Investigation of stimulated dynamics of vortex-matter in high-temperature superconductors

A simple mechanical method for the investigation of Abrikosov vortex lattice stimulated dynamics in superconductors has been used. By this method we studied the action of pulsed magnetic fields on the vortex lattice and established the resulting change of the course of relaxation processes in the vortex matter in high-temperature superconductors. This method can be used for investigation of phase transitions in vortex matter both high-temperature and exotic superconductors.Comment: 5 pages, 6 figure

Intrauterine devices and endometrial cancer risk : a pooled analysis of the Epidemiology of Endometrial Cancer Consortium

Intrauterine devices (IUDs), long-acting and reversible contraceptives, induce a number of immunological and biochemical changes in the uterine environment that could affect endometrial cancer (EC) risk. We addressed this relationship through a pooled analysis of data collected in the Epidemiology of Endometrial Cancer Consortium. We combined individual-level data from 4 cohort and 14 case-control studies, in total 8,801 EC cases and 15,357 controls. Using multivariable logistic regression, we estimated pooled odds ratios (pooled-ORs) and 95% confidence intervals (CIs) for EC risk associated with ever use, type of device, ages at first and last use, duration of use and time since last use, stratified by study and adjusted for confounders. Ever use of IUDs was inversely related to EC risk (pooled-OR = 0.81, 95% CI = 0.74-0.90). Compared with never use, reduced risk of EC was observed for inert IUDs (pooled-OR = 0.69, 95% CI = 0.58-0.82), older age at first use (≥35 years pooled-OR = 0.53, 95% CI = 0.43-0.67), older age at last use (≥45 years pooled-OR = 0.60, 95% CI = 0.50-0.72), longer duration of use (≥10 years pooled-OR = 0.61, 95% CI = 0.52-0.71) and recent use (within 1 year of study entry pooled-OR = 0.39, 95% CI = 0.30-0.49). Future studies are needed to assess the respective roles of detection biases and biologic effects related to foreign body responses in the endometrium, heavier bleeding (and increased clearance of carcinogenic cells) and localized hormonal changes

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Meta-analysis of type 2 Diabetes in African Americans Consortium

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)<P<5 × 10(-8), odds ratio (OR)  = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2 × 10(-23) < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.Peer reviewe

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

Background Genome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. Methods Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. Results Assuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92–0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88–0.94). Conclusions Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer