Schema mapping and query translation in heterogeneous P2P XML databases

Peers in a peer-to-peer data management system often have heterogeneous schemas and no mediated global schema. To translate queries across peers, we assume each peer provides correspondences between its schema and a small number of other peer schemas. We focus on query reformulation in the presence of heterogeneous XML schemas, including data–metadata conflicts. We develop an algorithm for inferring precise mapping rules from informal schema correspondences. We define the semantics of query answering in this setting and develop query translation algorithm. Our translation handles an expressive fragment of XQuery and works both along and against the direction of mapping rules. We describe the HePToX heterogeneous P2P XML data management system which incorporates our results. We report the results of extensive experiments on HePToX on both synthetic and real datasets. We demonstrate our system utility and scalability on different P2P distributions.     

Understanding online customer repurchasing intention and the mediating role of trust – an empirical investigation in two developed countries

Although e-commerce adoption and customers’ initial purchasing behavior have been well studied in the literature, repeat purchase intention and its antecedents remain understudied. This study proposes a model to understand the extent to which trust mediates the effects of vendor-specific factors on customers’ intention to repurchase from an online vendor. The model was tested and validated in two different country settings. We found that trust fully mediates the relationships between perceived reputation, perceived capability of order fulfillment, and repurchasing intention, and partially mediates the relationship between perceived website quality and repurchasing intention in both countries. Moreover, multi-group analysis reveals no significant between-country differences of the model with regards to the antecedents and outcomes of trust, except the effect of reputation on trust. Academic and practical implications and future research are discussed.     

Automated High Throughput Screening of Fluorescent Imaging Plate Reader (FLIPR) Assays Using Assay Platform™ Integrated with Activity Base for ‘on the fly’ Compound Reconfirmation

Advances in the field of automation have meant hitherto complex manual cell-based assays can now be automated. These improvements have brought significant enhancements in throughput, data fidelity and consistency, and allowed a reallocation of constrained resources.Building upon these improvements, we have linked our automated cell-based screening system, Assay Platform™, to Activity Base (IDBS), a software package designed to automate the analysis of HTS data. Customisation of this package has resulted in software that can identify ‘active’ compounds and re-pick them ‘on the fly’ from the original compound plates for triplicate re-testing without operator intervention.Based on an operator initially defining ‘normal’ parameters for assay activity in Activity Base, combined with an automated quality control software module that checks data fidelity, wells containing ‘active’ compounds can be re-picked and re-tested at the end of an automated screening run. Automating cell-based assays has significantly improved productivity, and, with the synergism of Activity Base, has given us greater power to complete each screening run and report ‘active’ compounds to Chemistry more rapidly. This article presents our approach to the automation of cell-based Fluorescent Imaging Plate Reader (FLIPR) screening together with automated active re-test confirmation using Activity Base.     

Urinary proteomic analysis of chronic allograft nephropathy

The pathogenesis of progressive renal allograft injury, which is termed chronic allograft nephropathy (CAN), remains obscure and is currently defined by histology. Prospective protocol-biopsy trials have demonstrated that clinical and standard laboratory tests are insufficiently sensitive indicators of the development and progression of CAN. The study aim was to determine if CAN could be characterized by urinary proteomic data and identify the proteins associated with disease. The urinary proteome of 75 renal transplant recipients and 20 healthy volunteers was analyzed using surface enhanced laser desorption and ionization MS. Patients could be classified into subgroups with normal histology and Banff CAN grades 2-3 with a sensitivity of 86% and a specificity of 92% by applying the classification algorithm Adaboost to urinary proteomic data. Several urinary proteins associated with advanced CAN were identified including α1-microglobulin, β2-microglobulin, prealbumin, and endorepellin, the antiangiogenic C-terminal fragment of perlecan. Increased urinary endorepellin was confirmed by ELISA and increased tissue expression of the endorepellin/perlecan ratio by immunofluoresence analysis of renal biopsies. In conclusion, analysis of urinary proteomic data has further characterized the more severe CAN grades and identified urinary endorepellin, as a potential biomarker of advanced CAN.     

Examining spatial patterns in affordable housing: the case of California density bonus implementation

California State Density Bonus Law §65915–18 financially incentivizes housing developers to produce affordable housing by granting density bonuses to those who designate a percentage of the total units for residence by low or moderate income households. By incorporating affordable housing units alongside market-rate units, state density bonus law fosters opportunities to enhance neighborhood level socio-economic diversity. This paper investigates the effectiveness of density bonus policy at promoting socio-economic diversity within the City of San Diego by examining locational patterns of density bonus implementation and neighborhood demographic characteristics. This study utilizes spatial and non-spatial statistical analyses to identify trends and correlations in density bonus usage, housing stock, and racial and economic characteristics. The results indicate that density bonus usage in San Diego has not fostered socio-economic integration; rather its usage is clustered in neighborhoods characterized by high concentrations of Hispanics, Blacks, and multi-family housing units. The findings underscore the need to refine supply-side affordable housing tools so that they are effective in a range of land markets, and not only in the traditionally lower value land markets where minority households tend to reside.     

Partially Hydrolyzed Soy Protein Shows Enhanced Transport of Amino Acids Compared to Nonhydrolyzed Protein across an Intestinal Epithelial Cell Monolayer

Consumption of protein hydrolysates has been proposed to stimulate muscle anabolism more than intact (nonhydrolyzed) proteins via accelerated delivery of amino acids for muscle protein synthesis (MPS). We evaluated whether the rate of amino acid uptake and transport across intestinal cells was enhanced for soy protein hydrolysates versus nonhydrolyzed soy protein. Intact and partially hydrolyzed proteins were subject to simulated gut digestion and applied to the apical surface of Caco‐2 monolayers. Basolateral media was harvested after 3 h and quantitatively analyzed for free amino acids using ion‐exchange chromatography and comparison to an included reference standard. Basolateral concentrations of all amino acids were higher (mean 32%) for hydrolyzed versus nonhydrolyzed protein with the greatest differences in histidine, lysine, and valine. Scale‐up production of the soy protein hydrolysate did not diminish its enhanced absorption properties. These data support the hypothesis that hydrolyzed soy protein may provide dietary amino acids that are more rapidly transported across the intestinal epithelium versus intact soy protein. This would be important under conditions where rapid and increased levels of amino acids are needed such as in the stimulation of MPS.     

Investigation of the temperature homogeneity of die melt flows in polymer extrusion

Polymer extrusion is fundamental to the processing of polymeric materials and melt flow temperature homogeneity is a major factor which influences product quality. Undesirable thermal conditions can cause problems such as melt degradation, dimensional instability, weaknesses in mechanical/optical/geometrical properties, and so forth. It has been revealed that melt temperature varies with time and with radial position across the die. However, the majority of polymer processes use only single‐point techniques whose thermal measurements are limited to the single point at which they are fixed. Therefore, it is impossible for such techniques to determine thermal homogeneity across the melt flow. In this work, an extensive investigation was carried out into melt flow thermal behavior of the output of a single extruder with different polymers and screw geometries over a wide range of processing conditions. Melt temperature profiles of the process output were observed using a thermocouple mesh placed in the flow and results confirmed that the melt flow thermal behavior is different at different radial positions. The uniformity of temperature across the melt flow deteriorated considerably with increase in screw rotational speed while it was also shown to be dependent on process settings, screw geometry, and material properties. Moreover, it appears that the effects of the material, machine, and process settings on the quantity and quality of the process output are heavily coupled with each other and this may cause the process to be difficult to predict and variable in nature. POLYM. ENG. SCI., 54:2430–2440, 2014. © 2013 Society of Plastics Engineers     

ModerateDrinking.Com and moderation management: Outcomes of a randomized clinical trial with non-dependent problem drinkers.

Objective: To evaluate the effectiveness of a web-based protocol, ModerateDrinking.com (MD; www.moderatedrinking.com) combined with use of the online resources of Moderation Management (MM; www.moderation.org) as opposed to the use of the online resources of MM alone. Method: We randomly assigned 80 problem drinkers to either the experimental or control group with follow-ups at 3, 6, and 12 months. Results: Seventy-five participants (94%) had outcome data at 1 or more follow-up points, and 59 participants (73%) were assessed at all 3 follow-ups. Comparing baseline measures to the average outcomes at follow-ups indicated a significant overall reduction in both groups in alcohol-related problems and consumption variables. Compared with the control group, the experimental group had better outcomes on percent days abstinent. There was an interaction between intensity of drinking at baseline and treatment in determining outcomes assessing drinking. Less heavy drinkers in the experimental group had better outcomes on log mean blood alcohol content (BAC) per drinking day compared with the control group. Heavier drinkers did not differentially benefit from the MD program on this measure. Mixed model analyses in general corroborated these outcomes. Conclusion: The outcome data provide partial evidence for the effectiveness of the MD web application combined with MM, compared with the effectiveness of the resources available online at MM by themselves. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Comparing the effectiveness of several modeling methods for fault prediction

We compare the effectiveness of four modeling methods—negative binomial regression, recursive partitioning, random forests and Bayesian additive regression trees—for predicting the files likely to contain the most faults for 28 to 35 releases of three large industrial software systems. Predictor variables included lines of code, file age, faults in the previous release, changes in the previous two releases, and programming language. To compare the effectiveness of the different models, we use two metrics—the percent of faults contained in the top 20% of files identified by the model, and a new, more general metric, the fault-percentile-average. The negative binomial regression and random forests models performed significantly better than recursive partitioning and Bayesian additive regression trees, as assessed by either of the metrics. For each of the three systems, the negative binomial and random forests models identified 20% of the files in each release that contained an average of 76% to 94% of the faults.