Biology subjects

Biswas, S.

Publications and source records attributed to Biswas, S..

10 recordsLinked to original sources

bioRxiv2026

Who rests with whom? Sex composition and group demography shape resting associations in free-ranging dogs

Free-ranging dogs frequently rest near conspecifics, but the demographic factors structuring their resting associations remain poorly understood. We quantified dyadic resting associations in 26 free-ranging dog groups in West Bengal, India, observed between 2019 and 2023. Association strength was estimated from scan based resting co-occurrences using the Half-Weight Index. We tested whether dyadic association strength varied with dyad sex composition, dyad life stage composition, group size, and group sex ratio using a generalised additive model for location, scale and shape that accounted for group identity and repeated occurrence of individuals across dyads. Male-male dyads had lower association strengths than female-female dyads, whereas mixed-sex dyads did not differ from female-female dyads. Association strength decreased with increasing group size but increased as the male-to-female ratio within the group increased, while life-stage composition had no detectable effect. Individual level network metrics, including strength, reach, clustering coefficient, affinity, and eigenvector centrality, did not vary with sex or season. Mixed-sex pairs were also frequently represented among the strongest dyadic associations within groups. These findings indicate that resting associations in free-ranging dogs vary with dyad sex composition and group demography. Further opportunity-controlled analyses are required to determine whether the prominence of mixed-sex dyads reflects preferential association rather than group composition alone.

animal behavior and cognition↗

bioRxiv2018

Association of the types of alcoholic beverages and blood lipids in a local population in Jharkhand, India

Although light-to-moderate alcohol consumption is considered beneficial, alcohol in binge doses or high cumulative lifetime consumption leads to cardiovascular diseases, metabolic syndrome and structural damage to various organs. Alcohol is known to alter blood lipid concentrations; however, the association of the types of alcohol on the lipid profile has not been investigated extensively. A cross-sectional study involving male participants (n = 86) aged 20 to 60 from the Ranchi and Dhanbad zone of Jharkhand, India, was carried out to investigate the effects of cumulative lifetime consumption of Haria, a local rice-based fermented alcohol, Indian made foreign liquor (IMFL), and a combination of the two on the blood lipid profiles. Demographic characteristics, dietary intake and medical history were obtained from the participants by questionnaire, and lipid levels were determined by analysis of blood samples. The effect of Haria alone on the blood lipids was also investigated on the local female population (n = 31). After adjusting for demographic and dietary factors, IMFL and combination of IMFL and Haria consumption was associated with increased serum total cholesterol, triglyceride, and low density lipoprotein (LDL) cholesterol levels (P < 0.05) and decreased high density lipoprotein (HDL) cholesterol levels (P < 0.05). None of the blood lipids changed significantly in Haria consumers in both male and female groups. This study suggests that Haria, a popular alcoholic beverage of West Bengal and east-central India, is a relatively safe local alcoholic beverage and does not alter the lipid profile in consumers.

biochemistry↗

bioRxiv2018

Anemia Diagnosis on a Simple Paper-based Assay

In developing countries, the maternal and neonatal mortality rate is often affected by prenatal period anemia, a preventable and ubiquitous impairment attributed due to low hemoglobin (Hgb) concentration. We report the development of a simple, frugal (~ 0.02 $ per test), rapid and high fidelity paper-based colorimetric microfluidic device for point-of-care (POC) detection of anemia. We validate our findings with 32 blood samples collected from different patients covering a wide spectrum of anemia and subsequently, compare with standard pathological results measured using a hematology analyzer. POC based Hgb estimates are correlated with the pathological gold standard estimates of Hgb levels (r = 0.909), and the POC test method yielded similar sensitivity and specificity for detecting mild anemia (n = 8) (<11 g/dl) (sensitivity: 87.5%, specificity: 100 %) and for severe anemia (n = 3) (<7 g/dl) (sensitivity: 100 %, specificity: 100 %). The estimated Hgb levels are, within 1.5 g/dl from the pathological estimate, for 91 % of the blood samples. Results demonstrate the elevated efficacy and viability of this POC colorimetric diagnostic test, in comparison to the state-of-the-art complex and expensive diagnostic tests for anemia detection.

bioengineering↗

bioRxiv2018

Toward machine-guided design of proteins

Proteins--molecular machines that underpin all biological life--are of significant therapeutic and industrial value. Directed evolution is a high-throughput experimental approach for improving protein function, but has difficulty escaping local maxima in the fitness landscape. Here, we investigate how supervised learning in a closed loop with DNA synthesis and high-throughput screening can be used to improve protein design. Using the green fluorescent protein (GFP) as an illustrative example, we demonstrate the opportunities and challenges of generating training datasets conducive to selecting strongly generalizing models. With prospectively designed wet lab experiments, we then validate that these models can generalize to unseen regions of the fitness landscape, even when constrained to explore combinations of non-trivial mutations. Taken together, this suggests a hybrid optimization strategy for protein design in which a predictive model is used to explore difficult-to-access but promising regions of the fitness landscape that directed evolution can then exploit at scale.

synthetic biology↗

bioRxiv2017

A Regression-based Framework for Scalable Pathway-guided Search in Genome-wide Association Studies.

Traditional unbiased genome-wide association studies (GWAS) have successfully identified thousands of loci associated with various complex diseases but there is evidence to suggest that many variants were missed at stringent genome-wide thresholds. Fortunately, there is a rapidly increasing amount of prior knowledge in publicly available genomic datasets and biological databases that can be harnessed to enhance the power of discovering SNPs/Genes from existing or new GWAS datasets. For most diseases, many of the identified loci tend to cluster into a few specific biological pathways/networks. From the point of view of disease etiology, such clustering is generally to be expected. This phenomenon can be exploited to conduct a more powerful genome-wide scan that is tailored to identify loci that are interconnected in pathways. We propose a scalable regression-based analytical framework to enable such a pathway-guided GWAS and demonstrate that it provides significant gains in power to detect disease associated SNPs. Our method requires two inputs, namely a) genome-wide summary level data (e.g., SNP p-values) and b) a grouping of genes into biologically meaningful categories (e.g., a database of pathways). It automatically adjusts the input p-values by incorporating the knowledge derived adaptively from the data and the pathways specified. The method involves a regularized logistic regression analysis to derive priors of each SNP and then re-weights the p-values of SNPs so as to maximize overall power of making discoveries. It increases the power to discover SNPs co-clustering into some of these pathways, while maintaining the global type-1 error (FWER) at the desired level. We used whole-genome simulations and summary data from real GWA studies of psoriasis, SLE, coronary artery disease and type-2 diabetes to illustrate the power improvement achieved by pathway-guided search. Our pipeline implemented as an R package can flexibly handle large number of prior annotations possibly derived from multiple databases.

genetics↗

bioRxiv2017

Active deep learning reduces annotation burden in automatic cell segmentation

The relationship between cellular architecture and cellular state and function is apparent, but not yet completely understood. Precise characterization of cellular state is important in many fields, from pathology to synthetic biology. High-content high-throughput microscopy is now more than ever accessible to researchers. This allows for collection of large amount of cellular images. Naturally, the analysis of this data cannot be left to manual investigation and needs to resort to the use of efficient computing algorithms for cellular detection, segmentation, and tracking. Annotation is required for building high quality algorithms. Medical professionals and researchers spend a lot of effort and time in annotating cells. This task has proved to be very repetitive and time consuming. The experts time is valuable and should be used effectively. Our hypothesis is that active deep learning will help to share some of the burden that researchers face in their everyday work. In this paper, we focus specifically on the problem of cellular segmentation.\n\nWe approach the segmentation task using a classification framework. Each pixel in the image is classified based on whether the patch around it resides on the interior, boundary or exterior of the cell. Deep convolutional neural networks (CNN) are used to perform the classification task. Active learning is the method used to reduce the annotation burden. Uncertainty sampling, a popular active learning framework is used in conjunction with CNN to segment the cells in the image. Three datasets of mammalian nuclei and cytoplasm are used for this work. We show that active deep learning significantly reduces the number of training samples required and also improves the quality of segmentation.

bioinformatics↗

bioRxiv2017

Investigating the existence of periodicity in activity of neural network by novel neural signal processing technique - quantifying induced learning in cell culture

The network forming ability of neurons are huge for their sparking ability to form new connections and break existing ones. This sheer ability allows dynamic nature of the network for which this network are ever changing. The neurons being cells that are chemically and electrically excitable, electrical excitation of these cells cause variation of voltage in vicinity of the active neurons. These variation captured through electrical recording device records to activity points in the network. Cultured neuron cells on Multi electrode array dish is used to study disassociated cultures. A novel integrative model of neural signal processing termed as Activity Index is applied. AI variation is plotted graphically to show the evidence in periodicity of network analysis. The finding on periodicity are discussed along with how could it be used as a potential parameter to quantify learning ability of a cell culture.\n\nIndex Termsneurons, dynamic, variation of voltage, Multi electrode array, Activity Index train, Periodicity, learning

systems biology↗

bioRxiv2017

Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals

Epilepsy is a neurological condition which affects the nervous system. It is a general term used for a group of disorders in which nerve cells of the brain discharge anomalous electrical impulses from time to time, causing a temporary malfunction of the other nerve cells of the brain. EEG signal provides an important cue for diagnosis and interpretation related to prognosis of epilepsy. In this work we envisage to provide novel tool which can be used to detect the prognosis of epileptic disorder by comparing linear and nonlinear modalities of EEG analysis conventionally used Power spectral analysis and a robust non linear method, Detrended Fluctuation Analysis (DFA). Publicly available dataset is used for this work consisting of 100 normal patients EEG data as control group and 100 epileptic patients EEG data for comparison. Response for different frequency bands (alpha, theta, beta) of the EEG spectrum have been analyzed using Detrended Fluctuation Analysis (DFA) and Power Spectral Intensity (PSI). The comparison of the DFA scaling exponent with the spectral power data is calculated for all the 3 different frequency bands of EEG signal provide new and interesting results which have been discussed in detail.

neuroscience↗

bioRxiv2017

The G-box transcriptional regulatory code in Arabidopsis

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes--these expansions are linked to adaptation to environmental stressors (1, 2). Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determine that the flanking sequences near G-boxes help determine in vitro specificity, but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we construct a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the gene expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a website that provides interactive visualisations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

plant biology↗

bioRxiv2016

Genome sequence of a diabetes-prone desert rodent reveals a mutation hotspot around the ParaHox gene cluster

The sand rat Psammomys obesus is a gerbil native to deserts of North Africa and the Middle East1. Sand rats survive with low caloric intake and when given high carbohydrate diets can become obese and develop type II diabetes2 which, in extreme cases, leads to pancreatic failure and death3,4. Previous studies have reported inability to detect the Pdx1 gene or protein in gerbils5-7, suggesting that absence of this key insulin-regulating homeobox gene might underlie diabetes susceptibility. Here we report sequencing of the sand rat genome and discovery of an extensive, mutationally-biased GC-rich genomic domain encompassing many essential genes, including the elusive Pdx1. The sequence of Pdx1 has been grossly affected by GC-biased mutation leading to the highest divergence observed in the animal kingdom. In addition to molecular insights into restricted caloric intake in a desert species, the discovery that specific chromosomal regions can be subject to elevated mutation rate has widespread significance to evolution.

evolutionary biology↗