Механические свойства и электропроводность холоднодеформированного сплава Al–Y–Sc–Er

Aluminum alloys alloyed with rare earth and transition metal are promising materials for electric energy transportation due to their high properties of strength, thermal stability, and electrical conductivity. The features of strengthening, their mechanical properties and electrical conductivity of Al–0.2Y–0.2Sc–0.3Er alloy after cold rolling have been established. The alloy as a cast structure is presented by aluminum solid solution (Al) and dispersed eutectics with τ2 (Al75-76Er11-17Y7-14) phase upon complete dissolution of scandium in (Al), and a content of yttrium and erbium at the level of 0.2–0.3 % each. Cold rolling the ingot accelerates strengthening upon annealing at 270 and 300 °C, reducing the time of achieving peak hardness. The maximum strengthening due to precipitation of L12 dispersoid of Al3(Sc,Y,Er) phase with the average particle size up to 10 nm is achieved after 7 h of annealing at 300 °C after cold rolling. This shows the prevailing heterogeneous mechanism of nucleation due to defects accumulated during cold rolling which stimulates strengthening. The eutectic particles are located mainly along the boundaries, elongated in the rolling direction. Irrespective of the mode of sheet fabrication, the alloy demonstrates high thermal stability up to 400 °C. During annealing of the sheets to 450 °C, their non-recrystallized structure is retained. Ingot annealing at t = 300 °C in 7 h and cold rolling with subsequent annealing under the same conditions provide a high level of mechanical properties and electrical conductivity: σ0.2 = 194 MPa, σu = 210 MPa, δ = 12.1 % and IACS – 60,1 %. The alloy has demonstrated high yield stress up to 100 h of annealing at t = 300 °C.Алюминиевые сплавы, легированные редкоземельными и переходными металлами, являются перспективными материалами для транспортировки электроэнергии ввиду высоких показателей прочности, термической стабильности и электропроводности. В работе определены особенности упрочнения, механические свойства и электропроводность сплава Al–0,2Y–0,2Sc–0,3Er после холодной прокатки. Литая структура сплава представлена алюминиевым твердым раствором (Al) и дисперсной эвтектикой с фазой τ2 (Al75-76Er11-17Y7-14) при полном растворении скандия в (Al) и содержании иттрия и эрбия на уровне 0,2–0,3 % каждого. Холодная прокатка слитка ускоряет упрочнение при отжиге при температурах 270 и 300 °C, уменьшая время достижения пиковой твердости. Максимальное упрочнение за счет выделения L12-дисперсоидов фазы Al3(Sc,Y,Er) со средним размером частиц до 10 нм достигается после 7 ч отжига при температуре 300 °С после холодной прокатки, что говорит о превалировании гетерогенного механизма зарождения за счет дефектов, накопленных в процессе холодной прокатки, стимулирующих упрочнение. Частицы эвтектики располагаются преимущественно вдоль границ, вытягиваясь в направлении прокатки, и вне зависимости от режима получения листа сплав демонстрирует высокую термическую стабильность до 400 °С. В процессе отжига листов до 450 °С сохраняется нерекристаллизованная структура. Отжиг слитка при t = 300 °С в течение 7 ч и холодная прокатка с последующим отжигом в тех же условиях обеспечивают высокий уровень механических свойств и электропроводности: σ0,2 = 194 МПа, σв = 210 МПа, δ = 12,1 % и IACS – 60,1 %. Сплав продемонстрировал высокую стабильность предела текучести вплоть до 100 ч отжига при t = 300 °С

Disease risk prediction with rare and common variants

A number of studies have been conducted to investigate the predictive value of common genetic variants for complex diseases. To date, these studies have generally shown that common variants have no appreciable added predictive value over classical risk factors. New sequencing technology has enhanced the ability to identify rare variants that may have larger functional effects than common variants. One would expect rare variants to improve the discrimination power for disease risk by permitting more detailed quantification of genetic risk. Using the Genetic Analysis Workshop 17 simulated data sets for unrelated individuals, we evaluate the predictive value of rare variants by comparing prediction models built using the support vector machine algorithm with or without rare variants. Empirical results suggest that rare variants have appreciable effects on disease risk prediction

Effect of BRCA2 sequence variants predicted to disrupt exonic splice enhancers on BRCA2 transcripts

Background: Genetic screening of breast cancer patients and their families have identified a number of variants of unknown clinical significance in the breast cancer susceptibility genes, BRCA1 and BRCA2. Evaluation of such unclassified variants may be assisted by web-based bioinformatic prediction tools, although accurate prediction of aberrant splicing by unclassified variants affecting exonic splice enhancers (ESEs) remains a challenge

Hierarchical Generalized Linear Models for Multiple Groups of Rare and Common Variants: Jointly Estimating Group and Individual-Variant Effects

Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/)

