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Taken together, our results support the first isolation of virulent NDV isolates with a lentogenic motif; isolate Ck/ME5/Eg/16 might be generated in nature from LaSota live vaccine, whereas isolate Ck/ME3/Eg/16 is emergent from NDV/Ck/Egypt/3/2006. We conclude that the current diagnostic evaluation of the virulence of NDV isolates by characteristic amino acid residues at the F protein cleavage site is insufficient. There is a need to link virologic and epidemiologic data together, and routine and emergency LaSota vaccination protocols should be carefully and optimally applied, with regards to the timing and presence of co-infecting agents in the field. This article is protected by copyright. All rights reserved.This review was conducted to synthesize and critically appraise the literature on knowledge, attitudes, understanding, perceptions, and expectations of mental health professionals (MHPs) and mental health professional (MHP) students' regarding recovery. A systematic search in Scopus, CINAHL, PsycINFO, Web of Science, Medline, and Embase as well as Google scholar and web-based repositories was conducted. The searches were conducted using a combination of key terms "mental health professionals", "students", 'knowledge', "understanding", "perception" "attitude", "expectation", "recovery". After screening and quality assessment, the review included 29 studies (18 quantitative, 8 qualitative, and 3 mixed-method studies) published in English, from January 2006 to June 2019, and was analysed systematically using a mixed-method synthesis. The findings revealed that there is increasing evidence (especially among MHPs) of knowledge, attitudes, understanding, perceptions, and expectations regarding recovery. However, there are disparities in how MHPs perceive and understand recovery. While some understood it to mean a personal process, others explained it as a clinical process. In addition, there was limited knowledge among the MHPs and MHP students regarding the nonlinearity nature of the recovery process and expectations regarding recovery. The implications from these findings are the need for more in-service training for MHPs, and examination of the curriculum used to educate MHP students. In particular, they should be sufficiently informed about the nonlinearity nature of the recovery process and how to develop hopeful and realistic expectations for consumers throughout the recovery process. The review was preregistered with PROSPERO (Registration No CRD42019136543). © 2020 Australian College of Mental Health Nurses Inc.Research on transition metal dichalcogenides (TMDs) has been accelerated by the development of large-scale synthesis based on chemical vapor deposition (CVD) growth. However, in most cases, CVD-grown TMDs are composed of randomly oriented grains, and thus contain many distorted grain boundaries (GBs), which seriously degrade their electrical and photoelectrical properties. Here, the epitaxial growth of highly aligned MoS2 grains is reported on a twofold symmetry a-plane sapphire substrate. The obtained MoS2 grains have an unusual rectangle shape with perfect orientation alignment along the [1-100] crystallographic direction of a-plane sapphire. It is found that the growth temperature plays a key role in its orientation alignment and morphology evolution, and high temperature is beneficial to the initial MoS2 seeds rotate to the favorable orientation configurations. In addition, the photoluminescence quenching of the well-aligned MoS2 grains indicates a strong MoS2 -substrate interaction which induces the anisotropic growth of MoS2 , and thus brings the formation of rectangle shape grains. Moreover, the well-aligned MoS2 grains splice together without GB formation, and thus that has negligible effect on its electrical transport properties. The progress achieved in this work could promote the controlled synthesis of large-area TMDs single crystal film and the scalable fabrication of high-performance electronic devices. © 2020 WILEY-VCH Verlag GmbH &amp; Co. [https://www.selleckchem.com/EGFR(HER).html EGFR inhibitor] KGaA, Weinheim.Advances in scientific technology in the early 20th century have facilitated the development of synthetic plastics that are lightweight, rigid, and can be easily molded to a desirable shape without changing their material properties. Thus, plastics become ubiquitous and indispensable materials that are used in various manufacturing sectors, including clothing, automotive, medical, and electronic industries. However, strong physical durability and chemical stability of synthetic plastics, most of which are produced from fossil fuels, hinders their complete degradation when they are improperly discarded after use. In addition, accumulated plastic wastes without degradation have caused severe environmental problems, such as microplastics pollution and plastic islands. Thus, the usage and production of plastics is not free from environmental pollution or resource depletion. In order to lessen the impact of climate change and reduce plastic pollution, it is necessary to understand and address the current plastic life cycles. In this review, we suggest "sustainable biopolymer" as a promising solution to current plastic crisis. Desired properties of sustainable biopolymers and bio-based and bio/chemical hybrid technologies for the development of sustainable biopolymers are mainly discussed. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.