The HelioMont method for assessing solar irradiance over complex terrain: Validation and improvements

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Comparative Risk Assessment to Inform Adaptation Priorities for the Natural Environment: Observations from the First UK Climate Change Risk Assessment

Risk assessment can potentially provide an objective framework to synthesise and prioritise climate change risks to inform adaptation policy. However, there are significant challenges in the application of comparative risk assessment procedures to climate change, particularly for the natural environment. These challenges are evaluated with particular reference to the first statutory Climate Change Risk Assessment (CCRA) and evidence review procedures used to guide policy for the UK government. More progress was achieved on risk identification, screening and prioritisation compared to risk quantification. This was due to the inherent complexity and interdependence of ecological risks and their interaction with socio-economic drivers as well as a climate change. Robust strategies to manage risk were identified as those that coordinate organisational resources to enhance ecosystem resilience, and to accommodate inevitable change, rather than to meet specific species or habitats targets. The assessment also highlighted subjective and contextual components of risk appraisal including ethical issues regarding the level of human intervention in the natural environment and the proposed outcomes of any intervention. This suggests that goals for risk assessment need to be more clearly explicated and assumptions on tolerable risk declared as a primer for further dialogue on expectations for managed outcomes. Ecosystem-based adaptation may mean that traditional habitats and species conservation goals and existing regulatory frameworks no longer provide the best guide for long-term risk management thereby challenging the viability of some existing practices

Structural and molecular basis of ZNRF3/RNF43 transmembrane ubiquitin ligase inhibition by the Wnt agonist R-spondin

The four R-spondin (Rspo) proteins are secreted agonists of Wnt signalling in vertebrates, functioning in embryogenesis and adult stem cell biology. Through ubiquitination and degradation of Wnt receptors, the transmembrane E3 ubiquitin ligase ZNRF3 and related RNF43 antagonize Wnt signalling. Rspo ligands have been reported to inhibit the ligase activity through direct interaction with ZNRF3 and RNF43. Here we report multiple crystal structures of the ZNRF3 ectodomain (ZNRF3ecto), a signalling-competent Furin1–Furin2 (Fu1–Fu2) fragment of Rspo2 (Rspo2Fu1–Fu2), and Rspo2Fu1–Fu2 in complex with ZNRF3ecto, or RNF43ecto. A prominent loop in Fu1 clamps into equivalent grooves in the ZNRF3ecto and RNF43ecto surface. Rspo binding enhances dimerization of ZNRF3ecto but not of RNF43ecto. Comparison of the four Rspo proteins, mutants and chimeras in biophysical and cellular assays shows that their signalling potency depends on their ability to recruit ZNRF3 or RNF43 via Fu1 into a complex with LGR receptors, which interact with Rspo via Fu2

Real-Time qPCR-Based Detection of Circulating Tumor Cells from Blood Samples of Adjuvant Breast Cancer Patients: A Preliminary Study

Background: Circulating tumor cells (CTCs) are cells that detach from a primary tumor, circulate through the blood stream and lymphatic vessels, and are considered to be the main reason for remote metastasis. Due to their origin, tumor cells have different gene expression levels than the surrounding blood cells. Therefore, they might be detectable in blood samples from breast cancer patients by real-time quantitative polymerase chain reaction (RT-qPCR). Materials and Methods: Blood samples of healthy donors and adjuvant breast cancer patients were withdrawn and the cell fraction containing white blood cells and tumor cells was enriched by density gradient centrifugation. RNA was isolated and reverse transcribed to cDNA, which was then used in TaqMan real-time PCR against cytokeratin (CK)8, CK18 and CK19. 18S and GAPDH were used as controls. Results: All 3 CKs were, on average, found to be significantly higher expressed in adjuvant breast cancer samples compared to negative controls, probably due to the presence of CTCs. Unfortunately, gene expression levels could not be correlated to tumor characteristics. Conclusions: RT-qPCR could make up a new approach for the detection of CTCs from blood samples of breast cancer patients, but a correlation of the PCR data to gold standard methods in CTC detection would help to further improve the informative value of the qPCR results. (C) 2016 S. Karger GmbH, Freibur

A Novel Data Fusion Technique for Snow Cover Retrieval

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the moderate-resolution imaging spectroradiometer (MODIS) data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well-known underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season

Alpine forest biodiversity estimated from the space: testing the Spectral Variation Hypothesis comparing Landsat 8 and Sentinel 2 using a multi-temporal Rao Q index

Forests cover about 30 percent of the earth surface, they are the most biodiverse terrestrial ecosystems and they are at the base of many ecological processes and services. The loss of forest biodiversity makes in risk the benefits that the humans derived from theme. The assessment of biodiversity is therefore an important and essential goal to achieve, that however can result difficult, time consuming and expensive if estimated through field data. Through the remote sensing it is possible to estimate in a more objectively way the species diversity, using limited resources, covering broad surfaces with high quality and standardized data. One of the method to estimate biodiversity from remote sensing data is through the Spectral Variation Hypothesis (SVH) , which states that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of that area. The SVH has been tested using different indexes and measures; recently in literature, the Rao’s Q index, applied to remote sensing data has been theoretically tested as a new and innovative spectral variation measure. In this paper for the first time, the SVH through the Rao’s Q index has been tested with an NDVI time series derived from the Sentinel 2 (with a spatial resolution of 10m) and Landsat 8 satellites (spatial resolution of 30m) and correlated with data of species diversity (through Shannon’s H) collected in forest. The results showed that the Rao’s Q is a grateful spectral variation index. For both the sensors, the correlation with the field data had the same tendency as the NDVI trend, reaching the highest value of correlation (through the coefficient of determination R2) in June, when the NDVI was at its peak. In this case the correlation reached a value of R2=0.61 for the Sentinel 2 and of R2=0.45 for the Landsat 8, showing that the SVH is scale and sensor dependent. The SVH tested with optical images through the Rao’s Q index showed grateful and promising results in alpine forests and could lead to as much good results with other remote sensing data or in other ecosystems