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Purpose: The present scoping review examines research data management (RDM) best practices and empirical studies in academic libraries between 2010 and 2021. Method: The current study developed systematic database searches to locate potential articles for inclusion and designed a detailed and systematic coding scheme to examine the substantive features of RDM and characteristics of RDM practice, with an emphasis on RDM instruction. Results and Discussion: The results from the current study demonstrated that there is great demand for RDM training after 2011. Furthermore, research about RDM training spread across North America, Europe, Asia Pacific, and elsewhere. The findings also proved that RDM training is essential for both STEM and non- STEM subjects but simultaneously indicated that non-STEM subjects such as the social sciences in particular lack RDM training. Results from the current literature also found that a large number of RDM training programs focused on the introduction of RDM or an RDM overview, without in-depth and discipline-based curriculum for researchers across domains. Additionally, this study identified a lack of quantitative research, especially statistical analysis, on the effect of RDM interventions. Conclusion: This study contributes to our comprehensive understanding of some essential elements associated with RDM training, with the primary finding that future practitioners in the RDM field would benefit from stronger collaboration with faculty or researchers to develop more discipline-based curriculums for RDM and more application-based approaches for teaching RDM.

Mycobacterium abscessus is a rapidly growing nontuberculous mycobacterium that causes severe pulmonary infections. Recent studies indicate that ferroptosis may play a critical role in the pathogenesis of M . abscessus pulmonary disease. We obtained gene expression microarray data from the Gene Expression Omnibus database, focusing on THP-1-derived macrophages infected with M . abscessus and uninfected controls. Differentially expressed genes related to ferroptosis were identified through weighted gene co-expression network analysis and the \"limma\" package, followed by gene set variation analysis and gene set enrichment analysis for enrichment assessment. To explore regulatory network relationships among hub genes, we constructed RBP-mRNA, ceRNA, and TF-mRNA networks. Additionally, a protein-protein interaction network was built, and functional enrichment analyses were conducted for the hub genes. The diagnostic value of these genes was assessed using receiver operating characteristic curves. Six differentially expressed genes associated with ferroptosis were identified in M . abscessus infection. The receiver operating characteristic curves demonstrated that these genes had excellent predictive value for the infection. Functional enrichment analysis showed that these genes were involved in immune responses, inflammation, cellular metabolism, cell death, and apoptosis. Pathway enrichment analysis revealed significant enrichment in pathways related to apoptosis, inflammation, and hypoxia. The RBP-mRNA network highlighted significant interactions between hub genes and key RNA-binding proteins, while the ceRNA network predicted that miRNAs and lncRNAs regulate ferroptosis-related genes NACC2 and ITPKB . Furthermore, interactions between the hub gene HSD3B7 and transcription factors LMNB1 and ASCL1 may promote ferroptosis in macrophages by influencing iron metabolism and reactive oxygen species production, contributing to the M . abscessus infection process. Our findings identified biomarkers linked to ferroptosis in M . abscessus infection, providing new insights into its pathogenic mechanisms and potential therapeutic strategies.

The complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Data management training has not fully addressed the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a school of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, RDM confidence and preparation differ between populations so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction.

There are 25 auxin response factors (ARFs) in the rice genome, which play critical roles in regulating myriad aspects of plant development, but their role (s) in host antiviral immune defense and the underneath mechanism remain largely unknown. By using the rice- rice dwarf virus (RDV) model system, here we report that auxin signaling enhances rice defense against RDV infection. In turn, RDV infection triggers increased auxin biosynthesis and accumulation in rice, and that treatment with exogenous auxin reduces OsIAA10 protein level, thereby unleashing a group of OsIAA10-interacting OsARFs to mediate downstream antiviral responses. Strikingly, our genetic data showed that loss-of-function mutants of osarf12 or osarf16 exhibit reduced resistance whereas osarf11 mutants display enhanced resistance to RDV. In turn, OsARF12 activates the down-stream OsWRKY13 expression through direct binding to its promoter, loss-of-function mutants of oswrky13 exhibit reduced resistance. These results demonstrated that OsARF 11, 12 and 16 differentially regulate rice antiviral defense. Together with our previous discovery that the viral P2 protein stabilizes OsIAA10 protein via thwarting its interaction with OsTIR1 to enhance viral infection and pathogenesis, our results reveal a novel auxin-IAA10-ARFs-mediated signaling mechanism employed by rice and RDV for defense and counter defense responses.

Fringe projection profilometry (FPP) and digital image correlation (DIC) are widely applied in three-dimensional (3D) measurements. The combination of DIC and FPP can effectively overcome their respective shortcomings. However, the speckle on the surface of an object seriously affects the quality and modulation of fringe images captured by cameras, which will lead to non-negligible errors in the measurement results. In this paper, we propose a fringe image extraction method based on deep learning technology, which transforms speckle-embedded fringe images into speckle-free fringe images. The principle of the proposed method, 3D coordinate calculation, and deformation measurements are introduced. Compared with the traditional 3D-DIC method, the experimental results show that this method is effective and precise.

Biochar application to soils may increase carbon (C) sequestration due to the inputs of recalcitrant organic C. However, the effects of biochar application on the soil greenhouse gas (GHG) fluxes appear variable among many case studies; therefore, the efficacy of biochar as a carbon sequestration agent for climate change mitigation remains uncertain. We performed a meta‐analysis of 91 published papers with 552 paired comparisons to obtain a central tendency of three main GHG fluxes (i.e., CO2, CH4, and N2O) in response to biochar application. Our results showed that biochar application significantly increased soil CO2 fluxes by 22.14%, but decreased N2O fluxes by 30.92% and did not affect CH4 fluxes. As a consequence, biochar application may significantly contribute to an increased global warming potential (GWP) of total soil GHG fluxes due to the large stimulation of CO2 fluxes. However, soil CO2 fluxes were suppressed when biochar was added to fertilized soils, indicating that biochar application is unlikely to stimulate CO2 fluxes in the agriculture sector, in which N fertilizer inputs are common. Responses of soil GHG fluxes mainly varied with biochar feedstock source and soil texture and the pyrolysis temperature of biochar. Soil and biochar pH, biochar applied rate, and latitude also influence soil GHG fluxes, but to a more limited extent. Our findings provide a scientific basis for developing more rational strategies toward widespread adoption of biochar as a soil amendment for climate change mitigation.

This meta-analysis examined the effectiveness of teacher professional development (PD) training on improving in-service STEM teachers’ self-efficacy in the K-12 teaching context. Twenty-one articles yielded 48 effect size estimates, incorporating 1412 teachers in the final analyses. Results indicated that the overall effect size of PD training on K-12 STEM teachers’ self-efficacy was strong at g = 0.64 (p < 0.01). This review contributes to the STEM teacher self-efficacy literature and confirms the significant impact of PD on improving STEM teachers’ self-efficacy. Results regarding the impact of substantive PD characteristics provide policymakers and practitioners seeking to improve STEM teacher self-efficacy with information for improving PD. Recommendations for researchers are also discussed in the paper.

Agricultural extension plays a crucial role in disseminating knowledge, empowering farmers, and advancing agricultural development. The effectiveness of these roles can be greatly improved by integrating technology. These technologies, often grouped into two categories–agricultural technology and educational technology–work together to yield the best outcomes. While several studies have been conducted using technologies in agricultural extension programs, no previous reviews have solely examined the impact of these technologies in agricultural extension, and this leaves a significant knowledge gap especially for professionals in this field. For this scoping review, we searched the five most relevant, reliable, and comprehensive databases (CAB Abstracts (Ovid), AGRICOLA (EBSCO), ERIC (EBSCO), Education Source (EBSCO), and Web of Science Core Collection) for articles focused on the use of technology for training farmers in agricultural extension settings. Fifty-four studies published between 2000 and 2022 on the use of technology in agricultural extension programs were included in this review. Our findings show that: (1) most studies were conducted in the last seven years (2016–2022) in the field of agronomy, with India being the most frequent country and Africa being the most notable region for the studies; (2) the quantitative research method was the most employed, while most of the included studies used more than one data collection approach; (3) multimedia was the most widely used educational technology, while most of the studies combined more than one agricultural technology such as pest and disease control, crop cultivation and harvesting practices; (4) the impacts of technology in agricultural extension were mostly mixed, while only the educational technology type had a statistically significant effect or impact of the intervention outcome. From an analysis of the results, we identified potential limitations in included studies’ methodology and reporting that should be considered in the future like the need to further analyze the specific interactions between the two technology types and their impacts of some aspects of agricultural extension. We also looked at the characteristics of interventions, the impact of technology on agricultural extension programs, and current and future trends. We emphasized the gaps in the literature that need to be addressed.

Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents. This paper will review some natural-product-related potent anticancer, anti-HIV, antibacterial and antimalarial drugs or lead compounds mainly discovered from 2016 to 2022. Structurally typical marine bioactive products are also included. Molecular modeling, machine learning, bioinformatics and other computer-assisted techniques that are very important in narrowing down bioactive core structural scaffolds and helping to design new structures to fight against key disease-associated molecular targets based on available natural products are considered and briefly reviewed.

A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed pixels resulted from fragmented patches and difficulty in obtaining optical satellites due to a frequently cloudy and rainy climate. Here we propose a crop type and cropping pattern mapping framework in subtropical hilly and mountainous areas, considering multiple sources of satellites (i.e., Landsat 8/9, Sentinel-2, and Sentinel-1 images and GF 1/2/7). To develop this framework, six types of variables from multi-sources data were applied in a random forest classifier to map major summer crop types (singe-cropped rice and double-cropped rice) and winter crop types (rapeseed). Multi-scale segmentation methods were applied to improve the boundaries of the classified results. The results show the following: (1) Each type of satellite data has at least one variable selected as an important feature for both winter and summer crop type classification. Apart from the endmember variables, the other five extracted variable types are selected by the RF classifier for both winter and summer crop classifications. (2) SAR data can capture the key information of summer crops when optical data is limited, and the addition of SAR data can significantly improve the accuracy as to summer crop types. (3) The overall accuracy (OA) of both summer and winter crop type mapping exceeded 95%, with clear and relatively accurate cropland boundaries. Area evaluation showed a small bias in terms of the classified area of rapeseed, single-cropped rice, and double-cropped rice from statistical records. (4) Further visual examination of the spatial distribution showed a better performance of the classified crop types compared to three existing products. The results suggest that the proposed method has great potential in accurately mapping crop types in a complex subtropical planting environment.