Lipid Metabolism-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Advanced Gastric Cancer.

Objective: Abnormal lipid metabolism is known to influence the malignant behavior of gastric cancer. However, the underlying mechanism remains elusive. In this study, we comprehensively analyzed the biological significance of genes involved in lipid metabolism in advanced gastric cancer (AGC). Methods: We obtained gene expression profiles from The Cancer Genome Atlas (TCGA) database for early and advanced gastric cancer samples and performed differential expression analysis to identify specific lipid metabolism-related genes in AGC. We then used consensus cluster analysis to classify AGC patients into molecular subtypes based on lipid metabolism and constructed a diagnostic model using least absolute shrinkage and selection operator- (LASSO-) Cox regression analysis and Gene Set Enrichment Analysis (GSEA). We evaluated the discriminative ability and clinical significance of the model using the Kaplan-Meier (KM) curve, ROC curve, DCA curve, and nomogram. We also estimated immune levels based on immune microenvironment expression, immune checkpoints, and immune cell infiltration and obtained hub genes by weighted gene co-expression network analysis (WGCNA) of differential genes from the two molecular subtypes. Results: We identified 6 lipid metabolism genes that were associated with the prognosis of AGC and used consistent clustering to classify AGC patients into two subgroups with significantly different overall survival and immune microenvironment. Our risk model successfully classified patients in the training and validation sets into high-risk and low-risk groups. The high-risk score predicted poor prognosis and indicated low degree of immune infiltration. Subgroup analysis showed that the risk model was an independent predictor of prognosis in AGC. Furthermore, our results indicated that most chemotherapeutic agents are more effective for AGC patients in the low-risk group than in the high-risk group, and risk scores for AGC are strongly correlated with drug sensitivity. Finally, we performed qRT-PCR experiments to verify the relevant results. Conclusion: Our findings suggest that lipid metabolism-related genes play an important role in predicting the prognosis of AGC and regulating immune invasion. These results have important implications for the development of targeted therapies for AGC patients.

Uncertainty Modeling for Gaze Estimation.

Gaze estimation is an important fundamental task in computer vision and medical research. Existing works have explored various effective paradigms and modules for precisely predicting eye gazes. However, the uncertainty for gaze estimation, e.g., input uncertainty and annotation uncertainty, have been neglected in previous research. Existing models use a deterministic function to estimate the gaze, which cannot reflect the actual situation in gaze estimation. To address this issue, we propose a probabilistic framework for gaze estimation by modeling the input uncertainty and annotation uncertainty. We first utilize probabilistic embeddings to model the input uncertainty, representing the input image as a Gaussian distribution in the embedding space. Based on the input uncertainty modeling, we give an instance-wise uncertainty estimation to measure the confidence of prediction results, which is critical in practical applications. Then, we propose a new label distribution learning method, probabilistic annotations, to model the annotation uncertainty, representing the raw hard labels as Gaussian distributions. In addition, we develop an Embedding Distribution Smoothing (EDS) module and a hard example mining method to improve the consistency between embedding distribution and label distribution. We conduct extensive experiments, demonstrating that the proposed approach achieves significant improvements over baseline and state-of-the-art methods on two widely used benchmark datasets, GazeCapture and MPIIFaceGaze, as well as our collected dataset using mobile devices.

Combustion Characteristic Prediction of a Supercritical CO2 Circulating Fluidized Bed Boiler Based on Adaptive GWO-SVM.

The development of a new and efficient supercritical carbon dioxide (S-CO2) power cycle system is one of the important technical ways to break through the bottleneck of coal power development, improve the efficiency of power generation, and realize energy saving and emission reduction. In order to simplify the complicated workload and save the huge time cost of numerical simulations on combustion characteristics, it is of great significance to accurately make the combustion characteristic prediction according to the operating performance of the S-CO2 CFB boiler. This study proposed a combustion characteristic prediction model corresponding to the S-CO2 CFB boiler based on the adaptive gray wolf optimizer support vector machine (AGWO-SVM). The parameters of the gray wolf optimizer algorithm were processed adaptively first combined with the boiler characteristics, and then the adaptive gray wolf optimizer algorithm was integrated with the support vector machine to solve the imbalance of local and global search problems of particles being easy to gather in a certain position in the process of pattern recognition. The novel method effectively predicts the boiler in the scaling process from the aspect of boiler capacity, optimizes the combustion characteristic expression by numerical simulations, greatly saves time cost and applicability of enlarged design by altering complex numerical simulations, and lays the application foundation of the S-CO2 CFB boiler in the industrial field with acceptable operation accuracy.

