Automatic pathology classification using a single feature machine learning - support vector machines

International audienceMagnetic Resonance Imaging (MRI) has been gaining popularity in the clinic in recent years as a safe in-vivo imaging technique. As a result, large troves of data are being gathered and stored daily that may be used as clinical training sets in hospitals. While numerous machine learning (ML) algorithms have been implemented for Alzheimer's disease classification, their outputs are usually difficult to interpret in the clinical setting. Here, we propose a simple method of rapid diagnostic classification for the clinic using Support Vector Machines (SVM) and easy to obtain geometrical measurements that, together with a cortical and sub-cortical brain parcellation, create a robust framework capable of automatic diagnosis with high accuracy. On a significantly large imaging dataset consisting of over 800 subjects taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, classification-success indexes of up to 99.2% are reached with a single measurement

Neurobehavioral Disorder Associated With Prenatal Alcohol Exposure

Children and adolescents affected by prenatal exposure to alcohol who have brain damage that is manifested in functional impairments of neurocognition, self-regulation, and adaptive functioning may most appropriately be diagnosed with neurobehavioral disorder associated with prenatal exposure. This Special Article outlines clinical implications and guidelines for pediatric medical home clinicians to identify, diagnose, and refer children regarding neurobehavioral disorder associated with prenatal exposure. Emphasis is given to reported or observable behaviors that can be identified as part of care in pediatric medical homes, differential diagnosis, and potential comorbidities. In addition, brief guidance is provided on the management of affected children in the pediatric medical home. Finally, suggestions are given for obtaining prenatal history of in utero exposure to alcohol for the pediatric patient

Respiratory Syncytial Virus infection promotes necroptosis and HMGB1 release by airway epithelial cells

Rationale: Respiratory syncytial virus (RSV) bronchiolitis causes significant infant mortality. Bronchiolitis is characterized by airway epithelial cell (AEC) death; however, the mode of death remains unknown. Objectives: To determine whether necroptosis contributes to RSV b r onchiolitis pathogenesis via HMGB1 (high mobility group box 1) release. Methods: Nasopharyngeal samples were collected from children presenting to the hospital with acute respiratory infection. Primary human AECs and neonatal mice were inoculated with RSV and murine Pneumovirus, respectively. Necroptosis was determined via viability assays and immunohistochemistry for RIPK1 (receptor-interacting protein kinase-1), MLKL (mixed lineage kinase domain-like pseudokinase) protein, and caspase-3. Necroptosis was blocked using pharmacological inhibitors and RIPK1 kinase-dead knockin mice. Measurements and Main Results: HMGB1 levels were elevated in nasopharyngeal samples of children with acute RSV infection. RSV-induced epithelial cell death was associated with increased phosphorylated RIPK1 and phosphorylated MLKL but not active caspase-3 expression. Inhibition of RIPK1 or MLKL attenuated RSV-induced HMGBI translocation and release, and lowered viral load. MLKL inhibition increased active caspase-3 expression in a caspase-8/9-dependent manner. In susceptible mice, Pneumovirus infection upregulated RIPK1 and MLKL expression in the airway epithelium at 8 to 10 days after infection, coinciding with AEC sloughing, HMGB1 release, and neutrophilic inflammation. Genetic or pharmacological inhibition of RIPK1 or MLKL attenuated these pathologies, lowered viral load, and prevented type 2 inflammation and airway remodeling. Necroptosis inhibition in early life ameliorated asthma progression induced by viral or allergen challenge in later life. Conclusions: Pneumovirus infection induces AEC necroptosis. Inhibition of necroptosis may be a viable strategy to limit the severity of viral bronchiolitis and break its nexus with asthma

Nilearn for new use cases: Scaling up computational and community efforts

Introduction Nilearn (https://nilearn.github.io) is a well-established Python package that provides statistical and machine learning tools for fast and easy analysis of brain images with instructive documentation and a friendly community. This focus has led to its current position as a crucial part of the neuroimaging community’s open-source software ecosystem, supporting efficient and reproducible science [1]. It has been continuously developed over the past 10 years, currently with 900 stars, 500 forks, and 176 contributors on GitHub. Nilearn leverages and builds upon other central Python machine learning packages, such as Scikit-Learn [2], that are extensively used, tested, and optimized by a large scientific and industrial community. In recent years, efforts in Nilearn have been focused on meeting evolving community needs by increasing General Linear Model (GLM) support, interfacing with initiatives like fMRIPrep and BIDS, and improving the user documentation. Here we report on progress regarding our current priorities. Methods Nilearn is developed to be accessible and easy-to-use for researchers and the open-source community. It features user-focused documentation that includes a user guide and an example gallery as well as comprehensive contribution guidelines. Nilearn is also presented in tutorials and workshops throughout the year including the Montreal Artificial Intelligence and Neuroscience (MAIN) Educational Workshop, the OHBM Brainhack event, and for the Chinese Open Science Network. The community is encouraged to ask questions, report bugs, make suggestions for improvements or new features, and make direct contributions to the source code. We use the platforms Neurostars, GitHub, and Discord to interact with contributors and users on a daily basis. Nilearn adheres to best practices in software development including using version control, unit testing, and requiring multiple reviews of contributions. We also have a continuous integration infrastructure set up to automate many aspects of our development process and make sure our code is continuously tested and up-to-date. Results Nilearn supports methods such as image manipulation and processing, decoding, functional connectivity analysis, GLM, multivariate pattern analysis, along with plotting volumetric and surface data. In the latest release, cluster-level and TFCE-based family-wise error rate (FWER) control have been added to support the mass univariate and GLM analysis modules, expanding from the already implemented voxel-level correction method (see Fig1). Optimizing Nilearn’s maskers is also underway such as the recently added classes for handling multi-subject 4D image data. These also provide the option to use parallelization to speed up computation. In addition, Nilearn has introduced a new theme to update the documentation making the website more readable and accessible (https://nilearn.github.io/). This change also sets the stage for further improvement and modernisation of several aspects of the documentation, like the user guide. Development on Nilearn’s interfaces module added a new function to write BIDS-compatible model results to disk. This and further development of the BIDS interface will facilitate interaction with other relevant community tools such as FitLins [3]. Finally, several surface plotting enhancements are in progress including improving the API for background maps (see Fig2). Conclusion Nilearn is extensively used by researchers of the neuroimaging community due to its implementations of well-founded methods and visualization tools which are often essential in brain imaging research for quality control and communicating results. Recent work has highlighted areas where more active work is needed to scale the project both technically and socially, including: working with large datasets, better supporting analyses on the cortical surface, and advancing standard practice in neuroimaging statistics through active community outreach. References [1] Poldrack, R., Gorgolewski, K., Varoquaux, G. (2019). Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging Annual Review of Biomedical Data Science 2(1), 119-138. https://dx.doi.org/10.1146/annurev-biodatasci-072018-021237 [2] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825-2830. [3] Markiewicz, C. J., De La Vega, A., Wagner, A., Halchenko, Y. O., Finc, K., Ciric, R., Goncalves, M., Nielson, D. M., Kent, J. D., Lee, J. A., Bansal, S., Poldrack, R. A., Gorgolewski, K. J. (2022). poldracklab/fitlins: 0.11.0 (0.11.0). Zenodo. https://doi.org/10.5281/zenodo.7217447This poster was presented at OHBM 2023

Sequential Isotopic Signature Along Gladius Highlights Contrasted Individual Foraging Strategies of Jumbo Squid (Dosidicus gigas)

International audienceBackground: Cephalopods play a major role in marine ecosystems, but knowledge of their feeding ecology is limited. In particular, intra- and inter-individual variations in their use of resources has not been adequatly explored, although there is growing evidence that individual organisms can vary considerably in the way they use their habitats and resources. Methodology/Principal Findings: Using d13C and d15N values of serially sampled gladius (an archival tissue), we examined high resolution variations in the trophic niche of five large (.60 cm mantle length) jumbo squids (Dosidicus gigas) that were collected off the coast of Peru. We report the first evidence of large inter-individual differences in jumbo squid foraging strategies with no systematic increase of trophic level with size. Overall, gladius d13C values indicated one or several migrations through the squid's lifetime (,8-9 months), during which d15N values also fluctuated (range: 1 to 5%). One individual showed an unexpected terminal 4.6% d15N decrease (more than one trophic level), thus indicating a shift from higher- to lower-trophic level prey at that time. The data illustrate the high diversity of prey types and foraging histories of this species at the individual level. Conclusions/Significance: The isotopic signature of gladii proved to be a powerful tool to depict high resolution and ontogenic variations in individual foraging strategies of squids, thus complementing traditional information offered by stomach content analysis and stable isotopes on metabolically active tissues. The observed differences in life history strategies highlight the high degree of plasticity of the jumbo squid and its high potential to adapt to environmental changes

Dominant-negative mutations in human IL6ST underlie hyper-IgE syndrome

Autosomal dominant hyper-IgE syndrome (AD-HIES) is typically caused by dominant-negative (DN) STAT3 mutations. Patients suffer from cold staphylococcal lesions and mucocutaneous candidiasis, severe allergy, and skeletal abnormalities. We report 12 patients from 8 unrelated kindreds with AD-HIES due to DN IL6ST mutations. We identified seven different truncating mutations, one of which was recurrent. The mutant alleles encode GP130 receptors bearing the transmembrane domain but lacking both the recycling motif and all four STAT3-recruiting tyrosine residues. Upon overexpression, the mutant proteins accumulate at the cell surface and are loss of function and DN for cellular responses to IL-6, IL-11, LIF, and OSM. Moreover, the patients’ heterozygous leukocytes and fibroblasts respond poorly to IL-6 and IL-11. Consistently, patients with STAT3 and IL6ST mutations display infectious and allergic manifestations of IL-6R deficiency, and some of the skeletal abnormalities of IL-11R deficiency. DN STAT3 and IL6ST mutations thus appear to underlie clinical phenocopies through impairment of the IL-6 and IL-11 response pathways

A planet within the debris disk around the pre-main-sequence star AU Microscopii

AU Microscopii (AU Mic) is the second closest pre main sequence star, at a distance of 9.79 parsecs and with an age of 22 million years. AU Mic possesses a relatively rare and spatially resolved3 edge-on debris disk extending from about 35 to 210 astronomical units from the star, and with clumps exhibiting non-Keplerian motion. Detection of newly formed planets around such a star is challenged by the presence of spots, plage, flares and other manifestations of magnetic activity on the star. Here we report observations of a planet transiting AU Mic. The transiting planet, AU Mic b, has an orbital period of 8.46 days, an orbital distance of 0.07 astronomical units, a radius of 0.4 Jupiter radii, and a mass of less than 0.18 Jupiter masses at 3 sigma confidence. Our observations of a planet co-existing with a debris disk offer the opportunity to test the predictions of current models of planet formation and evolution.Comment: Nature, published June 24th [author spelling name fix

Association of respiratory symptoms and lung function with occupation in the multinational Burden of Obstructive Lung Disease (BOLD) study

Background Chronic obstructive pulmonary disease has been associated with exposures in the workplace. We aimed to assess the association of respiratory symptoms and lung function with occupation in the Burden of Obstructive Lung Disease study. Methods We analysed cross-sectional data from 28 823 adults (≥40 years) in 34 countries. We considered 11 occupations and grouped them by likelihood of exposure to organic dusts, inorganic dusts and fumes. The association of chronic cough, chronic phlegm, wheeze, dyspnoea, forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1)/FVC with occupation was assessed, per study site, using multivariable regression. These estimates were then meta-analysed. Sensitivity analyses explored differences between sexes and gross national income. Results Overall, working in settings with potentially high exposure to dusts or fumes was associated with respiratory symptoms but not lung function differences. The most common occupation was farming. Compared to people not working in any of the 11 considered occupations, those who were farmers for ≥20 years were more likely to have chronic cough (OR 1.52, 95% CI 1.19–1.94), wheeze (OR 1.37, 95% CI 1.16–1.63) and dyspnoea (OR 1.83, 95% CI 1.53–2.20), but not lower FVC (β=0.02 L, 95% CI −0.02–0.06 L) or lower FEV1/FVC (β=0.04%, 95% CI −0.49–0.58%). Some findings differed by sex and gross national income. Conclusion At a population level, the occupational exposures considered in this study do not appear to be major determinants of differences in lung function, although they are associated with more respiratory symptoms. Because not all work settings were included in this study, respiratory surveillance should still be encouraged among high-risk dusty and fume job workers, especially in low- and middle-income countries.publishedVersio

Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardised protocol and definition. Methods: We analysed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population attributable risk (PAR) associated with each of the identifed risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington,KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors.info:eu-repo/semantics/publishedVersio

Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardized protocol and definition. Methods: We analyzed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population-attributable risk (PAR) associated with each of the identified risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington, KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education, and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors.info:eu-repo/semantics/publishedVersio