Abstract: Pigments synthesised by Chryseobacterium sp. kr6 growing on feather waste were extracted and characterised. The pigment extract was characterised by KOH test, UV–vis, CIELAB colour system, HPLCDAD- MS, FTIR and its antioxidant capacity was evaluated. A positive bathochromic shift was observed when kr6 colonies or pigment extracts were subjected to alkaline solution (20% KOH) and a λmax at 450 nm was detected for acetone extracts, although no typical fine structure of carotenoids was detected in the electomagnetic spectra. The HPLC profile of the extracted pigment showed that the compound has three different peaks with λmax near 450 nm. The FTIR analysis shows some principal functional groups from a flexirubinlike molecule. The pigmented compound also presents antioxidant activity evaluated by the scavenging of the ABTS radical.

Abstract: The world demands new solutions and products to be used as dyes for industrial applications. Microbial pigments represent an eco-friendly alternative as they can be produced in large amounts through biotechnological processes and do not present environmental risks, as they are easily decomposable. Moreover, some of these metabolites are recognized for their biological activities, which qualify them for potential uses as food colorants and nutraceuticals, protecting against degenerative diseases related with oxidative stress. Because of their genetic simplicity as compared with plants, microorganisms may be a better source to understand biosynthetic mechanisms and to be engineered for producing high pigment yields. Despite the origin of the pigmented microorganism,it seems very important to develop protocols using organic industrial residues and agricultural byproducts as substrates for pigment production and fi nd novel green strategies for rapid pigment extraction. This review looks for the most recent studies that describe microbial pigments from microalgae, fungi, and bacteria. In particular, the underexploited tools of omics science such as proteomics and metabolomics are addressed. The use of techniques involving mass spectrometry, allows to identify different protein and metabolite profi les that may be associated with a variety of biotechnologically-relevant pathways of pigment synthesis.

Resumen: Durante años, expertos en Estrategia han resaltado la importancia de definir claramente el marco estratégico de una empresa debido a que éste le permite crear su identidad, propósito y dirección. Asimismo, porque a la vez dicho marco sirve como instrumento para apropiarse de una mayor porción de valor creado a partir de, por ejemplo, la implementación de más y mejores prácticas de innovación empresarial. Sin embargo, ¿es realmente determinante la construcción acuciosa de un marco estratégico para efectos de generar resultados adecuados en términos de innovación y competitividad? El presente trabajo tiene como propósito abordar dicho cuestionamiento. Para ello se pretende desplegar un planteamiento teórico y conceptual sobre el impacto de la calidad del marco estratégico (i.e., misión y visión organizacional) sobre la creación de ventaja competitiva y posterior consecución de una cultura de innovación en las organizaciones. Para ello, en principio se desarrollará una revisión sistemática sobre el impacto de estos dos componentes sobre los resultados organizacionales, y se propondrá el vínculo teórico que puede existir entre las variables involucradas. Los resultados obtenidos serán proposiciones teóricas susceptibles de ser comprobadas empíricamente en un estudio posterior.

Abstract: Using the theoretical-methodological perspective based on experience frameworks, this paper aims at analyzing and understanding the way in which organizations communicate aspects that are related to occupational safety and health. In this sense, we carry out an analysis of the narratives related to this topic and propose a diagnosis mechanism to subjectively measure the burden of the regulatory, strategic, and moral frameworks of these narratives. A panel of Latin American experts applied a three-round Delphi analysis on five case studies in Colombia to illustrate how this mechanism works. In addition to the use of a robust research paradigm, this work contributes by proposing a scheme capable of presenting the motivations and interests of occupational safety and health professionals in perspective, finding imbalances in the way relevant information is transmitted through official messages and channels. Therefore, its use is susceptible to study in this and other areas of knowledge of the organizational discipline, and its adoption could be worthwhile in order to generate more and better knowledge in this regard. Resumen: Haciendo uso de la perspectiva teórico-metodológica basada en los marcos de la experiencia, el presente trabajo tiene como propósito analizar y entender la forma en la que, usualmente, se comunican los aspectos relacionados con la seguridad y la salud en el trabajo en el contexto organizacional. En este sentido, se despliega un análisis sobre las narrativas propias de esta temática y se propone un mecanismo de diagnóstico para medir subjetivamente la carga de los marcos reglamentario, estratégico y moral de dichas narrativas. Un análisis Delphi de tres rondas aplicado por un panel de expertos latinoamericanos sobre cinco casos de estudio en Colombia ilustra la manera en la que el mecanismo en mención puede funcionar. Adicional al uso de un paradigma investigativo robusto, el trabajo contribuye, toda vez que propone un esquema capaz de exponer en perspectiva, las motivaciones y los intereses de los profesionales de la seguridad y salud en el trabajo, encontrando desequilibrios en cuanto a la forma de transmitir información relevante por medio de mensajes y canales oficiales. Por tanto, su uso es susceptible de estudio en esta y otras áreas del conocimiento de la disciplina organizacional, y su adopción podría valer la pena en aras de generar más y mejor conocimiento al respecto.

