Search

Regenerative AI is transforming breast cancer diagnosis and treatment through enhanced imaging analysis, personalized medicine, drug discovery, and remote patient monitoring. AI algorithms can detect subtle patterns in mammograms and other imaging modalities with high accuracy, potentially leading to earlier diagnoses. In treatment planning, AI integrates patient-specific data to predict individual responses and optimize therapies. For drug discovery, generative AI models rapidly design and screen novel molecules targeting breast cancer pathways. Remote monitoring tools powered by AI provide real-time insights to guide care. Examples include Google's LYNA for analyzing pathology slides, Kheiron's Mia for mammogram interpretation, and Tempus's platform for integrating clinical and genomic data. While promising, challenges remain, including limited high-quality training data, integration into clinical workflows, interpretability of AI decisions, and regulatory/ethical concerns. Strategies to address these include collaborative data-sharing initiatives, user-centered design, explainable AI techniques, and robust oversight frameworks. In developing countries, AI tools like MammoAssist and Niramai's thermal imaging system are improving access to screening. Overall, regenerative AI offers significant potential to enhance breast cancer care, but judicious implementation with awareness of limitations is crucial. Coordinated efforts across the healthcare ecosystem are needed to fully realize AI's benefits while addressing challenges.

Abstract The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.

Scientific machine learning and physics-informed neural networks are novel conceptual approaches that integrate scientific knowledge with methods from data science and deep learning. This emerging field has garnered increasing interest due to its unique features and potential applications. However, key topics and subject-specific applications remain under explored. To address this gap, we conducted a scientometric analysis using large-scale bibliographic and citation data from Scopus. Our study provides a global overview of publication and citation trends, explores the co-occurrence of subject areas to uncover multidisciplinary relationships, and identifies the most collaborative fields. Additionally, we employ a Latent Dirichlet Allocation (LDA) model for topic modeling, introducing a novel information-theoretic approach to determine the optimal number of topics. Furthermore, we conduct a trend analysis based on the convergence behavior of entropy and an interpretation grounded in statistical physics, revealing a consolidation process of research directions indicated as an equilibrium state. Lastly, by analyzing Dirichlet distributions from LDA, we estimate the dimension of the transdisciplinarity of scientific machine learning, offering insights into the primary coherent research areas within this field.

Background: In modern times, metabolic disorders are most common and one of them is polycystic ovarian syndrome (PCOS) in women, which causes high morbidity and complications. PCOS has largely been a neglected and less researched area; however, it is gaining importance in recent times as PCOS is increasing as well as it can be prevented to a considerable extent. Methods: A cross-sectional study was conducted to assess knowledge among females in government hospitals in Northern India. Data were collected using a pretested structured questionnaire on 300 women (completed 270), selected by proportionate sampling technique, and admitted in government hospitals. Data were analyzed by SPSS version 23. Results: The results showed that the mean age of respondents was 33.02 ± 9.039 years, the mean age at menarche was 12.33 ± 2.13 years, and the mean of gravida status was 1.82 ± 0.78 years. Only half of the respondents had good knowledge about PCOS. Conclusion: This study suggests that efforts are needed to reinforce women’s knowledge through setting-based awareness campaigns and health education in this context to enable them to identify and seek timely treatment and improve their quality of life.

This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.

Currently, studies involving a digital twin are gaining widespread interest. While the first fields adopting such a concept were in manufacturing and engineering, lately, interest extends also beyond these fields across all academic disciplines. Given the inviting idea behind a digital twin which allows the efficient exploitation and utilization of simulations such a trend is understandable. The purpose of this paper is to use a scientometrics approach to study the early publication history of the digital twin across academia. Our analysis is based on large-scale bibliographic and citation data from Scopus that provides authoritative information about high-quality publications in essentially all fields of science, engineering and humanities. This paper has four major objectives. First, we obtain a global overview of all publications related to a digital twin across all major subject areas. This analysis provides insights into the structure of the entire publication corpus. Second, we investigate the co-occurrence of subject areas appearing together on publications. This reveals interdisciplinary relations of the publications and identifies the most collaborative fields. Third, we conduct a trend and keyword analysis to gain insights into the evolution of the concept and the importance of keywords. Fourth, based on results from topic modeling using a Latent Dirichlet Allocation (LDA) model we introduce the definition of a scientometric dimension (SD) of digital twin research that allows to summarize an important aspect of the bound diversity of the academic literature.

“Health for All” will never be attained if sociocultural bias and pervasive hypocrisy are not eliminated. The patient mentioned in this case study had difficulty gaining access to health care for more than two decades. The seizure history was modified due to social pressure. The primary healthcare workers’ ability to provide timely access to healthcare regardless of caste, religion, or gender, even in the most remote regions of the country, is of paramount importance. The patient was diagnosed with hypoparathyroidism and treated with calcium and vitamin D in high oral doses. The case also illustrates the significance of medical examination in preventing future difficulties in patients with presenile cataract.

Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype–environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia–North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype–environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which–won–where pattern of genotype plus genotype–environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.

Background: By examining the recent advancements in technology, particularly the transformation of material into digital flows and its impact on customer demands, the aim of this paper is to investigate supply chain management (SCM) by analyzing seven key constructs: uncertainty, perspective, topicality, coordination, flow, job to be done, and connection. These constructs were chosen since they reflect evolving SCM practices that motivate this study. Methods: We conducted a broad, structured narrative review to comprehensively address the extensive SCM literature. This approach allowed us to evaluate the current state of SCM research and offer recommendations for overcoming prevailing challenges. Results: Our findings reveal the significant impact of technological advancements on SCM operations, requiring companies to adapt and remain competitive. We envision future supply chains as dynamic networks of networks, necessitating the adoption of a value architecture concept that extends a firm’s business model to an ecosystem business model. Conclusions: Considering these changes, our study recommends exploiting uncertainty, adopting demand-driven systems, offering on-demand customized services and products, utilizing prescriptive analytics, prioritizing information flows and services, and embracing open systems with high interoperability. Summarizing these opportunities and challenges that arise with changes in SCM provides interesting venues for future research and valuable insights for practitioners.

Abstract Chickpea (Cicer arietinum L.) is predominantly an indeterminate plant and tends to generate vegetative growth when the ambient is conducive for soil moisture, temperature and certain other environmental conditions. The semi-determinate (SDT) types are comparatively early, resistant to lodging and found to be similar in their yield potential to indeterminate (IDT) lines. Indeterminate and semi-determinate genotypes are found to be similar during early stage, which makes it difficult to distinguish between them. Thus, there is a need to identify molecular markers linked either to indeterminate or semi-determinate plant types. The present study was carried out to study the genetics of semi-determinacy and identify molecular markers linked to stem growth habit. The study was undertaken in the cross involving BG 362(IDT) × BG 3078-1(SDT). All F1 plants were indeterminate, which indicates that indeterminate stem type is dominant over semi-determinate. In further advancement to F2 generation, F2 plants are segregated in the ratio of 3(Indeterminate): 1(Semi-determinate) that indicates that the IDT and SDT parents which are involved in the cross differed for a single gene. The segregation pattern observed in F2 is confirmed in F3 generation. The parental polymorphic survey was undertaken for molecular analysis using total of 245 SSR markers, out of which 41 polymorphic markers were found to distinguish the parents and were utilized for bulked segregant analysis (BSA). The segregation pattern in F2 indicates that the IDT (Indeterminate) and SDT (Semi-determinate) parents which are involved in the cross differed for single gene. The segregation pattern of F2 and F3 derived from the cross BG 362 (IDT) × BG 3078-1 (SDT) confirmed the genotypic structure of the newly found SDT genotype BG 3078-1 as dt1dt1Dt2Dt2. Three SSR markers TA42, Ca_GPSSR00560 and H3DO5 were found to be putatively linked to Dt1 locus regulating IDT stem growth habit. Our results indicate that the SSR markers identified for Dt1 locus helps to differentiate stem growth habit of chickpea in its early growth stage itself and can be efficiently utilized in Marker Assisted Selection (MAS) for changed plant type in chickpea.

Global food security, both in terms of quantity and quality remains as a challenge with the increasing population. In parallel, micronutrient deficiency in the human diet leads to malnutrition and several health-related problems collectively known as “hidden hunger” more prominent in developing countries around the globe. Biofortification is a potential tool to fortify grain legumes with micronutrients to mitigate the food and nutritional security of the ever-increasing population. Anti-nutritional factors like phytates, raffinose (RFO’s), oxalates, tannin, etc. have adverse effects on human health upon consumption. Reduction of the anti-nutritional factors or preventing their accumulation offers opportunity for enhancing the intake of legumes in diet besides increasing the bioavailability of micronutrients. Integrated breeding methods are routinely being used to exploit the available genetic variability for micronutrients through modern “omic” technologies such as genomics, transcriptomics, ionomics, and metabolomics for developing biofortified grain legumes. Molecular mechanism of Fe/Zn uptake, phytate, and raffinose family oligosaccharides (RFOs) biosynthesis pathways have been elucidated. Transgenic, microRNAs and genome editing tools hold great promise for designing nutrient-dense and anti-nutrient-free grain legumes. In this review, we present the recent efforts toward manipulation of genes/QTLs regulating biofortification and Anti-nutrient accumulation in legumes using genetics-, genomics-, microRNA-, and genome editing-based approaches. We also discuss the success stories in legumes enrichment and recent advances in development of low Anti-nutrient lines. We hope that these emerging tools and techniques will expedite the efforts to develop micronutrient dense legume crop varieties devoid of Anti-nutritional factors that will serve to address the challenges like malnutrition and hidden hunger.

In recent years, the development of RNA-guided genome editing (CRISPR-Cas9 technology) has revolutionized plant genome editing. Under nutrient deficiency conditions, different transcription factors and regulatory gene networks work together to maintain nutrient homeostasis. Improvement in the use efficiency of nitrogen (N), phosphorus (P) and potassium (K) is essential to ensure sustainable yield with enhanced quality and tolerance to stresses. This review outlines potential targets suitable for genome editing for understanding and improving nutrient use (NtUE) efficiency and nutrient stress tolerance. The different genome editing strategies for employing crucial negative and positive regulators are also described. Negative regulators of nutrient signalling are the potential targets for genome editing, that may improve nutrient uptake and stress signalling under resource-poor conditions. The promoter engineering by CRISPR/dead (d) Cas9 (dCas9) cytosine and adenine base editing and prime editing is a successful strategy to generate precise changes. CRISPR/dCas9 system also offers the added advantage of exploiting transcriptional activators/repressors for overexpression of genes of interest in a targeted manner. CRISPR activation (CRISPRa) and CRISPR interference (CRISPRi) are variants of CRISPR in which a dCas9 dependent transcription activation or interference is achieved. dCas9-SunTag system can be employed to engineer targeted gene activation and DNA methylation in plants. The development of nutrient use efficient plants through CRISPR-Cas technology will enhance the pace of genetic improvement for nutrient stress tolerance of crops and improve the sustainability of agriculture.

Non-communicable diseases are already acknowledged as a double burden, and now childhood obesity is putting extra strain on our health system. The current paper aimed to analyze the ongoing health and related programmes/policies in India, and we discussed the existing opportunities in the programmes to address the issue of childhood obesity in India. We searched the “MEDLINE,” “PsycINFO,” “Scopus,” “Web of Science,” and “Google Scholar” databases using the following keywords: (”overweight”) and (”obesity”), (”childhood obesity”), (”nutritional programmes in India”), (”Health policies in India”), (”malnourished children in India”) in combination with each other and in truncated form. All the relevant articles and policy documents (MOHFW, INDIA) available in the public domain were included to support the argument for this narrative review. We found that we have programme gaps like guidelines issues by Food Safety Standards Authority of India to tackle childhood obesity and it has not been strictly implemented due to multiple reasons. School health programme has an opportunity to address the issue of childhood obesity, but at the ground level the outcomes are not very promising. The National Nutrition Mission have only focussed on undernutrition and anemia problem, ignoring the overweight/childhood obesity. Primary care physicians are key players in the treatment of childhood obesity, yet rates of obesity management in the primary care setting are low. National Programme for prevention & Control of Cancer, Diabetes, Cardiovascular Diseases & stroke is dealing with health promotion and prevention, early diagnosis, and management of all ages, except children. Diet provided in Integrated Child Development Scheme is calculated based on calories, not by the quality which is a concern to us. The breastfeeding promotion programme named Mothers Absolute Affection programme has not been implemented with letter and spirit. Other than health programmes, we assume that Ministry of Urban Planning, Foreign Direct Investment policy, Advertisement Council of India and many more sector/policy/programme are indirectly responsible for the increasing burden of childhood obesity in India. Lack of awareness and wrong perception also responsible for the development of childhood obesity. We have multiple National Health Programmes and Policies to address the childhood malnutrition, but are focussing the undernutrition component only, ignoring overnutrition problem in the children, which is emerging as quadruple burden to our health system. Appropriate actions and inclusion of suggestions provided in this study for the improvement of the programmes at the practical level needs to be considered by the policy makers to halt the ever-rising trend of childhood obesity and primary care physicians should play a leadership role.

Background: Pregnancy is a positive, fulfilling experience for many, but it can put the mother at the risk of suffering ill-health, disability, or even maternal death. The common causes of maternal death include pregnancy-induced hypertension (PIH) or gestational hypertension or hypertensive disorders in pregnancy (HDP), post-partum hemorrhage, and pulmonary embolism. Modifications in lifestyle and diet have been widely advocated as a cost-effective strategy to prevent PIH. Objectives: To assess the role of dietary factors in PIH attending antenatal care services at a teaching hospital. Materials and Methods: Pregnant women who were attending the antenatal clinic who were diagnosed with hypertension in pregnancy and normotensive before the pregnancy were included in the study. As per the protocol, informed written consent, demographic details with clinical data were obtained from the patients. A total of 225 antenatal cases having hypertension in pregnancy were included in our study. Results: Majority of the study subjects (81.8%) were taking mixed diets, 50.2% consumed additional salt in their diet, the intake history of visible fat was given by 25.3% and 96.4% consumed tea while 52.9% had a history of consuming junk food. Among the 41 vegetarian study subjects with hypertension in pregnancy, the maximum subjects (73.1%) had preeclampsia followed by gestational hypertension in 23.6% the subjects. The relation between the type of diet and hypertension in pregnancy was found to be statistically insignificant. The relation between visible fat consumption and hypertension in pregnancy was found to be statistically significant. Discussion: The association of dietary factors with HDP could be explained by several factors. One being the high-calorie intake by women with known HDP and those without HDP. The imbalance between the energy intake and expenditure is a potential risk factor and leads to overweight or obesity. Conclusion: The consumption of additional salt in the diet, visible fat, and obesity was found to be associated with HDP in our study.

Abstract The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.

Background: Cancer is a major public health problem across the globe. According to the Malaysian National Cancer Registry Report (MNCR), between 2007 and 2011, there has been a greater increase in the number of reported cancer cases among females (99.3 per 100,000 people) than males (86.9 cases per 100,000 people). Nearly 15 million new cancer cases were projected by 2020. We conducted this study to assess the quality of life (QoL) among cancer patients. Design and Methods: This was a cross-sectional study conducted in the oncology department of Malaysian hospitals from October to November 2019. The study population comprised Malaysians in the age range of18 to 100 years diagnosed with cancer, irrespective of their treatment status, cancer type and prognosis. A pre-tested structured questionnaire was used in seventy cancer patients’ using a convenient sampling method. Data were collected using a questionnaire survey after obtaining permission from the hospital administrators. Data were entered in MS Excel and analysis was done using the SPSS-version 23. Participants’ socio-demographic characteristics were described using descriptive statistics. Results are presented using graphs, tables, mean, percentages, standard deviation, frequency, and significance. Analysis of variance (ANOVA) test was performed to find out the association between socio-demographic variables and overall QoL scores of the patients. Results: Of the seventycancer patients, 29 (41.43%) were males and 41 (58.57%) were females. The most affected socio-demographic factors were marital status and occupation (employment). It was found that there were 55 (78.57%) married patients and 49 (70%) employed patients. QoL of most of the patients was influenced by the education level and treatment. It was found that 35 (50%) of them had low QoL scores. It was observed that QoL among the study participants was significantly (p

Health systems in low- and middle-income countries like India continue to struggle with the overwhelming burden of noncommunicable diseases (NCDs) alongside the coexistence of multiple medical conditions. Such cases are challenging to diagnose and treat, especially in places where electronic health records are not readily available. In such contexts, using colour coding system for recording health conditions may ensure optimal documentation, effective patient-provider communication, adherence to treatment and follow up, quality of health services, and an overall improvement in health systems performance for NCDs. Colour coding is a common tool used in several service industries including public health programmes locally and globally. Despite such promising aspects, colour coding is not widely used for NCDs in health services organizations, which necessitates a translation of evidence from other sectors and the adoption of innovative and evidence-based approaches to promote the use of colour coding for better addressing NCD epidemic.

Introduction: Virtual clinic is a new concept in India. This summary describes that how a virtual clinic is transforming the healthcare scenario in rural India. Virtual clinic is based on a social business model, which may involve diverse stakeholders to promote primary care. Aim: This virtual e-clinic aims to expand health outreach in rural and hard-to-reach areas of India and provide primary health care services by connecting local practitioners and health workers visiting patients with qualified allopathic doctors in city through video conferencing technologies. Methodology: This was a cross-sectional study evaluating the feasibility and acceptance of virtual primary care. A convenient sampling method was used. Data on demographic profiles, morbidity patterns, and referrals were collected with proper consent. This virtual clinic comprises of smartphones, monitors, and assistive devices so that patient can interact with the doctors through video-conferencing and can have authenticated prescriptions with standardized protocols. The private organization who initiated the virtual care program had two centers at the beginning and gradually expanded them to 20 in Uttar Pradesh. Consultations charges were kept minimum to no-profit, no-loss. Data were collected from January 2019 to June 2010. Results: Total number of consultations made was 800. Out of 800 patients, 157 patients belonged to age group of ≥60 years. Mean age the patient was 56 ± 1.56 years, among them 421 (52.62%) were male. The participants actively engaged in clinical interactions and completed full sessions of consultations, which highlight the acceptability of the virtual care system and feasibility of effective patient-provider communication and service delivery using digital technologies. Conclusion: The concept of virtual primary care is becoming very popular in rural region where no qualified doctors are available. The initial results of this technological startup appears to be promising; however, it is necessary to evaluate the quality of care, health outcomes, potentials to integrate such innovations in existing primary care, and the legal as well as ethical issues in the future research.

Preserving health requires a holistic approach involving the component of physical, mental, social, and spiritual well-being as stated by World Health Organisation. Salutogenesis concept focuses on the factors responsible for well-being rather than the disease pathogenesis in contrary to pathogenesis concept. This evidence-based summary tries to shed a light on existing concept called salutogenesis which is much required in the current scenario.

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery framework provides an orderly partition of the often complex data mining processes to ensure a practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data- and model development-related issues. These issues need to be carefully addressed by allowing a flexible, customized and industry-specific knowledge discovery framework. For this reason, extensions of CRISP-DM are needed. In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. Furthermore, it emphasizes also the need for a detailed business understanding and the interdependencies with the developed models and data quality for fulfilling higher business objectives. Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues.

In this paper, we have described the health care problem (maldistribution of doctors) in India. Later, we have introduced the concept of artificial intelligence and we have described this technology with various examples, how it is rapidly changing the health care scenario across the world. We have also described the various advantages of artificial intelligence technology. At the end of the paper, we have raised some serious concerns regarding complete replacement of human based health care technology with artificial intelligence technology. Lastly, we concluded that we have to use artificial intelligent technology to prevent human sufferings/health care problems with proper caution.

Abstract With an aim of enhancing drought tolerance using a marker‐assisted backcrossing (MABC) approach, we introgressed the “QTL‐hotspot” region from ICC 4958 accession that harbors quantitative trait loci (QTLs) for several drought‐tolerance related traits into three elite Indian chickpea (Cicer arietinum L.) cultivars: Pusa 372, Pusa 362, and DCP 92‐3. Of eight simple sequence repeat (SSR) markers in the QTL‐hotspot region, two to three polymorphic markers were used for foreground selection with respective cross‐combinations. A total of 47, 53, and 46 SSRs were used for background selection in case of introgression lines (ILs) developed in genetic backgrounds of Pusa 372, Pusa 362, and DCP 92‐3, respectively. In total, 61 ILs (20 BC3F3 in Pusa 372; 20 BC2F3 in Pusa 362, and 21 BC3F3 in DCP 92‐3), with >90% recurrent parent genome recovery were developed. Six improved lines in different genetic backgrounds (e.g. BGM 10216 in Pusa 372; BG 3097 and BG 4005 in Pusa 362; IPC(L4‐14), IPC(L4‐16), and IPC(L19‐1) in DCP 92‐3) showed better performance than their respective recurrent parents. BGM 10216, with 16% yield gain over Pusa 372, has been released as Pusa Chickpea 10216 by the Central Sub‐Committees on Crop Standards, Notification and Release of Varieties of Agricultural Crops, Ministry of Agriculture and Farmers Welfare, Government of India, for commercial cultivation in India. In summary, this study reports introgression of the QTL‐hotspot for enhancing yield under rainfed conditions, development of several introgression lines, and release of Pusa Chickpea 10216 developed through molecular breeding in India.

Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible. On a downside, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. For this reason, we present in this paper an introductory review of deep learning approaches including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks. These models form the major core architectures of deep learning models currently used and should belong in any data scientist's toolbox. Importantly, those core architectural building blocks can be composed flexibly—in an almost Lego-like manner—to build new application-specific network architectures. Hence, a basic understanding of these network architectures is important to be prepared for future developments in AI.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a considerably common childhood-onset neurodevelopmental disorder, often associated with socio-behavioral and academic difficulties. There is an increased risk for development of a range of future problems such as psychiatric disorders, difficulties in employment, and relationships. The aim of this study was to know the prevalence and determinants of ADHD among primary school children in district Dehradun and to assess the learning difficulties and classroom behavior in these students. MATERIALS AND METHODS: In this cross-sectional study, overall, 228 students (aged 6–12 years) studying in a primary school were assessed for ADHD after seeking the written informed consent from their parents. The data were analyzed using Excel sheet and SPSS software (22.0 version). For all qualitative measures, frequency and percentages were calculated, and for quantitative measures, mean and standard deviation were calculated. For analysis of categorical values, Chi-square test was used. P < 0.05 was considered statistically significant. RESULTS: The prevalence of ADHD was found to be 11.8% based on the teacher tool only and 1.75% based on the parent and teacher tools combined. ADHD was found to be significantly more in males. Inattention was the most prevalent subtype of ADHD, and children screened positive for ADHD had significant learning difficulties at various levels. CONCLUSION: The possibility of ADHD in students with academic difficulties should not be ignored as children with ADHD usually face significant problems at school such as learning difficulties and have been shown to be at increased risk for a broad range of negative outcomes. Early identification and treatment of ADHD can significantly reduce the rates of some of these poor outcomes. Screening these children in the early years will help the parents, children, teachers, and community at large.

Referral is a dynamic process, in which a health worker at one level of the health system, having insufficient resources (drugs, equipment, skills) to manage a clinical condition, seeks the help of a better or differently resourced facility at the same or higher level to assist in. Health care systems of every country are designed in such a way to encourage patients to first attempt to get care at the primary level and then to approach a higher level of care according to the need. This protocol minimizes the costs for the caretaker/patients. However, in most of the countries, patients often bypass primary care facilities and directly go to the higher center thereby, increasing the burden on higher level facilities, the picture is not very different in India also. Health care system in India is plugged by: overpopulation, lack of expert clinicians, skewed distribution of physicians, lack of motivation among existing health care personnel and an ineffective referral mechanism. Due to failure of conventional paper-based referral systems in our country, we can introduce an e-referral system in the era of internet. It is evident from our experiences, that this artificial intelligence enabled e-referral system has many advantages over the traditional paper-based referral system. It will aid health workers for timely management of cases. Most importantly, it will streamline the existing unorganized referral process. Although, for effective e-Referral system, there should be a collaborative platform where easy search and discovery for health care providers is possible and help in decision making. e- Referral should be incorporated in our health system to strengthen it by bridging the access gap may be through Public Private Partnership model.

Abstract Background sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles. Results The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected. Conclusions sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

The LINCS L1000 data repository contains almost two million gene expression profiles for thousands of small molecules and drugs. However, due to the complexity and the size of the data repository and a lack of an interoperable interface, the creation of pharmacologically meaningful workflows utilizing these data is severely hampered. In order to overcome this limitation, we developed the L1000 Viewer, a search engine and graphical web interface for the LINCS data repository. The web interface serves as an interactive platform allowing the user to select different forms of perturbation profiles, e.g., for specific cell lines, drugs, dosages, time points and combinations thereof. At its core, our method has a database we created from inferring and utilizing the intricate dependency graph structure among the data files. The L1000 Viewer is accessible via http://L1000viewer.bio-complexity.com/.

High-dimensional data from molecular biology possess an intricate correlation structure that is imposed by the molecular interactions between genes and their products forming various different types of gene networks. This fact is particularly well-known for gene expression data, because there is a sufficient number of large-scale data sets available that are amenable for a sensible statistical analysis confirming this assertion. The purpose of this paper is two fold. First, we investigate three methods for generating constrained covariance matrices with a biologically realistic structure. Such covariance matrices are playing a pivotal role in designing novel statistical methods for high-dimensional biological data, because they allow to define Gaussian graphical models (GGM) for the simulation of realistic data; including their correlation structure. We study local and global characteristics of these covariance matrices, and derived concentration/partial correlation matrices. Second, we connect these results, obtained from a probabilistic perspective, to statistical results of studies aiming to estimate gene regulatory networks from biological data. This connection allows to shed light on the well-known heterogeneity of statistical estimation methods for inferring gene regulatory networks and provides an explanation for the difficulties inferring molecular interactions between highly connected genes.

In this paper, we define novel graph measures for directed networks. The measures are based on graph polynomials utilizing the out- and in-degrees of directed graphs. Based on these polynomial, we define another polynomial and use their positive zeros as graph measures. The measures have meaningful properties that we investigate based on analytical and numerical results. As the computational complexity to compute the measures is polynomial, our approach is efficient and can be applied to large networks. We emphasize that our approach clearly complements the literature in this field as, to the best of our knowledge, existing complexity measures for directed graphs have never been applied on a large scale.

BACKGROUND: The acceptability of hospital staff in the use of hospital information management system (HIMS) is an emerging research area it can explain the fate of any HIMS development and implementation project in hospitals. The aim of this study was to observe the level of acceptance of HMIS among nursing officials working at a teaching hospital. MATERIALS AND METHODS: This cross-sectional study was conducted for 1 year in a teaching hospital of northern India by using a pretested questionnaire. Our study participants were nursing officers who were not under the probation period and we used a purposive sampling (10% nurses from each ward). Our sample size was 256. RESULTS: We have observed that majority of 174 (67.96%) participants had good acceptability to the HIMS system. Our study revealed that most of the participants were aware of HIMS. Among all participants, nearly half of them had good acceptability to the HIMS system. This is may be due to their job profiles, distribution of their working places, and their past experiences with HMIS. The bottlenecks such as connectivity problem, error prevention, and lack of training can be addressed by the hospital management by proper measures. CONCLUSION: The acceptance level of HIMS among the nursing officials working in a teaching hospital was good.

The purpose of this paper is to survey studies for estimating and analyzing different types of economic networks We focus on data-based approaches that allow the direct estimation of the networks from empirical data without the need of relying on theoretical assumptions. Due to the fact that there is a large variety of different economic networks, e.g., interbank, investment, director, ownership, financial, product or trade networks, we present a systematic categorization of these by the meaning of the “nodes” within these networks. These can correspond to banks, firms, investors, products, stocks etc. Furthermore, we review practical methods for graphically exploring such networks and discuss useful databases for obtaining the empirical data for the computational construction of economic networks.

