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The cyclin-dependent kinases, CDK4 and CDK6 are upregulated in various cancers, including pancreatic ductal adenocarcinoma. However, monotherapy with CDK4/6 inhibitors has been unsatisfactory and several oncogenic pathways might be activated. We demonstrated that PD-0332991, an FDA-approved CDK4/6 inhibitor, moderately suppressed tumor growth and induced the epithelial-to-mesenchymal transition (EMT). However, the performance of this inhibitor was significantly improved and the EMT partly reversed when bromodomain and extra-terminal motif (BET) was inhibited by JQ1. Mechanistically, CDK4/6 inhibition activated the canonical Wnt/β-catenin pathway through GSK3β Ser9 phosphorylation. Crosstalk between the TGFβ/Smad and Wnt/β-catenin pathways based on GSK3β was disrupted by JQ1 and levels of active β-catenin were suppressed by regulating the phosphorylation status of GSK3β. The simultaneous inhibition of CDK4/6 and BET synergistically suppressed tumor growth in vitro and in vivo. Moreover, CDK4 and CDK6 negatively correlated with overall survival, and the predictive accuracy of a CDK4/6-based nomogram was higher than that of tumor size, nodes, and metastasis (TNM) classification alone. Our findings provide theoretical evidence for the construction of predictive models and new targets with which to treat pancreatic cancer.

The COVID-19 pandemic has imposed tremendous impacts on the tourism industry worldwide. The tourism sector can take advantage of the new technology (e.g. virtual tourism), to respond to the challen...

Photovoltaic power generation relies on power electronics and therefore does not have natural inertia and damping characteristics. In order to make the capacitance of the medium time scale participate in the grid frequency response without adding additional equipment, this paper takes the grid-connected photovoltaic power generation system based on DC voltage droop control as the research object, and establishes the static synchronous generator (SSG) model of the system. The model is used to analyze the main parameters affecting the inertia, damping and synchronization characteristics of the system and their influence laws. The research results show that the energy storage effect of the capacitor on the medium time scale can also make the system exhibit certain inertia characteristics. From the point of view of control parameters, as the droop coefficient $D_{\mathrm {p}}$ decreases, the inertia characteristic exhibited by the system is stronger. The larger the DC voltage outer loop proportional coefficient $K_{\mathrm {p}}$ is, the stronger the damping effect of the system is. The larger the DC voltage outer loop integral coefficient $K_{\mathrm {i}}$ , the stronger the synchronization capability of the system. In addition, the MATLAB/Simulink simulation platform is used to verify the correctness of the theoretical analysis results.

Sox9 has been previously characterized as a transcription factor responsible for the extracellular matrix production during liver fibrosis. However, the deregulation and functional role of hepatocyte Sox9 in the progression of liver fibrosis remains elusive. Here, we found a significant increase of Sox9 in the hepatocytes isolated from CCl4-induced fibrotic liver and showed that antisense oligoribonucleotides depletion of Sox9 was sufficient to attenuate CCl4-induced liver fibrosis. Notably, the increase of Sox9 in hepatocyte was associated with the upregulation of long noncoding RNA H19 in both in vitro and in vivo systems. Mechanistic studies revealed that Sox9 induced H19 by binding to a conserved promoter region of H19. In vitro, hepatocyte injury triggered the increase of Sox9/H19 axis, whereas silence of H19 greatly alleviated the H2O2-induced hepatocyte apoptosis, suggesting that H19 functions as a downstream effector of Sox9 signaling and is involved in hepatocyte apoptosis. In animal experiments, inhibition of H19 alleviated the activation of hepatic stellate cells and reduced the extent of liver fibrosis, whereas ectopic expression of H19 abolished the inhibitory effects of Sox9 depletion on liver fibrosis, suggesting that the profibrotic effect of hepatocyte Sox9 depends on H19. Finally, we investigated the clinical relevance of Sox9/H19 axis to liver fibrosis and identified the increase of Sox9/H19 axis in liver cirrhosis patients. In conclusion, our findings link Sox9/H19 axis to the intrinsic mechanisms of hepatocyte apoptosis and may represent a hitherto unknown paradigm in hepatocyte injury associated with the progression of liver fibrosis.

