Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 109 records · Page 6Linked to original sources

bioRxiv2026

Brain organoid computing for robotic decision-making

Biomimicry has inspired the evolution of robotics toward greater autonomy, adaptability, and symbiosis with humans and dynamic environments. However, current robotic systems still face major challenges in recapitulating the high-efficiency decision-making capabilities of the human brain under complex and dynamic conditions. Here, we present Brainobot, a biohybrid robotic system that establishes a brain organoid controller as a high-level robotic decision-making layer for closed-loop embodiment. By leveraging brain organoid reservoir computing, Brainobot interacts with dynamic environments by receiving and processing sensory inputs and generating motor actions. As a proof-of-concept demonstration, Brainobot is implemented in a humanoid robotic system to perform real-world tasks, including object grasping and laser chasing. Interestingly, Brainobot exhibits unique features, including cross-task adaptivity, high computing efficiency, and low energy consumption. Thus, our approach may provide insights for advancing robotic embodiment and understanding biological decision-making.

bioengineering↗

bioRxiv2026

Rtel1 in hypothalamic oxytocin neurons regulates oxytocin output and social behaviors in mice

RTEL1, a DNA helicase that resolves G-quadruplexes to maintain telomere and genome stability, has emerged as a candidate autism spectrum disorder risk gene, but its neural functions remain unknown. Here, we show that Rtel1 haploinsufficiency in mice impairs social novelty and increases marble burying and anxiety-related behaviors without affecting locomotion or spatial learning. RTEL1 is expressed in oxytocin (OXT) neurons of the hypothalamic paraventricular nucleus, where Rtel1 haploinsufficiency reduces OXT abundance and blunts social-evoked OXT sensor responses. RTEL1 associates with a G-quadruplex-forming sequence near the Oxt transcription start site, and purified RTEL1 binds and unwinds this structure in an ATP-dependent manner. Rtel1 reduction in Oxt-lineage cells recapitulates the social and OXT phenotypes, whereas adult RTEL1 re-expression in paraventricular OXT neurons increases OXT abundance and ameliorates behavioral abnormalities. These findings uncover a non-telomeric function for RTEL1 linking promoter DNA structure to hypothalamic oxytocin output and social behavior.

neuroscience↗

bioRxiv2026

Emergence of tolerance and avoidance strategies from local and systemic responses to nitrogen: insights from modelling of auxin-mediated root plasticity

Understanding how root phenotypic plasticity enhances resource-use efficiency can help understand the outcomes of plant competition and identify suitable genotypes for medium- to low-input agricultural systems. Auxin regulates multiple root growth processes, including the root architectural responses to nitrogen (N). We explored the extent to which auxin-mediated local and systemic responses to external and internal N influences plant N uptake and use, using a functional-structural-plant (FSP) modelling approach. A simplified auxin module was developed at the level of the organ and integrated into an FSP model to represent physiological plastic root responses to N. Model performance was evaluated against experimental data. We then ran simulations under various N conditions with local or systemic responses enabled or disabled, to quantify their contribution to N uptake and use. Simulations showed that local auxin responses enhanced N uptake by distributing more roots towards deeper soil layers and increased N forage, thereby avoiding N stress. Systemic auxin responses reduced N uptake by distributing more roots near the top soil layers, which reduced plant size and N demand, thereby enhancing stress tolerance. This points to a trade-off in N uptake between the tolerance and avoidance strategies, which can be traced back to biomass and N investment resulting from source-sink relationships in the plant. Representing hormone-mediated root plasticity in an FSP model provides mechanistic insights into plant strategies for resource capture.

plant biology↗

bioRxiv2026

A T2T Benchmark Reveals How Reference Choice Shapes Human Genome Interpretation

The completion of telomere-to-telomere (T2T) human genomes has expanded the accessible landscape of human genetic variation, yet benchmark resources remain limited to conventional high-confidence regions defined by existing reference frameworks. Here, we generated a near-perfect diploid T2T genome (T2T-LIN) from a Chinese individual and established assembly-based truth sets by comparison with T2T-YAO, an ancestry-matched near-perfect T2T reference genome. The benchmark showed a heterozygous/homozygous SNV ratio of ~2, consistent with expectations under Hardy-Weinberg equilibrium, and enabled genome-wide evaluation of reference-dependent biases. We found that reference choice substantially influences genome interpretation: ancestry-matched linear T2T references provided the most faithful representation of individual genomic variation and enabled more accurate genome reconstruction than unmatched linear, diploid and graph-based references. Benchmarking previously inaccessible repetitive and structurally complex regions revealed substantial limitations of current variant callers that were masked by conventional metrics. The T2T-LIN and YAO-LIN benchmarks establish a T2T-era framework for evaluating reference-dependent genome interpretation and variant discovery across nearly the complete human genome.

