The brain solves a problem that current AI architectures struggle to manage: storing specific episodic facts without corrupting general semantic knowledge. Neuroscience explains this through Complementary Learning Systems theory - a fast hippocampal system for episodic storage using pattern-separated representations, and a slow neocortical system for extracting statistical regularities. Current AI systems lack this separation, attempting both functions through neural weights alone. We identify the 'Stability Gap' in online neural memory: fast-weight mechanisms that write facts into shared continuous parameters collapse to near-random accuracy within tens of semantically related facts. Through semantic density (rho), we show collapse occurs with as few as N=5 facts at high density (rho > 0.6) or N ~ 20-75 at moderate density - a phenomenon we formalise as the Orthogonality Constraint. This failure persists even with perfect attention and unlimited context, arising from write-time interference when storage and retrieval share the same substrate. We also identify schema drift and version ambiguity as primary failure modes in production systems, observing 40-70% schema consistency and 0-100% clean correction rates. Context-based memory incurs 30-300% cost premium over selective retrieval. We propose Knowledge Objects (KOs): discrete, typed memory units with controlled vocabularies and explicit version chains. Paired with neural weights, KOs enable a true complementary learning architecture, suggesting reliable AI memory may require this bicameral design.

Recent high-precision experimental confirmations of quantum complementarity have revitalized foundational debates about measurement, description, and realism. This article argues that complementarity is most productively interpreted as an epistemic principle--constraining what can be simultaneously accessed and represented--rather than as an ontological claim about quantum reality. Reexamining the Einstein-Bohr debate through this lens reveals a persistent tension between descriptive completeness and contextual meaning, a tension experiments clarify but do not dissolve. Building on this analysis, we introduce cognitive complementarity as a structural principle governing reasoning under non-classical uncertainty, where mutually constraining representations cannot be jointly optimized. Within this framework, we propose quantum intuition as a testable cognitive capacity: the ability to sustain representational plurality, regulate commitment timing, and resolve perspective-incompatibilities in a context-sensitive manner. Formulated as a naturalistic construct grounded in shared informational constraints, quantum intuition offers a principled bridge between quantum measurement theory and cognition. This work reframes the historical debate, extends epistemic lessons from quantum foundations into cognitive science, and outlines empirical pathways for studying decision-making in contexts of irreducible uncertainty.

Adult neurodivergence, including Attention-Deficit/Hyperactivity Disorder (ADHD), high-functioning Autism Spectrum Disorder (ASD), and Cognitive Disengagement Syndrome (CDS), is marked by substantial symptom overlap that limits the discriminant sensitivity of standard psychometric instruments. While recent work suggests that Large Language Models (LLMs) can simulate human psychometric responses from qualitative data, it remains unclear whether they can accurately and stably model neurodevelopmental traits rather than broad personality characteristics. This study examines whether LLMs can generate psychometric responses that approximate those of real individuals when grounded in a structured qualitative interview, and whether such simulations are sensitive to variations in trait intensity. Twenty-six adults completed a 29-item open-ended interview and four standardized self-report measures (ASRS, BAARS-IV, AQ, RAADS-R). Two LLMs (GPT-4o and Qwen3-235B-A22B) were prompted to infer an individual psychological profile from interview content and then respond to each questionnaire in-role. Accuracy, reliability, and sensitivity were assessed using group-level comparisons, error metrics, exact-match scoring, and a randomized baseline. Both models outperformed random responses across instruments, with GPT-4o showing higher accuracy and reproducibility. Simulated responses closely matched human data for ASRS, BAARS-IV, and RAADS-R, while the AQ revealed subscale-specific limitations, particularly in Attention to Detail. Overall, the findings indicate that interview-grounded LLMs can produce coherent and above-chance simulations of neurodevelopmental traits, supporting their potential use as synthetic participants in early-stage psychometric research, while highlighting clear domain-specific constraints.

We present a mathematical and philosophical framework in which brain function is modeled using sheaf theory over neural state spaces. Local neural or cognitive functions are represented as sections of a sheaf, while global coherence corresponds to the existence of global sections. Brain pathologies are interpreted as obstructions to such global integration and are classified using tools from sheaf cohomology. The framework builds on the neural manifold program in contemporary neuroscience and on standard results in sheaf theory, and is further interpreted through a Leibnizian lens \cite{Churchland2012, Leibniz1714, MacLaneMoerdijk, Perich2025}. This paper is intended as a conceptual and formal proposal rather than a complete empirical theory.

Ventriloquism After-Effect is the phenomenon where sustained exposure to the ventriloquist illusion causes a change in unisensory auditory localization towards the location where the visual stimulus was present. We investigate the recalibration in EEG networks that causes this change and the track the timeline of changes in the auditory processing pathway. Our results obtained using network analysis, non-stationary time series analysis and multivariate pattern classification show that recalibration takes place early in the auditory processing pathway and the after-effect decays with time after exposure to the illusion.

Background: Conventional electrocardiogram (ECG) analysis faces a persistent dichotomy: expert-driven features ensure interpretability but lack sensitivity to latent patterns, while deep learning offers high accuracy but functions as a black box with high data dependency. We introduce ECGomics, a systematic paradigm and open-source platform for the multidimensional deconstruction of cardiac signals into digital biomarker. Methods: Inspired by the taxonomic rigor of genomics, ECGomics deconstructs cardiac activity across four dimensions: Structural, Intensity, Functional, and Comparative. This taxonomy synergizes expert-defined morphological rules with data-driven latent representations, effectively bridging the gap between handcrafted features and deep learning embeddings. Results: We operationalized this framework into a scalable ecosystem consisting of a web-based research platform and a mobile-integrated solution (this https URL). The web platform facilitates high-throughput analysis via precision parameter configuration, high-fidelity data ingestion, and 12-lead visualization, allowing for the systematic extraction of biomarkers across the four ECGomics dimensions. Complementarily, the mobile interface, integrated with portable sensors and a cloud-based engine, enables real-time signal acquisition and near-instantaneous delivery of structured diagnostic reports. This dual-interface architecture successfully transitions ECGomics from theoretical discovery to decentralized, real-world health management, ensuring professional-grade monitoring in diverse clinical and home-based settings. Conclusion: ECGomics harmonizes diagnostic precision, interpretability, and data efficiency. By providing a deployable software ecosystem, this paradigm establishes a robust foundation for digital biomarker discovery and personalized cardiovascular medicine.

Continuous attractor networks (CANs) are a well-established class of models for representing low-dimensional continuous variables such as head direction, spatial position, and phase. In canonical spatial domains, transitions along the attractor manifold are driven by continuous displacement signals, such as angular velocity-provided by sensorimotor systems external to the CAN itself. When such signals are not explicitly provided as dedicated displacement inputs, it remains unclear whether attractor-based circuits can reliably acquire recurrent dynamics that support stable state transitions, or whether alternative predictive strategies dominate. In this work, we present an experimental framework for training CANs to perform successor-like transitions between stable attractor states in the absence of externally provided displacement signals. We compare two recurrent topologies, a circular ring and a folded snake manifold, and systematically vary the temporal regime under which stability is evaluated. We find that, under short evaluation windows, networks consistently converge to impulse-driven associative solutions that achieve high apparent accuracy yet lack persistent attractor dynamics. Only when stability is explicitly enforced over extended free-run periods do genuine attractor-based transition dynamics emerge. This suggests that shortcut solutions are the default outcome of local learning in recurrent networks, while attractor dynamics represent a constrained regime rather than a generic result. Furthermore, we demonstrate that topology strictly limits the capacity for learned transitions. While the continuous ring topology achieves perfect stability over long horizons, the folded snake topology hits a geometric limit characterized by failure at manifold discontinuities, which neither curriculum learning nor basal ganglia-inspired gating can fully overcome.

Perturbation screens hold the potential to systematically map regulatory processes at single-cell resolution, yet modeling and predicting transcriptome-wide responses to perturbations remains a major computational challenge. Existing methods often underperform simple baselines, fail to disentangle measurement noise from biological signal, and provide limited insight into the causal structure governing cellular responses. Here, we present the latent causal diffusion (LCD), a generative model that frames single-cell gene expression as a stationary diffusion process observed under measurement noise. LCD outperforms established approaches in predicting the distributional shifts of unseen perturbation combinations in single-cell RNA-sequencing screens while simultaneously learning a mechanistic dynamical system of gene regulation. To interpret these learned dynamics, we develop an approach we call causal linearization via perturbation responses (CLIPR), which yields an approximation of the direct causal effects between all genes modeled by the diffusion. CLIPR provably identifies causal effects under a linear drift assumption and recovers causal structure in both simulated systems and a genome-wide perturbation screen, where it clusters genes into coherent functional modules and resolves causal relationships that standard differential expression analysis cannot. The LCD-CLIPR framework bridges generative modeling with causal inference to predict unseen perturbation effects and map the underlying regulatory mechanisms of the transcriptome.

Precision psychiatry aspires to elucidate brain-based biomarkers of psychopathology to bolster disease risk assessment and treatment development. To this end, functional magnetic resonance imaging (fMRI) has helped triangulate brain circuits whose functional features are correlated with or even predictive of forms of psychopathology. Yet, fMRI biomarkers to date remain largely descriptive identifiers of where, rather than how, neurobiology is aberrant, limiting their utility for guiding treatment. We present a method for decomposing fMRI-based functional connectivity (FC) into constituent biomechanisms - output drive, input responsivity, modulator gating - with clearer alignment to differentiable therapeutic interventions. Neurocircuit mechanism decomposition (NMD) integrates (i) a graph-constrained, lag-aware transformer to estimate directed, pathway-specific routing distributions and drive signals, with (ii) a measurement-aware state-space model (SSM) that models hemodynamic convolution and recovers intrinsic latent dynamics. This dual-head architecture yields interpretable circuit parameters that may provide a more direct bridge from fMRI to treatment strategy selection. We instantiate the model in an anatomically and electrophysiologically well-defined circuit: the cortico-basal ganglia-thalamo-cortical loop.

