Patchy layered structure of tropical troposphere as seen by Indian MST radar

The MST radar observations at Gadanki (13.47° N, 79.18° E) show, almost every day throughout the year, stratified layers of intense reflectivity near the tropopause level (17 km) and also at a couple of levels between 4 km and 10 km. Highest individual reflectivity values occur near 17 km, but they occur for a short while. The region between 11 km and 15 km shows the lowest values of reflectivity alongwith vertical downward motion almost on all days of the year. High values of reflectivity are attributed to the existence of visible or sub-visible clouds; the layered structure of clouds is attributed to inertio-gravity waves with vertical wavelength of 2-3 km. It is suggested that each high reflectivity layer consists mainly of thin sheets and patches of visible and sub-visible cloud material. Hydrometeors inside the cloud material go up and down due to gravity, precipitation-loading, Brunt-Vaisala oscillations, and Kelvin-Helmholtz waves. In these small-scale motions, thin air sheets and patches get formed with sharp temperature and humidity discontinuities through contact cooling, melting, evaporation, condensation and freezing. Also, melting and freezing at low temperatures generate electrical charges in these thin sheets and patches. These thin sheets and patches have vertical dimensions ranging from a few centimetres to several metres and horizontal dimensions of the order of 1km. These thin sheets and patches have corresponding vertical and horizontal discontinuities and sharp gradients in refractive index for the MST radar beam. These show up as regions of high values of reflectivity

Characterization of heterogeneity and spatial distribution of phases in complex solid dispersions by thermal analysis by structural characterization and X-ray micro computed tomography

Purpose: This study investigated the effect of drug-excipient miscibility on the heterogeneity and spatial distribution of phase separation in pharmaceutical solid dispersions at a micron-scale using two novel and complementary characterization techniques, thermal analysis by structural characterization (TASC) and X-ray micro-computed tomography (XCT) in conjunction with conventional characterization methods. Method: Complex dispersions containing felodipine, TPGS, PEG and PEO were prepared using hot melt extrusion-injection moulding. The phase separation behavior of the samples was characterized using TASC and XCT in conjunction with conventional thermal, microscopic and spectroscopic techniques. The in vitro drug release study was performed to demonstrate the impact of phase separation on dissolution of the dispersions. Results: The conventional characterization results indicated the phase separating nature of the carrier materials in the patches and the presence of crystalline drug in the patches with the highest drug loading (30% w/w). TASC and XCT where used to provide insight into the spatial configuration of the separate phases. TASC enabled assessment of the increased heterogeneity of the dispersions with increasing the drug loading. XCT allowed the visualization of the accumulation of phase separated (crystalline) drug clusters at the interface of air pockets in the patches with highest drug loading which led to poor dissolution performance. Semi-quantitative assessment of the phase separated drug clusters in the patches were attempted using XCT. Conclusion: TASC and XμCT can provide unique information regarding the phase separation behavior of solid dispersions which can be closely associated with important product quality indicators such as heterogeneity and microstructure

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data

Multiple micronutrient supplementation improves vitamin B12 and folate concentrations of HIV infected children in Uganda: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>The effect of multiple micronutrient supplementation on vitamin B₁₂and folate has hither to not been reported in African HIV infected children. This paper describes vitamin B₁₂and folate status of Ugandan HIV infected children aged 1-5 years and reports the effect of multiple micronutrient supplementation on serum vitamin B₁₂and folate concentrations.</p> <p>Methods</p> <p>Of 847 children who participated in a multiple micronutrient supplementation trial, 214 were assessed for vitamin B₁₂and folate concentrations pre and post supplementation. One hundred and four children were randomised to two times the recommended dietary allowance (RDA) of a 14 multiple micronutrient supplement (MMS) and 114 to a 'standard of care' supplement of 6 multivitamins (MV). Serum vitamin B₁₂was measured by an electrochemiluminescence immunoassay and folate by a competitive protein-binding assay using Modular E (Roche) automatic analyzer. Vitamin B₁₂concentrations were considered low if less than 221picomoles per litre (pmol/L) and folate if < 13.4 nanomoles per litre (nmol/L). The Wilcoxon Signed Ranks test was used to measure the difference between pre and post supplementation concentrations.</p> <p>Results</p> <p>Vitamin B₁₂was low in 60/214 (28%) and folate in 62/214 (29.0%) children. In the MMS group, the median concentration (IQR) of vitamin B₁₂at 6 months was 401.5 (264.3 - 518.8) pmol/L compared to the baseline of 285.5 (216.5 - 371.8) pmol/L, p < 0.001. The median (IQR) folate concentrations increased from 17.3 (13.5 - 26.6) nmol/L to 27.7 (21.1 - 33.4) nmol/L, p < 0.001. In the 'standard of care' MV supplemented group, the median concentration (IQR) of vitamin B₁₂at 6 months was 288.5 (198.8 - 391.0) pmol/L compared to the baseline of 280.0 (211.5 - 386.3) pmol/L while the median (IQR) folate concentrations at 6 months were 16.5 (11.7 - 22.1) nmol/L compared to 15.7 (11.9 - 22.1) nmol/L at baseline. There was a significant difference in the MMS group in both vitamin B₁₂and folate concentrations but no difference in the MV group.</p> <p>Conclusions</p> <p>Almost a third of the HIV infected Ugandan children aged 1-5 years had low serum concentrations of vitamin B₁₂and folate. Multiple micronutrient supplementation compared to the 'standard of care' supplement of 6 multivitamins improved the vitamin B₁₂and folate status of HIV infected children in Uganda.</p> <p>Trial registration</p> <p><url>http://ClinicalTrials.gov</url><a href="http://www.clinicaltrials.gov/ct2/show/NCT00122941">NCT00122941</a>)</p

Viral hepatitis and HIV-associated tuberculosis: Risk factors and TB treatment outcomes in Thailand

<p>Abstract</p> <p>Background</p> <p>The occurrence of tuberculosis (TB), human immunodeficiency virus (HIV), and viral hepatitis infections in the same patient poses unique clinical and public health challenges, because medications to treat TB and HIV are hepatotoxic. We conducted an observational study to evaluate risk factors for HBsAg and/or anti-HCV reactivity and to assess differences in adverse events and TB treatment outcomes among HIV-infected TB patients.</p> <p>Methods</p> <p>Patients were evaluated at the beginning, during, and at the end of TB treatment. Blood samples were tested for aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin (BR), complete blood count, and CD4+ T lymphocyte cell count. TB treatment outcomes were assessed at the end of TB treatment according to international guidelines.</p> <p>Results</p> <p>Of 769 enrolled patients, 752 (98%) had serologic testing performed for viral hepatitis: 70 (9%) were reactive for HBsAg, 237 (31%) for anti-HCV, and 472 (63%) non-reactive for both markers. At the beginning of TB treatment, 18 (26%) patients with HBsAg reactivity had elevated liver function tests compared with 69 (15%) patients non-reactive to any viral marker (p = 0.02). At the end of TB treatment, 493 (64%) were successfully treated. Factors independently associated with HBsAg reactivity included being a man who had sex with men (adjusted odds ratio [AOR], 2.1; 95% confidence interval [CI], 1.1–4.3) and having low TB knowledge (AOR, 1.8; CI, 1.0–3.0). Factors most strongly associated with anti-HCV reactivity were having injection drug use history (AOR, 12.8; CI, 7.0–23.2) and living in Bangkok (AOR, 15.8; CI, 9.4–26.5). The rate of clinical hepatitis and death during TB treatment was similar in patients HBsAg reactive, anti-HCV reactive, both HBsAg and anti-HCV reactive, and non-reactive to any viral marker.</p> <p>Conclusion</p> <p>Among HIV-infected TB patients living in Thailand, markers of viral hepatitis infection, particularly hepatitis C virus infection, were common and strongly associated with known behavioral risk factors. Viral hepatitis infection markers were not strongly associated with death or the development of clinical hepatitis during TB treatment.</p

