Identification of Vehicle Health Assurance Related Trends

Trend analysis in aviation as related to vehicle health management (VHM) was performed by reviewing the most current statistical and prognostics data available from the National Transportation Safety Board (NTSB) accident, the Federal Aviation Administration (FAA) incident, and the NASA Aviation Safety Reporting System (ASRS) incident datasets. In addition, future directions in aviation technology related to VHM research areas were assessed through the Commercial Aviation Safety Team (CAST) Safety Enhancements Reserved for Future Implementations (SERFIs), the National Transportation Safety Board (NTSB) Most-Wanted List and recent open safety recommendations, the National Research Council (NRC) Decadal Survey of Civil Aeronautics, and the Future Aviation Safety Team (FAST) areas of change. Future research direction in the VHM research areas is evidently strong as seen from recent research solicitations from the Naval Air Systems Command (NAVAIR), and VHM-related technologies actively being developed by aviation industry leaders, including GE, Boeing, Airbus, and UTC Aerospace Systems. Given the highly complex VHM systems, modifications can be made in the future so that the Vehicle Systems Safety Technology Project (VSST) technical challenges address inadequate maintenance crew's trainings and skills, and the certification methods of such systems as recommended by the NTSB, NRC, and FAST areas of change

Aviation Trends Related to Atmospheric Environment Safety Technologies Project Technical Challenges

Current and future aviation safety trends related to the National Aeronautics and Space Administration's Atmospheric Environment Safety Technologies Project's three technical challenges (engine icing characterization and simulation capability; airframe icing simulation and engineering tool capability; and atmospheric hazard sensing and mitigation technology capability) were assessed by examining the National Transportation Safety Board (NTSB) accident database (1989 to 2008), incidents from the Federal Aviation Administration (FAA) accident/incident database (1989 to 2006), and literature from various industry and government sources. The accident and incident data were examined for events involving fixed-wing airplanes operating under Federal Aviation Regulation (FAR) Parts 121, 135, and 91 for atmospheric conditions related to airframe icing, ice-crystal engine icing, turbulence, clear air turbulence, wake vortex, lightning, and low visibility (fog, low ceiling, clouds, precipitation, and low lighting). Five future aviation safety risk areas associated with the three AEST technical challenges were identified after an exhaustive survey of a variety of sources and include: approach and landing accident reduction, icing/ice detection, loss of control in flight, super density operations, and runway safety

Improved tractography using asymmetric fibre orientation distributions

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity

Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models

International audienceCurrent theories hold that brain function is highly related with long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcella-tion based on extrinsic connectivity remains challenging. Current par-cellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity. To tackle these problems, we propose a parsimonious model for the extrinsic con-nectivity and an efficient parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with anatomical and functional par-cellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with an anatomical atlas and the motor strip mapping included in the Human Connectome Project data

Virtual Partner Interaction (VPI): Exploring Novel Behaviors via Coordination Dynamics

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI—Virtual Partner Interaction—a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease

Measuring macroscopic brain connections in vivo

Decades of detailed anatomical tracer studies in non-human animals point to a rich and complex organization of long-range white matter connections in the brain. State-of-the art in vivo imaging techniques are striving to achieve a similar level of detail in humans, but multiple technical factors can limit their sensitivity and fidelity. In this review, we mostly focus on magnetic resonance imaging of the brain. We highlight some of the key challenges in analyzing and interpreting in vivo connectomics data, particularly in relation to what is known from classical neuroanatomy in laboratory animals. We further illustrate that, despite the challenges, in vivo imaging methods can be very powerful and provide information on connections that is not available by any other means

A Comparison of Accuracy between A New Commercial ELISA Test, GenediaTM Test and Other Commercial ELISA Tests for Serological Diagnosis of Helicobacter pylori Infection in Korea

Background/Aims : A new commercial enzyme linked immunosorbent assay (ELISA) test using Korean Helicobacter pylori (H. pylori) as an antigen, GenediaTM test, was compared to other serologic tests for H. pylori infection. Methods: Among two hundred seventy three subjects, H. pylori-positive group was consisted of 132 patients (50 peptic ulcer diseases, 52 chronic gastritis, and 30 gastric cancers) and H. pylori-negative group was consisted of 141 patients (121 adults and 20 pediatric patients). Endoscopic antral biopsy specimens were obtained for microscopy and rapid urease test (CLOTM test). We also performed GenediaTM IgG, IgA ELISA, G.A.P IgG, IgA ELISA, and Cobas-core IgG EIA. H. pylori infection was defermined when H. pylori was detected histologically or the results of CLOTM tests were positive. Results : The sensitivities and specificities of the serologic tests were 96.2% and 46.1% in GenediaTM IgG, 91.7% and 52.5% in GenediaTM IgA, 81.8% and 46.8% in G.A.P IgG, 25.0% and 85.1% in G.A.P IgA, 96.9% and 38.6% in Cobas-core test, respectively. In H. pylori-negative pediatric patients, the specificity of the tests was 80% in GenediaTM IgG, 95% in GenediaTM IgA, 60% in G.A.P. IgG, 100% in G.A.P IgA, and 75% in Cobas-core test. Conclusions: In Korea, GenediaTM test was comparable or superior to general serologic tests used for diagnosing H. pylori infection. However, it is necessary to improve the specificity of the GenediaTM test. (Kor J Gastroenterol 2000;36:20 - 28)ope

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments