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019) while there were no differences in trait anger (p = .16). Prospectively, the interaction between mean anger recognition and trait anger independently predicted BP increases from baseline to follow-up (ps ≤ .043), in that overrating displayed anger predicted future BP increases only if trait anger was high.

Our findings indicate an anger recognition bias in men with essential hypertension and that overrating displayed anger in combination with higher trait anger seems to predict future BP increases. This might be of clinical relevance for the development and progression of hypertension and cardiovascular disease.

Our findings indicate an anger recognition bias in men with essential hypertension and that overrating displayed anger in combination with higher trait anger seems to predict future BP increases. This might be of clinical relevance for the development and progression of hypertension and cardiovascular disease.

MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases.

In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense eodel, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Many packages serve as an interface between R language and the Application Programming Interface (API) of databases and web services. There is usually a 'one-package to one-service' correspondence which poses challenges such as consistency to the users and scalability to the developers. This, among other issues, has motivated us to develop a package as a framework to facilitate the implementation of API resources in the R language. This R package, rbioapi, is a consistent, user-friendly, and scalable interface to biological and medical databases and web services. To date, rbioapi fully supports Enrichr, JASPAR, miEAA, PANTHER, Reactome, STRING, and UniProt. We aim to expand this list by collaborations and contributions and gradually make rbioapi as comprehensive as possible.

rbioapi is deposited in CRAN under the https//cran.r-project.org/package=rbioapi address. The source code is publicly available in a GitHub repository at https//github.com/moosa-r/rbioapi/. Also, the documentation website is available at https//rbioapi.moosa-r.com.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.Breeding for improved leaf photosynthesis is considered as a viable approach to increase crop yield. Whether it should be improved in combination with other traits has not been assessed critically. Based on the quantitative crop model GECROS that interconnects various traits to crop productivity, we review natural variation in relevant traits, from biochemical aspects of leaf photosynthesis to morpho-physiological crop characteristics. While large phenotypic variations (sometimes >2-fold) for leaf photosynthesis and its underlying biochemical parameters were reported, few quantitative trait loci (QTL) were identified, accounting for a small percentage of phenotypic variation. More QTL were reported for sink size (that feeds back on photosynthesis) or morpho-physiological traits (that affect canopy productivity and duration), together explaining a much greater percentage of their phenotypic variation. Traits for both photosynthetic rate and sustaining it during grain filling were strongly related to nitrogen-related traits. Much of the molecular basis of known photosynthesis QTL thus resides in genes controlling photosynthesis indirectly. Simulation using GECROS demonstrated the overwhelming importance of electron transport parameters, compared with the maximum Rubisco activity that largely determines the commonly studied light-saturated photosynthetic rate. Exploiting photosynthetic natural variation might significantly improve crop yield if nitrogen uptake, sink capacity, and other morpho-physiological traits are co-selected synergistically.

Determine the incidence of GBS disease among extremely preterm infants and assess risk of death or neurodevelopmental impairment (NDI) at 18-26 months' corrected age.

Observational cohort study of infants enrolled in a multicenter registry. GBS disease incidence was assessed in infants born 1998-2016 at 22-28 weeks' gestation surviving >12 hours. The composite outcome, death or NDI, was assessed in infants born 1998-2014 at 22-26 weeks' gestation. Infection was defined as GBS isolation in blood/CSF culture at ≤72 hours (early-onset disease, EOD) and >72 hours (late-onset disease, LOD) after birth. The outcome was compared in infants with GBS disease, infants infected with other pathogens, and uninfected infants using Poisson regression models.

Incidence of GBS EOD (2.70/1000 births [95% CI 2.15-3.36]) and LOD (8.47/1000 infants [7.45-9.59]) did not change significantly over time. The adjusted relative risk (aRR, 95% CI) of death/NDI was higher among GBS EOD cases compared to infants with other infections (1.22, [1.02-1.45]) and uninfected infants (1.44, [1.23-1.69]). Death/NDI did not differ between infants with GBS LOD and comparator groups. GBS LOD occurred at a significantly later age than non-GBS late-onset infection. Among infants surviving >30 days, the risk of death was higher with GBS LOD (1.90, [1.36-2.67]), compared to uninfected infants.

In a cohort of extremely preterm infants, incidence of GBS disease did not change during the study period. Increased risk of death/NDI with GBS EOD, and of death among some infants with GBS LOD, supports the need for novel preventive strategies for disease reduction.

In a cohort of extremely preterm infants, incidence of GBS disease did not change during the study period. Increased risk of death/NDI with GBS EOD, and of death among some infants with GBS LOD, supports the need for novel preventive strategies for disease reduction.While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) andssociations for medical research and reduce the blindness of wet-lab experiments.Sufficient water is essential for plant growth and production. Root hairs connect roots to the soil, extend the effective root radius, and greatly enlarge the absorbing surface area. Although the efficacy of root hairs in nutrient uptake, especially phosphorus, has been well recognized, their role in water uptake remains contentious. Here we review recent advances in this field, discuss the factors affecting the role of root hairs in water uptake, and propose future directions. We argue that root hair length and shrinkage, in response to soil drying, explain the apparently contradictory evidence currently available. Our analysis revealed that shorter and vulnerable root hairs (i.e. rice and maize) made little, if any, contribution to root water uptake. Selleck Trichostatin A In contrast, relatively longer root hairs (i.e. barley) had a clear influence on root water uptake, transpiration, and hence plant response to soil drying. We conclude that the role of root hairs in water uptake is species (and probably soil) specific. We propose that a holistic understanding of the efficacy of root hairs in water uptake will require detailed studies of root hair length, turnover, and shrinkage in different species and contrasting soil textures.Increasing biological evidence indicated that microRNAs (miRNAs) play a vital role in exploring the pathogenesis of various human diseases (especially in tumors). Mining disease-related miRNAs is of great significance for the clinical diagnosis and treatment of diseases. Compared with the traditional experimental methods with the significant limitations of high cost, long cycle and small scale, the methods based on computing have the advantages of being cost-effective. However, although the current methods based on computational biology can accurately predict the correlation between miRNAs and disease, they can not predict the detailed association information at a fine level. We propose a knowledge-driven approach to the fine-grained prediction of disease-related miRNAs (KDFGMDA). Different from the previous methods, this method can finely predict the clear associations between miRNA and disease, such as upregulation, downregulation or dysregulation. Specifically, KDFGMDA extracts triple information from massive experimental data and existing datasets to construct a knowledge graph and then trains a depth graph representation learning model based on knowledge graph to complete fine-grained prediction tasks.