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Abstract: Lot Quality Assurance Sampling (LQAS) is strongly advocated for use in monitoring the health status of populations, largely in the developing world. It is advocated both for the monitoring of small areas as well as for making global assessments of the health status of a larger region. This paper contrasts the interpretation offered by LQAS methods to that offered by Bayesian hierarchical models. It considers applications to previously reported local area data and presents a reanalysis of published data on vaccine coverage in Peru as well as HTLV-1 prevalence in Benin. The desirability of using Bayesian methods in the field may be challenged; nevertheless this work amplifies previously expressed concerns about the way the LQAS method can be used. It raises questions about the ability of the LQAS approach to make, sufficiently often, the correct decisions in order to be useful in monitoring health programmes at the local level. Keywords: Cluster Sampling, Bayesian Hierarchical Model, Overdisperson, Hypergeometric distribution, Classification.Download Full Article |
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Abstract: Autism spectrum disorder (autism) is a common and heterogeneous neurodevelopmental disorder of genetic origins defined by challenges in social communication and clusters of restrictive and repetitive behaviors. An emerging hypothesis of autism pathogenesis describes symptoms as the results from deviations from normative developmental processes. In this account, symptoms represent the outcome of variable instantiation of genetic liabilities – in terms of dosage and timing – leading to disruptions in the developmental trajectories of foundational social adaptive skills. Given the fast pace of change in behavior and brain development in the first two years of life, we pose that the currently prevalent cross-sectional experimental designs are ill-suited to capture changes from normative benchmarks that might be small at any data point but which inexorably and cumulatively increase divergences in developmental trajectories that ultimately culminate in the unmistakable cluster of atypical behaviors we now call autism. We argue that only densely-sampled longitudinal experimental designs can capture the underlying dynamic processes moving the individual child’s development towards or away from normative benchmarks. We illustrate this phenomenon via a detailed example in which a cross-sectional comparison between a clinical and a control cohort failed to find differences, which could only be detected by ascertaining that the developmental trajectory of one cohort was moving upwards while the other was moving downwards, with the developmental lines intersecting at the cross-sectional data point. We conclude by magnifying Karmiloff-Smith’s assertion, oft-quoted but seldom followed, that “development itself is the key to understanding developmental disorders” [1]. Keywords: Autism, Autism Spectrum Disorder, Social Visual Engagement, Eye Fixation, Infancy, Prodromal, developmental trajectories, growth curve, growth charts. Download Full Article |
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Abstract: This article focuses on model based sparse feature extraction of biomedical signals for classification problems, which stems from sparse representation in modern signal processing. In the presented work, a novel approach based on sparse principal component analysis (SPCA) is proposed to extract signal features. This method involves partitioning signals and utilizing SPCA to select only a limited number of signal segments in order to construct signal principal components during the training stage. For signal classification purposes, a set of regression models based on sparse principal components of the selected training signal segments is constructed. Within this approach, model residuals are estimated and used as signal features for classification. The applications of the proposed approach are demonstrated by using both the synthetic data and real EEG signals. The high classification accuracy results suggest that the proposed methods may be useful for automatic event detection using long-term observational signals. keywords: Sparse Principal Component Analysis, Sparse Feature Extraction, Signal Classification, Long-term Signals. Keywords: Sparse Principal Component Analysis, Sparse Representation, Signal Classification, Long-term Signals. |
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Abstract: The calculation of net dietary energy is in great triumph on the helm of designing an apt dieting for both the therapeutic and normal diet. There are some procedures in this connection in nutritional science which is relatively time consuming, laboratory tests induced and often the misleading data contributors in view of assuring balanced dieting. The dietician is often at bay to approve an exact dieting to sustain health and nutritional soundness adhering to the existing dietary energy measuring methods because the frequently using methods are not informing the net dietary energy level required at all in correct amount for the sample at a population in a community. The aim of the current study is to make a dot over these ongoing panics exploring an easy and accurate way in prescribing a confounding free diet. The study can divulge an open secret in measuring net dietary energy which is mandatory for dieting practices worldwide to resist the possible health horrors in nutritional epidemiology. The study finding is the Modified Mass Energy Equivalence [equation (xi)] can be an outstanding biophysical model in measuring net dietary energy as a dieting tool in health pedagogy of health science. Keywords: Mass Energy Equivalence, Health Pedagogy, Biophysical Modeling, Nutritional Epidemiology, Health Physics.Download Full Article |
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Abstract: This article focuses on model based sparse feature extraction of biomedical signals for classification problems, which stems from sparse representation in modern signal processing. In the presented work, a novel approach based on sparse principal component analysis (SPCA) is proposed to extract signal features. This method involves partitioning signals and utilizing SPCA to select only a limited number of signal segments in order to construct signal principal components during the training stage. For signal classification purposes, a set of regression models based on sparse principal components of the selected training signal segments is constructed. Within this approach, model residuals are estimated and used as signal features for classification. The applications of the proposed approach are demonstrated by using both the synthetic data and real EEG signals. The high classification accuracy results suggest that the proposed methods may be useful for automatic event detection using long-term observational signals. keywords: Sparse Principal Component Analysis, Sparse Feature Extraction, Signal Classification, Long-term Signals. Keywords: Sparse Principal Component Analysis, Sparse Representation, Signal Classification, Long-term Signals.Download Full Article |