Prostaglandin E2 promotes features of replicative senescence in chronically activated human CD8+ T cells.

Prostaglandin E2 (PGE2), a pleiotropic immunomodulatory molecule, and its free radical catalyzed isoform, iso-PGE2, are frequently elevated in the context of cancer and chronic infection. Previous studies have documented the effects of PGE2 on the various CD4+ T cell functions, but little is known about its impact on cytotoxic CD8+ T lymphocytes, the immune cells responsible for eliminating virally infected and tumor cells. Here we provide the first demonstration of the dramatic effects of PGE2 on the progression of human CD8+ T cells toward replicative senescence, a terminal dysfunctional state associated multiple pathologies during aging and chronic HIV-1 infection. Our data show that exposure of chronically activated CD8+ T cells to physiological levels of PGE2 and iso-PGE2 promotes accelerated acquisition of markers of senescence, including loss of CD28 expression, increased expression of p16 cell cycle inhibitor, reduced telomerase activity, telomere shortening and diminished production of key cytotoxic and survival cytokines. Moreover, the CD8+ T cells also produced higher levels of reactive oxygen species, suggesting that the resultant oxidative stress may have further enhanced telomere loss. Interestingly, we observed that even chronic activation per se resulted in increased CD8+ T cell production of PGE2, mediated by higher COX-2 activity, thus inducing a negative feedback loop that further inhibits effector function. Collectively, our data suggest that the elevated levels of PGE2 and iso-PGE2, seen in various cancers and HIV-1 infection, may accelerate progression of CD8+ T cells towards replicative senescence in vivo. Inhibition of COX-2 activity may, therefore, provide a strategy to counteract this effect

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys

Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data

In this paper we introduce fuzzy forests, a novel machine learning algorithm for ranking the importance of features in high-dimensional classification and regression problems. Fuzzy forests is specifically designed to provide relatively unbiased rankings of variable importance in the presence of highly correlated features, especially when the number of features, p, is much larger than the sample size, n (p n). We introduce our implementation of fuzzy forests in the R package, fuzzyforest. Fuzzy forests works by taking advantage of the network structure between features. First, the features are partitioned into separate modules such that the correlation within modules is high and the correlation between modules is low. The package fuzzyforest allows for easy use of the package WGCNA (weighted gene coexpression network analysis, alternatively known as weighted correlation network analysis) to form modules of features such that the modules are roughly uncorrelated. Then recursive feature elimination random forests (RFE-RFs) are used on each module, separately. From the surviving features, a final group is selected and ranked using one last round of RFE-RFs. This procedure results in a ranked variable importance list whose size is pre-specified by the user. The selected features can then be used to construct a predictive model

Who Voted in 2016? Using Fuzzy Forests to Understand Voter Turnout

Objective: What can machine learning tell us about who voted in 2016? There are numerous competing voter turnout theories, and a large number of covariates are required to assess which theory best explains turnout. This article is a proof of concept that machine learning can help overcome this curse of dimensionality and reveal important insights in studies of political phenomena. Methods: We use fuzzy forests, an extension of random forests, to screen variables for a parsimonious but accurate prediction. Fuzzy forests achieve accurate variable importance measures in the face of high‐dimensional and highly correlated data. The data that we use are from the 2016 Cooperative Congressional Election Study. Results: Fuzzy forests chose only a small number of covariates as major correlates of 2016 turnout and still boasted high predictive performance. Conclusion: Our analysis provides three important conclusions about turnout in 2016: registration and voting procedures were important, political issues were important (especially Obamacare, climate change, and fiscal policy), but few demographic variables other than age were strongly associated with turnout. We conclude that fuzzy forests is an important methodology for studying overdetermined questions in social sciences

Who Voted in 2016? Using Fuzzy Forests to Understand Voter Turnout

Objective: What can machine learning tell us about who voted in 2016? There are numerous competing voter turnout theories, and a large number of covariates are required to assess which theory best explains turnout. This article is a proof of concept that machine learning can help overcome this curse of dimensionality and reveal important insights in studies of political phenomena. Methods: We use fuzzy forests, an extension of random forests, to screen variables for a parsimonious but accurate prediction. Fuzzy forests achieve accurate variable importance measures in the face of high‐dimensional and highly correlated data. The data that we use are from the 2016 Cooperative Congressional Election Study. Results: Fuzzy forests chose only a small number of covariates as major correlates of 2016 turnout and still boasted high predictive performance. Conclusion: Our analysis provides three important conclusions about turnout in 2016: registration and voting procedures were important, political issues were important (especially Obamacare, climate change, and fiscal policy), but few demographic variables other than age were strongly associated with turnout. We conclude that fuzzy forests is an important methodology for studying overdetermined questions in social sciences

Characterization of designed, synthetically accessible bryostatin analog HIV latency reversing agents.

HIV latency in resting CD4+ T cell represents a key barrier preventing cure of the infection with antiretroviral drugs alone. Latency reversing agents (LRAs) can activate HIV expression in latently infected cells, potentially leading to their elimination through virus-mediated cytopathic effects, host immune responses, and/or therapeutic strategies targeting cells actively expressing virus. We have recently described several structurally simplified analogs of the PKC modulator LRA bryostatin (termed bryologs) designed to improve synthetic accessibility, tolerability in vivo, and efficacy in inducing HIV latency reversal. Here we report the comparative performance of lead bryologs, including their effects in reducing cell surface expression of HIV entry receptors, inducing proinflammatory cytokines, inhibiting short-term HIV replication, and synergizing with histone deacetylase inhibitors to reverse HIV latency. These data provide unique insights into structure-function relationships between A- and B-ring bryolog modifications and activities in primary cells, and suggest that bryologs represent promising leads for preclinical advancement