Total Twenty-Four Samples were collected from the Four different depths of Prayagraj, Uttar Pradesh near the bank of Yamuna River and determined Phosphorous (P) fractions and their relationship with soil properties, The soils were Neutral, low salinity and medium to rich in organic matter in the surface to subsurface horizons. Most of the soils showed Low range in nitrogen and phosphorous and high in potassium. Among the P fractions, the most predominant were Ca-P and followed by Fe-P > S-P > Occl-P > Red-P > Al-P. The Overall results showed that the available-P, organic-P and total-P decreases with increase the depth, Where the Ca-P was significantly positively correlated with pore space (r = 0.396*), Organic Carbon ( r = 0.555*), Available Nitrogen ( r = 0.55*) and significantly negatively correlated with Clay (r = -0.324*), pH ( r = -0.226*), EC ( r = -0.232*). Total-P and Occ-P are main contributing factors for the availability of P. The regression equations showed that soil pH was the main soil property that influences the different fraction of P. The Nutrient Index of available P for Inceptisols was found under medium to low category, about 60% of Inceptisols are likely to respond of P-fertilizer.

Iron and zinc are essential micronutrients required for growth and health. Deficiencies of these nutrients are highly prevalent among populations, but can be alleviated by supplementation. Cross-sectional studies in humans showed positive association of serum zinc levels with hemoglobin and markers of iron status. Dietary restriction of zinc or intestinal specific conditional knock out of ZIP4 (SLC39A4), an intestinal zinc transporter, in experimental animals demonstrated iron deficiency anemia and tissue iron accumulation. Similarly increased iron accumulation has been observed in cultured cells exposed to zinc deficient media. These results together suggest a potential role of zinc in modulating whole body iron metabolism. Studies in intestinal cell culture models demonstrate that zinc induces iron uptake and transcellular transport via induction of divalent metal iron transporter-1 (DMT1) and ferroportin (FPN) expression, respectively. It is interesting to note that intestinal cells are exposed to very high levels of zinc through pancreatic secretions, which is a major route of zinc excretion from the body. Therefore, zinc appears to be modulating the iron metabolism possibly via regulating the DMT1 and FPN1 levels. Herein we critically reviewed the available evidence to hypothesize novel mechanism of Zinc-DMT1/FPN axis in regulating intestinal iron absorption and tissue iron accumulation to facilitate future research aimed at understanding the yet elusive mechanisms of iron and zinc interactions.

The threat of antimicrobial resistance to society is compounded by a relative lack of new clinically effective licensed therapies reaching patients over the past three decades. This has been particularly problematic within antifungal drug development leading to a rise in fungal infection rates and associated mortality. This paper highlights the potential of an ultrashort peptide, (naphthalene-2-ly)-acetyl-diphenylalanine-dilysine-OH (NapFFKK-OH), encompassing hydrogel-forming and antifungal properties within a single peptide motif, thus overcoming formulation (e.g. solubility, drug loading) issues associated with many current employed highly hydrophobic antifungals. A range of fungal susceptibility (colony counts) and cell cytotoxicity (MTS cell viability, LIVE/DEAD staining® with fluorescent microscopy, haemolysis) assays were employed. Scanning electron microscopy confirmed the nanofibrous architecture of our self-assembling peptide, existing as a hydrogel at concentrations of 1% w/v and above. Broad-spectrum activity was demonstrated against a range of fungi clinically relevant to infection (Aspergillus niger, Candida glabrata, Candida albicans, Candida parapsilosis and Candida dubliniensis) with greater than 4 log10 CFU/mL reduction at concentrations of 0.5% w/v and above. We hypothesise antifungal activity is due to targeting of anionic components present within fungal cell membranes resulting in membrane disruption and cell lysis. NapFFKK-OH demonstrated reduced toxicity against mammalian cells (NCTC 929, ARPE-19) suggesting increased selectivity for fungal cells. However, further studies relating to safety for systemic administration is required, given the challenges toxicity has presented in the wider context of antimicrobial peptide drug development. Overall this study highlights the promise of NapFFKK-OH hydrogels, particularly as a topical formulation for the treatment of fungal infections relating to the skin and eyes, or as a hydrogel coating for the prevention of biomaterial related infection.

The Covid-19 pandemic has resulted in the closure of schools at every level, globally, forcing education to move online. Meeting the needs of students online for Science Lab classes, in particular, is a challenge since the physical labs are not available to the teachers or students. OLabs is a virtual Science Lab providing a complete learning environment of theory, experimental procedures, videos, animations, simulations, and assessments that capture real lab experiences with the relevant pedagogy. This study looks at the acquisition and behaviors of users, on the OLabs platform, during pre and Covid-19 times. Using Google Analytics, we observe that, during the pandemic time, users increasingly adopted OLabs as a new learning pedagogy for performing experiments as indicated by parameters like the number of users; the number of unique pages viewed per session; time spent on viewing content; bounce rate; and preference for content types such as theory, simulations, videos, and animations.

India is ranked 5th in world in terms of Covid-19 publications accounting for 6.7% of the total. About 60% of the Covid-19 publications in the year 2020 are from United States, China, UK, Italy, and India. We present a bibliometrics analysis of the publi-cation trends and citation structure along with identification of major research clusters. By performing network analysis of authors, citations, institutions, key-words, and countries, we explore semantic associations by applying visualization techniques. Our study shows lead taken by United States, China, UK, Italy, India in Covid-19 research may be attributed to the high prevalence of Covid-19 cases in those countries witnessing the first outbreak and also due to access to Covid-19 data, access to labs for experimental trials, immediate funding, and overall support from the govt. agencies. Large number of publications and citations from India are due to co-authored publications with countries like United States, UK, China, and Saudi Arabia. Findings show health sciences with highest the number of publications and citations, while physical sciences and social sciences and humanities counts were low. A large proportion of publications fall into the open access category. With India as focus, by comparing three major pandemics SARS, MERS, Covid-19 from biblio-metrics perspective, we observe much broader involvement of authors from multiple countries for Covid-19 studies as compared to SARS and MERS. Finally, by applying bibliometric indicators, we see an increasing number of sustainable develop-ment-related studies from the Covid-19 domain, particularly concerning the topic of good health and well-being. This study allows for a deeper understanding on how the scholarly community from a populous country like India pursued research in the midst of a major pandemic which resulted in closure of scientific institutions.

Climate change is a buzzword in the world. Scientist has approved it as global warming with its projection of undesired and unpredicted frequent extreme events and their vulnerabilities not only at present but also at future. There is an assumption of occurrence of adaptive capacity and behavior of farmers in agriculture production activity at some extent to neutralize climate change vulnerabilities of flood and landslides on paddy production. This paper empirically examines the effects of climate change in paddy production and farmer’s adaptive behaviors to neutralize such climatic shocks and events in paddy production by employing CD production function based econometric model. The study employed primary data collected through 642 household surveys. The study finds that climatic shocks and events have huge loss (60%) in paddy production and revenue income in such plot where farmers have not indigenous knowledge and practices. But both small and larger farmers who have adaptive capacity and behavior with their indigenous knowledge have less loss in paddy production and revenue income, although they have heterogeneity in their socio-economic characteristics (income, asset holding, literacy, experience, land holding and age). The farmers who have used adaptive behavior have indigenous knowledge and experiences including bamboo wall construction to control flood and landslides and seed change to resist climatic shocks and events. In hilly region, the farmers have not sufficient alternative measures, except both adaptive measures because of their poverty, illiteracy and remote locations. The study finds their higher effective level to minimize vulnerabilities to paddy production and revenue per farm plot, although these adaptive behaviors are cost effective and local entity. Comparatively, bamboo wall construction is more effective measure in the paddy production than others are (seed switch) to minimize the flooding materials from the flood and the landslides. Thus, low cost indigenous adaption behavior of farmers is effective measure to climate change and climate change induced disasters and events vulnerability in paddy production.

Interpretive Structural Modelling (ISM) is widely used to understand the complex connections between different components. This study presents a bibliometric overview of ISM research with a focus on its linkages to the Sustainable Development Goals (SDG) and the impact of COVID-19. The study analyzed 1988 publications on ISM published between 2012 and 2021, of which 1202 were directly mapped to the SDG and 59 were related to COVID-19. The study identified key authors, institutions, countries, and journals involved in the research and their linkages to the SDG. The results showed that ISM research is strongly linked to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). We also identified influential SDG based on centrality measures like betweenness and eigenvector. The top four countries contributing to ISM publications were India, China, the United Kingdom, and the United States. The most frequently cited journals were Benchmarking: An International Journal, Sustainability, Journal of Modelling in Management, and Journal of Cleaner Production. Four main clusters were identified in the ISM research, including (1) integration with AHP & Fuzzy Logic for promoting sustainability alignment, (2) ISM-based strategy development for various stakeholders, (3) ISM-based decision-making in various fields, and (4) ISM-based risk evaluation. For the first time studies that used ISM approach to understand the epidemiological characteristics of COVID-19 were identified and their key findings were discussed. The study also identified several emerging topics for future ISM research, such as blockchain & IoT, environmental management systems, climate change adaptation, smart cities, and humanitarian logistics and their potential linkages to SDG.

Drugs are known to cause substantial liver injury, with hepatotoxicity ranging from asymptomatic increase of liver enzymes to hepatic failure. HIV/AIDS patients, with compromised immune systems, are especially vulnerable to opportunistic infections such as tuberculosis (TB). Patients with co-infection frequently get combined antiretroviral (ART) and antitubercular (ATD) regimens, which can cause hepatotoxicity. Hepatotoxicity has been linked to commonly administered antiretroviral drug regimens as well as first-line antitubercular agents such isoniazid, rifampin, and pyrazinamide, which are required for the treatment of both drug-sensitive and drug-resistant tuberculosis. As a result, there is an urgent need to investigate treatments based from traditional herbs known for their hepatoprotective abilities against ART and ATD-induced liver injury. Many researchers have investigated the effectiveness of medicinal plants in treating chemically caused hepatotoxicity. Medicinal plants are high in phytochemicals and secondary metabolites, both of which have a variety of medicinal benefits. Currently, there is a lack of specific therapeutic regimens to address this adverse effect, and management often relies on symptomatic treatment or therapy withdrawal. Traditional medicinal plants may have the potential to restore normal liver function, enzyme activity, and cellular structure in response to ART and ATD-induced hepatotoxicity. This review aims to examine the scientific evidence supporting the use of medicinal plants as hepatoprotective agents against liver damage induced by ART and ATDs.

Gas turbine engines are widely used in various industries such as aviation, power generation, and marine propulsion due to their high efficiency and reliability. The combustion chamber of a gas turbine engine design contains a number of elements, including as the chamber's size and form, the fuel injection system, the ignition system, and the cooling system and it plays a critical role in the performance and efficiency of the engine. This research discusses the fabrication of a combustion chamber model for a gas turbine engine. The aim of this research is to develop a combustion chamber that is cost-effective, easily available, and can be fabricated in less time. The research covers the different types of combustion chambers used in gas turbine engines and compares their advantages and disadvantages. It then selects the best type of combustion chamber for the gas turbine engine based on its cost, availability, and fabrication time, Efficiency and high power to fuel ratio. The report also describes the fabrication process involved, including the techniques and materials used, along with safety precautions and environmental protection measures. Lastly, the research suggests future improvements and potential applications for the fabricated combustion chamber model.

Simulation-Based Medical Education that uses Virtual Patients has become increasingly important during the COVID-19 pandemic. With the need for social distancing and minimizing contact, medical simulation technology has provided a valuable tool for healthcare professionals to practice and improve their skills without the need for face-to-face interactions. MedSIM is a medically accurate simulation platform with Virtual Patients designed for undergraduate medical education. Our study involved two groups of students. The PreCOVID group, before the pandemic, underwent conventional teaching methods. The COVID group, during the pandemic, had students exposed to conventions skills taught earlier and were taught again with MedSIM. Students indicated high satisfaction with the clinical skills demonstrated by the Virtual Patients. More than half agreed that MedSIM had enabled them to perform all kinds of procedures on patients (PreCOVID group 68.8%, COVID group 71.3%), showed cues and consequences much like those in natural clinical environments (PreCOVID 68.4%, COVID 71.3%). Also, MedSIM allowed them to have a repetitive practice that helps in critical skills transfer to actual patients (PreCOVID 72.7%, COVID 74.7%). MedSIM met the expectations of most of the students. Students from both groups rated the online performance of the MedSIM simulator as "Very good." Analysis from a customized word cloud indicated that most students found MedSIM to be good and of educational value. MedSIM platform enhances healthcare professionals' skills and knowledge, leading to better patient outcomes and increased access to healthcare, supporting SDG 3 (Good Health and Well-being). It also provides a safe and controlled environment for healthcare professionals to learn and practice essential skills, supporting SDG 4 (Quality Education).

