Non-negative connectivity causes bow-tie architecture in neural circuits.

Bow-tie architecture (BTA) is widely observed in biological neural systems, yet the underlying mechanism driving its spontaneous emergence remains unclear. In this study, we identify a novel formation mechanism by training multi-layer neural networks under biologically inspired non-negative connectivity constraints across diverse classification tasks. We show that non-negative weights reshape network dynamics by amplifying back-propagated error signals and suppressing hidden-layer activity, leading to the self-organization of BTA without pre-defined architecture.

Communicating with two vehicles immediately ahead boosts traffic capacity sixfold in connected autonomous vehicle platoons.

Addressing urban congestion through enhanced traffic capacity has emerged as a critical objective for connected autonomous driving technologies. An irredundant communication connectivity topology is essential for ensuring the high efficiency and stability of the traffic system, which has not been fully validated due to the scarcity of real-world tests. Motivated by this fact, this paper deploys a connected autonomous vehicle platoon without relying on the information of a platoon leader to preserve the possibility of extending the platoon in future practical applications.

Effectiveness of Technology-Based Interventions in Promoting Lung Cancer Screening Uptake and Decision-Making Among Patients.

This study reviews how technology-based interventions have been designed and implemented to promote lung cancer screening (LCS), support shared decision-making, and enhance patient engagement. A systematic search of six databases in February 2025 identified 28 eligible studies published between 2014 and 2025. Most interventions were home-based and self-guided, including videos, websites, mobile apps, telehealth, and patient portal messages.

Enhancing Youth Access to Reproductive Health Services in Rural Communities Through Clinical and Educational Interventions in Upstate New York, USA.

Introduction: Access to sexual and reproductive health care has been a critical issue affecting public health outcomes, particularly for the youth population. This project identified common barriers to accessing sexual and reproductive health care for youth in rural areas and applied multiple educational and clinical interventions that helped enhance access to care.

Methodology: We designed a three-year-long program at Finger Lakes Community Health (FLCH) in rural upstate New York that involved youth, their parents or caregivers, educators, and medical professionals to improve youth access to care.

A small set of critical hyper-motifs governs heterogeneous flow-weighted network resilience.

Flow-weighted networks are widespread in real-world systems, capturing the essence of flow interactions among various entities. Examples are food webs, social networks, transportation systems, and financial transactions. These networks are vulnerable to degradation when subjected to disturbances, often triggering cascading failures that severely impact their functionality.

CANA v1.0.0: efficient quantification of canalization in automata networks.

Summary: The biomolecular networks underpinning cell function exhibit canalization, or the buffering of fluctuations required to function in a noisy environment. We present a new major release of CANA, v1.0.

Systematic review on the technology's role in supporting lung cancer patients in the treatment journey.

This systematic review examines the role of technology-based interventions in supporting lung cancer patients during their treatment. It identifies (1) the different technologies utilized, (2) their functions and benefits, and (3) the barriers encountered by patients. The authors searched six databases for literature examining the use of technology to support treatment among lung cancer patients.

Tumor-adipose assembloids reveal cell-fate-transition-triggered multistage collective invasions.

Tumor invasion constitutes a multifaceted process encompassing collective cellular migration and dynamic cell-fate transitions. Although these aspects have been studied separately by physicists and biologists, their spatiotemporal coupling remains unclear. To bridge this gap, we introduce a tumor-adipose assembloid model that facilitates live tracking and temporal analysis of cancer cells and adipocytes.

Fuels Mediate the Influence of Climate Teleconnections on Wildfires in Dryland Ecosystems.

Climate teleconnections modulate regional wildfire occurrence. Understanding the underlying mechanisms is critical for sub-seasonal to annual wildfire predictions since the magnitude of certain teleconnection climate modes (TCMs) intensifies or they may undergo phase shifts. Here, we study how TCMs govern wildfire activity and compare the effects of weather and fuels in mediating the influence of TCMs on wildfires.

Quantifying edge relevance for epidemic spreading via the semi-metric topology of complex networks.

Sparsification aims at extracting a reduced core of associations that best preserves both the dynamics and topology of networks while reducing the computational cost of simulations. We show that the semi-metric topology of complex networks yields a natural and algebraically-principled sparsification that outperforms existing methods on those goals. Weighted graphs whose edges represent distances between nodes are when at least one edge breaks the triangle inequality (transitivity).

Deep learning for predicting the occurrence of tipping points.

Tipping points occur in many real-world systems, at which the system shifts suddenly from one state to another. The ability to predict the occurrence of tipping points from time series data remains an outstanding challenge and a major interest in a broad range of research fields. Particularly, the widely used methods based on bifurcation theory are neither reliable in prediction accuracy nor applicable for irregularly sampled time series which are commonly observed from real-world systems.

Dissecting human cortical similarity networks across the lifespan.

The human cortex exhibits remarkable morphometric similarity between regions; however, the form and extent of lifespan network remodeling remain unknown. Here, we show the spatiotemporal maturation of morphometric brain networks, using multimodal neuroimaging data from 33,937 healthy participants aged 0-80 years. Global architecture matures from birth to early adulthood through enhanced modularity and small worldness.