Collective movement is observed widely in nature, where individuals interact locally to produce globally ordered, coherent motion. In typical models of collective motion, each individual takes the average direction of multiple neighbors, resulting in ordered movement. In small flocks, noise induced order can also emerge with individuals copying only a randomly chosen single neighbor at a time. We propose a new model of collective movement, inspired by how real animals move, where individuals can move in two directions or remain stationary. We demonstrate that when individuals interact with a single neighbor through a novel form of halting interaction -- where an individual may stop upon encountering an oppositely moving neighbor rather than instantly aligning -- persistent collective order can emerge even in large populations. This represents a fundamentally different mechanism from conventional averaging-based or noise-induced ordering. Using deterministic and stochastic mean-field approximations, we characterize the conditions under which such ``flocking by stopping'' behavior can occur, and confirm the mean-field predictions using individual-based simulations. Our results highlight how incorporating a stopped state and halting interactions can generate new routes to order in collective movement.

We consider a SIRD epidemic model for a population composed of two groups of individuals with asymmetric interactions, where the force of infection depends on the active (alive) population in each group, rather than on the total population, as in the classical formulation. We prove that the final state for susceptible individuals is always positive and characterize it as the unique fixed point of a map. We also relate the final size to the basic reproduction number and show that the final number of susceptibles decreases when transmission rates increase. Numerical simulations compare the active-population and classical two-group SIRD models, showing differences in final size and the occurrence of multiple epidemic waves. The convergence of the fixed point approach is also illustrated.

Strong, correlated noise in recurrent neural circuits often passes through nonlinear transfer functions, complicating dynamical mean-field analyses of complex phenomena such as transients and bifurcations. We introduce a method that replaces nonlinear functions of Ornstein-Uhlenbeck (OU) noise with a Gaussian-equivalent process matched in mean and covariance, and combine this with a lognormal moment closure for expansive nonlinearities to derive a closed dynamical mean-field theory for recurrent neuronal networks. The resulting theory captures order-one transients, fixed points, and noise-induced shifts of bifurcation structure, and outperforms standard linearization-based approximations in the strong-fluctuation regime. More broadly, the approach applies whenever dynamics depend smoothly on OU processes via nonlinear transformations, offering a tractable route to noise-dependent phase diagrams in computational neuroscience models.

This study presents the first large-scale empirical evaluation of quantum machine learning for predicting antibiotic resistance in clinical urine cultures. Antibiotic resistance is amongst the top threats to humanity, and inappropriate antibiotic use is a main driver of resistance. We developed a Quantum Projective Learning (QPL) approach and executed 60 qubit experiments on IBM Eagle and Heron quantum processing units. While QPL did not consistently outperform classical baselines, potentially reflecting current quantum hardware limitations, it did achieve parity or superiority in specific scenarios, notably for the antibiotic nitrofurantoin and selected data splits, revealing that quantum advantage may be data-dependent. Analysis of data complexity measures uncovered a multivariate signature, which comprised Shannon entropy, Fisher Discriminant Ratio, standard deviation of kurtosis, number of low-variance features, and total correlations. The multivariate model accurately (AUC = 0.88, $p$-value = 0.03) distinguished cases wherein QPL executed on quantum hardware would outperform classical models. This signature suggests that quantum kernels excel in feature spaces with high entropy and structural complexity. These findings point to complexity-driven adaptive model selection as a promising strategy for optimizing hybrid quantum-classical workflows in healthcare. Overall, this investigation marks the first application of quantum machine learning in urology, and in antibiotic resistance prediction. Further, this work highlights conditional quantum utility and introduces a principled approach for leveraging data complexity signatures to guide quantum machine learning deployment in biomedical applications.

We study the ChorHendySnir2006 evolutionary model, which consists of a rooted phylogenetic tree with three leaves, subject to the Jukes--Cantor (JC69) molecular evolutionary model and molecular clock. We show that the likelihood function associated with this model has a unique maximum which depends analytically of the parameters (as it was conjectured in ChorHendySnir2006), assuming that these parameters verify some very precise inequalities; some of which arise naturally from the model. With a typical argument of differential topology we reduce the proof to answer a question of algebra, very simple, although computationally involved, that we solve using some Maple libraries. We are very indebted to Marta Casanellas, who presented the problem to us and gave us the first insights on it.

Volitional memory control supports adaptive cognition by enabling intentional Recall of goal-relevant information and Suppression of unwanted memories. While neural mechanisms underlying Recall and Suppression have been studied largely in isolation, less is known about the large-scale brain networks supporting these processes under competing cognitive demands, particularly as a function of subclinical anxiety. Here, we examined control of emotionally valenced memories during directed Recall and Suppression while 47 participants concurrently performed an independent visual working memory task. Cognitive control efficiency was quantified using the Balanced Integration Score (BIS), and seed-to-voxel resting-state functional connectivity (rsFC) was used to characterize intrinsic network organization. Dissociable rsFC profiles were associated with memory control efficiency across emotional valences and were selectively moderated by anxiety. More efficient Suppression of positive memories was linked to reduced connectivity between the anterior cingulate cortex and posterior perceptual-midline regions, as well as diminished hippocampal-frontal pole coupling. In contrast, efficient Suppression of negative memories was associated with increased connectivity between posterior parietal and lateral occipital regions. Anxiety moderated relationships between cognitive efficiency and prefrontal connectivity during Suppression of positive memories and Recall of positive and neutral memories. Direct comparisons further revealed stronger hippocampal-thalamic rsFC during Suppression relative to Recall of positive memories. Together, these findings delineate the functional brain architecture supporting volitional control of emotional memories under cognitive load and demonstrate that anxiety severity selectively shapes these network-level mechanisms across the anxiety continuum.

Physics relies on mathematical spaces carefully matched to the phenomena under study. Phase space in classical mechanics, Hilbert space in quantum theory, configuration spaces in field theory all provide representations in which physical laws, stability and invariants become expressible and testable. In contrast, biology lacks an agreed-upon notion of space capturing context dependence, partial observability, degeneracy and irreversible dynamics. To address this gap, we introduce a unified mathematical space tailored to biological processes where states are represented in locally convex spaces indexed by context, where context includes both environment and history. Within our setting, proximity is defined through families of seminorms rather than a single global metric, allowing biological relevance to vary across conditions. Admissible sets encode biological constraints, observation maps formalize partial observability and many-to-one relations between state and dynamics capture irreversibility without requiring convergence to fixed points. Stabilization is characterized by neighborhood inclusion and degeneracy arises naturally through quotient structures induced by observation. We develop explicit constructions, operators and bounds within this space, yielding quantitative predictions dictated by its structure. A worked example based on EGFR-mutant non-small-cell lung cancer shows how single-cell data can be mapped into our framework, how numerical thresholds can be calibrated from the literature and how testable predictions can be formulated concerning rare tolerant states, context-dependent proximity and early stabilization. Overall, by providing biology with a space playing a role analogous to those used in physics, we aim to support structurally grounded and quantitative analyses of biological systems across contexts.

Bacteriophages (phages) are key regulators of bacterial populations and hold great promise for applications such as phage therapy, biocontrol, and industrial fermentation. The success of these applications depends on accurately determining phage host range, which is often specific at the strain level rather than the species level. However, existing computational approaches face major limitations: many rely on genus-specific features that do not generalize across taxa, while others require large amounts of training data that are unavailable for most bacterial lineages. These challenges create a critical need for methods that can accurately predict strain-level phage-host interactions across diverse bacterial genera, particularly under data-limited conditions. We present PhageMind, a learning framework designed to address this challenge by enabling efficient transfer of knowledge across bacterial genera. PhageMind is trained to identify shared principles of phage-bacterium interactions from well-studied systems and to rapidly adapt these principles to new genera using only a small number of known interactions. To reflect the biological basis of infection, we represent phage-host relationships using a knowledge graph that explicitly incorporates phage tail fiber proteins and bacterial O-antigen biosynthesis gene clusters, and we use this representation to guide interaction prediction. Across four bacterial genera (Escherichia, Klebsiella, Vibrio, and Alteromonas), PhageMind achieves high prediction accuracy and shows strong adaptability to new lineages. In particular, in leave-one-genus-out evaluations, the model maintains robust performance when only limited reference data are available, demonstrating its potential as a scalable and practical tool for studying phage-host interactions across the global phageome.

Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited by insufficient understanding of protein structures and unpredictable molecular generation. To address these challenges, we propose Exploration-Augmented Latent Inference for LLMs (ELILLM), a framework that reinterprets the LLM generation process as an encoding, latent space exploration, and decoding workflow. ELILLM explicitly explores portions of the design problem beyond the model's current knowledge while using a decoding module to handle familiar regions, generating chemically valid and synthetically reasonable molecules. In our implementation, Bayesian optimization guides the systematic exploration of latent embeddings, and a position-aware surrogate model efficiently predicts binding affinity distributions to inform the search. Knowledge-guided decoding further reduces randomness and effectively imposes chemical validity constraints. We demonstrate ELILLM on the CrossDocked2020 benchmark, showing strong controlled exploration and high binding affinity scores compared with seven baseline methods. These results demonstrate that ELILLM can effectively enhance LLMs capabilities for SBDD.

This study investigates the dynamics of red blood cells (RBCs) under the influence of evanescent waves generated by total internal reflection (TIR). Using a 1064 nm laser system and a dual-chamber prism setup, we quantified the mobility of erythrocytes in different glucose environments. Our methodology integrates automated tracking via TrackMate\c{opyright} to analyze over 60 trajectory sets. The results reveal a significant decrease in mean velocity, from 11.8 {\mu}m/s in 5 mM glucose to 8.8 {\mu}m/s in 50 mM glucose (p = 0.019). These findings suggest that evanescent waves can serve as a non-invasive tool to probe the mechanical properties of cell membranes influenced by biochemical changes.

Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM, a lightweight spatial transcriptomics foundation model based on graph convolutional networks (GCNs) trained with a masked central spot prediction objective. Trained on 416 human Visium samples spanning 15 organs, SAGE-FM learns spatially coherent embeddings that robustly recover masked genes, with 91% of masked genes showing significant correlations (p < 0.05). The embeddings generated by SAGE-FM outperform MOFA and existing spatial transcriptomics methods in unsupervised clustering and preservation of biological heterogeneity. SAGE-FM generalizes to downstream tasks, enabling 81% accuracy in pathologist-defined spot annotation in oropharyngeal squamous cell carcinoma and improving glioblastoma subtype prediction relative to MOFA. In silico perturbation experiments further demonstrate that the model captures directional ligand-receptor and upstream-downstream regulatory effects consistent with ground truth. These results demonstrate that simple, parameter-efficient GCNs can serve as biologically interpretable and spatially aware foundation models for large-scale spatial transcriptomics.

Alzheimer's disease (AD), defined as an abnormal buildup of amyloid plaques and tau tangles in the brain can be diagnosed with high accuracy based on protein biomarkers via PET or CSF analysis. However, due to the invasive nature of biomarker collection, most AD diagnoses are made in memory clinics using cognitive tests and evaluation of hippocampal atrophy based on MRI. While clinical assessment and hippocampal volume show high diagnostic accuracy for amnestic or typical AD (tAD), a substantial subgroup of AD patients with atypical presentation (atAD) are routinely misdiagnosed. To improve diagnosis of atAD patients, we propose a machine learning approach to distinguish between atAD and non-AD cognitive impairment using clinical testing battery and MRI data collected as standard-of-care. We develop and evaluate our approach using 1410 subjects across four groups (273 tAD, 184 atAD, 235 non-AD, and 685 cognitively normal) collected from one private data set and two public data sets from the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). We perform multiple atAD vs. non-AD classification experiments using clinical features and hippocampal volume as well as a comprehensive set of MRI features from across the brain. The best performance is achieved by incorporating additional important MRI features, which outperforms using hippocampal volume alone. Furthermore, we use the Boruta statistical approach to identify and visualize significant brain regions distinguishing between diagnostic groups. Our ML approach improves the percentage of correctly diagnosed atAD cases (the recall) from 52% to 69% for NACC and from 34% to 77% for ADNI, while achieving high precision. The proposed approach has important implications for improving diagnostic accuracy for non-amnestic atAD in clinical settings using only clinical testing battery and MRI.

Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmarks, they often fail to generalize to realistic deployment scenarios, where most candidate drug pairs involve previously unseen drugs and validated interactions are scarce. We demonstrate that proximity in the embedding spaces of prevailing molecule-centric DDI models does not reliably correspond to interaction labels, and that simply scaling up model capacity therefore fails to improve generalization. To address these limitations, we propose GenRel-DDI, a generalizable relation learning framework that reformulates DDI prediction as a relation-centric learning problem, in which interaction representations are learned independently of drug identities. This relation-level abstraction enables the capture of transferable interaction patterns that generalize to unseen drugs and novel drug pairs. Extensive experiments across multiple benchmark demonstrate that GenRel-DDI consistently and significantly outperforms state-of-the-art methods, with particularly large gains on strict entity-disjoint evaluations, highlighting the effectiveness and practical utility of relation learning for robust DDI prediction. The code is available at this https URL.

The last decade has seen significant advances in computer-aided diagnostics for cytological screening, mainly through the improvement and integration of scanning techniques such as whole slide imaging (WSI) and the combination with deep learning. Simultaneously, new imaging techniques such as quantitative phase imaging (QPI) are being developed to capture richer cell information with less sample preparation. So far, the two worlds of WSI and QPI have not been combined. In this work, we present a reconstruction algorithm which makes whole slide imaging of cervical smears possible by using a self-referencing three-wave digital holographic microscope. Since a WSI is constructed by combining multiple patches, the algorithm is adaptive and can be used on partial holograms and patched holograms. We present the algorithm for a single shot hologram, the adaptations to make it flexible to various inputs and show that the algorithm performs well for the tested epithelial cells. This is a preprint of our paper, which has been accepted for publication in 2026 IEEE International Symposium on Biomedical Imaging (ISBI).

