Biology subjects

Meng, Z.

Publications and source records attributed to Meng, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Genome-wide analysis of the annexin gene family in alfalfa (Medicago sativa L.) and functional characterization of MsANN22 under drought stress

Annexins are a conserved family of calcium-dependent phospholipid-binding proteins that participate in diverse plant stress responses. However, the annexin gene family in Medicago sativa and the molecular mechanisms underlying annexin-mediated drought tolerance remain largely unexplored. In this study, 73 MsANN genes were identified in the alfalfa genome. Their phylogenetic relationships, synteny, conserved motifs, gene structures, and promoter cis-acting elements were systematically analyzed. Synteny analysis revealed 75 segmental/whole-genome duplication-associated gene pairs but no tandem duplications, indicating that large-scale duplication was the major driver of MsANN family expansion. The MsANN promoters contained ABA- and drought-responsive elements. Expression profiling identified MsANN22 as a strongly drought-responsive gene encoding a cytoplasm-localized protein. MsANN22 overexpression enhanced drought tolerance in both Arabidopsis thaliana and alfalfa: in A. thaliana, MsANN22 overexpression promoted seed germination under osmotic stress, whereas in alfalfa it enhanced antioxidant capacity, with superoxide dismutase and peroxidase activities reaching 1.38- and 1.70-fold those of wild-type plants, respectively. Consistently, MsANN22-overexpressing alfalfa accumulated approximately 20% less malondialdehyde than wild-type plants and exhibited reduced H2O2 and O2*- accumulation, as indicated by DAB and NBT staining. Yeast two-hybrid and luciferase complementation assays further supported physical interactions of MsANN22 with MsIAA and MsPR-10. These findings provide insight into the molecular mechanism by which MsANN22 mediates drought tolerance and offer a candidate gene for improving drought tolerance in alfalfa.

plant biology↗

bioRxiv2026

Dual blockade of IL-18 and IL-4Rα by a bispecific antibody for improved treatment of atopic dermatitis

Background: Atopic dermatitis (AD) is a chronic inflammatory dermatosis primarily driven by type 2 immunity, with additional type 1 and type 3 components that may limit the benifit of ILRalpha blockade alone. Interleukin18 (IL18), an epithelial alarmin genetically associated with AD and correlated with disease severity, has been implicated as an upstream amplifier of this mixed inflammatory response. Objective: To assess whether concurrent IL18 neutralization and IL4Ralpha; blockade with a bispecific antibody provides broader and more balanced suppression of AD relevant type 1, type 2, and type 3 pathways than single target inhibition. Methods: We generated FJ0125, a bispecific antibody comprising human IL18 binding protein fused to a novel humanized anti IL4Ralpha antibody, and characterized its binding, signaling, and functional activity in vitro and in vivo. The activity was evaluated in human group 2 innate lymphoid cell and primed Th2 cell assays, a keratinocyte & dendritic cell & T cell coculture system, and an MC903/Staphylococcus aureus induced dermatitis model in human IL4/IL4Ralpha; knock in mice. Results: FJ0125 bound to IL18 and IL4Ralpha; with high affinity, engaged both targets, inhibited IL4/IL13 signaling with dupilumab comparable potency, and neutralized human and mouse IL18. In vitro, FJ0125 more effectively suppressed IL18 driven lymphocyte activation and type 1, type 2, and type 3 cytokine production than IL18BPFc or IL4Ralpha; monotherapy. In vivo, FJ0125 reduced multitype cytokines, improved dermatitis, lowered serum IgE, and abrogated the paradoxical IL22 increase observed with selective IL4Ralpha; blockade. Conclusion: Dual IL18/IL4Ralpha; blockade with FJ0125 achieves coordinated suppression of multitype inflammation and represents a mechanistically substantiated therapeutic strategy for AD.

immunology↗

bioRxiv2026

A multi-tissue epigenomic atlas links the sheep non-coding genome to domestication and complex traits

Non-coding regulatory variation drives complex traits, domestication, and evolutionary adaptation, yet the sheep genome lacks high-resolution functional annotation. Here we present SheepEpimap, a multi-tissue regulatory atlas harmonizing 516 CUT&Tag histone modifications, ATAC-seq, and RNA-seq datasets across 43 adult tissues in sheep. We annotated 2.93 million cis-regulatory elements, yielding 557,441 enhancer-gene pairs and 145,407 variants with allele-specific effects. By training a sequence-to-function deep-learning model, we decoded the base-pair syntax of chromatin accessibility, annotated transcription factor motif instances genome-wide, and constructed 12,210 tissue-specific gene regulatory networks (GRNs). Integrating this resource with multi-tissue expression quantitative trait loci, selection sweeps, and genome-wide association studies prioritized non-coding variants driving domestication and complex traits. Finally, cross-species analysis revealed that sequence-conserved, tissue-matched enhancers were significantly enriched in the heritability of complex traits and diseases in humans. In summary, SheepEpimap (https://genome.ucsc.edu/s/mengzhu/SheepEpimap) provides an open-access foundational ecosystem for sheep functional genomics, precision breeding, and comparative biology.

genetics↗

bioRxiv2026

Genome-resolved and kinetic evidence for low-DO comammox-anammox synergy and acetate-stimulated nitrate reduction in IFAS biofilms

Mainstream anammox implementation for nitrogen removal is constrained by unstable nitrite supply and organic carbon requirements for nitrate byproduct removal. This study evaluated integrated fixed-film activated sludge (IFAS) biofilms to enhance anammox activity by coupling low-oxygen ammonium oxidation with volatile fatty acid (VFA)-driven nitrate reduction. Nanopore long-read metagenomic assembly recovered a high-quality, circular single-contig Candidatus Brocadia sapporoensis metagenome-assembled genome (MAG) from full-scale IFAS biofilms. This MAG encodes complete anammox metabolism, dissimilatory nitrate reduction to ammonium (DNRA) and acetate/propionate carbon transformation pathways. Metatranscriptomics showed that low dissolved oxygen (DO) upregulated Ca. B. sapporoensis genes involved in anammox, nitrate reduction, and carbon metabolism. Microaerobic assays established a DO level of 0.7 mg/L as optimal for sustaining near-maximal ammonium oxidation alongside anammox-driven total inorganic nitrogen (TIN) loss. Anoxic tests conducted in secondary effluent indicated that external acetate amendment promoted greater partial nitrate reduction and TIN loss than additional propionate amendment. Integrating this dissolved oxygen concentration with external acetate amendment in a two-stage microaerobic-anoxic system successfully achieved sequential ammonium oxidation, partial nitrate reduction, and anammox-mediated TIN removal. Stage-specific expression suggested Ca. B. sapporoensis could contribute to nitrite self-supplementation via nxrAB-mediated nitrate reduction. Overall, microaerobic ammonium oxidation and Ca. B. sapporoensis-driven partial nitrate reduction jointly sustain mainstream anammox activity. Furthermore, this study demonstrates that successful metabolic synergy depends fundamentally upon precise dissolved oxygen control and effective external acetate amendment.

microbiology↗

bioRxiv2026

PIP2 stabilizes Nav1.5 gating and links receptor signaling to cardiac late sodium current

The cardiac sodium channel NaV1.5 initiates each heartbeat by generating the rapid depolarizing upstroke of the action potential. Dysregulation of NaV1.5 gating can produce cardiac arrhythmias by slowing inactivation, increasing late sodium current (INa,L), and impairing electrical stability. Here, we show that phosphatidylinositol-4,5-bisphosphate (PIP2) is a critical membrane cofactor that stabilizes NaV1.5 gating. Acute PIP2 depletion in human iPSC-derived cardiomyocytes, produced by activation of endogenous AT1 receptors, activation of an engineered M3q-DREADD, or optogenetic recruitment of CRY2-pseudojanin, shifted voltage dependence, slowed fast inactivation, and increased INa,L. These effects were prevented by augmenting intracellular PIP2, required PLC activity when driven by Gq-coupled receptors, and were independent of downstream Ca{superscript 2} or PKC signaling. Unlike the skeletal-muscle isoform NaV1.4, NaV1.5 displayed PIP2-dependent shifts in both activation and steady-state inactivation, indicating isoform-specific lipid coupling. Induced-fit docking and molecular dynamics simulations identified a PIP2-interaction interface between the domain IV voltage sensor and pore that contains disease-linked residues. The disease-reported variant R1644C weakened and redistributed the predicted PIP2-contact network, produced elevated basal INa,L, showed enhanced sensitivity to PIP2 depletion, and caused an approximately 30-fold reduction in apparent functional PIP2 sensitivity in excised patches. These findings define a lipid-dependent mechanism that stabilizes NaV1.5 gating and reveal how physiological Gq signaling and inherited channel variants can converge on the channel-PIP2 axis to promote proarrhythmic late sodium current.

physiology↗

bioRxiv2026

High-accuracy hierarchical rRNA operon profiling resolves genomovar-level taxonomy and microdiversity using Nanopore sequencing.