Using homologous network to identify reassortment risk in H5Nx avian influenza viruses.

The resurgence of H5Nx reassortment has caused multiple epidemics resulting in severe disease even death in wild birds and poultry. Assessing H5Nx reassortment risk is crucial for designing targeted interventions and enhancing preparedness efforts to manage H5Nx outbreaks effectively. However, the complexity in H5Nx reassortment, driven by the diversity of influenza A viruses (IAVs) and wide range of hosts, has hindered the effective quantification of reassortment risk.

Postnatal critical-period brain plasticity and neurodevelopmental disorders: revisited circuit mechanisms.

Critical periods (CPs) are defined as postnatal developmental windows during which brain circuits exhibit heightened sensitivity to altered experiences or sensory inputs, particularly during brain development in humans and animals. During the CP, experience-induced refinements of neural connections are crucial for establishing adaptive and mature brain functions, and aberrant CPs are often accompanied by many neurodevelopmental disorders (NDDs), including autism spectrum disorders and schizophrenia. Understanding neural mechanisms underlying the CP regulation is key to delineating the etiology of NDDs caused by abnormal postnatal neurodevelopment.

Temporal dynamics of quantity processing: distinct time course and representational patterns revealed by multivariate pattern analysis.

People employ both discrete and continuous quantities to quantify aspects of their environment. However, the temporal dynamics and interactions underlying the processing of these quantitative information remain insufficiently understood. Our study aimed to address this gap by employing a one-back task in conjunction with magnetoencephalography (MEG) to investigate neural responses to dot stimuli representing both discrete (e.

Investigate channel rectifications and neural dynamics by an electrodiffusive Gauss-Nernst-Planck approach.

Electrodiffusion plays a crucial role in modulating ion channel conductivity and neural firing dynamics within the nervous system. However, the relationship among ion electrodiffusion, concentration changes, as well as channel conductivity and neuronal discharge behaviors is not quite clear. In this work, we introduce a novel Gauss-Nernst-Planck (GNP) approach to investigate how electrodiffusive dynamics influence ion channel rectification and neural activity.

A hippocampal navigation model through hierarchical memory organization.

Unlabelled: Animals in nature exhibit exceptional navigational abilities, primarily due to the hippocampus's capacity to form and utilize spatial and non-spatial memories. However, existing models often fail to accurately capture the dynamic interplay between different hippocampal regions. This study presents a unified navigation model inspired by the functional interactions between the hippocampus and surrounding neural circuits, with a focus on the transition mechanisms between vector-based navigation, controlled by grid cells, and hierarchical memory-based navigation, coordinated by the ventral-dorsal hippocampal axis.

Matrix-Weighted Networks for Modeling Multidimensional Dynamics: Theoretical Foundations and Applications to Network Coherence.

Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices.

Impact of lifting school mask mandates on community SARS-CoV-2 cases, hospitalizations, and deaths: a retrospective observational study.

Background: School masking mandates were widely adopted as a pandemic control measure, however, limited data are available regarding their effectiveness as a strategy for reducing burden of disease in the surrounding community.

Objective: To evaluate the impact of school masking policy de-adoption (mask-lifting) on SARS-CoV-2 incidence rates, hospitalizations, and deaths in the surrounding community.

Methods: : Retrospective observational study with an event study design, a difference-in-difference method; a target trial emulation (TTE) framework was applied as a secondary analysis.

Geometric properties of the additional third-order transitions in the two-dimensional Potts model.

Within the canonical ensemble framework, this paper investigates the presence of higher-order transition signals in the q-state Potts model (for q≥3), using two geometric order parameters: isolated spins number and the average perimeter of clusters. Our results confirm that higher-order transitions exist in the Potts model, where the number of isolated spins reliably indicates third-order independent transitions. This signal persists regardless of the system's phase transition order, even at higher values of q.

Universality of the complete-graph Potts model with 0

Universality is a fundamental concept in modern physics. For the q-state Potts model, the critical exponents are merely determined by the order-parameter symmetry S_{q}, spatial dimensionality and interaction range, independent of microscopic details. In a simplest and mean-field treatment, i.

Global patterns of extreme temperature teleconnections using climate network analysis.

Extreme weather events, rare yet profoundly impactful, are often accompanied by severe conditions. Increasing global temperatures are poised to exacerbate these events, resulting in greater human casualties, economic losses, and ecological destruction. Complex global climate interactions, known as teleconnections, can lead to widespread repercussions triggered by localized extreme weather.

Prediction of functional neural circuits in caenorhabditis elegans based on overlapping community detection.

The identification of functional neural circuits is crucial for understanding brain functions. However, experimental methods are often labor-intensive and resource-intensive. In this study, we modified the BIGCLAM algorithm to detect overlapping communities in directed and weighted networks and applied it to the neural networks of hermaphrodite and male Caenorhabditis elegans (C.

Multiply robust causal inference in the presence of an error-prone treatment.

Numerous estimation procedures employed in causal inference often rely on accurately measured data. However, the prevalence of measurement errors in practical studies may yield biased effect estimates. It is common to employ validation samples to rectify such biases in the measurement error literature.