Uncommon protein half-life prediction with noise.

<p>Uncommon protein half-life prediction with noise.</p

Artificial neural network between the protein properties and clusters.

<p>Artificial neural network between the protein properties and clusters.</p

Coefficients of the linear regression between the common liver protein half-life in the tissue and cell of each cluster (Fig 4).

<p>Coefficients of the linear regression between the common liver protein half-life in the tissue and cell of each cluster (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180428#pone.0180428.g004" target="_blank">Fig 4</a>).</p

Performance analysis of the protein half-life prediction of the model with two types of protein properties (e.g. PCH, ACH) using two-thirds (e.g. ()) of total data sets (common liver proteins) of each cluster.

<p>All protein properties are designated by ACH, and positively-correlated proteins are designated by PCH. The best result provided is the <b>C</b>_<b>2</b> cluster. It has predicted 44% of protein half-lives between 10% deviation from the experimental value.</p

Linear regression between the half-lives of proteins present in the murine liver tissue and cell culture (e.g. NIH3T3[2]) data sets.

<p>Linear regression between the half-lives of proteins present in the murine liver tissue and cell culture (e.g. NIH3T3[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180428#pone.0180428.ref002" target="_blank">2</a>]) data sets.</p

Common liver protein subset with the highest correlation coefficient between the protein half-life in the tissue and cells.

<p>Common liver protein subset with the highest correlation coefficient between the protein half-life in the tissue and cells.</p

Tobacco use prevalence for DYRAD

The data file provides the prevalence of tobacco use and cessation training indicators by countries surveyed in GHPSS and six WHO regions as well as aggregate estimates for WHO regions reported by gender for four health professions disciplines namely medicine, dentistry, nursing and pharmacy

Gaussian Process Modeling of Protein Turnover

We describe a stochastic model to compute in vivo protein turnover rate constants from stable-isotope labeling and high-throughput liquid chromatography–mass spectrometry experiments. We show that the often-used one- and two-compartment nonstochastic models allow explicit solutions from the corresponding stochastic differential equations. The resulting stochastic process is a Gaussian processes with Ornstein–Uhlenbeck covariance matrix. We applied the stochastic model to a large-scale data set from ¹⁵N labeling and compared its performance metrics with those of the nonstochastic curve fitting. The comparison showed that for more than 99% of proteins, the stochastic model produced better fits to the experimental data (based on residual sum of squares). The model was used for extracting protein-decay rate constants from mouse brain (slow turnover) and liver (fast turnover) samples. We found that the most affected (compared to two-exponent curve fitting) results were those for liver proteins. The ratio of the median of degradation rate constants of liver proteins to those of brain proteins increased 4-fold in stochastic modeling compared to the two-exponent fitting. Stochastic modeling predicted stronger differences of protein turnover processes between mouse liver and brain than previously estimated. The model is independent of the labeling isotope. To show this, we also applied the model to protein turnover studied in induced heart failure in rats, in which metabolic labeling was achieved by administering heavy water. No changes in the model were necessary for adapting to heavy-water labeling. The approach has been implemented in a freely available R code

Gaussian Process Modeling of Protein Turnover

We describe a stochastic model to compute in vivo protein turnover rate constants from stable-isotope labeling and high-throughput liquid chromatography–mass spectrometry experiments. We show that the often-used one- and two-compartment nonstochastic models allow explicit solutions from the corresponding stochastic differential equations. The resulting stochastic process is a Gaussian processes with Ornstein–Uhlenbeck covariance matrix. We applied the stochastic model to a large-scale data set from ¹⁵N labeling and compared its performance metrics with those of the nonstochastic curve fitting. The comparison showed that for more than 99% of proteins, the stochastic model produced better fits to the experimental data (based on residual sum of squares). The model was used for extracting protein-decay rate constants from mouse brain (slow turnover) and liver (fast turnover) samples. We found that the most affected (compared to two-exponent curve fitting) results were those for liver proteins. The ratio of the median of degradation rate constants of liver proteins to those of brain proteins increased 4-fold in stochastic modeling compared to the two-exponent fitting. Stochastic modeling predicted stronger differences of protein turnover processes between mouse liver and brain than previously estimated. The model is independent of the labeling isotope. To show this, we also applied the model to protein turnover studied in induced heart failure in rats, in which metabolic labeling was achieved by administering heavy water. No changes in the model were necessary for adapting to heavy-water labeling. The approach has been implemented in a freely available R code

The HIV care cascade: Japanese perspectives

<div><p>Japan has been known as a low HIV-prevalence country with a concentrated epidemic among high-risk groups. However, it has not been determined whether Japan meets the 90-90-90 goals set by the Joint United Nations Programme on HIV/AIDS (UNAIDS)/World Health Organization (WHO). Moreover, to date, the HIV care cascade has not been examined. We estimated the total number of diagnosed people living with HIV/AIDS (PLWHA) (n = 22,840) based on legal reports to the Ministry of Health, Labour and Welfare by subtracting the number of foreigners who left Japan (n = 2,273) and deaths (n = 2,321) from the cumulative diagnosis report (n = 27,434). The number of total undiagnosed PLWHA was estimated by age and sex specific HIV-positive rates observed among first-time blood donors between 2011–2015 in Japan. Our estimates show that 14.4% (n = 3,830) of all PLWHA (n = 26,670) were undiagnosed in Japan at the end of 2015. The number of patients retained in care (n = 20,615: 77.3% of PLWHA), the percentage of those on antiretroviral therapy (n = 18,921: 70.9% of PLWHA) and those with suppressed viral loads (<200 copies/mL; n = 18,756: 70.3% of PLWHA) were obtained through a questionnaire survey conducted in the AIDS Core Hospitals throughout the country. According to these estimates, Japan failed to achieve the first two of the three UNAIDS/WHO targets (22,840/26,670 = 85.6% of HIV-positive cases were diagnosed; 18,921/22,840 = 82.8% of those diagnosed were treated; 18,756/18,921 = 99.1% of those treated experienced viral suppression). Although the antiretroviral treatment uptake and success after retention in medical care appears to be excellent in Japan, there are unmet needs, mainly at the surveillance level before patients are retained in care. The promotion of HIV testing and treatment programs among the key affected populations (especially men who have sex with men) may contribute to further decreasing the HIV epidemic and achieving the UNAIDS/WHO targets in Japan.</p></div