Influence of Analysis Technique on Measurement of Diffusion Tensor Imaging Parameters

We compared results from various methods of analysis of diffusion tensor imaging (DTI) data from a single data set consisting of 10 healthy adolescents

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Resistivity and Induced Polarization Monitoring of Biogas combined with Microbial Ecology on a Brown field Site

The accumulation of biogenic greenhouse gases (methane, carbon dioxide) in organic sediments is an important factor in the redevelopment and risk management of many brownfield sites. Good practice with brownfield site characterization requires the identification of free-gas phases and pathways that allow its migration and release at the ground surface. Gas pockets trapped in the subsurface have contrasting properties with the surrounding porous media that favor their detection using geophysical methods. We have developed a case study in which pockets of gas were intercepted with multilevel monitoring wells, and their lateral continuity was monitored over time using resistivity. We have developed a novel interpretation procedure based on Archie’s law to evaluate changes in water and gas content with respect to a mean background medium. We have used induced polarization data to account for errors in applying Archie’s law due to the contribution of surface conductivity effects. Mosaics defined by changes in water saturation allowed the recognition of gas migration and groundwater infiltration routes and the association of gas and groundwater fluxes. The inference on flux patterns was analyzed by taking into account pressure measurements in trapped gas reservoirs and by metagenomic analysis of the microbiological content, which was retrieved from suspended sediments in groundwater sampled in multilevel monitoring wells. A conceptual model combining physical and microbiological subsurface processes suggested that biogas trapped at depth may have the ability to quickly travel to the surface. </jats:p

An ontology-guided approach to process formation and coordination of demand-driven collaborations

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine exe-cutes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering

An ontology-guided approach to process formation and coordination of demand-driven collaborations

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine executes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering.Elastic Manufacturing Systems (EP/T024429/1) European Commission under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 723336

Influence of Analysis Technique on Measurement of Diffusion Tensor Imaging Parameters

PURPOSE: We compared results from various methods of analysis of diffusion tensor imaging (DTI) data from a single data set consisting of 10 healthy adolescents. METHODS: All subjects were imaged on a single 3T MRI system (single-shot echo-planar imaging (EPI) pulse sequence, b value 1000). We measured fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity and radial diffusivity values using 64 pixel rectangular regions of interest (ROIs) in the right-side, midline and left-side of the central portion of the splenium of the corpus callosum for fixed (i.e., at same sites in all subjects) and targeted (i.e., at sites of highest FA values) locations, We compared results with those obtained using 64 pixel oval ROIs and 100 pixel rectangular ROIs in same locations. Finally, we compared results from ROI-based methods and from tractography. All comparisons used the Wilcoxon signed rank test and the intraclass correlation of individual values. RESULTS: Compared to tractography, the average of mean ROI-based values was significantly higher for fixed FA (14%) and targeted FA (39%) values and significantly lower for ADC (16%) and radial diffusivity (38%) values. For solely ROI-based comparisons, significant differences were found in the following comparisons: 64 pixel ROI vs. 100 pixel ROI, oval ROI vs. rectangular ROI, targeted FA left of midline vs. mean targeted FA value, and targeted ROI right of midline vs. mean targeted FA value. CONCLUSION: Markedly different values were obtained when using either ROI-based or tractography-based techniques, or ROI analysis techniques that differ only relatively slightly