Both Stevens Johnson Syndrome (SJS) and toxic epidermal necrolysis (TEN) are main injurious cutaneous medication reactions that mostly affect the epidermis and mucus membranes. TEN and SJS affecting nearly 1 or 2/1,000,000 people per year, and can recognized as medical crises since they may be deadly. Mucocutaneous discomfort, hemorrhagic erosions, erythema, and more or less severe epidermal separation that appear as ulcer and patches of dermic loss are their defining characteristics. The sole difference between TEN and SJS at this time is the degree of skin detachment, making them two extremes of a spectrum of severe cutaneous adverse drug reactions (cADRs). In the majority of cases, drugs are considered as the principal reason of SJS/TEN, but herpes simplex virus and Mycoplasma pneumoniae infections are also recognized causes, along with lesser number of cases in which the cause is still unknown. Among the drugs with a "high" likelihood of producing TEN/SJS are carbamazepine (CBZ), trimethoprim-sulfamethoxazole, phenytoin, aminopenicillins, allopurinol, cephalosporins, other sulfonamide antibiotics, quinolones, phenobarbital, and NSAIDs of the oxicam variety. There is strong genetic evidence for SJS and TEN in Han Chinese due to the substantial association between the human leukocyte antigen (HLA-B*1502) and SJS brought on by CBZ. The diagnosis is made mostly based on clinical symptoms and the histological study of a dermal biopsy. Pemphigus vulgaris, bullous pemphigoid, linear IgA dermatosis, paraneoplastic pemphigus, disseminated fixed bullous drug eruption, acute generalized exanthematous pustulosis (AGEP), and staphylococcal scalded skin syndrome (SSSS) are among the differential diagnoses. The management of patients with SJS/TEN is complicated by the high risk of mortality, necessitating early diagnosis, estimation of the SCORTEN prognosis, identification and discontinuation of the causative drug, specialized supportive care, and high-dose injectable Ig therapeutic interventions. The reported fatality rates for SJS are 1-5% on average and 25-35% for TEN; it can be even higher in patients who are elderly or who have a significant amount of epidermal detachment on their skin. More than 50% of TEN patients who survive the disease experience long-term consequences.

Apart from their established role in embryonic development Nerve Growth Factors (NGFs) have diverse functions in the nervous system. Their role in integration of physiological functioning of the nervous system is now attracting attention. In the present analysis, we propose a novel paradigm about a novel role of NGFs: NGFs play imperative role in maintaining psychological integrity of an individual as a biological system. This function may be mediated through HPA-axis- operated homeostatic mechanisms; stress induced disruption of which may lead to psychiatric disorders. Current literature suggests existence of constitutive homeostatic regulatory mechanisms for NGFs disruption which may lead to pertinent and imperative behavioural effects. NGFs are known to play crucial role in endocrine regulation. This is especially true with the prototype ‘NGF’ and Brain Derived Neurotrophic Factor (BDNF). These moieties have been observed to play important function in maintaining neuro-endocrine homeostasis thereby having a profound impact on the psychological health of an individual. Role of NGFs and HPA-axis activation (in separate studies) in developing psychiatric disorders - especially those born of stress - have been reported. Literature suggests their unique interplay for producing a common effect which might be implicated in stress induced genesis of psychiatric disorders. This aspect, therefore, needs to be elucidated further as a disease etiogenesis model. This model may yield important insights into the biology of psychiatric disorders and may open ways for new therapeutic approaches.

Translational medicine requires facile experimental systems to replicate the dynamic biological systems of diseases. Drug approval continues to lag, partly due to incongruencies in the research pipeline that traditionally involve 2D models, which could be improved with 3D models. The bone marrow (BM) poses challenges to harvest as an intact organ making it difficult to study disease processes such as breast cancer (BC) survival in BM, and to effective evaluation of drug response in BM. Furthermore, it is a challenge to develop 3D BM structures due to its weak physical properties, and complex hierarchical structure and cellular landscape. To address this, we leveraged 3D bioprinting to create a BM structure with varied methylcellulose (M):alginate (A) ratios. We selected hydrogels containing 4% (w/v) M and 2% (w/v) A, which recapitulates rheological and ultrastructural features of the BM while maintaining stability in culture. This hydrogel sustained the culture of two key primary BM microenvironmental cells found at the perivascular region, mesenchymal stem cells and endothelial cells. More importantly, the scaffold showed evidence of cell autonomous dedifferentiation of BC cells to cancer stem cell properties. This scaffold could be the platform to create BM models for various disease and also for drug screening.

Background: Mucoadhesive polymers represent a major part of site-specific and localized retention strategies in oral drug delivery. Present research was designed to synthesize and characterize a novel mucoadhesive carbohydrate polymer (thiolated gum ghatti; TGG) which was further employed to fabricate mucoadhesive tablets of domperidone using an industrially viable compression coating technique. Methods: Thiolation of gum ghatti was achieved by the ester formation (esterification) between hydroxyl group and carboxyl group of gum ghatti and thioglycolic acid. Results: TGG was characterized by various physico-chemical techniques such as FTIR, XRD, SEM and DSC techniques. In rheological studies, the observed viscosities of pure gum-mucin were 45.45 and 71.75 mPas and that of thiolated gum were 78.7 and 112.58 mPas respectively in water and simulated gastric fluid. Signifi-cant increase in viscosity for thiolated gum may be attributed to increased macromolecular interactions responsible for enhanced mucoadhesive potential of thiolated gum. In-silico studies corroborate the role of mucin gum interaction and energetic stabilization for en-hanced mucoadhesion properties of thiolated gum. Ex-vivo mucoadhesion strength of gum ghatti and thiolated gum ghatti coated tablets was found to be ranging between 4.67± 0.79 to 8.99± 0.75 g and 11.76± 1.34 to 18.83± 2.07 g respectively. Conclusion: Thiolated gum ghatti may be regarded as a promising polymer for developing different mucoadhesive drug delivery systems.

Zika virus (ZIKV) has been characterized as one of the potential pathogens and placed under future epidemic outbreaks by the WHO. However, lack of potential therapeutics can result in uncontrolled pandemic like other human pandemic viruses; therefore, prioritized effective therapeutics development has been recommended against ZIKV. In this context, the present study adopted the strategy to explore the lead compounds from Azadirachta indica against ZIKV via concurrent inhibition of the ZIKVpro and ZIKVRdRp proteins using molecular simulations. Initially, structure-based virtual screening of 44 bioflavonoids reported in Azadirachta indica against the crystal structures of targeted ZIKV proteins resulted in the identification of top four common bioflavonoids, viz. rutin, nicotiflorin, isoquercitrin, and hyperoside. These compounds showed substantial docking energy (-7.9 to -11.01 kcal/mol) and intermolecular interactions with essential residues of ZIKVpro (His51, Asp74, and Ser135) and ZIKVRdRp (Asp540, Ile799, and Asp665) by comparison to the reference compounds, O7N inhibitor (ZIKVpro) and Sofosbuvir inhibitor (ZIKVRdRp). Long interval molecular dynamics simulation (500 ns) on the selected docked poses reveals the stability of respective docked complexes contributed by intermolecular hydrogen bonds and hydrophobic interactions. The predicted complex stability was further supported by calculated end-point binding free energy using molecular mechanics generalized born surface area (MM/GBSA) method. Consequently, the identified common bioflavonoids are recommended as promising therapeutic inhibitors of ZIKVpro and ZIKVRdRp for further experimental assessment.

Staphylococcus aureus is a common pathogen in human. Methicillin resistant Staphylococcus aureus (MRSA) infection poses a big and perplexing difficulty in terms of therapy. The acquisition of the non-native gene PBP2a, which has a decreased tolerance for β-lactam antibiotics, frequently confers resistance. PBP2a has a less attraction for methicillin and it helps bacteria to continue peptidoglycan biosynthesis, and it is cell wall’s core component in bacteria. So, even in the presence of methicillin or any other antibiotic, bacteria develop resistance. Due to resistance-causing genes, S. aureus becomes MRSA. The main premise of the resistance mechanism is well understood. The current demand for novel antibiotics is legitimate in the face of therapeutic concerns posed by resistant micro-organisms. The emphasis of this review is on PBP2a scaffolds and the different screening approaches used to find PBP2a inhibitors. Penicillin, Cephalosporins, Pyrazole-Benzimidazole based derivatives, Oxadiazole containing derivatives, non-β-lactam allosteric inhibitors, 4-(3H)-Quinazolinones, Pyrrolylated chalcone, Bis-2-Oxoazetidinyl macrocycle (β-lactam antibiotics with 1, 3-Bridges), Macrocycle-embedded β-lactams as novel inhibitors, Pyridine-Coupled Pyrimidinones, novel Naphthalimide corbelled aminothiazoximes, non-covalent inhibitors, Investigational-β-lactam antibiotics Carbapenem, novel Benzoxazole derivatives, Pyrazolylpyridine analogues, and other miscellaneous classes of scaffolds for PBP2a are also represented as well as with their biological activity is discussed. The penicillin-binding protein is also discussed, which is the crucial target for the cell wall of MRSA. Various aspects of PBP2a, the cell wall of bacteria, peptidoglycans, different crystal structures of PBP2a, synthetic routes for PBP2a inhibitors, and future perspectives of MRSA inhibitors are also enumerated.

The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.

Objective: In India, yearly, estimated one million TB cases are missing from notification, mostly from private sector. The large number of patients in private sector has raised concerns for suboptimal quality of care. This study was conducted to find out the treatment adherence status among the private TB patients and factors associated with poor adherence.Data Source: Secondary project data, obtained through adherence monitoring house visit by NGO workers. Data collected by reviewing different records available with the patients & data was entered into the CommCare HQ, an open-source mobile platform designed for data collection.Methods: Descriptive observational study.Results: Default rate among private patients was 5%. Commonest reasons stated for being a defaulter were ‘Medicine is not good’ (30%), ‘Travel’ (28.6%), ‘Cost of treatment’ (21.8%) and ‘Side effects’ (11.6%). Despite best of efforts only 36.9% defaulter could be retrieved. Higher default rate was associated with 15-59 years age, males, earning member of the family, addiction, DR-TB, continuation phase of treatment, previous history of TB, presence of symptoms and inability to walk.Conclusion: Privately treated TB patients are vulnerable to non-adherence. Once defaulted, it is difficult to retrieve them. Economically productive age group is at higher risk of being defaulter. Strict adherence monitoring for private TB patients and extensive advocacy communication & social mobilization program in the community, workplaces and institutions is a need of hour.

Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages. hor[RU,RA]{\large Richard Anderson} \address[RU]{\large Rutgers University, USA} \address[RA]{\large [email protected]} \author[RU]{\large Carmela Scala} \author[RU]{\large Jim Samuel} \author[UC]{\large Vivek Kumar} \address[UC]{\large University of Cagliari, Italy} \author[RU]{\large Parth Jain}

Leptospirosis is a zoonotic disease of global importance. The current killed vaccine provides serovar-specific protection without sterilizing immunity. Several surface antigens, including Leptospira immunoglobulin-like proteins (LigA and LigB), have been identified as subunit vaccine candidates; however, they require potent adjuvants. Bacterial Lipopolysaccharide (LPS), including lipid A, is a well-known immunostimulatory agent, and the formulation of Monophosphoryl Lipid A (MPLA) in Alum is a clinical adjuvant. Being less endotoxic, we tested the adjuvant activity of lipid A purified from L. interrogans serovar Pomona (PLA) for its ability to activate innate and enhance antigen-specific adaptive immune response. PLA-induced activation of macrophages similar to levels induced by MPLA, albeit at a much higher dose, indicating that it is less stimulatory than MPLA. Mice immunized with a Variable portion of LigA (LAV) formulated in Alum and PLA (LAV-Alum-PLA) induced significantly higher levels of LAV-specific humoral and cellular immune response than Alum but similar to levels induced by Alum-MPLA. The adjuvant activity of PLA seems to be quite similar to MPLA and primarily mediated through enhanced recruitment, activation, and uptake of antigens by innate immune cells. Moreover, like MPLA, the PLA formulation was able to generate a long-term memory response. Most importantly, PLA demonstrated better potency than MPLA formulation and generated sterilizing immunity against challenge in a hamster model of leptospirosis. Altogether, our study has provided important insight into the adjuvant activity of Leptospira lipid A and has opened avenues for the development of LPS-based vaccines against this dreadful zoonosis.

Antimicrobial resistance is a major public health concern. Rodents and shrews live in closer proximity to humans and animals and serve as reservoirs for the transmission of several diseases, including AMR pathogens. There is no data available from India on surveillance of AMR bacteria in rodents and shrews. In this study, bacterial pathogens viz, Staphylococcus sp, Escherichia coli and Salmonella sp were isolated from rodents and shrews and genetic elements conferring antimicrobial resistance in the pathogens were screened by PCR. Bacterial pathogens in the oropharyngeal and anal swabs collected from the rodents and shrews trapped (n=100) were cultured on selective media such as Mannitol salt, Mac Conkey and Xylose lysine deoxycholate media to isolate Staphylococcus sp., E. coli and Salmonella sp. respectively. A total of 76 (80%) S. aureus and 19 (20%) Staphylococcus non aureus were isolated. E. coli was isolated in 89 samples and among the Salmonella sp (n=59), 16 (27%) were S. enteritidis and 29 (49.1%) were S. typhimurium. In S. aureus isolates, mec A and mec C (MRSA) were detected in 40 (52.6%) and 6 (7.9%) isolates respectively. Also, 7 (36.84%) and 1 (5.3%) Staphylococcus non aureus isolates were tested to harbour mec A and mec C genes. A total of 77 (86.5%) E. coli isolates had genes encoding ESBL [TEM in 21 (23.59%), SHV in 45 (50.6%) and CTXM in 11 (12.4%)]. Among the 45 pathogenic Salmonella sp, 44 (97.78%) harboured AMR genes [tet in 13 (28.9%), sul3 & sul4 in 20 (44.4%) and qnrA in 11 (24.4%)]. Thirty-eight animals harboured more than one zoonotic pathogen and 21 with multi-drug resistant genes. Our findings signify that these rodents and shrews may serve as sentinels to screen environment spillover of AMR pathogens and also act as reservoir to transmit MRSA, ESBL, Salmonella sp. human/animals. Also these animals aid in the transfer of antimicrobial resistance genetic elements to non-pathogenic organisms.

SARS-CoV2 popularly known as (COVID-19) has presently received worldwide attention. It has been considered a pandemic by the World Health Organisation. Owing to its high transmittance factor the virus has brought about many deaths and spread to all the major countries of the world. Scientists and Researchers worldwide are giving their full efforts to develop a vaccine. In our present study, we have included the comparative analysis of the different spike glycoprotein sequences of the patients suffering from COVID-19 from different countries where this pandemic has occurred. Spike glycoproteins are the structural proteins that bring about the binding of the SARS-CoV-2 viral molecule to the ACE2 receptor of the host following which infection occurs. Through this data, we have shown the different point mutations in the spike glycoproteins that occurred over time in different countries as the disease progressed.

This paper starts with hypothesis (and presents some evidence) that anxiety in migrants is sufficiently important to be modelled. It presents a small (and very incomplete) review of emotion modelling in literature. It asks the question of how to translate these into agent-based modelling, and whether this can be orthogonal to specific modelling of goals and capabilities of agents. This short paper is offered as a motivator for discussion, rather than a discussion of results.

Enteroaggregative Escherichia coli (EAEC) are highly heterogeneous in virulence; we wanted to understand the pathogenic potential of EAEC isolated various clinical and non-clinical sources in an animal model. We infected male BALB/c mice in six mice/group with 50 EAEC isolates, isolated from clinical and non-clinical sources. We studied colonization, weight loss, stool shedding, inflammatory markers and its relationship with 21 virulence genes and phylogroups, EAEC organ burden and histopathological changes. We detected significantly more inflammatory changes and fecal lactoferrin and calprotectin levels in mice infected with EAEC isolated from symptomatic cases. In clinical EAEC isolates, the presence of chromosomal genes {(aap (46%), aaiC (23.3%), and SPATEs (pet (13.3%), sat (20%), sigA and pic (6.6%)}, adhesive variants {(agg4A (53.3%), aggA (53.3%), aafA (36.6%), agg3A (40%)} of EAEC, and master regulator gene aggR (66.6%) were associated with higher levels of lactoferrin and calprotectin. Also, 70% (9/13) of EAEC isolated from acute diarrheal cases bearing chuA (70%) in our study were assigned to group B2 (4 isolates) and D (5 isolates). Real-time PCR analysis revealed that colonization by EAEC strains from different clinical and non-clinical sources occurs up to 10-15 days of life. Even from non-diarrhoeal stools and non-clinical sources, had the potential to cause prolonged colonization, weight loss, and inflammation in the intestine though the degree varied. Moreover, better understanding of EAEC pathogenic pathways is desperately needed in different clinical scenarios.

Ayurveda as a healthcare system has survived for thousands of years but continues to be dogged by reported lack of efficacy of the treatments in clinical trials. The reported lack of efficacy could be due to a real lack of efficacy (which then contradicts the survival of Ayurveda as a functional medical system enjoying considerable public patronage) or could be attributed to inadequacies in the efforts towards evidence generation or in a larger context the overall scientific conduct of research in Ayurveda. In an effort towards better evidence generation, there is an immediate need for standardizing the design, conduct and reporting of clinical trials of Ayurveda but it is a daunting task. For this effort to benefit the scientific endeavors of Ayurveda researchers, it should allow the researchers to be able to apply Ayurveda’s multi-component, individualized and inherently holistic approach. Statistical principles can benefit this effort. Statistical hypothesis testing (SHT) is central to these statistical principles and also aligns well with conventional scientific principles of evidence generation. Although there are challenges with SHT, good practitioners engaged in it do much more than just apply the mathematical theory behind it. As a particular example, lot of time in clinical trial designing is spent in addressing biases and designing trials prudently by minimizing the effect of such biases. SHT can benefit such an effort objectively. There is a need for Ayurveda researchers to engage deeply and mindfully about biases in study design in order to gain scientific validity and acceptability. The article highlights issues that arise in Ayurveda research, and discusses few ways of dealing with these issues using statistical principles.

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.

The master molecular regulators and mechanisms determining longevity and health span include nitric oxide (NO) and superoxide anion radicals (SOR). L-arginine, the NO synthase (NOS) substrate, can restore a healthy ratio between the dangerous SOR and the protective NO radical to promote healthy aging. Antioxidant supplementation orchestrates protection against oxidative stress and damage—L-arginine and antioxidants such as vitamin C increase NO production and bioavailability. Uncoupling of NO generation with the appearance of SOR can be induced by asymmetric dimethylarginine (ADMA). L-arginine can displace ADMA from the site of NO formation if sufficient amounts of the amino acid are available. Antioxidants such as ascorbic acids can scavenge SOR and increase the bioavailability of NO. The topics of this review are the complex interactions of antioxidant agents with L-arginine, which determine NO bioactivity and protection against age-related degeneration.

The master molecular regulators and mechanisms determining longevity and health span include nitric oxide (NO) and superoxide anion radicals (SOR). L-arginine, the NO synthase (NOS) substrate, can restore a healthy ratio between the dangerous SOR and the protective NO radical to promote healthy aging. Antioxidant supplementation orchestrates protection against oxidative stress and damage—L-arginine and antioxidants such as vitamin C increase NO production and bioavailability. Uncoupling of NO generation with the appearance of SOR can be induced by asymmetric dimethylarginine (ADMA). L-arginine can displace ADMA from the site of NO formation if sufficient amounts of the amino acid are available. Antioxidants such as ascorbic acids can scavenge SOR and increase the bioavailability of NO. The topics of this review are the complex interactions of antioxidant agents with L-arginine, which determine NO bioactivity and protection against age-related degeneration.

Conjugated polymers (CPs) offer potentials for sustainable semiconductor devices due to their low cost, and inherent molecular self-assembly. Enhanced crystallinity and molecular orientation in thin films of solution-processable CPs have significantly improved organic electronic device per-formance. In this work, three methods namely spin-coating, dip-coating, and unidirectional float-ing-film transfer method (UFTM) were utilized with their parametric optimization for fabricating RR-P3HT films. These films were then utilized for their characterization via optical and micro-structural analysis to elucidate dominant roles of molecular orientation, and crystallinity in con-trolling charge transport in organic field-effect transistors (OFETs). OFETs fabricated by RR-P3HT thin films using spin-coating and dip-coating displayed field-effect mobility (μ) of 8.0 × 10-4 cm2V-1s-1 and 1.3 × 10-3 cm2V-1s-1, respectively. This two-time enhancement in (µ for dip-coated films was attributed to its enhanced crystallinity. Interestingly, UFTM films based OFETs demon-strated μ of 7.0 × 10-2 cm2V-1s-1, >100 times increment as compared to its spin-coated counterpart This superior device performance is attributed to synergistic influence of higher crystallinity and molecular orientation. Since the crystallinity of dip-coated and UFTM-thin films are similar ~50 times improved µ of UFTM thin films suggest a dominant role of molecular orientation as com-pared to crystallinity in controlling the charge transport.

Many ethical issues arise when robots are introduced into elder-care settings. When ethically charged situations occur, robots ought to be able to handle them appropriately. Some experimental approaches use (top-down) moral generalist approaches, like Deontology and Utilitarianism, to implement ethical decision-making. Others have advocated the use of bottom-up approaches, such as learning algorithms, to learn ethical patterns from human behaviour. Both approaches have their shortcomings when it comes to real-world implementations. Human beings have been observed to use a hybrid form of ethical reasoning called Pro-Social Rule Bending, where top-down rules and constraints broadly apply, but in particular situations, certain rules are temporarily bent. This paper reports on implementing such a hybrid ethical reasoning approach in elder-care robots. We show through simulation studies that it leads to better upholding of human values such as autonomy, whilst not sacrificing beneficence.

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (Predictability, Computability, Stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning (Yu and Kumbier, 2020), to the design of RL algorithms for the digital interventions setting. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We illustrate the use of the PCS framework for designing an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

The growing importance of sustainability in organizational success, particularly in the pharma-ceutical industry, underscores the need for leveraging technologies like blockchain to enhance sustainability indicators across environmental, social, and economic pillars. This study aims to identify and understand the challenges hindering the adoption of blockchain in the pharmaceuti-cal sector for improving sustainability performance, addressing two research questions: the spe-cific challenges faced by blockchain adoption in this context and the interdependencies among these challenges. Employing a two-step approach, the study compiles challenges through a litera-ture review, refines them via expert opinions, and establishes their interrelationships using methodologies like Fuzzy Interpretive Structural Modeling (FISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). The research contributes to unraveling the complex relationships and dependencies within the system, providing a structured framework for improved decision-making and strategic planning. It fills a literature gap by being the first attempt to outline driving and dependent factors related to the challenges of adopting blockchain technology for sustainability enhancement in the pharmaceutical sector, offering insights that can significantly impact brand image, company perception, and consumer value.

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit (IMU) sensors installed as wearable device. The objective is to learn classification of multiple classes of human activities in real-time. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based, and distance-based, are employed to achieve the desired classification performance. A comparative study of these strategies has been presented in terms of classifiability and classification accuracies. The proposed system is shown to be computationally efficient, which is relevant in the context of limited computing resources on typical wearable devices. The trade-off between classification accuracy and computation complexity is analyzed for different algorithmic hyper-parameters and other system parameters. Extensive experimentation and simulation study have been conducted on a multi-user human activity data from the public do-main in order to validate the proposed learning paradigm.

Global growth and sustainability highly depend upon the output of industries, corporations, governments, and non-governmental organisations requiring comprehensive energy transition in the face of a worldwide crisis demanding carbon neutralisation. The carbon neutralisation challenges required for energy transition could be overcome by detailed surveying, recording and the analysis of the carbon dioxide emissions. It is crucial to study innovative materials like metal-organic frameworks for their transdisciplinary applications towards carbon capture to have a sustainable strategy to capture harmful emissions. In addition, the CO₂ solubility, adsorption and absorption capacity, and several reinforcing characteristics and factors for carbon dioxide capture using MOFs are discussed in detail. Further, for various multidisciplinary applications towards commercialisation, MOFs solubility, adsorption and absorption parameters were considered. Moreover, the essential organic qualities of MOFs and their organo-derivatives are considerably elaborated. Overall, to align with the current sustainable development goals, it is significant that carbon dioxide emissions must be reduced along with its capture in a scientific manner. Relevant projections for carbon capture through MOFs have been reviewed industrially and universally in alignment with the United Nations Sustainable Development Goals (UNSDGs).

