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Highcarrier concentration and low mobility in Bi2Se3 hide thetopological surface states (TSS). In the 2D ternary topological insulator (TI) Bi2–xSbxSe3,compensatory Sb doping regulates the carrier concentration and mobility withambipolar performance, together with the ultrathin thickness; these factorsmake the TSS in the 2D ternary TI Bi2–xSbxSe3 more observable. Here, a chemical vapor deposition (CVD) method is provided for synthesizing ultrathin Sb‐doped Bi2Se3 nanoplates with dimensions of 2–126 nm in thickness, 3–100 µm in lateral size, and an average Sb doping ranging from 0.15 ≤ x ≤ 0.75. Bi2–xSbxSe3 field effect transistors and Hall devices are manufactured to determine the carrier concentration and mobility of the obtained Bi2–xSbxSe3 nanoplates. These findings demonstrate that the 2D carrier concentration for Bi2–xSbxSe3 nanoplates can decrease up to 1.6 × 1012 cm–2. Furthermore, field‐effect mobility and Hall mobility of up to 3411 cm2 V–1s–1 and 6462 cm2 V–1 s–1, respectively, are realized. A strong ambipolar field effect is found in low‐carrier‐density Bi2–xSbxSe3 nanoplates, proving that these nanostructures may be freely controlled in terms of carrier type and concentration. The synthesis of high‐quality Bi2–xSbxSe3 nanoplates with low‐carrier concentration and high‐mobility provides a platform for investigating TI characteristics more clearly. [ABSTRACT FROM AUTHOR]

Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for efficient market management and consumer health. The commonly used method to classify AS grades depends on the evaluator's observation and experience. However, this method has issues such as unquantifiable parameters and inconsistent identification results among different evaluators, resulting in a relatively chaotic classification of AS in the market. To address these issues, this study introduced a computer vision-based approach to intelligently grade AS. Images of AS at five grades were acquired, denoised, and segmented, followed by extraction of shape, color, and texture features. Thirteen feature parameters were selected based on difference and correlation analysis, including tail area, whole body area, head diameter, G average, B average, R variances, G variances, B variances, R skewness, G skewness, B skewness, S average, and V average, which exhibited significant differences and correlated with grades. These parameters were then used to train and test both the traditional back propagation neural network (BPNN) and the BPNN model improved with a growing optimizer (GOBPNN). Results showed that the GOBPNN model achieved significantly higher average testing precision, recall, F-score, and accuracy (97.1%, 95.9%, 96.5%, and 95.0%, respectively) compared to the BPNN model. The method combining machine vision technology with GOBPNN enabled efficient, objective, rapid, non-destructive, and cost effective AS grading. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy. [ABSTRACT FROM AUTHOR]

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) conformations of a small molecule profoundly affect its binding to the target of interest, the resulting biological effects, and its disposition in living organisms, but it is challenging to accurately characterize the conformational ensemble experimentally. Here, we proposed an autoregressive torsion angle prediction model Tora3D for molecular 3D conformer generation. Rather than directly predicting the conformations in an end-to-end way, Tora3D predicts a set of torsion angles of rotatable bonds by an interpretable autoregressive method and reconstructs the 3D conformations from them, which keeps structural validity during reconstruction. Another advancement of our method over other conformational generation methods is the ability to use energy to guide the conformation generation. In addition, we propose a new message-passing mechanism that applies the Transformer to the graph to solve the difficulty of remote message passing. Tora3D shows superior performance to prior computational models in the trade-off between accuracy and efficiency, and ensures conformational validity, accuracy, and diversity in an interpretable way. Overall, Tora3D can be used for the quick generation of diverse molecular conformations and 3D-based molecular representation, contributing to a wide range of downstream drug design tasks. [ABSTRACT FROM AUTHOR]

Overexpression of a dominant-negative allele of YPT1 has been found to inhibit Candida albicans growth and aspartyl protease secretion. The long-term goal of the study, in which the C. albicans homologue of S. cerevisiae YPT1 was cloned, was to characterize key steps in the processing and secretion of C. albicans virulence-associated proteins. Molecular genetic approaches were used to investigate the gene and its function and to look at its effects on Sap secretion.

Signaling through mitogen-activated protein kinases (MPKs) cascades is a complex and fundamental process in eukaryotes, requiring MPK-activating kinases (MKKs) and MKK-activating kinases (MKKKs). However, to date only a limited number of MKK-MPK interactions and MPK phosphorylation substrates have been revealed. We determined which Arabidopsis thaliana MKKs preferentially activate 10 different MPKs in vivo and used the activated MPKs to probe high-density protein microarrays to determine their phosphorylation targets. Our analyses revealed known and novel signaling modules encompassing 570 MPK phosphorylation substrates; these substrates were enriched in transcription factors involved in the regulation of development, defense, and stress responses. Selected MPK substrates were validated by in planta reconstitution experiments. A subset of activated and wild-type MKKs induced cell death, indicating a possible role for these MKKs in the regulation of cell death. Interestingly, MKK7- and MKK9-induced death requires Sgt1, a known regulator of cell death induced during plant innate immunity. Our predicted MKK-MPK phosphorylation network constitutes a valuable resource to understand the function and specificity of MPK signaling systems.