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Genomic determinants of antibody response to a typhoid vaccine in Indian recipients
No full textTyphoid is endemic in India and has high global incidence. There were large outbreaks of typhoid in India between 1990 and 2018. Available typhoid vaccines induce variable levels of protective antibodies among recipients; thus, there is variability in response to the vaccine. Interindividual genomic differences is hypothesized to be a determinant of the variability in response. We studied the antibody response of ~1000 recipients of the Vi-polysaccharide typhoid vaccine from Kolkata, India, who showed considerable variability of antibody response, i.e., anti-Vi-polysaccharide antibody level 28 days postvaccination relative to prevaccination. For each vaccinee, whole-genome genotyping was performed using the Infinium Global Screening Array (Illumina). We identified 39 SNPs that mapped to 13 chromosomal regions to be associated with antibody response to the vaccine; these included SNPs on genes LRRC28 (15q26.3), RGS7 (1q43), PTPRD (9p23), CERKL (2q31.3), DGKB (7p21.2), and TCF4 (18q21.2). Many of these loci are known to be associated with various blood cell traits, autoimmune traits and responses to other vaccines; these genes are involved in immune related functions, including TLR response, JAK–STAT signalling, phagocytosis and immune homeostasis
Granulated mask RCNN and eye detection index (EDI) for detection and localization of eye of tropical cyclone from satellite imagery
No full textThe eye usually located at the center of the tropical cyclone is connected to the rapid intensification of tropical cyclone and prediction of its track and intensity. The problem of its localization and detection from satellite imagery in deep learning framework is considered here, where the study conducted deals with 35 named tropical cyclones of different categories over the Bay of Bengal and the Arabian Sea of the North Indian Ocean (NIO). Mask region-based convolutional neural network (MRCNN), which is a variant of RCNN with an addition of a mask branch that produces a mask around the detected object, is considered as the backbone architecture. Since deep learning is time-consuming, we propose embedding the concept of granular computing into MRCNN to speed up its learning mechanism. The granulated mask region-based convolutional neural network (G-MRCNN), thus developed, provides better object(s) localization and increases eye detection accuracy, apart from speedy learning. Two novel indices for eye detection, viz, compactness and eye detection index (EDI), are defined incorporating the shape, area, and compactness (circular) of the predicted mask as well as the detection score. The larger the value of EDI, the more circular and compact the shape of the eye region, and the better the prediction. Different types of granulations ranging from regular to arbitrary shapes have been incorporated in G-MRCNN and the prediction accuracy of each model has been compared against a set of testing data as well as during the time of validation using the aforesaid two indices. EDI is seen to reflect well the eye detection performance, as also judged visually. In that sense it is unique. The results reveal that the performance of the G-MRCNN with k-means (k = 5) clustering-based granulation is better than other methods for the detection and localization of the eye of the tropical cyclone over NIO. The prediction skill of the model is then validated with 4 named tropical cyclones of extremely severe, very severe, and severe categories over NIO
Hierarchical Bayesian Integrated Modeling of Age- and Sex-Structured Wildlife Population Dynamics
No full textBiodiversity of large wild mammals is declining at alarming rates worldwide. It is therefore imperative to develop effective population conservation and recovery strategies. Population dynamics models can provide insights into processes driving declines of particular populations of a species and their relative importance. But there are insufficient tools, namely population dynamics models for wild herbivores, for characterizing their decline and for guiding conservation and management actions. Therefore, we have developed a model which can serve as a tool to fill that void. Specifically, we develop an integrated Bayesian state-space population dynamics model for wildlife populations and illustrate it using a topi population inhabiting the Greater Mara-Serengeti Ecosystem in Kenya and Tanzania. The model integrates ground demographic survey with aerial survey monitoring data. It incorporates population age and sex structure and life history traits and strategies and relates birth rates, age-specific survival rates and sex ratios with meteorological covariates, prior population density, environmental seasonality and predation risk. It runs on a monthly time step, enabling accurate characterization of reproductive seasonality, phenology, synchrony and prolificacy of births, juvenile and adult recruitments. Model performance is evaluated using balanced bootstrap sampling and by comparing model predictions with empirical aerial population size estimates. The hierarchical Bayesian model is implemented using MCMC methods for parameter estimation, prediction and inference and reproduces several well-known features of the Mara topi population, including striking and persistent population decline, seasonality of births, juvenile and adult recruitments. It is general and can be readily adapted for other wildlife species and extended to incorporate several additional useful features. Supplementary materials accompanying this paper appear on-line
High capacity secure dynamic multi-bit data hiding using Fibonacci Energetic pixels
No full textSteganography and Steganalysis are becoming increasingly relevant in information forensics and hiding data in the higher bitplanes without keeping any perceptible signature into the image is a challenging problem in this area. In this paper, we propose a unique solution to this problem using Fibonacci numbers as base. The pixels are selected from the busy part of the image where noticeable changes in pixel intensities occur. The business of the pixels is determined by their Fibonacci energy. The pixels values are converted into Fibonacci base and their corresponding Fibonacci energies are estimated by the Fibonacci expansion of pixel intensities. The set of energetic pixels are considered according to the descending order of their energy values. The binary data are concealed into higher bitplanes (up to 5) of the Fibonacci base of the pixel intensities. We theoretically derive some nice combinatorial properties related to distortion of pixel intensities and also experimentally show that our algorithm withstands against visual, structural and statistical attacks. The average embedding capacity is 3.98 bpp and average PSNR is 39.59 dB. We also demonstrate that our method is capable of resisting from the series of benchmark tests provided by StirMark 4.0
High-latitude platform carbonate deposition constitutes a climate conundrum at the terminal Mesoproterozoic
No full textDuring the Mesoproterozoic Era, 1600 to 1000 million years ago, global climate was warm with very little evidence of glaciation. Substantial greenhouse warming would have been required to sustain this ice-free state given 5-18% lower solar luminosity. Paleomagnetic data reported here place voluminous ca. 1.2 Ga shallow marine carbonate deposits from India at an unexpectedly high latitude of around 70° from the equator. Previous studies noted high latitudes, but their implication was never considered. Here, we evaluate the temporal-latitudinal distribution of neritic carbonate deposits across the Proterozoic and identify similar deposits from North China that together with those from India are seemingly unique to the late Mesoproterozoic. A uniformitarian interpretation implies that this is cold-water carbonate deposition, but facies similarity with low-latitude neritic deposits rather suggests a hotter climate and elevated polar ocean temperatures of 15–20° or higher. This interpretation represents a climate conundrum that would require much greater greenhouse warming than documented for the Mesoproterozoic
How combined pairwise and higher-order interactions shape transient dynamics
No full textUnderstanding how species interactions shape biodiversity is a core challenge in ecology. While much focus has been on long-term stability, there is rising interest in transient dynamics—the short-lived periods when ecosystems respond to disturbances and adjust toward stability. These transitions are crucial for predicting ecosystem reactions and guiding effective conservation. Our study introduces a model that uses convex combinations to blend pairwise and higher-order interactions (HOIs), offering a more realistic view of natural ecosystems. We find that pairwise interactions slow the journey to stability, while HOIs speed it up. Employing global stability analysis and numerical simulations, we establish that as the proportion of HOIs increases, mean transient times exhibit a significant reduction, thereby underscoring the essential role of HOIs in enhancing biodiversity stabilization. Our results reveal a robust correlation between the most negative real part of the eigenvalues of the Jacobian matrix associated with the linearized system at the coexistence equilibrium and the mean transient times. This indicates that a more negative leading eigenvalue correlates with accelerated convergence to stable coexistence abundances. This insight is vital for comprehending ecosystem resilience and recovery, emphasizing the key role of HOIs in promoting stabilization. Amid growing interest in transient dynamics and its implications for biodiversity and ecological stability, our study enhances the understanding of how species interactions affect both transient and long-term ecosystem behavior. By addressing a critical gap in ecological theory and offering a practical framework for ecosystem management, our work advances knowledge of transient dynamics, ultimately informing effective conservation strategies
Improving Women’s Mental Health during a Pandemic
No full textThis paper evaluates a randomized, over-the-phone counseling intervention aimed at mitigating the mental health impact of COVID-19 on a sample of 2, 402 women across 357 villages in Bangladesh. We find that the provision of two hours of mental support plus information on COVID-19 improves mental health ten months postintervention, leading to reductions of 20 percent in the prevalence of moderate and severe stress and 33 percent in depression. Our results suggest that this type of low-cost intervention ($14 per person) can be effective in providing rapid psychological support to vulnerable groups in times of crises
MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification
No full textScript identification plays a vital role in applications that involve handwriting and document analysis within a multi-script and multi-lingual environment. Moreover, it exhibits a profound connection with human cognition. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documents collected from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu, and Thai. The dataset consists of 1,135 documents scanned from local newspaper and handwritten letters as well as notes from different native writers. Further, these documents are segmented into lines and words, comprising a total of 13,979 and 86,655 lines and words, respectively, in the dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning methods. The benchmark includes results at the document, line, and word levels with printed and handwritten documents. Results of script identification independent of the document/line/word level and independent of the printed/handwritten letters are also given. The new multi-lingual database is expected to create new script identifiers, present various challenges, including identifying handwritten and printed samples and serve as a foundation for future research in script identification based on the reported results of the three benchmarks
Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey
No full textAim: This study aimed to evaluate the position of Turkey in the field of Medical Informatics and assess the general structure of research by analyzing Medical Informatics research with bibliometric methods. Material and Methods: In this study, we conducted a bibliometric analysis of research and review articles generated between 1980 and 2023 from the Web of Science bibliometric data source, utilizing bibliometric methods through the R bibliometrix tool and VosViewer. Results: In the field of medical informatics research in Turkey, the country holds the 27th position with 905 articles, 15,610 citations, and an impressive impact factor of 51, along with an average citation rate of 17.25 per article, based on bibliometric analysis conducted between 1980 and 2023. Notable institutions in this field include Middle East Technical University, Hacettepe University, and Selçuk University. The prominent research topics encompass neural network(s), machine learning, support vector, health care, decision support, deep learning, EEG signals, classification accuracy, reflecting the areas of intensive investigation. Conclusion: In Turkey, the field of medical informatics has lagged slightly behind basic engineering sciences or medical sciences. The domain exhibits a multidisciplinary structure intersecting with various engineering fields such as computer science, software engineering, industrial engineering, artificial intelligence engineering, and electronic engineering. To enhance productivity in this field, greater collaboration with other research areas can be pursued. Additionally, it is recommended to urgently establish four-year undergraduate programs specifically dedicated to medical informatics or health informatics at universities
Meta-analysis of exponential lifetime data from Type-I hybrid censored samples
No full textIn this study, in order to do a life testing experiment, sampled units are divided into a prefixed number of groups with equal number of units. Units in all the groups are tested simultaneously and independently and, in each group the experiment is terminated as soon as a prefixed time elapses or a prefixed number of failures occurs. We provide the meta-analysis of an exponential lifetime data from Type-I hybrid censored samples. The main goal of this study is to obtain optimal schemes based on some optimality criteria by minimizing certain cost function that is based on a maximum likelihood estimator of mean lifetime. We provide the maximum likelihood estimator of mean lifetime and its probability density function under this set-up. Various optimal schemes have been provided by minimizing expected total cost incurred during the experiment as the raw moments can be obtained explicitly. Numerical results on bias and mean squared error of the maximum likelihood estimator have been reported. We also provide confidence intervals of the unknown parameter. For illustration, meta-analysis for a real data set of three groups is presented