A Bayesian method for calculating real-time quantitative PCR calibration curves using absolute plasmid DNA standards

<p>Abstract</p> <p>Background</p> <p>In real-time quantitative PCR studies using absolute plasmid DNA standards, a calibration curve is developed to estimate an unknown DNA concentration. However, potential differences in the amplification performance of plasmid DNA compared to genomic DNA standards are often ignored in calibration calculations and in some cases impossible to characterize. A flexible statistical method that can account for uncertainty between plasmid and genomic DNA targets, replicate testing, and experiment-to-experiment variability is needed to estimate calibration curve parameters such as intercept and slope. Here we report the use of a Bayesian approach to generate calibration curves for the enumeration of target DNA from genomic DNA samples using absolute plasmid DNA standards.</p> <p>Results</p> <p>Instead of the two traditional methods (classical and inverse), a Monte Carlo Markov Chain (MCMC) estimation was used to generate single, master, and modified calibration curves. The mean and the percentiles of the posterior distribution were used as point and interval estimates of unknown parameters such as intercepts, slopes and DNA concentrations. The software WinBUGS was used to perform all simulations and to generate the posterior distributions of all the unknown parameters of interest.</p> <p>Conclusion</p> <p>The Bayesian approach defined in this study allowed for the estimation of DNA concentrations from environmental samples using absolute standard curves generated by real-time qPCR. The approach accounted for uncertainty from multiple sources such as experiment-to-experiment variation, variability between replicate measurements, as well as uncertainty introduced when employing calibration curves generated from absolute plasmid DNA standards.</p

Sequestration of free cholesterol in cell membranes by prions correlates with cytoplasmic phospholipase A2 activation

<p>Abstract</p> <p>Background</p> <p>The transmissible spongiform encephalopathies (TSEs), otherwise known as the prion diseases, occur following the conversion of the normal cellular prion protein (PrP^C) to an alternatively folded isoform (PrP^Sc). The accumulation of PrP^Scwithin the brain leads to neurodegeneration through an unidentified mechanism. Since many neurodegenerative disorders including prion, Parkinson's and Alzheimer's diseases may be modified by cholesterol synthesis inhibitors, the effects of prion infection on the cholesterol balance within neuronal cells were examined.</p> <p>Results</p> <p>We report the novel observation that prion infection altered the membrane composition and significantly increased total cholesterol levels in two neuronal cell lines (ScGT1 and ScN2a cells). There was a significant correlation between the concentration of free cholesterol in ScGT1 cells and the amounts of PrP^Sc. This increase was entirely a result of increased amounts of free cholesterol, as prion infection reduced the amounts of cholesterol esters in cells. These effects were reproduced in primary cortical neurons by the addition of partially purified PrP^Sc, but not by PrP^C. Crucially, the effects of prion infection were not a result of increased cholesterol synthesis. Stimulating cholesterol synthesis via the addition of mevalonate, or adding exogenous cholesterol, had the opposite effect to prion infection on the cholesterol balance. It did not affect the amounts of free cholesterol within neurons; rather, it significantly increased the amounts of cholesterol esters. Immunoprecipitation studies have shown that cytoplasmic phospholipase A₂(cPLA₂) co-precipitated with PrP^Scin ScGT1 cells. Furthermore, prion infection greatly increased both the phosphorylation of cPLA₂and prostaglandin E₂production.</p> <p>Conclusion</p> <p>Prion infection, or the addition of PrP^Sc, increased the free cholesterol content of cells, a process that could not be replicated by the stimulation of cholesterol synthesis. The presence of PrP^Scincreased solubilisation of free cholesterol in cell membranes and affected their function. It increased activation of the PLA₂pathway, previously implicated in PrP^Scformation and in PrP^Sc-mediated neurotoxicity. These observations suggest that the neuropathogenesis of prion diseases results from PrP^Scaltering cholesterol-sensitive processes. Furthermore, they raise the possibility that disturbances in membrane cholesterol are major triggering events in neurodegenerative diseases.</p