Influence of NiO nano-flakes dispersion on the viscosity of lubricating oil

Protecting interacting surfaces of mechanical systems against friction and wear have a wide range of industrial applications. Viscosity is the supreme property of any lubricant which overpowers viscous drag in hydro-dynamically lubricated mechanical systems. The dispersion stability of NiO-nanolubricants is achieved by ultrasonication technique. The detailed study of the viscosity of NiO nano-flakes dispersed in SN500 lubricants with weight fraction of 0.25-1.5% was performed in the temperature between 40-90 °C. The results show that increasing the weight fraction of NiO nano-flakes resulted in consistence viscosity increment. Further, the measured viscosity is compared with different concentration and temperature dependent theoretical models. On the basis on experimental viscosity data a theoretical correlation is recommended to predict the viscosity of NiO-nanolubricants with less than 5% margin of deviation.   Bull. Chem. Soc. Ethiop. 2020, 34(1), 203-214. DOI: https://dx.doi.org/10.4314/bcse.v34i1.1

A semi-parametric Bayesian model for unsupervised differential co-expression analysis

<p>Abstract</p> <p>Background</p> <p>Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples.</p> <p>Results</p> <p>We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ER<it>α </it>regulatory network.</p> <p>Conclusions</p> <p>We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.</p

COVID-19 susceptibility variants associate with blood clots, thrombophlebitis and circulatory diseases.

Epidemiological studies suggest that individuals with comorbid conditions including diabetes, chronic lung, inflammatory and vascular disease, are at higher risk of adverse COVID-19 outcomes. Genome-wide association studies have identified several loci associated with increased susceptibility and severity for COVID-19. However, it is not clear whether these associations are genetically determined or not. We used a Phenome-Wide Association (PheWAS) approach to investigate the role of genetically determined COVID-19 susceptibility on disease related outcomes. PheWAS analyses were performed in order to identify traits and diseases related to COVID-19 susceptibility and severity, evaluated through a predictive COVID-19 risk score. We utilised phenotypic data in up to 400,000 individuals from the UK Biobank, including Hospital Episode Statistics and General Practice data. We identified a spectrum of associations between both genetically determined COVID-19 susceptibility and severity with a number of traits. COVID-19 risk was associated with increased risk for phlebitis and thrombophlebitis (OR = 1.11, p = 5.36e-08). We also identified significant signals between COVID-19 susceptibility with blood clots in the leg (OR = 1.1, p = 1.66e-16) and with increased risk for blood clots in the lung (OR = 1.12, p = 1.45 e-10). Our study identifies significant association of genetically determined COVID-19 with increased blood clot events in leg and lungs. The reported associations between both COVID-19 susceptibility and severity and other diseases adds to the identification and stratification of individuals at increased risk, adverse outcomes and long-term effects

Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

BACKGROUND: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework. RESULTS: We present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available at . CONCLUSION: We use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment

"Erlebnisgarten für Menschen mit einer Behinderung": Erstellung eines Konzepts auf der Grundlage einer Bedürfnisanalyse: Projektbericht

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Robotic Guided Minimally Invasive Spine Surgery

Minimally invasive spine surgery (MISS) continues to evolve, and the advent of robotic spine technology may play a role in further facilitating MISS techniques, increasing safety, and improving patient outcomes. In this chapter we review early limitations of spinal robotic systems and go over currently available spinal robotic systems. We then summarize the evidence-based advantages of robotic spine surgery, with an emphasis on pedicle screw placement. Additionally, we review some common and expanded clinical applications of robotic spine technology to facilitate MISS. The chapter concludes with a discussion regarding the current limitations and future directions of this relatively novel technology as it applies to MISS

Differential decay of human faecal Bacteroides in marine and freshwater

Genetic markers from Bacteroides and other faecal bacteria are being tested for inclusion in regulations to quantify aquatic faecal contamination and estimate public health risk. For the method to be used quantitatively across environments, persistence and decay of markers must be understood. We measured concentrations of contaminant molecular markers targeting Enterococcus and Bacteroides spp. in marine and freshwater microcosms spiked with human sewage and exposed to either sunlight or dark treatments. We used Bayesian statistics with a delayed Chick-Watson model to estimate kinetic parameters for target decay. DNA-and RNA-based targets decayed at approximately the same rate. Molecular markers persisted (could be detected) longer in marine water. Sunlight increased the decay rates of cultured indicators more than those of molecular markers; sunlight also limited persistence of molecular markers. Within each treatment, Bacteroides markers had similar decay profiles, but some Bacteroides markers significantly differed in decay rates. The role of extracellular DNA in persistence appeared unimportant in the microcosms. Because conditions were controlled, microcosms allowed the effects of specific environmental variables on marker persistence and decay to be measured. While marker decay profiles in more complex environments would be expected to vary from those observed here, the differences we measured suggest that water matrix is an important factor affecting quantitative source tracking and microbial risk assessment applications.Keywords: Propidium monoazide, Microbial ecology, Sunlight inactivation, Quantitative PCR, Real time PCR, Bayesian method, 16S ribosomal RNA, Genetic markers, Bacteria, Growth rateKeywords: Propidium monoazide, Microbial ecology, Sunlight inactivation, Quantitative PCR, Real time PCR, Bayesian method, 16S ribosomal RNA, Genetic markers, Bacteria, Growth rat

Chlorine Inactivation of Highly Pathogenic Avian Influenza Virus (H5N1)

To determine resistance of highly pathogenic avian influenza (H5N1) virus to chlorination, we exposed allantoic fluid containing 2 virus strains to chlorinated buffer at pH 7 and 8, at 5°C. Free chlorine concentrations typically used in drinking water treatment are sufficient to inactivate the virus by >3 orders of magnitude

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens