Vitamin A Transport Mechanism of the Multitransmembrane Cell-Surface Receptor STRA6.

Vitamin A has biological functions as diverse as sensing light for vision, regulating stem cell differentiation, maintaining epithelial integrity, promoting immune competency, regulating learning and memory, and acting as a key developmental morphogen. Vitamin A derivatives have also been used in treating human diseases. If vitamin A is considered a drug that everyone needs to take to survive, evolution has come up with a natural drug delivery system that combines sustained release with precise and controlled delivery to the cells or tissues that depend on it. This "drug delivery system" is mediated by plasma retinol binding protein (RBP), the principle and specific vitamin A carrier protein in the blood, and STRA6, the cell-surface receptor for RBP that mediates cellular vitamin A uptake. The mechanism by which the RBP receptor absorbs vitamin A from the blood is distinct from other known cellular uptake mechanisms. This review summarizes recent progress in elucidating the fundamental molecular mechanism mediated by the RBP receptor and multiple newly discovered catalytic activities of this receptor, and compares this transport system with retinoid transport independent of RBP/STRA6. How to target this new type of transmembrane receptor using small molecules in treating diseases is also discussed

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically, annotation and labeling of the medical images is much more expensive and time-consuming than other applications and often involves manual labor from multiple domain experts. In this work, we propose a multi-stage, self-paced learning framework utilizing a convolutional neural network (CNN) to classify Computed Tomography (CT) image patches. The key contribution of this approach is that we augment the size of training samples by refining the unlabeled instances with a self-paced learning CNN. By implementing the framework on high performance computing servers including the NVIDIA DGX1 machine, we obtained the experimental result, showing that the self-pace boosted network consistently outperformed the original network even with very scarce manual labels. The performance gain indicates that applications with limited training samples such as medical image analysis can benefit from using the proposed framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017

Identification of PLXDC1 and PLXDC2 as the transmembrane receptors for the multifunctional factor PEDF.

Pigment Epithelium Derived Factor (PEDF) is a secreted factor that has broad biological activities. It was first identified as a neurotrophic factor and later as the most potent natural antiangiogenic factor, a stem cell niche factor, and an inhibitor of cancer cell growth. Numerous animal models demonstrated its therapeutic value in treating blinding diseases and diverse cancer types. A long-standing challenge is to reveal how PEDF acts on its target cells and the identities of the cell-surface receptors responsible for its activities. Here we report the identification of transmembrane proteins PLXDC1 and PLXDC2 as cell-surface receptors for PEDF. Using distinct cellular models, we demonstrate their cell type-specific receptor activities through loss of function and gain of function studies. Our experiments suggest that PEDF receptors form homooligomers under basal conditions, and PEDF dissociates the homooligomer to activate the receptors. Mutations in the intracellular domain can have profound effects on receptor activities

Loss of soil carbon and nitrogen indicates climate change-induced alterations in a temperate forest ecosystem

Climate warming is expected to influence terrestrial biogeochemical cycles by modifying the quality and quantity of plant litter input to soils. Although a growing number of studies recognize the importance of plant litter input in influencing the loss of soil organic matter (SOM) through a phenomenon called the priming effect (PE), the exact mechanisms behind PE are not well known. Importantly, most PE research is based on short term pot experiments in which fresh organic matter (FOM) input is represented by a single addition of compounds of unnaturally simple chemical composition. Furthermore, only a few studies exist in which the PE was explored in terms of organic C (SOC) and total N content in the soil. Here, we report results of a 3-year long litter manip-ulation study conducted under natural conditions in a broadleaved Korean pine forest in N-E China. We show that the extra supply (twice the normal input) of aboveground tree litter composing of conifer needles, leaves and small twigs was associated not only with slightly decreased SOC (by 5%) but especially that of soil total N (STN) (by 15%) content in the top soil (0-5 cm depth). In contrast, removal of litter resulted in an increased (ca. 15%) amount of both SOC and STN during the study when compared to control soils receiving natural litter input. Despite the enhanced leaf litter decomposition rate in the treatment receiving extra litter, the changes in SOC and STN were related neither to soil microbial biomass nor to community composition. The amount of N lost (40.0 g m- 2) in the soil due to litter addition was ca. three times the amount of N added (12.3 g m- 2) via the litter, while the amount of C lost (238 g m- 2) was about one third of that added (940 g m- 2), suggesting that soil N in our research site is more prone to the PE than soil C. As we did not manipulate belowground FOM input, our results suggest that input of aboveground litter rather than that by roots explained the PE in our study. Results of our long-term study conducted under natural conditions in undisturbed forest soils highlight the large potential of recalcitrant, aboveground litter to affect the PE, which should not go unnoticed when predicting the role of forest soils under conditions (such as climate warming) when these soils act as C sinks.Peer reviewe

Notas sobre toponímia portuguesa medieval

Usando como fonte o Reportório Toponímico de Portugal Continental (1967) e o programa de georreferenciação toponímica notvs (Silva 2002) como ferramenta de trabalho, propomo-nos traçar um esboço da paisagem linguística medieval, através da identificação das áreas de implantação toponímica das línguas faladas durante a Reconquista no futuro território português (nomeadamente o árabe e o romance andalusis1 e o galego-português) e da relação entre essa implantação e a geografia humana e ocupação do espaço.info:eu-repo/semantics/publishedVersio