Gene expression profiling using transcriptional drug perturbations are useful for many biomedical discovery studies including drug repurposing and elucidation of drug mechanisms (MoA) and many other pharmacogenomic applications. However, limited data availability across cell types has severely hindered our capacity to progress in these areas. To fill this gap, recently, the LINCS program generated almost 1.3 million profiles for over 40,000 drug and genetic perturbations for over 70 different human cell types, including meta information about the experimental conditions and cell lines. Unfortunately, Big Data like the ones generated from the ongoing LINCS program do not enable easy insights from the data but possess considerable challenges toward their analysis. In this paper, we address some of these challenges. Specifically, first, we study the gene expression signature profiles from all cell lines and their perturbagents in order to obtain insights in the distributional characteristics of available conditions. Second, we investigate the differential expression of genes for all cell lines obtaining an understanding of condition dependent differential expression manifesting the biological complexity of perturbagents. As a result, our analysis helps the experimental design of follow-up studies, e.g., by selecting appropriate cell lines.

Identification of functionally relevant potential genomic loci using an economical, simpler and user-friendly genomics-assisted breeding strategy is vital for rapid genetic dissection of complex flowering time quantitative trait in chickpea. A high-throughput multiple QTL-seq strategy was employed in two inter (Cicer arietinum desi accession ICC 4958 × C reticulatum wild accession ICC 17160)- and intra (ICC 4958 × C. arietinum kabuli accession ICC 8261)-specific RIL mapping populations to identify the major QTL genomic regions governing flowering time in chickpea. The whole genome resequencing discovered 1635117 and 592486 SNPs exhibiting differentiation between early- and late-flowering mapping parents and bulks, constituted by pooling the homozygous individuals of extreme flowering time phenotypic trait from each of two aforesaid RIL populations. The multiple QTL-seq analysis using these mined SNPs in two RIL mapping populations narrowed-down two longer (907.1 kb and 1.99 Mb) major flowering time QTL genomic regions into the high-resolution shorter (757.7 kb and 1.39 Mb) QTL intervals on chickpea chromosome 4. This essentially identified regulatory as well as coding (non-synonymous/synonymous) novel SNP allelic variants from two efl1 (early flowering 1) and GI (GIGANTEA) genes regulating flowering time in chickpea. Interestingly, strong natural allelic diversity reduction (88–91%) of two known flowering genes especially mapped at major QTL intervals as compared to that of background genomic regions (where no flowering time QTLs were mapped; 61.8%) in cultivated vis-à-vis wild Cicer gene pools was evident inferring the significant impact of evolutionary bottlenecks on these loci during chickpea domestication. Higher association potential of coding non-synonymous and regulatory SNP alleles mined from efl1 (36–49%) and GI (33–42%) flowering genes for early and late flowering time differentiation among chickpea accessions was evident. The robustness and validity of two functional allelic variants-containing genes localized at major flowering time QTLs was apparent by their identification from multiple intra-/inter-specific mapping populations of chickpea. The functionally relevant molecular tags delineated can be of immense use for deciphering the natural allelic diversity-based domestication pattern of flowering time and expediting genomics-aided crop improvement to develop early flowering cultivars of chickpea.

Time-resolved spectroscopic experiments have been performed with protein in solution and in crystalline form using a newly designed microspectrophotometer. The time-resolution of these experiments can be as good as two nanoseconds (ns), which is the minimal response time of the image intensifier used. With the current setup, the effective time-resolution is about seven ns, determined mainly by the pulse duration of the nanosecond laser. The amount of protein required is small, on the order of 100 nanograms. Bleaching, which is an undesirable effect common to photoreceptor proteins, is minimized by using a millisecond shutter to avoid extensive exposure to the probing light. We investigate two model photoreceptors, photoactive yellow protein (PYP), and α-phycoerythrocyanin (α-PEC), on different time scales and at different temperatures. Relaxation times obtained from kinetic time-series of difference absorption spectra collected from PYP are consistent with previous results. The comparison with these results validates the capability of this spectrophotometer to deliver high quality time-resolved absorption spectra.

Chickpea (Cicer arietinum L.) is known to be sensitive to many herbicides and, therefore, choices for using post-emergence herbicides for weed control are limited. The present study was aimed at identifying sources of tolerance to two herbicides with different modes of action (imazethapyr—amino acid synthesis inhibitor; and metribuzin—photosynthesis inhibitor) for use in breeding herbicide tolerant cultivars. Screening of 300 diverse chickpea genotypes (278 accessions from the reference set and 22 breeding lines) revealed large genetic variations for tolerance to herbicides imazethapyr and metribuzin. In general, the sensitivity of the genotypes to metribuzin was higher compared to that for imazethapyr. Several genotypes tolerant to metribuzin (ICC 1205, ICC 1164, ICC 1161, ICC 8195, ICC 11498, ICC 9586, ICC 14402 ICC 283) and imazethapyr (ICC 3239, ICC 7867, ICC 1710, ICC 13441, ICC 13461, ICC 13357, ICC 7668, ICC 13187) were identified, based on average herbicide tolerance scores from two experimental locations each. The herbicide tolerant lines identified in this study will be useful resources for development of herbicide tolerant cultivars and for undertaking genetic and physiological studies on herbicide tolerance in chickpea.

