Enhancing the organizational citizenship behavior for the environment: the roles of green training and organizational culture

The environmental concern has become an emerging topic in organization's human resource management strategy, especially in improving employee's environmental behavior at work Thus, the organizational citizenship behavior toward the environment (OCBE) has been currently attracting scholars in management. However, published studies contain research gaps in investigation of the relationships between both green training and organizational culture and OCBE, especially in the hotel industry. Following the social exchange and Ability-Motivation Opportunity theories, this study aims to examine the effects of two green practices on OCBE and the moderating role of green organizational culture to the effect of green training on OCBE. A quantitative approach with survey strategy is employed and conducted in 4-5 star hotels to test these relationships. The PLS-SEM and K-means Cluster Analysis techniques are applied to analyze data. The findings reveal that green training and organizational culture positively influence OCBE. Also, the effect of green training on OCBE is moderated by green organizational culture. Finally, our study provides limitations of research and further studies, and the implications for management practices concerned with improving employee's voluntary eco-behavior in the hotel industry.Internal Grant Agency of FaME TBU [IGA/FaME/2018/009

Fcγ受容体発現細胞を用いたデング熱流行期前の健康人血清中におけるデングウイルス感染増強抗体の解析

Background: Antibodies are critical responses to protect the host from dengue virus(DENV) infection. Antibodies target DENV by two pathologic mechanisms: virus neutralization and infection enhancement. In dengue patients, the absence of neutralizing activity in the presence of FcγR implies that infection-enhancing activity hampers the neutralizing activity of antibodies, which could potentially lead to symptomatic presentations and severe clinical outcomes. Methods: A total of 100 pair serum samples from adult healthy volunteers were obtained during the dengue season in Ha Noi in 2015 for evaluation of neutralizing and infection-enhancing activity. Additionally, 20 serum samples from acute secondary DENV infection patients were also used as the patient group in this study. PRNT was performed on BHK cells and FcγR-expressing BHK cell lines for all serum samples. Results: Out of 100 residents, positive neutralizing antibodies (N.A) were found in 44.23 and 76.92% for DENV-1; 38.46 and 75% for DENV-2; 19.23 and 15.38% for DENV-3; and 1.92 and 9.62% for DENV-4 for pre and post-dengue season respectively. The percentage of post-exposure residents having positive responses against single, two, or more than three DENV serotypes were 38.46, 44.23 and 15.38%, respectively. A total of 34 residents were DENV seropositive before the dengue season and these individuals demonstrated further elevation of IgG antibodies after the dengue season. At the end of the season, 18 residents were confirmed to be new asymptomatic DENV infection cases. In both groups, N.A titers determined on BHK cells were higher than that on FcγR-expressing BHK cells. In heterotypic N.A responses, N.A titers to the infecting serotype from the samples obtained from pre-exposure group were significantly higher than those of the patient group. However, fold enhancement to the infecting serotypes from the samples in the pre-exposure group was substantially lower as compared to that of the patient group. Conclusion: Before and after the dengue season, serum samples from healthy volunteers demonstrated high levels of neutralizing antibodies and low or absence of infection-enhancement activity. The results suggest that while infection-enhancement activity hampers neutralizing activity of antibodies, high levels of DENV neutralizing antibodies set a critical threshold in facilitating the prevention of disease progression.長崎大学学位論文 学位記番号:博(医歯薬)甲第1058号 学位授与年月日:平成30年3月20日Author: Minh Huong Phu Ly, Meng Ling MoiEmail author, Thi Bich Hau Vu, Mya Myat Ngwe Tun, Todd Saunders, Cam Nhat Nguyen, Anh Kieu Thi Nguyen, Hung Manh Nguyen, Than Huu Dao, Do Quyen Pham, Thi Thu Thuy Nguyen, Thi Quynh Mai Le, Futoshi Hasebe and Kouichi MoritaCitation: BMC Infectious Diseases, 18, 31; 2018Nagasaki University (長崎大学)課程博

Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit

BackgroundInterpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in a low resource ICU.MethodsThis was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool.ResultsThe average accuracy of beginners' LUS interpretation was 68.7% [95% CI 66.8-70.7%] compared to 72.2% [95% CI 70.0-75.6%] in intermediate, and 73.4% [95% CI 62.2-87.8%] in advanced users. Experts had an average accuracy of 95.0% [95% CI 88.2-100.0%], which was significantly better than beginners, intermediate and advanced users (p < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% [95% CI 65.6-73.9%] to 82.9% [95% CI 79.1-86.7%], (p < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% [95% CI 57.9-78.2%] to 93.4% [95% CI 89.0-97.8%], (p < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5-20.6) to 5.0 s (IQR 3.5-8.8), (p < 0.001) and clinicians' median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool.ConclusionsAI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently