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Abstract To analyse mortality associated to emergency admissions on weekends, differentiating whether the patients were admitted to the Internal Medicine department or to the hospital as a whole. Retrospective follow-up study of patients discharged between 2015 and 2019 in: (a) the Internal Medicine department (n = 7656) and (b) the hospital as a whole (n = 83,146). Logistic regression models were fitted to analyse the risk of death, adjusting for age, sex, severity, Charlson index, sepsis, pneumonia, heart failure and day of admission. Cox models were also adjusted for the time from admission until normal inpatient care. There was a significant increase in mortality for patients admitted in weekends with short stays in Internal Medicine (48, 72 and 96 h: OR = 2.50, 1.89 and 1.62, respectively), and hospital-wide (OR = 2.02, 1.41 and 1.13, respectively). The highest risk in weekends occurred on Fridays (stays ≤ 48 h: OR = 3.92 [95% CI 2.06–7.48]), being no significative on Sundays. The risk increased with the time elapsed from admission until the inpatient department took over care (OR = 5.51 [95% CI 1.42–21.40] when this time reached 4 days). In Cox models patients reached HR = 2.74 (1.00–7.54) when the delay was 4 days. Whether it was Internal Medicine or hospital-wide patients, the risk of death associated with emergency admission in WE increased with the time between admission and transfer of care to the inpatient department; consequently, Friday was the day with the highest risk while Sunday lacked a weekend effect. Healthcare systems should correct this serious problem.

Abstract Background To investigate the incidence of non-cancer mortalities and prognostic factors associated with competitive causes of death in a homogeneous cohort of patients with locally advanced head and neck cancer treated with radiotherapy and systemic treatment. Methods This study included 284 patients with locally advanced head and neck cancer treated with radiotherapy and systemic treatment between 2005 and 2017. The cumulative incidence of death associated with tumour, second tumours, treatment, side effects and comorbidity was calculated. A Fine and Gray regression model was used to investigate factors associated with cancer and competitive mortality. Results The cumulative incidence of tumoral death at 5 and 10 years were 35 and 47% respectively, whereas the cumulative incidence of competitive mortality were 10 and 12% respectively. In the multivariate analysis, age and comorbidity were independent factors for non-cancer mortality. Patients with a high risk of non-cancer mortality presented a cumulative incidence of 17.3% at 5 years and 18.4% at 10 years. Conclusions This study demonstrated a high incidence of competing mortality in older patients with comorbidities. Non-cancer deaths should be considered when selecting patients for combination therapies and in the study design ofclinical trials.

Participatory Geographic Information Systems (PGIS) combine geographic information with citizen or community participation to integrate local knowledge with issues related to natural resource management, territorial planning, and environmental pollution that are fundamental to decision-making. However, the current challenge is involving and educating the population to succeed in the PGIS. For this reason, a PGIS system was implemented to identify areas with higher solid waste accumulation and to determine the accuracy of the reports made by the trained population. A participatory platform was implemented using ArcGIS (Online, Desktop, Survey123, Web App Builder, and Experience Builder) for daily reporting of areas contaminated by solid waste. The information obtained for two months was used to generate Kernel density maps, which made it possible to identify the most contaminated regions of the city. A total of 2778 areas contaminated by solid waste were reported during the evaluation period, where 53 % (1486) presented a low level of contamination (area less than 1 m2), 16 % (442) had a medium level of contamination (area equal to 1 m2) and 31 % (850) reported a high level of contamination (area greater than 1 m2). The results allowed the generation of Kernel density maps showing 24 areas classified as 'Extremely High'. Saturdays and Sundays reported higher contamination concerning the other days of the week. The accumulation of contaminants is mainly in the water bodies located in the study area.

