Several challenges arise in DNA extraction and gene amplification for airborne fungal metagenome analysis from a particulate matter (PM) samples. In this study, various conditions were tested to optimize the DNA extraction method from PM samples and polymerase chain reaction (PCR) conditions with primer set and annealing temperature. As a result of comparative evaluation of DNA extraction under various conditions, chemical cell lysis using buffer and proteinase K for 20 minutes and bead beating treatment were followed by using a commercial DNA extraction kit to efficiently extract DNA from the PM filter samples. To optimize the PCR conditions, PCR was performed using 10 primer sets for amplifying the ITS2 gene region. The concentration of the PCR amplicon was relatively high when the annealing temperature was 58℃ with the ITS3tagmix3/ITS4 primer set. Even under these conditions, when the concentration of the PCR product was low, nested PCR was performed using the primary PCR amplicon as the template DNA to amplify the ITS2 gene at a satisfactory concentration. Using the methods optimized in this study, DNA extraction and PCR were performed on 15 filter samples that collected PM2.5 in Seoul, and the ITS2 gene was successfully amplified in all samples. The optimized methods can be used for research on analyzing and interpreting the fungal metagenome of atmospheric PM samples.
Hand-foot-and-mouth disease (HFMD) is a viral infectious disease that occurs in children under 5 years of age. Its main causes are coxsackievirus (CV) and enterovirus (EV). Since there are no efficient therapeutics for HFMD, vaccines are effective in preventing the disease. To develop broad coverage against CV and EV, the development of a bivalent vaccine form is needed. The Mongolian gerbil is an efficient and suitable animal model of EV71 C4a and CVA16 infection used to investigate vaccine efficacy following direct immunization. In this study, Mongolian gerbils were immunized with a bivalent inactivated EV71 C4a and inactivated CVA16 vaccine to test their effectiveness against viral infection. Bivalent vaccine immunization resulted in increased Ag-specific IgG antibody production; specifically, EV71 C4a-specific IgG was increased with medium and high doses and CVA16-specific IgG was increased with all doses of immunization. When gene expression of T cell-biased cytokines was analysed, Th1, Th2, and Th17 responses were found to be highly activated in the high-dose immunization group. Moreover, bivalent vaccine immunization mitigated paralytic signs and increased the survival rate following lethal viral challenges. When the viral RNA content was determined from various organs, all three doses of bivalent vaccine immunization were found to significantly decrease viral amplification. Upon histologic examination, EV71 C4a and CVA16 induced tissue damage to the heart and muscle. However, bivalent vaccine immunization alleviated this in a dose-dependent manner. These results suggest that the bivalent inactivated EV71 C4a/CVA16 vaccine could be a safe and effective candidate HFMD vaccine.
Energy plays a significant role in modern lifestyle because of our extensive reliance over energy-operating devices. Therefore, there is a need for alternative and green energy resources that can fulfill the energy demand. For this, fuel cell (FCs) especially anion exchange membrane fuel cells (AEMFCs) have gained tremendous attention over the other (FCs) due to their fast reaction kinetics without using noble catalyst and allow to use of cheaper polymers with high performance. But lack of highly conductive, chemically, and mechanically stable anion exchange membrane (AEM) still main obstacle to the development of high performance AEMFCs. Therefore, graphene-based polymer composite membranes came into the existence as AEMs for the FCs. The exceptional properties of the graphene help to improve the performance of AEMs. Still, there are lot of challenges in the graphene derivatives based AEMs because of their high tendency of agglomeration in polymer matrix which reduced their potential. To overcome this issue surface modification of graphene derivatives is necessary to restrict their agglomeration and conserved their potential features that can help to improve the performance of AEM. Therefore, this review focus on the surface modification of graphene derivatives and their role in the fabrication of AEMs for the FCs.
Byeonguk Ahn;Fahimeh Yavartanoo;Jang-Keun Yoon;Su-Min Kang;Seungjun Kim;Thomas H.-K. Kang
Computers and Concrete
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v.31
no.4
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pp.337-348
/
2023
Shear wall is commonly used as a lateral force resisting system of concrete mid-rise and high-rise buildings, but it brings challenges in providing relatively large space throughout the building height. For this reason, the structure system where the upper structure with bearing, non-bearing and/or shear walls that sits on top of a transfer plate system supported by widely spaced columns at the lower stories is preferred in some regions, particularly in low to moderate seismic regions in Asia. A thick reinforced concrete (RC) plate has often been used as a transfer system, along with RC transfer girders; however, the RC plate becomes very thick for tall buildings. Applying the post-tensioning (PT) technique to RC plates can effectively reduce the thickness and reinforcement as an economical design method. Currently, a simplified model is used for numerical modeling of PT transfer plate, which does not consider the interaction of the plate and the upper structure. To observe the actual behavior of PT transfer plate under seismic loads, it is necessary to model whole parts of the structure and tendons to precisely include the interaction and the secondary effect of PT tendons in the results. This research evaluated the seismic behavior of shear wall-type residential buildings with PT transfer plates for the condition that PT tendons are included or excluded in the modeling. Three-dimensional finite element models were developed, which includes prestressing tendon elements, and response spectrum analyses were carried out to evaluate seismic forces. Two buildings with flat-shape and L-shape plans were considered, and design forces of shear walls and transfer columns for a system with and without PT tendons were compared. The results showed that, in some cases, excluding PT tendons from the model leads to an unrealistic estimation of the demands for shear walls sit on transfer plate and transfer columns due to excluding the secondary effect of PT tendons. Based on the results, generally, the secondary effect reduces shear force demand and axial-flexural demands of transfer columns but increases the shear force demand of shear walls. The results of this study suggested that, in addition to the effect of PT on the resistance of transfer plate, it is necessary to include PT tendons in the modeling to consider its effect on force demand.
Innopolis start-up has become a representative model and path for commercialization of public technology. Along with the quantitative growth of innopolis start-up, the importance of innopolis start-up in national policies and institutional strategies related to public technology commercialization has also increased. However, over the past 16 years, innopolis start-up's establishment and growth have taken place in different ways at different times. This study aims to compare and analyze changes in innopolis start-up over the past 16 years, focusing on comparisons before and after 2014, when the establishment of innopolis start-up began to increase rapidly. Main findings are as follows. First, in the early stage of the quantitative expansion period, policy changes related to innopolis start-up were the main factors for the increase in innopolis start-ups. In addition, the rapid increase in the establishment of innopolis start-up after 2016 was largely influenced by changes in the start-up environment and institutional changes related to innopolis start-up. Second, the time of registration and size of the capital of innopolis start-up had a statistically significant effect on the sales for 3 years after registration. This result shows that with the rapid increase in innopolis start-ups, the need to build a customized support system for innopolis start-ups by size or growth stage has increased.
Hyo-Sun Kwak;Jun-Hyeok Ham;Eiseul Kim;Yinhua Cai;Sang-Hee Jeong;Hae-Yeong Kim
Journal of Food Hygiene and Safety
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v.38
no.4
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pp.193-201
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2023
Antimicrobials in human medicine are classified by The World Health Organization (WHO) into three groups: critically important antimicrobials (CIA), highly important antimicrobials (HIA), and important antimicrobials (IA). CIA are antibiotic classes that satisfy two main criteria: that they are the sole or the only available limited therapeutic option to effectively treat severe bacterial infections in humans (Criterion 1), and infections where bacteria are transmitted to humans from non-human sources or have the potential to acquire resistance genes from non-human sources (Criterion 2). WHO emphasizes the need for cautious and responsible use of the CIA to mitigate risk and safeguard human health. Specific antimicrobials within the CIA with a high priority for management are reclassified as "highest priority critically important antimicrobials (HP-CIA)" and include the 3rd generation of cephalosporins and the next generation of macrolides, quinolones, glycopeptides, and polymyxins. The CIA list is the scientific basis for risk assessment and risk management policies that warrant using antimicrobials to reduce antimicrobial resistance in several countries. In addition, the CIA list ensures food safety in the food industry, including for the popular food chain companies McDonald's and KFC. The continuous update of the CIA list reflects the advancement in research and emerging future challenges. Thus, active and deliberate evaluation of antimicrobial resistance and the construction of a list that reflects the specific circumstances of a country are essential to safeguarding food security.