In automated manufacturing systems, most of the manufacturing processes including machining are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Ceramic cutting tools are used to machine hard materials. Ti[C,N] mixed alumina ceramic cutting tools are widely used to machine hardened steel and Stainless Steel due to its superior mechanical properties. Martensitic stainless steel has wide applications in screws, bolts, nuts and other engineering applications. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Using the dataset obtained from experimental machining tool life model has been developed using Gradient Descent algorithm. The model was validated using co-efficient of determination. The accuracy of the machine learning model was tested using the test data and 99.83% accuracy was obtained. Tool life model based on Gradient Descent Algorithm was successfully implemented for the tool life of Ti[C,N] mixed alumina ceramic cutting tool.Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article; yet reasonably common within the subject discipline.)

Global population is predicted to approach 10 billion by 2050, an increase of over 2 billion from today. To meet the demands of growing, geographically and socio-economically diversified nations, we need to diversity and expand agricultural production. This expansion of agricultural productivity will need to occur under increasing biotic, and environmental constraints driven by climate change. Clustered regularly interspaced short palindromic repeats-site directed nucleases (CRISPR-SDN) and similar genome editing technologies will likely be key enablers to meet future agricultural needs. While the application of CRISPR-Cas9 mediated genome editing has led the way, the use of CRISPR-Cas12a is also increasing significantly for genome engineering of plants. The popularity of the CRISPR-Cas12a, the type V (class-II) system, is gaining momentum because of its versatility and simplified features. These include the use of a small guide RNA devoid of trans-activating crispr RNA (tracrRNA), targeting of T-rich regions of the genome where Cas9 is not suitable for use, RNA processing capability facilitating simpler multiplexing, and its ability to generate double strand breaks (DSB) with staggered ends. Many monocot and dicot species have been successfully edited using this Cas12a system and further research is ongoing to improve its efficiency in plants, including improving the temperature stability of the Cas12a enzyme, identifying new variants of Cas12a or synthetically producing Cas12a with flexible PAM sequences. In this review we provide a comparative survey of CRISPR-Cas12a and Cas9, and provide a perspective on applications of CRISPR-Cas12 in agriculture.

This study investigates novel ferrocene-based electrochemical sensors for metal cation detection through the design, synthesis and characterisation of ferrocene derivatives. Specifically, the re-search determines the redox potentials of ferrocene versus decamethylferrocene to provide in-sight into the redox potential variations between them. The investigation also examines how electrochemical oxidation of the ferrocene moiety can modulate host affinity for transition metals cations via effects such as electrostatic interactions and changes to coordination chemistry. Metal ion coordination to receptors containing functional groups like imine and quinoline is explored to elucidate selectivity mechanisms. These findings advance the fundamental understanding of ferrocene electrochemistry and host-guest interactions, supporting the development of improved cation sensors with optimised recognition properties, sensitivity, and selectivity. Overall, the work lays the necessary groundwork for applications in analytical chemistry and sensor technologies through customised ferrocene-derived materials.

The 2019 COVID-19 pandemic caused by SARS-CoV-2 has resulted in many fatalities worldwide. Despite various types of supportive care, mortality rates for patients with comorbidities remain high. To explore alternative treatment options, interferons (IFNs) have emerged as promising therapeutic drugs for SARS-CoV-2. This review aims to investigate the potential of IFNs as a drug with details on their mechanisms of action, and available data on their use with ongoing clinical trials, results, potential limitations, and challenges. Recently published research articles, which were systematically searched through online databases, have been selected and found that IFNs have colossal potential in treating SARS-CoV-2 infection by modulating the host's immune response and inhibiting viral replication and decreasing the severity of disease and hospitalization (p = 0.03, ±0.05) and (p = 0.04, ±0.05) respectively. However, due to less available data, more controlled and randomized trials are needed to confirm the efficacy and safety of IFN therapy. The optimal dosing and duration of IFN therapy also remain to be determined. Although further research is needed the wait for ongoing clinical trial results under investigation is also important for a better understanding of IFN therapy.

Long COVID, also referred to as post-COVID-19 condition, describes the lingering after-effects of COVID-19 infection. This complex syndrome encompasses a range of symptoms affecting physical well-being, cognitive function, and mental health. The widespread impact of long COVID extends beyond individual sufferers, placing significant strain on healthcare systems and causing far-reaching socioeconomic consequences. The scale of this multifaceted health crisis is unparalleled in recent history, affecting millions globally and challenging our understanding of post-viral conditions. This study aimed to explore the possibility of targeting G protein-coupled receptors (GPCRs) signaling pathway-related genes as therapeutics for long COVID using computational analysis. Targeting GPCRs and their signaling pathways holds significant promise for long COVID therapeutics, as these receptors are involved in crucial physiological processes including immune regulation, inflammation control, and neurotransmission. All of these are implicated in the diverse and persistent symptoms of long COVID, potentially offering a versatile approach to addressing multiple aspects of the condition simultaneously. This study identified 21 genes associated with GPCRs for further exploration as potential targets. Notably, GPCR genes such as HRAS, KRAS, GNAQ, and GNA11 were determined to be possible therapeutic targets for the pathophysiological mechanisms of long COVID. FDA-approved drugs targeting these genes were identified, including Binimetinib, Cabozantinib, Selumetinib, Panitumumab, Cetuximab, Adagrasib, Tipifarnib, and Sotorasib. Additionally, Naltrexone and Tipifarnib have emerged as potential new treatment options for long COVID. SARS-CoV-2 may hijack GPCR signaling pathways to dysregulate lung ion and fluid transport, contributing to the pathophysiology of pulmonary edema in patients with COVID-19. Further research is required to translate the findings into the development of therapeutics and to investigate the relationship between viral infection and GPCR signalling.

Choice of the salts of therapeutic agents or active pharmaceutical ingredients (API) is based on the physicochemical properties of API and the dosage form considerations. The appropriate salt can have positive effect on overall therapeutic and pharmaceutical effects of API. However, the incorrect salt form can negatively affect the overall pharmaceutical outcomes of the API. This review addresses various criteria for choosing appropriate salt form along with the effect of salt forms on API’s pharmaceutical properties. In addition to comprehensive review of the criteria, this review also gives a brief historic perspective of the salt selection process.

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

1) Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict Chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India; 2) The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points; 3) The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R² value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. 4) This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.

Bisphenols are widely used in the synthesis of polycarbonate plastics, epoxy resins and thermal paper, which are used in manufacturing items of daily use. Packaged foods and drinks are the main sources of exposure to bisphenols. These chemicals affect humans and animals by disrupting the estrogen, androgen, progesterone, thyroid, and aryl hydrocarbon receptor functions. Bisphenols exert numerous harmful effects because of their interaction with receptors, ROS formation, lipid peroxidation, mitochondrial dysfunction, and cell signal alterations. Both cohort and case-control studies have determined an association between bisphenol exposure and increased risk of cardiovascular diseases, neurological disorders, reproductive abnormalities, obesity, and diabetes. Prenatal exposure to bisphenol results in developmental disorders in animals. These chemicals also affect the immune cells and play a significant role in initiating the inflammatory response. Exposure to bisphenols exhibit age, gender, and dose-dependent effects. Even at low concentrations, bisphenols exert toxicity, and hence deserve a critical assessment for their uses. Since bisphenols have a global influence on human health, the need to discover the underlying pathways involved in all disease conditions is essential. Furthermore, it is important to promote the use of alternatives for bisphenols, thereby restricting their uses.

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

The Internet of Healthcare Things (IoHT) is an essential IoT domain that encompasses the necessary capabilities to transform the current healthcare system. IoHT generates, transfers, manages, stores, and processes highly confidential medical data and sensitive personal information. Thus, IoHT is a critical application from a security perspective and is most favorably targeted by attackers. Several security-breaching attempts can be carried out due to the vulnerabilities of the IoHT that put classified data at constant risk. Henceforth, this work aims to inspect the performance of 6LoWPAN-RPL-based `Patient-Centric IoHT’ in the presence of malicious intruders conducting blackhole, rank, and DoDAG attacks in both normal and emergency operational modes of the mentioned resource-constraint network. PC-IoHT is simulated on the Cooja simulator and Contiki-OS for each attack with two and four intruders (out of 10 motes) for both operation modes. Several performance evaluation parameters are used to assess the overall performance of the considered PC-IoHT. The attacks’ impact on PC-IoHT is analyzed by comparing blackhole, rank, and DoDAG attacks with the no-attack scenario.

The present study aimed to undertake a detailed investigation of pathological epidemiology and molecular confirmation of Ehrlichia canis among pet dogs in smart city Bhubaneswar, Odisha, a state in eastern India. A total of 178 dogs were screened for Ehrlichiosis on the basis of history, clinical signs, blood, and buffy coat smear examination, resulting in only 56 dogs (31.46%) screening positive. The epidemiological study recorded an increase in incidences among dogs less than one year of age (41%), males (68%), German Shepherds (25%), dogs more than 20 kg body weight (75%), summer months (55%) and dogs housed on pukka houses with exposure to the outside (59%). The majority of the infected dogs had a history of tick infestation (79%) at some point in their life. Clinical signs showed non-typical manifestations like fever, lethargy, diarrhoea, epistaxis, hind limb edema, and corneal opacity. Haematological studies revealed anaemia and thrombocytopenia along with neutrophilia with relative lymphopenia and monocytosis. A decreasing trend was observed in the levels of total protein and albumin with an increase in the levels of globulin, AST, ALT, BUN, and creatinine. The ultrasonography studies revealed hepatosplenomegaly along with hyper-echogenicity in various organs. Pro-teinuria and haematuria were consistent, along with the presence of bile salts in the urine of affected dogs. Molecular confirmation from n-type PCR data using Ehrlichia-specific primers targeting p28 gene (843 bp) was done, and the identified gene sequences submitted to NCBI databases have accession numbers OQ383671-OQ383674 and OP886674-OP886677. Ticks collected from dogs were identified morphologically through microscopy and scanning electron microscopy as Rhipicephalus sanguineus.

Waste-to-Hydrogen (Wahh) is a process of converting waste into hydrogen gas through various methods such as thermal conversion, biological conversion, and chemical conversion. This process not only helps in managing waste but also provides a clean source of energy as hydrogen is a renewable energy source that produces zero greenhouse gas emissions during combustion. Wahh has the potential to provide a sustainable solution for energy generation and waste management in the current scenario of globally increasing energy demands. This study aims to examine the properties and energy efficiencies of various renewable fuels including fuelwood, biogas, biomethane, and biohydrogen with a specific focus on differentiating the characteristics of biohydrogen. The paper explores the emerging concepts of waste-to-hydrogen microgrids, biohydrogen refineries, biohydrogen prosumer networks, and a biohydrogen circular economy, as well as Public-Private-People Partnerships (4Ps) and the hydrogen-centric renewable energy revolution, which are taking shape in industrialized countries. The study presents a comprehensive feasibility analysis of these concepts in the context of Nepal. The results of this research will provide valuable insights into the potential for adopting these innovative technologies in Nepal and will contribute to the development of a sustainable and efficient energy system.

In a search for a reliable, inexpensive, and versatile technique for high-throughput kinetic assays of drug metabolism, we elected to rehire an old-school approach based on the determination of formaldehyde (FA) formed in cytochrome P450-dependent demethylation reactions. After evaluating several fluorometric techniques of FA detection, we choose the method based on the Hantzsch reaction with acetoacetanilide as the most sensitive, robust, and adaptable to high-throughput implementation. Here we provide a detailed protocol for the use of our new technique for automatized assays of cytochrome P450-dependent drug demethylations and discuss its applicability for high-throughput scanning of the pathways of drug metabolism in the human liver. To probe our method further, we applied it to re-evaluating the pathways of metabolism of ketamine, a dissociative anesthetic, and potent antidepressant increasingly used in the treatment of alcohol withdrawal syndrome. Probing the kinetic parameters of ketamine demethylation by 11 major cytochrome P450 (CYP) enzymes, we demonstrate that besides CYP2B6 and CYP3A enzymes, which were initially recognized as the primary metabolizers of ketamine, an important role is also played by CYP2C19, and CYP2D6, while the involvement of CYP2C9 suggested in the previous reports deemed insignificant.

