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. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.

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. RESULTS: We used Env+ pseudovirions that carried a GFP reporter gene to measure infection of the first cells targeted in ectocervical explant cultures. In straight titrations of Env+ pseudovirus supernatants, mac-tropic R5 Envs from late disease mediated slightly higher infectivities for ectocervical explants although this was not significant. Surprisingly, explant infection by several T/F/acute Envs was lower than for Envs from late disease. However, when infectivity for explants was corrected to account for differences in the overall infectivity of each Env+ pseudovirus (measured on highly permissive HeLa TZM-bl cells), non-mac-tropic early and late disease Env+ pseudoviruses mediated significantly higher infection. This observation suggests that cervical tissue preferentially supports non-mac-tropic Env+ viruses compared to mac-tropic viruses. Finally, we show that T-cells were the main targets for infection regardless of whether explants were stimulated with T-cell or monocyte/macrophage cytokines. There was no evidence of macrophage infection even for pseudovirions carrying highly mac-tropic Envs from brain tissue or for the highly mac-tropic, laboratory strain, BaL, which targeted T-cells in the explant tissue. CONCLUSIONS: Our data support ectocervical tissue as a favorable environment for non-mac-tropic HIV-1 R5 variants and emphasize the role of T-cells as initial targets for infection even for highly mac-tropic variants.

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 0.5 octaves. These shifts were attributed to voicing cue changes, and may reflect increased reliance on low-frequency information (apart from periodicity cues) for correct voicing perception. The purpose of this study was to determine whether increasing channel envelope bandwidth would reverse these shifts by improving access to voicing and pitch cues. Importance functions were measured for 48 subjects with normal hearing, who listened to vowel-consonant-vowel tokens either as recorded or as output from five different vocoders that simulated implant processing. Envelopes were constructed using filters that either included or excluded pitch information. Results indicate that vocoding-based shifts are only partially counteracted by including pitch information; moreover, a substantial baseline shift is present even for vocoders with high spectral resolution. The results also suggest that vocoded speech intelligibility is most sensitive to a loss of spectral resolution in high-importance regions, a finding with possible implications for cochlear implant electrode mapping.

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. Here, we investigated the sensitivities of macrophage-tropic and non-macrophage-tropic R5 envelopes to neutralizing antibodies. We observed striking differences in the sensitivities of Env(+) pseudovirions to soluble CD4 (sCD4) and to neutralizing monoclonal antibodies (MAbs) that target the CD4 binding site. Macrophage-tropic R5 Envs were sensitive to sCD4, while non-macrophage-tropic Envs were significantly more resistant. In contrast, all Envs were sensitive to VRC01 regardless of tropism, while MAb b12 conferred an intermediate neutralization pattern where all the macrophage-tropic and about half of the non-macrophage-tropic Envs were sensitive. CD4, b12, and VRC01 share binding specificities on the outer domain of gp120. However, these antibodies differ in their ability to induce conformational changes on the trimeric envelope and in specificity for residues on the V1V2 loop stem and ß20-21 junction that are targets for CD4 in recruiting the bridging sheet. These distinct specificities of CD4, b12, and VRC01 likely explain the observed differences in Env sensitivity to inhibition by these reagents and provide an insight into the envelope mechanisms that control macrophage tropism. We present a model where the efficiency of bridging-sheet recruitment by CD4 is a major determinant of HIV-1 R5 envelope sensitivity to soluble CD4 and macrophage tropism.

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. RESULTS: Almost all envelopes amplified were R5. There was clear compartmentalization of envelope sequences for four of the five subjects. However, strong compartmentalization of macrophage-tropism in brain was observed even when brain and immune tissue envelope sequences were not segregated. R5 envelopes from immune tissue of four subjects carried a higher positive charge compared to brain envelopes. We also confirm a significant correlation between macrophage tropism and sensitivity to soluble CD4, a weak association with sensitivity to the CD4 binding site antibody, b12, but no clear relationship with maraviroc sensitivity. CONCLUSIONS: Our study shows that non-macrophage-tropic R5 envelopes carrying gp120s with an increased positive charge were predominant in immune tissue in late disease. However, highly macrophage-tropic variants with lower charged gp120s were nearly universal in the brain. These results are consistent with HIV-1 R5 envelopes evolving gp120s with an increased positive charge in immune tissue or sites outside the brain that likely reflect an adaptation for increased replication or fitness for CD4+ T-cells. Our data are consistent with the presence of powerful pressures in brain and in immune tissues selecting for R5 envelopes with very different properties; high macrophage-tropism, sCD4 sensitivity and low positive charge in brain and non-macrophage-tropism, sCD4 resistance and high positive charge in immune tissue.

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. We measured total bone mass and areal bone density using dual-energy X-ray absorptiometry, and used established formulas to calculate estimates of volumetric (size-adjusted) bone density. LM tended to be positively associated with bone both before and after adjustment for size-related factors. FM and body fat percentage, however, were positively associated with size-dependent bone measures, but adjusting for size removed or reversed this association. These findings suggest that the association between bone mass and body composition, especially FM, is dependent on the bone measures analyzed, and that determining the most appropriate size-adjustment techniques is critical for understanding this relationship.

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. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed. RESULTS: For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing. CONCLUSION: The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets.

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. This review investigates recent research efforts and conducts a comprehensive overview of the work on medical big data, especially as related to chronic diseases and health monitoring. It focuses on the full cycles of the big data processing, which includes medical big data preprocessing, big data tools and algorithms, big data visualization, and security issues in big data. It also attempts to combine common big data technologies with special medical needs by analyzing in detail existing works of medical big data. To the best of our knowledge, this is the first survey that targets chronic diseases and health monitoring big data technologies.

