q-bio
q-bio
11-26 00:00
arXiv:2511.20155v1 Announce Type: cross Abstract: Quantifying the in-plane rheology of epithelial monolayers remains challenging due to the difficulty of imposing controlled shear. We introduce a self-driven, rheometer-like assay in which collective migration generates stationary shear flows, allowing rheological parameters to be inferred directly from image sequences. The assay relies on two sets of ring-shaped fibronectin patches, micropatterned in arrays for high-throughput imaging. Within isolated rings, the epithelial tissue exhibits persistent rotation, from which we infer active migration stresses and substrate friction. Within partially overlapping rings, the tissue exhibits sustained shear, from which we infer the elastic and viscous responses of the cells. The emergence of a Maxwell-like viscoelastic relation --characterized by a linear relationship between mean cell deformation and neighbor-exchange rate-- is specifically recapitulated within a wet vertex-model framework, which reproduces experimental measurements only when intercellular viscous dissipation is included alongside substrate friction. We apply our method to discriminate the respective roles of two myosin~II isoforms in tissue mechanics. Overall, by harnessing self-generated stresses instead of externally imposed ones, we propose a noninvasive route to rheological inference in migrating epithelial tissues and, more generally, in actively flowing granular materials.
cond-mat.softq-bio.to
q-bio
q-bio
11-26 00:00
arXiv:2511.20162v1 Announce Type: cross Abstract: Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given a video sequence, such models are able to describe in detail objects, the surroundings and dynamic actions. In this study, we explored the extent to which these models ground their semantic understanding in the actual visual input. Specifically, given sequences of hands interacting with objects, we asked models when and where the interaction begins or ends. For this purpose, we introduce a first of its kind, large-scale dataset with more than 20K annotated interactions on videos from the Something-Something-V2 dataset. 250 AMTurk human annotators labeled core interaction events, particularly when and where objects and agents become attached ('contact') or detached ('release'). We asked two LMMs (Qwen-2.5VL and GPT-4o) to locate these events in short videos, each with a single event. The results show that although the models can reliably name the target objects, identify the action and provide coherent reasoning, they consistently fail to identify the frame where the interaction begins or ends and cannot localize the event within the scene. Our findings suggest that in struggling to pinpoint the moment and location of physical contact that defines the interaction, the models lack the perceptual grounding required for deeper understanding of dynamic scenes.
q-bio.nccs.cvcs.ai
q-bio
q-bio
11-26 00:00
arXiv:2511.20382v1 Announce Type: cross Abstract: Representation learning on multi-omics data is challenging due to extreme dimensionality, modality heterogeneity, and cohort-specific batch effects. While pre-trained transformer backbones have shown broad generalization capabilities in biological sequence modeling, their application to multi-omics integration remains underexplored. We present MoRE (Multi-Omics Representation Embedding), a framework that repurposes frozen pre-trained transformers to align heterogeneous assays into a shared latent space. Unlike purely generative approaches, MoRE employs a parameter-efficient fine-tuning (PEFT) strategy, prioritizing cross-sample and cross-modality alignment over simple sequence reconstruction. Specifically, MoRE attaches lightweight, modality-specific adapters and a task-adaptive fusion layer to the frozen backbone. It optimizes a masked modeling objective jointly with supervised contrastive and batch-invariant alignment losses, yielding structure-preserving embeddings that generalize across unseen cell types and platforms. We benchmark MoRE against established baselines, including scGPT, scVI, and Harmony with scArches, evaluating integration fidelity, rare population detection, and modality transfer. Our results demonstrate that MoRE achieves competitive batch robustness and biological conservation while significantly reducing trainable parameters compared to fully fine-tuned models. This work positions MoRE as a practical step toward general-purpose omics foundation models.
