for various environmental, food and industrial processes.

This study aimed to evaluate the role of chest radiographs and electrocardiograms in predicting the hemodynamics of congenital heart disease (CHD). This retrospective study included 50 patients with a diagnosis of CHD who had undergone any form of cardiac intervention, either surgical or nonsurgical between September 2019 and September 2020. Chest radiographs and electrocardiograms were evaluated and compared with the diagnostic gold standard echocardiography. Chest radiographs had the highest sensitivity, specificity, and accuracy, with all being 100%, in detecting situs and cardiac position. There was a very good agreement between chest radiographs and echocardiography in the detection of both situs and cardiac position (κ = 1.00, p &lt; 0.001), while moderate agreement was observed for the detection of cardiomegaly, position of aortic knuckle, main pulmonary artery dilation, and right pulmonary artery dilation. Electrocardiograms had a high sensitivity (100.00%), but modest specificity and accuracy for the detection of left ventricle pressure overload. For the detection of left atrial enlargement and left ventricle volume overload, electrocardiograms had high specificity (94.12% and 94.29%, respectively) but low sensitivity and modest accuracy. There was a moderate agreement between electrocardiograms and echocardiography in the detection of right ventricle pressure overload (κ = 0.43, p = 0.002) and left ventricle volume overload (κ = 0.46, p &lt; 0.001). The study findings indicate that chest radiographs and electrocardiograms alone are not adequate for the assessment of hemodynamics of CHD and reinstates the recommendation that in addition to routine chest radiographs and electrocardiograms, echocardiography should be performed.

An analysis of the curve of Spee in relation to muscle activity, assessed by electromyographic analysis, on a group of 16 young adults with previous fixed orthodontic treatment is proposed as a pilot study. Given the high incidence of this type of dental treatment, it is important to point out how, in this field of dentistry, there is a marked tendency to level and flatten this anatomical occlusal curve, as proposed by Andrews. If in orthodontics there is a standardized levelling of the curve of Spee, in the prosthetic and restorative field, the management of the curve of Spee requires greater attention and often even an individualized approach. The purpose of this study is to investigate what the levelling of the antero-posterior occlusal curve entails in terms of masticatory effectiveness and harmony. From the results obtained, it can be seen that in order to optimize the harmony of the stomatognathic apparatus, the curve of Spee is a parameter that requires individual attention: modifying it through standardized levelling has inevitable consequences.

We present a very computationally light and fast approximation algorithm and then verify it with genetic algorithm and simulated annealing. We show that our algorithm is on par with GA and SA in terms of output produced while having a tightly bounded time complexity. Our algorithm works best when there is a strong positive correlation between the reliability of a component and its cost. We present two algorithms with the same essence. One of them is system cost bounded and the other is target reliability bounded. Our proposed algorithm works on a subsystem level redundancy instead of component level redundancy.

Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. This study aimed to test the efficacy of a newly-developed symptom self-management program on health outcomes among inpatients post-AMI. A pilot randomised, controlled trial was utilised. A convenience sample of 90 was recruited from a tertiary hospital in Singapore. Adult inpatients with post-AMI were randomly assigned to the three groups: IManage-HSet, IManage-FF or control group. Sample size was computed using power analysis. Data were collected through self-reported questionnaires and physiological instruments. Data analyses included univariate statistics, analysis of variance and repeated measure analysis of variance. Ninety participants were recruited with 30 in each group. Participants in the IManage-HSet and IManage-FF group had significantly greater relaxation (ηp2 = 0.11), lower objective stress (ηp2 =0.14), and greater knowledge (ηp2 = 0.62) than those of the control group. The two intervention groups had lower subjective stress (ηp2 = 0.04), depression (ηp2 = 0.01), and anxiety (ηp2 = 0.03) than the control but such differences did not achieve statistical significance. Furthermore, most participants reported that they were satisfied with the programs and they provided positive comments on the contents, educational materials and relaxation practice. This pilot study supports the potential benefits and feasibility of the IManage-VR and IManage-FF interventions on stress, perceived relaxation, knowledge and patient satisfaction. Contents of the symptom management interventions could be added into current cardiac rehabilitation programs. Future RCTs with multi-centered sites and larger sample size are required to further test the intervention. This study was prospectively registered with the International Standard Randomised Controlled Trial on the 5th August 2014 with registration number ISRCTN61027816.

Antibody responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are multi-targeted and variable over time. Multiplex quantitative serological assays are needed to provide accurate and robust seropositivity data for establishment of serological signatures during vaccination and or infection. We describe here, validation and evaluation of an electro-chemiluminescence (ECL) based Mesoscale Discovery assays (MSD), for estimation of total and functional IgG to SARS-CoV-2 spike, nucleocapsid, and receptor binding (RBD) proteins in human serum samples to establish serological signatures of SARS-CoV-2 natural infection and breakthrough cases. The 9-PLEX assay was validated as per ICH, EMA and US FDA guidelines using a panel of sera samples including the NIBSC/WHO international reference panel (20/268). Assay demonstrated high specificity and selectivity in inhibition assays, wherein the homologous inhibition was more than 85% and heterologous inhibition was below 10 %. Assay also met predetermined acceptance criteria for precision (CV < 30 %), accuracy (70-130 %) and dilutional linearity. Method applicability to serological signatures was demonstrated using sera samples (N=45) representing vaccinated, infected and breakthrough cases. The method was able to establish distinct serological signatures and thus provide potential tool for seroprevalence of SARS-COV-2 during vaccination or infection.

The Yamuna River in Delhi is a prevalent epitome of the depleted ecosystem that has transformed into a sewage drain due to intense pollution and blooming anthropogenic pressure. As a leading polluter, Delhi contributes to more than 70% of Yamuna's pollution load. Its drains discharge a massive B.O.D. load daily into the river, making it severely polluted, as reflected by the water quality index. The paper uses secondary data to analyze the parameters to determine the water quality of Yamuna at different monitoring sites. It performs correlation analysis to determine the relationship among factors contributing to river water pollution. The results of correlation analysis suggest a highly significant association between parameters COD-BOD, but the association is mild to a minimum among other parameters such as BOD-DO, BOD-pH, COD-DO, COD-pH and DO-pH. Most parameters were observed to be above the hazardous level acceptable for using river water. The analysis of S.T.P.s indicates the need to increase the capacity in terms of treatment, storage, reopening of closed plants and efficient operation to meet the increasing demand for fresh Water. Also, there is a need to create demand for wastewater in different sectors of urban areas like construction, horticulture, industrial coolants, etc.

(1) Background: Prevention of acute cardiovascular events in patients with cardiovascular diseases (CVDs) requires promoting health-protective behaviors (e.g., physical activity) and preventing health-compromising behaviors (sitting). Digital health interventions provide much potential. Based on multiple behavioral change theory, an intervention (reCardial app) was evaluated, and the following hypotheses were tested: H1: Health behaviors (physical activity, sitting) and disease self-management (self-care maintenance, self-care confidence) are closely interrelated. H2: Change in health behaviors and disease self-management indicators over time is more pronounced in reCardial app-users. H3: In the intervention group, changes in systolic and diastolic blood pressure indicate a positive trajectory. (2) Methods: A study with the design of a randomized controlled trial over 12 weeks was conducted. 40 CVD-patients were randomized equally to the Intervention Group (35% women; Mage=60.6years) and Control Group (45% women; Mage=61.5years). (3) Results: Findings support H1 with significant correlations between health behaviors (r=0.45-0.63**), and disease self-management (r=-0.32-0.54**), H2 with Eta²=0.21 (not statistically significant) and H3 partially with d=0.101 for systolic blood pressure but not regarding diastolic blood pressure. (4) Conclusions: Replications are needed with larger samples and more objective measures. The app can help prevent and manage CVD by means of promoting health-protective behaviors and preventing health-compromising behaviors. Taking different behaviors into account can increase intervention effects and with that support of users.

This Article demonstrates how an eigendecomposition problem is inputted into a quantum circuit, how gates are applied in the quantum circuit, and how the output measurements are the correct eigenvalues. This process is known as quantum phase estimation (QPE). A quantum harmonic oscillator example, a foundational quantum physical chemistry problem, is demonstrated within the context of QPE. A particle in a box example, another quantum physical chemistry problem, may be solved by QPE with a caveat. These examples are of the limiting cases of diagonal matrices. Future advances in taking matrix inverses for solving linear sets of equations or finding ground state energies in the Schrödinger equation will use the principles implemented in this Article.

A special vibration damper is proposed for material property identification through a scratching process on a vibrating object. This paper presents the design and working principle of a scratching damper based on a scratch test device. A non-contact electromagnetic shaker is used to generate the shaking force for the vibration of the tested object. The required pressure on the scratched material during the scratching process is generated by a compression spring and adjusted by changing the amount of compression of the spring. The proposed damper is designed and tested on extruded aluminum samples for the determination of its Young’s modulus and hardness and validated by using the standard test instruments. Observations of physical dimensions of the scratching tracks are determined by microscope and were utilized to compute the scratching energy factor. Load curves are obtained at different pressure of the scratching process. The hysteresis loop energy during the scratching process of the tested object is measured and used for the determination of material properties of the object. Furthermore, energy conservation law, scratch energy release rate and loop energy release rate are used to determine the Young’s modulus and hardness of the sample. The measurement accuracy of the modulus and hardness of the tested sample is evaluated. The proposed method of material testing has several advantages over the traditional methods such as low cost and high repeatability, and may be used as an alternative to the standard modulus and hardness testers.

Glioblastoma multiforme (GBM) is a highly heterogeneous brain tumor with limited treatment options and a poor prognosis. Cancer stem cells (CSCs) have emerged as a critical factor in GBM resistance and management, contributing to tumor growth, heterogeneity, and immunosuppression. The transcription factor FOXM1 has been identified as a key player in the progression, spread, and therapy resistance of various cancers, including GBM. In this study, researchers conducted structure-based in silico screening to identify natural compounds that could target the DNA-binding domain (DBD) of the FOXM1 protein. Through molecular docking analyses, identified Silybin B as a potential inhibitor of FOXM1, exhibiting strong interaction with the protein. MD simulations were performed to validate the binding stability of the FOXM1-Silybin B complex. The study provides valuable insights into the potential of Silybin B as a FOXM1 inhibitor and its ability to induce senescence in GBM stem cells. These findings contribute to the development of structure-based design strategies for FOXM1 inhibitors and innovative therapeutic approaches for the treatment of Glioblastoma.

Background: Maternal influenza vaccination provides effective protection against influenza in-fections in pregnant women and new-borns. In India, the influenza vaccine has not yet been of-fered through immunization programs, owing to the lack of sufficient safety data for Indian pregnant women. Methods: This cross-sectional observational study enrolled 558 women ad-mitted to the obstetrics ward of a civic hospital in Pune. Study-related information was obtained from the participants through interviews using structured questionnaires and hospital records. Univariate and multivariate analysis were used and chi-square with unadjusted and adjusted odds ratio were estimated to account for vaccine exposure and the temporal nature of each out-come, respectively. Results: The protective effect of maternal influenza vaccination against delivering very low birth weight infants was observed. (aOR 2.29, 95% CI 1.03 to 5.58, p=0.03). No association was observed between maternal influenza vaccination for spontaneous abortion (OR 1.42, 95%CI 0.75, 2.68), chorioamnionitis (OR 0.60, 95% CI, 0.1, 3.63), gestational hypertension (OR 1.73, 95% CI 0.72, 4.16) and preterm birth (OR 1.64, 95% CI 0.91, 2.97). Interpretation: These results show that the in-fluenza vaccine administered during pregnancy is safe and has a lower risk of negative birth outcomes.

