arXiv Quantitative Biology @arxiv_bio@qoto.org

Chemotaxis describes the intricate interplay of cellular motion in response to a chemical signal. We here consider the case of slab geometry which models chemotactic motion between two infinite membranes. Like previous works, we are particularly interested in the asymptotic regime of high tumbling rates. We establish local existence and uniqueness of solutions to the kinetic equation and show their convergence towards solutions of a parabolic Keller-Segel model in the asymptotic limit. In addition, we prove convergence rates with respect to the asymptotic parameter under additional regularity assumptions on the problem data. Particular difficulties in our analysis are caused by vanishing velocities in the kinetic model as well as the occurrence of boundary terms.

The study presents a novel approach for quantifying cellular interactions in digital pathology using deep learning-based image cytometry. Traditional methods struggle with the diversity and heterogeneity of cells within tissues. To address this, we introduce the Spatial Interaction Potential (SIP) and the Co-Localization Index (CLI), leveraging deep learning classification probabilities. SIP assesses the potential for cell-to-cell interactions, similar to an electric field, while CLI incorporates distances between cells, accounting for dynamic cell movements. Our approach enhances traditional methods, providing a more sophisticated analysis of cellular interactions. We validate SIP and CLI through simulations and apply them to colorectal cancer specimens, demonstrating strong correlations with actual biological data. This innovative method offers significant improvements in understanding cellular interactions and has potential applications in various fields of digital pathology.

Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in non-smoking females using gene expression profiling from the GDS3837 dataset. Utilizing XGBoost, a machine learning algorithm, the analysis achieved a strong predictive performance with an AUC score of 0.835. The top biomarkers identified - CCAAT enhancer binding protein alpha (C/EBP-alpha), lactate dehydrogenase A4 (LDHA), UNC-45 myosin chaperone B (UNC-45B), checkpoint kinase 1 (CHK1), and hypoxia-inducible factor 1 subunit alpha (HIF-1-alpha) - have been validated in the literature as being significantly linked to lung cancer. These findings highlight the potential of these biomarkers for early diagnosis and personalized therapy, emphasizing the value of integrating machine learning with molecular profiling in cancer research.

We present a method to convert weather data into probabilistic forecasts of Aedes aegypti abundance. The approach, which relies on the Aedes-AI suite of neural networks, produces weekly point predictions with corresponding uncertainty estimates. Once calibrated on past trap and weather data, the model is designed to use weather forecasts to estimate future trap catches. We demonstrate that when reliable input data are used, the resulting predictions have high skill. This technique may therefore be used to supplement vector surveillance efforts or identify periods of elevated risk for vector-borne disease outbreaks.

The evolutionary relationships between species are typically represented in the biological literature by rooted phylogenetic trees. However, a tree fails to capture ancestral reticulate processes, such as the formation of hybrid species or lateral gene transfer events between lineages, and so the history of life is more accurately described by a rooted phylogenetic network. Nevertheless, phylogenetic networks may be complex and difficult to interpret, so biologists sometimes prefer a tree that summarises the central tree-like trend of evolution. In this paper, we formally investigate methods for transforming an arbitrary phylogenetic network into a tree (on the same set of leaves) and ask which ones (if any) satisfy a simple consistency condition. This consistency condition states that if we add additional species into a phylogenetic network (without otherwise changing this original network) then transforming this enlarged network into a rooted phylogenetic tree induces the same tree on the original set of species as transforming the original network. We show that the LSA (lowest stable ancestor) tree method satisfies this consistency property, whereas several other commonly used methods (and a new one we introduce) do not. We also briefly consider transformations that convert arbitrary phylogenetic networks to another simpler class, namely normal networks.