The incidence of pancreatic ductal adenocarcinoma (PDAC) has been on the rise in recent years; however, its clinical diagnosis and treatment remain challenging. Although surgical resection remains the only chance for long-term patient survival, the likelihood of initial resectability is no higher than 20%. Neoadjuvant therapy (NAT) in PDAC aims to transform the proportion of inoperable PDACs into operable cases and reduce the likelihood of recurrence to improve overall survival. Ongoing phase 3 clinical trial aims to validate the role of NAT in PDAC therapy, including prolongation of survival, increased R0 resection, and a higher proportion of negative lymph nodes. Controversies surrounding the role of NAT in PDAC treatment include applicability to different stages of PDAC, chemotherapy regimens, radiation, duration of treatment, and assessment of effect. This review aims to summarize the current progress and controversies of NAT in PDAC.

In response to the current phenomenon of severe wind curtailment during the winter heating period in China’s "Three Northern Regions", deep adjustment of thermal power units can be achieved to reduce wind curtailment. Thus, this paper presents a bi-level optimization model for the combined heat and power system operation and multi-energy storage configuration. The lower model is the combined heat and power operation model of wind curtailment consumption with thermal storage electric boilers and electric energy storage devices. The goal is to minimize the total cost of combined heat and power unit operation, wind curtailment, environmental pollution, and large grid interaction. The upper model aims at the lowest cost of multi-energy storage configuration. Finally, the IEEE 9-node system is simulated using YALMIP and GUROBI. The results show that the proposed model is able to reduce the cost of the generation unit operation and environmental pollution, to guarantee full wind power consumption, and to reduce investment costs for energy storage systems.

Tropical cyclone (TC) activity has significant seasonal, interannual and interdecadal variations. Accurate prediction of TC seasonal activities before the onset of the coming TC season (June-November) can provide sufficient time for the government and the public to prepare for tropical cyclone disasters and minimize risks and life losses.Based on COAWST model, we developed a new regional coupled seasonal forecasting system for the Northwest Pacific Ocean including a series of technology improvements. The results of multi-year hindcast experiments show that the coupled seasonal forecasting system can effectively improve the tropical cyclone frequency and intensity forecast compared to the CFSv2 real-time seasonal forecast, especially the tropical cyclone frequency forecast of the TC exceeding the typhoon level, but there is still a certain gap between the results in the forecasting system and the observed TC frequency and intensity, which is mainly reflected in the fact that the forecasting season has a higher frequency of TCs and the peak of strong TCs is relatively weaker. This gap may be caused by the forecasting bias of the sea surface temperature.

In order to accurately understand the chrematistics of wind farm’ output, it is necessary to consider the correlation of the outputs of multiple wind farms. In this paper, a method for analyzing the correlation of multiple wind farms’ outputs based on fluctuation division and time shift technology is proposed. Firstly, the overall correlation characteristics of multiple wind farms are obtained by using sequential scatter diagram analysis and regression analysis. Then, the fluctuation process of wind farms is divided and paired by output fluctuation division and output fluctuation pairing algorithm. Finally, the time shift technique of Pearson correlation coefficient and granger causality test are used to obtain the optimal time shift quantity and direction. It can be verified by concrete examples that this method can accurately extract the local characteristics of wind farm output fluctuation, and then more accurately describe the relevant characteristics of wind farm output.

With the development of economy, the power grid structure becomes more and more complex, and many new business scenarios and new requirements has emerged. The power system stability analysis method based on physical model was unable to accurately model the uncertain factors in the power system, which make difficulty to analysis the system. In this paper, a method of grid stability identification based on XGboost method has been proposed, and the state of power grid was determined from the grid multidimensional operation data. Firstly, the decision tree method was introduced. Secondly, introduced the principle and characteristics of classification and regression tree, and then introduced the XGboost principle and characteristics. Finally, the IEEE39 node system has been used to verified the correctness of the XGboost.

In this paper, the features and possible causes of sea surface temperature (SST) biases over the Northwest Pacific are investigated based on a mixed-layer heat budget analysis in 21 coupled general circulation models (CGCMs) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5). Most CMIP5 models show cold SST biases throughout the year over the Northwest Pacific. The largest biases appear during summer, and the smallest biases occur during winter. These cold SST biases are seen at the basin scale and are mainly located in the inner region of the low and mid-latitudes. According to the mixed-layer heat budget analysis, overestimation of upward net sea surface heat fluxes associated with atmospheric processes are primarily responsible for the cold SST biases. Among the different components of surface heat fluxes, overestimated upward latent heat fluxes induced by the excessively strong surface winds contribute the most to the cold SST biases during the spring, autumn, and winter seasons. Conversely, during the summer, overestimated upward latent heat fluxes and underestimated downward solar radiations at the sea surface are equally important. Further analysis suggests that the overly strong surface winds over the Northwest Pacific during winter and spring are associated with excessive precipitation over the Maritime Continent region, whereas those occurring during summer and autumn are associated with the excessive northward extension of the intertropical convergence zone (ITCZ). The excessive precipitation over the Maritime Continent region and the biases in the simulated ITCZ induce anomalous northeasterlies, which are in favor of enhancing low-level winds over the North Pacific. The enhanced surface wind increases the sea surface evaporation, which contributes to the excessive upward latent heat fluxes. Thus, the SST over the Northwest Pacific cools.

The swimming crab is an important commercial fishery species in China, Korea and Japan. Overfishing and destruction of habitats in coastal waters mean that the natural resources cannot meet the swimming crab’s increased demands for food, caused by large-scale stock enhancement in Liaoning, China over the last eight years. The genetic response of the local wild populations to such a large-scale release has not been investigated. Here, we monitored the temporal variations in genetic diversity and structure in Panjin and Yingkou using eighteen microsatellite markers across six years. A high level of genetic diversity was maintained in these populations, but significant deviation from HWE was observed (P

Objective: To evaluate the therapeutic efficacy and safety of S1 monotherapy or combination with nab-paclitaxel for the treatment of elderly patients with metastatic or locally advanced pancreatic adenocarcinoma. Method: PubMed, Embase, Cochrane Central Library, China Biology Medicine, and China National Knowledge Infrastructure databases were searched without time limits according to the inclusion criteria. RevMan (Version 5.3) software was used for data extraction and meta-analysis. Objective response rate (ORR) and disease control rate (DCR) were used to evaluate therapeutic effects while side effects including leukopenia, thrombocytopenia, neurotoxicity, vomit, and alopecia were extracted for evaluation. There was no need for ethical review in this study because no ethical experiments were conducted and all data used were public data. All relevant data are within the paper and its Supporting Information files. Results: Four retrospective studies comprising 308 elderly patients with metastatic or locally advanced pancreatic adenocarcinoma were included in the analysis. One hundred fifty-one patients underwent S1 monotherapy and 157 received S1 combined nab-paclitaxel. Meta-analysis indicated that compared with S1 monotherapy, S1 combined with nab-paclitaxel had higher ORR (OR 2.25, 95% CI: 1.42–3.55; P = .0005) and DCR (OR 2.94, 95% CI: 1.55–5.58; P = .0009). The adverse reaction of leukopenia was higher in the combined therapy group (OR 1.85, 95% CI: 1.09–3.13, P = .02), but no significant difference was found in thrombocytopenia, neurotoxicity, vomiting, and alopecia between the 2 groups (P > .05). Conclusion: Nab-paclitaxel plus S1 was more efficient in terms of ORR and DCR than S1 monotherapy in elderly pancreatic ductal adenocarcinoma patients while the side effect was controllable with a higher probability of leukopenia. Thus, combined nab-paclitaxel and S1 could be safely used in elderly patients.