Nutritional Component Analyses in Different Varieties of Actinidia eriantha Kiwifruit by Transcriptomic and Metabolomic Approaches.

Actinidia eriantha is a unique germplasm resource for kiwifruit breeding. Genetic diversity and nutrient content need to be evaluated prior to breeding. In this study, we looked at the metabolites of three elite A. eriantha varieties (MM-11, MM-13 and MM-16) selected from natural individuals by using a UPLC-MS/MS-based metabolomics approach and transcriptome, with a total of 417 metabolites identified. The biosynthesis and metabolism of phenolic acid, flavonoids, sugars, organic acid and AsA in A. eriantha fruit were further analyzed. The phenolic compounds accounted for 32.37% of the total metabolites, including 48 phenolic acids, 60 flavonoids, 7 tannins and 20 lignans and coumarins. Correlation analysis of metabolites and transcripts showed PAL (DTZ79_15g06470), 4CL (DTZ79_26g05660 and DTZ79_29g0271), CAD (DTZ79_06g11810), COMT (DTZ79_14g02670) and FLS (DTZ79_23g14660) correlated with polyphenols. There are twenty-three metabolites belonging to sugars, the majority being sucrose, glucose arabinose and melibiose. The starch biosynthesis-related genes (AeglgC, AeglgA and AeGEB1) were expressed at lower levels compared with metabolism-related genes (AeamyA and AeamyB) in three mature fruits of three varieties, indicating that starch was converted to soluble sugar during fruit maturation, and the expression level of SUS (DTZ79_23g00730) and TPS (DTZ79_18g05470) was correlated with trehalose 6-phosphate. The main organic acids in A. eriantha fruit are citric acid, quinic acid, succinic acid and D-xylonic acid. Correlation analysis of metabolites and transcripts showed ACO (DTZ79_17g07470) was highly correlated with citric acid, CS (DTZ79_17g00890) with oxaloacetic acid, and MDH1 (DTZ79_23g14440) with malic acid. Based on the gene expression, the metabolism of AsA acid was primarily through the L-galactose pathway, and the expression level of GMP (DTZ79_24g08440) and MDHAR (DTZ79_27g01630) highly correlated with L-Ascorbic acid. Our study provides additional evidence for the correlation between the genes and metabolites involved in phenolic acid, flavonoids, sugars, organic acid and AsA synthesis and will help to accelerate the kiwifruit molecular breeding approaches.

Effects of S and Al on K Migration and Transformation during Coal and Biomass Co-combustion.

The co-combustion of biomass and coal has both environmental and economic benefits in terms of pollutants and greenhouse gas emissions. However, one of the key factors affecting the feasibility of this technology is the ash deposition and corrosion caused by the high alkali metal content of biomass, especially K. After the addition of elemental S to corn stalk/Xiaolongtan lignite blended fuel and Al2O3 to corn stalk/Datong lignite, combustion experiments were carried out in a tubular furnace to explore the effects of S and Al in coal on K migration and transformation. The experimental results show that when S/K < 6, an increase in the S/K ratio inhibited the release of K. When S/K > 6, the sulfation become saturated, and an increase in S promoted the release of K. When S/K = 6, the higher the temperature was, and the more obvious the inhibitory effect on the release of K was. Increasing the S/K ratio not only increased the CaSO4 content of the ash but also increased the content of water-soluble K compounds, such as K2SO4, and decreased the contents of acid-soluble K compounds and insoluble K compounds, such as KAlSi3O8. After Al2O3 was added, as the Al/K ratio increased, the K release rate gradually decreased. When the sample with Al/K = 2.5 and the original samples were burned at 600-700 °C, the difference in the K release rates of the two samples was relatively small. When the temperature was higher than 700 °C, the higher the temperature was, and the greater the difference in the K release rates of the samples was, which indicates that a high temperature promotes the formation of aluminosilicates containing K.

Genome-wide identification and characterization of the TIFY gene family in kiwifruit.

BACKGROUND: The TIFY gene family is a group of plant-specific transcription factors involved in regulation of plant growth and development and a variety of stress responses. However, the TIFY family has not yet been well characterized in kiwifruit, a popular fruit with important nutritional and economic value. RESULTS: A total of 27 and 21 TIFY genes were identified in the genomes of Actinidia eriantha and A. chinensis, respectively. Phylogenetic analyses showed that kiwifruit TIFY genes could be classified into four major groups, JAZ, ZML, TIFY and PPD, and the JAZ group could be further clustered into six subgroups (JAZ I to JAZ VI). Members within the same group or subgroup have similar exon-intron structures and conserved motif compositions. The kiwifruit TIFY genes are unevenly distributed on the chromosomes, and the segmental duplication events played a vital role in the expansion of the TIFY genes in kiwifruit. Syntenic analyses of TIFY genes between kiwifruit and other five plant species (including Arabidopsis thaliana, Camellia sinensis, Oryza sativa, Solanum lycopersicum and Vitis vinifera) and between the two kiwifruit species provided valuable clues for understanding the potential evolution of the kiwifruit TIFY family. Molecular evolutionary analysis showed that the evolution of kiwifruit TIFY genes was primarily constrained by intense purifying selection. Promoter cis-element analysis showed that most kiwifruit TIFY genes possess multiple cis-elements related to stress-response, phytohormone signal transduction and plant growth and development. The expression pattern analyses indicated that TIFY genes might play a role in different kiwifruit tissues, including fruit at specific development stages. In addition, several TIFY genes with high expression levels during Psa (Pseudomonas syringae pv. actinidiae) infection were identified, suggesting a role in the process of Pas infection. CONCLUSIONS: In this study, the kiwifruit TIFY genes were identified from two assembled kiwifruit genomes. In addition, their basic physiochemical properties, chromosomal localization, phylogeny, gene structures and conserved motifs, synteny analyses, promoter cis-elements and expression patters were systematically examined. The results laid a foundation for further understanding the function of TIFY genes in kiwifruit, and provided a new potential approach for the prevention and treatment of Psa infection.

Influence of CYP2D6 and ß2-adrenergic receptor gene polymorphisms on the hemodynamic response to propranolol in Chinese Han patients with cirrhosis.

BACKGROUND AND AIM: Propranolol is widely used to prevent gastroesophageal variceal bleeding; however, some patients could not benefit from propranolol. This study is to evaluate the relationship between CYP2D6 and ß2-adrenergic receptor (ß2-AR) gene polymorphisms and the hemodynamic response to propranolol in Chinese Han patients. METHODS: The clinical data of patients with gastroesophageal varices undergoing hepatic venous pressure gradient (HVPG) measurement before and 7 days after oral propranolol administration in our department were collected. Four single nucleotide polymorphisms of CYP2D6 and ß2-AR genes were detected. The relationship was identified by logistic regression model. RESULTS: Thirty patients were involved in the analysis. Sixty milligram propranolol twice each day was well tolerated by all the patients. The initial and secondary average of HVPG was 17.4 ± 5.8 mmHg vs. 13.2 ± 4.8 mmHg, respectively (t = 5.726, P < 0.001). Twenty patients responded to propranolol. The mean reduction value of HVPG was 6.6 ± 3.6 mmHg (range from 3 to 19). Genotype analysis showed: 20 homozygotes for C/C188 and 10 for heterozygous C/T188, 8 homozygotes for G/G4268 and 22 heterozygotes for G/C4268, 14 homozygotes for Gly16 and 10 heterozygotes, and 6 homozygotes for Arg16, 27 homozygotes for Gln27 and 3 heterozygotes. The multivariate logistic regression analysis indicated that CYP2D6 (188C>T) genotype was an independent predicting factor for HVPG response to propranolol (P = 0.033). CONCLUSIONS: CYP2D6 (188C>T) gene polymorphisms influence the hemodynamic response to propranolol in this population of Chinese Han patients with gastroesophageal varices. However, HVPG response cannot be completely predicted from CYP2D6 and ß2-AR gene polymorphisms.

Preparation of a silver nanoparticle-based dual-functional sensor using a complexation-reduction method.

A dual-functional sensor based on silver nanoparticles was synthesized by a two-stage procedure consisting of a low-temperature chitosan-Ag(+) complexation followed by a high-temperature reduction of the complex to form chitosan-capped silver nanoparticles (CS-capped Ag NPs). The surface plasmon resonance (SPR) absorption and fluorescence emission of the silver nanoparticles were influenced by the concentration and degradation time of chitosan, and the temperatures of the complexation and reduction reactions. The SPR absorption band was blue-shifted while the intensities of emission and absorption were decreased after reacting the silver nanoparticles with Hg(2+) ions. The silver nanoparticles reacted with Hg(2+) were characterized by high resolution transmission electron microscopy (HRTEM), energy dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman scattering spectroscopy (SERS). The results suggested that the particle growth and aggregation of the silver nanoparticles were caused by the adsorption of Hg(2+) and deposition of Hg(0) on the nanoparticle surface. Direct correlations of the SPR absorption and fluorescence emission with the concentration of Hg(2+) were useful for quantitative analysis of Hg(2+). It was possible to use the dual-functional silver nanoparticles as a colorimetric and fluorescent sensor for sensitive and selective detection of Hg(2+) ions.

Simulation on gasification of forestry residues in fluidized beds by Eulerian-Lagrangian approach.

A comprehensive three-dimensional numerical model is developed to simulate forestry residues gasification in a fluidized bed reactor using Eulerian-Lagrangian approach. The complex granular flow behaviors and chemical reaction characteristics are addressed simultaneously. The model uses an Eulerian method for fluid phase and a discrete particle method for solid phase, which takes particle contact force into account. Heterogeneous and homogenous reaction rates are solved on the Eulerian grid. The numerical model is employed to study the gasification performance in a lab-scale pine gasifier. A series of simulations have been performed with some critical parameters including temperature, equivalence ratio and steam to biomass ratio. The model predicts product gas composition and carbon conversion efficiency in good agreement with experimental data. The formation and development of flow regimes, profiles of particle species, and distributions of gas compositions inside the reactor are also discussed.

Efficacy analysis of methylprednisolone plus adrenaline to treat wasp sting injury.

OBJECTIVE: To observe the effect of methylprednisolone (MP) plus adrenaline to treat patients injured by wasp stings. METHODS: This study was carried out at The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei Province, China, from June to November 2008. A total of 45 cases were divided into 2 groups. The MP was used alone in the control group, and MP plus adrenaline was used in the observation group. We compared the clinical effect between the 2 groups. RESULTS: The alanine aminotransferase (ALT) (64.05 +/- 59.31 versus 124.14 +/- 80.01 U/L), and creatine kinase isoenzyme (CKMB) (90.32 +/- 85.19 versus 216.68 +/- 277.58 umol/L) levels of the observation group were significantly lower than those of the control group. The length of hospital stay of the observation group was significantly shorter than that of the control group (7.23 +/- 2.93 versus 11.23 +/- 8.02), while complications from the wasp sting were fewer than those of the control group. In addition, the level of peripheral blood leukocytes was positively correlated with the number of stings (r=0.733, p=0.001), levels of ALT and CKMB (r=0.627, p=0.001, and r=0.705, p=0.001), and length of hospital stay (r=0.667, p=0.001). CONCLUSION: Allergic and inflammatory responses play an important role in addition to the direct effect of wasp venom on the human body. Compared with MP alone, early combination of adrenaline helps to further inhibit the diffusion of allergy and inflammatory cytokines, and therefore reduce the severity of injury.

Characteristic and mercury adsorption of activated carbon produced by CO2 of chicken waste.

Preparation of activated carbon from chicken waste is a promising way to produce a useful adsorbent for Hg removal. A three-stage activation process (drying at 200 degrees C, pyrolysis in N2 atmosphere, followed by CO2 activation) was used for the production of activated samples. The effects of carbonization temperature (400-600 degrees C), activation temperature (700-900 degrees C), and activation time (1-2.5 h) on the physicochemical properties (weight-loss and BET surface) of the prepared carbon were investigated. Adsorptive removal of mercury from real flue gas onto activated carbon has been studied. The activated carbon from chicken waste has the same mercury capacity as commercial activated carbon (Darco LH) (Hg(v): 38.7% vs. 53.5%, Hg(0): 50.5% vs. 68.8%), although its surface area is around 10 times smaller, 89.5 m2/g vs. 862 m2/g. The low cost activated carbon can be produced from chicken waste, and the procedure is suitable.