Biology subjects

Xin, Y.

Publications and source records attributed to Xin, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Assessing the Reliability of LLM-Generated Phenotype-Genotype Associations Through External Validation

BackgroundPhenotype-genotype associations underpin precision medicine by enabling disease prevention, early diagnosis, risk stratification, therapeutic target discovery, and personalized treatment. However, the rapid growth of scientific evidence has made manual curation of these associations increasingly labor-intensive, time-consuming, and incomplete. Large Language Models (LLMs) offer a potential path to scalable genomic generation and synthesis of this knowledge, but their ability to accurately identify phenotype-genotype associations and the extent to which these outputs are supported by established genomic knowledge bases remain unclear. Materials and MethodsFour LLMs, Claude Sonnet 4.6, DeepSeek V4 Flash, Gemini 3 Flash Preview, and GPT-5.5, were benchmarked on six zero-shot task categories covering forward and reverse phenotype-gene and phenotype-SNP generation. A total of 4,196 associations were identified from curated inputs and evaluated through a multistage external verification pipeline comprising phenotype normalization, ontology mapping, genomic identifier validation against Ensembl, and evidence verification using both the GWAS Catalog and OMIM. Associations were assigned a fused evidence level of strong, moderate, weak, or none. ResultsOverall, 74.19% of generated associations were matched to at least one external genomic knowledge base; 9.15% received strong support and 54.46% moderate support. Phenotype-gene associations were more verifiable than phenotype-SNP associations (strong or moderate: 67.19% vs 54.06%). Among existing associations, Claude Sonnet 4.6 achieved the highest overall strong or moderate rate (69.2%), followed by GPT-5.5 (65.1%), DeepSeek V4 Flash (61.7%), and Gemini 3 Flash Preview (56.9%). ConclusionLLMs can support scalable generation of candidate phenotype-genotype associations. Performance varied substantially by relation type and was lower for SNP-level and rare disease associations, highlighting both the limitations of current genomic resources and the need for rigorous validation pipelines.

bioinformatics↗

bioRxiv2026

Shared but temporally distinct neural representations support semantic matching across word and picture formats: evidence from EEG decoding and temporal generalization analyses

Audiovisual semantic matching can be achieved using either written words or pictures, yet whether these formats engage shared semantic matching representations with similar temporal dynamics remains unclear. We recorded electroencephalography from 27 participants while they performed audiovisual semantic matching tasks in which spoken words were paired with either written words or pictures. Stimuli included both natural and man-made objects. Time-resolved multivariate pattern analyses (MVPA or decoding), cross-decoding, and temporal generalization analyses were used to characterize the temporal dynamics of semantic processing. Reliable decoding of matching versus mismatching judgments emerged in both word and picture conditions. Decoding onset that significant above chance level occurred earlier for written words than for pictures and cross-decoding analyses revealed successful generalization between word and picture formats. Temporal generalization analyses further demonstrated distinct representational dynamics across formats, with word processing characterized by predominantly time-specific neural representations and picture processing showing more sustained and temporally stable representations. In addition, matching-related discrimination emerged earlier for natural objects than for man-made objects across both formats. The results suggest that speech-word matching shows earlier neural evidence of audiovisual alignment than speech-picture matching, potentially reflecting differences in how auditory linguistic input is integrated with visual information across representational formats.

neuroscience↗

bioRxiv2026

Antagonists Perturb the MC2R MRAP Complex and Reshape Receptor Conformations

Melanocortin 2 receptor (MC2R) is a G protein-coupled receptor (GPCR) for adrenocorticotropic hormone (ACTH), and its trafficking and signalling are associated with the melanocortin receptor accessory protein (MRAP). Mutations in either MC2R or MRAP disrupt this signalling and cause familial glucocorticoid deficiency. Here, we combine native mass spectrometry (MS) and hydrogen deuterium exchange mass spectrometry (HDX MS) to uncover how MRAP association and post-translational modification status shape the conformations of MC2R. Using native MS, we demonstrate that MC2R associates with MRAP or when MRAP is depleted the protein is extensively palmitoylated at the C-terminus. ACTH binding is restricted to the MC2R MRAP complex. By contrast antagonists shift the equilibrium toward MRAP-independent receptor populations. Our HDX MS analysis shows that ACTH binding induces global stabilisation of MC2R and the MRAP N terminus, consistent with reinforcement of the receptor accessory protein interface. Antagonist binding by contrast destabilises this interface and increases dynamics in transmembrane helix 2 (TM2). Notably, TM2 destabilisation is retained when the MRAP complex is depleted. Together, MRAP association and palmitoylation define distinct MC2R assemblies with ligand-dependent dynamics, suggesting new ways to influence MC2R pharmacology.

biochemistry↗

bioRxiv2026

A dataset of simultaneous two-photon calcium imaging and auditory discrimination behavior

The emerging field of artificial intelligence for neuroscience relies heavily on high-quality, standardized datasets. Data linking high-dimensional neural population dynamics with complex behavior is crucial for advancing the field of brain-computer interfaces. However, few existing datasets provide these paired modalities in a standardized, AI-ready format, limiting their immediate utility for computational modeling. Here, we present a large-scale dataset comprising simultaneous two-photon calcium imaging of the primary auditory cortex (A1) and detailed behavioral records in mice. The animals performed an auditory two-alternative forced-choice (2AFC) categorization task, classifying tones as low or high frequency by licking left or right water ports. The raw neural and behavioral data were preprocessed, synchronized, and formatted into trial-aligned multi-dimensional tensors. Comprehensive validation confirms that the recorded populations exhibit consistent, high-fidelity temporal dynamics and distinct frequency tuning across all experimental conditions. To evaluate the datasets capacity for neural decoding, we benchmarked it across diverse machine learning architectures and deep learning networks. By bridging biological recordings and behavior, this open-access dataset serves as a valuable benchmark for developing novel decoding algorithms and testing brain-inspired computational models.

neuroscience↗

bioRxiv2026

Gut-derived metabolic reprogramming drives immune aging and tissue degeneration

Aging is characterized by changes in gut microbiome, metabolic imbalance and chronic inflammation, yet how these processes integrate to drive tissue degeneration remains poorly defined. Using age-related macular degeneration (AMD) as a model of tissue aging, we identify a diet-induced metabolic-immune axis that promotes systemic and retinal degeneration. In mice, a high-fat, cholesterol-enriched (HFC) diet induced perturbations in the gut structural integrity and microbiome repertoire, as well as systemic metabolic aging signatures, prominently marked by reduced circulating histidine. Plasma histidine levels were similarly decreased in AMD patients and inversely correlated with body mass index (BMI) in control donors. These diet-induced gut microbiome changes and subsequent metabolic alterations promoted peripheral innate immune reprogramming, with expansion of inflammatory neutrophils and monocytes that infiltrated the outer retina in a mouse model. Mechanistically, the gut-derived IGF1R/AKT2 signaling acts as a central regulator of global epigenetic remodeling and systemic immune aging under high-fat conditions in C. elegans. In a mouse model with an age-dependent dry AMD-like pathology, distinct retinal pigment epithelium (RPE) subpopulations exhibited downregulation of the histidine transporter SLC7A5, linking metabolic stress to activation of MIF/CD74-dependent inflammatory signaling between RPE and infiltrating immune cells. Histidine supplementation or AKT2 phospho-state modulation attenuated systemic immune activation and rescued retinal degeneration. These findings identify histidine-axis dysregulation as a mechanistic bridge between diet-induced microbiome changes, metabolic stress, immune aging, and retinal degeneration.

cell biology↗

bioRxiv2026

Prophylactic Lipoxin A4 Attenuates Clostridioides difficile Infection by Augmenting Epithelial Barrier and Resolving Inflammation

Clostridioides difficile infection (CDI) is a leading healthcare-associated diarrhea with high recurrence rates, partially due to antibiotic-induced dysbiosis and dysregulated host inflammation. Specialized pro-resolving mediators (SPMs), such as Lipoxin A4 (LXA4), offer promise in controlling excessive inflammation and promoting tissue repair, yet their role in CDI remains unexplored. Here, we developed a compact, gas-tight gut-on-a-chip (GOC) system that reconciles the anaerobic requirements of C. difficile with the oxic needs of human intestinal epithelium, enabling physiologically relevant co-culture within a standard incubator. A CDI in vitro model was established based on this GOC system. Using the model, we demonstrated that prophylactic administration of LXA4 significantly preserved epithelial barrier integrity, attenuated pro-inflammatory cytokine secretion (IL-8 and IFN-{gamma}), and reduced bacterial colonization. Transcriptomic analysis revealed that LXA4 pretreatment upregulated genes involved in cell junction organization while downregulated immune activation pathways. These protective effects were validated in a murine CDI model, where LXA4 pretreatment reduced weight loss, pathological damage, and fecal bacterial burden. Furthermore, prophylactic administration of LXA4 synergized with vancomycin treatment further enhanced antibiotic efficacy while allowing a 50% dose reduction without compromising therapeutic outcomes. Our study establishes a robust approach for CDI research and highlights the prophylactic and adjuvant potential of inflammation-resolving strategies, offering a novel approach to mitigate CDI incidence and improve treatment outcomes.

