Heparan sulfate Proteoglycans (HSPG) are ubiquitous molecules with indispensable functions in various biological processes. Glypicans are a family of HSPG's, characterized by a Gpi-anchor which ...directs them to the cell surface and/or extracellular matrix where they regulate growth factor signaling during development and disease. We report the identification and expression pattern of glypican genes from zebrafish. The zebrafish genome contains 10 glypican homologs, as opposed to six in mammals, which are highly conserved and are phylogenetically related to the mammalian genes. Some of the fish glypicans like Gpc1a, Gpc3, Gpc4, Gpc6a and Gpc6b show conserved synteny with their mammalian cognate genes. Many glypicans are expressed during the gastrulation stage, but their expression becomes more tissue specific and defined during somitogenesis stages, particularly in the developing central nervous system. Existence of multiple glypican orthologs in fish with diverse expression pattern suggests highly specialized and/or redundant function of these genes during embryonic development.
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•Relationship between energy and economic indicators over 26 years.•Long-run equilibrium relationship among all the five variables.•Renewable energy consumption plays a significant ...role in the process of economic development.•Role of non-renewable energy consumption is not found to be statistically significant.
This study explores the connections between renewable energy consumption (REC), non-renewable energy consumption (NREC), gross fixed capital formation (GFCF), the labor force (LF), and economic growth (GDP) in Renewable Energy Country Attractiveness Index (RECAI) countries for 1991–2016. We quantify the nexus between REC, NREC, and GDP while utilizing a production model framework and including the measures of labor and capital, for suggesting a phase-wise strategy to attain the sustainable development goals. We use robust methodologies including Lagrange Multiplier (LM) panel unit root tests with trend shifts, Westerlund cointegration test, LM bootstrap technique for cointegration with breaks, continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) estimators, Augmented Mean Group (AMG) approach, fully modified ordinary least squares, dynamic ordinary least squares, Canonical Cointegrating Regression (CCR), and panel causality test proposed by Canning & Pedroni. We compute non-parametric time-varying coefficients with fixed effects for seeing the impact of GFCF, LF, REC, and NREC on GDP. Our results press upon policymakers to shift toward clean energy and REC for attaining the environmental goals (SDGs 6, 7, 13, and 15) and the economic goals (SDGs 1, 2, 8, and 10). While this shift would help developed economies, which have already attained the economic goals, to progress on the front of environmental goals, it would enable developing countries to progress on both fronts in a balanced manner.
Clustering is a crucial and, at the same time, challenging task in several application domains. It is important to incorporate the optimum feature finding into our clustering algorithms for better ...exploration of features and to draw meaningful conclusions, but this is difficult when there is no or little information about the importance or relevance of features. To tackle this task in an efficient manner, we employ the natural evolution process inherent in genetic algorithms (GAs) to find the optimum features for clustering the healthy aging dataset. To empirically verify the findings, genetic algorithms were combined with a number of clustering algorithms, including partitional, density-based, and agglomerative clustering algorithms. A variant of the popular KMeans algorithm, named KMeans++, gave the best performance on all performance metrics when combined with GAs.
Abstract Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated biologists. Microbes play a crucial role ...in the material and elemental cycles of the natural world. Growing own microbes for research purposes requires a significant time and financial investment. On the other hand, high-throughput sequencing technology cannot advance at the same clip as the culture method. The area of microbiology has made substantial use of machine learning (ML) methods to tackle this problem. Classification and prediction have emerged as key avenues for advancing microbial community research in computational biology. This research compares the advantages and disadvantages of using different algorithmic approaches in four subfields of microbiology (pathogen and epidemiology; microbial ecology; drug development; microbiome and taxonomy).
