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Long non-coding RNAs (lncRNAs) have emerged as important regulators of gene expression and plant development. Here, we identified 6,510 lncRNAs in Arabidopsis under normal or stress conditions. We found that the expression of natural antisense transcripts (NATs) that are transcribed in the opposite direction of protein-coding genes often positively correlates with and is required for the expression of their cognate sense genes. We further characterized MAS , a NAT-lncRNA produced from the MADS AFFECTING FLOWERING4 ( MAF4) locus. MAS is induced by cold and indispensable for the activation of MAF4 transcription and suppression of precocious flowering. MAS activates MAF4 by interacting with WDR5a, one core component of the COMPASS-like complexes, and recruiting WDR5a to MAF4 to enhance histone 3 lysine 4 trimethylation (H3K4me3). Our study greatly extends the repertoire of lncRNAs in Arabidopsis and reveals a role for NAT-lncRNAs in regulating gene expression in vernalization response and likely in other biological processes. Long non-coding RNAs regulate developmental transitions and stress responses in plants. Here Zhao et al. show that a non-coding antisense transcript MAS transcribed from the Arabidopsis MAF4 locus activates H3K4me3 deposition and MAF4 transcription to suppress precocious flowering.

Defect detection of petrochemical pipelines is an important task for industrial production safety. At present, pipeline defect detection mainly relies on closed circuit television method (CCTV) to take video of the pipeline inner wall and then detect the defective area manually, so the detection is very time-consuming and has a high rate of false and missed detections. To solve the above issues, we proposed an automatic defect detection system for petrochemical pipeline based on Cycle-GAN and improved YOLO v5. Firstly, in order to create the pipeline defect dataset, the original pipeline videos need pre-processing, which includes frame extraction, unfolding, illumination balancing, and image stitching to create coherent and tiled pipeline inner wall images. Secondly, aiming at the problems of small amount of samples and the imbalance of defect and non-defect classes, a sample enhancement strategy based on Cycle-GAN is proposed to generate defect images and expand the data set. Finally, in order to detect defective areas on the pipeline and improve the detection accuracy, a robust defect detection model based on improved YOLO v5 and Transformer attention mechanism is proposed, with the average precision and recall as 93.10% and 90.96%, and the F1-score as 0.920 on the test set. The proposed system can provide reference for operators in pipeline health inspection, improving the efficiency and accuracy of detection.

The vector propulsion system with a universal coupling joint exhibits complex spatial positioning and dynamic interactions. Under challenging sea conditions, the installation foundation of autonomous underwater vehicles (AUVs) rolls with the hull motion, transmitting complex alternating loads to the steering mechanism through the support structure. This significantly impacts the vibrational response of the propulsion shafting. First, based on coordinate transformation methods, the stiffness matrix and the inclination and rolling angles of key parameters are analyzed. The absolute velocity of the propeller is derived using velocity synthesis principles and coordinate transformation matrices. Next, a unified dynamic model of the propulsion system under basic motion is established using the Lagrange equation, with centroid displacement as the generalized coordinate. Finally, the vibration characteristics of the propeller and its corresponding propulsion performance under AUV rolling motion are investigated through numerical simulation and experimental methods. The results demonstrate that rolling conditions not only transmit low-frequency vibrations but also excite high-frequency structural-roll coupled vibrations. Shorter rolling periods and larger inclination angles increase vibration amplitudes and cause significant thrust losses. This effect is a critical consideration for the process control of vector propulsion systems.

With rapid industrialization and urbanization, the increasing prevalence of air and water pollutants poses a significant threat to public health. Traditional research methods, such as epidemiological studies and in vitro/in vivo experiments, provide valuable biological insights but are often costly, time-consuming, and limited in scale. To address this gap, this study develops a machine learning-based approach to predict the carcinogenicity of pollutants. Using the dataset of carcinogenic and non-carcinogenic molecules that we collected, the pretrained KPGT model trained with molecular fingerprints and descriptors achieved an AUC of 0.83, surpassing traditional machine learning models. To validate this model, common pollutants from air and water sources were analyzed. Further clustering classified these pollutants into five distinct groups. Target prediction analysis identified key genes associated with representative pollutant molecules, such as MAPK1, MTOR, and PTPN11. GO and KEGG pathway analyses, along with survival analysis, revealed potential carcinogenic mechanisms and prognostic implications. Our findings contribute to improved pollution risk assessment and evidence-based environmental policy development, ultimately aiding in the mitigation of pollutant-related health risks.

