Altered flow of information in temporal lobe epilepsy.

Phytochromes (PHYs) are a major group of photoreceptors, described as red and far-red light sensors in land plants. Recent genomic and metagenomic explorations have revealed the presence of PHYs also in various eukaryotic microalgae originating from distinct endosymbiotic events. Growing evidence indicates that these PHYs are spectrally and functionally tuned to shorter wavelengths, which are prevalent in the aquatic environments as depth increases. Investigations using emerging phytoplankton model species, along with environmental surveys, are uncovering new PHY-mediated responses that likely influence their growth and distribution in marine environments. This Tansley Insight explores the implications of these discoveries for understanding the evolution and functional significance of this major photoreceptor class in the upper ocean, where light drives both energy and information flow.

BlotDx: A deep learning tool for Western blot-based diagnostics.

Distinct neurodynamics underlie empathy for infant pain: An EEG study of temporal and oscillatory mechanisms.

• Introduces a flow-aware training strategy for PINNs based on the information flow in the domain. • Decomposes the domain into geodesic subdomains that are progressively supervised via an epoch-based schedule. • Matches or improves baseline accuracy while reducing computational costs across seven PDE benchmarks. • Prevents uninformative updates and misleading gradients in regions where the solution is not yet determined. Physics-Informed Neural Networks (PINNs) typically update parameters at all collocation points from the first epoch, even in regions still unreachable from boundary or initial conditions. In these zones, early updates may be uninformative or harmful, as residual gradients often correlate poorly with the true error. We introduce a flow-aware training strategy that delays supervision until the governing physics can propagate meaningful information. The computational mesh is decomposed into geodesic subdomains ranked by distance from an information boundary, where initial or boundary conditions are applied, and progressively activated according to an epoch schedule. This selective exposure concentrates optimization where updates are most effective, preventing wasted capacity and misleading gradients in causally disconnected regions. The method requires no architectural changes and uses the standard PINN loss; only the sampling mask evolves over time. Benchmarks on seven PDE problems show that flow-aware training matches or improves baseline accuracy while reducing computational cost.

Semantic digital twins for masonry bridges: Structuring geometrical, material and assessment field data

Long-term exercise is associated with distinct patterns of effective connectivity in the executive control network linked to cognitive flexibility aging.

Traditional inspection approaches often face challenges related to integration and interoperability across system boundaries. To overcome these issues, knowledge-based systems have increasingly been adopted for their ability to formalize domain expertise and support data-driven decision-making. However, a persistent gap remains between knowledge engineering and system modeling, which hinders information flow and semantic consistency throughout the system lifecycle. To bridge this gap, this study introduces a domain knowledge-enhanced Model-Based Systems Engineering (MBSE) framework that establishes a semantic connection between the system design phase and operational phase. The approach embeds a domain inspection ontology into the MBSE workflow at the metamodel level through the construction of domain-specific metamodels that integrate ontological concepts into core modeling constructs. Once developed, these semantically enriched system models are systematically transformed into application ontologies, which serve as schemas for operational knowledge bases. The framework is validated through a real-world overhead crane inspection case study. The evaluation demonstrates enhanced lifecycle traceability, efficient cross-domain interoperability, reliable rule-based reasoning, and reduced maintenance effort. Overall, this work contributes a unified methodology that bridges system modeling and operational knowledge for intelligent inspection systems. • Proposes an MBSE framework that integrates domain knowledge at the metamodel level. • Creates a semantic bridge from design-time system models to operational knowledge bases. • Automates the generation of application ontologies from semantically enriched system models. • Feasibility is demonstrated via an overhead crane inspection case study.

Artificial intelligence resolves transboundary water conflicts under climate uncertainty.

This paper examines the causal role of face-to-face (F2F) interactions in generating local informational advantages for mutual fund managers. Using COVID-19 lockdowns as an exogenous shock, I show that fund managers’ performance on local stocks declined relative to distant stocks when in-person meetings were curtailed, driven by impaired investment timing rather than changes in firm fundamentals. I investigate two distinct benefits of F2F interactions arising from interpersonal cues: trust-building, which enhances the transmission of soft information, and impression management, which facilitates the transmission of favorable information. The results cannot be fully explained by changes in internal information flows or the use of public information, and are more pronounced for stocks in less transparent information environments and in regions with stronger social traits.

How education and institutional support shape university students' digital sustainable entrepreneurial intentions: The moderating role of sustainable development goals knowledge.

GVCG-YOLO: A fast and lightweight Twin-fusion fiber-core algorithm for efficient kitchen waste detection

Information theory is a powerful framework for quantifying complexity, uncertainty and dynamical structure in time-series data, with widespread applicability across disciplines such as physics, neuroscience and finance. However, the literature on these measures remains fragmented, with domain-specific terminologies, inconsistent mathematical notation and disparate visualization conventions that hinder interdisciplinary integration. This work addresses these challenges by unifying key information-theoretic time-series measures through shared semantic definitions, standardized mathematical notation and cohesive visual representations. We compare these measures in terms of their theoretical foundations, computational formulations and practical interpretability-mapping them onto a common conceptual space through an illustrative case study with functional magnetic resonance imaging time series in the brain. This case study exemplifies the complementary insights these measures offer in characterizing the dynamics of complex neural systems, such as signal complexity and information flow. By providing a structured synthesis, our work aims to enhance interdisciplinary dialogue and methodological adoption, which is particularly critical for accessibility and reproducibility in computational neuroscience. More broadly, our framework serves as a resource for researchers seeking to navigate and apply information-theoretic time-series measures to diverse complex systems.

ABSTRACT This article provides a genealogy of ‘smart’ border policing before computational and digital technologies, such as Blockchain or Artificial Intelligence gained traction in public administration. First, I analyse the European Union’s plans and desires for an interoperability framework in the areas of migration, border policing and security. I show that database interoperability is one of the largest concerns of European public administration in general, and border and migration policing in particular. Second, I focus on plans and implementations of database interoperability between public administration, border control, and policing, as they were articulated in the 1970s and 80s in Germany. Looking at archival documents about the reform of the Central Register of Foreigners, I analyse how public administration imagined and designed data interoperability between migration and policing institutions. I situate these plans as part of larger political concerns about anti-terrorism, labour migration, and European integration. I present three interrelated arguments. First, historically, I show how interoperability, as a technocratic administrative project, was and is continuously reproduced through the fabrication of political urgency and the governmental desire for seamless and unhindered information flow, a process I call infrastructural opportunism. Second, media-theoretically, I characterize interoperability as a theory of communication that is entangled with histories of modernity and empire. I argue that it is not only interoperability in itself that has efficacy in debates on reforming public administration, but that talking about interoperability produces its efficacy. Third, politically, I argue that we must strengthen abolitionist approaches in the study of databases and migration policing, as they provide fundamental structural critiques of border violence beyond reformist approaches to privacy and data protection. As such, this article contributes to the literature on digital migration and border policing, as well as the history of media technology.

The human brain's capacity for visual information processing is essential for higher-order cognitive functions, including facial recognition and spatial navigation. However, the millisecond-scale dynamics of cortico-subcortical information flow during novel visual stimulation remain unclear. To investigate this, we conducted intracranial electroencephalography (iEEG) recordings in 22 patients with refractory epilepsy as they underwent a picture-viewing task. Our findings showed region-specific modulations in high-frequency broadband (HFB, 60-160 Hz) activity across multiple brain regions, such as the dorsal and ventral visual streams, the limbic system, and higher-order cortical areas. Initial activation followed a hierarchical, back-to-front propagation pattern, starting from the primary visual areas, progressing through the ventral and dorsal streams, and ultimately engaging high-order cortical and limbic structures. Extensive top-down connectivity from higher-order cortex to limbic areas further revealed their role in novel visual processing, providing a high-resolution spatiotemporal map of visual information flow across brain regions.

Objective. Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. Biological systems, including the heart, exhibit complex nonlinear dynamics arising from interactions between their subsystems. Information-theoretic measures, particularly entropy-based methods, provide a framework to quantify these interactions. Using ECG recordings, we investigate information flow between heart rhythm and ventricular repolarization to identify potential markers of pathological alterations in cardiac electrical activity.Approach. Entropy-based measures of information transfer were derived from beat-to-beat ECG time series using a window-based approach and subsequently averaged at the subject level. These features were used as inputs to supervised machine learning models to discriminate patients with congenital long QT syndrome (LQTS) from healthy controls. Model performance was evaluated using repeated stratified train-test splits, and classification robustness was assessed across multiple runs using standard performance metrics, including the area under the receiver operating characteristic curve. The explainable artificial intelligence techniques were applied. SHapley Additive exPlanations were used to quantify the contribution of entropy-based features to the model predictions. This post-hoc explainability analysis enabled systematic assessment of feature importance while preserving the predictive performance of the models.Results. The proposed approach achieved high and stable classification performance across repeated validation runs. Both random forest (RF) and support vector machine (SVM) classifiers demonstrated high discrimination between LQTS patients and healthy controls, with consistently high area under the curve. For RF a mean accuracy of 95.9%, mean sensitivity of 95.9%, and mean specificity of 92.9% were achieved across repeated runs. For SVM the corresponding mean values were 93.1%, 93.1%, and 92.0%, respectively.Conclusions.Explainability analysis revealed a dominant contribution of multivariate and conditional information flow features compared with single-source entropy measures, highlighting the relevance of joint and conditional interactions in the classification patterns.

Major depressive disorder is known to disturb the hippocampus, but how this impacts signal processing performed by the structure remains poorly understood. Here, we report that single housing (7-10 days) promotes a depression-like phenotype in young adult mice that is associated with a robust, yet surprisingly discreet defect in information flow across the primary hippocampal circuit. In addition to sociability disturbances and despair-like behavior, single housing eliminated preference for novelty and impaired episodic memory encoding. Additionally, the lateral habenula, an epithalamic structure critically involved in depression, was hyperactive. Although the CA1 waveform and associated spike output elicited by single-pulse lateral perforant path (LPP) activation of hippocampus was largely unaffected by single housing, pronounced disturbances emerged when the circuit was activated with physiologically relevant frequencies and patterns. The characteristic 'theta/gamma' pattern was distorted such that a pronounced facilitation was present in the single-housed group, while the filtering of CA1 output to brief beta (25 Hz) and gamma (50 Hz) frequency LPP stimulation evident in group-housed slices was absent. Within field CA3, the recruitment of inhibitory interneurons suppresses spike output, and subsequent signal propagation to CA1, in response to beta frequency LPP inputs but not those arriving at gamma frequencies. This CA3 beta filter was significantly impaired following single housing. These results suggest that a depression phenotype is associated with a highly selective and partial loss of inhibition within the CA3 and CA1 links of the hippocampal circuit, providing new insights into the relationship between depression and hippocampal function.

We present a new method for deriving the dynamics of chemical reaction networks using the Laplacian matrix of the corresponding species-reaction graph, in contrast to previous works that use the Laplacian of the graph of complexes. Species-reaction graphs are bipartite graphs that contain two sets of vertices, one representing species and the other representing reactions, connected by directed edges that indicate relationships between them. Our approach starts by assigning appropriate edge weights to this bipartite graph, which are then used to compute the weighted graph Laplacian. This Laplacian reformulation of the system of differential equations governing the network dynamics emphasizes the flow of information throughout the chemical reaction network considered as causal network. As an application of this framework, we introduce a novel model reduction technique based on the Kron reduction of the weighted Laplacian matrix associated with the species-reaction graphs. Our systematic approach involves identifying nodes for deletion while preserving the bipartite structure, followed by constructing the Kron-reduced model. To demonstrate the effectiveness of our method, we apply it to a complex biochemical network, showing how model simplification facilitates analysis and interpretation of these systems.

For decades canonical models proposed that striatal influences on cortex are conveyed indirectly, through inhibitory projections from the striatum to the globus pallidus, which in turn controls thalamic output to the cortex. Accordingly, information flow between the cortex and the striatum has traditionally been considered unidirectional. Here, we demonstrate a direct striato-cortical projection, revising this view. Using anatomical, electrophysiological and optogenetic techniques in mice, we identified a population of striatal cholinergic neurons (SC-ChAT) in the dorsal tail of the striatum that directly project to auditory, somatosensory and motor cortices. Auditory SC-ChAT axons extend across all cortical layers, preferentially targeting layer 6 neurons via fast nicotinic receptor-mediated transmission of acetylcholine. Functionally, these inputs increase spike probability and advance action potential onset, exerting precise control over the output of cortical pyramidal neurons and demonstrating that the striatum not only receives cortical input but also directly modulates cortical processing.

Classification of autism spectrum disorder using a directional graph attention network on brain effective connectivity.