Study on Quality Control of Concrete Raw Materials in Road and Bridge Construction

The main material of concrete is a construction building material composed of water and mineral mixture and cement and chemical additives in the corresponding proportion and below the standard. In the process of making concrete material, slurry and cement are needed to mix, then cement slurry and sand are mixed into mortar according to the corresponding proportion, and aggregate is added to mortar to form concrete building material. In the process of concrete preparation, the most important construction link is mixing, which needs to be fully stirred to make the performance of concrete meet the construction needs. In the process of concrete construction technology development, both mix ratio and production technology have become more and more mature, but there are still some problems, which have an impact on the quality of concrete. Therefore, this paper discusses the quality control of concrete raw materials according to the construction process of road and bridge

SVH-B interacts directly with p53 and suppresses the transcriptional activity of p53

AbstractWe previously reported that inhibition of SVH-B, a specific splicing variant of SVH, results in apoptotic cell death. In this study, we reveal that this apoptosis may be dependent on the presence of p53. Co-immunoprecipitation and GST pull-down assays have demonstrated that SVH-B directly interacts with p53. In both BEL-7404 cells and p53-null Saos-2 cells transfected with a temperature-sensitive mutant of p53, V143A, ectopically expressed SVH-B suppresses the transcriptional activity of p53, and suppression of SVH by RNA interference increases the transcriptional activity of p53. Our results suggested the function of SVH-B in accelerating growth and inhibition of apoptosis is related to its inhibitory binding to p53

Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort

Rational Design of a Chalcogenopyrylium-Based Surface-Enhanced Resonance Raman Scattering-Nanoprobe with Attomolar Sensitivity

High sensitivity and specificity are two desirable features in biomedical imaging. Raman imaging has surfaced as a promising optical modality that offers both. Here, we report the design and synthesis of a group of near infrared absorbing 2-thienyl-substituted chalcogenopyrylium dyes tailored to have high affinity for gold. When adsorbed onto gold nanoparticles, these dyes produce biocompatible SERRS-nanoprobes with attomolar limits of detection amenable to ultrasensitive in vivo multiplexed tumor and disease marker detection

Transcriptome analysis of differential sugar accumulation in the developing embryo of contrasting two Castanea mollissima cultivars

Chinese chestnut (Castanea mollissima) is an important nut tree species, and its embryo is rich in sugar. We combined metabolomic and transcriptomic data to analyze metabolites and genes related to sugar in two Chinese chestnut cultivars at 60, 70, 80, 90 and 100 days after flowering (DAF). The soluble sugar content of high-sugar cultivar at maturity is 1.5 times that of low-sugar cultivar. Thirty sugar metabolites were identified in embryo, with the most dominant being sucrose. Analysis of the gene expression patterns revealed that the high-sugar cultivar promoted the conversion of starch to sucrose by up-regulating genes related to starch degradation and sucrose synthesis at 90-100 DAF. It also strongly increased the enzyme activity of SUS-synthetic, which may promote sucrose synthesis. Gene co-expression network analysis showed that ABA and peroxide were related to starch decomposition during Chinese chestnut ripening. Our study analyzed the composition and molecular synthesis mechanism of sugar in Chinese chestnut embryos, and provided a new insight into the regulation pattern of high sugar accumulation in Chinese chestnut nuts

Two novel hierarchical homogeneous nanoarchitectures of TiO2 nanorods branched and P25-coated TiO2 nanotube arrays and their photocurrent performances

We report here for the first time the synthesis of two novel hierarchical homogeneous nanoarchitectures of TiO2 nanorods branched TiO2 nanotube arrays (BTs) and P25-coated TiO2 nanotube arrays (PCTs) using two-step method including electrochemical anodization and hydrothermal modification process. Then the photocurrent densities versus applied potentials of BTs, PCTs, and pure TiO2 nanotube arrays (TNTAs) were investigated as well. Interestingly, at -0.11 V and under the same illumination condition, the photocurrent densities of BTs and PCTs show more than 1.5 and 1 times higher than that of pure TNTAs, respectively, which can be mainly attributed to significant improvement of the light-absorbing and charge-harvesting efficiency resulting from both larger and rougher surface areas of BTs and PCTs. Furthermore, these dramatic improvements suggest that BTs and PCTs will achieve better photoelectric conversion efficiency and become the promising candidates for applications in DSSCs, sensors, and photocatalysis

TGFβƒ1 Promotes Gemcitabine Resistance Through Regulating the LncRNA-LET/NF90/miR-145 Signaling Axis in Bladder Cancer

High tumor recurrence is frequently observed in patients with urinary bladder cancers (UBCs), with the need for biomarkers of prognosis and drug response. Chemoresistance and subsequent recurrence of cancers are driven by a subpopulation of tumor initiating cells, namely cancer stem-like cells (CSCs). However, the underlying molecular mechanism in chemotherapy-induced CSCs enrichment remains largely unclear. In this study, we found that during gemcitabine treatment lncRNA-Low Expression in Tumor (lncRNA-LET) was downregulated in chemoresistant UBC, accompanied with the enrichment of CSC population. Knockdown of lncRNA-LET increased UBC cell stemness, whereas forced expression of lncRNA-LET delayed gemcitabine-induced tumor recurrence. Furthermore, lncRNA-LET was directly repressed by gemcitabine treatment-induced overactivation of TGFβ/SMAD signaling through SMAD binding element (SBE) in the lncRNA-LET promoter. Consequently, reduced lncRNA-LET increased the NF90 protein stability, which in turn repressed biogenesis of miR-145 and subsequently resulted in accumulation of CSCs evidenced by the elevated levels of stemness markers HMGA2 and KLF4. Treatment of gemcitabine resistant xenografts with LY2157299, a clinically relevant specific inhibitor of TGFβRI, sensitized them to gemcitabine and significantly reduced tumorigenecity in vivo. Notably, overexpression of TGFβ1, combined with decreased levels of lncRNA-LET and miR-145 predicted poor prognosis in UBC patients. Collectively, we proved that the dysregulated lncRNA-LET/NF90/miR-145 axis by gemcitabine-induced TGFβ1 promotes UBC chemoresistance through enhancing cancer cell stemness. The combined changes in TGFβ1/lncRNA-LET/miR-145 provide novel molecular prognostic markers in UBC outcome. Therefore, targeting this axis could be a promising therapeutic approach in treating UBC patients

BoostTree and BoostForest for Ensemble Learning

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It achieves high randomness (diversity) by sampling its parameters randomly from a parameter pool, and selecting a subset of features randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 34 classification and regression datasets. Remarkably, BoostForest has only one hyper-parameter (the number of BoostTrees), which can be easily specified. Our code is publicly available, and the proposed ensemble learning framework can also be used to combine many other base learners