Functional outline of the epidemic process

The present study has shown that on the level of the parasitic system the epidemic process is a biological system, wherein the host population serves as the internal regulator, the mechanism of transmission serves as the external regulator and the parasite population, as the regulated object. The biological regulating mechanisms of the epidemic process have fundamental differences in the groups of infectious with various mechanisms of transmission, and the specific nature of the mechanism of transmission determines the peculiar features of the biological mechanism which governs the self-regulation of the epidemic process. In contrast, on a higher level of the organization of the epidemic process, i. e. on the level of the socio-ecological system, the epidemic process is a biosocial system, wherein the human society serves as the regulator, the parasitic system serves as the regulated object and the mechanism of transmission plays the role of the filter which determines the scope of social factors, most important in the regulation of the epidemic process in a given infection. The spontaneous regulation of the epidemic process is the freed forward channel from the regulator to the regulated object, and the controlled regulation is the feedback channel.     

Chewing lice from high‐altitude and migrating birds in Yunnan,China, with descriptions of two new species of Guimaraesiella

In total, 366 birds representing 55 species in 24 families and eight orders, were examined for chewing lice (Phthiraptera: Amblycera, Ischnocera) in two high‐altitude localities in Yunnan Province, China. In Ailaoshan, almost all of the birds examined were resident passeriforms, of which 36% were parasitized by chewing lice. In Jinshanyakou, most birds were on migration, and included both passerine and non‐passerine birds. Of the passerine birds caught in Jinshanyakou, only one bird (0.7%) was parasitized by chewing lice. The prevalence of Myrsidea and Brueelia‐complex lice on birds caught in Ailaoshan was higher than in previous reports. Of the chewing lice identifiable to species level, three represent new records for China: Actornithophilus hoplopteri (Mjöberg, 1910), Maculinirmus ljosalfar Gustafsson & Bush, 2017 and Quadraceps sinensis Timmermann, 1954. In total, 17 new host records are included, of which we describe two as new species in the Brueelia‐complex: Guimaraesiella (Cicchinella) ailaoshanensis sp. nov. ex Schoeniparus dubius dubius (Hume, 1874) and G. (C.) montisodalis sp. nov. ex Fulvetta manipurensis tonkinensis Delacour & Jabouille, 1930. This published work has been registered in ZooBank, http://zoobank.org/urn:lsid:zoobank.org:pub:9FC3D8EE‐2CED‐4DBE‐A1DB‐471B71260D27 .     

Inhibition of RNA synthesis in yeast protoplasts by a peptide factor from Tetrahymena cells

Protoplasts of Schizosaccharomyces pombe, grown on a rich nutrient medium, were treated with a peptide factor isolated from cultures of the protozoan Tetrahymena pyriformis. The peptide factor is known to inhibit RNA synthesis in Tetrahymena. It has now been shown that the peptide factor also inhibits RNA synthesis in yeast protoplasts without affecting protein synthesis.     

A cross-talk between epithelium and endothelium mediates human alveolar–capillary injury during SARS-CoV-2 infection

COVID-19, caused by SARS-CoV-2, is an acute and rapidly developing pandemic, which leads to a global health crisis. SARS-CoV-2 primarily attacks human alveoli and causes severe lung infection and damage. To better understand the molecular basis of this disease, we sought to characterize the responses of alveolar epithelium and its adjacent microvascular endothelium to viral infection under a co-culture system. SARS-CoV-2 infection caused massive virus replication and dramatic organelles remodeling in alveolar epithelial cells, alone. While, viral infection affected endothelial cells in an indirect manner, which was mediated by infected alveolar epithelium. Proteomics analysis and TEM examinations showed viral infection caused global proteomic modulations and marked ultrastructural changes in both epithelial cells and endothelial cells under the co-culture system. In particular, viral infection elicited global protein changes and structural reorganizations across many sub-cellular compartments in epithelial cells. Among the affected organelles, mitochondrion seems to be a primary target organelle. Besides, according to EM and proteomic results, we identified Daurisoline, a potent autophagy inhibitor, could inhibit virus replication effectively in host cells. Collectively, our study revealed an unrecognized cross-talk between epithelium and endothelium, which contributed to alveolar–capillary injury during SARS-CoV-2 infection. These new findings will expand our understanding of COVID-19 and may also be helpful for targeted drug development.Subject terms: Mechanisms of disease, Viral infection     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.