15 research outputs found

    Geospatial-based analysis for soil erosion susceptibility evaluation : application of a hybrid decision model

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    DATA AVAILABILITY : Data will be made available on request.Erosion hazard is a major environmental change in developing countries and therefore necessitates investigations for effective erosion control measures. This study is hinged on the numerous advantages of a hybrid Multi-Criteria Decision Model (MCDM) to assess erosion vulnerability using remote-sensed data and the application of Geographical Information System (GIS). Nine risk factors of erosion were selected for this study and their thematic maps were utilized to produce a spatial distribution of erosion hazard in the state. An integrated IVFRN–DEMATEL–ANP model was used to investigate the interrelationships between the risk factors and also obtain their final weights. The assessment model identified Rainfall, Erosivity Index, Stream Power Index, Sediment Transport Index, Topographic Wetness Index, and Soil as the most influential factors of erosion in the study area. The weighted linear combination method was used to integrate the risk factors to produce the spatial distribution of erosion vulnerability model. The method was validated using Anambra State of Nigeria. The findings from the study revealed that Anambra State is vulnerable to erosion hazard with 45% of the state lying between Very High and Medium vulnerable zones. A good predictive model performance of 89.7% was obtained using the AUC-ROC method. The feasibility of integrating the IVFRN, DEMATEL, and ANP models as an assessment model for mapping erosion vulnerability has been determined in this study, and this is vital in managing the impact of erosion hazards globally. The model’s identification of hydrological and topographical factors as major causes of erosion hazard emphasizes the importance of critical analysis of risk factors as done in this study for effective management of erosion. This study is a veritable tool for implementation of erosion mitigation measures.https://link.springer.com/journal/40808hj2023Future Afric

    Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness

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    Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer's disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer's Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer's Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation. (C) 2014 Elsevier Inc. All rights reserved.11Nsciescopu

    Pathological Computed Tomography Features Associated with Adverse Outcomes after Mild Traumatic Brain Injury: A TRACK-TBI Study with External Validation in CENTER-TBI

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    Importance: A head computed tomography (CT) with positive results for acute intracranial hemorrhage is the gold-standard diagnostic biomarker for acute traumatic brain injury (TBI). In moderate to severe TBI (Glasgow Coma Scale [GCS] scores 3-12), some CT features have been shown to be associated with outcomes. In mild TBI (mTBI; GCS scores 13-15), distribution and co-occurrence of pathological CT features and their prognostic importance are not well understood. Objective: To identify pathological CT features associated with adverse outcomes after mTBI. Design, Setting, and Participants: The longitudinal, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled patients with TBI, including those 17 years and older with GCS scores of 13 to 15 who presented to emergency departments at 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018, and underwent head CT imaging within 24 hours of TBI. Evaluations of CT imaging used TBI Common Data Elements. Glasgow Outcome Scale-Extended (GOSE) scores were assessed at 2 weeks and 3, 6, and 12 months postinjury. External validation of results was performed via the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Data analyses were completed from February 2020 to February 2021. Exposures: Acute nonpenetrating head trauma. Main Outcomes and Measures: Frequency, co-occurrence, and clustering of CT features; incomplete recovery (GOSE scores <8 vs 8); and an unfavorable outcome (GOSE scores <5 vs ≥5) at 2 weeks and 3, 6, and 12 months. Results: In 1935 patients with mTBI (mean [SD] age, 41.5 [17.6] years; 1286 men [66.5%]) in the TRACK-TBI cohort and 2594 patients with mTBI (mean [SD] age, 51.8 [20.3] years; 1658 men [63.9%]) in an external validation cohort, hierarchical cluster analysis identified 3 major clusters of CT features: contusion, subarachnoid hemorrhage, and/or subdural hematoma; intraventricular and/or petechial hemorrhage; and epidural hematoma. Contusion, subarachnoid hemorrhage, and/or subdural hematoma features were associated with incomplete recovery (odds ratios [ORs] for GOSE scores <8 at 1 year: TRACK-TBI, 1.80 [95% CI, 1.39-2.33]; CENTER-TBI, 2.73 [95% CI, 2.18-3.41]) and greater degrees of unfavorable outcomes (ORs for GOSE scores <5 at 1 year: TRACK-TBI, 3.23 [95% CI, 1.59-6.58]; CENTER-TBI, 1.68 [95% CI, 1.13-2.49]) out to 12 months after injury, but epidural hematoma was not. Intraventricular and/or petechial hemorrhage was associated with greater degrees of unfavorable outcomes up to 12 months after injury (eg, OR for GOSE scores <5 at 1 year in TRACK-TBI: 3.47 [95% CI, 1.66-7.26]). Some CT features were more strongly associated with outcomes than previously validated variables (eg, ORs for GOSE scores <5 at 1 year in TRACK-TBI: neuropsychiatric history, 1.43 [95% CI.98-2.10] vs contusion, subarachnoid hemorrhage, and/or subdural hematoma, 3.23 [95% CI 1.59-6.58]). Findings were externally validated in 2594 patients with mTBI enrolled in the CENTER-TBI study. Conclusions and Relevance: In this study, pathological CT features carried different prognostic implications after mTBI to 1 year postinjury. Some patterns of injury were associated with worse outcomes than others. These results support that patients with mTBI and these CT features need TBI-specific education and systematic follow-up.
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