81 research outputs found

    Business opportunities analysis using GIS: the retail distribution sector

    Full text link
    [EN] The retail distribution sector is facing a difficult time as the current landscape is characterized by ever-increasing competition. In these conditions, the search for an appropriate location strategy has the potential to become a differentiating and competitive factor. Although, in theory, an increasing level of importance is placed on geography because of its key role in understanding the success of a business, this is not the case in practice. For this reason, the process outlined in this paper has been specifically developed to detect new business locations. The methodology consists of a range of analyzes with Geographical Information Systems (GISs) from a marketing point of view. This new approach is called geomarketing. First, geodemand and geocompetition are located on two separate digital maps using spatial and non-spatial databases. Second, a third map is obtained by matching this information with the demand not dealt with properly by the current commercial offer. Third, the Kernel density allows users to visualize results, thus facilitating decision-making by managers, regardless of their professional background. The advantage of this methodology is the capacity of GIS to handle large amounts of information, both spatial and non-spatial. A practical application is performed in Murcia (Spain) with 100 supermarkets and data at a city block level, which is the highest possible level of detail. This detection process can be used in any commercial distribution company, so it can be generalized and considered a global solution for retailers.Roig Tierno, H.; Baviera-Puig, A.; Buitrago Vera, JM. (2013). Business opportunities analysis using GIS: the retail distribution sector. Global Business Perspectives. 1(3):226-238. doi:10.1007/s40196-013-0015-6S22623813Alarcón, S. (2011). The trade credit in the Spanish agrofood industry. Mediterranean Journal of Economics, Agriculture and Environment (New Medit), 10(2), 51–57.Alcaide, J. C., Calero, R., & Hernández, R. (2012). Geomarketing. Marketing territorial para vender y fidelizar más. Madrid: ESIC.Applebaum, W., & Cohen, S. B. (1961). The dynamics of store trading areas and market equilibrium. Annals of the Association of American Geographers, 51(1), 73–101.Baviera-Puig, A., Buitrago-Vera, J. M., Escriba, C., & Clemente, J. S. (2009). Geomarketing: Aplicación de los sistemas de información geográfica al marketing. Paper presented at the Octava Conferencia Iberoamericana en Sistemas, Cibernética e Informática, Orlando, FL.Baviera-Puig, A., Buitrago-Vera, J. M., & Mas-Verdú, F. (2012). Trade areas and knowledge-intensive services: The case of a technology centre. Management Decision, 50(8), 1412–1424.Baviera-Puig, A., Buitrago-Vera, J. M., & Rodríguez-Barrio, J. E. (2013). Un modelo de geomarketing para la localización de supermercados: Diseño y aplicación práctica. Documentos de Trabajo de la Cátedra Fundación Ramón Areces de Distribución Comercial (DOCFRADIS), 1, 1–27.Berumen, S. A., & Llamazares, F. (2007). La utilidad los métodos de decisión multicriterio (como el AHP) en un entorno de competitividad creciente. Cuadernos de administración, 20(34), 65–87.Birkin, M., Clarke, G., & Clarke, M. (2002). Retail geography and intelligent network planning. Chichester: Wiley.Chasco, C. (2003). El geomarketing y la distribución commercial. Investigación y Márketing, 79, 6–13.Chen, R. J. C. (2007). Significance and variety of geographic information system (GIS) applications in retail, hospitality, tourism, and consumer services. Journal of Retailing and Consumer Services, 14, 247–248.Church, R. L. (2002). Geographical information systems and location science. Computers and Operations Research, 29, 541–562.Church, R. L., & Murray, A. T. (2009). Business site selection, location analysis and GIS. Hoboken, NJ: Wiley.Clarke, G. (1998). Changing methods of location planning for retail companies. GeoJournal, 45, 289–298.Clarkson, R. M., Clarke-Hill, C. M., & Robinson, T. (1996). UK