genomics↗

bioRxiv2026

From pose to behavior: SABER integrates identity-resolved multi-animal pose tracking with language-model-based behavioral factor discovery

Quantifying social behavior requires accurate assignment of posture and actions to individual animals, which is often hindered by close contact, occlusion, and identity switches. Meanwhile, current behavioral recognition pipelines still lack stable predictive accuracy. Here we developed SABER, a locally deployable framework that couples multianimal pose estimation with identity preserving tracking, interpretable behavioral factor mining, and multiscale temporal behavior prediction from a single overhead video stream. Across spontaneous two mice social interaction, mating, aggression, and four mice recordings, SABER improved pose estimation accuracy and tracking continuity relative to comparator pipelines. Its behavioral factor-mining procedure identified interpretable kinematic, postural, and social descriptors, and temporal integration improved classification of behavioral categories. SABER offers an intuitive, open-source interface to facilitate use. Applied to social defeat stress mice, SABER detected reduced approach behavior and a multivariate behavioral profile that distinguished depression susceptible from control animals. SABER outputs could also be synchronized with miniscope calcium recordings, enabling joint analysis of behavioral states and neuronal population activity. SABER therefore provides an accessible, identity resolved route from single view social interaction video to behavioral phenotyping and brain behavior analysis.

animal behavior and cognition↗

bioRxiv2026

CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

Understanding how different cell types assemble into tissues and organs, as well as how they interact to transmit and receive biological signals, is essential for advancing biomedical and biological research. Recent advancements in spatial transcriptomics (ST) technologies have opened new avenues for investigating biological systems by achieving subcellular spatial resolution. Since cells are the fundamental units of life, extracting single-cell information from high-resolution ST data is crucial. However, existing ST platforms often capture sparse transcript counts per spot or measure only a limited number of genes, complicating the extraction of comprehensive single-cell information. In this study, we introduce CellART, a unified framework designed to extract single-cell information across diverse high-resolution ST platforms, including VisiumHD, Xenium, MERFISH, and Stereo-seq. By leveraging multimodal data, such as staining images, spatial transcriptomics data, and single-cell RNA sequencing references, CellART simultaneously performs cell segmentation and cell type annotation through a seamless integration of deep learning and probabilistic modeling. We demonstrate the efficiency, generalizability, and robustness of CellART across various high-resolution spatial transcriptomics platforms, capable of processing datasets containing millions of spots. Comprehensive experiments validate the biological relevance and accuracy of the recovered cellular information within spatial configurations. Notably, we highlight the utility of CellART in breast and colorectal cancer datasets, showcasing its ability to fully leverage high-resolution ST data. By enhancing cellular resolution, CellART facilitates the identification of transient cancer cell states and immune cell subtypes. Furthermore, CellART enables investigations into cancer-immune cell communication, uncovering both established interactions and novel ligand-receptor pairs. The outputs of CellART are compatible with widely used community tools, facilitating a variety of downstream analyses.

bioinformatics↗

bioRxiv2026

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework. Accordingly, we propose a physics-informed hybrid gated recurrent unit (PI-HGRU) model for spatiotemporal dynamic prediction of rice sheath blight disease, caused by a fungus. Our model embeds differential equations describing disease transmission dynamics into a hybrid gated recurrent unit (HGRU) framework as mechanistic constraints, thereby enabling collaborative modelling between epidemiological processes and data-driven learning. We conducted model training and evaluation using a long-term, multi-source dataset spanning 17 years (2000-2016) and covering 16 major rice-producing provinces in southern China. These rich data include spatiotemporally aligned field disease observations, remote sensing data, meteorological data, and soil property data. In addition, to address the challenges of irregular sampling intervals and inconsistent sequence lengths in disease survey data, we adopted a sliding time-window-based prediction framework. We further conducted a time-window sensitivity analysis to determine appropriate configurations of the input time window and lag time, enabling the model to represent the cumulative and delayed effects of environmental factors. Our PI-HGRU framework substantially outperforms the purely data-driven HGRU baseline model, improving the squared Pearson correlation coefficient (r2) by 22.8% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by 10.2% and 18.0%, respectively. Furthermore, analysis of the models intermediate variables showed that the transmission rate {beta}(t) exhibited interpretable relationships with environmental conditions within the input time window, providing a process-related link between environmental drivers and modeled disease transmission dynamics. Overall, our work demonstrates that integrating epidemiological mechanisms into deep learning models in a physics-informed manner can improve predictive accuracy and stability while enhancing model interpretability, highlighting its potential for large-scale disease forecasting and precision disease management.

