Longitudinal changes in functional capacity in frontotemporal dementia and Alzheimer's disease.

Introduction: This study investigated the changes in functional capacity with disease progression in a well-characterised cohort of patients diagnosed with frontotemporal dementia (FTD) and Alzheimer's disease (AD) presentations.

Methods: We recruited 126 behavioural variant FTD (bvFTD), 40 progressive nonfluent aphasia (PNFA), 64 semantic dementia (SD), 45 logopenic progressive aphasia (LPA), and 115 AD patients. Functional capacity was measured annually over ∼7 years using the Disability Assessment for Dementia.

Implications of Applying "Clinically Significant Impairment" to Autism Assessment: Commentary on Six Problems Encountered in Clinical Practice.

The addition of 'clinically significant impairment' (American Psychiatric Association, Diagnostic and statistical manual of mental disorders, Author, 2013) to the diagnostic criteria for autism in DSM-5 attempts to establish a threshold for the condition. However, the increased prominence of the neurodiversity paradigm and social model of disability runs counter to the idea that characteristics of autism are fundamentally impairing. Consequently, diagnostic criteria for autism are becoming misaligned with the contemporary views of 'disorder' and 'disability'.

On Image Fusion of Ground Surface Vibration for Mapping and Locating Underground Pipeline Leakage: An Experimental Investigation.

This paper is concerned with imaging techniques for mapping and locating underground pipeline leakage. Ground surface vibrations induced by the propagating axisymmetric wave can be measured by an array of acoustic/vibration sensors, with the extraction of magnitude information used to determine the position of leak source. A method of connected graph traversal is incorporated into the vibroacoustic technique to obtain the spatial image with better accuracy compared to the conventional magnitude contour plot.

3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion.

We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced.