Estudi de l'herbivorisme del Senecio mitjançant models zero-inflats

Els estudis ecològics que involucren recomptes d'abundància o de presència-absència sovint produeixen dades amb un excés de zeros. Una forma de tractar amb l'excés de zeros és considerar els models zeros inflats. Aquests models assumeixen que l'excés de zeros prové d'un procés amb dues parts que es poden modelar de forma conjunta emprant una mixtura de distribucions. Per una banda, es modela el procés de recompte mitjançant una distribució de Poisson o Binomial Negativa on queden recollits una part dels zeros observats. Per altra banda, es modelen els zeros addicionals a través d'un model logit. En aquest treball aquesta metodologia es compara amb un model de regressió amb distribució Zipfian. Aquesta distribució de probabilitat exhibeix una elevada probabilitat al primer valor, té una cua pesada i mostra un comportament lineal en l'escala log-log

Ultra high-pressure homogenized emulsions stabilized by sodium caseinate: effects of protein concentration and pressure on emulsions structure and stability

Microstructure, physical properties and oxidative stability of emulsions treated by colloid mill (CM), conventional homogenization (CH, 15 MPa) and ultra-high-pressure homogenization (UHPH, 100–300 MPa) by using different concentrations of 1, 3 and 5 g/100 g of sodium caseinate (SC), were evaluated. The application of UHPH treatment at 200 and 300 MPa resulted in emulsions that were highly stable to creaming and oxidation, especially when the protein content increased from 1 to 3 and 5 g/100 g. Further, increasing the protein content to 3 and 5 g/100 g in UHPH emulsions tended to change the rheological behavior from Newtonian to shear thinning. CH emulsions containing 1 g/100 g of protein exhibited Newtonian flow behavior with lower tendencies to creaming compared to those formulated with 3 or 5 g/100 g. This study has proved that UHPH processing at pressures (200–300 MPa) and in the presence of sufficient amount of sodium caseinate (5 g/100 g), produces emulsions with oil droplets in nano-/submicron scale with a narrow size distribution and high physical and oxidative stabilities, compared to CM and CH treatments

Impact of oil phase concentration on physical and oxidative stability of oil-in-water emulsions stabilized by sodium caseinate and ultra-high pressure homogenization

In the present study, oil-in-water emulsions were formulated using 5.0% (w/v) of sodium caseinate (SC) and different oil concentrations (10–30%, v/v) by conventional homogenization (CH) and ultra-high pressure homogenization (UHPH, 200–300 MPa). The effect of oil concentration and pressure of treatment on emulsions characteristics and stability was studied. Emulsions were characterized assessing their microstructure, droplet size distribution, rheological properties, emulsifying activity index (EAI), creaming stability by Turbiscan®, and photo-oxidation. UHPH emulsions, especially those treated at 200 MPa, showed smaller droplet size and greater physical stability than CH emulsions. In addition, emulsions containing higher oil volume fractions (20 and 30%) exhibited greater physical and oxidative stability. UHPH emulsions treated at 200 MPa and containing 20% oil content were the most stable emulsions against physical separation and photo-oxidation. These results show that UHPH is a potential technology to enhance the physical and oxidative stability of emulsions containing sodium caseinate as emulsifier for several applications

Precariedad, exclusión social y diversidad funcional (discapacidad): lógicas y efectos subjetivos del sufrimiento social contemporáneo (II). Innovación docente en Filosofía

El PIMCD "Precariedad, exclusión social y diversidad funcional (discapacidad): lógicas y efectos subjetivos del sufrimiento social contemporáneo (II). Innovación docente en Filosofía" se ocupa de conceptos generalmente eludidos por la tradición teórica (contando como núcleos aglutinantes los de la precariedad laboral, la exclusión social y diversidad funcional o discapacidad), cuyo análisis propicia nuevas prácticas en la enseñanza universitaria de filosofía, adoptando como meta principal el aprendizaje centrado en el estudiantado, el diseño de nuevas herramientas de enseñanza y el fomento de una universidad inclusiva. El proyecto cuenta con 26 docentes de la UCM y otros 28 docentes de otras 17 universidades españolas (UV, UNED, UGR, UNIZAR, UAH, UC3M, UCA, UNIOVI, ULL, EHU/UPV, UA, UAM, Deusto, IFS/CSIC, UCJC, URJC y Univ. Pontificia de Comillas), que permitirán dotar a las actividades programadas de un alcance idóneo para consolidar la adquisición de competencias argumentativas y dialécticas por parte de lxs estudiantes implicados en el marco de los seminarios previstos. Se integrarán en el PIMCD, aparte de PDI, al menos 26 estudiantes de máster y doctorado de la Facultad de Filosofía, a lxs que acompañarán durante el desarrollo del PIMCD 4 Alumni de la Facultad de Filosofía de la UCM, actualmente investigadores post-doc y profesorxs de IES, cuya experiencia será beneficiosa para su introducción en la investigación. Asimismo, el equipo cuenta con el apoyo de varixs profesorxs asociadxs, que en algunos casos son también profesores de IES. Varixs docentes externos a la UCM participantes en el PIMCD poseen una dilatada experiencia en la coordinación de proyectos de innovación de otras universidades, lo que redundará en beneficio de las actividades a desarrollar. La coordinadora y otrxs miembros del PIMCD pertenecen a la Red de Innovación Docente en Filosofia (RIEF), puesta en marcha desde la Universitat de València (http://rief.blogs.uv.es/encuentros-de-la-rief/), a la que mantendremos informada de las actividades realizadas en el proyecto. Asimismo, lxs 6 miembros del PAS permitirán difundir debidamente las actividades realizadas en el PIMCD entre lxs estudiantes Erasmus IN del curso 2019/20 en la Facultad de Filosofía, de la misma manera que orientar en las tareas de maquetación y edición que puedan ser necesarias de cara a la publicación de lxs resultados del PIMCD y en las tareas de pesquisa bibliográfica necesarias para el desarrollo de los objetivos propuestos. Han manifestado su interés en los resultados derivados del PIMCD editoriales especializadas en la difusión de investigaciones predoctorales como Ápeiron y CTK E-Books