Genomovar-level and intragenomic diversity cannot be resolved by conventional amplicon sequencing due to the limitation of fragment lengths and read accuracy, while the application of metagenomic profiling to a large number of samples can be resource intensive. Here, we report UltraRes-rrn, an integrated wet-lab and computational workflow for high-accuracy rrn (i.e., 16S- ITS-23S rRNA) operon profiling using Nanopore sequencing. By integrating ultra-long DNA recovery, long-read amplicon sequencing, and unique molecular identifiers (UMI)-based consensus correction, UltraRes-rrn obtains full-length 16S-ITS-23S rRNA operon consensus sequences with mean accuracies exceeding 99.98%. To achieve higher resolution, we propose a hierarchical rRNA operon profiling strategy in which concatenated 16S+23S rRNA genes (16S23S) serve as a primary and the internal transcribed spacer (ITS) provides a secondary marker. The 16S23S marker achieved discrimination at the genomovar level compared to either 16S or 23S rRNA genes alone, which mitigates the ITS-driven over-splitting observed with the full-length rrn operon and allows for larger proportion of data being classified at higher confidence thresholds. Further, ITS variation was strongly structured by tRNA occurrence patterns, suggesting that ITS can capture taxon microdiversity missed by either 16S or 23S rRNA gene sequences alone. The UltraRes-rrn workflow was applied to full-scale nitrogen removal reactors, revealing intraspecies diversity variation driven by different carbon regimes, which would not have been possible with a shorter gene sequence. In summary, UltraRes-rrn enables cost-effective community profiling at genomovar-level resolution in complex ecosystems.

microbiology↗

bioRxiv2026

Fatty acid scavenging enables cancer escape from KRAS inhibition

Although inhibitors of oncogenic KRAS have shown clinical efficacy1, resistance to KRAS inhibition is common2, and its molecular basis remains unclear. Here we show that KRASi-resistant cancer cells sustain mitochondrial bioenergetics through enhanced fatty acid (FA) metabolism, despite suppression of canonical KRAS signaling. Specifically, KRASi-resistant pancreatic cancer cells exploit macropinocytosis to scavenge FA released from adipose tissue, fueling beta-oxidation independently of KRAS-PI3K signaling. This adaptive metabolic program is driven by the adhesion G protein-coupled receptor ADGRB1, which activates non-canonical PI3K{gamma}-PAK1 signaling to stimulate macropinocytosis and maintain metabolic homeostasis under KRASi. Disruption of ADGRB1-PI3K{gamma} signaling dismantles this metabolic program and restores KRASi sensitivity. This pathway operates across multiple KRAS-mutated cancers and is associated with poor therapeutic response and outcome. These findings offer a promising strategy for overcoming KRASi resistance.

cancer biology↗

bioRxiv2026

Explainable Physicochemical Determinants of Protein Ligand Binding via Non-Covalent Interactions

Protein-ligand binding governs enzymatic catalysis, metabolic homeostasis, and therapeutic modulation. Thus, the accurate prediction of these interactions underpins modern rational drug discovery. However, existing deep-learning frameworks largely operate as black-box predictors that fail to resolve the individual residues mediating binding or decode the fundamental non-covalent forces that drive molecular recognition. To address these limitations, we present ExplainBind, an interaction-aware framework that predicts binding likelihood, localizes specific binding residues at single-amino-acid resolution rather than coarse pocket-level regions, and decodes the underlying non-covalent interaction patterns, all zero-shot, without requiring prior three-dimensional structural inputs. To support residue- and interaction-level training and evaluation, we construct InteractBind, a protein-ligand benchmark with residue-atom interaction maps. Simultaneously, benchmarking experiments demonstrate that ExplainBind consistently outperforms state-of-the-art baselines across diverse protein and ligand spaces, maintaining high precision when generalized to entirely novel sequences and chemical scaffolds. When applied to two unseen therapeutic targets, ExplainBind successfully ranks potent angiotensin-converting enzyme (ACE) inhibitors and clarifies differences in their potency via affinity-stratified interaction landscapes. Furthermore, we demonstrate the prospective utility of ExplainBind by discovering novel inhibitors and activators of L-2-hydroxyglutarate dehydrogenase (L2HGDH) through wet-lab validation, with mechanistically distinct interaction profiles providing a clear molecular rationale for their divergent functional outcomes. Collectively, these results establish ExplainBind as a powerful, generalizable tool for mechanistically informed, interpretable drug discovery.

molecular biology↗

bioRxiv2026

Peptide-to-protein data aggregation using Fisher's method improves target identification in chemical proteomics

Protein-level statistical tests in proteomics aimed at obtaining p-value are conventionally made on protein abundances aggregated from peptide data. This integral approach overlooks peptide-level heterogeneity and ignores important information coded in individual peptide data, while protein p-value can also be obtained by Fishers method of combining peptide p-values using chi-square statistics. Here we test this latter approach across diverse chemical proteomics datasets based on assessments of protein expression, solubility and protease accessibility. Using the top four peptides ranked by their p-values consistently outperformed protein-level analysis and avoided biases introduced by inclusion of deviant peptides or imputation of missing peptide values. Fishers method provides a simple and robust strategy, improving identification of regulated/shifted proteins in diverse proteomics assays.

bioinformatics↗

bioRxiv2026

Discovery of potent oligopeptides for various metabolic diseases using deep learning

Artificial intelligence (AI)-based methods are increasingly critical in peptide drug discovery, but their applications are limited in a narrow scope such as antimicrobial peptides. For indications where peptide therapy inherently excels, such as metabolism, endocrinology, and tissue regeneration, AI-based pipeline for therapeutic peptide discovery is long awaited but still unmet. Here, we propose a deep learning-based pipeline, Deepeptide, capable of identifying therapeutic oligopeptides for various metabolism-related indications. Leveraging the intrinsical relationship of disease indication -- biological processes -- molecular functions, Deepeptide discovers oligopeptides with indication-ameliorating-related molecular functions as lead candidates for indication of interest. Deepeptide was applied in five representative indications of metabolism, endocrinology, and tissue regeneration: angiogenesis, lipid metabolism, osteogenesis, glucose metabolism, and anti-angiogenesis. Overall, 62% of the identified oligopeptide candidates demonstrated significant bioactivity in vitro, with most of them showing comparable potency to the first-line drugs. Notably, the heptapeptide AP7 exhibited angiogenic potency comparable to VEGF in excisional wound splinting mouse model by promoting cell migration rather than proliferation, and hexapeptide TP6 showed significant dual-efficacy against hyperlipidemia and obesity in high-fat diet mice by inhibiting lipid synthesis and regulating gut microbiota. These findings highlight the potential and generalizability of Deepeptide in therapeutic oligopeptide discovery for metabolic diseases.

bioinformatics↗

bioRxiv2025

Brain-wide Genome Editing via STEP-RNPs for Treatment of Angelman Syndrome

Brain-wide genome-editing remains a major hurdle for the treatment of neurogenetic disorders. Here, we report a non-viral, non-nanoparticle, chemical modification-based method, called Stimuli-Responsive Traceless Engineering Platform (STEP) that achieves highly efficient and brain-wide genome-editing of neurons in mice. Using cholesterol-based STEP as a lead, we show that a single administration of STEP-ribonucleoproteins (RNPs) results in functional genetic rescue with significant improvements across a battery of neurobehavioral domains in the Angelman syndrome (AS) mouse model. No significant off-target events or general toxicity effects are observed. STEP-RNPs are also highly efficient at editing human neurons and cortical brain organoids differentiated from AS patient-derived iPSCs. scRNA-seq analysis confirms functional genetic rescue via reactivation of Ube3a/UBE3A expression in human and mouse STEP-RNP-treated neuronal cells. Genome editing via STEP-RNPs has broad applications and the potential to treat many other neurogenetic disorders.

