In situ exploration of large dynamic networks

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.     

Status,causes and controls of cyanobacterial blooms in Lake Erie

The Laurentian Great Lakes are among the most prominent sources of fresh water in the world. Lake Erie's infamous cyanobacterial blooms have, however, threatened the health of this valuable freshwater resource for decades. Toxic blooms dominated by the cyanobacterium Microcystis aeruginosa have most recently been one of primary ecological concerns for the lake. These toxic blooms impact the availability of potable water, as well as public health and revenues from the tourism and fishery industries. The socioeconomic effects of these blooms have spurred research efforts to pinpoint factors that drive bloom events. Despite decades of research and mitigation efforts, these blooms have expanded both in size and duration in recent years. However, through continued joint efforts between the Canadian and United States governments, scientists, and environmental managers, identification of the factors that drive bloom events is within reach. This review provides a summary of historical and contemporary research efforts in the realm of Lake Erie's harmful cyanobacterial blooms, both in terms of experimental and management achievements and insufficiencies, as well as future directions on the horizon for the lake's research community.     

Abiotic transformation of toxaphene by superreduced vitamin B12 and dicyanocobinamide

Toxaphene is a complex organochlorine pesticide mixture, residues of which are widespread in the environment. Previous studies with the isolated bacterium Sulfurospirillum (formerly Dehalospirillum) multivorans resulted in an effective anaerobic biotransformation of toxaphene. Since the bacterium contains a corrinoid derivative in the active center of the tetrachloroethene dehalogenase, we attempted to use superreduced corrinoids for abiotic transformation of toxaphene. The two corrinoids studied were dicyanocobinamide and cyanocobalamin (vitamin B12). Superreduced dicyanocobinamide mediated a rapid transformation of toxaphene. More than 90% of the initial pool was transformed within 6 h. The transformation was nonselective, and even the most persistent metabolite in environmental samples, the so-called dead-end metabolite 2-exo,3-endo,6-exo,8,9,10-hexachlorobornane (B6-923 or Hx-Sed) was transformed within hours. Superreduced cyanocobalamin was also able to transform toxaphene albeit at significantly lower velocity. The lack of transformation products detectable in gas chromatograms of hexanes-extracted fractions of the assays suggests rapid, sequential dehalogenation and/or destruction of the C10-hydrocarbon backbone of the compounds of technical toxaphene.     

Chip-based analysis of protein-protein interactions by fluorescence detection and on-chip immunoprecipitation combined with microLC-MS/MS analysis

A new chip-based method to identify protein-protein interactions was developed using the guanine nucleotide exchange factor GRF2 and two interacting proteins, Ras and calmodulin, as model proteins. A generic immobilization strategy for FLAG-tagged bait proteins on a protein-repellent streptavidin chip surface was implemented by presentation of an oriented anti-FLAG antibody. A flow cell device, integrating different chip surfaces, was developed, and the interaction of immobilized GRF2 with the two analytes was verified by fluorescence assays. On-chip tryptic digest assays were then performed on the capture surface and analyzed by microLC-MS/MS. The interaction of GRF2 with calmodulin and Ras was demonstrated, and the lower limit of detection was determined. We also implemented an on-chip immunoprecipitation assay to identify GRF2-binding partners from complex protein mixtures. Cells overexpressing FLAG-GRF2 were lysed and then incubated with the anti-FLAG chip. In addition to detecting GRF2, we also identified calmodulin, demonstrating that this technique can successfully identify endogenous levels of proteins, bound to recombinant bait proteins. This chip-based method has the advantage that no subsequent gel separations of protein complexes prior to LC-MS analysis are required and is therefore amenable to miniaturized high-throughput determination of protein-protein interactions.     

The Semantic Web: Collective Intelligence on the Web

The World Wide Web has turned hypertext into a success story by enabling world-wide sharing of unstructured information and informal knowledge. The Semantic Web targets the sharing of structured information and formal knowledge pursuing objectives of achieving collective intelligence on the Web. Germane to the structure of the Semantic Web is a layering and standardization of concerns. These concerns are reflected by an architecture of the Semantic Web that we present through a common use case. Semantic Web data for the use case is now found on the Web and is part of a quickly growing set of Semantic Web resources available for formal processing.     

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from ? 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.     

On stability of signature-based similarity measures for content-based image retrieval

Retrieving similar images from large image databases is a challenging task for today’s content-based retrieval systems. Aiming at high retrieval performance, these systems frequently capture the user’s notion of similarity through expressive image models and adaptive similarity measures. On the query side, image models can significantly differ in quality compared to those stored on the database side. Thus, similarity measures have to be robust against these individual quality changes in order to maintain high retrieval performance. In this paper, we investigate the robustness of the family of signature-based similarity measures in the context of content-based image retrieval. To this end, we introduce the generic concept of average precision stability, which measures the stability of a similarity measure with respect to changes in quality between the query and database side. In addition to the mathematical definition of average precision stability, we include a performance evaluation of the major signature-based similarity measures focusing on their stability with respect to querying image databases by examples of varying quality. Our performance evaluation on recent benchmark image databases reveals that the highest retrieval performance does not necessarily coincide with the highest stability.     

Tetrahydroindoles as Multipurpose Screening Compounds and Novel Sirtuin Inhibitors

Indoles are privileged structures in medicinal and bioorganic chemistry that are particularly well suited to serve as platforms for diversity. Among many other therapeutic areas, the indole scaffold has been used to design aromatic compounds useful to interfere with enzymes engaged in the regulation of substrate acylation status, such as sirtuins. However, the planarity of the indole ring is not necessarily optimal for all target enzymes, especially when functionalization with aromatic side chains is required. Replacement of flat scaffolds by nonplanar molecular cores dominated by sp³ hybridization is a common strategy to avoid the disadvantages associated with poor solubility and high promiscuity, while covering less-well-explored areas of chemical space. Thus, we synthesized fragment-like tetrahydroindoles suitable for fragment-based drug discovery as well as a well-characterized small library intended as multipurpose screening compounds. For proof of principle, these compounds were screened against sirtuins 1–3, enzymes known to be addressable by indoles. We found that 2,6,6-trimethyl-4-oxo-4,5,6,7-tetrahydro-1H-indole-3-carboxamides are potent and selective SIRT2 inhibitors. Compound 16 t displayed an IC₅₀ value of 0.98 μm and could serve as exquisite starting point for hit-to-lead profiling.