6D pose estimation is a common and important task in industry. Obtaining the 6D pose of objects is the basis for many other functions such as bin picking, autopilot, etc. Therefore, many corresponding studies have been made in order to improve the accuracy and enlarge the range of application of various approaches. After several years of development, the methods of 6D pose estimation have been enriched and improved. Although some predecessors have analyzed the methods and summarized them in detailed, there have been many new breakthroughs in recent years. To understand 6D pose estimation better, this paper will make a new and more detailed review of 6D pose estimation. We divided these methods into two approaches: Learning-based approaches and non-learning-based approaches, including 2D-information-based approach and 3D-information-based approach. Additionally, we introduce the challenges that exist in 6D pose estimation. Finally, we compare the performance of different methods qualitatively and discuss the future development trends of the 6D pose estimation.

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.

Electrochemical uranium extraction from seawater provides a new opportunity for a sustainable supply of nuclear fuel. However, there is still room for studying flexible electrode materials in this field. Herein, we construct amidoxime group modified porous aromatic frameworks (PAF-144-AO) on flexible carbon cloths in situ using an easy to scale-up electropolymerization method followed by postdecoration to fabricate the self-standing, binder-free, metal-free electrodes (PAF-E). Based on the architectural design, adsorption sites (amidoxime groups) and catalytic sites (carbazole groups) are integrated into PAF-144-AO. Under the action of an alternating electric field, uranyl ions are selectively captured by PAN-E and subsequently transformed into Na2O­(UO3·H2O) x precipitates in the presence of Na+ via reversible electron transfer, with an extraction capacity of 12.6 mg g–1 over 24 days from natural seawater. This adsorption–electrocatalysis mechanism is also demonstrated at the molecular level by ex situ spectroscopy. Our work offers an effective approach to designing flexible porous organic polymer electrodes, which hold great potential in the field of electrochemical uranium extraction from seawater.

The development of monoclonal antibody treatments for successful tumor-targeted therapies took several decades. However, the efficacy of antibody-based therapy is still confined and desperately needs further improvement. Nanobodies are the recombinant variable domains of heavy-chain-only antibodies, with many unique properties such as small size (~15kDa), excellent solubility, superior stability, ease of manufacture, quick clearance from blood, and deep tissue penetration, which gain increasing acceptance as therapeutical tools and are considered also as building blocks for chimeric antigen receptors as well as for targeted drug delivery. Thus, one of the promising novel developments that may address the deficiency of monoclonal antibody-based therapies is the utilization of nanobodies. This article provides readers the significant factors that the structural and biochemical properties of nanobodies and the research progress on nanobodies in the fields of tumor treatment, as well as their application prospect.

CCL8, a chemokine overexpressed in ovarian cancer (OC), has drawn attention for its role in tumor progression. This study aimed to explore the function of CCL8 in OC and its effects on tumor-associated macrophages (TAMs) and related mechanisms. Bioinformatics analysis revealed a correlation between high CCL8 expression and M1 macrophage infiltration, as well as a favourable prognosis in OC patients. In vitro, CCL8 polarised THP1-derived macrophages towards an M1 phenotype, and the conditioned medium from these macrophages suppressed ES2 cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT). Mechanistically, CCL8-induced macrophages promoted apoptosis in OC cells via activation of the NF-κB p65 pathway, as evidenced by increased Bax and Caspase3 expression, and these effects were reversed by p65 inhibition. The findings demonstrate that CCL8 exerts a tumor-uppressive effect by inducing M1 macrophage polarisation and activating the NF-κB pathway, positioning it as a potential immunotherapeutic target in OC.

The growing concern for the exhaustion of fossil energy and the rapid revolution of electronics have created a rising demand for electrical energy storage devices with high energy density, for example, lithium secondary batteries (LSBs). With high surface area, low cost, excellent mechanical strength, and electrochemical stability, amorphous carbon-based materials (ACMs) have been widely investigated as promising platform for anode materials in the LSBs. In this review, we firstly summarize recent advances in the synthesis of the ACMs with various morphologies, ranging from zero- to three-dimensional structures. Then, the use of ACMs in Li-ion batteries and Li metal batteries is discussed respectively with the focus on the relationship between the structural features of the as-prepared ACMs and their roles in promoting electrochemical performances. Finally, the remaining challenges and the possible prospects for the use of ACMs in the LSBs are proposed to provide some useful clews for the future developments of this attractive area.