The overuse of antibiotics has become a major global concern due to its role in diminishing treatment effectiveness and positively selecting antibiotic-resistant bacterial strains. This issue is particularly important in the beef cattle sector, where Bovine Respiratory Diseases (BRD) impose significant economic and welfare burdens. BRD are complex, multifactorial conditions primarily affecting young calves and feedlot cattle, caused by a combination of viral and bacterial pathogens, environmental factors, and stressors. Despite efforts to reduce antimicrobial use (AMU), the cattle production system remains heavily reliant on antibiotics to control BRD, often through the implementation of collective treatments to prevent outbreaks. This study aimed at evaluating the impact of various treatment practices on the spread of BRD, specifically focusing on criteria for implementing collective treatments. Using a mechanistic stochastic model, we simulated the spread of \textit{Mannheimia haemolytica} in a multi-pen fattening operation under sixteen different scenarios, considering pen composition, individual risk levels, and treatment strategies. Our findings suggest that an alternative criterion for collective treatments based on the speed of the disease spread, could reduce BRD incidence and AMU more effectively than conventional methods. This research highlights the importance of responsible treatment practices and the potential benefits of novel criteria for collective treatment strategies in improving animal health. Moreover, it emphasizes the need for transparency on the exposure to risk factors along the production chain.

This study addresses the TAUKADIAL challenge, focusing on the classification of speech from people with Mild Cognitive Impairment (MCI) and neurotypical controls. We conducted three experiments comparing five machine-learning methods: Random Forests, Sparse Logistic Regression, k-Nearest Neighbors, Sparse Support Vector Machine, and Decision Tree, utilizing 1076 acoustic features automatically extracted using openSMILE. In Experiment 1, the entire dataset was used to train a language-agnostic model. Experiment 2 introduced a language detection step, leading to separate model training for each language. Experiment 3 further enhanced the language-agnostic model from Experiment 1, with a specific focus on evaluating the robustness of the models using out-of-sample test data. Across all three experiments, results consistently favored models capable of handling high-dimensional data, such as Random Forest and Sparse Logistic Regression, in classifying speech from MCI and controls.

The rapid growth of the drone industry, particularly in the use of small unmanned aerial systems (sUAS) and unmanned aerial vehicles (UAVs), requires the development of advanced training protocols for remote pilots. Remote pilots must develop a combination of technical and cognitive skills to manage the complexities of modern drone operations. This paper explores the integration of neurotechnology, specifically auricular vagus nerve stimulation (aVNS), as a method to enhance remote pilot training and performance. The scientific literature shows aVNS can safely improve cognitive functions such as attention, learning, and memory. It has also been shown useful to manage stress responses. For safe and efficient sUAS/UAV operation, it is essential for pilots to maintain high levels of vigilance and decision-making under pressure. By modulating sympathetic stress and cortical arousal, aVNS can prime cognitive faculties before training, help maintain focus during training and improve stress recovery post-training. Furthermore, aVNS has demonstrated the potential to enhance multitasking and cognitive control. This may help remote pilots during complex sUAS operations by potentially reducing the risk of impulsive decision-making or cognitive errors. This paper advocates for the inclusion of aVNS in remote pilot training programs by proposing that it can provide significant benefits in improving cognitive readiness, skill and knowledge acquisition, as well as operational safety and efficiency. Future research should focus on optimizing aVNS protocols for drone pilots while assessing long-term benefits to industrial safety and workforce readiness in real-world scenarios.

We consider the relay version of the generalized Hutchinson's equation as a phenomenological model of an isolated neuron. After an exponential substitution, the equation takes the form of a differential-difference equation with a piecewise-constant right-hand side. In the work of A. Yu. Kolesov and others, this equation was studied with a negative continuous initial function, and the existence and orbital stability of a periodic solution with a period longer than the delay were proven. In the present work, all possible solutions with continuous on the interval of the delay length initial functions, containing no more than two zeros, are constructed. It is proven that the equation has a periodically unstable solution, the period of which is shorter than the delay.