genetics↗

bioRxiv2025

A multi-tissue atlas of genetic regulatory effects in sheep

Characterizing the impact of genomic variants on genome function and ultimately complex traits in livestock is essential for the development of sustainable precision agriculture and comparative genomics. Here, as part of the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we present the pilot phase of the SheepGTEx resource through analyzing 6,761 RNA-sequencing samples of 51 primary tissues in a multi-breed population of sheep. We identify millions of regulatory variants associated with seven types of molecular phenotypes, and fine-map 322,468 primary and 114,017 non-primary effects, revealing a high degree of regulatory allelic heterogeneity. We systematically characterize the pleiotropic effects of these variants on molecular phenotypes, assess their context-specific regulatory patterns across tissues, breeds, sexes, and developmental stages, as well as explore their evolutionary constraints across mammals. Finally, we demonstrate the substantial potential of the SheepGTEx resource (https://sheepgtex.farmgtex.org), by providing examples of regulatory mechanisms underpinning 34 complex traits, population divergence between European and Asian breeds, and adaptive evolution in sheep over the past ten millennia.

genomics↗

bioRxiv2025

Stochastic resonance is behind single cell proteomics success

Stochastic resonance is a phenomenon of noise-aided detection of below-threshold signal at the expense of the linearity of signal quantification. Here we demonstrate that peptide signal from single cell proteomes multiplexed by tandem mass tag (TMT) with a carrier proteome is below the nominal detection threshold in the Orbitrap mass analyzer and is registered in the mass spectra largely due to stochastic resonance amplification. This amplification is of the order of 0.5-2.5 single cell equivalents. Consequently, spiking by the corresponding amount of a constant "pedestal" proteome lifts the signal of the analyte single cell proteome above the detection threshold, improving detection and largely restoring linearity of quantification.

biochemistry↗

bioRxiv2025

High-ratiO partiaL proteolysiS with carriER proteome (HOLSER) Enables Global Structure Profiling and Site-resolved Elucidation of Ligand-Protein Interactions

Understanding how cellular proteins interact with their environment, including endogenous and exogeneous molecules, is critical for elucidating mechanisms of cellular regulation and drug action. Partial proteolysis-based techniques offer peptide-level resolution of ligand-induced conformational changes but are limited by modest proteome coverage and depth, as well as sensitivity to the experimental conditions. To overcome these limitations, we developed higH ratiO partiaL proteolysiS with carriER proteome (HOLSER), an efficient workflow that features extended digestion time for reduced peptide yield variability as well as tandem mass tag multiplexing that includes full digests for enhanced proteome depth and sequence coverage as well as higher precision of peptide abundance measurements. We demonstrate HOLSER capabilities of probing structural changes on the scale of specific binding sites for kinase target mapping, individual protein domains for structural mapping of the FKBP-mTOR complex in response to rapamycin as well as global proteome structure profiling.

biochemistry↗

bioRxiv2025

Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance

The rapid evolution of extended-spectrum {beta}-lactamases (ESBLs) represents a global health threat, undermining the efficacy of {beta}-lactams, the most extensively used antibiotic class. To elucidate the evolutionary dynamics underlying {beta}-lactam resistance, we constructed a comprehensive combinatorial mutant library comprising all 55,296 possible TEM-1 {beta}-lactamase variants integrating 18 clinically observed mutations across 13 key residues. Over eight million empirical fitness measurements were obtained under selection pressure with both a native antibiotic substrate (ampicillin) and a novel antibiotic (aztreonam), establishing the largest experimentally determined fitness landscape for antibiotic resistance to date. Through graph-theoretic and epistatic analyses, we discovered that selection with ampicillin resulted in weak epistasis, with mutants rarely surpassing the fitness of the wild-type enzyme. Conversely, aztreonam selection elicited extensive higher-order epistasis, generating a rugged fitness landscape characterized by increased phenotypic unpredictability. Interpretable machine-learning analyses identified context-dependent epistatic interactions necessary for achieving high-level aztreonam resistance. Further evolutionary statistical analyses, including direct coupling analysis and latent generative landscapes, showed that top-performing TEM-1 variants consistently adhered to conserved epistatic patterns found in naturally occurring {beta}-lactamases. Our findings demonstrate that higher-order epistasis critically shapes fitness landscape ruggedness when enzymes adapt to novel substrates, whereas adaptations to native substrates exhibit predictably smoother landscapes. This integrated experimental and computational framework provides a foundation for predictive evolutionary pharmacology, enabling assessments of newly developed {beta}-lactams or emerging {beta}-lactamase variants for their potential contribution to ESBL evolution. Importantly, incorporating graph-theoretically informed evolutionary constraints can strategically disrupt evolutionary pathways, presenting a viable approach to mitigate the rise of antibiotic resistance.

