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Owing to the effects of camera, illumination, extraction algorithm defect, and other reasons, vector data for reservoir waterbodies extracted from remote sensing data may have quality issues, impacting the efficiency of data utilization in areas such as water resource management and reservoir monitoring. To efficiently detect abnormal data from massive vector products of reservoir waterbodies, a semi-automatic detection method for reservoir waterbody vector data is presented. The method has three phases. First, the original reservoir vector data are preprocessed to obtain the time series of the area of reservoir waterbodies. Second, data modeling with time series of reservoir waterbodies area data is done using the extensible generic anomaly detection system (EGADS) plug-in framework and time series modeling is conducted using the Olympic model. Third, data that have quality problems are identified with KσK\sigma model was used to determine the outliers; thereby, the date of the outliers is detected. Results of accuracy verification show that the sensitivity and specificity of the method were 94.44 and 83.87%, respectively, showing its feasibility for use in anomaly detection in polygonal reservoir waterbody vector data with far greater efficiency than traditional manual inspection.

People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry's quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished. [ABSTRACT FROM AUTHOR]

Hepatic inflammation is always accompanied with abnormal lipid metabolism. Whether N6-methyladenosine (m6A) mRNA methylation affects irregular inflammatory lipid level is unclear. Here, the m6A modification patterns in chicken liver at the acute stage of LPS-stimulated inflammation and at the normal state were explored via m6A and RNA sequencing and bioinformatics analysis. A total of 7,815 m6A peaks distributed in 5,066 genes were identified in the normal chicken liver and were mostly located in the CDS, 3′UTR region, and around the stop codon. At 2 h after the LPS intraperitoneal injection, the m6A modification pattern changed and showed 1,200 different m6A peaks. The hyper- and hypo-m6A peaks were differentially located, with the former mostly located in the CDS region and the latter in the 3′UTR and in the region near the stop codon. The hyper- or hypo-methylated genes were enriched in different GO ontology and pathways. Co-analysis revealed a significantly positive relationship between the fold change of m6A methylation level and the relative fold change of mRNA expression. Moreover, computational prediction of protein–protein interaction (PPI) showed that genes with altered m6A methylation and mRNA expression levels were clustered in processes involved in lipid metabolism, immune response, DNA replication, and protein ubiquitination. CD18 and SREBP-1 were the two hub genes clustered in the immune process and lipid metabolism, respectively. Hub gene AGPAT2 was suggested to link the immune response and lipid metabolism clusters in the PPI network. This study presented the first m6A map of broiler chicken liver at the acute stage of LPS induced inflammation. The findings may shed lights on the possible mechanisms of m6A-mediated lipid metabolism disorder in inflammation. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is a very common cancer among women and one of the primary causes of death in women worldwide. Because BC has different molecular subtypes, the challenges associated with targeted therapy have increased significantly, and the identification of new therapeutic targets has become increasingly urgent. Blocking apoptosis and inhibiting cell death are important characteristics of malignant tumours, including BC. Under adverse conditions, including exposure to antitumour therapy, inhibition of cell death programmes can promote cancerous transformation and the survival of cancer cells. Therefore, inducing cell death in cancer cells is fundamentally important and provides new opportunities for potential therapeutic interventions. Lytic forms of cell death, primarily pyroptosis, necroptosis and ferroptosis, are different from apoptosis owing to their characteristic lysis, that is, the production of cellular components, to guide beneficial immune responses, and the application of lytic cell death (LCD) in the field of tumour therapy has attracted considerable interest from researchers. The latest clinical research results confirm that lytic death signalling cascades involve the BC cell immune response and resistance to therapies used in clinical practice. In this review, we discuss the current knowledge regarding the various forms of LCD, placing a special emphasis on signalling pathways and their implications in BC, which may facilitate the development of novel and optimal strategies for the clinical treatment of BC. [ABSTRACT FROM AUTHOR]

Clarifying the spatiotemporal characteristics of urban traffic congestion can help to manage and alleviate such congestion more effectively. In this study, we used real-time traffic data provided by the Gaode LBS open platform to model and analyze urban traffic congestion in Xi'an, China, focusing on the spatiotemporal characteristics of single roads, different road grades, and the whole road network at different moments and time periods. Weekday real-time traffic data were collected from 23 April to 11 May, 2018, and the congestion index for different types of roads within Xi'an's Third Ring Road was calculated. The results allowed us to thoroughly analyze the spatiotemporal characteristics of traffic congestion in Xi'an and suggest methods for congestion control and improvement, including enhanced traffic control during special periods and more balanced urban development. Our results show the feasibility of this evaluation approach, which can provide timely decision-making support for urban traffic congestion management. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) molecularly imprinted polymer (MIP) arrays were used for electrochemical sensor to detect sulfadimidine (SM2) residue in food. NiCo2O4 nanoneedle arrays were decorated on the free-standing and highly conductive 3D graphene by a hydrothermal process. Polypyrrole (PPy) was coated onto NiCo2O4 nanoneedle arrays via electropolymerization in the presence of template molecule SM2 to obtain MIP/NiCo2O4 nanoneedle/3D graphene electrode. The desirable detectability of the composite electrode can be ascribed to the unique structure and the synergistic effects of the components: Nanoneedle arrays on 3D graphene offered the matrix to MIP and each nanoneedle as sensing unit was accessible to analyte, resulting in high specific surface area, desirable conductivity, short ion diffusion path and excellent adsorption capacity, which could powerfully boost the electrochemical property for the detection of SM2. Under optimized conditions, a wide linear range over SM2 of 0.2-1000 ng/mL with a detection limit of 0.169 ng/mL (S/N = 3) was obtained. The developed sensor also had favorable recovery of 92.3-102.23% and the relative standard deviation of 2.27-4.10%. The MIP array sensor provided an efficient tool for the selective and rapid detection of SM2 in food. [ABSTRACT FROM AUTHOR]

In this work, we reported the highly ordered NiCoO nanoneedle@polypyrrole arrays anchored on three-dimensional graphene (NiCoO@PPy/3D graphene) for high-sensitivity detection of trace lead ions (Pb). The 3D graphene was prepared by a hydrothermal process and then decorated with NiCoO nanoneedle arrays by another hydrothermal process. This two-step hydrothermal method is simple and mild. Furthermore, the highly conductive PPy was coated on NiCoO via a chemical vapor-phase polymerization to prepare NiCoO@PPy/3D graphene. The free-standing NiCoO@PPy/3D graphene can be directly utilized as 3D electrochemical working electrode without being decorated onto working electrode like Au and glassy carbon electrode. High conductivity, large specific surface area, short ion diffusion path and excellent adsorption capacity of NiCoO@PPy/3D graphene efficiently improved electrochemical property for the detection of Pb. Using square-wave anodic stripping voltammetry (SWASV), a linear range between the currents and the concentrations of Pb of 0.0125-0.709 μM with a high sensitivity of 115.621 μA μM was obtained. The limit of detection can reach to 0.2 nM. In addition, the fabricated sensor of Pb also had good selectivity, reproducibility and long-term stability. Finally, NiCoO@PPy/3D graphene electrode was utilized for determining Pb in tap water sample using the standard addition method, revealing a promising application for the quantitative detection of certain concentration ranges of Pb in real sample. [ABSTRACT FROM AUTHOR]