The proteins of flotillin-1 and flotillin-2 were originally discovered in axon regeneration of goldfish retinal ganglion cells. They are generally used as marker proteins of lipid rafts and considered to be scaffolding proteins of lipid microdomains. Although they are ubiquitously expressed and well-conserved from fly to man, their exact functions remain controversial. In this review, we summarize the structure of flotillins and some functions of them, such as regulating axon regeneration, endocytosis, T cell activation, insulin signaling, membrane protein recruitment, roles in the progression of some diseases and so on.

Background/Aims: The gut-vascular barrier (GVB) has recently been depicted to dampen the bacterial invasion of the bloodstream. The intestinal mucosa is a tissue rich in small vessels including capillaries. In this study, the protective effect of berberine on GVB in small bowel mucosa was investigated. Methods: The rat cecal ligation and puncture (CLP) sepsis model was employed to evaluate the effect of berberine on serum endotoxin level and intestinal vascular permeability to Evans blue in vivo. The rat intestinal microvascular endothelial cells (RIMECs) treated by lipopolysaccharide (LPS) were used to assess the effect of berberine on endothelial permeability to FITC-labeled dextran, transendothelial electrical resistance (TEER), and tight junction (TJ) and adherens junction (AJ) expression in vitro. Results: After 24-hr CLP operation the serum endotoxin concentration and gut vascular permeability were significantly increased, while berberine markedly reduced endotoxin level and vascular leakage. In vitro, LPS not only dramatically increased endothelial permeability of RIMECs to FITC-dextran, but also decreased TEER and inhibited claudin-12, beta-catenin and VE-cadherin expression. These effects of LPS were antagonized by berberine. In addition, our in vivo and vitro studies also confirmed that the effect of berberine on GVB could be partially abolished by ICG001. Conclusion: Berberine exerted a protective effect on GVB function in sepsis, which was strictly related to the modulation of the Wnt/beta-catenin signaling pathway.

Premature senescence greatly affects the yield production and the grain quality in plants, although the molecular mechanisms are largely unknown. Here, we identified a novel rice premature senescence leaf 85 (psl85) mutant from ethyl methane sulfonate (EMS) mutagenesis of cultivar Zhongjian100 (the wild-type, WT). The psl85 mutant presented a distinct dwarfism and premature senescence leaf phenotype, starting from the seedling stage to the mature stage, with decreasing level of chlorophyll and degradation of chloroplast, declined photosynthetic capacity, increased content of malonaldehyde (MDA), upregulated expression of senescence-associated genes, and disrupted reactive oxygen species (ROS) scavenging system. Moreover, endogenous abscisic acid (ABA) level was significantly increased in psl85 at the late aging phase, and the detached leaves of psl85 showed more rapid chlorophyll deterioration than that of WT under ABA treatment, indicating that PSL85 was involved in ABA-induced leaf senescence. Genetic analysis revealed that the premature senescence leaf phenotype was controlled by a single recessive nuclear gene which was finally mapped in a 47 kb region on the short arm of chromosome 7, covering eight candidate open reading frames (ORFs). No similar genes controlling a premature senescence leaf phenotype have been identified in the region, and cloning and functional analysis of the gene is currently underway.

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Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%-8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.Remote Sens. 2020, 12, 987 2 of 21 various homogeneous or heterogeneous data sources, advanced machine learning algorithms, and a variety of sampling and classification strategies have been introduced into land cover mapping researches [1,[5][6][7].Supervised classification is the most commonly used method in land cover mapping studies, especially for generating a regional or even a global land cover product [8]. Samples are the foundation of this method. The quantity and quality of samples would have a significant impact on the accuracy of land cover products [9,10]. There are many methods to collect samples, such as field survey, crowdsourcing, and manual interpretation [11]. In general, field survey has been a simple and practical method, but it cannot be conducted in inaccessible regions [12]. More importantly, it is time-consuming and labor-intensive. Manual interpretation of high-resolution remote sensing images, relying on the interpretation experiences of the interpreters, has been an effective way to fill the blank of field surveys. However, the subjectivity of the interpreter might affect the objectivity of samples [10]. Crowdsourcing was a new technology that helps to collect samples [13,14]. Unfortunately, it is still in the experimental stage at present, with few available samples and a lack of objective evaluation for their reliability...