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The characteristics of health literate organizations have been variously described in recognition that it is important for organizations to respond to the diversity of people's health literacy strengths and challenges. A systematic scoping review was conducted to identify, assess and classify international self-assessment tools aimed at measuring the capability of organizations to embody health literate characteristics. Following the JBI Scoping Manual, a search was conducted in six databases and identified 2693 articles. After screening, 16 studies published between 2007 and 2023 across eight countries were eligible for inclusion. Results were summarized and a finite list of items from existing tools was generated. Content analysis was performed to classify these items. Whilst most assessment tools in the included studies were healthcare-focused, other settings included schools and government departments. The 16 assessment tools included a total of 661 items, and 647 items were retained that met the definition of health literacy responsiveness. Items were classified into six domains (communication; navigation of resources; culture; policies and practice; involvement or engagement and workforce development), with high agreement between two researchers (91.5%). The 647 items were reviewed to exclude items that were too contextually specific, focused solely on service users, were too broad or had suitable alternatives; 210 items were finally retained. This research is two-fold: provides a synthesis of existing organizational health literacy responsiveness assessment tools across settings; and provides a list of items, which will be essential to developing context specific assessment tools through Delphi methods in the future., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Summary: There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis., Availability and Implementation: LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM., (Published by Oxford University Press 2023.)

Motivation: PERMANOVA is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias but at the potential costs of information loss and the introduction of a stochastic component into the analysis., Results: Here, we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix and averaging the F-statistic. Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease in which samples from case participants have systematically smaller library sizes than samples from control participants., Availability and Implementation: We have implemented all the approaches described above, including the function for calculating the analytical average of the squared or unsquared distance matrix, in our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results., Results: We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then, the P-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g. allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa., Availability and Implementation: Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Summary: We previously developed the LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. The LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here, we propose LDM-omni3 that combines LDM analyses at the relative abundance and presence-absence data scales, thereby offering optimal power across scenarios with different association mechanisms. The new LDM-omni3 test is available for the wide range of data types and analyses that are supported by the LDM., Availability and Implementation: The LDM-omni3 test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In drug discovery, one of the most important tasks is to find novel and biologically active molecules. Given that only a tip of iceberg of drugs was founded in nearly one-century's experimental exploration, it shows great significance to use in silico methods to expand chemical database and profile drug-target linkages. In this study, a web server named ChemGenerator was proposed to generate novel activates for specific targets based on users' input. The ChemGenerator relies on an autoencoder-based algorithm of Recurrent Neural Networks with Long Short-Term Memory by training of 7 million of molecular Simplified Molecular-Input Line-Entry System as the basic model, and further develops target guided generation by transfer learning. As results, ChemGenerator gains lower loss (<0.01) than existing reference model (0.2~0.4) and shows good performance in the case of Epidermal Growth Factor Receptor. Meanwhile, ChemGenerator is now freely accessible to the public by http://smiles.tcmobile.org. In proportion to endless molecular enumeration and time-consuming expensive experiments, this work demonstrates an efficient alternative way for the first virtual screening in drug discovery., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Motivation: Many methods for testing association between the microbiome and covariates of interest (e.g. clinical outcomes, environmental factors) assume that these associations are driven by changes in the relative abundance of taxa. However, these associations may also result from changes in which taxa are present and which are absent. Analyses of such presence-absence associations face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias, but at the potential cost of information loss as well as the introduction of a stochastic component into the analysis. Currently, there is a need for robust and efficient methods for testing presence-absence associations in the presence of such confounding, both at the community level and at the individual-taxon level, that avoid the drawbacks of rarefaction., Results: We have previously developed the linear decomposition model (LDM) that unifies the community-level and taxon-level tests into one framework. Here, we present an extension of the LDM for testing presence-absence associations. The extended LDM is a non-stochastic approach that repeatedly applies the LDM to all rarefied taxa count tables, averages the residual sum-of-squares (RSS) terms over the rarefaction replicates, and then forms an F-statistic based on these average RSS terms. We show that this approach compares favorably to averaging the F-statistic from R rarefaction replicates, which can only be calculated stochastically. The flexible nature of the LDM allows discrete or continuous traits or interactions to be tested while allowing confounding covariates to be adjusted for. Our simulations indicate that our proposed method is robust to any systematic differences in library size and has better power than alternative approaches. We illustrate our method using an analysis of data on inflammatory bowel disease (IBD) in which cases have systematically smaller library sizes than controls., Availabilityand Implementation: The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Air pollution has been associated with an increase in cardiovascular diseases incidence. To evaluate whether air pollution can accelerate atherogenic processes, we assessed the effects of air pollution on important surrogate markers of atherosclerosis [brachial flow-mediated dilation (FMD) and carotid intima-media thickness (IMT)]., Methods: A total of 1656 Han Chinese (mean age 46.0 + 11.2 years; male 47%) in Hong Kong, Macau, Pun Yu, Yu County and the 3-Gorges Territories (Yangtze River) were studied between 1996 and 2007 [Chinese Atherosclerosis in the Aged and Young Project (the CATHAY Study)]. Cardiovascular risk profiles were evaluated. Particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) parameters were computed from satellite sensors. Brachial FMD and carotid IMT were measured by ultrasound., Results: Health parameters [age, gender, body mass index, waist : hip ratio (WHR) and glucose)] were similar in lowest and highest PM2.5 exposure tertiles, systolic and diastolic blood pressures and triglycerides were higher (P < 0.001) and low-density cholesterol (LDL-C) was lower in the top PM2.5 tertile (P < 0.001). Brachial FMD [7.84 ± 1.77, 95% confidence interval (CI) 7.59-8.10%, vs 8.50 ± 2.52, 95% CI 8.23-8.77%, P < 0.0001) was significantly lower and carotid IMT (0.68 ± 0.13 mm, 95% CI 0.67-0.69 mm vs 0.63 mm ± 0.15 mm 95% CI 0.62-0.64 mm; P < 0.0001) was significantly thicker in the top PM2.5 tertile compared with the lowest tertile. On multiple regression, FMD was inversely related to PM2.5 (beta = 0.134, P = 0.015) independent of gender, age and blood pressure (model R2 = 0.156, F-value = 7.6, P < 0.0001). Carotid IMT was significantly correlated with PM2.5 exposure (beta = 0.381, P < 0.0001) independent of age, location, gender, WHR, blood pressure and LDL-C (model R2 = 0.408, F-value = 51.4, P-value <0.0001)., Conclusions: Air pollution is strongly associated with markers of early atherosclerosis, suggesting a potential target for preventive intervention., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: There is a lack of studies comparing hypertension and comorbidities in rural and urban older people in China. This study aimed to investigate the prevalence of hypertension and comorbidities in older people and to describe differences between patients from rural and urban areas in China., Methods: A cross-sectional study on global AGEing and adult health (SAGE) was conducted from 2007 to 2010 across 8 provinces in China. The current study included a nationally representative subsample of 7,403 respondents aged 60 years and above. Hypertension was either self-reported diagnosed hypertension or 1 elevated measured blood pressure measurement (≥140/90 mm Hg). Comorbidities were self-reported and included angina, arthritis, asthma, stroke, diabetes, depression, and chronic lung disease., Results: The prevalence of hypertension was 66.1%. Hypertension associated with 1 comorbidity was recorded in 35.9% of all participants. A large majority (73.6%) of participants with hypertension reported "good" or "very good" general health status. The prevalence of hypertension and the number of comorbidities increased with age. The prevalence rates of multiple comorbidities were higher for urban participants than their rural counterparts (27.8 vs. 42.4% for 1 morbidity; 7.7 vs. 16.4% for 2 comorbidities; 1.4 vs. 4.7% for 3 comorbidities; 0.4 vs. 1.2% for ≥4 comorbidities) after adjustment for sociodemographic factors. However, urban participants reported better general health status, irrespective of the number of comorbidities., Conclusions: Our study implies that health promotion policies targeting older people should be developed. Rural areas should be a priority, and investment in the quality of health care in rural areas is urgently needed., (© American Journal of Hypertension, Ltd 2020. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Accumulating evidence suggested that long-term antibiotic use may alter the gut microbiome, which has, in turn, been linked to type 2 diabetes. We undertook this study to investigate whether antibiotic use was associated with increased risk of type 2 diabetes., Methods: This prospective cohort study included women free of diabetes, cardiovascular disease and cancer in the Nurses' Health Study (NHS 2008-2014) and NHS II (2009-2017). We evaluated the overall duration of antibiotics use in the past 4 years and subsequent diabetes risk with Cox proportional-hazards regression adjusting for demography, family history of diabetes and lifestyle factors., Results: Pooled analyses of NHS and NHS II (2837 cases, 703 934 person-years) revealed that a longer duration of antibiotic use in the past 4 years was associated with higher risk of diabetes [Trend-coefficient = 0.09, 95% confidence interval (CI) 0.04 to 0.13]. Participants who received antibiotics treatment for a medium duration of 15 days to 2 months [hazard ratio (HR) 1.23, 95% CI 1.10 to 1.39] or long duration of >2 months (HR 1.20, 95% CI 1.02 to 1.38) had higher risk of type 2 diabetes as compared with non-users. Subgroup analyses suggested that the associations were unlikely to be modified by age, family history of diabetes, obesity, smoking, alcohol drinking, physical activity and overall diet quality., Conclusions: A longer duration of antibiotic use in recent years was associated with increased risk of type 2 diabetes in women. Physicians should exercise caution when prescribing antibiotics, particularly for long-term use., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Motivation: Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR)., Results: We introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g. clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based P-values that can control for sample correlation. The LDM can also be applied to transformed data, and an 'omnibus' test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations., Availability and Implementation: The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows., Contact: yijuan.hu@emory.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objectives: Neuroprotective potential of 7-methoxyflavanone (7MF) and its underlying mechanism was investigated., Methods: Inhibitory effects of 7MF on microglial activation and neuroinflammation were evaluated by employment of lipopolysaccharide (LPS)-induced BV2 microglial cells. Changes in expression of genes and proteins of interest were investigated by RT-qPCR analysis and Western blot analysis. Inhibitory effects of 7MF on microglial overactivation were verified in LPS-treated C57BL/6J mice using ionized calcium-binding adaptor molecule-1 (Iba1) in the brain and interleukin-6 (IL-6) in serum as indicators., Key Findings: In BV2 cells, pretreatment with 7MF antagonized LPS-induced production of inflammatory factors IL-6, tumour necrosis factor-α (TNF-α), cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), intercellular adhesion molecule-1 (ICAM-1) and monocyte chemoattractant protein-1 (MCP-1). Mechanistic studies revealed reduced expression of Toll-like receptor 4 (TLR4), myeloid differentiation factor-88 (MyD88), phosphorylated forms of c-Jun N-terminal kinase (p-JNK) and extracellular signal-regulated kinases 1/2 (p-ERK) but increased nuclear accumulation of nuclear factor erythroid 2-related factor 2 (Nrf2) and cellular expression of NAD(P)H quinone dehydrogenase-1 (NQO-1) by 7MF. In LPS-treated mice, pretreatment with 7MF reduced the brain level of Iba1 and serum level of IL-6., Conclusions: 7-methoxyflavanone inhibited LPS-stimulated TLR4/MyD88/MAPK signalling and activated Nrf2-mediated transcription of antioxidant protein NQO-1, showing antineuroinflammatory effect, so it is a potential neuroprotective agent., (© 2019 Royal Pharmaceutical Society.)

Motivation: Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project., Results: To allow inference on population structure using PCA in these situations, we provide an approach that is based on using sequencing reads directly without calling genotypes. Our approach is to adjust the data from different sequencing groups to have the same read depth and error rate so that PCA does not generate spurious components representing sequencing quality. To accomplish this, we have developed a subsampling procedure to match the depth distributions in different sequencing groups, and a read-flipping procedure to match the error rates. We average over subsamples and read flips to minimize loss of information. We demonstrate the utility of our approach using two datasets from 1000 Genomes, and further evaluate it using simulation studies., Availability and Implementation: TASER-PC software is publicly available at http://web1.sph.emory.edu/users/yhu30/software.html., Contact: yijuan.hu@emory.edu., Supplementary Information: Supplementary data are available at Bioinformatics online.

Background: There is an urgent need for studies of viral persistence and immunity during human Zika infections to inform planning and conduct of vaccine clinical trials., Methods: In 5 returned US travelers with acute symptomatic Zika infection, clinical features, viral RNA levels, and immune responses were characterized., Results: Two pregnant, flavivirus-experienced patients had viral RNA persist in plasma for >44 and >26 days. Three days after symptom onset, transient increases in proinflammatory monocytes began followed at 5 days by transient decreases in myeloid dendritic cells. Anti-Zika virus immunoglobulin M was detected at day 7 after symptom onset, persisted beyond 103 days, and remained equivocal through day 172. Zika virus-specific plasmablasts and neutralizing antibodies developed quickly; dengue virus-specific plasmablasts and neutralizing antibodies at high titers developed only in flavivirus-experienced patients. Zika virus- and dengue virus-specific memory B cells developed in both flavivirus-naive and -experienced patients. CD4+ T cells were moderately activated and produced antiviral cytokines after stimulation with Zika virus C, prM, E, and NS5 peptides in 4/4 patients. In contrast, CD8+ T cells were massively activated, but virus-specific cells that produced cytokines were present in only 2/4 patients assessed., Conclusions: Acute infections with Zika virus modulated antigen-presenting cell populations early. Flavivirus-experienced patients quickly recalled cross-reactive MBCs to secrete antibodies. Dengue virus-naive patients made little dengue-specific antibody but developed MBCs that cross-reacted against dengue virus. Zika virus-specific functional CD4+ T cells were readily detected, but few CD8+ T cells specific for the tested peptides were found., (© The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.)

In order to clarify the mitochondrial toxicity mechanism of the organic arsenical MOPIMP (2-methoxy-4-(((4-(oxoarsanyl) phenyl) imino) methyl) phenol), research was carried out at the sub-cell level based on the previous finding that the compound MOPIMP can damage the mitochondria by triggering a burst of ROS. After investigating its influence on isolated mitochondria in vitro , it was demonstrated that a high dose of MOPIMP with short-term exposure can induce mitochondrial swelling, decrease the membrane potential, enhance the permeability of H + and K + , and induce membrane lipid peroxidation, indicating that it can result in an MPT process in a ROS-mediated and Ca 2+ -independent manner. Additionally, MPT was also aggravated as a result of impairment of the membrane integrity and membrane fluidity. In addition, short-term incubation between mitochondria and compound MOPIMP promoted the inhibition of respiratory chain complexes I, II, III and IV, as well as damage to the respiration process, which supported the previous finding about the burst of ROS. On the other hand, after long-term exposure by the organic arsenical MOPIMP , mitochondrial metabolic dysfunction was triggered, which was in accordance with perturbation of the respiratory chain complexes as well as the respiration process. This work systematically sheds light on the mitochondrial toxicity mechanism of the organic arsenical MOPIMP , including induction of the MPT process and inhibition of respiratory metabolism, which provides a potential target for organic arsenicals as anti-tumor drugs.

In this study, three different oil phases include 1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (BMPy[NTf2]), 1-butyl-3-methylimidazolium hexafluorophosphate (BMImPF6) and n-octane were compared for the MEEKC analysis (SDS as surfactant and n-butanol as co-surfactant) of eight isoflavones from pueraria. The investigated isoflavones can be well separated by all of those three microemulsion systems after careful optimization, and the MEEKC with n-octane as the oil phase was the best choice (good symmetry and high resolutions of peaks with short analysis time) for the analysis. The optimum conditions of MEEKC method were as follows: 70 mM SDS, 0.7 M n-butanol and 0.5% (w/v) n-octane in 10 mM sodium tetraborate (STB) at pH 8.5, applied voltage was 23 kV and cassette temperature was set at 30°C. And then the developed method was fully validated (limit of detection, limit of quantification, intraday precision, interday precision and recovery) and successfully applied to determine the eight analytes in three Radix Puerariae samples. In addition, although the MEEKC with classic oil phase (n-octane) showed better results for isoflavones analysis in this study, the MEEKC with ionic liquids (BMPy[NTf2] and BMImPF6) also showed great separation potential for analytes, which may be further applied in the analysis of other natural products., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: The human immunodeficiency virus (HIV) care continuum has become an important tool for evaluating HIV care. Current depictions of the care continuum are often cross-sectional and evaluate retention and viral suppression (VS) in a single year, yet the National HIV/AIDS Strategy calls for programs with long-lasting outcomes., Methods: Retrospective chart review of HIV-infected patients enrolled in a large, urban clinic in 2010 followed longitudinally for 36 months. McNemar comparisons and logistic regression analyses were conducted to evaluate covariate association with continuous retention and VS. Generalized estimating equation log-linear models were used to integrate time into the model., Results: Among 655 patients (77% male, 83% black, 54% men who have sex with men (MSM), 78% uninsured) continuous retention/VS at 12 months (84%/64%), 24 months (60%/48%), and 36 months (49%/39%) showed significant attrition (P < .0001) over time. Continuous retention was associated with prevalent VS at the end of 36 months (adjusted prevalence ratio 3.12; 95% confidence interval [CI], 2.40, 4.07). 12-month retention for black (84%) and nonblack (85%) patients was equivalent, yet fewer blacks (46%) than nonblacks (63%) achieved 36-month continuous retention due to a significant interaction between race and time (aOR 0.75, 95% CI, .59, .95)., Conclusions: Continuous retention is a critically important measure of long-term success in HIV treatment and the crucial component of successful treatment-as-prevention but is infrequently evaluated. Single cross-sections may overestimate successful retention and virologic outcomes. A longitudinal HIV care continuum provides greater insight into long-term outcomes and exposes disparities not evident with traditional cross-sectional care continua., (© The Author 2015. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.)

Jumonji domain-containing protein 6 (JMJD6) is a nuclear protein involved in histone modification, transcription and RNA processing. Although JMJD6 is crucial for tissue development, the link between its molecular functions and its roles in any given differentiation process is unknown. We report that JMJD6 is required for adipogenic gene expression and differentiation in a manner independent of Jumonji C domain catalytic activity. JMJD6 knockdown led to a reduction of C/EBPβ and C/EBPδ protein expression without affecting mRNA levels in the early phase of differentiation. However, ectopic expression of C/EBPβ and C/EBPδ did not rescue differentiation. Further analysis demonstrated that JMJD6 was associated with the Pparγ2 and Cebpα loci and putative enhancers. JMJD6 was previously found associated with bromodomain and extra-terminal domain (BET) proteins, which can be targeted by the bromodomain inhibitor JQ1. JQ1 treatment prevented chromatin binding of JMJD6, Pparγ2 and Cebpα expression, and adipogenic differentiation, yet had no effect on C/EBPβ and C/EBPδ expression or chromatin binding. These results indicate dual roles for JMJD6 in promoting adipogenic gene expression program by post-transcriptional regulation of C/EBPβ and C/EBPδ and direct transcriptional activation of Pparγ2 and Cebpα during adipocyte differentiation., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Cell migration is a critical determinant of cancer metastasis, and a better understanding of the genes involved will lead to the identification of novel targets aimed at preventing cancer dissemination. KIAA1199 has been shown to be upregulated in human cancers, yet its role in cancer progression was hitherto unknown., Methods: Clinical relevance was assessed by examining KIAA1199 expression in human cancer specimens. In vitro and in vivo studies were employed to determine the function of KIAA1199 in cancer progression. Cellular localization of KIAA1199 was microscopically determined. SNAP-tag pull-down assays were used to identify binding partner(s) of KIAA1199. Calcium levels were evaluated using spectrofluorometric and fluorescence resonance energy transfer analyses. Signaling pathways were dissected by Western blotting. Student t test was used to assess differences. All statistical tests were two-sided., Results: KIAA1199 was upregulated in invasive breast cancer specimens and inversely associated with patient survival rate. Silencing of KIAA1199 in MDA-MB-435 cancer cells resulted in a mesenchymal-to-epithelial transition that reduced cell migratory ability in vitro (75% reduction; P < .001) and decreased metastasis in vivo (80% reduction; P < .001). Gain-of-function assays further demonstrated the role of KIAA1199 in cell migration. KIAA1199-enhanced cell migration required endoplasmic reticulum (ER) localization, where it forms a stable complex with the chaperone binding immunoglobulin protein (BiP). A novel ER-retention motif within KIAA1199 that is required for its ER localization, BiP interaction, and enhanced cell migration was identified. Mechanistically, KIAA1199 was found to mediate ER calcium leakage, and the resultant increase in cytosolic calcium ultimately led to protein kinase C alpha activation and cell migration., Conclusions: KIAA1199 serves as a novel cell migration-promoting gene and plays a critical role in maintaining cancer mesenchymal status.

Myogenic microRNAs are important regulators of muscle development and differentiation. To better understand the roles of chromatin-modifying and remodeling enzymes in the activation of myogenic microRNA expression, we have functionally analyzed two different protein arginine methyltransferases, Prmt5 and Prmt4, both of which have previously been implicated in the regulation of myogenic mRNA expression. Both Prmts are required for myogenic microRNA induction during differentiation. Prmt5 is indirectly required due to the necessity of Prmt5 for expression of the transcriptional regulator, myogenin, as ectopic expression of myogenin eliminates Prmt5 dependency. By contrast, Prmt4 binds to the upstream regulatory regions of myogenic microRNAs and is required for dimethylation of the Prmt4 substrate, H3R17, at microRNA regulatory sequences. Deletion of Prmt4 does not alter MyoD binding at myogenic microRNA regulatory sequences but prevents the binding of both myogenin and the Brg1 ATPase that catalyzes SWI/SNF-dependent chromatin remodeling, resulting in an inhibition of microRNA expression.

Missing data arise in genetic association studies when genotypes are unknown or when haplotypes are of direct interest. We provide a general likelihood-based framework for making inference on genetic effects and gene-environment interactions with such missing data. We allow genetic and environmental variables to be correlated while leaving the distribution of environmental variables completely unspecified. We consider 3 major study designs-cross-sectional, case-control, and cohort designs-and construct appropriate likelihood functions for all common phenotypes (e.g. case-control status, quantitative traits, and potentially censored ages at onset of disease). The likelihood functions involve both finite- and infinite-dimensional parameters. The maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Expectation-Maximization (EM) algorithms are developed to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed inferential and numerical methods perform well in practical settings. Illustration with a genome-wide association study of lung cancer is provided.

RNA molecules play an important role in many biological activities. Knowing its secondary structure can help us better understand the molecule's ability to function. The methods for RNA structure determination have traditionally been implemented through biochemical, biophysical and phylogenetic analyses. As the advance of computer technology, an increasing number of computational approaches have recently been developed. They have different goals and apply various algorithms. For example, some focus on secondary structure prediction for a single sequence; some aim at finding a global alignment of multiple sequences. Some predict the structure based on free energy minimization; some make comparative sequence analyses to determine the structure. In this paper, we describe how to correctly use GPRM, a genetic programming approach to finding common secondary structure elements in a set of unaligned coregulated or homologous RNA sequences. GPRM can be accessed at http://bioinfo.cis.nctu.edu.tw/service/gprm/.

Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stem-loops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.

Unlabelled: Most of the current bioinformatics literature lacks an explicit and clear description of the data used in experiments when introducing or evaluating computational tools. Without the exact data set that was fed into computational tools in experiments, any mistaken preparation of the data for later experiments may lead to discrepancy in conclusion. The NCTU BioInfo Archive is a new web-based bioinformatic data archive. It serves as a test bed for evaluating computational tools, as a bridge to link other research communities with bioinformatics, and also as an environment full of motivations and possibilities to encourage more exploratory research in bioinformatics., Availability: http://bioinfo.cis.nctu.edu.tw

Motivation: Computer-assisted methods are essential for the analysis of biosequences. Gene activity is regulated in part by the binding of regulatory molecules (transcription factors) to combinations of short motifs. The goal of our analysis is the development of algorithms to identify regulatory motifs and to predict the activity of combinations of those motifs., Approach: Our research begins with a new motif-finding method, using multiple objective functions and an improved stochastic iterative sampling strategy. Combinatorial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. The hypothesis is generated by applying standard inductive learning algorithms., Results: Tests using 10 previously identified regulons from budding yeast and 14 artificial families of sequences demonstrated the effectiveness of the new motif-finding method. Motif combination and classification approaches were used in the analysis of a sample DNA array data set derived from genome-wide gene expression analysis., Availability: Programs will be available as executable files upon request., Contact: yhu@ics.uci.eduor yhu@cse.ttu.edu.tw