Climate change is a buzzword in the world. Scientist has approved it as global warming with its projection of undesired and unpredicted frequent extreme events and their vulnerabilities not only at present but also at future. There is an assumption of occurrence of adaptive capacity and behavior of farmers in agriculture production activity at some extent to neutralize climate change vulnerabilities of flood and landslides on paddy production. This paper empirically examines the effects of climate change in paddy production and farmer’s adaptive behaviors to neutralize such climatic shocks and events in paddy production by employing CD production function based econometric model. The study employed primary data collected through 642 household surveys. The study finds that climatic shocks and events have huge loss (60%) in paddy production and revenue income in such plot where farmers have not indigenous knowledge and practices. But both small and larger farmers who have adaptive capacity and behavior with their indigenous knowledge have less loss in paddy production and revenue income, although they have heterogeneity in their socio economic characteristics (income, asset holding, literacy, experience, land holding and age). The farmers who have used adaptive behavior have indigenous knowledge and experiences including bamboo wall construction to control flood and landslides and seed change to resist climatic shocks and events. In hilly region, the farmers have not sufficient alternative measures, except both adaptive measures because of their poverty, illiteracy and remote locations. The study finds their higher effective level to minimize vulnerabilities to paddy production and revenue per farm plot, although these adaptive behaviors are cost effective and local entity. Comparatively, bamboo wall construction is more effective measure in the paddy production than others are (seed switch) to minimize the flooding materials from the flood and the landslides. Thus, low cost indigenous adaption behavior of farmers is effective measure to climate change and climate change induced disasters and events vulnerability in paddy production.

The Epithelial- Mesenchymal Transition (EMT) is a biological phenomenon associated with explicit phenotypic and molecular changes in cellular traits. Unlike the earlier-held popular belief of it being a binary process, EMT is now thought of as a landscape including diverse hybrid E/M phenotypes manifested by varying degrees of the transition. These hybrid cells can co-express both epithelial and mesenchymal markers and/or functional traits, and can possess the property of collective cell migration, enhanced tumor-initiating ability, and immune/targeted therapy-evasive features, all of which are often associated with worse patient outcomes. These characteristics of the hybrid E/M cells have led to a surge in studies that map their biophysical and biochemical hallmarks that can be helpful in exploiting their therapeutic vulnerabilities. This review discusses recent advances made in investigating hybrid E/M phenotype(s) from diverse biophysical and biochemical aspects by integrating live cell-imaging, cellular morphology quantification and mathematical modeling, and highlights a set of questions that remain unanswered about the dynamics of hybrid E/M states.

COVID-19 patients suffer from detrimental effects of cytokine storm and not much success has been achieved to overcome this issue. We sought to test the ability of selenium in reducing the impact of two important cytokine storm players; IL-6 and TNF-a. The effects of four selenium compounds were evaluated on the secretion of these cytokines from THP-1 macrophages in vitro following LPS challenge. Also, potential impact of methylseleninic acid (MSeA) on Nrf2 and IkBa was determined following short treatment of THP-1 macrophages. MSeA was observed to be the most potent selenium form to reduce IL-6 and TNF-a levels among the four selenium compounds tested. In addition, an increase in Nrf2 and decrease in pIkBa in human macrophages was observed following MSeA treatment. Our data indicate that COVID-19 patients might benefit by suppressing their cytokine storm with addition of MSeA to the standard therapy.

Cytokine and AMH profiling were done in prepubertal Murrah buffalo heifers post-progesterone supplementation and co-synchronization protocol. Cytokines: IFNγ, IL6, IL1, IL13, TNFα, and TGFβ, and, AMH, Progesterone and Estrogen hormone estimations were done. Ovarian follicular pattern and fertility outcome were recorded. Eighteen prepubertal heifers, 15-17 months of age, 250-300 kg BWt were randomly divided into 3 groups, Group1: n=6, supplemented with altrenogest 0.044mg/kg/day/heifer orally for 14 days along with Co-synch programme; Group2: n=6, administered CIDR for 14 days and co-synch programme, Group3: n=6, received co-synch programme. Ultrasonography was done to ovarian follicle status on respective days of sampling. Pregnancy diagnosis was done around 45 and 60 post-AI. Estradiol 17-β level remained constant in altrenogest group, CIDR implanted and Control groups during the Progesterone treatment and Co-synch treatment. Estradiol levels during Co-synch were significantly less on day 9 of Co-synch (14.41 ± 1.97 pg/mL) than day 0 and on day7 in pre pubertal buffalo heifers in altrenogest group. However, no significance was observed in other groups. Progesterone levels in buffalo heifers subjected to synchronisation protocols varied significantly (P< 0.05) on day 7 of P4 treatment between altrenogest supplemented and CIDR implanted groups, as well as, Co-synch groups. Supplementation of altrenogest showed a significant (P= 0.043) increase in Progesterone levels by day 14 of altrenogest treatment. Progesterone varied significantly in all Groups on day 9 of co synch protocol [ Group 1 (P= 0.02), Group 2 (P= 0.041) and Group 3 (P= 0.007)]. Cytokine IFN-γ showed high correlation with progesterone, indicating the role of IFN-γ in puberty in buffalo heifers (r= 0.626, P< 0.01). AMH had a significant positive correlation supplemented with altrenogest with IFN-γ (r= 0.673, P< 0.01) and TGF-β (r= 0.463, P< 0.01), whereas, with TNF-α it was a negatively correlated (r= -0.34, P< 0.05). Based on Karl Pearson correlation coefficients, IL-13 and TGF-β could be considered as markers for puberty in buffalo heifers.

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics and spatial transcriptomics now allow recording the patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multi-scale dynamics of its evolution. Here, we review latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed a burgeoning growth in recent past in terms of mapping heterogeneity within tumor cell types as well as stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor, and a more systematic investigation of implications of heterogeneity for the patient outcomes.

Despite advances in algorithms and computational platforms, de-novo genome assembly remains a challenging process. Due to the constant innovation in sequencing technologies (Sanger, SOLiD, Illumina, 454, PacBio and Oxford Nanopore), genome assembly has evolved to respond to the changes in input data type. This paper includes a broad and comparative review of the most recent short-read, long-read and hybrid assembly techniques. In this review, we provide (1) an algorithmic description of the important processes in the workflow that introduces fundamental concepts and improvements; (2) a review of existing software that explains possible options for genome assembly; and (3) a comparison of the accuracy and the performance of existing methods executed on the same computer using the same processing capabilities and using the same set of real and synthetic datasets. Such evaluation allows a fair and precise comparison of accuracy in all aspects. As a result, this paper identifies both the strengths and weaknesses of each method. This comparative review is unique in providing a detailed comparison of a broad spectrum of cutting-edge algorithms and methods.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed nonalcoholic fatty liver disease (NAFLD), is a widespread global health concern that affects around 25% of the global population. Its influence is expanding, and it is anticipated to overtake alcohol as the leading cause of liver failure and liver-related death worldwide. Unfortunately, there are no approved therapies for MASLD; as such, national and international regulatory health agencies undertook strategies and action plans designed to expedite the development of drugs for treatment of MASLD. A sedentary lifestyle and an unhealthy diet intake are important risk factors. Western countries have a greater estimated prevalence of MASLD partly due to lifestyle habits. Mitochondrial dysfunction is strongly linked to the development of MASLD. Further, it has been speculated that mitophagy, a type of mitochondrial quality control, may be impaired in MASLD. Thyroid hormone (TH) coordinates signals from the nuclear and mitochondrial genomes to control mitochondrial biogenesis and function in hepatocytes. Mitochondria are known TH targets and preclinical and clinical studies suggest that TH, thyroid receptor β (TR-β) analogs, and synthetic analogs specific to liver could be of therapeutic benefit in treating MASLD. In this review, we highlight how mitochondrial dysfunction contributes to development of MASLD, and how understanding the role of TH in improving mitochondrial function paved the way for innovative drug development programs of TH-based therapies targeting MASLD.

Machine Learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a Support Vector Machine (SVM) for classification. Results showed that this approach achieved an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates the mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

Coronavirus Disease-19 (COVID-19) is presently wreaking havoc on public health and socio-economic development. Besides the upper and lower respiratory tract involvement, gastrointestinal symptoms are also reported in COVID-19 patients through gut-lung axis. Finding its way through the feces of infected individuals and other sources, the genetic material of SARS-CoV-2 (ssRNA) is reported widely in wastewater and is being used as a fingerprint for its detection. With millions of cases arriving every day, there is a need to level up the testing speed efficiency. Due to the restricted sampling potential of testing laboratories, clinical testing is unable to track all the symptomatic and asymptomatic cases. Wastewater-based epidemiology (WBE) bestows an auxiliary monitoring tool that will contribute in community level screening. Sample collection, concentration, RNA extraction, quantification and data analysis are the main steps involved in implementation of WBE that can be relied upon as an alarm call for an upcoming wave, emergence of a new variant or any future pandemic. WBE can be a cheaper and more practical alternative to high end and sophisticated clinical testing for community transmission detection. Worldwide, there are more than 300 reports entailing the occurrence of SARS-CoV-2 in wastewater exhibiting unique temporal trends with five of them in India. This review aims to address the present knowledge on surveillance of SARS-CoV-2 in wastewater and its implications.

Cancer is a highly heterogeneous disease and remains a global health challenge affecting millions of human lives worldwide. Despite advancements in conventional treatments like surgery, chemotherapy, and immunotherapy, the rise of multidrug resistance, tumor recurrence, and severe side effects necessitates innovative therapeutic approaches. The complex nature of the tumor microenvironment (TME) further compromises the efficacy of traditional chemotherapy drugs. Recently, stimulus-responsive nanomedicines designed to target TME characteristics (e.g., pH, redox, enzymes) have gained attention for their potential to enhance anticancer efficacy while minimizing adverse effects. Among various nanocarriers, hydrogels are an interesting class of nanocarriers used in cancer therapy due to their high water content, adjustable mechanical characteristics, and ability to respond to external/internal stimuli. These properties make hydrogels an ideal nanocarrier for controlled drug release within the TME. This review comprehensively surveys the latest advancements in stimulus-responsive hydrogels for cancer therapy, exploring various stimuli-responsive mechanisms, including biological (e.g., pH, redox), chemical (e.g., enzymes, glucose), and physical (e.g., temperature, light), as well as dual- or multi-stimuli responsiveness. This review will offer novel perspectives on the development of stimulus-responsive hydrogels for cancer therapy.

MiRNAs are 20-22 nucleotide long single-stranded non-coding RNA sequences, which can regulate post transcriptional activity of mRNA by binding with it at 3’UTR region (untranslated region). Thus deregulation of miRNA expression is responsible for dysregulating mRNA function which contributes in developing various diseases as well as cancerous phenotypes. Alteration of single nucleotide in miRNA sequence is one of the reasons behind deregulation of miRNA expression. The most frequent carcinoma in current day is breast cancer which causes a high mortality among women around the world as well as India. Despite of the advancement of diagnostic tools, strategies and treatment, the cases of breast cancer is increasing every year. There are plenty of biomarkers like ER, PR, Her2, Ki-67, etc available which are frequently used in diagnosis and treatment of breast cancer. After the discovery of MiRNA in 1993 in Caenorhabiditis elegans, it is attracting all the limelight in diagnosis and treatment of different carcinomas as well as breast cancer. In this review we will discuss on involvement of different types of MiRNAs and miR SNPs in breast cancer occurrence and susceptibility in a detailed manner.

Metastasis is the leading cause of cancer death and can be realized through the phenomenon of tumor cell fusion. The fusion of tumor cells with other tumor or normal cells leads to the appearance of tumor hybrid cells (THCs) exhibiting novel properties such as increased proliferation and migration, drug resistance, decreased apoptosis rate and avoiding immune surveillance. Experimental studies showed the association of THCs with a high frequency of cancer metastasis; however, the underlying mechanisms remain unclear. Many other questions also remain to be answered: the role of genetic alterations in tumor cell fusion, the molecular landscape of cells after fusion, the lifetime and fate of different THCs, and the specific markers of THCs, and their correlation with various cancers and clinicopathological parameters. In this review, we discuss the factors and potential mechanisms involved in the occurrence of THCs, the types of THCs, and their role in cancer drug resistance and metastasis, as well as potential therapeutic approaches for the prevention and targeting of tumor cell fusion. In conclusion, we emphasize the current knowledge gaps in the biology of THCs that should be addressed to develop highly effective therapeutics and strategies for metastasis suppression.

We developed a novel hybrid approach for solving global optimization, computer science, bio-medical and engineering real life applications that is based on the coupling of the Whale Optimizer and Sine Cosine Algorithms via a surrogate model. We relate the whale optimizer algorithm to balance between the exploitation and the exploration process in the proposed method. There exist confirmed techniques for searching approximate best optimal solutions, but our algorithm will further guarantee that such numerical and statistical solutions satisfy physical bounds of the standard and real life functions. Our experiments with the benchmark, bio-medical, computer science and engineering real life problems have illustrated the advantages of using a newly hybrid approach based on mixing Whale Optimizer and Sine Cosine algorithms. It holds considerable potential for reducing execution time for solving standard and real life problems and at the same time improving the quality of the solution.

Despite identical genetic constitution, a cancer cell population can exhibit phenotypic variations termed as non-genetic/non-mutational heterogeneity. Such heterogeneity – a ubiquitous nature of biological systems – has been implicated in metastasis, therapy resistance and tumour relapse. Here, we review the evidence for existence, sources and implications of non-genetic heterogeneity in multiple cancer types. Stochasticity/ noise in transcription, protein conformation and/or external microenvironment can underlie such heterogeneity. Moreover, the existence of multiple possible cell states (phenotypes) as a consequence of the emergent dynamics of gene regulatory networks may enable reversible cell-state transitions (phenotypic plasticity) that can facilitate adaptive drug resistance and higher metastatic fitness. Finally, we highlight how computational and mathematical models can drive a better understanding of non-genetic heterogeneity and how a systems-level approach integrating mathematical modelling and in vitro/in vivo experiments can map the diverse phenotypic repertoire, and identify therapeutic vulnerabilities of an otherwise clonal cell population.

Lineage plasticity, the switching of cells from one lineage to another has been recognized to be a cardinal property essential for embryonic development, tissue repair and homeostasis. However, such a highly regulated process goes awry when cancer cells exploit this inherent ability to their advantage, resulting in tumorigenesis, relapse, metastasis and therapy resistance. In this review, we summarize our current understanding on the role of lineage plasticity in tumor progression and therapeutic resistance in multiple cancers. Lineage plasticity can be triggered by treatment itself and is reported across various solid as well as liquid tumors. Here we focus on the importance of lineage switching in tumor progression and therapeutic resistance of solid tumors such as the prostate, lung, hepatocellular and colorectal carcinoma and the myeloid and lymphoid lineage switch observed in leukemias. Besides this, we also discuss the role of Epithelial-Mesenchymal Transition (EMT) in facilitating the lineage switch in biphasic cancers such as aggressive carcinosarcomas. We also discuss the mechanisms involved, current therapeutic approaches and challenges that lie ahead in taming the scourge of lineage plasticity in cancer.

Leaf crude extract (aqueous) of Oroxylum indicum (L.) Kurz induces genomic DNA fragmentation, comet formation, and inhibition of cell proliferation in prostate cancer cell line, PC3 as assessed by agarose gel electrophoresis, comet assay, and MTT assay respectively. The bioactive compound was purified through bioassay-guided fractionation using preparative HPLC and MTT as-say. The brown and water-soluble compound was characterized using 1H and 13C nuclear magnetic resonance (NMR), fourier transform infrared (FT-IR) and electrospray ionization (ESI) mass spectrometry, and the compound was iden-tified as a glycosylated hydroquinone derivative, 2-[p-(2-Carboxyhydrazino)phenoxy]-6-(hydroxymethyl) tetrahy-dro-2H-pyran-3,4,5-triol (molecular formula, C13H18N2O8; molecular mass = 330). The identified phytocompound has not been reported earlier elsewhere. Therefore, the common name of the novel anticancer phytocompound isolated from oroxylum indicum in this current study is named as oroxyquinone. The half-maximal inhibitory concentration (IC50) of oroxyquinone on PC3 cells was 19.44 µg/ml (95% CI = 17.97 to 21.04). Oroxyquinone induced cell cycle arrest at S phases and inhibition of cell migration on PC3 as assessed by flow cytome-try and wound healing assay respectively. On investigating the molecular mechanism of inducing apoptosis, the results indicated that the oroxyquinone induced apoptosis through the p38 pathway and cell cycle arrest, however, not through caspase-3 and PARP pathways. The present study identifies a novel an-ticancer molecule and provides scientific evidence supporting the therapeutic potency of OI for ethnomedicinal uses.

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.

With the emergence of COVID-19 pandemic in 2019, the world saw a humungous loss of human life and economic resources globally but also the rapid appearance of SARS-CoV-2 variants which have exhibited a higher transmissibility and/or virulence and which also evade immune system to such an extent that it raises a big question mark on the efficacy of current diagnostics, vaccines and convalescent plasma and mAb therapies. This has been attributed to the emergence of huge spectrum of mutations, especially in the virus’s spike (S) protein, occurring in regions harboring high concentration of B cell epitopes thus allowing neutralizing antibody escape. The mutations resulting in ACE 2 receptor recognition failure (T19R), unfavorable electrostatic interactions (E484K), structural change (∆69-70), disruption of hydrogen bonds, salt bridges or hydrophobic interactions (K417N, N501Y, ∆Y145) and change in orientation (N501Y) cause strong immune evasion by these variants. Further, the recent emergence of Omicron with more than 30 mutations in the S protein VOC allows it to escape and fail diagnosis as well as immune system and the protection generated by different vaccination regimes. Yet Omicron may not be the end of the story. This review presents an insight of the immunity escape and its mechanisms followed by different SARS-CoV-2 variant of concerns.

Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight.

Background –The brinjal is of much importance in the warm areas of Far East, being grown extensively in India, Bangladesh, Pakistan, China and the Philippines. It is also popular in Egypt, France, Italy and United States. Preserving soil fertility throughout time by preserving organic matter and soil biological diversity, mechanical intervention, as well as exercise is highly needed in upcoming future. Effect of six different fertilizer, five organic and one synthetic fertilizers listing vermicompost, poultry manure, mustard cake, neem cake, bonemeal and N:P:K study were main concern of this investigation to find out the most suitable fertilizer for the brinjal.Result - the final growth and development through N:P:K @ 1.5%(19:19:19) outperformed all the organic fertilizers but when considering only organic fertilizer performance vermicompost@5 ton/ha was outperforming all the other organic fertilizers. Coming close to it, mustard [email protected]/ha has shown its inheritance in providing nutrients. Use of other organic fertilizers are also effective use of poultry manure can provide nitrogen in higher amount in short time. Higher temperature above 25 degree Celsius cause slow growth, temperature range between 21 to 14 degrees Celsius was found better in terms of growth of flower and fruit formation.Conclusion – our experiment demonstrate specific organic fertilizer use over other fertilizers in terms of brinjal different developemental parameters and there is even comparision between synthetic fertilizer with organic fertilizer readily available in market. Vermicompost showed promising result followed by mustard cake for this specific brinjal genotype in northern plain of Indian subcontinent.

The main goal of the study is to assess the Decision criteria, and the determining factor for the sustainability of entrepreneurial startups in order to contribute towards social inclusion and capacity building. Both concepts are in the development phase and are the outcome of entrepreneurial ecosystem and individual behavior and traits. The current study observed the research problem as entrepreneurship and the entrepreneurial startups are the continuous phenomena required for every economy. The lack of an efficient ecosystem and incompetent trait of an entrepreneur drags the entrepreneurial startup to a failure. Therefore, an assessment on decision criteria and determining factors categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup contributing to the social inclusion and capacity building. The study solved the research problem by statistical assessment of decision criteria and determining factors and categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup. The research is built on research questions, objectives, conceptual model, and hypothesis which are tested based on the data collected. The collection of data was done through a survey questionnaire on a sample of established entrepreneurs. The study concludes that five components of decision criteria are Region, Competition, Funding Opportunities, Tax System, and Country Economic Situation, whereas the eight components Consciousness and Reliable, Pursuit Results, Flexibility, Stress Resistance, Skills Identification Exploitation Potential Market Opportunities, Leadership, Creativity and Innovation, and Delegation Decision Making of determining factor are required for a successful entrepreneurial startup to be able to work towards social inclusion and capacity building.

Despite the demonstrated benefits of data monetization initiatives in achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to significant data quality issues. This reflects a disconnect between advancements in data monetization research—spanning digitization, digitalization, and digital transformation—and practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company engaged in preparing pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach to addressing data cleanliness challenges and to distill a general framework for the customer master data cleansing process. This framework illuminates a relatively unexplored aspect of data monetization supplementing existing literature on digitization, digitalization, and digital transformation.

Breast cancer is the second cause of fatality among all cancers for women. Automatic classification of breast cancer lesions in mammograms is a challenging task due to the irregularity and complexity of the location, size, shape, and texture of these lesions. The intensity dissimilarity has been found between breast cancer tissues and normal tissues, when a multi-spectral anatomical mammographic screening scans have been done. In this work, two approaches have been evaluated to classify the breast tumor lesions. The first one is through Gabor wavelet features and the second one is Statistical features. Subsequently, support vector machine, Multilayer Perceptron and KNN classifiers have been used with computer based method for breast tumor classification.

We introduce an information theoretic framework for a quantitative measure of originality to model the impact of various classes of biases, errors and error corrections on scientific research. Some of the open problems are also outlined.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

The landscape of clinical management for metastatic melanoma (MM) and other solid tumors has been modernized by the advent of immune checkpoint inhibitors (ICI), including programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors. While these agents demonstrate efficacy in suppressing tumor growth, they also lead to immune-related adverse events (irAEs), resulting in exacerbation of autoimmune diseases such as Rheumatoid arthritis (RA), Ulcerative colitis (UC), and Crohn’s disease (CD). The immune checkpoint inhibitors offer promising advancements in the treatment of melanoma and other cancers, but they also present significant challenges related to irAEs and autoimmune diseases. Ongoing research is crucial to better understand these challenges and develop strategies for mitigating adverse effects while maximizing therapeutic benefits. In this manuscript, we addressed this challenge using network-based approaches by constructing and analyzing molecular and signaling networks associated with tumor-immune cross-talk.. Our analysis revealed that IL6 is the key regulator responsible for irAEs during ICI therapies. Furthermore, we conducted an integrative network and molecular-level analysis, including virtual screening, of drug libraries, such as the Collection of Open Natural Products (COCONUT) and the Zinc15 FDA-approved library, to identify potential IL-6 inhibitors.. Subsequently, the compound amprenavir was identified as the best molecule that may disrupt essential interactions between IL6 and IL6R responsible for initiating signaling cascades underlying irAEs in ICI therapies.

Aortic dissection is a clinicopathological entity caused by rupture of the intima leading to a high mortality if not treated. Over time, diagnostic and investigative methods, antihypertensive therapy, and early referrals have resulted in improving outcomes according to registry data. Some data has also emerged from recent studies suggesting a link between Human Cytomegalovirus (HCMV) infection and aortic dissection. Furthermore, the use of microRNAs have also become increasingly widespread in the literature. These have been noted to play a role in aortic dissections with elevated levels noted in studies as early as 2017. This review aims to provide a broad and holistic overview of the role of miRNAs, while studying the role of HCMV infection in the context of aortic dissections. The role of long non-coding RNAs, circular RNAs and microRNAs are explored to identify changes in expression during aortic dissections. The use of such biomarkers may one day be translated into clinical practice to allow early detection and prognostication of outcomes and drive preventative and therapeutic options in the future.

The Coronavirus 2 (SARS-CoV-2) infection is a global pandemic that has affected millions of people worldwide. The advent of vaccines, however, has permitted some restitution. Aside from the respiratory complications of the infection, there is also a thrombotic risk attributed to both the disease alongside the vaccine. There are no reliable data for the risk of thromboembolism in SARS-CoV-2 infection in patients managed out with the hospital setting. A literature review was performed to identify the pathophysiological mechanism of thrombosis from the SARS-CoV-2 infection including the role of Angiotensin-Converting Enzyme receptors. The impact of the vaccine and likely mechanisms from thrombosis following vaccination was also clarified. Finally, the utility of the vaccines available against the multiple variants is also highlighted. The systemic response to SARS-CoV-2 infection is still relatively poorly understood, but several risk factors have been identified. The roll-out of the vaccines worldwide has also allowed the lifting of lockdown measures and a reduction in the spread of the disease. The experience of the SARS-CoV-2 infection, however, has highlighted the crucial role of epidemiological research and the need for ongoing studies within this field.

Background: The practices of various health-care professionals have been improvised to accommodate the on-going covid-19 pandemic situation. Different guidelines have been set in place to ease the process of re-opening of non-elective healthcare services like out-patient physiotherapy clinics. Although the measures taken should be guided by evidence based information, major consensus amongst practicing therapists needs to guide the India physiotherapy clinics. Objective: To identify and present the opinions of different physiotherapists about the various strategies for re-opening the out-patient physiotherapy clinics. Methods: An online cross-sectional survey was conducted. Over 169 participants were selected to participate in the survey according to the pre-decided inclusion and exclusion criteria. The data was collected and saved via google forms. Result and conclusion: A majority of respondents had a consensus over different strategies for re-opening the physiotherapy OPDs. These were regarding different measures to be adapted including modifications in the clinic infrastructure and the practice pattern. This would help in smoothly re-instating the physiotherapy services post the covid-19 lockdown.

Genetic variability in rice breeding program plays the very crucial role. It provides outstanding pool of superior alleles governing better agronomic and quality characters through association mapping. For the understanding of population structure and genetic relationship among the different rice lines is indispensable prior to setting of correlation among dynamic alleles and traits. In the present investigation, genetic diversity and population structure of 116 rice accessions by using 64 polymorphic SSR markers was targeted for the evaluation of the genetic relatedness and diversity. Genotyping assessment based on SSR markers revealed a total of 225 alleles, with an average PIC value of 0.755. The germplasm lines were classified into three distinct subgroups through population structure analysis, utilizing both model and distance-based approaches. AMOVA analysis showed that 11% of the total variation could be attributed to differences between groups, while the remaining 89% of the variation was likely due to differences within groups. The study suggests that the population structure and genetic relatedness should be considered when working with the core collection of 116 rice germplasm lines for association mapping, aiming to establish marker-trait associations.

DevOps is an emerging practice to be followed in the Software Development life cycle. The name DevOps indicates that it’s an integration of the Development and Operation team. It is followed to integrate the various stages of the development cycle. DevOps is an extended version of the existing Agile method. DevOps aims at continuous integration, Continuous Delivery, Continuous Improvement, faster feedback and security. This paper reviews the building blocks of DevOps, challenges in adopting DevOps, Models to improve DevOps practices and Future works on DevOps

This article introduces ten similarity measures that utilize cosine and cotangent functions to compare (m,n)-rung orthopair fuzzy sets, this a super class of q-rung orthopair fuzzy sets. Further, we applied our established weighted similarity measures to medical diagnosis, pattern recognition,and building material problems and also presented the comparison between established measures with the existing cosine and cotangent measures of q-rung orthopair, pythagorean and intuitionistic fuzzy sets. Lastly, we considered a numerical example that demonstrates the effective and scientific application of these similarity measures in plant leaf disease classification. Furthermore, we present graphical interpretations to demonstrate the effectiveness of our established measures.

Deficiencies of vitamin B12 (B12) and folate (FA) are of particular interest due to their pleiotropic role in 1-carbon metabolism. In addition to adverse birth outcomes, deficiencies of B12 and FA, or an imbalance in FA/B12 status, are linked to metabolic disorders. Indian diets that are predominantly plant food-based could be deficient in these vitamins, but there are no national estimates of the prevalence of B12 and FA deficiency in Indian children and adolescents, nor of their associations with age, sex, and growth indicators. The recent Comprehensive National Nutrition Survey (CNNS-2016-18) provided estimates of the prevalence of B12 and FA deficiency at the national and state level among preschool (1-4y: 9,976 and 11,004 children respectively), school-age children (5-9y: 12,156 and 14,125) and adolescents (10-19y: 11,748 and 13,621). Serum B12 and erythrocyte FA were measured by the direct chemiluminescence method and their deficiency was defined using WHO cut-offs. The prevalence of B12 and FA deficiency was high among adolescents (31.0%, CI: 28.7-33.5 and 35.6%, CI: 33.1-8.2), compared to school-age (17.3%, CI: 15.4-19.3 and 27.6%, CI: 25.5-29.9) and preschool children (13.8%, CI: 11.7-16.2 and 22.8%, CI: 20.5-25.2, respectively). The prevalence of both B12 and FA deficiency was significantly higher by 8 and 5% points respectively, in adolescent boys compared to girls. The prevalence of B12 deficiency was higher in moderately stunted school children (by 18.9% points) than in normal children, but no such difference was observed for FA deficiency. There was wide regional variation in the prevalence of B12 and FA deficiency, but no rural-urban differences were observed across all age groups. The national prevalence of B12 deficiency among preschool or school-age children was <20% (the cut-off that indicates a public health problem). However, FA deficiency in these age groups and both FA and B12 deficiencies in adolescents were >20%, which warrants further investigation.

Global emerge of zoonotic novel corona virus (COVID-19) became a pandemic and its effect to mankind is talk of the town now a days. This tiny, invisible enemy has affected every country in the world and almost every living directly or indirectly and nationwide complete lockdown has triggered a short-term environmental impact. Since 2003, corona virus came into existence in the form of Severe Acute Respiratory Syndrome (SARS) and more evolved Middle East Respiratory Syndrome (MERS) in 2012. This time, at the end of December 2019, outbreak of novel corona virus COVID-19 (also known as SARS-CoV₂, nCoV-2019) draw attention as global health emergency. World Health Organization (WHO) report says that the outbreak of this virus is so immense, it has already affected 35,57,235 people and caused death to 2,45,150 people worldwide and 46,433 Indians got affected with 1568 death as on 5^th May 2020 (2:00 am) and these numbers are increasing exponentially day by day. Virologist, micro-biologist and science community are hammering their head very hard to find out cure and vaccine against this powerful virus and to prevent mass demise of mankind. In order to curb the spread of COVID-19, Janta curfew on 22.03.2020 and nationwide complete lockdown was implemented in India for 21 days (phase-I, from 25.03.2020 to 14.04.2020) to stop community transmission of third stage, for 19 days (phase-II, 15.04.2020 to 03.05.2020) and 14 days (phase-III, 04.05.2020 to 17.05.2020) complete lockdown to minimize the community transmission effect. During complete lockdown and quarantine period a drastic change in Earth’s atmosphere, including reduction in emission of greenhouse gases, air pollution (~50% fall in air quality index), noise pollution, water pollution and solid waste pollution, have been recorded by government agencies as well as private agencies. In this paper we considered data of Janta curfew, phase-I and phase-II lockdown to link between geological and environmental aspect related to environmental impact due to emerge of COVID-19 and massive reduction in pollution level during complete lockdown in India. We propose future lockdown strategies to minimize the emission of greenhouse gas by ~100 Mt to ~200Mt (3.33% to 6.66%) of GHG_total per year by 2-4 days per month nationwide lockdown or ~70 Mt to ~140 Mt (2.33% to 4.66%) of GHG_total per year by 2-4 days per month complete lockdown of energy sectors only.

The exceptional Jordan algebra is the algebra of $3 \times 3$ Hermitian matrices with octonionic entries. It is the only one from Jordan’s algebraic formulation of quantum mechanics which is not equivalent to the conventional formulation of quantum theory. It has often been suggested that this exceptional algebra could explain physical phenomena not currently explained by the conventional approach, such as values of the fundamental constants of the standard model of particle physics, and their relation to gravitation. We show that this is indeed the case; and this also unravels the connection between general relativity and the weak force. The exceptional Jordan algebra also predicts a new U(1) gravitational interaction which modifies general relativity, and which provides a theoretical basis for understanding the Modified Newtonian Dynamics (MOND).

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome is one of the most frequent maternally inherited mitochondrial disorders.MELAS syndrome is a multi-organ disease that presents with a wide range of symptoms, such as dementia, epilepsy, lactic acidemia, myopathy, hearing loss, stroke-like episodes, diabetes, short stature, and myopathy. MELAS primarily affects tissue with high energy demands such as the brain, muscles, and heart. With 65–76% of affected people presenting at or before the age of 20, childhood is the typical age of onset. Genetic testing, imaging tests such as magnetic resonance imaging (MRI) scans, and other techniques, tests of blood, urine, and cerebrospinal fluid in addition to muscle biopsy (if necessary) may be used to diagnose MELAS syndrome. Management of MELAS remains largely supportive, focusing on symptom alleviation and metabolic stabilization. The long-term prognosis of MELAS is variable, influenced by factors such as age of onset, severity of symptoms, and presence of comorbidities. While some patients experience a relatively stable course with intermittent exacerbations, others may exhibit progressive neurological deterioration and multiorgan involvement. Early recognition, prompt intervention, and close monitoring are essential for optimizing outcomes and mitigating complications.Presenting here a case report of an occurrence of MELAS in a 31-year old male patient with chief complaints of perspiration, uneasiness and weight loss with increase in appetite and past history of bilateral hearing loss and vision loss in left eye.

Since the onset of coronavirus disease 2019 (COVID-19) pandemic, the virus kept developing and mutating into different variants over time that also gained increased transmissibility and spread in populations at a higher pace, culminating in successive waves of COVID-19 cases. The scientific community has developed vaccines and antiviral agents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease. Realizing that growing SARS-CoV-2 variations significantly impact the efficacy of antiviral therapies and vaccines, we have summarized the appearance and attributes of SARS-CoV-2 variants for future perspectives in drug design, bringing up-to-date hints for developing therapeutic agents targeting the variants. The Omicron variant is among the most mutated form; its strong transmissibility and immune resistance capacity have prompted international worry. Most mutation sites currently being studied are located in the BCOV_S1_CTD of the S protein. Despite this, several hurdles remain, such as developing vaccination and pharmacological treatment efficacies for emerging mutants of SARS-CoV-2 strains. In this review, we present an updated viewpoint on the current issues faced by the emergence of various SARS-CoV-2 variants. Furthermore, we discuss the clinical studies conducted to assist the development and dissemination of vaccines, small molecule therapeutics, and therapeutic antibodies having broad-spectrum action against SARS-CoV-2 strains.

The paper presents attribute based characterization of nanomaterials method for computer storage and retrieval as knowledgebase. The knowledgebase permits indepth understanding and comparison between nanomaterials available with the scientists and product developers to satisfy their research and development (R & D) needs. Techniques for order preference by similarity to ideal solution (TOPSIS) is proposed to evaluate nanomaterials in the presence of multiple attributes. The method normalizes attributes to nullify the effect of different units and their values in the range of 0 to 1. The relative importance of different attributes for different applications is considered. The weight vector is derived using Eigen value formulation. The positive and negative benchmark solutions are derived. Euclidean distance of alternatives from these best and worst solution leads to the development of proximity /goodness/suitability index for ranking. Final decision is taken by decision makers by SWOT analysis and short and long term strategies of the organisation. The methodology is illustrated with the help of an example and step-by-step procedure. Results, discussion and conclusion highlights the importance and practical application.

There must exist a reformulation of quantum field theory, even at low energies, which does not depend on classical time. The octonionic theory proposes such a reformulation, leading to a pre-quantum pre-spacetime theory. The ingredients for constructing such a theory, which is also a unification of the standard model with gravitation, are : (i) the pre-quantum theory of trace dynamics – a matrix-valued Lagrangian dynamics, (ii) the spectral action principle of non-commutative geometry, (iii) the number system known as the octonions, for constructing a non-commutative manifold and for defining elementary particles via Clifford algebras, (iv) a Lagrangian with $E_8 \times E_8$ symmetry. The split bioctonions define a sixteen dimensional space (with left-right symmetry) whose geometry (evolving in Connes time) relates to the four known fundamental forces, while predicting two new forces, $SU(3)_{grav}$ and $U(1)_{grav}$. This latter interaction is possibly the theoretical origin of MOND. Coupling constants of the standard model result from left-right symmetry breaking, and their values are theoretically determined by the characteristic equation of the exceptional Jordan algebra of the octonions. The quantum-to-classical transition, precipitated by the entanglement of a critical number of fermions, is responsible for the emergence of classical spacetime, and also for the familiar formulation of quantum theory on a spacetime background.

The antibiotic or antimicrobial resistance is rapidly spreading in microbes relevant to human health. Two visible major contributory factors have been the indiscriminate overuse of antimicrobials for preventing diseases in human and to enhance the productivity in agriculture sector. To mitigate the potential threat posed by post-antibiotic era, the global health stakeholders have been making extra efforts at a war footing to formulate and implement global and national plans of action. In the current article, an endeavour is made to provide a perspective to look beyond the current focus on just use of the antimicrobials. Attention has been drawn towards various obvious and not-so-obvious self-preservation infection-prevention practices in vogue from the pre-antibiotic era whose usage has been on decline in the antibiotic era for various reasons. Particularly, the practices with a clear potential to effectively decrease the spread of pathogens through contact, curtail the evolution and dissemination of the antimicrobial resistance in local environment and its introduction into the global community, should be Identified and strengthened to make them part of comprehensive hygiene and quarantine practices. Broadly, the suggestions pertaining to the personal and community hygiene including bereavement practices, isolation and quarantine of suspected pathogen carriers, and water and environment security have been made to invoke a constructive debate and discussion among various stakeholders for their evaluation and implementation to effectively delay the development of antimicrobial resistance wherever possible and disrupt its spread to pathogens.

miRNAs have recently attracted investigators' interest as regulators of valvular diseases pathogenesis, diagnostic biomarkers, and therapeutical targets. Evidence from in-vivo and in-vitro studies demonstrated stimulatory or inhibitory roles in mitral valve prolapse development, aortic leaflet fusion, and calcification pathways, specifically osteoblastic differentiation and transcription factors modulation. Tissue expression assessment and comparison between physiological and pathological phenotypes of different disease entities, including mitral valve prolapse and mitral chordae tendineae rupture, emerged as the best strategies to address miRNAs over or under-representation and thus, their impact on pathogeneses. In this review, we discuss the fundamental intra- and intercellular signals regulated by miRNAs leading to defects in mitral and aortic valves, congenital heart diseases, and the possible therapeutic strategies targeting them. These miRNAs inhibitors comprise of antisense oligonucleotides and sponge vectors. The miRNA mimics, miRNA expression vectors, and small molecules are instead possible practical strategies to increase specific miRNA activity. Advantages and technical limitations of these new drugs, including instability and complex pharmacokinetics, are also presented. Novel delivery strategies, such as nanoparticles and liposomes, are described to improve knowledge on future personalized treatment directions.

In this paper the dynamics of spatially extended infected phytoplankton with the Holling type II functional response and logistically growing susceptible phytoplankton system is studied. The proposed model is an extension of temporal model available [6], in spatiotemporal domain. The reaction diffusion system exhibits spatiotemporal chaos in phytoplankton dynamics. This is particularly important for the spatially extended systems that are studied in this paper as they display a wide spectrum of ecologically relevant behavior, including chaos. In this system stability of the system is studied with respect to disease contact rate and the growth fraction of infected phytoplankton indirectly rejoin the susceptible phytoplankton population. The results of numerical experiments in one dimension and two dimensions in space as well as time series in temporal models are presented using MATLAB simulation. Moreover, the stability of the corresponding temporal model is studied analytically. Finally, the comparison of the three types of numerical experimentations are discussed in conclusion.

We have recently proposed a pre-quantum, pre-space-time theory as a matrix-valued Lagrangian dynamics on an octonionic space-time. This pre-theory offers the prospect of unifying the internal symmetries of the standard model with gravity. It can also predict the values of free parameters of the standard model, because these parameters arising in the Lagrangian are related to the algebra of the octonions which define the underlying non-commutative space-time on which the dynamical degrees of freedom evolve. These free parameters are related to the algebra $J_3(\mathbb O)$ [exceptional Jordan algebra] which in turn is related to the three fermion generations. The exceptional Jordan algebra [also known as the Albert algebra] is the finite dimensional algebra of 3x3 Hermitean matrices with octonionic entries. Its automorphism group is the exceptional Lie group $F_4$. These matrices admit a cubic characteristic equation whose eigenvalues are real and depend on the invariant trace, determinant, and an inner product made from the Jordan matrix. Also, there is some evidence in the literature that the groups $F_4$ and $E_6$ could play a role in the unification of the standard model symmetries, including the Lorentz symmetry. The octonion algebra is known to correctly yield the electric charge values (0, 1/3, 2/3, 1) for standard model fermions, via the eigenvalues of a $U(1)$ number operator, identified with $U(1)_{em}$. In the present article, we use the same octonionic representation of the fermions to compute the eigenvalues of the characteristic equation of the Albert algebra, and compare the resulting eigenvalues with the known mass ratios for quarks and leptons. We find that the ratios of the eigenvalues correctly reproduce the [square root of the] known mass ratios for quarks and charged leptons. We also propose a diagrammatic representation of the standard model bosons, Higgs and three fermion generations, in terms of the octonions, exhibiting an $F_4$ and $E_6$ symmetry. In conjunction with the trace dynamics Lagrangian, the Jordan eigenvalues also provide a first principles theoretical derivation of the low energy value of the fine structure constant, yielding the value $1/137.04006$. The Karolyhazy correction to this value gives an exact match with the measured value of the constant, after assuming a specific value for the electro-weak symmetry breaking energy scale.

In this work we study the energy consumption by various modern secured hash functions (MD2, MD5, SHA-1, and SHA-2) and modern symmetric key encryption protocols (Blowfish, DES, 3DES, and AES) from the algorithmic perspective. We identify various parameters that moderate energy consumption of these hashes and protocols. Our work is directed towards redesigning or modifying these algorithms to make them consume lesser energy. As a first step, we try to determine the applicability of the asymptotic energy complexity model by Roy et. al. on these hashes and protocols. Specifically, we try to observe whether parallelizing the access of blocks of data in these algorithms reduces their energy consumption based on the energy model. Our results confirm the applicability of the energy model on these hashes and protocols. Our work is motivated by the relevance and importance of cryptographic hashes and symmetric key protocols for modern ICT (Information and Communication Technology), and ICT enabled industry to keep them protected from dynamically changing threat scenarios. Hence the design of more energy efficient hashes and protocols will definitely contribute in reducing the ICT energy consumption that is continuously increasing.

Enteroccoccus faecalis is a commensal that is responsible for numerous infections in surgical sites, urinary tract and bloodstream. It is of growing concern given its propensity to affect the elderly population where there is a high prevalence of undiagnosed degenerative valvular disease and the with more invasive procedures and implanted medical devices. Its actions are mediated by specific characteristics of the bacterium such as pili activity and biofilm formation. This confers significant resistance toward a multitude of antibiotic therapy, especially in endocarditis. The current guidelines advocate antimicrobial therapy but the emergence of resistant strains has added a degree of complexity to the management of these patients, especially with the advent of transcatheter therapies which are often used in patients who are poor surgical candidates. Their presentations are often variable with generalized symptoms and can be diagnostically challenging. We present our experience of a case study involving an octogenarian with a TAVI valve who developed endocarditis and performed a literature review to highlight treatment algorithms for these cases.

Endothelial integrity plays a major role in homeostasis and is responsive to the numerous endogenous factors released. While its functional role in vascular tone is well described, its role in the pathophysiology of cardiovascular disease is of interest as a potential therapeutic target. We performed a systematic review to provide an overview of new therapeutic and diagnostic targets for the treatment of coronary artery disease related to endothelial dysfunction. Databases of PubMed, Ovid’s version of MEDLINE, and EMBASE were interrogated with appropriate search terms. 28 studies met inclusion criteria and were included in the final systematic review. We identified inflammation, pulmonary hypertension, diabetes mellitus and Fabry disease as pathophysiological mechanisms and explored the therapeutic options related to these conditions including medications such as Canakinumab. Endothelial dysfunction has a key role in several different pathophysiological processes which can be targeted for therapeutic options. Ongoing research should be targeted at making the transition to clinical practice. Further research is also needed on understanding the amelioration of endothelial dysfunction with the use of cardiovascular medications.

Staphylococci sp. have become the primary pathogens implicated in infective endocarditis, especially within high-income nations. Coupled with the increasing burden of healthcare with aging populations and the protracted course the infections may take, these infections contribute to a significant challenge for healthcare. A systematic review was conducted using relevant search criteria from PubMed, Ovid’s version of MEDLINE, and EMBASE, and data were tabulated from randomized controlled trials (RCT), observational cohort studies, meta-analysis, and basic research articles. The review was registered with the OSF register of systematic reviews and followed the PRISMA reporting guidelines. 35 studies met the inclusion criteria and were included in the final systematic review. The role of Staphylococcus aureus and its interaction with the protective shield and host protection functions is identified and highlighted in several studies. The interaction between infective endocarditis pathogens, vascular endothelium, and blood constituents is also explored giving rise to the potential use of antiplatelets as preventative and/or curative agents. Several factors allow Staphylococcus aureus infections to proliferate within the host with numerous promoting and perpetuating agents. The complex interaction with the hosts' innate immunity also potentiates its virulence. Ameliorating these molecular pathways may serve as a therapeutic avenue for the prevention and treatment of these infections in the near future.

One of the hallmarks of the SARS-CoV-2 infection has been the inflammatory process that played a role in its pathogenesis, resulting in mortality within susceptible individuals. This uncontrolled inflammatory process leads to severe systemic symptoms via multiple pathways, however, the role of endothelial dysfunction and thrombosis have not been truly explored. This review aims to highlight the pathogenic mechanisms of these inflammatory triggers leading to thrombogenic complications. There are direct and indirect pathogenic pathways of the infection that are examined in detail. We also describe the case of carotid artery thrombosis in a patient following the SARS-CoV-2 infection, while reviewing the literature on the role of ACE2, the endothelium, and the different mechanisms by which SARS-CoV-2 may manifest both acutely and chronically. We also highlight differences from the other coronaviruses that have made this infection pandemic with similarities to the influenza virus.

Landslides are a frequent and recurrent problem in hilly regions of India and predicting them is always a challenging task. In this paper, an attempt was made to deal with this problem using advanced physical and numerical modeling methods. Detailed understanding of the initial slope failures is very interesting, and challenging at the same time, in the design and development of wireless sensor network based on early warning of landslide monitoring. A small scale physical model was developed to assess the instability through a sensor network with variable rain fall intensity. This was achieved by increasing the simulated rain water flow intensity in different time spans (dry condition, at t=0 to t= 30 min, 0.5 mm/min at t=30 to t= 60 min, 0.75 mm/min at t=60 to t=91 min and 1 mm/min at t=91 to t= 120 min). The water level and movement in the slope was recorded by rainfall sensor, vibration sensor, soil moisture sensor and a digital camera. The following changes were observed during the slope failure: a) movement of small particles at top of the slope; b) initial failure of medium size soil particle; c) scouring of soil mass; d) whole slope collapse. The obtained results clearly indicated the superiority and effectiveness of the proposed system in providing a factor of safety for the progressive slope.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of a cancer or transformed cell, the initiation and progression were shown to be driven by only a few mutational events viz. driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient’s cancer cell gained large attention recently for designing better targeting therapies towards paving way for the precision cancer medicine. With rolling advancements in high-throughput omics technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures, and their potential application in developing precision cancer therapies.Keywords:

Low carbon supply chain management (LCSCM) strategies are required in the Indian manufacturing industries due to rapidly growing energy usage for financial and environmental concerns. LCSCM technologies face a number of challenges that limit their adoption. Eleven hurdles were selected from the literature as well as the recommendations of business and academic experts. These barriers are defined by a number of factors, including a lack of information, financial worries, environmental concerns, and governmental limits, among others. In addition, efforts are being made to quantify the most optimal relevant barriers using Multi criteria decision making (MCDM) techniques such as interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL). MICMAC analysis is used to estimate the sensitivity and priority of barriers based on dependency power and driving power. One of these barriers is a needed factor; three are driving elements; four barriers are linked factors; and three are self-sufficient factors. The findings of this study can help academic specialists, industry representatives, and practitioners in their ongoing attempts to adopt 'LCSCM techniques' in Indian manufacturing industries. The model is appropriately explained and applies the link between the barriers.The objective of this paper is the Evaluation and selection of most influential of Barriers to Adopt Low Carbon Supply Chain Management (LCSCM) in the Indian Manufacturing Sector is interrelated.

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

Background: The Covid-19 pandemic abruptly disrupted global healthcare systems, necessitating rapid adaptations. This study evaluates the impact on health systems and services in the aftermath of the first wave in the Indian context. It analyses the disruptions, adaptive measures, and changes made, as well as the challenges faced by healthcare providers and seekers. Valuable insights from this study will enhance the resilience and preparedness of healthcare systems for future challenges. Methods: The eligible studies included primary studies conducted in the Indian context that explore the impact of COVID-19 on health services provision, utilisation, and the well-being of healthcare providers and seekers. Electronic searches were conducted in six databases: PubMed, MEDLINE, Embase, Global Health, CINAHL, and the WHO database on COVID-19. The results were analysed using detailed narrative synthesis. Results: The review includes 38 articles, incorporating a total of 22,502 subjects. There has been a substantial impact on health service provision, particularly in outpatient departments (OPD) (n=19) and elective services (n=16), while emergency services continued at sub-optimal levels (n=20). Various adaptations and changes were implemented in precautionary measures, protocols, staff allocation and training, usage of personal protective equipment (PPE), preoperative, operative, and postoperative measures, as well as physical infrastructure and resources. Depression and stress (n=14), fear of contracting the infection (n=6), stigmatisation (n=5), and financial repercussions (n=5) significantly affected the mental health of healthcare providers, and healthcare seekers also faced significant challenges (n=11). Conclusion: The study reveals COVID-19's substantial impact on health services. The healthcare system responded by quickly adjusting staff management, resource allocation, and infection prevention measures. The study also highlights the mental health challenges faced by healthcare providers and the concerns of healthcare seekers regarding delays and suboptimal care. Looking ahead, the findings underscore the importance of preparedness for future pandemics, including improved healthcare infrastructure, resource optimisation, and comprehensive protocols. Lessons learned from COVID-19 should inform strategies to mitigate disruptions and ensure the well-being of healthcare providers and seekers in future outbreaks.

Tourette syndrome is a complex neuropsychiatric condition that manifests in childhood and is often associated with other psychiatric comorbidities. This case describes a young male with Tourette syndrome with major depressive disorder and attention deficit hyperactivity disorder (ADHD), who experienced troublesome side-effects due to his existing medications (escitalopram, risperidone and methylphenidate). In order to control his tics, ameliorate depressive symptoms, and eliminate side-effects of stiffness and sedation, risperidone was switched to cariprazine, a third-generation antipsychotic medication with D3-D2 partial agonism. In addition, the antidepressant dose was also increased. With the new combination, the patient reported good control of his tics, together with significant improvement in depressive symptoms and no side-effects. Based on this case and the reviewed literature, cariprazine might be a viable option for patients with Tourette syndrome with other comorbid illnesses, whom are prone to side effects of medication.

The signaling complex around voltage-gated sodium (Nav) channels includes accessory proteins and kinases crucial for regulating neuronal firing. Previous studies showed that one such kinase, WEE1—critical to the cell cycle—selectively modulates Nav1.2 channel activity through the accessory protein fibroblast growth factor 14 (FGF14). Here, we tested whether WEE1 exhibits crosstalk with the AKT/GSK3 pathway for coordinated regulation of FGF14/Nav1.2 channel complex assembly and function. Using the in-cell split luciferase complementation assay (LCA), we found that the WEE1 inhibitor II and GSK3 inhibitor XIII reduce FGF14/Nav1.2 complex formation, while the AKT inhibitor triciribine increases it. However, combining WEE1 inhibitor II with either one of the other two inhibitors abolished its effect on FGF14/Nav1.2 complex formation. Whole-cell voltage-clamp recordings of sodium currents (INa) in HEK293 cells co-expressing Nav1.2 channels and FGF14-GFP showed that WEE1 inhibitor II significantly suppresses peak INa density, both alone and in the presence of triciribine or GSK3 inhibitor XIII, despite the latter inhibitors’ opposite effects on INa. Additionally, WEE1 inhibitor II slowed the tau of fast inactivation, and caused depolarizing shifts in the voltage dependence of activation and inactivation. These phenotypes either prevailed or were additive when combined with triciribine but were outcompeted when both WEE1 inhibitor II and GSK3 inhibitor XIII were present. Concerted regulation by WEE1 inhibitor II, triciribine and GSK3 inhibitor XIII were also observed on long-term inactivation and use-dependence of Nav1.2 currents. Overall, these findings suggest a complex role for WEE1 kinase—in concert with the AKT/GSK3 pathway—in regulating the Nav1.2 channelosome.

There must exist a reformulation of quantum field theory which does not refer to classical time. We propose a pre-quantum, pre-spacetime theory, which is a matrix-valued Lagrangian dynamics for gravity, Yang-Mills fields, and fermions. The definition of spin in this theory leads us to an eight dimensional octonionic space-time. The algebra of the octonions reveals the standard model; model parameters are determined by roots of the cubic characteristic equation of the exceptional Jordan algebra. We derive the asymptotic low energy value 1/137 of the fine structure constant, and predict the existence of universally interacting spin one Lorentz bosons, which replace the hypothesised graviton. Gravity is not to be quantized, but is an emergent four-dimensional classical phenomenon, precipitated by the spontaneous localisation of highly entangled fermions.

The flipped classroom is gaining prominence as an active learning pedagogy to engage a new generation of students. However, all courses do not lend themselves to a fully flipped design and instructors are often reluctant to flip lectures. In this study, I experimented with a “partial” flipped classroom design in a first-year undergraduate economics course. In this partial flipped format, traditional lectures were substituted with micro-lectures and the remaining class time was devoted to activities like quizzes, group work and student presentations. The full lectures were panopto recorded and put up on the e-learning site, Blackboard. This format enabled me to combine the benefits of a traditional lecture with a flipped classroom design. In order to evaluate the effectiveness of the partial flipped classroom format, I compared the final exam scores of students in the partial flipped classroom with those in the control group, which followed a traditional lecture-based approach. The key results from the analysis revealed that students in the partial flipped classroom performed better in the final exams vis-à-vis students in the traditional classroom format. Furthermore, the partial flipped classroom format was associated with lower odds of students failing in the module. This format also resulted in better student engagement, more flexibility and enhanced student-tutor interaction within the classroom.

The growing demand for high-power DC Fast Charging (DCFC) stations for Electric Vehicles (EVs) is expected to lead to increased peak power demand and reduction of grid power quality. To maximize the economic benefits and station utilization under practical constraints set by regulatory authorities and DCFC station operators, this study explores and provides methods for connecting DCFC stations to the grid employing low power interconnection rules and Distributed Energy Resources (DERs). The system uses automotive Second Life Batteries (SLBs) and Photovoltaic (PV) systems as energy buffers and local energy resource to support EV charging and improve the station techno-economic feasibility through load shifting and charge sustaining. The optimal sizing of the DERs and the selection of the grid interconnection topology is achieved by means of a Design Space Exploration (DSE) by means of exhaustive search approach to maximize the economic benefits of the charging station and to mitigate high-power demand to the grid. Without loosing of generality, this study considers a range of DER sizes, grid interconnection specifications, and related electricity tariffs of American Electric Power (AEP) Ohio and the Public Utility Commission of Ohio (PUCO). Various scenarios and strategies have been defined to account for the interconnection requirements of the grid to the DCFC with DERs. The system’s techno-economic performance of different scenarios has been analyzed and compared using a multitude of metrics.

Abstract: A. baumannii exhibits resilience in harsh environments and progressively develops resistance to diverse antibacterial agents over time. This adaptability poses a significant challenge in treating an infection caused by this bacterium within healthcare settings. The presence of peptidoglycan, a crucial precursor for cell wall synthesis, contributes to this challenge. Peptidoglycan serves not only as a structural support in bacterial cells but also provides protection against adverse environmental conditions. The synthesis of the peptidoglycan layer involves a group of substrates such as N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc), within a three-partitioned process occurring in the membrane, periplasmic space, and cytoplasm of each bacterium. Initiated in the cytoplasm and extending to the inner cell membrane, this synthesis starts with the production of nucleotide precursors. The first step involves the synthesis of UDP-GlcNAc, orchestrated by Glm (GlmS, GlmM, and GlmU) enzymes using fructose-6-phosphate. Subsequently, enzymes from the Mur family, namely MurB, MurA, MurD, MurC, MurI, MurF, and MurE, synthesize UDP-N-acetylmuramyl-pentapeptide (UDP-Mpp) from UDP-GlcNAc. Concurrently, on the cytosolic side, the production of membrane-surrounded undecaprenyl phosphate occurs, involving membrane-side proteins MraY and MurG in the biosynthesis of peptidoglycan. This review emphasizes updated information on Mur family proteins, encompassing physicochemical properties, nature of binding site, number and type of binding site residues, functional domain details, three-dimensional structural aspects, and their functions within bacterial cells. Special attention is directed towards their enzymatic role in peptidoglycan biosynthesis due to the limited availability of precursors in the market. Additionally, the review provides a comprehensive summary of existing inhibitors and ongoing efforts in identifying new targeted inhibitors, recognizing these enzymes as potential candidates for antibacterial drug design and discovery.

Fever remains an integral part of the acute clinical diseases management, esp. viral, for which effective therapeutics remain desired. However, the presence of often confusing fever reduction recommendations for COVID-19 in the public domain during the pandemic, as late as 28 April 2021, seems to suggest the reduction of any ‘uncomfortable’ fever ranging from 37.8 - 39oC, as opposed to WHO fever reduction guidelines (≥39oC), urgently need attention. The confusion could percolate down into different agencies who look up to these agencies for guidance in framing their own, denying the benefits of fever to populations, and effectively undo whatever successive WHO’s guidelines have achieved in the last two decades. The existence of conflicting guidelines in public domains which are open to interpretations has consequences to public health and the healthcare infrastructure, on implementation. For controlling acute infectious diseases, esp. viral, the fever remains the most important enabler. Historically, our chief obstacles to harnessing the benefits of fever in acute clinical diseases with limited therapeutics had been: a) widespread myths about ‘fevers’ arising from a general misunderstanding of basic facts; b) presence of confusing guidelines by different agencies which are open to alternate interpretation. The article attempts to briefly indicate the benefits of fever in disease resolution, dispel myths, underline vagueness in illustrative national guidelines and the need to align them with evidence-based WHO guidelines, as it has the potential to perpetuate myths/confusion in masses leading to adverse impact on disease management – more morbidity and mortality from diseases including COVID-19.