Immune-mediated bowel diseases (IMBD), including Ulcerative colitis and Crohn's disease, represent a significant global health burden with their complex etiology and increasing prevalence. The connection between intestinal parasites and the gut microbiome in immune-mediated bowel disease is a complex and evolving field of research. Several studies have demonstrated that intestinal parasites can modulate the composition and function of the gut microbiome. Parasitic infections can result in alterations in the gut microbial community, including changes in microbial diversity, abundance, and metabolic activity. These changes can influence the immune response and contribute to the development of IMBDs. In contrast, the gut microbiome serves a pivotal function in maintaining intestinal homeostasis and immune regulation. Dysbiosis, characterized by changes in the gut microbial composition, has been associated with the pathogenesis of IMBDs. Imbalances in the gut microbiota can result in increased gut permeability, chronic inflammation, and aberrant immune responses, all of which are hallmarks of IMBDs. The bidirectional interaction between intestinal parasites and the gut microbiome further complicates the understanding of immune-mediated bowel diseases. Certain parasites, such as hookworms and Necator americanus, have been found to downregulate immune responses and may have therapeutic potential in treating celiac disease. On the other hand, infections with parasites like Strongyloides stercoralis and Blastocystis have been shown to mimic the symptoms of IBD, highlighting the intricate relationship between parasites and the pathogenesis of these diseases. Additional investigation is required to comprehensively elucidate the mechanisms that underlie the association between intestinal parasites and the gut microbiome in immune-mediated bowel disease. This knowledge could potentially lead to the development of targeted therapeutic strategies that aim to restore gut microbiota homeostasis and alleviate the symptoms of these debilitating conditions. By understanding and harnessing the complex interplay between parasites, the gut microbiome, and the host immune system, researchers may uncover novel approaches for the management and treatment of immune-mediated bowel diseases.

Aim: Glioblastoma multiform (GBM) is a prevalent brain cancer which accounts for 80% of cases globally with a noticeably short life expectancy subsequent diagnosis. There are several factors associated with GBM including alterations and over expressions of oncoproteins. Analysis of SEC61G expression in GBM patient’s favor tumour progression, which promotes an environment of oxidative stress, while preventing apoptosis. Methods: In this article using in silico tools including GEPIA2, ULCAN, LinkedOmics, Gene MANIA and mirTEd, we observed the role of SEC61G gene in GBM progression by analyzing the expression, positive correlation associated with metabolic, mitochondrial gene, immune cell infiltration level, gene interaction networks, miRNA association as well as survival analysis. Results: The expression of the mitochondrial, metabolic genes PGLS, PGK1, EIF6, and EIF3K which are involved in redox metabolism and CD63 immunoregulation, as well as other genes that promote tumour progression and invasion, including EGFR, LANCL2, BCL2L12, SEC61G-DT, KDELR1, CDKN2A, IFNA16, IFNA10, and BCL7C were positively correlated with the increased/high expression of SEC61G.We also found hsa-mir-380-5p to be unregulated in glioblastoma phenotypes. Conclusion: Our findings suggest that SEC61G associated redox metabolism, complement system and immunoregulation, as well as miRNA expression can be targeted to develop a potential therapeutic approach for GBM patients.

When we talk about the Internet of Things, we are referring to the connecting of things to a1 physical network that is embedded with software, sensors, and other devices that allow information2 to be exchanged between devices. It is possible that the interconnection of devices will present3 issues in terms of security, trustworthiness, reliability, and confidentiality, among other things.4 The proposed approach is effective at detecting intrusions into the Internet of Things network.5 Initially, the privacy-preserving technology was deployed utilising a Blockchain-based methodology6 to ensure that personal information was protected. Patients’ health records (PHR) security is the7 most crucial component of encryption over the Internet because of the value and importance of these8 records, particularly in the context of the Internet of Medical Things (IoMT). The search terms access9 mechanism is one of the most common approaches used to access personal health records from a10 database, but it is vulnerable to a number of security flaws. However, while blockchain-enabled11 healthcare systems provide increased security, they may also introduce weaknesses into the current12 state of the art. Blockchain-enabled frameworks have been proposed in the literature as a means13 of resolving those challenges. These solutions, on the other hand, are primarily concerned with14 data storage, with Blockchain serving as a database. To enable secure search and keyword-based15 access to a distributed database, this study proposes the use of blockchain technology as a distributed16 database, together with a homomorphic encryption mechanism. Aside from that, the suggested17 system includes a secure key revocation mechanism that can be used to automatically update various18 policies.As a result, our proposed approach provides greater security, efficiency, and transparency19 while also being more cost-effective. We have compared the findings of our proposed models with20 those of the benchmark models, if appropriate. Our comparison research demonstrates that our21 suggested framework provides a more secure and searchable mechanism for the healthcare system22 than the current state of the art.

We introduce a series of twisted donor-acceptor (D-A) carbazole-benzophenone based electroactive bipolar derivatives that were synthesized through the reaction of 4-fluorobenzophenone with various partially alkylated 3,3’-bicarbazoles. The comprehensive structural characterization of these compounds is provided. These amorphous materials exhibit suitable glass transition temperatures ranging from 57 to 102 °C and demonstrate high thermal stability, with decomposition temperatures reaching 400 °C. Moreover, the developed materials exhibit elevated photoluminescence quantum yields (PLQY) of up to 75.5% and favorable HOMO-LUMO levels, along with suitable triplet-singlet state energy values. Due to their good solubility and suitable film-forming properties, all the compounds were evaluated as blue emitters dispersed in 4,4’-bis(N-carbazolyl)-1,10-biphenyl (CBP) host for the fabrication of organic light-emitting diodes (OLEDs) in concentration dependent experiments. Among these experiments, the device containing 15 wt% of the guest 4-(9’-{2-ethylhexyl}-[3,3’]-bicarbazol-9-yl)benzophenone demonstrated the best characteristics, achieving maximum brightness of 3581 cd/m2, current efficiency of 5.7 cd/A, power efficiency of 4.1 lm/W and external quantum efficiency of 2.7%.

There are different treatment options in modern dentistry for the replacement of lost dentition. Of these the most upcoming and acceptable treatment option is Dental implants. The common problem usually with immediate implant placement in the anterior region is the post-operative soft tissue contour as a part of the bone modelling during healing. Hurzeler et al in 2010 introduced a new technique called the “socket shield technique”. This technique has been used as an alternative treatment modality for immediate implant placement in the aesthetic zone.This review articles provides a detailed information regarding the clinical concept of Root membrane technique.

Sustainable Development Goal (SDG) 6.2 sets an ambitious target of leaving no one without adequate sanitation by 2030. The key concern is the lack of local human and financial capital to fund the collection of reliable information to monitor progress towards the goal. As a result, national and local records may be telling a different story of the proportion of safely managed sanitation that counts towards achieving the SDGs. This paper unveils such inconsistency in sanitation data generated by urban authorities and proposes a simple approach for collecting reliable and verifiable information on access to safely managed sanitation. The paper is based on a study conducted in Babati Town Council in Tanzania. Using a smartphone-based survey tool, city health officers were trained to map 17,383 housing units in the town. A housing unit may comprise of two or more households. The findings show that 5% practice open defecation, while 82% of the housing units have some forms of sanitation. Despite the extensive coverage, only 31% of the faecal sludge generated is safely contained, while 64% is not. This study demonstrates the possibility of using simple survey tools to collect reliable data for monitoring progress towards safely managed sanitation in the towns of global south.

Fast neutron (FN) radiation mediated mutagenesis is a unique approach among the several induced mutagenesis methods being used in plant science in terms of impacted mutations. The FN mutagenesis usually creates deletions from a few bases to several million bases (Mb). A library of random deletion generated using FN mutagenesis lines can provide indispensable resources for the reverse genetic approaches. In this review, information from several efforts made using FN mutagenesis has been compiled to understand the type of induced mutations, frequency, and genetic stability. Concerns regarding the utilization of FN mutagenesis technique for a plant with different level of ploidy and genome complexity are discussed. We have highlighted the utility of next-generation sequencing techniques which can be efficiently utilized for the characterization of mutant lines as well as for the mapping of causal mutations. Pros and cons of Mut-map, MutChromSeq, exon capture, whole genome sequencing, MutRen-Seq, and different tilling approaches can be used for the detection of FN-induced mutation has also been discussed. Genomic resources developed using the FN mutagenesis have been catalogued wooing to meaningful utilization of the available resources. The information provided here will be helpful for the efficient exploration for the crop improvement programs and for better understanding of genetic regulations.

Background: The burden of resistant fungal infection is rising in patients with pulmonary disease. Options for antifungal therapy are limited, and the only orally-available antifungals, the triazoles, demonstrate inter and intra-patient variability, non-linear kinetics, toxicity, drug interactions and increasing antifungal resistance. Therapeutic drug monitoring (TDM) of itraconazole, voriconazole and posaconazole has been necessary to ensure their safety and efficacy, but is considered unnecessary for the newest triazole isavuconazole, use of which is increasing. Aims: To characterise isavuconazole susceptibility of Aspergillus fumigatus isolates in a tertiary respiratory referral centre to understand prevalence of isavuconazole antimicrobial resistance. To retrospectively review experience of isavuconazole use in this setting, assessing tolerability and therapeutic drug monitoring. Methods: A retrospective observational analysis of adult patients with respiratory disease in a tertiary hospital setting between Sept 2016 and Aug 2021. Clinical cultures were collected and triazole Minimum inhibitory concentration (MIC) were recorded (based on Clinical &amp; Laboratory Standards Institute (CLSI method)). Isavuconazole trough drug levels were carried out as part of the standard of care. Clinical outcomes of treatment were evaluated, along with drug tolerance and TDM. Results: During the study period, isavuconazole susceptibility testing was performed on 26 Aspergillus spp isolates. 80.8% of Aspergillus fumigatus isolates were non-wild type and had isavuconazole MIC &gt; 1mg/L, and 73.0% had MIC above the EUCAST (European Committee on Antimicrobial Susceptibility Testing) epidemiological cut-off (ECOFF) of 2mg/L. There was good correlation between isavuconazole MIC and voriconazole MIC (r =0.7, p=0.0002). 54 patients had isavuconazole therapy over the study period with a median duration of 7.7 months (IQR 0.79 - 16.42). 67% of patients were able to tolerate isavuconazole, despite toxicity with prior azole treatment being the primary indication for use (in 61.8%). Increased age (r=0.29; p=0.03 (95%CI 0.02,0.52)) and gender (r for female sex=-0.31; p=0.027 (95%CI -0.52,0.036) were associated risk factors for development of adverse events (AEs). 127 Isavuconazole TDM levels were performed over the study period with 90% &gt;1mg/L and 72% &gt;2mg/L. Dose change from manufacturer’s dose recommendation, however, was required in 15% of patients to achieve a serum drug concentration above the EUCAST ECOFF or Area of technical uncertainty (ATU) value of 2mg/L. Conclusion: In our study, we show use of Isavuconazole as salvage therapy in chronic pulmonary fungal disease setting with high prevalence of azole resistance. Isavuconazole MICs demonstrated good correlation with voriconazole MICs suggesting the latter could be a useful surrogate marker for isavuconazole susceptibility. Although Isavuconazole achieved excellent serum drug concentrations at standard dose compared to other azole drugs, we highlight the importance of antifungal stewardship and TDM monitoring to optimise therapy in this setting.

Background: Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed because of a lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated comorbidities. Methodology: The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites of diabetes (DB) and diabetes-related comorbidity (DC) along with the healthy control (HC) subjects. A combined and pairwise analysis was performed, between the serum samples of HC (n=50), and DB (n=38), and DC (n=35) individuals to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites. Results: The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline and threonine). The metabolites were further used to identify the perturbed metabolic pathway and correlation of metabolites in DC patients. Conclusion: The 1H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its comorbidities on their onset or progression by determining the altered levels of the metabolites in serum.

Secondary transportation of raw and comminuted forest products is a major component in forest harvesting operations in terms of economics, public perception, and safety. Consequently, there is a substantial amount of literature on this topic. The existing literature has dealt with many of the technical aspects of transportation with a majority of them focusing on improving supply chain issues; However, there are only few specific to secondary transportation issues in general. This annotated bibliography will help practitioners, researchers, and stakeholders gain a better understanding of the existing literature from 2000 to 2015. To this end, we began by classifying the selected literature into six themes: cost, roads and routes, trucking, efficiency & safety, other modes of transportation, and supply chain & optimization. Woody biomass for bioenergy production was the most researched forest product with respect to transportation. About one-third of the articles were presented in the context of supply chain modeling and optimization. More than half of the studies originated from Europe while the United States had the most publications for any given country. The most articles (16) were published in 2013. Biomass and Bioenergy published the highest number of articles (29) during the timeframe.

