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A novel rice mutant lmi1 showed increased resistance to bacterial blight. LMI1 encodes a DUF292 protein and regulates defense immune responses and cell death via vesicle trafficking in chloroplasts.

Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.

The study of loaded meshing characteristics and dynamic behavior is significant for evaluating the stability of hypoid gear under actual working conditions. Loaded tooth contact analysis (LTCA) of a hypoid gear drive under different loads is carried out. The equivalent meshing force, loaded transmission error (LTE), comprehensive elastic deformation, and time-varying meshing stiffness (TVMS) are investigated. Results show that the LTE and TVMS are sensitive to load, and the LTE and TVMS curves show obvious asymmetric as the load increases. Under low load conditions, the actual contact ratio increases significantly with the increase of load, but tends to be flat when the load reaches a certain range. The obtained TVMS and LTE are fitted by higher Fourier series and introduced into an 8-DOF dynamic model. By comparing the time history diagram, phase diagram, Poincaré section, and spectrum, the dynamic characteristics of the system under different loads are contrasted. On low load condition, the system may exhibit chaotic motion in certain meshing frequency bands and damping ratio intervals. While the tooth backlash increases to a critical value, the system may change to chaotic motion from periodic motion. The results provided a basis for improving the meshing performance of hypoid gear.

Abstract IL-33 belongs to the inflammatory factor family and is closely associated with the inflammatory response. However, its role in the development of intrauterine adhesions (IUAs) remains unclear. In this study, the role of IL-33 in the formation of IUAs after endometrial injury was identified via RNA sequencing after mouse endometrial organoids were transplanted into an IUA mouse model. Major pathological changes in the mouse uterus, consistent with the expression of fibrotic markers, such as TGF-β, were observed in response to treatment with IL-33. This finding may be attributed to activation of the phosphorylation of downstream MAPK signaling pathway components, which are activated by the release of IL-33 in macrophages. Our study provides a novel mechanism for elucidating IUA formation, suggesting a new therapeutic strategy for the prevention and clinical treatment of IUAs.

Abstract Memristive devices with threshold switching characteristics can be effectively utilized to mimic biological neurons acting as one of the key building blocks for constructing advanced hardware neural networks. In this work, the emulation of leaky integrate‐and‐fire memristive neuron is realized in one single cell with Ag/Ag−In−Zn−S/silk sericin/W architecture without the need for additional auxiliary circuits. The studied devices demonstrate excellent electrical properties, such as stably repeatable threshold switching, concentratedly low threshold voltage (≈0.4 V), and relatively small device‐to‐device variation. In addition, multiple neural features, such as leaky integrate‐and‐fire neuron functionality and strength‐modulated spike frequency characteristic, have been successfully emulated owing to the forming‐free volatile threshold switching effect. The stable volatile threshold switching behaviors and regular firing event may be attributed to the controllable metallic Ag filamentary mechanism. Furthermore, a solid accuracy of 91.44% of the pattern recognition of Modified National Institute of Standards and Technology (MNIST) data is obtained via a trained spiking neural network (SNN) based on the leaky integrate‐and‐fire behavior of sericin‐based device. These achievements shed light on the fact that employing sericin biomaterials has great application potential in advanced neuromorphic computation.

Recent increases in emissions from freight transport have caused strong concerns about air quality in Pakistan, following the rapid development of projects related to the China-Pakistan Economic Corridor (CPEC). This study reported the first measurements of on-road truck emissions in Pakistan and investigated their dependence on altitude along CPEC routes. Emissions from 70 trucks were measured on CPEC highways located in Islamabad (540 m above sea level), Sost (2800 m above sea level), and at the Khunjerab Pass (4693 m above sea level). Calculated emission factors for carbon monoxide, hydrocarbons, and nitrogen oxides from heavy-duty trucks in Islamabad were 12.94 ± 1.46, 15.21 ± 1.67, and 10.69 ± 1.34 g km−1 (95% confidence level), respectively, for pre-Pak-II trucks, and 12.75 ± 2.80, 14.24 ± 3.53, and 10.24 ± 2.34 g km−1 (95% confidence level), respectively, for Pak-II trucks, representing 2–20 times higher values than the emission standards in Pakistan and India. An altitude increase of approximately 4000 m, with the associated changes in meteorology and fleet characteristics, induced an average increase of 103.6%, 86.3%, 124.5%, and 133.6% in the emission factors of carbon monoxide, hydrocarbons, nitrogen oxides, and carbon dioxide, respectively. Moreover, on-road emissions along the CPEC were mainly influenced by truck types. This study will support the budget evaluation of transport emissions from the CPEC trade fleet.

