During narrative experiences, identification with a fictional character can alter one's attitudes and self-beliefs to be more similar to those of the character. The ventral medial prefrontal cortex (vMPFC) is a brain region that shows increased activity when introspecting about the self but also when thinking of close friends. Here, we test whether identification with fictional characters is associated with increased neural overlap between self and fictional others. Nineteen fans of the HBO series Game of Thrones performed trait evaluations for the self, 9 real-world friends and 9 fictional characters during functional neuroimaging. Overall, the participants showed a larger response in the vMPFC for self compared to friends and fictional others. However, among the participants higher in trait identification, we observed a greater neural overlap in the vMPFC between self and fictional characters. Moreover, the magnitude of this association was greater for the character that participants reported feeling closest to/liked the most as compared to those they felt least close to/liked the least. These results suggest that identification with fictional characters leads people to incorporate these characters into their self-concept the greater the immersion into experiences of 'becoming' characters, the more accessing knowledge about characters resembles accessing knowledge about the self.
  The discovery of cancer subtyping can help explore cancer pathogenesis, determine clinical actionability in treatment, and improve patients' survival rates. However, due to the diversity and complexity of multi-omics data, it is still challenging to develop integrated clustering algorithms for tumor molecular subtyping.
 
  We propose Subtype-GAN, a deep adversarial learning approach based on the multiple-input multiple-output neural network to model the complex omics data accurately. With the latent variables extracted from the neural network, Subtype-GAN uses consensus clustering and the Gaussian Mixture model to identify tumor samples' molecular subtypes. Compared with other state-of-the-art subtyping approaches, Subtype-GAN achieved outstanding performance on the benchmark data sets consisting of ∼4,000 TCGA tumors from 10 types of cancer. We found that on the comparison data set, the clustering scheme of Subtype-GAN is not always similar to that of the deep learning method AE but is identical to that of NEMO, MCCA, VAE, and other excellent approaches. Finally, we applied Subtype-GAN to the BRCA data set and automatically obtained the number of subtypes and the subtype labels of 1031 BRCA tumors.  https://www.selleckchem.com/products/SP600125.html  Through the detailed analysis, we found that the identified subtypes are clinically meaningful and show distinct patterns in the feature space, demonstrating the practicality of Subtype-GAN.
 
  The source codes, the clustering results of Subtype-GAN across the benchmark data sets are available at https//github.com/haiyang1986/Subtype-GAN.
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.Colour Doppler ultrasonography is the first measure to allow objective bedside assessment of GCA. This article discusses the evidence using the OMERACT filter. Consensus definitions for ultrasonographic changes were agreed upon by a Delphi process, with the 'halo' and 'compression' signs being characteristic. The halo is sensitive to change, disappearing within 2-4 weeks of starting glucocorticoids. Ultrasonography has moderate convergent validity with temporal artery biopsy in a pooled analysis of 12 studies including 965 participants [κ = 0.44 (95% CI 0.38, 0.50)]. The interobserver and intra-observer reliabilities are good (κ = 0.6 and κ = 0.76-0.78, respectively) in live exercises and excellent when assessing acquired images and videos (κ = 0.83-0.87 and κ = 0.88, respectively). Discriminant validity has been tested against stroke and diabetes mellitus (κ=-0.16 for diabetes). Machine familiarity and adequate examination time improves performance. Ultrasonography in follow-up is not yet adequately defined. Some patients have persistent changes in the larger arteries but these do not necessarily imply treatment failure or predict relapses.
  Off-target predictions are crucial in gene editing research. Recently, significant progress has been made in the field of prediction of off-target mutations, particularly with CRISPR-Cas9 data, thanks to the use of deep learning. CRISPR-Cas9 is a gene editing technique which allows manipulation of DNA fragments. The sgRNA-DNA (single guide RNA-DNA) sequence encoding for deep neural networks, however, has a strong impact on the prediction accuracy. We propose a novel encoding of sgRNA-DNA sequences that aggregates sequence data with no loss of information.
 
  In our experiments, we compare the proposed sgRNA-DNA sequence encoding applied in a deep learning prediction framework with state-of-the-art encoding and prediction methods. We demonstrate the superior accuracy of our approach in a simulation study involving Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) as well as the traditional Random Forest (RF), Naive Bayes (NB) and Logistic Regression (LR) classifiers.We highlight the quality of our results by building several FNNs, CNNs and RNNs with various layer depths and performing predictions on two popular CRISPOR and GUIDE-seq gene editing data sets. In all our experiments, the new encoding led to more accurate off-target prediction results, providing an improvement of the area under the Receiver Operating Characteristic (ROC) curve up to 35%.
 
  The code and data used in this study are available at https//github.com/dagrate/dl-offtarget.
 The code and data used in this study are available at https//github.com/dagrate/dl-offtarget.
  After a myocardial infarction, the adult human heart lacks sufficient regenerative capacity to restore lost tissue, leading to heart failure progression. Finding novel ways to reprogram adult cardiomyocytes into a regenerative state is a major therapeutic goal. The epicardium, the outermost layer of the heart, contributes cardiovascular cell types to the forming heart and is a source of trophic signals to promote heart muscle growth during embryonic development. The epicardium is also essential for heart regeneration in zebrafish and neonatal mice and can be reactivated after injury in adult hearts to improve outcome. A recently identified mechanism of cell-cell communication and signalling is that mediated by extracellular vesicles (EVs). Here, we aimed to investigate epicardial signalling via EV release in response to cardiac injury and as a means to optimize cardiac repair and regeneration.
 
  We isolated epicardial EVs from mouse and human sources and targeted the cardiomyocyte population. Epicardial EVs enhanced proliferation in H9C2 cells and in primary neonatal murine cardiomyocytes in vitro and promoted cell cycle re-entry when injected into the injured area of infarcted neonatal hearts.