bioengineering↗

bioRxiv2025

Multimodal AI for Single cfDNA Profiling and Cancer Screening

Cell-free DNA (cfDNA) serves as a non-invasive biomarker for cancer detection, but conventional methods face challenges due to the ultra-low abundance of tumor-derived cfDNA (ctDNA) among normal cfDNA. Though nucleosome-bound cfDNA harbors rich epigenomic features that could enable ctDNA identification by single-molecule multi-omics cross-validation, this remains unexplored due to methodological limits. Here, we developed a cfDNA sequencing approach integrating methylation, fragmentomics, and histone modifications at the single-molecule level; together with gene semantics and epigenomic annotations, these modalities were vectorized and fused to represent each cfDNA molecule. We trained a Transformer-based model (cfAI) to profile and evaluate ctDNA likelihood at molecule, gene, and sample levels. cfAI achieved [~]10-fold enrichment of cancer-derived signals over noise and reached 72.6% sensitivity at 93.1% specificity for multi-cancer detection. Our study establishes an innovative framework that overcomes the inherent signal-to-noise limitations of conventional assays and reveals biological features at molecular resolution for cancer detection.

bioinformatics↗

bioRxiv2025

The impact of downsampling on data quality, univariate measurement and multivariate pattern analysis in event-related potential research

The choice of sampling rate is a critical preprocessing step in event-related potential (ERP) research, yet its impact on different analytic approaches remains underexplored. In this study, we systematically evaluated how downsampling affects data quality measured via Standardized Measurement Error (SME), conventional univariate ERP metrics (mean amplitude, peak amplitude, peak latency, and 50% area latency), and multivariate pattern analysis (MVPA; decoding). We analyzed seven commonly studied ERP components: P3, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential collected from neurotypical young adults. Results showed that both amplitude- and latency-based scores were significantly affected by sampling rate changes, particularly for 50% area latency that presented increased SME (reduced data quality) at lower sampling rates. Amplitude-based measures and their corresponding effect sizes were more influenced by sampling rate variations, whereas latency-based measures were comparatively stable across different temporal resolutions. In contrast, multivariate decoding performance remained highly robust, with decoding accuracy and effect sizes showing minimal variation even at the lowest sampling rate (e.g., 64 Hz). Overall, these findings suggest that caution should be exercised when employing lower sampling rates for data quality and conventional univariate ERP analyses. Nevertheless, lower sampling rates (e.g., 64 Hz) may still be appropriate for decoding analyses, particularly in studies where precise temporal resolution is not critical. For researchers analyzing ERP data with similar components, noise levels, and participant populations as in this study, following these recommendations should yield robust statistical power.

neuroscience↗

bioRxiv2025

Class A PBPs reinforce the septal cell wall following initial synthesis by SEDS-bPBP pairs during bacterial cytokinesis

Bacterial cytokinesis requires the coordinated action of septal peptidoglycan (PG) synthases to ensure efficient and accurate septum formation. Two major types of synthases contribute: SEDS-bPBP and bifunctional class A PBPs (aPBPs). How their activities are organized in space and time has remained unclear. Using Deinococcus radiodurans, which often contains both constricting and closed septa in the same cell, we show that septal PG synthesis proceeds through distinct steps at different regions. Primary synthesis at the leading edge by SEDS-bPBP pairs drives constriction, whereas aPBPs act later at the lagging edge and in closed septa to thicken the wall, enhance cross-linking, and reinforce septal integrity. At the single-molecule level, both synthase types move directionally to perform processive PG synthesis at distinct sites. We also observed spatiotemporal separation of SEDS-bPBP and aPBP synthases in Staphylococcus aureus, suggesting that secondary septal PG synthesis is a conserved mechanism securing successful cytokinesis.

microbiology↗

bioRxiv2025

ComBat-Predict enhances generalizability of neuroimaging models to new sites

Neuroimaging is vital in quantifying brain atrophy due to typical aging and due to neurodegenerative diseases. To collect large samples necessary to model lifespan brain development, research consortiums aggregate images acquired across multiple study sites. Previous studies have demonstrated that this multi-site study design can lead to site-related bias, necessitating harmonization of these "site effects". However, current methodologies are unable to generalize to new sites outside the original harmonized sample, limiting translation to new sites or clinical practice. Here, we propose a method called ComBat-Predict (CB-Predict) building upon the ComBat method for site effect adjustment, which extends to data from a new site with smaller sample sizes and unknown site effects. In data from the Alzheimers Disease Neuroimaging Initiative (ADNI), our proposed method mitigates bias and yields high accuracy in predicting cortical thickness measures when generalizing the model to new data. Furthermore, we demonstrate that our proposed harmonization method can reduce site-related variance in centile scores estimated using data from the Lifespan Brain Chart Consortium (LBCC). Altogether, our results demonstrate that CB-Predict effectively harmonizes new sites and thereby enables effective translation of neuroimaging models to additional samples.

neuroscience↗

bioRxiv2025

Advancing Target Discovery Through Disease-Specific Integration of Multi-Modal Target Identification Models and Comprehensive Target Benchmarking System

Target identification is crucial for drug development. AI-driven approaches leveraging multi-omics and computational modeling can accelerate this process. However, integrating multi-modal data for disease-specific target identification and predicting translational potential remains challenging. Moreover, the absence of a systematic evaluation framework for model performance limits confidence in target reliability. We present a unified platform combining machine learning-based target identification with comprehensive benchmarking. As a testbed, we developed Target Identification Pro (TargetPro), a disease-specific model spanning 38 diseases across oncology, metabolic, immune, fibrotic, and neurological categories. TargetPro shows strong predictive performance for clinical-stage targets and reveals disease-specific patterns, underscoring the need for tailored target detection models. We next created Target Identification Benchmark (TargetBench 1.0) to rigorously assess target identification systems, including large language models, based on their ability to recover established targets and find high-quality novel candidates. This integrated approach offers a streamlined strategy to evaluate target discovery models, ultimately improving drug development efficiency.

bioinformatics↗

bioRxiv2025

Dendritic computation for rule-based flexible categorization

A hallmark of intelligent behavior is the ability to flexibly respond to external sensory inputs based on dynamically changing rules. A central question is how neurons in the brain implement computations underlying intelligent behaviors. The neocortical pyramidal neurons use their elaborated dendritic arbors to segregate a plethora of inputs and dynamically integrate them--a process known as dendritic computation--which may play important roles in rule-dependent sensory processing. However, evidence directly linking dendritic computation with intelligent cognitive behaviors has been absent. Here we combine two-photon imaging and a rule-switching flexible categorization task in mice to show that a projectome-defined extratelencephalic (ET) cortical layer 5 (L5) neurons in the auditory cortex integrate dendritic rule information and somatic sensory input to enable rule-dependent flexible categorization. The apical dendrite and soma within the same ET neurons exhibit distinct compartmental representations for sensory and rule information, with the soma predominantly encoding sensory information and the dendrites representing inferred task rule. Simultaneous optogenetic dendritic inhibition and two-photon imaging revealed that dendritic rule coding is essential for somatic output of flexible categorization. Our findings indicate that nonlinear dendritic integration of rule and sensory information constitutes a neuronal computational mechanism underlying rule-switching flexible decision-making.

neuroscience↗

bioRxiv2025

Lignature: A Comprehensive Database of Ligand Signatures to Predict Cell-Cell Communication

Ligand-receptor interactions mediate intercellular communication, inducing transcriptional changes that regulate physiological and pathological processes. Ligand-induced transcriptomic signatures can be used to predict active ligands; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we developed Lignature, a curated database encompassing intracellular transcriptomic signatures for 362 human ligands, significantly expanding the repertoire of ligands with available intracellular response signatures. Lignature compiles signatures from published transcriptomic datasets and established resources such as CytoSig and ImmuneDictionary, generating both gene- and pathway-based signatures for each ligand. We applied Lignature to predict active ligands driving transcriptomic changes in controlled in vitro experiments and real-world single-cell sequencing datasets. Lignature outperformed existing methods such as NicheNet, achieving higher accuracy in identifying active ligands at both the gene and pathway levels. These results establish Lignature as a robust platform for ligand signaling inference, providing a powerful tool to explore ligand-receptor interactions across diverse experimental and physiological contexts.

