Cuproptosis-related lncRNA signatures: Predicting prognosis and evaluating the tumor immune microenvironment in lung adenocarcinoma

BackgroundCuproptosis, a unique kind of cell death, has implications for cancer therapy, particularly lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) have been demonstrated to influence cancer cell activity by binding to a wide variety of targets, including DNA, RNA, and proteins.MethodsCuproptosis-related lncRNAs (CRlncRNAs) were utilized to build a risk model that classified patients into high-and low-risk groups. Based on the CRlncRNAs in the model, Consensus clustering analysis was used to classify LUAD patients into different subtypes. Next, we explored the differences in overall survival (OS), the tumor immune microenvironment (TIME), and the mutation landscape between different risk groups and molecular subtypes. Finally, the functions of LINC00592 were verified through in vitro experiments.ResultsPatients in various risk categories and molecular subtypes showed statistically significant variations in terms of OS, immune cell infiltration, pathway activity, and mutation patterns. Cell experiments revealed that LINC00592 knockdown significantly reduced LUAD cell proliferation, invasion, and migration ability.ConclusionThe development of a trustworthy prediction model based on CRlncRNAs may significantly aid in the assessment of patient prognosis, molecular features, and therapeutic modalities and may eventually be used in clinical applications

High-Precision, Whole-Genome Sequencing of Laboratory Strains Facilitates Genetic Studies

Whole-genome sequencing is a powerful technique for obtaining the reference sequence information of multiple organisms. Its use can be dramatically expanded to rapidly identify genomic variations, which can be linked with phenotypes to obtain biological insights. We explored these potential applications using the emerging next-generation sequencing platform Solexa Genome Analyzer, and the well-characterized model bacterium Bacillus subtilis. Combining sequencing with experimental verification, we first improved the accuracy of the published sequence of the B. subtilis reference strain 168, then obtained sequences of multiple related laboratory strains and different isolates of each strain. This provides a framework for comparing the divergence between different laboratory strains and between their individual isolates. We also demonstrated the power of Solexa sequencing by using its results to predict a defect in the citrate signal transduction pathway of a common laboratory strain, which we verified experimentally. Finally, we examined the molecular nature of spontaneously generated mutations that suppress the growth defect caused by deletion of the stringent response mediator relA. Using whole-genome sequencing, we rapidly mapped these suppressor mutations to two small homologs of relA. Interestingly, stable suppressor strains had mutations in both genes, with each mutation alone partially relieving the relA growth defect. This supports an intriguing three-locus interaction module that is not easily identifiable through traditional suppressor mapping. We conclude that whole-genome sequencing can drastically accelerate the identification of suppressor mutations and complex genetic interactions, and it can be applied as a standard tool to investigate the genetic traits of model organisms

A Multimodal Framework with Co-Attention for Fake Review Detection

On online shopping platforms, product reviews matter given their impact on consumers\u27 shopping decisions and product sales. However, a large number of fake reviews are created to lure customers to buy products, which brings poor shopping experiences to customers and further damages the platform\u27s reputation. Prior studies mainly leveraged textual features to detect fake reviews, failing to deal with multi-modal reviews with images. Therefore, to bridge this gap, we propose a new end-to-end framework to detect fake reviews in e-commerce platforms. We first design flexible encoders for unimodal feature extraction. Then we imitate the human browsing behaviors by designing a co-attention mechanism. The co-attention explores the dependency between textual and visual modalities, thus further enhancing the performance of fake review detection. Experiments conducted on a real-world dataset demonstrate the effectiveness of our model

Research on the Aeroelastic Response of Tower Effects for Great Grade Wind Turbine

International audience(Com. 17 mai 2017, n° 15-21.260, inédit ; Civ. 2e, 18 mai 2017, n° 16-17.754, à paraître au Bulletin ; D. 2017. 1120 ; Com. 4 mai 2017, n° 15-22.830, inédit

Experimental and Numerical Studies on Flow and Turbulence Characteristics of Impinging Stream Reactors with Dynamic Inlet Velocity Variation

Impinging stream technique has been widely used in engineering industries. Insufficient data are available on the effects of dynamic inflow conditions on the flow and turbulence characteristics of an impinging stream reactor. In this study, we investigate and discuss the flow and turbulence characteristics of an impinging stream reactor with dynamic inlet velocity variation, e.g., sinusoidal, parabolic, step or triangular variation. The effects of period, amplitude, phase difference, mean inlet velocity and type of dynamic inlet velocity variation on the motional behaviors of the impinging surface and the mean turbulence kinetic energy (k) of the impingement region are investigated and discussed using particle image velocimetry (PIV) and computational fluid dynamics (CFD) at various values of L/D (the ratio of impinging spacing to nozzle diameter). The results show that the impinging surface makes back-and-forth motions in impinging stream reactors with dynamic inlet velocity variation. The mean k of the impingement region during one period is dominated by both the inlet velocity conditions and the geometric configuration. Dynamic inflow conditions bring more turbulence energy and pulsating characteristics to impinging zones over constant inlet velocity for an instantaneously moving impinging surface. Impinging stream reactors with dynamic inlet velocity variation provides more intense turbulence properties over conventional impinging stream reactors at the same mean inlet velocity. This work shows that the impinging streams with dynamic inlet velocity variation has strong potential for future relevant reactors and processes for engineering applications