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Diagenetic facies play a significant role in the evaluation and prediction of reservoirs as they comprehensively reflect the spatial distribution characteristics of deposition, diagenesis, and petrophysical properties. Based on the petrographic observation and considering pores, lithology, and diagenesis types, the diagenetic facies types were identified, and the pore evolution process and its influence on the quality of low permeability tight gas reservoirs were analyzed. The results show that six types of diagenetic facies were identified, including siliceous cementation intergranular pore facies (A), carbonate dissolution pore facies (B), matrix dissolution pore facies (C), clastic dissolution pore facies (D), matrix filling and strong compaction tight facies (E), and carbonate cementation and metasomatism tight facies (F). The evolution process of porosity shows that the restored original porosity is indicated to be between 34.2% and 36.0%. The average porosity loss caused by compaction was 17.1%, while the average porosity loss caused by cementation is 14.1%. The average porosity increased by 2.5% on diagenetic facies B, C, and D due to dissolution. Consequently, diagenetic facies A with weak compaction and diagenetic facies B, C, and D with strong dissolution are effective reservoirs with porosity greater than 6.6% and permeability greater than 0.25 − 0.6 × 10 − 3 μm2. The distribution areas of A, B, C, and D are favorable areas for tight gas exploration and are important indicators for the prediction of tight gas in the Upper Paleozoic Shanxi Formation in the eastern Ordos Basin. This study provides a new petrographic method for reservoir prediction and exploration of natural gas, which has practical value and economic significance.

Gut microbes may be the critical mediators for the cognitive enhancing effects of exercise. Via fecal microbiota transplantation (FMT), this study is aimed at determining the mechanism of how voluntary exercise improved learning and memory ability impairment post a high-fat, high-cholesterol (HFHC) diet. The learning and memory abilities assessed via the Morris water maze in the FMT recipient group of voluntary exercising mice were improved compared to sedentary group. 16S rRNA gene sequencing results indicated that exercise-induced changes in gut microbiota distribution were transmissible, mainly in terms of elevated Lactobacillus, Lactobacillus, and Eubacterium nodatum, as well as decreased Clostrida_UCG-014 and Akkermansia after FMT. The neuroprotective effects of FMT were mainly related to the improved insulin signaling pathway (IRS2/PI3K/AKT) and mitochondrial function; inhibition of AQP4; decreased p-Tau at serine 396 and 404; increased BDNF, PSD95, and synaptophysin in the hippocampus; and also decreased HDAC2 and HDAC3 protein expressions in the nuclear and cytoplasmic fractions of the hippocampus. The findings of qRT-PCR suggested that exercise-induced gut microbes, on the one hand, elevated GPR109A and decreased GPR43 and TNF-α in the hippocampus. On the other hand, it increased GPR109A and GPR41 expressions in the proximal colon tissue. In addition, total short-chain fatty acid (SCFA), acetic acid, propionic acid, isobutyric acid, valeric acid, and isovaleric acid contents were also elevated in the cecum. In conclusion, exercise-induced alterations in gut microbiota play a decisive role in ameliorating HFHC diet-induced cognitive deficits. FMT treatment may be a new considerable direction in ameliorating cognitive impairment induced by exposure to HFHC diet.

Deep neural networks have been proved vulnerable to being attacked by adversarial examples, which have attracted extensive attention from researchers. Existing GAN-based object detection adversarial example generation methods are efficient in generating speed but ignore the visual imperceptibility of adversarial examples. In this paper, to improve the visual imperceptibility of adversarial examples, we propose an object detection adversarial example generation method based on weakly perceptual perturbations in key regions. First, a positioning module based on the gradient-weighted activation mapping method is designed to analyze the key region of the object from the perspective of gradient propagation and use the key region in order to limit the range and amplitude of the perturbation. Second, the deep feature of the convolutional network is introduced to constrain the content of adversarial perturbation and improve the similarity between adversarial examples and original images. Finally, a postprocessing method based on median filtering is introduced to further correct the color deviation of adversarial examples and improve imperceptibility. The experimental results for VOC datasets show that the attack success rate increased by 4%, the PSNR increased by 8.6%, and the MSE and LPIPS decreased by 92.3% and 59.5%, respectively. It demonstrates that the proposed method can significantly improve the imperceptibility of the adversarial example with a high attack success rate.

At present, the frequent occurrence of coal mine accidents in China is a key concern in the field of safety. Although the coal mine accident rate is decreasing due to the progress of technology and the improvement of management, there is still a lack of comprehensive evaluation of the role of various factors in the coal mine. Therefore, the purpose of this study is to establish a game theory combination weight-TOPSIS comprehensive evaluation model based on the analysis of various factors affecting coal mine safety. The model combines the weights obtained from fuzzy analytic hierarchy process (FAHP), entropy weight method (EWM), and back propagation neural network (BP-NN) by using the combination method of game theory and then combines with the TOPSIS method to evaluate 20 indicators in four aspects of a coal mine: personnel, equipment, environment, and management. Taking five mines under Zheng Coal Group as an example, the evaluation results of the game theory combination weight-TOPSIS comprehensive evaluation model are compared with those of the fuzzy analytic hierarchy process and the improved composite weight-TOPSIS method. The results show that the safety grade of the five mine samples under Zhengmei Group is ranked as follows: mine 4 > grade II (relatively safe) > mine 2 > mine 5 > grade III (moderately safe) > mine 3 > mine 1 > grade IV (generally safe). The evaluation result of the game theory combination weight-TOPSIS method is basically consistent with the actual safety situation of the coal mine, which reflects the superiority and practicality of the evaluation model. The model has greater differentiation in the process of determining the coal mine safety grade, and the evaluation results are more intuitive and reasonable, which can provide a more effective evaluation tool for the comprehensive evaluation of coal mine safety. Finally, based on the evaluation results, corresponding policy recommendations to improve the status quo of coal mine safety are proposed, aiming to provide decision-making reference for coal mine safety management personnel.

Since most orthopedic patients’ wounds are open in clinical practice, postoperative wound infection and other conditions are prone to occur, which pose varying degrees of threat to patients’ prognosis and life and health. In this paper, a retrospective study is conducted on orthopedic trauma patients in our hospital from January 2020 to January 2022, including 98 patients with postoperative infection and 98 patients without infection, to detect and compare the levels of inflammatory factors and the level of T cell lymphatic group index difference. The ROC curve is drawn to analyze the diagnostic efficacy of postoperative infection indicators for patients with orthopedic trauma. The differences in baseline data between the two groups are compared. Multivariate Logistic regression analysis is performed on infection status. The experimental results show that the IL-6, IL-2, and CRP of the infection group of patients are significantly higher than uninfected group, and the CD3, CD4, and CD8 are significantly lower than uninfected group, which means that patients with infection after orthopedic trauma are in a disordered state of immune cytokines and function. Therefore, postoperative infection can be effectively assessed by early combined detection of the above indicators. In addition, the analysis of other clinical data showed that the operation time, the number of underlying diseases, and the surgical method are also risk factors for postoperative infection.

According to the needs generated by the industry, there is an urgent need to investigate the flow vibration response law of the elastic tube bundle of heat exchangers subjected to the coupling effect of shell and tube domain at different inlet flow velocities. In this paper, based on the continuity equation, momentum equation, turbulence model, and dynamic grid technology, the vibration response of the elastic tube bundle under the mutual induction of shell and tube fluids is studied by using pressure-velocity solver and two-way fluid-structure coupling (FSC) method. The results show that the monitoring points on the same connection block have the same vibration frequency, while the vibration amplitude is different. Monitoring point A is the most deformed by the impact of fluid in the shell and tube domain. When the inlet velocity( v shell = v tube = 0.15 , 0.4, 0.5 m/s) of shell and tube is low, the amplitude of tube bundle vibration in the Y direction is greater than that in X and Z direction, and the tube bundle produces periodic vibration in the vertical direction. The vibration equilibrium position of the tube bundle along the shell flow direction gradually moves up with the increase of the inlet velocity. The amplitude in the Y -direction of the elastic bundle decreases with the increase of shell-side and tube-side velocity. The relationship between the vibration amplitude in the Y direction and the entrance velocity is a linear function.

Purpose. To identify significant radiomics features based on MRI and establish effective models for predicting the response to bortezomib-based regimens. Materials and Methods. In total, 95 MM patients treated with bortezomib-based therapy were enrolled, including 77 with bortezomib, cyclophosphamide, and dexamethasone (BCD) and 18 with bortezomib, lenalidomide, and dexamethasone (VRD). Based on T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-fs), radiomics features were extracted and then selected. The random forest (RF), k -nearest neighbor, support vector machine, logistic regression, decision tree, and Bayes models were built using the selected features. The predictive power of six models for response to BCD and VRD regimens were evaluated. The correlation between the selected features and progression-free survival (PFS) was also analyzed. Results. Four wavelet features were correlated with BCD treatment response. The six models all showed predictive power for BCD regimen (AUC: 0.84-0.896 in the training set, 0.801-0.885 in the validation set), and RF performed relatively better than others. Nevertheless, all the BCD-based models were incapable of predicting the VRD treatment response. The wavelet-HLH_firstorder_kurtosis was also associated with PFS (log-rank P = 0.019 ). Conclusion. The four wavelet features were valuable biomarkers for predicting the response to BCD regimen. The six models based on these features showed predictive power, and RF was the best. One wavelet feature was also a survival-related biomarker. MRI-based radiomics had the potential to guide clinicians in MM management.

In order to solve the problems of order reduction and customer churn caused by sorting delays, the author proposes a method for e-commerce sorting equipment based on cloud computing. This method mainly adopts double-layer sorting equipment; compared with single-layer automatic sorting equipment, double-layer sorting equipment has the characteristics of higher efficiency and smaller floor space. The sorting method adopts the “group sorting” method, which can effectively improve the sorting efficiency of the sorting equipment. The algorithm method adopts the mathematical model based on cloud computing for calculation. Experimental Results. The author adopts the cloud computing-based “composition sorting” double-layer sorting equipment; compared with the traditional single-layer sorting equipment, the throughput of the single-layer sorting strategy is 0.72 pieces/s when the conveying speed of the conveyor belt is the same, the throughput of the two-layer same-direction strategy is 1.46 pieces/s, the throughput of the balanced load strategy is 1.97 pieces/s, and the throughput of the group sorting load strategy is 2.57 pieces/s. This method can effectively solve the problems of order reduction and customer churn caused by sorting delays.

The Permian strata in the southwest Tarim Basin contain organic reefs with good exploration prospects. Based on observations of outcrops and thin section examination of the Permian Keziliqiman Formation reef in the Sangzhu section of Pishan County, Xinjiang, northwest China, the basic characteristics of the reef are studied. This organic reef, as a point reef which grew along the platform margin, has a complete subfacies system of base, core, front, back, and cap. The lithology of the lower zone of the core is mainly sponge bafflestone, while that of the upper zone is coral framestone. The development of reefs was controlled by the rise and fall of sea level. It was when the sea level rose in southwest Tarim Basin that the Keziliqiman reef gradually came into existence. Later, with rapid decline of sea level in that area, the Sangzhu section turned into a hot and dry supratidal zone, where algae began to flourish, dolomitization intensified, reef building organisms died en masse, and eventually the reef died. Paleontological and geochemical characteristics of the Sangzhu reef indicate that the sea-level decline in southwest Tarim Basin may be related to the Early Permian glaciers. Since then, the reef began to be transformed by diagenesis. But on the whole, its effect was not significant. Furthermore, its diagenetic process had more to do with marine fluids than with nonmarine fluids, for nonmarine fluids was powerful enough to affect bioclasts but powerless to affect micritic matrix.

A class of arbitrary time convergence controllers based on a special time-varying scaling function provide designers with a good choice to realize prescribed time stable systems. However, these controllers have the problem of conservative control parameter range and lack a uniform formula for systems of different order. Herein, the arbitrary time convergence controller is improved, the unified formula for n -th order system is given, and a more accurate control parameter range is obtained. By constructing an ingenious auxiliary function and a novel Lyapunov function, the arbitrary time convergence of the n -th order controller is proved, and the reasonable parameter selection range is obtained using the integrator backstepping and the mathematical induction method. The effectiveness and advantages of the proposed arbitrary time convergence controllers under saturated input constraints are illustrated through numerical simulations by comparative studies in unmanned aerial vehicle (UAV) formation control.

Waxy rice has a long history of being cultivated and consumed in China. In this study, the effect of different factors including α-amylase activity, protein, and amylopectin structure on the pasting properties of four waxy rice varieties were investigated. Rice flours were divided into four groups (Vietnam indica (VI), Jiangxi indica (JI), Anhui japonica (AJ), and Dongbei japonica (DJ) group) and treated with AgNO3 solution, DL-dithiothreitol (DTT), or protease (n = 3). Results suggested that both α-amylase activity and protein significantly decrease the pasting viscosity of waxy rice flours. Chain length distribution of amylopectin as measured by high performance ion exchange chromatography (HPAEC-PAD) showed that starch with a higher ratio of short chain leads to a higher pasting viscosity. X-Ray diffractograms showed that the crystal type of all the four varieties of rice starches were characteristic A-type. Relative crystallinity of each rice starch was further calculated, and higher crystallization resulted in a higher viscosity. Our study would provide a fundamental knowledge of the relationship between different factors and waxy starch pasting properties, as well as be a reference for controlling the quality of waxy rice starch-based food products.

With the acceleration of population aging, the detection rate of aortic dissection has increased. The incidence rate of aortic dissection has increased year by year and has become a serious threat to human health. However, the current clinical treatment of aortic dissection is mainly limited to surgery (including intracavity), but the complexity of the disease and the high risk of surgery seriously affect the overall treatment effect of the disease. Therefore, an in-depth study of the pathogenesis of aortic dissection and the development of early diagnosis methods is not only expected to control the development of aortic dissection but also to improve the existing clinical treatment effect. Based on the bioinformatics analysis of the related mRNA sequence data of aortic dissection in GEO database, the gene expression regulatory network of aortic dissection was constructed. Through the screening of key node genes, the key factors (molecular markers) that may affect the occurrence of aortic dissection were obtained, and their functions were tested in human aortic smooth muscle cells (HAoSMC). Finally, it was concluded that SERPINE1 gene is a reliable molecular marker for the early diagnosis of aortic dissection.

The traditional perimeter-based network protection model cannot adapt to the development of current technology. Zero trust is a new type of network security model, which is based on the concept of never trust and always verify. Whether the access subject is in the internal network or the external network, it needs to be authenticated to access resources. The zero trust model has received extensive attention in research and practice because it can meet the new network security requirements. However, the application of zero trust is still in its infancy, and enterprises, organizations, and individuals are not fully aware of the advantages and disadvantages of zero trust, which greatly hinders the application of zero trust. This paper introduces the existing zero trust architecture and analyzes the core technologies including identity authentication, access control, and trust assessment, which are mainly relied on in the zero trust architecture. The main solutions under each technology are compared and analyzed to summarize the advantages and disadvantages, as well as the current challenges and future research trends. Our goal is to provide support for the research and application of future zero trust architectures.

In order to optimize robot road planning, automation will increase production efficiency and reduce production costs. An improved ant colony algorithm based on a two-dimensional flat path planning study divided the three-dimensional space into planes, rasterized each plane, and replaced the original shape by storing a pheromone at the intersection. The pheromone storage space along the route will be reduced, and a three-dimensional space route planning study will be gradually conducted. As complex 3D environments and landscape features change, obstacle avoidance strategies have increased, road heuristics have improved, and new heuristic features have emerged. The initial value of the road node pheromone increases the efficiency of early ant search because of the uneven distribution of starting point, target point location information, and forward direction. After each iteration, the stuck ants that did not reach the target point are discarded according to the high-quality ant renewal rules, the iteration threshold is set, and the pheromone fluctuation coefficient is adjusted as the algorithm tends to merge. Compared with the basic ant colony algorithm, the convergence iteration times of the improved ant colony algorithm in this paper are reduced by about 40%, and the optimal path length is shortened by about 10. The duration of the algorithm increases. In terms of algorithm performance, it takes some time to improve the ant colony algorithm. Because of the complexity of the algorithm, some search strategies are added to the algorithm. The contribution of this article is the basis for the mobile robot to walk accurately from the initial position to the working position and perform various tasks independently.

Rock brittleness is a critical factor affecting the stimulation of shale oil reservoirs. For efficient development of the shale oil in Lucaogou Formation, Jimusar Sag, the brittleness of the sweet spots needs to be evaluated. In this paper, the triaxial compression, acoustic wave measurements, and three-point bending tests were carried out on the reservoir cores. Based on the prepeak energy characteristics of the stress-strain curve, the brittleness of different horizons was calculated, with the largest difference of 24.89%. An improved model based on dynamic elastic properties was proposed to evaluate the brittleness along the vertical pay zones, by which the continuous brittleness in the upper sweet spot was found more changeful than that in the lower sweet spot. The linear correlation coefficient between the brittleness from the improved model and that from the laboratory tests is 0.85, improving the accuracy by 21% and 27% respectively, compared with the conventional elastic property methods. From the characteristics of compression fractures and the length of the fracture process zone, it was found that the compression fractures were more complex and the fracture process zone length was shorter in a more brittle rock, verifying the reliability of the improvement model. The improved method based on dynamic elastic properties proposed in this paper is expected to guide the brittleness evaluation in other regions.

The self-impedance of the steel rail, as the signal transmission medium in the electrified railway track circuit, has a direct impact on the track circuit’s transmission performance. The finite element method is the most common method for calculating rail impedance, although it has a number of drawbacks, including a complicated model, a long computation time, and poor accuracy. The partial element equivalent circuit (PEEC) approach is used in this paper to provide a method for determining rail self-impedance. Firstly, the rail equivalent method is determined by analyzing the physical model of the rail. Considering the skin effect, the PEEC model of the rail is established. The internal impedance of the rail can be obtained by solving the equivalent circuit. The external impedance considering the influence of the earth is calculated by the Carson impedance calculation formula, which is processed by the segmented linear approximation method. The rail’s self-impedance is determined using the two procedures together. Finally, the actual measurement data verified the PEEC method to calculate the rail impedance. Compared with the finite element method (FEM), the calculation accuracy of the PEEC method is higher. The current frequency, the height of the rail from the ground, and the earth’s conductivity impact on the rail’s self-impedance are analyzed. The results show that the PEEC technique can be used to calculate the rail’s self-impedance and that the impact of current frequency, rail height above the ground, and ground conductivity on the rail’s self-impedance may be accurately represented. The self-impedance computation of the track circuit provides a theoretical basis.

In two-dimensional (2D) electron systems, the viscous flow is dominant when electron-electron collisions occur more frequently than the impurity or phonon scattering. In this work, a quantum hydrodynamic model, considering viscosity, is proposed to investigate the interaction of a charged particle moving above the two-dimensional viscous electron gas. The stopping power, perturbed electron gas density, and the spatial distribution of the velocity vector field have been theoretically analyzed and numerically calculated. The calculation results show that viscosity affects the spatial distribution and amplitude of the velocity field. The stopping power, which is an essential quantity for describing the interactions of ions with the 2D electron gas, is calculated, indicating that the incident particle will suffer less energy loss due to the weakening of the dynamic electron polarization and induced electric field in 2D electron gas with the viscosity. The values of the stopping power may be more accurate after considering the effect of viscosity. Our results may open up new possibilities to control the interaction of ions with 2D electron gas in the surface of metal or semiconductor heterostructure by variation of the viscosity.

