Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis

BackgroundLung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.Materials and methodsLUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.ResultsA total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.ConclusionImmune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients

An experimental study of clogging fault diagnosis in heat exchangers based on vibration signals

The water-circulating heat exchangers employed in petrochemical industrials have attracted great attentions in condition monitoring and fault diagnosis. In this paper, an approach based on vibration signals is proposed. By the proposed method, vibration signals are collected for different conditions through various high-precision wireless sensors mounted on the surface of the heat exchanger. Furthermore, by analyzing the characteristics of the vibration signals, a database of fault patterns is established, which therefore provides a scheme for conditional monitoring of the heat exchanger. An experimental platform is set up to evaluate the feasibility and effectiveness of the proposed approach, and support vector machine based on dimensionless parameters is developed for fault classification. The results have shown that the proposed method is efficient and has achieved a high accuracy for benchmarking vibration signals under both normal and faulty conditions

Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures

Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e. normal bearing and three different types of defected bearings on outer race, inner race and roller separately. Particle Swarm Optimization (PSO) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modelling based Continuous Wavelet Transform (IMCWT) model. Then, a three-dimensional feature space of the statistical parameters and a Nearest Neighbor (NN) classifier are respectively applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment

Impact of fouling on flow-induced vibration characteristics in fluid-conveying pipelines

This paper addresses monitoring problems commonly encountered in petrochemical enterprises caused by fouling and clogging in the circulating water heat exchangers by monitoring the heat exchanger’s wall vibration signal for early failure detection. Due to the difficulties encountered in simulation caused by the large number of tubes inside the heat exchanger, such methods were discussed by studying in the fluid-conveying pipeline fouling. ANSYS was used to establish the normal model and fouling model of a fluid-conveying pipeline so as to analyze the changing rule of various parameters that are influenced by different inlet velocities. As the inlet velocity and fouling severity continuously increased, the wall load and the vibration acceleration increased as well, leading variations in wall vibration signals. This paper conduct extensive experiments by using straight pipes to compare the results from simulation and from normal fluid-conveying pipelines, under the same working conditions. By such comparison, we estimate the accuracy of the simulation model

NAD metabolism-related genes provide prognostic value and potential therapeutic insights for acute myeloid leukemia

IntroductionAcute myeloid leukemia (AML) is an aggressive blood cancer with high heterogeneity and poor prognosis. Although the metabolic reprogramming of nicotinamide adenine dinucleotide (NAD) has been reported to play a pivotal role in the pathogenesis of acute myeloid leukemia (AML), the prognostic value of NAD metabolism and its correlation with the immune microenvironment in AML remains unclear.MethodsWe utilized our large-scale RNA-seq data on 655 patients with AML and the NAD metabolism-related genes to establish a prognostic NAD metabolism score based on the sparse regression analysis. The signature was validated across three independent datasets including a total of 1,215 AML patients. ssGSEA and ESTIMATE algorithms were employed to dissect the tumor immune microenvironment. Ex vivo drug screening and in vitro experimental validation were performed to identify potential therapeutic approaches for the high-risk patients. In vitro knockdown and functional experiments were employed to investigate the role of SLC25A51, a mitochondrial NAD+ transporter gene implicated in the signature.ResultsAn 8-gene NAD metabolism signature (NADM8) was generated and demonstrated a robust prognostic value in more than 1,800 patients with AML. High NADM8 score could efficiently discriminate AML patients with adverse clinical characteristics and genetic lesions and serve as an independent factor predicting a poor prognosis. Immune microenvironment analysis revealed significant enrichment of distinct tumor-infiltrating immune cells and activation of immune checkpoints in patients with high NADM8 scores, acting as a potential biomarker for immune response evaluation in AML. Furthermore, ex vivo drug screening and in vitro experimental validation in a panel of 9 AML cell lines demonstrated that the patients with high NADM8 scores were more sensitive to the PI3K inhibitor, GDC-0914. Finally, functional experiments also substantiated the critical pathogenic role of the SLC25A51 in AML, which could be a promising therapeutic target.ConclusionOur study demonstrated that NAD metabolism-related signature can facilitate risk stratification and prognosis prediction in AML and guide therapeutic decisions including both immunotherapy and targeted therapies

Phase-dependent study of near-infrared disk emission lines in LB-1

The mass, origin and evolutionary stage of the binary system LB-1 has been the subject of intense debate, following the claim that it hosts an $\sim$70$M_{\odot}$ black hole, in stark contrast with the expectations for stellar remnants in the Milky Way. We conducted a high-resolution, phase-resolved spectroscopic study of the near-infrared Paschen lines in this system, using the 3.5-m telescope at Calar Alto Observatory. We find that Pa$\beta$ and Pa$\gamma$ (after proper subtraction of the stellar absorption component) are well fitted with a standard double-peaked model, typical of disk emission. We measured the velocity shifts of the red and blue peaks at 28 orbital phases: the line center has an orbital motion in perfect antiphase with the stellar motion, and the radial velocity amplitude ranges from 8 to 13 km/s for different choices of lines and profile modelling. We interpret this curve as proof that the disk is tracing the orbital motion of the primary, ruling out the circumbinary disk and the hierarchical triple scenarios. The phase-averaged peak-to-peak half-separation (proxy for the projected rotational velocity of the outer disk) is $\sim$70 km s$^{-1}$, larger than the stellar orbital velocity and also inconsistent with a circumbinary disk. From those results, we infer a primary mass 4--8 times higher than the secondary mass. Moreover, we show that the ratio of the blue and red peaks (V/R intensity ratio) has a sinusoidal behaviour in phase with the secondary star, which can be interpreted as the effect of external irradiation by the secondary star on the outer disk. Finally, we briefly discuss our findings in the context of alternative scenarios recently proposed for LB-1. Definitive tests between alternative solutions will require further astrometric data from $Gaia$.Comment: To be submitted to ApJ. Comments are welcom

Human Influenza A (H5N1) Cases, Urban Areas of People’s Republic of China, 2005–2006

We investigated potential sources of infection for 6 confirmed influenza A (H5N1) patients who resided in urban areas of People’s Republic of China. None had known exposure to sick poultry or poultry that died from illness, but all had visited wet poultry markets before illness

Transient Receptor Potential V Channels Are Essential for Glucose Sensing by Aldolase and AMPK

Fructose-1,6-bisphosphate (FBP) aldolase links sensing of declining glucose availability to AMPK activation via the lysosomal pathway. However, how aldolase transmits lack of occupancy by FBP to AMPK activation remains unclear. Here, we show that FBP-unoccupied aldolase interacts with and inhibits endoplasmic reticulum (ER)-localized transient receptor potential channel subfamily V, inhibiting calcium release in low glucose. The decrease of calcium at contact sites between ER and lysosome renders the inhibited TRPV accessible to bind the lysosomal v-ATPase that then recruits AXIN:LKB1 to activate AMPK independently of AMP. Genetic depletion of TRPVs blocks glucose starvation-induced AMPK activation in cells and liver of mice, and in nematodes, indicative of physical requirement of TRPVs. Pharmacological inhibition of TRPVs activates AMPK and elevates NAD(+) levels in aged muscles, rejuvenating the animals' running capacity. Our study elucidates that TRPVs relay the FBP-free status of aldolase to the reconfiguration of v-ATPase, leading to AMPK activation in low glucose