The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software maintenance ...effort. Cognitive informatics is important in quantifying the degree of difficulty or the efforts made by developers to understand the source code. Several empirical studies were conducted in 2003 to assign cognitive weights to each possible basic control structure of software, and these cognitive weights are used by several researchers to evaluate the cognitive complexity of software systems. In this paper, an effort has been made to categorize the Control Flow Graphs (CFGs) nodes according to their node features. In our case, we extracted seven unique features from the program, and each unique feature was assigned an integer value that we evaluated through Cognitive Complexity Measures (CCMs). We then incorporated CCMs' results as a node feature value in CFGs and generated the same based on the node connectivity for a graph. In order to obtain the feature representation of the graph, a node vector matrix is then created for the graph and passed to the Graph Convolutional Network (GCN). We prepared our data sets using GCN output and then built Deep Neural Network Defect Prediction (DNN-DP) and Convolutional Neural Network Defect Prediction (CNN-DP) models to predict software defects. The Python programming language is used, along with Keras and TensorFlow. Three hundred twenty Python programs were written by our talented UG and PG students, and all experiments were carried out during laboratory classes. Together with three skilled lab programmers, they compiled and ran each individual program and detected defect/no-defect programs before categorizing them into three different classes, namely Simple, Medium, and Complex programs. Accuracy, Receiver Operating Characteristics (ROC), Area Under Curve (AUC), F-measure, Precision and hyper-parameter tuning procedures are used to evaluate the approaches. The experimental results show that the proposed models outperformed state-of-the-art methods such as Nave Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) in all evaluation criteria.
Blockchain is gaining attention since its development due to its features like immutability, transparency, decentralization.It can also be used as decentralized storage. Blockchain offers a wide ...range of digital services both in financial and nonfinancial sectors. In this paper, we discuss the basic concepts and terminologies associated with blockchain and discuss theapplication areas both in financial and non- financial sectors such as Digital voting, Digital identity, Education, etc. anddiscuss the existing framework in these areas, also the progress and challenges of global acquisition. The main highlight ofthis paper is COVID-19 and we discussed existing solutions to fight the COVID-19 pandemic by using such technology.This survey paper will be helpful for a data scientist as well as researchers to explore in the area COVID-19.
Pregnant women are considered a "high-risk" group with limited access to health facilities in urban slums in India. Barriers to using health services appropriately may lead to maternal and child ...mortality, morbidity, low birth weight, and children with stunted growth. With the increase in the use of artificial intelligence (AI) and machine learning in the health sector, we plan to develop a predictive model that can enable substantial uptake of maternal health services and improvements in adverse pregnancy health care outcomes from early diagnostics to treatment in urban slum settings.
The objective of our study is to develop and evaluate the AI-guided citizen-centric platform that will support the uptake of maternal health services among pregnant women seeking antenatal care living in urban slum settings.
We will conduct a cross-sectional study using a mixed methods approach to enroll 225 pregnant women aged 18-44 years, living in the urban slums of Delhi for more than 6 months, seeking antenatal care, and who have smartphones. Quantitative and qualitative data will be collected using an Open Data Kit Android-based tool. Variables gathered will include sociodemographics, clinical history, pregnancy history, dietary history, COVID-19 history, health care facility data, socioeconomic status, and pregnancy outcomes. All data gathered will be aggregated into a common database. We will use AI to predict the early at-risk pregnancy outcomes (in terms of the type of delivery method, term, and related complications) depending on the needs of the beneficiaries translating into effective service-delivery improvements in enhancing the use of maternal health services among pregnant women seeking antenatal care. The proposed research will help policy makers to prioritize resource planning, resource allocation, and the development of programs and policies to enhance maternal health outcomes. The academic research study has received ethical approval from the University Research Ethics Committee of Dehradun Institute of Technology (DIT) University, Dehradun, India.
The study was approved by the University Research Ethics Committee of DIT University, Dehradun, on July 4, 2021. Enrollment of the eligible participants will begin by April 2022 followed by the development of the predictive model by October 2022 till January 2023. The proposed AI-guided citizen-centric tool will be designed, developed, implemented, and evaluated using principles of human-centered design that will help to predict early at-risk pregnancy outcomes.
The proposed internet-enabled AI-guided prediction model will help identify the potential risk associated with pregnancies and enhance the uptake of maternal health services among those seeking antenatal care for safer deliveries. We will explore the scalability of the proposed platform up to different geographic locations for adoption for similar and other health conditions.
PRR1-10.2196/35452.
Well-being is multidimensional, complex, and dynamic in nature. It is an amalgam of physical and mental health, essential for disease prevention and the promotion of a healthy life.