bioinformatics↗

bioRxiv2026

RFOptimization: Guiding Design Optimization with All-Atom Structure Prediction

Biomolecular interactions, including protein--protein interactions, protein--nucleic acid recognition, and protein--small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent \emph{de novo} design methods can generate candidate binders for diverse molecular targets, practical design campaigns remain limited by low filter-passing rates and model-specific biases that arise when designs are optimized against a single predictor. Here, we present RFOptimization (RFO), a training-free framework for all-atom biomolecular binder optimization. RFO formulates binder improvement as a residue-wise mutational search problem, sampling candidate substitutions alternately based on gradient-guided sequence optimization using all-atom structure prediction models and a cycling-based sequence redesign strategy that alternates structure generation with an orthogonal predictor and MPNN-based sequence design to improve the \emph{in silico} success rate of RFdiffusion-generated binders within minutes of computation. To reduce overfitting to any individual structure model, candidate mutations are further evaluated with orthogonal AlphaFold3 metrics as final filters. We demonstrate the generality of RFO across diverse design settings, including classical protein binder design, ligand-binding biosensor design, cyclic peptide design, and active site-aware enzyme design.

biochemistry↗

bioRxiv2026

Selective collateralization of transcriptomically distinct neurons organizes visual-stream output from the primary visual cortex

Parallel visual streams segregate information into pathways specialized for distinct computations, but how this segregation is achieved by anatomical segregation of primary visual cortex output remains unclear. This problem is complicated because individual neurons frequently send broadcasting projections to multiple cortical areas, and the target choice depends on both topographical location and molecular identity. Here we developed axonal BARseq2 to jointly map gene expression and high-resolution axonal projections from 1,448 neurons spanning the mouse primary visual cortex (VISp). Axonal BARseq2 recapitulated projection patterns observed by bulk tracing and single-neuron reconstruction, and recovered transcriptomic identities consistent with reference snRNA-seq datasets. Retinotopy strongly predicted projections to individual cortical targets, particularly for areas proximal to VISp, but explained little of which areas are frequently co-innervated. Instead, co-innervation patterns defined three preferential output pathways that largely corresponded to the ventral stream and two subdivisions of the dorsal stream. These pathways were associated with fine-grained transcriptional identities of L4/5 intra-telencephalic neurons, which were further validated with an external MERFISH dataset. Thus, VISp output is organized by two distinct rules: retinotopy constrains where neurons project, whereas cell-type-associated collateralization constrains which targets are co-innervated. This selective broadcasting, in which single neurons reach many higher visual areas in cell-type-specific combinations, could provide an anatomical substrate for visual-stream segregation at the level of VISp output in mice.

neuroscience↗

bioRxiv2026

Shank3 mutation disrupts affective touch encoding in the dorsal medial prefrontal cortex of Beagle dogs

Individuals with autism spectrum disorder (ASD) often show aversion to affective touch (AT). However, the neural mechanism of this abnormality in cortices remains poorly understood probably due to the lack of effective animal models. Here, we used a canine model to address this issue by leveraging the intimate dog-human interactions. In a newly-designed heterospecific AT paradigm, we found that dogs carrying mutations in Shank3, a high-risk gene for ASD, avoided human AT. In vivo single-unit recording analysis showed that AT-evoked oscillations in the dorsal medial prefrontal cortex (dmPFC) were significantly altered in Shank3 mutant dogs. Shank3 mutation also reduced the number of neurons encoding AT in the dmPFC. Importantly, the aversion to AT and altered neural processing in Shank3 mutants were largely rescued by a GABAA receptor antagonist pentylenetetrazole. Together, these findings provide neural mechanisms for abnormal AT processing in ASD and suggest potential biomarkers for therapeutic strategies.

