Microbiota Gut-Brain Axis in Ischemic Stroke: A Narrative Review with a Focus about the Relationship with Inflammatory Bowel Disease.

The gut microbiota is emerging as an important player in neurodevelopment and aging as well as in brain diseases including stroke, Alzheimer's disease, and Parkinson's disease. The complex interplay between gut microbiota and the brain, and vice versa, has recently become not only the focus of neuroscience, but also the starting point for research regarding many diseases such as inflammatory bowel diseases (IBD). The bi-directional interaction between gut microbiota and the brain is not completely understood. The aim of this review is to sum up the evidencesconcerningthe role of the gut-brain microbiota axis in ischemic stroke and to highlight the more recent evidences about the potential role of the gut-brain microbiota axis in the interaction between inflammatory bowel disease and ischemic stroke

Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides

A multi-scale regional landslide susceptibility assessment approach: the SUFRA_SICILIA (SUscettibilit\ue0 da FRAna in Sicilia) project

The SUFRA project is based on a three level susceptibility mapping. According to the availability of more detailed data, the three scale for susceptibility mapping are increased respect to the ones suggested by the TIER group to 1:100,000, 1:50,000 and 1:25,000/1:10,000. The mapping levels exploit climatic, soil use (CORINE2009) and seismic informative layers, differentiating in the details of the core data (geology and topography), in the quality and resolution of the landslide inventory and in the modelling approach (Tab. 1). SUFRA_100 is based on a heuristic approach which is applied by processing a geologic layer (produced by ARTA integrating pre-CARG 1:100,000 geologic maps); the DEM exploited are IGMI 250m and the mapping units are 1km side square cells. Models are validated with respect to the PAI LIPs (Landslide Identification Points) which are reclassified adopting a simplified scheme. Output cuts of SUFRA100 will be referred to administrative boundaries (provinces). SUFRA50 is based on statistical analysis of new CARG geologic maps and 20m (ITA2000) - 2m (ATA2007) DEM. The mapping units are 500m and 50m cells, hydrographic and hydro-morphometric units. The landslide inventory is the IFFI2012_LIPs (first level) which is the result of the conversion in IFFI format of the PAI archive, which will be supported by remote landslide mapping (exploiting the ATA2007 aerial photos), according to the IFFI first level approach. Validation of the models will be performed exploiting both random spatial partition and temporal partition methods. Output cuts of SUFRA50 will be based on physiographic (basin) and administrative (municipalities) boundaries. SUFRA10/25 is based on statistical analysis of new CARG geologic maps (remotely and field adapted) and 2m (ATA2007) DEM. The mapping units are the slope units (SLUs) which are derived by further partitioning the hydro-morphometric units so to obtain closed morphodynamic units. The landslide inventories is the IFFI2012 which is the results of a field supported (on focus) landslide remote systematic mapping, according to the IFFI full level approach. Examples of SUFRA_100, SUFRA_50 and SUFRA_10 are presented for some representative key sector of Sicily. First results attest for the feasibility and goodness of the proposed protocol. The SUFRA program aims at enabling the regional governmental administration to cope with landslide prevision, which is the required operational concept in land management and planning. PAI has been a great advance with respect to the \u201cpre-SARNO\u201d conditions, but it is very exposed to fail: it is a blind approach for new activations; it is critically dependent on the quality of the landslide inventories; it cannot project the susceptibility outside the landslide area

Development and Initial Validation of a Self-Scored COPD Population Screener Questionnaire (COPD-PS)

COPD has a profound impact on daily life, yet remains underdiagnosed and undertreated. We set out to develop a brief, reliable, self-scored questionnaire to identify individuals likely to have COPD. COPD-PS™ development began with a list of concepts identified for inclusion using expert opinion from a clinician working group comprised of pulmonologists (n = 5) and primary care clinicians (n = 5). A national survey of 697 patients was conducted at 12 practitioner sites. Logistic regression identified items discriminating between patients with and without fixed airflow obstruction (AO, postbronchodilator FEV1/FVC < 70%). ROC analyses evaluated screening accuracy, compared scoring options, and assessed concurrent validity. Convergent and discriminant validity were assessed via COPD-PS and SF-12v2 score correlations. For known-groups validation, COPD-PS differences between clinical groups were tested. Test-retest reliability was evaluated in a 20% sample. Of 697 patients surveyed, 295 patients met expert review criteria for spirometry performance; 38% of these (n = 113) had results indicating AO. Five items positively predicted AO (p < 0.0001): breathlessness, productive cough, activity limitation, smoking history, and age. COPD-PS scores accurately classified AO status (area under ROC curve = 0.81) and reliable (r = 0.91). Patients with spirometry indicative of AO scored significantly higher (6.8, SD = 1.9; p < 0.0001) than patients without AO (4.0, SD = 2.3). Higher scores were associated with more severe AO, bronchodilator use, and overnight hospitalization for breathing problems. With the prevalence of COPD in the studied cohort, a score on the COPD-PS of greater than five was associated with a positive predictive value of 56.8% and negative predictive value of 86.4%. The COPD-PS accurately classified physician-reported COPD (AUC = 0.89). The COPD-PS is a brief, accurate questionnaire that can identify individuals likely to have COPD

Computed tomographic biomarkers in idiopathic pulmonary fibrosis: The future of quantitative analysis

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Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017

Landslides are among the most dangerous natural processes. Debris avalanches and debris flows in particular have often caused casualties and severe damage to infrastructures in a wide range of environments. The assessment of susceptibility to these phenomena may help policy makers in mitigating the associated risk and thus it has attracted special attention in the last decades. In this experiment, we assessed susceptibility to debris-avalanche and -flow landslides by using a stochastic approach. Two different modeling techniques were employed: i) Multivariate Adaptive Regression Splines (MARS) and ii) Logistic Regression (LR). Both MARS and LR allow for calculating the probability of landslide occurrence by building statistical relationships between a set of environmental variables and the target variable, i.e. presence/absence of the landslide event. The target variable was extracted from an inventory of debris-avalanche and - flow landslides which were triggered by the tropical storm that hit the area of Mocoa (Colombia) on 1 April 2017. As predictor variables, we employed nine terrain attributes derived from a 5-m resolution DEM (i.e. elevation, slope angle, northness, eastness, upslope slope angle, convergence index, topographic position index, valley depth and topographic wetness index), in addition to lithology, distance from faults and presence/absence of soil creep processes. In our experiment, we used three different landslide datasets which contain i) the highest point of each recognized landslide crown-lines (dataset LIP), ii) the highest 10% of cells of each landslide area (dataset SOURCE), and iii) the entire landslide areas, which include initiation and accumulation zones (dataset MASS). In order to evaluate their predictive ability, LR and MARS models were submitted to k-fold spatial cross-validation strategy, which consists in extracting random training and test subsets from k spatially disjoint sub-areas. The results of model validation, expressed in terms of Area Under the ROC Curve (AUC), demonstrate better predictive performance of MARS models with respect to LR models, for all the three landslide datasets. The mean AUC values calculated for the datasets LIP, SOURCE and MASS of the MARS models are 0.776, 0.788 and 0.768, respectively, whereas AUC values of the LR models are 0.748, 0.751 and 0.703, respectively. Model validation also shows that the predictive skill of the models is better when landslide data are sampled from the highest portions of the landslides (dataset SOURCE). Maps of susceptibility to debris-avalanche and -flow landslides for the Mocoa area were produced by using both LR and MARS and the three landslide datasets. The analysis of the distribution of events versus the susceptibility classes of the maps confirm that MARS and the dataset SOURCE provide the best ability to discriminate between event and non-event cells

Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017

Landslides are among the most dangerous natural processes. Debris avalanches and debris flows in particular have often caused casualties and severe damage to infrastructures in a wide range of environments. The assessment of susceptibility to these phenomena may help policy makers in mitigating the associated risk and thus it has attracted special attention in the last decades. In this experiment, we assessed susceptibility to debris-avalanche and -flow landslides by using a stochastic approach. Two different modeling techniques were employed: i) Multivariate Adaptive Regression Splines (MARS) and ii) Logistic Regression (LR). Both MARS and LR allow for calculating the probability of landslide occurrence by building statistical relationships between a set of environmental variables and the target variable, i.e. presence/absence of the landslide event. The target variable was extracted from an inventory of debris-avalanche and - flow landslides which were triggered by the tropical storm that hit the area of Mocoa (Colombia) on 1 April 2017. As predictor variables, we employed nine terrain attributes derived from a 5-m resolution DEM (i.e. elevation, slope angle, northness, eastness, upslope slope angle, convergence index, topographic position index, valley depth and topographic wetness index), in addition to lithology, distance from faults and presence/absence of soil creep processes. In our experiment, we used three different landslide datasets which contain i) the highest point of each recognized landslide crown-lines (dataset LIP), ii) the highest 10% of cells of each landslide area (dataset SOURCE), and iii) the entire landslide areas, which include initiation and accumulation zones (dataset MASS). In order to evaluate their predictive ability, LR and MARS models were submitted to k-fold spatial cross-validation strategy, which consists in extracting random training and test subsets from k spatially disjoint sub-areas. The results of model validation, expressed in terms of Area Under the ROC Curve (AUC), demonstrate better predictive performance of MARS models with respect to LR models, for all the three landslide datasets. The mean AUC values calculated for the datasets LIP, SOURCE and MASS of the MARS models are 0.776, 0.788 and 0.768, respectively, whereas AUC values of the LR models are 0.748, 0.751 and 0.703, respectively. Model validation also shows that the predictive skill of the models is better when landslide data are sampled from the highest portions of the landslides (dataset SOURCE). Maps of susceptibility to debris-avalanche and -flow landslides for the Mocoa area were produced by using both LR and MARS and the three landslide datasets. The analysis of the distribution of events versus the susceptibility classes of the maps confirm that MARS and the dataset SOURCE provide the best ability to discriminate between event and non-event cells