cs.lgq-bio.gn
q-bio
q-bio
11-26 00:00
arXiv:2511.20493v1 Announce Type: cross Abstract: Objectives. The aim of the present study was to develop a fully deep learning model to reduce the intra- and inter-operator reproducibility of sector classification systems for predicting unerupted maxillary canine likelihood of impaction. Methods. Three orthodontists (Os) and three general dental practitioners (GDPs) classified the position of unerupted maxillary canines on 306 radiographs (T0) according to the three different sector classification systems (5-, 4-, and 3-sector classification system). The assessment was repeated after four weeks (T1). Intra- and inter-observer agreement were evaluated with Cohen's K and Fleiss K, and between group differences with a z-test. The same radiographs were tested on different artificial intelligence (AI) models, pre-trained on an extended dataset of 1,222 radiographs. The best-performing model was identified based on its sensitivity and precision. Results. The 3-sector system was found to be the classification method with highest reproducibility, with an agreement (Cohen's K values) between observations (T0 versus T1) for each examiner ranged from 0.80 to 0.92, and an overall agreement of 0.85 [95% confidence interval (CI) = 0.83-0.87]. The overall inter-observer agreement (Fleiss K) ranged from 0.69 to 0.7. The educational background did not affect either intra- or inter-observer agreement (p>0.05). DenseNet121 proved to be the best-performing model in allocating impacted canines in the three different classes, with an overall accuracy of 76.8%. Conclusion. AI models can be designed to automatically classify the position of unerupted maxillary canines.
eess.ivcs.cvq-bio.qm
q-bio
q-bio
11-26 00:00
arXiv:2412.15947v4 Announce Type: replace Abstract: Study Objectives: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and chest temperature, and finger photoplethysmography and finger temperature. Methods: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography (PSG) at a tertiary care sleep lab. Each PSG recording was manually scored and these annotations served as ground truth labels for training and evaluation of our models. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. Results: After ensembling, the model attains a 3-class (wake, non rapid eye movement [NREM] sleep, rapid eye movement [REM] sleep) balanced accuracy of 84.02%, F1 score of 84.23%, Cohen's $\kappa$ of 72.89%, and a Matthews correlation coefficient (MCC) score of 73.00%; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30%, F1 score of 74.10%, Cohen's $\kappa$ of 61.51%, and MCC score of 61.95%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11%, F1 score of 66.15%, Cohen's $\kappa$ of 53.23%, MCC score of 54.38%. Conclusions: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography (EEG), and can be applied to data from adults attending a tertiary care sleep clinic.
cs.lgq-bio.qm
q-bio
q-bio
11-26 00:00
arXiv:2412.19329v2 Announce Type: replace Abstract: Background: Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. Objective: This study evaluates bifurcation parameters from a whole-brain network model as biomarkers for distinguishing brain states associated with resting-state and task-based cognitive conditions. Methods: Synthetic BOLD signals were generated using a supercritical Hopf brain network model to train deep learning models for bifurcation parameter prediction. Inference was performed on Human Connectome Project data, including both resting-state and task-based conditions. Statistical analyses assessed the separability of brain states based on bifurcation parameter distributions. Results: Bifurcation parameter distributions differed significantly across task and resting-state conditions ($p < 0.0001$ for all but one comparison). Task-based brain states exhibited higher bifurcation values compared to rest. Conclusion: Bifurcation parameters effectively differentiate cognitive and resting states, warranting further investigation as biomarkers for brain state characterization and neurological disorder assessment.
cs.lgq-bio.nc
q-bio
q-bio
11-26 00:00
arXiv:2504.11402v3 Announce Type: replace Abstract: The epidemiological dynamics of Mycoplasma pneumoniae is characterized by poorly understood complex multiannual cycles. The origins of these cycles have long been debated, and multiple explanations of varying complexity have been suggested. Using Bayesian methods, we fit a dynamical model to half a century of M. pneumoniae surveillance data from Denmark (1958-1995, 2010-2025) and uncover a parsimonious explanation for the persistent cycles, based on the theory of quasicycles. The period of the multiannual cycle (approx. 5 years in Denmark) is explained by susceptible replenishment due, primarily, to loss of immunity. While an excellent fit to shorter time series (a few decades), the deterministic model eventually settles into an annual cycle, unable to reproduce the persistent cycles. We find that environmental stochasticity (e.g., varying contact rates) stabilizes the multiannual cycles and so does demographic noise, at least in smaller or incompletely mixing populations. The temporary disappearance of cycles during 1979-1985 is explained as a consequence of stochastic mode-hopping. The circulation of M. pneumoniae was recently disrupted by COVID-19 non-pharmaceutical interventions (NPIs), providing a natural experiment on the effects of large perturbations. Consequently, the effects of NPIs are included in the model and medium-term predictions are explored. Our findings highlight the intrinsic sensitivity of M. pneumoniae dynamics to perturbations and interventions, underscoring the limitations for long-term prediction. More generally, our findings provide further evidence for the role of stochasticity as a driver of complex cycles across endemic and recurring pathogens.
