Biology subjects

Markus, A.

Publications and source records attributed to Markus, A..

3 recordsLinked to original sources

bioRxiv2026

Deep learning framework for kinematic event detection and stimulation decoding in primate reaching behavior

Accurate analysis of motor behavior requires the reliable detection of ongoing kinematic events and a granular characterization of the changes in motor output that occur in response to neural impairments. This article describes a deep learning framework based on bidirectional long short-term memory (BiLSTM) networks developed to analyze single-trial three-dimensional reaching trajectories recorded from two non-human primates. The framework was used to detect movement onset and corrective turning points, and to differentiate control from the perturbed trials during which cerebellar output was blocked. This approach was compared to manually annotated data. Only small within-animal errors in detecting movement onset times were observed (8.92 {+/-} 2.03 ms and 9.56 {+/-} 3.64 ms for the two monkeys). These errors were significantly smaller (p < 0.001) than those obtained using conventional velocity-threshold methods. Transferring the same detection algorithm from one animal to the other resulted in large errors because the reconstructed workspaces were represented in different coordinate frames. Orthogonal Procrustes alignment substantially reduced the between-animal event-detection errors, and brought performance closer to the within-animal range. Decoding of the perturbed vs. the control trials achieved above-chance levels of accuracy for each animal (accuracies of 71.0% and 61.9% respectively). However, the between-animal generalization was poor (near chance level) and was not improved by geometric alignment. These findings suggest that geometric alignment can support the transfer of shared kinematic event structure between animals, but that perturbation-related changes in movements reflect animal-specific compensatory strategies which cannot be generalized.

animal behavior and cognition↗

bioRxiv2026

Label-Free Multimodal Volumetric Imaging of Colon Cancer Tissue via Registration of Propagation-Based Phase-Contrast CT, Light-Sheet, and Three-Photon Microscopy

Multimodal 3D imaging has emerged as a powerful approach for investigating complex tissue architecture in pathological specimens. Techniques such as propagation-based phase-contrast computed tomography (PCT), light-sheet microscopy (LSM), and three-photon microscopy (3PM) provide complementary information on unlabeled tissue morphology based on distinct intrinsic contrast mechanisms. However, integrating these heterogeneous datasets into a unified spatial framework remains challenging due to differences in imaging geometry, spatial resolution, and modality-specific distortions. In this study, we present a registration pipeline for spatially aligning volumetric datasets acquired with PCT, LSM, and 3PM from formalin-fixed paraffin-embedded (FFPE) human colon cancer specimens. Biopsies from theses specimens were optically cleared and imaged sequentially using the three high-resolution modalities. To compensate for large positional differences between acquisitions, a three-stage cascade registration strategy was developed, consisting of coarse global alignment on down-sampled data, followed by rigid refinement at intermediate resolution. Mutual information was used as the similarity metric to ensure robust multimodal registration. The resulting framework enables the generation of spatially aligned multi-channel 3D datasets that combine structural information from X-ray phase-contrast imaging with complementary optical contrast signals. Beyond registration, we demonstrate that the fused six-dimensional feature space can be further exploited for unsupervised tissue characterization using a Gaussian Mixture Model (GMM), enabling data-driven identification of spatially coherent tissue regions without manual annotation. Qualitative evaluation confirms consistent alignment of major anatomical structures across modalities, while the unsupervised clustering reveals biologically meaningful patterns despite modality-specific noise and resolution differences. While further optimization and validation across larger datasets will enhance its computational efficiency and breadth of application, the approach already demonstrates strong potential for comprehensive tissue analysis and enables scalable, label-free 3D characterization of colon cancer tissue architecture.

pathology↗

bioRxiv2026

A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology

Compared to immortalized cell lines, patient-derived organoids and other ex vivo models have been shown to better recapitulate patient responses to therapy. High cost and technical complexity have prevented the creation of pan-cancer ex vivo datasets, limiting comprehensive analyses and predictive modeling for ex vivo drug response. We present the Pan-PreClinical (PPC) project: a drug screen atlas of 2.1M experiments across 1,982 ex vivo samples and 3,100 drugs spanning 134 cancer indications tested across 26 studies. We develop a contrastive Bayesian model to harmonize across studies, identifying 303 tissue-specific drug sensitivities and demonstrating drug sensitivities are predictive of clinically-relevant molecular profiles. Integrating established cell line databases reveals systematic biases across 55 cancer subtypes, with cell line screens favoring drugs targeting highly proliferative cells and undervaluing cell-cell communication targets. We leverage PPC to establish an ex vivo foundation model and computational platform for scalable ex vivo cancer biology and predictive oncology.

cancer biology↗