The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolecules. Almost all research on large-scale biosequence transformers has focused on one domain at a time (single-omic), usually nucleotides or peptides. These models have seen incredible success in downstream tasks in each domain and have achieved particularly noteworthy breakthroughs in sequences of peptides and structural modeling. However, these single-omic models are naturally incapable of modeling multi-omic tasks, one of the most biologically critical being nucleotide-peptide interactions. We present our work training the first multi-omic nucleotide-peptide foundation models. We show that these multi-omic models (MOMs) can learn joint representations between various single-omic distributions that are emergently consistent with the Central Dogma of molecular biology, despite only being trained on unlabeled biosequences. We further demonstrate that MOMs can be fine-tuned to achieve state-of-the-art results on peptide-nucleotide interaction tasks, namely predicting the change in Gibbs free energy (ΔG) of the binding interaction between a given oligonucleotide and peptide, as well as the effect on this binding interaction due to mutations in the oligonucleotide sequence (ΔΔG). Remarkably, we show that multi-omic biosequence transformers emergently learn useful structural information without any prior structural training, allowing us to predict which peptide residues are most involved in the peptide-nucleotide binding interaction. Lastly, we provide evidence that multi-omic biosequence models are non-inferior to foundation models trained on single-omics distributions, suggesting a more generalized or foundational approach to building these models.

Breast cancer is one of the two cancers responsible for the most deaths in women, with about 42,000 deaths each year in the US. That there are over 300,000 breast cancers newly diagnosed each year suggests that only a fraction of the cancers result in mortality. Thus, most of the women undergo seemingly curative treatment for localized cancers, but a significant later succumb to metastatic disease for which current treatments are only temporizing for the vast majority. The current prognostic metrics are of little actionable value for 4 of the 5 women seemingly cured after local treatment, and many women are exposed to morbid and even mortal adjuvant therapies unnecessarily, with these adjuvant therapies reducing metastatic recurrence by only a third. Thus, there is a need for better prognostics to target aggressive treatment at those who are likely to relapse and spare those who were actually cured. While there is a plethora of molecular and tumor-marker assays in use and under-development to detect recurrence early, these are time consuming, expensive and still often un-validated as to actionable prognostic utility. A different approach would use large data techniques to determine clinical and histopathological parameters that would provide accurate prognostics using existing data. Herein, we report on machine learning, together with grid search and Bayesian Networks to develop algorithms that present a AUC of up to 0.9 in ROC analyses, using only extant data. Such algorithms could be rapidly translated to clinical management as they do not require testing beyond routine tumor evaluations.

Circadian disruption contributes to adverse effects on sleep, performance, and health. One accepted method to track continuous daily changes in circadian timing is to measure core body temperature (CBT), and establish daily, circadian-related CBT minimum time (Tmin). This method typically applies cosine-model fits to measured CBT data, which may not adequately account for substantial wake metabolic activity and sleep effects on CBT that confound and mask circadian effects, and thus estimates of the circadian-related Tmin. This study introduced a novel physiology-grounded analytic approach to separate circadian from non-circadian effects on CBT, which we compared against traditional cosine-based methods. The dataset comprised 33 healthy participants attending a 39-hour in-laboratory study with an initial overnight sleep followed by an extended wake period. CBT data were collected at 30-second intervals via ingestible capsules. Our design captured CBT during both the baseline sleep period and during extended wake period (without sleep) and allowed us to model the influence of circadian and non-circadian effects of sleep, wake, and activity on CBT using physiology-guided generalized additive models. Model fits and estimated Tmin inferred from extended wake without sleep were compared with traditional cosine-based models fits. Compared to the traditional cosine model, the new model exhibited superior fits to CBT (Pearson R 0.90 [95%CI; [0.83 - 0.96] versus 0.81 [0.55-0.93]). The difference between estimated vs measured circadian Tmin, derived from the day without sleep, was better fit with our method (0.2 [-0.5,0.3] hours) versus previous methods (1.4 [1.1 to 1.7] hours). This new method provides superior demasking of non-circadian influences compared to traditional cosine methods, including the removal of a sleep-related bias towards an earlier estimate of circadian Tmin.

