Automated imaging coupled with AI-powered analysis accelerates the assessment of plant resistance to Tetranychus urticae.

The two-spotted spider mite (TSSM), Tetranychus urticae, is among the most destructive piercing-sucking herbivores, infesting more than 1100 plant species, including numerous greenhouse and open-field crops of significant economic importance. Its prolific fecundity and short life cycle contribute to the development of resistance to pesticides. However, effective resistance loci in plants are still unknown. To advance research on plant-mite interactions and identify genes contributing to plant immunity against TSSM, efficient methods are required to screen large, genetically diverse populations. In this study, we propose an analytical pipeline utilizing high-resolution imaging of infested leaves and an artificial intelligence-based computer program, MITESPOTTER, for the precise analysis of plant susceptibility. Our system accurately identifies and quantifies eggs, feces and damaged areas on leaves without expert intervention. Evaluation of 14 TSSM-infested Arabidopsis thaliana ecotypes originating from diverse global locations revealed significant variations in symptom quantity and distribution across leaf surfaces. This analytical pipeline can be adapted to various pest and host species, facilitating diverse experiments with large specimen numbers, including screening mutagenized plant populations or phenotyping polymorphic plant populations for genetic association studies. We anticipate that such methods will expedite the identification of loci crucial for breeding TSSM-resistant plants.

Large extracellular vesicles do not mitigate the harmful effect of hyperglycemia on endothelial cell mobility.

Extracellular vesicles, especially the larger fraction (LEVs - large extracellular vesicles), are believed to be an important means of intercellular communication. Earlier studies on LEVs have shown their healing properties, especially in the vascular cells of diabetic patients. Uptake of LEVs by endothelial cells and internalization of their cargo have also been demonstrated. Endothelial cells change their properties under hyperglycemic conditions (HGC), which reduces their activity and is the cause of endothelial dysfunction. The aim of our study was to investigate how human umbilical vein endothelial cells (HUVECs) change their biological properties: shape, mobility, cell surface stiffness, as well as describe the activation of metabolic pathways after exposure to the harmful effects of HGC and the administration of LEVs released by endothelial cells. We obtained LEVs from HUVEC cultures in HGC and normoglycemia (NGC) using the filtration and ultracentrifugation methods. We assessed the size of LEVs and the presence of biomarkers such as phosphatidylserine, CD63, beta-actin and HSP70. We analyzed the LEVs uptake efficiency by HUVECs, HUVEC shape, actin cytoskeleton remodeling, surface stiffness and finally gene expression by mRNA analysis. Under HGC conditions, HUVECs were larger and had a stiffened surface and a strengthened actin cortex compared to cells under NGC condition. HGC also altered the activation of metabolic pathways, especially those related to intracellular transport, metabolism, and organization of cellular components. The most interesting observation in our study is that LEVs did not restore cell motility disturbed by HGC. Although, LEVs were not able to reverse this deleterious effect of HGC, they activated transcription of genes involved in protein synthesis and vesicle trafficking in HUVECs.

The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.

With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. In this work, we propose a new dataset which we call COVID-19 CT & Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. Moreover, it is supplemented with a carefully defined split. The aim of COVID-19 CT & Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models.

Deep learning approach to bacterial colony classification.

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.