High-titer biosurfactant production in aerated fermenters using hydrophilic substrates is often hampered by excessive foaming. Ethanol has been shown to efficiently destabilize foam of rhamnolipids, a popular group of biosurfactants. To exploit this feature, we used ethanol as carbon source and defoamer, without introducing novel challenges for rhamnolipid purification. In detail, we engineered the non-pathogenic Pseudomonas putida KT2440 for heterologous rhamnolipid production from ethanol. To obtain a strain with high growth rate on ethanol as sole carbon source at elevated ethanol concentrations, adaptive laboratory evolution (ALE) was performed. Genome re-sequencing allowed to allocate the phenotypic changes to emerged mutations. Several genes were affected and differentially expressed including alcohol and aldehyde dehydrogenases, potentially contributing to the increased growth rate on ethanol of 0.51 h-1 after ALE. Further, mutations in genes were found, which possibly led to increased ethanol tolerance. The engineered rhamnolipid producer was used in a fed-batch fermentation with automated ethanol addition over 23 h, which resulted in a 3-(3-hydroxyalkanoyloxy)alkanoates and mono-rhamnolipids concentration of about 5 g L-1. The ethanol concomitantly served as carbon source and defoamer with the advantage of increased rhamnolipid and biomass production. In summary, we present a unique combination of strain and process engineering that facilitated the development of a stable fed-batch fermentation for rhamnolipid production, circumventing mechanical or chemical foam disruption.
  Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19.
 
  Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutiing CT imaging, offering promise for guiding clinical treatment.Circulating tumor cells (CTCs) derived from primary tumors and/or metastatic tumors are markers for tumor prognosis, and can also be used to monitor therapeutic efficacy and tumor recurrence. Circulating tumor cells enrichment and screening can be automated, but the final counting of CTCs currently requires manual intervention. This not only requires the participation of experienced pathologists, but also easily causes artificial misjudgment. Medical image recognition based on machine learning can effectively reduce the workload and improve the level of automation. So, we use machine learning to identify CTCs. First, we collected the CTC test results of 600 patients. After immunofluorescence staining, each picture presented a positive CTC cell nucleus and several negative controls. The images of CTCs were then segmented by image denoising, image filtering, edge detection, image expansion and contraction techniques using python's openCV scheme. Subsequently, traditional image recognition methods and machine learning were used to identify CTCs. Machine learning algorithms are implemented using convolutional neural network deep learning networks for training. We took 2300 cells from 600 patients for training and testing. About 1300 cells were used for training and the others were used for testing. The sensitivity and specificity of recognition reached 90.3 and 91.3%, respectively. We will further revise our models, hoping to achieve a higher sensitivity and specificity.Plants recruit specific microorganisms to live inside and outside their roots that provide essential functions for plant growth and health. The study of the microbial communities living in close association with plants helps in understanding the mechanisms involved in these beneficial interactions. Currently, most of the research in this field has been focusing on the description of the taxonomic composition of the microbiome. Therefore, a focus on the plant-associated microbiome functions is pivotal for the development of novel agricultural practices which, in turn, will increase plant fitness. Recent advances in microbiome research using model plant species started to shed light on the functions of specific microorganisms and the underlying mechanisms of plant-microbial interaction. Here, we review (1) microbiome-mediated functions associated with plant growth and protection, (2) insights from native and agricultural habitats that can be used to improve soil health and crop productivity, (3) current -omics and new approaches for studying the plant microbiome, and (4) challenges and future perspectives for exploiting the plant microbiome for beneficial outcomes. We posit that integrated approaches will help in translating fundamental knowledge into agricultural practices.Studying effects of milk components on bone may have a clinical impact as milk is highly associated with bone maintenance, and clinical studies provided controversial associations with dairy consumption. We aimed to evaluate the impact of milk extracellular vesicles (mEVs) on the dynamics of bone loss in mice. MEVs are nanoparticles containing proteins, mRNA and microRNA, and were supplemented into the drinking water of mice, either receiving diet-induced obesity or ovariectomy (OVX). Mice receiving mEVs were protected from the bone loss caused by diet-induced obesity. In a more severe model of bone loss, OVX, higher osteoclast numbers in the femur were found, which were lowered by mEV treatment. Additionally, the osteoclastogenic potential of bone marrow-derived precursor cells was lowered in mEV-treated mice. The reduced stiffness in the femur of OVX mice was consequently reversed by mEV treatment, accompanied by improvement in the bone microarchitecture.  https://www.selleckchem.com/products/piperacillin.html  In general, the RANKL/OPG ratio increased systemically and locally in both models and was rescued by mEV treatment.