Machine reading comprehension aims to teach machines to understand a text like a human, and is a new challenging direction in Artificial Intelligence. Datasets play an important role while describing or building an algorithm for machine reading comprehension. The type of answers we required from developed algorithm depends on datasets.The datasets are classified into two types, namely datasets with extractive answers and datasets with descriptive answers. This article summarize both datasets with an example of each type to get better insight of datasets in machine reading comprehension and which datasets to use depending the requirements.

In this study, the complex interactions between the well-known Mittag-Leffler functions with two, three, and four parameters are explored. We first study the integral forms of the Mittag-Leffler function $\mathsf{E}_{\alpha, \beta}^{\rho,\kappa}(z)$ with the goal of clarifying its mathematical behaviours and features. Moreover, we create relations between these functions and generalised hypergeometric functions and Fox-Wright functions, expanding the range of their use and comprehension. We explore a number of unique situations by in-depth examination, providing insight into the subtle characteristics of these functions. This investigation advances our theoretical knowledge and opens up possibilities for future applications in a number of scientific fields. As such, this study adds to the current conversation in mathematical analysis and is an important tool for both practitioners and researchers.

Proprotein convertase subtilisin/Kexin type 9 (PCSK9) is a proteolytic enzyme that indirectly regulates serum LDL cholesterol by destroying LDL receptors. In clinical studies, the main role of the proprotein convertase subtilisin/Kexin type9 (PCSK9) inhibitor in cholesterol regulation was elucidated. It is produced in the liver but is also present in the kidney and intestine. It prevents HMGCo from synthesizing cholesterol. SREBP-2 is a reductase that is induced by statins. In a dose-dependent manner, increasing SREBP-2 levels enhanced LDL-R and PCSK9 gene expression. At the minimum, two procedures have been developed to overcome the plasma level of PCSK9. This is the LDLR test, polyclonal antibodies, and sentience oligonucleotide. Lower dosage statin treatment with a proprotein convertase subtilisin/Kexin type9 inhibitor will be most efficient in lowering LDL and avoiding statin adverse effects. In multiple long-term trials, statins have been found to reduce cardiovascular mortality by 30% and stroke incidence by 20%. In this way, we conclude the role of PCSK9 in hypercholesterolemia.

The system design for Sustainable manufacturing is to consider both ecological and financial constraints. Manufacturing industry demands to advance in sustainable manufacturing by accounting in environment factors in it. All the factors that affect the environment need to be analyzed so that proper amendments or suggestions can be provided. To favour this, Computer aided life cycle inventory model has been presented for CNC milling and turning processes. Based on utilization of resources and stages, whole machining operation time can be divided into three phases named as process (milling or turning), idle and basic times. As parameters are different for evaluating the process times i.e. depth and width of cut in case of milling and initial and final diameters for turning, two different case studies has been presented one for each milling and turning process. Effect of material choice has been studied for different processes. Highly dense and hard materials takes more time in finishing the job due to low cutting speed and feed rates as compared to that of sot materials. In addition, face milling takes more time and consumes more power as compared to peripheral milling due to more retraction time caused by over travel distance and lower vertical transverse speeds than the horizontal transverse speed used in peripheral retraction process.

Natural Fibre Reinforced Composites (NFRCs) represent a significant category of materials that has the capacity to contribute to the worldwide objectives of sustainability and environmental preservation. To maximise the potential of composite materials, it is essential to establish their high quality and cost-effective manufacture. While the basic production procedures for producing items based on NFRCs have reached a high level of development, more research efforts are needed to advance their secondary manufacturing processes, namely in the areas of machining and joining. The primary factor leading to the rejection of composite goods with holes is the damage generated during the drilling process. The present experimental study aims to examine the effectiveness of hole production during the moulding phase. A comparative analysis has been conducted to assess the mechanical characteristics of moulded and drilled holes in jute fibre reinforced epoxy composite. It was observed that the moulded holes exhibit superior performance compared to the drilled holes for jute/epoxy materials. Additionally, the load extension curves indicate that the moulded holes experience greater extension in comparison to the drilled holes.

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

Spin dependent quantum transport properties in twisted carbon nanotube and stretched carbon nanotube are calculated using density functional theory (DFT) and non-equilibrium green’s function (NEGF) formulation. Twisting and stretching have no effect on spin transport in CNTs at low bias voltages. However, at high bias voltages the effects are significant. Stretching restricts any spin-up current in antiparallel configuration (APC) which results in higher magneto resistance (MR). Twisting allows spin-up current almost equivalent to the pristine CNT case resulting in lower MR. High spin filtration is observed in PC and APC for pristine, stretched and twisted structures at all applied voltages. In APC, at low voltages spin filtration in stretched CNT is higher than in pristine and twisted ones with pristine giving higher spin filtration than twisted CNT.

Comprehensive studies into the various causes of this ubiquitous disease have been prompted by the worldwide increase in cancer incidence. There are many potential causes of cancer, but environmental factors are of particular concern now. This abstract explores the complex link between environmental factors and the growing cancer epidemic, shedding light on the role that our immediate environment plays in the emergence of cancers. The environment can contain carcinogenic agents that gradually influence the course of cancer, including air pollutants, water contaminants, workplace exposures, and industrial activities. The ways through which environmental carcinogens may cause DNA damage and unchecked cell proliferation are discussed in this abstract. Environmental justice and the moral necessity to reduce inequities in exposure and health effects are highlighted to underline the disproportionate impact on vulnerable groups. The study presented here highlights the need for preemptive action to reduce environmental risks. Society can reduce the hidden threat posed by environmental carcinogens through strict legislation, sustainable practices, and community-driven initiatives. In order to lessen the cancer burden and secure a healthier future for all, the authors of this abstract argue that there must be a concerted effort to create surroundings that promote health and well-being.

In the present paper, we introduced a quadratically convergent Newton’s like normal S2 iteration method free from the second derivative for the solution of nonlinear equations permitting 3 f'(x) = 0 at some points in the neighborhood of the root. Our proposed method works well 4 when the Newton method fails. Numerically it has been verified that the Newton’s like normal 5 S-iteration method converges faster than Fang et al. method [A cubically convergent Newton-type 6 method under weak conditions, J. Compute. and Appl. Math., 220 (2008), 409-412]. We studied 7 different aspects of normal S-iteration method. Lastly, fractal patterns support the numerical 8 results and explain the convergence, divergence, and stability of method.

The Indian Himalayan Region (IHR), due to its topography, geography, and active tectonics, a rough mountain zone, is among the most vulnerable zones to the landslip danger. The most cutting-edge and accurate ways for creating a landslip susceptibility model (LSM) are advanced statistical techniques. The goal of the current work was to use advanced statistical techniques to analyse and evaluate the updated landslip susceptibility for East District in the NE Himalayas of Sikkim, India. The spatiotemporal landslip inventory for the years are produced using literature surveys, historical satellite imageries and on-site observations. Slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalised difference vegetation index (NDVI), rainfall, drainage density and land use/ land cover (LULC) are some of the topographic, environmental, geologic, and anthropogenic factors that were included in the spatial database. These LCFs were chosen to study the area's periodic landslip vulnerability. An inventory of 151 landslides from historical published records, field visits and Imagery interpretations, respectively, were used in the experimental design. Information Value Model (IVM), was used to evaluate the vulnerability to landslides as determined by fifteen LCFs. The goal of the study is to reduce the number of fatalities and possible economic harm caused by the region's frequent slope instabilities. It is expected that the application of statistical algorithms would assist relevant authorities and organisations in properly planning for and managing the region's landslip threat.

The body's billions of bacteria—the microbiota—influence health and illness. The microbiome—the genetic material of these microbes—affects digestion, immunological function, and mental health. Due to bacterial variety, complicated relationships, and research technique constraints, investigating the microbiota is difficult. AI and machine learning have expanded microbiome research. Artificial intelligence (AI) systems can efficiently scan large microbiome datasets to help researchers understand microbial populations and their functions. AI-based predictive algorithms can analyze food impacts on microbial communities and forecast illness risks based on gut microbiota composition. AI helps find microbial biomarkers linked to specific health disorders, enabling early illness identification and targeted therapy. AI-driven medication development platforms also modulate microbiome to treat microbiota-related diseases. Understanding microbial populations' involvement in health and illness requires understanding complicated microbial interactions within the microbiome and between it and the host. AI systems improve human health by comprehending these complex relationships. AI in microbiota study must overcome various obstacles. Data quality and AI model interpretability are essential for accurate findings. Diverse and representative datasets avoid biases and strengthen AI-driven conclusions. AI in microbiota research requires data protection, informed permission, and appropriate use of sensitive data. AI is improving our understanding of the human microbiome and its health effects, changing microbiota research. Overcoming difficulties and adhering to ethical norms will enable the proper use of AI-driven findings, leading to microbiome insights and precision medical breakthroughs.

This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.

Maize (Zea mays subsp. mays) is the staple food crop in the world. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of developed models was evaluated based on statistical indices and graphical representation. Results of gamma test based on the least value of gamma and standard error indices show that day of anthesis (DOA), day of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined as efficient information vector combination for drought-tolerant index (DTI) as well as the stress-tolerant index (STI). The results of MLP, SVM, MLP-GA, and SVM-GA algorithms were compared based on statistical indices and visual interpretation that have satisfactory for prediction of the drought-tolerant index and stress-tolerant index in maize crop. It has also seemed that genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found a better prediction of the drought-tolerant index and stress-tolerant index in maize crop. Similarly, the SVM-GA model has the highest potential to forecast the DTI and STI in maize crops as compared to MLP, SVM, MLP-GA models.

The aim of this work is to assess the influence of Friction Stir Welding (FSW), process parameters, optimized tool traveling speed, and corrosion resistance of the 0.95 Mg-Al-alloy and pure copper weldment. Samples of aluminum-copper with and without deformation were characterized to investigate the metallurgical effects created during the welding deformation process. Effect of process parameters on microstructure and corrosion rate have been investigated for all the samples. All the electrochemical and polarization tests were done in 3.5 wt.% NaCl solution. Scanning Kelvin Probe (SKP) was done to detect the localized corrosion on the surface. Optical micrography observation indicated that the primary α-Al phase, which was formed during solidification can effectively limit the growth of Cu₉Al₄ phase. Finer acicular α-Al precipitates were observed in CuAl matrix during joining process that tends to coarser with the increase in tools travel speed. The electrochemical and polarization results showed that among all the tool travelling speed the specimen joined at tool travelling speed of 40 mm/min shows the best non-corrosive property.

Artificial Intelligence (AI) has become a hopeful tool for helping parents with different parts of raising children by giving them personalized tips, evidence-based methods, and timely solutions. This paper talks about the difficulties and possibilities of using AI to help parents. It also talks about the possible benefits and its impact on child psychology. There are a lot of ways that AI can help parents, from giving personalized advice that fits the needs of each family to intervening quickly when it spots early signs of developmental problems or parenting stress. AI makes it easier for parents to find tools, workshops, and interactive modules that are based on research. This gives parents the knowledge and trust they need to make choices about their child's growth that are based on facts. Also, AI-powered support can connect parents with similar problems, creating a sense of community and making it less embarrassing to ask for help with parenting or mental health. But there are a few problems that need to be carefully dealt with before AI can be used to help parents in a responsible and effective way. Data privacy and security are very important to protect families' safety and well-being. Important things to think about are the ethical use of data, transparency, and educated permission. Also, the accuracy and dependability of AI programs are very important when it comes to giving good advise and suggestions. To make sure everyone gets fair and equal help, efforts must be made to find and get rid of flaws. Also, AI shouldn't replace human sense and experience; it should add to them. When parents rely too much on AI, they might forget how important human contact and mental support are. To accommodate different parenting styles and views, it is important to be sensitive to culture and circumstances. In conclusion, AI-powered parenting support has a lot of promise to help parents be better parents and help children grow and learn. AI can be a useful tool for helping parents if it can solve problems related to data privacy, ethics, reducing bias, and working together with humans. Finding a balance between technology progress and human connection is important if AI is to be used to its fullest potential to give parents more power and help children grow in the best way possible.

