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The evaporation behavior of 233Pa in the FLiBeZr molten salt was investigated during a vacuum distillation process.

Purpose To develop and validate a nomogram for individualized prediction of the long-term prognosis of patients with non-B, non-C hepatocellular carcinoma (NBNC-HCC) who underwent hepatectomy. Materials and methods Five hundred ninety-four patients who met the criteria were included in the research and randomly categorized into the training or validation cohort. The nomogram was constructed on the basis of the independent risk variables that were acquired via multivariate Cox proportional hazard regression analysis. Several complementary methods included the Harrell c-index, time-dependent areas under the receiver operating characteristic curve (tdAUC), and calibration plot, and the Kaplan-Meier curve with Log rank test were used to test predictive performance of the model. The clinical utility of the model was tested by the decision cure analysis (DCA). Results Tumor diameter, tumor number, elevated serum gamma-glutamyl transpeptidase (GGT) level, microvascular invasion (MVI), and macrovascular invasion were independent risk factors of prognosis of NBNC-HCC. C-indexes of the nomogram were 0.702 (95% confidence interval [CI], 0.662-0.741) in the training cohort and 0.700 (95% CI, 0.643-0.758) in the validation cohort, and median tdAUC values of the nomogram were 0.743 (range, 0.736-0.775) in the training cohort and 0.751 (range, 0.686-0.793) in the validation cohort, which were both higher than those in the conventionally used Barcelona Clinic Liver Cancer staging system, American Joint Committee on Cancer, and eighth edition and the model of Zhang et al. The calibration plot depicted a good consistency between prediction of the model and observed outcome. The Kaplan-Meier curve analysis showed that the model was able to separate patients into three distinct risk subgroups. The DCA analysis also demonstrated that the nomogram was clinically useful. Conclusion We developed and validated a nomogram that was accurate and clinically useful in patients with NBNC-HCC who underwent hepatectomy.

In thorium molten salt reactors (TMSR)

Obesity is often associated with intestinal microbiota imbalance and increased Gram-negative bacteria characterized by higher endotoxin levels. Therefore, a study on the joint effects of a high-fat diet and Gram-negative bacteria prechallenge might provide further information on the interactive effects of intestinal microbiota and obesity as well as the effects of a probiotic intervention on these processes. This study focused on the joint effects of a high-fat diet and Escherichia coli on mouse inflammatory cytokines, intestinal microbiota, hepatic pathological changes, and the alleviating capacity of probiotics (Lactobacillus plantarum, Bifidobacterium breve, and Lactobacillus fermentum). Our results showed that E. coli administration and the high-fat diet exacerbated the inflammatory syndrome by increasing the visceral fat content, the inflammatory cell infiltration, and intestinal microbiota disorder in mice. E. coli administration caused a decrease in short-chain fatty acids in mouse feces, and probiotics effectively improved this phenomenon. Compound probiotic intervention reduced LPS and IL-1β levels, while increased IL-10 levels in mice improved the degeneration and inflammatory infiltration of mouse liver cells. The intestinal microbiota showed great differences at 3 weeks and 6 weeks post-administration. High fat and E. coli alone or in combination caused intestinal microbiota disorder, with increased harmful bacteria, and the probiotics effectively improved the intestinal flora structure and increased the fecal short-chain fatty acid (SCFA) content. In conclusion, a high-fat diet and Gram-negative bacteria challenge exacerbated the inflammatory syndrome, which can be alleviated by compound probiotic intervention.

Based on the known deprotection reaction of thioacetal initiated by Hg2+, a new type of fluorescent probe 3-(4-(1,3-dithiolan-2-yl) phenyl)-9H-carbazole (DTCB) is designed and synthesized successfully. The probe shows a good fluorescence response to Hg2+ in THF-H2O (fw = 5%) mixed system. The addition of Hg2+ greatly enhances the fluorescence intensity of the probe and changed from dark blue to green fluorescence. Within a large Hg2+ concentration range (0–6×10−5 mol·L−1), the fluorescence intensity at 480nm presents good linear with the Hg2+ concentration (R2 = 0.993), and the detection limit of the probe to Hg2+ is below 2.05×10−7 mol·L−1. The DTCB probe shows good selectivity and anti-interference performance, and many metal ions and anions hardly effect on its detection performance. The sensing mechanism is confirmed by theory calculation based on DFT and TD-DFT theories, and the intramolecular charge transfer (ICT) is responsible for the significant fluorescence change of probe DTCB. Meanwhile, the probe is applied to the detection of Hg2+ in real water samples; and it is interesting that the DTCB-based test strip firstly reveals the strong fluorescence from solid-state 3-(4-formylphenyl)-9H-carbazole (FCB) with Hg2+ adding. The results show that the probe based on DTCB has potential application prospect in actual sample analysis.

In China, a variety of non-motor vehicles (NM-Vehicle) exist on the road in recent years, some are similar to pedestrian, others are similar to motor vehicle (M-vehicle) in monitor video, which brings a great number of trouble to policeman when case tracking. In this paper, we proposed a traffic object classification pipeline based on state-of-the-art deep convolutional neural networks (CNNs).Unfortunately, there is no traffic object image database which typically includes motor vehicle (M-vehicle), non-motor vehicle (NM-vehicle) and pedestrian images as we know, and CNNs will be suffered over-fitting without enough training images. Therefore, we first construct a large-scale traffic object image dataset with more than 50000 training images. Then histogram equalization was used to compensate the poor illumination caused by the diversity in image scene. Considering the computing time, we finally choose the 3-convlayer network to our cooperative engagement information system (CEIS), and the value of mAP is 93.3%. With the model fusion, the result is improved to 96%.

A fast and robust single-view action recognition system is needed in more and more applications, especially on mobile devices. In this paper, we propose a key pose classification based method for action recognition. Firstly, we use a fast feature extraction method to represent these key poses and use a fast boosting method for key pose classification. Secondly, we propose a fast recognition approach based on the results of classification and order constraint. Due to the quick feature extraction method and a fast and robust sequences recognition method, our system can implement real-time pose recognition on mobile devices. Our experiments have indicated that this method has a good performance on many videos in a wild scene.