.The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods perform well on distinguishing majority long noncoding RNAs (lncRNAs) and coding RNAs (mRNAs) but poorly on RNAs with small open reading frames (sORFs). Here, we present DeepCPP (deep neural network for coding potential prediction), a deep learning method for RNA coding potential prediction. Extensive evaluations on four previous datasets and six new datasets constructed in different species show that DeepCPP outperforms other state-of-the-art methods, especially on sORF type data, which overcomes the bottleneck of sORF mRNA identification by improving more than 4.31, 37.24 and 5.89% on its accuracy for newly discovered human, vertebrate and insect data, respectively. Additionally, we also revealed that discontinuous k-mer, and our newly proposed nucleotide bias and minimal distribution similarity feature selection method play crucial roles in this classification problem.  https://www.selleckchem.com/products/ipi-145-ink1197.html  Taken together, DeepCPP is an effective method for RNA coding potential prediction. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Early hospital readmission (EHR), defined as all readmissions within 30 days of initial hospital discharge, is a health care quality measure. It is influenced by the demographic characteristics of the population at risk, the multidisciplinary approach for hospital discharge, the access, coverage, and comprehensiveness of the health care system, and reimbursement policies. EHR is associated with higher morbidity, mortality, and increased health care costs. Monitoring EHR enables the identification of hospital and outpatient healthcare weaknesses and the implementation of corrective interventions. Among kidney transplant recipients in the USA, EHR ranges between 18 and 47%, and is associated with one-year increased mortality and graft loss. One study in Brazil showed an incidence of 19.8% of EHR. The main causes of readmission were infections and surgical and metabolic complications. Strategies to reduce early hospital readmission are therefore essential and should consider the local factors, including socio-economic conditions, epidemiology and endemic diseases, and mobility.There are more than 150 different rare genetic kidney diseases. They can be classified according to diagnostic findings as (i) disorders of growth and structure, (ii) glomerular diseases, (iii) tubular, and (iv) metabolic diseases. In recent years, there has been a shift of paradigm in this field. Molecular testing has become more accessible, our understanding of the underlying pathophysiologic mechanisms of these diseases has evolved, and new therapeutic strategies have become more available. Therefore, the role of nephrologists has progressively shifted from a mere spectator to an active player, part of a multidisciplinary team in the diagnosis and treatment of these disorders. This article provides an overview of the recent advances in rare hereditary kidney disorders by discussing the genetic aspects, clinical manifestations, diagnostic, and therapeutic approaches of some of these disorders, named familial focal and segmental glomerulosclerosis, tuberous sclerosis complex, Fabry nephropathy, and MYH-9 related disorder.INTRODUCTION Chronic hemodialysis (HD) patients are considered to be at high risk for infection. Here, we describe the clinical outcomes of chronic HD patients with influenza A (H1N1) infection and the strategies adopted to control an outbreak of influenza A in a dialysis unit. METHODS Among a total of 62 chronic HD patients, H1N1 infection was identified in 12 (19.4%). Of the 32 staff members, four (12.5%) were found to be infected with the H1N1 virus. Outcomes included symptoms at presentation, comorbidities, occurrence of hypoxemia, hospital admission, and clinical evaluation. Infection was confirmed by real-time reverse transcriptase polymerase chain reaction. RESULTS The 12 patients who had H1N1 infection did not differ significantly from the other 50 non-infected patients with respect to age, ***, dialysis vintage, dialysis modality, or proportion of comorbidities. Obesity was higher in the H1N1-infected group (41.5 vs. 4%, p less then 0.002). The most common symptoms were fever (92%), cough (92%), and rhinorrhea (83%).