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Mass production of high-quality synthetic SAR training imagery is essential for boosting the performance of deep-learning (DL)-based SAR automatic target recognition (ATR) algorithms in an open-world environment. To address this problem, we exploit both the widely used Moving and Stationary Target Acquisition and Recognition (MSTAR) SAR dataset and the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset, which consists of selected samples from the MSTAR dataset and their computer-generated synthetic counterparts. A series of data augmentation experiments are carried out. First, the sparsity of the scattering centers of the targets is exploited for new target pose synthesis. Additionally, training data with various clutter backgrounds are synthesized via clutter transfer, so that the neural networks are better prepared to cope with background changes in the test samples. To effectively augment the synthetic SAR imagery in the SAMPLE dataset, a novel contrast-based data augmentation technique is proposed. To improve the robustness of neural networks against out-of-distribution (OOD) samples, the SAR images of ground military vehicles collected by the self-developed MiniSAR system are used as the training data for the adversarial outlier exposure procedure. Simulation results show that the proposed data augmentation methods are effective in improving both the target classification accuracy and the OOD detection performance. The purpose of this work is to establish the foundation for large-scale, open-field implementation of DL-based SAR-ATR systems, which is not only of great value in the sense of theoretical research, but is also potentially meaningful in the aspect of military application.

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-Feature Pyramid Network, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts and fuses multi-scale voxel information in a bottom-up manner, whereas decoder fuses multiple feature maps from various scales by Feature Pyramid Network in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios.

The establishment of rural study bases offers a promising opportunity to improve educational opportunities and community development in remote areas. This research investigates the development path of rural study bases using service design concepts that emphasize user-centric methods, stakeholder interaction, and iterative improvement procedures. Understanding rural communities' particular needs and goals allows remote study bases to plan and implement meaningful and impactful educational programs and services. Rural study bases strive to build favourable learning settings that encourage active engagement and cooperation by combining physical infrastructure development, digital resource integration, and community outreach efforts. This paper, using examples from diverse industries and situations, discusses essential issues and best practices for efficiently adopting service design principles in the establishment of rural study bases. Finally, rural studies based on service design concepts show promise for improving socioeconomic outcomes and empowering rural people through education. As an outcome, it provides high user satisfaction (94%), effective community outreach, excellent digital resource integration, enhanced academic performance, and strong stakeholder engagement.

Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA activity for two highly specific SpCas9 variants (eSpCas9(1.1) and SpCas9-HF1) and wild-type SpCas9 (WT-SpCas9) in human cells, and obtain indel rates of over 50,000 gRNAs for each nuclease, covering ~20,000 genes. We evaluate the contribution of 1,031 features to gRNA activity and develope models for activity prediction. Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate that our model outperforms other popular gRNA design tools. Finally, we develop an online design tool DeepHF for the three Cas9 nucleases. The database, as well as the designer tool, is freely accessible via a web server, http://www.DeepHF.com/ . Application of highly specific Cas9 variants can be restricted by the design of the guide RNA. Here the authors present DeepHF, a gRNA activity prediction tool built from genome-scale screens of 50,000 guides covering 20,000 genes.

The automatic segmentation of field-road using artificial intelligence (AI) is imperative for intelligence agriculture, allowing for the distinction between operational patterns (e.g., turning and transporting) through the analysis of global navigation satellite system (GNSS) data. This AI-driven discrimination is crucial for accurately monitoring the field operations of agricultural machinery. This study presents a deep learning framework that implements a novel semantic segmentation model designed to segment field-road trajectories, addressing the marked differences in spatial characteristics. The developed AI approach integrates transformer and semantic technologies to create an advanced semantic encoder that generates high-quality semantic prior maps and associated mask features. These features are effectively combined through a novel lightweight up-sampling mechanism paired with a semantic feature pyramid network (FPN) decoder, resulting in improved prediction outputs. The class imbalance issue between field-road pixels is effectively addressed by employing a pixel-wise weighted cross-entropy loss function in this study. Additionally, the model identifies unique features by evaluating GNSS points’ similarities to adjacent points and their global class counterparts. The proposed method was evaluated using the dataset comprising 6,380 GNSS trajectory images of wheat and rice in this study. The experimental results demonstrate that the mean intersection-over-union (mIoU) and F1-score of the model achieved 92.46% and 92.65%, respectively. Consequently, this study contributes significantly to refined field-operation cost analysis and is instrumental for advancements in precision mechanization management and agricultural intelligence.

N6‐methyladenosine (m6A) is the most prevalent internal modification in mammalian messenger RNAs and is associated with numerous biological processes. However, its role in chromosomal instability remains to be established. Here, we report that an RNA m6A methyltransferase, METTL16, plays an indispensable role in the progression of chromosome segregation and is required to preserve chromosome stability in colorectal cancer (CRC) cells. Depletion or inhibition of the methyltransferase activity of METTL16 results in abnormal kinetochore‐microtubule attachment during mitosis, leading to delayed mitosis, lagging chromosomes, chromosome mis‐segregation and chromosomal instability. Mechanistically, METTL16 exerts its oncogenic effects by enhancing the expression of suppressor of glucose by autophagy 1 (Soga1) in an m6A‐dependent manner. CDK1 phosphorylates Soga1, thereby triggering its direct interaction with the polo box domain of PLK1. This interaction facilitates PLK1 activation and promotes mitotic progression. Therefore, targeting the METTL16‐Soga1 pathway may provide a potential treatment strategy against CRC because of its essential role in maintaining chromosomal stability. METTL16 catalyses the addition of m6A to Soga1 messenger RNA (mRNA) transcripts, leading to stabilization and increased levels of Soga1 mRNA through IGF2BP1‐mediated mechanisms. CDK1‐mediated phosphorylation of Soga1 generates a PLK1 docking site that promotes PLK1 activation. This further promotes the activation of Aurora B kinase, which mediates erroneous kinetochore‐microtubule attachments. These results indicate that METTL16 promotes colorectal cancer proliferation by activating the METTL16/m6A/Soga1 axis.

Background Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies worldwide because of rapid progression and high incidence of metastasis or recurrence. Accumulating evidence shows that CD73-expressing tumor cell is implicated in development of several types of cancer. However, the role of CD73 in HCC cell has not been systematically investigated and its underlying mechanism remains elusive. Methods CD73 expression in HCC cell was determined by RT-PCR, Western blot, and immunohistochemistry staining. Clinical significance of CD73 was evaluated by Cox regression analysis. Cell counting kit-8 and colony formation assays were used for proliferation evaluation. Transwell assays were used for motility evaluations. Co-immunoprecipitation, cytosolic and plasma membrane fractionation separation, and ELISA were applied for evaluating membrane localization of P110β and its catalytic activity. NOD/SCID/γc(null) (NOG) mice model was used to investigate the in vivo functions of CD73. Results In the present study, we demonstrate that CD73 was crucial for epithelial-mesenchymal transition (EMT), progression and metastasis in HCC. CD73 expression is increased in HCC cells and correlated with aggressive clinicopathological characteristics. Clinically, CD73 is identified as an independent poor prognostic indicator for both time to recurrence and overall survival. CD73 knockdown dramatically inhibits HCC cells proliferation, migration, invasion, and EMT in vitro and hinders tumor growth and metastasis in vivo. Opposite results could be observed when CD73 is overexpressed. Mechanistically, adenosine produced by CD73 binds to adenosine A2A receptor (A2AR) and activates Rap1, which recruits P110β to the plasma membrane and triggers PIP3 production, thereby promoting AKT phosphorylation in HCC cells. Notably, a combination of anti-CD73 and anti-A2AR achieves synergistic depression effects on HCC growth and metastasis than single agent alone. Conclusions CD73 promotes progression and metastasis through activating PI3K/AKT signaling, indicating a novel prognostic biomarker for HCC. Our data demonstrate the importance of CD73 in HCC in addition to its immunosuppressive functions and revealed that co-targeting CD73 and A2AR strategy may be a promising novel therapeutic strategy for future HCC management.

The arachidonic acid (AA) pathway plays a key role in cardiovascular biology, carcinogenesis, and many inflammatory diseases, such as asthma, arthritis, etc. Esterified AA on the inner surface of the cell membrane is hydrolyzed to its free form by phospholipase A2 (PLA2), which is in turn further metabolized by cyclooxygenases (COXs) and lipoxygenases (LOXs) and cytochrome P450 (CYP) enzymes to a spectrum of bioactive mediators that includes prostanoids, leukotrienes (LTs), epoxyeicosatrienoic acids (EETs), dihydroxyeicosatetraenoic acid (diHETEs), eicosatetraenoic acids (ETEs), and lipoxins (LXs). Many of the latter mediators are considered to be novel preventive and therapeutic targets for cardiovascular diseases (CVD), cancers, and inflammatory diseases. This review sets out to summarize the physiological and pathophysiological importance of the AA metabolizing pathways and outline the molecular mechanisms underlying the actions of AA related to its three main metabolic pathways in CVD and cancer progression will provide valuable insight for developing new therapeutic drugs for CVD and anti-cancer agents such as inhibitors of EETs or 2J2. Thus, we herein present a synopsis of AA metabolism in human health, cardiovascular and cancer biology, and the signaling pathways involved in these processes. To explore the role of the AA metabolism and potential therapies, we also introduce the current newly clinical studies targeting AA metabolisms in the different disease conditions.

With the development of coronary angiography, more and more attention has been paid to coronary slow flow phenomenon (CSFP). Recent studies have found that the correlation between homocysteine (Hcy) levels and CSFP was contradictory, so we conducted this meta-analysis to investigate the correlation. By March 2022, studies that meet the research requirements were identified by searching multiple databases including Embase, Web of Science, and PubMed. We included studies evaluating the correlation between Hcy levels and CSFP. Random or fixed effect meta-analyses were performed according to heterogeneity among included studies. A leave-out method and subgroup analyses were conducted to determine the source of heterogeneity. Thirteen studies involving 625 CSFP and 550 subjects were included. After pooling data from each study, Hcy levels were higher in the CSFP groups (standard mean difference [SMD], 1.45; 95% CI, 0.94 to 1.96, P < .00001) than in the control group. In the meta-analysis, there was significant heterogeneity (I2 = 93%), which was further explored through leave-out method and and subgroup analyses. Specifically, pooling data from studies with a mean thrombolysis in myocardial infarction (TIMI) frame count ≥ 46 (SMD, 1.31; 95% CI, 1.00 to 1.63, P < .00001) resulted in no heterogeneity (0%), indicating that the TIMI frame count ≥ 46 was the source of heterogeneity. Our study found that elevated Hcy levels are strongly associated with CSFP. More importantly, the association was stronger in CSFP patients with mean TIMI frame count ≥ 46.

Based on infrastructure and computing power provided by cloud computing service providers, more and more colleges and universities have developed information-based educational resources and implemented information-based educational applications in a virtualized environment. After accessing cloud computing services, universities do not need to spend a lot of money to purchase commercial software licenses, because cloud computing can provide a large number of commonly used application software. However, there are few cases about these educational information software resources applied to actual teaching and combined with specific courses, which makes the information software resources disconnected from the courses and reduces the teaching efficiency and effectiveness of the classroom. Based on aforementioned background, this study is aimed at the existing teaching mode in colleges and universities, to conduct research on the specific cloud computing application and practice in the teaching of colleges and universities. With rapid development of information technology, the education informatization will certainly transit from the computer-aided education to the education centered on calculation, data and service, of which possibility and technical support can be provided by the development of cloud computing. With the continuous development and popularization of cloud computing technology, its advantages of low cost, convenience and security will surely attract more colleges and universities to carry out daily education on cloud platform.