Uso de nanoestructuras híbridas de acetato de celulosa/nanoarcilla para el desarrollo de lubricantes ecólogicos

Departamento de Ingeniería Química, Química Física y Ciencias de los Materiale

Electrohydrodynamic Processing of PVP-Doped Kraft Lignin Micro- and Nano-Structures and Application of Electrospun Nanofiber Templates to Produce Oleogels

The present work focuses on the development of lignin micro- and nano-structures obtained by means of electrohydrodynamic techniques aimed to be potentially applicable as thickening or structuring agents in vegetable oils. The micro- and nano-structures used were mainly composed of eucalyptus kraft lignin (EKL), which were doped to some extent with polyvinylpyrrolidone (PVP). EKL/PVP solutions were prepared at different concentrations (10–40 wt.%) and EKL:PVP ratios (95:5–100:0) in N, N-dimethylformamide (DMF) and further physico-chemically and rheologically characterized. Electrosprayed micro-sized particles were obtained from solutions with low EKL/PVP concentrations (10 and 20 wt.%) and/or high EKL:PVP ratios, whereas beaded nanofiber mats were produced by increasing the solution concentration and/or decreasing EKL:PVP ratio, as a consequence of improved extensional viscoelastic properties. EKL/PVP electrospun nanofibers were able to form oleogels by simply dispersing them into castor oil at nanofiber concentrations higher than 15 wt.%. The rheological properties of these oleogels were assessed by means of small-amplitude oscillatory shear (SAOS) and viscous flow tests. The values of SAOS functions and viscosity depended on both the nanofiber concentration and the morphology of nanofiber templates and resemble those exhibited by commercial lubricating greases made from traditional metallic soaps and mineral oilsThis work is part of a research project (Ref. RTI2018-096080-B-C21) sponsored by the MICINN-FEDER I+D+i Spanish Programme. The authors gratefully acknowledge their financial support. J.F.R.-V. acknowledges receiving the Ph.D. Research Grant PRE2019-090632 from MICINN (Spain

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology

Evaluating the impact of image preprocessing on iris segmentation

La segmentación del iris es una de las etapas más importantes en los sistemas de reconocimiento del iris. En este trabajo se aplican algoritmos de preprocesamiento de la imagen con el objetivo de evaluar su impacto en los porcentajes de segmentación exitosa del iris. Los algoritmos utilizados se basan en el ajuste del histograma, filtros Gaussianos y en la eliminación del reflejo especular en imágenes del ojo humano. Se aplica el método de segmentación introducido por Masek a 199 imágenes tomadas bajo condiciones no controladas, pertenecientes a la base de datos CASIA-irisV3, antes y después de aplicar los algoritmos de preprocesamiento. Posteriormente se evalúa el impacto de los algoritmos de preprocesamiento en el porcentaje de segmentación exitosa del iris por medio de una inspección visual de las imágenes, para determinar si las circunferencias detectadas del iris y de la pupila corresponden adecuadamente con el iris y la pupila de la imagen real. El algoritmo que generó uno de los mayores incrementos de los porcentajes de segmentación exitosa (pasa de 59% a 73%) es aquel que combina la eliminación de reflejos especulares, seguido por la aplicación de un filtro Gaussiano con máscara 5x5. Los resultados obtenidos señalan la importancia de una etapa previa de preprocesamiento de la imagen como paso previo para garantizar una mayor efectividad en el proceso de detección de bordes y segmentación del iris.Segmentation is one of the most important stages in iris recognition systems. In this paper, image preprocessing algorithms are applied in order to evaluate their impact on successful iris segmentation. The preprocessing algorithms are based on histogram adjustment, Gaussian filters and suppression of specular reflections in human eye images. The segmentation method introduced by Masek is applied on 199 images acquired under unconstrained conditions, belonging to the CASIA-irisV3 database, before and after applying the preprocessing algorithms. Then, the impact of image preprocessing algorithms on the percentage of successful iris segmentation is evaluated by means of a visual inspection of images in order to determine if circumferences of iris and pupil were detected correctly. An increase from 59% to 73% in percentage of successful iris segmentation is obtained with an algorithm that combine elimination of specular reflections, followed by the implementation of a Gaussian filter having a 5x5 kernel. The results highlight the importance of a preprocessing stage as a previous step in order to improve the performance during the edge detection and iris segmentation processes

