The L-embedded OTU did not influence IFN signaling, the sensitivity to IFN, or IFN induction. Moreover, the attenuation of OTU C40A-mutated L could not be relieved by inactivating the IFN response, but after overexpression of conjugation-competent ISG15 the polymerase activity recovered to wild-type levels. Consequently, ISG15 was used to produce OTU-deficient tc-VLPs, a potential vaccine candidate. Our data thus indicate that in the context of full-length L the OTU domain is important for the regulation of CCHFV polymerase by ISG15.Following the Fukushima Daiichi Nuclear Power Plant accident in March 2011, radionuclides such as iodine-131, cesium-134 and cesium-137 were released into environment. In this study, we collected wild mushrooms from the Kawauchi Village of Fukushima Prefecture, located less than 30 km southwest of the Fukushima nuclear power plant, to evaluate their radiocesium (134Cs+137Cs) concentrations and the risk of internal radiation exposure in local residents. 342 mushroom samples were collected from 2016 to 2019. All samples were analysed for radiocesium content by a high-purity germanium detector. Among 342 mushroom samples, 260 mushroom samples (76%) were detected the radiocesium exceeding the regulatory limit of radiocesium (100 Bq/kg for general foods in Japan). The median of committed effective dose from ingestion of wild mushrooms was in the range of 0.015-0.053 mSv in 2016, 0.0025-0.0087 mSv in 2017, 0.029-0.110 mSv in 2018 and 0.011-0.036 mSv in 2019 based on the assumption that Japanese citizens consumed wild mushrooms for 1 year. https://www.selleckchem.com/products/ad80.html Thus, our study showed that although radiocesium is still detected in mushrooms collected in Kawauchi village even after 5 to 9 years later, the committed effective dose due to consuming mushrooms was lower than 1 mSv per year. Long-term comprehensive follow-up should monitor radiocesium concentrations in wild mushrooms to support the recovery of the community after the nuclear disaster.The identification of sick leave determinants could positively influence decision making to improve worker quality of life and to reduce consequently costs for society. Sick leave is a research topic of interest in economics, psychology, health and social behaviour. The question of choosing an appropriate statistical tool to analyse sick leave data can be challenging. In fact, sick leave data have a complex structure, characterized by two dimensions frequency and duration, and involve numerous features related to individual and environmental factors. We conducted a scoping review to characterize statistical approaches to analyse individual sick leave data in order to synthesise key insights from the extensive literature, as well as to identify gaps in research. We followed the PRISMA methodology for scoping reviews and searched Medline, World of Science, Science Direct, Psycinfo and EconLit for publications using statistical modeling for explaining or predicting sick leave at the individual level. We selected 469 articles from the 5983 retrieved, dated from 1981 to 2019. In total, three types of model were identified univariate outcome modeling using for the most part count models (438 articles), bivariate outcome modeling (14 articles), such as multistate models and structural equation modeling (22 articles). The review shows that there was a lack of evaluation of the models as predictive accuracy was only evaluated in 18 articles and the explanatory accuracy in 43 articles. Further research based on joint models could bring more insights on sick leave spells, considering both their frequency and duration.Over the past decade, pastoralists in Kunene Region, Namibia, have endured recurrent drought and flood events that have culminated in the loss of their primary form of livelihood-pastoralism. Most pastoralists are finding it difficult to sustain their livelihoods, and their communities have fallen into extreme poverty. Ecosystem-based Adaptation (EbA) approaches are increasingly acknowledged as having the potential to enhance the adaptive capacity of vulnerable communities. The first step is to develop an understanding of how affected communities live, their perceptions of and how they respond to climate change and the biophysical impacts of climate change in their communities. This study aims to collect this information in order to explore the use of EbA to help pastoralists adapt to climate change. We examined an isolated pastoral Himba community, to understand their perceptions, experiences and understanding of climate change and its related impacts on their livelihoods. A nested mixed-methods approach usicapacity.To manage coronavirus disease 2019 (COVID-19), a national health authority has implemented a case definition of patients under investigation (PUIs) to guide clinicians' diagnoses. We aimed to determine characteristics among all PUIs and those with and without COVID-19. We retrospectively reviewed clinical characteristics and risk factors for laboratory-confirmed COVID-19 cases among PUIs at a tertiary care center in Bangkok, Thailand, between March 23 and April 7, 2020. Reverse transcription-polymerase chain reaction for SARS-CoV-2 RNA was performed. There were 405 evaluable PUIs; 157 (38.8%) were men, with a mean age ± SD of 36.2 ± 12.6 years. The majority (68.9%) reported no comorbidities. There were 53 (13.1%) confirmed COVID-19 cases. The most common symptoms among those were cough (73.6%), fever (58.5%), sore throat (39.6%), and muscle pain (37.4%). Among these patients, diagnoses were upper respiratory tract infection (69.8%), viral syndrome (15.1%), pneumonia (11.3%), and asymptomatic infection (3.8%). Multivariate analysis identified close contact with an index case (OR, 3.49; 95%CI, 1.49-8.15; P = 0.004), visiting high-risk places (OR, 1.92; 95%CI, 1.03-3.56; P = 0.039), productive cough (OR, 2.03; 95%CI, 1.05-3.92; P = 0.034), and no medical coverage (OR, 3.91; 95%CI, 1.35-11.32; P = 0.012) as independent risk factors for COVID-19 among the PUIs. The majority had favorable outcomes, though one (1.9%) died from severe pneumonia. COVID-19 was identified in 13% of PUIs defined per a national health authority's case definition. History of contact with a COVID-19 patient, visiting a high-risk place, having no medical coverage, and productive cough may identify individuals at risk of COVID-19 in Thailand.