q-bio.penlin.cd
q-bio
q-bio
11-26 00:00
arXiv:2507.09045v2 Announce Type: replace Abstract: Autism spectrum disorder (ASD) involves atypical brain organization, yet the large-scale functional principles underlying these alterations remain incompletely understood. Here we examine whether coevolutionary balance-a network-level energy measure derived from signed interactions and nodal activity states-captures disruptions in resting-state functional connectivity in autistic adults. Using ABIDE I resting-state fMRI data, we constructed whole-brain networks by combining binarized fALFF activity with signed functional correlations and quantified their coevolutionary energy. Compared with matched typically developing adults, the ASD group showed a characteristic redistribution of coevolutionary energy, with more negative global energy but higher (less negative) energy within the default mode network and altered energy in its interactions with dorsal attention and salience networks, indicating a reorganization rather than a uniform loss of balance in intrinsic network organization. These effects replicated across validation analyses with null models designed to disrupt link or node structure. Coevolutionary energy also showed modest but significant associations with ADI-R social and communication scores. Finally, incorporating coevolutionary features into a leakage-safe machine-learning classifier supported above-chance ASD versus typically developing (TD) discrimination on a held-out test set. These findings suggest that coevolutionary balance offers a compact, interpretable descriptor of altered resting-state network dynamics in autism.
q-bio.ncphysics.bio-ph
q-bio
q-bio
11-26 00:00
arXiv:2511.19444v1 Announce Type: new Abstract: Dabas et al. in Science 2025 report that approximately 117 human kinases directly phosphorylate the C-terminal domain (CTD) of RNA polymerase II (Pol II), proposing an extensive, direct biochemical bridge between signal transduction and transcriptional control. Such a sweeping claim that one-fourth of the human kinome directly targets the CTD represents a profound revision of canonical transcriptional biology. However, the evidence presented relies primarily on in vitro kinase assays using short CTD peptides, sparse in-cell validation, and mechanistically incomplete models of nuclear trafficking, chromatin targeting, structural compatibility, and catalytic specificity. In this extended critique, we demonstrate that the conclusions of this study are not supported by current biochemical, structural, cell biological, or genomic data. We outline severe shortcomings in assay design, lack of quantitative kinetics, incompatibilities with known Pol II structural constraints, unsupported assumptions about nuclear localization, inappropriate extension to "direct-at-gene" mechanisms, absence of global transcriptional effects, failure to align with the essential role of canonical CDKs, and missing transparency in dataset reporting. We conclude that the central claims of the study are premature and contradicted by decades of established transcriptional research. Substantial new evidence is required before revising the mechanistic model of Pol II CTD regulation.
q-bio.scq-bio.mnq-bio.cb
q-bio
q-bio
11-26 00:00
arXiv:2511.19535v1 Announce Type: new Abstract: Accurate diagnosis of spitzoid tumors (ST) is critical to ensure a favorable prognosis and to avoid both under- and over-treatment. Epigenetic data, particularly DNA methylation, provide a valuable source of information for this task. However, prior studies assume complete data, an unrealistic setting as methylation profiles frequently contain missing entries due to limited coverage and experimental artifacts. Our work challenges these favorable scenarios and introduces ReMAC, an extension of ReMasker designed to tackle classification tasks on high-dimensional data under complete and incomplete regimes. Evaluation on real clinical data demonstrates that ReMAC achieves strong and robust performance compared to competing classification methods in the stratification of ST. Code is available: https://github.com/roshni-mahtani/ReMAC.
