Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques.

Splitting tensile strength (STS) is an important mechanical property of concrete. Modeling and predicting the STS of concrete containing Metakaolin is an important method for analyzing the mechanical properties. In this paper, four machine learning models, namely, Artificial Neural Network (ANN), support vector regression (SVR), random forest (RF), and Gradient Boosting Decision Tree (GBDT) were employed to predict the STS. The comprehensive comparison of predictive performance was conducted using evaluation metrics. The results indicate that, compared to other models, the GBDT model exhibits the best test performance with an R2 of 0.967, surpassing the values for ANN at 0.949, SVR at 0.963, and RF at 0.947. The other four error metrics are also the smallest among the models, with MSE = 0.041, RMSE = 0.204, MAE = 0.146, and MAPE = 4.856%. This model can serve as a prediction tool for STS in concrete containing Metakaolin, assisting or partially replacing laboratory compression tests, thereby saving costs and time. Moreover, the feature importance of input variables was investigated.

Comparative transcriptomic and metabolomic analysis revealed molecular mechanism of two wheat near-isogenic lines response to nitrogen application.

Nitrogen (N) is an essential nutrient element required for plant growth, and the development of wheat varieties with high nitrogen use efficiency (NUE) is an urgent need for sustainable crop production. However, the molecular mechanism of NUE between diverse wheat varieties in response to N application remains unclear. To reveal the possible molecular mechanisms underlying this complex phenomenon, we investigated the transcriptional and metabolic changes of flag leaves of two wheat near-isogenic lines (NILs) differing in NUE under two N fertilizer treatments. Comparative transcriptome analysis indicated that the expression levels of the genes responsible for carbon and nitrogen metabolism were significantly higher in high-NUE wheat. The metabolome comparison revealed that the activity of the tricarboxylic acid (TCA) cycle was enhanced in high-NUE wheat, while reduced in low-NUE wheat after the N fertilizer application. Additionally, amino acid metabolism increased in both wheat NILs but more increased in high-NUE wheat. In summary, more upregulated transcripts and metabolites were identified in high-NUE wheat, and this study provides valuable new insights for improving NUE in wheat.

Design of Environmentally Friendly Ca-Alginate Beads for Self-Healing Cement-Based Materials.

Ca-alginate beads have strong hygroscopicity, which have been used for the self-healing and internal curing of cement-based materials. However, ca-alginate beads in cement will chelate with calcium ions, which decreases the swelling rate of ca-alginate beads in the healing environment and is detrimental to the self-healing of cement-based materials. In this paper, the mechanism and steps for preparing ca-alginate beads with a lower ability to chelate with calcium ions were proposed based on protonation theory. In addition, the molecular structure and the swelling rates in cement filtrate and healing environment of ca-alginate beads prepared by the proposed method were characterized. The results showed that the ca-alginate beads prepared by the proposed method had higher molecular density and a lower ability to chelate with calcium ions. The swelling rate in the healing environment is not decreased. Furthermore, the equilibrium swelling rate in cement filtrate can satisfy the need for internal curing of cement-based materials.

Transcriptome Analysis Reveals Different Responsive Patterns to Nitrogen Deficiency in Two Wheat Near-Isogenic Lines Contrasting for Nitrogen Use Efficiency.

The development of crop cultivars with high nitrogen use efficiency (NUE) under low-N fertilizer inputs is imperative for sustainable agriculture. However, there has been little research on the molecular mechanisms underlying enhanced resilience to low N in high-NUE plants. The comparison of the transcriptional responses of genotypes contrasting for NUE will facilitate an understanding of the key molecular mechanism of wheat resilience to low-N stress. In the current study, the RNA sequencing (RNA-seq) technique was employed to investigate the genotypic difference in response to N deficiency between two wheat NILs (1Y, high-NUE, and 1W, low-NUE). In our research, high- and low-NUE wheat NILs showed different patterns of gene expression under N-deficient conditions, and these N-responsive genes were classified into two major classes, including "frontloaded genes" and "relatively upregulated genes". In total, 103 and 45 genes were identified as frontloaded genes in high-NUE and low-NUE wheat, respectively. In summary, our study might provide potential directions for further understanding the molecular mechanism of high-NUE genotypes adapting to low-N stress.

Transcriptome Analysis of Two Near-Isogenic Lines with Different NUE under Normal Nitrogen Conditions in Wheat.