Transcription factors (TFs) regulate gene expression by binding to specific genomic loci determined by DNA sequence. Their sequence specificity is commonly summarized by a consensus binding motif. However, eukaryotic genomes contain billions of low-affinity DNA sequences to which TFs associate with a sequence-dependent binding energy. We currently lack insight into how the genomic sequence defines this spectrum of binding energies and the resulting pattern of TF localization. Here, we set out to obtain a quantitative understanding of sequence-dependent TF binding to both motif and non-motif sequences. We achieve this by first pursuing accurate measurements of physical binding energies of the human TF Klf4 to a library of short DNA sequences in a fluorescence-anisotropy-based bulk competitive binding assay. Second, we show that the highly non-linear sequence dependence of Klf4 binding energies can be captured by combining a linear model of binding energies with an Ising model of the coupled recognition of nucleotides by a TF. We find that this statistical mechanics model parametrized by our in vitro measurements captures Klf4 binding patterns on individual long DNA molecules stretched in the optical tweezer, and is predictive for Klf4 occupancy across the entire human genome without additional fit parameters.

The effectiveness of oncolytic virotherapy is significantly affected by several elements of the tumour microenvironment, which reduce the ability of the virus to infect cancer cells. In this work, we focus on the influence of hypoxia on this therapy and develop a novel continuous mathematical model that considers both the spatial and epigenetic heterogeneity of the tumour. We investigate how oxygen gradients within tumours affect the spatial distribution and replication of both the tumour and oncolytic viruses, focusing on regions of severe hypoxia versus normoxic areas. Additionally, we analyse the evolutionary dynamics of tumour cells under hypoxic conditions and their influence on susceptibility to viral infection. Our findings show that the reduced metabolic activity of hypoxic cells may significantly impact the virotherapy effectiveness; the knowledge of the tumour's oxygenation could, therefore, suggest the most suitable type of virus to optimise the outcome. The combination of numerical simulations and theoretical results for the model equilibrium values allows us to elucidate the complex interplay between viruses, tumour evolution and oxygen dynamics, ultimately contributing to developing more effective and personalised cancer treatments.

Stockart et al. (2025) recommend guidelines for best practices in the field of unconscious cognition. However, they condone the repeatedly criticized technique of excluding trials with high visibility ratings or of participants with high sensitivity for the critical stimulus. Based on standard signal detection theory for discrimination judgments, we show that post-hoc trial selection only isolates points of neutral response bias but remains consistent with uncomfortably high levels of sensitivity. We argue that post-hoc selection constitutes a sampling fallacy that capitalizes on chance, generates regression artifacts, and wrongly ascribes unconscious processing to stimulus conditions that may be far from indiscriminable. As an alternative, we advocate the study of functional dissociations, where direct (D) and indirect (I) measures are conceptualized as spanning up a two-dimensional D-I-space and where single, sensitivity, and double dissociations appear as distinct curve patterns. While Stockart et al.'s recommendations cover only a single line of that space where D is close to zero, functional dissociations can utilize the entire space, circumventing requirements like null visibility and exhaustive reliability, and allowing for the planful measurement of theoretically meaningful functional relationships between experimentally controlled variables.

This article presents a comprehensive study of the continuous McKendrick model, which serves as a foundational framework in population dynamics and epidemiology. The model is formulated through partial differential equations that describe the temporal evolution of the age distribution of a population using continuously defined birth and death rates. In this work, we provide rigorous derivations of the renewal equation, establish the appropriate boundary conditions, and perform a detailed analysis of the survival functions. The central result demonstrates that the population approaches extinction if and only if the net reproduction number $R_{n}$ is strictly less than unity. We present two independent proofs: one based on Laplace transform techniques and Tauberian theorems, and another employing a reformulation as a system of ordinary differential equations with eigenvalue analysis. Additionally, we establish the connection between the deterministic framework and stochastic process formulations, showing that the McKendrick equation emerges as the fluid limit of an individual-based stochastic model.

Neural activity data can be associated with behavioral and physiological variables by analyzing their changes in the temporal domain. However, such relationships are often difficult to quantify and test, requiring advanced computational modeling approaches. Here, we provide a protocol for the statistical analysis of brain dynamics and for testing their associations with behavioral, physiological and other non-imaging variables. The protocol is based on an open-source Python package built on a generalization of the hidden Markov model (HMM) - the Gaussian-linear HMM - and supports multiple experimental modalities, including task-based and resting-state studies, often used to explore a wide range of questions in neuroscience and mental health. Our toolbox is available as both a Python library and a graphical interface, so it can be used by researchers with or without programming experience. Statistical inference is performed by using permutation-based methods and structured Monte Carlo resampling, and the framework can easily handle confounding variables, multiple testing corrections and hierarchical relationships within the data, among other features. The package includes tools developed to facilitate the intuitive visualization of statistical results, along with comprehensive documentation and step-by-step tutorials for data interpretation. Overall, the protocol covers the full workflow for the statistical analysis of functional neural data and their temporal dynamics.

The mapping from sound to neural activity that underlies hearing is highly non-linear. The first few stages of this mapping in the cochlea have been modelled successfully, with biophysical models built by hand and, more recently, with DNN models trained on datasets simulated by biophysical models. Modelling the auditory brain has been a challenge because central auditory processing is too complex for models to be built by hand, and datasets for training DNN models directly have not been available. Recent work has taken advantage of large-scale high resolution neural recordings from the auditory midbrain to build a DNN model of normal hearing with great success. But this model assumes that auditory processing is the same in all brains, and therefore it cannot capture the widely varying effects of hearing loss. We propose a novel variational-conditional model to learn to encode the space of hearing loss directly from recordings of neural activity in the auditory midbrain of healthy and noise exposed animals. With hearing loss parametrised by only 6 free parameters per animal, our model accurately predicts 62% of the explainable variance in neural responses from normal hearing animals and 68% for hearing impaired animals, within a few percentage points of state of the art animal specific models. We demonstrate that the model can be used to simulate realistic activity from out of sample animals by fitting only the learned conditioning parameters with Bayesian optimisation, achieving crossentropy loss within 2% of the optimum in 15-30 iterations. Including more animals in the training data slightly improved the performance on unseen animals. This model will enable future development of parametrised hearing loss compensation models trained to directly restore normal neural coding in hearing impaired brains, which can be quickly fitted for a new user by human in the loop optimisation.

In the last century, most sensorimotor studies of cortical neurons relied on average firing rates. Rate coding is efficient for fast sensorimotor processing that occurs within a few seconds. Much less is known about long-term working memory with a time scale of hours (Ericsson and Kintsch, 1995). The discovery of millisecond-precision spike initiation in cortical neurons was unexpected (Mainen and Sejnowski, 1995). Even more striking was the precision of spiking in vivo, in response to rapidly fluctuating sensory inputs, suggesting that neural circuits could preserve and manipulate sensory information through spike timing. High temporal resolution enables a broader range of neural codes. It could also support spike-timing-dependent plasticity (STDP), which is triggered by the relative timing of spikes between presynaptic and postsynaptic neurons in the millisecond range. What spike-timing mechanisms could regulate STDP in vivo? Cortical traveling waves have been observed across many frequency bands with high temporal precision. Traveling waves have wave fronts that could link spike timing to STDP. As a wave front passes through a cortical column, excitatory synapses on the dendrites of both pyramidal and basket cells are stimulated synchronously. Inhibitory basket cells form a calyx on pyramidal cell bodies, and inhibitory rebound following a strong transient hyperpolarization can trigger a backpropagating action potential, which arrives shortly after the excitatory inputs on pyramidal dendrites. STDP activated in this way could persist for hours, creating a second-tier network. This temporary network could support long-term working memory, a cognitive network riding above the long-term sensorimotor network. On their own, traveling waves and STDP have not yet yielded new insights into cortical function. Together, they could be responsible for how we think (Sejnowski, 2025).

Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a useful approach to map the anatomic features of brain senescence. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) can reveal spatially localized patterns of aging. Although previous studies have examined anatomical contributors to GBA, no framework has been established to compute LBA using cortical morphology. To address this gap, we introduce a novel graph neural network (GNN) that uses morphometric features (cortical thickness, curvature, surface area, gray/white matter intensity ratio and sulcal depth) to estimate LBA across the cortical surface at high spatial resolution (mean inter-vertex distance = 1.37 mm). Trained on cortical surface meshes extracted from the MRIs of cognitively normal adults (N = 14,250), our GNN identifies prefrontal and parietal association cortices as early sites of morphometric aging, in concordance with biological theories of brain aging. Feature comparison using integrated gradients reveals that morphological aging is driven primarily by changes in surface area (gyral crowns and highly folded regions) and cortical thickness (occipital lobes), with additional contributions from gray/white matter intensity ratio (frontal lobes and sulcal troughs) and curvature (sulcal troughs). In Alzheimers disease (AD), as expected, the model identifies widespread, excessive morphological aging in parahippocampal gyri and related temporal structures. Significant associations are found between regional LBA gaps and neuropsychological measures descriptive of AD-related cognitive impairment, suggesting an intimate relationship between morphological cortical aging and cognitive decline. These results highlight the ability of GNN-derived gero-morphometry to provide insights into local brain aging.

