RESULTS A total of 189 patients were recruited and 33% developed functional decline during hospitalisation. A score chart was developed with five predictors that were measured on hospital admission mobility impairment = 9 points, cognitive impairment = 7 points, loss of appetite = 6 points, depressive symptoms = 5 points, use of physical restraints or having an indwelling urinary catheter = 5 points. The score chart of the developed model demonstrated good calibration and discriminated adequately (C-index = 0.75, 95% CI (0.68-0.83) and better between patients with and without functional decline (chi2 = 12.8, p = 0.005) than the three previously developed models (range of C-index = 0.65-0.68). CONCLUSION Functional decline is a prevalent complication and can be adequately predicted on hospital admission. A score chart can be used in clinical practice to identify patients who could benefit from preventive interventions. Independent external validation is needed.BACKGROUND The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.BACKGROUND The stripe rust pathogen, Puccinia striiformis f. sp. tritici (Pst), threats world wheat production. Resistance to Pst is often overcome by pathogen virulence changes, but the mechanisms of variation are not clearly understood. To determine the role of mutation in Pst virulence changes, in previous studies 30 mutant isolates were developed from a least virulent isolate using ethyl methanesulfonate (EMS) mutagenesis and phenotyped for virulence changes. The progenitor isolate was sequenced, assembled and annotated for establishing a high-quality reference genome. In the present study, the 30 mutant isolates were sequenced and compared to the wide-type isolate to determine the genomic variation and identify candidates for avirulence (Avr) genes. RESULTS The sequence reads of the 30 mutant isolates were mapped to the wild-type reference genome to identify genomic changes. After selecting EMS preferred mutations, 264,630 and 118,913 single nucleotide polymorphism (SNP) sites and 89,078 and 72,513 Indel Since the avirulence gene candidates were identified from associated SNPs and Indels caused by artificial mutagenesis, these avirulence gene candidates are valuable resources for elucidating the mechanisms of the pathogen pathogenicity, and will be studied to determine their functions in the interactions between the wheat host and the Pst pathogen.BACKGROUND Previous epidemiological evidence has identified many risk factors for coronary artery disease (CAD). Pulse pressure (PP) was reported to be associated with CAD. https://www.selleckchem.com/products/bos172722.html However, more attention was paid to aortic PP than to brachial PP. This cross-sectional study aimed to investigate the direct relationship between brachial PP and the presence and extent of CAD in stable angina patients. METHODS We recruited a total of 1118 consecutive patients with stable chest pain suspected of CAD. After screening with exclusion criteria, 654 patients were finally included in our study. Every patient underwent both blood pressure measurement and selective coronary angiography. Univariate and multivariate analysis were performed to analyze the association between PP and the presence and extent of CAD. RESULTS This study revealed that brachial PP was an independent correlate of multivessel CAD. In multivariate generalized linear regression model, increasing brachial PP (per 1 mmHg) were associated with the increased number of diseased vessels (β = 0.01, SE = 0.00, P  less then  0.0001). Binary logistic regression analysis further confirmed this association. The risk of multivessel CAD increased significantly in patients with brachial PP ≥ 60 mmHg (OR = 1.69, 95% CI = 1.14-2.48, P = 0.0084) and as per 1 mmHg increased in brachial PP (OR = 1.02, 95% CI = 1.01-1.03, P = 0.0002), independent of age, gender, body mass index (BMI), smoking, diabetes, hypercholesterolemia and creatinine (Cr). This association was still of statistical significance in subgroup analysis of hypertension and diabetes. CONCLUSION Increasing brachial PP was significantly and independently associated with increased risk of multivessel coronary disease in stable angina patients. The association of brachial PP with CAD was more pronounced in hypertension group than in non-hypertension one.