Навчальна програма з дисципліни "Геометричне моделювання інженерних об'єктів"

Вивчення дисципліни “Геометричне моделювання інженерних об'єктів” це одна з важливих складових дисциплін підготовки спеціалістів напрямку “Комп’ютерні науки” включена до списку професійно – орієнтованих дисциплін. Студентам спеціальності «Інформаційні технології проектування» дисципліна викладається у 2-му семестрі, що дає можливість використовувати одержані знання, практичні навички при виконанні курсових робіт за дисциплінами з напрямків «Теорія механізмів і машин», «Деталі машин», «Основи автоматизованого проектування». Таким чином з молодших курсів студенти отримують необхідні знання та практичні навички що знадобляться їм при виконанні бакалаврських та дипломних робіт. Дисципліна розвиває просторову уяву студента, дає теоретичну основу геометричного моделювання, навички створення збірок, а також навички виконання креслень різноманітних інженерних об’єктів. Цей курс має бути вступним при наданні інженерної освіти, має інженерну спрямованість

A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles.

Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish 'direct' versus 'indirect' TF interactions. We applied four algorithms to measure 'direct' dependence to a compendium of 367 mouse haematopoietic TF ChIP-seq samples and obtained a consensus network known as a 'TF association network' where edges in the network corresponded to likely causal pairwise relationships between TFs. The 'TF association network' illustrates the role of TFs in developmental pathways, is reminiscent of combinatorial TF regulation, corresponds to known protein-protein interactions and indicates substantial TF-binding reorganization in leukemic cell types. With the rapid increase in TF ChIP-Seq data sets, the approach presented here will be a powerful tool to study transcriptional programmes across a wide range of biological systems.Bloodwise, the Biotechnology and Biological Sciences Research Council, the Leukaemia and Lymphoma Society, Cancer Research UK, the National Institute for Health Research Cambridge Biomedical Research Centre, and the Wellcome Trust and MRC Cambridge Institute for Medical Research and Wellcome Trust—Medical Research Council Cambridge Stem Cell Institute; Yousef Jameel scholarship awarded by the Cambridge Commonwealth, European and International Trust (to F.S.L.N.).This is the final version of the article. It first appeared from Oxford University Press via https://doi.org/10.1093/bib/bbw10

TRES predicts transcription control in embryonic stem cells.

SUMMARY: Unraveling transcriptional circuits controlling embryonic stem cell maintenance and fate has great potential for improving our understanding of normal development as well as disease. To facilitate this, we have developed a novel web tool called 'TRES' that predicts the likely upstream regulators for a given gene list. This is achieved by integrating transcription factor (TF) binding events from 187 ChIP-sequencing and ChIP-on-chip datasets in murine and human embryonic stem (ES) cells with over 1000 mammalian TF sequence motifs. Using 114 TF perturbation gene sets, as well as 115 co-expression clusters in ES cells, we validate the utility of this approach. AVAILABILITY AND IMPLEMENTATION: TRES is freely available at http://www.tres.roslin.ed.ac.uk. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work was supported by a University of Edinburgh Chancellors Fellowship awarded to AJ and strategic funding from the BBSRC. CP was funded by the Scottish Government through the Strategic Partnership for Animal Science Excellence (SPASE). The Gottgens’ lab is supported by LLR, the MRC, BBSRC, Cancer Research UK, and Wellcome Trust core support to the Cambridge Institute for Medical Research and Wellcome Trust–MRC Cambridge Stem Cell Institute.This version is the author accepted manuscript. The published advanced access version can be viewed on the journals website at: http://bioinformatics.oxfordjournals.org/content/early/2014/06/23/bioinformatics.btu399.full.pdf+htm

Comparison of automated and human assignment of MeSH terms on publicly-available molecular datasets

AbstractPublicly available molecular datasets can be used for independent verification or investigative repurposing, but depends on the presence, consistency and quality of descriptive annotations. Annotation and indexing of molecular datasets using well-defined controlled vocabularies or ontologies enables accurate and systematic data discovery, yet the majority of molecular datasets available through public data repositories lack such annotations. A number of automated annotation methods have been developed; however few systematic evaluations of the quality of annotations supplied by application of these methods have been performed using annotations from standing public data repositories. Here, we compared manually-assigned Medical Subject Heading (MeSH) annotations associated with experiments by data submitters in the PRoteomics IDEntification (PRIDE) proteomics data repository to automated MeSH annotations derived through the National Center for Biomedical Ontology Annotator and National Library of Medicine MetaMap programs. These programs were applied to free-text annotations for experiments in PRIDE. As many submitted datasets were referenced in publications, we used the manually curated MeSH annotations of those linked publications in MEDLINE as “gold standard”. Annotator and MetaMap exhibited recall performance 3-fold greater than that of the manual annotations. We connected PRIDE experiments in a network topology according to shared MeSH annotations and found 373 distinct clusters, many of which were found to be biologically coherent by network analysis. The results of this study suggest that both Annotator and MetaMap are capable of annotating public molecular datasets with a quality comparable, and often exceeding, that of the actual data submitters, highlighting a continuous need to improve and apply automated methods to molecular datasets in public data repositories to maximize their value and utility

