Correlation of Cytokine Levels and Microglial Cell Infiltration during Retinal Degeneration in RCS Rats

Microglial cells, which are immunocompetent cells, are involved in all diseases of the central nervous system. During their activation in various diseases, a variety of soluble factors are released. In the present study, the correlation between cytokine levels and microglial cell migration in the course of retinal degeneration of Royal College of Surgeons (RCS) rats was evaluated. MFG-E8 and CD11b were used to confirm the microglial cells. In the retina of RCS rats, the mRNA expression of seven genes (MFG-E8 and its integrins αυ and ß5, CD11b and the cytokines TNF-α, IL-1ß, and MCP-1) formed almost similar bimodal peak distributions, which were centred at P7 and P45 to P60. In contrast, in rdy rats, which comprised the control group, a unimodal peak distribution centred at P14 was observed. The gene expression accompanied the activation and migration of microglial cells from the inner to the outer layer of the retina during the process of degeneration. Principal component analysis and discriminant function analysis revealed that the expression of these seven genes, especially TNF-α and CD11b, positively correlated with retinal degeneration and microglial activity during retinal degeneration in RCS rats, but not in the control rats. Furthermore, linear regression analysis demonstrated a significant correlation between the expression of these genes and the activation of microglial cells in the dystrophic retina. Our findings suggest that the suppression of microglial cells and the blockade of their cytotoxic effects may constitute a novel therapeutic strategy for treating photoreceptor death in various retinal disorders

Essential Role of MFG-E8 for Phagocytic Properties of Microglial Cells

Milk fat globule factor-E8 (MFG-E8) has been regarded as a key factor involved in the phagocytosis of apoptotic cells. We induced a lentivirus into the microglial cells for the augmentation or abrogation of MFG-E8 expression in mouse microglial cells, and investigated phagocytosis of phosphatidylserine tagged human red blood cells (hRBCs) in co-cultures. Increased MFG-E8 levels were associated with a significant increase in phagocytic activity compared to the controls. Conversely, phagocytosis dramitically decreased due to the abrogation of MFG-E8. In addition, the expression of the inflammatory cytokines, TNF-α and IL-1β, also increased or decreased in the microglial cells with the augmentation or abrogation of MFG-E8, respectively. Our findings indicate that the enhanced expression of MFG-E8 could increase phagocytosis of apoptotic cells; conversely, the rate of phagocytosis and the expression of inflammatory cytokines decreased when MFG-E8 expression was knocked down. Our results confirm that MFG-E8 plays an important role in phagocytosis, and possibly serves as an essential signal molecule for microglial cells

Introduction. Translating and Interpreting between Chinese and Spanish in the Contemporary World: Current Perspectives and Horizons for the Future

Translating and Interpreting between Chinese and Spanish in the Contemporary World: Current Perspectives and Horizons for the Future/nLa traducción e interpretación entre el chino y el español en la época contemporánea: perspectivas actuales y horizontes de futur

DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience

Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests

Identification and experimental validation of key m6A modification regulators as potential biomarkers of osteoporosis

Osteoporosis (OP) is a severe systemic bone metabolic disease that occurs worldwide. During the coronavirus pandemic, prioritization of urgent services and delay of elective care attenuated routine screening and monitoring of OP patients. There is an urgent need for novel and effective screening diagnostic biomarkers that require minimal technical and time investments. Several studies have indicated that N6-methyladenosine (m6A) regulators play essential roles in metabolic diseases, including OP. The aim of this study was to identify key m6A regulators as biomarkers of OP through gene expression data analysis and experimental verification. GSE56815 dataset was served as the training dataset for 40 women with high bone mineral density (BMD) and 40 women with low BMD. The expression levels of 14 major m6A regulators were analyzed to screen for differentially expressed m6A regulators in the two groups. The impact of m6A modification on bone metabolism microenvironment characteristics was explored, including osteoblast-related and osteoclast-related gene sets. Most m6A regulators and bone metabolism-related gene sets were dysregulated in the low-BMD samples, and their relationship was also tightly linked. In addition, consensus cluster analysis was performed, and two distinct m6A modification patterns were identified in the low-BMD samples. Subsequently, by univariate and multivariate logistic regression analyses, we identified four key m6A regulators, namely, METTL16, CBLL1, FTO, and YTHDF2. We built a diagnostic model based on the four m6A regulators. CBLL1 and YTHDF2 were protective factors, whereas METTL16 and FTO were risk factors, and the ROC curve and test dataset validated that this model had moderate accuracy in distinguishing high- and low-BMD samples. Furthermore, a regulatory network was constructed of the four hub m6A regulators and 26 m6A target bone metabolism-related genes, which enhanced our understanding of the regulatory mechanisms of m6A modification in OP. Finally, the expression of the four key m6A regulators was validated in vivo and in vitro, which is consistent with the bioinformatic analysis results. Our findings identified four key m6A regulators that are essential for bone metabolism and have specific diagnostic value in OP. These modules could be used as biomarkers of OP in the future

SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation

Self-supervised pre-training bears potential to generate expressive representations without human annotation. Most pre-training in Earth observation (EO) are based on ImageNet or medium-size, labeled remote sensing (RS) datasets. We share an unlabeled RS dataset SSL4EO-S12 (Self-Supervised Learning for Earth Observation - Sentinel-1/2) to assemble a large-scale, global, multimodal, and multi-seasonal corpus of satellite imagery from the ESA Sentinel-1 \& -2 satellite missions. For EO applications we demonstrate SSL4EO-S12 to succeed in self-supervised pre-training for a set of methods: MoCo-v2, DINO, MAE, and data2vec. Resulting models yield downstream performance close to, or surpassing accuracy measures of supervised learning. In addition, pre-training on SSL4EO-S12 excels compared to existing datasets. We make openly available the dataset, related source code, and pre-trained models at https://github.com/zhu-xlab/SSL4EO-S12.Comment: Accepted by IEEE Geoscience and Remote Sensing Magazine. 18 page

FOXO1 Expression in Keratinocytes Promotes Connective Tissue Healing

Wound healing is complex and highly orchestrated. It is well appreciated that leukocytes, particularly macrophages, are essential for inducing the formation of new connective tissue, which requires the generation of signals that stimulate mesenchymal stem cells (MSC), myofibroblasts and fibroblasts. A key role for keratinocytes in this complex process has yet to be established. To this end, we investigated possible involvement of keratinocytes in connective tissue healing. By lineage-specific deletion of the forkhead box-O 1 (FOXO1) transcription factor, we demonstrate for the first time that keratinocytes regulate proliferation of fibroblasts and MSCs, formation of myofibroblasts and production of collagen matrix in wound healing. This stimulation is mediated by a FOXO1 induced TGFβ1/CTGF axis. The results provide direct evidence that epithelial cells play a key role in stimulating connective tissue healing through a FOXO1-dependent mechanism. Thus, FOXO1 and keratinocytes may be an important therapeutic target where healing is deficient or compromised by a fibrotic outcome

Peaks Fusion assisted Early-stopping Strategy for Overhead Imagery Segmentation with Noisy Labels

Automatic label generation systems, which are capable to generate huge amounts of labels with limited human efforts, enjoy lots of potential in the deep learning era. These easy-to-come-by labels inevitably bear label noises due to a lack of human supervision and can bias model training to some inferior solutions. However, models can still learn some plausible features, before they start to overfit on noisy patterns. Inspired by this phenomenon, we propose a new Peaks fusion assisted EArly-Stopping (PEAS) approach for imagery segmentation with noisy labels, which is mainly composed of two parts. First, a fitting based early-stopping criterion is used to detect the turning phase from which models are about to mimic noise details. After that, a peaks fusion strategy is applied to select reliable models in the detection zone to generate final fusion results. Here, validation accuracies are utilized as indicators in model selection. The proposed method was evaluated on New York City dataset whose labels were automatically collected by a rule-based label generation system, thus noisy to some extent due to a lack of human supervision. The experimental results showed that the proposed PEAS method can achieve both promising statistical and visual results when trained with noisy labels