Biotransformation of major ginsenosides into compound K by a new Penicillium dipodomyicola strain isolated from the soil of wild ginseng

A new strain, GH9, having ß-glucosidase activity was isolated from the soil of wild ginseng using Esculin-R2A agar. It shows the strongest activities to convert ginsenoside Rb1 to minor ginsenosides compound K. The transformation products were identified by thin layer chromatography (TLC) and high performance liquid chromatography (HPLC), and strain GH9 was found to transform major ginsenoside to minor ginsenoside compound K as the key sole product. The optimal biotransformation conditions of GH9 with C-K were obtained as follows: media, yeast broth (YB); transforming temperature, 40°C; pH of the medium, 4 - 6; transforming time, 7 days. At these optimum conditions, the maximum yield was 86.1%. Strain GH9 was identified as a Penicillium dipodomyicola species based on the internal transcribed spacers (ITS) ITS1-5.8S-ITS2 rRNA gene sequences constructed phylogenetic trees.Key words: Biotransformation, Panax ginseng, ginsenoside Rb1, ginsenoside compound K, minor ginsenoside

Interactive Contrastive Learning for Self-supervised Entity Alignment

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, KGs contain rich side information (e.g., entity description), and how to effectively leverage those information has not been adequately investigated in self-supervised EA. In this paper, we propose an interactive contrastive learning model for self-supervised EA. The model encodes not only structures and semantics of entities (including entity name, entity description, and entity neighborhood), but also conducts cross-KG contrastive learning by building pseudo-aligned entity pairs. Experimental results show that our approach outperforms previous best self-supervised results by a large margin (over 9% average improvement) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA.Comment: Accepted by CIKM 202

Analysis of tumor-related features of non-small cell lung cancer based on TCR repertoire workflow

Objective·To explore the immune-related characteristics of non-small cell lung cancer (NSCLC), discover potential tumor markers in V-J genes, and lay the foundation for establishing a TCR-antigen recognition prediction model.Methods·A total of 704 NSCLC samples were collected to establish a comprehensive T-cell receptor (TCR) repertoire analysis workflow. The upstream analysis included steps such as raw data processing, quality control, filtering, TCR sequence identification, and extraction. The downstream analysis included repertoire clone distribution, clone typing, V-J gene sharing, CDR3 distribution characteristics, and clone tracking. The sample clone distribution was analyzed by using indices such as Shannon-Weiner index and Chao1 index. Clone typing was performed based on the number of clone amplifications to explore differences among different types. The degree of V-J gene segment sharing was analyzed, and the sharing of low-frequency clone types was determined through clone amplification weight analysis of V-J genes by using two samples of papillary thyroid carcinoma. Finally, analysis of the distribution characteristics of V genes and high-frequency clone type CDR3, and clone tracking analysis were conducted to monitor changes in tumor immune clone frequencies before and after analysis, aiming to identify potential tumor markers.Results·① Significant differences were observed in clone distribution and clone typing among different NSCLC tissues, as well as among different ages and genders. ② Specific highly-shared V-J genes were identified in the analysis of V-J gene sharing, and non-normal distribution of high-clone V genes and amino acid high-frequency clone types were found in the CDR3 distribution analysis. ③ In the analysis of high-frequency clone type clone tracking, highly expressed or newly expressed high-frequency clone types were observed in NSCLC, suggesting that these clone types could serve as potential tumor-associated antigens or bind with CDR3 reference sequences of new antigens. ④ It was found that the expression frequency of TRBJ2-5 gene, originally low-expressed, significantly increased, indicating its potential role as a key low-frequency gene in tumor immune response.Conclusion·The TRAV21 and TRBV6.5 genes show high clone amplification in NSCLC and could serve as potential tumor biomarkers

Rational Design of a Two-Photon Fluorescent Probe for Human Cytochrome P450 3A and the Visualization of Mechanism-Based Inactivation