Journal of Korea Society of Industrial Information Systems
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v.28
no.4
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pp.45-58
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2023
In a world of uncertainty and complexity, leadership is essential to lead collaborative and positive interactions among employees. In other words, if members share opinions and work through voluntary leadership, they will respond more effectively to uncertain challenges and get closer to the targeted management performance. Therefore, in this study, we would like to elucidate the importance of shared leadership, which has recently become an issue. We will examine the impact of shared leadership on team efficacy, team organizational citizenship behavior, and turnover intention. A survey was conducted among members working in a team organization in Busan, and the results were as follows. First, the effects of shared leadership on team efficacy were found to have significant positive(+) effects, such as the hypotheses set at planning and organizing 0.202(C.R.=2.853), problem solving 0.463(C.R.=5.620), support and caring 0.237(C.R.=3.326), and development and mentoring 0.366(C.R.=5.132), respectively. Second, the effects of team efficacy on team organizational citizenship behavior and turnover intention were 0.545(C.R.=5.895) and -0.143(C.R.=-0.817), respectively, and team efficacy was found to have a positive(+)positive(+) effect on team organizational citizenship behavior, but team efficacy did not have a significant effect on turnover intention.
Early morning delivery possesses distinct characteristics that differentiate it from standard delivery services. This service typically involves delivering products to customers during the early morning hours, primarily before 7 AM. While online early morning delivery offers various advantages from a customer perspective, it also presents challenges that sellers and online shopping companies need to overcome. The early morning delivery market is experiencing significant growth in the online food retail sector, incorporating both PC-based online shopping and mobile shopping. The objective of this research is to identify the factors influencing customer satisfaction and the intention to reuse in the context of early morning delivery for online shopping. To model the online shopping environment with early morning delivery, independent factors were categorized into three types: System Properties, Product Characteristics, and Delivery Characteristics. This study examined the relationships among these three independent factors, the mediating factor of customer satisfaction, and the dependent variable of the intention to reuse. To conduct this research, empirical validation of the research hypotheses was carried out using the final dataset for analysis. Within this study, the previously explored System Properties, Product Characteristics, and Delivery Characteristics were established. Summarizing the findings of the analysis, it was discovered that System Properties and Product Characteristics played a significant role in determining the quality of early morning delivery services for online shopping. While product diversity and convenience had a positive impact, it is noteworthy that Delivery Characteristics did not influence customer satisfaction. Consequently, it can be concluded that there is no effect on the intention to reuse.
With the increasing emphasis on anomaly detection across various fields, diverse anomaly detection algorithms have been developed for various data types and anomaly patterns. However, the performance of anomaly detection algorithms is generally evaluated on publicly available datasets, and the specific performance of each algorithm on anomalies of particular types remains unexplored. Consequently, selecting an appropriate anomaly detection algorithm for specific analytical contexts poses challenges. Therefore, in this paper, we aim to investigate the types of anomalies and various attributes of data. Subsequently, we intend to propose approaches that can assist in the selection of appropriate anomaly detection algorithms based on this understanding. Specifically, this study compares the performance of anomaly detection algorithms for four types of anomalies: local, global, contextual, and clustered anomalies. Through further analysis, the impact of label availability, data quantity, and dimensionality on algorithm performance is examined. Experimental results demonstrate that the most effective algorithm varies depending on the type of anomaly, and certain algorithms exhibit stable performance even in the absence of anomaly-specific information. Furthermore, in some types of anomalies, the performance of unsupervised anomaly detection algorithms was observed to be lower than that of supervised and semi-supervised learning algorithms. Lastly, we found that the performance of most algorithms is more strongly influenced by the type of anomalies when the data quantity is relatively scarce or abundant. Additionally, in cases of higher dimensionality, it was noted that excellent performance was exhibited in detecting local and global anomalies, while lower performance was observed for clustered anomaly types.
This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.
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