Association between diabetes, diabetes treatment and risk of developing endometrial cancer.

BackgroundA growing body of evidence suggests that diabetes is a risk factor for endometrial cancer incidence. However, most of these studies used case-control study designs and did not adjust for obesity, an established risk factor for endometrial cancer. In addition, few epidemiological studies have examined the association between diabetes treatment and endometrial cancer risk. The objective of this study was to assess the relationships among diabetes, diabetes treatment and endometrial cancer risk in postmenopausal women participating in the Women's Health Initiative (WHI).MethodsA total of 88 107 postmenopausal women aged 50-79 years who were free of cancer and had no hysterectomy at baseline were followed until date of endometrial cancer diagnosis, death, hysterectomy or loss to follow-up, whichever came first. Endometrial cancers were confirmed by central medical record and pathology report review. Multivariate Cox proportional hazards regression models were used to estimate hazard ratios (HRs) (95% confidence interval (CI)) for diagnosis of diabetes and metformin treatment as risk factors for endometrial cancer.ResultsOver a mean of 11 years of follow-up, 1241 endometrial cancers developed. In the primary analysis that focused on prevalent diabetes at enrolment, compared with women without diabetes, women with self-reported diabetes, and the subset of women with treated diabetes, had significantly higher risk of endometrial cancer without adjusting for BMI (HR=1.44, 95% CI: 1.13-1.85 for diabetes, HR=1.57, 95% CI: 1.19-2.07 for treated diabetes). However after adjusting for BMI, the associations between diabetes, diabetes treatment, diabetes duration and the risk of endometrial cancer became non-significant. Elevated risk was noted when considering combining diabetes diagnosed at baseline and during follow-up as time-dependent exposure (HR=1.31, 95% CI: 1.08-1.59) even after adjusting for BMI. No significant association was observed between metformin use and endometrial cancer risk.ConclusionsOur results suggest that the relationship observed in previous research between diabetes and endometrial cancer incidence may be largely confounded by body weight, although some modest independent elevated risk remains

Fast predictive maintenance in Industrial Internet of Things (IIoT) with Deep Learning (DL): A review

Applying Deep Learning in the field of Industrial Internet of Things is a very active research field. The prediction of failures of machines and equipment in industrial environments before their possible occurrence is also a very popular topic, significantly because of its cost saving potential. Predictive Maintenance (PdM) applications can benefit from DL, especially because of the fact that high complex, non-linear and unlabeled (or partially labeled) data is the normal case. Especially with PdM applications being used in connected smart factories, low latency predictions are essential. Because of this real-time processing becomes more important. The aim of this paper is to provide a narrative review of the most current research covering trends and projects regarding the application of DL methods in IoT environments. Especially papers discussing the area of predictions and real-time processing with DL models are selected because of their potential use for PdM applications. The reviewed papers were selected by the authors based on a qualitative rather than a quantitative level

Medical genetics and genomic medicine in the United States. Part 2: Reproductive genetics, newborn screening, genetic counseling, training, and registries

eview of genetics in the United States with emphasis on the prenatal, metabolic, genetic counseling, and training aspects of the field

Indirect evidence and the poverty of the stimulus: the case of anaphoric one

It is widely held that children’s linguistic input underdetermines the correct grammar, and that language learning must therefore be guided by innate linguistic constraints. In contrast, a recent counterproposal holds that apparently impoverished input may contain indirect sources of evidence that allow the child to learn without such constraints. Here, we support this latter view by showing that a Bayesian model can learn a standard “poverty-of-stimulus” example, anaphoric one, from realistic input without a constraint traditionally assumed to be necessary, by relying on indirect evidence. Our demonstration does however assume other linguistic knowledge; thus we reduce the problem of learning anaphoric one to that of learning this other knowledge. We discuss whether this other knowledge may itself be acquired without linguistic constraints.Stephani Foraker, Terry Regier, Naveen Khetarpal, Amy Perfors and Joshua B. Tenenbau

A draft human pangenome reference

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individual

Deep generative modeling for single-cell transcriptomics.

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task

Co3O4 Nanocrystals on Graphene as a Synergistic Catalyst for Oxygen Reduction Reaction

Catalysts for oxygen reduction and evolution reactions are at the heart of key renewable energy technologies including fuel cells and water splitting. Despite tremendous efforts, developing oxygen electrode catalysts with high activity at low costs remains a grand challenge. Here, we report a hybrid material of Co3O4 nanocrystals grown on reduced graphene oxide (GO) as a high-performance bi-functional catalyst for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). While Co3O4 or graphene oxide alone has little catalytic activity, their hybrid exhibits an unexpected, surprisingly high ORR activity that is further enhanced by nitrogen-doping of graphene. The Co3O4/N-doped graphene hybrid exhibits similar catalytic activity but superior stability to Pt in alkaline solutions. The same hybrid is also highly active for OER, making it a high performance non-precious metal based bi-catalyst for both ORR and OER. The unusual catalytic activity arises from synergetic chemical coupling effects between Co3O4 and graphene.Comment: published in Nature Material