Biology subjects

Khandelwal, P.

Publications and source records attributed to Khandelwal, P..

6 recordsLinked to original sources

bioRxiv2026

Benchmarking Large Language Models for Assay-Aware Drug-Target Binding Affinity Prediction

Machine learning models for molecular discovery often train on bioactivity data from heterogeneous assays, yet assay context is rarely explicitly represented. We investigate whether natural-language assay descriptions provide predictive information beyond molecular and protein representations. We develop ACL-DTI (Assay-aware Contrastive Learning-based Drug Target Interaction) and MAMMAL-AsAP, an assay-context extension of MAMMAL-DTI. Across five triplet-disjoint random splits, assay-aware models achieve competitive or improved performance over assay-agnostic baselines. Among the evaluated models, MAMMAL-AsAP achieves the highest (R^2) of 0.7198 +/- 0.0085. Masking experiments show that ACL-DTI is robust to incomplete assay descriptions, exhibiting the smallest reductions in R^2 among the evaluated models when masking the target name, assay technique, or both: 0.0228 +/- 0.0064, 0.0406 +/- 0.0113, and 0.0620 +/- 0.0190, respectively. These results indicate that assay descriptions contain predictive information not fully captured by molecular structures and protein sequences, and reveal architecture-dependent differences in how this information is exploited.

bioinformatics↗

bioRxiv2026

Cortical reconstruction and anatomical parcellation of high-resolution multi-modal postmortem ex vivo MRI of the human infant brain

High-resolutionpostmortemmagneticresonanceimagingen- ables detailed examination of brain anatomy at spatial scales not achiev- able in vivo and provides a unique opportunity to link morphomet- ric measurements with the underlying pathology. Despite these advan- tages, robust computational tools for automated anatomical segmen- tation and cortical surface reconstruction remain limited, particularly in postmortem infant brains. Incomplete myelination, thinner cortical ribbons, small-scale neuroanatomy, evolving tissue contrast, fixation- induced signal alterations, and variability in postmortem preparation make standard neuroimaging pipelines unsuitable for postmortem in- fant MRI. In this work, we introduce a unique high-resolution multi- sequence postmortem infant MRI dataset and a unified computational framework that combines deep learning-based volumetric segmentation with surface-based cortical reconstruction and anatomical parcellation in native subject-space resolution. The framework is designed to general- ize across diverse postmortem MRI acquisition protocols, spatial resolu- tions, tissue preparation conditions, and specimen characteristics while remaining robust to substantial variability in image contrast, tissue de- formation, fixation-induced intensity changes, background signal char- acteristics, and anatomical variability encountered in postmortem in- fant MRI. We benchmark our framework against widely used contrast- agnostic and foundational brain segmentation models, demonstrating improved anatomical consistency and segmentation performance across heterogeneous high-resolution postmortem infant datasets. Our method enables morphometric analysis in native postmortem space, providing the same downstream quantitative analyses routinely available for in vivo developmental neuroimaging. The complete framework is released as open-source software with command-line workflows, containers, and comprehensive documentation to facilitate reproducible postmortem in- fant MRI analysis as part of the purple-mri package: https://purple-mri.readthedocs.io

neuroscience↗

bioRxiv2025

Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neuropathology patterns

Structured AbstractO_ST_ABSIntroductionC_ST_ABSThe impact of different neuropathologies on deep brain structures remains to be understood. We examine subcortical and limbic volumetry in neurodegenerative diseases involving p-tau, -synuclein and TDP-43. MethodsWe acquired neuropathological measures and brain segmentations from postmortem analysis of 132 donors with Alzheimers disease (AD), Lewy body disease (LBD), Frontotemporal Lobar Degeneration with TDP-43 (FTLD-TDP) and FTLD-Tau. ResultsLBD had the least subcortical, limbic and cortical atrophy compared to AD, FTLD-TDP and FTLD-Tau. In donors with both AD and LBD pathologies, primary LBD was associated with less atrophy than primary AD. While AD had cortico-subcortical and cortico-limbic morphometric associations, LBD had more limited parieto-occipital cortico-limbic associations. FTLD-TDP had cortico-subcortical while FTLD-Tau had cortico-subcortical and cortico-limbic associations. In AD and FTLD-Tau, hippocampal volumes correlated with p-tau burden, neuron loss and gliosis. In LBD, thalamic -synuclein severity was associated with subcortical/limbic volumes. DiscussionPostmortem neuroimaging reveals disease- and region-specific structure-pathology relationships.

neuroscience↗

bioRxiv2025

Scaly-Tail Organ Enhances Static Stability during Pel's Scaly-tailed Flying Squirrels' Arboreal Locomotion

Scaly-tailed squirrels (Anomaluridae) are one of the least studied mammalian families. Their namesake is due to a peculiar and unique scaly-tail organ extruding from the caudal vertebrate that has been predicted to help reduce skidding. This study investigates the function of the scaly-tail organ found in Anomalurus pelii, investigating its potential role in enhancing arboreal locomotion. As these animals glide from tree to tree in a habitat abundant with smooth-bark trees, we hypothesize that the scaly-tail organ assists with friction enhancement in their native smooth-bark habitat. Through a combination of analyses using mathematical and physical models for experimental validation, we explore whether the scaly-tail organ could improve the sliding and overturning stability during perching. Our experimental results showed that the scaly-tail organ can act as a skid-reduction mechanism by enhancing substrate engagement on intermediate roughness substrates by 58%. Mathematical models showed the scaly-tail organ enhances overturning stability by acting as an additional support point. Our model showed that the scaly-tailed squirrel can reach up to 82.5{whitebullet} inclination without claw force; however, without scales, it reduces to 79.6{whitebullet}. Overall, this research highlights the functional significance of scaly-tail organs in adaptations in scaly-tailed flying squirrels and contributes to our understanding of their locomotion strategies and environmental stresses. Our study also provides insights into innovative perching mechanisms for robots operating in arboreal environments.

bioengineering↗

bioRxiv2024

Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality

ObjectivesVolumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimers disease. However, poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. MethodMRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimers Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. ResultsThe multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. ConclusionThe multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

bioengineering↗

bioRxiv2021

Morphological principles of neuronal mitochondria

In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like ATP and heat often represent mitochondria as idealized geometries and therefore can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial TEM tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.6 nanometer isotropic voxels. The resulting in silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs that are present across this population of mitochondria; observations which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons.

neuroscience↗