evolutionary biology↗

bioRxiv2025

PatternExtract: A facile, scalable pipeline for point pattern generation from spatial imaging data

We present PatternExtract, an open-source pipeline that generates accurate spatial point patterns from RGB pathology images and cell coordinate data without relying on composite channels or proprietary software. Using a novel two-kernel tissue segmentation method combined with automated pixel classification in QuPath, PatternExtract precisely excludes tissue artifacts such as necrosis and blood vessels to define spatial windows. Optimized on 568 diffuse large B-cell lymphoma images and validated on an independent cohort, the pipeline enables robust spatial analyses.

bioinformatics↗

bioRxiv2025

Hippocampal single-cell RNA Atlas of chronic methamphetamine abuse-induced cognitive decline in mice

BackgroundChronic methamphetamine abuse leads to cognitive decline, posing a significant threat to human health and contributing to loss of productivity. However, the intricate and multifaceted mechanisms underlying methamphetamine-induced neurotoxicity have impeded the development of effective therapeutic interventions. MethodsTo establish a mouse model of cognitive decline induced by chronic methamphetamine exposure, we employed a large sample size and conducted two behavioral tests (Y-maze and novel object recognition test) at 2 and 4 weeks post-exposure. Subsequently, single-cell RNA sequencing was utilized to delineate the mRNA expression profiles of individual cells within the hippocampus. Comprehensive bioinformatics analyses, including cell clustering and identification, differential gene expression analysis, cellular communication analysis, pseudotemporal trajectory analysis, and transcription factor regulation analysis, were performed to elucidate the cellular-level changes in mRNA profiles caused by chronic methamphetamine exposure. ResultsOur findings demonstrated impairments in working memory, spatial cognition, learning, and cognitive memory. After 4 weeks of behavioral testing, we identified diverse cell types in the hippocampi of METH- and saline-treated mice through scRNA-seq, including glial cells, stromal cells, vascular cells, and immune cells. We observed that methamphetamine exerts cell-specific effects on gene expression changes associated with neuroinflammation, blood-brain barrier disruption, neuronal support dysfunction, and immune dysregulation. Furthermore, cross-talk analysis revealed extensive alterations in cellular communication patterns and signal changes within the hippocampal microenvironment induced by methamphetamine exposure. Pseudotime analysis predicted hippocampal neurogenesis disorders and identified key regulatory genes implicated in chronic methamphetamine abuse. Transcription factor analysis uncovered regulators and pathways linked to astrocyte-mediated neuroinflammation, endothelial junction integrity, microglial synaptic remodeling, and oligodendrocyte-supported neuronal cell bodies and axons. Additionally, it highlighted the role of neural precursor cells in various forms of neurodegeneration. ConclusionsThis study establishes a robust mouse model of cognitive impairment induced by chronic methamphetamine exposure. It provides valuable biological insights, characterizes the single-cell atlas of the hippocampus, and offers novel directions for investigating neurological damage associated with chronic methamphetamine-induced cognitive decline.

pharmacology and toxicology↗

bioRxiv2025

BABA: Naturalistic fMRI and MEG recordings during viewing of a reality TV show

Prior neuroimaging datasets using naturalistic listening paradigms have predominantly focused on single-talker scenarios. While these studies have been invaluable for advancing our understanding of speech and language processing in the brain, they do not capture the complexities of real-world multi-talker environments. Here, we introduce the "Le Petit Prince (LPP) Multi-talker Dataset", a high-quality, naturalistic neuroimaging dataset featuring 40 minutes of electroencephalogram (EEG) and 7T functional magnetic resonance imaging (fMRI) recordings from 26 native Mandarin Chinese speakers as they listened to both single-talker and multi-talker speech streams. Validation analyses conducted on both EEG and fMRI data demonstrate the datasets high quality and robustness. Additionally, the dataset includes detailed transcriptions and prosodic and linguistic annotations of the speech stimuli, enabling fine-grained analyses of neural responses to specific linguistic and acoustic features. The LPP Multi-talker Dataset is well-suited for addressing a wide range of research questions in cognitive neuroscience, including selective attention, auditory stream segregation, and working memory processes in naturalistic listening contexts.

neuroscience↗