Business Management Strategies to Address Retail Theft

Theft and fraud in the retail sector contribute to significant financial losses, operational disruptions, and reduced employee morale. Retail leaders are particularly concerned about these deviant behaviors, as they threaten profitability, business sustainability, and long-term workforce stability. Grounded in the retaillance conceptual framework, the purpose of this qualitative pragmatic inquiry was to explore strategies leaders used to mitigate shoplifting and fraud. The participants were six retail loss prevention professionals who had successfully mitigated retail crime. Data sources consisted of semistructured interviews and public records. Through thematic analysis, four themes were identified: (a) team dynamics, (b) organizational training and policies, (c) economic influence, and (d) technology benefits. A key recommendation is that the training for loss prevention professionals and other store employees be highly effective for successful outcomes to prevent theft. The implications of positive social change include the potential for retail business leaders to enhance business sustainability by fostering workforce retention and promoting community growth through economic contributions such as retail sales taxes

Business Management Strategies to Address Retail Theft

Theft and fraud in the retail sector contribute to significant financial losses, operational disruptions, and reduced employee morale. Retail leaders are particularly concerned about these deviant behaviors, as they threaten profitability, business sustainability, and long-term workforce stability. Grounded in the retaillance conceptual framework, the purpose of this qualitative pragmatic inquiry was to explore strategies leaders used to mitigate shoplifting and fraud. The participants were six retail loss prevention professionals who had successfully mitigated retail crime. Data sources consisted of semistructured interviews and public records. Through thematic analysis, four themes were identified: (a) team dynamics, (b) organizational training and policies, (c) economic influence, and (d) technology benefits. A key recommendation is that the training for loss prevention professionals and other store employees be highly effective for successful outcomes to prevent theft. The implications of positive social change include the potential for retail business leaders to enhance business sustainability by fostering workforce retention and promoting community growth through economic contributions such as retail sales taxes

3-Oxocyclo­butane­carboxylic acid: hydrogen bonding in a small-ring γ-keto acid

The title ketocarboxylic acid, C5H6O3, is the smallest carboxy­cyclanone to have its crystal structure determined. It adopts a chiral conformation, by rotation of its carboxyl O atoms away from the plane of skeletal symmetry that passes through the carboxyl carbon and both atoms of the ketone carbonyl. The four-membered ring is non-planar, with a shallow fold of 14.3 (1)° along a line connecting the two α-carbons of the ketone group. In the crystal, the molecules are linked by centrosymmetric hydrogen-bond pairing of ordered carboxylic acid groups [O⋯O = 2.6392 (12) Å and O—H⋯O = 175.74 (15)°], yielding two different sets of dimers, related by by a 21 screw axis in c, in the cell. A C—H⋯O interaction is also present

Removal of 2-butoxyethanol gaseous emissions by biotrickling filtration packed with polyurethane foam

The removal of 2-butoxyethanol from gaseous emissions was studied using two biotrickling filters (BTF1 and BTF2) packed with polyurethane foam. Two different inoculum sources were used: a pure culture of Pseudomonas sp. BOE200 (BTF1) and activated sludge from a municipal wastewater treatment plant (BTF2). The bioreactors were operated at inlet loads (ILs) of 130 and 195 g m−3 hour−1 and at an empty bed residence time (EBRT) of 12.5 s. Under an IL of ∼130 g m−3 hour−1, BTF1 presented higher elimination capacities (ECs) than BTF2, with average values of 106 ± 7 and 68 ± 8 g m−3 hour−1, respectively. However, differences in ECs between BTFs were decreased by reducing the irrigation intervals from 1 min every 12 min to 1 min every 2 hours in BTF2. Average values of EC were 111 ± 25 and 90 ± 7 g m−3 hour−1 for BTF1 and BTF2, respectively, when working at an IL of ∼195 g m−3 hour−1. Microbial analysis revealed a significant shift in the microbial community of BTF1 inoculated with Pseudomonas sp. BOE200. At the end of the experiment, the species Microbacterium sp., Chryseobacterium sp., Acinetobacter sp., Pseudomonas sp. and Mycobacterium sp. were detected. In BTF2 inoculated with activated sludge, the denaturing gradient gel electrophoresis (DGGE) technique showed a diverse microbial community including species that was able to use 2-butoxyethanol as its carbon source, such as Pseudomonas aeruginosa and Pseudomonas putida as representative species. Although BTF1 inoculated with Pseudomonas sp. BOE200 and higher gas velocity (probably greater gas/liquid mass transfer rate) showed a slight improvement in performance, the use of activated sludge as inoculum seems to be a more feasible option for the industrial application of this technology

Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production.

The use of kinetic models of metabolism in design-build-learn-test cycles is limited despite their potential to guide and accelerate the optimization of cell factories. This is primarily due to difficulties in constructing kinetic models capable of capturing the complexities of the fermentation conditions. Building on recent advances in kinetic-model-based strain design, we present the rational metabolic engineering of an S. cerevisiae strain designed to overproduce p-coumaric acid (p-CA), an aromatic amino acid with valuable nutritional and therapeutic applications. To this end, we built nine kinetic models of an already engineered p-CA-producing strain by integrating different types of omics data and imposing physiological constraints pertinent to the strain. These nine models contained 268 mass balances involved in 303 reactions across four compartments and could reproduce the dynamic characteristics of the strain in batch fermentation simulations. We used constraint-based metabolic control analysis to generate combinatorial designs of 3 enzyme manipulations that could increase p-CA yield on glucose while ensuring that the resulting engineering strains did not deviate far from the reference phenotype. Among 39 unique designs, 10 proved robust across the phenotypic uncertainty of the models and could reliably increase p-CA yield in nonlinear simulations. We implemented these top 10 designs in a batch fermentation setting using a promoter-swapping strategy for down-regulations and plasmids for up-regulations. Eight out of the ten designs produced higher p-CA titers than the reference strain, with 19-32 % increases at the end of fermentation. All eight designs also maintained at least 90 % of the reference strain's growth rate, indicating the critical role of the phenotypic constraint. The high experimental success of our in-silico predictions lays the foundation for accelerated design-build-test-learn cycles enabled by large-scale kinetic modeling

4-Oxocyclo­hexa­neacetic acid: catemeric hydrogen bonding and spontaneous resolution of a single conformational enanti­omer in an achiral ∊-keto acid

The asymmetric unit of the title compound, C8H12O3, consists of a single conformational enanti­omer, which aggregates in the catemeric acid-to-ketone hydrogen-bonding mode [O⋯O = 2.682 (4) Å and O—H⋯O = 172 (6)°]. Four hydrogen-bonding chains of translationally related mol­ecules pass through the cell orthogonal to the 43 screw axis along c, alternating in the 110 and the 10 direction, with alignment with respect to this axis of + + − −. Successive chains are rotated by 90° around the c axis. One C—H⋯O=C close contact, involving the carboxyl group, exists

7-Meth­oxy-3,4-dihydro­naphthalen-1(2H)-one

In the title compound, C11H12O2, the six-membered ketone ring fused to the 7-meth­oxy benzene ring adopts a slightly distorted envelope configuration with the central methyl­ene C atom being the flap. The crystal packing is stabilized by weak inter­molecular C—H⋯O and C—H⋯π inter­actions, which lead to supra­molecular layers in the bc plane

Redetermination of 3-methyl­benzoic acid

The asymmetric unit of the title compound, C8H8O2, contains two crystallographically independent mol­ecules, which form dimers linked by O⋯H—O hydrogen bonds. The benzene rings in the dimers are inclined at a dihedral angle of 7.30 (8)° and both methyl groups display rotational disorder. This redetermination results in a crystal structure with significantly higher precision than the original determination [Ellas & García-Blanco (1963 ▶). Acta Cryst. 16, 434], in which the authors reported only the unit-cell parameters and space group, without any detailed information on the atomic arrangement. In the crystal, dimers are connected by weak C—H⋯O inter­actions, forming R 2 2(10) and R 4 4(18) rings along [110] and an infinite zigzag chain of dimers along the [001] direction also occurs

Modeling metabolic and signaling pathways in cancer cells

Cancer is a leading cause of death in the world, and the mechanisms that underlie this disease are still not completely understood. As cancer develops and progresses, cells undergo a diversity of mutations that sustain their rapid proliferation and the evasion of the immune system. Cancer cells alter their configuration and organization, exhibiting abnormal phenotypes and changes in functionality. The complexity of cancer lies in their heterogeneity and variability among patients, which challenges the current therapies and drug targets. In the last decades, ten hallmarks of cancer cells have been recognized, including alterations in metabolism and the signaling pathways. The sequencing of the human genome and the advances in omics data processing allowed to generate metabolic and signaling networks for human cells at a genome-scale, enlightening the detailed biochemistry and signal transduction processes occurring in human cells, and enabling to study human metabolism and signaling pathways at a systems level. However, the complexity of these networks hinders a consistent and concise physiological representation. In the field of systems biology, mathematical models and computational methods are derived to describe cellular processes based on experimental data and the biological networks. Furthermore, these models have proven to be valuable in understanding the genotype-phenotype relationship of cells and to formulate new hypotheses to guide experimental design. In this thesis, we present modeling approaches and computational methods to investigate the metabolic and signaling alterations in cancer cells and overcome the challenges arising from biological networks of such size and complexity. Firstly, we curated the thermodynamic properties for all the compounds and reactions in the human metabolic genome-scale models (GEMs) Recon 2 and Recon 3D to guarantee the consistency of the predictions with the bioenergetics of the cell. Moreover, we developed a workflow (redHUMAN) for reconstructing reduced-size models that focus on parts of metabolism relevant to a specific physiology, and we introduce a novel method to account for the cellular interactions with the extracellular medium. Using redHUMAN, we reduced the human GEMs around pathways that are altered in cancer physiology. Secondly, we applied a set of computational methods to integrate omics data into the reduced version of Recon 3D to build metabolic models for breast, colon, and ovarian cancers. These models were used to study how different cancer cells use the metabolic pathways to function and survive and how the underlying genetic deregulations affect the metabolic tasks. Thirdly, we developed a method (CONSIGN) to contextualize signaling networks to a specific type of cell under particular conditions, maximizing the consistency with experimental data. We used this method to generate a breast cancer-specific signaling network for the transcription factor MYC. Finally, we created an integrated model of signaling and metabolic models by accounting for the regulation of metabolic genes by transcription factors. We analyzed the interactions of the MYC signaling network in the breast cancer metabolic model. The work in this thesis demonstrates the potential of metabolic and signaling models to identify and infer the genetic origins and the microenvironment effects in the transformed phenotype of cancer cells, marking a step forward towards the study of drug targets and biomarkers