Laboratory rearing systems are useful models for studying Rhinotermitid behavior. Information on the biology of fungus-growing termites, however, is limited because of the difficulty of rearing colonies in the laboratory settings. The physical structure of termite nests makes it impossible to photograph or to observe colonies in the field. In this study, an artificial rearing system for field-collected colonies of the fungus-growing termite Odontotermes formosanus (Shiraki) was developed to facilitate observation in the laboratory. We recorded colony activity within the artificial rearing system and documented a variety of social behaviors that occurred throughout the food processing of the colony. This complex miniature ecosystem was cooperatively organized via division of labor in the foraging and processing of plant materials, and the observed patterns largely resembled the caste and age-based principles present in Macrotermes colonies. This work extends our insights into polyethism in the subfamily Macrotermitinae.
There are no studies assessing the epidemiology and burden of decubitus ulcers at global, regional, and national levels. We aim to report this issue from 1990 to 2019 by extracting data from the Global Burden of Disease Study (GBD) 2019 and stratifying it by age, gender, and socio-demographic index (SDI). Globally, the number of prevalent cases of decubitus ulcers in 2019 is 0.85 (95% UI 0.78 to 0.94) million. The age-standardized rates of prevalence, incidence, and years lived with disability (YLDs) in 2019 are 11.3 (95% UI 10.2 to 12.5), 41.8 (37.8 to 46.2), and 1.7 (1.2 to 2.2) per 100,000 population, and compared with 1990, it has decreased by 10.6% (95% UI 8.7% to 12.3%), 10.2% (8.2 to 11.9%), and 10.4% (8.1 to 12.5%), respectively. In addition, the global prevalence rate of decubitus ulcers increases with age, peaking at the > 95 age group among men and women. At the regional and national levels, we observe a positive correlation between age-standardized YLDs and SDI. Malaysia, Saudi Arabia, and Thailand experienced the most significant increases in age-standardized prevalence rates at the national level. Finally, we concluded that the age-standardized prevalence, incidence, and YLDs rates of decubitus ulcer declined from 1990 to 2019, with significant regional differences. In order to monitor the dynamic changes of decubitus ulcers burden, it is recommended to improve the quality of decubitus ulcer health data in all regions and countries.
The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources.