Aldose reductase regulates microglia/macrophages polarization through the cAMP response element-binding protein after spinal cord injury in mice.

Inflammatory reactions are the most critical pathological processes occurring after spinal cord injury (SCI). Activated microglia/macrophages have either detrimental or beneficial effects on neural regeneration based on their functional polarized M1/M2 subsets. However, the mechanism of microglia/macrophage polarization to M1/M2 at the injured spinal cord environment remains unknown. In this study, wild-type (WT) or aldose reductase (AR)-knockout (KO) mice were subjected to SCI by a spinal crush injury model. The expression pattern of AR, behavior tests for locomotor activity, and lesion size were assessed at between 4 h and 28 days after SCI. We found that the expression of AR is upregulated in microglia/macrophages after SCI in WT mice. In AR KO mice, SCI led to smaller injury lesion areas compared to WT. AR deficiency-induced microglia/macrophages induce the M2 rather than the M1 response and promote locomotion recovery after SCI in mice. In the in vitro experiments, microglia cell lines (N9 or BV2) were treated with the AR inhibitor (ARI) fidarestat. AR inhibition caused 4-hydroxynonenal (HNE) accumulation, which induced the phosphorylation of the cAMP response element-binding protein (CREB) to promote Arg1 expression. KG501, the specific inhibitor of phosphorylated CREB, could cancel the upregulation of Arg1 by ARI or HNE stimulation. Our results suggest that AR works as a switch which can regulate microglia by polarizing cells to either the M1 or the M2 phenotype under M1 stimulation based on its states of activity. We suggest that inhibiting AR may be a promising therapeutic method for SCI in the future

Snx3 Regulates Recycling of the Transferrin Receptor and Iron Assimilation

Sorting of endocytic ligands and receptors is critical for diverse cellular processes. The physiological significance of endosomal sorting proteins in vertebrates, however, remains largely unknown. Here we report that sorting nexin 3 (Snx3) facilitates the recycling of transferrin receptor (Tfrc) and thus is required for the proper delivery of iron to erythroid progenitors. Snx3 is highly expressed in vertebrate hematopoietic tissues. Silencing of Snx3 results in anemia and hemoglobin defects in vertebrates due to impaired transferrin (Tf)-mediated iron uptake and its accumulation in early endosomes. This impaired iron assimilation can be complemented with non-Tf iron chelates. We show that Snx3 and Vps35, a component of the retromer, interact with Tfrc to sort it to the recycling endosomes. Our findings uncover a role of Snx3 in regulating Tfrc recycling, iron homeostasis, and erythropoiesis. Thus, the identification of Snx3 provides a genetic tool for exploring erythropoiesis and disorders of iron metabolism.National Institutes of Health (U.S.) (P01 HL032262

A Deep Learning-Based Satellite Target Recognition Method Using Radar Data

A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition

Integrating satellite-based passive microwave and optically sensed observations to evaluating the spatio-temporal dynamics of vegetation health in the red soil regions of southern China

Attentions over the health of evergreen vegetation are increasing owing to frequently occurrence of recent disturbance events (i.e. soil erosion, logging activities, and afforestation). However, vegetation indices that characterize canopy greenness have limitations in spectral saturation for representing the growth states of densely vegetated areas, and the continuous acquisition of satellite-derived vegetation functional metrics depends on the availability of clear image observations. This study investigated the vegetation health dynamics (1993–2012) in the red soil regions of southern China using satellite observations based task-oriented metrics, including the Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Aboveground Biomass Carbon (ABC). The results indicated that the total number of pixels with significant changes (SC) was 214, 1,186, and 794 for the NDVI, VWC, and ABC indices, respectively. More than 90% of the SC pixels in the three metrics exhibited increasing trends, which were primarily observed in mountainous areas. Pixels that exhibited a continuously declining trend were discretely distributed throughout the entire study area. Among the SC pixels, vegetation in major parts of the study area was disturbed by abrupt events. In the NDVI, VWC, and ABC, the frequency of abrupt changes increased after 2000, coinciding with the launch of the Natural Forest Conservation Program (NFCP) in 2000–2001. For regions with abrupt changes, four patterns were further observed based on the indices: the continued increases (pattern-1), sustained decreases (pattern-2), recovery growth after an initial decline (pattern-3), and significant decreases after initial growth (pattern-4). Pattern-1 appeared more frequently than the other three patterns. This study indicates that vegetation in most areas was optimally developed and exhibited a healthier tendency compared with previous growth states. Notably, the presence of an increasingly unhealthy vegetation state was observed in the northeastern region of the study area. Satellite derived datasets and synergetic use of indicators contribute to understanding the changes in the vegetation health in the entire red soil regions in southern China. Thus, this study acts as a reference for forest management and soil erosion control

