2,096 research outputs found

    Predicting brain age with complex networks: From adolescence to adulthood.

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    In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging

    Physics-Based Simulations of Flow and Fire Development Downstream of a Canopy

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    International audienceThe behavior of a grassland fire propagating downstream of a forest canopy has been simulated numerically using the fully physics-based wildfire model FIRESTAR3D. This configuration reproduces quite accurately the situation encountered when a wildfire spreads from a forest to an open grassland, as can be the case in a fuel break or a clearing, or during a prescribed burning operation. One of the objectives of this study was to evaluate the impact of the presence of a canopy upstream of a grassfire, especially the modifications of the local wind conditions before and inside a clearing or a fuel break. The knowledge of this kind of information constitutes a major element in improving the safety conditions of forest managers and firefighters in charge of firefighting or prescribed burning operations in such configurations. Another objective was to study the behavior of the fire under realistic turbulent flow conditions, i.e., flow resulting from the interaction between an atmospheric boundary layer (ABL) with a surrounding canopy. Therefore, the study was divided into two phases. The first phase consisted of generating an ABL/canopy turbulent flow above a pine forest (10 m high, 200 m long) using periodic boundary conditions along the streamwise direction. Large Eddy Simulations (LES) were carried out for a sufficiently long time to achieve a quasi-fully developed turbulence. The second phase consisted of simulating the propagation of a surface fire through a grassland, bordered upstream by a forest section (having the same characteristics used for the first step), while imposing the turbulent flow obtained from the first step as a dynamic inlet condition to the domain. The simulations were carried out for a wind speed that ranged between 1 and 12 m/s; these values have allowed the simulations to cover the two regimes of propagation of surfaces fires, namely plume-dominated and wind-driven fires

    Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients

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    Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms of the disorder, and the development of antiepileptogenic interventions could potentially prevent or cure epilepsy in many of them. However, the discovery of potential antiepileptogenic treatments and clinical validation would require a means to identify populations of patients at very high risk for epilepsy after a potential epileptogenic insult, to know when to treat and to document prevention or cure. A fundamental challenge in discovering biomarkers of epileptogenesis is that this process is likely multifactorial and crosses multiple modalities. Investigators must have access to a large number of high quality, well-curated data points and study subjects for biomarker signals to be detectable above the noise inherent in complex phenomena, such as epileptogenesis, traumatic brain injury (TBI), and conditions of data collection. Additionally, data generating and collecting sites are spread worldwide among different laboratories, clinical sites, heterogeneous data types, formats, and across multi-center preclinical trials. Before the data can even be analyzed, these data must be standardized. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-center project with the overarching goal that epileptogenesis after TBI can be prevented with specific treatments. The identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. We have been analyzing human data collected from UCLA and rat data collected from the University of Eastern Finland, both centers collecting data for EpiBioS4Rx, to identify biomarkers of epileptogenesis. Big data techniques and rigorous analysis are brought to longitudinal data collected from humans and an animal model of TBI, epilepsy, and their interaction. The prolonged continuous data streams of intracranial, cortical surface, and scalp EEG from humans and an animal model of epilepsy span months. By applying our innovative mathematical tools via supervised and unsupervised learning methods, we are able to subject a robust dataset to recently pioneered data analysis tools and visualize multivariable interactions with novel graphical methods

    A Comparison of Neuroelectrophysiology Databases

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    As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. These archives provide researchers with tools to store, share, and reanalyze neurophysiology data though the means of accomplishing these objectives differ. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. While many tools are available to reanalyze data on and off the archives' platforms, this article features Reproducible Analysis and Visualization of Intracranial EEG (RAVE) toolkit, developed specifically for the analysis of intracranial signal data and integrated with the discussed standards and archives. Neuroelectrophysiology data archives improve how researchers can aggregate, analyze, distribute, and parse these data, which can lead to more significant findings in neuroscience research.Comment: 25 pages, 8 figures, 1 tabl

    Pathocenosis: A Holistic Approach to Disease Ecology

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    The History of medicine describes the emergence and recognition of infectious diseases, and human attempts to stem them. It also throws light on the role of changing environmental conditions on disease emergence/re-emergence, establishment and, sometimes, disappearance. However, the dynamics of infectious diseases is also influenced by the relationships between the community of interacting infectious agents present at a given time in a given territory, a concept that Mirko Grmek, an historian of medicine, conceptualized with the word “pathocenosis”. The spatial and temporal evolution of diseases, when observed at the appropriate scales, illustrates how a change in the pathocenosis, whether of “natural” or anthropic origin, can lead to the emergence and spread of diseases

    Predicting where a radiation will occur: Acoustic and molecular surveys reveal overlooked diversity in Indian Ocean Island crickets (Mogoplistinae: Ornebius)

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    Recent theory suggests that the geographic location of island radiations (local accumulation of species diversity due to cladogenesis) can be predicted based on island area and isolation. Crickets are a suitable group for testing these predictions, as they show both the ability to reach some of the most isolated islands in the world, and to speciate at small spatial scales. Despite substantial song variation between closely related species in many island cricket lineages worldwide, to date this characteristic has not received attention in the western Indian Ocean islands; existing species descriptions are based on morphology alone. Here we use a combination of acoustics and DNA sequencing to survey these islands for Ornebius crickets. We uncover a small but previously unknown radiation in the Mascarenes, constituting a three-fold increase in the Ornebius species diversity of this archipelago (from two to six species). A further new species is detected in the Comoros. Although double archipelago colonisation is the best explanation for species diversity in the Seychelles, in situ cladogenesis is the best explanation for the six species in the Mascarenes and two species of the Comoros. Whether the radiation of Mascarene Ornebius results from intra- or purely inter- island speciation cannot be determined on the basis of the phylogenetic data alone. However, the existence of genetic, song and ecological divergence at the intra-island scale is suggestive of an intra-island speciation scenario in which ecological and mating traits diverge hand-in-hand. Our results suggest that the geographic location of Ornebius radiations is partially but not fully explained by island area and isolation. A notable anomaly is Madagascar, where our surveys are consistent with existing accounts in finding no Ornebius species present. Possible explanations are discussed, invoking ecological differences between species and differences in environmental history between islands. (Résumé d'auteur

    Imbalanced Lignin Biosynthesis Promotes the Sexual Reproduction of Homothallic Oomycete Pathogens

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    Lignin is incorporated into plant cell walls to maintain plant architecture and to ensure long-distance water transport. Lignin composition affects the industrial value of plant material for forage, wood and paper production, and biofuel technologies. Industrial demands have resulted in an increase in the use of genetic engineering to modify lignified plant cell wall composition. However, the interaction of the resulting plants with the environment must be analyzed carefully to ensure that there are no undesirable side effects of lignin modification. We show here that Arabidopsis thaliana mutants with impaired 5-hydroxyguaiacyl O-methyltransferase (known as caffeate O-methyltransferase; COMT) function were more susceptible to various bacterial and fungal pathogens. Unexpectedly, asexual sporulation of the downy mildew pathogen, Hyaloperonospora arabidopsidis, was impaired on these mutants. Enhanced resistance to downy mildew was not correlated with increased plant defense responses in comt1 mutants but coincided with a higher frequency of oomycete sexual reproduction within mutant tissues. Comt1 mutants but not wild-type Arabidopsis accumulated soluble 2-O-5-hydroxyferuloyl-l-malate. The compound weakened mycelium vigor and promoted sexual oomycete reproduction when applied to a homothallic oomycete in vitro. These findings suggested that the accumulation of 2-O-5-hydroxyferuloyl-l-malate accounted for the observed comt1 mutant phenotypes during the interaction with H. arabidopsidis. Taken together, our study shows that an artificial downregulation of COMT can drastically alter the interaction of a plant with the biotic environment
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