This paper reports a novel fabric phase sorptive extraction-high performance liquid chromatography-ultra violet detection (FPSE-HPLC-UV) method for the simultaneous extraction and analysis of four phenyltin derivatives that include triphenyltin hydroxide, triphenyltin acetate, triphenyltin chloride and tetraphenyltin in environmental water (agricultural waste water and municipal waste water) and canned food samples. The selected analytes were well resolved by Waters Nova pack C18 column (3.9 x 150 mm, 4 µm particle size) in isocratic elution mode within 15 minutes. The new microextraction media has been analytically evaluated using phenyltin derivatives as model compounds. The factors affecting the extraction efficiency of FPSE have been evaluated and the optimum extraction conditions were determined. Under these optimum conditions, the limits of detection (LODs) for sol-gel C18 coated fabric phase sorptive extraction (FPSE) media in combination with HPLC-UV for the analysis of the phenyltin derivatives were in the range of 10-100 ng/mL with high precision (low relative standard deviation) at 10 ng/mL concentration with good absolute recoveries. To the best of our knowledge, this is the first FPSE extraction procedure applied to environmental water and canned food samples for the simultaneous determination of phenyltin derivatives and could be readily adopted as a rapid and robust green analytical tool for routine environmental and food analytical laboratories.

Aim: This study aimed to investigate the relationship between dental caries, dental anxiety, and salivary cortisol levels in children visiting pediatric dental clinics and their implications on pediatric oral health. Materials and Methods: A cross-sectional study was conducted at a dental university in the UAE. A total of 60 children, aged 4-12 years, were included. Salivary cortisol levels were measured using an Enzyme-Linked Immunosorbent Assay (ELISA) kit. Dental caries status was evaluated, and dental anxiety levels were assessed using the Modified Dental Anxiety Scale (MDAS). Statistical analyses, including Mann-Whitney U test and Pearson's correlation coefficient, were performed to determine significant differences and associations. Results: The study group showed significantly higher salivary cortisol levels compared to the control group (p<0.0001). A strong positive correlation was found between salivary cortisol levels and dental caries status (p<0.001). However, no significant difference in dental anxiety levels was observed between the study and control groups (p=0.85). A strong positive correlation was found between dental anxiety levels and dental caries status (p<0.001). Conclusion: The findings indicate a significant association between salivary cortisol levels and dental caries, suggesting that higher cortisol levels are associated with active caries. Dental anxiety levels were positively correlated with dental caries. Understanding the relationship between these variables can contribute to better oral health strategies and interventions for children, emphasizing the importance of managing dental anxiety and stress in pediatric dental care. Clinical Significance: This study highlights the potential of salivary cortisol as a biomarker for assessing stress and its impact on oral health in children. By addressing dental anxiety and stress, dental professionals can provide child-friendly dental care, enhance preventive measures, and improve oral health outcomes in pediatric patients.

Micro-grids’ operations offer local reliability; in the event of faults or low voltage/frequency events on the utility side, micro-grids can disconnect from the main grid and operate autonomously while providing the continued supply of power to local customers. With the ever-increasing penetration of renewable generation, however, the operations of micro-grids become increasingly complicated because of the associated fluctuations of voltages. As a result, transformer taps are adjusted frequently, thereby leading to the fast degradation of expensive tap-changer transformers. In the islanding mode, the difficulties also come from the drop of voltage and frequency upon disconnecting from the main grid. To appropriately model the above, the nonlinear AC power flow constraints are necessary. Computationally, the discrete nature of tap-changer operations and the stochasticity caused by renewables add two layers of difficulty on top of a complicated AC-OPF problem. To resolve the above computational difficulties, the main principles of the recently-developed "l1-proximal" Surrogate Lagrangian Relaxation are extended. Testing results based on 9-bus system demonstrate the efficiency of the method to obtain the exact feasible solutions for micro-grid operations thereby avoiding approximations inherent to existing methods, while demonstrating that through the optimization, 1. the number of tap changes is drastically reduced, and 2. the method is capable of handling networks with meshed topologies.

This study explores the synthesis and application of BODIPY-functionalized gold nanoparticles (AuNPs) for the sensitive detection of biothiols via an indicator displacement assay coupled with Surface-Enhanced Raman Scattering (SERS) techniques, alongside their efficacy for in vitro cancer cell imaging. Moreover, the assay allowed a visible colorimetric detection of biothiols under normal and ultraviolet light conditions. The BODIPY (boron-dipyrromethene) fluorophores were strategically conjugated to the surface of gold nanoparticles, forming a robust nanohybrid that leverages the plasmonic properties of AuNPs for enhanced spectroscopic sensitivity. The detection mechanism exploits the displacement of the BODIPY indicator upon interaction with biothiols, triggering a measurable change in fluorescence and SERS signals. This dual-mode sensing approach provides high selectivity and sensitivity for biothiol detection, with detection limits reaching nanomolar concentrations using fluorescence and femtomolar concentration for cysteine using SERS. Furthermore, the BODIPY-AuNP complexes demonstrated excellent biocompatibility and photostability, facilitating their use in fluorescence imaging of biothiol distribution within cellular environments and highlighting their potential for diagnostic and therapeutic applications in biomedical research.

This paper offers a novel geometric and visual approach to the renowned Four Color Map Theorem and K5 non-planarity problem while unveiling their profound connection to the "kissing number" problem in 2D. We represent planar graphs through circles and tangents, simplifying complex structures and shedding light on these classic problems. Our proofs by contradiction, rooted in the kissing number concept, reveal that both the Four Color Map Theorem and K5 non-planarity are fundamentally linked, as they pivot around the concept of coloring. This study bridges the realms of geometry and graph theory, providing fresh insights and emphasizing the significance of the kissing number problem in various fields.

Long COVID is the collective term used to describe the persistence of symptoms in individuals who have recovered from SARS-CoV-2 infection. Some people who have healed from COVID-19 continue to experience new or persistent symptoms that last for weeks or months. While Long COVID is becoming more widely recognized, its fundamental causes and effective therapies remain largely unclear. This study aims to explore the potential of phytochemical medications as a cutting-edge therapeutic strategy for treating Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC).G protein-coupled receptors (GPCRs), which play a critical role in cell signaling, were the focus of this study. GPCRs are known to be involved in immunological and inflammatory responses and are considered potential targets for Long COVID therapy. The GPCRs examined included chemokine receptors, serotonin receptors, adrenergic receptors, and histamine receptors. The docking results demonstrated high binding affinity between phytochemical drug structures and these receptors for most drug-receptor combinations.The study consists of three main parts. First, data on receptors and drugs were retrieved from the Protein Data Bank (PDB) and PubChem, respectively. The receptors selected are those predicted to be linked to Long COVID. The structure of the drugs was converted from SDF to PDB format using OpenBabel from ChemInfo. Docking studies were performed to assess the binding affinity between the drugs (ligands) and their respective receptors (binding sites) using iGemDock v2.1.In conclusion, the findings of this study could have significant implications for the development of alternative therapies for Long COVID.

There are many modern and traditional methods in valuing the performance of the company among them EVA, CVA are modern methods where as EPS, ROI, ROE etc. are some of the traditional methods. modern and traditional methods are not only used to check the performance of a company but it also used in investment decisions by the investors. Stern Stewart, managing partner of M/s Stern Stewart & Co. introduced a modified concept of economic profit in 1990 in the name of Economic Value Added (EVA) as measure of business performance and CVA is only a cash consideration in EVA. In this paper an attempt is made to study which method is used by the investors while taking the investment decisions i.e. traditional methods are modern methods. 100 respondents (Investors) are questioned and collected their views, we are assuming that there is no impact of traditional or modern methods while taking investment decisions by the investors.

Structure - function interdependence is a universal phenomenon in biological systems. Any alteration in structural features may result in change in functions–leading to natural selection of a particular trait, or dysfunctions thereof. Many such alterations arise during the course of evolution of a species and may meticulously be traced during embryonic development of an organism. Through the theoretical construct of morphological integration, a set of phenotypic traits alter in a coordinated and integrated manner during evolution and embryonic development of an organism yielding efficient environmentally adapted physiological functions pertinent to those structures. Such integration may go awry sometimes, setting the basis for genesis of diseases. Morphological integration in human skull has been established through various methods. The brain-skull co-development is handcuffed through evolution and development, and the very basis of a neuro-psychiatric disorder could be underlying in dysmorphogenesis of the skull, its consequent effect on structures, and thus functions of the pertinent brain components. Here we propose that morphological integration in human skull may be mechanistically implied in etiogenesis of certain neuro-psychiatric disorders and should be borne in mind during clinical diagnosis and therapeutic interventions.

Climate-smart agriculture (CSA) is a new and emerging concept in climate discourse to address the issues of climate change impacts in agriculture. Few researchers and organizations have concentrated on CSA policies and practices in Nepal. This study aims to review and analyze the policies and practices in relation to CSA and define local level CSA indicators in the Central and Western Nepal. This study adopted the scoping review of climate policies and practices in Nepal and also utilized the household survey information particularly focusing on climate adaptation relating to agriculture. Through the scoping review, it is recognized that the number of climate policies in Nepal has specifically prioritized agriculture and food security as an important thematic area. Among those policies, National Adaptation Programme of Action (NAPA), National Framework for Local Adaptation Plan of Action (LAPA) and National Adaptation Plan (NAP) have specific sections on CSA practices, however, these policies/plans lack specific indicators. Different categories of CSA practices are common in Nepal, but many of these practices are popular as agricultural and environmental practices prior to the CSA concept and approach. The output indicators specific to CSA practices help the communities and policymakers to measure and monitor the progress of its implementation. Additional researches are required in this field to better understand the CSA concept, approaches, and mechanisms in the Nepalese context.

The study, conducted at the Department of Medicine, Maulana Azad Medical College and associated Lok Nayak Hospital, aimed to investigate the prevalence of iron deficiency anaemia among anaemic overweight and obese individuals. Fifty participants were included after screening and consent, undergoing socio-demographic assessment, clinical evaluation, and laboratory investigations. Middle to old-aged individuals predominated (Mean ± SD: 54.54 ± 11.32 years), with females comprising 62%. All subjects were obese (mean BMI: 37.27 ± 5.71 kg/m²), with common comorbidities including hypertension (68%), diabetes (36%), and obstructive sleep apnea-hypopnea syndrome (32%). Nutritional deficiencies, particularly iron deficiency, were prevalent, affecting 62% of subjects, with 48% having iron deficiency alone. Anemia of chronic illness was observed in 38%. Laboratory parameters showed low hemoglobin (8.53 ± 1.23 g/dL), MCV (78.75 ± 10.13 fL), and elevated ESR (20.56 ± 21.47 mm/hour) and CRP levels (4.71 ± 3.32 mg/dL), indicating inflammation. Correlation analyses revealed negative associations between BMI and serum iron/ferritin and positive associations with serum hepcidin. The study underscores the significance of addressing nutritional deficiencies and monitoring iron status in obese populations, suggesting potential associations between obesity and iron metabolism. Further research is needed to explore these relationships for clinical management implications.