This study aims ...to explore the features that impact the well-being of individuals between 18 and 24 years of age in an Indian setting. It further aims to design, develop, and evaluate the usefulness and effectiveness of a web-based informatics platform or stand-alone intervention to enhance the well-being of individuals aged 18-24 years in an Indian setting.
This study follows a mixed method approach to identify factors influencing the well-being of individuals in the age group of 18-24 years in an Indian setting. The college-going students in this age group from the states of Uttarakhand (urban settings of Dehradun) and Uttar Pradesh (urban settings of Meerut) will be enrolled. They will be randomly allocated to the control and intervention groups. The participants in the intervention group will have access to the web-based well-being platform.
This study will examine the factors that influence the well-being of individuals aged 18-24 years. It will also facilitate the design and development of the web-based platform or stand-alone intervention, which will enhance the well-being of individuals in the age group of 18-24 years in an Indian setting. Furthermore, the results of this study will help generate a well-being index for individuals to plan tailored interventions. The 60 in-depth interviews have been conducted as of September 30, 2022.
The study will help understand the factors that influence the well-being of individuals. The findings of this study will help in the design and development of the web-based platform or stand-alone intervention to enhance the well-being of individuals in the age group of 18-24 years in an Indian setting.
PRR1-10.2196/38632.
Coronavirus disease 2019 (COVID-19) pandemic has emerged as the major public health threat in recent times. Although associated with high morbidity and mortality affecting all age groups across ...populations, "pregnant women" represent a subgroup that needs extra surveillance. We present the case of a primigravida in her advanced pregnancy presenting with acute febrile illness with flu-like symptoms. The clinico-radiological picture was suspicious for COVID-19; however, she tested negative for COVID-19 on two occasions. On further investigations, she tested positive for Scrub typhus (IgM-ELISA) and responded to treatment with doxycycline. However, due to the ongoing COVID-19 pandemic, much time was lost before suspecting and reaching the final diagnosis. Therefore, the patient had to suffer due to delayed medical intervention and intrauterine fetal death. Despite the unprecedented rise of COVID-19 in pregnant women in recent times, we should not forget about other tropical illnesses, which can mimic COVID-19 in clinical presentation and affect feto-maternal outcomes adversely.
Portable sleep monitoring (PSM) is a promising alternative diagnostic tool for Obstructive Sleep Apnea (OSA) especially in high burden resource limited settings. We aimed to determine the diagnostic ...accuracy and feasibility of PSM device-based studies in patients presenting for evaluation of OSA at a tertiary care hospital in North-India. PSM studies (using a Type-III PSM device) were compared for technical reliability and diagnostic accuracy with the standard laboratory-based Type-I polysomnography (PSG). Patients were also interviewed about their experience on undergoing an unsupervised PSM studies. Fifty patients (68% males) were enrolled in the study, of which only 30% patients expressed their concerns about undergoing unsupervised PSM studies which included safety issues, ease of use, diagnostic accuracy, etc. Technical acceptability criteria were easily met by the PSM studies with signal loss in 12% studies (complete data loss and inaccessible data in 6% studies), warranting repetition sleep studies in four patients. The overall sensitivity of PSM device (AHI ≥5) was 93.5% (area under curve; AUC: 0.87). The diagnostic accuracy was 68.5%, 80%, and 91.4% for mild, moderate, and severe cases of OSA, respectively. An overall strong correlation was observed between PSM-AHI (apnoea-hypopnoea index) and PSG (r>0.85, p≤0.001), especially in severe OSA. The observed sensitivity was >90% for AHI>20 (clinically significant OSA), with high specificity of 91% for severe OSA (AUC: 0.94, 0.97 for AHI>20, AHI>30 respectively). The overall Bland-Altman concordance analysis also demonstrated only a small dispersion for PSM studies with a Cronbach's coefficient of 0.95. Therefore, there is good diagnostic accuracy as well as feasibility of home-based portable sleep studies in Indian patients. It can be promoted for widespread use in high burden countries like India for diagnosing and managing appropriately selected stable patients with high clinical probability of OSA, especially during the ongoing crises of COVID-19 pandemic.
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