Минералогические и технологические особенности легкоплавкого глинистого сырья Красноярского края

In the last few years, the growing complexity of the electrical power networks, mainly due to the increased use of electronic converters together with the requirements of a higher level of reliability and security, pushed the development of new techniques for the state estimation of the power systems. In this paper, the authors focus their attention on the implementation and experimental validation of a decentralized observer for the state estimation in an electric ship, whose power network is characterized by fast dynamics and by the presence of many electronic devices. The proposed solution implements a decentralized information filter (DIF)

Polysaccharide peptide from Coriolus versicolor induces interleukin 6-related extension of endotoxin fever in rats

Purpose: Polysaccharide peptide (PSP) extracted from the Coriolus versicolor mushroom is frequently suggested as an adjunct to the chemo- or radiotherapy in cancer patients. In a previous study we showed that PSP induced a tumour necrosis factor-a (TNF-a)-dependent anapyrexia-like response in rats. Thus, PSP appears to be a factor which modifies a number of pathophysiological responses. Because of this, PSP is suggested as a potential adjuvant for cancer therapy during which cancer patients frequently contract microbial infections accompanied by fever. The aim of the present study was to investigate whether or not PSP can modulate the course of the fever in response to an antigen such as lipopolysaccharide (LPS). Materials and methods: Body temperature (Tb) of male Wistar rats was measured by biotelemetry. PSP was injected intraperitoneally (i.p.) at a dose of 100mgkg 1, 2 h before LPS administration (50 mgkg 1, i.p.). The levels of interleukin (IL)-6 and TNF-a in the plasma of rats were estimated 3 h and 14 h post-injection of PSP using a standard sandwich ELISA kit. Results: We report that i.p. pre-injection of PSP 2 h before LPS administration expanded the duration of endotoxin fever in rats. This phenomenon was accompanied by a significant elevation of the blood IL-6 level of rats both 3 h and 14 h post-injection of PSP. Pre-treatment i.p. of the rats with anti-IL-6 antibody (30 mg/rat) prevented the PSP-induced prolongation of endotoxin fever. Conclusions: Based on these data, we conclude that PSP modifies the LPS-induced fever in IL-6-related fashion

Flavouring Group Evaluation 76 Revision 2 (FGE.76Rev2): Consideration of sulfur-containing heterocyclic compounds, evaluated by JECFA, structurally related to thiazoles, thiophenes, thiazoline and thienyl derivatives from chemical group 29 and miscellaneous substances from chemical group 30 evaluated by EFSA in FGE.21Rev5

The Panel on Food Additives and Flavourings (FAF) was requested to consider the JECFA evaluations of 28 flavouring substances in the Flavouring Group Evaluation 76 (FGE.76Rev2). Twenty-one of these substances have been considered in FGE.76Rev1. Seven substances could not be evaluated, because of concerns with respect to genotoxicity. New genotoxicity data have been provided for 4-methyl-5-vinylthiazole [FL-no: 15.018] and 4,5-dimethyl-2-isobutyl-3-thiazoline [FL-no: 15.032], which are representative substances of [FL-no: 15.005] and [FL-no: 15.029, 15.030, 15.130 and 15.131], respectively. The Panel concluded that the concern for genotoxicity is ruled out for [FL-no: 15.018 and 15.005]. The concerns for gene mutations and clastogenicity are ruled out for [FL-no: 15.032, 15.029, 15.030, 15.130 and 15.131]. In vitro, [FL-no: 15.032] induced micronuclei through an aneugenic mode of action. The available in vivo micronucleus study was not adequate to rule out the concern for potential aneugenicity in vivo. The Panel compared the lowest concentration resulting in aneugenicity in vitro with the use levels reported for [FL-no: 15.032]. Based on this comparison, the Panel concluded that the use of [FL-no: 15.032] at the maximum reported use levels does not raise a concern for aneugenicity. Based on structural similarity, for the remaining four substances [FL-no: 15.029, 15.030, 15.130 and 15.131], an aneugenic potential may also be anticipated. Individual genotoxicity data are needed to establish whether they have aneugenic potential. The Panel agrees with JECFA conclusions for 24 flavouring substances 'No safety concern at estimated levels of intake as flavouring substances' when based on the MSDI approach. For six substances, more reliable information on uses and use levels are needed to refine the mTAMDI estimates. For 15 substances, use levels are needed to calculate the mTAMDIs. For [FL-no: 15.109 and 15.113], information on the actual stereochemical composition is inadequate and the conclusion reached for the named substances cannot be applied to the materials of commerce

Scientific Guidance on the data required for the risk assessment of flavourings to be used in or on foods