supermarket location assessment. International Journal of Retail and Distribution Management, 24(6), 22–33.Davis, P. (2006). Spatial competition in retail markets: Movie theaters. The RAND Journal of Economics, 37(4), 964–982.Ghosh, A., & McLafferty, S. L. (1982). Locating stores in uncertain environments: A scenario planning approach. Journal of Retailing, 58(4), 5–22.Härdle, W. (1991). Smoothing techniques with implementation in S. Nueva York, NY: Springer.Harris, B., & Batty, M. (1993). Locational models, geographical information, and planning support systems. Journal of Planning Education and Research, 12, 184–198.Hernandez, T. (2007). Enhancing retail location decision support: The development and application of geovisualization. Journal of Retailing and Consumer Services, 14, 249–258.Hernandez, T., & Bennison, D. (2000). The art and science of retail location decisions. International Journal of Retail and Distribution Management, 28(8), 357–367.Huff, D. (1963). Defining and estimating a trade area. Journal of Marketing, 28, 34–38.Instituto Nacional de Estadística (INE). (2011). Padrón de habitantes 2011. http://www.ine.es . Accessed 9 Oct 2012.Kelly, J. P., Freeman, D. C., & Emlen, J. M. (1993). Competitive impact model for site selection: The impact of competition, sales generators and own store cannibalization. The International Review of Retail, Distribution and Consumer Research, 3, 237–259.Latour, P., & Le Floc’h, J. (2001). Géomarketing: Principes, méthodes et applications. París: Éditions d’Organisation.Mendes, A. B., & Themido, I. H. (2004). Multi-outlet retail site location assessment. International Transactions in Operational Research, 11, 1–18.Moreno, A. (1991). Modelización cartográfica de densidades mediante estimadores Kernel. Treballs de la Societat Catalana de Geografia, 6(30), 155–170.Moreno, A. (2007). Obtención de capas raster de densidad. In A. Moreno (Coord.), Sistemas y Análisis de la información Geográfica. Manual de autoaprendizaje con ArcGIS (pp. 685–691). Madrid: Editorial RA-MA.Murad, A. A. (2003). Creating a GIS application for retail centers in Jeddah City. International Journal of Applied Earth Observation and Geoinformation, 4, 329–338.Murad, A. A. (2007). Using GIS for retail planning in Jeddah City. American Journal of Applied Sciences, 4(10), 820–826.Musyoka, S. M., Mutyauvyu, S. M., Kiema, J. B. K., Karanja, F. N., & Siriba, D. N. (2007). Market segmentation using geographic information systems (GIS). A case study of the soft drink industry in Kenya. Marketing Intelligence and Planning, 25(6), 632–642.Nielsen Database. (2012). Retailers Database. http://www.nielsen.com/global/en.html . Accessed 12 Oct 2012.Ozimec, A. M., Natter, M., & Reutterer, T. (2010). Geographical information systems-based marketing decisions: Effects of alternative visualizations on decision quality. Journal of Marketing, 74, 94–110.Reilly, W. J. (1931). The law of retail gravitation. New York: Knickerbocker Press.Rob, M. A. (2003). Some challenges of integrating spatial and non-spatial datasets using a geographical information system. Information Technology for Development, 10, 171–178.Rosenblatt, M. (1956). Remarks on some nonparametric estimates of a density functions. Annals of Mathematical Statistic, 27, 832–837.Sede Electrónica del Catastro. (2012). Datos Catastrales. https://www.sedecatastro.gob.es . Accessed 10 Oct 2012.Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall.Sleight, P., Harris, R., & Webber, R. (2005). Geodemographics, GIS and neighbourhood targeting. Chichester: Wiley.Suárez-Vega, R., Santos-Peñate, D. R., & Dorta-González, P. (2012). Location models and GIS tools for retail site location. Applied Geography, 35, 12–22.Thaler, R. (1986). The psychology and economics conference handbook: Comments on Simon, on Einhorn and Hogarth, and on Tversky and Kahneman. The Journal of Business, 59(4), 279–284.Wood, S., & Reynolds, J. (2012). Leveraging locational insights within retail store development? Assessing the use of location planners’ knowledge in retail marketing. Geoforum, 43, 1076–1087

    VLDL Hydrolysis by Hepatic Lipase Regulates PPARδ Transcriptional Responses

    Get PDF
    PPARs (α,γ,δ) are a family of ligand-activated transcription factors that regulate energy balance, including lipid metabolism. Despite these critical functions, the integration between specific pathways of lipid metabolism and distinct PPAR responses remains obscure. Previous work has revealed that lipolytic pathways can activate PPARs. Whether hepatic lipase (HL), an enzyme that regulates VLDL and HDL catabolism, participates in PPAR responses is unknown.Using PPAR ligand binding domain transactivation assays, we found that HL interacted with triglyceride-rich VLDL (>HDL≫LDL, IDL) to activate PPARδ preferentially over PPARα or PPARγ, an effect dependent on HL catalytic activity. In cell free ligand displacement assays, VLDL hydrolysis by HL activated PPARδ in a VLDL-concentration dependent manner. Extended further, VLDL stimulation of HL-expressing HUVECs and FAO hepatoma cells increased mRNA expression of canonical PPARδ target genes, including adipocyte differentiation related protein (ADRP), angiopoietin like protein 4 and pyruvate dehydrogenase kinase-4. HL/VLDL regulated ADRP through a PPRE in the promoter region of this gene. In vivo, adenoviral-mediated hepatic HL expression in C57BL/6 mice increased hepatic ADRP mRNA levels by 30%. In ob/ob mice, a model with higher triglycerides than C57BL/6 mice, HL overexpression increased ADRP expression by 70%, demonstrating the importance of triglyceride substrate for HL-mediated PPARδ activation. Global metabolite profiling identified HL/VLDL released fatty acids including oleic acid and palmitoleic acid that were capable of recapitulating PPARδ activation and ADRP gene regulation in vitro.These data define a novel pathway involving HL hydrolysis of VLDL that activates PPARδ through generation of specific monounsaturated fatty acids. These data also demonstrate how integrating cell biology with metabolomic approaches provides insight into specific lipid mediators and pathways of lipid metabolism that regulate transcription

    Assessment and validation of a suite of reverse transcription-quantitative PCR reference genes for analyses of density-dependent behavioural plasticity in the Australian plague locust

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>The Australian plague locust, <it>Chortoicetes terminifera</it>, is among the most promising species to unravel the suites of genes underling the density-dependent shift from shy and cryptic solitarious behaviour to the highly active and aggregating gregarious behaviour that is characteristic of locusts. This is because it lacks many of the major phenotypic changes in colour and morphology that accompany phase change in other locust species. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the most sensitive method available for determining changes in gene expression. However, to accurately monitor the expression of target genes, it is essential to select an appropriate normalization strategy to control for non-specific variation between samples. Here we identify eight potential reference genes and examine their expression stability at different rearing density treatments in neural tissue of the Australian plague locust.</p> <p>Results</p> <p>Taking advantage of the new orthologous DNA sequences available in locusts, we developed primers for genes encoding 18SrRNA, ribosomal protein L32 (RpL32), armadillo (Arm), actin 5C (Actin), succinate dehydrogenase (SDHa), glyceraldehyde-3P-dehydrogenase (GAPDH), elongation factor 1 alpha (EF1a) and annexin IX (AnnIX). The relative transcription levels of these eight genes were then analyzed in three treatment groups differing in rearing density (isolated, short- and long-term crowded), each made up of five pools of four neural tissue samples from 5<sup>th </sup>instar nymphs. SDHa and GAPDH, which are both involved in metabolic pathways, were identified as the least stable in expression levels, challenging their usefulness in normalization. Based on calculations performed with the geNorm and NormFinder programs, the best combination of two genes for normalization of gene expression data following crowding in the Australian plague locust was EF1a and Arm. We applied their use to studying a target gene that encodes a Ca<sup>2+ </sup>binding glycoprotein, <it>SPARC</it>, which was previously found to be up-regulated in brains of gregarious desert locusts, <it>Schistocerca gregaria</it>. Interestingly, expression of this gene did not vary with rearing density in the same way in brains of the two locust species. Unlike <it>S. gregaria</it>, there was no effect of any crowding treatment in the Australian plague locust.</p> <p>Conclusion</p> <p>Arm and EF1a is the most stably expressed combination of two reference genes of the eight examined for reliable normalization of RT-qPCR assays studying density-dependent behavioural change in the Australian plague locust. Such normalization allowed us to show that <it>C. terminifera </it>crowding did not change the neuronal expression of the <it>SPARC </it>gene, a gregarious phase-specific gene identified in brains of the desert locust, <it>S. gregaria</it>. Such comparative results on density-dependent gene regulation provide insights into the evolution of gregarious behaviour and mass migration of locusts. The eight identified genes we evaluated are also candidates as normalization genes for use in experiments involving other Oedipodinae species, but the rank order of gene stability must necessarily be determined on a case-by-case basis.</p