Mechanism-based inactivation (MBI) can mediate adverse reactions and hepatotoxicity from drugs, which is a result of their conversion into highly reactive metabolites catalyzed by enzymes such as cytochrome P450 3A (CYP3A). In the present research, we optimized the key interaction domain of the fluorophore with the target protein to develop a two-photon fluorescent probe for CYP3A that is involved in the metabolism of more than half of all clinical drugs. The developed BN-1 probe exhibited appropriate selectivity and sensitivity for the semi-quantitative detection and imaging of endogenous CYP3A activity in various living systems, thereby providing a high-throughput screening system enabling evaluation of MBI-associated hepatotoxicity by CYP3A. Using BN-1 as a fluorescent molecular tool facilitates the efficient discovery and characterization of CYP3A-induced MBI in natural systems.</p

Co-design Hardware and Algorithm for Vector Search

Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents. As performance demands for vector search systems surge, accelerated hardware offers a promising solution in the post-Moore's Law era. We introduce \textit{FANNS}, an end-to-end and scalable vector search framework on FPGAs. Given a user-provided recall requirement on a dataset and a hardware resource budget, \textit{FANNS} automatically co-designs hardware and algorithm, subsequently generating the corresponding accelerator. The framework also supports scale-out by incorporating a hardware TCP/IP stack in the accelerator. \textit{FANNS} attains up to 23.0$\times$ and 37.2$\times$ speedup compared to FPGA and CPU baselines, respectively, and demonstrates superior scalability to GPUs, achieving 5.5$\times$ and 7.6$\times$ speedup in median and 95\textsuperscript{th} percentile (P95) latency within an eight-accelerator configuration. The remarkable performance of \textit{FANNS} lays a robust groundwork for future FPGA integration in data centers and AI supercomputers.Comment: 11 page

A gate-tunable quantum phase transition in a topological excitonic insulator

Coulomb interactions among electrons and holes in two-dimensional (2D) semimetals with overlapping valence and conduction bands can give rise to a correlated insulating ground state via exciton formation and condensation. One candidate material in which such excitonic state uniquely combines with non-trivial band topology are atomic monolayers of tungsten ditelluride (WTe2), in which a 2D topological excitonic insulator (2D TEI) forms. However, the detailed mechanism of the 2D bulk gap formation in WTe2, in particular with regard to the role of Coulomb interactions, has remained a subject of ongoing debate. Here, we show that WTe2 is susceptible to a gate-tunable quantum phase transition, evident from an abrupt collapse of its 2D bulk energy gap upon ambipolar field-effect doping. Such gate tunability of a 2D TEI, into either n- and p-type semimetals, promises novel handles of control over non-trivial 2D superconductivity with excitonic pairing.Comment: 8 pages, 4 figures, under submissio

Seasonal variations of C-1-C-4 alkyl nitrates at a coastal site in Hong Kong: Influence of photochemical formation and oceanic emissions

Five C-1-C-4 alkyl nitrates (RONO2) were measured at a coastal site in Hong Kong in four selected months of 2011 and 2012. The total mixing ratios of C-1-C-4 RONO2 (Sigma 5RONO2) ranged from 15.4 to 143.7 pptv with an average of 65.9 +/- 33.0 pptv. C-3-C-4 RONO2 (2-butyl nitrate and 2-propyl nitrate) were the most abundant RONO2 during the entire sampling period. The mixing ratios of C-3-C-4 RONO2 were higher in winter than those in summer, while the ones of methyl nitrate (MeONO2) were higher in summer than those in winter. Source analysis suggests that C-2-C-4 RONO2 were mainly derived from photochemical formation along with biomass burning (58.3-71.6%), while ocean was a major contributor to MeONO2 (53.8%) during the whole sampling period. The photochemical evolution of C-2-C-4 RONO2 was investigated, and found to be dominantly produced by the parent hydrocarbon oxidation. The notable enrichment of MeONO2 over C-3-C-4 RONO2 was observed in a summer episode when the air masses originating from the South China Sea (SCS) and MeONO2 was dominantly derived from oceanic emissions. In order to improve the accuracy of ozone (O-3) prediction in coastal environment, the relative contribution of RONO2 from oceanic emissions versus photochemical formation and their coupling effects on O-3 production should be taken into account in future studies. (C) 2017 Elsevier Ltd. All rights reserved