Production of lignin/cellulose acetate fiber-bead structures by electrospinning and exploration of their potential as green structuring agents for vegetable lubricating oils

In this work we developed electrospun lignin/cellulose acetate fiber-bead nanostructures and explored their potential as structuring agents for vegetable oils to be used as eco-friendly lubricating oleogels. A variety of nanostructures were obtained from solutions containing 20 or 30 wt. % eucalyptus Kraft lignin (EKL) and cellulose acetate (CA) in variable weight ratios from 100:0 to 60:40 in an N,N-dimethylformamide/acetone mixture. The EKL/CA solutions were characterized in physicochemical terms from viscosity, surface tension and electrical conductivity measurements. Also, the electrospun nanostructures were characterized morphologically by scanning electron microscopy. Their morphology was found to be strongly dependent on the rheological properties of the biopolymer solution. Electrospun EKL/CA beaded nanofibers and well-developed uniform nanofiber mats allowed oleogels to be easily obtained by simply dispersing them in castor oil whilst nanoparticle clusters gave rise to unstable dispersions. The rheological properties of these gel-like dispersions can be tailored through the membrane concentration and/or EKL/CA ratio and depend to a large extent on the morphology of the electrospun nanostructures. The rheological and tribological properties of the oleogels were comparable to those previously reported for conventional and other bio-based lubricating greases. Overall, electrospun EKL/CA nanofibers allow easy, efficient structuring of vegetable oils to obtain oleogels holding potential for use as lubricants.Research Project RTI2018–096080-B-C21, funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”. PhD Research Grant PRE2019–090632 from Spain’s Ministry of Science and Innovation. Funding for open access charge: Universidad de Huelva / CBU

Oil structuring properties of electrospun Kraft lignin/ cellulose acetate nanofibers for lubricating applications: influence of lignin source and lignin/cellulose acetate ratio

In the present work, electrospun Kraft lignin/cellulose acetate nanostructures were produced, assessed and proposed as structuring or thickening agents of castor oil for lubricating applications. Solutions of Kraft lignins (KL) derived from different sources (eucalyptus, poplar and olive tree pruning) and cellulose acetate (CA) were prepared and used as feed for electrospinning. The rheological properties (shear and extensional viscosity), electrical conductivity and surface tension of KL/CA solutions influence the morphology of the electrospun nanofibers, which in turn is affected by the chemical structure and composition of the Kraft lignins. Electrospun KL/CA nanostructures consisting of filament-interconnected nanoparticles, beaded nanofibers or uniform nanofiber mats were able to form gel-like homogeneous fine dispersions by simply mechanically dispersing them into castor oil. The swelling of KL/ CA nanofibers in the percolation network was demonstrated. The rheological, tribological and microstructural properties of these oleogels are essentially governed by the morphological characteristics of the electrospun nanostructures, i.e. fiber diameter, number of beads and porosity. Rheological properties of the resulting oleogels may be tailored by modifying the lignin source and KL:CA weight ratio. According to their rheological and tribological properties, KL/ CA electrospun nanostructures-based oleogels can be proposed as a sustainable alternative to conventional lubricating greases.This work is part of a research project (RTI2018-096080-B-C21) funded by MCIN/AEI/10. 13039/501100011033 and by “ERDF A way of making Europe”. J.F. Rubio-Valle has also received a Ph.D. Research Grant PRE2019-090632 from Ministerio de Ciencia e Innovación (Spain). The financial support is gratefully acknowledged. Universidad de Huelva/CBUA thanks to the CRUE-CSIC agreement with Springer Nature

