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Mushroom fly, Lycoriella mali (Diptera: Sciaridae), is the primary pest in imported mushrooms. The amount of Tricholoma matsutake imported from China increases every fall when it is harvested. When importing T. matsutake, disinfestation using methyl bromide (MB) or phosphine (PH3) is performed to prevent the introduction of L. mali. However, MB will be phased out due to ozone-depletion, chronic toxicity to workers, and residual issues. PH3 fumigation in mushroom disinfestation requires a long exposure time (24 h). In this study, we used ethyl formate (EF), which can replace MB and reduce exposure time. The efficacy of EF, PH3 and EF + PH3 on L. mali was evaluated. Using 4-h EF fumigation at 5 °C, the 3rd and 4th instar was the most tolerant stage in terms of 99% killed lethal concentration × time products (LCt99%). When 4-h EF fumigation at 5 °C was applied on all stages of L. mali, the LCt99% values of EF were 73.1 g h/m3 to the 1st and 2nd instar, 112.9 g h/m3 to the 3rd and 4th instar, 68.9 g h/m3 to pupae, and 20.1 g h/m3 to adult. It was confirmed that combination treatment with EF + PH3 had a synergistic effect on L. mali. The LCt99% of EF + 0.5 g/m3 of PH3 to the 3rd and 4th instar was 48.3 g h/m3. When only 140 g/m3 of EF was applied for 4 h at > 5 °C and 35 g/m3 of EF + 0.5 g/m3 of PH3 for 4 h at > 5 °C in commercial trials containing T. matsutake, proven efficacy (100%) on L. mali was confirmed. In the case of EF treatment only, phytotoxic damage occurred due to high Ct products, and there was no phytotoxic damage in combination treatment with EF + PH3. This study provides a new guideline for EF + PH3 combination treatment within a shorter exposure time (4 h) than existing PH3 treatment (24 h) and replacement of MB use. [ABSTRACT FROM AUTHOR]

Background: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments., Objective: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission., Methods: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions., Results: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results., Conclusions: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' outcomes., (©Hoon Ko, Heewon Chung, Wu Seong Kang, Chul Park, Do Wan Kim, Seong Eun Kim, Chi Ryang Chung, Ryoung Eun Ko, Hooseok Lee, Jae Ho Seo, Tae-Young Choi, Rafael Jaimes, Kyung Won Kim, Jinseok Lee. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.12.2020.)