Loss Function Based Ranking in Two-Stage, Hierarchical Models

Several authors have studied the performance of optimal, squared error loss (SEL) estimated ranks. Though these are effective, in many applications interest focuses on identifying the relatively good (e.g., in the upper 10%) or relatively poor performers. We construct loss functions that address this goal and evaluate candidate rank estimates, some of which optimize specific loss functions. We study performance for a fully parametric hierarchical model with a Gaussian prior and Gaussian sampling distributions, evaluating performance for several loss functions. Results show that though SEL-optimal ranks and percentiles do not specifically focus on classifying with respect to a percentile cut point, they perform very well over a broad range of loss functions. We compare inferences produced by the candidate estimates using data from The Community Tracking Study

Ranking USRDS Provider-Specific SMRs from 1998-2001

Provider profiling (ranking, league tables ) is prevalent in health services research. Similarly, comparing educational institutions and identifying differentially expressed genes depend on ranking. Effective ranking procedures must be structured by a hierarchical (Bayesian) model and guided by a ranking-specific loss function, however even optimal methods can perform poorly and estimates must be accompanied by uncertainty assessments. We use the 1998-2001 Standardized Mortality Ratio (SMR) data from United States Renal Data System (USRDS) as a platform to identify issues and approaches. Our analyses extend Liu et al. (2004) by combining evidence over multiple years via an AR(1) model; by considering estimates that minimize errors in classifying providers above or below a percentile cutpoint in addition to those that minimize rank-based, squared-error loss; by considering ranks based on the posterior probability that a provider\u27s SMR exceeds a threshold; by comparing these ranks to those produced by ranking MLEs and ranking P-values associated with testing whether a provider\u27s SMR = 1; by comparing results for a parametric and a non-parametric prior; by reporting on a suite of uncertainty measures. Results show that MLE-based and hypothesis test based ranks are far from optimal, that uncertainty measures effectively calibrate performance; that in the USRDS context ranks based on single-year data perform poorly, but that performance improves substantially when using the AR(1) model; that ranks based on posterior probabilities of exceeding a properly chosen SMR threshold are essentially identical to those produced by minimizing classification loss. These findings highlight areas requiring additional research and the need to educate stakeholders on the uses and abuses of ranks; on their proper role in science and policy; on the absolute necessity of accompanying estimated ranks with uncertainty assessments and ensuring that these uncertainties influence decisions

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

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

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

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 beta20-21 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

A bat algorithm for SDN network scheduling

Abstract With the development of SDN, end-to-end network reservation becomes a reality. Resource reservation can become an online service for network users, and the network faces new challenges on how to allocate users’ resource dynamically. To solve the problem, this paper proposed a bat-based algorithm for SDN network scheduling along with a network request model. The network resource request model is for characterizing users’ simultaneous network resource request in SDN. Based on the model, this paper transforms the resource reservation problem into a multiple-knapsack problem and proposes a bat algorithm to optimize the solution. Experiments show that the proposed algorithm is better than greedy and dynamic programming algorithm. The major contribution of this paper is to model the SDN users’ resource requests and apply bat algorithm for the solution

An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

Load curve data from advanced metering infrastructure record the consumers&rsquo; behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the &lsquo;trend alteration point&rsquo; concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering

A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data

Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy