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The implementation of the research results is seen as a crucial step in the development of innovation in the transport sector. Moving to such an implementation is not always easy or straightforward. It requires a suitable organizational framework both inside as well as outside research producing entities and a number of other facilitating factors that are usually found within an innovation ecosystem. The paper examines systematically the conditions and prevailing practices for transport research implementation in Europe (the European Union) and China and draws useful insights as to the factors that influence such implementation, the incentives, and other facilitating provisions that the research funding organizations can take. It also analyses the current practice and lessons learned for research implementation on the road to innovation production in four major areas of transport research namely: Automated Mobility, Intelligent Railways, Shared and Micromobility applications, and Electromobility.

Abstract Background Multidrug resistance (MDR) mediated by ATP binding cassette subfamily B member 1 (ABCB1/P-gp) is a major cause of cancer chemotherapy failure, but the regulation mechanisms are largely unknown. Methods Based on single gene knockout, we studied the regulation of CDK6-PI3K axis on ABCB1-mediated MDR in human cancer cells. CRISPR/Cas9 technique was performed in KB-C2 cells to knockout cdk6 or cdk4 gene. Western blot, RT-PCR and transcriptome analysis were performed to investigate target gene deletion and expression of critical signaling factors. The effect of cdk4 or cdk6 deficiency on cell apoptosis and the cell cycle was analyzed using flow cytometry. In vivo studies were performed to study the sensitivity of KB-C2 tumors to doxorubicin, tumor growth and metastasis. Results Deficiency of cdk6 led to remarkable downregulation of ABCB1 expression and reversal of ABCB1-mediated MDR. Transcriptomic analysis revealed that CDK6 knockout regulated a series of signaling factors, among them, PI3K 110α and 110β, KRAS and MAPK10 were downregulated, and FOS-promoting cell autophagy and CXCL1-regulating multiple factors were upregulated. Notably, PI3K 110α/110β deficiency in-return downregulated CDK6 and the CDK6-PI3K axis synergizes in regulating ABCB1 expression, which strengthened the regulation of ABCB1 over single regulation by either CDK6 or PI3K 110α/110β. High frequency of alternative splicing (AS) of premature ABCB1 mRNA induced by CDK6, CDK4 or PI3K 110α/110β level change was confirmed to alter the ABCB1 level, among them 10 common skipped exon (SE) events were found. In vivo experiments demonstrated that loss of cdk6 remarkably increased the sensitivity of KB-C2 tumors to doxorubicin by increasing drug accumulation of the tumors, resulting in remarkable inhibition of tumor growth and metastasis, as well as KB-C2 survival in the nude mice. Conclusions CDK6-PI3K as a new target signaling axis to reverse ABCB1-mediated MDR is reported for the first time in cancers. Pathways leading to inhibition of cancer cell proliferation were revealed to be accompanied by CDK6 deficiency.

In order to meet the rapid growth of railway transportation demand, the technology that can improve its capacity has been widely concerned. Virtual Coupling (VC) is a new technology to improve capacity by decreasing headways between successive trains. Its basic principle is to control the train formation operation in coordination with the goal of keeping the same speed under the support of data transmission technology such as train-to-train communication. Our paper first reviews the existing theoretical research on train formation operation control, which is divided into four categories: train following, feedback control, optimal control and computational intelligent method. Secondly, by reviewing the related projects in Europe and China, based on the scenario analysis method, five general scenarios and two emergency scenarios in the whole process of VC operation are sorted out. Then, an original, complete list of performance indicators (PIs) is proposed for evaluating the performance of VC in different scenarios. Additionally, the paper gives a brief insight into VC operation by providing an adequate context to understand the proposed PIs.

The control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. However, it is challenging since traffic dynamics are complicated in real-world scenarios. Because of the high complexity of the optimisation problem for modeling the traffic, experimental settings of existing works are often inconsistent. Moreover, it is not trivial to control multiple intersections properly in real complex traffic scenarios due to its vast state and action space. Failing to take intersection topology relations into account also results in inferior solutions. To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios. Additionally, we propose a novel baseline model with strong performance. It is based on deep reinforcement learning with an encoder-decoder structure: an edge-weighted graph convolutional encoder to excavate multi-intersection relations; and an unified structure decoder to jointly model multiple junctions in a comprehensive manner, which significantly reduces the number of the model parameters. By doing so, the proposed model is able to effectively deal with the multi-intersection traffic control optimisation problem. Models are trained/tested on both synthetic and real maps and traffic data with the Simulation of Urban Mobility (SUMO) simulator. Experimental results show that the proposed model surpasses multiple competitive methods. The traffic data and the code can be found at https://git.io/JPdU1.