The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains. While large language models (LLMs) have achieved much progress in the domain of natural language processing, their potential in protein engineering remains largely unexplored. Previous approaches have equipped LLMs with protein understanding capabilities by incorporating external protein encoders, but this fails to fully leverage the inherent similarities between protein sequences and natural languages, resulting in sub-optimal performance and increased model complexity. To address this gap, we present TourSynbio-7B, the first multi-modal large model specifically designed for protein engineering tasks without external protein encoders. TourSynbio-7B demonstrates that LLMs can inherently learn to understand proteins as language. The model is post-trained and instruction fine-tuned on InternLM2-7B using ProteinLMDataset, a dataset comprising 17.46 billion tokens of text and protein sequence for self-supervised pretraining and 893K instructions for supervised fine-tuning. TourSynbio-7B outperforms GPT-4 on the ProteinLMBench, a benchmark of 944 manually verified multiple-choice questions, with 62.18% accuracy. Leveraging TourSynbio-7B's enhanced protein sequence understanding capability, we introduce TourSynbio-Agent, an innovative framework capable of performing various protein engineering tasks, including mutation analysis, inverse folding, protein folding, and visualization. TourSynbio-Agent integrates previously disconnected deep learning models in the protein engineering domain, offering a unified conversational user interface for improved usability. Finally, we demonstrate the efficacy of TourSynbio-7B and TourSynbio-Agent through two wet lab case studies on vanilla key enzyme modification and steroid compound catalysis.

Proteins can form droplets via liquid-liquid phase separation (LLPS) in cells. Recent experiments demonstrate that LLPS is qualitatively different on two-dimensional (2d) surfaces compared to three-dimensional (3d) solutions. In this paper, we use mathematical modeling to investigate the causes of the discrepancies between LLPS in 2d versus 3d. We model the number of proteins and droplets inducing LLPS by continuous-time Markov chains and use chemical reaction network theory to analyze the model. To reflect the influence of space dimension, droplet formation and dissociation rates are determined using the first hitting times of diffusing proteins. We first show that our stochastic model reproduces the appropriate phase diagram and is consistent with the relevant thermodynamic constraints. After further analyzing the model, we find that it predicts that the space dimension induces qualitatively different features of LLPS which are consistent with recent experiments. While it has been claimed that the differences between 2d and 3d LLPS stems mainly from different diffusion coefficients, our analysis is independent of the diffusion coefficients of the proteins since we use the stationary model behavior. Therefore, our results give new hypotheses about how space dimension affects LLPS.

Recent studies suggest that drug-drug interaction (DDI) prediction via computational approaches has significant importance for understanding the functions and co-prescriptions of multiple drugs. However, the existing silico DDI prediction methods either ignore the potential interactions among drug-drug pairs (DDPs), or fail to explicitly model and fuse the multi-scale drug feature representations for better prediction. In this study, we propose RGDA-DDI, a residual graph attention network (residual-GAT) and dual-attention based framework for drug-drug interaction prediction. A residual-GAT module is introduced to simultaneously learn multi-scale feature representations from drugs and DDPs. In addition, a dual-attention based feature fusion block is constructed to learn local joint interaction representations. A series of evaluation metrics demonstrate that the RGDA-DDI significantly improved DDI prediction performance on two public benchmark datasets, which provides a new insight into drug development.

In the present paper, our goal is to establish a framework for the mathematical modelling and the analysis of the spread of an epidemic in a large population commuting regularly, typically along a time-periodic pattern, as is roughly speaking the case in populous urban center. We consider a large number of distinct homogeneous groups of individuals of various sizes, called subpopulations, and focus on the modelling of the changing conditions of their mixing along time and of the induced disease transmission. We propose a general class of models in which the 'force of infection' plays a central role, which attempts to 'reconcile' the classical modelling approaches in mathematical epidemiology, based on compartmental models, with some widely used analysis results (including those by P. van den Driessche and J. Watmough in 2002), established for apparently less structured systems of nonlinear ordinary-differential equations. We take special care in explaining the modelling approach in details, and provide analysis results that allow to compute or estimate the value of the basic reproduction number for such general periodic epidemic systems.

Sequential Monte Carlo methods are a powerful framework for approximating the posterior distribution of a state variable in a sequential manner. They provide an attractive way of analyzing dynamic systems in real-time, taking into account the limitations of traditional approaches such as Markov Chain Monte Carlo methods, which are not well suited to data that arrives incrementally. This paper reviews and explores the application of Sequential Monte Carlo in dynamic disease modeling, highlighting its capacity for online inference and real-time adaptation to evolving disease dynamics. The integration of kernel density approximation techniques within the stochastic Susceptible-Exposed-Infectious-Recovered (SEIR) compartment model is examined, demonstrating the algorithm's effectiveness in monitoring time-varying parameters such as the effective reproduction number. Case studies, including simulations with synthetic data and analysis of real-world COVID-19 data from Ireland, demonstrate the practical applicability of this approach for informing timely public health interventions.

Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neural information processing. Information storage (IS), the main measure quantifying the active utilization of memory in a dynamic system, is only defined for discrete-time processes. While recent theoretical work laid the foundations for the continuous-time analysis of the predictive capacity stored in a process, methods for the effective computation of the related measures are needed to favor widespread utilization on neural data. This work introduces a method for the model-free estimation of the so-called memory utilization rate (MUR), the continuous-time counterpart of the IS, specifically designed to quantify the predictive capacity stored in neural point processes. The method employs nearest-neighbor entropy estimation applied to the inter-spike intervals measured from point-process realizations to quantify the extent of memory used by a spike train. An empirical procedure based on surrogate data is implemented to compensate the estimation bias and detect statistically significant levels of memory. The method is validated in simulated Poisson processes and in realistic models of coupled cortical dynamics and heartbeat dynamics. It is then applied to real spike trains reflecting central and autonomic nervous system activities: in spontaneously growing cortical neuron cultures, the MUR detected increasing memory utilization across maturation stages, associated to emergent bursting synchronized activity; in the study of the neuro-autonomic modulation of human heartbeats, the MUR reflected the sympathetic activation occurring with postural but not with mental stress. The proposed approach offers a computationally reliable tool to analyze spike train data in computational neuroscience and physiology.

Accurately predicting the geographic ranges of species is crucial for assisting conservation efforts. Traditionally, range maps were manually created by experts. However, species distribution models (SDMs) and, more recently, deep learning-based variants offer a potential automated alternative. Deep learning-based SDMs generate a continuous probability representing the predicted presence of a species at a given location, which must be binarized by setting per-species thresholds to obtain binary range maps. However, selecting appropriate per-species thresholds to binarize these predictions is non-trivial as different species can require distinct thresholds. In this work, we evaluate different approaches for automatically identifying the best thresholds for binarizing range maps using presence-only data. This includes approaches that require the generation of additional pseudo-absence data, along with ones that only require presence data. We also propose an extension of an existing presence-only technique that is more robust to outliers. We perform a detailed evaluation of different thresholding techniques on the tasks of binary range estimation and large-scale fine-grained visual classification, and we demonstrate improved performance over existing pseudo-absence free approaches using our method.

The emergence of cognition requires a framework that bridges evolutionary principles with neurocomputational mechanisms. This paper introduces the "thoughtseed" framework, proposing that cognition arises from the dynamic interaction of self-organizing units of embodied knowledge called "thoughtseeds." We leverage evolutionary theory, "neuronal packets," and the "Inner Screen" hypothesis within Free Energy Principle, and propose a four-level hierarchical model of the cognitive agent's internal states: Neuronal Packet Domains (NPDs), Knowledge Domains (KDs), thoughtseeds network, and meta-cognition. The dynamic interplay within this hierarchy, mediated by nested Markov blankets and reciprocal message passing, facilitates the emergence of thoughtseeds as coherent patterns of activity that guide perception, action, and learning. The framework further explores the role of the organism's Umwelt and the principles of active inference, especially the generative model at each nested level, in shaping the selection and activation of thoughtseeds, leading to adaptive behavior through surprise minimization. The "Inner Screen" is posited as the locus of conscious experience, where the content of the dominant thoughtseed is projected, maintaining a unitary conscious experience. Active thoughtseeds are proposed as the fundamental units of thought that contribute to the "content of consciousness." We present a mathematical framework grounded in active inference and dynamical systems theory. The thoughtseed framework represents an initial but promising step towards a novel, biologically-grounded model for understanding the organizing principles and emergence of embodied cognition, offering a unified account of cognitive phenomena, from basic physiological regulation to higher-order thought processes, and potentially bridge neuroscience and contemplative traditions.