Biology subjects

Bajaj, C.

Publications and source records attributed to Bajaj, C..

4 recordsLinked to original sources

bioRxiv2026

Signed Dopaminergic Asymmetry Tracks Contralateral Motor Laterality in Parkinsons Disease

Parkinson's disease is asymmetric, but representations that collapse left--right measurements discard that direction. We separated overall dopamine-transporter availability from hemispheric distribution: mean putamen specific-binding ratio (SBR) indexed overall DaT signal, whereas signed asymmetry retained which hemisphere was relatively more affected. Signed asymmetry aligned with contralateral MDS-UPDRS Part III laterality in PPMI (n=1,367; r=0.676) and S4 (n=56; r=0.551). In 1,204 PPMI landmarks, each 0.1 asymmetry corresponded to a 0.0507 increase in 12-month laterality (95\% CI 0.0387--0.0626); held-out \(R^2\) improved 0.0176 over non-directional imaging but was uncertain under a bounded link. Exploratory imaging--clinical disagreement marked lower side persistence (OR=0.491 per IQR-standardized unit; 0.297--0.711). Signed direction therefore distinguishes mirror-image and conflicting dopaminergic--motor states erased by mean-only summaries. This is a reproducible biological coordinate, not global progression, treatment benefit or clinical utility.

neuroscience↗

bioRxiv2025

Pathway Anchored Multimodal Clustering Reveals Circuit Level Signatures in Parkinsons Disease

Parkinsons disease is increasingly understood as a disorder of distributed brain circuits, yet most imaging analyses do not explicitly respect pathway structure. We introduce a pathway-anchored, multimodal clustering framework based on Scalable Robust Variational Compositional Co-clustering (SRVCC) that integrates structural MRI, free-water-corrected diffusion MRI, and DAT-SPECT in anatomically defined circuits. For each pathway, we derive a simple Multimodal Pathway Integrity Score (MPIS) that aggregates z-normalised volume, microstructural, and dopaminergic measures into an interpretable summary of imaging integrity. In the PPMI cohort, SRVCC identifies stable imaging-derived patient clusters and feature modules under explicit model selection and bootstrap/stability checks, with covariate-adjusted analyzes controlling for age, sex, education, and medication. MPIS shows coherent but modest structure-function associations: lower nigrostriatal and frontostriatal integrity relates to higher motor burden (UPDRS-III), while reduced sensory/visuospatial and limbic integrity is linked to lower global cognition (MoCA); microvascular markers robustly stratify imaging profiles but display minimal cross-sectional coupling to these global scales. Feature-level reports highlight dominant region-by-modality contributors (e.g., striatal DAT-SBR, thalamic and cerebellar morphology, white-matter hyperintensity metrics), providing a transparent bridge from multimodal data to circuit-level signatures. This pathway-aware representation offers a principled, reproducible way to summarise multimodal imaging in PD and may support future work on circuit-informed stratification, prognosis, and targeted outcome measures.

neuroscience↗

bioRxiv2018

Cryo-Electron tomography 3D structure and nanoscale model of Arabidopsis thaliana cell wall

Using cryo-electron tomography of vitrified sections of one month-old Arabidopsis thaliana inflorescence stem tissue, we visualized primary and secondary cell walls of xylem tissue. Extensive quantitative and statistical analysis of segmented 3D tomographic data allowed geometrically idealized 3D-CAD model building of prototypic microfibrils, cross-links, and their supramolecular microfibril 3D organization. We propose a prototypic microfibril model where a cellulose core is heavily decorated by a thin sheath of hemicellulose with infrequent but sturdy hemicellulose-based cross-links. Such prototypic microfibrils then adopt a rather unexpected 3D supramolecular organization of high order and complexity. We discuss a possible new role for lignin in plant cell walls at low concentrations with lignin not acting as a matrix but rather as a reinforcement of microfibrils and cross-links. Extensive computational simulations of mechanical properties further revealed that this 3D organization of the cell wall is not optimized for load bearing but instead for flexibility and ductility.\n\nOne Sentence SummaryCryo-electron tomography and mechanical simulations revealed cell wall 3D architecture, optimized for flexibility/ductility.

plant biology↗

bioRxiv2018

A Fully Automated, Faster Noise Reduction Approach to Increasing the Analytical Capability of Chemical Imaging for Digital Histopathology

High dimensional data, for example from infrared spectral imaging, involves an inherent trade-off in the acquisition time and quality of spatial-spectral data. Minimum Noise Fraction (MNF) developed by Green et al. [1] has been extensively studied as an algorithm for noise removal in HSI (Hyper-Spectral Imaging) data. However, there is a speed-accuracy trade-off in the process of manually deciding the relevant bands in the MNF space, which by current methods could become a person month time for analyzing an entire TMA (Tissue Micro Array). We propose three approaches termed Fast MNF, Approx MNF and Rand MNF where the computational time of the algorithm is reduced, as well as the entire process of band selection is fully automated. This automated approach is shown to perform at the same level of reconstruction accuracy as MNF with large speedup factors, resulting in the same task to be accomplished in hours. The different approximations of the algorithm, show the reconstruction accuracy vs storage (50x) and runtime speed (60x) trade-off. We apply the approach for automating the denoising of different tissue histology samples, in which the accuracy of classification (differentiating between the different histologic and pathologic classes) strongly depends on the SNR (signal to noise ratio) of recovered data. Therefore, we also compare the effect of the proposed denoising algorithms on classification accuracy. Since denoising HSI data is done without any ground truth, we also use a metric that assesses the quality of denoising in the image domain between the noisy and denoised image in absence of ground truth.

bioinformatics↗