Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

<p>Abstract</p> <p>Background</p> <p>Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.</p> <p>Results</p> <p>In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile.</p> <p>Conclusion</p> <p>Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.</p

LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis

Automated log analysis is crucial in modern software-intensive systems for ensuring reliability and resilience throughout software maintenance and engineering life cycles. Existing methods perform tasks such as log parsing and log anomaly detection by providing a single prediction value without interpretation. However, given the increasing volume of system events, the limited interpretability of analysis results hinders analysts' trust and their ability to take appropriate actions. Moreover, these methods require substantial in-domain training data, and their performance declines sharply (by up to 62.5%) in online scenarios involving unseen logs from new domains, a common occurrence due to rapid software updates. In this paper, we propose LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt employs large language models (LLMs) to perform zero-shot log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 107.5% compared with simple prompts. Experiments on nine publicly available evaluation datasets across two tasks demonstrate that LogPrompt, despite using no training data, outperforms existing approaches trained on thousands of logs by up to around 50%. We also conduct a human evaluation of LogPrompt's interpretability, with six practitioners possessing over 10 years of experience, who highly rated the generated content in terms of usefulness and readability (averagely 4.42/5). LogPrompt also exhibits remarkable compatibility with open-source and smaller-scale LLMs, making it flexible for practical deployment

LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application

Biological control of potato common scab and growth promotion of potato by Bacillus velezensis Y6

Potato common scab, caused mainly by Streptomyces scabies, causes surface necrosis and reduces the economic value of potato tubers, but effective chemical control is still lacking. In this study, an attempt was made to control potato common scab by inoculating potatoes with Bacillus velezensis (B. velezensis) and to further investigate the mechanism of biological control. The results showed that B. velezensis Y6 could reduce the disease severity of potato common scab from 49.92 ± 25.74% [inoculated with Streptomyces scabies (S. scabies) only] to 5.56 ± 1.89% (inoculated with S. scabies and Y6 on the same day) and increase the potato yield by 37.32% compared with the control under pot experiment in this study. Moreover, in the field trial, it was found that Y6 could also significantly reduce disease severity from 13.20 ± 1.00% to 4.00 ± 0.70% and increase the potato yield from 2.07 ± 0.10 ton/mu to 2.87 ± 0.28 ton/mu (p &lt; 0.01; Tukey’s test). Furthermore, RNA-seq analysis indicated that 256 potato genes were upregulated and 183 potato genes were downregulated in response to B. velezensis Y6 inoculation. In addition, strain Y6 was found to induce the expression of plant growth-related genes in potato, including cell wall organization, biogenesis, brassinosteroid biosynthesis, and plant hormone transduction genes, by 1.01–4.29 times. As well as up-regulate hydroquinone metabolism-related genes and several transcription factors (bHLH, MYB, and NAC) by 1.13–4.21 times. In summary, our study will help to understand the molecular mechanism of biological control of potato common scab and improve potato yield

Network pharmacology and experimental verification reveal the mechanism of safranal against glioblastoma (GBM)

IntroductionSafranal is an active component of the traditional Tibetan medicine (TTM) saffron, which has potential anticancer activity.Methods and resultsHere, we studied the therapeutic effect and mechanism of safranal on GBM. CCK-8, GBM-brain organoid coculture experiments and 3D tumour spheroid invasion assays showed that safranal inhibited GBM cell proliferation and invasion in vitro. Network pharmacology, RNA-seq, molecular docking analysis, western blotting, apoptosis, and cell cycle assays predicted and verified that safranal could promote GBM cell apoptosis and G2/M phase arrest and inhibit the PI3K/AKT/mTOR axis. In vivo experiments showed that safranal could inhibit GBM cell growth alone and in combination with TMZ.ConclusionThis study revealed that safranal inhibits GBM cell growth in vivo and in vitro, promotes GBM cell apoptosis and G2/M phase arrest, inhibits the PI3K/AKT/mTOR axis and cooperate with TMZ