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Alfalfa (Medicago sativa. L.) is a globally significant autotetraploid legume forage crop. However, despite its importance, establishing efficient gene editing systems for cultivated alfalfa remains a formidable challenge. In this study, we pioneered the development of a highly effective ultrasonic-assisted leaf disc transformation system for Gongnong 1 alfalfa, a variety widely cultivated in Northeast China. Subsequently, we created a single transcript CRISPR/Cas9 (CRISPR_2.0) toolkit, incorporating multiplex gRNAs, designed for gene editing in Gongnong 1. Both Cas9 and gRNA scaffolds were under the control of the Arabidopsis ubiquitin-10 promoter, a widely employed polymerase II constitutive promoter known for strong transgene expression in dicots. To assess the toolkit’s efficiency, we targeted PALM1, a gene associated with a recognizable multifoliate phenotype. Utilizing the CRISPR_2.0 toolkit, we directed PALM1 editing at two sites in the wild-type Gongnong 1. Results indicated a 35.1% occurrence of editing events all in target 2 alleles, while no mutations were detected at target 1 in the transgenic-positive lines. To explore more efficient sgRNAs, we developed a rapid, reliable screening system based on Agrobacterium rhizogenes-mediated hairy root transformation, incorporating the visible reporter MtLAP1. This screening system demonstrated that most purple visible hairy roots underwent gene editing. Notably, sgRNA3, with an 83.0% editing efficiency, was selected using the visible hairy root system. As anticipated, tetra-allelic homozygous palm1 mutations exhibited a clear multifoliate phenotype. These palm1 lines demonstrated an average crude protein yield increase of 21.5% compared to trifoliolate alfalfa. Our findings highlight the modified CRISPR_2.0 system as a highly efficient and robust gene editing tool for autotetraploid alfalfa.

Background: Interdigital tinea pedis is the most common type of foot infection, which is often treated by topical or systemic antifungals. Due to the increase in antifungal resistance, antifungal socks are becoming potential alternatives for the daily management of tinea pedis. Methods: In this study, antifungal fibres were adopted to produce interdigital hygiene socks to split the third and fourth toe seams of the feet. In vitro antifungal activity was first examined to verify the effectiveness of the socks. Preventive efficacy against tinea pedis was then evaluated among healthy participants, followed by therapeutic effect detection in patients diagnosed with tinea pedis by analysing the improvement in total symptom scores (TTS). Results: The interdigital-type hygiene socks exhibited apparent antifungal activities in vitro. An in vivo study demonstrated significant preventive effects against tinea pedis for interdigital socks compared to plain socks (P = 0.011) and a lower TTS than noninterdigital (P = 0.04) or plain socks (P

BackgroundJob burnout has become an important factor affecting the mental and physical health and work efficiency of college counselors, and indirectly affects the quality and development of talent cultivation for college students. ObjectiveTo explore the relationship between job stress, job crafting, and job burnout among college counselors, and to test the mediating role of job crafting between job stress and job burnout, in order to take targeted measures to alleviate job stress and job burnout of college counselors, reduce associated health risks, and improve the effectiveness of higher education. MethodsAn anonymous questionnaire survey was conducted among 400 counselors from social network communication groups by convenience sampling. The Counselor Work Stress Scale, Job Crafting Scale, and Maslach Burnout Inventory-General Survey were used. Harman's single-factor method was used to evaluate common method bias in the survey data. One-way ANOVA was applied to test the difference in job stress, job crafting, and job burnout among college counselors by demographic characteristics, and chi-square test was used to analyze the difference in reporting job burnout. Partial correlation analysis was used to evaluate the correlation between selected variables. Structural equation modeling was used to analyze the relationship of job stress, job crafting, and job burnout among college counselors, and Bootstrap analysis was used to test if there was a mediating effect of job crafting on the relationship between job stress and job burnout. ResultsOf the 390 questionnaires recovered, there were 338 valid questionnaires (86.67%). Among the included subjects, the mean scores of job stress, job crafting, and job burnout were (2.70±0.62), (3.77±0.62), and (2.09±1.09), respectively. The positive rate of job burnout was 76.9% (260/338), with a positive rate of 72.8% in exhaustion dimension and 59.8% in cynicism dimension. There were significant differences in job crafting scores among the college counselors by different genders and professional titles (P

Background. Coronary artery disease (CAD) is a type of cardiovascular disease which is one of the leading causes of death around the world. The presence of coronary stenosis is considered a pivotal indicator in the diagnosis of various CADs. The main purpose of this paper was to investigate the feasibility of an anchor-free deep learning (DL) method, fully convolutional one-stage object detection (FCOS), in coronary artery stenosis automatic detection. Methods. First, 2786 invasive coronary angiography (ICA) images from 130 patients were randomly divided into training, validation, and testing datasets using the 10-fold cross-validation approach. Then, FCOS was compared with other three widely used anchor-based DL models: single shot multibox detector (SSD), faster region-based convolutional network (Faster R-CNN), and you only look once (YOLOv3), in terms of precision, recall, F1 score, average precision (AP), and average recall (AR). Finally, the performances of different models in the detection of stenosis were compared in either single or multiple lesion scenarios using statistical tests. Results. FCOS achieved significantly superior precision (96.14% ± 0.53%), recall (94.36% ± 0.79%), F1 score (95.22% ± 0.56%), AP0.50 (93.36% ± 0.93%), AR0.50:0.95 (64.73% ± 1.46%), APsmall (55.04 ± 0.96%), APmedium (59.97 ± 1.13%), and APlarge (68.09 ± 5.18%) compared to Faster R-CNN and YOLOv3. Moreover, FCOS demonstrated significantly higher AR0.50:0.95 and APsmall compared to SSD. Regardless of the presence of single or multiple coronary stenoses in ICA images, FCOS also outperformed Faster R-CNN and YOLOv3. Furthermore, it showed significantly higher AR0.50:0.95 compared to SSD when in the multiple stenosis scenario. Conclusions. It is feasible to use the anchor-free DL model FCOS in detecting coronary stenosis based on ICA images.

The escalating degradation of urban eco-environments has underscored the significance of ecological security in sustainable urban development. Green infrastructure bridges green spaces in cities and increases ecosystem connectivity, thereby optimizing urban ecological security patterns. This study uses Nanjing as a case study and adopts a research paradigm that involves identifying ecological sources, constructing resistance surfaces, and subsequently extracting corridors within the ecological security pattern. This method amalgamates the evaluation of green infrastructure supply and demand, leading to the identification of both ecological corridors and nodes. The findings reveal that while the supply of green infrastructure in Nanjing is low in the city center and high in the suburbs, demand is high in the central area and low in the periphery, indicating a spatial mismatch between supply and demand. Ecological corridors and nodes are categorized into the core, important, and general levels based on their centrality and areas of supply–demand optimization. The connectivity, supply capacity, and supply–demand relationship of green infrastructure in Nanjing have been enhanced to varying degrees through the ecological security pattern optimization. The results of this study can serve as a decision-making reference for optimizing green infrastructure network patterns and enhancing urban ecological security.

Current point cloud registration methods predominantly focus on extracting geometric information from point clouds. In certain scenarios, i.e., when the target objects to be registered contain a large number of repetitive planar structures, the point-only based methods struggle to extract distinctive features from the similar structures, which greatly limits the accuracy of registration. Moreover, the deep learning-based approaches achieve commendable results on public datasets, but they face challenges in generalizing to unseen few-shot datasets with significant domain differences from the training data, and that is especially common in industrial applications where samples are generally scarce. Moreover, existing registration methods can achieve high accuracy on complete point clouds. However, for partial point cloud registration, many methods are incapable of accurately identifying correspondences, making it challenging to estimate precise rigid transformations. This paper introduces a domain-adaptive multimodal feature fusion method for partial point cloud registration in an unsupervised manner, named DAMF-Net, that significantly addresses registration challenges in scenes dominated by repetitive planar structures, and it can generalize well-trained networks on public datasets to unseen few-shot datasets. Specifically, we first introduce a point-guided two-stage multimodal feature fusion module that utilizes the geometric information contained in point clouds to guide the texture information in images for preliminary and supplementary feature fusion. Secondly, we incorporate a gradient-inverse domain-aware module to construct a domain classifier in a generative adversarial manner, weakening the feature extractor’s ability to distinguish between source and target domain samples, thereby achieving generalization across different domains. Experiments on a public dataset and our industrial components dataset demonstrate that our method improves the registration accuracy in specific scenarios with numerous repetitive planar structures and achieves high accuracy on unseen few-shot datasets, compared with the results of state-of-the-art traditional and deep learning-based point cloud registration methods.

In the face of geopolitical challenges and climate change, economic progress, safe production, and environmental protection have emerged as important directions for chemical industry development. However, the rational optimization of the chemical industry layout under the backdrop of ecological environmental protection necessitates further exploration. This study explores the evolution and future development direction of the chemical industry layout within the coastal region of Jiangsu Province, China, using the CA–Markov model. The findings reveal a trend of spatial agglomeration growth among coastal chemical enterprises, with Moran’s Index increasing from 0.109 in 2007 to 0.206 in 2017. The petrochemical industry, in particular, demonstrated the most significant agglomeration effect, with approximately 52.10% being concentrated in 14 coastal industrial parks in 2017. Under the constraints of the ecological environment and policy guidance, the land area allocated for the chemical industry experienced a reduction of over 10%, further strengthening the emphasis on spatial agglomeration. Chemical industries along Jiangsu’s coast have become agglomerated and concentrated near industrial parks and ports. Their spatial distribution and connectivity were mainly influenced by factors such as convenient transportation, the ecological environment, local policies, the distance from residential areas, and industrial agglomeration. Under different scenarios—including natural growth, ecological environment constraints, and policy guidance—chemical industries show diverse spatial patterns. Ecological environmental constraints and policy guidance can provide various intervention methods for the government to promote the optimization direction and focus of the chemical industry layout while minimizing its impact on the ecological environment.

In the late Qing Dynasty, religious periodicals by Western missionaries were made legal in China, and subsequently became an important manner of their missionary cause. Among them, Child’s Paper 小孩月报 (1875–1881) by John Marshall Willoughby Farnham, a Protestant missionary from the United States, endeavoured to convert child readers by carrying children’s stories of moral and emotional education. By concentrating on the educational elements of Child’s Paper, this article inspects how conversion was achieved via the intertextual interpretation of Christian doctrines within these educational elements. Specifically, how the image of little Christians and urchins, respectively, represents salvation and redemption in Christian morals. This article holds that the missionaries’ stress on the authority of Christian discourse in the education of Chinese children makes evident an increasing emphasis on the reformative effects of Christianity on Chinese children. Moreover, the conversion-education efforts by missionaries also construed helping Chinese children gain a cross-cultural perspective on Western religion, and arguably inspired later Chinese intellectuals’ to create newspapers for the purpose of the pre-primary education of Chinese children.

A palladium catalysed construction of fluoroalkyl indoles and isoquinolones through aryl/monofluoroalkylation of allenamides has been developed. Monofluoromethyl-substituted heterocycles could be accessed under mild conditions with broad functional group tolerance. In addition, indole-oxindole bisheterocyclic scaffolds bearing a fluorine atom were successfully synthesized with 3-fluoro-oxindole as the nucleophile by applying this method. [ABSTRACT FROM AUTHOR]