Capabilities of the Attenuated Total Reflection (ATR) at THz wavelengths for increased sub-surface depth characterisation of (bio-)materials is presented. The penetration depth of a THz evanescent wave in biological samples is dependent on the wavelength and temperature and can reach 0.1-0.5 mm depth due to strong refractive index change ∼0.4 of the ice-water transition; this is quite significant and important when studying biological samples. Technical challenges are discussed when using ATR for uneven, heterogeneous, high refractive index samples with possibility of frustrated total internal reflection (a breakdown of the ATR reflection-mode into transmission-mode). Local field enhancements at the interface are discussed with numerical/analytical examples. Maxwell’s scaling was used to model behaviour of absorber-scatterer inside materials at the interface with ATR prism for realistic complex refractive indices of bio-materials. Modality of ATR with polarisation analysis is proposed and its principle illustrated, opening an invitation for its experimental validation. The sensitivity of the polarised ATR mode to the refractive index between the sample and ATR prism is revealed. Design principles of polarisation active optical elements and spectral filters are outlined. The results and concepts are based on experiments carried out at the THz beamline of the Australian Synchrotron.

Organic light-emitting diodes (OLEDs) have ushered in a technological revolution with their remarkable impact on diverse sectors of our daily lives. These versatile devices known for their outstanding display and lighting capabilities, have become indispensable components in various industries including smartphones, tablets, televisions, and automotive applications. This paper delves into the development of a group of twisted donor-acceptor-donor (D-A-D) derivatives incorporating bicarbazole and benzophenone structures for potential use as blue emitters in OLEDs. The synthesized compounds, namely DB14, DB23 and DB29, were designed with various alkyl side chains to enhance their film forming properties as for blue emitters. Characterization and evaluation of these derivatives included photophysical, electrochemical, thermal, and electroluminescent analysis. Some of the OLED devices incorporating these emitters demonstrated promising performance with maximum current efficiency (CEmax) of 2.6 cd/A and external quantum efficiency (EQEmax) of 5.3%. Notably, the DB23 emitter exhibited the highest EQE among all devices. The study also emphasizes the influence of host-guest energy transfer, optimal doping concentrations, and molecular structures on device performance. This research not only contributes to the understanding of advanced OLED materials but also provides insights into designing efficient blue emitters for future display and lighting applications, thereby advancing the field of organic electronics.

This review focuses on an extensive synopsis of the recent improvements in the field of CO2 hydrogenation over structured zeolites including their properties, synthesis methods, and characterization approaches. The role of bimodal mesoporous structures surface oxygen vacancies, and the Si/Al ratio in improving catalytic activity is highlighted. The porosity, thermal stability, and structural integrity are also discussed in the review along with how structured zeolites are affected by electrical and plasma interactions. The synthesis methods are analyzed in detail by comparing the benefits and drawbacks of the bottom-up techniques, such as hard templating, soft templating, and non-templating on the one side; and the top-down approaches, e.g. dealumination, desilication, and recrystallization on the other. This review also draws attention to the necessity of overcoming the difficulties arising from these synthesis techniques, for example, micropore-induced diffusion restrictions and morphological constraints, employing innovative approaches and optimization strategies. Conventional and advanced characterization techniques are pointed out in this respect as crucial for understanding the catalytic mechanism and the dynamic behavior, hence, beneficial in the advancements of research on structured zeolites with higher efficiency and activity. The review ends with a perspective that highlights the necessity of further research in the improvement of synthesis approaches and characterization procedures of the structured zeolites to increase their catalytic activity in CO2 hydrogenation, a critical process of paramount importance in the global effort to reduce excessive carbon emissions and combat climate change..

Coded aperture 3D imaging techniques have been rapidly evolving in the recent years. The two main directions of evolution are in aperture engineering to generate the optimal optical field and in development of computational reconstruction to reconstruct the object’s image from the intensity distribution with a minimal noise. The goal is to find the ideal aperture-reconstruction method pair and if not, to optimize one to match the other for designing an imaging system with required 3D imaging characteristics. Lucy-Richardson-Rosen algorithm (LR²A), a recently developed computational reconstruction method was found to perform better than its predecessors such as matched filter, Weiner filter, phase-only filter, Lucy-Richardson algorithm and non-linear reconstruction (NLR) for certain apertures when the point spread function (PSF) is a real and symmetric function. For other cases of PSF, NLR performed better than the rest of the methods. In this tutorial, LR²A has been presented as a generalized approach for any optical field along with MATLAB codes for reconstruction of any image when the PSF is known. The common problems and pitfalls in using LR²A has been discussed. Simulation and experimental studies for common optical fields such as spherical, Bessel, vortex beams and exotic optical fields such as Airy, scattered and self-rotating beams have been presented. From this study, it can be seen that it is possible to transfer the 3D imaging characteristics from non-imaging type exotic fields to indirect imaging systems faithfully using LR²A. The application of LR²A to medical images such as colonoscopy images and cone beam computed tomography images with synthetic PSF has been demonstrated. We believe that the tutorial will provide a deeper understanding of computational reconstruction using LR²A.

The knowledge of what separates us genetically from our less-evolved relatives is crucial for gaining new biomedical insights about the human-chimpanzee relatedness for the use of appropriate stand-in towards the development of new treatments and diagnostic aids for various ailments. Although the genomes of humans and chimpanzees share 99% similarity, significant differences exist between the two species in their non-coding intronic regions. However, no work has been carried out in the aspects of target prediction concerning the ‘predicted homology’ in their microRNA sequences. Non-coding miRNAs which are post-transcriptional regulators of development, differentiation, growth, and metabolism, harboring the intronic regions may be crucial for expanding the horizons of our understanding. In this study, we proposed to perform the target prediction for the human-chimp miRNA homologs in the PHEX gene of the human X chromosome using various computational tools and databases. We identified a total of 1296 human miRNAs, 46, 957 gene targets, and 30, 563 targets of human and homologous chimp miRNAs respectively. Furthermore, we analysed gene interacting networks to identify the top interacting targets in both the species. Finally, we interpreted the biological importance of top-interacting miRNAs and their targets. The results demonstrated varying levels of multiplicity and cooperativity between the predicted miRNAs and target genes in the two genera. Such miRNAs may be responsible for the dysregulation of gene expression in several signaling pathways.

