The Role of Polymorphisms at the Interleukin-1, Interleukin-4, GATA-3 and Cyclooxygenase-2 Genes in Non-Surgical Periodontal Therapy

Periodontitis is a multifactorial disease. The aim of this explorative study was to investigate the role of Interleukin-(IL)-1, IL-4, GATA-3 and Cyclooxygenase-(COX)-2 polymorphisms after non-surgical periodontal therapy with adjunctive systemic antibiotics (amoxicillin/metronidazole) and subsequent maintenance in a Caucasian population. Analyses were performed using blood samples from periodontitis patients of a multi-center trial (ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00707369","term_id":"NCT00707369"}}NCT00707369=ABPARO-study). Polymorphisms were analyzed using quantitative real-time PCR. Clinical attachment levels (CAL), percentage of sites showing further attachment loss (PSAL) ≥1.3 mm, bleeding on probing (BOP) and plaque score were assessed. Exploratory statistical analysis was performed. A total of 209 samples were genotyped. Patients carrying heterozygous genotypes and single-nucleotide-polymorphisms (SNP) on the GATA-3-IVS4 +1468 gene locus showed less CAL loss than patients carrying wild type. Heterozygous genotypes and SNPs on the IL-1A-889, IL-1B +3954, IL-4-34, IL-4-590, GATA-3-IVS4 +1468 and COX-2-1195 gene loci did not influence CAL. In multivariate analysis, CAL was lower in patients carrying GATA-3 heterozygous genotypes and SNPs than those carrying wild-types. For the first time, effects of different genotypes were analyzed in periodontitis progression after periodontal therapy and during supportive treatment using systemic antibiotics demonstrating a slight association of GATA-3 gene locus with CAL. This result suggests that GATA-3 genotypes are a contributory but non-essential risk factor for periodontal disease progression.     

GD&T-Based Characterization of Short-Range Non-contact 3D Imaging Systems

We present a series of test metrics, artifacts, and procedures for characterizing and verifying the operating limits of a short-range non-contact three-dimensional imaging system. These metrics have been designed to correspond to dimensioning and tolerancing metrics that are widely used in industry (e.g. automotive, aerospace, etc.). We introduce operating limit metrics that correspond with the geometric dimensioning and tolerancing (GD&T) metrics of Form (Flatness and Circularity), Orientation (Angularity), Location (Sphere, Corner, and Hole Position Errors), and Size (Diameter, Sphere-spacing, Plane-spacing and Angle Errors). An example is presented to illustrate how these metrics, artifacts, and associated test procedures can be used in practice.     

Evolutionary functional black-box testing in an industrial setting

During the past years, evolutionary testing research has reported encouraging results for automated functional (i.e. black-box) testing. However, despite promising results, these techniques have hardly been applied to complex, real-world systems and as such, little is known about their scalability, applicability, and acceptability in industry. In this paper, we describe the empirical setup used to study the use of evolutionary functional testing in industry through two case studies, drawn from serial production development environments at Daimler and Berner & Mattner Systemtechnik, respectively. Results of the case studies are presented, and research questions are assessed based on them. In summary, the results indicate that evolutionary functional testing in an industrial setting is both scalable and applicable. However, the creation of fitness functions is time-consuming. Although in some cases, this is compensated by the results, it is still a significant factor preventing functional evolutionary testing from more widespread use in industry.     

Growing a list

It is easy to find expert knowledge on the Internet on almost any topic, but obtaining a complete overview of a given topic is not always easy: information can be scattered across many sources and must be aggregated to be useful. We introduce a method for intelligently growing a list of relevant items, starting from a small seed of examples. Our algorithm takes advantage of the wisdom of the crowd, in the sense that there are many experts who post lists of things on the Internet. We use a collection of simple machine learning components to find these experts and aggregate their lists to produce a single complete and meaningful list. We use experiments with gold standards and open-ended experiments without gold standards to show that our method significantly outperforms the state of the art. Our method uses the ranking algorithm Bayesian Sets even when its underlying independence assumption is violated, and we provide a theoretical generalization bound to motivate its use.     

Game based capacity allocation for utility computing environments

Utility computing has the potential to greatly increase the efficiency of IT operations by sharing resources across multiple users. This sharing, however, introduces complex problems with regards to pricing and allocating these resources in a way that is fair, easy to implement, and economically efficient. In this paper, we study a queue-based model that attempts to address these issues. Each client/user has a continuous flow of jobs that need to be processed. The service rate each receives, however, is proportional to a bid it submits to the system operator. Assuming that user costs are some function of their average backlogs plus their bid amounts, we use this allocation mechanism to construct an economic game. Much previous research has shown that these types of allocation games have desirable properties if the cost functions are well-defined and convex over the space of possible outcomes. Because of its queueing interface, however, our model induces functions that do not satisfy the latter, commonly assumed properties. In spite of these complications, we show that the game still has a unique equilibrium and that the system will converge to this point if users iteratively make ??best response?? updates to their bids. Finally, we explore the ??price of anarchy?? in our model, proving a bound on efficiency losses as a function of several fundamental system parameters. Thus, our scheme results in equilibria with a number of highly desirably properties.     

Thermally Stable, Latent Olefin Metathesis Catalysts

Highly thermally stable N-aryl,N-alkyl N-heterocyclic carbene (NHC) ruthenium catalysts were designed and synthesized for latent olefin metathesis. These catalysts showed excellent latent behavior toward metathesis reactions, whereby the complexes were inactive at ambient temperature and initiated at elevated temperatures, a challenging property to achieve with second generation catalysts. A sterically hindered N-tert-butyl substituent on the NHC ligand of the ruthenium complex was found to induce latent behavior toward cross-metathesis reactions, and exchange of the chloride ligands for iodide ligands was necessary to attain latent behavior during ring-opening metathesis polymerization (ROMP). Iodide-based catalysts showed no reactivity toward ROMP of norbornene-derived monomers at 25 °C, and upon heating to 85 °C gave complete conversion of monomer to polymer in less than 2 hours. All of the complexes were very stable to air, moisture, and elevated temperatures up to at least 90 °C, and exhibited a long catalyst lifetime in solution at elevated temperatures.     

Large-scale biomedical image analysis in grid environments

This paper presents the application of a component-based Grid middleware system for processing extremely large images obtained from digital microscopy devices. We have developed parallel, out-of-core techniques for different classes of data processing operations employed on images from confocal microscopy scanners. These techniques are combined into a data preprocessing and analysis pipeline using the component-based middleware system. The experimental results show that: 1) our implementation achieves good performance and can handle very large datasets on high-performance Grid nodes, consisting of computation and/or storage clusters and 2) it can take advantage of Grid nodes connected over high-bandwidth wide-area networks by combining task and data parallelism.     

Functionalized Graphene as Extracellular Matrix Mimics: Toward Well‐Defined 2D Nanomaterials for Multivalent Virus Interactions

Polysulfated nanomaterials that mimic the extracellular cell matrix are of great interest for their potential to modulate cellular responses and to bind and neutralize pathogens. However, control over the density of active functional groups on such biomimetics is essential for efficient interactions, and this remains a challenge. In this regard, producing polysulfated graphene derivatives with control over their functionality is an intriguing accomplishment in order to obtain highly effective 2D platforms for pathogen interactions. Here, a facile and efficient method for the controlled attachment of a heparin sulfate mimic on the surface of graphene is reported. Dichlorotriazine groups are conjugated to the surface of graphene by a one‐pot [2+1] nitrene cycloaddition reaction at ambient conditions, providing derivatives with defined functionality. Consecutive step by step conjugation of hyperbranched polyglycerol to the dichlorotriazine groups and eventual conversion to the polyglycerol sulfate result in the graphene based heparin biomimetics. Scanning force microscopy, cryo‐transmission electron microscopy, and in vitro bioassays reveal strong interactions between the functionalized graphene (thoroughly covered by a sulfated polymer) and vesicular stomatitis virus. Infection experiments with highly sulfated versions of graphene drastically promote the infection process, leading to higher viral titers compared to nonsulfated analogues.