生物学最新文献

This study examines competition models based on the Lotka-Volterra form that incorporate starvation-driven diffusions (SDD). Such dispersal assumes that species disperse in response to resource abundance or scarcity in a heterogeneous habitat. The primary objective of this study is to examine how SDD, in combination with diverse interspecific interactions, affects species' fitness and coexistence states. To this end, the study introduces a refined classification for competing interactions based on a novel metric that quantifies the variability of resource heterogeneity across the environment. This approach contrasts with traditional models that assume uniform diffusion within homogeneous environments. This study investigates the local stability of two semitrivial steady states and establishes the existence and uniqueness of positive steady states by eigenvalue analysis and monotone dynamical systems theory. Through this analytical exploration, the study reveals that the interplay between species' dispersal strategies and the varying intensities of interspecific competition significantly impacts ecological outcomes.

This work examines the dynamics of a discrete-time plankton interaction model, in which phytoplankton generate toxins and are vulnerable to external contamination. The model includes a Holling Type-II predation response and uses a piecewise constant argument approach to break it up into smaller pieces. This keeps the ecological realism of the continuous system while making it possible to study complex discrete-time behaviors. Our focus is on the formation of Neimark-Sacker bifurcation, a phenomena associated with the initiation of quasi-periodic oscillations in population densities. We show how toxin buildup and outside contamination can make plankton populations unstable, which could cause blooms to happen in an irregular way, using stability analysis and numerical simulations. The results show how useful discrete-time models are for capturing rapid changes in ecosystems, such damaging algal blooms. They also give ideas for managing ecosystems and reducing blooms.

Proper adjustment for population substructure is essential in epigenome-wide association studies (EWAS), particularly in cohorts with diverse ancestries. EPISTRUCTURE offers a genotype-free approach to ancestry inference, originally developed using a European reference population from the Cooperative Health Research in the Region of Augsburg (KORA) study. However, its effectiveness in genetically diverse, multi-ancestry cohorts remains insufficiently evaluated. For EWAS using cord-blood samples from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we systematically assessed the ancestry adjustment performance of EPISTRUCTURE principal components (PCs) derived from the widely used KORA-based reference set versus new reference sets generated from genotyping data of the multi-ancestry HAPO cohort. HAPO-based reference sets were defined by varying SNP - CpG $R^2$ thresholds (e.g. RS30: $R^2>0.3$) to identify ancestry-informative CpGs. We applied these reference sets for population substructure adjustment in EWAS of three newborn adiposity traits, birthweight, cord C-peptide, and sum of skinfolds, to evaluate their impact on association detection and biological interpretation. Compared to the KORA reference, the HAPO RS30 reference consistently produced lower genomic inflation and identified more biologically relevant associations for birthweight and cord blood C-peptide in EWAS of HAPO cord blood samples (n = 3,116). Pathway enrichment analyses revealed strong immune and metabolic signals, including pathways uniquely captured by EWAS when using the HAPO-derived reference for ancestry adjustment. Trait enrichment using the EWAS Catalog further confirmed associations with fetal growth, maternal metabolic traits, and glucose regulation. Our findings demonstrate that reference sets derived from multi-ancestry cohorts like HAPO better capture underlying population substructure and improve ancestry adjustment in diverse EWAS settings.

Despite advances in antiviral therapy, the rate of functional cure for chronic hepatitis B (CHB) remains unsatisfactory, and developing an applicable prediction model is pivotal to improving it. Thus, we aimed to identify key predictive factors and develop prognostic models for functional cure in HBeAg-negative patients and the advantaged populations. This retrospective study included 202 HBeAg-negative CHB patients (114 classified as advantaged populations) receiving pegylated interferon-alpha (PEG-IFNα) therapy for model derivation and internal validation, and 183 HBeAg-negative CHB patients (117 classified as advantaged populations) for model external validation. Using 48 routinely collected clinical indicators, we constructed prediction models through LASSO regression followed by multivariable logistic regression. Two nomogram-based models were developed: the SHAN model, based on four independent predictors - ln (HBsAg +1), age, neutrophil percentage (NE%), and sex - was tailored for HBeAg-negative patients. For the advantaged populations, two additional variables - alpha-fetoprotein (AFP) and lactate dehydrogenase (LDH) - were incorporated into the FLASH-N model. Both models demonstrated strong discrimination, with AUCs of 0.908 in the training set and 0.949 in the test set for the SHAN model and an AUC of 0.920 (bootstrap-corrected to 0.889) for the FLASH-N model in the advantaged populations. In external validation, SHAN model achieved an AUC of 0.861, and FLASH-N achieved an AUC of 0.800. Calibration plots and decision curve analysis further confirmed the robustness, accuracy, and clinical utility of both nomograms. By leveraging routinely available baseline variables, these models offer powerful tools for predicting functional cure in CHB, enabling refined risk stratification and more personalized clinical decision-making.

5' RNA capping is one of the major post-transcriptional modifications for the mobility and stability of RNA molecules. Measuring 5' caps of RNAs can help quantify expression levels of mRNAs and lncRNAs. One of the most successful RNAseq methods that has used capping as a tool to quantify expression of transcription is Cap Analysis of Gene Expression (CAGE). Computational prediction of capping can therefore be used as a precursor to the prediction of transcriptional expression. Unfortunately, there is hardly any computational technique that has focused purely on predicting 5' capping. We have developed a transformer-based method for computational prediction of capping from DNA sequences. Our Llama and ReLoRA-based pre-training model, and Llama and LoRA-based fine-tuning model predict capping associated regions. We have used Leave-one-chromosome-out-cross-validation for our model. The average accuracy, and F1-score after fine-tuning the human genome hg19 (mouse genome mm9) for sequence classification is 79.12% (78.09%) and 78.11% (76.17%), respectively. We noted attention peak-based motifs having an aggregate Wilcoxon rank-sum p-value of 1.075e-10 between the attention peak region and the entire context window for the predicted positive motifs; an aggregate p-value of 7.17e-18 for the predicted negative motifs; and an aggregate p-value of 6.70e-08 between the attention peaks of the predicted positive and the predicted negative motifs. Our Llama-based approach aims to create a sequence-based framework to identify capping associated regions corresponding to CAGE peaks. Our analysis reveals statistically significant motifs from the regions of peak attention scores, which demonstrates biological relevance for some through their resident sites matching with known TF motifs.

To study the macroscopic dynamics of susceptible host cells, infected host cells, free virus particles and antibodies after viral infection within-host, this study develops a novel dynamic model that integrates three key mechanisms: a general incidence function capturing the complexity of antibody production, the inhibitory effect of antibodies on viral infectivity and the cytokine-mediated self-cure of infected cells. The basic reproduction numbers for both the virus and immune response are derived, along with sufficient conditions for the stability of equilibria. Bifurcation analysis revealed that a Hopf bifurcation may occur when the basic reproduction number of the immune response exceeds one. Numerical simulations highlight the critical role of saturation effects in viral replication and the immune response for infection control. Antibody immunity, once depleted, may not be replenished and neglecting saturation effects could overestimate both the oscillatory parameter range and the severity of infection.

COVID-19 infection exhibits significant age-related differences. In this paper, we consider an infectious disease model with age-structure in susceptibility and evolutionary game and analyze the impact of mandatory and voluntary vaccination strategies on disease progression. We derive the conditions for the existence of equilibria and confirm that the basic reproduction number $R_0$ serves as a threshold parameter that fully determines the dynamical properties of the model. Theoretical analyses indicate that the persistence of COVID-19 is contingent upon the value of the basic reproduction number. By conducting numerical simulations, we investigate the impacts of various factors, including relative vaccine cost and vaccine effectiveness, on disease dynamics under a voluntary vaccination policy. Our analysis reveals that enhancing vaccine effectiveness does not reduce disease transmission when vaccination rates are extremely low. Under voluntary vaccination policies, it is crucial to keep relative vaccine costs below a certain threshold to promote higher vaccination uptake.