Parkinson’s disease (PD) is a common neurodegenerative disorder that primarily affects the elderly. While the etiology of PD is likely multifactorial with the involvement of genetic, environmental, aging and other factors, α-synuclein (α-syn) pathology is a pivotal mechanism underlying the development of PD. In recent years, astrocytes have attracted considerable attention in the field. Although astrocytes perform a variety of physiological functions in the brain, they are pivotal mediators of α-syn toxicity since they internalize α-syn released from damaged neurons, and this triggers an inflammatory response, protein degradation dysfunction, mitochondrial dysfunction and endoplasmic reticulum stress. Astrocytes are indispensable coordinators in the background of several genetic mutations, including PARK7, GBA1, LRRK2, ATP13A2, PINK1, PRKN and PLA2G6. As the most abundant glial cells in the brain, functional astrocytes can be replenished and even converted to functional neurons. In this review, we discuss astrocyte dysfunction in PD with an emphasis on α-syn toxicity and genetic modulation and conclude that astrocyte replenishment is a valuable therapeutic approach in PD.

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In this paper, the tuning characteristics of locally bent microfiber taper covered with a nanosized high-refractive-index liquid crystal (LC) layer under different temperatures and electric field intensities have been theoretically analyzed and experimentally investigated. A locally bent microfiber taper interferometer with a waist diameter of ∼3.72  μm is fabricated by using the flame brushing technique, followed by bending the transition region of the taper to form a modal interferometer and later by placing a ∼200  nm LC layer over the uniform taper waist region. Experimental results indicate that a high-efficiency thermal or electric tuning of an LC-coated locally bent microfiber taper interferometer could be achieved. This suggests a potential application of this device as tunable all-fiber photonic devices, such as filters, modulators, and sensing elements.

Background: Immunotherapy is a treatment that can significantly improve the prognosis of patients with colon cancer, but the response to immunotherapy is different in patients with colon cancer because of the heterogeneity of colon carcinoma and the complex nature of the tumor microenvironment (TME). In the precision therapy mode, finding predictive biomarkers that can accurately identify immunotherapy-sensitive types of colon cancer is essential. Hypoxia plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism, and chemotherapy and immunotherapy resistance. Thus, understanding the mechanism of hypoxia-related genes (HRGs) in colon cancer progression and constructing hypoxia-related signatures will help enrich our treatment strategies and improve patient prognosis.Methods: We obtained the gene expression data and corresponding clinical information of 1,025 colon carcinoma patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct hypoxia subtypes (subtype A and subtype B) according to unsupervised clustering analysis and assessed the clinical parameters, prognosis, and TME cell-infiltrating characteristics of patients in the two subtypes. We identified 1,132 differentially expressed genes (DEGs) between the two hypoxia subtypes, and all patients were randomly divided into the training group (n = 513) and testing groups (n = 512). Following univariate Cox regression with DEGs, we construct the prognostic model (HRG-score) including six genes (S1PR3, ETV5, CD36, FOXC1, CXCL10, and MMP12) through the LASSO–multivariate cox method in the training group. We comprehensively evaluated the sensitivity and applicability of the HRG-score model from the training group and the testing group, respectively. We explored the correlation between HRG-score and clinical parameters, tumor microenvironment, cancer stem cells (CSCs), and MMR status. In order to evaluate the value of the risk model in clinical application, we further analyzed the sensitivity of chemotherapeutics and immunotherapy between the low-risk group and high-risk group and constructed a nomogram for improving the clinical application of the HRG-score.Result: Subtype A was significantly enriched in metabolism-related pathways, and subtype B was significantly enriched in immune activation and several tumor-associated pathways. The level of immune cell infiltration and immune checkpoint-related genes, stromal score, estimate score, and immune dysfunction and exclusion (TIDE) prediction score was significantly different in subtype A and subtype B. The level of immune checkpoint-related genes and TIDE score was significantly lower in subtype A than that in subtype B, indicating that subtype A might benefit from immune checkpoint inhibitors. Finally, an HRG-score signature for predicting prognosis was constructed through the training group, and the predictive capability was validated through the testing group. The survival analysis and correlation analysis of clinical parameters revealed that the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. There were also significant differences in immune status, mismatch repair status (MMR), and cancer stem cell index (CSC), between the two risk groups. The correlation analysis of risk scores with IC50 and IPS showed that patients in the low-risk group had a higher benefit from chemotherapy and immunotherapy than those in the high-risk group, and the external validation IMvigor210 demonstrated that patients with low risk were more sensitive to immunotherapy.Conclusion: We identified two novel molecular subgroups based on HRGs and constructed an HRG-score model consisting of six genes, which can help us to better understand the mechanisms of hypoxia-related genes in the progression of colon cancer and identify patients susceptible to chemotherapy or immunotherapy, so as to achieve precision therapy for colon cancer.

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The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination ( R 2 ) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm ( R 2 = 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an R 2 larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation.

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo“ approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model . The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.