Motivation: Opioid use disorder (OUD) often arises after prescription opioid exposure and follows transitions among onset, remission, and relapse. Linked EHR-survey resources such as the All of Us Research Program enable stage-specific risk modeling and connection to intervention options. Results: We built a multi-stage framework to model time-to-onset, time-to-remission, and time-to-relapse after remission using All of Us EHR and survey data. For each participant we derived longitudinal predictors from clinical conditions and survey concepts, including recent (1/3/12-month) event counts, cumulative exposures, and time since last event. We fit regularized Cox models for each transition and aggregated selection frequencies and hazard ratios to identify a compact set of high-confidence predictors. Pain, mental health, and polysubstance use contributed across stages: chronic pain syndromes, tobacco/nicotine dependence, anxiety and depressive disorders, and cannabis dependence prominently predicted onset and relapse, whereas tobacco dependence during remission and other remission-coded conditions were strongly associated with transition to remission. To support therapeutic prioritization, we constructed a therapy knowledge graph integrating genetic targets, biological pathways, and published evidence to map identified risk factors to candidate treatments in recent OUD studies and clinical guidelines.

The rapid advancement of DNA sequencing has produced vast genomic datasets, yet interpreting and engineering genomic function remain fundamental challenges. Recent large language models have opened new avenues for genomic analysis, but existing approaches are often limited by restricted training scope, constrained generative capability, or prohibitive computational cost. We introduce GENErator, a generative genomic foundation model for long-context DNA modeling, with a context length of 98k nucleotides, pre-trained on 386 billion nucleotides of eukaryotic DNA. Without task-specific fine-tuning, GENERator exhibits strong intrinsic capabilities: unsupervised embedding analyses reveal phylogenetically coherent structure, and sequence recovery benchmarks demonstrate generative accuracy comparable to or exceeding state-of-the-art models with substantially improved computational efficiency. In a zero-shot setting, GENERator achieves competitive variant effect prediction performance relative to alignment-based methods, while remaining fully alignment-free and broadly applicable across species. With task-specific fine-tuning, the model attains leading performance on established genomic benchmarks. We further demonstrate practical generative applications. GENERator can generate protein-coding DNA sequences that translate into structurally plausible proteins and, through a prompt-guided design framework, design cis-regulatory elements with targeted activity profiles, including synthetic super-enhancers validated by high-throughput UMI-STARR-seq assays. Together, these results establish GENERator as an efficient and biologically grounded framework for genomic interpretation and programmable sequence design. Code and supplementary resources are available at this https URL.

Neural networks storing multiple discrete attractors are canonical models of biological memory. Previously, the dynamical stability of such networks could only be guaranteed under highly restrictive conditions. Here, we derive a theory of the local stability of discrete fixed points in a broad class of networks with graded neural activities and in the presence of noise. By directly analyzing the bulk and the outliers of the Jacobian spectrum, we show that all fixed points are stable below a critical load that is distinct from the classical \textit{critical capacity} and depends on the statistics of neural activities in the fixed points as well as the single-neuron activation function. Our analysis highlights the computational benefits of threshold-linear activation and sparse-like patterns.

Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help address these challenges, we developed Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. We evaluated Mantis against 48 forecasting models across six diseases with diverse modes of transmission, assessing both point forecast accuracy (mean absolute error) and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower mean absolute error than all models in the CDC's COVID-19 Forecast Hub when backtested on early pandemic forecasts which it had not previously seen. Across all other diseases tested, Mantis consistently ranked in the top two models across evaluation metrics. Mantis further generalized to diseases with transmission mechanisms not represented in its training data, demonstrating that it can capture fundamental contagion dynamics rather than memorizing disease-specific patterns. These capabilities illustrate that purely simulation-based foundation models such as Mantis can provide a practical foundation for disease forecasting: general-purpose, accurate, and deployable where traditional models struggle.

Ensemble forecasts have become a cornerstone of large-scale disease response, underpinning decision making at agencies such as the US Centers for Disease Control and Prevention (CDC). Their growing use reflects the goal of combining multiple models to improve accuracy and stability versus relying on any single model. However, while ensembles regularly demonstrate stability against individual model failures, improved accuracy is not guaranteed. During the COVID-19 pandemic, the CDC's multi-model ensemble outperformed the best single model by only 1\%, and CDC flu ensembles have often ranked below individual models. Prior work has established that ensemble performance depends critically on diversity: when models make independent errors, combining them yields substantial gains. In practice, however, this diversity is often lacking. Here, we propose that this is due in part to how models are developed and selected: both modelers and ensemble builders optimize for stand-alone accuracy rather than ensemble contribution, and most epidemic forecasts are built from a small set of approaches trained on the same surveillance data. The result is highly correlated errors, limiting the benefit of ensembling. This suggests that in developing models and ensembles, we should prioritize models that contribute complementary information rather than replicating existing approaches. We present a toy example illustrating the theoretical cost of correlated errors, analyze correlations among COVID-19 forecasting models, and propose improvements to model fitting and ensemble construction that foster genuine diversity. Ensembles built with this principle in mind produce forecasts that are more robust and more valuable for epidemic preparedness and response.