MicroMotility: State of the art, recent accomplishments and perspectives on the mathematical modeling of bio-motility at microscopic scales

Mathematical modeling and quantitative study of biological motility (in particular, of motility at microscopic scales) is producing new biophysical insight and is offering opportunities for new discoveries at the level of both fundamental science and technology. These range from the explanation of how complex behavior at the level of a single organism emerges from body architecture, to the understanding of collective phenomena in groups of organisms and tissues, and of how these forms of swarm intelligence can be controlled and harnessed in engineering applications, to the elucidation of processes of fundamental biological relevance at the cellular and sub-cellular level. In this paper, some of the most exciting new developments in the fields of locomotion of unicellular organisms, of soft adhesive locomotion across scales, of the study of pore translocation properties of knotted DNA, of the development of synthetic active solid sheets, of the mechanics of the unjamming transition in dense cell collectives, of the mechanics of cell sheet folding in volvocalean algae, and of the self-propulsion of topological defects in active matter are discussed. For each of these topics, we provide a brief state of the art, an example of recent achievements, and some directions for future research

Serological cross-sectional survey of equine infectious anemia in Saudi Arabia

The equine infectious anaemia virus (EIAV) is one of the most serious equine diseases worldwide. There is scarce information on the epizootiology of equine infectious anaemia (EIA) in Saudi Arabia. Given the importance of the equine industry in Saudi Arabia, this cross- -sectional study aims to provide information about the prevalence of EIAV based on serological surveillance of the equine population in the country. A total of 4728 sera samples were collected (4523 horses and 205 donkeys) between December 2017 and November 2019. All samples were tested using commercially available EIAV ELISA. All tested samples showed negative results for EIAV antibodies with a 95% confidence interval. The results provided evidence that Saudi Arabia’s equine populations (horses and donkeys) are currently free of EIAV. The results also suggest the need for continuous monitoring of EIAV and strict regulation when importing horses from other countries

Evolving deep forest with automatic feature extraction for image classification using genetic programming

© Springer Nature Switzerland AG 2020. Deep forest is an alternative to deep neural networks to use multiple layers of random forests without back-propagation for solving various problems. In this study, we propose a genetic programming-based approach to automatically and simultaneously evolving effective structures of deep forest connections and extracting informative features for image classification. First, in the new approach we define two types of modules: forest modules and feature extraction modules. Second, an encoding strategy is developed to integrate forest modules and feature extraction modules into a tree and the search strategy is introduced to search for the best solution. With these designs, the proposed approach can automatically extract image features and find forests with effective structures simultaneously for image classification. The parameters in the forest can be dynamically determined during the learning process of the new approach. The results show that the new approach can achieve better performance on the datasets having a small number of training instances and competitive performance on the datasets having a large number of training instances. The analysis of evolved solutions shows that the proposed approach uses a smaller number of random forests over the deep forest method