Biology subjects

Bhagwat, N.

Publications and source records attributed to Bhagwat, N..

9 recordsLinked to original sources

bioRxiv2026

A large-scale crowd-sourced annotated acoustic dataset of Indian fauna

Global rates of biodiversity loss warrant conservation action and monitoring at large geographic scales. Conservation technologies such as acoustic monitoring in conjunction with deep learning now enable us to monitor wildlife simultaneously across space and time. However, for a significant proportion of biodiversity in tropical regions, we cannot yet rely on automated recognition approaches because we lack acoustic templates to robustly train deep learning algorithms. In this paper, we relied on a novel participatory approach, enlisting researchers, conservation practitioners, and nature enthusiasts to create a unique crowd-sourced, open-access dataset of acoustic annotations across taxonomic groups for biodiversity in India. Our dataset comprises 3311 minutes of strongly labelled data (bounding boxes or annotations for a species vocalization) and 2504 minutes of weakly labelled data (indicating the presence of a species within an audio file but lacking bounding boxes) for 518 species across India, spanning 25 of 36 states and union territories. We present metadata and code for data processing and highlight the strengths of a participatory approach to biodiversity monitoring.

ecology↗

bioRxiv2026

Nipoppy: A framework for standardizing neuroimaging studies to facilitate international derived-data sharing

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

bioinformatics↗

bioRxiv2026

Open neuroinformatics infrastructure ecosystem for federated multisite studies

Despite growing understanding of the benefits of having Findable, Accessible, Interoperable, and Reusable (FAIR) data, many datasets still cannot be shared. Federated analysis methods can enable multisite studies that do not require the sharing of participant-level information. However, there are many practical hurdles that prevent the large-scale adoption of federated methods. We discuss challenges related to cross-site data preparation for federated learning, present solutions offered by recent neuroinformatics projects, and showcase an example of tool integration applied to neurodegenerative disease data.

neuroscience↗

bioRxiv2024

The impact of FreeSurfer versions on structural neuroimaging analyses of Parkinson's disease

Image processing software impacts the quantification of brain measures, playing an important role in the search for clinical biomarkers. We investigated the impact of the variability between FreeSurfer releases on the estimation of structural brain measures in Parkinsons disease (PD). Structural brain scans from 106 controls and 209 patients were analyzed with FreeSurfer versions 5.3, 6.0.1, and 7.3.2, including longitudinal data from 125 patients. First, we measured the differences in the estimation of volume, surface area, and cortical thickness between FreeSurfer versions. Second, we focused on the relationship between MRI-derived brain measures and group differences as well as disease severity clinical outcomes, which were evaluated both cross-sectionally and longitudinally. We found high software-induced variability in the estimation of all three structural measures, which impacted clinical outcomes. There were differences between software versions in group differences between patients and healthy controls in subcortical volume and vertex-wise cortical thickness. Software variability also impacted the estimated relationship between brain structure and disease severity in patients. Hence, software variability not only relates to the estimation of structural measures, but it also impacts clinically- relevant MRI measures. Our study provides insight into the reproducibility of structural neuroimaging studies in PD populations.

neuroscience↗

bioRxiv2023

Endogenous pathology in tauopathy mice progresses via brain networks

Neurodegenerative tauopathies are hypothesized to propagate via brain networks. This is uncertain because we have lacked precise network resolution of pathology. We therefore developed whole-brain staining methods with anti-p-tau nanobodies and imaged in 3D PS19 tauopathy mice, which have pan-neuronal expression of full-length human tau containing the P301S mutation. We analyzed patterns of p-tau deposition across established brain networks at multiple ages, testing the relationship between structural connectivity and patterns of progressive pathology. We identified core regions with early tau deposition, and used network propagation modeling to determine the link between tau pathology and connectivity strength. We discovered a bias towards retrograde network-based propagation of tau. This novel approach establishes a fundamental role for brain networks in tau propagation, with implications for human disease. One-Sentence SummaryNovel whole brain imaging of p-tau deposition reveals retrograde-dominant network propagation in a tauopathy mouse model.

