Share of Wallet in Loyalty Research: Issues and a Methodology to Address Them

In a competitive market environment, share of wallet is a key measure for customer relationship management. Share of wallet analysis enables the firm to be more proactive in their ability to target customers to increase additional spending. Although share of wallet is important as a measure of customer loyalty, there is a dearth of research which focuses on share of wallet per se. In this paper, we briefly review the share of wallet literature in the context of its value to loyalty researchers. We illustrate some issues regarding share of wallet in identifying loyalty and then discuss the development of a methodology to alleviate the issues by introducing an adjusted share of wallet approach, “Share of Wallet Index” (SOWI). SOWI is calculated by multiplying by the square root of the percent that each customer is above and below the median spend in the category times the raw share of wallet measure. The SOWI approach offers a useful tradeoff between modeling total dollars and modeling raw share of wallet by taking into account both category spend and category share. It also contributes to the literature on customer relationship management and loyalty and advances the empirical analysis on the customer loyalty behavior. This research provides several managerial implications. First, this share of wallet index may help firms to identify additional revenue opportunities when more effort is used with specific customers. Second, firms can understand their relative competitive positions in the market place. We discuss limitations and future research opportunities

Ultra High Performance Liquid Chromatography-High Resolution Mass Spectrometry plasma lipidomics can distinguish between canine breeds despite uncontrolled environmental variability and non-standardized diets:Plasma lipidome of dog breeds using UHPLC-HRM

INTRODUCTION AND OBJECTIVES: The purpose of this study was to use high accurate mass metabolomic profiling to investigate differences within a phenotypically diverse canine population, with breed-related morphological, physiological and behavioural differences. Previously, using a broad metabolite fingerprinting approach, lipids appear to dominate inter- and intra- breed discrimination. The purpose here was to use Ultra High Performance Liquid Chromatography–High Resolution Mass Spectrometry (UHPLC–HRMS) to identify in more detail, inter-breed signatures in plasma lipidomic profiles of home-based, client-owned dogs maintained on different diets and fed according to their owners’ feeding regimens. METHODS: Nine dog breeds were recruited in this study (Beagle, Chihuahua, Cocker Spaniel, Dachshund, Golden Retriever, Greyhound, German Shepherd, Labrador Retriever and Maltese: 7–12 dogs per breed). Metabolite profiling on a MTBE lipid extract of fasted plasma was performed using UHPLC-HRMS. RESULTS: Multivariate modelling and classification indicated that the main source of lipidome variance was between the three breeds Chihuahua, Dachshund and Greyhound and the other six breeds, however some intra-breed variance was evident in Labrador Retrievers. Metabolites associated with dietary intake impacted on breed-associated variance and following filtering of these signals out of the data-set unique inter-breed lipidome differences for Chihuahua, Golden Retriever and Greyhound were identified. CONCLUSION: By using a phenotypically diverse home-based canine population, we were able to show that high accurate mass lipidomics can enable identification of metabolites in the first pass plasma profile, capturing distinct metabolomic variability associated with genetic differences, despite environmental and dietary variability. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-016-1152-0) contains supplementary material, which is available to authorized users

Carprofen inhibits the release of matrix metalloproteinases 1, 3, and 13 in the secretome of an explant model of articular cartilage stimulated with interleukin 1β