    Up-regulation of brain-derived neurotrophic factor in primary afferent pathway regulates colon-to-bladder cross-sensitization in rat

    Get PDF
    Background In humans, inflammation of either the urinary bladder or the distal colon often results in sensory cross-sensitization between these organs. Limited information is known about the mechanisms underlying this clinical syndrome. Studies with animal models have demonstrated that activation of primary afferent pathways may have a role in mediating viscero-visceral cross-organ sensitization. Methods Colonic inflammation was induced by a single dose of tri-nitrobenzene sulfonic acid (TNBS) instilled intracolonically. The histology of the colon and the urinary bladder was examined by hematoxylin and eosin (H&E) stain. The protein expression of transient receptor potential (TRP) ion channel of the vanilloid type 1 (TRPV1) and brain-derived neurotrophic factor (BDNF) were examined by immunohistochemistry and/or western blot. The inter-micturition intervals and the quantity of urine voided were obtained from analysis of cystometrograms. Results At 3 days post TNBS treatment, the protein level of TRPV1 was increased by 2-fold (p \u3c 0.05) in the inflamed distal colon when examined with western blot. TRPV1 was mainly expressed in the axonal terminals in submucosal area of the distal colon, and was co-localized with the neural marker PGP9.5. In sensory neurons in the dorsal root ganglia (DRG), BDNF expression was augmented by colonic inflammation examined in the L1 DRG, and was expressed in TRPV1 positive neurons. The elevated level of BDNF in L1 DRG by colonic inflammation was blunted by prolonged pre-treatment of the animals with the neurotoxin resiniferatoxin (RTX). Colonic inflammation did not alter either the morphology of the urinary bladder or the expression level of TRPV1 in this viscus. However, colonic inflammation decreased the inter-micturition intervals and decreased the quantities of urine voided. The increased bladder activity by colonic inflammation was attenuated by prolonged intraluminal treatment with RTX or treatment with intrathecal BDNF neutralizing antibody. Conclusion Acute colonic inflammation increases bladder activity without affecting bladder morphology. Primary afferent-mediated BDNF up-regulation in the sensory neurons regulates, at least in part, the bladder activity during colonic inflammation

    De Novo Analysis of Transcriptome Dynamics in the Migratory Locust during the Development of Phase Traits

    Get PDF
    Locusts exhibit remarkable density-dependent phenotype (phase) changes from the solitary to the gregarious, making them one of the most destructive agricultural pests. This phenotype polyphenism arises from a single genome and diverse transcriptomes in different conditions. Here we report a de novo transcriptome for the migratory locust and a comprehensive, representative core gene set. We carried out assembly of 21.5 Gb Illumina reads, generated 72,977 transcripts with N50 2,275 bp and identified 11,490 locust protein-coding genes. Comparative genomics analysis with eight other sequenced insects was carried out to indentify the genomic divergence between hemimetabolous and holometabolous insects for the first time and 18 genes relevant to development was found. We further utilized the quantitative feature of RNA-seq to measure and compare gene expression among libraries. We first discovered how divergence in gene expression between two phases progresses as locusts develop and identified 242 transcripts as candidates for phase marker genes. Together with the detailed analysis of deep sequencing data of the 4th instar, we discovered a phase-dependent divergence of biological investment in the molecular level. Solitary locusts have higher activity in biosynthetic pathways while gregarious locusts show higher activity in environmental interaction, in which genes and pathways associated with regulation of neurotransmitter activities, such as neurotransmitter receptors, synthetase, transporters, and GPCR signaling pathways, are strongly involved. Our study, as the largest de novo transcriptome to date, with optimization of sequencing and assembly strategy, can further facilitate the application of de novo transcriptome. The locust transcriptome enriches genetic resources for hemimetabolous insects and our understanding of the origin of insect metamorphosis. Most importantly, we identified genes and pathways that might be involved in locust development and phase change, and may thus benefit pest management

    QCD and strongly coupled gauge theories : challenges and perspectives

    Get PDF
    We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

    Impact of food processing and detoxification treatments on mycotoxin contamination

    Get PDF
    corecore