RESUMEN
The outbreak of the COVID-19 pandemic has brought massive shifts in human activities through a global blockade, directly affecting wildlife survival. However, the indirect impacts of changes in human activities are often easily overlooked. We conducted surveys of Reeves's Pheasant (Syrmaticus reevesii) and its sympatric species by camera traps in forest-type nature reserves in three different scenarios: pre-lockdown, lockdown and post-lockdown. An increase in livestock activities observed during the lockdown and post-lockdown period in our study area provided us an opportunity to investigate the indirect impact of the lockdown on wildlife. The pre-lockdown period was used as a baseline to compare any changes in trends of relative abundance index, activity patterns and temporal spacing of targeted species and livestock. During the lockdown period, the relative abundance index of livestock increased by 50% and there was an increase in daytime activity. Reeves's Pheasant showed avoidance responses to almost all sympatric species and livestock in three different periods, and the livestock avoidance level of Reeves's Pheasant during the lockdown period was significantly and positively correlated with the relative abundance index of livestock. Species-specific changes in activity patterns of study species were observed, with reduced daytime activities of Hog Badger and Raccoon Dog during and after the confinement periods. This study highlights the effect of the COVID-19 lockdown on the responses of wildlife by considering the changes in their temporal and spatial use before, during and after lockdown. The knowledge gained on wildlife during reduced human mobility because of the pandemic aids in understanding the effect of human disturbances and developing future conservation strategies in the shared space, to manage both wildlife and livestock.
RESUMEN
The outbreak of the COVID-19 pandemic has brought massive shifts in human activities through a global blockade, directly affecting wildlife survival. However, the indirect impacts of changes in human activities are often easily overlooked. We conducted Reeves's Pheasant (Syrmaticus reevesii) and sympatric species by camera traps in forest-type nature reserves in three different scenarios: pre-lockdown, lockdown and post-lockdown. An increase in livestock activities observed during the lockdown and post-lockdown period in our study area provided us an opportunity to investigate the indirect impact of the lockdown on wildlife. The pre-lockdown period was used as a baseline to compare any changes in trends of relative abundance index, activity patterns and temporal spacing of targeted species and livestock. During the lockdown period, the relative abundance index of livestock increased by 50% and there was an increase in daytime activity. The relative abundance index of Hog Badger dropped rapidly to its lowest level after the closure began. The naive occupancy of Wild Boar and sympatric muntjac increased after the lockdown began, while that of the other four relatively small-bodied species decreased to varying degrees. Species-specific changes in activity patterns of study species were observed, with reduced daytime activities of Hog Badger and Raccoon Dog during and after the confinement periods. This study highlights the effect of the COVID-19 lockdown on the responses of wildlife by considering the changes in their temporal and spatial use before, during and after lockdown. The knowledge gained on wildlife during reduced human mobility because of the pandemic aids in understanding the effect of human disturbances and developing future conservation strategies in the shared space, to manage both wildlife and livestock.
RESUMEN
Deep learning is an obvious method for the detection of disease, analyzing medical images and many researchers have looked into it. However, the performance of deep learning algorithms is frequently influenced by hyperparameter selection, the question of which combination of hyperparameters are best emerges. To address this challenge, we proposed a novel algorithm for Adaptive Hyperparameter Tuning (AHT) that automates the selection of optimal hyperparameters for Convolutional Neural Network (CNN) training. All of the optimal hyperparameters for the CNN models were instantaneously selected and allocated using a novel proposed algorithm Adaptive Hyperparameter Tuning (AHT). Using AHT, enables CNN models to be highly autonomous to choose optimal hyperparameters for classifying medical images into various classifications. The CNN model (Deep-Hist) categorizes medical images into basic classes: malignant and benign, with an accuracy of 95.71%. The most dominant CNN models such as ResNet, DenseNet, and MobileNetV2 are all compared to the already proposed CNN model (Deep-Hist). Plausible classification results were obtained using large, publicly available clinical datasets such as BreakHis, BraTS, NIH-Xray and COVID-19 X-ray. Medical practitioners and clinicians can utilize the CNN model to corroborate their first malignant and benign classification assessment. The recommended Adaptive high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist's aid tool.
RESUMEN
During 2017–2019, we observed Trichothecium spp. causing fruit rot in the field and in storage. This study was conducted to examine morphological differences of the species from different hosts, reveal the species' potential host range, and evaluate the efficacy of five fungicides. Six strains of Trichothecium spp. isolated from nectarine, peach and walnut were selected. Although the colony morphology, mycelial growth rate and spore size differed among hosts, phylogenetic analysis based on the internal transcribed spacer and part of the 5ʹ end of the β‐tubulin gene showed that all tested strains belonged to Trichothecium roseum. For its host range, 23 kinds of fruit were examined using T. roseum strain YT‐1 as an inoculum;10 kinds of fruit, including pear, apple, mango, Chinese chestnut, pepino melon, fig and durian, were susceptible to T. roseum, with minimum inoculation concentrations ranging from 104 to 105 spores/ml. The fungicides that most effectively inhibited the six isolates were fluazinam and fludioxonil, with EC50 values of 0.07–0.1 and 0.01–0.04 μg/ml, respectively, followed by difenoconazole (0.81–2.96 μg/ml), boscalid (5.43–13.51 μg/ml) and azoxystrobin (9.18–27.25 μg/ml). Improvement of the shelf life of nectarines held in plastic trays was explored using allyl isothiocyanate (AITC) against T. roseum YT‐1. The application of 10 μl/L AITC significantly suppressed disease symptoms. The findings provide useful information for future disease emergency management in the field and for food preservation. [ FROM AUTHOR] Copyright of Plant Pathology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
RESUMEN
As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.