Explore the vast range of titles available.

To encourage energy saving and emission reduction and improve traffic efficiency in the multiple signalized intersections area, an eco-driving strategy for connected and automated vehicles (CAVs) considering the effects of traffic flow is proposed for the mixed traffic environment. Firstly, the formation and dissipation process of signalized intersection queues are analyzed based on traffic wave theory, and a traffic flow situation estimation model is constructed, which can estimate intersection queue length and rear obstructed fleet length. Secondly, a feasible speed set calculation method for multiple signalized intersections is proposed to enable vehicles to pass through intersections without stopping and obstructing the following vehicles, adopting a trigonometric profile to generate smooth speed trajectory to ensure good riding comfort, and the speed trajectory is optimized with comprehensive consideration of fuel consumption, emissions, and traffic efficiency costs. Finally, the effectiveness of the strategy is verified. The results show that traffic performance and fuel consumption benefits increase as the penetration rate of CAVs increases. When all vehicles on the road are CAVs, the proposed strategy can increase the average speed by 9.5%, reduce the number of stops by 78.2%, reduce the stopped delay by 82.0%, and reduce the fuel consumption, NOx, and HC emissions by 20.4%, 39.4%, and 46.6%, respectively.

Road scene understanding is crucial to the safe driving of autonomous vehicles. Comprehensive road scene understanding requires a visual perception system to deal with a large number of tasks at the same time, which needs a perception model with a small size, fast speed, and high accuracy. As multi-task learning has evident advantages in performance and computational resources, in this paper, a multi-task model YOLO-Object, Drivable Area, and Lane Line Detection (YOLO-ODL) based on hard parameter sharing is proposed to realize joint and efficient detection of traffic objects, drivable areas, and lane lines. In order to balance tasks of YOLO-ODL, a weight balancing strategy is introduced so that the weight parameters of the model can be automatically adjusted during training, and a Mosaic migration optimization scheme is adopted to improve the evaluation indicators of the model. Our YOLO-ODL model performs well on the challenging BDD100K dataset, achieving the state of the art in terms of accuracy and computational efficiency.

As autonomous vehicles and traditional vehicles will coexist for several decades, how to efficiently manage the mixed traffic, while enhancing road throughput, fuel consumption and traffic stability becomes a challenge. This is due to the randomness and heterogeneity of traditional vehicles interspersed among autonomous vehicles. Moreover, communication delays arising from the shared wireless communication network substantially degrade the performance of platooning control for connected autonomous vehicles. To address these challenging problems, this paper proposes a knowledge-guided self-learning mixed platoon control strategy. Firstly, the proposed strategy extracts key features of the continuous and aggregated behavior of traditional vehicles, such as desired time-varying time gap and standstill spacing, by integrating knowledge from the kinematic wave model and Newell’s car-following model. This helps autonomous vehicles predict traditional vehicles’ trajectories. Secondly, to tackle delayed current state information, the study incorporates previous control instructions into the state representation of the soft actor-critic algorithm. Simulations show the proposed strategy outperforms existing methods in traffic stability, passenger comfort, energy consumption cost and traffic oscillation dampening, with a zero collision rate in vehicle merging and diverging scenarios. The framework provides a generalizable and scalable solution for the development and adoption of connected autonomous vehicle systems. This paper proposes a knowledge-guided self-learning mixed platoon control strategy for the coexistence of autonomous and traditional vehicles. The framework provides a generalizable and scalable solution for the development and adoption of connected autonomous vehicle systems.

Dysregulation of the Wnt/β-catenin signaling pathway is critically involved in gastric cancer (GC) progression. However, current Wnt pathway inhibitors being studied in preclinical or clinical settings for other cancers such as colorectal and pancreatic cancers are either too cytotoxic or insufficiently efficacious for GC. Thus, we screened new potent targets from β-catenin destruction complex associated with GC progression from clinical samples, and found that scaffolding protein RACK1 deficiency plays a significant role in GC progression, but not APC, AXIN, and GSK3β. Then, we identified its upstream regulator UBE2T which promotes GC progression via hyperactivating the Wnt/β-catenin signaling pathway through the ubiquitination and degradation of RACK1 at the lysine K172, K225, and K257 residues independent of an E3 ligase. Indeed, UBE2T protein level is negatively associated with prognosis in GC patients, suggesting that UBE2T is a promising target for GC therapy. Furthermore, we identified a novel UBE2T inhibitor, M435-1279, and suggested that M435-1279 acts inhibit the Wnt/β-catenin signaling pathway hyperactivation through blocking UBE2T-mediated degradation of RACK1, resulting in suppression of GC progression with lower cytotoxicity in the meantime. Overall, we found that increased UBE2T levels promote GC progression via the ubiquitination of RACK1 and identified a novel potent inhibitor providing a balance between growth inhibition and cytotoxicity as well, which offer a new opportunity for the specific GC patients with aberrant Wnt/β-catenin signaling.

Ethical approval was obtained from the Ethics Committee of Lanzhou University Second Hospital (2021A-606) in accordance with the principles of the Declaration of Helsinki and the Good Clinical Practice guidelines. The secondary endpoints included the major pathological response (MPR) rate, objective response rate (ORR), disease control rate (DCR), tumor down-staging rate, R0 resection rate, safety, disease-free survival (DFS), and overall survival (OS). During the neoadjuvant treatment, 2 patients were excluded due to diarrhea or refusal to continue, resulting 28 patients in the full-analysis set (FAS); besides, one patient with type 2 diabetes died of diabetic ketoacidosis before receiving adjuvant treatment [Supplementary Figure 1, http://links.lww.com/CM9/C151] The radiological responses assessed in FAS indicated a range of tumor shrinkage from –100.0% to 1.1% [Figure 1A]. [5] While the survival data from this study are preliminary, the encouraging 2-year OS rate, 18-month DFS rate, and 100.0% R0 resection rate suggest potential long-term survival benefits for patients with LAGC.

Background Hepatocellular carcinoma (HCC) one of the most common digestive system tumors, threatens the tens of thousands of people with high morbidity and mortality world widely. The purpose of our study was to investigate the related genes of HCC and discover their potential abilities to predict the prognosis of the patients. Methods We obtained RNA sequencing data of HCC from The Cancer Genome Atlas (TCGA) database and performed analysis on protein coding genes. Differentially expressed genes (DEGs) were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were conducted to discover biological functions of DEGs. Protein and protein interaction (PPI) was performed to investigate hub genes. In addition, a method of supervised machine learning, recursive feature elimination (RFE) based on random forest (RF) classifier, was used to screen for significant biomarkers. And the basic experiment was conducted by lab, we constructe a clinical patients’ database, and obtained the data and results of immunohistochemistry. Results We identified five biomarkers with significantly high expression to predict survival risk of the HCC patients. These prognostic biomarkers included SPC25, NUF2, MCM2, BLM and AURKA. We also defined a risk score model with these biomarkers to identify the patients who is in high risk. In our single-center experiment, 95 pairs of clinical samples were used to explore the expression levels of NUF2 and BLM in HCC. Immunohistochemical staining results showed that NUF2 and BLM were significantly up-regulated in immunohistochemical staining. High expression levels of NUF2 and BLM indicated poor prognosis. Conclusion Our investigation provided novel prognostic biomarkers and model in HCC and aimed to improve the understanding of HCC. In the results obtained, we also conducted a part of experiments to verify the theory described earlier, The experimental results did verify our theory.

Background Numerous essential algorithms and methods, including entropy-based quantitative methods, have been developed to analyze complex DNA sequences since the last decade. Exons and introns are the most notable components of DNA and their identification and prediction are always the focus of state-of-the-art research. Results In this study, we designed an integrated entropy-based analysis approach, which involves modified topological entropy calculation, genomic signal processing (GSP) method and singular value decomposition (SVD), to investigate exons and introns in DNA sequences. We optimized and implemented the topological entropy and the generalized topological entropy to calculate the complexity of DNA sequences, highlighting the characteristics of repetition sequences. By comparing digitalizing entropy values of exons and introns, we observed that they are significantly different. After we converted DNA data to numerical topological entropy value, we applied SVD method to effectively investigate exon and intron regions on a single gene sequence. Additionally, several genes across five species are used for exon predictions. Conclusions Our approach not only helps to explore the complexity of DNA sequence and its functional elements, but also provides an entropy-based GSP method to analyze exon and intron regions. Our work is feasible across different species and extendable to analyze other components in both coding and noncoding region of DNA sequences.

Gastric cancer (GC) is characterised by a dense stromal microenvironment, lack of therapeutic targets, and limited effective treatment options, collectively leading to a poor prognosis. Here, we identify UL16 binding protein 2 (ULBP2) as a potential therapeutic target in GC. Mechanistically, ULBP2 overexpression activates the TGF-β signalling pathway, promoting the activation of cancer-associated fibroblasts (CAFs) and tumor progression in GC. Furthermore, we developed ULBP2 CAR-T cells and assessed their therapeutic potential in GC cell lines, organoids, cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) mouse models. We showed that ULBP2 CAR-T cells effectively eliminated GC cell lines and organoids and, either alone or in combination with an anti-PD-1 antibody, significantly inhibited tumor growth and prolonged survival in both CDX and PDX mouse models. In conclusion, ULBP2 contributes to GC progression by promoting TGF-β mediated CAF activation, which collectively reinforce the dense stromal microenvironment. Targeting ULBP2 suppresses tumor growth, reduces stromal deposition, and promotes T cell infiltration, thereby enhancing the efficacy of immunotherapy in GC.

Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

Despite the advancements in synthetic methods, it is still challenging to prepare white light‐emitting carbon dots for solid‐state white light‐emitting diodes (LEDs). Herein, the synthesis and characterization of a carbonized polymer dot (CPD) that emits white light in the solid state are presented. The reaction of an oxygen‐rich precursor, trimethylolpropane tri(cyclic carbonate) ether, with nitrogen‐rich melamine in an alcohol via a single‐step solvothermal method affords CPDs with hydroxyurethane and CN groups. The morphology of the CPDs can be regulated by changing the reaction time. Transmission electron microscopy reveals structural evolution from flocculent polymers to dandelion‐like and spherical particles with an increase in the carbonization time from 6 to 12 and 18 h, respectively. The dandelion‐like CPDs exhibit a relatively high quantum yield of 7.5% in the solid state, which is ascribed to the abundant surface poly(hydroxyurethane) chains that restrict the aggregation‐caused quenching of luminescence. It is proposed that multiple coexistent clusters generate different emission sites, thereby leading to white light emission. Loose clusters formed from hydroxyurethane and CN bonds, which have a low degree of conjugation, emit blue light, whereas compact clusters generated through interactions between the hydroxyurethane and CN bonds of poly(hydroxyurethane) and carbon cores emit yellow light. A carbonized polymer dot that emits white light in the solid state is prepared from trimethylolpropane tri(cyclic carbonate) ether and melamine as precursors by a one‐step solvothermal method. A multi‐cluster‐induced luminescence mechanism is proposed to account for the observed luminescence.