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Abstract Precise regulation of gene expression is crucial to development. Enhancers, the core of gene regulation, determine the spatiotemporal pattern of gene transcription. Since many disease-associated mutations are characterized in enhancers, the research on enhancer will provide clues to precise medicine. Rapid advances in high-throughput sequencing technology facilitate the characterization of enhancers at genome wide, but understanding the functional mechanisms of enhancers remains challenging. Herein, we provide a panorama of enhancer characteristics, including epigenetic modifications, enhancer transcripts, and enhancer-promoter interaction patterns. Furthermore, we outline the applications of high-throughput sequencing technology and functional genomics methods in enhancer research. Finally, we discuss the role of enhancers in human disease and their potential as targets for disease prevention and treatment strategies.

Abstract Objective We compared the long-term outcomes of percutaneous endoscopic lumbar discectomy (PELD) with those of open fenestration discectomy (OFD) in treating lumbar disc herniation (LDH). Methods 281 patients were included from January 2013 to December 2018, of whom 228 underwent PELD and 53 underwent OFD. General information of patients was collected and imbalances in covariates between groups were corrected using propensity score matching (PSM). Then, the clinical function scores, imaging data, and complications were compared. Results Among participants, 102 patients were successfully matched and were followed for 62–128 months. Compared to the OFD group, the PELD group indicated greater improvements in visual analog scale score of low back pain (VAS-BP), Japanese orthopedic association (JOA), and Oswestry disability index (ODI) at 7 days postoperatively and the last follow-up (P 0.05). Conclusion PELD and OFD both provide acceptable clinical outcomes for LDH. However, PELD is superior to OFD in terms of relieving low back pain, delaying disc degeneration, and maintaining segmental stability.

Abstract Background Enhancers are important gene regulatory elements that promote the expression of critical genes in development and disease. Aberrant enhancer can modulate cancer risk and activate oncogenes that lead to the occurrence of various cancers. However, the underlying mechanism of most enhancers in cancer remains unclear. Here, we aim to explore the function and mechanism of a crucial enhancer in melanoma. Methods Multi-omics data were applied to identify an enhancer (enh17) involved in melanoma progression. To evaluate the function of enh17, CRISPR/Cas9 technology were applied to knockout enh17 in melanoma cell line A375. RNA-seq, ChIP-seq and Hi-C data analysis integrated with luciferase reporter assay were performed to identify the potential target gene of enh17. Functional experiments were conducted to further validate the function of the target gene ETV4. Multi-omics data integrated with CUT&Tag sequencing were performed to validate the binding profile of the inferred transcription factor STAT3. Results An enhancer, named enh17 here, was found to be aberrantly activated and involved in melanoma progression. CRISPR/Cas9-mediated deletion of enh17 inhibited cell proliferation, migration, and tumor growth of melanoma both in vitro and in vivo. Mechanistically, we identified ETV4 as a target gene regulated by enh17, and functional experiments further support ETV4 as a target gene that is involved in cancer-associated phenotypes. In addition, STAT3 acts as a transcription factor binding with enh17 to regulate the transcription of ETV4. Conclusions Our findings revealed that enh17 plays an oncogenic role and promotes tumor progression in melanoma, and its transcriptional regulatory mechanisms were fully elucidated, which may open a promising window for melanoma prevention and treatment.

In automatic driving, the recognition of Front-Vehicle taillights plays a key role in predicting the intentions of the vehicle ahead. In order to accurately identify the Front-Vehicle taillights, we first analyze the different characteristics of the vehicle taillight signal, and then propose an improved taillight recognition model based on YOLOv5s. First, CA(coordinate attention) is inserted into the backbone network of YOLOv5s model to improve small target recognition and reduce interference from other light sources; Then, the EIOU Loss is used to solve the class imbalance problem; Finally, EIOU-NMS is used to solve the problem of anchor box error suppression in the recognition process. We use the actual scene video and vehicle taillights dataset to conduct ablation experiments to verify the effectiveness of the improved algorithm. The experimental results show that the mAP value of the model is 9.2% higher than YOLOv5s.

Abstract Background Excessive oxidative stress has been accepted as one of the critical factors for intervertebral disc degeneration (IDD), which is associated with low back pain (LBP). Fisetin (Fis) is a bioactive flavonoid that possesses strong bioactive activity. In present study, we aimed to illuminate the role of Fis on nucleus pulposus mesenchymal stem cells (NPMSCs). Methods NPMSCs were isolated and cultured from rat NP tissues and identified by flow cytometry and multilinear differentiation. The cytotoxicity of Fis, EX‐527, and hydrogen peroxide (H2O2) on NPMSCs was validated using Cell Counting Kit‐8 tests. Cell apoptosis was tested by flow cytometry and TUNEL assay. Inflammatory mediators were assessed by Elisa tests, RT‐PCR. Extracellular matrix (ECM) metabolism was measured by Western blot analysis and RT‐qPCR. The expression of the SIRT1 was evaluated by Western blot analysis. Results NPMSCs were successfully isolated and cultured from rat NP tissues, and it has been identified by flow cytometry and multilinear differentiation. The results showed that Fis attenuated H2O2‐induced apoptosis, inflammation, and ECM degradation of NPMSCs. Moreover, the above protective effects of Fis can be inhibited by EX‐527, a unique SIRT1 inhibitor, indicating that SIRT1 may involve in the mechanism of Fis in protecting NPMSCs from oxidative stress. Conclusions As a natural compound with little cytotoxicity on NPMSCs, Fis alleviate H2O2‐induced apoptosis, inflammation, and ECM degradation by suppressing oxidative stress, this finding may add the theoretical basis for research on new treatment of IDD based on NPMSCs.

In skeleton-based human action recognition, Transformer, which models the correlations between joint pairs in global topology, has achieved remarkable results. However, compared to many researches on changing graph topology learning in graph convolution network (GCN), Transformer self-attention ignores the topology of the skeleton graph when capturing the dependencies between joints. To address these problems, we propose a novel two-stream spatial Graphormer network (2s-SGR), which uses self-attention incorporating structural encodings to model joint and bone information, and which consists of two networks, the joint stream spatial Graphormer network (Js-SGR) and the bone stream spatial Graphormer network (Bs-SGR). First, in the Js-SGR, while Transformer models joint correlations in the global topology of the space, the topology of the joints and the edge information of the bones are introduced into the self-attention through custom structural encodings. At the same time, joint motion information is modeled in spatial-temporal blocks. The added information on structure and motion can effectively capture the dependencies of nodes between frames and enhance feature representation. Second, for the second-order information of the skeleton, the Bs-SGR adapts to the structure of the bone by adjusting the custom structural encodings. Finally, the global spatial-temporal features of joints and bones in the skeleton are fused and input into the classification network to obtain action recognition results. Extensive experiments on three large-scale datasets, NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics, demonstrate that the performance of the 2s-SGR proposed in this paper is at the state-of-the-art level and is effectively validated by ablation experiments.

Although driverless technology belongs to the frontier of science and technology, there is no sufficient actual data. From the lack of a comprehensive systematic evaluation method of traffic safety under driverless penetration, considering the impact of the three core systems of driverless perception, decision-making, control, and the complex road factors on the safety of driving, we review the main risk causal factors through the analysis of the accident causal model STAMP and put forward the fusion of the Leaky Noisy-OR Gate and Bayesian network model. The Bayesian network professional analysis tool GeNIe 2.0 was used to simulate, analyze, and evaluate the driverless traffic risk Bayesian network model, which accurately assessed the traffic safety risk under driverless penetration and diagnosed and identified the sensitive risk factors. The results of this study concluded that, in order to effectively deal with the future traffic safety risks of driverless vehicles, vehicle enterprises, research institutions, software and hardware suppliers in the field of driverless driving should strengthen the research and development and manufacturing of key components such as perception, and enhance the depth of research and development of AI decision-making software, which provides a new way of thinking about the management of the safety risk of driverless traffic and a theoretical basis for the development and implementation of risk control measures.

How decisions are made when autonomous vehicles (AVs) are faced with moral dilemmas is still a challenge. For this problem, this paper proposed the concept of common principles, which were drawn from the general public choice and could be generally accepted by society. This study established five moral dilemma scenarios with variables including the number of sacrifices, passenger status, presence of children, decision-making power subjects, and laws. Based on existing questionnaire data, we used gray correlation analysis to analyze the influence of the individual and psychological factors of participants in decision-making. Then, an independent sample t-test and analysis of covariance were selected to analyze the influence relationship between individual and psychological factors. Finally, by induction statistics of decision choices and related parameters of participants, we obtain common principles of autonomous vehicles, including the principle of protecting law-abiding people, the principle of protecting the majority, and the principle of protecting children. The principles have different priorities in different scenarios and can meet the complex changes in moral dilemmas. This study can alleviate the contradiction between utilitarianism and deontology, the conflict between public needs and individualized needs, and it can provide a code of conduct for ethical decision-making in future autonomous vehicles.

Autonomous vehicles (AVs) are supposed to make appropriate strategies to ensure driving safety and improve traffic efficiency. However, not all collisions will be avoidable. In a typical dilemma scenario of this paper, we have to make hard decisions from two evils, sparing a child with red-light running behavior or crashing into a well-equipped motorcycle driver without violation of the traffic law. Combined with the existing traffic laws and the accident liability judgment cases in China, 12 typical dilemma scenarios are established. In order to acquire data to analyze the driving decision-making factors involving ethics and legal under the moral dilemma of AVs, we conduct a series of experiments in virtual reality environments. Furthermore, key factors are extracted to characterize the driving decisions under different scenarios and quantified by the gray relation entropy analysis method. Particularly, the ethical factor is represented by the quantity and type of collision targets, the legal factor is qualified by rights of way. The results showed that priority levels of the influencing factors in each moral dilemma were considerably different. Nevertheless, when the quantity of collision targets on two sides is equal, more participants prefer to protect the ones that comply with traffic rules. The research results can provide the basis for designing moral algorithms for the AVs.

Hollow fiber membranes were produced from a commercial polyvinylidene fluoride (PVDF) polymer, Kynar HSV 900, with a unique sandwich structure consisting of two sponge-like layers connected to the outer and inner skin layers while the middle layer comprises macrovoids. The sponge-like layer allows the membrane to have good mechanical strength even at low skin thickness and favors water vapor transportation during vacuum membrane distillation (VMD). The middle layer with macrovoids helps to significantly reduce the trans-membrane resistance during water vapor transportation from the feed side to the permeate side. Together, these novel structural characteristics are expected to render the PVDF hollow fiber membranes more efficient in terms of vapor flux as well as mechanical integrity. Using the chemistry and process conditions adopted from previous work, we were able to scale up the membrane fabrication from a laboratory scale of 1.5 kg to a manufacturing scale of 50 kg with consistent membrane performance. The produced PVDF membrane, with a liquid entry pressure (LEPw) of >3 bar and a pure water flux of >30 L/m2·hr (LMH) under VMD conditions at 70–80 °C, is perfectly suitable for next-generation high-efficiency membranes for desalination and industrial wastewater applications. The technology translation efforts, including membrane and module scale-up as well as the preliminary pilot-scale validation study, are discussed in detail in this paper.

Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis. Unfortunately, any one kind of household traffic surveys has its own problems. Even all household traffic survey data is accurate, it is difficult to get the trip routes information. To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey’s defect. Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis. In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed. Results show that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is worth noting that students have the shortest travel distance and company business’s travel distance distribution is dispersed, which has the longest travel distance. Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.

Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.

Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.

In order to study the fatigue properties of basic Magnesium Sulfate cement reinforced concrete beams, specimens of the basic magnesium sulfate cement (BMSC) and ordinary portland cement (PO·C) were in strength grade of C40 and C50 were prepared, respectively. Response surface methodology is applied to this study. The results show that under the fatigue load, the deflection value of the specimen decreases with the increase of the number of fatigue cycles. The fatigue cycle life of the BMSC specimens with higher strength grades is more than that of the PO·C specimens, and the higher the strength grade, the more obvious. Under the fatigue load, the development of the midspan deflection of the specimen showed an S-shaped trend, and the deflection in the BMSC specimen was smaller than that of the PO·C specimen. The spacing of the cracks in BMSC columns was smaller than that of PO·C columns because of the better bond performance between the reinforcing bars and the surrounding BMS concrete. But the crack widths of BMSC columns were close to the width of PO·C columns. It shows that the BMSC specimen has a high rigidity and good resistance to deformation. [ABSTRACT FROM AUTHOR]

The first enantioenriched synthesis of 1,1,3-trisubstituted (trifluoromethyl)indene derivatives, bearing a quaternary stereogenic carbon center, is reported using a simple chiral sulfinamide phosphine-catalyzed asymmetric allylic alkylation of a mixture of indenes with Morita-Baylis-Hillman carbonates. The resulting derivatives can serve as a valuable synthetic building block for some drugs and natural products. Broad substrate scope and high regio- and enantioselectivity of this reaction were particularly remarkable. [ABSTRACT FROM AUTHOR]

The cusped field thruster is a new electric propulsion device that is expected to have a non-uniform radial current density at the anode. To further study the anode current density distribution, a multiannulus anode is designed to directly measure the anode current density for the first time. The anode current density decreases sharply at larger radii; the magnitude of collected current density at the center is far higher compared with the outer annuli. The anode current density non-uniformity does not demonstrate a significant change with varying working conditions. [ABSTRACT FROM AUTHOR]

The artificial potential field algorithm for path control of unmanned ground vehicles formation in highway is proposed. First, the basic unit is derived from decomposition-iteration principle to improve formation stability and flexibility. Then, the virtual-leader potential field (VLPF) and vehicle-vehicle potential field are combined to generate the formation path in straight highway. Especially the VLPF function boundary is set as ellipse to adapt the highway environment. Finally, the formation in curved highway can be achieved through changing the ellipse's orientation and dimension. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]