Nitrogen (N) is an essential nutrient element for crop productivity. Unfortunately, the nitrogen use efficiency (NUE) of crop plants gradually decreases with the increase of the N application rate. Nevertheless, little has been known about the molecular mechanisms of differences in NUE among genotypes of wheat. In this study, we used RNA-Sequencing (RNA-Seq) to compare the transcriptome profiling of flag leaves at the stage of anthesis in wheat NILs (1Y, high-NUE, and 1W, low-NUE) under normal nitrogen conditions (300 kg N ha-1, corresponding to 1.6 g N pot-1). We identified 7023 DEGs (4738 upregulated and 2285 downregulated) in the comparison between lines 1Y and 1W. The responses of 1Y and 1W to normal N differed in the transcriptional regulatory mechanisms. Several genes belonging to the GS and GOGAT gene families were upregulated in 1Y compared with 1W, and the enhanced carbon metabolism might lead 1Y to produce more C skeletons, metabolic energy, and reductants for nitrogen metabolism. A subset of transcription factors (TFs) family members, such as ERF, WRKY, NAC, and MYB, were also identified. Collectively, these identified candidate genes provided new information for a further understanding of the genotypic difference in NUE.

Identification of key candidate genes and pathways in anaplastic thyroid cancer by bioinformatics analysis.

BACKGROUND: Anaplastic thyroid carcinoma (ATC) is a refractory and poor prognosis tumor Present study aimed to investigate the underlying biological functions and pathways involved in the development of ATC and to identify potential hub genes and candidate biomarkers of ATC. MATERIALS AND METHODS: Bioinformatics analyses were performed to identify the differentially expressed genes (DEGs) between ATC tissue samples and adjacent normal tissue samples. Protein-protein interaction (PPI) networks of the DEGs were constructed using Search Tool for the Retrieval of Interacting Genes online tool and Cytoscape software and divided into sub-networks using the Molecular Complex Detection (MCODE) plug-in. DEGs in each module was analyzed by enrichment analysis of the KEGG Orthology Based Annotation System (KOBAS) web software version 3.0. Eventually, the hub genes from bioinformatics analysis were verified by qRT-PCR assay in different ATC cell lines. RESULTS: Thirty hub genes were selected and three modules were built by the Cytoscape software from the PPI network. Seven genes (CDK1, CCNB2, BUB1B, CDC20, RRM2, CHEK1 and CDC45) were screened from thirty hub genes. Enrichment analysis showed that these hub genes were primarily accumulated in 'cell cycle', 'p53 signaling pathway', 'viral carcinogenesis', 'pyrimidine metabolism' and 'ubiquitin mediated proteolysis'. The results of qRT-PCR indicated that seven hub genes were unregulated in three ATC cell lines compared with normal thyroid gland cell. CONCLUSIONS: These findings suggest that CDK1, CCNB2, BUB1B, CDC20, RRM2, CHEK1 and CDC45 may serve as novel diagnosis biomarkers and potential therapeutic target for ATC.

Photochemical Vapor Generation for Colorimetric Speciation of Inorganic Selenium.

Gold nanoparticles (AuNPs) are widely used as optical probes in colorimetric detection, thanks to their high molar extinction coefficient. However, sample matrixes of high salinity or strong acidity/alkalinity often break the electrostatic repulsion of AuNPs suspension, or/and the surface functionality of AuNPs, causing strong and unfavorable interferences. Photochemical vapor generation (PVG) is an efficient technique for the sample matrix separation. Besides, it possesses distinct features of green reducing reagent, reduced interferences from concomitant elements, and direct speciation by the assistance of photocatalyst. Herein, we developed a photochemical vapor generation (PVG) method for the green and direct speciation analysis of inorganic selenium (i.e., Se(IV) and Se(VI)), by colorimetric or visual monitoring of unmodified AuNPs. The generated Se species from PVG were directed into the AuNPs solution for a reaction to take place, which produced a specific new absorption band at 600 nm for detection. The experimental parameters, including the concentration of organic acid, the sample flow rate, the concentration of AuNPs, and the flow rate of carries gas, were optimized in detail. Under optimized conditions, the limits of detection (LOD) for Se(IV) and Se(VI) were 0.007 and 0.006 µg mL-1 by UV-vis detection, respectively. It is worth mentioning that 0.08 µg mL-1 Se can induce an obvious color change, which can be directly observed with the naked eye. Relative standard deviations (RSDs) of 4.5% and 4.3% were obtained from seven replicate measurements of 0.15 µg mL-1 Se(IV) and Se(VI) standard solution, respectively. The developed assay has been successfully applied for the speciation of Se in a dietary supplement sample and environmental water samples including lake water, seawater, simulated water reference materials, and tap water.

Enzyme-free amplified DNA assay: five orders of linearity provided by metal stable isotope detection.

For the quantification of mutated genes at greatly varied concentrations in body fluids, nucleic acid amplification strategies are often challenged by limited dynamic linear ranges. The proposed metal stable isotope detection strategy demonstrates five orders of linear range and a lower attomol detection limit, showing promising potential in clinical diagnosis.