Information entropy can be utilized to assess the complexity of the Banking System and the uncertainty of association information between banks. Based on information entropy and the balance sheet data of 167 banks from 2011 to 2020, we proposed a centrality indicator based on Adjacency Information Entropy, then applied it to directed weighted and directed unweighted interbank networks to identify banks with systemically important status, and compared the results with those of traditional centrality indicators. The outcomes show that the results of centrality indicator based on Adjacency Information Entropy are more accurate than those of traditional centrality indicators in identifying banks with systemically important status in interbank networks. Six large state-owned commercial banks, nine joint-stock commercial banks, two policy banks, and two urban commercial banks with large assets are identified as systemically important banks based on the proposed centrality indicator, which is highly consistent with the list of systemically important banks published by the Chinese banking regulator. Among the systemically important banks, changes in systemic risk levels of joint-stock commercial banks and urban commercial banks are more susceptible to national policies than those of large state-owned commercial banks. The findings of this paper have certain reference significance for strengthening the supervision of banks with systemically important status in interbank networks and formulating targeted policies for regulating large commercial banks and small- and medium-sized commercial banks.

Drugs can treat different diseases but also bring side effects. Undetected and unaccepted side effects for approved drugs can greatly harm the human body and bring huge risks for pharmaceutical companies. Traditional experimental methods used to determine the side effects have several drawbacks, such as low efficiency and high cost. One alternative to achieve this purpose is to design computational methods. Previous studies modeled a binary classification problem by pairing drugs and side effects; however, their classifiers can only extract one feature from each type of drug association. The present work proposed a novel multiple-feature sampling scheme that can extract several features from one type of drug association. Thirteen classification algorithms were employed to construct classifiers with features yielded by such scheme. Their performance was greatly improved compared with that of the classifiers that use the features yielded by the original scheme. Best performance was observed for the classifier based on random forest with MCC of 0.8661, AUROC of 0.969, and AUPR of 0.977. Finally, one key parameter in the multiple-feature sampling scheme was analyzed.

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

Background. Exosomes are extracellular vesicles that play important roles in various physiological and pathological functions. Previous studies have demonstrated that exosome-derived contents are promising biomarkers to inform the pathogenesis and diagnosis of major depressive disorder and schizophrenia. Methods. We used ultraperformance liquid chromatography-tandem mass spectrometry to analyze the differentially expressed metabolites in serum exosomes of patients with bipolar disorder (BD) and evaluated the potential of exosomal metabolites as biomarkers for BD. Results. Our results showed 26 differentially expressed serum exosomal metabolites in patients with BD ( n = 32 ) when compared with healthy control (HC) subjects ( n = 40 ), and these differentially expressed metabolites were enriched in pathways related to sugar metabolism. We then utilized random forest classifier and identified 15 exosomal metabolites that can be used to classify samples from patients with BD and HC subjects with 0.838 accuracy (95% CI, 0.604–1.00) in the training set of participants. These 15 metabolites showed excellent performance in differentiating between patients with BD and HC subjects in the testing set of participants, with 0.971 accuracy (95% CI, 0.865–1.00). Importantly, the 15 exosomal metabolites also showed good to excellent performance in differentiating between BD patients and other major psychiatric diseases (major depressive disorder and schizophrenia). Conclusion. Collectively, our findings for the first time revealed a potential role of exosomal metabolite dysregulations in the onset and/or development of BD and suggested that blood exosomal metabolites are strong candidates to inform the diagnosis of BD.

Major depressive disorder (MDD) has become the second most common disease worldwide, making it a threat to human health. Cyperi Rhizoma (CR) is a traditional herbal medicine with antidepressant properties. Traditional Chinese medicine theory states that CR relieves MDD by dispersing stagnated liver qi to soothe the liver, but the material basis and underlying mechanism have not been elucidated. In this study, we identified the active compounds and potential anti-MDD targets of CR by network pharmacology-based approaches. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we hypothesized that the anti-MDD effect of CR may be mediated by an altered response of the liver to lipopolysaccharide (LPS) and glucose metabolism. Through bioinformatics analysis, comparing normal and MDD liver tissue in rats with spontaneous diabetes, we identified differentially expressed genes (DEGs) and selected PAI-1 (SERPINE1) as a target of CR in combating MDD. Molecular docking and molecular dynamics analysis also verified the binding of the active compound quercetin to PAI-1. It can be concluded that quercetin is the active compound of CR that acts against MDD by targeting PAI-1 to enhance the liver response to LPS and glucose metabolism. This study not only reveals the material basis and underlying mechanism of CR against MDD through soothing the liver but also provides evidence for PAI-1 as a potential target and quercetin as a potential agent for MDD treatment.

Background. Transarterial chemoembolization (TACE) combined with sorafenib (TACE-S) or apatinib (TACE-A) is used in the treatment of hepatocellular carcinoma (HCC). However, to date, no study has compared the efficacy and safety of both treatments. The objective of this study was to compare the efficacy and safety of patients with advanced HCC who received either TACE-S or TACE-A. Methods. 193 patients with advanced HCC were included in the study between June 2015 and December 2019. Propensity score matching (PSM) analysis was used in the study to reduce selection bias. Results. Before PSM, the median overall survival (mOS) and median progression-free survival (mPFS) of patients treated with TACE-S were not significantly longer than in patients treated with TACE-A ( P = 0.703 , P = 0.514 ). TACE-A did not increase the mortality risk compared with TACE-S in the first 12 months (HR: 1.255, 95%CI: 0.796–1.978, P = 0.329 ) or after the 12-month mark (HR: 0.832, 95%CI: 0.482–1.436, p = 0.508 ). Similarly, TACE-A did not increase the tumor recurrence risk relative to TACE-S in the first 12 months (HR: 1.054, 95%CI: 0.744–1.493, P = 0.767 ) or after the 12-month mark (HR: 1.730, 95%CI: 0.592–5.049, P = 0.316 ). The subgroups analysis showed that TACE-A did not increase mortality risk or tumor recurrence risk relative to TACE-S. After PSM, similar results were presented. The III and IV stage adverse events in the TACE-A group were similar to those in the TACE-S group before PSM. Conclusions. Patients with advanced hepatocellular carcinoma could get similar survival benefits from treatment with either transarterial chemoembolization plus apatinib or transarterial chemoembolization plus sorafenib.

Accurate estimation of the state of charge (SOC) of Li-ion battery can ensure the reliability of the storage system. A combined estimator of online full-parameter identification and adaptive unscented particle filter for Li-ion battery SOC based on an improved fractional-order model is proposed, which overcomes the shortcomings of the traditional SOC cumulative error and the difficulty of OCV acquisition. The proposed adaptive fractional unscented particle filter algorithm introduces fractional parameters as hidden parameters and reduces the complexity of the algorithm iteration by reducing the number of particles. At the same time, the noise adaptive algorithm based on the residual sequence can solve the divergence problem of the filter and improve the adaptability of the algorithm. To verify the feasibility of the algorithm under complex operating conditions, the urban dynamometer driving schedule dynamic working conditions of Li-ion batteries are verified. The experimental results show that the evaluation index of the algorithm is the best, the RMSE is 0.67%, and the SOC estimation is more accurate. It shows that the algorithm has strong robustness and fast convergence.

Purpose. To evaluate the clinical outcome of the three-random-point (TRP) marking method for toric intraocular lens (IOL) alignment using the iTrace aberrometer (Tracey Technologies Corp., Houston, TX). Setting. Department of Ophthalmology, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangdong Provincial People’s Hospital, Guangzhou, China. Design. Prospective, randomized comparative trial. Method. Thirty eyes of 30 patients undergoing cataract surgery with coexisting corneal astigmatism of over 1.0 D were included in this study. All patients were prospectively randomized into the TRP marking group or slit-lamp horizontal meridian (SHM) marking group. TRP marking involved marking three points randomly in the corneal limbus of the patients and accurately marking the horizontal meridian was not required. The follow-up duration was 3 months after cataract surgery. Results. Fifteen eyes of 15 patients were in the TRP marking group and 15 eyes of 15 patients in the SHM marking group. There was no statistically significant difference in BCVA, UDVA, preexisting corneal astigmatism, or residual astigmatism between the groups before or after surgery P > 0.05 . The mean toric IOL misalignment was lesser but without significance in the TRP marking group than in the SHM marking group after 3 postoperative months (2.66° ± 1.42° versus 3.29° ± 1.67°; P = 0.295 ). Conclusion. The TRP marking method using the iTrace aberrometer is simple and accurate for preoperative marking of toric IOLs. It can eliminate the potential systematic errors resulting from varying head positions during the preoperative keratometry measurement and from manual marking.

Convexity plays an important role in many areas of mathematics, especially in the study of optimization problems where they are distinguished by a number of convenient properties. Our aim is to introduce a more extended version of convexity. In this paper, we introduced interval-valued generalized η h convex function and proved Hermite–Hadamard-, Jensen-, and Ostrowski-type inequalities in this generalization. The presented results are generalizations of many existing results of literature.

Metabolic pathway is an important type of biological pathways. It produces essential molecules and energies to maintain the life of living organisms. Each metabolic pathway consists of a chain of chemical reactions, which always need enzymes to participate in. Thus, chemicals and enzymes are two major components for each metabolic pathway. Although several metabolic pathways have been uncovered, the metabolic pathway system is still far from complete. Some hidden chemicals or enzymes are not discovered in a certain metabolic pathway. Besides the traditional experiments to detect hidden chemicals or enzymes, an alternative pipeline is to design efficient computational methods. In this study, we proposed a powerful multilabel classifier, called iMPTCE-Hnetwork, to uniformly assign chemicals and enzymes to metabolic pathway types reported in KEGG. Such classifier adopted the embedding features derived from a heterogeneous network, which defined chemicals and enzymes as nodes and the interactions between chemicals and enzymes as edges, through a powerful network embedding algorithm, Mashup. The popular RAndom k-labELsets (RAKEL) algorithm was employed to construct the classifier, which incorporated the support vector machine (polynomial kernel) as the basic classifier. The ten-fold cross-validation results indicated that such a classifier had good performance with accuracy higher than 0.800 and exact match higher than 0.750. Several comparisons were done to indicate the superiority of the iMPTCE-Hnetwork.

