Notes

Structurel experience in a flavin-dependent dehalogenase HadA describe catalysis and also substrate inhibition by means of quadruple π-stacking.
These results demonstrated that the in vivo performance is the addition of many parameters that did not always correlate with the in vitro behavior of the protein of interest, becoming especially relevant at evaluating the potential of IBs as a protein-based nanomaterial for therapeutic purposes.Salmonella enterica is a pathogenic bacterium that causes foodborne illness. One of the vehicle foods of S. enterica are chicken eggs. Efficient collection of the bacterium is necessary to detect it specifically. We developed a method to detect S. enterica by PCR on a microfluidic disc device using a fluorescent probe. Salmonella enterica cells were isolated in the microchambers on the device, followed by thermal lysis and PCR targeting with the invA gene, a gene specific to S. enterica, were observed by measurement of the fluorescent signal that resulted from gene amplification. However, the developed method was unable to discriminate viable cells from dead cells. Consequently, in this study, magnetic beads modified with anti-Salmonella antibody were utilized to detect viable Salmonella cells from egg yolk prior to PCR on the device. While using the antibody-modified beads, egg yolk components, which inhibit PCR, were removed. The collected cells were subsequently detected by PCR of the invA gene on a microfluidic disc device. This method enabled the detection of viable cells without the inhibition of PCR by any egg component. S. enterica was detected at 5.0×104 cells mL-1 or at a higher concentration of egg yolk within 6 h including the sampling time.In this paper, a silica aerogel support was prepared by two-step sol-gel method, and the active component K2CO3 was supported on the support by wet loading to obtain a modified potassium-based CO2 adsorbent. As the influences of reaction conditions on the CO2 capture characteristics of modified potassium-based adsorbents, the reaction temperature (50 °C, 60 °C, 70 °C, 80 °C), water vapor concentration (10%, 15%, 20%), CO2 concentration (5%, 10%, 12.5%, 15%), and total gas flow rate (400 mL/min, 500 mL/min, 600 mL/min) were studied in a self-designed fixed-bed reactor. At the same time, the low-temperature nitrogen adsorption experiment, scanning electron microscope, and X-ray diffractometer were used to study the microscopic characteristics of modified potassium-based adsorbents before and after the reaction. The results show that the silica aerogel prepared by the two-step sol-gel method has an excellent microstructure, and its specific surface area and specific pore volume are as high as 838.9 m2/g and 0.85 cm3/g, respectively. The microstructure of K2CO3 loaded on the support is improved, which promotes the CO2 adsorption performance of potassium-based adsorbents. The adsorption of CO2 by potassium-based adsorbents can be better described by the Avrami fractional kinetic model and the modified Avrami fractional kinetic model, and it is a complex multi-path adsorption process, which is related to the adsorption site and activity. check details The optimal adsorption temperature, water vapor concentration, CO2 concentration, and total gas volume were 60 °C, 15%, 12.5%, and 500 mL/min, respectively.WRKY transcription factors play central roles in developmental processes and stress responses of wheat. Most WRKY proteins of the same group (Group III) have a similar function in abiotic stress responses in plants. TaWRKY46, a member of Group III, was up-regulated by PEG treatment. TaWRKY46-GFP fusion proteins localize to the nucleus in wheat mesophyll protoplasts. Overexpression of TaWRKY46 enhanced osmotic stress tolerance in transgenic Arabidopsis thaliana plants, which was mainly demonstrated by transgenic Arabidopsis plants forming higher germination rate and longer root length on 1/2 Murashige and Skoog (MS) medium containing mannitol. Furthermore, the expression of several stress-related genes (P5CS1, RD29B, DREB2A, ABF3, CBF2, and CBF3) was significantly increased in TaWRKY46-overexpressing transgenic Arabidopsis plants after mannitol treatment. Taken together, these findings proposed that TaWRKY46 possesses vital functions in improving drought tolerance through ABA-dependent and ABA-independent pathways when plants are exposed to adverse osmotic conditions. TaWRKY46 can be taken as a candidate gene for transgenic breeding against osmotic stress in wheat. It can further complement and improve the information of the WRKY family members of Group III.Colorectal cancer (CRC) is one of the leading causes of world cancer deaths. To improve the survival rate of CRC, diagnosis and post-operative monitoring is necessary. Currently, biomarkers are used for CRC diagnosis and prognosis. However, these biomarkers have limitations of specificity and sensitivity. Levels of plasma lysyl-tRNA synthetase (KARS1), which was reported to be secreted from colon cancer cells by stimuli, along with other secreted aminoacyl-tRNA synthetases (ARSs), were analyzed in CRC and compared with the currently used biomarkers. The KARS1 levels of CRC patients (n = 164) plasma were shown to be higher than those of healthy volunteers (n = 32). The diagnostic values of plasma KARS1 were also evaluated by receiving operating characteristic (ROC) curve. Compared with other biomarkers and ARSs, KARS1 showed the best diagnostic value for CRC. The cancer specificity and burden correlation of plasma KARS1 level were validated using azoxymethane (AOM)/dextran sodium sulfate (DSS) model, and paired pre- and post-surgery CRC patient plasma. In the AOM/DSS model, the plasma level of KARS1 showed high correlation with number of polyps, but not for inflammation. Using paired pre- and post-surgery CRC plasma samples (n = 60), the plasma level of KARS1 was significantly decreased in post-surgery samples. Based on these evidence, KARS1, a surrogate biomarker reflecting CRC burden, can be used as a novel diagnostic and post-operative monitoring biomarker for CRC.Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on one-dimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF-1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios.
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