Estudi de l'herbivorisme del Senecio mitjançant models zero-inflats

Els estudis ecològics que involucren recomptes d'abundància o de presència-absència sovint produeixen dades amb un excés de zeros. Una forma de tractar amb l'excés de zeros és considerar els models zeros inflats. Aquests models assumeixen que l'excés de zeros prové d'un procés amb dues parts que es poden modelar de forma conjunta emprant una mixtura de distribucions. Per una banda, es modela el procés de recompte mitjançant una distribució de Poisson o Binomial Negativa on queden recollits una part dels zeros observats. Per altra banda, es modelen els zeros addicionals a través d'un model logit. En aquest treball aquesta metodologia es compara amb un model de regressió amb distribució Zipfian. Aquesta distribució de probabilitat exhibeix una elevada probabilitat al primer valor, té una cua pesada i mostra un comportament lineal en l'escala log-log

Returning after taking a break in online higher education: from intention to effective re-enrolment

Dropout is a major concern in higher education, especially in distance education, which experiences higher dropout rates. Taking into account that the flexibility of online higher education allows students to opt for periods of non-enrolment (i.e. breaks), an approach is adopted at a programme level that analyses the intention to continue of those students that have not enrolled in the second semester. Thanks to this longitudinal analysis, re-enrolment intention can be compared with the effective restart of the studies in the third semester. An e-mail survey was sent to 1,216 non-active second-term students and bivariate analysis is used to explore the relation of the variables collected in the survey with the two explained variables mentioned (intention and effective re-enrolment). An analysis of the results shows that satisfaction with course-programme variables such as learning resources, the support received or the learning platform, all of which the institution can have influence over, are very closely related with re-enrolment intention. Additionally, re-enrolment intention turns out to be one of the necessary although not sole conditions for effective re-enrolment, as the latter is also related to other external variables such as the student's age or previous university experience. This knowledge makes it possible for institutions to design personalized actions to re-engage students who are taking a break, according to the observed variables

Returning after taking a break in online higher education: from intention to effective re-enrolment

Dropout is a major concern in higher education, especially in distance education, which experiences higher dropout rates. Taking into account that the flexibility of online higher education allows students to opt for periods of non-enrolment (i.e. breaks), an approach is adopted at a programme level that analyses the intention to continue of those students that have not enrolled in the second semester. Thanks to this longitudinal analysis, re-enrolment intention can be compared with the effective restart of the studies in the third semester. An e-mail survey was sent to 1,216 non-active second-term students and bivariate analysis is used to explore the relation of the variables collected in the survey with the two explained variables mentioned (intention and effective re-enrolment). An analysis of the results shows that satisfaction with course-programme variables such as learning resources, the support received or the learning platform, all of which the institution can have influence over, are very closely related with re-enrolment intention. Additionally, re-enrolment intention turns out to be one of the necessary although not sole conditions for effective re-enrolment, as the latter is also related to other external variables such as the student's age or previous university experience. This knowledge makes it possible for institutions to design personalized actions to re-engage students who are taking a break, according to the observed variables

What does a zero mean? Understanding false, random and structural zeros in ecology

Data Descriptio

What does a zero mean? understanding false, random and structural zeros in ecology

1. Zeros (i.e. events that do not happen) are the source of two common phenomena in count data: overdispersion and zero-inflation. Zeros have multiple origins in a dataset: false zeros occur due to errors in the experimental design or the observer; structural zeros are related to the ecological or evolutionary restrictions of the system under study; and random zeros are the result of the sampling variability. Identifying the type of zeros and their relation with overdispersion and/or zero inflation is key to select the most appropriate statistical model. 2. We first introduce the dispersion and zero inflation indeces and review the theory of the Zero-inflated (ZI) models and the use of the score tests to assess overdispersion and zero inflation over a model. Then, we present the following protocol to assist with the analysis of count data: Step 1) classify the zeros and minimize the presence of false zeros; Step 2) identify suitable covariates; Step 3) test the data for overdispersion and zero-inflation; and Step 4) choose the most adequate model based on the results of step 3 and use score tests to determine whether more complex models should be implemented. 3. We applied the recommended protocol on a real dataset on plant-herbivore interactions to evaluate the suitability of six different models (Poisson, NB and their zero-inflated versions –ZIP, ZINB–) in the light of overdispersion and zero inflation. Finally, we discuss the consequences of adjusting suboptimal models. 4. Our data was overdispersed and zero-inflated, and the ZINB was the model with the best fit, as predicted. Ignoring overdispersion and/or zero inflation during data analyses caused biased estimates of the statistical parameters and serious errors in the interpretation of the results. Our results are a clear example on how the conclusions of an ecological hypothesis can change depending on the model applied. Understanding of how zeros arise in count data, for example identifying the potential sources of structural zeros, is essential to select the best statistical design. A good model not only fits the data correctly but also takes into account the idiosyncrasies of the biological system under study.Peer ReviewedPostprint (author's final draft