Se han descrito hasta ochos estructuras anatómicas causas de compresión del nervio mediano en su recorrido, siendo muy raras las originadas por un proceso supracondíleo y ligamento de Struthers, que desde un punto de vista anatómico hay que diferenciarlo de la Arcada de Struthers, aunque no descrita por este autor, y sitio de compresión del nervio cubital en el codo. En el síndrome de Struthers, a diferencia de la compresión del nervio mediano en otras localizaciones, el tratamiento quirúrgico inicial consigue la desaparición de la sintomatología neurológica en menos de un mes.

En la zona de canteras de Valdeinfierno, en el sector de Batallones-Malcovadeso, parte centro-meridional de la Cuenca de Madrid, se ha reconocido una serie de depósitos carbonáticos que presentan geometría dómica y estructura interna columnar. Dentro de estos depósitos de carbonato se distinguen dos unidades. La inferior (U2) es mayoritariamente dolomítica y de estructura brechoide, y petrográficamente consiste en dolomicritas con cemento calcítico. La superior (U3) está formada tanto por dolomita como por calcita, y se caracteriza por el desarrollo de morfologías columnares de aspecto travertínico. Petrográficamente, los depósitos de esta unidad consisten en caliza con texturas alveolar y biogénica. Entre las columnas de carbonato se distinguen abundantes masas arcillosas. Hacia la base de estas unidades carbonáticas se sitúa una unidad detrítica (U1), formada por arenas feldespáticas y a su techo se dispone un conjunto lutítico con alguna intercalación carbonática de composición dolomítica (Unidad U4) y, por encima de estas lutitas, un conjunto calizo (Unidad U5). Las arcillas presentes en la unidad U3 tienen una composición (sepiolita > esmectita trioctaédrica ± illita) netamente diferente a las de la unidad U4 (esmectita dioctaédrica > illita ± caolinita). La composición isotópica de los carbonatos de las unidades U2 y U3, así como de los niveles carbonatados de la unidad U4, es bastante similar en el caso de la dolomita, con valores δ13C que oscilan entre –6,21 y –7,78‰ y entre –3,23 y –4,05‰ para el δ18O. Por otro lado, desde un punto de vista de su composición isotópica, se diferencian los carbonatos de la unidad 4 y del techo de la unidad 2, exclusivamente calcíticos, en los que los valores de δ13C oscilan entre –8,07 y –11,75‰, y los de δ18O entre –5,27 y –7,35‰. La calcita existente en los niveles dolomíticos presenta valores intermedios entre los dos grupos anteriores. Los rasgos de la sucesión sedimentaria y los resultados analíticos obtenidos indican una fase de expansión lacustre en la zona estudiada con un cambio en la química de las aguas, todo ello favorecido por la entrada de aguas subterráneas a través de surgencias.

El polvo de aluminio (pAl) es un residuo peligroso procedente de la metalurgia secundaría del aluminio. Con objeto de reducir la reactividad del mismo, en el CENIM se ha desarrollado un proceso de estabilización/solidificación mediante su fraguado con yeso. En este trabajo se presenta el estudio del comportamiento térmico del pAl estabilizado con el yeso, con el fin de establecer la influencia del residuo en las reacciones de hidratación/deshidratación del yeso durante el proceso del fraguado, a saber: deshidratación parcial del sulfato cálcico dihidrato, deshidratación del hemihidrato, deshidratación del hidróxido de aluminio y transformación de anhidrita soluble en anhidrita insoluble. Asimismo se ha estudiado la cinética asociada a cada una de las reacciones anteriormente mencionadas mediante Calorimetría Diferencial de Barrido.

Wildfire represents a significant threat to ecosystems and communities in the Department of Amazonas, Peru, causing losses in biodiversity and land degradation and affecting socioeconomic security. The objective of this study was to analyze the spatial and temporal distribution of burned areas (BAs) from 1986 to 2023 to identify recurrence patterns and their impact on different types of land use and land cover (LULC). Landsat 5, 7, and 8 satellite images, processed by Google Earth Engine (GEE) using a decision tree approach, were used to map and quantify the affected areas. The results showed that the BAs were mainly concentrated in the provinces of Utcubamba, Luya, and Rodríguez de Mendoza, with a total of 1208.85 km2 burned in 38 years. The most affected land covers were pasture/grassland (38.25%), natural cover (forest, dry forest, and shrubland) (29.55%) and agricultural areas (14.74%). Fires were most frequent between June and November, with the highest peaks in September and August. This study provides crucial evidence for the implementation of sustainable management strategies, fire prevention, and restoration of degraded areas, contributing to the protection and resilience of Amazonian ecosystems against future wildfire threats.

