Biology subjects

Jiang, R.

Publications and source records attributed to Jiang, R..

At least 19 recordsLinked to original sources

bioRxiv2026

ProtCell: a foundation model for single-cell proteomics

Single-cell proteomics provides a direct functional readout of cellular states, but targeted assays measure limited and non-identical protein panels, fragmenting information across experiments. Here, we present ProtCell, a foundation model developed using approximately 380 million cells from 98 datasets for pretraining and evaluation. ProtCell combines protein-identity and continuous-abundance embeddings in a transformer encoder and learns cell- and protein-level representations through masked abundance modeling. ProtCell first connects fragmented profiles through transferable annotation and batch correction while preserving biological organization. It then extends limited profiles through abundance reconstruction and inference of an unmeasured cytotoxic protein program enriched in natural killer cells and memory CD8$^+$ T cells. The learned protein dependencies further guide the selection of compact panels that retain information for profile reconstruction and biological classification. ProtCell thus provides a framework for connecting fragmented proteomic profiles, extending information beyond measured panels and guiding more informative measurements.

bioinformatics↗

bioRxiv2026

EpiZoo: a DNA sequence-aware foundation model for cross-species single-cell epigenomics

Large-scale single-cell epigenomic atlases characterize chromatin regulatory landscapes across diverse biological contexts, spanning cell types, tissues, individuals and species. Foundation models provide an opportunity to capture the full spectrum of cellular diversity in these atlases, yet current models remain largely confined to individual species by genomic coordinate dependence and overlook regulatory information encoded in DNA sequences. Here we introduce EpiZoo, a DNA sequence-aware foundation model for cross-species single-cell epigenomics. EpiZoo converts million-dimensional single-cell epigenomic profiles from diverse species into compact cell sentences that integrate DNA-encoded regulatory information, sequence-independent epigenomic context and accessibility-based importance. Built around a mixture-of-experts transformer and containing 2.6 billion parameters in total, EpiZoo is pretrained on our manually curated multi-species Omni-scATAC corpus of approximately 20.9 million cells to learn regulatory programs across species. On external datasets excluded from pretraining, EpiZoo achieves state-of-the-art performance in fundamental single-cell analysis tasks, including feature extraction, cell type annotation and data imputation. Its sequence-aware architecture enables extension to evolutionarily diverse species, and supports comparative analysis of regulatory conservation and divergence during primate evolution. Benefiting from this modeling design, EpiZoo enables context-aware prioritization of somatic mutations in cancer and prediction of cell-type-specific chromatin accessibility from DNA sequences across genomic regions and species.

bioinformatics↗

bioRxiv2026

Interocular suppression weakens binocular disparity integration in the dorsal visual stream

A unified percept depends on the coordinated interplay of sensory integration and suppression, yet how this interaction is implemented across cortical representations remains unclear. Using binocular vision as a tractable model system, we combined fMRI with multivariate analyses to test how interocular suppression reshapes binocular disparity integration across human visual pathways. Local binocular rivalry weakened near-far disparity decoding, with disruption increasing along the dorsal pathway from V1 to V3A and intraparietal sulcus (IPS), but no reliable loss in ventral regions. In V3A, anticorrelated random-dot stereograms showed an inverted representational relationship relative to correlated stimuli, and this structure was abolished under suppression, indicating disrupted non-canonical disparity integration. Informational connectivity further revealed that suppression selectively weakened V3A-centered coordination while largely sparing coupling among early visual areas. Together, these findings identify higher-level dorsal regions as a key substrate for coordinating integrative and suppressive computations during unified percept formation.

neuroscience↗

bioRxiv2026

SRGAP2 limits experience-dependent structural synaptic plasticity in adult cortical circuits

In cortical circuits, synaptic plasticity involves either changes in the weight of pre-existing synapses, referred to as functional synaptic plasticity, or synapse formation and elimination, referred to as structural synaptic plasticity. Experience-dependent structural synaptic plasticity is prominent in juvenile cortical circuits during critical periods of development but drastically decreases in adult cortical circuits. The molecular mechanisms limiting experience-dependent structural synaptic plasticity in adult cortical circuits remain largely unknown. During development, the postsynaptic protein SRGAP2 limits the formation of both excitatory (E) and inhibitory (I) synapses in cortical pyramidal neurons (CPNs) and promotes their maturation. SRGAP2 expression is maintained throughout adulthood but its synaptic function in the adult cortex has not been explored. Using longitudinal 2-photon (2P) imaging of dendritic spine dynamics in layer 2/3 CPNs and found that this form of sensory deprivation induces a striking increase in structural synaptic plasticity favoring spine formation in adult constitutive SRGAP2+/- mice, in contrast to wild-type adult mice, where whisker trimming does not induce significant structural synaptic plasticity. Using conditional, cell-type specific, deletion of SRGAP2, we demonstrate that this experience-dependent structural synaptic plasticity requires both of SRGAP2 in expression L2/3 CPNs and in microglia. We previously demonstrated that the human-specific paralogs SRGAP2B/C inhibit all known functions of SRGAP2, phenocopying SRGAP2 haploinsufficiency, our results suggest that SRGAP2B/C might endow increased levels of experience-dependent structural synaptic plasticity to human pyramidal neurons in adult cortical circuits.

neuroscience↗

bioRxiv2026

RegFM: an interpretable context-aware foundation model for human transcriptional regulation

Transcriptional regulation is governed by interactions between cis-regulatory elements (CREs) and trans-acting regulators in a context-specific manner. Although DNA and single-cell foundation models have enabled modeling regulatory biology at scale, most represent either sequence or cellular state alone, limiting their ability to capture context-dependent gene regulation. Here we present RegFM, a context-aware foundation model for human transcriptional regulation. RegFM treats transcriptional regulation as a dialogue between cis-regulatory sequences (e.g., CREs) and trans-acting regulators (e.g., transcription factors (TFs) and chromatin regulators (CRs)) by coupling long-range CRE representations with TFs and CRs activity. Trained on large-scale ENCODE and CELLxGENE transcriptomic profiles, RegFM learns gene-centered regulatory representations that generalize across unseen cellular contexts. In a wide range of tasks, including gene expression prediction, cis-regulatory element annotation, bivalent promoter and dosage-sensitivity classification, and perturbation-response prediction, RegFM consistently improves over existing methods. RegFM emerges as a scalable and interpretable framework for modeling human transcriptional regulation and provides insights into context-dependent gene regulatory programs.

genomics↗

bioRxiv2026

HoloCell: A Generative Foundation Model for Holistic Cellular Modeling

Single-cell multi-omics technologies have recently advanced to enable the profiling of epigenomic, transcriptomic, and proteomic layers within individual cells, offering new opportunities to characterize cellular states as integrated biological systems. However, developing a unified framework that can seamlessly integrate diverse omics modalities and remain robust to heterogeneous modality missingness remains challenging. Existing methods are often designed for specific modalities or modality pairs, relying on dataset-specific training or paired measurements. Here we present HoloCell, to our knowledge the first generative foundation model for joint representation learning and generative modeling across all three major single-cell omics modalities, i.e., epigenomics, transcriptomics, and proteomics. HoloCell contains over 860 million parameters and is pretrained on the Human-Multi-Omics-Corpus, which comprises approximately 468 million single-cell profiles across these three omics layers, corresponding to over 425 billion tokens. HoloCell introduces a a simple yet biologically motivated hierarchical tokenization strategy that encodes cis-regulatory elements, genes, and proteins as structured tokens within a shared modeling framework. We evaluated HoloCell across single-omics representation learning, paired multi-omics integration, unpaired multi-omics alignment, and cross-modal generation via iterative diffusion and remasking, demonstrating its superior performance and flexibility across diverse omics tasks. From a representation perspective, HoloCell provides a unified digital mapping of cellular states across multiple omics layers, capturing cell heterogeneity as an integrated system. From a generation perspective, its iterative diffusion and remasking frame-work permits flexible generation orders beyond fixed left-to-right causality, enabling in silico simulation of multi-omics information flow. Together, these capabilities position HoloCell as a versatile foundation model toward the emerging concept of a virtual cell, offering both systematic characterization and generative simulation of cellular systems within a unified framework.

