{"id":3087,"date":"2023-01-17T11:53:53","date_gmt":"2023-01-17T17:53:53","guid":{"rendered":"https:\/\/kermitmurray.com\/msblog\/?page_id=3087"},"modified":"2023-01-17T20:57:06","modified_gmt":"2023-01-18T02:57:06","slug":"biorxiv","status":"publish","type":"page","link":"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/","title":{"rendered":"bioRxiv"},"content":{"rendered":"\n<div class=\"wp-block-caxton-grid relative\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-columns caxton-grid-block\" style=\"padding-top:0;padding-left:0;padding-bottom:0;padding-right:0;grid-template-columns:repeat(12, 1fr)\" data-tablet-css=\"padding-left:em;padding-right:em;\" data-mobile-css=\"padding-left:em;padding-right:em;\">\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 8\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"https:\/\/www.biorxiv.org\/alertsrss\" target=\"_blank\" rel=\"noreferrer noopener\">Journal Home<\/a><\/strong><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 4\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"http:\/\/connect.biorxiv.org\/biorxiv_xml.php?subject=all\" target=\"_blank\" rel=\"noreferrer noopener\">RSS<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n<ul class=\"su-subpages\"><li class=\"page_item page-item-3099\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-biochemistry\/\">BioRxiv Biochemistry<\/a><\/li>\n<li class=\"page_item page-item-3112\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-bioinformatics\/\">BioRxiv Bioinformatics<\/a><\/li>\n<li class=\"page_item page-item-3132\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-biophysics\/\">BioRxiv Biophysics<\/a><\/li>\n<li class=\"page_item page-item-3188\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-cancer-biology\/\">BioRxiv Cancer Biology<\/a><\/li>\n<li class=\"page_item page-item-3190\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-pharmacology-and-toxicology\/\">BioRxiv Pharmacology and Toxicology<\/a><\/li>\n<li class=\"page_item page-item-3114\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-systems-biology\/\">BioRxiv Systems Biology<\/a><\/li>\n<li class=\"page_item page-item-3193\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-zoology\/\">BioRxiv Zoology<\/a><\/li>\n<\/ul>\n\n\n<ul class=\"has-dates has-authors has-excerpts wp-block-rss\"><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.11.724385v1?rss=1'>Inflammation-induced epigenetic memory restores oligodendrocyte progenitor cell regenerative capacity in the aged central nervous system<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Cabeza-Fernandez, S., Ninerola, S., Armengol-Gomis, A., Paraiso-Luna, J., Casillas-Bajo, A., Gomez-Sanchez, J. A., Cabedo, H., Barco, A., de la Fuente, A. G.<\/span><div class=\"wp-block-rss__item-excerpt\">Although remyelination, a central nervous system (CNS) regenerative process mediated by oligodendrocyte progenitor cells (OPCs), takes place in an inflammatory environment the long-term impact of inflammation on OPC remyelination capacity remains unclear. Here, we studied the short- and long-term impact of systemic inflammation on adult OPCs to assess whether transient inflammation triggers enduring chromatin remodelling indicative of inflammatory memory in OPCs. We observed long-lasting epigenetic modifications in response to both lipopolyssaccharide (LPS) and polyinosinic:polycytidylic acid (Poly(I:C)), but only LPS induced [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.10.724074v1?rss=1'>Astrocyte immunosuppressive activity in glioblastoma depends on ZEB1 and is counteracted by CXCL14<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Clement, M., Gibbs, A., Begum, A., Siebzehnrubl, D., Kaushik, S., Singh, N., Gupta, B., Eftychidis, V., Siebzehnrubl, F. A.<\/span><div class=\"wp-block-rss__item-excerpt\">Glioblastomas are incurable and lethal brain cancers. Immunotherapies offer new and promising treatment options for glioblastoma patients, but the highly immunosuppressive nature of these cancers presents a challenging clinical obstacle. Glioblastoma immune evasion is driven by cell-cell interactions in the tumor microenvironment and recent studies have identified astrocytes as important contributors to immune silencing [1, 2]. Cell plasticity is a key feature of reactive astrocytes that drives heterogeneous, pro- or anti-inflammatory states [3], but the molecular regulators of astrocyte-immune interactions [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.11.724286v1?rss=1'>Spatiotemporally dynamic noradrenergic regulation of cortical networks<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Barnes, C., Cini da Silva, F. A., Xu, P., Cardin, J. A.