It is enormously challenging to achieve a satisfactory balance between quality of service (QoS) and users’ privacy protection along with measuring privacy disclosure in social Internet of Things (IoT). We propose a privacy-preserving personalized service framework (Persian) based on static Bayesian game to provide privacy protection according to users’ individual security requirements in social IoT. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries.

This research aims to investigate the efficiency of strengthening of plain concrete (PLC) arches using UHPC. Thirteen segment arch models reinforced using normal concrete (NC, group N) or ultra-high performance concrete (UHPC, group U) were tested in this study. The failure mode, strain distribution, and calculation of bearing capacity of PLC arch bridges reinforced with UHPC arch circle were analyzed. The experimental results showed that the technology of chiseling and planting bars could provide sufficient bond strength on the interface between the UHPC reinforcement layer and NC substrate. Both groups showed one or two failure modes of the concrete crushing of the original structure and the interface failure. However, no cracks appeared in the UHPC reinforcement layer, indicating that there was still large bearing potential. The strain distribution of the whole section in group N was consistent with the plane section assumption. But, this phenomenon was not observed in group U since the strain of the reinforcement layer was ahead (R-side loading) or behind (L-side loading) that of original structures. A simplified calculation formula was used for calculating the bearing capacity of group U. It was accurate for specimens loaded on the L-side, and an enhancement coefficient of 0.15 should be considered for R-side loading specimens.

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease. Sophora flavescens (S. flavescens), also named Kushen, is a famous Chinese herbal medicine that has been used to prevent and cure T2DM both in folk medicine and in medical institution. However, its mechanism of action remains unclear. In this study, the pharmacodynamic effects of S. flavescens EtOAc extract (SFE) on high-fat diet and low-dose streptozotocin-induced T2DM rats were examined. Fecal metabolomics analysis and 16S rRNA gene sequencing were applied to determine the influence of T2DM and SFE treatment on gut microbiota and host metabolism. Based on the consistency of the results of metabolic pathways in metabolomics analysis and phylogenetic investigation of communities by reconstruction of unobserved state (PICRUSt) analysis of 16S rRNA gene sequencing, the level of metabolites and the operational taxonomic units of gut bacteria were combined, and Spearman’s analysis was implemented. Our data showed that SFE significantly decreased fasted blood glucose levels and improved lipid profile, glycosylated serum protein, glycosylated hemoglobin index, and pancreas damage. Metabolomics and 16S rRNA gene sequencing analysis indicated gut bacteria disorder, disturbed lipid metabolism, carbohydrate metabolism, and especially amino acid metabolism in T2DM and that SFE can regulated these metabolic pathways through the influence on gut bacteria. Spearman’s analysis indicated that the amino acid metabolism that included tryptophan, branched chain amino acid, aromatic amino acid, beta-alanine, and glycine, serine and threonine metabolism, lipid metabolism, including lysophosphatidylcholines and lysophosphatidylethanolamines, primary bile acid and linoleic acid metabolism, carbohydrate metabolism, and nucleotide metabolism positively correlated with Faecalibacterium, Flexispira, Phascolarctobacterium, Prevotella, Roseburia, and [Prevotella]. In addition, arginine and proline metabolism, steroid hormone, steroid biosynthesis, and sphingolipid metabolism positively correlated with Lactobacillus, Oscillospira, Parabacteroides, Ruminococcus, and Streptococcus. Taken together, we speculated that SFE may have an effect on T2DM by mediating host-microbial metabolic axis. Exploration of SFE treatment for T2DM by multiomics is expected to provide a reference for clinical treatment.

Brain metastasis is a major cause of death in breast cancer patients. The greatest event for brain metastasis is the breaching of the blood-brain barrier (BBB) by cancer cells. The role of exosomes in cancer metastasis is clear, whereas the role of exosomes in the integrity of the BBB is unknown. Here, we established a highly brain metastatic breast cancer cell line by three cycles of in vivo selection. The effect of exosomes on the BBB was evaluated in vitro by tracking, transepithelial/transendothelial electrical resistance (TEER), and permeability assays. BBB-associated exosomal long noncoding RNA (lncRNA) was selected from the GEO dataset and verified by real-time PCR, TEER, permeability, and Transwell assays. The cells obtained by the in vivo selection showed higher brain metastatic capacity in vivo and higher migration and invasion in vitro compared to the parental cells. Exosomes from the highly brain metastatic cells were internalized by brain microvascular endothelial cells (BMECs), which reduced TEER and increased permeability of BBB. The exosomes derived from the highly metastatic cells promoted invasion of the breast cancer cells in the BBB model. lncRNA GS1-600G8.5 was highly expressed in the highly brain metastatic cells and their exosomes, as compared to the samples with reduced metastatic behavior. Silencing of GS1-600G8.5 significantly abrogated the BBB destructive effect of exosomes. GS1-600G8.5-deficient exosomes failed to promote the infiltration of cancer cells through the BBB. Furthermore, BMECs treated with GS1-600G8.5-deprived exosomes expressed higher tight junction proteins than those treated with the control exosomes. These data suggest the exosomes derived from highly brain metastatic breast cancer cells might destroy the BBB system and promote the passage of cancer cells across the BBB, by transferring lncRNA GS1-600G8.5.

