The Intentional Use of Service Recovery Strategies to Influence Consumer Emotion, Cognition and Behaviour

Service recovery strategies have been identified as a critical factor in the success of. service organizations. This study develops a conceptual frame work to investigate how specific service recovery strategies influence the emotional, cognitive and negative behavioural responses of . consumers., as well as how emotion and cognition influence negative behavior. Understanding the impact of specific service recovery strategies will allow service providers' to more deliberately and intentionally engage in strategies that result in positive organizational outcomes. This study was conducted using a 2 x 2 between-subjects quasi-experimental design. The results suggest that service recovery has a significant impact on emotion, cognition and negative behavior. Similarly, satisfaction, negative emotion and positive emotion all influence negative behavior but distributive justice has no effect

Development of a measure of model fidelity for mental health Crisis Resolution Teams

Background Crisis Resolution Teams (CRTs) provide short-term intensive home treatment to people experiencing mental health crisis. Trial evidence suggests CRTs can be effective at reducing hospital admissions and increasing satisfaction with acute care. When scaled up to national level however, CRT implementation and outcomes have been variable. We aimed to develop and test a fidelity scale to assess adherence to a model of best practice for CRTs, based on best available evidence. Methods A concept mapping process was used to develop a CRT fidelity scale. Participants (n = 68) from a range of stakeholder groups prioritised and grouped statements (n = 72) about important components of the CRT model, generated from a literature review, national survey and qualitative interviews. These data were analysed using Ariadne software and the resultant cluster solution informed item selection for a CRT fidelity scale. Operational criteria and scoring anchor points were developed for each item. The CORE CRT fidelity scale was then piloted in 75 CRTs in the UK to assess the range of scores achieved and feasibility for use in a 1-day fidelity review process. Trained reviewers (n = 16) rated CRT service fidelity in a vignette exercise to test the scale’s inter-rater reliability. Results There were high levels of agreement within and between stakeholder groups regarding the most important components of the CRT model. A 39-item measure of CRT model fidelity was developed. Piloting indicated that the scale was feasible for use to assess CRT model fidelity and had good face validity. The wide range of item scores and total scores across CRT services in the pilot demonstrate the measure can distinguish lower and higher fidelity services. Moderately good inter-rater reliability was found, with an estimated correlation between individual ratings of 0.65 (95% CI: 0.54 to 0.76). Conclusions The CORE CRT Fidelity Scale has been developed through a rigorous and systematic process. Promising initial testing indicates its value in assessing adherence to a model of CRT best practice and to support service improvement monitoring and planning. Further research is required to establish its psychometric properties and international applicability

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Crystal structure of protein tyrosine phosphatase-2 from Cydia pomonella granulovirus

Many viral genomes encode kinase and phosphatase enzymes to manipulate pathways that are controlled by phosphorylation events. The majority of viral phosphatase genes occur in the Baculoviridae and Poxviridae families of large DNA viruses. The corresponding protein sequences belong to four major homology groups, and structures are currently available for only two of these. Here, the first structure from the third group, the protein tyrosine phosphatase-2 (PTP-2) class of viral phosphatases, is described. It is shown that Cydia pomonella granulovirus PTP-2 has the same general fold and active-site architecture as described previously for other phosphatases, in the absence of significant sequence homology. Additionally, it has a novel C-terminal extension in an area corresponding to the interface of dimeric poxvirus phosphatases belonging to the Tyr–Ser protein phosphatase homology group

Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study

Multi-omics research is a key ingredient of data-intensive life sciences research, permitting measurement of biological molecules at different functional levels in the same individual. For a complete picture at the biological systems level, appropriate statistical techniques must however be developed to integrate different 'omics' data sets (e.g., genomics and proteomics). We report here multivariate projection-based analyses approaches to genomics and proteomics data sets, using the case study of and applications to observations in kidney transplant patients who experienced an acute rejection event (n=20) versus non-rejecting controls (n=20). In this data sets, we show how these novel methodologies might serve as promising tools for dimension reduction and selection of relevant features for different analytical frameworks. Unsupervised analyses highlighted the importance of post transplant time-of-rejection, while supervised analyses identified gene and protein signatures that together predicted rejection status with little time effect. The selected genes are part of biological pathways that are representative of immune responses. Gene enrichment profiles revealed increases in innate immune responses and neutrophil activities and a depletion of T lymphocyte related processes in rejection samples as compared to controls. In all, this article offers candidate biomarkers for future detection and monitoring of acute kidney transplant rejection, as well as ways forward for methodological advances to better harness multi-omics data sets

Alteration of human blood cell transcriptome in uremia

Background: End-stage renal failure is associated with profound changes in physiology and health, but the molecular causation of these pleomorphic effects termed “uremia” is poorly understood. The genomic changes of uremia were explored in a whole genome microarray case-control comparison of 95 subjects with end-stage renal failure (n = 75) or healthy controls (n = 20). Methods: RNA was separated from blood drawn in PAXgene tubes and gene expression analyzed using Affymetrix Human Genome U133 Plus 2.0 arrays. Quality control and normalization was performed, and statistical significance determined with multiple test corrections (qFDR). Biological interpretation was aided by knowledge mining using NIH DAVID, MetaCore and PubGene Results: Over 9,000 genes were differentially expressed in uremic subjects compared to normal controls (fold change: -5.3 to +6.8), and more than 65% were lower in uremia. Changes appeared to be regulated through key gene networks involving cMYC, SP1, P53, AP1, NFkB, HNF4 alpha, HIF1A, c-Jun, STAT1, STAT3 and CREB1. Gene set enrichment analysis showed that mRNA processing and transport, protein transport, chaperone functions, the unfolded protein response and genes involved in tumor genesis were prominently lower in uremia, while insulin-like growth factor activity, neuroactive receptor interaction, the complement system, lipoprotein metabolism and lipid transport were higher in uremia. Pathways involving cytoskeletal remodeling, the clathrin-coated endosomal pathway, T-cell receptor signaling and CD28 pathways, and many immune and biological mechanisms were significantly down-regulated, while the ubiquitin pathway and certain others were up-regulated. Conclusions: End-stage renal failure is associated with profound changes in human gene expression which appears to be mediated through key transcription factors. Dialysis and primary kidney disease had minor effects on gene regulation, but uremia was the dominant influence in the changes observed. This data provides important insight into the changes in cellular biology and function, opportunities for biomarkers of disease progression and therapy, and potential targets for intervention in uremia.Computer Science, Department ofMedical Genetics, Department ofPathology and Laboratory Medicine, Department ofScience, Faculty ofStatistics, Department ofOther UBCNon UBCMedicine, Faculty ofReviewedFacult

A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers

Background: Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble? Results The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity. Conclusion Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.Computer Science, Department ofMedical Genetics, Department ofMedicine, Department ofPathology and Laboratory Medicine, Department ofRespiratory Medicine, Division ofScience, Faculty ofStatistics, Department ofNon UBCMedicine, Faculty ofReviewedFacult