<\/span><div class=\"wp-block-rss__item-excerpt\">Brain activity and cognition exhibit state-dependent fluctuations that may reflect the influence of neuromodulatory systems, including norepinephrine (NE). Although the activity of noradrenergic neurons is strongly coupled to sleep-wake cycles and pupil dynamics, suggesting a global arousal signal, recent evidence suggests potentially modular partitioning of these cells. In addition, it is unclear whether different neuromodulatory signals exhibit distinct spatiotemporal patterns. We performed simultaneous, dual color mesoscopic imaging of either NE and calcium or NE and acetylcholine (ACh) signals across the [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.12.724497v1?rss=1'>Differential maturation in vestibular neuronal groups related to developmental motor reorganization in amphibians<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Barrios, G., Olechowski-Bessaguet, A., Cardoit, L., Fevrier, T., Wattignier, A., Tostivint, H., Cattaert, D., Thoby-Brisson, M., Lambert, F. M.<\/span><div class=\"wp-block-rss__item-excerpt\">Vestibular neurons are core elements of the pathways involved in vestibulo-motor functions, such as vestibulo-spinal and vestibulo-ocular reflexes. To meet behavioral needs, electrophysiological neuronal properties are adequately adapted to the sensory-motor computation sustaining these distinct vestibular reflexes. During frog metamorphosis, there is a complete reorganization of the posturo-locomotor system while the oculomotor system remains minimally changed, probably associated to so far unknown changes in vestibular neuronal properties. We used this unique model to investigate the central developmental mechanisms underlying such [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.11.724160v1?rss=1'>Postweaning social isolation induces gene expression alterations and histone modification dysregulations in nucleus accumbens (NAc) neurons<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by You, J., Uematsu, A., Jouji-Nishino, A., Saeki, M., Kishi, Y.<\/span><div class=\"wp-block-rss__item-excerpt\">Lack of social interaction results in various behavioral abnormalities in rodents, including increased anxiety levels, altered sociability, and impaired cognitive ability. Epigenetic factors regulate gene expression, however, how they contribute to juvenile social isolation (jSI)-induced behavioral alterations remains largely unknown. Here, we focused on the nucleus accumbens (NAc), a critical brain region of the reward system that regulates motivation-related behaviors. We first performed RNA-seq on neuronal nuclei and found alterations in genes related to neuronal function, as well as in [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.10.723768v1?rss=1'>Maternal high-fat diet drives sex-specific microglia remodeling of serotonergic reward circuits<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Bilbo, S., Patton, M., Sun, W., Stanley, L., Paredes, A., Kang, J. Y., Schettewi, Z., Horvath, B., Dziabis, J. E., Devlin, B., Vaidyanathan, T. V.<\/span><div class=\"wp-block-rss__item-excerpt\">Maternal nutrition shapes offspring brain development and influences lifelong risk for neurological disorders, yet the circuit-level mechanisms linking maternal metabolic state to offspring behavior remain poorly defined. Here we show that maternal high-fat diet (mHFD) disrupts microglia-serotonin interactions during a critical postnatal window to drive persistent, sex-specific alterations in mesolimbic circuitry. In mice, mHFD selectively increased serotonergic fiber density in the nucleus accumbens (NAc) of male, but not female, offspring at postnatal day 14, coincident with reduced microglial phagocytosis of [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.11.705002v1?rss=1'>Behavioral evidence challenges species-specific ocular morphology as a primary constraint on human gaze-following<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Shafiei, M., Arnous, Y., Taubert, N., Giese, M., Thier, P.