cs.lgq-bio.qm
q-bio
q-bio
11-26 00:00
arXiv:2511.19549v1 Announce Type: new Abstract: Biomarkers are critical tools in the diagnosis and monitoring of neurodegenerative diseases. Reliable quantification depends on assay validity, especially the demonstration of parallelism between diluted biological samples and the assay's standard curve. Inadequate parallelism can lead to biased concentration estimates, jeopardizing both clinical and research applications. Here we systematically review the evidence of analytical parallelism in body fluid (serum, plasma, cerebrospinal fluid) biomarker assays for neurodegeneration and evaluate the extent, reproducibility, and reporting quality of partial parallelism. This systematic review was registered on PROSPERO (CRD42024568766) and conducted in accordance with PRISMA guidelines. We included studies published between December 2010 to July 2024 without language restrictions. ... In conclusion, partial parallelism was infrequently observed and inconsistently reported in most biomarker assays for neurodegeneration. Narrow dilution ranges and variable methodologies limit generalizability. Transparent reporting of dilution protocols and adherence to established analytical validation guidelines is needed. This systematic review has practical implications for clinical trial design, regulatory approval processes, and the reliability of biomarker-based diagnostics.
stat.apq-bio.qm
q-bio
q-bio
11-26 00:00
arXiv:2511.19743v1 Announce Type: new Abstract: We look at the interaction of dispersal and environmental stochasticity in $n$-patch models. We are able to prove persistence and extinction results even in the setting when the dispersal rates are stochastic. As applications we look at Beverton-Holt and Hassell functional responses. We find explicit approximations for the total population size at stationarity when we look at slow and fast dispersal. In particular, we show that if dispersal is small then in the Beverton-Holt setting, if the carrying capacity is random, then environmental fluctuations are always detrimental and decrease the total population size. Instead, in the Hassell setting, if the inverse of the carrying capacity is made random, then environmental fluctuations always increase the population size. Fast dispersal can save populations from extinction and therefore increase the total population size. We also analyze a different type of environmental fluctuation which comes from switching environmental states according to a Markov chain and find explicit approximations when the switching is either fast or slow - in examples we are able to show that slow switching leads to a higher population size than fast switching. Using and modifying some approximation results due to Cuello, we find expressions for the total population size in the $n=2$ patch setting when the growth rates, carrying capacities, and dispersal rates are influenced by random fluctuations. We find that there is a complicated interaction between the various terms and that the covariances between the various random parameters (growth rate, carrying capacity, dispersal rate) play a key role in whether we get an increase or a decrease in the total population size. Environmental fluctuations turn to sometimes be beneficial -- this show that not only dispersal, but also environmental stochasticity can lead to an increase in population size.
q-bio.pemath.pr
q-bio
q-bio
11-26 00:00
arXiv:2511.19813v1 Announce Type: new Abstract: Identifying key driver genes governing biological processes such as development and disease progression remains a challenge. While existing methods can reconstruct cellular trajectories or infer static gene regulatory networks (GRNs), they often fail to quantify time-resolved regulatory effects within specific temporal windows. Here, we present Time-varying Network Driver Estimation (TNDE), a computational framework quantifying dynamic gene driver effects from single-cell snapshot data under a linear Markov assumption. TNDE leverages a shared graph attention encoder to preserve the local topological structure of the data. Furthermore, by incorporating partial optimal transport, TNDE accounts for unmatched cells arising from proliferation or apoptosis, thereby enabling trajectory alignment in non-equilibrium processes. Benchmarking on simulated datasets demonstrates that TNDE outperforms existing baseline methods across diverse complex regulatory scenarios. Applied to mouse erythropoiesis data, TNDE identifies stage-specific driver genes, the functional relevance of which is corroborated by biological validation. TNDE offers an effective quantitative tool for dissecting dynamic regulatory mechanisms underlying complex biological processes.
q-bio.mnstat.apstat.ml
q-bio
q-bio
11-26 00:00
arXiv:2511.20179v1 Announce Type: new Abstract: Scalable assessments of mental illness, the leading driver of disability worldwide, remain a critical roadblock toward accessible and equitable care. Here, we show that human-computer interactions encode multiple dimensions of self-reported mental health and their changes over time. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA to predict 1.3 million mental-health self-reports from 20,000 cursor and touchscreen recordings recorded in 9,000 online participants. The dataset includes 2,000 individuals assessed longitudinally, 1,500 diagnosed with depression, and 500 with obsessive-compulsive disorder. MAILA tracks dynamic mental states along three orthogonal dimensions, generalizes across contexts, and achieves near-ceiling accuracy when predicting group-level mental health. The model translates from general to clinical populations, identifies individuals living with mental illness, and captures signatures of psychological function that are not conveyed by language. Our results demonstrate how everyday human-computer interactions can power passive, reliable, dynamic, and maximally scalable mental health assessments. The ability to decode mental states at zero marginal cost sets new benchmarks for precision medicine and public health, while raising important questions about privacy, agency, and autonomy online.