neuroscience↗

bioRxiv2026

Unlocking Sensitive Data with SPHERE in the Age of AI

Sensitive human data underpin discoveries across medicine, biology and the social sciences, yet privacy regulation often prevents sharing them with collaborators or artificial intelligence (AI) systems. We introduce SPHERE, a model-free method that makes sensitive datasets directly usable by AI and shareable for open science as a synthetic twin, while the original records never leave the local environment. Across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving the data's statistical structure: means, variances and correlations are reproduced exactly, effect size and P value in linear statistical analysis is numerically identical, nonlinear machine-learning utility is retained, and each twin is generated in seconds on a laptop. Frontier AI agents running on the twin reach the same scientific conclusions as on the original records. Analyses of the twin reproduce genome- and proteome-wide results at UK Biobank scale and recover the findings of landmark studies across three independent cohorts and consortia. The approach also extends to deep-learning embeddings across language, vision and time-series, with minimal utility loss. We make the Stanford Alzheimer's Disease Research Center cohort openly available for the first time, as a SPHERE twin spanning nine modalities that any registered researcher can analyze without an approval process. We release SPHERE with certification of each twin's privacy and fidelity, and an AI agent that autonomously executes research tasks on sensitive data without ever accessing it. Sensitive datasets that are currently closed to research could thus become routine inputs to open science and AI to enable key discoveries.

bioinformatics↗

bioRxiv2026

Haplotype-resolved genome assembly of barberry reveals structural divergence and allele-specific expression during infection by wheat stripe rust pathogen

Barberry is an ecologically and medicinally important perennial shrub and an alternate host of Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust. However, its genome architecture and molecular responses to Pst infection remain poorly understood. Here, we generated a haplotype-resolved, chromosome-level genome assembly of Berberis aggregata using PacBio HiFi and Hi-C sequencing. The two haplotypes, Ba5A and Ba5B, were assembled into 14 pseudochromosomes each, with genome sizes of approximately 1.13 and 1.16 Gb, respectively, and showed high continuity and completeness. Comparative analyses revealed extensive divergence between the haplotypes, including widespread structural variation, substantial hemizygosity, and TE-rich, gene-poor non-alignable regions. Haplotype-specific hemizygous genes also exhibited distinct functional biases, with Ba5A enriched in immune-related processes and Ba5B associated with transport and cell-envelope functions. Dual RNA-seq across infection stages revealed stage-dependent host transcriptional reprogramming, characterized by early pathogen perception followed by stronger defense activation and photosynthesis repression, whereas Pst displayed a coordinated colonization program consistent with its biotrophic lifestyle. Allele-specific expression analysis showed that ~25-28% of genes exhibited significant haplotype-biased expression, and a subset transitioned from unbiased expression at 0 dpi to Ba5A- or Ba5B-biased expression during infection. Integration with differential expression analysis further identified infection-responsive induced-biased genes associated with defense-related processes, including responses to fungal pathogens. These findings show that haplotype divergence and dynamic allele-specific regulation jointly contribute to transcriptional responses during Pst infection, providing new insights into the genomic basis of barberry-rust pathogen interactions.

genomics↗

bioRxiv2026

BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images

Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.

bioinformatics↗

bioRxiv2026

A Hymenoptera-restricted gene mediating ant castes co-opts deeply conserved machinery to control organ size

Lineage-specific genes are widespread and have been implicated as phenotypic innovation inducers, but how they acquire complex developmental functions remains poorly understood. Ant queens and workers develop dramatically different organ sizes from identical genomes under juvenile hormone (JH) control, yet the molecular effectors translating JH signalling into caste-specific organ growth remain unknown. Here we identify torch, a Hymenoptera-restricted gene, as the most consistently gyne-biased and JH-responsive gene across 68 ant species. Knockdown of torch in virgin queens of Monomorium pharaonis produces a worker-like, multi-organ growth-restricted phenotype. Mechanistically, torch harbours an E-box-like motif activated by the JH receptor Gce-Tai and acts as a GA-repeat-binding transcription factor that regulates Hippo signalling, the deeply conserved organ-size control pathway in animals. Expressing torch heterologously in mice and a growth-restricted Drosophila background shows that the gene retained its general growth-promoting activity across more than 700 million years of animal evolution in lineages that lack the gene, establishing that its function is mediated through conserved rather than ant-specific machinery. A lineage-specific gene can therefore acquire complex morphogenetic function by co-opting ancient organ-size circuitry, providing a general route by which novel genes can drive phenotypic innovation.

evolutionary biology↗

bioRxiv2026

DAG-HEART: Directed Acyclic Graph-Guided Health Equity-Aware Representation Transfer Learning Framework for Breast Cancer

Breast cancer outcome prediction remains challenging for underrepresented populations because genomic datasets are demographically imbalanced and conventional multi-omics integration largely relies on undirected molecular similarity. We developed DAG-HEART, a directed acyclic graph-guided multi-omics transfer-learning framework that extends our previous transfer learning strategy with data augmentation. Using TCGA-BRCA mRNA, miRNA, and DNA-methylation data, DAG-HEART was evaluated for progression-free interval prediction in a data-minority group. DAG-guided nonlinear integration consistently improved predictive performance relative to direction-agnostic and correlation-based representations, while biologically motivated directional constraints generally outperformed reversed or unconstrained structures. Recurrently selected features converged on extracellular-matrix and regulatory pathways and supported clinically meaningful risk stratification. DAG-HEART provides an interpretable strategy for combining directed multi-omics structure with transfer learning under data imbalance across racial groups.

bioinformatics↗

bioRxiv2026

Gaussian accelerated Molecular Dynamics - Thermodynamic Integration (GaMD-TI): Improved alchemical free energy calculations with enhanced sampling

It is valuable to calculate alchemical free energy changes in drug discovery and development. Thermodynamics Integration (TI) has been widely used in computational chemistry for estimating free energy changes with alchemical transformations. However, TI based on usually short Molecular Dynamics (MD) simulations often suffers from insufficient conformational sampling. Here, we have integrated Gaussian accelerated MD and TI (GaMD-TI) to enhance the conformational sampling and improve accuracy of free energy calculations. GaMD-TI has been demonstrated in model systems of alchemical changes in the Valine dipeptide and mutation cycle of the Alanine <-> Valine <-> Isoleucine (AVI) residues. Simulations showed that when GaMD boost potentials followed near-Gaussian distribution, the free energy change could be reweighted accurately through generalized cumulant expansion to the second order. The total free energy change often exhibited faster convergence using Selective GaMD (SGaMD) than using conventional MD (cMD). Accuracy of the free energy estimates from SGaMD-TI simulations was similar to or higher than those from cMD-TI simulations, although the differences were subtle for these small model systems. Meanwhile, dihedral angles in the model systems underwent significantly more frequent conformational transitions in SGaMD than in cMD, indicating improved sampling. Future studies are planned on larger systems with more complicated alchemical changes, such as ligand binding to proteins/nucleic acids and mutations at biomolecular binding interfaces. GaMD-TI should be broadly applicable to alchemical free energy calculations and therapeutic design.

biochemistry↗

bioRxiv2026

Multi-kingdom microbial diversity and interaction landscapes in mosquitoes revealed by 5,163 individual meta-transcriptomes

Mosquitoes are pathogen vectors embedded within diverse microbial ecosystems. However, the nature and interactions among their multi-kingdom microbiome remain poorly understood. We conducted a nationwide single-mosquito meta-transcriptomic survey of 5,163 mosquitoes representing 100 species across China, integrating viral discovery with marker-gene profiling of bacteria, archaea, fungi, and other eukaryotic microbes. From this, we identified 1,606 microbial species-level taxa, including extensive novel diversity, and revealed pronounced host species-specific organization of mosquito-associated communities. We detected 34 pathogens or potential pathogens of human or animal relevance, whose prevalence, abundance, host range, and geographic distribution defined distinct epidemiological patterns. Network analysis uncovered pervasive cross-kingdom microbial associations, including candidate antiviral relationships involving Wolbachia and other microbial taxa. Our study establishes a detailed view of the full-spectrum microbiome and provides a resource and conceptual framework for studying vector competence, pathogen emergence, and microbiome-informed mosquito-borne disease control.

microbiology↗

bioRxiv2026

Multi-parametric NIR-II fluorescence perfusion imaging towards quantitative stroke evaluation

Preclinical stroke research is severely hampered by the lack of high-throughput, quantitative perfusion imaging tools capable of bridging the gap between functional hemodynamics and structural injury. The emerging second near-infrared (NIR-II) fluorescence imaging offers superior spatial resolution and tissue penetration, but its utility remains restricted by a lack of standardized analysis protocols and inability to resolve in-depth infarct regions. Here, we introduce FIMPA, an integrated analytical framework for standardized, multi-parametric stroke evaluation based on NIR-II fluorescence perfusion imaging. FIMPA systematically processes the image sequence through motion correction and atlas registration to generate an anatomically aligned library of 125 vascular and hemodynamic parameters. Statistical screening identifies 67 stroke-correlated features, enabling objective, data-driven quantification of perfusion deficits. To resolve spatial infarct boundaries, we developed FIMPA-SI (FIMPA Stroke Index), a deep learning-based index utilizing a ViT-UNETR framework. By leveraging self-supervised pre-training with MRI data, FIMPA-SI suppresses dominant vascular signals to translate multi-parametric dynamics into anatomically precise stroke severity maps. Validated in tMCAO mice, FIMPA-SI achieves high spatial concordance with MRI and significantly outperforms conventional laser speckle contrast imaging and single-parameter metrics. FIMPA provides a robust solution for accelerating preclinical therapeutics and advancing optical neuroimaging.

bioengineering↗