As a mixed-methods research in economics and psychology, this study aimed to analyze the influence from the intergenerational succession on the financialization level including asset financialization and revenue financialization, and further test the moderating effect of the heirs’ typical growing experience according to The Imprinting Theory, based on the 2009–2020 annual data of listed family enterprises of China. There were two key findings. First, the effect of Chinese family enterprises’ intergenerational succession on asset financialization was positively significant while the effect on revenue financialization was not significant, indicating that the financialization behavior has not brought about effective financial profits. Second, among the heirs’ typical growing experiences, their parents’ entrepreneurial experience during their childhood, oversea study experience, and MBA education experience had the significantly positive moderating effects on the influence from intergeneration succession to asset financialization level of Chinese family enterprises, which was an important internal mechanism for the heirs to promote the financialization process of family enterprises.

As one of the important national infrastructures, more and more attentions are attracted by the industrial control system for rail (ICS-R), with the rapid development of information technology and the increasingly severe network situation.The system composition of ICS-R was analyzed, the types of security threats faced by the ICS-R were summarized and analyzed, the practical cases of threat propagation were given, and the information security threat trend of ICS-R was analyzed considering the development trend of technology.Finally, several development suggestions were prospected as promising techniques.

Developing technologies such as AI and big data are changing the landscape of cybercrime for both attackers and defenders.Traditional defense technology mainly use the passive detect-then-defend model,which is hard to meet the security protection requirements brought by new attack characteristics.A novel security framework,named parallel security was proposed,which aimed at providing a comprehensive solution with descriptive security,predictive security and prescriptive security.The main idea was based on the ACP theory of parallel intelligent,and integrated new methods such as generative adversarial leaning model,parallel blockchain.Human elements,sociology and psychology were taken as an important component in the framework,in order to enhance the immunity of the system.In addition,a parallel security based platform was design and developed for several typical scenarios in the field of industrial internet as a proof of concept.

Given a vehicle image,vehicle re-identification aims to find the same vehicle caught by other cameras,it can be regarded as a sub-problem of image retrieval.In the real traffic surveillance system,vehicle re-identification can play a role in locating,supervising and criminal investigation of target vehicles.With the rise of deep neural networks and the release of large-scale dataset,improving the accuracy and efficiency of vehicle re-identification has become a research focus in the field of computer vision and multimedia in recent years.The vehicle re-identification methods from different perspectives were classified,and the overview,comparison and analysis in terms of feature extraction,design and performance were given in detail,and the challenges and future trends of vehicle re-identification were predicted.

Abstract Background PI3K/AKT is a vital signaling pathway in humans. Recently, several PI3K/AKT inhibitors were reported to have the ability to reverse cancer multidrug resistance (MDR); however, specific targets in the PI3K/AKT pathways and the mechanisms associated with MDR have not been found because many of the inhibitors have multiple targets within a large candidate protein pool. AKT activation is one presumed mechanism by which MDR develops during cancer treatment. Methods The effects of inhibiting PI3K 110α and 110β by BAY-1082439 treatment and CRISPR/Cas9 knockout were examined to determine the possible functions of BAY-1082439 and the roles of PI3K 110α and 110β in the reversal of MDR that is mediated by the downregulation of P-gp and BCRP. Inhibition of AKT with GSK-2110183 showed that the downregulation of P-gp and BCRP is independent of generalized AKT inactivation. Immunofluorescence, immunoprecipitation, MTT, flow cytometry and JC-1 staining analyses were conducted to study the reversal of MDR that is mediated by P-gp and BCRP in cancer cells. An ATPase assay and a structural analysis were also used to analyze the potential mechanisms by which BAY-1082439 specifically targets PI3K 110α and 110β and nonspecifically influences P-gp and BCRP. Results By inhibiting the activation of the PI3K 110α and 110β catalytic subunits through both the administration of BAY-1082439 and the CRISPR/Cas9 deletion of Pik3ca and Pik3cb, the ATP-binding cassette transporters P-gp/ABCB1 and BCRP/ABCG2 were downregulated, thereby reestablishing the drug sensitivity of human epidermoid carcinoma and non-small cell lung cancer (NSCLC) MDR cells. Inhibition of AKT did not reverse the MDR mediated by P-gp or BCRP. The ABC family proteins and AKT may play MDR-enhancing roles independently. Conclusions The reversal of the dual functions of ABC-transporter-mediated and AKT-activation-enhanced MDR through the inhibition or knockout of PI3K 110α or 110β promises to improve current strategies based on combined drug treatments to overcome MDR challenges.

