The dominating set problem asks for a small subset D of nodes in a graph such that every node is either in D or adjacent to a node in D. This problem arises in a number of distributed network applications, where it is important to locate a small number of centers in the network such that every node is nearby at least one center. Finding a dominating set of minimum size is NP-complete, and the best known approximation is logarithmic in the maximum degree of the graph and is provided by the same simple greedy approach that gives the well-known logarithmic approximation result for the closely related set cover problem.We describe and analyze new randomized distributed algorithms for the dominating set problem that run in polylogarithmic time, independent of the diameter of the network, and that return a dominating set of size within a logarithmic factor from optimal, with high probability. In particular, our best algorithm runs in O(log n log ∆) rounds with high probability, where n is the number of nodes, ∆ is one plus the maximum degree of any node, and each round involves a constant number of message exchanges among any two neighbors; the size of the dominating set obtained is within O(log ∆) of the optimal in expectation and within O(log n) of the optimal with high probability. We also describe generalizations to the weighted case and the case of multiple covering requirements.

Understanding groundwater storage (GWS) changes is vital to the utilization and control of water resources in the Tibetan Plateau. However, well level observations are rare in this big area, and reliable hydrology models including GWS are not available. We use hydro-geodesy to quantitate GWS changes in the Tibetan Plateau and surroundings from 2003 to 2009 using a combined analysis of satellite gravity and satellite altimetry data, hydrology models as well as a model of glacial isostatic adjustment (GIA). Release-5 GRACE gravity data are jointly used in a mascon fitting method to estimate the terrestrial water storage (TWS) changes during the period, from which the hydrology contributions and the GIA effects are effectively deducted to give the estimates of GWS changes for 12 selected regions of interest. The hydrology contributions are carefully calculated from glaciers and lakes by ICESat-1 satellite altimetry data, permafrost degradation by an Active-Layer Depth (ALD) model, soil moisture and snow water equivalent by multiple hydrology models, and the GIA effects are calculated with the new ICE-6G_C (VM5a) model. Taking into account the measurement errors and the variability of the models, the uncertainties are rigorously estimated for the TWS changes, the hydrology contributions (including GWS changes) and the GIA effect. For the first time, we show explicitly separated GWS changes in the Tibetan Plateau and adjacent areas except for those to the south of the Himalayas. We find increasing trend rates for eight basins: +2.46 ± 2.24 Gt/yr for the Jinsha River basin, +1.77 ± 2.09 Gt/yr for the Nujiang-Lancangjiang Rivers Source Region, +1.86 ± 1.69 Gt/yr for the Yangtze River Source Region, +1.14 ± 1.39 Gt/yr for the Yellow River Source Region, +1.52 ± 0.95 Gt/yr for the Qaidam basin, +1.66 ± 1.52 Gt/yr for the central Qiangtang Nature Reserve, +5.37 ± 2.17 Gt/yr for the Upper Indus basin and +2.77 ± 0.99 Gt/yr for the Aksu River basin. All these increasing trends are most likely caused by increased runoff recharges from melt water and/or precipitation in the surroundings. We also find that the administrative actions such as the Chinese Ecological Protection and Construction Project help to store more groundwater in the Three Rivers Source Region, and suggest that seepages from the Endorheic basin to the west of it are a possible source for GWS increase in this region. In addition, our estimates for GWS changes basically confirm previous results along Afghanistan, Pakistan, north India and Bangladesh, and clearly reflect the excessive use of groundwater. Our results will benefit the water resource management in the study area, and are of particular significance for the ecological restoration in the Tibetan Plateau.

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Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases. Molecular & Cellular