IntroductionDespite the growing attention paid to the research of translation technology teaching (TTT), there is still a lack of studies on students’ attitudes and the motivational factors in relation to it. To this end, the paper reports on a questionnaire-based study that describes students’ attitudes towards translation technology (in the Chinese MTI context) and explores its structural relations with translation mindsets and future work self.MethodsData were collected from 108 grade 2021 MTI students of three selected Chinese universities and analyzed using descriptive statistics and structural equation modeling (SEM).ResultsThe results demonstrate that Chinese MTI students’ overall attitudes towards translation technology are slightly positive. So far, they perceive translation technology to be slightly effective for translation and are slightly mindful of it. They are slightly influenced by teachers and still feel inhibited when learning and using it. Furthermore, the results also indicate that growth translation mindsets positively influence students’ attitudes towards the effectiveness of translation technology, teacher influence, exhibition to translation technology, and mindfulness about translation technology, whereas fixed translation mindsets only negatively predict students’ teacher influence. Likewise, future work self-salience positively associates with students’ attitudes towards the effectiveness of translation technology and mindfulness about translation technology, while future work self-elaboration positively relates to students’ exhibition to translation technology. Among them, growth translation mindsets are the strongest predictor for all attitudes components.DiscussionTheoretical and pedagogical implications are also discussed.

The issue of employability has already become a well-delineated topic of study among interpreting educators. However, the current literature still lacks descriptive research on interpreting students' employability development and ignores the developmental effects of interpreter competences in this process. Moreover, the advantage of using career adaptability for measurement is also under-researched. This exploratory case study aims at taking an initial step forward, surveying interpreting students' career adaptability development and the developmental effects of different interpreter competences on major adaptability resources, and ultimately diagnosing curriculum problems and making modifications accordingly. Thirty grade 2019 interpreting students from three Chinese universities contributed to data collection, through six questionnaires in a two-wave survey. The results highlight that, throughout the Chinese MTI program, interpreting students could become more concerned and well prepared for their future (concern), more curious to explore themselves and their surroundings (curiosity), and more capable of solving problems (confidence). The results also indicate that students' knowledge and language competence serve as the major facilitators in this process, and that other interpreter competences, such as psychological competence, transfer competence, professionalism, and cross-cultural competence, are also instrumental. In order to further boost their adaptability constructs, the results suggest that students' language and knowledge competence, professionalism, and cross-cultural and mental agility still need to be improved. Five suggestions for curriculum revision have been raised accordingly. As an initial effort, the current study will hopefully inspire further studies on interpreting students' career adaptability and add more knowledge to the curriculum design from this viewpoint.

Low-level features contain spatial detail information, and high-level features contain rich semantic information. Semantic segmentation research focuses on fully acquiring and effectively fusing spatial detail with semantic information. This paper proposes a multiscale feature-enhanced adaptive fusion network named MFEAFN to improve semantic segmentation performance. First, we designed a Double Spatial Pyramid Module named DSPM to extract more high-level semantic information. Second, we designed a Focusing Selective Fusion Module named FSFM to fuse different scales and levels of feature maps. Specifically, the feature maps are enhanced to adaptively fuse these features by generating attention weights through a spatial attention mechanism and a two-dimensional discrete cosine transform, respectively. To validate the effectiveness of FSFM, we designed different fusion modules for comparison and ablation experiments. MFEAFN achieved 82.64% and 78.46% mIoU on the PASCAL VOC2012 and Cityscapes datasets. In addition, our method has better segmentation results than state-of-the-art methods.

Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method's F1 - score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method's F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.

