p53 signaling pathway.
 
  These transcriptomic data provide insights into the molecular mechanisms of ovarian development in P. clarkii. The results will be helpful for improving the reproduction and development of this aquatic species.
 These transcriptomic data provide insights into the molecular mechanisms of ovarian development in P. clarkii. The results will be helpful for improving the reproduction and development of this aquatic species.
  Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately.
 
  We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction.  https://www.selleckchem.com/products/dinaciclib-sch727965.html  VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The intmethods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https//github.com/zhanglabNKU/VGAELDA .
  We suggest an adaptive sample size calculation method for developing clinical prediction models, in which model performance is monitored sequentially as new data comes in.
 
  We illustrate the approach using data for the diagnosis of ovarian cancer (n = 5914, 33% event fraction) and obstructive coronary artery disease (CAD; n = 4888, 44% event fraction). We used logistic regression to develop a prediction model consisting only of a priori selected predictors and assumed linear relations for continuous predictors. We mimicked prospective patient recruitment by developing the model on 100 randomly selected patients, and we used bootstrapping to internally validate the model. We sequentially added 50 random new patients until we reached a sample size of 3000 and re-estimated model performance at each step. We examined the required sample size for satisfying the following stopping rule obtaining a calibration slope ≥ 0.9 and optimism in the c-statistic (or AUC) < = 0.02 at two consecutive sample sizes. This pre modeled, and lower sample sizes when Firth's correction was used.
 
  Adaptive sample size determination can be a useful supplement to fixed a priori sample size calculations, because it allows to tailor the sample size to the specific prediction modeling context in a dynamic fashion.
 Adaptive sample size determination can be a useful supplement to fixed a priori sample size calculations, because it allows to tailor the sample size to the specific prediction modeling context in a dynamic fashion.
  The National Institute for Health and Care Excellence (NICE) recommend that men on androgen deprivation therapy (ADT) for prostate cancer should receive supervised exercise to manage the side-effects of treatment. However, these recommendations are rarely implemented into practice. Community-based exercise professionals (CBEPs) represent an important target group to deliver the recommendations nationally, yet their standard training does not address the core competencies required to work with clinical populations, highlighting a need for further professional training. This paper describes the development of a training package to support CBEPs to deliver NICE recommendations.
 
  Development of the intervention was guided by the Medical Research Council guidance for complex interventions and the Behaviour Change Wheel. In step one, target behaviours, together with their barriers and facilitators were identified from a literature review and focus groups with CBEPs (n = 22) and men on androgen deprivation therapructured and transparent guide to intervention development. A training package for CBEPs was developed and should increase trust amongst patients and health care professionals when implementing exercise into prostate cancer care. Furthermore, if proven effective, the development and approach taken may provide a blueprint for replication in other clinical populations where exercise has proven efficacy but is insufficiently implemented.
 Established intervention development approaches provided a structured and transparent guide to intervention development. A training package for CBEPs was developed and should increase trust amongst patients and health care professionals when implementing exercise into prostate cancer care. Furthermore, if proven effective, the development and approach taken may provide a blueprint for replication in other clinical populations where exercise has proven efficacy but is insufficiently implemented.
  Diagnostic precision and the identification of rare diseases is a daily challenge, which needs specialized expertise. We hypothesized, that there is a correlation between the distance of residence to the next tertiary medical facility with highly specialized care and the diagnostic precision, especially for rare diseases.
 
  Using a nation-wide hospitalization database, we found a negative association between diagnostic diversity and travel time to the next tertiary referral hospital when including all cases throughout the overall International Classification of Diseases version 10 German Modification (ICD-10-GM) diagnosis codes. This was paralleled with a negative association of standardized incidence rates in all groups of rare diseases defined by the Orphanet rare disease nomenclature, except for rare teratologic and rare allergic diseases.
 
  Our findings indicate a higher risk of being mis-, under- or late diagnosed especially in rare diseases when living more distant to a tertiary medical facility. Greater distance to the next tertiary medical facility basically increases the chance for hospitalization in a non-comprehensive regional hospital with less diagnostic capacity, and, thus, impacts on adapted health care access.