The trillions of bacteria that live in and on our bodies make up the human microbiota, which is a very important part of keeping us healthy and protecting us from pathogenic infections. Dysbiosis is when there are changes in the composition and variety of the microbiota. This can upset the delicate balance between the host and its microbes, making the host more vulnerable to pathogens. Understanding the complicated link between microbiota and pathogens is important for coming up with effective ways to change microbiota and boost the body's defenses against infections. This study looks at how microbiota modulation affects the spread of pathogens and infections. It talks about how the makeup of microbiota affects how susceptible you are to pathogens. It shows how a healthy microbiota can protect you by making it hard for pathogens to grow and take over. The review also goes into detail about how the microbiota protects against pathogens, such as through competitive exclusion, the production of antimicrobial compounds, and the regulation of immune reactions. The study also looks at the idea of dysbiosis and how it is linked to pathogenic shifts. Disruptions in the microbiota can make it easier for pathogens to grow and make it harder for the host to fight off infections. It focuses on the need to recover microbial balance to improve the host's defenses and lower risks caused by pathogens. There is also talk about the use of probiotics, prebiotics, and fecal microbiota transplantation (FMT) as ways to change the microbiome. The goal of these interventions is to restore the diversity and function of microbes, improve the integrity of barriers, and boost the immune system's reaction to pathogens. The review shows how these methods could be used to change the microbiota and make it easier for the body to fight off infections. In conclusion, knowing how the microbiota and pathogens work together in a complicated way can help a lot when it comes to making specific interventions for microbiota modulation. Strategies that restore the balance of microbes and boost the host's defenses could stop pathogens from taking over and making people sick. More study needs to be done in this area to improve therapeutic approaches and use the microbiota to its fullest extent to fight infectious diseases.

Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment for carcinogenesis.

In our recently proposed theory of quantum gravity, a black hole arises from the spontaneous localisation of an entangled state of a large number of atoms of space-time-matter [STM]. Prior to localisation, the non-commutative curvature of an STM atom is described by the spectral action of non-commutative geometry. By using the techniques of statistical thermodynamics from trace dynamics, we show that the gravitational entropy of a Schwarzschild black hole results from the microstates of the entangled STM atoms and is given (subject to certain assumptions) by the classical Euclidean gravitational action. This action, in turn, equals the Bekenstein-Hawking entropy (Area/$4{L_P}^2$) of the black hole. We argue that spontaneous localisation is related to black-hole evaporation through the fluctuation-dissipation theorem.

This paper focuses on the relationship between Mental Health and the Landscape. It aims to find out how people respond to Biophilic inspired landscape design in a healthcare setting. The focal point is not to cure diseases but to help them deal with the physiological, psychological, and psycho-social imbalance and provide a healing environment for the overall well-being of an individual. For this study, a Multispecialty Hospital was selected where an Indoor Healing Garden is used as a retrofitting tool to reduce stress and thus reconnect humans with nature. A multi-method approach is being used for this study. Initially, a questionnaire was conducted for the targeted users categorized into three types – patients, staff, and visitors to know their longing for the landscape. Based on this data and available literature, an evidence-based design was proposed. This conceptual design model is then shown to the targeted user and the response is recorded. The data has then collaborated with similar studies done earlier and design elements are highlighted which helps in creating a restorative environment by reducing stress and increasing recovery rate and thus approaching sustainable development.

Managing sidelobe levels (SLLs) in metasurface-driven beam-steering antennas poses a significant challenge due to intrinsic factors leading to grating lobes. Our proposed method employs an equivalent model to optimize large periodic metasurfaces efficiently. This model predicts complete metasurface performance, accounting for mutual coupling between patches. We introduce an evolutionary optimization algorithm based on the Cross Entropy (CE) method to enhance PGM-based beam-steering antennas and suppress sidelobes. Two strategies were employed: first is to optimize the patch dimensions for a sidelobe-free pattern and the second is to maintain the PGM dimensions while optimizing feed array amplitudes. Both strategies effectively suppressed sidelobes, offering insights into the CE method's applicability and effectiveness for CPU-intensive electromagnetic optimization challenges. The proposed CE method variant retains its simplicity while improving monitoring capabilities, addressing this limitation. Smaller generations yield better improvements per evaluation

Modulating the gut microbiota with probiotics has been identified as a promising therapeutic method for the management of gastrointestinal illnesses. The effectiveness of probiotics in the treatment of gastrointestinal disorders can only be determined by delving into their underlying mechanisms of action. Through an analysis of the underlying processes, this study hopes to provide light on the potential benefits of probiotics for treating a variety of gastrointestinal conditions. The study covers a wide variety of gastrointestinal conditions, such as IBS, IBD, and antibiotic-associated diarrhea. Modifying the gut microbiota composition, strengthening the gut barrier function, modulating the immune system, and producing beneficial metabolites are among mechanisms of action that have been investigated. The strain- and dose-specific variables essential to getting the best treatment results are also highlighted. This review, which synthesizes the current information, gives a thorough overview of the effectiveness of probiotics in treating a variety of gastrointestinal illnesses and illuminates potential avenues for further study and therapeutic use.

The report details out programming approach for automating warehouse using Answer Set Programming.

The article reviews the constituent’s physico-chemical parameters in the water of the Mansagar Lake of the Jaipur City. The water quality is also investigated using evaluated physico-chemical parameters with in-depth study of their distribution as per sample location-wise and season-wise. A detailed discussion on the associationship among parameters (pH, Conductivity, turbidity, dissolved Oxygen, chemical and Biochemical Oxygen demand, etc.) using the Pearson’s analytical method is also presented. The strength of the article is the graphical presentation of evaluated physico-chemical parameters and the water quality indices for sample station-wise and the season-wise. To our best of understanding, first time such details have been clubbed together and discussion in brief for studied lake.

Plants are immobile, and, to overcome harsh environmental conditions, such as drought, salt, and cold, they have evolved complex signaling pathways. Abscisic acid (ABA), an isoprenoid phytohormone, is a critical signaling mediator that regulates diverse biological processes in various organisms. Significant progress has been made in the determination and characterization of key ABA-mediated molecular factors involved in different stress responses, including stomatal closure and developmental processes, such as seed germination and bud dormancy. Since ABA-signaling is a complex signaling network that integrates with other signaling pathways, the dissection of its intricate regulatory network is necessary to understand the function of essential regulatory genes involved in ABA signaling. In the present review, we focus on two aspects of ABA signaling. First, the perception of the stress signal (abiotic and biotic) and the response network of ABA-signaling components that transduce the signal to the downstream pathway to respond to stress tolerance, regulation of stomata, and ABA signaling component ubiquitination. Second, ABA-signaling in plant development processes, such as lateral root growth regulation, seed germination, and flowering time regulation. Examining such diverse signal integration dynamics could enhance our understanding of the underlying genetic, biochemical, and molecular mechanisms of ABA signaling networks in plants.

Transcatheter aortic valve implantation (TAVI) has grown exponentially within the cardiology and cardiac surgical spheres. It has now become a routine approach for treating aortic stenosis. Several uncertainties have been raised about TAVI in comparison to conventional surgical aortic valve replacement (SAVR). The primary concerns are with regards to the longevity of the valves. Several factors have been identified that may predict poor outcomes following TAVI. These include the lesser-used finite element analysis (FEA) to quantify the properties of calcifications that affect TAVI valves. This can also be used in conjunction with other integrated software to ascertain the functionality of these valves. Other imaging modalities are now widely available such as Multi-detector row computed tomography(MDCT) which can accurately size the aortic valve annulus.. This may help reduce the incidence of paravalvular leaks and regurgitation which may necessitate further intervention. Structural valve degeneration (SVD) remains a key factor with varying results from current studies. The true incidence of SVD in TAVI compared to SAVR remains unclear with the lack of long term data. It is now widely accepted that both are part of the armamentarium and are not mutually exclusive. Decision-making for the appropriate intervention should be made via shared decision-making involving the heart team.

Studies at the molecular, systemic, and epidemiological levels have shown that chronic metal exposure is linked to significant health consequences, including cancer, affecting hundreds of millions worldwide. Subtle and convoluted mechanisms underline metals' toxicity and carcinogenicity. The use of sensors for carcinogenic metals' trace detection is on the rise due to their selectivity, simplicity, and affordability. Biotechnology and microelectronics in the development of sensors have grown complementarily in recent years. This study offers a comprehensive overview of current research and advancements in developing sensors for detecting carcinogenic metals. Here, we have focussed on the developed biosensor platforms for group 1 carcinogens, i.e., arsenic, nickel, cadmium, chromium, and beryllium, along with their brief roles in human carcinogenesis. This review also looks at the importance of sensing such metal exposure in humans from a larger perspective, hoping to influence future research toward early prevention and treatment of illnesses like cancer.

Zero-shot learning ability robotics enables robots to generalize their knowledge and perform tasks for which they have not been explicitly trained. This ability is extremely helpful because in classic learning approaches, robots are trained on specific tasks and require extensive data and labeled examples to perform accurately. However, in real-world scenarios, robots often encounter new or unseen tasks that were not part of their training data. We briefly survey works in this area to give the reader a perspective about progresses in this area.

This is an article or technical note which is intended to provides an insight journey of Machine Learning Systems (MLS) testing, its evolution, current paradigm and future work. Machine Learning Models, used in critical applications such as healthcare industry[1], Automobile [2], [3] and Air Traffic control, Share Trading etc., and failure of ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, developers, scientists, and ML community around the world, must build a highly reliable test architecture for critical ML application. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. This attribute comes from the software engineering [5], [6], but the same cannot be applied in as-is form to the ML testing and we will tell you “why”.

One of the biggest demanding situations for food security in the 21^st century is to enhance crop yield stability through the improvement of diseases-resistant crops. Managing plant health is a major challenge for modern food production and compounded by the lack of common ground among the many disease control disciplines involved. All plants simultaneously engage with billions of microbes which can be collectively referred to as the plant microbiome. Most microbes inside the plant microbiome are harmless or even beneficial to the plant as they promote plant growth or provide protection in opposition to diseases. However, some of these microbes also cause disease with devastating effects on crop yields. To prevent pathogen infection, plants have evolved an advanced innate immune system that recognizes conserved cell surface molecules that most pathogen possesses. Activation of the plant immune system stops the invading pathogen, however this comes with fitness cost that significantly reduces plant growth and leads to yield penalty. Apart from their innate immune system controlling pre-programmed defense reactions, plants can also increase the responsiveness of their immune system in response to selected environmental signals. This phenomenon is known as “defense priming”. Although defense priming rarely provides full protection, its broad-spectrum effectiveness, low-fitness cost, long‐lasting durability and inherited to future generations make it attractive for sustainable crop protection.

Currently, energy security and environmental degradation are the two biggest challenges before humanity that can be surmounted with the use of green and sustainable biofuels produced from lignocellulosic crops. In the future, to ensure adequate and cost-effective supply of biofuels, it requires a sufficient amount of amenable and quality lignocellulosic feedstocks. Therefore, agricultural yields of lignocellulosic biomass crops should be substantially increased by intense genetic maneuvering of key gene regulatory mechanisms and signaling pathways that control plant biomass yield. Recently, numerous miRNAs families are identified, characterized, and validated across the plant kingdom. Plant microRNAs (miRNAs) are 21 to 24 nucleotides long, non-coding small RNAs, act as regulators of their target genes via inducing modifications in transcription, translation, and epigenome. MiRNAs represent many hallmark characteristics like sequence-specific regulation, tissue, and species-specific expression, evolutionary conservation, and functional diversity. They coordinate well physiological and life cycle processes in plants under adverse environmental conditions. Hence, miRNAs offer accurate, precise, and efficient regulatory switches in the miRNA-targeted genetic networks. It is evident from the study of the miR156 family and its target SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) genes network that controls highly significant agronomic traits in crop plants. The miR156/SPL module acts as a master circuit that synchronizes many intricate complex biological functions such as growth and development, and metabolic processes by sensing internal and external environmental signals in plants. Therefore, miR156 can prove a potential target for miRNAs based plant biotechnology to harmonize complex biofuel traits and improve biomass yield in lignocellulosic biomass crops.