Relationship between Store Loyalty and Shopping Behavior: A Conceptual Study

The primary focus of this paper is to measure the relationship among the three constructs Consumer Self Concept, Store Loyalty and Shopping Behavior. The paper is the beginning of the doctoral study and concentrating on the conceptual mapping of idea. For this paper the extensive literature review is the base and the concept derived from the secondary sources only. The effort is expected to give an insight of the problems and would attempt to suggest the importance of Self Concept & Store Image congruity’s role in generating Store Loyalty. This in turn greatly impacts shopping behavior too. The paper seeks to understand the role of Brands that the store carries on the resultant impact on Store Loyalty, Seek to extend our understanding of the impact of self-concept congruity by assessing the relative effects of two types of congruity variables on brand commitment, trust and retail loyalty. The role of Brand & Trust works as mediating variables in shaping Store Image & thus Store Loyalty and to devise marketing strategies for retailers i.e. seek to change consumer’s reaction to a store favorably or re-position the images of the stores to the self-image of the target group of consumers. Key Words: Consumer, Self Concept, Store Loyalty, Shopping Behavior, Store Image

Retail Store Brand Commitment Study of Big Bazaar and Pantaloons

The study is all about the brand commitment by retail stores in the eyes of customers. The extensive literature review done finds the problem that in this competitive environment where the consumer choices fluctuate due to various reasons, it is getting very challenging for stores to maintain the customers commitment towards brand at the one end. At the other end to sustain in this war business houses need to hold their customer base, making the customers more loyal. The objectives defined to find the solution for this research problem are to know the level brand commitment of the brands taken in study, to get the comparison of brands commitment and to find the opportunity to expand the brands business. A structured questionnaire administered among 550 respondents. The data analyzed using SPSS with t- test and paired t- test. The outcome of paired t- test for the 6 pairs shows that there is no significant difference in opinion of respondents for the variables of brand commitment for both the brands big bazaar and pantaloons

Brain tumour segmentation with incomplete data

Brain tumour segmentation remains a challenging task, complicated by the marked heterogeneity of imaging appearances and their distribution across multiple modalities: FLAIR, T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences (T1CE). This has compelled a research focus on uniformly multimodal models trained on complete acquisition sets rare in real-world clinical practice. Consider, for example, patients with renal failure who cannot receive contrast, artefact-spoiled sequences, or patients undergoing single-sequence intraoperative imaging. How well do segmentation models perform with such incomplete data, and what features of the lesion are identifiable under these circumstances? In a large-scale analysis involving 30 distinct segmentation models, we answer these questions with a state-of-the-art tumour segmentation modelling ensemble, nnU-Net-derived (Isensee et al, Nature Methods, 2020), deployed across all possible combinations of imaging modalities, trained, and tested with five-fold cross-validation on the 2021 BraTS-RSNA glioma population of 1251 patients. Segmentation performances for whole lesions range from Dice scores of 0.907 (single sequence) to 0.945 (full datasets) (Figure 1). When segmenting lesions by tissue type (enhancing tumour, non-enhancing tumour and oedema), Dice scores range from 0.701 (single sequence) to 0.891 (full datasets). Models missing postcontrast imaging still achieve a Dice coefficient for the whole tumour of 0.942 and identify the enhancing tumour component with Dice of up to 0.790 (Figure 2). Segmentation models can identify tumours with missing data, and can be used in clinical situations where partial data is frequent

The legibility of the imaged human brain

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications, and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including multilayer perceptrons of demographic, psychological, serological, chronic morbidity, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted individual psychology better than the coincidence of common chronic morbidity (p<0.05). Serology predicted common morbidity (p<0.05) and was best predicted by it (p<0.001), followed by structural neuroimaging (p<0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the brain.Comment: 36 pages, 6 figures, 1 table, 2 supplementary figure

Brain tumour genetic network signatures of survival

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterised by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic, and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH- wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH- mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours, and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.Comment: Main article: 52 pages, 1 table, 7 figures. Supplementary material: 13 pages, 11 supplementary figure

Genetic Mechanisms in Apc-Mediated Mammary Tumorigenesis

Many components of Wnt/β-catenin signaling pathway also play critical roles in mammary tumor development, yet the role of the tumor suppressor gene APC (adenomatous polyposis coli) in breast oncongenesis is unclear. To better understand the role of Apc in mammary tumorigenesis, we introduced conditional Apc mutations specifically into two different mammary epithelial populations using K14-cre and WAP-cre transgenic mice that express Cre-recombinase in mammary progenitor cells and lactating luminal cells, respectively. Only the K14-cre–mediated Apc heterozygosity developed mammary adenocarcinomas demonstrating histological heterogeneity, suggesting the multilineage progenitor cell origin of these tumors. These tumors harbored truncation mutation in a defined region in the remaining wild-type allele of Apc that would retain some down-regulating activity of β-catenin signaling. Activating mutations at codons 12 and 61 of either H-Ras or K-Ras were also found in a subset of these tumors. Expression profiles of acinar-type mammary tumors from K14-cre; ApcCKO/+ mice showed luminal epithelial gene expression pattern, and clustering analysis demonstrated more correlation to MMTV-neu model than to MMTV-Wnt1. In contrast, neither WAP-cre–induced Apc heterozygous nor homozygous mutations resulted in predisposition to mammary tumorigenesis, although WAP-cre–mediated Apc deficiency resulted in severe squamous metaplasia of mammary glands. Collectively, our results suggest that not only the epithelial origin but also a certain Apc mutations are selected to achieve a specific level of β-catenin signaling optimal for mammary tumor development and explain partially the colon- but not mammary-specific tumor development in patients that carry germline mutations in APC