This thesis presents a comprehensive review of cancer, with a particular focus on its physical and biochemical basis. Cancer, a leading cause of mortality worldwide, is characterized by the uncontrolled growth and spread of abnormal cells. Understanding the fundamental differences between cancerous and normal cells is essential for developing effective diagnostic tools and therapeutic strategies. The review begins by exploring the physical anomalies of cancer cells, including their irregular shape, size, and structural organization. These cells often exhibit altered cell membranes, disrupted cytoskeletal architecture, and irregularities in cell adhesion and communication, which contribute to their invasive and metastatic potential. The biochemical basis of cancer is examined in detail, with an emphasis on the dysregulation of key metabolic pathways. Cancer cells undergo profound metabolic reprogramming, often referred to as the Warburg effect, where they preferentially utilize glycolysis over oxidative phosphorylation, even in the presence of oxygen. This metabolic shift supports the rapid proliferation of cancer cells and their survival in hypoxic conditions. Additionally, cancer cells produce abnormal proteins that contribute to tumor progression, such as mutant p53, and fail to produce critical tumor suppressor proteins like RB1, leading to unchecked cell division and evasion of apoptosis. Furthermore, the thesis reviews the role of oncogenes and tumor suppressor genes in cancer development, highlighting how mutations and epigenetic alterations drive the transformation of normal cells into malignant ones. The complex interplay between these genetic changes and the tumor microenvironment is also discussed, emphasizing the importance of signaling pathways, immune evasion, and angiogenesis in cancer progression. This detailed review synthesizes current research and insights from authoritative sources, including peer-reviewed journals and foundational medical texts, to provide a thorough understanding of the physical and biochemical underpinnings of cancer. The findings underscore the importance of continued research in these areas to advance the diagnosis, treatment, and prevention of cancer.

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

The orthodontic treatment brings numerous benefits and, in most cases, the benefits outweigh the possible disadvantages. Root resorption (RR) is a common adverse phenomenon associated with orthodontic treatment. This study evaluates the role of low-level laser emission / Photobiomodu-lation (LE/P) in quantitative measurements of root resorption (QRR). The application of LE/P was done after each orthodontic activation with 4 types of treatment intervention (TI) on the RR after fixed orthodontic treatment (FOT) of the upper arch with ectopic eye tooth/teeth [EET]. 32 Orthodontic patients scheduled for FOT were selected and assigned to the 4 groups. These were LE/P+Self ligating bracket (SLB), LE/P+Conventional bracket (CB), Non-Photobiomodulation (non-LE/P)+SLB, and non-LE/P+CB. Standard management stages of FOT were followed in the maxilla. Each patient received a single application of LE/P labially/buccally and palatally, a total of 5 different points during each activation or appointment. The main outcome measure was QRR in maxillary anteriors before and after FOT assessed via cone-beam computed tomography (CBCT) using 3D OnDemand software. Insignificant QRR was found between before and after FOT in SLB, CLB, and LE/P, non-LE/P groups (p > 0.05). QRR in the SLB vs CB and LE/P vs non-LE/P group was significantly different in 11, 13, and 23 (p < 0.05). QRR in the LE/P+SLB group (p < 0.05) was significantly different in 11, 13, and 23 than that in the other groups. The most severe QRR was found on the 13 (0.88 ± 0.28mm and 0.87±0.27mm) and 23 (1.19 ± 0.14 mm and 1.16±0.13mm) in the CB and non-LE/P group (p < 0.001). LE/P+SLB showed highly significant superior outcome (p < 0.001) in relation to non-LE/P+CB, the QRR of 23 were 0.813± 0.114mm and 1.156± 0.166mm respectively. Significantly higher amount of QRR found in EET patients after FOT treated with the CB, non-LE/P, and non-LE/P+CB system and warrants further investigation to explore potential specific causes.

Deforestation and forest degradation mostly caused by human interventions affects the capacity of forest ecosystem to provide ecosystem services and livelihood benefits. Forest Land Restoration (FLR) is an emerging concept which focuses on the improvement of ecosystem as well as livelihood of the people at the landscape level. Nepal has successfully recovered degraded forest land mainly from the hilly region through forest restoration initiatives especially community based forestry. However, the Terai region is still experiencing deforestation and forest degradation. This study navigated the gaps related to forest restoration in the existing policies and practices and revealed that the persistence of deforestation and forest degradation in Terai is a result of a complex socio-economic structure, limitation of government to implement appropriate management modality, unplanned infrastructure, and urban development. We suggest that forest restoration should focus on ecological and social wellbeing pathways at the landscape level, to reverse the trend of deforestation and forest degradation in the Terai regions of Nepal. The study provides a critical insight to the policy makers and practitioners of Nepal and other countries (with similar context) who are engaged in forest/ecosystem restoration enterprise.

The notion of enhancement of the image is to ameliorate the perceptibility of information contained in an image. In the present research, a novel technique for the enhancement of image quality is propounded using fuzzy logic technique with a cuckoo optimization algorithm. Generally, the image is transformed from RGB domain to HSV domain keeping the color information intact within the image. The image has been categorized into three regions: underexposed, overexposed and mixed region on the basis of two threshold values. For the fuzzification of under and overexposed area the degree of membership is defined by the Gaussian membership, while the mixed area is fuzzified by parametric sigmoid function. The key parameters like visual factors and fuzzy contrast provide the quantitative analysis of an image. An objective function is framed which involves entropy and visual factor has been optimized by a new evolutionary cuckoo optimization algorithm. The results procured after simulation by the cuckoo optimization algorithm are compared with Bacterial foraging algorithm and ant colony optimization based image enhancement and this approach is found to be improved.

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

The probability of the evolution of a character depends on two factors: the probability of moving from one character state to another character state and the probability of the new character state fixation. More the evolution of a character is probable more convergent evolution will be witnessed, consequently, convergent evolution could mean that the convergent character evolution result as a combination of these two factors. We investigate this phenomenon by studying the convergent evolution of biochemical functions. We use for the investigation the case of β-lactamases. β-lactamases hydrolyzes β-lactams which are antimicrobials able to block the DD-peptidases involved in bacterial cell wall synthesis. β-lactamase activity is present in two different superfamilies: the metallo-β-lactamase and the serine β-lactamase superfamily. The mechanism used to hydrolyze the β-lactam is different for the two superfamilies. We named this kind of evolution an allo-convergent evolution. We further show that the β-lactamase activity evolved several times within each superfamily, a convergent evolution type that we named iso-convergent evolution. Both types of convergent evolution can be explained by the two evolutionary mechanisms discussed above. The probability of moving from one state to another is explaining the promiscuous β-lactamase activity present in the ancestral sequences of each superfamily, while the probability of fixation is explained in part, by positive selection as the organisms having β-lactamase activity allows them to resist to organism secreting β-lactams. Indeed a mutation increasing the β-lactamases activity will be selected as the organisms having this activity will have an advantage over the others.

Industrial effluents contain contaminants that pose threats to the environment and human health. Bioremediation is an environmentally friendly, sustainable, and cost-effective approach to degrade or transform pollutants using microbial enzymes. Laccase is a versatile enzyme that oxidizes a wide range of substrates, such as dyes, phenolic compounds, pesticides, and polycyclic aromatic hydrocarbons (PAHs), with the consumption of molecular oxygen. Laccase can also remove heavy metals by forming complexes with organic ligands or reducing them to fewer toxic forms. However, the application of laccase in bioremediation is limited by its low production, stability, and specificity. Recombinant technology has been employed to enhance the expression, activity, and stability of laccase in various bacterial hosts. Immobilization techniques have been developed to improve the reusability and stability of laccase in different environmental conditions. This review summarizes the recent developments in the production optimization of recombinant laccase enzymes and their role in the bioremediation of industrial effluents. It also discusses the challenges of laccase-based biocatalytic systems for environmental cleanup.

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.

An emissions baseline for a city is the foundation to building an informed clean air action plan. This includes an understanding of the sources and their intensities to device cost-effective measures with the least health impacts. In an urban environment, emissions from road transport is one of the major contributors to the air pollution problem, and accounting for this sector requires a detailed profile of in-use vehicles, covering their age-mix, usage, and fuel economy. In this paper, we present a simplified framework to survey at select fuel stations to establish the necessary inputs and use them to build a localized vehicle emissions inventory. The methodology is illustrated with an application in the city of Patna, India. Here, the FuSS was employed to profile the in-use passenger vehicle characteristics by 19 trained students, over nine days and at ten fuel stations, to collect 9,775 survey points. The methodology outlined in this paper, along with the tools and training framework, is also replicable in settings other than the fuel stations.

Novel Coronavirus (2019-nCOV) causes inflammatory response with worsening symptoms. Classification of potential anti-viral and anti-inflammatory drugs in managing the symptoms of the COVID-19 and reducing morbidity is important. The objective of this study is to identify a group of drugs, best suited for COVID-19 treatment based on recent developments in clinical trials, FDA drug evaluation, directions and developments and from drug therapies globally. Online literature search was done on Medline, PubMed and google scholar databases for studies on various treatments and drug therapies for COVID-19 and relevant studies were identified and the identified drugs are described in detail as per their Pharmacological, pharmaceutical properties of the drugs, mechanism of action, current COVID-19 drug therapy, contraindications and drug-drug interactions Certain drugs can inhibit action against viral infection and protect lungs from severe inflammatory response. This article summarizes several drugs like Hydroxychloroquine, Chloroquine, Remdesivir, Favipiravir, Lopinavir, Ritonavir, Dexamethasone, Ivermectin, Baricitinib, Casirivimab / imdevimab, Bamlanivimab along with auxiliary treatment like convalescent plasma transfusion. Remdesivir is first drug approved by FDA. Hydroxychloroquine, dexamethasone and remdesivir are showing results against COVID-19 but it is important to test the efficacy and safety of such drugs though some drugs have shown remarkable results.

Covid-19 has given a halt to all the activities in the world. Europe was most affected, followed by the United States of America. It has taken more than 350000 lives until now. In this study, we have assessed the severity of Covid-19 by analyzing the mortality rate of Covid-19 and other chronic diseases. The Covid-19 data and “death rate” data caused by other diseases were downloaded from the world health organization (WHO) website. A normalized method was used to see the mortality rate of Covid-19 in comparison to other diseases. The deaths caused by Covid-19 in April 2020 have overtaken the average number of deaths caused by Cancer, Cardiovascular diseases, and other diseases in Belgium, the United Kingdom (UK), Spain, France, and Ireland. Covid-19 was found to be strongly correlated with non-communicable respiratory diseases and Cancer with correlation coefficients 0.73 and 0.67 respectively. The severity of Covid-19 in the United States of America (USA) was moderate. The severity of Covid-19 in Asian countries was found to be low. Europe showed the highest diversity in the mortality rate of Covid-19. On average, except for a few European countries, Cardiovascular diseases, cancer, and non-communicable respiratory diseases were still more lethal and caused more deaths than Covid-19.

The treatment of petroleum refinery wastewater (PRWW) is of great interest in the field of industrial wastewater management. The wastewater contains a diverse concentration of contaminants such as oil and grease, petroleum hydrocarbons, phenol, ammonia, sulfides, as well as other organic and inorganic composites etc. The treatment of refinery wastewater has been treated through various processes including; physical, biological, chemical or hybrid methods which combine two or more techniques. Hence, the objective of this review is to summarize the recent research studies applied in the treatment of petroleum refinery wastewater using conventional, advanced as well as integrated treatment techniques. Furthermore, critically highlight the efficiencies and major limitations of each technique as well as prospects for improvement. Several conventional treatment techniques (basically, the physicochemical and biological processes) were discussed. In this context, advanced oxidation processes (AoPs) especially electrochemical oxidation and photocatalysis as well as the integrated/hybrid processes are found effective to remove the recalcitrant fraction of organic pollutants because of their various inherent mechanisms. These techniques could effectively remove COD and phenols concentration with an average removal efficiency exceeding 90%. Hence, the review also presented an elaborate discussion of the photocatalytic process as one of the advanced techniques and highlighted some basic concepts to optimize the degradation efficiency of a photocatalyst. Finally, a brief recommendation for research prospects is also presented.

We report cyclic growth/retraction phenomena observed for saline droplets placed on the cured PDMS membrane with the thickness of 7.8±0.1 µm floating on pure water surface. Osmotic mass transport across the micro-scaled floating PDMS membrane provided the growth of the sessile saline droplets followed by evaporation of the saline droplets. The observed growth/retraction cycle was reversible. The model of the osmotic mass transfer across the cured PDMS membrane is suggested. The model explains semi-quantitatively the time evolution of a droplet.

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.

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that “chatgpt” occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.

Long COVID is a phenomenon in which individuals experience persistent symptoms after recovering from COVID-19. The symptoms are discovered to be unique for every individual and can affect multiple organs systems in the body. This study aims to identify effective natural remedies for long COVID by analyzing hub genes associated with the symptoms of the condition and evaluating the repurposed drugs catered and used for treating the symptoms of long COVID. The most common and prevalent symptoms of long COVID were identified; Fatigue, Shortness of Breath, Loss of Smell, Headache, Brain Fog, Chest Pain, Insomnia, Heart Palpitations, Dizziness, Joint Pain, Depression, Anxiety, Tinnitus, Loss of Appetite. Hub genes for each of the symptoms provided an insight on the key biological pathways of the symptom. Repurposed drugs identified, provided the template to identify the natural compounds with similar structure as a potential therapeutic drug. The natural compounds were retrieved using fingerprint search of the repurposed drugs from the NPASS Database. The findings of this study suggest several natural remedies for each symptom based on the molecular docking of the hub gene and natural compound using iGEMDOCK. The identified natural remedies may hold promise in treating long COVID, but further research is required to explore the efficacy and effectiveness of the proposed natural compounds. The results of the study pose important implications for the development of effective treatments for long COVID.

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.

Antimicrobial resistance (AMR) is a major global concern; antibiotics and other regular treatment methods have failed to overcome the increasing number of infectious diseases. Bacteriophages (phages) are viruses that specifically target/ kill bacterial hosts without affecting other human microbiome. Phage therapy provides optimism in the current global healthcare scenario with a long history of its applications in humans that has now reached various clinical trials. Phages in clinical trials have specific requirements of being exclusively lytic, free from toxic genes with an enhanced host range that adds an advantage to this requisite. This review explains in detail the various phage engineering methods and their potential applications in therapy. To make phages more efficient, engineering has been attempted using techniques like conventional homologous recombination, Bacteriophage Recombineering of Electroporated DNA (BRED), clustered regularly interspaced short palindromic repeats (CRISPR)-Cas, CRISPY-BRED/Bacteriophage Recombineering with Infectious Particles (BRIP), chemically accelerated viral evolution (CAVE), and phage genome rebooting. Phages are administered in cocktail form in combination with antibiotics, vaccines, and purified proteins, such as endolysins. Thus, phage therapy is proving to be a better alternative for treating life-threatening infections, with more specificity and fewer detrimental consequences.

COVID-19 (2019-nCoV) is a pandemic disease with an estimated mortality rate of 3.4% (estimated by the WHO as of March 3, 2020). Until now there is no antiviral drug and vaccine for COVID-19. The current overwhelming situation by COVID-19 patients in hospitals is likely to increase in the next few months. About 15 percent of patients with serious disease in COVID-19 require immediate health services. Rather than waiting for new anti-viral drugs or vaccines that take a few months to years to develop and test, several researchers and public health agencies are attempting to repurpose medicines that are already approved for another similar disease and have proved to be fairly effective. This study aims to identify FDA approved drugs that can be used for drug repurposing and identify biomarkers among high- risk and asymptomatic groups. In this study gene-disease association related to COVID-19 reported mild, severe symptoms and clinical outcomes were determined. The high-risk group was studied related to SARS-CoV-2 viral entry and life cycle by using Disgenet and compared with curated COVID-19 gene data sets from the CTD database. The overlapped gene sets were enriched and the selected genes were constructed for protein-protein interaction networks. Through interactome, key genes were identified for COVID-19 and also for high risk and asymptomatic groups. The key hub genes involved in COVID-19 were VEGFA, TNF, IL-6, CXCL8, IL10, CCL2, IL1B, TLR4, ICAM1, MMP9. The identified key genes were used for drug-gene interaction for drug repurposing. The chloroquine, lenalidomide, pentoxifylline, thalidome, sorafenib, pacitaxel, rapamycin, cortisol, statins were proposed to be probable drug repurposing candidates for the treatment of COVID-19. However, these predicted drug candidates need to be validated through randomized clinical trials. Also, a key gene involved in high risk and the asymptomatic group were identified, which can be used as probable biomarkers for early identification.

In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring Leaf Wetness Duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida's Plant Science Research and Education Unit (PSREU) in Citra, Florida, USA and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.

Nipah virus (NiV) is responsible to cause various outbreaks in Asian countries, with latest from Kerala state of India. Till date there is no drug available despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed “anti-Nipah” web resource, which comprised of a data repository, prediction method, and data visualization modules. The database comprised of 313 (181 unique) inhibitors from different strains and outbreaks of NiV extracted from research articles and patents. However, the quantitative structure–activity relationship (QSAR) based predictors were accomplished using classification approach employing 10-fold cross validation through support vector machine with 120 (68p + 52n) inhibitors. The overall predictor showed the accuracy and Matthew’s correlation coefficient of 88.89% and 0.77 on training/testing dataset respectively. The independent validation dataset also performed equally well. The data visualization modules from chemical clustering and principal component analyses displayed the diversity in the NiV inhibitors. Therefore, our web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly webserver is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/

Plants have the ability to decrease the pollution level from the environment by absorbing inorganic/organic pollutants through their roots. After that, these pollutants are translocated or accumulated in less toxic forms in various parts of plants. This ability of plants is known as phytoremediation. It is an eco-friendly, and cost-effective approach for controlling environmental pollution. It can provide a sustainable way to improve the economics of developing countries. However, the biomass formed during this process can re-contaminate the environment through secondary pollutants. The contamination due to heavy metals (HMs) has become an environmental challenge globally. Metals such as cadmium (Cd), chromium (Cr), mercury (Hg), arsenic (As), nickel (Ni), lead (Pb), etc. can enter the water and soil largely through anthropogenic activities as well as other natural processes. After that, these metals pose a severe threat to living organisms. Due to their non-biodegradable nature, these metals can remain as such for a long time. These metals can initiate oxidative pressure in plants as a result affects agricultural production and yield. To mitigate this problem, phytoremediation has come forward as a safe, affordable, and ecologically sustainable solution than conventional physicochemical decontamination methods. Initially, several edible crops had been identified for the remediation. But at the present time, scientists are focusing on non-edible crops like aromatic/medicinal plants. There is no risk of food contamination by using these plants. This chapter describes the classification & uses of Tulsi and the phytoremediation mechanism of plants in detail.

Magnesium and Aluminum alloys continue to be important in the context of modern and lightweight technologies. With the advancement of additive manufacturing (AM), components can be produced directly in a net shape, widen up the usage of magnesium and aluminum alloys as well as holding new ideas for the application of unique physical structures made feasible by 3D printing. Laser-based approach, one of the metal additive manufacturing (AM) methods, enables the formation of arbitrary 3D structures. With promising findings, research in this area is advancing quickly, bringing up a variety of potential applications in both the scientific and industrial sectors. Complex structures can now be manufactured easily utilizing AM technologies to meet the pre-requisite objectives like reduced part numbers, greater functionality, and lightweight, among others. AM has the ability to meet demands by lowering costs and speeding up the manufacturing process. Due to their popularity in numerous high-value applications, aluminum, and magnesium alloys are one of the key material systems being researched in the laser-based additive manufacturing approaches. The review here aims to comprehensively examine the additive manufacturing of magnesium and aluminum alloys, highlighting the influence of the laser-based additive manufacturing approach on the mechanical characteristics, microstructure, and tribological performance of magnesium and aluminum alloys.

Around the world, every year, about 17 million people death cause happen due to CardioVascular Diseases (CVD). As per clinical records, primarily sufferers exhibit myocardial infarctions and Heart Failures (HF). Creatinine is a Musculo - skeletal waste product. The kidneys filter creatinine from the blood and excrete it through the urine in a healthy body. High creatinine levels can suggest renal problems. Elevated Serum Creatinine (SC) has been well established in the HF. Patients’ electronic medical records can be used to quantify symptoms and other related clinical laboratory test values, which would then be utilized to direct biostatistics exploration to uncover patterns and associations that doctors would otherwise miss. The latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B with HF. In this article, we used a dataset of the year 2015 of heart patients records of 299 patients. The present paper used the data analytic and statistical tools to verify the significant differences between alive and dead patients’ SC and Serum Sodium (SS). It also demonstrates the impact of significant features on abnormal SC and SS on the Survival-Status levels. The Age-Group feature, which is derived from age attribute and, Ejection Fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), Blood-Pressure (BP), gender, diabetes, and smoking-status were utilized to determine the potential contributing features to mortality with Cox regression model. The Kaplan Meier plot was used to investigate the overall pattern of survival concerning age-group. During pre-processing of the dataset, Age and SS were removed due to multicollinear features during performing machine learning algorithms experiments. This paper also predicted patients’ survival, age group, and gender using supervised machine learning classifiers. Detection of significant features would help in making informed decisions to balance the lifestyle of heart patients. The author revealed that the patient’s follow-up months, as well as SC, EF, CPK, and platelets, are sufficient key features to predict heart patient survival using Random Forest (RF) stratified 10-fold CV method with accuracy (96%) with 5% Standard Deviation (SD) from medical records dataset. We identified the age-group and gender of the patient, and the RF model outperformed others with the best accuracy 96% and 94% in both cases having 11% SD. Also, prominent features such as CPK, SC, follow-up month, platelets, and ejection were found to be significant factors in predicting the patient’s age-group. Smoking habits, CPK, platelets, follow-up month, and SC of each patient were discovered to be significant predictors of patient gender. The hypothetical study proved that SC and SS making substantial differences in the survival of patients (p &lt; 0.05) and failed to reject that anemia, diabetes, and BP making a significant impact on the creatinine and sodium of each patient (p &gt; 0.05). With χ2(1) = 8.565, the Kaplan Meier plot revealed that mortality was high in the extremely elder age-group. The finding has possible effects on clinical practice and becomes a new medical support system when predicting whether a patient can survive a heart attack or not. The doctor should primarily concentrate on follow-up month, SC and EF, CPK, and platelet count since the aim is to understand whether a patient survives after HF.

As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19 Indian data using various models. Based on the collected COVID-19 outbreak data, we try to find the propagation rule of this outbreak disease and predict the outbreak situations in India. For India data, the time series model gives the best results in the form of predication as compared to other models for all variables of COVID-19. For new cases, new deaths, total cases and total deaths, the best fitted ARIMA models are as follows: ARIMA(0,2,3), ARIMA(0,1,1), ARIMA(0,2,0) and ARIMA(0,2,1). Based on time series analysis, we predict all variables for next month and conclude that the predictive value of Indian COVID-19 data of total cases is more than 20 lakhs with more than 43 thousand total deaths. The present chapter recommended that a comparison between various predictive models provide the accurate and better forecast value of the COVID-19 outbreak for all study variables.

To utilize heterosis and combining ability for grain yield and its attributing traits along with three physiological traits viz., leaf area index, chlorophyll fluorescence and chlorophyll content in bread wheat (Tritium aestivum L.) the crosses were attempted during rabi, 2019-20 in a half diallel mating scheme among 8 wheat varieties and the study was carried out during rabi, 2020-21 at Block No. D-13 field of Mahua block, Simradha research farm, Rani Lakshmi Bai Central Agricultural University, Jhansi. Analysis of variance showed significant differences for treatments. Based on per se performance the hybrids NW 5054 x PBW 723 (23.3 g), and HI 1544 x NIAW 34 (23.14 g), were identified for the higher grain yield. The hybrid HI 544 x NIAW 34 (18.41 %) was identified as the best heterotic cross for grain yield per plant and its component traits. The hybrids HI 1544 x NIAW 34 (3.40) and HI 1544 x GW 322 (2.76), as well as the parents HD 3086 (21.85) and PBW 723 (20.68), were identified as superior economic hybrids and parents based on SCA/GCA effects, per se performance for grain yield per plant, and its component trait in bread wheat. Days to heading, flag leaf length, biological yield per plant, harvest index, number of grains per spike, 1000 grain weight, spike length, leaf area index, chlorophyll fluorescence, and chlorophyll content were all positively correlated with grain yield per plant.

The once-held wisdom of the supreme efficiency of one-way streets has been gradually sup-planted by the perceived sustainability of two-way streets in the design of livable cities that prioritizes the safety of pedestrians and thriving of local businesses. However, it is rarely dis-cussed on whether one-way street conversions have truly improved the long-term traffic effi-ciencies on urban street networks, as conflating socioeconomic factors such as vehicular popula-tion growth and induced travel demand may render empirical analysis inconclusive. In this study, microscopic traffic simulations implemented on SUMO platform was performed to ana-lyze the effect of street conversion in Downtown Brickfields, Kuala Lumpur. This approach can control and standardize travel demand in both one-way and two-way street networks, and would therefore give a fairer evaluation by precluding all socioeconomic factors. It was found that one-way streets do not necessarily improve the traffic efficiency of the network, as it is very dependent on the traffic scenario evolution over time. One-way streets perform better at the on-set of traffic congestion due to its higher capacity, but on average, the 4-fold longer travel times that made it harder to clear traffic by getting vehicles to their destinations compared to two-way streets. As time progresses, congestion in one-way streets may become twice as worse compared to two-way streets. This study may contribute to a more holistic assessment of traffic circulation plan designed for smart and livable cities

Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

As the demand of heating and air conditioning is increasing day by day due to getting a comfortable life, for that reason, the market for heat pump and air conditioning systems or both, is seeing a steady increase in low global warming potential. All researchers on heat pump and air conditioning systems are searching for environmentally friendly refrigerants. However, a significant number of contemporary refrigerants (HCFCs) have the potential to cause global warming and ozone depletion, such as those found in heat pump air conditioning systems. Using more environmentally friendly refrigerants, including hydrocarbon and blend refrigerant mixtures, is one way to lower GWP and Ozone depletion potential. This study examines the performance of several alternative refrigerants, including R32, R134A, R410A, R22, and R290. In order to examine the performance of R290, theoretical and simulation results are compared on various refrigerants while taking into account a number of performance parameters, including the refrigerant energy efficiency ratio, mass flow rate, coefficient of performance, cooling capacity, and compressor work for one ton of refrigeration (1TR) air conditioners. One player with a very low GWP is R290. However, the only drawback is that it can catch fire and is hazardous to use in split-type commercial heat pump air conditioning applications. The environmental and thermo physical properties of HC-290 are significantly better than those of other materials, according to theoretical and simulation studies utilizing Cool Pack software, hence it is a viable replacement.

Background and objectives: Main aim of the study is to analyze the outcomes of NSTEMI admissions and testing the relevance of TIMI as a risk score in a real-world setting. Also looking at any potential social or health care disparities involved with outcomes of NSTEMI admissions. The study also investigates factors associated with mortality in NSTEMI admissions and correlation between in-hospital mortality and heart catheterization. . Materials and Methods: NSTEMI admissions were analyzed using the National Inpatient Sample. TIMI scores were calculated in the admissions and association between all-cause mortality and calculated TIMI score >=2 was analyzed. Difference in outcomes based on heart catheterization was studied in the subgroup with TIMI score >=2. Correlation between time to heart catheterization and all-cause mortality in NSTEMI admissions were analyzed. Results: No significant social or healthcare disparities were noted amongst outcomes for NSTEMI admissions. NSTEMI admissions with a calculated TIMI score >=2 had a statistically significant association with all-cause mortality during the admission. Prevalence of mortality among NSTEMI admissions with calculated TIMI score >2, who did not undergo heart catheterization was higher with statistical significance when compared to admissions that underwent heart catheterization. NSTEMI admissions with a calculated TIMI score >2 who underwent catheterization on or beyond the 4th day of hospitalization had statistically significant association with all-cause mortality during the admission. Age at admission, presence of CKD and CHF were associated with mortality in NSTEMI admissions, with statistical significance. Keywords: NSTEMI; TMI score; heart catheterization

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

Paraffin wax stores energy in the form of latent heat at a nearly constant temperature during melting and releases this energy during solidification. This effect is used in industrial energy storage. An unusual change in the shape of a melted droplet of paraffin wax placed on a relatively cold glass plate is studied. As the droplet solidifies, its upper surface becomes nearly flat and a dimple is formed in the center of this surface, making the droplet look like a fruit (pumpkins are more commonly shaped like this, but the authors prefer apples). A series of experiments, as well as physical and numerical modeling of the droplet's thermal state, taking into account the formation of a mushy zone between liquidus and solidus, made it possible to understand the role of gravity and gradual increase in viscosity and density of paraffin wax on changing the droplet shape and, in particular, to clarify the mechanism of formation of the dimple on its upper.

Covid-19 infections are rapidly spreading worldwide with more than 100000 death and thus understanding the molecular mechanism of tropism of human cells is an urgent need for drug design. We have described here a bioinformatics approach to predict the functional aspects of non-structural nsp16 protein of Corona virus. The covid-19 7098 AA large polyprotein was degraded into sixteen proteins and last nsp16 protein was found an RlmE type rRNA methyltransferase. Nsp16 has no similarity to bacterial RlmABCD but has 25 percent similarity to the bacterial RlmE protein which methylates the U2551 2-hydroxy group of Ribose. The nsp16 proteins of different corona viruses like covid-19, bat-coronavirus, SARS and MERS have strong homology. Mrm2 and Dim1 like yeast and mammalian rRNA methyltransferases have 26-33 percent homologies but not with 2-O-capping MTase as reported previously. Rrp8 MTases also has no similarity to nsp16. We postulated that mitochondrial rRNA methylation of bronchial cells were mediated by the nsp16 protein causing inhibition of protein synthesis due to poor assembly of aminoacyl-tRNA or mRNA and peptidyl transferase at the PTC. This is one of the new molecular mechanism of corona virus cellular tropism and different than ACE-2 mediated blockage of cellular signalling to inhibit aldesterone biosynthesis with abnormal Na⁺ ions in cells. We also designed primers based on nsp16 cDNA sequence (nt 20659-21552, accession no MT121215) specific for Covid-19 diagnosis by RT-PCR.

In this letter, we construct an effective field theory of gravity at large distances. We obtain to the leading order for large distances: a cosmological constant term and a logarithmic term in addition to the usual General Relativistic terms. The novel logarithmic term has important implications in explaining phenomena such as flat galactic rotation curves and stronger gravitational lensing than predicted that are generally attributed to the presence of dark matter.

Introduction: We undertook the present study to analyze morphological features of a skull supposed to be that of Alum Bheg, a martyr from 1857 Indian Freedom Struggle (also called Sepoy Mutiny), using established methods to validate identity with regards to age and height as available in the note found with the skull (about 32 year and 5 feet 7½ inch). Methods: Identification of sex of the skull was done based on established criteria. Analysis for closure of skull sutures (cranial and facial) and measurement of orbitomedial (OM) & maxillomedial (MM) facial anthropometric lines were undertaken to provide an estimated age against each examined suture as well as group of sutures through established scoring systems. Further, approximate height of individual was estimated from skull length using regression equations from a reference adult Indian male population. Results: Established criteria confirmed that the skull was of a male individual and skull sutures and age related morphological changes in bones indicate that it belonged to an individual in age range 20 -50 years with an average of 30 years and approximate height between 5 feet 8.2 inch to 6 feet 1 inch. Discussion: Based on our observations we suggest that the skull belonged to a male individual around 30 years of age and height 5 feet 8.2 inch to 6 feet 1 inch. The observed values are in approximation with that mentioned in the historical note and slight differences may be attributed either to gross reporting of original values or limitations of anthropometric analysis.

A precise understanding of the genes and associated genomes of SARS-CoV2 is important for various reasons such as discovering origin of the virus and virulence and so on. A thorough descriptive understanding of the SARS-CoV2 genomes and other coronavirus of the beta-coronavirus genus is primarily important. In this article, a set of ten genomes of four CoVs and their associated genes are considered for this present study. A spatial representations of nucleotide bases including purine-pyrimidine representations of the different genes of the corresponding genomes are quantified using Hurst exponent, Shannon entropy and density estimation of different nucleotides including GC content, in order to draw a comparison and contrast among the ten genomes of different types of CoVs which include MERS, SARS-CoV, HKU1 (Human Coronavirus) and associated their genes.

Background: Complementary and alternative medicines (CAM) are widely used, with pharmacies accounting for a significant share of the distribution market. Despite their popularity, many CAMs lack scientific evidence for effectiveness, and some can cause adverse health effects. Limited research exists on CAM-related adverse events and the effectiveness of CAM labels in conveying crucial information. Methods: This cross-sectional study aimed to investigate CAM utilization, consumer perspectives, and the influence of labels on CAM usage in Melbourne, Australia. Data were collected through an online questionnaire and analysed using descriptive statistics, Pearson's correlation, MANOVA, and regression analyses. Results: The study enrolled 125 current CAM users, primarily recruited from pharmacy and supermarket customers (age: predominantly 31-50; gender: 20.8% male, 79.2% female). Participants' perceptions of effectiveness and safety were positively correlated. Label warnings prompted information-seeking, but consultation with healthcare professionals (HCPs), particularly pharmacists, was infrequent (24%). Adverse reactions were reported by 18.5% of participants, with 8.3% experiencing severe reactions. The analysis revealed relationships between CAM usage and demographics. Ordinal logistic regression showed a significant association between calcium consumption and higher age groups (coefficient = 1.658, p = 0.006), while binary logistic regression identified higher iron consumption among females (coefficient = 2.177, p = 0.007). Conclusions: Label warnings significantly prompted consumers to seek more information about CAMs. However, a limited engagement of HCPs, especially pharmacists, suggests an opportunity for improved consumer education and pharmacist involvement in CAMs-related discussions. The findings highlight demographic influences on CAM usage being essential for developing targeted health interventions and policies. Addressing these aspects may lead to safer CAM practices and informed consumer decision-making.

Rice (Oryza sativa L.) supplies nourishment to about half of the population of the world's inhabitants. Of them, more than 2 billion people suffer from "hidden hunger" in which they are unable to meet the recommended nutrients or micronutrients from their daily dietary intake. Bio-fortification refers to developing micronutrient-rich diet foods using traditional breeding methods and modern biotechnology, a promising approach to nutrition enrichment as part of an integrated strategy for food systems. To improve the profile of rice grain for the biofortification related traits, understanding the genetics of important biofortification traits is required. Moreover, these attributes are quantitative in nature and are influenced by several genes and environmental variables. In the course of past decades, several endeavours such, as finding the important quantitative trait loci (QTLs) for improving the nutrient profile of rice seeds were successfully undertaken. In this review, we have presented the information regarding the QTLs identified for the biofortification traits in the rice.

Social networking applications such as Twitter have increasingly gained significance in terms of socio-economic, political, and religious as well as entertainment sectors. This in turn, has witnessed a wide gamut of information explosion in the social networking realm that can tend to be both useful as well as misleading at the same point of time. Spam detection is one such solution that caters to this problem through identification of irrelevant users and their data. However, existing research has so far laid primary focus on user profile information through activity detection and relevant techniques that may underperform when these profiles exhibit characteristics of temporal dependency, poor reflection of generated content from the user profile, etc. This is the primary motivation for this paper that addresses the aforementioned problem of user profiles by focusing on both profile information and content-based spam detection. To this end, this work delivers three significant contributions. Firstly, exhaustive use of Natural language processing (NLP) techniques has been rendered towards creation of a new comprehensive dataset with a wide range of content-based features. Secondly, this dataset has been fed into a customized state-of-art hybrid machine learning model that has been exclusively built using a combination of both machine learning and deep learning techniques. Extensive simulation based analysis not only records over 98% accuracy but also establishes the practical applicability of this proposal by proving that modeling based on the mixed profile and content-generated data is more capable of spam detection in contrast to each of these standalone approaches. Finally, a novel methodology based on logistic regression is proposed and supported by analytical formulations. This paves the way for the custom-built dataset to be analyzed and corresponding probabilities to be obtained that differentiate legitimate users from spammers. The obtained mathematical outcome can henceforth be used for future prediction of user categories through appropriate parameter tuning for any given dataset. This makes our method a truly generic one capable of identifying and classifying different user categories.

Urinary peptides are the products of systemic protein turn over which are excreted out of the body and hence can serve as an important biomarker for various pathophysiology. These peptides in other species of bovine have been reported to possess several bioactive properties. To investigate the potential of urinary peptides we generated a peptidome profile from the urine of Murrah Buffaloes (n=10). Urine samples were processed using &lt;10 kDa MWCO filter, filtrate obtained was used for peptide extraction using Solid Phase Extraction (SPE). Eluate, obtained after SPE was extracted using ethyl acetate and the peptide-containing aqueous phase was collected. We extensively assessed the antimicrobial properties of the urinary aqueous phase. The nLC-MS/MS of the aqueous phase from 10 animals resulted in the identification of 8165 peptides originating from 6041 parent proteins. 76 Proteases responsible for the release of these sequences from precursor proteins were also identified. We further analyzed these peptide sequences to identify bioactive peptides and classify them into anti-cancerous, anti-hypertensive, anti-microbial, and anti-inflammatory groups with a special emphasis on antimicrobial properties. With this in mind, we simultaneously conducted experiments to evaluate antimicrobial properties of urinary aqueous extract on three pathogenic bacterial strains S. aureus, E. coli, &amp; S. agalactiae.

