Researchers achieve de novo biosynthesis of plant lignans using synthetic yeast consortia
Peer-Reviewed Publication
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Research team proposed the e-calculus, a process calculus for modeling epistemic interactions between agents in concurrency situations. It captures asynchrony via a shared buffer pool and has advantages in modeling asynchrony compared to existing methods.
Researchers Uncover the Role of Soil Amendments in Enhancing Plant Defense Mechanisms and Biological Control
This review provides a cutting-edge perspective on recent advances in tandem ECR (T-ECR) technology, highlighting the rational design of nanostructured multifunctional catalysts in tandem configurations while discussing optimization strategies for both tandem electrocatalytic pathways and cascade reactor engineering.
BACKGROUND
Urinary tract infections (UTIs) rank among the most prevalent bacterial infections globally. Traditional urine culture methods have significant limitations in detection time and sensitivity, prompting the need to evaluate targeted next-generation sequencing (tNGS) as a potential diagnostic tool.
METHODS
The study included a discovery cohort of 400 suspected UTI patients (202 analyzed) and a validation cohort of 200 patients (110 analyzed). The study assessed detection time, concordance rates, ability to identify polymicrobial infections, and antibiotic resistance genes (ARGs). Both clear and turbid urine samples were evaluated across different clinical settings.
RESULTS
In the discovery cohort, tNGS demonstrated 96.5% concordance with culture-positive samples, while showing superior specificity in culture-negative specimens (53.1% vs 28.1% for mNGS). Detection time for tNGS (12.89h) was notably shorter than mNGS (17.38h) and traditional culture (61.48h). tNGS exhibited remarkable capability in identifying polymicrobial infections (55.4% of samples), significantly outperforming both mNGS (27.7%) and traditional culture methods, which failed to detect any co-infections. The method showed particular strength in detecting fastidious organisms like Ureaplasma parvum and fungal species such as Candida tropicalis. For antibiotic resistance prediction, tNGS detected more ARGs (52.67% vs 41.22% for mNGS) and achieved 100% sensitivity for vancomycin and methicillin resistance in Gram-positive pathogens. The validation cohort confirmed tNGS's robust performance, maintaining high concordance rates for both culture-positive (90.00%) and culture-negative samples (55.00%), demonstrating consistent reliability across different clinical settings
CONCLUSIONS
tNGS demonstrates advantages in rapid and accurate UTI diagnosis, particularly in detecting polymicrobial infections and analyzing antibiotic resistance genes. It shows promise as an effective complementary tool for UTI diagnostics.
The precise regulation of the RIG-I-like receptors (RLRs)-mediated type I interferon (IFN-I) activation is crucial in antiviral immunity and maintaining host immune homeostasis in the meantime. Here, the authors identify an E3 ubiquitin ligase, namely RNF167, as a negative regulator of RLR-triggered IFN signaling. Mechanistically, RNF167 facilitates both atypical K6- and K11-linked polyubiquitination of RIG-I/MDA5 within CARD and CTD domains, respectively, which leads to degradation of the viral RNA sensors through dual proteolytic pathways. RIG-I/MDA5 conjugated with K6-linked ubiquitin chains in CARD domains is recognized by the autophagy cargo adaptor p62, that delivers the substrates to autolysosomes for selective autophagic degradation. In contrast, K11-linked polyubiquitination in CTD domains leads to proteasome-dependent degradation of RLRs.
Thus, this study clarifies a function of atypical K6- and K11-linked polyubiquitination in the regulation of RLR signaling. The authors also unveil an elaborate synergistic effect of dual proteolysis systems to control amplitude and duration of IFN-I activation, hereby providing insights into physiological roles of the cross-talk between these two protein quality control pathways.
AI tools are transforming how we observe the world around us — and even the stars beyond. Recently, an international team proved that deep learning techniques and large language models can help astronomers classify stars with high accuracy and efficiency. Their study, “Deep Learning and Methods Based on Large Language Models Applied to Stellar Light Curve Classification,” was published Feb. 26 in Intelligent Computing, a Science Partner Journal.