Literature review. Generation and storage of electricity through biological systems: microbial fuel cells

Traballo fin de grao (UDC.CIE). Bioloxía. Curso 2016/2017[Resumen] Debido a la mayor concienciación actual del elevado ritmo de consumo de energía y grado de contaminación asociado, se ha incrementado la investigación de energías renovables. Una alternativa potencialmente interesante son las células de combustible microbiano o microbial fuel cells (MFCs), que generan electricidad que puede ser almacenada o utilizada en diversas aplicaciones, gracias al aprovechamiento de la actividad de una fuente biológica. Desde su origen, en 1911, cuando Michael Cresse Potter empleó microorganismos para la consecución de electricidad, diversos estudios enriquecieron paulatinamente el campo, incrementándose entre finales del siglo XX y comienzos del siglo XXI. En la presente revisión bibliográfica se estudia el estado actual de investigación en este ámbito, valorando las características, aplicaciones y limitaciones de cuatro configuraciones de MFCs, para determinar su potencial uso como tecnología renovable en base a su sostenibilidad energética. Por último, se concluye que, de las expuestas, la célula de combustible microbiano sedimentaria o sediment microbial fuel cell (SMFC) es la alternativa más sostenible y se presentan los parámetros que condicionan su rendimiento, proponiéndose vías de investigaciones futuras que contribuyan a su mejoría y mayor aplicación en la vida real.[Resumo] Debido á maior concienciación actual do elevado ritmo de consumo de enerxía e grao de contaminación asociado, aumentou a investigación de enerxías renovables. Unha alternativa potencialmente interesante son as células de combustible microbiano ou microbial fuel cells (MFCs), que xeran electricidade que pode ser almacenada ou empregada en diversas aplicacións, grazas ao aproveitamento da actividade dunha fonte biolóxica. Dende a súa orixe, en 1911, cando Michael Cresse Potter empregou microorganismos para a consecución de electricidade, diversos estudos enriqueceron paulatinamente o campo, incrementándose entre finais do século XX e comezos do século XXI. Na presente revisión bibliográfica estúdase o estado actual de investigación neste ámbito, valorando as características, aplicacións e limitacións de catro configuracións de MFCs, para determinar o seu potencial uso como tecnoloxía renovable en base a súa sustentabilidade enerxética. Por último, conclúese que, das expostas, a célula de combustible microbiano sedimentaria ou sediment microbial fuel cell (SMFC) é a alternativa máis sostible e preséntanse os parámetros que condicionan o seu rendimento, propoñéndose vías de investigación futuras que contribúan a súa melloría e maior aplicación na vida real.[Abstract] Due to the greater current awareness about energy consumption and the pollution associated, research on renewable energies has increased. A potentially interesting alternative are microbial fuel cells (MFCs), which generate electricity that can be storage or used in a variety of applications thanks to the activity exploitation of a biological source. Since its origin, around 1911, when Michael Cresse Potter used microorganisms to produce electricity, diverse studies have been gradually developed, increasing between the end of XX century and the beginning of XXI century. In this literature review, the actual state of investigation in this area is being presented, considering the characteristics, applications and limitations of four MFC configurations. The objective is to determine their potential usage as renewable technology considering their energy sustainability. Lastly, it is concluded that, among the previously explained devices, the sediment microbial fuel cell (SMFC) is the most sustainable alternative, and the parameters conditioning its efficiency are also being presented, as well as the proposition of future investigation ways to contribute to its improvement and greater application in real life

Poverty Mapping Under Area-Level Random Regression Coefficient Poisson Models

[Abstract]: Under an area-level random regression coefficient Poisson model, this article derives small area predictors of counts and proportions and introduces bootstrap estimators of the mean squared errors (MSEs). The maximum likelihood estimators of the model parameters and the mode predictors of the random effects are calculated by a Laplace approximation algorithm. Simulation experiments are implemented to investigate the behavior of the fitting algorithm, the predictors, and the MSE estimators with and without bias correction. The new statistical methodology is applied to data from the Spanish Living Conditions Survey. The target is to estimate the proportions of women and men under the poverty line by province.This work was supported by the Ministry of Science and Innovation and the State Research Agency of the Spanish Government through the European Regional Development Fund (PID2022-136878NB-I00, PID2020-113578RB-I00 and PRE2021-100857 to Naomi Diz-Rosales funded by MCIN/AEI/10.13039/501100011033); by the Conselleria d’Innovació, Universitats, Ciéncia i Societat Digital of the Generalitat Valenciana (Prometeo/2021/063); by the Consellería de Cultura, Educación, Formación Profesional e Universidades of the Xunta de Galicia through the European Regional Development Fund (Competitive Reference Groups ED431C/2020/14, COV20/00604, and ED431G/2019/01); and by Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC) that is supported by Xunta de Galicia, collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centers of the Sistema Universitario de Galicia (CIGUS).Xunta de Galicia; ED431C/2020/14Xunta de Galicia; COV20/00604Xunta de Galicia; ED431G/2019/0