Study in black rice has gain prominence in recent times due to its high nutritive value, curative effect, and anti-oxidant properties. However, its poor agronomic traits, including low yield necessitates the incorporation of the colour-grain trait into elite varieties through plant breeding techniques. SSR markers play an important role in plant identification and breeding. Here, the generation of reference-based transcriptome, annotation of transcriptome datasets, and a large set of simple sequence repeat (SSR) markers derived from Black rice have been described. In all 28664 SSRs were predicted in 34978 (48.59%) expressed transcripts. However, 7068 (20.20%) transcripts were found to have more than one SSR. The identified SSRs were dominated by tri-nucleotide and tetra-nucleotide repeats representing about 54.11% and 33.31% respectively, of total SSRs. Validation of selected markers associated with anthocyanin trait performed across different black rice accessions established the reliability of the process used for mining SSR markers. The SSR markers identified in this study could be used to select varieties with desired traits, and to investigate the genetic mechanism underlying anthocyanin accumulation in the pericarps of black rice. Furthermore, the findings from this study may prove beneficial in future genetic diversity studies, primer development, and selective breeding programs.

Hypertension (HTN) is the foremost contributor to cardiovascular diseases (CVD), which remain a leading cause of death. However, adherence to the properly prescribed dosage form is critical and challenging for patients with chronic diseases such as HTN to prevent illness progression and death. The study’s objective was to develop AMB-loaded subcutaneous implants to reduce the frequency of administration, thus improving patient compliance during HTN management. AMB subcutaneous implants were prepared using continuous hot melt extrusion technology using PCL and PLGA with dimensions of 7.5 cm (length) by 2 mm (diameter). The implants were characterized for thermal characteristics, drug-excipient incompatibilities, surface morphology, fracturability, in vitro release, and stability studies. DSC showed that the drug was in the crystalline state in the fabricated implants. Scanning Electron Microscopy (SEM) revealed a smooth surface. The lead formulation presented good drug content, showed an extended drug release profile over 30 days, and followed zero-order release kinetics with a mean dissolution time of 15 days. The formulation was stable over 30 days in accelerated stability conditions (the last time point was tested, 40℃/75% RH), and the release profile was similar to the initial drug release profile. The lead formulation showed good fracturability during textural analysis. Therefore, the hot melt extruded implants could be an alternative management strategy for conventional amlodipine besylate (AMB) formulation.

The present novel corona virus (2019-nCoV) infection has created a global emergency situation by spreading all over the world in a large scale within very short time period. The infection induced death rate is also very high. There is no vaccine or anti-viral medicine for such infection. So at this moment a major worldwide problem is that how we can control this pandemic. On the other hand, India is a high population density country, where the corona virus disease (COVID-19) has started to spread from $1^{st}$ week of March, 2020 in a significant number of COVID-19 positive cases. Due to this high population density human to human social contact rate is very high in India. So control of the pandemic COVID-19 in early stage is very urgent and challenging problem. Mathematical models are employed in this paper to study the COVID-19 dynamics, to identify the influential parameters and to find the proper prevention strategies to reduce the outbreak size. In this work, we have formulated a deterministic compartmental model to study the spreading of COVID-19 and estimated the model parameters by fitting the model with reported data of ongoing pandemic in India. Sensitivity analysis has been done to identify the key model parameters. The basic reproduction number has been estimated from actual data and the effective basic reproduction number has been studied on the basis of reported cases. Some effective preventive measures and their impacts on the disease dynamics have also been studied. Future trends of the disease transmission has been Predicted from our model with some control measures. Finally, the positive measures to control the disease have been summarized.

The technology-driven world of the 21^st century is currently confronted with a major threat to humankind in the form of the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). As of April 22, 2020, COVID-19 has claimed 169, 006 human lives and had spread to over 200 countries with more than 2,471,136 confirmed cases. The perpetually increasing figures associated with COVID-19 are disrupting the social and economic systems globally. The losses are unmatched and significantly higher compared to those from previously encountered pathogenic infections. Previously, two CoVs (SARS-CoV and Middle East respiratory syndrome-CoV) affected the human population in 2002 and 2012 in China and Saudi Arabia, respectively. Based on genomic similarities, animal-origin CoVs, primarily those infecting bats, civet cats, and pangolins, were presumed to be the source of emerging human CoVs, including the SARS-CoV-2. The cohesive approach amongst virologists, bioinformaticians, big data analysts, epidemiologists, and public health researchers across the globe has delivered high-end viral diagnostics. Similarly, vaccines and therapeutics against COVID-19 are currently in the pipeline for clinical trials. The rapidly evolving and popular technology of artificial intelligence played a major role in confirming and countering the COVID-19 pandemic using digital technologies and mathematical algorithms. In this review, we discuss the noteworthy advancements in the mitigation of the COVID-19 pandemic, focusing on the etiological viral agent, comparative genomic analysis, population susceptibility, disease epidemiology, animal reservoirs, laboratory animal models, disease transmission, diagnosis using artificial intelligence interventions, therapeutics and vaccines, and disease mitigation measures to combat disease dissemination.

The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).

Abstract: Background: With the growing trends in recreational marijuana use, our study aims at analyzing the association between Acute coronary syndromes (ACS) and Ventricular Fibrillation (Vfib) and cannabis use disorder in young adults (18 – 45) Methods: National In-patient Sample was used for population analysis. Probit logistic regression was used for analyzing the associations between diagnosis of ACS/ Vfib and documented cannabis use disorder. Propensity Score matched samples were made from the ACS/Vfib admissions with Cannabis use disorder to analyze the in-hospital outcomes. Results: Statistically significant association was found between admission diagnosis of ACS/ vfib, and documented cannabis use disorder in the study population. The association remained sig-nificant after accounting for potential confounders. Documented race as black, documented di-agnosis of uncontrolled hypertension and diabetes were found to have significant association with admissions for ACS among the population with documented Cannabis use disorder. No difference in mortality were found between ACS admissions with and without documented cannabis use disorder in the propensity matched cohorts. Conclusions: Documented diagnosis of Cannabis use disorder has statistically significant associ-ation with admission diagnosis of ACS/Vfib even after accounting for confounders. Documented race as black, and co existing diagnosis of uncontrolled hypertension and diabetes had significant association with admission diagnosis of ACS in population with documented cannabis use dis-order. Keywords: Cannabis use disorder; ACS; Ventricular fibrillation

In this paper, we present a theory of gravity in Minkowski space. The basic idea is that the presence of mass affects the worldline of particles in the flat Minkowski background. Although General Relativity is in complete agreement with observations so far, the theory discussed in this paper is capable of solving some of the conceptual difficulties of GR, such as the energy problem, and is at the core of constructing this theory. We also discuss the cosmic expansion of the present universe and comment on the early universe. We also argue how the flatness problem and horizon problem are solved within this framework. As it will become clear, this is a theory of principle and not a theory of construction.

Valorization of food and fruit wastes has the potential for the production of sustainable energy and biochemicals. Approximately 70% of the weight of the original jackfruit (Artocarpus heterophyllus L.) fruit is lost during processing as waste in the form of peeled skin and core, both of which have not been utilized and, thus contribute to disposal as well as pollution issues. The major components, cellulose, and hemicellulose, can be biologically transformed easily into bioenergy sources like ethanol, methanol, and butanol; valuable phenolics and biotechnological products like pectin, citric acid, bromelain, ferulic acid, and vanillin; and many other products. These residues can also be utilized as essential sources for the biological transformation process leading to the production of numerous products with added value, such as phenolic antioxidants, phenolic flavor compounds, and organic acids. Thus, the value addition of jackfruit waste can support the sustainable solution towards food and nutritional security. In this way, zero waste can be achieved through novel biorefineries which are critically highlighted in this paper. Furthermore, novel technologies for the conversion of jackfruit wastes are summarized with recent findings.

In this study, global response analysis was performed to explore the mechanism of action of Usnic acid and its synergy with Norfloxacin, a well-known quinolone antibiotic to which MRSA clinical isolates showed resistance (MIC, 500 µg/mL). Microdilution assay, growth kinetics, microscopic analysis, and cell-based assays consistently showed that Usnic acid possess strong anti-staphylococcal activity (MIC, 7.8 µg/ml), causes cell leakage, modulates efflux pump activity, and synergizes with Norfloxacin against multi-drug resistant clinical isolate MRSA 2071. Whole cell proteome profiling using gel-free based nano-LC-ESI-QTOF-MS/MS revealed several proteins whose expression was significantly modulated by Usnic acid and Norfloxacin alone or in combination. Usnic acid downregulated the abundance of RNA polymerase subunits (RpoB, RpoC), carbamoyl phosphate synthase large subunit (PyrAB), chaperone (GroEL), and adenylosuccinate synthetase (PurA). Interestingly, proteins found to be upregulated in the presence of Usnic acid and Norfloxacin included oxidative stress-related proteins such as peroxidase (Tpx), alkyl hydroperoxide reductase (AphC), and general stress protein (UspA). This study clearly showed that Usnic acid affects numerous cellular targets and can potentiate the action of Norfloxacin. Furthermore, an in-vivo study emphasized the use of Usnic acid to develop as a safe phytopharmaceutical to repurpose old antibiotics through combination therapy against MRSA infections.

Traditional end-effector robots for arm rehabilitation are usually attached a­t the hand, primarily focusing on coordinated multi-joint training. Therapy at an individual joint level of the arm for severely impaired stroke survivors is not always possible with existing end-effector robots. The Arm Rehabilitation Robot (AREBO)—an end-effector robot—was designed to provide both single and multi-joint assisted training while retaining the advantages of traditional end-effector robots, such as ease of use and cost-effectiveness (compared to exoskeletons). This work presents the design, optimization, and characterization of AREBO for training single-joint movements of the arm. The AREBO has three actuated and three unactuated degrees of freedom, allowing it to apply forces in any arbitrary direction at its endpoint and self-align to arbitrary orientations within its workspace. The AREBO’s link lengths were optimized to maximize its workspace and manipulability. The AREBO provides single-joint training in both unassisted and adaptive weight support modes using a human arm model to estimate the human arm's kinematics and dynamics without using additional sensors. The characterization of the robot’s controller and the algorithm for estimating the human arm parameters were performed using a two degrees of freedom mechatronic model of the human shoulder joint. The results demonstrate that: (a) the movements of the human arm can be estimated using a model of the human arm and robot’s kinematics, (b) the AREBO has similar transparency to that of existing arm therapy robots in the literature, and (c) the adaptive weight support mode control can adapt to different levels of impairment in the arm. This work demonstrates how an appropriately designed end-effector robot can be used for single-joint training, which can be easily extended to multi-joint training. Future work will focus on the evaluation of the system on patients with any neurological condition requiring arm training.

Artificial Intelligence (AI) tools and techniques are comparatively recent advances of present-day globalized world. With their easy availability and friendly user interface AI has affected every sphere of human being. These tools are increasingly becoming popular because of the numerous benefits occurred to the society in many ways. However, there are not only the positive side of these technologies, but also many negative sides. Along with the development and adoption of AI, cyber-attacks are increased and disrupting the information system. Threat actors may misuse artificial intelligence. Hence, these tool designers are working and applying the ability of generative AI can tip the scales in cybersecurity. So, in the age of AI, there is always tension between the cyber attackers and the defenders to make a balanced information system. It is a general rule that defenders must be well in advance in their favor and keep ahead of adversaries. The United National and several governments for example the US, UK, and the big AI giants like Microsoft along with OpenAI, are working on emerging cyberattacks. This research paper explores the concept of information resilience in ML and AI systems, focusing on strategies to enhance their ability to withstand uncertainties, adversarial attacks, and data perturbations.