Introduction: Arthritic diseases are characterized by the degradation of collagenous and noncollagenous extracellular matrix (ECM) components in articular cartilage. The increased expression and activity of matrix metalloproteinases (MMPs) is partly responsible for cartilage degradation. This study used proteomics to identify inflammatory proteins and catabolic enzymes released in a serum-free explant model of articular cartilage stimulated with the pro-inflammatory cytokine interleukin 1β (IL-1β). Western blotting was used to quantify the release of selected proteins in the presence or absence of the cyclooxygenase-2 specific nonsteroidal pro-inflammatory drug carprofen. Methods: Cartilage explant cultures were established by using metacarpophalangeal joints from horses euthanized for purposes other than research. Samples were treated as follows: no treatment (control), IL-1β (10 ng/ml), carprofen (100 μg/ml), and carprofen (100 μg/ml) + IL-1β (10 ng/ml). Explants were incubated (37°C, 5% CO2) over twelve day time courses. High-throughput nano liquid chromatography/mass spectrometry/mass spectrometry uncovered candidate proteins for quantitative western blot analysis. Proteoglycan loss was assessed by using the dimethylmethylene blue (DMMB) assay, which measures the release of sulfated glycosaminoglycans (GAGs). Results: Mass spectrometry identified MMP-1, -3, -13, and the ECM constituents thrombospondin-1 (TSP-1) and fibronectin-1 (FN1). IL-1β stimulation increased the release of all three MMPs. IL-1β also stimulated the fragmentation of FN1 and increased chondrocyte cell death (as assessed by β-actin release). Addition of carprofen significantly decreased MMP release and the appearance of a 60 kDa fragment of FN1 without causing any detectable cytotoxicity to chondrocytes. DMMB assays suggested that carprofen initially inhibited IL-1β-induced GAG release, but this effect was transient. Overall, during the two time courses, GAG release was 58.67% ± 10.91% (SD) for IL-1β versus 52.91% ± 9.35% (SD) with carprofen + IL-1β. Conclusions: Carprofen exhibits beneficial anti-inflammatory and anti-catabolic effects in vitro without causing any detectable cytotoxicity. Combining proteomics with this explant model provides a sensitive screening system for anti-inflammatory compounds

Characterisation of the main drivers of intra- and inter- breed variability in the plasma metabolome of dogs

INTRODUCTION: Dog breeds are a consequence of artificial selection for specific attributes. These closed genetic populations have metabolic and physiological characteristics that may be revealed by metabolomic analysis. OBJECTIVES: To identify and characterise the drivers of metabolic differences in the fasted plasma metabolome and then determine metabolites differentiating breeds. METHODS: Fasted plasma samples were collected from dogs maintained under two environmental conditions (controlled and client-owned at home). The former (n = 33) consisted of three breeds (Labrador Retriever, Cocker Spaniel and Miniature Schnauzer) fed a single diet batch, the latter (n = 96), client-owned dogs consisted of 9 breeds (Beagle, Chihuahua, Cocker Spaniel, Dachshund, Golden Retriever, Greyhound, German Shepherd, Labrador Retriever and Maltese) consuming various diets under differing feeding regimens. Triplicate samples were taken from Beagle (n = 10) and Labrador Retriever (n = 9) over 3 months. Non-targeted metabolite fingerprinting was performed using flow infusion electrospray-ionization mass spectrometry which was coupled with multivariate data analysis. Metadata factors including age, gender, sexual status, weight, diet and breed were investigated. RESULTS: Breed differences were identified in the plasma metabolome of dogs housed in a controlled environment. Triplicate samples from two breeds identified intra-individual variability, yet breed separation was still observed. The main drivers of variance in dogs maintained in the home environment were associated with breed and gender. Furthermore, metabolite signals were identified that discriminated between Labrador Retriever and Cocker Spaniels in both environments. CONCLUSION: Metabolite fingerprinting of plasma samples can be used to investigate breed differences in client-owned dogs, despite added variance of diet, sexual status and environment. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-016-0997-6) contains supplementary material, which is available to authorized users

Clusterin secretion is attenuated by the proinflammatory cytokines interleukin‐1β and tumor necrosis factor‐α in models of cartilage degradation

The protein clusterin has been implicated in the molecular alterations that occur in articular cartilage during osteoarthritis (OA). Clusterin exists in two isoforms with opposing functions, and their roles in cartilage have not been explored. The secreted form of clusterin (sCLU) is a cytoprotective extracellular chaperone that prevents protein aggregation, enhances cell proliferation and promotes viability, whereas nuclear clusterin acts as a pro-death signal. Therefore, these two clusterin isoforms may be putative molecular markers of repair and catabolic responses in cartilage and the ratio between them may be important. In this study, we focused on sCLU and used established, pathophysiologically relevant, in vitro models to understand its role in cytokine-stimulated cartilage degradation. The secretome of equine cartilage explants, osteochondral biopsies and isolated unpassaged chondrocytes was analyzed by western blotting for released sCLU, cartilage oligomeric protein (COMP) and matrix metalloproteinases (MMP) 3 and 13, following treatment with the proinflammatory cytokines interleukin-1β (IL-1β) and tumor necrosis factor-α. Release of sulfated glycosaminoglycans (sGAG) was determined using the dimethylmethylene blue assay. Clusterin messenger RNA (mRNA) expression was quantified by quantitative real-time polymerase chain reaction. MMP-3, MMP-13, COMP, and sGAG release from explants and osteochondral biopsies was elevated with cytokine treatment, confirming cartilage degradation in these models. sCLU release was attenuated with cytokine treatment in all models, potentially limiting its cytoprotective function. Clusterin mRNA expression was down-regulated 7-days post cytokine stimulation. These observations implicate sCLU in catabolic responses of chondrocytes, but further studies are required to evaluate its role in OA and its potential as an investigative biomarker