Polydatin, an active ingredient from the roots of Polygonum cuspidatum, is considered to have protective effects on the cardiovascular system and liver. In this study, we demonstrated that polydatin has antitumor activity against human cervical cancer. Polydatin efficiently inhibited cervical cancer cell proliferation by regulating cell cycle-related proteins including p21, p27, CDK2, CDK4, Cyclin D1, and Cyclin E1. Furthermore, polydatin suppressed cell invasion and migration by regulating epithelial–mesenchymal transition (EMT) markers, including E-cadherin, N-cadherin, Snail and Slug. The c-Myc, as a proto-oncogene, is considered to be closely associated with the proliferation and metastasis of tumor cells. After polydatin treatment, the protein expression of c-Myc showed a significant decrease. Based on these data, we overexpressed c-Myc in cervical cancer cells and observed that the overexpression of c-Myc rescued the inhibitory effect of polydatin on cell proliferation and metastasis. These results indicated that polydatin can inhibit cell proliferation and metastasis through suppressing the c-Myc expression in human cervical cancer.

A second wave of coronavirus disease 2019 (COVID-19) infections emerged in Beijing in summer 2020, which provided an opportunity to explore the response of air pollution to reduced human activity. Proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) coupled with positive matrix factorization (PMF) source apportionment were applied to evaluate the pollution pattern and capture the detailed dynamic emission characteristics of volatile organic compounds (VOCs) during the representative period, with the occurrence of O _3 pollution episodes and the Beijing resurgence of COVID-19. The level of anthropogenic VOC was lower than during the same period in previous years due to the pandemic and emission reduction measures. More than two thirds of the days during the observation period were identified as high-O _3 days and VOCs exhibited higher mixing ratios and faster consumption rates in the daytime on high-O _3 days. The identified VOC emission sources and the corresponding contributions during the whole observation period included: vehicle + fuel (12.41 ± 9.43%), industrial process (9.40 ± 8.65%), solvent usage (19.58 ± 13.46%), biogenic (6.03 ± 5.40%), background + long-lived (5.62 ± 11.37%), and two groups of oxygenated VOC (OVOC) factors (primary emission and secondary formation, 26.14 ± 15.20% and 20.84 ± 14.0%, respectively). Refined dynamic source apportionment results show that the ‘stay at home’ tendency led to decreased emission (−34.47 ± 1.90%) and a weakened morning peak of vehicle + fuel during the Beijing resurgence. However, a growing emission of primary OVOCs (+51.10 ± 8.28%) with similar diurnal variation was observed in the new outbreak and afterwards, which might be related to the enhanced usage of products intended to clean and disinfect. The present study illustrated that more stringent VOC reduction measures towards pandemic products should be carried out to achieve the balanced emission abatement of NO _x and VOC when adhering to regular epidemic prevention and control measures.

Free phenolics (FP), esterified phenolics (EP), glycosidized phenolics (GP) and insoluble bound phenolics (IBP) were successively extracted from two varieties of peony flowers: Danfeng and Xiangyu. The contents of total phenolics, total flavonoids and the major components in different phenolics fractions were determined, and their antioxidant activity and α-glucosidase inhibitory activity were also studied. It was found that for the two varieties, the contents of total phenols (31.45 and 32.64 mg/g, respectively) and total flavonoids (41.11 and 40.67 mg/g, respectively) in FP were both the highest, followed by EP and GP, and the lower values were found in IBP. Each of the two FPs contained 17 components, with the major ones being resveratrol, 1,2,3,4,6-O-pentagalloylglucose and apigetrin. The major components of EP and GP were kaempferol-7-O-β-D-glucoside and apigetrin. The antioxidant activity of FP was the strongest among the four phenolics fractions. The 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH), 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) cation radical scavenging capacity and ferric reducing power of FP were 855.03, 367.10, and 230.54 µmol/g for Danfeng, and 499.06, 290.64, and 196.39 µmol/g for Xiangyu, respectively. For both varieties, FP exhibited the strongest inhibitory activity against α-glucosidase with half maximal inhibitory concentrations of 12.15 and 13.87 μg/mL, respectively. The research results are of reference significance for the utilization of polyphenols in Danfeng and Xiangyu.