Alberto Garre López (08/04/2014 - 30/06/2015) (PP). Discurso de investidura: Sesión Plenaria número 83, celebrada los días 7 y 8 de Abril de 2014

Abstract not availabl

Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

Food production is a complex process where uncertainty is very relevant (e.g. stochastic yield and demand, variability in raw materials and ingredients…), resulting in differences between planned production and actual output. These discrepancies have an economic cost for the company (e.g. waste disposal), as well as an environmental impact (food waste and increased carbon footprint). This research aims to develop tools based on data analytics to predict the magnitude of these discrepancies, improving enterprise profitability while, at the same time, reducing environmental impact aiding food waste management. A food company that produces liquid products based on fruits and vegetables was analyzed. Data was gathered on 1,795 batches, including the characteristics of the product (recipe, components used…) and the difference between the input and the output weight. Machine Learning (ML) algorithms were used to predict deviations in production, reducing uncertainties related to the amount of waste produced. The ML models had greater predictive capacity than a linear model with stepwise parameter selection. Then, uncertainty is included in the predictions using a normal distribution based on the residuals of the model. Furthermore, we also demonstrate that ML models can be used as a tool to identify possible production anomalies. This research shows innovative ways to deal with uncertainty in production planning using modern methods in the field of operation research. These tools improve classical methods and provide production managers with valuable information to assess the economic benefits of improved machinery or process controls. As a consequence, accurate predictive models can potentially improve the profitability of food companies, also reducing their environmental impact.</p

Comparison of Two Network-Theory-Based Methods for detecting Functional Regions

Background: Functional regions are abstract, uniformly defined territorial units that form an important basis for many development strategies of a country or a region. Objectives: This study analyses the application of network theory to the detection of such regions. Methods/Approach: Functional regions are analysed using two methods based on the graph theory: the Walktrap algorithm and the chain approach. The quality of the two regionalization methods is analysed using the fuzzy set theory with the revised method. Slovenia was used as a case study. Results: The Walktrap algorithm generated eight functional regions; seven of them corresponded to those identified in previous studies. The only difference occurred in the northwestern mountainous part of Slovenia. The chain approach led to similar results, although it resulted in a huge functional urban region of the capital Ljubljana. Conclusions: The results show that the Walktrap algorithm calculates regions that are more closed, where more workers find work in the home region, than the chain approach

Describing the heat shock response of Bacillus spp. under isothermal inactivation conditions

[ESP] El estrés ambiental y los métodos de procesamiento de alimentos, como el calentamiento, la acidez, son responsables de provocar respuestas adaptativas a las bacterias. La respuesta general al estrés en la mayoría de las bacterias Gram positivas, incluidas B. subtilis, L. monocytogenes, está regulada por el factor sigma alternativo σΒ que induce la transcripción de genes capaces de proporcionar a las células vegetativas resistencia al estrés. En este estudio, se analizó la resistencia al calor de las células vegetativas de B. subtilis bajo calentamiento isotérmico, así como la influencia de la ausencia del gen sigB en la resistencia al calor bacteriano. Los experimentos isotérmicos se llevaron a cabo en agua peptonada (pH 7) a 51, 52,5, 55 y 57,5°C y mostraron que ambas cepas eran bastante sensibles al calor. El mutante sigB presentó mayor inactivación a 51 y 52.5°C. [ENG] Adaptive responses to bacteria are triggered by environmental conditions and food processing processes such as heating and acidity. In the majority of Gram positive bacteria, including B. subtilis and L. monocytogenes, the overall stress response is governed by the alternative sigma factor σB (sigB), which stimulates the transcription of genes that provide resistance to stress to the vegetative cells. The heat resistance of B. subtilis vegetative cells was investigated under isothermal heating, as well as the effect of the sigB gene absence on bacterial heat resistance. Isothermal studies at 51, 52.5, 55, and 57.5°C in peptone water (pH 7) demonstrated that both strains were extremely heat sensitive. At 51 and 52.5°C, the sigB mutant presented more inactivation.Leonidas Georgalis is grateful for the “beca asociada a actividades de I+D+I”, convocatoria B- 077/20, for awarding him a pre−doctoral grant

Modeling Population Growth in R with the biogrowth Package

The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in the scientific literature, their application usually requires advanced knowledge of mathematical programming and statistical inference, especially when modelling growth under dynamic environmental conditions. This article presents the biogrowth package for R, which implements functions for modelling the growth of populations. It can predict growth under static or dynamic environments, considering the effect of an arbitrary number of environmental factors. Moreover, it can be used to fit growth models to data gathered under static or dynamic environmental conditions. The package allows the user to fix any model parameter prior to the fit, an approach that can mitigate identifiability issues associated to growth models. The package includes common S3 methods for visualization and statistical analysis (summary of the fit, predictions, . . . ), easing result interpretation. It also includes functions for model comparison/selection. We illustrate the functions in biogrowth using examples from food science and economy

Different model hypotheses are needed to account for qualitative variability in the response of two strains of Salmonella spp. under dynamic conditions

In this article, the thermal inactivation of two Salmonella strains (Salmonella Enteritidis CECT4300 and Salmonella Senftenberg CECT4565) was studied under both isothermal and dynamic conditions. We observed large differences between these two strains, with S. Senftenberg being much more resistant than S. Enteritidis. Under isothermal conditions, S. Senftenberg had non-linear survivor curves, whereas the response of S. Enteritidis was log-linear. Therefore, weibullian inactivation models were used to describe the response of S. Senftenberg, with the Mafart model being the more suitable one. For S. Enteritidis, the Bigelow (log-linear) inactivation model was successful at describing the isothermal response. Under dynamic conditions, a combination of the Peleg and Mafart models (secondary model of Mafart; t* of Peleg) fitted to the isothermal data could predict the response of S. Senftenberg to the dynamic treatments tested (heating rates between 0.5 and 10 °C/min). This was not the case for S. Enteritidis, where the model predictions based on isothermal data underestimated the microbial concentrations. Therefore, a dynamic model that considers stress acclimation to one of the dynamic profiles was fitted, using the remaining profiles as validation. In light of this, besides its quantitative impact, variability between strains of bacterial species can also cause qualitative differences in microbial inactivation. This is demonstrated by S. Enteritidis being able to develop stress acclimation where S. Senftenbenberg could not. This has important implications for the development of microbial inactivation models to support process design, as every industrial treatment is dynamic. Consequently, it is crucial to consider different model hypotheses, and how they affect the model predictions both under isothermal and dynamic conditions.The financial support of this research was provided by the Ministerio de Economía, Industria y Competitividad, Spain, through Project PID2020-116318RB-C32 and by FEDER funds. Dr. Alberto Garre was supported by a Maria Zambrano scholarship

Dynamics of microbial Inactivation and acrylamide production in high-temperature heat treatments

In food processes, optimizing processing parameters is crucial to ensure food safety, maximize food quality, and minimize the formation of potentially toxigenic compounds. This research focuses on the simultaneous impacts that severe heat treatments applied to food may have on the formation of harmful chemicals and on microbiological safety. The case studies analysed consider the appearance/synthesis of acrylamide after a sterilization heat treatment for two different foods: pureed potato and prune juice, using Geobacillus stearothermophilus as an indicator. It presents two contradictory situations: on the one hand, the application of a high-temperature treatment to a low acid food with G. stearothermophilus spores causes their inactivation, reaching food safety and stability from a microbiological point of view. On the other hand, high temperatures favour the appearance of acrylamide. In this way, the two objectives (microbiological safety and acrylamide production) are opposed. In this work, we analyse the effects of high-temperature thermal treatments (isothermal conditions between 120 and 135 _C) in food from two perspectives: microbiological safety/stability and acrylamide production. After analysing both objectives simultaneously, it is concluded that, contrary to what is expected, heat treatments at higher temperatures result in lower acrylamide production for the same level of microbial inactivation. This is due to the different dynamics and sensitivities of the processes at high temperatures. These results, as well as the presented methodology, can be a basis of analysis for decision makers to design heat treatments that ensure food safety while minimizing the amount of acrylamide (or other harmful substances) produced.The financial support of this research work was provided by the Ministry of Science, Innovation and Universities of the Spanish Government and European Regional Development Fund (ERDF) through project AGL2017-86840-C2-1-R. J.L.P.-S. is grateful to the JAE-INTRO program from CSIC (Grant no JAEINT19_EX_0797). A.G. was supported by a postdoctoral grant from the Fundación Séneca (20900/PD/18)