En este trabajo se estudia la variabilidad composicional (mineralógica y geoquímica) y textural de la capa principal de bentonita (saponita) del depósito de Yuncos (Toledo), en el sector suroccidental de la cuenca de Madrid. Desde un punto de vista sedimentológico se han diferenciado en la capa bentonítica dos episodios: uno inferior (1) caracterizado por litofacies de lutitas masivas, que muestran lateralmente microfacies brechoides-bioturbadas (columna A) a intraclásticas con «glébulas» y materia orgánica (columnas B y C), y otro episodio superior (11) constituido por lutitas laminadas-Iajeadas con ooides calcíticos, que muestra un escaso desarrollo en las columnas estudiadas. El primer episodio se interpreta como depósitos de encharcamiento palustre somero sobre facies de «mud-flat» lacustre, y el segundo como correspondiente a depósitos en pequeños encharcamientos rellenando depresiones sobre la bentonita del episodio inferior que es parcialmente removilizada. De los datos texturales se infiere que la columna A corresponde a la zona con mayor grado de somerización, mientras que la C es la que presenta rasgos de un mayor período de encharcamiento, siendo la B transicional entre ambas. Mineralógicamente se observa una disminución progresiva en el contenido de minerales heredados (cuarzo, feldespatos, illita-mica) en la dirección A-C, que coincide con un incremento en la proporción de fracción arcilla en las granulometrías. La arcilla está constituida fundamentalmente por saponita, con illita abundante en la columna A y sepiolita mejor desarrollada en la zona transicional (columna B). Estas características se reflejan en el comportamiento geoquímico, con A12O3, K2O, Fe2O3, Na2O y TiO2 decreciendo en la dirección A-C (correlación positiva), mientras que MgO es su antagonista (correlación negativa). Los elementos traza, especialmente Ba, Nb, Pb, V y Zn, siguen también una evolución que, como en los elementos mayores, se relaciona con la proporción de minerales detríticos. Las diferencias observadas en la mineralogía, quimismo y texturas de la capa bentonítica se relacionan con: a) La posición respecto a la entrada de aportes. b) El grado de participación de procesos paleoedáficos vérticos durante episodios de somerización-exposición. Los estudios con SEM-EDAX han puesto de manifiesto la existencia de saponitas con rasgos texturales y composicionales diferentes, lo que sugiere dos generaciones: una asociada a procesos de transformación (Al/Mg > 0,3) y otra a neoformación (Al/Mg < 0,3).

Deep learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect-prognosis (PP) approach. Different convolutional neural networks (CNNs) have been applied under present-day conditions with promising results, but little is known about their suitability for extrapolating future climate change conditions. Here, we analyze this problem from a multi-model perspective, developing and evaluating an ensemble of CNN-based downscaled projections (hereafter DeepESD) for temperature and precipitation over the European EUR-44i (0.5∘) domain, based on eight global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). To our knowledge, this is the first time that CNNs have been used to produce downscaled multi-model ensembles based on the perfect-prognosis approach, allowing us to quantify inter-model uncertainty in climate change signals. The results are compared with those corresponding to an EUR-44 ensemble of regional climate models (RCMs) showing that DeepESD reduces distributional biases in the historical period. Moreover, the resulting climate change signals are broadly comparable to those obtained with the RCMs, with similar spatial structures. As for the uncertainty of the climate change signal (measured on the basis of inter-model spread), DeepESD preserves the uncertainty for temperature and results in a reduced uncertainty for precipitation. To facilitate further studies of this downscaling approach, we follow FAIR principles and make publicly available the code (a Jupyter notebook) and the DeepESD dataset. In particular, DeepESD is published at the Earth System Grid Federation (ESGF), as the first continental-wide PP dataset contributing to CORDEX (EUR-44).

