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Matsumuraeses phaseoli is a Lepidopteran pest that primarily feeds on numerous species of cultivated legumes, such as Glycine and Phaseolus. It is widely distributed in northeast Asia. A novel granulovirus, designated as Matsumuraeses phaseoli granulovirus (MaphGV), was isolated from pathogenic M. phaseoli larvae that dwell in rolled leaves of Astragalus membranaceus, a Chinese medicinal herb. In this study, using next-generation sequencing, we report the complete genome of MaphGV. MaphGV genome comprises a double-stranded DNA of 116,875 bp, with 37.18% GC content. It has 128 hypothetical open reading frames (ORFs). Among them, 38 are baculovirus core genes, 18 are lepidopteran baculovirus conserved genes, and 5 are unique to Baculoviridae. MaphGV has one baculovirus repeat ORF (bro) and three inhibitors of apoptosis proteins (iap), including a newfound iap-6. We found two atypical baculoviral homologous regions (hrs) and four direct repeats (drs) in the MaphGV genome. Based on phylogenetic analysis, MaphGV belongs to Clade b of Betabaculovirus and is closely related to Cydia pomonellagranulovirus (CpGV) and Cryptophlebia leucotretagranulovirus (CrleGV). This novel baculovirus discovery and sequencing are invaluable in understanding the evolution of baculovirus and MaphGV may be a potential biocontrol agent against the bean ravaging pest.

Based on the earned value management theory, the project duration and cost forecast data are obtained through Monte Carlo simulation, combined with the knowledge of mathematical statistics to in-depth analysis of the data, and then the project quality calculation is completed by establishing the quality correlation function. On the basis of existing scholars’ research, at the process level, the management focus is identified by calculating process influence and quality, and at the project level, the operation of the entire project is simulated and predicted by the calculation of the total construction period, cost, and quality.

Purpose This study aimed to use an 18 F-FDG PET/CT multiparametric quantitative analysis to determine the efficacy of neoadjuvant chemotherapy in patients with locally progressive gastric cancer. Materials and methods We conducted a retrospective analysis of 34 patients with pathologically identified gastric cancer who received neoadjuvant chemotherapy and surgery. Chemotherapy regimens were followed and 18 F-FDG PET/CT was conducted. We ascertained multiparamaters of the target lesions pre- and post-treatment and determined the ideal cutoff values for the percentage change in biomarkers. Independent factors were evaluated using binary logistic regression. A response classification system was used to explore the association between metabolic and anatomical responses and the degree of pathological remission. Results Binary logistic regression analysis showed that Lauren bowel type and change in total lesion glycolysis >45.2% were risk predictors for the efficacy of neoadjuvant chemotherapy; total lesion glycolysis demonstrated the best predictive efficacy. The categorical variable system of the two-module response (metabolic and anatomical response) group had a higher predictive accuracy than that of the single-module response (metabolic or anatomical response) group. Conclusions Using 18 F-FDG PET/CT multiparametric quantitative analysis, Lauren bowel type and change in total lesion glycolysis >45.2% were independent predictors of the efficacy of neoadjuvant chemotherapy in patients with gastric adenocarcinoma. Additionally, the dual-module assessment demonstrated high predictive efficacy.

Microstructure and solute distribution have a significant impact on the mechanical properties of wire and arc additive manufacturing (WAAM) deposits. In this study, a multiscale model, consisting of a macroscopic finite element (FE) model and a microscopic phase field (PF) model, was used to predict the 2319 Al alloy microstructure evolution with epitaxial growth. Temperature fields, and the corresponding temperature gradient under the selected process parameters, were calculated by the FE model. Based on the results of macroscopic thermal simulation on the WAAM process, a PF model with a misorientation angle was employed to simulate the microstructure and competitive behaviors under the effect of epitaxial growth of grains. The dendrites with high misorientation angles experienced competitive growth and tended to be eliminated in the solidification process. The inclined dendrites are commonly hindered by other grains in front of the dendrite tip. Moreover, the solute enrichment near the solid/liquid interface reduced the driving force of solidification. The inclined angle of dendrites increased with the misorientation angle, and the solute distributions near the interface had similar patterns, but various concentrations, with different misorientation angles. Finally, metallographic experiments were conducted on the WAAM specimen to validate the morphology and size of the dendrites, and electron backscattered diffraction was used to indicate the preferred orientation of grains near the fusion line, proving the existence of epitaxial growth.

