Biology subjects

Jha, A. K.

Publications and source records attributed to Jha, A. K..

10 recordsLinked to original sources

bioRxiv2026

A Personalized Approach to Student Mental Health: Real-Time EEG Biofeedback for Stress and Focus Management

Real-time monitoring of student stress and focus requires low-cost EEG systems, but fixed spectral thresholds may not transfer reliably across users because EEG amplitude and band power vary between individuals and recording conditions. This study investigates a subject-specific thresholding approach using a two-channel Brain BioAmp acquisition setup (FP1 and FP2) and an ESP32-based FFT pipeline. Beta/Alpha and Theta/Alpha ratios are calculated from spectral band estimates and compared with thresholds derived from Baseline, Focused, and Stressed task-condition recordings. In the reported pilot results, the mean Beta/Alpha ratio increased from 2.76 during Baseline to 2.80 during Focused and 2.88 during Stressed conditions. The Theta/Alpha ratio was lowest during Focused (0.95) and highest during Stressed (1.04), compared with 0.99 during Baseline. These are descriptive observations; classification accuracy, statistical significance, and generalization across participants have not been established. The signal-processing calculations and plotted frequency ranges should be checked against the original implementation before these values are treated as final.

bioengineering↗

bioRxiv2026

Genetic Perturbation Prediction Models Fail Out-of-Distribution: A Rigorous Benchmark and Systematic Evaluation

Perturbation prediction models - which forecast transcriptional responses to gene inactivation - are increasingly proposed as in silico surrogates for genome-scale drug discovery screens. Yet, rigorous evaluation of whether these models genuinely generalize beyond their training distribution has been lacking. Existing benchmarks rely predominantly on random train-test splits, obscuring systematic failure modes that only emerge under realistic out-of-distribution (OOD) conditions. Here we introduce EL-PERTURB (Extrapolative Learning for PERTURBation Prediction), a benchmarking framework that stress-tests 20 perturbation prediction models spanning six architecture classes on both essential gene and genome-wide Perturb-seq screens, using a novel stratified OOD splitting strategy that partitions train and test sets into functionally distinct regions of transcriptional space. We find that model performance degrades substantially under OOD conditions, and that architectural complexity - including single-cell foundation model embeddings - confers no reliable advantage over well-tuned simpler baselines. EL-PERTURB establishes a rigorous benchmark for perturbation effect prediction across a gradient of generalization difficulty, providing a guide for building models that can genuinely serve as surrogates for wet-lab screening.

systems biology↗

bioRxiv2026

A Guided AI Framework for Customizable and Efficient Harmonisation to the OMOP Common Data Model

Getting clinical data from different sources to "talk" to each other within the OMOP Common Data Model (CDM) is arguably the most tedious part of multi-center research. While this integration is essential, the transformation process is frequently a manual grind, requiring a rare overlap of deep clinical knowledge and technical expertise. In this paper, we present a framework designed to alleviate some of the burden on the researcher by automating data harmonization through two distinct steps: structural schema mapping and terminological standardization. For the structural piece, we moved away from "black box" logic in favor of a stateful workflow managed by large language models (LLMs) and directed acyclic graphs. By profiling EHR data at the source, our system generates context-aware dictionaries that offer ranked mapping suggestionsalongside confidence scores. While our benchmarking showed a 97.5% agreement rate at the schema level and an 84% agreement rate at the value level when compared with human experts, the system appears most effective when treated as a "co-pilot" rather than a total replacement for human oversight. To handle value-level standardization, we implemented a hybrid search strategy that pairs the semantic depth of SapBERT embeddings with the literal precision of fuzzy string matching. By using FAISS for rapid similarity retrieval, the engine attempts to resolve messy or "noisy" clinical descriptionsto standard OMOP concepts. This approach seems particularly promising for handling the non- standardized labels that often plague smaller, local datasets. Ultimately, our results suggest that this guided approach can shift the timeline for OHDSI-compliant warehousing from weeks of manual curation to a more manageable and scalable pipeline, potentially lowering the barrier to entry for smaller research teams.

bioinformatics↗

bioRxiv2024

Faster amylin aggregation on fibrillar collagen hastens diabetic progression through β cell death and loss of function

Amyloid deposition of the neuroendocrine peptide amylin in islet tissues is a hallmark of type 2 diabetes (T2DM), leading to {beta}-cell toxicity through nutrient deprivation, membrane rupture and apoptosis. Though accumulation of toxic amylin aggregates in islet matrices is well documented, the role of the islet extracellular matrix in mediating amylin aggregation and its pathological consequences remains elusive. Here, we address this question by probing amylin interaction with collagen I (Col I)--whose expression in the islet tissue increases during diabetes progression. By combining multiple biophysical techniques, we show that hydrophobic, hydrophilic & cation-{pi} interactions regulate amylin binding to Col I, with fibrillar collagen driving faster amylin aggregation. Amylin-entangled Col I matrices containing high amounts of amylin induce death and loss of function of INS1E {beta}-cells. Together, our results illustrate how amylin incorporation in islet matrices through amylin-Col interactions drives T2DM progression by impacting {beta}-cell viability and insulin secretion.

biophysics↗

bioRxiv2021

Using synthetic MR images for field map-less distortion correction

Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion combined with underlying differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) structural images makes the alignment of functional and anatomical images a challenge. Typically, separately acquired field map data are used to correct fMRI distortions and a flexible cost function insensitive to cross-modal differences in image contrast and intensity is used for aligning fMRI and anatomical images. The quality of alignment achieved with this approach can vary greatly and depends on the quality of field map data. In addition, many publicly available datasets lack field map data entirely. To address this issue, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require separately acquired field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image that has similar contrast properties to fMRI data. The undistorted synthetic image then serves as an effective reference for individual-specific nonlinear unwarping to correct fMRI distortions. We demonstrate, in both pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club) data that Synth performs comparably well to other leading distortion correction approaches that utilize field map data, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.

neuroscience↗

bioRxiv2020

Corna - An Open Source Python Tool For Natural Abundance Correction In Isotope Tracer Experiments

BackgroundStable isotope-based approaches are used in the field of metabolomics for quantification and identification of metabolites, discovery of new pathways and measurement of intracellular fluxes. In these experiments, often performed with mass spectrometry (MS), data must be corrected for natural abundance of isotopes. Various stand-alone tools with their own separate data formats and learning curves exist for correction of data collected at different resolutions, for tandem MS, and for different number of tracer elements. ResultsWe present a Python package, Corna, that combines natural abundance correction workflows for several experimental conditions and can be used as a one-stop-shop for stable isotope labeled experiments. We validate the algorithms in Corna with published tools, where available, and include new features, such as correction of two tracer elements, that are not yet implemented in any existing software application as per our knowledge. We also present the integration of Corna with an existing open source peak integration software. The integrated workflow can reduce processing times for a typical stable isotope based workflow from days to hours for a familiar user. ConclusionsAlgorithmic advancements have been keeping up with the developments in mass spectrometry technologies and have been the focus of most existing tools for natural abundance correction. However, in this high throughput era, it is also important to recognize user experience, and integrated and reproducible workflows. Corna has been written in Python and is designed for users who have access to large amounts of data from different kinds of experiments and want to integrate a natural abundance correction tool seamlessly in their pipelines. The latest version of Corna can be accessed at https://github.com/raaisakuk/NA_Correction.

bioinformatics↗

bioRxiv2020

Inferring metabolic rewiring in embryonic neural development using single cell data

Metabolism is intricately linked with cell fate changes. Much of this understanding comes from detailed metabolomics studies averaged across a population of cells which may be composed of multiple cell types. Currently, there are no quantitative techniques sensitive enough to assess metabolomics broadly at the single cell level. Here we present scMetNet, a technique that interrogates metabolic rewiring at the single cell resolution and we apply it to murine embryonic development. Our method first confirms the key metabolic pathways, categorized into bioenergetic, epigenetic and biosynthetic, that change as embryonic neural stem cells differentiate and age. It then goes beyond to identify specific sub-networks, such as the cholesterol and mevalonate biosynthesis pathway, that drive the global metabolic changes during neural cortical development. Having such contextual information about metabolic rewiring provides putative mechanisms driving stem cell differentiation and identifies potential targets for regulating neural stem cell and neuronal biology.

systems biology↗

bioRxiv2019

Integrated Metabolomic and Transcriptomic Profiling Reveals Novel Activation-Induced Metabolic Networks in Human T cells

The targeting of metabolic pathways is emerging as an exciting new approach for modulating immune cell function and polarization states. In this study, carbon tracing and systems biology approaches integrating metabolomic and transcriptomic profiling data were used to identify adaptations in human T cell metabolism important for fueling pro-inflammatory T cell function. Results of this study demonstrate that T cell receptor (TCR) stimulation leads to a significant increase in glucose and amino acid metabolism that trigger downstream biosynthetic processes. Specifically, increased expression of several enzymes such as CTPS1, IL4I1, and ASL results in the reprogramming of amino acid metabolism. Additionally, the strength of TCR signaling resulted in different metabolic enzymes utilized by T cells to facilitate similar biochemical endpoints. Furthermore, this study shows that cyclosporine represses the pathways involved in amino acid and glucose metabolism, providing novel insights on the immunosuppressive mechanisms of this drug. To explore the implications of the findings of this study in clinical settings, conventional immunosuppressants were tested in combination with drugs that target metabolic pathways. Results showed that such combinations increased efficacy of conventional immunosuppressants. Overall, the results of this study provide a comprehensive resource for identifying metabolic targets for novel combinatorial regimens in the treatment of intractable immune diseases.

immunology↗

bioRxiv2018

Optimization of Experiment Design for Mass Spectrometric Isotopic Labeling Kinetics

Determination of metabolic fluxes by measurement of time-dependent sampling of isotopic enrichments during the administration of labeled substrates provides rich information. Because such experiments are resource-intensive and frequently push the limits of sensitivity of the measurement techniques, optimization of experiment design can improve feasibility with respect to financial and labor costs, time to completion, and increase precision and accuracy of the results. Here we used a previously published set of data acquired in cultured insulinoma cells to evaluate contributions to the sensitivity and variability of the rate of citrate synthase (CS). Specifically, we calculated changes in uncertainty in CS if sample times were dropped or new ones were added, and we observed that some sampling times can be dropped with little effect, while improvements can be made with a strategic choice of when to add samples. We measured the contributions of data sampled at different times on the sensitivity of CS, finding that CS had greater sensitivity at early time points. We tested the concept that if two estimated parameters are correlated significantly, then refining one might constrain the other. In this case, the rate of Beta-oxs was found to be correlated with CS, and narrower variability in Beta-ox did indeed improve the sensitivity of CS. The tests described here might be applied at the initial design stage and then after a pilot phase to improve sensitivities of targeted fluxes and the reduction of materials, time, labor, and other experimental resources. The correlation analyses can be used to consider what orthogonal measurements might be beneficial for further improvement of measurements. While this study used a specific example of a set of time-dependent kinetic isotopic measurements, the results illustrate some generalizable behaviors that can be tested in other experimental systems.

bioinformatics↗

bioRxiv2017

Structural basis for EarP-mediated arginine glycosylation of translation elongation factor EF-P

Glycosylation is a universal strategy to post-translationally modify proteins. The recently discovered arginine rhamnosylation activates the polyproline specific bacterial translation elongation factor EF-P. EF-P is rhamnosylated on arginine 32 by the glycosyltransferase EarP. However, the enzymatic mechanism remains elusive. In the present study, we solved the crystal structure of EarP from Pseudomonas putida. The enzyme is composed of two opposing domains with Rossmann-folds, thus constituting a GT-B glycosyltransferase. While TDP-rhamnose is located within a highly conserved pocket of the C-domain, EarP recognizes the EF-P via its KOW-like N-domain. Based on our structural data combined with an in vitro /in vivo enzyme characterization, we propose a mechanism of inverting arginine glycosylation. As EarP is essential for pathogenicity in P. aeruginosa our study provides the basis for targeted inhibitor design.

biochemistry↗