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. Triple goal estimates [Shen and Louis, 1998. Triple-goal estimates in two-stage hierarchical models. J. Roy. Statist. Soc. Ser. B 60, 455-471] have this performance and are appealing for performance evaluations. Because triple-goal estimates rely more heavily on the entire distribution than do posterior means, they are more sensitive to misspecification of the population distribution and we present various strategies to robustify triple-goal estimates by using nonparametric distributions. We evaluate performance based on the correctness and efficiency of the robustified estimates under several scenarios and compare empirical Bayes and fully Bayesian approaches to model the population distribution. We find that when data are quite informative, conclusions are robust to model misspecification. However, with less information in the data, conclusions can be quite sensitive to the choice of population distribution. Generally, use of a nonparametric distribution pays very little in efficiency when a parametric population distribution is valid, but successfully protects against model misspecification.

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. The output of MixMAP is comprised of locus level estimates and tests of association. In application of MixMAP to summary data from the Global Lipids Gene Consortium, we suggest twelve new loci (PKN, FN1, UGT1A1, PPARG, DMDGH, PPARD, CDK6, VPS13B, GAD2, GAB2, APOH and NPC1) for low-density lipoprotein cholesterol (LDL-C), a causal risk factor for cardiovascular disease and we also demonstrate the potential utility of MixMAP in small data settings. Overall, MixMAP offers novel and complementary information as compared to SNP-based analysis approaches and is straightforward to implement with existing open-source statistical software tools.

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. An application to data arising from a study of antiretroviral-associated dyslipidaemia in human immunodeficiency virus infection is presented. Extensive simulations studies are also implemented to investigate the performance of this approach.

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. However, even optimal methods might not lead to definitive results and ranks should be accompanied by valid uncertainty assessments. We outline the Bayesian approach and use estimated Standardized Mortality Ratios (SMRs) in 1998-2001 from the United States Renal Data System (USRDS) as a platform to identify issues and demonstrate approaches. Our analyses extend Liu et al. (2004) by computing estimates developed by Lin et al. (2006) that minimize errors in classifying providers above or below a percentile cut-point, by combining evidence over multiple years via a first-order, autoregressive model on log(SMR), and by use of a nonparametric prior. Results show that ranks/percentiles based on maximum likelihood estimates of the SMRs and those based on testing whether an SMR = 1 substantially under-perform the optimal estimates. Combining evidence over the four years using the autoregressive model reduces uncertainty, improving performance over percentiles based on only one year. Furthermore, percentiles based on posterior probabilities of exceeding a properly chosen SMR threshold are essentially identical to those produced by minimizing classification loss. Uncertainty measures effectively calibrate performance, showing that considerable uncertainty remains even when using optimal methods. Findings highlight the importance of using loss function guided percentiles and the necessity of accompanying estimates with uncertainty assessments.

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. A rapid and cost effective method to diagnose and quantify HIV-1 from DBS is urgently needed to facilitate early diagnosis of HIV-1 infection and monitoring of antiretroviral therapy. METHODS AND FINDINGS: We have developed a real-time LightCycler (rtLC) PCR assay to detect and quantify HIV-1 from DBS. HIV-1 RNA extracted from DBS was amplified in a one-step, single-tube system using primers specific for long-terminal repeat sequences that are conserved across all HIV-1 clades. SYBR Green dye was used to quantify PCR amplicons and HIV-1 RNA copy numbers were determined from a standard curve generated using serially diluted known copies of HIV-1 RNA. This assay detected samples across clades, has a dynamic range of 5 log(10), and %CV <8% up to 4 log(10) dilution. Plasma HIV-1 RNA copy numbers obtained using this method correlated well with the Roche Ultrasensitive (r = 0.91) and branched DNA (r = 0.89) assays. The lower limit of detection (95%) was estimated to be 136 copies. The rtLC DBS assay was 2.5 fold rapid as well as 40-fold cheaper when compared to commercial assays. Adaptation of the assay into other real-time systems demonstrated similar performance. CONCLUSIONS: The accuracy, reliability, genotype inclusivity and affordability, along with the small volumes of blood required for the assay suggest that the rtLC DBS assay will be useful for early diagnosis and monitoring of pediatric HIV-1 infection in resource-limited settings.

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). These data and inferential goals require a hierarchical, Bayesian model that accounts for nesting relations and identifies both population values and random effects for unit-specific parameters. Furthermore, the Bayesian approach coupled with optimizing a loss function provides a framework for computing non-standard inferences such as ranks and histograms.Estimated ranks that minimize Squared Error Loss (SEL) between the true and estimated ranks have been investigated. The posterior mean ranks minimize SEL and are "general purpose," relevant to a broad spectrum of ranking goals. However, other loss functions and optimizing ranks that are tuned to application-specific goals require identification and evaluation. For example, when the goal is to identify the relatively good (e.g., in the upper 10%) or relatively poor performers, a loss function that penalizes classification errors produces estimates that minimize the error rate. We construct loss functions that address this and other goals, developing a unified framework that facilitates generating candidate estimates, comparing approaches and producing data analytic performance summaries. We compare performance for a fully parametric, hierarchical model with Gaussian sampling distribution under Gaussian and a mixture of Gaussians prior distributions. We illustrate approaches via analysis of standardized mortality ratio data from the United States Renal Data System.Results show that SEL-optimal ranks perform well over a broad class of loss functions but can be improved upon when classifying units above or below a percentile cut-point. Importantly, even optimal rank estimates can perform poorly in many real-world settings; therefore, data-analytic performance summaries should always be reported.