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011-12 and 2012-13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate data on 3,000 polymorphic markers. Phenotypic and genotypic data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits viz. seed yield, 100 seed weight, days to 50% flowering and days to maturity. Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.

Bromodomain containing proteins recognize the level of histone acetylation and regulate epigenetically controlled processes like gene transcription and chromatin modification. The BET (bromodomain and extra-terminal) family proteins, which are transcriptional co-regulators, have been implicated in the pathogenesis of cancer, neurodegenerative disorders, and defects in embryonic stem cell differentiation. Inhibitors selectively targeting the BET bromodomains can pave the path for new drug discovery against several forms of major diseases. By a rational structure-based approach, we have identified a new inhibitor (NSC127133) of the second bromodomain (BD2) of the BET family protein BRD2 using the NCI Diversity Set III library. A high-resolution crystal structure of the BRD2-BD2 in complex with this compound and in apo- form is refined to 0.91 and 0.94 Å, respectively. The compound, which is a phenanthridinone derivative, binds well to the acetyl-lysine binding pocket of BD2 and displays significant hydrophobic and hydrophilic interactions. Moreover, the atomic resolution data obtained in this study allowed us to visualize certain structural features of BD2 which remained unobserved so far. We propose that the discovered compound may be a potential molecule to develop a new library for inhibiting the BRD2-BD2 function.

In this paper, we study the discrimination power of graph measures that are based on graph-theoretical matrices. The paper generalizes the work of [M. Dehmer, M. Moosbrugger. Y. Shi, Encoding structural information uniquely with polynomial-based descriptors by employing the Randić matrix, Applied Mathematics and Computation, 268(2015), 164-168]. We demonstrate that by using the new functional matrix approach, exhaustively generated graphs can be discriminated more uniquely than shown in the mentioned previous work.

A “” containing quantitative trait loci (QTL) for several root and drought tolerance traits was transferred through marker-assisted backcrossing into JG 11, a leading variety of chickpea ( L.) in India from the donor parent ICC 4958. Foreground selection with up to three simple sequence repeat markers, namely TAA170, ICCM0249, and STMS11, and background selection with up to 10 amplified fragment length polymorphism primer combinations was undertaken. After undertaking three backcrosses with foreground and background selection and selfing, 29 BCF plants homozygous for two markers (ICCM0249 and TAA170) were selected and referred as introgression lines (ILs). Root trait phenotyping of these ILs showed higher rooting depth (RDp) (average 115.21 ± 2.24 cm) in all 29 ILs, better root length density (RLD) (average 0.41 ± 0.02 cm cm) in 26 ILs, and higher root dry weight (RDW) (average 1.25 ± 0.08 g per cylinder) as compared to the recurrent parent, JG 11 (111.70 cm for RDp, 0.39 cm cm for RLD, and 1.10 g per cylinder for RDW), as well as the donor parent, ICC 4958 (114.20 cm for RDp, 0.45 cm cm for RLD, and 1.25 g per cylinder for RDW). These ILs, developed in 3 yr, after multilocation field trials may be released as improved variety with enhanced drought tolerance.

Molecular descriptors have been explored extensively. From these studies, it is known that a large number of descriptors are strongly correlated and capture similar characteristics of molecules. In this paper, we evaluate 919 Dragon-descriptors of 6 different categories by means of clustering. Also, we analyze these different categories of descriptors also find a subset of descriptors which are least correlated among each other and, hence, characterize molecular graphs distinctively.

Gene expression data can provide a very rich source of information for elucidating the biological function on the pathway level if the experimental design considers the needs of the statistical analysis methods. The purpose of this paper is to provide a comparative analysis of statistical methods for detecting the differentially expression of pathways (DEP). In contrast to many other studies conducted so far, we use three novel simulation types, producing a more realistic correlation structure than previous simulation methods. This includes also the generation of surrogate data from two large-scale microarray experiments from prostate cancer and ALL. As a result from our comprehensive analysis of 41,004 parameter configurations, we find that each method should only be applied if certain conditions of the data from a pathway are met. Further, we provide method-specific estimates for the optimal sample size for microarray experiments aiming to identify DEP in order to avoid an underpowered design. Our study highlights the sensitivity of the studied methods on the parameters of the system.