Object-based change detection for vegetation disturbance and recovery using Landsat time series

Accurate characterization of historical trends in vegetation change at the landscape scale is necessary for resource management and ecological assessment. Vegetation disturbance and recovery are coherent spatial and temporal processes. Pixel-based change detection methods often struggle to provide reliable estimates of change events because they neglect spatial contextual information and are affected by salt-and-pepper noise. To address such problems, we propose a new approach, “object-based change detection of trends in disturbance and recovery” (Object-LT), which introduces object-based image analysis (OBIA) into the current framework of LandTrendr algorithm. We then applied this approach to detect vegetation changes during 2000–2020 in the ecologically fragile region of Guyuan, Ning Xia, China. Accuracy assessment indicated that Object-LT could accurately identify disturbance and recovery trends in vegetation with overall accuracies of 90.05% and 87.50%, respectively. Compare with pixel-based LandTrendr algorithm, Object-LT significantly improved user’s accuracy and removed salt-and-pepper noise. Spatial–temporal maps of vegetation change showed that the recovery area was 571.27 km2 while the disturbed area was 297.65 km2, accounting for 5.44% and 2.83% of the study area, respectively. This indicates a general vegetation recovery trend in the study area. Object-LT allowed for an accurate and comprehensive characterization of vegetation change over large areas, which contributes to a better understanding of change processes of vegetation landscape over time

Both Complexity and Location of DNA Damage Contribute to Cellular Senescence Induced by Ionizing Radiation

<div><p>Persistent DNA damage is considered as a main cause of cellular senescence induced by ionizing radiation. However, the molecular bases of the DNA damage and their contribution to cellular senescence are not completely clear. In this study, we found that both heavy ions and X-rays induced senescence in human uveal melanoma 92–1 cells. By measuring senescence associated-β-galactosidase and cell proliferation, we identified that heavy ions were more effective at inducing senescence than X-rays. We observed less efficient repair when DNA damage was induced by heavy ions compared with X-rays and most of the irreparable damage was complex of single strand breaks and double strand breaks, while DNA damage induced by X-rays was mostly repaired in 24 hours and the remained damage was preferentially associated with telomeric DNA. Our results suggest that DNA damage induced by heavy ion is often complex and difficult to repair, thus presents as persistent DNA damage and pushes the cell into senescence. In contrast, persistent DNA damage induced by X-rays is preferentially associated with telomeric DNA and the telomere-favored persistent DNA damage contributes to X-rays induced cellular senescence. These findings provide new insight into the understanding of high relative biological effectiveness of heavy ions relevant to cancer therapy and space radiation research.</p></div

High dose of ionizing radiation induces persistent DDR activation in 92–1 cells.

<p>(A, B) Micrographs of DDR foci in 92–1 cells following exposure to 10 Gy of X-rays. Persistent DDR activations are detectable even on the 5th day post-irradiation in the form of pATM foci (A) and 53BP1 foci (B). Scale bar, 10 μm. (C) The fraction of 53BP1foci positive cells (± s.e.m.) and (D) the average number of 53BP1 foci per cell (± s.e.m.) at the indicated time points after irradiation. For the quantification analysis, 100 cells per time point were analyzed (*p<0.05). (E) Micrographs of apoptosis in irradiated 92–1 cells measured by Hochest33342/PI staining; Scale bar, 20mm. (F) Intracellular ROS levels measured by fluorescence microscopy after staining with the fluorescent probe DCF. Scar bar: 20μm.</p