Explore the vast range of titles available.

Tools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges. Here, we develop efficient algorithms for approximate inference of the model underlying the STRUCTURE program using a variational Bayesian framework. Variational methods pose the problem of computing relevant posterior distributions as an optimization problem, allowing us to build on recent advances in optimization theory to develop fast inference tools. In addition, we propose useful heuristic scores to identify the number of populations represented in a data set and a new hierarchical prior to detect weak population structure in the data. We test the variational algorithms on simulated data and illustrate using genotype data from the CEPH–Human Genome Diversity Panel. The variational algorithms are almost two orders of magnitude faster than STRUCTURE and achieve accuracies comparable to those of ADMIXTURE. Furthermore, our results show that the heuristic scores for choosing model complexity provide a reasonable range of values for the number of populations represented in the data, with minimal bias toward detecting structure when it is very weak. Our algorithm, fastSTRUCTURE, is freely available online at http://pritchardlab.stanford.edu/structure.html.

The neurodevelopmental disorder known as autism spectrum disorder (ASD) affects people of all racial, ethnic, and socioeconomic backgrounds. Although the study of autism is burgeoning with important implications both for public health and society, there is little research exploring the experiences of raising a child with autism spectrum disorder (ASD) from the Parent's perspectives. The aim of this study was to explore experiences and coping strategies of parents of children with ASD in Nepal. A descriptive phenomenological design explored the lived experiences of nine parents raising children with ASD in Nepal. Participants were purposively selected from the Pediatric OPD of Patan Academy of Health Sciences. Data were collected through in-depth, semi-structured interviews in Nepali and analyzed using Colaizzi's method. The interview guide, adapted from Batchelor (2017) and the Chronic Sorrow Instrument, explored emotional, social, financial, and caregiving experiences. Researcher bias was minimized through bracketing and reflexive journaling, and trustworthiness ensured via member checking, audit trails, and peer debriefing. Ethical approval was obtained from the PAHS Ethical Review Board, with informed consent and confidentiality maintained. Five major themes emerged: psychological impact (emotional burden, hope and uncertainty, guilt), physical impact (fatigue, sleep deprivation, safety concerns), social impact (isolation, stigma, family sacrifices), career impact (job loss, reduced opportunities), and financial impact (high costs, limited resources). Coping strategies included crying, listening to religious music (bhajans), meditation, and practicing positive thinking. Some parents reframed their experience as an opportunity to help others. Parents raising children with autism in Nepal face profound emotional strain, physical fatigue, social isolation, career disruption, and financial pressure. These challenges were especially evident among mothers, who formed the majority of caregivers. Despite early diagnosis and continued care, families reported limited resources and persistent social stigma. Yet, many parents showed determination and resilience in supporting their children's development. These findings emphasize the need to strengthen family-centered support, accessible services, and community awareness to better address the lived realities of caregivers.

Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution of cis-acting genetic effects at all major stages of gene regulation from chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65% of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereas the remaining eQTLs are enriched in transcribed regions. Using a novel method, we also detected 2893 splicing QTLs, most of which have little or no effect on gene-level expression. These splicing QTLs are major contributors to complex traits, roughly on a par with variants that affect gene expression levels. Our study provides a comprehensive view of the mechanisms linking genetic variation to variation in human gene regulation.

Deep learning-based ship detection from SAR data is one of the challenging problems in the remote sensing area. Also, SAR ship detection is precise object detection and pattern recognition task under the computer vision area. The main problems are false detection, primarily due to speckle presence and multi-scale SAR image availability. We propose a novel real-time system with a preprocessing technique exclusively for SAR ship detection to address this problem. The proposed SarNeDe preprocessing stage is specially designed using image processing techniques and lee filter to reduce the false prediction and improve the SAR image quality, which increases the detection accuracy because the lee filter alone could increase missed detections. The SarNeDe image is generated from raw SAR image and is given to a novel multi-scale lightweight deep learning model to predict all ships’ positions. The proposed model has a feature merging & boosting network and three detection parts for detecting big, medium, & small ships. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method’s feasibility. The experimental results indicated that our proposed method’s ship detection accuracy is superior to other state-of-the-art ship detectors with reduced false detections.

Caloric restriction extends healthy lifespan in multiple species 1 . Intermittent fasting, an alternative form of dietary restriction, is potentially more sustainable in humans, but its effectiveness remains largely unexplored 2 – 8 . Identifying the most efficacious forms of dietary restriction is key for developing interventions to improve human health and longevity 9 . Here we performed an extensive assessment of graded levels of caloric restriction (20% and 40%) and intermittent fasting (1 and 2 days fasting per week) on the health and survival of 960 genetically diverse female mice. We show that caloric restriction and intermittent fasting both resulted in lifespan extension in proportion to the degree of restriction. Lifespan was heritable and genetics had a larger influence on lifespan than dietary restriction. The strongest trait associations with lifespan included retention of body weight through periods of handling—an indicator of stress resilience, high lymphocyte proportion, low red blood cell distribution width and high adiposity in late life. Health effects differed between interventions and exhibited inconsistent relationships with lifespan extension. 40% caloric restriction had the strongest lifespan extension effect but led to a loss of lean mass and changes in the immune repertoire that could confer susceptibility to infections. Intermittent fasting did not extend the lifespan of mice with high pre-intervention body weight, and two-day intermittent fasting was associated with disruption of erythroid cell populations. Metabolic responses to dietary restriction, including reduced adiposity and lower fasting glucose, were not associated with increased lifespan, suggesting that dietary restriction does more than just counteract the negative effects of obesity. Our findings indicate that improving health and extending lifespan are not synonymous and raise questions about which end points are the most relevant for evaluating aging interventions in preclinical models and clinical trials. Health effects of dietary restriction are uncoupled from longevity.

Epoxy nanocomposites reinforced with various grades of multilayer graphene nanoplatelets (GNPs) are manufactured and tested. The effects of size, surface area, and concentration of GNP, as well as alternating current (AC) frequency on the electrical and dielectric properties of epoxy nanocomposites are experimentally investigated. GNPs with larger size and surface area are always beneficial to increase the electrical conductivity of the composites. However, their effects on the dielectric constant are highly dependent on GNP concentration and AC frequency. At lower GNP concentration, the dielectric constant increases proportionally with the increase in GNP size, while decreasing as the AC frequency increases. At higher GNP concentration in epoxy, the dielectric constant first increases with the increase of the GNP size, but decreases thereafter. This trend is also observed for varying the processed GNP surface area on the dielectric constant. Moreover, the variations of the electrical conductivity and dielectric constant with the GNP concentration and AC frequency are then correlated with the measured interfiller spacing and GNP diameter.

Many research works using deep learning techniques for automatic ship detection from SAR images have good detection accuracy. But the main problem in these methods is false detection, mostly due to speckle presence. Therefore, we propose a new deep learning model with a novel preprocessing stage to address this problem. We are introducing a deep learning architecture to detect and localize ships in the SAR image. First, generate a three-channel image from gray-scale SAR image. Then, this image is used to train the model to predict the ship’s position in the SAR image. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method’s feasibility. We used python 3.5 for coding with the Keras framework in the NVIDIA Tesla K80 GPU hardware platform. The experimental results indicated that our proposed method’s ship detection accuracy has increased with reduced false detection percentage.

Dense temporal measurements of physiological health, using simple and consistent assays, are essential to characterize biological processes associated with aging and evaluate the effectiveness of interventions on these processes. We measured body weight in 960 genetically diverse female mice, every 7-10 days over the full course of their lifespan. We used a state space model to characterize the trajectories of body weight throughout life and derived novel traits capturing the dynamics of body weight, 10 of which were both heritable and associated with lifespan. Genetic mapping of these body weight-derived traits identified 5 genomic loci, none of which were previously mapped to body weight. We observed that the ability to maintain stable body weight, despite fluctuations in energy intake and expenditure, was positively associated with lifespan in an age-dependent manner and mapped to a genomic locus linked to energy homeostasis. Our results highlight how dense longitudinal measurements of physiological phenotypes offer new insights into the biology of aging. This study reveals that the ability to maintain a stable body weight predicts longevity in mice. By tracking weight dynamics throughout life, the authors identify genetic loci linked to body weight, energy homeostasis and healthy aging.

Accurate annotation of protein coding regions is essential for understanding how genetic information is translated into function. We describe riboHMM, a new method that uses ribosome footprint data to accurately infer translated sequences. Applying riboHMM to human lymphoblastoid cell lines, we identified 7273 novel coding sequences, including 2442 translated upstream open reading frames. We observed an enrichment of footprints at inferred initiation sites after drug-induced arrest of translation initiation, validating many of the novel coding sequences. The novel proteins exhibit significant selective constraint in the inferred reading frames, suggesting that many are functional. Moreover, ~40% of bicistronic transcripts showed negative correlation in the translation levels of their two coding sequences, suggesting a potential regulatory role for these novel regions. Despite known limitations of mass spectrometry to detect protein expressed at low level, we estimated a 14% validation rate. Our work significantly expands the set of known coding regions in humans.

Understanding how genetic variation shapes a complex trait relies on accurately quantifying both the additive genetic and genotype–environment interaction effects in an age-dependent manner. We used a linear mixed model to quantify diet-dependent genetic contributions to body weight measured through adulthood in diversity outbred female mice under five diets. We observed that heritability of body weight declined with age under all diets, except the 40% calorie restriction diet. We identified 14 loci with age-dependent associations and 19 loci with age- and diet-dependent associations, with many diet-dependent loci previously linked to neurological function and behavior in mice or humans. We found their allelic effects to be dynamic with respect to genomic background, age, and diet, identifying several loci where distinct alleles affect body weight at different ages. These results enable us to more fully understand and predict the effectiveness of dietary intervention on overall health throughout age in distinct genetic backgrounds. Body weight is one trait influenced by genes, age and environmental factors. Both internal and external environmental pressures are known to affect genetic variation over time. However, it is largely unknown how all factors – including age – interact to shape metabolism and bodyweight. Wright et al. set out to quantify the interactions between genes and diet in ageing mice and found that the effect of genetics on mouse body weight changes with age. In the experiments, Wright et al. weighed 960 female mice with diverse genetic backgrounds, starting at two months of age into adulthood. The animals were randomized to different diets at six months of age. Some mice had unlimited food access, others received 20% or 40% less calories than a typical mouse diet, and some fasted one or two days per week. Variations in their genetic background explained about 80% of differences in mice’s weight, but the influence of genetics relative to non-genetic factors decreased as they aged. Mice on the 40% calorie restriction diet were an exception to this rule and genetics accounted for 80% of their weight throughout adulthood, likely due to reduced influence from diet and reduced interactions between diet and genes. Several genes involved in metabolism, neurological function, or behavior, were associated with mouse weight. The experiments highlight the importance of considering interactions between genetics, environment, and age in determining complex traits like body weight. The results and the approaches used by Wright et al. may help other scientists learn more about how the genetic predisposition to disease changes with environmental stimuli and age.