Network estimation for censored time-to-event data for multiple events based on multivariate survival analysis.

In general survival analysis, multiple studies have considered a single failure time corresponding to the time to the event of interest or to the occurrence of multiple events under the assumption that each event is independent. However, in real-world events, one event may impact others. Essentially, the potential structure of the occurrence of multiple events can be observed in several survival datasets. The interrelations between the times to the occurrences of events are immensely challenging to analyze because of the presence of censoring. Censoring commonly arises in longitudinal studies in which some events are often not observed for some of the subjects within the duration of research. Although this problem presents the obstacle of distortion caused by censoring, the advanced multivariate survival analysis methods that handle multiple events with censoring make it possible to measure a bivariate probability density function for a pair of events. Considering this improvement, this paper proposes a method called censored network estimation to discover partially correlated relationships and construct the corresponding network composed of edges representing non-zero partial correlations on multiple censored events. To demonstrate its superior performance compared to conventional methods, the selecting power for the partially correlated events was evaluated in two types of networks with iterative simulation experiments. Additionally, the correlation structure was investigated on the electronic health records dataset of the times to the first diagnosis for newborn babies in South Korea. The results show significantly improved performance as compared to edge measurement with competitive methods and reliability in terms of the interrelations of real-life diseases

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies

Structures of the GPR158-RGS7-Gb5 Complex: Fine-Tuning Mechanism of Signal Transduction

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Structure of the class C orphan GPCR GPR158 in complex with RGS7-Gβ5

The orphan GPR158 receptor belongs to the class C GPCR family and interacts with the regulator of G protein signaling 7 (RGS7)-Gβ5 complex. Here, the authors present the cryo-EM structure of human GPR158, which reveals that the extracellular domain contains a PAS domain, and they also determine the structures of GPR158 in complex with either one or two RGS7-Gβ5 heterodimers and discuss implications for the signaling mechanism

Time Evolution Studies on Strain and Doping of Graphene Grown on a Copper Substrate Using Raman Spectroscopy

© 2019 American Chemical Society.The enhanced growth of Cu oxides underneath graphene grown on a Cu substrate has been of great interest to many groups. In this work, the strain and doping status of graphene, based on the gradual growth of Cu oxides from underneath, were systematically studied using time evolution Raman spectroscopy. The compressive strain to graphene, due to the thermal expansion coefficient difference between graphene and the Cu substrate, was almost released by the nonuniform Cu2O growth; however, slight tensile strain was exerted. This induced p-doping in the graphene with a carrier density up to 1.7 × 1013 cm-2 when it was exposed to air for up to 30 days. With longer exposure to ambient conditions (>1 year), we observed that graphene/Cu2O hybrid structures significantly slow down the oxidation compared to that using a bare Cu substrate. The thickness of the CuO layer on the bare Cu substrate was increased to approximately 270 nm. These findings were confirmed through white light interference measurements and scanning electron microscop

Functional selectivity of insulin receptor revealed by aptamer-trapped receptor structures

Activation of insulin receptor (IR) initiates a cascade of conformational changes and autophosphorylation events. Herein, we determined three structures of IR trapped by aptamers using cryo-electron microscopy. The A62 agonist aptamer selectively activates metabolic signaling. In the absence of insulin, the two A62 aptamer agonists of IR adopt an insulin-accessible arrowhead conformation by mimicking site-1/site-2' insulin coordination. Insulin binding at one site triggers conformational changes in one protomer, but this movement is blocked in the other protomer by A62 at the opposite site. A62 binding captures two unique conformations of IR with a similar stalk arrangement, which underlie Tyr1150 mono-phosphorylation (m-pY1150) and selective activation for metabolic signaling. The A43 aptamer, a positive allosteric modulator, binds at the opposite side of the insulin-binding module, and stabilizes the single insulin-bound IR structure that brings two FnIII-3 regions into closer proximity for full activation. Our results suggest that spatial proximity of the two FnIII-3 ends is important for m-pY1150, but multi-phosphorylation of IR requires additional conformational rearrangement of intracellular domains mediated by coordination between extracellular and transmembrane domains. DNA aptamers can activate insulin receptor as selective agonists or positive allosteric modulators. Here, authors determine structures of the insulin receptor bound to aptamers and provide a basis for the selective activation or allosteric regulation of insulin receptor.11Ysciescopu

Three-Dimensional Graphene–RGD Peptide Nanoisland Composites That Enhance the Osteogenesis of Human Adipose-Derived Mesenchymal Stem Cells

Graphene derivatives have immense potential in stem cell research. Here, we report a three-dimensional graphene/arginine-glycine-aspartic acid (RGD) peptide nanoisland composite effective in guiding the osteogenesis of human adipose-derived mesenchymal stem cells (ADSCs). Amine-modified silica nanoparticles (SiNPs) were uniformly coated onto an indium tin oxide electrode (ITO), followed by graphene oxide (GO) encapsulation and electrochemical deposition of gold nanoparticles. A RGD–MAP–C peptide, with a triple-branched repeating RGD sequence and a terminal cysteine, was self-assembled onto the gold nanoparticles, generating the final three-dimensional graphene–RGD peptide nanoisland composite. We generated substrates with various gold nanoparticle–RGD peptide cluster densities, and found that the platform with the maximal number of clusters was most suitable for ADSC adhesion and spreading. Remarkably, the same platform was also highly efficient at guiding ADSC osteogenesis compared with other substrates, based on gene expression (alkaline phosphatase (ALP), runt-related transcription factor 2), enzyme activity (ALP), and calcium deposition. ADSCs induced to differentiate into osteoblasts showed higher calcium accumulations after 14–21 days than when grown on typical GO-SiNP complexes, suggesting that the platform can accelerate ADSC osteoblastic differentiation. The results demonstrate that a three-dimensional graphene–RGD peptide nanoisland composite can efficiently derive osteoblasts from mesenchymal stem cells

A pre-trained BERT for Korean medical natural language processing

Abstract With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score