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Tumor biomarkers, the substances which are produced by tumors or the body’s responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

At present, China’s rural sewage treatment technology is mostly used in municipal sewage treatment methods, with high energy consumption and complicated operation and maintenance. Through analyzing the problem of rural sewage management, we discussed the necessity and feasibility of rural sewage resource utilization, presented the principles that should be followed in the resource utilization according to local conditions, promoting the treatment, economic rationality, and ecological safety, and Summarized the technical models of irrigation utilization, ecological dissipation and decentralized on-site utilization for the resource utilization of rural domestic wastewater. The expansion of the resource utilization model will be more conducive to reducing the cost of rural sewage management and enhancing the quality of the rural environment and human settlements.

Traffic sign detection is a challenging task for unmanned driving systems. In the traffic sign detection process, the object size and weather conditions vary widely, which will have a certain impact on the detection accuracy. In order to solve the problem of balanced detecting precision of traffic sign recognition model in different weather conditions, and it is difficult to detect occluded objects and small objects, this paper proposes a small object detection algorithm based on improved YOLOv5s in complex weather. First, we add the coordinate attention(CA) mechanism in the backbone, a light-weight yet effective module, embedding the location information of traffic signs into the channel attention to improve the feature extraction ability of the network. Second, we exploit effectively fine-grained features about small traffic signs from the shallower layers by adding one prediction head to YOLOv5s. Finally, we use Alpha-IoU to improve the original positioning loss CIoU, improving the accuracy of bbox regression. Applying this model to the recently proposed CCTSDB 2021 dataset, for small objects, the precision is 88.1%, and the recall rate is 79.8%, compared with the original YOLOv5s model, it is improved by 12.5% and 23.9% respectively, and small traffic signs can be effectively detected under different weather conditions, with low miss rate and high detection accuracy. The source code will be made publicly available at https://github.com/yang-0706/ImprovedYOLOv5s .

Multi-center heterogeneous data are a hot topic in federated learning. The data of clients and centers do not follow a normal distribution, posing significant challenges to learning. Based on the assumption that the client data have a multivariate skewed normal distribution, we improve the DP-Fed-mv-PPCA model. We use a Bayesian framework to construct prior distributions of local parameters and use expectation maximization and pseudo-Newton algorithms to obtain robust parameter estimates. Then, the clipping algorithm and differential privacy algorithm are used to solve the problem in which the model parameters do not have a display solution and achieve privacy guarantee. Furthermore, we verified the effectiveness of our model using synthetic and actual data from the Internet of vehicles.

Intersection management is a sophisticated event in the intelligent transportation system due to a variety of behavior for traffic participants. This paper primarily overviews recent studies on the scenes of intersection, aiming at improving the efficiency or guaranteeing the safety when vehicles pass the crossing. These studies are respectively surveyed from the perspectives of efficiency and safety. Firstly, recent contributions to efficiency-oriented, intersection management overviews from four scenes, including congestion avoidance, green light optimized speed advisory (GLOSA), trajectory planning, and emergency vehicle priority preemption control. Furthermore, the studies on intersection collision detection and abnormal information warning are surveyed in the safety category. The corresponding algorithms for velocity and route management presented in the surveyed works are discussed.

Silicosis is a global problem, and it has brought about great burdens to society and patients’ families. The etiology of silicosis is clear, preventable, and controllable, but the onset is hidden and the duration is long. Thus, it is difficult to diagnose it early and treat it effectively, leaving workers unaware of the consequences of dust exposure. As such, a lack of details in the work history and a slow progression of lung disease contribute to the deterioration of patients until silicosis has advanced to fibrosis. These issues are the key factors impeding the diagnosis and the treatment of silicosis. This article reviews the literature on the early identification, diagnosis, and treatment of silicosis as well as analyzes the difficulties in the diagnosis and the treatment of silicosis and discusses its direction of future development.

The three-amino-acid-loop-extension (TALE) superfamily genes broadly existed in plants, which played important roles in plant growth, development and abiotic stress responses. In this study, we identified 68 Glycine max TALE (GmTALE) superfamily members. Phylogenetic analysis divided the GmTALE superfamily into the BEL1-like (BLH/BELL homeodomain) and the KNOX (KNOTTED-like homeodomain) subfamilies. Moreover, the KNOX subfamily could be further categorized into three clades (KNOX Class I, KNOX Class II and KNOX Class III). The GmTALE genes showed similarities in the gene structures in the same subfamily or clade, whose coding proteins exhibited analogous motif and conserved domain compositions. Besides, synteny analyses and evolutionary constraint evaluations of the TALE members among soybean and different species provided more clues for GmTALE superfamily evolution. The cis-element analyses in gene promoter regions and relevant gene expression profiling revealed different regulating roles of GmTALE genes during soybean plant development, saline and dehydration stresses. Genome-wide characterization, evolution, and expression profile analyses of GmTALE genes can pave the way for future gene functional research and facilitate their roles for applications in genetic improvement on soybean in saline and dehydration stresses.

The gut microbiota has been linked to many cancers, yet its role in acute myeloid leukaemia (AML) progression remains unclear. Here, we show decreased diversity in the gut microbiota of AML patients or murine models. Gut microbiota dysbiosis induced by antibiotic treatment accelerates murine AML progression while faecal microbiota transplantation reverses this process. Butyrate produced by the gut microbiota (especially Faecalibacterium ) significantly decreases in faeces of AML patients, while gavage with butyrate or Faecalibacterium postpones murine AML progression. Furthermore, we find the intestinal barrier is damaged in mice with AML, which accelerates lipopolysaccharide (LPS) leakage into the blood. The increased LPS exacerbates leukaemia progression in vitro and in vivo. Butyrate can repair intestinal barrier damage and inhibit LPS absorption in AML mice. Collectively, we demonstrate that the gut microbiota promotes AML progression in a metabolite-dependent manner and that targeting the gut microbiota might provide a therapeutic option for AML. The role of gut microbiota in acute myeloid leukaemia (AML) remains unclear. Here, the authors show disordered gut microbiota and reduced butyrate cause intestinal barrier damage in AML mice, with increased plasma LPS that accelerates AML progression.

Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we investigate the composition of ESCC tumors based on 208,659 single-cell transcriptomes derived from 60 individuals. We identify 8 common expression programs from malignant epithelial cells and discover 42 cell types, including 26 immune cell and 16 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and analyse the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we link the cancer cell transcriptomes to the somatic mutations and identify several markers significantly associated with patients’ survival, which may be relevant to precision care of ESCC patients. These results reveal the immunosuppressive status in the ESCC TME and further our understanding of ESCC. Esophageal squamous-cell carcinomas (ESCC) have poor prognosis, and detailed molecular profiles are necessary to identify prognostic markers. Here the authors analyse 60 ESCC patient samples using scRNA-seq, TCR-seq and genomics; they find mucosal immunity markers associated with survival and immunosuppressive microenvironments.