Oscillatory traveling waves provide evidence for predictive coding abnormalities in schizophrenia.

Background: The computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that schizophrenia patients have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms.

Methods: Here, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding.

A computational model reveals an early transient decrease in fiber cross-linking that unlocks adult regeneration.

The decline in regeneration efficiency after birth in mammals is a significant roadblock for regenerative medicine in tissue repair. We previously developed a computational agent based-model (ABM) that recapitulates mechanical interactions between cells and the extracellular-matrix (ECM), to investigate key drivers of tissue repair in adults. Time calibration alongside a parameter sensitivity analysis of the model suggested that an early and transient decrease in ECM cross-linking guides tissue repair toward regeneration.

Augmenting the availability of historical GDP per capita estimates through machine learning.

Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18 century, well-being in 1850, and church building activity in the 14 and 15 century.

Acceptability of Allogeneic Mesenchymal Stromal Cell-Based Tissue Engineering for the Treatment of Periodontitis: A Qualitative Study in France.

Introduction And Aims: Periodontitis, the main cause of tooth loss in adults, is a public health concern; its incidence increases with age, and its prevalence increases with increasing life expectancy of the population. Innovative therapies such as cell therapy represent promising future solutions for guided tissue regeneration. However, these therapies may be associated with fears and mistrust from the general public.

Generation of functionally active resident macrophages from adipose tissue by 3D cultures.

Introduction: Within adipose tissue (AT), different macrophage subsets have been described, which played pivotal and specific roles in upholding tissue homeostasis under both physiological and pathological conditions. Nonetheless, studying resident macrophages poses challenges, as the isolation process and the culture for extended periods can alter their inherent properties.

Methods: Stroma-vascular cells isolated from murine subcutaneous AT were seeded on ultra-low adherent plates in the presence of macrophage colony-stimulating factor.

A robust event-driven approach to always-on object recognition.

We propose a neuromimetic architecture capable of always-on pattern recognition, i.e. at any time during processing.

The oral organ: A new vision of the mouth as a whole for a gerophysiological approach to healthy aging.

This article brings a new perspective on oral physiology by presenting the oral organ as an integrated entity within the entire organism and its surrounding environment. Rather than considering the mouth solely as a collection of discrete functions, this novel approach emphasizes its role as a dynamic interphase, supporting interactions between the body and external factors. As a resilient ecosystem, the equilibrium of mouth ecological niches is the result of a large number of interconnected factors including the heterogeneity of different oral structures, diversity of resources, external and internal pressures and biological actors.

CRAFT: Concept Recursive Activation FacTorization for Explainability.

Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image - revealing "where" the model looks, but failing to elucidate "what" the model sees in those areas. In this work, we try to fill in this gap with CRAFT - a novel approach to identify both "what" and "where" by generating concept-based explanations.

Evolutionary design of explainable algorithms for biomedical image segmentation.

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting "black box" models are difficult to interpret.

A Benchmark for Compositional Visual Reasoning.

A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks.

Harmonizing the object recognition strategies of deep neural networks with humans.

The many successes of deep neural networks (DNNs) over the past decade have largely been driven by computational scale rather than insights from biological intelligence. Here, we explore if these trends have also carried concomitant improvements in explaining the visual strategies humans rely on for object recognition. We do this by comparing two related but distinct properties of visual strategies in humans and DNNs: they believe important visual features are in images and they use those features to categorize objects.

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars.

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g.

Humans feel too special for machines to score their morals.

Artificial intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems-enabling people and organizations to form judgments of others at scale. However, it also poses significant ethical challenges and is, subsequently, the subject of wide debate. As these technologies are developed and governing bodies face regulatory decisions, it is crucial that we understand the attraction or resistance that people have for AI moral scoring.

Explainable machine learning framework to predict personalized physiological aging.

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage.

Reasoning about climate change.

Why is disbelief in anthropogenic climate change common despite broad scientific consensus to the contrary? A widely held explanation involves politically motivated (system 2) reasoning: Rather than helping uncover the truth, people use their reasoning abilities to protect their partisan identities and reject beliefs that threaten those identities. Despite the popularity of this account, the evidence supporting it (i) does not account for the fact that partisanship is confounded with prior beliefs about the world and (ii) is entirely correlational with respect to the effect of reasoning. Here, we address these shortcomings by (i) measuring prior beliefs and (ii) experimentally manipulating participants' extent of reasoning using cognitive load and time pressure while they evaluate arguments for or against anthropogenic global warming.

Gingival Orofacial Granulomatosis Clinical and 2D/3D Microscopy Features after Orthodontic Therapy: A Pediatric Case Report.

Orofacial granulomatosis (OFG) represents a heterogeneous group of rare orofacial diseases. When affecting gingiva, it appears as a chronic soft tissue inflammation, sometimes combined with the enlargement and swelling of other intraoral sites, including the lips. Gingival biopsy highlights noncaseating granulomatous inflammation, similar to that observed in Crohn's disease and sarcoidosis.

reveals different types of cellular interactions predictive of response to immunotherapies and survival in cancer.

Spatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, performing a rational analysis including multiple algorithms while integrating different conditions such as clinical data is still not trivial. To make such investigations more accessible, we developed , a Python package to analyze spatial omics data with respect to clinical or biological data and to gain insight on cell interaction patterns or tissue architecture of biological relevance.

Distinct roles of forward and backward alpha-band waves in spatial visual attention.

Previous research has associated alpha-band [8-12 Hz] oscillations with inhibitory functions: for instance, several studies showed that visual attention increases alpha-band power in the hemisphere ipsilateral to the attended location. However, other studies demonstrated that alpha oscillations positively correlate with visual perception, hinting at different processes underlying their dynamics. Here, using an approach based on traveling waves, we demonstrate that there are two functionally distinct alpha-band oscillations propagating in different directions.

Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.

Brain-Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. Testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce.

Doctors in Medical Data Sciences: A New Curriculum.

Machine Learning (ML), a branch of Artificial Intelligence, which is competing with human experts in many specialized biomedical fields and will play an increasing role in precision medicine. As with any other technological advances in medicine, the keys to understanding must be integrated into practitioner training. To respond to this challenge, this viewpoint discusses some necessary changes in the health studies curriculum that could help practitioners to interpret decisions the made by a machine and question them in relation to the patient's medical context.

Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study.

Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis.

Explaining the Neuroevolution of Fighting Creatures Through Virtual fMRI.

While interest in artificial neural networks (ANNs) has been renewed by the ubiquitous use of deep learning to solve high-dimensional problems, we are still far from general artificial intelligence. In this article, we address the problem of emergent cognitive capabilities and, more crucially, of their detection, by relying on co-evolving creatures with mutable morphology and neural structure. The former is implemented via both static and mobile structures whose shapes are controlled by cubic splines.

Polarized Citizen Preferences for the Ethical Allocation of Scarce Medical Resources in 20 Countries.

Unlabelled: When medical resources are scarce, clinicians must make difficult triage decisions. When these decisions affect public trust and morale, as was the case during the COVID-19 pandemic, experts will benefit from knowing which triage metrics have citizen support. We conducted an online survey in 20 countries, comparing support for 5 common metrics (prognosis, age, quality of life, past and future contribution as a health care worker) to a benchmark consisting of support for 2 no-triage mechanisms (first-come-first-served and random allocation).