English for Korean postgraduate engineering students in the global academic community : perceptions of the importance of English, skills-based needs and sociocultural behaviours

This study aims to investigate the perceived needs for English of Korean postgraduate engineering students in an academic community. It questions the broader issues of needs in English for Academic Purposes (EAP), encompassing the importance of English, skills-based needs in English and sociocultural behaviours. In raising these issues, this research uses a comparative framework. I collected data in two contexts, the United Kingdom and Korea, to examine the perceptions of Korean postgraduate engineering students themselves and subject lecturers by using both questionnaires and semi-structured interviews.\ud The research showed that the current global world order has strongly influenced participants' perceptions in both the Korean and the UK academic contexts. The role of English was considered as being pivotal for communication, and a balanced command of English skills integrated with academic practices of the engineering discipline were seen to be required for students. However, there was a diversity of VIews among participants regarding the sociocultural behaviours which characterized the emerging global academic community. Participants in Korea tended to be self-critical of their own academic culture. In the United Kingdom, students struggled, resisted or attempted to reshape the dominant academic culture, while lecturers were frustrated by students' non-participatory and non-interactive attitudes in the community of practice.\ud Considering the demands of participation in a global academic community leads to the conclusion that Korean engineering students need to be equipped with multiple skills and discipline-specific literacy, forged to meet the needs of globalization. Students should also be expected to have critical awareness, sociocultural sensitivity and flexibility, in order to be genuine members of the engineering academic community. Finally, this thesis discusses the implications for upgraded EAP programmes adapted to the needs of Korean engineering students in the global age. \u

MCBT: Multi-Hop Cluster Based Stable Backbone Trees for Data Collection and Dissemination in WSNs

We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs). The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD) and Multicluster, Mobile, Multimedia radio network (MMM), consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes) of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime

Directed evolution of CRISPR-Cas9 to increase its specificity

The use of CRISPR-Cas9 as a therapeutic reagent is hampered by its off-target effects. Although rationally designed S. pyogenes Cas9 (SpCas9) variants that display higher specificities than the wild-type SpCas9 protein are available, these attenuated Cas9 variants are often poorly efficient in human cells. Here, we develop a directed evolution approach in E. coli to obtain Sniper-Cas9, which shows high specificities without killing on-target activities in human cells. Unlike other engineered Cas9 variants, Sniper-Cas9 shows WT-level on-target activities with extended or truncated sgRNAs with further reduced off-target activities and works well in a preassembled ribonucleoprotein (RNP) format to allow DNA-free genome editing.

MCBT: Multi-Hop Cluster Based Stable Backbone Trees for Data Collection and Dissemination in WSNs

We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs). The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD) and Multicluster, Mobile, Multimedia radio network (MMM), consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes) of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime

Development of examination objectives based on nursing competency for the Korean Nursing Licensing Examination: a validity study

Purpose This study aimed to develop the examination objectives based on nursing competency of the Korean Nursing Licensing Examination. Methods This is a validity study to develop the examination objectives based on nursing competency. Data were collected in December 2021. We reviewed the literature related to changing nurse roles and on the learning objectives for the Korea Medical Licensing Examination and other health personnel licensing examinations. Thereafter, we created a draft of the nursing problems list for examination objectives based on the literature review, and the content validity was evaluated by experts. A final draft of the examination objectives is presented and discussed. Results A total of 4 domains, 12 classes, and 85 nursing problems for the Korean Nursing Licensing Examination were developed. They included the essentials of objectives, related factors, evaluation goals, related activity statements, related clients, related settings, and specific outcomes. Conclusion This study developed a draft of the examination objectives based on clinical competency that were related to the clinical situations of nurses and comprised appropriate test items for the licensing examination. Above results may be able to provide fundamental data for item development that reflects future nursing practices

AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning

© 2022, The Author(s).Background: The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e., the number of patients. However, we are yet to understand the spread at the level of individual patients. As of March 2021, COVID-19 is wide-spread all over the world with new genetic variants. One important question is to track the early spreading patterns of COVID-19 until the virus has got spread all over the world. Results: In this work, we proposed AutoCoV, a deep learning method with multiple loss object, that can track the early spread of COVID-19 in terms of spatial and temporal patterns until the disease is fully spread over the world in July 2020. Performances in learning spatial or temporal patterns were measured with two clustering measures and one classification measure. For annotated SARS-CoV-2 sequences from the National Center for Biotechnology Information (NCBI), AutoCoV outperformed seven baseline methods in our experiments for learning either spatial or temporal patterns. For spatial patterns, AutoCoV had at least 1.7-fold higher clustering performances and an F1 score of 88.1%. For temporal patterns, AutoCoV had at least 1.6-fold higher clustering performances and an F1 score of 76.1%. Furthermore, AutoCoV demonstrated the robustness of the embedding space with an independent dataset, Global Initiative for Sharing All Influenza Data (GISAID). Conclusions: In summary, AutoCoV learns geographic and temporal spreading patterns successfully in experiments on NCBI and GISAID datasets and is the first of its kind that learns virus spreading patterns from the genome sequences, to the best of our knowledge. We expect that this type of embedding method will be helpful in characterizing fast-evolving pandemics.N

Communication Technology and Social Support to Navigate Work/Life Conflict During Covid-19 and Beyond

Drawing on a national survey of 447 U.S. workers who transitioned to remote work during COVID-19, this study examined how different types of communication technologies (CTs) used for work and private life were associated with work/life conflicts and perceptions of social support across different relationship types (coworker, family, and friends). Findings indicated that work/life conflicts became aggravated when the use of CTs violated relational norms (e.g., mobile texting with coworkers and emailing with family and friends). On the other hand, uses of CTs that were perceived to offer access to social support (e.g., instant messaging with coworkers and friends) were related to lower work/life conflict. Social media (e.g., Facebook) had a direct relationship to higher work/life conflict, but an indirect relationship to lower work/life conflict through social support. Overall, findings suggest that individuals attempt to create work/life boundaries by selecting specific CTs when physical work/life boundaries are collapsed

Ocurrence of Clubroot Caused by Plasmodiophora brassicae on Kohlrabi in Korea

From 2016 to 2018, approximately 15% of kohlrabi were observed displaying significant clubroot symptoms in farmer's fields in Jeju, Korea. The initial infection appeared as hypertrophy of root hairs, and as the disease progressed, galls formation occurred on the main roots, finally disease progress resulted in yellowing and wilting of leaves. Pathogenicity was proven by artificial inoculation of plants with resting spore suspension, fulfilling Koch's postulates. The resting spore is one-celled, spherical and subspherical, colorless, and 3-5 mm in diameter. On the basis of the morphological characteristics and phylogenetic analyses of internal transcribed spacer rDNA, the causal agent was identified as Plasmodiophora brassicae. To our knowledge, this is the first report on the occurrence of P. brassicae on kohlrabi in Korea

Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning

Abstract The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3–82.7%), the Cohen’s kappa score (κ) was 0.650 (95% CI: 0.570–0.730), and the quadratic-weighted kappa score (κ quad) was 0.897 (95% CI: 0.815–0.979). When trained on 621 cases from one institution and validated on 167 cases from the other institution, the system’s accuracy reached 67.4% (95% CI: 63.2–71.6%), κ 0.553 (95% CI: 0.495–0.610), and the κ quad 0.880 (95% CI: 0.822–0.938). In order to evaluate the impact of the proposed method, performance comparison with several baseline methods was also performed. While limited by case volume and a few more factors, the results of this study can contribute to the potential development of an artificial intelligence system to diagnose other cancers without extensive region-level annotations