Cancer whole-genome and transcriptome sequencing (cWGTS) has been challenging to implement in clinical settings. Here, the authors develop a workflow to deliver robust cWGTS analyses and reports within clinically-relevant timeframes for paediatric, adolescent and young adult solid tumour patients.

Se han estudiado diferentes procesos de alteración argílica avanzada en tobas dacíticas, ignimbritas riodacíticas y aglomerados andesíticos, observándose el desarrollo de procesos supergénicos posteriores que destacan por su influencia en la modificación de la cristalinidad y hábito de las fases silíceas y ferruginosas primarias. En los materiales tobáceos se desarrollan alteraciones masivas zonadas en la vertical. En las zonas inferiores se observan procesos de propilitización y silicificación (cuarzo, clorita, illita ± pirita) que evolucionan a paragénesis argílicas (illita, caolinita ± alunita) en las zonas intermedias y a materiales silíceos cuarzoaluníticos en las zonas superiores. En la transición entre las zonas intermedias y los horizontes silíceos se desarrollan aureolas ferruginosas y corazas limoníticas. En los materiales de tipo aglomerático e ignimbrítico se desarrollan alteraciones similares con carácter fisuralligadas a zonas de intensa fracturación. En las zonas fisurales predominan los procesos de removilización supergénica, observándose un mayor tamaño, grado de idiomorfismo y cristalinidad de agregados y cristales de cuarzo, en comparación con los niveles silíceos superiores de alteración masiva en las tobas. En los sectores de alteración masiva, las paragénesis argílicas con alunita, presentan importantes concentraciones de CI (0.4%), mientras que en las corazas ferruginosas se concentran Sb (30 ppm.) y As (500 ppm.). Este último también está presente en los horizontes silíceos (80 ppm.). Se discute un modelo genético en relación con la circulación hidrotermal de aguas marinas en consonancia con ambientes geoquímicos ligados a fuentes termales. Asimismo, se analiza la influencia de los procesos supergénicos y de meteorización responsables de la removilización de los elementos traza analizados (CI, As y Sb) junto con el Fe, así como de la evolución morfológica y de la cristalinidad de los materiales silíceos.

El análisis de la composición mineral de materiales arcillosos con sepiolita, pone de manifiesto variaciones significativas en el contenido de este mineral, con respecto a esmectitas e illitas, minerales terrígenos (cuarzo y feldespato) y carbonatos (calcita y dolomita) según la potencia de los materiales. La formación de sepiolita se relaciona con un proceso de silicificación, que controla el tipo de textura, el grado de cristalinidad y el tamaño de los agregados de sepiolita en función de la relación SiO22/MgO. La silicificación está favorecida por la presencia de CO3Ca, el carácter alternante de la tabla de agua, con mecanismos de disolución-precipitación, da lugar en las zonas más superficiales a suelos hidromórficos. En estos suelos la sepiolita se forma principalmente sobre lutitas verdosas ricas en saponita, con desarrollo de texturas glomerulares y laminares. En zonas más profundas se desarrollan texturas masivas de sepiolita, de tipo reticular sobre materiales arcillosos, y en forma de cémentos cuando predominan minerales terrígenos, ambos de carácter diagenético.

En el área estudiada, desde un punto de vista litológico se han diferenciado tres tramos: a) Lutítico. Lutitas verdes y rosáceas con intercalaciones samíticas e incipiente desarrollo de calcretas. la asociación de minerales de la arcilla es: esmectita-interestratificados (kerolitaestevensita)-sepiolita-illita-caolinita. b) Lutítico-calcáreo. Lutitas verdes y calcretas con un nivel samítico en la base del tramo. La asociación de minerales de la arcil1a es: esmectita-illita-paligorskita-sepiolita-eaolinita. c) Lutítico-dolomítico. Lutitas verdes y dolomicritas. La asociación de minerales de la arcilla es: esmectita-illita-paligorskita-sepiolita. Los resultados permiten establecer una génesis de sepiolita y paligorskita en relación con ambientes lacustres-palustres, donde los procesos edafodiagenéticos (carbonatación, dolomitización, silicificación, vertisolización) provocan la desestabilización de fases esmectíticas precursoras. De acuerdo con el carácter di o trioctaédrico de la esmectita se propicia la formación de paligorskita o sepiolita, respectivamente.

