Proteomics in chronic kidney disease: The issues clinical nephrologists need an answer for

A growing number of patients are recognised to have chronic kidney disease (CKD). However, only a minority will progress to end-stage renal disease requiring dialysis or transplantation. Currently available diagnostic and staging tools frequently fail to identify those at higher risk of progression or death. Furthermore within specific disease entities there are shortcomings in the prediction of the need for therapeutic interventions or the response to different forms of therapy. Kidney and urine proteomic biomarkers are considered as promising diagnostic tools to predict CKD progression early in diabetic nephropathy, facilitating timely and selective intervention that may reduce the related health-care expenditures. However, independent groups have not validated these findings and the technique is not currently available for routine clinical care. Furthermore, there are gaps in our understanding of predictors of progression or need for therapy in non-diabetic CKD. Presumably, a combination of tissue and urine biomarkers will be more informative than individual markers. This review identifies clinical questions in need of an answer, summarises current information on proteomic biomarkers and CKD, and describes the European Kidney and Urine Proteomics initiative that has been launched to carry out a clinical study aimed at identifying urinary proteomic biomarkers distinguishing between fast and slow progressors among patients with biopsy-proven primary glomerulopathies.     

Application of proteomic analysis to renal disease in the clinic

Chronic kidney disease accounts for a significant portion of many national health-care budgets. In this viewpoint, we highlight recent advances in urinary proteomic analyses that potentially may soon enhance the care of afflicted patients. To achieve this goal, cooperation of interdisciplinary teams from around the world will be vital, so as to standardize methodology for preparation urine samples and technical aspects of the analyses, and for better application of results. These collaborative activities will facilitate translational research in kidney diseases.     

Hybrid graph convolutional networks with multi-head attention for location recommendation

World Wide Web - Recommending yet-unvisited points of interest (POIs) which may be of interest to users is one of the fundamental applications in location-based social networks. It mainly replies...     

Electrocatalysis of the first and second kind: Theoretical and experimental study in conditions of square-wave voltammetry

A comparative analysis of two common electrocatalytic mechanisms in conditions of square-wave voltammetry (SWV) is presented from both theoretical and experimental points of view. The catalytic mechanism of the first kind is represented by a simple regenerative EC′ reaction scheme, where C′ is a follow-up irreversible chemical reaction through which the substrate is transformed and the catalyst regenerated. The second mechanism, known as electrocatalysis of the second kind, assumes the formation of an electroactive complex YS between the catalyst Y and the electroinactive substrate S in a chemically reversible reaction, followed by an irreversible electrochemical reaction of YS that yields the catalyst Y and the final electroinactive product P. The latter mechanism is mathematically modelled based on the semi-infinite diffusion model, deriving a general solution in the form of an integral equation that may be used for any voltammetric technique. A specific recurrent formula is derived based on the step-function method, enabling the simulation of SW voltammetric experiments with a wide range of experimental parameters. The properties of the response are thoroughly analyzed in terms of the kinetics and thermodynamics of the preceding chemical reaction, as well as the kinetics of the electrode reaction. The comparative analysis of the two reaction schemes reveals that both mechanisms are rather similar, exhibiting comparable sensitivity toward the kinetics of the catalytic reaction. Theoretical analysis is experimentally illustrated with the electrocatalytic reduction of bromate and nitrite ions catalyzed by Fe(II) (catalysis of the first kind) and Co(II) ions (catalysis of the second kind), respectively.     

Transformation design and nonlinear Hamiltonians

We study a class of nonlinear Hamiltonians, with applications in quantum optics. The interaction terms of these Hamiltonians are generated by taking a linear combination of powers of a simple ‘beam splitter’ Hamiltonian. The entanglement properties of the eigenstates are studied. Finally, we show how to use this class of Hamiltonians to perform special tasks such as conditional state swapping, which can be used to generate optical cat states and to sort photons.     

Ranking continuous nearest neighbors for uncertain trajectories

This article addresses the problem of performing Nearest Neighbor (NN) queries on uncertain trajectories. The answer to an NN query for certain trajectories is time parameterized due to the continuous nature of the motion. As a consequence of uncertainty, there may be several objects that have a non-zero probability of being a nearest neighbor to a given querying object, and the continuous nature further complicates the semantics of the answer. We capture the impact that the uncertainty of the trajectories has on the semantics of the answer to continuous NN queries and we propose a tree structure for representing the answers, along with efficient algorithms to compute them. We also address the issue of performing NN queries when the motion of the objects is restricted to road networks. Finally, we formally define and show how to efficiently execute several variants of continuous NN queries. Our experiments demonstrate that the proposed algorithms yield significant performance improvements when compared with the corresponding naïve approaches.     

Towards fusing uncertain location data from heterogeneous sources

Properly incorporating location-uncertainties – which is, fully considering their impact when processing queries of interest – is a paramount in any application dealing with spatio-temporal data. Typically, the location-uncertainty is a consequence of the fact that objects cannot be tracked continuously and the inherent imprecision of localization devices. Although there is a large body of works tackling various aspects of efficient management of uncertainty in spatio-temporal data – the settings consider homogeneous localization devices, e.g., either a Global Positioning System (GPS), or different sensors (roadside, indoor, etc.).In this work, we take a first step towards combining the uncertain location data – i.e., fusing the uncertainty of moving objects location – obtained from both GPS devices and roadside sensors. We develop a formal model for capturing the whereabouts in time in this setting and propose the Fused Bead (FB) model, extending the bead model based solely on GPS locations. We also present algorithms for answering traditional spatio-temporal range queries, as well as a special variant pertaining to objects locations with respect to lanes on road segments – augmenting the conventional graph based road network with the width attribute. In addition, pruning techniques are proposed in order to expedite the query processing. We evaluated the benefits of the proposed approach on both real (Beijing taxi) and synthetic (generated from a customized trajectory generator) data. Our experiments demonstrate that the proposed method of fusing the uncertainties may eliminate up to 26 % of the false positives in the Beijing taxi data, and up to 40 % of the false positives in the larger synthetic dataset, when compared to using the traditional bead uncertainty models.     

<Emphasis Type="SmallCaps">SILVERBACK+</Emphasis>: scalable association mining via fast list intersection for columnar social data

We present Silverback+, a scalable probabilistic framework for accurate association rule and frequent item-set mining of large-scale social behavioral data. Silverback+ tackles the problem of efficient storage utilization and management via: (1) probabilistic columnar infrastructure and (2) using Bloom filters and sampling techniques. In addition, probabilistic pruning techniques based on Apriori method are developed, for accelerating the mining of frequent item-sets. The proposed target-driven techniques yield a significant reduction of the size of the frequent item-set candidates, as well as the required number of repetitive membership checks through a novel list intersection algorithm. Extensive experimental evaluations demonstrate the benefits of this context-aware consideration and incorporation of the infrastructure limitations when utilizing the corresponding research techniques. When compared to the traditional Hadoop-based approach for improving scalability by straightforwardly adding more hosts, Silverback+ exhibits a much better runtime performance, with negligible loss of accuracy.     

2nd Combined Working Group and Management Committee Meeting of Urine and Kidney Proteomics COST Action 29-30 March 2009, Nafplio, Greece

EuroKUP (Urine and Kidney Proteomics; www.eurokup.org) is a COST (European Cooperation in the field of Scientific and Technical research: www.cost.esf.org Action fostering a multi-disciplinary network of investigators from 25 countries and focusing on facilitating translational proteomic research in kidney diseases. Four Working Groups focusing respectively on defining clinically important research questions in kidney diseases, kidney tissue proteomics, urine proteomics and bioinformatics have been generated. The EuroKUP members had their second combined Working Group and Management Committee (MC) meeting in Nafplio, Greece from March 29 to 30, 2009. This report summarizes the main presentations, discussions and agreed action points during this meeting. These refer to the design of collaborative projects and clinical center networks for specific kidney diseases; establishment of guidelines for kidney tissue proteomics analysis by laser-based imaging- and laser capture microdissection-MS; development and characterization of a "standard" urine specimen to be used for assessment of platform capability and data comparability in clinical proteomics applications; definition of statistical requirements in biomarker discovery studies; and development of a specialized kidney and urine ontology. Various training activities are planned involving training schools on laser capture microdissection- and imaging-MS, workshops on ontologies as well as short-term travel grants for junior investigators.