Pigment Epithelium-Derived Factor – An Angiostatic Factor with a Broader Function in Melanoma

Capítulo 8: Open Access.Supported by grants Ministerio de Educacion y Ciencia SAF2007-62292 and SAF2010-19256 to BJ. AFB has been supported by a CSIC-JAE fellowship and JLO by a SAF2007-62292 contract.Peer Reviewe

Bernoulli theorem, minimum specific energy and water wave celerity in open channel flow

One basic principle of fluid mechanics used to resolve practical problems in hydraulic engineering is the Bernoulli theorem along a streamline, deduced from the work-energy form of the Euler equation along a streamline. Some confusion exists about the applicability of the Bernoulli theorem and its generalization to open-channel hydraulics. In the present work, a detailed analysis of the Bernoulli theorem and its extension to flow in open channels are developed. The generalized depth-averaged Bernoulli theorem is proposed and it has been proved that the depth-averaged specific energy reaches a minimum in converging accelerating free surface flow over weirs and flumes. Further, in general, a channel control with minimum specific energy in curvilinear flow is not isolated from water waves, as customary state in open-channel hydraulics

Vitamin D receptor ChIP-seq in primary CD4+ cells: relationship to serum 25-hydroxyvitamin D levels and autoimmune disease

PMCID: PMC3710212This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Hypoxia Negatively Regulates Antimetastatic PEDF in Melanoma Cells by a Hypoxia Inducible Factor-Independent, Autophagy Dependent Mechanism

Pigment epithelium-derived factor (PEDF), a member of the serine protease inhibitor (SERPIN) superfamily, displays a potent antiangiogenic and antimetastatic activity in a broad range of tumor types. Melanocytes and low aggressive melanoma cells secrete high levels of PEDF, while its expression is lost in highly aggressive melanomas. PEDF efficiently abrogates a number of functional properties critical for the acquisition of metastatic ability by melanoma cells, such as neovascularization, proliferation, migration, invasiveness and extravasation. In this study, we identify hypoxia as a relevant negative regulator of PEDF in melanocytes and low aggressive melanoma cells. PEDF was regulated at the protein level. Importantly, although downregulation of PEDF was induced by inhibition of 2-oxoglutarate-dependent dioxygenases, it was independent of the hypoxia inducible factor (HIF), a key mediator of the adaptation to hypoxia. Decreased PEDF protein was not mediated by inhibition of translation through untranslated regions (UTRs) in melanoma cells. Degradation by metalloproteinases, implicated on PEDF degradation in retinal pigment epithelial cells, or by the proteasome, was also excluded as regulatory mechanism in melanoma cells. Instead, we found that degradation by autophagy was critical for PEDF downregulation under hypoxia in human melanoma cells. Our findings show that hypoxic conditions encountered during primary melanoma growth downregulate antiangiogenic and antimetastasic PEDF by a posttranslational mechanism involving degradation by autophagy and could therefore contribute to the acquisition of highly metastatic potential characteristic of aggressive melanoma cells

A Bayesian assessment of an approximate model for unconfined water flow in sloping layered porous media

The prediction of water table height in unconfined layered porous media is a difficult modelling problem that typically requires numerical simulation. This paper proposes an analytical model to approximate the exact solution based on a steady-state Dupuit–Forchheimer analysis. The key contribution in relation to a similar model in the literature relies in the ability of the proposed model to consider more than two layers with different thicknesses and slopes, so that the existing model becomes a special case of the proposed model herein. In addition, a model assessment methodology based on the Bayesian inverse problem is proposed to efficiently identify the values of the physical parameters for which the proposed model is accurate when compared against a reference model given by MODFLOW-NWT, the open-source finite-difference code by the U.S. Geological Survey. Based on numerical results for a representative case study, the ratio of vertical recharge rate to hydraulic conductivity emerges as a key parameter in terms of model accuracy so that, when appropriately bounded, both the proposed model and MODFLOW-NWT provide almost identical results

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors

A regulated deficit irrigation strategy for hedgerow olive orchards with high plant density

Background & Aims There is not a consensus on the best irrigation approach for super-high density (SHD) olive orchards. Our aim was to design and test a regulated deficit irrigation (RDI) strategy for a sustainable balance between water saving, tree vigour and oil production. Methods We tested our RDI strategy for 3 years in an ‘Arbequina’ orchard with 1,667 trees ha−1. Two levels of irrigation reduction were applied, 60RDI and 30RDI, scaled to replacing 60 % and 30 %, respectively, of the of irrigation needs (IN). We also had a full irrigation (FI) treatment as control, with IN totalling 4,701 m3 ha−1 Results The 30RDI treatment showed the best balance between water saving, tree vigour and oil production. With a yearly irrigation amount (IA) of 1,366 m3 ha−1, which meant 72 % water saving as compared to FI, the reduction in oil yield was 26 % only. Conclusions Our results, together with recent knowledge on the effect of water stress on fruit development, allowed us to suggest a potentially improved RDI strategy for which a total IA of ca. 2,100 m3 ha−1 was calculated. Both some management details and the benefits of this suggested RDI strategy are still to be tested

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data