The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics – including the so-called “bias correction” methods – and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.

Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes a proper assessment of the (possible) added value offered by these techniques difficult. As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied to downscale temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their potential application in climate change studies. To do this, we use a warm test period as a surrogate for possible future climate conditions. Our results show that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g., continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as Coordinated Regional Climate Downscaling Experiment (CORDEX).

The use of Sentinel-3 Ocean and Land Color Instrument (OLCI) images in estimating chlorophyll-a (total and class-differentiated)a concentration is promising owing to Sentinel-3’s 21 bands. This was investigated for the case of Laguna de Bay (or Laguna Lake), Philippines. Field surveys were conducted on 13–17 November 2018 using FluoroProbe, a submersible fluorimeter capable of quantifying concentrations of spectral classes of microalgae. These were regressed with reflectance data obtained from 10-day composite Sentinel-3 reflectance images as well as ten empirical algorithms (indices) for OLCI. Compared to band reflectance, the 10 indices yielded stronger correlations, especially with R665/R709, R674/R709, and (1/R665-1/R709)xR754 with the following respective correlation values: −0.623, −0.646, and 0.628. Multiple regression results indicates that 48% of the variability of total chl-a concentration is explained by five explanatory (reflectance) variables (R412, R443, R560, R681, and R754) with RMSE of 2.814 μg/l. In contrast, the two indices R674/R754 and (1/R665-1/R709)xR754 accounted for about 46% of the variability of total chl-a concentration with RMSE of 2.475 μg/l. For diatoms and bluegreen microalgae, R560/R665 and (1/R665-1/R709)xR754 constitute the models with R2 of 0.21 and 0.435, and RMSE of 2.516 and 2.163 ug/l, respectively. Green microalgal concentration is jointly described by three indices: R560/R665, R674/R754, and R709-R754, with R2 = 0.182 and RMSE = 1.219 μg/l. From cryptophytes, the model comprising of R560/R665, (1/R665-1/R709)xR754, and R709-R754 produced an R2 = 0.289 and RMSE = 0.767 μg/l. It can be said that the empirical algorithms can be used for Sentinel-3 OLCI data providing acceptable estimations of total and spectral class-differentiated chl-a concentration.

Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.

Recently, Neutrosophy theory and its application in decision-making has became in a significant issue for the scientific community. In this paper, an extension of the PESTEL (Political, Economic, Social, Technological, Ecological and Legal) tech-nique adapted to the neutrosophic environment is proposed for decision making in management. The proposal is specially tailored for the dynamic analysis of the different factors in an uncertain environment. The framework developed for the extension of the PESTEL technique to the neutrosophic environment is compound for six fundamental activities. A system of recommendation is designed for practical purposes. A case study is developed for the use of the neutrosophic PESTEL to show the applicability of the technique. The paper ends with conclusions and recommendations for future work.

Resumen: Objetivo: Presentar una metodología para optimizar, a través de la Z’-Score de Altman para empresas privadas, la predicción de entrada en situación de concurso de acreedores (bancarrota) en empresas privadas del sector sanitario español. Método: El método propuesto consiste en la aplicación de los algoritmos genéticos (AG) para encontrar los coeficientes de la fórmula de la cadena de ratios propuestos por Altman en su versión para empresas privadas que optimicen la predicción en empresas privadas sanitarias españolas, maximizando la sensibilidad y la especificidad, y con ello reduciendo los errores de tipo I y tipo II. Con este propósito se ha utilizado una muestra de 5903 empresas del sector sanitario privado español obtenidas de las bases de datos de Sistema de Análisis de Balances Ibéricos (SABI) entre los años 2007 y 2015. Resultados: El modelo predictivo obtenido con los AG presenta mayor exactitud, sensibilidad y especificidad que el propuesto por Altman para empresas privadas, tanto con los datos de test como con todos los datos de la muestra. Conclusiones: El hallazgo más importante del presente estudio es establecer una metodología que logra identificar unos coeficientes optimizados para la Z de Altman, lo cual permite realizar una predicción más precisa de la bancarrota en las empresas sanitarias privadas españolas. Abstract: Objective: This paper presents a methodology to optimize, using Altman's Z-Score for private companies, the prediction of private companies of the Spanish health sector entering a situation of bankruptcy. Method: The proposed method consists of the application of genetic algorithms (GA) to find the coefficients of the formula of the chain of ratios proposed by Altman in the version of the score for private companies which optimize the prediction for Spanish private health companies, maximizing sensitivity and specificity, and thereby reducing type I and type II errors. For this purpose, a sample of 5,903 companies from the Spanish private health sector obtained from the database of the Iberian Balance Analysis System (SABI) between 2007 and 2015 was used. Results: The results show that the predictive model obtained with the AG presents greater accuracy, sensitivity and specificity than that proposed by Altman for private companies with both test data and all sample data. Conclusions: The most important finding of this study was to establish a methodology that can identify the optimized coefficients for the Altman Z-Score, which allows a more accurate prediction of bankruptcy in Spanish private healthcare companies. Palabras clave: Empresas sanitarias, Concurso de acreedores, Z-Altman, Predicción, Algoritmos genéticos, Keywords: Health companies, Bankruptcy, Z-Altman, Prediction, Genetic algorithms

Fresh products demand natural preservatives without solvent traces. Calcium-based extraction of bioactive compounds from eggplant peels (EP), could be used as an alternative solvent-free natural antimicrobial food preservative agent. In this study, we extracted bioactive compounds from EP using different calcium salts (CaSO4, CaCO3, and CaCl2) and evaluated the total phenolic and flavonoid contents, antioxidant activity, and bactericidal activity against Salmonella Typhimurium. The EP extracts obtained using 1% CaCO3 exhibited the highest total phenolic content and pronounced antioxidant activity. All EP extracts decreased Salmonella concentration after 10 and 12 h. In infected-lettuce leaves, the 1% CaCO3 EP extract inhibited bacterial growth at similar levels as a commercial disinfectant. The main compound identified in this EP extract was the chlorogenic acid. The extracts from EP obtained using calcium salts, represent a natural novel alternative preservative agent for the food industry, with antioxidant properties and potentially positive effects on human health.

The production of vegetables on substrates has become a great alternative. Therefore, different substrates were evaluated in a greenhouse lettuce culture. A completely randomized block design (BCA) with three treatments was used as follows: T1: peat 30% coconut peat 70%, T2: peat 70% coconut peat 30% and T3: peat 50 % Coconut peat 50%, for a total of 9 experimental units each consisting of 10 plants. Every 8 days, during 32 days, the height of the plant, fresh and dry mass of leaves and root, leaf area and every 2 days, the SPAD units and the stomatal conductivity (SC) were determined. There were significant differences in the SPAD units in time for day 11 ddt with a value of 20.7. The SC presented the highest values in T3 and T2 with values of 354.2 and 366.2 mol m-2 s–1, respectively. T1 presented the highest value of stomatal resistance (RE) with 7.5 m2 s mol–1. The leaf area and height did not show significant differences. T2 at 15 days after transplant showed the highest values of fresh root mass (MFR), dry root mass (MSR) and fresh leaf mass (MFH) with 3.1 g, 0.106 g and 13.19 g respectively. In harvest, T3 showed 10% and 15% more production of MFH in lettuce than treatments T2 and T1, respectively.