An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System.

The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns.

Chronic Diseases and Health Monitoring Big Data: A Survey.

With the advancement of technology in data science and network technology, the world has stepped into the Era of Big Data, and the medical field is rich in data suitable for analysis. Thus, in recent years, there has been much research in medical big data, mainly targeting data collection, data analysis, and visualization. However, very few works provide a full survey of the medical big data on chronic diseases and health monitoring.

Infection of ectocervical tissue and universal targeting of T-cells mediated by primary non-macrophage-tropic and highly macrophage-tropic HIV-1 R5 envelopes.

Background: HIV-1 variants carrying non-macrophage-tropic HIV-1 R5 envelopes (Envs) are predominantly transmitted and persist in immune tissue even in AIDS patients who have highly macrophage-tropic variants in the brain. Non-macrophage-tropic R5 Envs require high levels of CD4 for infection contrasting with macrophage-tropic Envs, which can efficiently mediate infection of cells via low CD4. Here, we investigated whether non-macrophage-tropic R5 Envs from the acute stage of infection (including transmitted/founder Env) mediated more efficient infection of ectocervical explant cultures compared to non-macrophage-tropic and highly macrophage-tropic R5 Envs from late disease.

Effects of envelope bandwidth on importance functions for cochlear implant simulations.

Frequency-importance functions (FIFs) quantify intelligibility contributions of spectral regions of speech. In previous work, FIFs were considered as instruments for characterizing intelligibility contributions of individual cochlear implant electrode channels. Comparisons of FIFs for natural speech and vocoder-simulated implant processed speech showed that vocoding shifted peak importance regions downward in frequency by 0.

Mixed modeling of meta-analysis P-values (MixMAP) suggests multiple novel gene loci for low density lipoprotein cholesterol.

Informing missing heritability for complex disease will likely require leveraging information across multiple SNPs within a gene region simultaneously to characterize gene and locus-level contributions to disease phenotypes. To this aim, we introduce a novel strategy, termed Mixed modeling of Meta-Analysis P-values (MixMAP), that draws on a principled statistical modeling framework and the vast array of summary data now available from genetic association studies, to test formally for locus level association. The primary inputs to this approach are: (a) single SNP level p-values for tests of association; and (b) the mapping of SNPs to genomic regions.

Efficiency of bridging-sheet recruitment explains HIV-1 R5 envelope glycoprotein sensitivity to soluble CD4 and macrophage tropism.

HIV-1 R5 viruses vary extensively in their capacity to infect macrophages. R5 viruses that confer efficient infection of macrophages are able to exploit low levels of CD4 for infection and predominate in brain tissue, where macrophages are a major target for infection. HIV-1 R5 founder viruses that are transmitted were reported to be non-macrophage-tropic.

Independent evolution of macrophage-tropism and increased charge between HIV-1 R5 envelopes present in brain and immune tissue.

Background: Transmitted HIV-1 clade B or C R5 viruses have been reported to infect macrophages inefficiently, while other studies have described R5 viruses in late disease with either an enhanced macrophage-tropism or carrying envelopes with an increased positive charge and fitness. In contrast, our previous data suggested that viruses carrying non-macrophage-tropic R5 envelopes were still predominant in immune tissue of AIDS patients. To further investigate the tropism and charge of HIV-1 viruses in late disease, we evaluated the properties of HIV-1 envelopes amplified from immune and brain tissues of AIDS patients with neurological complications.

Mixture modelling as an exploratory framework for genotype-trait associations.

We propose a mixture modelling framework for both identifying and exploring the nature of genotype-trait associations. This framework extends the classical mixed effects modelling approach for this setting by incorporating a Gaussian mixture distribution for random genotype effects. The primary advantages of this paradigm over existing approaches include that the mixture modelling framework addresses the degrees-of-freedom challenge that is inherent in application of the usual fixed effects analysis of covariance, relaxes the restrictive single normal distribution assumption of the classical mixed effects models and offers an exploratory framework for discovery of underlying structure across multiple genetic loci.

The association of lean mass and fat mass with peak bone mass in young premenopausal women.

Total body mass is a major determinant of bone mass, but studies of the relative contributions of lean mass (LM) and fat mass (FM) to bone mass have yielded conflicting results. This is likely because of the use of bone measures that are not adequately adjusted for body size and, therefore, not appropriate for analyses related to body composition, which is also correlated with body size. We examined the relationship between body composition and peak bone mass in premenopausal women aged 18-30 yr using both size-dependent and size-adjusted measures of bone density and body composition, as well as statistical models adjusted for size-related factors.

Low-cost HIV-1 diagnosis and quantification in dried blood spots by real time PCR.

Background: Rapid and cost-effective methods for HIV-1 diagnosis and viral load monitoring would greatly enhance the clinical management of HIV-1 infected adults and children in limited-resource settings. Recent recommendations to treat perinatally infected infants within the first year of life are feasible only if early diagnosis is routinely available. Dried blood spots (DBS) on filter paper are an easy and convenient way to collect and transport blood samples.

Ranking USRDS provider specific SMRs from 1998-2001.

Provider profiling (ranking/percentiling) is prevalent in health services research. Bayesian models coupled with optimizing a loss function provide an effective framework for computing non-standard inferences such as ranks. Inferences depend on the posterior distribution and should be guided by inferential goals.

Gene set enrichment analysis for non-monotone association and multiple experimental categories.

Background: Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint.

Loss Function Based Ranking in Two-Stage, Hierarchical Models.

Performance evaluations of health services providers burgeons. Similarly, analyzing spatially related health information, ranking teachers and schools, and identification of differentially expressed genes are increasing in prevalence and importance. Goals include valid and efficient ranking of units for profiling and league tables, identification of excellent and poor performers, the most differentially expressed genes, and determining "exceedances" (how many and which unit-specific true parameters exceed a threshold).

Flexible distributions for triple-goal estimates in two-stage hierarchical models.

Performance evaluations often aim to achieve goals such as obtaining estimates of unit-specific means, ranks, and the distribution of unit-specific parameters. The Bayesian approach provides a powerful way to structure models for achieving these goals. While no single estimate can be optimal for achieving all three inferential goals, the communication and credibility of results will be enhanced by reporting a single estimate that performs well for all three.