Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep learning models using auxiliary hidden layers. Scalable MCMC is available for network training and inference. SDA provides a number of computational advantages over traditional algorithms, such as avoiding backtracking, local modes and can perform optimization with stochastic gradient descent (SGD) in TensorFlow. Standard deep neural networks with logit, ReLU and SVM activation functions are straightforward to implement. To illustrate our architectures and methodology, we use P\'{o}lya-Gamma logit data augmentation for a number of standard datasets. Finally, we conclude with directions for future research

Financial Risks of Russian Oil Companies in Conditions of Volatility of Global Oil Prices

The development of scientific approaches to assessing and diagnosing the financial risks of oil industry in the Russian Federation becomes a high priority task in conditions of high level of volatility in oil prices in the world energy market and preservation of sanctions regime. The article shows the main threats to financial stability of oil companies in Russia. Using cluster analysis, a system of indicators is proposed that determines the level of financial risk of oil companies in Russia. Based on the method of expert assessments and fuzzy sets, the classification of financial risk levels of oil industry is proposed. The integrated financial risk level of oil industry was calculated and scenarios of its development for 2018–2020 were forecast by means of regression modeling. The system of measures to improve the stability of oil companies and prevent functional financial risks is argued. The practical implementation of research results will be the basis for timely diagnosis of financial risks and qualitative development of preventive measures to neutralize them in the oil industry of Russia. Keywords: Oil Industry, Oil Companies, Financial Risks, Oil Prices, Financial Stability of Oil Industry JEL Classifications: Q43; Q41; G32; L52 DOI: https://doi.org/10.32479/ijeep.735

Study of the resistome of human microbial communities using a targeted panel of antibiotic resistance genes in COVID-19 patients

Aim. To study overall drug resistance genes (resistome) in the human gut microbiome and the changes in these genes during COVID-19 in-hospital therapy. Materials and methods. A single-center retrospective cohort study was conducted. Only cases with laboratory-confirmed SARS-CoV-2 RNA using polymerase chain reaction in oro-/nasopharyngeal swab samples were subject to analysis. The patients with a documented history of or current comorbidities of the hepatobiliary system, malignant neoplasms of any localization, systemic and autoimmune diseases, as well as pregnant women were excluded. Feces were collected from all study subjects for subsequent metagenomic sequencing. The final cohort was divided into two groups depending on the disease severity: mild (group 1) and severe (group 2). Within group 2, five subgroups were formed, depending on the use of antibacterial drugs (ABD): group 2A (receiving ABD), group 2AC (receiving ABD before hospitalization), group 2AD (receiving ABD during hospitalization), group 2AE (receiving ABD during and before hospitalization), group 2B (not receiving ABD). Results. The median number of antibiotic resistance (ABR) genes (cumulative at all time points) was significantly higher in the group of patients treated with ABD: 81.0 (95% CI 73.8–84.5) vs. 51.0 (95% CI 31.1–68.4). In the group of patients treated with ABD (2A), the average number of multidrug resistance genes (efflux systems) was significantly higher than in controls (group 2B): 47.0 (95% CI 46.0–51.2) vs. 21.5 (95% CI 7.0–43.9). Patients with severe coronavirus infection tended to have a higher median number of ABR genes but without statistical significance. Patients in the severe COVID-19 group who did not receive ABD before and during hospitalization also had more resistance genes than the patients in the comparison group. Conclusion. This study demonstrated that fewer ABR genes were identified in the group with a milder disease than in the group with a more severe disease associated with more ABR genes, with the following five being the most common: SULI, MSRC, ACRE, EFMA, SAT

Capacity of ceruloplasmin to scavenge products of the respiratory burst of neutrophils is not altered by the products of reactions catalyzed by myeloperoxidase

Ceruloplasmin (CP) is a copper-containing ferroxidase of blood plasma, acting as an acute phase reactant during inflammation. The effect of oxidative modification of CP induced by oxidants produced by myeloperoxidase, such as HOCl, HOBr and HOSCN, on its spectral, enzymatic and anti-inflammatory properties was studied. We monitored chemiluminescence of lucigenin and luminal along with fluorescence of hydroethidine and scopoletin to assay the inhibition by CP of the neutrophilic respiratory burst induced by phorbol 12-myristate 13-acetate (PMA) or formyl-methionyl-leucyl-phenylalanine (fMLP). Superoxide dismutase activity of CP and its capacity to reduce the production of oxidants in respiratory burst of neutrophils remained virtually unchanged upon modifications caused by HOCl, HOBr and HOSCN. Meanwhile, the absorption of type I copper ions at 610 nm became reduced along with a drop of the ferroxidase and amino oxidase activities of CP. Likewise its inhibitory effect on halogenating activity of myeloperoxidase was diminished. Sera of either healthy donors or patients with Wilson disease were co-incubated with neutrophils from healthy volunteers. In these experiments, we observed a reverse correlation between the content of CP in sera and the rate of hydrogen peroxide production by activated neutrophils. In conclusion, CP is likely to play a role of an anti-inflammatory factor tempering the neutrophil respiratory burst in the bloodstream despite the MPO-mediated oxidative modifications.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Interaction of extended loop 882-892 of Cp with Mpo.

<p>Cp is shown in blue, heavy chain of Mpo in palegreen, light chain of Mpo in pink. Copper ions are shown as orange spheres. Heme is shown in violet, ferrous is shown in gray sphere. (<b>A</b>) Overall view of Cp monomer contact with Mpo monomer. Selected contact is indicated by dash-line oval (<b>B</b>). Zoom of contact area of Cp with the entrance of heme pocket of Mpo. Cp is shown in cartoon; Mpo is shown in cartoon and semi-transparent surface representation. Ends of loop between domain 5 and 6 are marked. <b>(C)</b> Inhibition of ABTS-peroxidase activity of Mpo by synthetic peptide RPYLKVFNPR corresponding to the 883-892 fragment of Cp sequence.</p

Scheme of influence of Cp, Mpo, Lf on functions of each other due to interactions.

<p>Inhibition is shown by thin black arrows, activation or protection of function is shown by bold grey arrows. *Substrates of Mpo: o-DA, 4-chloro-1-naphol, tetramethylbenzidine, ABTS; **Substrates of Cp: <i>o</i>-DA, <i>p</i>-PD, DOPA.</p

Contact areas of Cp with Mpo near 3,4 and 5 domains of Cp.

<p>(<b>A</b>) Contact area near N-terminal of Mpo light-chain and loop 699-720 of Cp. (<b>B</b>) Contact of 699-720 loop of Cp with symmetrical monomer of Mpo. Symmetrical molecule of Cp is not shown for clearance. (<b>C</b>) Contact area near 542-557 and 618-624 loops of Cp. (<b>D</b>) Contact area near <i>p-</i>PD site of Cp (domain 4). Residues M668, W669 and H667 are shown in stick representation.</p

Overall parameters obtained by SAXS.

<p>R_g, D_max and V_p are, respectively, radius of gyration, maximum size and excluded particle volume obtained from experimental SAXS profiles. MM_est is the molecular mass estimated from the excluded volume (using empiric factor of 0.625) and MM_mon is the molecular mass expected from monomeric sequence (or equimolar monomeric assemblies in case of binary and ternary complexes) with glycosylation taken into account. Oligomeric state is defined by the ratio of MM_est to MM_mon. χ is discrepancy between the experimental SAXS data and the curve computed by CRYSOL from the rigid body model of corresponding construct with tentatively added carbohydrates.</p