<\/span><div class=\"wp-block-rss__item-excerpt\">Previous research suggests that humans are extremely sensitive to object-directed eye gaze, which effectively guides their attention toward objects of shared interest. This contrasts with non-human primates, who typically require much more salient eye-gaze cues to achieve comparable attentional orienting. However, it remains unclear whether cross-species differences in ocular morphology account for this performance gap. To address this question, we examined humans&#039; covert shifts of spatial attention in response to eye-gaze cues provided by either realistic human or rhesus monkey [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.11.724319v1?rss=1'>Simulating the spectrum, not the syndrome: Large scale individualized modeling of oral reading in stroke aphasia<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Staples, R., DeMarco, A. T., Laks, A. B., Turkeltaub, P. E.<\/span><div class=\"wp-block-rss__item-excerpt\">Computational models are a linchpin in our understanding of the neurocognitive basis of reading. These models can simulate idealized profiles of alexia syndromes, but in reality, individuals with alexia present with a wide range of mixed deficits rather than idealized syndromes. To provide a complete cognitive theory of reading, computational models must be able to account for this individual variation. However, this has never been demonstrated. We test oral reading and non-reading phonological and semantic processing in 83 left-hemisphere stroke [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.12.724670v1?rss=1'>Trajectories of hippocampal subregion development in the first years of life and their association with school-aged episodic memory outcomes<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Stoyell, S. M., Lundquist, J. T., Hantzsch, L., Bunnell, A., Bunnell, A., Thomas, K. M., Fair, D. A., Tervo-Clemmens, B., Feczko, E., Elison, J. T.<\/span><div class=\"wp-block-rss__item-excerpt\">Brain networks that support episodic memory development in the first years of life remain poorly understood. Protracted growth of regions such as the hippocampus have been suggested as a causal role in episodic memory development, but development of these memory brain networks and their role in episodic memory development is not yet fully elucidated. In this study, subcortical memory network regions (hippocampus, thalamus, amygdala) were segmented from MRI images in 835 visits spanning 0-4 years of age across 322 participants [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.12.724368v1?rss=1'>Deep Representation Learning on Whole-Brain Population Dynamics Uncovers Geometrically Separable Neural Codes<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Abdelbaki, A., Bandow, P., Cheng, K. Y., Grunwald Kadow, I. C., Nawrot, M. P., Rostami, V.<\/span><div class=\"wp-block-rss__item-excerpt\">Learning interpretable low-dimensional representations of whole-brain neuronal dynamics remains a major computational challenge in systems neuroscience. We present a wiring-agnostic deep-learning framework that couples a convolutional encoder with a temporal transformer to learn compact representations directly from volumetric calcium imaging of the entire Drosophila melanogaster brain. Trained to classify 16 experimental conditions that factorially combine metabolic state (fed, starved), sensory modality (olfaction, gustation, or combined), and stimulus valence (appetitive, aversive, or conflicting), the model organizes pan-neuronal whole-brain population activity into [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.10.724142v1?rss=1'>Demonstrating the ability of GABAergic cells in the zona incerta to modulate motivation<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Korobkova, L., Dias, B.<\/span><div class=\"wp-block-rss__item-excerpt\">Motivation to engage in goal-directed actions is crucial for survival and well-being. Dopaminergic and serotonergic systems have been the focus of efforts to understand neurobiological etiologies of normative and disrupted motivation. Understanding how the brain incorporates salient sensory cues into motivational drive outside of these neuromodulatory systems is less appreciated. We posited that given their afferent and efferent connections, GABAergic cells in the zona incerta (ZI) are ideally positioned to perform this function. Combining behavioral tasks in mice with chemogenetics [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.08.723772v1?rss=1'>ContextTAD: Context-aware boundary learning for TAD calling from Hi-C contact maps<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Long, W., Hou, Y., Zhang, Y.