cs.hcq-bio.nccs.ai
q-bio
q-bio
11-26 00:00
arXiv:2511.20392v1 Announce Type: new Abstract: Traumatic Brain Injury (TBI) results from an impact or concussion to the head with the injury being specifically characterized through pathological degradation at various biological length scales. Following injury, various mechanical modeling techniques have been proposed in the literature that seek to quantify neuronal-scale to tissue-scale metrics of brain damage. Broadly, the two categories of degradation encompass physiological deterioration of neurons and upregulation of chemical entities such as neurotransmitters which causes initiation of downstream pathophysiological effects. Despite the many contributing pathways, in this work, we delineate and model a potential glia-initiated injury pathway that leads to secondary injury. The goal of this work is to demonstrate a continuum framework which models the multiphysics of mechano-chemical interactions underlying TBI. Using a coupled PDE (partial differential equation) formulation and FEM (finite element method) discretization, the framework highlights evolution of field variables which spatio-temporally resolve mechanical metrics and chemical species across neuronal clusters. The modeling domain encompasses microglia, neurons and the extracellular matrix. The continuum framework used to model the mechano-chemical interactions assumes a three dimensional viscoelastic network to capture the mechanical response underlying proteins constituting the neuron microstructure and advection-diffusion equations modeling spatio-temporal evolution of chemical species. We use this framework to numerically estimate key concentrations of chemical species produced by the strain field. In this work, we identify key biomarkers within the labyrinth of molecular pathways and build a framework that captures the core mechano-chemical interactions. This framework is an attempt to quantify secondary injury and thus assist in developing targeted TBI treatments.
q-bio.ncq-bio.qm
q-bio
q-bio
11-26 00:00
arXiv:2511.20532v1 Announce Type: new Abstract: The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex behavior, kinematic trajectories alone provide only indirect access to the underlying control processes. Here we present MIMIC-MJX, a framework for learning biologically-plausible neural control policies from kinematics. MIMIC-MJX models the generative process of motor control by training neural controllers that learn to actuate biomechanically-realistic body models in physics simulation to reproduce real kinematic trajectories. We demonstrate that our implementation is accurate, fast, data-efficient, and generalizable to diverse animal body models. Policies trained with MIMIC-MJX can be utilized to both analyze neural control strategies and simulate behavioral experiments, illustrating its potential as an integrative modeling framework for neuroscience.
cs.roq-bio.nccs.ai
q-bio
q-bio
11-26 00:00
arXiv:2511.19520v1 Announce Type: cross Abstract: Understanding how glioblastoma (GBM) emerges from initially healthy glial tissue requires models that integrate bioelectrical, metabolic, and multicellular dynamics. This work introduces an ASAL-inspired agent-based framework that simulates bioelectric state transitions in glial cells as a function of mitochondrial efficiency (Meff), ion-channel conductances, gap-junction coupling, and ROS dynamics. Using a 64x64 multicellular grid over 60,000 simulation steps, we show that reducing Meff below a critical threshold (~0.6) drives sustained depolarization, ATP collapse, and elevated ROS, reproducing key electrophysiological signatures associated with GBM. We further apply evolutionary optimization (genetic algorithms and MAP-Elites) to explore resilience, parameter sensitivity, and the emergence of tumor-like attractors. Early evolutionary runs converge toward depolarized, ROS-dominated regimes characterized by weakened electrical coupling and altered ionic transport. These results highlight mitochondrial dysfunction and disrupted bioelectric signaling as sufficient drivers of malignant-like transitions and provide a computational basis for probing the bioelectrical origins of oncogenesis.
q-bio.ncphysics.bio-phcs.ne
q-bio
q-bio
11-26 00:00
arXiv:2511.19548v1 Announce Type: cross Abstract: Neuroeconomics promises to ground welfare analysis in neural and computational evidence about how people value outcomes, learn from experience and exercise self-control. At the same time, policy and commercial actors increasingly invoke neural data to justify paternalistic regulation, "brain-based" interventions and new welfare measures. This paper asks under what conditions neural data can legitimately inform welfare judgements for policy rather than merely describing behaviour. I develop a non-empirical, model-based framework that links three levels: neural signals, computational decision models and normative welfare criteria. Within an actor-critic reinforcement-learning model, I formalise the inference path from neural activity to latent values and prediction errors and then to welfare claims. I show that neural evidence constrains welfare judgements only when the neural-computational mapping is well validated, the decision model identifies "true" interests versus context-dependent mistakes, and the welfare criterion is explicitly specified and defended. Applying the framework to addiction, neuromarketing and environmental policy, I derive a Neuroeconomic Welfare Inference Checklist for regulators and for designers of NeuroAI systems. The analysis treats brains and artificial agents as value-learning systems while showing that internal reward signals, whether biological or artificial, are computational quantities and cannot be treated as welfare measures without an explicit normative model.
cs.lgq-bio.ncq-fin.eccs.cyecon.gncs.ai
q-bio
q-bio
11-26 00:00
arXiv:2511.19828v1 Announce Type: cross Abstract: Originating in evolutionary game theory, the class of "zero-determinant" strategies enables a player to unilaterally enforce linear payoff relationships in simple repeated games. An upshot of this kind of payoff constraint is that it can shape the incentives for the opponent in a predetermined way. An example is when a player ensures that the agents get equal payoffs. While extensively studied in infinite-horizon games, extensions to discounted games, nonlinear payoff relationships, richer strategic environments, and behaviors with long memory remain incompletely understood. In this paper, we provide necessary and sufficient conditions for a player to enforce arbitrary payoff relationships (linear or nonlinear), in expectation, in discounted games. These conditions characterize precisely which payoff relationships are enforceable using strategies of arbitrary complexity. Our main result establishes that any such enforceable relationship can actually be implemented using a simple two-point reactive learning strategy, which conditions on the opponent's most recent action and the player's own previous mixed action, using information from only one round into the past. For additive payoff constraints, we show that enforcement is possible using even simpler (reactive) strategies that depend solely on the opponent's last move. In other words, this tractable class is universal within expectation-enforcing strategies. As examples, we apply these results to characterize extortionate, generous, equalizer, and fair strategies in the iterated prisoner's dilemma, asymmetric donation game, nonlinear donation game, and the hawk-dove game, identifying precisely when each class of strategy is enforceable and with what minimum discount factor.
econ.thcs.gtq-bio.pe
q-bio
q-bio
11-26 00:00
arXiv:2506.02013v2 Announce Type: replace Abstract: Myoelectric interfaces enable intuitive and natural control by decoding residual muscle activity, providing an effective pathway for motor restoration in individuals with preserved musculature. However, in patients with severe muscular atrophy or high-level spinal cord injury, the absence of reliable muscle activity renders myoelectric control infeasible. In such cases, motor brain-computer interfaces (BCIs) offer an alternative route. However, conventional brain-computer interface systems rely mainly on noisy cortical signals and classification-based decoding algorithms, which often result in low signal fidelity, limited controllability, and unstable real-time performance. Inspired by the motor pathway--an evolutionarily optimized system that filters, integrates, and transmits motor commands from the brain to the muscles--this study proposes the Brain-Muscle-Hand Interface (BMHI). BMHI decodes cortical EEG signals to reconstruct muscle-level EMG activity, functionally substituting for the muscles and enabling regression-based, continuous, and natural control via a myoelectric interface. To validate this architecture, we performed offline verification, comparative analysis, and online control experiments. Results demonstrate that: (1) the BMHI achieves a prediction accuracy of 0.79; (2) compared with conventional end-to-end brain-hand interfaces, it reduces training time by approximately eighteenfold while improving decoding accuracy; and (3) in online operation, the BMHI enables stable and efficient manipulation of both a virtual hand and a robotic arm. Compared with conventional BCIs, the BMHI, by replicating the motor pathway, enables continuous, stable, and naturally intuitive control.
