Gd-Bi-M-Ce-O (M = Cu,Zr, Ni,Co, Mn) ceria-based solid solutions for low temperature CO oxidation

Ceria based solid solutions doped by Gd, Bi, and the third dopant were synthesized by the co-precipitation method with ultrasonic treatment, followed by calcination at the temperature of 500°С. Characterization of the synthesized nanosystems by XRD, TEM, TG, Raman spectroscopy, FTIR, XPS was carried out. It is shown that all obtained powders of solid solutions crystallized into a cubic structure of the fluorite type, with an average particle size of 5–15 nm. The samples had a mesoporous structure of the pore diameter of 2–5 nm. The catalytic activity of Gd-Bi-M-Ce-O (M = Cu, Zr, Ni, Co, Mn) solid solutions was carried out. The catalyst Gd_0.05Bi_0.15Mn_0.20Ce_0.60O₂ has the lowest oxidation temperature.     

From miRNA Target Gene Network to miRNA Function: miR-375 Might Regulate Apoptosis and Actin Dynamics in the Heart Muscle via Rho-GTPases-Dependent Pathways

MicroRNAs (miRNAs) are short, single-stranded, non-coding ribonucleic acid (RNA) molecules, which are involved in the regulation of main biological processes, such as apoptosis or cell proliferation and differentiation, through sequence-specific interaction with target mRNAs. In this study, we propose a workflow for predicting miRNAs function by analyzing the structure of the network of their target genes. This workflow was applied to study the functional role of miR-375 in the heart muscle (myocardium), since this miRNA was previously shown to be associated with heart diseases, and data on its function in the myocardium are mostly unclear. We identified PIK3CA, RHOA, MAPK3, PAFAH1B1, CTNNB1, MYC, PRKCA, ERBB2, and CDC42 as key genes in the miR-375 regulated network and predicted the possible function of miR-375 in the heart muscle, consisting mainly in the regulation of the Rho-GTPases-dependent signaling pathways. We implemented our algorithm for miRNA function prediction into a Python module, which is available at GitHub.     

Dependence of Fluid Catalytic Cracking Unit Performance on H-Oil Severity,Catalyst Activity,and Coke Selectivity

The effect of the quality of ebullated bed vacuum residue H-Oil hydrocracking gas oils cracked in a commercial fluid catalytic cracking unit (FCCU) on its performance was studied. Six different catalysts were employed in this study. Four catalysts were tested in a commercial FCCU, and two in a laboratory FCCU. An increase of the H-Oil hydrocracker reaction temperature was associated with a decrease in the K_W factor of the H-Oil gas oils. The diminished K_W factor of H-Oil gas oils resulted in lower FCCU conversion and higher regenerator temperatures. The FCC conversion at maximum gasoline yield is best predicted by the feed K_W factor. The higher-activity, higher-Δcoke catalyst is unfavorable for FCCU performance because the excessive regenerator temperature excursions require reduction of the throughput.     

Experimental Fuzzy/Split-Range Control: Novel Strategy for Biodiesel Batch Reactor Temperature Control

With respect to environmental concerns, biofuels have been used around the world, such as biodiesel, and any improvement in the biodiesel production process is desirable. This paper proposes three different control systems based on fuzzy logic and output control signal split-range. Regulating reaction temperature with low settling time and process variable oscillation are the process control aims, and smooth output control signal for actuators protection is also desired. An automated biodiesel production prototype was used and the experimental results demonstrate that a fuzzy/split-range structure with three input variables showed the best performance, resulting in process variable regulation in set-point value with small settling time and actuators premature wearing prevention.     

Determination of contact points between workpiece and fixture elements as a tool for augmented reality in fixture design

Wireless Networks - Fixtures play a significant role in ensuring the quality of products in manufacturing engineering. The paper considers fork-type parts that have a complex spatial configuration...     

Analysis of complexity indices for classification problems: Cancer gene expression data

Currently, cancer diagnosis at a molecular level has been made possible through the analysis of gene expression data. More specifically, one usually uses machine learning (ML) techniques to build, from cancer gene expression data, automatic diagnosis models (classifiers). Cancer gene expression data often present some characteristics that can have a negative impact in the generalization ability of the classifiers generated. Some of these properties are data sparsity and an unbalanced class distribution. We investigate the results of a set of indices able to extract the intrinsic complexity information from the data. Such measures can be used to analyze, among other things, which particular characteristics of cancer gene expression data mostly impact the prediction ability of support vector machine classifiers. In this context, we also show that, by applying a proper feature selection procedure to the data, one can reduce the influence of those characteristics in the error rates of the classifiers induced.     

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis

To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.