Oral cancer is a major public health burden worldwide. The lack of biomarkers for early diagnosis has increased the difficulty in managing this disease. Recent studies have reported that neutrophil gelatinase-associated lipocalin (NGAL), a secreted glycoprotein, is upregulated in various tumors. In our study we found that NGAL was significantly downregulated in primary malignant and metastatic tissues of oral cancer compared to normal tissues. The downregulation of NGAL was strongly correlated with the degree of differentiation and stage (I-IV), and can serve as a prognostic biomarker for oral cancer. Tobacco carcinogens were also found to be involved in the downregulation of NGAL. Mechanistic studies revealed that knockdown of NGAL increased oral cancer cell proliferation, survival, and migration, and also induced resistance against cisplatin. Silencing of NGAL activated mTOR signaling and reduced autophagy by the LKB1-AMPK-p53-Redd1 signaling axis. Moreover, cyclin-D1, Bcl-2, and MMP-9 were upregulated, and caspase-9 was downregulated, suggesting that silencing of NGAL increases oral cancer cell proliferation, survival, and migration. Thus, from our study it is evident that downregulation of NGAL activates the mTOR pathway and helps in the progression of oral cancer.

Direct imaging systems that create an image of an object directly on the sensor in a single step are prone to many constraints as a perfect image is required to be recorded within this step. In designing high resolution direct imaging systems with a diffractive lens, the outermost zone width either reaches the lithography limit or the diffraction limit itself imposing challenges in fabrication. However, if the imaging mode is switched to an indirect one consisting of multiple steps to complete imaging, then different possibilities open up. One such methods is the widely used indirect imaging method with Golay configuration telescopes. In this study, a Golay-like configuration has been adapted to realize a large area diffractive lens with three sub-aperture diffractive lenses. The sub-aperture diffractive lenses are not required to collect light and focus them to a single point as in a direct imaging system but to focus independently on different points within the sensor area. This approach of Large Area Diffractive lens with Integrated Sub-Apertures (LADISA) relaxes the fabrication constraints and allows the sub-aperture diffractive elements to have a larger outermost zone width and smaller area. The diffractive sub-apertures were manufactured using photolithography. The fabricated diffractive element has been implemented in indirect imaging mode using non-linear reconstruction and Lucy-Richardson-Rosen algorithm with synthesized point spread functions. The computational optical experiments revealed an improved optical and computational imaging resolutions compared to previous studies.

Ultra-short 230 fs laser pulses of 515 nm wavelength were tightly focused into 700 nm focal spots and utilised in opening ~ 400 nm nano-holes in a Cr etch mask that was tens-of-nm thick. Nano-holes ablated at slightly above the threshold of ablation irradiance became nano-disks and nano-rings at slightly lower pulse energies. Subtle sub-1 nJ pulse energy control was harnessed to pattern large surface areas with controlled nano-alloying of Si and Cr. This technique is extendable to vacuum-free large area patterning of nanolayers by alloying them at distinct locations with sub-diffraction resolution. Such metal masks with nano-hole opening can be used for formation of random patterns of nano-needles with sub-100 nm separation when applied to dry etching of Si.

A large body of evidence has linked unhealthy eating patterns with an alarming increase in obesity and chronic disease worldwide. However, existing methods of assessing dietary intake in nutritional epidemiology rely on food frequency questionnaires or dietary records that are prone to bias and selective reporting. Herein, metabolic phenotyping was performed on 42 healthy participants from the Diet and Gene Intervention (DIGEST) pilot study, a parallel two-arm randomized clinical trial that provided complete diets to all participants. Matching urine and plasma specimens were collected at baseline and following 2 weeks of provision of either a Prudent or Western diet with a weight-maintaining menu plan designed by a dietician. Targeted and nontargeted metabolite profiling was conducted using three complementary analytical platforms, where 80 plasma metabolites and 84 creatinine-normalized urinary metabolites were reliably measured (CV < 30%) in the majority of participants (> 75%) after implementing a rigorous data workflow for metabolite authentication with stringent quality control. We classified a panel of metabolites with distinctive trajectories following 2 weeks of food provisions when using complementary univariate and multivariate statistical models. Unknown metabolites associated with contrasting dietary patterns were identified with high resolution MS/MS and/or co-elution after spiking with authentic standards. Overall, 3-methylhistidine and proline betaine concentrations increased consistently after participants were assigned a Prudent diet (q< 0.05) in both plasma and urine samples with a corresponding decrease in the Western diet group. Similarly, creatinine-normalized urinary imidazole propionate, hydroxypipecolic acid, dihydroxybenzoic acid, and enterolactone glucuronide, as well as plasma ketoleucine and ketovaline increased with a Prudent diet (p< 0.05) after adjustments for age, sex and BMI. In contrast, plasma myristic acid, linoelaidic acid, linoleic acid, a-linoleic acid, pentadecanoic acid, alanine, proline, carnitine and deoxycarnitine, as well as urinary acesulfame K increased among participants following a Western diet. Most metabolites were also correlated (r > ±0.30, p< 0.05) to changes in average intake of specific nutrients from self-reported diet records reflecting good adherence to assigned food provisions. Our study revealed robust biomarkers sensitive to short-term changes in habitual diet for accurate monitoring of healthy eating patterns in free-living populations, which is required for validating evidence-based public health policies for chronic disease prevention.

Robotic path planning and navigation algorithms enable autonomous mobile robots to navigate complex environments. These algorithms allow robots to be deployed in search and rescue missions, where they can locate victims in hazardous areas inaccessible to humans. Additionally, autonomous driving algorithms hold the potential to revolutionize transportation by enabling self-driving cars to navigate roads safely and efficiently. This paper focuses on developing an algorithm for autonomous navigation of the mobile robot (Tortoise bot) using robot operating system (ROS), based on publish-subscribe communication facilitating seamless interaction between various nodes. Tortoise Bot is a differential wheeled robot whose movement is based on two separately driven wheels placed on either side of the robot body. The Raspberry Pi acts as the controller. A 360-degree LiDAR and RGB-D camera are used for creating a 3D map of the environment. Various state-of-the art software tools, such as Gazebo, Ubuntu, MATLAB and RPI imager are used. Initially, a simulation based on simple obstacle avoidance is done using gazebo and MATLAB. Another simulation based on trajectory tracking is implemented using MATLAB to study the kinematic model. Online navigation and path planning are executed with the help of teleoperation and Dynamic Window Approach algorithm. A novel Wander algorithm is created that avoids obstacles on its path. Object recognition is also implemented during autonomous navigation using YOLO.

2,5-Dihydroxyacetophenone (DHAP) is an active compound obtained from Radix Rehmanniae Preparata, which is widely used as a herbal medicine in many Asian countries. DHAP has been found to possess anti-inflammatory, anti-anxiety, and neuroprotective qualities. For the present study, we evaluated the anti-cancer effects of DHAP on multiple myeloma cells. It was discovered that DHAP downregulated the expression of oncogenic gene products like Bcl-xl, Bcl-2, Mcl-1, Survivin, Cyclin D1, IAP-1, Cyclin E, COX-2, and MMP-9, and upregulated the expression of Bax and p21 proteins, consistent with the induction of G2/M phase cell cycle arrest and apoptosis in U266 cells. DHAP inhibited cell proliferation and induced apoptosis, as characterized by the cleavage of PARP and the activation of caspase-3, caspase-8, and caspase-9. Mitogen-activated protein kinase (MAPK) pathways have been linked to the modulation of the angiogenesis, proliferation, metastasis, and invasion of tumors. We therefore attempted to determine the effect of DHAP on MAPK signaling pathways, and discovered that DHAP treatment induced a sustained activation of JNK, ERK1/2, and p38 MAPKs. DHAP also potentiated the pro-apoptotic and anti-proliferative effects of bortezomib in U266 cells. Our results suggest that DHAP can be an effective therapeutic agent to target multiple myeloma.

THz band-pass filters were fabricated by laser ablation of micro-foils of stainless steel and Kapton. Their spectral performance was tested in transmission and re- flection at the THz beamline at the Australian Synchrotron (AuSy). A 25 μm Kapton film performed as a Fabry-Pérot etalon with a free spectral range of FSR = 119 cm−1, high finesse Fc ≈ 17, and was tuneable over ~10 μm (at ~5 THz band) with β = 30° tilt. The structure of the THz beam focal region as extracted by the first mirror (slit) shows a complex polarisation-wavelength- position dependence across the beam. This is important for polarisation sensitive measurements (in transmission and reflection) and requires normalisation at each orientation of linear polarisation.

Several recent surges in COVID-19 cases due to newly emerging variant strains of SARS-CoV-2 with greater transmissibility have highlighted the virus’s capability to directly modulate spike-ACE2 interactions and promote immune evasion by sterically masking the immunogenic epitopes. Recently, there have also been reports of the bidirectional transfer of coronavirus between different animal species and humans. The ability of coronavirus to infect and adapt to a wide range of hosts can be attributed to new variants that modify the molecular recognition profile of the spike protein (S protein). The receptor-binding domain of the spike protein specifically interacts with key host receptor molecules present on the host cell membranes to gain entry into the host and begin the infection cycle. In this review, we discuss the molecular, structural, and functional diversity associated with the coronavirus receptors across their different phylogenetic lineages and its relevance to various symptomatology in the rapid human-to-human infection in COVID-19 patients, tropism, and zoonosis. Despite this seeming diversity of host receptors, there may be some common underlying mechanisms that influence the host range, virus transmissibility, and pathogenicity. Understanding these mechanisms may be crucial in not only controlling the ongoing pandemic but also help in stopping the resurgence of such virus threats in the future.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized mainly by the gradual decay in neuronal function as a consequence of diverse degenerating events primarily including mitochondria dysfunction and cascades of neuro-immune reactions. Besides the acquired harmful reactive oxygen species (ROS), neurotoxins, and amyloid-beta (Aβ) and TAU pathologies in neurons, accumulating evidence with time underlined the roles of cytokines and growth factors in the AD pathogenesis. It may help us in evaluating the propensities and specific mechanism(s) of cytokines and factors impacting neuron upon apoptotic decline. Proinflammatory cytokines often induce inflammation in AD and AD-like pathogenesis in response to the apoptotic scenarios where some growth factors are involved in cytokinetic reactions to activate microglia and causing inflammation in AD. In this report, we comprehensively reviewed role of cytokines and chemokines in immune response to AD and neuropsychiatry. We provided insights into the neuroinflammation and the role of diverse factors including the pro-/anti-inflammatory cytokines, APP, TAU phosphorylation, glycation end products, complement system, and the role of glial cells. Also, we discussed the pathogenic and protective role of macrophage migration inhibitory factors, choroid plexus-, neurotrophic- and hematopoietic -related growth factors in AD. We further shed light on the availability and accessibility of the cytokines across the blood-brain barrier in AD pathophysiology. Taken together, the emerging role of these factors in AD pathology emphasized the importance of building novel strategies for an effective therapeutic/neuropsychiatric management of AD in clinics.

In the last decade, the tremendous improvement in the sensitivity and also affordability of Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) have revolutionized its application in pharmaceutical analysis, resulting in wide-spread of employing LC-MS/MS for determining pharmaceutical compounds including anticancer drugs in pharmaceutical research and also industries. Currently, LC-MS/MS has been widely used to quantify small molecule oncology drugs in various biological matrices to support preclinical and clinical Pharmacokinetic studies in R & D of oncology drugs. This mini-review article will describe the state-of-the art LC-MS/MS and its application in rapid quantification of small molecule anticancer drugs. In addition, efforts have also been made in this review to address several key aspects in the development of rapid LC-MS/MS methods, including sample preparation, chromatographic separation and matrix effect evaluation.

A sensitive and robust liquid chromatography-tandem mass spectrometric (LC-MS/MS) method was developed and validated for the determination of nimbolide in mouse serum. Exemestane was used as the internal standard (IS). Here, we employed acetonitrile-based protein precipitation (PPT) for serum sample preparation, and performed chromatographic separation using an ODS Hypersil C18 column (100×2.1 mm, 5µm) with gradient elution (0.1% formic acid in water vs 100% acetonitrile). The run time was 6 min. Instrumental analysis was performed by electrospray ionization tandem mass spectrometry (ESI-MS/MS) in the multiple-reaction monitoring (MRM) under positive mode. A good linear calibration was achieved in the 5–1000 ng/ml range. The intra- and inter-day precisions for nimbolide were ≤ 12.6% and ≤ 13.9 % respectively. Intra-day accuracy ranged from 96.9% – 109.3% while inter-day accuracy ranged from 94.3% – 110.2%. The matrix effect of nimbolide, detected but consistent at low and high concentrations, do not affect linearity of standard curve. In conclusion, we have developed and validated a sensitive analytical method for determination of a novel natural compound nimbolide in mouse serum and it has been successfully applied to our preclinical study in investigating the pharmacokinetic properties of nimbolide, which could greatly facilitate the preclinical development of the promising lead compound for anticancer therapy.

Phase imaging of biochemical samples has been demonstrated for the first time at the Infrared Microspectroscopy (IRM) beamline of the Australian Synchrotron using the usually discarded Near-IR (NIR) region of the synchrotron-IR beam. The synchrotron-IR beam at the Australian Synchrotron IRM beamline has a unique fork shaped intensity distribution as a result of the gold coated extraction mirror shape, which includes a central slit for rejection of the intense X-ray beam. The resulting beam configuration makes any imaging task challenging. For intensity imaging, the fork shaped beam is usually tightly focused to a point on the sample plane followed by a pixel-by-pixel scanning approach to record the image. In this study, a pinhole was aligned with one of the lobes of the fork shaped beam and the Airy diffraction pattern was used to illuminate biochemical samples. The diffracted light from the samples was captured using a NIR sensitive lensless camera. A rapid phase-retrieval algorithm was applied to the recorded intensity distributions to reconstruct the phase information corresponding to different planes. The preliminary results are promising to develop multimodal imaging capabilities at the IRM beamline of the Australian Synchrotron.

Polarisation analysis in the mid-infrared fingerprint region was carried out on thin (∼1μm) Si and SiO2 films evaporated via glancing angle deposition (GLAD) method at 70∘ to the normal. Synchrotron-based infrared microspectroscopic measurements were carried out on the Infrared Microspectroscopy (IRM) beamline at Australian Synchrotron. Specific absorption bands, particularly Si-O-Si stretching vibration, was found to follow the angular dependence of ∼cos2θ, consistent with the absorption anisotropy. This unexpected anisotropy stems from the enhanced absorption in nano-crevices, which have orientation following the cos2θ angular dependence as revealed by Fourier transforming the image of the surface of 3D columnar films and numerical modeling of light field enhancement by sub-wavelength nano-crevices.

Springs play a significant role in maintaining hydrological balance in the mountain ecosystem. The communities living in the mountain tops rely entirely on spring water for their daily needs. In recent decades, spring water discharges have decreased drastically in the Himalayan regions and become seasonal. Remote sensing and geographical information system techniques have proved to be essential for understanding the sustainable development of groundwater, particularly where people rely solely on spring water in the mountainous regions of the Himalayas. This study employs the Analytical Hierarchy Process (AHP) to identify groundwater potential zones. It involves overlaying seven thematic layers concerning elements of physical features and land-use/land-cover of the region, using the weighted overlay toolbox in ArcGIS, with each layer being assigned a weight based on its importance. Based on the results, groundwater potential zones have been classified into five categories: poor, fair, good, very good, and excellent. The study found that poor groundwater potential zones cover 1.1% of the area, fair zones cover 27.8%, good zones cover 23.3%, very good zones cover 26.6%, and excellent zones cover 21.2% of the total area. The excellent to very good potential zones are associated with perennial springs that have higher discharge (4.30-30.92 l/m), lineaments, rainfall, forested land, and porous soils, favoring the potentiality of the springs. The fair and poor groundwater potential zones are associated with seasonal flowing springs, consisting of barren land, less rainfall, and low fracture density, which influence the potentiality of groundwater. The validation of the groundwater potential zones indicated an Area Under Curve (AUC) value of 0.88, which shows good agreement with observed spring water locations. The identified groundwater potential zones are valuable for prioritizing the area for the sustainable development of spring water. Particularly, it is helpful for policymakers and government bodies to be involved in developing strategies for sustainable water management at the micro-watershed level and safeguarding spring water resources using integrated watershed management in the headwater region of the Himalayas.

A novel, rapid and sensitive liquid chromatography-tandem mass spectrometric (LC-MS/MS) method was developed and validated for the evaluation of OTS167 pharmacokinetics in mouse serum. SN-38 was optimized to be selected as the internal standard. Chromatographic separation was performed on a BDS Hypersil C18 column (100 x 2.1 mm, 5 µm) using gradient elution with mobile phase solvent A as water containing 0.1% formic acid and solvent B as acetonitrile containing 0.1% formic acid. The analysis was carried out using multiple reaction monitoring (MRM) with a triple-quadrupole mass spectrometer operated in the positive electrospray ionization mode. Mass transitions of 487.2 > 348.0 and 393.2 > 349.2 were monitored for OTS167 and SN-38 respectively. The standard calibration curve demonstrated high linearity at a range of 5-1000 ng/mL, with a coefficient of determination (r2) ≥ 0.996. The accuracy for OTS167 ranged from 92.3% to 102.2% and the precision was ≤ 12.7%. Recovery was consistent at about 83% to 89%. No significant matrix effect was observed. This method was successfully applied to monitor the pharmacokinetic profiles in mice over 24 h after ingestion of 5 mg/kg of OTS167. Maximum serum concentrations (3197 ng/mL) of OTS167 was observed at 1.67 hour after OTS167 ingestion via garage at 5 mg/kg and area under the curve (AUC) was 28579 h*ng/mL, suggesting that OTS167 can be quickly absorbed after oral administration.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). This review paper provides a comprehensive overview of fNIRS, EEG, and fMRI, including their principles, advantages, limitations, and integration with other modalities for advanced BCIs. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Applications of advanced BCIs in clinical, assistive, gaming, and entertainment domains are also discussed. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature on data acquisition approaches and a comparison of the ease of use for each of the modalities.