Separation and Artificial Maturation of Macerals from Type II Kerogen

Immature Type II kerogen (HI= 660 mg/g) from the Lower Toarcian of the Paris Basin was separated into an alginite concentrate (HI = 952 mg/g) and an amorphous organic matter (AOM) concentrate (HI = 573 mg/g) by density centrifugation. The flash pyrolyzate of the alginite is characterized by high relative concentrations of several series of n-alkanones and n-alkenones (including mid-chain alkyl ketones), in addition to n-alkanes, n-alk-1-enes and n-alkadienes. To our knowledge, this Toarcian alginite is the oldest example of marine organic matter whose pyrolyzate contains mid-chain alkanones in such high relative concentrations. In sharp contrast, the AOM produced predominantly alkylbenzenes, alkylthiophenes, n-alkanes and n-alk-1-enes upon pyrolysis. Micro-FTIR spectroscopy indicated that the alginite was enriched in aliphatic C-H (particularly CH2) and depleted in aromatic C=C, relative to the AOM, consistent with the pyrolysis results. Aliquots of the concentrates were heated separately in gold tubes (24 h, 70 MPa) at fixed temperatures ranging between 250 and 375°C. Yields of liquid products as a function of temperature were initially greater for the AOM, reaching a maximum at 325°C. In contrast, the alginite yielded little liquid product at low temperatures, attaining its maximum at 350°C, at which temperature its yield greatly surpassed that of the AOM. This kerogen is a heterogeneous assemblage of fossil organic matter, exhibiting different degrees of preservation and petroleum potential. The alginite is fossilized marine algaenans with alkyl chains cross-linked by ether bridges, while the AOM component is at least in part a geopolymer with thioether linkages, the thermally labile nature of which is responsible for its lower temperature peak liquid generation. It is evident that the alginite concentrate is chemically distinct from its companion AOM in this kerogen and that the full extent of its uniqueness would not have been revealed without the density separation step

Features of teaching Russian as a foreign language on the basis of local history texts

The paper deals with topical issues of teaching Russian as a foreign language (RAFL) and the peculiarities of teaching, taking into account the Linguistic-cultural component. Linguistic-cultural component is considered as materials on regional studies, local history, history, culture and the basics of legislation. Mastering this material will allow students of RAFL courses to master a wide range of background knowledge about the country, traditions, etc. A typology of textbooks on RAFL is given. The most frequently used textbooks on RAFL are examined from the point of view of the material containing a linguistic-cultural component and features that must be taken into account when working with local history material

Integrative Approach to Pain Genetics Identifies Pain Sensitivity Loci across Diseases

Identifying human genes relevant for the processing of pain requires difficult-to-conduct and expensive large-scale clinical trials. Here, we examine a novel integrative paradigm for data-driven discovery of pain gene candidates, taking advantage of the vast amount of existing disease-related clinical literature and gene expression microarray data stored in large international repositories. First, thousands of diseases were ranked according to a disease-specific pain index (DSPI), derived from Medical Subject Heading (MESH) annotations in MEDLINE. Second, gene expression profiles of 121 of these human diseases were obtained from public sources. Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes. Finally, selected candidate pain genes were genotyped in an independent human cohort and prospectively evaluated for significant association between variants and measures of pain sensitivity. The strongest signal was with rs4512126 (5q32, ABLIM3, P = 1.3×10−10) for the sensitivity to cold pressor pain in males, but not in females. Significant associations were also observed with rs12548828, rs7826700 and rs1075791 on 8q22.2 within NCALD (P = 1.7×10−4, 1.8×10−4, and 2.2×10−4 respectively). Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes. This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates

DNMT3A Loss Drives Enhancer Hypomethylation in FLT3-ITD-Associated Leukemias.

DNMT3A, the gene encoding the de novo DNA methyltransferase 3A, is among the most frequently mutated genes in hematologic malignancies. However, the mechanisms through which DNMT3A normally suppresses malignancy development are unknown. Here, we show that DNMT3A loss synergizes with the FLT3 internal tandem duplication in a dose-influenced fashion to generate rapid lethal lymphoid or myeloid leukemias similar to their human counterparts. Loss of DNMT3A leads to reduced DNA methylation, predominantly at hematopoietic enhancer regions in both mouse and human samples. Myeloid and lymphoid diseases arise from transformed murine hematopoietic stem cells. Broadly, our findings support a role for DNMT3A as a guardian of the epigenetic state at enhancer regions, critical for inhibition of leukemic transformation.L.Y. is funded by the Robert and Janice McNair Foundation as an MD/PhD McNair Scholar. This project was funded by CPRIT (RP110028, RP110471 and RP150292 ), the NIH (DK092883 and HG007538), and the Samuel Waxman Cancer Research Foundation. We also thank the Cytometry and Cell Sorting and Genomic and RNA Profiling Cores (NCI P30CA125123, P30 AI036211, P30 CA125123, and S10 RR024574 ) at Baylor College of Medicine. Authors declare no conflicts of interest.This is the author accepted manuscript. The final version is available from Cell Press via http://dx.doi.org/10.1016/j.ccell.2016.05.00