Abstract: As stated by Velasco, Lara in the company of Hernández, Gómez and Gaytán, (2016) one of the problems derived from poultry farms is the emission of gases and odors, which can affect human, animal and plant health, as well as its productivity. Taking the above into account, the present study explored through a literature review, the possible adverse effects that can be generated in people who are in poultry farms and possible mitigation measures. It is developed by implementing a systematic review methodology, a bibliometric analysis and classification of adverse effects in reactions, symptoms and diseases. Additionally, recommendations for future research are proposed. Resumen: Como bien lo afirman Velasco, Lara en compañía de Hernández, Gómez y Gaytán, (2016) uno de los problemas que se deriva de las explotaciones avícolas, es la emisión de gases y olores, los cuales pueden afectar la salud humana, animal y vegetal, como también la productividad de esta. Teniendo en cuenta lo anterior, el presente estudio exploró a través de una revisión de literatura, los posibles efectos adversos que se pueden generar en las personas que se encuentran en las granjas avícolas y posibles medidas de mitigación. Se desarrolla implementando una metodología de revisión sistemática, un análisis bibliométrico y clasificación de los efectos adversos en reacciones, síntomas y enfermedades. Adicionalmente, se proponen recomendaciones para futuras investigaciones.

Abstract: 1. Global insect decline has recently become a cause for major concern, particularly in the tropics where the vast majority of species occur. Deforestation is suggested as being a major driver of this decline, but how anthropogenic changes in landscape structure affect tropical insect communities has rarely been addressed. 2. We sampled Saturniidae and Sphingidae moths on 27 farms located in Brazilian Amazonia (Pará state) and characterised by different deforestation histories. We used functional traits (forewing length, body mass, wing load, trophic niche breadth, and resource use strategy), analysed by combining RLQ and null model analyses, to investigate the responses of their taxonomic and functional diversity to landscape change dynamics and current structure. 3. We found that communities had a higher proportion of large and polyphagous species with low wing load in landscapes with low forest quality and relative cover and high land use turnover. This was mainly due to a significant response to deforestation by saturniids, whereas the more mobile sphingids showed no significant landscape-related pattern. We also observed an overall increase of species richness and functional dispersion in landscapes that have been deforested for a long time when compared with more recent agricultural settlements. 4. Our results highlight the complex way in which landscape structure and historical dynamics interact to shape Neotropical moth communities and that saturniid moths respond clearly to the structure of the surrounding landscape, confirming their potential use as an indicator group for environmental monitoring programs.

Abstract: Three new species of the hawkmoth genus Xylophanes Hübner, 1819 from Colombia are described based on morphological characters and DNA barcodes: Xylophanes camilae Correa-Carmona & Giusti sp. n., Xylophanes pijao Giusti & Correa-Carmona sp. n., and Xylophanes tayrona Correa-Carmona & Giusti sp. n. The new species are compared with the most morphologically similar species within the informal Xylophanes crotonis species-group: Xylophanes aristor (Boisduval, 1870), Xylophanes crotonis (Walker, 1856) and Xylophanes huloti Haxaire & Vaglia, 2008. Diagnoses, distribution maps, photographs of genital structures and habitus are provided for each new species. In addition, a dichotomous key is provided to identify the new species and the most morphologically similar species within the group. To stabilize the nomenclature and fi x the taxon concept, a neotype is designated for Xylophanes aristor.

Abstract: The conditional autoregressive model (CAR model) is the most popular distribution for jointly modeling the \textit{a priori} uncertainty over spatially correlated data. In general, it is used in hierarchical spatial models where it models the uncertainty about random spatial effects. A limitation of the CAR model is its inability to produce high correlations between neighboring areas. We propose a robust model for area data that alleviates this problem. We represent the map by an undirected graph where nodes represent areas and edges connect neighboring nodes on the map. We assign distinct and random weights to the edges. The model is based on a spatially structured $t-$Student multivariate distribution, in which the precision matrix is indirectly constructed assuming a multivariate distribution for the random weights of the edges. Such t- Student distribution spatially correlates the edge weights and induces another $t$-Student model for the spatial effects of the areas that correlates them and is able to accommodate outliers and heavy tail behavior for these effects. More importantly, the proposed model can produce a higher marginal correlation between spatial effects than the CAR model, overcoming one of the main limitations of this model. We adjusted the proposed model to map the incidence of some types of cancer in southern Brazil and compared its performance with several alternative models proposed in the literature. The results show that the proposed model is competitive and provides similar and, in some cases, better results than those obtained by fitting models commonly used to analyze this type of data. In the second proposal, we approach the problem of dimensionality reduction in regression models. One of the most used methods to avoid overfitting and to select relevant variables in regression models with many predictors is the penalized regression technique. Under suchapproaches, variable selection is performed in a non-probabilistic way using some optimization criterion. Bayesian approaches to penalized regression have been proposed assuming an \textit{a priori} distribution for the regression coefficients that plays a role similar to the penalty term in classical statistics: compressing towards zero non-significant coefficients and putting a probability mass significant in coefficients that can be grouped. Generally, such a priori distributions, called shrinkage priors, assume independence between the effects of the covariates, which may not bean appropriate assumption in many cases. In this work, we focus on the dimensionality reduction of categorical variables with many levels. These variables are included in the model through variables dummy inducing sparsity in the design matrix, which can generate overfitting and difficulties in interpreting the results. The effect of the levels of these categorical variables are naturally correlated. To deal with this problem, we propose two a priori shrinkage distributions for the coefficients associated with the levels of categorical variables, correlating them. The proposed distributions are properand, in addition to sparsity, they have the property of grouping similar effects. We illustrate the use of these distributions by applying them to dimensionality reduction in a linear regression. Their performances are analyzed and compared to pre-existing methods through simulated data studies and considering housing price data available on Airbnb. Resumen: El modelo autorregresivo condicional (modelo CAR) es la distribución más popular para modelar conjuntamente la incertidumbre \ textit {apriori} sobre datos correlacionados espacialmente. En general, se utiliza en modelos espaciales jerárquicos donde modela la incertidumbre sobre los efectos espaciales aleatorios. Una limitación del modelo CAR es su incapacidad para producir altas correlaciones entre áreas vecinas. Proponemos un modelo robusto para datos de área que alivia este problema. Representamos el mapa mediante un gráfico no dirigido donde los nodos representan áreas y los bordes conectan los nodos vecinos en el mapa. Asignamos pesos distintos y aleatorios a los bordes. El modelo se basa en una distribución multivariante $ t- $ Student estructurada espacialmente, en la que la matriz de precisión se construye indirectamente asumiendo una distribución multivariante para los pesos aleatorios de los bordes. Tal distribución t-Student correlaciona espacialmente los pesos de los bordes e induce otro modelo $ t $ -Student para los efectos espaciales de las áreas que los correlaciona y es capaz de acomodar valores atípicos y un comportamiento de cola pesada para estos efectos. Más importante aún, el modelo propuesto puede producir una mayor correlación marginal entre los efectos espaciales que el modelo CAR, superando una de las principales limitaciones de este modelo. Ajustamos el modelo propuesto para mapear la incidencia de algunos tipos de cáncer en el sur de Brasil y comparamos su desempeño con varios modelos alternativos propuestos en la literatura. Los resultados muestran que el modelo propuesto es competitivo y proporciona resultados similares y, en algunos casos, mejores que los obtenidos mediante el ajuste de modelos comúnmente utilizados para analizar este tipo de datos. En la segunda propuesta abordamos el problema de la reducción de dimensionalidad en modelos de regresión. Uno de los métodos más utilizados para evitar el sobreajuste y seleccionar variables relevantes en modelos de regresión con muchos predictores es la técnica de regresión penalizada. Bajo tales enfoques, la selección de variables se realiza de forma no probabilística utilizando algún criterio de optimización. Se han propuesto enfoques bayesianos para la regresión penalizada asumiendo una distribución \ textit {a priori} para los coeficientes de regresión que juega un papel similar al término de penalización en la estadística clásica: comprimir hacia cero coeficientes no significativos y poner una masa de probabilidad significativa en coeficientes que se pueden agrupar. Generalmente, tales distribuciones a priori, llamadas a priori de contracción, asumen independencia entre los efectos de las covariables, lo que puede no ser una suposición apropiada en muchos casos. En este trabajo, nos enfocamos en la reducción de dimensionalidad de variables categóricas con muchos niveles. Estas variables se incluyen en el modelo a través de variables ficticias que inducen escasez en la matriz de diseño, lo que puede generar sobreajustes y dificultades en la interpretación de los resultados. El efecto de los niveles de estas variables categóricas está naturalmente correlacionado. Para abordar este problema, proponemos dos distribuciones de contracción a priori para los coeficientes asociados a los niveles de variables categóricas, correlacionándolos. Las distribuciones propuestas son adecuadas y, además de escasa, tienen la propiedad de agrupar efectos similares. Ilustramos el uso de estas distribuciones aplicándolas a la reducción de dimensionalidad en una regresión lineal. Sus resultados se analizan y comparan con métodos preexistentes a través de estudios de datos simulados y teniendo en cuenta los datos de precios de la vivienda disponibles en Airbnb.

Abstract: Traditional models for areal data assume a hierarchical structure where one of the components is the random efects that spatially correlate the areas. The conditional autoregressive (CAR) model is the most popular distribution to jointly model the prior uncertainty about these spatial random efects. One limitation of the CAR distribution is the inability of producing high correlations between neighboring areas. We propose a robust model for areal data that alleviates this problem. We represent the map by an undirected graph where the nodes are the areas and randomly-weighted edges connect nodes that are neighbors. The model is based on a multivariate Student-t distribution, spatially structured, in which the precision matrix is indirectly built assuming a multivariate distribution for the random edges. The weights' joint distribution is a spatial multivariate Student-t that induces another t distribution for the areas' spatial efects which inherit its capacity to accommodate outliers and heavy-tail behavior. Most important, it can produce a higher marginal correlation between the spatial efects than the CAR model overcoming one of the main limitations to this model. We fit the proposed model to analyze real cancer maps and compared its performance with several state-of-art competitors. Our proposed model provides better fitting in almost all cases. Resumen: Los modelos tradicionales para datos de áreas asumen una estructura jerárquica donde uno de los componentes son los efectos aleatorios que correlacionan espacialmente las áreas. El modelo autorregresivo condicional (CAR) es la distribución más popular para modelar conjuntamente la incertidumbre previa sobre estos efectos espaciales aleatorios. Una limitación de la distribución CAR es la incapacidad de producir altas correlaciones entre áreas vecinas. Proponemos un modelo robusto para datos de área que alivia este problema. Representamos el mapa mediante un gráfico no dirigido donde los nodos son las áreas y los bordes ponderados aleatoriamente conectan los nodos que son vecinos. El modelo se basa en una distribución t de Student multivariante, estructurada espacialmente, en la que la matriz de precisión se construye indirectamente asumiendo una distribución multivariante para los bordes aleatorios. La distribución conjunta de los pesos es una t de Student multivariante espacial que induce otra distribución t para los efectos espaciales de las áreas que heredan su capacidad para acomodar valores atípicos y un comportamiento de cola pesada. Más importante aún, puede producir una mayor correlación marginal entre los efectos espaciales que el modelo CAR, superando una de las principales limitaciones de este modelo. Ajustamos el modelo propuesto para analizar mapas de cáncer reales y comparamos su desempeño con varios competidores de última generación. Nuestro modelo propuesto proporciona un mejor ajuste en casi todos los casos.

Abstract: Chain elongation fermentation in open cultures systems can tolerate complex organic waste and convert it into useful biochemicals (commodities) such as caproic acid. In previous chain elongation research, fatty alcohols such as hexanol have been also documented. Hexanol has a potential application as biofuel, is less corrosive, watersoluble, and toxic than caproic acid. Moreover, the hexanol market size seems more attractive than the one of caproic acid. This research aims to produce hexanol via a chain elongation process in an upflow anaerobic sludge blanket (UASB) system. Granular sludge formation was also considered due to its advantages such as high biomass concentration and settleability. The highest selectivity (based on total carbon products in the effluent) and productivity of caproate was 82.49 % on day 35 and 10.25 g L-1 d -1 on day 33, whereas for hexanol was 1.66 % and 0.19 g L-1 d -1 , both on day 77. CO2 supply had a positive effect on chain elongation activity and hexanol formation. Selectivity and productivity of hexanol was not negatively affected despite methane formation. Throughout the whole study, suspended biomass was the dominant form. A thick biofilm layer on the inner wall of the reactor was present from day 19 to 76 and black aggregates became more visible from day 76 onwards, although not significantly. The process was severely inhibited by substrate (ethanol) toxicity during the last phase (36.77 ± 3.23 g L-1 ).