Ionic liquids at electrified interfaces

Until recently, “room-temperature” (<100–150 °C) liquid-state electrochemistry was mostly electrochemistry of diluted electrolytes(1)–(4) where dissolved salt ions were surrounded by a considerable amount of solvent molecules. Highly concentrated liquid electrolytes were mostly considered in the narrow (albeit important) niche of high-temperature electrochemistry of molten inorganic salts(5-9) and in the even narrower niche of “first-generation” room temperature ionic liquids, RTILs (such as chloro-aluminates and alkylammonium nitrates).(10-14) The situation has changed dramatically in the 2000s after the discovery of new moisture- and temperature-stable RTILs.(15, 16) These days, the “later generation” RTILs attracted wide attention within the electrochemical community.(17-31) Indeed, RTILs, as a class of compounds, possess a unique combination of properties (high charge density, electrochemical stability, low/negligible volatility, tunable polarity, etc.) that make them very attractive substances from fundamental and application points of view.(32-38) Most importantly, they can mix with each other in “cocktails” of one’s choice to acquire the desired properties (e.g., wider temperature range of the liquid phase(39, 40)) and can serve as almost “universal” solvents.(37, 41, 42) It is worth noting here one of the advantages of RTILs as compared to their high-temperature molten salt (HTMS)(43) “sister-systems”.(44) In RTILs the dissolved molecules are not imbedded in a harsh high temperature environment which could be destructive for many classes of fragile (organic) molecules

InterMEL: An international biorepository and clinical database to uncover predictors of survival in early-stage melanoma

INTRODUCTION: We are conducting a multicenter study to identify classifiers predictive of disease-specific survival in patients with primary melanomas. Here we delineate the unique aspects, challenges, and best practices for optimizing a study of generally small-sized pigmented tumor samples including primary melanomas of at least 1.05mm from AJTCC TNM stage IIA-IIID patients. We also evaluated tissue-derived predictors of extracted nucleic acids' quality and success in downstream testing. This ongoing study will target 1,000 melanomas within the international InterMEL consortium. METHODS: Following a pre-established protocol, participating centers ship formalin-fixed paraffin embedded (FFPE) tissue sections to Memorial Sloan Kettering Cancer Center for the centralized handling, dermatopathology review and histology-guided coextraction of RNA and DNA. Samples are distributed for evaluation of somatic mutations using next gen sequencing (NGS) with the MSK-IMPACTTM assay, methylation-profiling (Infinium MethylationEPIC arrays), and miRNA expression (Nanostring nCounter Human v3 miRNA Expression Assay). RESULTS: Sufficient material was obtained for screening of miRNA expression in 683/685 (99%) eligible melanomas, methylation in 467 (68%), and somatic mutations in 560 (82%). In 446/685 (65%) cases, aliquots of RNA/DNA were sufficient for testing with all three platforms. Among samples evaluated by the time of this analysis, the mean NGS coverage was 249x, 59 (18.6%) samples had coverage below 100x, and 41/414 (10%) failed methylation QC due to low intensity probes or insufficient Meta-Mixed Interquartile (BMIQ)- and single sample (ss)- Noob normalizations. Six of 683 RNAs (1%) failed Nanostring QC due to the low proportion of probes above the minimum threshold. Age of the FFPE tissue blocks (p<0.001) and time elapsed from sectioning to co-extraction (p = 0.002) were associated with methylation screening failures. Melanin reduced the ability to amplify fragments of 200bp or greater (absent/lightly pigmented vs heavily pigmented, p<0.003). Conversely, heavily pigmented tumors rendered greater amounts of RNA (p<0.001), and of RNA above 200 nucleotides (p<0.001). CONCLUSION: Our experience with many archival tissues demonstrates that with careful management of tissue processing and quality control it is possible to conduct multi-omic studies in a complex multi-institutional setting for investigations involving minute quantities of FFPE tumors, as in studies of early-stage melanoma. The study describes, for the first time, the optimal strategy for obtaining archival and limited tumor tissue, the characteristics of the nucleic acids co-extracted from a unique cell lysate, and success rate in downstream applications. In addition, our findings provide an estimate of the anticipated attrition that will guide other large multicenter research and consortia

Comprehensive Profiling of N‑Linked Glycosylation Sites in HeLa Cells Using Hydrazide Enrichment

The adenocarcinoma cell line HeLa serves as a model system for cancer research in general and cervical cancer in particular. In this study, hydrazide enrichment in combination with state-of-the art nanoLC−MS/MS analysis was used to profile N-linked glycosites in HeLa cells. N-Linked glycoproteins were selectively enriched in HeLa cells by the hydrazide capture method, which isolates all glycoproteins independent of their glycans. Nonglycosylated proteins were removed by extensive washing. N-Linked glycoproteins were identified with the specific NXT/S motif and deamidated asparagine (N). Deglycosylation was carried out in both H_2 (^16)O and H_2 ^(18)O to confirm the deamidation. NanoLC−MS/MS analysis indicated that the method selectively enriched at least 100 fold N-linked glycosites in HeLa cells. When both the membrane and cytosolic fractions were used, a total of 268 unique N-glycosylation sites were identified corresponding to 106 glycoproteins. Bioinformatic analysis revealed that most of the glycoproteins identified are known to have an impact on cancer and have been proposed as biomarkers