q-bio.nc
q-bio
q-bio
11-26 00:00
arXiv:2511.19769v1 Announce Type: new Abstract: Blini is a tool for quick lookup of nucleotide sequences in databases, and for quick dereplication of sequence collections. It is meant to help clean and characterize large collections of assembled contigs or long sequences that would otherwise be too big to search with online tools, or too demanding for a local machine to process. Benchmarks on simulated data demonstrate that it is faster than existing tools and requires less RAM, while preserving search and clustering accuracy.
q-bio.qm
q-bio
q-bio
11-26 00:00
arXiv:2511.19894v1 Announce Type: new Abstract: In recent years there has been a paradigm shift from the study of local task-related activation to the organization and functioning of large-scale functional and structural brain networks. However, a long-standing challenge in this large-scale brain network analysis is how to compare network organizations irrespective of their complexity. The maximum spanning tree (MST) has served as a simple, unbiased, standardized representation of complex brain networks and effectively addressed this long-standing challenge. This tree representation, however, has been limited to individual networks. Group-level trees are always constructed from the average network or through a bootstrap procedure. Constructing the group-level tree from the average network introduces bias from individual subjects with outlying connectivities. The bootstrap method can be computationally prohibitive if a good approximation is desired. To address these issues, we propose a novel spectral representation of trees using the spanning tree basis. This spectral representation enables us to compute the average MST and demonstrate that this average tree captures the global properties of all the MSTs in the group and also overlaps with the union of the shortest paths in the functional brain networks.
q-bio.qm
q-bio
q-bio
11-26 00:00
arXiv:2511.19964v1 Announce Type: new Abstract: Clemmesen's hook refers to a commonly observed slowdown and rebound in breast cancer incidence around the age at menopause. It suggests a shift in the underlying carcinogenic dynamics, but the mechanistic basis remains poorly understood. Building on our previously developed Extended Multistage Clonal Expansion Tumor (MSCE-T) model, we perform a theoretical analysis to determine the conditions under which Clemmesen's hook would occur. Our results show that Clemmesen's hook can be quantitatively explained by time-specific changes in the proliferative and apoptotic balance of early-stage mutated cell populations, corresponding to the decline in progesterone levels and progesterone-driven proliferation due to reduced menstrual cycles preceding menopause, and changing dominant carcinogenic impact from alternative growth pathways post-menopause (e.g., adipose-derived growth signals). In contrast, variation in last-stage clonal dynamics cannot effectively reproduce the observed non-monotonic incidence pattern. Analytical results further demonstrate that midlife incidence dynamics corresponding to the hook are governed primarily by intrinsic proliferative processes rather than detection effects. Overall, this study provides a mechanistic and mathematical explanation for Clemmesen's hook and establishes a quantitative framework linking hormonal transitions during menopause to age-specific breast cancer incidence curve.
q-bio.pe
q-bio
q-bio
11-26 00:00
arXiv:2511.20339v1 Announce Type: new Abstract: Biological information processing often arises from mesoscopic molecular systems operating far from equilibrium, yet their complexity can make the underlying principles difficult to visualize. In this study, we introduce a macroscopic hydraulic model that serves as an intuitive analog for the molecular switching behavior exhibited by G protein- coupled receptors (GPCRs) on the cell membrane. The hydraulic system reproduces the essential structural and functional features of the molecular switch, including the presence of up to three distinct steady state solutions, the characteristic shapes of these solutions, and the physical interpretation of the control parameters governing the behavior of the system. By mapping water flow, energy barrier height, and siphoning dynamics onto biochemical flux, activation energy, and state transitions, the model provides a transparent representation of the mechanisms that regulate GPCR activation. The correspondence between the hydraulic analog and the molecular system suggests several experimentally testable hypotheses about GPCR function. In particular, the model highlights the central role of energy flux, driven by imbalances in ATP/ADP or GTP/GDP concentrations, in activating the molecular switch and maintaining nonequilibrium signaling states. It also identifies two key parameters that primarily determine switch behavior: the energy difference between the active and inactive states and the effective height of the energy barrier that separates them. These results imply that GPCR signaling dynamics may be governed by generalizable physical principles rather than by biochemical details alone. The hydraulic framework thus offers a tractable platform for interpreting complex molecular behavior and may aid in the development of predictive models of GPCR function in diverse physiological contexts.
q-bio.sc