Following a request from the European Commission, EFSA developed a new scientific guidance to assist applicants in the preparation of applications for the authorisation of flavourings to be used in or on foods. This guidance applies to applications for a new authorisation as well as for a modification of an existing authorisation of a food flavouring, submitted under Regulation (EC) No 1331/2008. It defines the scientific data required for the evaluation of those food flavourings for which an evaluation and approval is required according to Article 9 of Regulation (EC) No 1334/2008. This applies to flavouring substances, flavouring preparations, thermal process flavourings, flavour precursors, other flavourings and source materials, as defined in Article 3 of Regulation (EC) No 1334/2008. Information to be provided in all applications relates to: (a) the characterisation of the food flavouring, including the description of its identity, manufacturing process, chemical composition, specifications, stability and reaction and fate in foods; (b) the proposed uses and use levels and the assessment of the dietary exposure and (c) the safety data, including information on the genotoxic potential of the food flavouring, toxicological data other than genotoxicity and information on the safety for the environment. For the toxicological studies, a tiered approach is applied, for which the testing requirements, key issues and triggers are described. Applicants should generate the data requested in each section to support the safety assessment of the food flavouring. Based on the submitted data, EFSA will assess the safety of the food flavouring and conclude whether or not it presents risks to human health and to the environment, if applicable, under the proposed conditions of use

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70–90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens’ adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention

Involvement of PPAR-γ in the neuroprotective and anti-inflammatory effects of angiotensin type 1 receptor inhibition: effects of the receptor antagonist telmisartan and receptor deletion in a mouse MPTP model of Parkinson's disease

<p>Abstract</p> <p>Background</p> <p>Several recent studies have shown that angiotensin type 1 receptor (AT1) antagonists such as candesartan inhibit the microglial inflammatory response and dopaminergic cell loss in animal models of Parkinson's disease. However, the mechanisms involved in the neuroprotective and anti-inflammatory effects of AT1 blockers in the brain have not been clarified. A number of studies have reported that AT1 blockers activate peroxisome proliferator-activated receptor gamma (PPAR γ). PPAR-γ activation inhibits inflammation, and may be responsible for neuroprotective effects, independently of AT1 blocking actions.</p> <p>Methods</p> <p>We have investigated whether oral treatment with telmisartan (the most potent PPAR-γ activator among AT1 blockers) provides neuroprotection against dopaminergic cell death and neuroinflammation, and the possible role of PPAR-γ activation in any such neuroprotection. We used a mouse model of parkinsonism induced by the dopaminergic neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and co-administration of the PPAR-γ antagonist GW9662 to study the role of PPAR-γ activation. In addition, we used AT1a-null mice lesioned with MPTP to study whether deletion of AT1 in the absence of any pharmacological effect of AT1 blockers provides neuroprotection, and investigated whether PPAR-γ activation may also be involved in any such effect of AT1 deletion by co-administration of the PPAR-γ antagonist GW9662.</p> <p>Results</p> <p>We observed that telmisartan protects mouse dopaminergic neurons and inhibits the microglial response induced by administration of MPTP. The protective effects of telmisartan on dopaminergic cell death and microglial activation were inhibited by co-administration of GW9662. Dopaminergic cell death and microglial activation were significantly lower in AT1a-null mice treated with MPTP than in mice not subjected to AT1a deletion. Interestingly, the protective effects of AT1 deletion were also inhibited by co-administration of GW9662.</p> <p>Conclusion</p> <p>The results suggest that telmisartan provides effective neuroprotection against dopaminergic cell death and that the neuroprotective effect is mediated by PPAR-γ activation. However, the results in AT1-deficient mice show that blockage of AT1, unrelated to the pharmacological properties of AT1 blockers, also protects against dopaminergic cell death and neuroinflammation. Furthermore, the results show that PPAR-γ activation is involved in the anti-inflammatory and neuroprotective effects of AT1 deletion.</p

Shared polygenic risk and causal inferences in amyotrophic lateral sclerosis

Objective To identify shared polygenic risk and causal associations in amyotrophic lateral sclerosis (ALS). Methods Linkage disequilibrium score regression and Mendelian randomization were applied in a large-scale, data-driven manner to explore genetic correlations and causal relationships between >700 phenotypic traits and ALS. Exposures consisted of publicly available genome-wide association studies (GWASes) summary statistics from MR Base and LD-hub. The outcome data came from the recently published ALS GWAS involving 20,806 cases and 59,804 controls. Multivariate analyses, genetic risk profiling, and Bayesian colocalization analyses were also performed. Results We have shown, by linkage disequilibrium score regression, that ALS shares polygenic risk genetic factors with a number of traits and conditions, including positive correlations with smoking status and moderate levels of physical activity, and negative correlations with higher cognitive performance, higher educational attainment, and light levels of physical activity. Using Mendelian randomization, we found evidence that hyperlipidemia is a causal risk factor for ALS and localized putative functional signals within loci of interest. Interpretation Here, we have developed a public resource () which we hope will become a valuable tool for the ALS community, and that will be expanded and updated as new data become available. Shared polygenic risk exists between ALS and educational attainment, physical activity, smoking, and tenseness/restlessness. We also found evidence that elevated low-desnity lipoprotein cholesterol is a causal risk factor for ALS. Future randomized controlled trials should be considered as a proof of causality. Ann Neurol 2019;85:470-481Peer reviewe

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

dentification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 x 10(-8)) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.Peer reviewe