<\/span><div class=\"wp-block-rss__item-excerpt\">Motivation: Reliable topologically associating domain (TAD) calling from Hi-C contact maps remains difficult at high resolution and realistic sequencing depth. A central reason is that many callers learn boundary evidence largely from local signals, while domain compatibility is handled mainly during downstream decoding, so the learned boundary scores are not explicitly optimized for the TAD assembly step that ultimately determines the final calls. Results: We present ContextTAD, a deep-learning TAD caller that learns boundary evidence from broader local Hi-C windows [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.12.724534v1?rss=1'>Temporal-to-spatial patterning of embryonic structures can involve active transformation of temporal information rather than direct mapping<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Garcia-Guillen, J., Ahmadi, M., Frimpong, T., Pacheco, K., Ambuehl, I., Mau, C., Duah, G., Oraby, T., El-Sherif, E.<\/span><div class=\"wp-block-rss__item-excerpt\">How spatial patterns arise during embryonic development is classically explained by the French Flag model, in which cells acquire positional identities by interpreting morphogen concentration thresholds. However, in many developmental systems, spatial patterns instead emerge progressively through temporal programs of gene expression that are transformed into spatial organization. In the short-germ insect Tribolium castaneum, both periodic pair-rule gene expressions that generate body segments and non-periodic gap gene expressions that establish regional identities arise sequentially at the posterior and propagate anteriorly [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.11.724300v1?rss=1'>Pericyte Loss Reprogrammes Capillary Endothelium and Drives White Matter Injury in Small Vessel Disease<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Stefancova, D., Chagnot, A., Sewell, M., Fialova, N., McQuaid, C., Uweru, J. O., Becker, S., Romero-Bernal, M., Mungall, W., Walczak-Gillies, V., Farr, T. D., Lennen, R., Jansen, M. A., Dando, O., Cholewa-Waclaw, J., Montagne, A.<\/span><div class=\"wp-block-rss__item-excerpt\">Pericytes are critical regulators of cerebrovascular homeostasis, yet their contribution to small vessel disease (SVD) and white matter injury remains incompletely understood. Here, we use an inducible Atp13a5-driven genetic strategy to selectively label and ablate brain pericytes, enabling integrated morphological, functional, and transcriptomic analyses across the neurogliovascular unit. Single-cell RNA sequencing and tissue-level mapping identified distinct pericyte subtypes distributed along the vascular tree and revealed subtype-specific vulnerability following depletion. Moderate pericyte loss induced transient cerebrovascular dysfunction characterised by reduced cerebral [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.08.723622v1?rss=1'>SPECTRA: Spatial Inference for Tractometry Toward Precision Mapping of White Matter Microstructure<\/a><\/div><time datetime=\"2026-05-13T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 13, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Feng, Y., Villalon-Reina, J. E., Ba Gari, I., Alibrando, J. D., Thomopoulos, S., Liou, K., Somu, S., Yoo, H., Shuai, Y., Chehrzadeh, S., Nir, T. M., Jahanshad, N., Chandio, B. Q., Thompson, P. M.<\/span><div class=\"wp-block-rss__item-excerpt\">Diffusion MRI tractometry characterizes white matter microstructure along fiber bundles, but standard along-tract profiling collapses measurements across the bundle cross-section, obscuring radial heterogeneity and producing spatially inconsistent units of inference. We present SPECTRA (Spatial Inference for Tractometry), a framework designed to address these limitations through a unified design of parameterization and statistical inference. First, we propose a 2D bundle parameterization that extends along-tract profiling to include a radial dimension defined on the atlas bundle. Second, we develop a two-stage hierarchical [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.11.724158v1?rss=1'>The elusive resistome: a global comparison reveals large discrepancies among detection pipelines<\/a><\/div><time datetime=\"2026-05-12T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 12, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Inda-Diaz, J. S., Adegoke, F., Lo\u0308ber, U., Jarquin-Diaz, V. H., Duan, Y., Bengtsson-Palme, J., Ugarcina Perovic, S., Coelho, L. P.