Learning from minority class has been a significant and challenging task which has many potential applications. Weather classification is such a case of imbalanced label distribution. This is because in places like Beijing, some types of weather, such as rain and snow, are relatively rare compared to sunny and haze days. Existing methods are primary to classify the weather conditions relying on expensive sensors or human assistance, which however usually are expensive and time-consuming. In this paper, we propose a new ensemble framework based on the advanced generative adversarial network and an effective data cleaning way to address the class imbalance problem for weather classification. The proposed method not only generates new and reliable samples for the minority class to restore balance, but also filters those generated samples which are unreliable. Experiments show that our approach outperforms the state-of-the-art methods by a huge margin for imbalanced weather classification on several benchmark data sets.

With the development of intelligent transportation,it has become an urgent problem to quickly and accurately recognize complex traffic scene.In recent years,a large number of scene recognition methods have been proposed to improve the effectiveness of traffic scene recognition,however,most of these algorithms cannot extract the semantic characteristics of the concept of vision,leading to the low recognition accuracy in traffic scenes.Therefore,a novel traffic scene recognition algorithm which extracts the high-level semantic and structural information for improving the accuracy was proposed.A system to discover semantically meaningful descriptions of the scene classes to reduce the “semantic gap” between the high level and the low-level feature representation was built.Then,the multi-label network was trained by minimizing loss function (namely,element-wise logistic loss) to obtain the high-level semantic representation of traffic scene images.Finally,experiments on four large-scale scene recognition datasets show that the proposed algorithm considerably outperforms other state-of-the-art methods.

Scene classification is increasingly popular due to its extensive usage in many real-world applications such as object detection, image retrieval, and so on. Traditionally, the low-level hand-crafted image representations are adopted to describe the scene images. However, they usually fail to detect semantic features of visual concepts, especially in handling complex scenes. In this paper, we propose a novel high-level image representation which utilizes image attributes as features for scene classification. More specifically, the attributes of each image are firstly extracted by a deep convolution neural network (CNN), which is trained to be a multi-label classifier by minimizing an element-wise logistic loss function. The process of generating attributes can reduce the “semantic gap” between the low-level feature representation and the high level scene meaning. Based on the attributes, we then build a system to discover semantically meaningful descriptions of the scene classes. Extensive experiments on four large-scale scene classification datasets show that our proposed algorithm considerably outperforms other state-of-the-art methods.

Android platform has dominated the operating system of mobile devices. However, the dramatic increase of Android malicious applications (malapps) has caused serious software failures to Android system and posed a great threat to users. The effective detection of Android malapps has thus become an emerging yet crucial issue. Characterizing the behaviors of Android applications (apps) is essential to detecting malapps. Most existing works on detecting Android malapps were mainly based on string static features, such as permissions and API usage extracted from apps. There also exists work on the detection of Android malapps with structural features, such as control flow graph and data flow graph. As Android malapps have become increasingly polymorphic and sophisticated, using only one type of static features may result in false negatives. In this paper, we propose DroidEnsemble that takes advantages of both string features and structural features to systematically and comprehensively characterize the static behaviors of Android apps and thus build a more accurate detection model for the detection of Android malapps. We extract each app’s string features, including permissions, hardware features, filter intents, restricted API calls, used permissions, code patterns, as well as structural features like function call graph. We then use three machine learning algorithms, namely, support vector machine, k-nearest neighbor, and random forest, to evaluate the performance of these two types of features and of their ensemble. In the experiments, we evaluate our methods and models with 1386 benign apps and 1296 malapps. Extensive experimental results demonstrate the effectiveness of DroidEnsemble. It achieves the detection accuracy as 95.8% with only string features and as 90.68% with only structural features. DroidEnsemble reaches the detection accuracy as 98.4% with the ensemble of both types of features, reducing 9 false positives and 12 false negatives compared to the results with only string features.

Long-term exposure to therapeutic doses of glucocorticoids (GCs) results in bone remodeling, which frequently causes osteoporosis and fracture healing retardation because of the abnormality of osteoblastic proliferation and differentiation. The mechanisms of GCs' effect on osteoblasts are largely unknown. In this present study, we found that dexamethasone (Dex) could induce the expression of the small G protein, RhoB, in mRNA and protein levels in the osteoblast-derived osteosarcoma cell lines MG-63. The up-regulation of RhoB mRNA by Dex mainly occurs at posttranscriptional level by increasing its mRNA stability through PI-3K/Akt and p38 mitogen-activated protein kinase signaling pathways. Over-expression of RhoB in MG-63 cells magnified while down-regulation of RhoB level by RNA interference impaired Dex-induced growth inhibition but not differentiation. What's more, over-expression of RhoB mimicked the effect of Dex on cell adhesion and migration. And interfering RhoB expression partially suppressed Dex-induced pro-adhesion and anti-migration in MG-63 cells. In conclusion, these results indicate that RhoB plays an important role in the pathological effect of Dex on osteoblastic growth and migration, which is a part of the mechanisms of GCs' adverse effect on bone remodeling.

As a traditional Chinese drink, tea is favored for its rich flavor and its medicinal functionality. In this study, the in vitro bioactivities of Wushanshencha (WST; a local tea from Chongqing, China), which is processed mainly from the leaves of the wild Malus hupehensis (Pamp.) Rehd.). We assessed the scavenging capacity of tea extracts on 1, 1-diphenyl-2-picrylhydrazyl (DPPH); 2, 2′-azino-bis (3-ethylbenzthiazoline-6- sulphonic acid) diammonium salt (ABTS); and hydroxyl (OH) free radicals, and demonstrate the high antioxidant activity and dose-dependent relationship of these extracts. We also detail the anti-mutagenic effect of these tea extracts against the Salmonella typhimurium TA98 strain induced by the 2, 7-diaminofluorene (2, 7-AF) mutagen and the TA100 strain induced by the N-methyl-N′-nitro- N- nitrosoguanidine (MNNG) mutagen at concentrations of 1.25 and 2.50 mg/plate, respectively, with the high-dose groups showing better results. We investigated the anticancer mechanisms of WST extracts (40, 100, and 160 μg/mL) in HepG2 human hepatoma cells via 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT) assay and quantitative real-time reverse transcription-polymerase chain reaction (RT-qPCR). The results showed that the proliferation of HepG2 cells was significantly inhibited in a dose-dependent manner by the tea extracts. Moreover, apoptosis in HepG2 cells was induced via upregulation of Caspase-3, Caspase-7, Caspase-8, Caspase-9, p21, p53, and Bax as well as downregulation of Bcl-2 apoptosis-associated factors, as assessed via mRNA expression levels after treating with WST extracts. The expression of inflammation-related factors, e.g., NF-κB, and Cox-2, was significantly downregulated by the WST extracts, demonstrating its inflammatory properties. Together, these observations indicated that WST extracts have anti-inflammatory and anti-cancer properties. In addition, high-performance liquid chromatography (HPLC) analysis showed that WST extracts contained chlorogenic acid, 4-hydroxycinnamic acid, isoquercitrin, taxifolin, quercitrin, rosmarinic acid, myricetin, baicalin, neosperidin dihydrochalcone, and quercetin. As such, WST appears to be an effectively functional drink, due to its rich functional components and anti-cancer activity.

Nanocrystalline SrAl2Si2 O8 :Eu2+ phosphor layers were coated on nonaggregated, monodisperse and spherical SiO2 particles using a hydrothermal homogeneous precipitation. After annealing at 1100 °C, core-shell SiO2@SrAl2 Si2 O8 :Eu2+ particles were obtained. They were characterized with x-ray diffraction (XRD), scanning electron microscopy, transmission electron microscopy and photoluminescence techniques. XRD analysis confirmed the formation of SiO2 @SrAl2 Si2 O8 :Eu2+ particles; it indicated that the SrAl2 Si2 O8 :Eu2+ shells on SiO2 particles consisted of hexagonal crystallites. The core-shell phosphors obtained are well-dispersed submicron spherical particles with a narrow size distribution. The thickness of the coated layer is approximately 20–40 nm. Under ultraviolet excitation (361 nm), the particles emit blue light at about 440 nm due to the Eu2+ ions in their shells.