Mapping the Poverty Proportion in Small Areas under Random Regression Coefficient Poisson Models

Cursos e Congresos, C-155[Abstract] In a complex socio-economic context, policy makers need highly disaggregated poverty indicators. In this work, we develop a methodology in small area estimation to derive predictors of poverty proportions under a random regression coefficient Poisson model, introducing bootstrap estimators of mean squared errors. Maximum likelihood estimators of model parameters and random effects mode predictors are calculated using a Laplace approximation algorithm. Simulation experiments are conducted to investigate the behaviour of the fitting algorithm, the predictors and the mean squared error estimator. The new statistical methodology is applied to data from the Spanish survey of living conditions to map poverty proportions by province and sex, developing a tool to support policy decision makingXunta de Galicia; ED431C-2020/14This research is part of the grant PID2020-113578RB-I00, funded by MCIN/AEI/10.13039/501100011033/. It has also been supported by the Spanish grant PID2022-136878NB-I00, the Valencian grant Prometeo/2021/063, by the Xunta de Galicia (Competitive Reference ED431C-2020/14) and by CITIC that is supported by Xunta de Galicia, collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Sistema Universitario de Galicia (CIGUS). The first author was also sponsoredby the Spanish Grant for Predoctoral Research Trainees RD 103/2019 being this work part of grant PRE2021-100857, funded by MCIN/AEI/10.13039/501100011033/ and ESF

Association of serum anti-centromere protein F antibodies with clinical response to infliximab in patients with rheumatoid arthritis: a prospective study

[Abstract] Background: One-third of rheumatoid arthritis (RA) patients demonstrate no clinical improvement after receiving tumor necrosis factor inhibitors (TNFi). The presence of serum autoantibodies is a hallmark in RA and may provide information on future response to treatment. The aim of this prospective study was to search for novel serum autoantibodies useful to predict clinical response to TNFi. Methods: The autoantibody repertoire was profiled on RA patients treated with TNFi as a first line of biologic therapy (N = 185), who were recruited in three independent cohorts. The presence and levels of autoantibodies in serum at baseline were analysed in association with the clinical response after 24 weeks follow-up. A multiplex bead array built using antigens selected from an initial untargeted screening was employed to identify the autoantibodies on a discovery cohort (N = 50) and to verify and validate the results on verification (N = 61) and validation (N = 74) cohorts. Non-parametric tests, meta-analysis and Receiver Operating Curves (ROC) were performed in order to assess the clinical relevance of the observed findings. Results: Novel autoantibodies were associated with the clinical response to TNFi, showing different reactivity profiles among the different TNFi. The baseline levels of IgG antibodies against Centromere protein F (CENPF), a protein related to cell proliferation, were significantly (p<0.05) increased in responders (N = 111) to infliximab (IFX) compared to non-responders (N = 44). The addition of anti-CENPF antibodies to demographic and clinical variables (age, sex, DAS28-ESR) resulted in the best model to discriminate responders, showing an area under the curve (AUC) of 0.756 (95% CI [0.639-0.874], p = 0.001). A further meta-analysis demonstrated the significant association of anti-CENPF levels with the patient's subsequent response to IFX, showing a standardized mean difference (SMD) of -0.65 (95% CI [-1.02;-0. 27], p = 0.018). Conclusions: Our study reveals for the first time the potential of circulating anti-CENPF antibodies to predict the clinical response to IFX before starting the treatment. This finding could be potentially useful to guide therapeutic decisions and may lead to further studies focusing on the role of CENPF on RA pathology.Instituto de Salud Carlos III; PI14/01707Instituto de Salud Carlos III; PI16/02124Instituto de Salud Carlos III; PI17/00404Instituto de Salud Carlos III; PI19/01206Instituto de Salud Carlos III; CIBER-CB06/01/0040Instituto de Salud Carlos III; RETIC-RIER-RD16/0012/0002Instituto de Salud Carlos III; PRB3-ISCIII-PT17/0019/0014)