Upon treatment with sulfur hexafluoride, alkali metal diphenyl or dicyclohexyl phosphides are oxidized within seconds to tetraphenyl or tetracyclohexyl diphosphines. When bulky di-tert-butylphosphide is employed, fluorophosphine intermediates are detected. This is the first reported reaction of sulfur hexafluoride with metal phosphides, and a rare example of reactivity of sulfur hexafluoride at ambient temperature.Herein we report experimental evidence for the shortest intermolecular distance reported for two electronically-different hydrogen atoms in the solid state. The Hδ+Hδ- non-covalent interaction was studied using theoretical calculations indicating that electrostatic and dispersion forces are of paramount importance.Herein, we describe a CRISPR-Cas12a sensing platform activated by a DNA ligation reaction for the sensitive detection of non-nucleic acid targets, including NAD+, ATP and polynucleotide kinase (PNK). In this design, the DNA ligation reaction triggered by these biomolecules generates DNA duplexes, which can activate the nuclease activity of Cas12a to produce amplified fluorescence signals. As a result, this work provides an alternative strategy to expand the applicability of the CRISPR-Cas system into the detection of non-nucleic acid biomolecules.Summarization of clinical narratives is a long-standing research problem. Here, we introduce the task of hospital-course summarization. Given the documentation authored throughout a patient's hospitalization, generate a paragraph that tells the story of the patient admission. We construct an English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and their corresponding summary proxy the clinician-authored "Brief Hospital Course" paragraph written as part of a discharge note. Exploratory analyses reveal that the BHC paragraphs are highly abstractive with some long extracted fragments; are concise yet comprehensive; differ in style and content organization from the source notes; exhibit minimal lexical cohesion; and represent silver-standard references. Our analysis identifies multiple implications for modeling this complex, multi-document summarization task.Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.In cohort studies, non-random medication use can pose barriers to estimation of the natural history trend in a mean biomarker value-namely, the association between a predictor of interest and a biomarker outcome that would be observed in the total absence of biomarker-specific treatment. Common causes of treatment and outcomes are often unmeasured, obscuring our ability to easily account for medication use with assumptions commonly invoked in causal inference such as conditional ignorability. Further, without a high degree of confidence in the availability of a variable satisfying the exclusion restriction, use of instrumental variable approaches may be difficult to justify. Heckman's hybrid model with structural shift (sometimes referred to less specifically as the treatment effects model) can be used to correct endogeneity bias via a homogeneity assumption (i.e., that average treatment effects do not vary across covariates) and parametric specification of a joint model for the outcome and treatment. In rece endogenous treatment.Segmentation of the prostate bed, the residual tissue after the removal of the prostate gland, is an essential prerequisite for post-prostatectomy radiotherapy but also a challenging task due to its non-contrast boundaries and highly variable shapes relying on neighboring organs. In this work, we propose a novel deep learning-based method to automatically segment this "invisible target". As the main idea of our design, we expect to get reference from the surrounding normal structures (bladder&rectum) and take advantage of this information to facilitate the prostate bed segmentation. To achieve this goal, we first use a U-Net as the backbone network to perform the bladder&rectum segmentation, which serves as a low-level task that can provide references to the high-level task of the prostate bed segmentation. Based on the backbone network, we build a novel attention network with a series of cascaded attention modules to further extract discriminative features for the high-level prostate bed segmentation task. Since the attention network has one-sided dependency on the backbone network, simulating the clinical workflow to use normal structures to guide the segmentation of radiotherapy target, we name the final composition model asymmetrical multi-task attention U-Net.  https://www.selleckchem.com/Androgen-Receptor.html  Extensive experiments on a clinical dataset consisting of 186 CT images demonstrate the effectiveness of this new design and the superior performance of the model in comparison to the conventional atlas-based methods for prostate bed segmentation. The source code is publicly available at https//github.com/superxuang/amta-net.
  To estimate the prevalence of depression among stroke survivors in India.
 
  Stroke survivors diagnosed with depression.
 
  Prevalence of Depression.
 
  Cochrane systematic review methods were followed. The literature search was from 1960-2019. We searched the following electronic databases Medline, ERIC, Embase, IndMED, PsycEXTRA, Global Health, Cochrane, CENTRAL Register, Econ Lit, and conference abstracts to identify studies for inclusion. A search strategy was appropriately developed and performed from May 2019 to December 2019. All included studies were assessed for their content and methodological quality using JBI Critical Appraisal Checklist.
 
  A total of 15 studies were included in this study. Prevalence of post-stroke depression in the studies varied from 24% to 90%. The pooled prevalence was 55% (95% CI 43%, 65%) with high heterogeneity (I
  =94.83%). Prevalence also varied between the tools (HAMD -60%, GDS -70%, HADS -40%). The overall methodological quality of the included studies was very poor.
 
  It is evident from the meta-analysis that about half of those who survive a stroke experience post-stroke depression.