bioinformatics↗

bioRxiv2025

Attenuated adenosine mediated immune-dampening increases natural killer cell activity in early age-related macular degeneration

Non-exudative age-related macular degeneration (AMD) involves retinal pigment epithelium (RPE) dysfunction and has been linked to altered intraocular immunity. Our investigation focuses on immune cell subsets and inflammation-associated factors in the eyes with early and intermediate AMD. We observed elevated levels of activated natural killer (NK) cells and interferon-{gamma}, concurrent with reduced myeloid-derived suppressor cells (MDSCs) and adenosine in AMD eyes. Aqueous humor from AMD patients had diminished ability to dampen NK cell activation, an effect rescued by adenosine supplementation. The Cryba1 cKO mouse model recapitulated these immune alterations, and single-cell RNA-sequencing identified NK cell-related genes and NK cell-RPE interactions. Co-culture of activated NK cells with RPE cells induced barrier dysfunction and Gasdermin-E driven pyroptosis providing a functional link relevant to AMD. These findings suggest a double-hit model where elevated immune activation and loss of immune dampening mechanisms drive AMD progression. Resetting the intraocular immune balance may be a promising therapeutic strategy for managing early and intermediate AMD. O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/634301v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1516bfaorg.highwire.dtl.DTLVardef@87f505org.highwire.dtl.DTLVardef@1e725caorg.highwire.dtl.DTLVardef@622717_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Illustra tion of pro posed mechanism underlying NK cell-RPE interaction in early AMD pathogenesis. Dysregulated NK cell communicate s with stressed RPE in early AMD immunopathology. Aberrant AMD aqueous humor and retina shows increased NK cells and NK effector molecules like IFN{gamma} with reduced MDSC and adenosine in human subjects, don or eye and animal model. Activated NK cells interaction with RPE causes dysfunction and pyroptotic cell death via Gasdermin-E pathway in AMD.. Created with BioRender.com C_FIG

immunology↗

bioRxiv2025

MetaLigand: A database for predicting non-peptide ligand mediated cell-cell communication

Non-peptide ligands (NPLs), including lipids, amino acids, carbohydrates, and non-peptide neurotransmitters and hormones, play a critical role in ligand-receptor-mediated cell-cell communication, driving diverse physiological and pathological processes. To facilitate the study of NPL-dependent intercellular interactions, we introduce MetaLigand, an R-based and web-accessible tool designed to infer NPL production and predict NPL-receptor interactions using transcriptomic data. MetaLigand compiles data for 233 NPLs, including their biosynthetic enzymes, transporter genes, and receptor genes, through a combination of automated pipelines and manual curation from comprehensive databases. The tool integrates both de novo and salvage synthesis pathways, incorporating multiple biosynthetic steps and transport mechanisms to improve prediction accuracy. Comparisons with existing tools demonstrate MetaLigands superior ability to account for complex biogenesis pathways and model NPL abundance across diverse tissues and cell types. Furthermore, analysis of single-nucleus RNA-seq datasets from age-related macular degeneration samples revealed that distinct retinal cell types exhibit unique NPL profiles and participate in specific NPL-mediated pathological cell-cell interactions. Finally, MetaLigand supports single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data, enabling the visualization of predicted NPL production levels and heterogeneity at single-cell resolution.

bioinformatics↗

bioRxiv2024

Environmental polysulfides promote protein disulfide bond formation of microorganisms growing under anaerobic condition

Polysulfides are rich in anaerobic and microbial metabolism active environments. Anaerobic survival of microorganisms requires the formation of protein disulfide bond (DSB). The relation between environmental polysulfides and anaerobic DSB formation has not been studied so far. Herein, we discovered that environmental polysulfides can efficiently mediate protein DSB formation of microorganisms under anaerobic condition. We used polysulfides to treat proteins including roGFP2, Trx1, and DsbA under anaerobic condition and found that all three proteins formed intramolecular DSB in vitro. The growth of E. coli {Delta}dsbB was reduced and the amount of its intracellular protein DSB was decreased under anaerobic condition. However, treating the mutant strain with polysulfides recovered the growth and reversed DSB decrease. Treating E. coli {Delta}dsbA with polysulfides promoted DSB formation of its periplasmic roGFP2 and recovered its growth under anaerobic condition. In addition, treating Schizosaccharomyces pombe with polysulfides led to increase of the intracellular protein DSB content. Thus, our study reveals that environmental polysulfides can promote DSB formation independent of the enzymatic DSB mediating system and oxygen. In this aspect, environmental polysulfides are beneficial for the survival of microorganisms in anaerobic habitats. IMPORTANCEHow polysulfides benefit adaption of microorganisms to anaerobic environments are unclear. Our study reveals that environmental polysulfides efficiently facilitate protein DSB formation under anaerobic condition. Polysulfides contain zero valent sulfur atoms (S0), which can be transferred to the thiol group of cysteine residue. This S0 atom gets two electrons from two cysteine residues and becomes reduced H2S, leaving two cysteine residues in disulfide bond form. Anaerobic growth of microorganisms was benefited from the formation of DSB. This finding paves the way for a deeper understanding of the intricate relationship between polysulfides and microorganisms in environmental contexts.

molecular biology↗

bioRxiv2024

Dynamic epigenomic landscape of carbon-concentrating mechanisms in the model industrial oleaginous microalga Nannochloropsis oceanica

Despite their ecological and physiological significance, how carbon-concentrating mechanisms (CCM) are regulated in microalgae remains elusive. Here in the model industrial microalga Nannochloropsis oceanica, we uncovered an epigenetic regulatory mechanism for CCM via comprehensive, multi-dimensional epigenomic analyses. Our integrated study reveals the complex interplay among histone modifications, dynamic nucleosome positioning, and 3D chromatin structure in regulating gene expression during low CO2 adaptation, despite minimal DNA methylation. Histone modifications, including lysine acetylation (H3K9ac and H3K27ac), crotonylation (Kcr), and methylation (H3K4me2), were associated with active chromatin states. Significantly altered ChIP-Seq peaks were linked to 43.1% of the differentially expressed genes (DEGs). Notably, H3K4me2 exhibited a distinct dual-peak profile around the transcription start site (TSS), which is unique among microalgae and plants. Chromatin compartment dynamics were correlated with gene expression and histone modifications, particularly H3K4me2, while differentially positioned nucleosomes were associated with key CCM-related genes and transcription factors. To further elucidate the role of H3K4me2, we knocked out its methyltransferase, resulting in genome-wide H3K4me2 peak shifts, slower growth, and reduced photosynthesis. These changes were accompanied by differential expression of key genes of NoHINT and NoPMA2, whose subsequent deletion and overexpression revealed their subtle yet significant impacts on growth and photosynthetic efficiency under low CO2 conditions, with NoHINT regulating growth and NoPMA2 influencing photosynthesis. Finally, we proposed a comprehensive model for epigenetic regulation of CCM in N. oceanica, which established a foundation for enhancing microalgal productivity through targeted epigenetic modifications. HighlightsO_LIMulti-layered epigenetic modifications contribute to the regulation of CCM and its adaptation to low CO2 in N. oceanica. C_LIO_LIThe histone modification H3K4me2 regulates the growth and photosynthesis of N. oceanica under low CO2 conditions. C_LIO_LIH3K4me2 targets NoHINT and NoPMA2 in N. oceanica, with NoHINT influencing growth dynamics and NoPMA2 modulating photosynthetic efficiency under low CO2 conditions. C_LI

genomics↗

bioRxiv2024

Shared and Unique Neural Codes for Biological Motion Perception in Humans and Macaque Monkeys

Throughout evolution, living organisms have honed the ability to swiftly recognize biological motion (BM) across species. However, how the brain processes within- and cross-species BM, and the evolutionary progression of these processes, remain unclear. To investigate these questions, we examined brain activity in the lateral temporal areas of humans and monkeys as they passively observed upright and inverted human and macaque BM stimuli. In humans, the middle temporal area (hMT+) responded to both human and macaque BM stimuli, while the right posterior superior temporal sulcus (pSTS) exhibited selective responses to human BM stimuli. This selectivity was evidenced by an increased feedforward connection from hMT+ to pSTS during the processing of human BM stimuli. In monkeys, the MT region processed BM stimuli from both species, but no subregion in the STS anterior to MT was specific to conspecific BM stimuli. A comparison of these findings suggests that upstream brain regions (i.e., MT) may retain homologous functions across species, while downstream brain regions (i.e., STS) may have undergone differentiation and specialization throughout evolution. These results provide insights into the commonalities and differences in the specialized visual pathway engaged in processing within- and cross-species BMs, as well as their functional divergence during evolution.

neuroscience↗