<\/span><div class=\"wp-block-rss__item-excerpt\">Identifying antibiotic resistance genes (ARGs) from metagenomic data is critical for studying antimicrobial resistance across microbial communities and pathogens. However, there is no standardized methodology for ARG annotation. Here, we compare ten commonly used ARG detection pipelines by analysing over 270 million prokaryotic genes from the Global Microbial Gene Catalogue across 13 distinct habitats. We observed up to a 45-fold difference in the number of reported ARGs, with a mean Jaccard index of only 16% between pipelines. Pipeline selection profoundly [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.08.723611v1?rss=1'>ClaroAI-Bench: Evaluating Agentic Scientific Reproducibility on Real Biomedical Papers<\/a><\/div><time datetime=\"2026-05-12T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 12, 2026<\/time> <span class=\"wp-block-rss__item-author\">by O&#039;Connell, K. A.<\/span><div class=\"wp-block-rss__item-excerpt\">We introduce ClaroAI-Bench, an evaluation suite for measuring AI agents&#039; ability to reproduce computational findings from published biomedical research. The benchmark comprises 35 real NIH-funded papers spanning five modalities (genomics, imaging, clinical\/EHR, epidemiology, wet-lab) scored on a five-dimension rubric: data findability (D1), data accessibility (D2), code availability (D3), environment reconstructability (D4), and results reproducibility (D5). Each task requires an agent to locate data, obtain code, reconstruct the compute environment, execute the analysis, and verify results against published claims, mirroring the [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.08.723620v1?rss=1'>ProCAST: A Bioinformatics Suite for Mass Spectrometry-Based Protein Corona Proteomics Analysis<\/a><\/div><time datetime=\"2026-05-12T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 12, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Mun, H., Leamy, M., Kaushik, A., Kieslich, C., Douglas-Green, S. A.<\/span><div class=\"wp-block-rss__item-excerpt\">When nanoparticles are exposed to biological fluids, they spontaneously adsorb proteins, forming a protein corona that defines their biological identity and dictates cellular uptake, biodistribution, and toxicity. Characterizing protein coronas includes using proteomics approaches (e.g., LC-MS\/MS) to identify proteins and generate vast lists of adsorbed proteins, often visualized via complex heatmaps. While heatmaps display data they do not offer heuristic guide, leaving the driving mechanisms of adsorption unknown. Moreover, interpretation of protein corona proteomics data remains limited by fragmented workflows, [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.08.723825v1?rss=1'>Learning a reversed bicycle disrupts predictive control and induces interference with the normal bicycle<\/a><\/div><time datetime=\"2026-05-12T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 12, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Nietschmann, P., Franklin, D. W.<\/span><div class=\"wp-block-rss__item-excerpt\">Motor skills such as bicycle riding are considered robust and transferable across bicycle types. However, when the steering direction is inverted (reversed bicycle) control is disrupted to the extent that the bicycle cannot be ridden. With sufficient practice, the reversed bicycle can be learned, but this learning appears to produce impairment of normal bicycle riding suggesting modification of this long-established motor memory. Here we investigate the learning process of riding a reversed bicycle over four days of practice, while repeatedly [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.12.724576v1?rss=1'>Phenotypic Analysis of GGDEF\/EAL Domain Protein Function in Phytopathogenic Pantoea ananatis<\/a><\/div><time datetime=\"2026-05-12T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 12, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Choi, O., Lee, Y., Kang, B., Lee, Y., Kim, J.<\/span><div class=\"wp-block-rss__item-excerpt\">Cyclic diguanosine monophosphate (c-di-GMP) is a ubiquitous bacterial second messenger that regulates diverse cellular processes, including colony morphology, motility, biofilm formation, and virulence. It is synthesized by diguanylate cyclases (DGCs) containing the GGDEF domain and degraded by phosphodiesterases (PDEs) containing the EAL domain. However, studies on the genetic and physiological characteristics of c-di-GMP metabolism in Pantoea ananatis are lacking. 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