Early diagnosis of coronary heart disease (CHD) remains a huge challenge clinically due to the lack of biomarkers. Toll-like receptor 8 (TLR8) is an innate immune receptor that is involved in various diseases. This study aimed to investigate whether TLR8 was associated with CHD. Dysregulate genes were predicted using microarray analysis. A total of 85 patients with CHD and 85 non-CHD were enrolled in this study. Basic clinical data and hematological indicators were collected. The levels of TLR8 in the serum were detected using enzyme-linked immunosorbent assay. Correlations between TLR8 levels and hematological indicators were evaluated using Pearson correlation coefficient. The diagnostic performance of TLR8 was analyzed using the receiver operating characteristic (ROC) curve. The results showed that multiple genes were aberrantly expressed in CHD. Among them, TLR8 levels in the serum were higher in CHD than that in non-CHD. It was revealed that TLR8 levels were related to fasting plasma glucose, triglyceride, and low-density lipoprotein cholesterol. Moreover TLR8 was an independent risk factor for CHD. ROC curve results showed that the area under the curve value was 0.8737. Serum TLR8 is closely associated with CHD and has the potential to be a diagnostic biomarker for CHD.

患者男性，16岁。发作性意识丧失伴肢体抽搐4年、双上肢抖动2年、加重2个月，于2013年9月4日人院。患者于4年前玩游戏时出现双眼反复眨眼，随即意识丧失、呼之不应，伴四肢抽搐、双眼上翻、面部发紫，无大小便失禁和舌咬伤，每次发作持续约2min后自行缓解。当地医院诊断为“癫痫”，

In this paper, double-layer support vector machine (DLSVM) for binary classification is proposed that can expeditiously learn a classification model under the premise of ensuring accuracy. In the first layer, least squares support vector machine (LSSVM) is used to evaluate all samples in the data set. Support vector machine (SVM) utilize a sparse data set to train the binary classifier in the second layer. In order to obtain excellent sparse data set for SVM, the normalized norm sequence and the normalized Lagrange multiplier Shannon entropy sequence are introduced. DLSVM provides low computation cost because the two-layer structure reduces lots of iterative operations. Experimental results show that the proposed method not only has similar classification accuracy compared to SVM but also has higher efficiency.

In this paper, a novel prediction strategy based on firefly algorithm, echo state network and Savitzky-Golay filter for short-term prediction of fading channel is proposed and introduced in detail. This hybrid strategy includes two parts, that is, optimization part and repairing part. In the former, parameters of echo state network are optimized by firefly algorithm so that an optimal prediction echo state network is obtained, then in order to improve performances in prediction strategy, prediction sample of echo state network is repaired by repairing strategy based on Savitzky-Golay filter. Finally, the classic Rayleigh channel is tested and performances are analysed and compared with classic AR, which verified the validity and correctness of our proposed prediction strategy.

In this paper, we propose a novel total least squares (NTLS)-based data detection algorithm for TDD massive MIMO uplink systems, where the impact of the channel estimation errors and the characteristics of channel hardening are both taken into consideration. Firstly, aiming at the problems caused by the channel estimation error, the NTLS method is adopted to minimize the impact. Secondly, QR decomposition and channel hardening properties are employed respectively to simplify the computational complexity. Finally, experimental results show that the algorithm not only has lower computational complexity, but also performs better than conventional linear data detection algorithms without requiring channel statistics.