Functions of exogenous FGF signals in regulation of fibroblast to myofibroblast differentiation and extracellular matrix protein expression

Fibroblasts are widely distributed cells found in most tissues and upon tissue injury, they are able to differentiate into myofibroblasts, which express abundant extracellular matrix (ECM) proteins. Overexpression and unordered organization of ECM proteins cause tissue fibrosis in damaged tissue. Fibroblast growth factor (FGF) family proteins are well known to promote angiogenesis and tissue repair, but their activities in fibroblast differentiation and fibrosis have not been systematically reviewed. Here we summarize the effects of FGFs in fibroblast to myofibroblast differentiation and ECM protein expression and discuss the underlying potential regulatory mechanisms, to provide a basis for the clinical application of recombinant FGF protein drugs in treatment of tissue damage

Three Dimensional Nuclei Segmentation and Classification of Fluorescence Microscopy Images

Segmentation and classification of cell nuclei in fluorescence 3D microscopy image volumes are fundamental steps for image analysis. However, accurate cell nuclei segmentation and detection in microscopy image volumes are hampered by poor image quality, crowding of nuclei, and large variation in nuclei size and shape. In this paper, we present an unsupervised volume to volume translation approach adapted from the Recycle-GAN using modified Hausdorff distance loss for synthetically generating nuclei with better shapes. A 3D CNN with a regularization term is used for nuclei segmentation and classification followed by nuclei boundary refinement. Experimental results demonstrate that the proposed method can successfully segment nuclei and identify individual nuclei

Plant Wilting Estimation And Field-Based Plot Extraction

Plant phenotyping is the process of characterization and quantification of physical traitsof plants such as height, leaf area, biomass, wilting degree, or flowering time. Many plantsbecome limp or droop through heat, loss of water, or disease. This is also known as wilting.In this thesis, we propose multiple quantifiable wilting metrics that will be useful in studyingbacterial wilt and identifying resistance genes. In order to obtain the wilting metrics, we usemachine learning methods to identify the center of the stem. We also propose a fast groundtruthing method to speed up training data generation. We test our metrics on both tomatoplants and soybean plants with wilting caused by either bacteria or drought. We successfullydemonstrated that our metrics are effective at estimating wilting in plants.Field experiments often comprise thousands of plants. For many Unmanned Aerial Vehi-cles (UAVs) image-based plant phenotyping analyses, we need to examine smaller groups ofplants known as ”plots”. We propose a method to extract plots from images acquired fromUAVs. In addition, we proposed a system that will allow us to combine our plot extractionresults with field data such as plant ID, plant genotype, and experiment type provided bythe planters. We also developed a method to generate synthetic plant center location data.</p

Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery

The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-consuming and not feasible for real application. In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning. Results show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10\% of original training data

Surface display of PbrR on Escherichia coli and evaluation of the bioavailability of lead associated with engineered cells in mice

Abstract Human exposure to lead mainly occurs by ingestion of contaminated food, water and soil. Blocking lead uptake in the gastrointestinal tract is a novel prevention strategy. Whole-cell biosorbent for lead was constructed with PbrR genetically engineered on the cell surface of Escherichia coli (E. coli), a predominant strain among intestinal microflora, using lipoprotein (Lpp)-OmpA as the anchoring protein. In vitro, the PbrR displayed cells had an enhanced ability for immobilizing toxic lead(II) ions from the external media at both acidic and neutral pH, and exhibited a higher specific adsorption for lead compared to other physiological two valence metal ions. In vivo, the persistence of recombinant E. coli in the murine intestinal tract and the integrity of surface displayed PbrR were confirmed. In addition, oral administration of surface-engineered E. coli was safe in mice, in which the concentrations of physiological metal ions in blood were not affected. More importantly, lead associated with PbrR-displayed E. coli was demonstrated to be less bioavailable in the experimental mouse model with exposure to oral lead. This is reflected by significantly lower blood and femur lead concentrations in PbrR-displayed E. coli groups compared to the control. These results open up the possibility for the removal of toxic metal ions in vivo using engineered microorganisms as adsorbents

Image-based plant wilting estimation

Abstract Background Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress. Results We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress. Conclusion Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species

Super-enhancer-driven MLX mediates redox balance maintenance via SLC7A11 in osteosarcoma

Abstract Osteosarcoma (OS) is a common type of bone tumor for which there has been limited therapeutic progress over the past three decades. The prevalence of transcriptional addiction in cancer cells emphasizes the biological significance and clinical relevance of super-enhancers. In this study, we found that Max-like protein X (MLX), a member of the Myc-MLX network, is driven by super-enhancers. Upregulation of MLX predicts a poor prognosis in osteosarcoma. Knockdown of MLX impairs growth and metastasis of osteosarcoma in vivo and in vitro. Transcriptomic sequencing has revealed that MLX is involved in various metabolic pathways (e.g., lipid metabolism) and can induce metabolic reprogramming. Furthermore, knockdown of MLX results in disturbed transport and storage of ferrous iron, leading to an increase in the level of cellular ferrous iron and subsequent induction of ferroptosis. Mechanistically, MLX regulates the glutamate/cystine antiporter SLC7A11 to promote extracellular cysteine uptake required for the biosynthesis of the essential antioxidant GSH, thereby detoxifying reactive oxygen species (ROS) and maintaining the redox balance of osteosarcoma cells. Importantly, sulfasalazine, an FDA-approved anti-inflammatory drug, can inhibit SLC7A11, disrupt redox balance, and induce massive ferroptosis, leading to impaired tumor growth in vivo. Taken together, this study reveals a novel mechanism in which super-enhancer-driven MLX positively regulates SLC7A11 to meet the alleviated demand for cystine and maintain the redox balance, highlighting the feasibility and clinical promise of targeting SLC7A11 in osteosarcoma