neuroscience↗

bioRxiv2023

Longitudinal brain structure changes in Parkinson's disease: a replication study

ContextAn existing major challenge in Parkinsons disease (PD) research is the identification of biomarkers of disease progression. While Magnetic Resonance Imaging (MRI) is a potential source of PD biomarkers, none of the MRI measures of PD are robust enough to warrant their adoption in clinical research. This study is part of a project that aims to replicate 11 PD studies reviewed in a recent survey (JAMA neurology, 78(10) 2021) to investigate the robustness of PD neuroimaging findings to data and analytical variations. ObjectiveThis study attempts to replicate the results in Hanganu et al. (Brain, 137(4) 2014) using data from the Parkinsons Progression Markers Initiative (PPMI). MethodsUsing 25 PD subjects and 18 healthy controls, we analyzed the rate of change of cortical thickness and of the volume of subcortical structures, and we measured the relationship between MRI structural changes and cognitive decline. We compared our findings to the results in the original study. ResultsSimilarly to the original study, PD patients with mild cognitive impairment (MCI) exhibited increased cortical thinning over time compared to patients without MCI in the right middle temporal gyrus, insula, and precuneus. (2) The rate of cortical thinning in the left inferior temporal and precentral gyri in PD patients correlated with the change in cognitive performance. (3) There were no group differences in the change of subcortical volumes. (4) We did not find a relationship between the change in subcortical volumes and the change in cognitive performance. ConclusionDespite important differences in the dataset used in this replication study, and despite differences in sample size, we were able to partially replicate the original results. We produced a publicly available reproducible notebook allowing researchers to further investigate the reproducibility of the results in Hanganu et al. (2014) when more data becomes available in PPMI.

neuroscience↗

bioRxiv2020

Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses

The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MR imaging data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Here we use an open structural MR imaging dataset (ABIDE), supplemented with the Human Connectome Project (HCP), to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of 1) software tool (e.g. ANTs, CIVET, FreeSurfer), 2) cortical parcellation (DKT, Destrieux, Glasser), and 3) quality control procedure (manual, automatic). We divide our statistical analyses by 1) method type, i.e. task-free (unsupervised) versus task-driven (supervised), and 2) inference objective, i.e. neurobiological group differences versus individual prediction. Results show that software, parcellation, and quality control significantly impact task-driven neurobiological inference. Additionally, software selection strongly impacts neurobiological and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2018

Highly specific enrichment of rare nucleic acids using Thermus thermophilus Argonaute

Characterization of disease-associated, cell-free nucleic acids (liquid biopsy) provides a powerful, minimally-invasive means for early detection, genotyping, and personalized therapy; but is challenged by alleles of interest differing by single nucleotide from and residing among large abundance of wild-type alleles. We describe a new multiplexed enrichment assay, dubbed NAVIGATER, that utilizes short nucleic acid-guided endonucleases Argonaute (Ago), derived from the bacterium Thermus thermophilus (TtAgo), to specifically cleave complementary DNA and RNA while sparing alleles having single nucleotide mismatches with the guides. NAVIGATER greatly increases the fractions of rare alleles of interest in samples and enhances sensitivity of downstream procedures such ddPCR, sequencing, and clamped enzymatic amplification. We demonstrate 60-fold enrichment of KRAS G12D in blood samples from pancreatic cancer patients and detection of KRAS, EGFR, and BRAF mutants with XNA-PCR at 0.01% fraction.

molecular biology↗

bioRxiv2018

The crossover function of MutSγ is activated via Cdc7-dependent stabilization of Msh4

The MutS{gamma} complex, Msh4-Msh5, binds DNA joint-molecule (JM) intermediates during homologous recombination to promote crossing over and accurate chromosome segregation at the first division of meiosis. MutS{gamma} facilitates the formation and biased resolution of crossover-specific JM intermediates called double Holliday junctions. Here we show that these activities are governed by regulated proteasomal degradation. MutS{gamma} is initially inactive for crossing over due to an N-terminal degron on Msh4 that renders it unstable. Activation of MutS{gamma} requires the Dbf4-dependent kinase, Cdc7 (DDK), which directly phosphorylates and thereby neutralizes the Msh4 degron. Phosphorylated Msh4 is chromatin bound and requires DNA strand exchange and chromosome synapsis, implying that DDK specifically targets MutS{gamma} that has already bound nascent JMs. Our study establishes regulated protein degradation as a fundamental mechanism underlying meiotic crossover control.

genetics↗