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Ubiquitin ligases (E3s) confer specificity to ubiquitination by recognizing target substrates. However, the substrates of most E3s have not been extensively discovered, and new methods are needed to efficiently and comprehensively identify these substrates. Mostly, E3s specifically recognize substrates via their protein interaction domains. We developed a novel integrated strategy to identify substrates of E3s containing protein interaction domains on a proteomic scale. The binding properties of the protein interaction domains were characterized by screening a random peptide library using a yeast two-hybrid system. Artificial degrons, consisting of a preferential ubiquitination sequence and particular interaction domain-binding motifs, were tested as potential substrates by in vitro ubiquitination assays. Using this strategy, not only substrates but also nonsubstrate regulators can be discovered. The detailed substrate recognition mechanisms, which are useful for drug discovery, can also be characterized. We used the Ligand of Numb protein X (LNX) family of E3s, a group of PDZ domain-containing RING-type E3 ubiquitin ligases, to demonstrate the feasibility of this strategy. Many potential substrates of LNX E3s were identified. Eight of the nine selected candidates were ubiquitinated in vitro, and two novel endogenous substrates, PDZ-binding kinase (PBK) and breakpoint cluster region protein (BCR), were confirmed in vivo. We further revealed that the LNX1-mediated ubiquitination and degradation of PBK inhibited cell proliferation and enhanced sensitivity to doxorubicin-induced apoptosis. The substrate recognition mechanism of LNX E3s was also characterized; this process involves the recognition of substrates via their specific PDZ domains by binding to the C-termini of the target proteins. This strategy can potentially be extended to a variety of E3s that contain protein interaction domain(s), thereby serving as a powerful tool for the comprehensive identification of their substrates on a proteomic scale.

BackgroundWith the help of proteomics technology, the human plasma and urine proteomes, which closely represent the protein compositions of the input and output of the kidney, respectively, have been profiled in much greater detail by different research teams. Many datasets have been accumulated to form “reference profiles” of the plasma and urine proteomes. Comparing these two proteomes may help us understand the protein handling aspect of kidney function in a way, however, which has been unavailable until the recent advances in proteomics technology.Methodology/Principal FindingsAfter removing secreted proteins downstream of the kidney, 2611 proteins in plasma and 1522 in urine were identified with high confidence and compared based on available proteomic data to generate three subproteomes, the plasma-only subproteome, the plasma-and-urine subproteome, and the urine-only subproteome, and they correspond to three groups of proteins that are handled in three different ways by the kidney. The available experimental molecular weights of the proteins in the three subproteomes were collected and analyzed. Since the functions of the overrepresented proteins in the plasma-and-urine subproteome are probably the major functions that can be routinely regulated by excretion from the kidney in physiological conditions, Gene Ontology term enrichment in the plasma-and-urine subproteome versus the whole plasma proteome was analyzed. Protease activity, calcium and growth factor binding proteins, and coagulation and immune response-related proteins were found to be enriched.Conclusion/SignificanceThe comparison method described in this paper provides an illustration of a new approach for studying organ functions with a proteomics methodology. Because of its distinctive input (plasma) and output (urine), it is reasonable to predict that the kidney will be the first organ whose functions are further elucidated by proteomic methods in the near future. It can also be anticipated that there will be more applications for proteomics in organ function research.

Flocculation or restacking of different kinds of two-dimensional (2D) nanosheets into heterostructure nanocomposites is of interest for the development of high-performance electrode materials and catalysts. However, lacking a molecular-scale control on the layer sequence hinders enhancement of electrochemical activity. Herein, we conducted electrostatic layer-by-layer (LbL) assembly, employing oxide nanosheets (e.g., MnO2, RuO2.1, reduced graphene oxide (rGO)) and layered double hydroxide (LDH) nanosheets (e.g., NiFe-based LDH) to explore a series of mono- and bilayer films with various combinations of nanosheets and sequences toward oxygen evolution reaction (OER). The highest OER activity was attained in bilayer films of electrically conductive RuO2.1 nanosheets underlying catalytically active NiFe LDH nanosheets with mixed octahedral/tetrahedral coordination (NiFe LDHTd/Oh). At an overpotential of 300 mV, the RuO2.1/NiFe LDHTd/Oh film exhibited an electrochemical surface area (ECSA) normalized current density of 2.51 mA cm–2 ECSA and a mass activity of 3610 A g–1, which was, respectively, 2 and 5 times higher than that of flocculated RuO2.1/NiFe LDHTd/Oh aggregates with a random appearance of a surface layer. First-principles density functional theory calculations and COMSOL Multiphysics simulations further revealed that the improved catalytic performance was ascribed to a substantial electronic coupling effect in the heterostructure, in which electrons are transferred from exposed NiFe LDHTd/Oh nanosheets to underneath RuO2.1. The study provides insight into the rational control and manipulation of redox-active surface layers and conductive underlying layers in heteroassembled nanosheet films at molecular-scale precision for efficient electrocatalysis.