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

Background: Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets.Results and discussion: Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology.Conclusions: Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery

Suitability of Dried Blood Spots for Accelerating Veterinary Biobank Collections and Identifying Metabolomics Biomarkers With Minimal Resources

Biomarker discovery using biobank samples collected from veterinary clinics would deliver insights into the diverse population of pets and accelerate diagnostic development. The acquisition, preparation, processing, and storage of biofluid samples in sufficient volumes and at a quality suitable for later analysis with most suitable discovery methods remain challenging. Metabolomics analysis is a valuable approach to detect health/disease phenotypes. Pre-processing changes during preparation of plasma/serum samples may induce variability that may be overcome using dried blood spots (DBSs). We report a proof of principle study by metabolite fingerprinting applying UHPLC-MS of plasma and DBSs acquired from healthy adult dogs and cats (age range 1-9 years), representing each of 4 dog breeds (Labrador retriever, Beagle, Petit Basset Griffon Vendeen, and Norfolk terrier) and the British domestic shorthair cat (n = 10 per group). Blood samples (20 and 40 μL) for DBSs were loaded onto filter paper, air-dried at room temperature (3 h), and sealed and stored (4°C for ~72 h) prior to storage at -80°C. Plasma from the same blood draw (250 μL) was prepared and stored at -80°C within 1 h of sampling. Metabolite fingerprinting of the DBSs and plasma produced similar numbers of metabolite features that had similar abilities to discriminate between biological classes and correctly assign blinded samples. These provide evidence that DBSs, sampled in a manner amenable to application in in-clinic/in-field processing, are a suitable sample for biomarker discovery using UHPLC-MS metabolomics. Further, given appropriate owner consent, the volumes tested (20-40 μL) make the acquisition of remnant blood from blood samples drawn for other reasons available for biobanking and other research activities. Together, this makes possible large-scale biobanking of veterinary samples, gaining sufficient material sooner and enabling quicker identification of biomarkers of interest

Potent Activity of the HIV-1 Maturation Inhibitor Bevirimat in SCID-hu Thy/Liv Mice

The HIV-1 maturation inhibitor, 3-O-(3',3'-dimethylsuccinyl) betulinic acid (bevirimat, PA-457) is a promising drug candidate with 10 nM in vitro antiviral activity against multiple wild-type (WT) and drug-resistant HIV-1 isolates. Bevirimat has a novel mechanism of action, specifically inhibiting cleavage of spacer peptide 1 (SP1) from the C-terminus of capsid which results in defective core condensation.Oral administration of bevirimat to HIV-1-infected SCID-hu Thy/Liv mice reduced viral RNA by >2 log(10) and protected immature and mature T cells from virus-mediated depletion. This activity was observed at plasma concentrations that are achievable in humans after oral dosing, and bevirimat was active up to 3 days after inoculation with both WT HIV-1 and an AZT-resistant HIV-1 clinical isolate. Consistent with its mechanism of action, bevirimat caused a dose-dependent inhibition of capsid-SP1 cleavage in HIV-1-infected human thymocytes obtained from these mice. HIV-1 NL4-3 with an alanine-to-valine substitution at the N-terminus of SP1 (SP1/A1V), which is resistant to bevirimat in vitro, was also resistant to bevirimat treatment in the mice, and SP1/AIV had replication and thymocyte kinetics similar to that of WT NL4-3 with no evidence of fitness impairment in in vivo competition assays. Interestingly, protease inhibitor-resistant HIV-1 with impaired capsid-SP1 cleavage was hypersensitive to bevirimat in vitro with a 50% inhibitory concentration 140 times lower than for WT HIV-1.These results support further clinical development of this first-in-class maturation inhibitor and confirm the usefulness of the SCID-hu Thy/Liv model for evaluation of in vivo antiretroviral efficacy, drug resistance, and viral fitness

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation