Hasil untuk "Biology (General)"

J. Doorbar, W. Quint, L. Banks et al.

R. Clark, P. Henson

R. Marsell, T. Einhorn

P. Rosenkranz, P. Aumeier, B. Ziegelmann

Stuart H. Orkin, Leonard I. Zon

Edward A. Codling, M. Plank, S. Benhamou

Mathematical modelling of the movement of animals, micro-organisms and cells is of great relevance in the fields of biology, ecology and medicine. Movement models can take many different forms, but the most widely used are based on the extensions of simple random walk processes. In this review paper, our aim is twofold: to introduce the mathematics behind random walks in a straightforward manner and to explain how such models can be used to aid our understanding of biological processes. We introduce the mathematical theory behind the simple random walk and explain how this relates to Brownian motion and diffusive processes in general. We demonstrate how these simple models can be extended to include drift and waiting times or be used to calculate first passage times. We discuss biased random walks and show how hyperbolic models can be used to generate correlated random walks. We cover two main applications of the random walk model. Firstly, we review models and results relating to the movement, dispersal and population redistribution of animals and micro-organisms. This includes direct calculation of mean squared displacement, mean dispersal distance, tortuosity measures, as well as possible limitations of these model approaches. Secondly, oriented movement and chemotaxis models are reviewed. General hyperbolic models based on the linear transport equation are introduced and we show how a reinforced random walk can be used to model movement where the individual changes its environment. We discuss the applications of these models in the context of cell migration leading to blood vessel growth (angiogenesis). Finally, we discuss how the various random walk models and approaches are related and the connections that underpin many of the key processes involved.

L. G. Davis, W. Kuehl, James Battey

E. Martins, T. F. Hansen

J. Hadfield, S Nakagawa

O. Pearce, H. Läubli

J. Peever, P. Fuller

Lydia M. O’Sullivan, Clay J. Newton, Keith R. Underwood et al.

In the bison industry, both heifers and bulls are marketed and harvested at various ages, generally ranging from 20 to 30 months. Bulls represent the greatest proportion of the slaughter mix, as the bison industry does not routinely castrate, leaving males intact throughout the growing and finishing phase. This practice makes bulls available for use during the breeding season prior to the finishing phase. Therefore, the objective of this study was to evaluate the influence of bull age and use in the breeding herd on carcass characteristics, meat quality, and sensory characteristics of bison bulls. Grass-finished bison bulls were assigned to one of two finishing treatments: 1) Young bulls (n = 98) finished on fall pasture and harvested at 30 months of age with no exposure to the breeding herd, or 2) Mature bulls (n = 24) finished on early summer pasture and harvested at 36 months of age following use in the breeding herd. Bison were harvested, carcass data were recorded, and striploins were collected for the analysis of meat quality attributes. Mature bulls had greater hot carcass weight, ribeye area, kidney fat percentage, and marbling score compared to Young bulls. Objective tenderness was affected by the interaction of postmortem aging and finishing treatment. Steaks from Young bison bulls were more tender at all aging time points compared to steaks from Mature bison bulls. Variation in tenderness between treatment groups was likely not due to differences in collagen, as total collagen was greater in steaks from Young bulls. Consumer panelists rated steaks from Mature bulls higher for toughness intensity and flavor liking and lower for off-flavor intensity. Trained panelists rated steaks from Young bulls higher for flavor intensity, while ratings for toughness and juiciness were increased for Mature bulls. Collectively, results from this study indicate that bulls used in the breeding herd and marketed at 36 months of age produced heavier carcasses. However, Mature bulls were tougher at all postmortem aging days and required 21 days of aging to reach an acceptable level of tenderness, which was detected by both trained and consumer panelists.

J. Gordon, S. Amini

Fan Zhang, Tianyu Liu, Zhihong Zhu et al.

Recent studies have demonstrated the feasibility of modeling single-cell data as natural languages and the potential of leveraging powerful large language models (LLMs) for understanding cell biology. However, a comprehensive evaluation of LLMs' performance on language-driven single-cell analysis tasks still remains unexplored. Motivated by this challenge, we introduce CellVerse, a unified language-centric question-answering benchmark that integrates four types of single-cell multi-omics data and encompasses three hierarchical levels of single-cell analysis tasks: cell type annotation (cell-level), drug response prediction (drug-level), and perturbation analysis (gene-level). Going beyond this, we systematically evaluate the performance across 14 open-source and closed-source LLMs ranging from 160M to 671B on CellVerse. Remarkably, the experimental results reveal: (1) Existing specialist models (C2S-Pythia) fail to make reasonable decisions across all sub-tasks within CellVerse, while generalist models such as Qwen, Llama, GPT, and DeepSeek family models exhibit preliminary understanding capabilities within the realm of cell biology. (2) The performance of current LLMs falls short of expectations and has substantial room for improvement. Notably, in the widely studied drug response prediction task, none of the evaluated LLMs demonstrate significant performance improvement over random guessing. CellVerse offers the first large-scale empirical demonstration that significant challenges still remain in applying LLMs to cell biology. By introducing CellVerse, we lay the foundation for advancing cell biology through natural languages and hope this paradigm could facilitate next-generation single-cell analysis.

Sophia Daum, Lilith Decristoforo, Mira Mousa et al.

Abstract The dynamic interactions between tumor endothelial cells (TECs) and the immune microenvironment play a critical role in the progression of non-small cell lung cancer (NSCLC). In general, endothelial cells exhibit diverse immunomodulatory properties, influencing immune cell recruitment, antigen presentation, and regulation of immune checkpoint expression. Understanding the multifaceted roles of TECs as well as assigning specific functional hallmarks to various TEC phenotypes offer new avenues for targeted development of therapeutic interventions, particularly in the context of advanced immunotherapy and anti-angiogenic treatments. This review provides insights into the complex interplay between TECs and the immune system in NSCLC including discussion of potential optimized therapeutic opportunities.

Laurenz De Cock, Erika D’haenens, Lies Vantomme et al.

Abstract RNA sequencing (RNA-seq) has become key to complementing exome and genome sequencing for variant interpretation. We present a minimally invasive RNA-seq protocol using short-term cultured peripheral blood mononuclear cells (PBMCs) with and without cycloheximide treatment, enabling detection of transcripts subject to nonsense-mediated decay. While broadly applicable, this protocol is particularly suited for neurodevelopmental disorders, as up to 80% of the genes in our intellectual disability and epilepsy gene panel are expressed in PBMCs. Applied to 46 affected individuals and 15 parents, RNA-seq revealed splicing defects in six of nine individuals with splice variants, allowing reclassification of seven variants. Targeted cDNA analysis confirmed aberrant splicing in four individuals but missed intron retention in two. Global analyses (FRASER, OUTRIDER, and monoallelic expression) supported findings but did not yield new diagnoses. We propose a flowchart integrating RNA-seq into diagnostic workflows. Overall, our protocol is easily implementable, captures complex splicing events, and enhances variant classification.

Adam Bayes, G. Tavella, G. Parker

Abstract Objectives Burnout is a state of exhaustion resulting from prolonged and excessive workplace stress. We sought to examine biological underpinnings of burnout, focussing on mechanisms and physical consequences. Methods We searched the literature on burnout and evaluated studies examining biological parameters in patient populations (i.e. ‘clinical’ burnout) as well as in individuals from the general population judged as having some degree of burnout evaluated using a dimensional approach. Results Findings suggest that burnout is associated with sustained activation of the autonomic nervous system and dysfunction of the sympathetic adrenal medullary axis, with alterations in cortisol levels. Limited studies have also shown altered immune function and changes in other endocrine systems. Consequences of burnout include increased allostatic load, structural and functional brain changes, excito-toxicity, systemic inflammation, immunosuppression, metabolic syndrome, cardiovascular disease and premature death. Limitations of studies include variability in study populations, low specificity of burnout measures, and mostly cross-sectional studies precluding examination of changes across the course of burnout. Conclusions Further examination of biological mechanisms of burnout would benefit from more homogeneous clinical samples, challenge tests and prospective studies. This would assist in differentiation from conditions such as depression and aid with development of specific treatment targets for burnout.

C. Deo, A. Abdelfattah, Hersh K. Bhargava et al.

C. Lundby, D. Montero, M. Joyner

Lu Zhou, Qingli Zhang, Huihuan Luo et al.

To investigate the association of long-term exposure to air pollution with incident arrhythmia from various causes, this prospective cohort study included 442,386 participants from the UK Biobank cohort. Residential annual average exposures at baseline were evaluated, including fine particles (PM2.5), coarse particles (PM2.5–10), nitrogen dioxide (NO2), and nitrogen oxides (NOx). We further constructed a composite air pollution score (APS) to evaluate the concomitant exposure to these four pollutants. The associations of air pollutants with various arrhythmia subtypes were assessed utilizing the Cox proportional hazards model, and the hazard ratios (HRs) for incident arrhythmias were estimated. A total of 41,021 patients with incident arrhythmia were recorded. The HRs of overall arrhythmia associated with a 10 μg/m3 increment in PM2.5, PM2.5–10, NO2, and NOx were 1.26, 0.95, 1.03, and 1.02, respectively. The HR was 1.08 in the highest quintile of the APS compared to the lowest one. For cause-specific arrhythmias, the HRs per unit increment in APS were 1.45, 1.67, 1.51, 1.80, 2.63, and 4.66 for atrial fibrillation, atrioventricular block, ventricular fibrillation/tachycardia, intraventricular block, supraventricular tachycardia, and ventricular premature beats, respectively. Females, older individuals, overweight or obese individuals, and those with low education attainment, low income, or cardiometabolic morbidities had higher HRs associated with pollutants. Long-term exposure to air pollution is linked to increased incidence risks of atrial and ventricular arrhythmias. More focus should be shifted to the impact of air pollution on other arrhythmias besides atrial fibrillation.