MicroRNA-Driven Developmental Remodeling in the Brain Distinguishes Humans from Other Primates

Comparison of human, chimpanzee, and macaque brain transcriptomes reveals a significant developmental remodeling in the human prefrontal cortex, potentially shaped by microRNA

Primate-specific endogenous retrovirus-driven transcription defines naive-like stem cells

Naive embryonic stem cells hold great promise for research and therapeutics as they have broad and robust developmental potential. While such cells are readily derived from mouse blastocysts it has not been possible to isolate human equivalents easily, although human naive-like cells have been artificially generated (rather than extracted) by coercion of human primed embryonic stem cells by modifying culture conditions or through transgenic modification. Here we show that a sub-population within cultures of human embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs) manifests key properties of naive state cells. These naive-like cells can be genetically tagged, and are associated with elevated transcription of HERVH, a primate-specific endogenous retrovirus. HERVH elements provide functional binding sites for a combination of naive pluripotency transcription factors, including LBP9, recently recognized as relevant to naivety in mice. LBP9-HERVH drives hESC-specific alternative and chimaeric transcripts, including pluripotency-modulating long non-coding RNAs. Disruption of LBP9, HERVH and HERVH-derived transcripts compromises self-renewal. These observations define HERVH expression as a hallmark of naive-like hESCs, and establish novel primate-specific transcriptional circuitry regulating pluripotency

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available

Nanopore Sequencing and Hi-C Based De Novo Assembly of Trachidermus fasciatus Genome

Trachidermus fasciatus is a roughskin sculpin fish widespread across the coastal areas of East Asia. Due to environmental destruction and overfishing, the population of this species is under threat. In order to protect this endangered species, it is important to have the genome sequenced. Reference genomes are essential for studying population genetics, domestic farming, and genetic resource protection. However, currently, no reference genome is available for Trachidermus fasciatus, and this has greatly hindered the research on this species. In this study, we integrated nanopore long-read sequencing, Illumina short-read sequencing, and Hi-C methods to thoroughly assemble the Trachidermus fasciatus genome. Our results provided a chromosome-level high-quality genome assembly with a predicted genome size of 542.6 Mbp (2n = 40) and a scaffold N50 of 24.9 Mbp. The BUSCO value for genome assembly completeness was higher than 96%, and the single-base accuracy was 99.997%. Based on EVM-StringTie genome annotation, a total of 19,147 protein-coding genes were identified, including 35,093 mRNA transcripts. In addition, a novel gene-finding strategy named RNR was introduced, and in total, 51 (82) novel genes (transcripts) were identified. Lastly, we present here the first reference genome for Trachidermus fasciatus; this sequence is expected to greatly facilitate future research on this species

Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features

The intelligent classification of heart-sound signals can assist clinicians in the rapid diagnosis of cardiovascular diseases. Mel-frequency cepstral coefficients (MelSpectrums) and log Mel-frequency cepstral coefficients (Log-MelSpectrums) based on a short-time Fourier transform (STFT) can represent the temporal and spectral structures of original heart-sound signals. Recently, various systems based on convolutional neural networks (CNNs) trained on the MelSpectrum and Log-MelSpectrum of segmental heart-sound frames that outperform systems using handcrafted features have been presented and classified heart-sound signals accurately. However, there is no a priori evidence of the best input representation for classifying heart sounds when using CNN models. Therefore, in this study, the MelSpectrum and Log-MelSpectrum features of heart-sound signals combined with a mathematical model of cardiac-sound acquisition were analysed theoretically. Both the experimental results and theoretical analysis demonstrated that the Log-MelSpectrum features can reduce the classification difference between domains and improve the performance of CNNs for heart-sound classification

A germline chromothripsis event stably segregating in 11 individuals through three generations

Purpose: Parentally transmitted germ-line chromothripsis (G-CTH) has been identified in only a few cases. Most of these rearrangements were stably transmitted, in an unbalanced form, from a healthy mother to her child with congenital abnormalities probably caused by de novo copy-number changes of dosage sensitive genes. We describe a G-CTH transmitted through three generations in 11 healthy carriers. Methods: Conventional cytogenetic analysis, mate-pair sequencing, and polymerase chain reaction (PCR) were used to identify the chromosome rearrangement and characterize the breakpoints in all three generations. Results: We identified an apparently balanced translocation t(3;5), later shown to be a G-CTH, in all individuals of a three-generation family. The G-CTH stably segregated without occurrence of additional rearrangements; however, several spontaneous abortions were reported, possibly due to unbalanced transmission. Although seven protein-coding genes are interrupted, no clinical features can be definitively attributed to the affected genes. However, it can be speculated that truncation of one of these genes, encoding ataxia-telangiectasia and Rad3-related protein kinase (ATR), a key component of the DNA damage response, may be related to G-CTH formation. Conclusion: G-CTH rearrangements are not always associated with abnormal phenotypes and may be misinterpreted as balanced two-way translocations, suggesting that G-CTH is an underdiagnosed phenomenon