Due to small capacity, limited size and low inertia of isolated island operation microgrid, the frequency of microgrid is greatly affected by the change of load. Islanding operation for multi sources micro grid, traditional virtual synchronous generator control can overcome the shortcomings of low inertia of micro grid, but it belongs to the droop control, it will be possible to make the micro grid's frequency cannot recover after a large disturbance and cause system frequency deviation, and affecting the frequency stability of the whole system. The improvement of virtual synchronous generator control is proposed with the integral controller in damping based on the traditional virtual synchronous generator control, it can automatically track the load fluctuation and adjust the power output to achieve power balance and zero-error frequency. Micro grid simulation model made in Matlab/Simulink platform wind /storages s, in the islanding mode, the traditional and improved virtual synchro-nous generator control is adopted to study the frequency change of load load switching and verify the correctness of the proposed control strategy.

With the vigorous development of renewable energy, large-scale wind power is connected to the power grid. Compared with conventional energy sources, wind power generation has the stochastic and intermittent characteristics, which brings great challenges to the safe and stable operation of power grids. Forecasting is essential for the rational, safe and effective use of wind power. Thus, the ability to predict wind power in a timely and accurate manner is particularly significant.The short-term prediction error of wind power is mainly caused by inherent stochastic factors and extrinsic stochastic factors. The inherent stochastic factor refers to the defect or imperfection of the prediction system itself. The external stochastic factor refers to the imperfect data input by the system or the error of the input data. Artificial neural network (ANN) algorithms are supposed to be with better performance to solve such problems. This paper reviews the development of wind power generation and the research status of wind power prediction technology. On this basis, a wind power prediction model of convolutional neural network (CNN) and genetic algorithm (GA) is constructed to predict the short-term wind power. The learning accuracy, the number of hidden nodes, and the training function are discussed. The selection of the optimal neural network and GA prediction model are presented. The simulation results show that the prediction accuracy and stability of the proposed prediction system are improved, compared with the BP neural network and CNN.

Nano-aerogels have been used in various fields due to their ultralow density, ultrahigh porosity and ultralow thermal conductivity. However, common nano-aerogels with poor thermal stability are scarcely used in high- and low-temperature environments. Polyimide (PI) nano-aerogels that are developed from PIs can meet the requirements and have been studied in recent years, especially in the field of deep space exploration. In this review, we discuss the current state of the fabrication, classification, methods of improving properties and fields of application of the PI nano-aerogels. As one of the best materials with outstanding comprehensive properties under harsh temperature environments, PI nano-aerogels will play important roles in technology in the future.

Safety is the primary consideration of underground operation. At present, many large mineral countries are committed to using wireless sensors to reduce the accident rate and prevent casualties and major economic losses through early warning. The existing monitoring and early warning system of wired coal mine can only be used in the main tunnel, while the complex underground working area is still the “blind area” of monitoring and early warning. This paper proposes a smart safety monitoring system based on improved DV-Hop localization algorithm for randomly deployed wireless sensor networks (WSNs), which can position accurate personal tracking in industrial fields, such as mines, underground operations, tunnel engineering, and underground engineering. By choosing anchor nodes and calculating the average per-hop distance between anchor nodes are considerably different from the original DV-Hop. Based on the improved DV-Hop positioning algorithm, the critical safety data related to underground coal mining and associated with the location of the staff can be extracted precisely and analyzed quickly. The simulations show that the enhanced algorithms are able to effectively improve localization accuracy for randomly distributed sensor networks and ensure its safety in complex environment and improve the level of safety production.

With the continuous improvement of renewable energy penetration, the inertia and primary frequency modulation capability of synchronous power grid are declining. If the virtual synchronous generator (VSG) technology is used for renewable energy generation, it can make due contribution to reducing the frequency variation and frequency deviation of the system under the impact of high power gap. The inertia support and primary frequency modulation function positioning in VSG research are not clear. In this paper, the inertia support function of VSG and its physical significance are analyzed, the expression of inertia support power of VSG is deduced, the location distinction between inertia support function of VSG and primary frequency modulation function is analyzed, and the different control function pairs of VSG in the process of large synchronous power grid frequency accident are simulated and analyzed,and study the effect of system frequency variation.

Automatically recognizing rear light signals of front vehicles can significantly improve driving safety by automatic alarm and taking actions proactively to prevent collisions and accidents. Much previous research only focuses on the detection of brake signals at night. In this paper, the authors propose a novel and robust framework to detect rear lights of vehicles and estimate their signal states at daytime. Comparing with existing state-of-the-art works, these experimental results show the high detection rate and robustness of the system in complicated light conditions.

Traffic congestion in urban areas is a severe problem in many cities around the world. Conventional infrastructure-based solutions to detect traffic congestion, such as surveillance cameras and road surface inductive loops, have the limitations of high deployment costs and limited coverage. In recent years, due to the popularity of mobile devices, solutions that do not require pre-deployed infrastructure start to emerge; in these solutions, sensor data is collected by mobile devices onboard the vehicles, sent to a central server via vehicle-to-infrastructure (V2I) or cellular communications, and used collectively to determine the traffic states of the roads. However, existing solutions require data from a considerably large number of vehicles on the same road to accurately detect traffic congestion of a particular road. In this paper, we propose a novel approach to detect the traffic states of the roads with only the data from a single vehicle. The biggest advantage of such an approach is that, unlike previously proposed solutions, the system can function properly even if there is only a smaller number of vehicles equipped with the system, which is usually the case at the early stage of the deployment of a vehicle-to-vehicle (V2V) network or a large-scale intelligent transportation system. In our solution, machine learning mechanisms are utilized to classify the traffic state by extracting the movement behaviors of a vehicle. Our model development and performance evaluation utilize highly accurate vehicle traces collected at several real-world intersections with lidar. In addition, to properly label the obtained data traces to either congested or free-flow and accurately reflect the reality, a previously proposed theoretical method is used in combination with human labeling. Evaluation shows that our approach can achieve a detection accuracy of 88.94%.

To investigate the anatomic features of the perforating branch flap of the medial vastus muscle, so as to provide a new perforating branch flap for repairing the soft tissue defect.Six fresh donated lower limb specimens underwent an intra-arterial injection of a lead oxide and lactoprene preparation. The integument of the thigh was dissected to observe the origin, course, size, and location of the perforating branch of the medial vastus muscle by angiography and photography. Based on the anatomic study, the free perforating branch flaps of the medial vastus muscle (14 cm x 6 cm to 20 cm x 5 cm) were used to repair skin and soft tissue defects (8 cm x 6 cm to 12 cm x 8 cm) of the feet in 4 patients between June 2009 and August 2011.The artery of the medial vastus was sent out constantly from the femoral artery, and then descended in the vastus muscle to lateral patella where it anastomosed with the terminal branches of lateral femoral circumflex artery to form prepatellar vascular network. The artery of the medial vastus sent out 3-5 musculocutaneous perforating branches into the deep fascia and then extended superficially to the overlying skin. Four flaps survived after surgery; wounds at the donor site and recipient site healed by first intention. After follow-up of 6-12 months, the flaps had good appearance and texture. All ankles had normal movement range of plantarflexion and dorsiflexion.The free perforating branch flaps of the medial vastus muscle can be harvested easily, and have the advantage of good texture and abundant donor site.

With the development of financial liberty and integration, Chinese commercial bank will be confronted with severe circumstance in the course of financial revolution, which will bring about the commercial bank's more and more credit risk. It is very necessary to research and grasp completely and systematically the characteristic, manifest and reason, and construct the early warning system of the commercial bank's credit risk to control completely the risk. Whether in thoughts or technology, application of the Artificial Neural Network in early warning of commercial bank credit risk is a breakthrough. Commercial bank credit risk management can be divided into two sections, early warning before loan and early warning after loan. The purpose of this paper is to study the early warning after loan. We discuss the early warning signal about financial factor, using the factor analysis in SPSS. Then we introduce the back-propagation neural network to design network framework, carry on the network training and test, then make positive analysis to the samples with Matlab. According to the results of B-P model analysis, carry on the general analysis, thus achieve the goal of credit loan early warning.

This paper proposes time-optimal trajectory generation method for industrial robots. The proposed time-optimal trajectory generation method studies the relationship between the maximum acceleration and the maximum velocity, and presents an algorithm for deriving the optimal trajectory based on this relationship. In order to verify this method, a new planar parallel robotic manipulator with fast motion and high precision is designed. Finally, the proposed method is applied to the designed robot, and the simulation results prove that the method is effective for the industrial robot.