Modelos matemáticos para la descripción del crecimiento de microorganismos patógenos en alimentos

[ESP] Los requerimientos actuales de calidad y seguridad microbiana en los alimentos sólo pueden ser satisfechos a través de una descripción detallada del comportamiento de los microorganismos patógenos durante el ciclo de vida del producto. La microbiología predictiva es clave en este aspecto, ya que describe por medio de modelos matemáticos la evolución de la población microbiana bajo diferentes condiciones ambientales. En esta contribución se presentan los modelos matemáticos más utilizados actualmente para la descripción de crecimiento microbiano. [ENG] Current standards on food quality and microbial safety can only be fulfilled through a detailed description of the behaviour of the pathogen microorganism during the life cycle of the product. Predictive microbiology serves a key role in this aspect. This science describes through mathematical models the evolution of a microbial population under different environmental conditions. This contribution presents the mathematical models most commonly used for the description of microbial growth.Escuela Técnica Superior de Ingeniería de Telecomunicación (ETSIT), Escuela Técnica Superior de Ingeniería Agronómica (ETSIA), Escuela Técnica Superior de Ingeniería Industrial (ETSII), Escuela Técnica Superior de Arquitectura y Edificación (ETSAE), Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos y de Ingeniería de Minas (ETSICCPIM), Facultad de Ciencias de la Empresa (FCCE), Parque Tecnológico de Fuente Álamo (PTFA), Vicerrectorado de Estudiantes y Extensión de la UPCT, Vicerrectorado de Investigación e Innovación de la UPCT, y Vicerrectorado de Internacionalización y Cooperación al Desarrollo de la UPCT

Expression of Smac/Diablo in tubular epithelial cells and during acute renal failure

Expression of Smac/Diablo in tubular epithelial cells and during acute renal failure.BackgroundApoptosis contributes to tubular cell loss in the course of renal injury. However, the mechanisms regulating tubular cell apoptosis are not well understood. Smac/Diablo is a mitochondrial protein that is released to the cytosol during apoptosis, where it blocks the antiapoptotic activity of inhibitor of apoptosis proteins (IAPs).MethodsWe have studied the regulation of Smac/Diablo mRNA and protein expression in murine toxic acute tubular necrosis, and in cultured tubular epithelial cells exposed to the lethal cytokine tumor necrosis factor (TNF).ResultsFolic acid–induced acute renal failure was associated with tubular cell apoptosis. Smac/Diablo mRNA and protein levels increased by 50% at 24 hours. TNF, a cytokine whose renal expression increases in folic acid nephropathy, induced apoptosis in cultured tubular epithelial cells in a time-dependent manner. In addition, TNF increased the mRNA and protein expression of Smac/Diablo.ConclusionThese findings support the concept that regulation of Smac/Diablo mRNA and protein expression is a mechanism by which lethal stimuli amplify their lethal potential in renal cells

A European questionnaire-based study on population awareness and risk perception of antimicrobial resistance

Versión post-printTo tackle antimicrobial resistance (AMR) is of outmost importance for the general population to understand the severity and the relevance of different routes of transmission. Respondents of different age groups, educational and occupational backgrounds, area of living, diet and household composition participated in an online survey with questions concerning socio-demographics, personal use of antibiotics, awareness, general knowledge, sources of information, behavior and attitude toward antibiotics, and risk perception on antibiotics and AMR. Descriptive and logistic regression analyses were carried out. A total of 1252 respondents, mainly from EU, participated in the survey. About 57.7% declared they consumed antibiotics in the last year and some misguided behaviors were identified, especially for those not having a food- or health-related background, who more frequently failed in giving the right answer to uncontroversial true/false questions (ANOVA, P < 0.05). The youngest respondents were less confident on the information received from traditional media (OR = 0.425), the national government (OR = 0.462), and consumer organizations (OR = 0.497), while they frequently obtained information from social networks and online media, which could therefore be exploited as a channel for educational campaigns targeting this population group. New measures, strategies and policy agenda at a European level aimed at improving awareness on AMR among targeted community groups must be taken into consideration.S