Effect of cool vs. warm dialysate on toxin removal: rationale and study design

10.1186/s12882-015-0017-5BMC Nephrology16

Health Monitoring of Pressure Regulating Stations in Gas Distribution Networks Using Mathematical Models

Many cities have extensive distribution networks that supply natural or town gas to domestic, industrial, and power plant consumers. A typical network may have hundreds of pressure regulating stations that are of different types and capacities, but most legacy networks are sparsely instrumented. The reliability of these stations is the first priority for ensuring uninterrupted gas supplies; hence, condition monitoring and prescriptive maintenance are critical. In this study, mathematical models were developed for two types of commonly used regulators: spring-loaded and lever-type regulators. We also considered three faults that are typically of interest: filter choking, valve seat damage, and diaphragm deterioration. The proposed methodologies used the available measured data and mathematical models to diagnose faults, track prognoses, and estimate the remaining useful life of the regulators. The applicability of our proposed methodologies was demonstrated using real data from an existing distribution network. To facilitate industrial use, the methodologies were packaged into a user-friendly dashboard that could act as an interface with the operational database and display the health status of the regulators

Machine learning based framework for multi-class diagnosis of neurodegenerative disease: a study on parkinson disease

by Meet Prakashbhai Vadera et.a

Multi-class diagnosis of Neurodegenerative diseases: a Neuroimaging machine learning based approach

With the advent of powerful analysis tools, intelligent medical diagnostics for neurodegenerative disease (NDs) diagnosis are coming close to becoming a reality. In this work, we describe a state-of-the-art machine-learning system with multiclass diagnostic capabilities for the diagnosis of NDs. Our framework for multiclass subject classification comprises feature extraction using principal component analysis, feature selection using Fisher discriminant ratio, and subject classification using least-squares support vector machines. A multisite, multiscanner data set containing 2540 patients clinically diagnosed as Alzheimer Disease (AD), healthy controls (HC), Parkinson disease (PD), mild cognitive impairment (MCI), and scans without evidence of dopaminergic deficit (SWEDD) was obtained from Parkinson’s Progression Marker Initiative and Alzheimer’s Disease Neuroimaging Initiative. Our work assumes significance since studies have primarily focused on comparing only two subject classes at once, i.e., as binary classes. To profile the diagnostic capabilities for real-time clinical practice, we tested our framework for multiclass disease diagnostic capabilities. The proposed method has been trained and tested on this cohort (2540 subjects), the largest reported so far in the literature. For multiclass diagnosis, our method results in highest reported classification accuracy of 87.89 ± 03.98% with a precision of 82.54 ± 08.85%. Also, we have obtained accuracy of up to 100% for binary class classification of NDs. We believe that this study takes us one step closer to translating machine learning into routine clinical settings as a decision support system for ND diagnosis.by Gurpreet Singh, Meet Vadera, Lakshminarayanan Samavedham and Erle Chuen Hian Li

VPMCD: Variable interaction modeling approach for class discrimination in biological systems

10.1016/j.febslet.2007.01.052FEBS Letters5815826-830FEBL

A regional blood flow model for β2-microglobulin kinetics and for simulating intra-dialytic exercise effect

10.1007/s10439-011-0383-5Annals of Biomedical Engineering39122879-2890ABME