Different Kraft lignin sources for electrospun nanostructures production: Influence of chemical structure and composition

This work focuses on the structural features and physicochemical properties of different Kraft lignins and how they can influence the electrospinning process to obtain nanostructures. Structural features of Kraft lignins were characterized by nuclear magnetic resonance, size exclusion chromatography, fourier-transform infrared spectroscopy, and thermal analysis, whereas chemical composition was analyzed by standard method. The addition of cellulose acetate (CA) improves the electrospinning process of Kraft lignins (KL). Thus, solutions of KL/CA at 30 wt% with a KL:CA weight ratio of 70:30 were prepared and then physicochemical and rheologically characterized. The morphology of electrospun nanostructures depends on the intrinsic properties of the solutions and the chemical structure and composition of Kraft lignins. Then, surface tension, electrical conductivity and viscosity of eucalypt/CA and poplar/CA solutions were suitable to obtain electrospun nanostructures based on uniform cross-linked nanofibers with a few beaded fibers. It could be related with the higher purity and higher linear structure, phenolic content and S/G ratios of lignin samples. However, the higher values of electrical conductivity and viscosity of OTP/CA solutions resulted in electrospun nanostructure with micro-sized particles connected by thin fibers, due to a lower purity, S/G ratio and phenolic content and higher branched structure in OTP lignin.This work is part of two coordinated research projects (RTI2018-096080-B-C21 and RTI2018-096080-B-C22) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. The authors also wish to thank the Comunidad de Madrid and MCIU/AEI/FEDER, EU for funding this study via Projects SUSTEC-CM S2018/EMT-4348. The authors also acknowledge the pre-doctoral grants from José Fernando Rubio Valle (Ref. PRE2019-090632). The contribution of COST Action LignoCOST (CA17128), supported by COST (European Cooperation in Science and Technology), in promoting interaction, exchange of knowledge and collaborations in the field of lignin valorization is gratefully acknowledged

SISTEMA INFORMÁTICO PARA ESTIMAR EL RIESGO DE CÁNCER DE MAMA APLICANDO TÉCNICAS DE APRENDIZAJE AUTOMÁTICO / COMPUTER SYSTEM TO ESTIMATE THE RISK OF BREAST CANCER BY APPLYING MACHINE LEARNING TECHNIQUES

El cáncer de mama es una enfermedad que afecta a las personas de todo el mundo, con mayores tasas de incidencia en el sexo femenino, aunque no excluye al sexo masculino. Se encuentra entre los cinco tipos de cáncer más mortíferos, teniendo mayor afluencia en los países menos desarrollados donde el acceso a los programas de salud es más deficiente. Las altas cifras de afectación y muerte son alarmantes, en la mayoría de los casos porque se detecta en etapas tardías. Se conoce clínicamente que en pacientes donde se logra detectar la enfermedad en etapas I y II se aumenta considerablemente el pronóstico de vida y las tasas de supervivencia. En el presente trabajo se propone el desarrollo de un sistema informático de carácter preventivo como proceso hospitalario orientado a Latinoamérica y el Caribe basado en factores de riesgo característicos de la población a fin de estimar la posibilidad de que las personas sean propensas a tener la enfermedad y así contribuir al diagnóstico clínico de la enfermedad en etapas iniciales. Como punto de partida se tienen los modelos estadísticos basados en cálculos sobre factores de riesgos, donde en 1989 Gail creó un modelo específicamente en la población blanca norteamericana utilizando cinco factores. Para la estimación se utilizan técnicas de aprendizaje automático que confluyen en la elaboración de un modelo computacional que permite calcular las estimaciones y arrojar dichos resultados nutriéndose de los factores de riesgos característicos para poblaciones latinas específicamente los definidos en la norma mexicana NOM-041-SSA2-2011

Molecular phylogeny of atractus (Serpentes, dipsadidae), with emphasis on Ecuadorian species and the description of three new taxa

Volume: 661Start Page: 91End Page: 12