다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).Combining 32 genome-wide association studies with high-density imputation provides a comprehensive view of the genetic contribution to type 2 diabetes in individuals of European ancestry with respect to locus discovery, causal-variant resolution, and mechanistic insight.

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

Chemotherapy, radiation therapy, as well as targeted anticancer agents can induce clinically relevant tumor-targeting immune responses, which critically rely on the antigenicity of malignant cells and their capacity to generate adjuvant signals. In particular, immunogenic cell death (ICD) is accompanied by the exposure and release of numerous damage-associated molecular patterns (DAMPs), which altogether confer a robust adjuvanticity to dying cancer cells, as they favor the recruitment and activation of antigen-presenting cells. ICD-associated DAMPs include surface-exposed calreticulin (CALR) as well as secreted ATP, annexin A1 (ANXA1), type I interferon, and high-mobility group box 1 (HMGB1). Additional hallmarks of ICD encompass the phosphorylation of eukaryotic translation initiation factor 2 subunit-α (EIF2S1, better known as eIF2α), the activation of autophagy, and a global arrest in transcription and translation. Here, we outline methodological approaches for measuring ICD markers in vitro and ex vivo for the discovery of next-generation antineoplastic agents, the development of personalized anticancer regimens, and the identification of optimal therapeutic combinations for the clinical management of cancer.

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Detection of immunogenic cell death and its relevance for cancer therapy

Mutation testing realizes the idea of using artificial defects to support testing activities. Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the generation of test cases, and to support experimentation. Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a “mini-handbook”-style roadmap for the application of mutation testing as experimental methodology.

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Mutation Testing Advances: An Analysis and Survey

We can only test software effectively if we under- stand how it is intended to behave. For some categories of programs, such as scientific models, it is not obvious what the output of the software should be. New techniques are needed to help domain experts, such as scientists, gather the knowledge they need to construct suitable tests and oracles. This paper introduces a new interactive tool for iterative test suite construction that is based upon the scientific method paradigm that scientists are familiar with. We apply our technique to a deterministic mathematical model, used to predict the spread of disease, and show how it helps scientists uncover situations they had not yet considered. Of the 15 hypotheses originally created by modellers, our technique found discrepancies in all but one, allowing us to refine them into a more rigorous test suite. Scientific software is particularly difficult to test, because its correct behaviour is not normally known in advance. The software is developed to investigate new research questions and the perceptions of researchers change as their hypotheses are explored (5). If we already knew the answers to these questions, there would be no need to create the software in the first place. Intrinsic difficulties occur in evaluating the output of scientific software, due to the stochasticity and uncertain- ties in the underlying model. Scientific software frequently incorporates complex models with nonlinear dynamics that are difficult to test. Each numerical approximation has the potential to introduce new errors (5), inaccuracies may arise due to the way in which experimental data are collected, and then when the model is implemented on a finite precision computer, some further accuracy is inevitably lost.

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Interactive Tool for Iterative Test Suite Construction

Background Large cell transformation (LCT) of Sezary Syndrome (SS) is a rare phenomenon. To date, there are no rigorous studies identifying risk factors for its development. Objectives Here, we seek to characterize the clinicopathologic risk factors that predispose patients with SS to develop LCT. Methods We retrospectively evaluated all SS patient records available in the Michigan Medicine Cancer Registry from 2010–2021. Clinical and pathologic variables were compared between groups. The Kaplan-Meier method and log-rank test were used to assess overall survival. Results Of 28 SS patients identified, eight patients experienced LCT, and 20 did not (NLCT). Peak lactate dehydrogenase (LDH) before LCT (p = 0.0012), maximum total body surface area (TBSA) involvement before LCT (p = 0.0114), absolute CD8+ cell count measured on flow cytometry at diagnosis of SS (p = 0.0455) and at the most recent blood draw (p = 0.00736), and ulceration on biopsy (p = 0.0034) were significant clinicopathologic variables identified between the SS patients that developed LCT versus those that did not. Conclusions Maximum TBSA involvement, peak LDH, presence of ulceration, and decreased levels of CD8+ cells in the peripheral blood may predict the development of LCT in patients with SS.

/pdf/predictors-of-large-cell-transformation-in-patients-with-2byw1dze.pdf

Predictors of large cell transformation in patients with Sezary Syndrome—A retrospective analysis

Making New Connections-Chromosome Conformation Capture for Identification of Disease-Associated Target Genes.

Scientific software is more difficult to test than many other software products, but scientists are not usually trained in software engineering techniques. Considering how often software is used to produce scientific results, how can we be sure the predictions made from these results are correct? Software engineering techniques should be useful for computational scientists. The problem is they find it difficult to know how to apply domain-independent techniques to the specific problems they face in their work. Nevertheless, we have discovered scientists use their own intuition to reinvent techniques surprisingly similar to those in software engineering. This seems like a good place to start our training.

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Software testing in a scientific research group

http://www.jidonline.org/article/S0022202X23010035/pdf

Matthew Patrick

Papers

Interactive Tool for Iterative Test Suite Construction

Predictors of large cell transformation in patients with Sezary Syndrome—A retrospective analysis

Making New Connections-Chromosome Conformation Capture for Identification of Disease-Associated Target Genes.

Software testing in a scientific research group

818 Multi-condition TWAS for inflammatory skin disorders highlights roles of genetic signals in cytokine-stimulated keratinocytes