Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community

Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90°N/90°S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100%. The CCI global map of open water bodies provided the best water class representation (F-score of 89%) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89%. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km2 ± 0.24 million km2. The dataset is freely available through the ESA CCI Land Cover viewer

Natural and projectively equivariant quantizations by means of Cartan Connections

The existence of a natural and projectively equivariant quantization in the sense of Lecomte [20] was proved recently by M. Bordemann [4], using the framework of Thomas-Whitehead connections. We give a new proof of existence using the notion of Cartan projective connections and we obtain an explicit formula in terms of these connections. Our method yields the existence of a projectively equivariant quantization if and only if an \sl(m+1,\R)-equivariant quantization exists in the flat situation in the sense of [18], thus solving one of the problems left open by M. Bordemann.Comment: 13 page

Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins.

Funder: ExscientiaFunder: Diamond Light SourceFunder: Kungliga Tekniska HoegskolanFunder: Chinese Center for Disease Control and PreventionFunder: European Federation of Pharmaceutical Industries and AssociationsFunder: European CommissionFunder: Kennedy Trust for Rheumatology ResearchFunder: Ontario Institute for Cancer ResearchFunder: Royal Institution for the Advancement of Learning McGill UniversityFunder: UCBSelectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family

Conformal geometry of the supercotangent and spinor bundles

We study the actions of local conformal vector fields X∈conf(M,g) on the spinor bundle of (M,g) and on its classical counterpart: the supercotangent bundle M of (M,g). We first deal with the classical framework and determine the Hamiltonian lift of conf(M,g) to M. We then perform the geometric quantization of the supercotangent bundle of (M,g), which constructs the spinor bundle as the quantum representation space. The Kosmann Lie derivative of spinors is btained by quantization of the comoment map. The quantum and classical actions of conf(M,g) turn, respectively, the space of differential operators acting on spinor densities and the space of their symbols into conf(M,g)-modules. They are filtered and admit a common associated graded module. In the conformally flat case, the latter helps us determine the conformal invariants of both conf(M,g)-modules, in particular the conformally odd powers of the Dirac operator.Peer reviewe

Improved building roof type classification using correlation-based feature selection and gain ratio algorithms

Of late, application of data mining for pattern recognition and feature classification is fast becoming an essential technique in remote sensing research. Accurate feature selection is a necessary step to improve the accuracy of classification. This process depends on the number of feature attributes available for interactive synthesis of common characteristics that discriminate different features. Geographic object-based image analysis (GEOBIA) has made it possible to derive varieties of object attribute for this purpose; however, the analysis is more computationally intensive. The aim of this study is to develop feature selection technique that will provide the most suitable attributes to identify different roofing materials and their conditions. First, the feature importance was evaluated using gain ratio algorithm, and the result was ranked, leading to selection of the optimal feature subset. Then, the quality of the selected features was assessed using correlation-based feature selection (CFS). The classification results using SVM classifier produced an overall accuracy of 83.16%. The study has shown that the ability to exploit rich image feature attribute through optimization process improves accurate extraction of roof material with greater reliability

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy

Scrapie-Specific Pathology of Sheep Lymphoid Tissues

Transmissible spongiform encephalopathies (TSEs) or prion diseases often result in accumulation of disease-associated PrP (PrPd) in the lymphoreticular system (LRS), specifically in association with follicular dendritic cells (FDCs) and tingible body macrophages (TBMs) of secondary follicles. We studied the effects of sheep scrapie on lymphoid tissue in tonsils and lymph nodes by light and electron microscopy. FDCs of sheep were grouped according to morphology as immature, mature or regressing. Scrapie was associated with FDC dendrite hypertrophy and electron dense deposit or vesicles. PrPd was located using immunogold labelling at the plasmalemma of FDC dendrites and, infrequently, mature B cells. Abnormal electron dense deposits surrounding FDC dendrites were identified as immunoglobulins suggesting that excess immune complexes are retained and are indicative of an FDC dysfunction. Within scrapie-affected lymph nodes, macrophages outside the follicle and a proportion of germinal centre TBMs accumulated PrPd within endosomes and lysosomes. In addition, TBMs showed PrPd in association with the cell membrane, non-coated pits and vesicles, and also with discrete, large and random endoplasmic reticulum networks, which co-localised with ubiquitin. These observations suggest that PrPd is internalised via the caveolin-mediated pathway, and causes an abnormal disease-related alteration in endoplasmic reticulum structure. In contrast to current dogma, this study shows that sheep scrapie is associated with cytopathology of germinal centres, which we attribute to abnormal antigen complex trapping by FDCs and abnormal endocytic events in TBMs. The nature of the sub-cellular changes in FDCs and TBMs differs from those of scrapie infected neurones and glial cells suggesting that different PrPd/cell membrane interactions occur in different cell types

Supersymmetric QCD corrections to e+e−→tbˉH−e^+e^-\to t\bar{b}H^- and the Bernstein-Tkachov method of loop integration

The discovery of charged Higgs bosons is of particular importance, since their existence is predicted by supersymmetry and they are absent in the Standard Model (SM). If the charged Higgs bosons are too heavy to be produced in pairs at future linear colliders, single production associated with a top and a bottom quark is enhanced in parts of the parameter space. We present the next-to-leading-order calculation in supersymmetric QCD within the minimal supersymmetric SM (MSSM), completing a previous calculation of the SM-QCD corrections. In addition to the usual approach to perform the loop integration analytically, we apply a numerical approach based on the Bernstein-Tkachov theorem. In this framework, we avoid some of the generic problems connected with the analytical method.Comment: 14 pages, 6 figures, accepted for publication in Phys. Rev.

Clinical anticancer drug development: targeting the cyclin-dependent kinases

Cell division involves a cyclical biochemical process composed of several step-wise reactions that have to occur once per cell cycle. Dysregulation of cell division is a hallmark of all cancers. Genetic and epigenetic mechanisms frequently result in deranged expression and/or activity of cell-cycle proteins including the cyclins, cyclin-dependent kinases (Cdks), Cdk inhibitors and checkpoint control proteins. The critical nature of these proteins in cell cycling raises hope that targeting them may result in selective cytotoxicity and valuable anticancer activity