bioinformatics↗

bioRxiv2026

scMTG reconstructs single-cell temporal dynamics with Markov transition generators

Single-cell time-series data offer a unique opportunity to study how cell states emerge, evolve, and diversify over time. However, cells are destructively measured across time points and individual cells cannot be directly tracked, making it challenging to reconstruct cell-state transitions and uncover the dynamic regulatory programs. Existing methods are predominantly based on optimal transport and typically require predefined low-dimensional representations, which can limit scalability, flexibility, and mechanistic interpretability. Here we present scMTG, a single-cell Markov transition generative framework that jointly learns cell representations and temporal cell-state transitions from unpaired single-cell time-series data. In a series of experiments, scMTG demonstrates superior performance in interpolating held-out missing-time-point data and inferring cross-time-point transition matrices, compared with state-of-the-art methods. More importantly, the learned Markov transition generator provides an interpretable framework for identifying the molecular programs that drive cell-state changes, enabling characterization of cell fate decisions and construction of time-resolved regulatory networks. Together, scMTG provides a unified generative framework for reconstructing cellular dynamics and uncovering regulatory programs that shape development, differentiation, and disease progression. scMTG is available as open-source software at https://github.com/liuq-lab/scMTG.

bioinformatics↗

bioRxiv2026

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

Functional neuroimaging aims to uncover brain processes underlying behavior and disease, yet studies are often underpowered to detect these effects. How this literature has shaped our understanding of brain function remains unknown, and little guidance exists for planning better powered studies. An underappreciated barrier is that commonly reported effect sizes across the brain are inflated, biasing study planning. Here, we introduce a correction for this inflation bias and show how more accurate studies can be planned using corrected effect size benchmarks from a mega-analysis of 63 typical studies across seven large datasets (52,979 participants). We find that common methods of planning studies based on uncorrected effects lead to roughly half the expected detections at typical sample sizes, with limited spatial overlap with original findings. These missed effects collectively explain meaningful additional variance in the desired outcome. We show how to recover missed effects by planning not only for power but also for a target number of detections via corrected benchmarks, or by taking a whole-brain approach with multivariate effects that individual research groups can detect (n < 50 compared to n > 1,000 for a typical univariate effect). These findings lay the groundwork for more informed study planning and a richer understanding of the widespread nature of brain effects, with implications for shared challenges (and solutions) across biomedicine.

neuroscience↗

bioRxiv2026

A Deep Learning Framework for Predicting Gut Microbe-Host Receptor Interactions

MotivationGut microbiota regulates host health through complex protein-protein interactions. However, deciphering this specific interactions between microbiota and human receptors remains a significant challenge due to the lack of specialized computational tools. ResultsLeveraging the hypothesis of cell communication and relevant data, HMI-Pred initially builds an ensemble classifier to screen for potential ligand sequences within microbial genomes. It then jointly evaluates sequence semantics and molecular docking to predict potential microbe-host receptor interactions.HMI-Pred achieved robust performance with F1-scores of 0.901 for microbial ligand identification and 0.883 for interaction prediction. Application to 332,381 microbial proteins revealed distinct interaction patterns: histone deacetylases (HDACs) served as broad-spectrum targets (mean score > 0.80), while G protein-coupled receptors (GPCRs) exhibited high specificity (scores 0.42-0.61). Furthermore, literature mining validated over 47% of the functional predictions, and specific immunomodulatory interactions were confirmed in Akkermansia muciniphila.HMI-Pred provides a valuable computational tool for decoding host-microbe signaling networks and facilitating the discovery of microbiome-based therapeutic targets. AvailabilityThe source code and documentation are available at https://github.com/YangLab-BUPT/HMI-Pred. Contactlihm@bupt.edu.cn

microbiology↗

bioRxiv2026

HitAnno: Atlas-level cell type annotation based on scATAC-seq data via a hierarchical language model

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) has emerged as a core technology for dissecting cellular epigenomic heterogeneity and gene regulatory programs. With the emergence of atlas-level scATAC-seq datasets, cell type annotation increasingly faces challenges arising from unprecedented data scale and increased cell-type diversity, which together place stringent demands on model reliability and robustness. Here, we introduce HitAnno, a hierarchical language model capable of accurate and scalable cell type annotation in atlas-level scATAC-seq data. Leveraging selected cell-type-specific peaks to construct "cell sentences", HitAnno employs a two-level attention mechanism that captures accessibility profiles hierarchically. Extensive evaluations show that HitAnno robustly annotates both major and rare cell types across multiple settings, including intra-dataset, cross-donor and inter-dataset annotation. The hierarchical attention mechanisms of the model reveal co-accessibility patterns among peaks and dependencies across higher-order peak sets, ensuring an interpretable annotation process. Training on a 31-cell-type human atlas, HitAnno can directly annotate new query datasets without retraining and is accessible through an online interface. Our model identifies heterogeneous subgroups within mixed labeled cells from unseen datasets, demonstrating its potential to assist researchers in refining existing cell atlases.

bioinformatics↗

bioRxiv2026

MacroH2A-Mediated Gene Repression through Nucleosome Compaction and Remodeling Inhibition

MacroH2A (mH2A) is a histone variant primarily implicated in heterochromatin maintenance and transcriptional repression, yet how its conserved histone-fold and its variant-specific domains reshape chromatin to enforce gene silencing remains poorly understood. Here, we dissect the domain-specific contributions of mH2A to nucleosome dynamics and chromatin remodeling. We show that, in addition to its linker region, the C-terminal tail of mH2A histone-fold also stabilizes nucleosome entry/exit DNA. Cryo-EM analysis reveals that this C-terminus tracks along nucleosomal DNA toward the dyad, adopting an on-dyad binding mode reminiscent of linker histone H1. Additionally, the mH2A linker potently inhibits DNA translocation by both the INO80 and Chd1 chromatin remodelers, while the histone fold selectively suppresses INO80 activity. In contrast, the macro domain has no detectable impact on terminal DNA accessibility or remodeling. Together, our results uncover a previously unappreciated architectural role for the mH2A histone fold and establish the linker domain as a dominant regulator of nucleosome dynamics and chromatin remodeling, providing a mechanistic framework for how mH2A enforces transcriptional repression.

biochemistry↗

bioRxiv2025

Transcriptomic and functional characterization indicate sexual dimorphism of discrete circadian neuron subtypes

While many sexually dimorphic behaviors exhibit distinct time-of-day preferences, our understanding of how sex shapes the molecular and circuit properties of central brain neurons remain limited. Here, we uncover the transcriptomic and circuit basis of sexual dimorphism within the Drosophila circadian network. By leveraging single-cell RNA sequencing of male and female clock neurons, we identify specific subsets of dorsal lateral neurons (LNds), dorsal neurons 1p (DN1ps), and dorsal neurons 3 (DN3s) with dramatic dimorphic gene expression profiles. These sex differences are primarily characterized by cell-type-specific expression of genes involved in neural connectivity, particularly cell adhesion molecules (CAMs). Focusing on the dimorphic Cry-negative E3 LNds, we show that they form functionally active, synaptic connections with downstream doublesex-expressing pC1 and pCd-1 neurons, which serve as central regulators of dimorphic behaviors. Moreover, we demonstrate that formation and maintenance of these connections are mediated at least in part by sex-enriched CAMs, dpr9 in males and dpr3 in females. Thus, our work reveals sexual differentiation mechanisms at both the molecular and circuit levels, identifying specific molecules that sculpt sex-specific pathways to link the circadian clock to dimorphic outputs.

molecular biology↗

bioRxiv2025

Comparative genomics of epiphytic and endophytic bacterial culture collections from Arabidopsis thaliana in Otautahi (Christchurch), Aotearoa New Zealand

Bacterial culture collections are essential resources for exploring the diversity of microorganisms and their interactions with each other and their hosts. Here, we report on the sequencing of the first 129 bacterial isolates, representing 34 genera, from a culture collection of more than 600 bacterial strains originally isolated from leaves of a naturalised Arabidopsis thaliana population from [O]tautahi (Christchurch), Aotearoa New Zealand. Epiphytic (leaf surface), and endophytic (apoplastic) bacteria were isolated separately from the same leaves, providing complementary insights into both compartments. The recovered isolates encompass the dominant taxa typically associated with the Arabidopsis phyllosphere, including Pseudomonas, Sphingomonas, Methylobacterium, and Flavobacterium. Their full genome assemblies (BUSCO average completeness > 99%, checkM average completeness > 97% and average contamination < 1%) were analysed and compared to assess genomic features across epiphytic and endophytic lineages. While the epiphytic and endophytic strain collections did not show large genomic differences, certain functional categories differ, such as terpene biosynthesis and biofilm formation being enriched in epiphytic strains, while arginine biosynthesis and carbohydrate degradation were associated with endophytic strains. These data provide a genomic foundation for future experimental work on leaf-associated microbial ecology and plant-microbe interactions. To our knowledge, this is the first Arabidopsis leaf culture collection established from a Southern Hemisphere source.

microbiology↗

bioRxiv2025

Information Network Flux interprets the importance of regulatory circuits in the protein and gene spaces

Cells receive information from environment, make decisions, and execute biological functions. This sensor-actuator controlling mode is common, and has benefits for system stability especially when there exist feedback-loops for action correction. In cells, transcription factors (TFs) play the role of "brain" since they control the expression levels of about 20,000 protein-coding genes. However, the detailed topological structure of the regulating relationships among them is far from clear. Circuitlike pathways, which usually contain protein-protein interactions (PPIs), TF-gene regulations and gene expressions, are pervasive in cells, however, their importance is yet to be systematically elucidated by unifying information at both protein and gene levels. Here we developed Information Network Flux (INF), an algorithm that could simulate information transmit on multi-layer networks, and integrate protein and gene level information for cell type specific pathways that respond to given perturbation. We used topological analysis to identify regulatory circuits and found that the TFs participating in these circuits, especially those acted as "sensor" or "actuator" are highly relevant to cellular response to external signals. At the gene level, circuit genes serve as efficient cell type indicators, which showed better performance than commonly used gene expression levels. At the circuit level, shorter circuits enrich housekeeping functions and longer circuits exhibit increased tissue-specificity. These findings suggest that these circuit pathways may contribute to the identity and stability of cell states.

biophysics↗

bioRxiv2025

Hidden-driver inference reveals synergistic brain-penetrant therapies for medulloblastoma

Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood-brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis), a systems biology framework that computationally prioritizes synergistic, BBB-permeable drug combinations by identifying hidden drivers sustaining oncogenic programs. Integrating MB-specific networks, transcriptomic data, and drug-gene interactions, SINBA nominated 32 candidates, of which 19 were experimentally validated as synergistic. Through iterative prioritization and experimental refinement, the MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in xenograft and immunocompetent models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of developmental origins and immune reprogramming. These findings establish SINBA as a computationally assisted discovery framework for clinically actionable combinations in high-risk MB.

cancer biology↗

bioRxiv2025

Polyclonal origins of human premalignant colorectal lesions

Cancer is generally thought to be caused by expansion of a single mutant cell1. However, analyses of early colorectal cancer lesions suggest that tumors may instead originate from multiple, genetically distinct cell populations2,3. Detecting polyclonal tumor initiation is challenging in patients, as it requires profiling early-stage lesions before clonal sweeps obscure diversity. To investigate this, we analyzed normal colorectal mucosa, benign and dysplastic premalignant polyps, and malignant adenocarcinomas (123 samples) from six individuals with familial adenomatous polyposis (FAP). Individuals with FAP have a germline heterozygous APC mutation, predisposing them to colorectal cancer and numerous premalignant polyps by early adulthood4. Whole-genome and/or whole-exome sequencing revealed that many premalignant polyps--40% with benign histology and 28% with dysplasia--were composed of multiple genetic lineages that diverged early, consistent with polyclonal origins. This conclusion was reinforced by whole-genome sequencing of single crypts from multiple polyps in additional patients which showed limited sharing of mutations among crypts within the same lesion. In some cases, multiple distinct APC mutations co-existed in different lineages of a single polyp, consistent with polyclonality. These findings reshape our understanding of early neoplastic events, demonstrating that tumor initiation can arise from the convergence of diverse mutant clones. They also suggest that cell-intrinsic growth advantages alone may not fully explain tumor initiation, highlighting the importance of microenvironmental and tissue-level factors in early cancer evolution.

cancer biology↗

bioRxiv2025

Human-scATAC-Corpus: a comprehensive database of scATAC-seq data

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) profiles chromatin accessibility at cellular resolution, making it possible to reveal epigenomic landscapes that govern gene regulation in a variety of cells. Nevertheless, heterogeneous feature spaces and complex processing pipelines have impeded the construction of an ensemble resource capable of supporting diverse downstream analytical scenarios. To address this gap, we present Human-scATAC-Corpus (https://health.tsinghua.edu.cn/human-scatac-corpus/), a comprehensive database of human scATAC-seq comprising 5,407,621 cells from 35 datasets across 37 tissues or cell lines. To support complementary use cases, each dataset is distributed in three aligned formats: cell-by-candidate cis-regulatory element matrices for cross-dataset integration, raw fragment files for flexible processing, and cell-by-peak matrices for within-dataset analyses. This resource spans diverse biological contexts and includes rich metadata, enabling method benchmarking and development, as well as pretraining of foundation models. The website offers searchable browsing, detailed dataset pages, on-demand downloads, and tutorials. EpiAgent, a foundation model pretrained on Human-scATAC-Corpus, is further integrated to provide online analyses, including reference mapping, embedding extraction, and cell type annotation. Human-scATAC-Corpus establishes a unified and scalable substrate for single-cell epigenomics and is intended to accelerate discovery while standardizing evaluation across tasks. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/674505v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@e2c4c3org.highwire.dtl.DTLVardef@1e3eaf8org.highwire.dtl.DTLVardef@5768c1org.highwire.dtl.DTLVardef@9e96ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Large-language-model-based antibiotic resistance gene prediction and resistomes mining in cyanobacterial blooms

The rapid spread of antibiotic resistance genes (ARGs) in aquatic ecosystems poses a serious public health threat. Conventional ARG detection methods, including sequence-alignment and machine learning, are limited by high false-negative or false-positive rates, especially due to sequence diversity and class imbalance in metagenomic data. Moreover, few tools offer comprehensive solutions for ARG identification, classification, and functional annotation. To address these limitations, we developed ESMARG, a novel protein language model framework based on ESM1v, for accurate ARG identification, classification, and annotation. Trained with both ARG and abundant non-ARG sequences from real-world metagenomes to enhance robustness and reduce false positives, ESMARG significantly outperformed traditional alignment-based (BLAST, DIAMOND) and recent deep learning models (ARGNet, ARG-SHINE), achieving 0.998 precision, 0.939 recall, and 0.968 F1-score for ARG identification. Functional and mechanism classification modules also demonstrated high accuracy (0.986 and 0.987) and computational efficiency. We applied ESMARG to analyze 26 cyanobacterial aggregate (CA) metagenomes collected from Lake Taihu across a full annual cyanobacterial harmful algal bloom (CyanoHAB) cycle. A total of 110 ARGs, spanning 24 drug classes and multiple resistance mechanisms, were detected, with seasonal shifts in ARG abundance and composition. Strong correlations were identified between CA resistomes and microbial community structure, especially with cyanobacteria. Further, ARG abundance was shaped by both biotic factors--such as cyanobacterial dominance and bacterial functional profiles--and abiotic variables, including biochemical oxygen demand and water temperature. Our findings demonstrate the power of ESMARG for high-resolution ARG profiling and reveal complex ecological interactions between resistomes, microbiomes, and environmental factors in CyanoHAB. HighlightsO_LIDeveloped ESMARG model outperforms BLAST/other deep learning models in ARG detection C_LIO_LIARG showed seasonal dynamics in cyanobacterial metagenomes across algal bloom cycle C_LIO_LIARG was strongly correlate with microbiome structure and cyanobacteria dominance C_LIO_LIARG abundance was shaped by cyanobacteria, bacterial functions, BOD and temperature C_LI

bioinformatics↗