The kelp aquaculture production in China is the largest in the world, and a large amount of kelp residue is produced by kelp processing. Kelp residues contain substantial quantities of crude fibre, protein, and residual alginic acid, and may be used as feedstuff for aquaculture animals. In this study, we used probiotics to ferment kelp residues to improve kelp nutrient content and then fed the fermented kelp to the sea cucumber, Apostichopus japonicus. To study the effect of fermented feed on sea cucumber, its growth performance, digestive enzyme activity, diversity of intestinal microbiota and water quality of the sea cucumber culture water were determined. Growth performance of sea cucumber fed with fermented feed significantly (p

Over 1,000 compounds, including ecteinascidin-743 and didemnin B, have been isolated from ascidians, with most having bioactive properties such as antimicrobial, antitumor, and enzyme-inhibiting activities. In recent years, direct and indirect evidence has shown that some bioactive compounds isolated from ascidians are not produced by ascidians themselves but by their symbiotic microorganisms. Isolated culturable bacteria associated with ascidians and investigating their potential bioactivity are an important approach for discovering novel compounds. In this study, a total of 269 bacteria were isolated from the ascidian Styela clava collected from the coast of Weihai in the north of the Yellow Sea, China. Phylogenetic relationships among 183 isolates were determined using their 16S rRNA gene sequences. Isolates were tested for antimicrobial activity against seven indicator strains, and an antiproliferative activity assay was performed to test for inhibition of human hepatocellular carcinoma Bel 7402 and human cervical carcinoma HeLa cell proliferation. Our results showed that the isolates belonged to 26 genera from 18 families in four phyla (Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes). Bacillus and Streptomyces were the most dominant genera; 146 strains had potent antimicrobial activities and inhibited at least one of the indicator strains. Crude extracts from 29 strains showed antiproliferative activity against Bel 7402 cells with IC50 values below 500 μg·mL−1, and 53 strains showed antiproliferative activity against HeLa cells, with IC50 values less than 500 μg·mL−1. Our results suggest that culturable bacteria associated with the ascidian Styela clava may be a promising source of novel bioactive compounds.

Reciprocating compressors are widely used in petroleum industry. Due to containing complex nonlinear signal, it is difficult to extract the fault features from its vibration signals. This paper proposes a new method named Convolutional Neural Network based on Multisource Raw vibration signals (MSRCNN). The proposed method uses multisource raw vibration signals collected by several sensors as input and uses the designed CNN to operate both the feature extraction and classification. The gas valve signals of reciprocating compressor in different states are used as the experimental data. In order to test the effectiveness of the proposed method, it is compared with the traditional BP (Back-Propagation) neural network fault diagnosis method based on power spectrum energy and wavelet packet energy. In order to further test the antinoise performance of the proposed method, some noisy signals with different signal-to-noise ratios were constructed by adding white noise into sampled signals for testing. The results show that the MSRCNN model has higher fault recognition rate than the traditional methods. This indicates that the MSRCNN method not only has good fault recognition effect, but also has certain antinoise performance.

To establish a nomogram for predicting the overall survival (OS) of patients with newly diagnosed multiple myeloma (MM), 304 patients with newly diagnosed MM were recruited between June 1, 2010, and June 30, 2015, from the Beijing Chaoyang Hospital, Capital Medical University, and randomly divided into training (n=214) and validation (n=90) cohorts. The Kaplan-Meier method and the Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinical and laboratory parameters on survival. Significant prognostic factors were combined to build a nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves and compared with the five staging systems currently used for MM. Multivariate analysis of the training cohort revealed that the age at diagnosis, clonal bone marrow plasma cells, serum lactate dehydrogenase, serumβ2-microglobulin, and del (17p) were independent risk factors for OS and were used to establish the nomogram. The C-index value of the nomogram for predicting OS was 0.749, which was significantly higher than the C-indices of the five most common staging systems currently used for MM. In the validation cohort, the C-index for nomogram-based predictions was 0.711 for OS, and the nomogram discrimination was better than the above mentioned five staging systems (P

In this paper, finite-time tracking problem of nonholonomic mobile robots for a moving target is considered. First of all, polar coordinates are used to characterize the distance and azimuth between the moving target and the robot. Then, based on the distance and azimuth transported from the sensor installed on the robot, a finite-time tracking control law is designed for the nonholonomic mobile robot by the switching control method. Rigorous proof shows that the tracking error converges to zero in a finite time. Numerical simulation demonstrates the effectiveness of the proposed control method.