Development and validation of the consumer disillusionment toward marketing activity scale

This study examines disillusioned consumers. The theory proposes that this is a group learning to lower their expectations of firm integrity and who, to avoid being let down, ignore marketing activity directly from the firm. This kind of exchange orientation develops as a response to consistent failure in perceptions of firm integrity. The research includes six studies, including over 600 adult consumers, to outline the development and validation of a measure of consumer disillusionment toward marketing activity. Completing the process provides a valid and reliable four-item measure. In addition, the study includes the assessment of the nomological validity of the construct. The nomological validation includes using cue utilization theory to predict that disillusioned consumers favor advertising that provides evidence of verifiable integrity. The validation experiment uses print advertising containing high and low verifiable integrity stimuli. Results confirm the theory with disillusioned consumers focusing less on the firm as source of information. Further, these consumers respond more favorably than non-disillusioned consumers to third party endorsers who serve to verify the firm's attempts to show integrity.

Sequential slotted amplify-and-forward with partial relay isolation

In this paper, we propose a novel relay ordering and scheduling strategy for the sequential slotted amplify-and-forward (SAF) protocol and evaluate its performance in terms of diversity-multiplexing trade-off (DMT). The relays between the source and destination are grouped into two relay clusters based on their respective locations. The proposed strategy achieves partial relay isolation and decreases the decoding complexity at the destination. We show that the DMT upper bound of sequential-SAF with the proposed strategy outperforms other amplify and forward protocols and is more practical compared to the relay isolation assumption made in the original paper [1]. Simulation result shows that the sequential-SAF protocol with the proposed strategy has better outage performance compared to the existing AF and non-cooperative protocols in high SNR regime.

A web application to perform linkage disequilibrium and linkage analyses on a computational grid

Motivation: Unravelling the genetic architecture of complex traits requires large amounts of data, sophisticated models and large computational resources. The lack of user-friendly software incorporating all these requisites is delaying progress in the analysis of complex traits. Methods: Linkage disequilibrium and linkage analysis (LDLA) is a high-resolution gene mapping approach based on sophisticated mixed linear models, applicable to any population structure. LDLA can use population history information in addition to pedigree and molecular markers to decompose traits into genetic components. Analyses are distributed in parallel over a large public grid of computers in the UK. Results: We have proven the performance of LDLA with analyses of simulated data. There are real gains in statistical power to detect quantitative trait loci when using historical information compared with traditional linkage analysis. Moreover, the use of a grid of computers significantly increases computational speed, hence allowing analyses that would have been prohibitive on a single computer. © The Author 2009. Published by Oxford University Press. All rights reserved.

Validation tool for traction force microscopy

Traction force microscopy (TFM) is commonly used to estimate cells’ traction forces from the deformation that they cause on their substrate. The accuracy of TFM highly depends on the computational methods used to measure the deformation of the substrate and estimate the forces, and also on the specifics of the experimental set-up. Computer simulations can be used to evaluate the effect of both the computational methods and the experimental set-up without the need to perform numerous experiments. Here, we present one such TFM simulator that addresses several limitations of the existing ones. As a proof of principle, we recreate a TFM experimental set-up, and apply a classic 2D TFM algorithm to recover the forces. In summary, our simulator provides a valuable tool to study the performance, refine experimentally, and guide the extraction of biological conclusions from TFM experiments.

Characteristics of microbial drug resistance and its correlates in chronic diabetic foot ulcer infections

While virulence factors and the biofilm-forming capabilities of microbes are the key regulators of the wound healing process, the host immune response may also contribute in the events following wound closure or exacerbation of non-closure. We examined samples from diabetic and non-diabetic foot ulcers/wounds for microbial association and tested the microbes for their antibiotic susceptibility and ability to produce biofilms. A total of 1074 bacterial strains were obtained with staphylococci, Pseudomonas, Citrobacter and enterococci as major colonizers in diabetic samples. Though non-diabetic samples had a similar assemblage, the frequency of occurrence of different groups of bacteria was different. Gram-negative bacteria were found to be more prevalent in the diabetic wound environment while Gram-positive bacteria were predominant in non-diabetic ulcers. A higher frequency of monomicrobial infection was observed in samples from non-diabetic individuals when compared to samples from diabetic patients. The prevalence of different groups of bacteria varied when the samples were stratified according to age and sex of the individuals. Several multidrug-resistant strains were observed among the samples tested and most of these strains produced moderate to high levels of biofilms. The weakened immune response in diabetic individuals and synergism among pathogenic micro-organisms may be the critical factors that determine the delicate balance of the wound healing process.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.