Eco-innovation : tools to facilitate early-stage workshops

This thesis presents research carried out into the use of creative tools at the early stages of eco-innovation. Eco-innovation is a practical approach aiming to develop new products and processes which significantly decrease our impact on the environment. Designers are trained to develop profitable products that increase production and consumption. Eco-innovation is a new discipline in which designers can radically reduce the environmental burdens of production and consumption through the innovation of new types of products and services. The main aim of this research was to develop an approach that would promote significant environmental improvements whilst remaining a practical, design-focused discipline. Problems and under-investigated aspects of eco-innovation were identified: • Creative approaches at early stages of eco-innovation were under-investigated and few tools had been developed for use at the early stages. • Empirical design research techniques had rarely been used to assess new eco-innovation tools or to inform their subsequent development. The focus of the research work was the development and testing of tools to facilitate workshops at the early stages of eco-innovation. Not only was the goal to facilitate the generation of radical ideas but also to ensure that these were developed into appropriate solutions having the potential to be taken up in industry. The development of the tools was based on literature research, worked examples and interviews. The tools were tested in controlled workshop experiments and the results were analysed using various empirical techniques. First, an idea-recording technique to improve the efficiency of generating and harvesting ideas in a team design process was developed. This novel tool was called the Product Ideas Tree (PIT) diagram. The tool was tested for its ability to facilitate design workshops. Secondly, a structured approach to innovation - the theory of inventive problem solving (TRIZ) - was investigated. Worked examples using some of the tools from TRIZ were presented and a limited number of tools were selected and simplified for testing in team design workshops. The PIT diagram and TRIZ tools experiments established which attributes of the tools and approaches were most beneficial. The development and testing of these specific tools provided the following general contributions to eco-innovation: • A model for eco-innovation that describes the factors influencing the discipline and the attributes of good practice. • A recommended process to transform radical ideas into appropriate solutions to improve their potential to be taken up in industry. • General insights into the use of tools in early-stage workshops such as: tool selection, integration into existing processes, system-level problem solving and providing thematic information. • Suggested improvements for testing tools in controlled workshop experiments.EThOS - Electronic Theses Online ServiceEPSRCGBUnited Kingdo

Criteris tècnics per a l’autorització dels centres que realitzen cirurgia refractiva

Cirurgia refractiva; Criteris tècnics; Protocols d'actuació; AutoritzacióCirugía refractiva; Criterios técnicos; Protocolos de actuación; AutorizaciónRefractive surgery; Technical criteria; Action protocols; AuthorizationAquest document presenta una sèrie de recomanacions per tal les noves tècniques de cirurgia refractiva es realitzin en unes instal·lacions adequades i en unes condicions de seguretat òptimes per garantir l’accés de tots els ciutadans a uns serveis de salut de qualitat

Recommendations for ophthalmologic practice during the easing of COVID-19 control measures

In the context of the COVID-19 pandemic, this paper provides recommendations for medical eye care during the easing of control measures after lockdown. The guidelines presented are based on a literature review and consensus among all Spanish Ophthalmology Societies regarding protection measures recommended for the ophthalmologic care of patients with or without confirmed COVID-19 in outpatient, inpatient, emergency and surgery settings. We recommend that all measures be adapted to the circumstances and availability of personal protective equipment at each centre and also highlight the need to periodically update recommendations as we may need to readopt more restrictive measures depending on the local epidemiology of the virus. These guidelines are designed to avoid the transmission of SARS-CoV-2 among both patients and healthcare staff as we gradually return to normal medical practice, to prevent postoperative complications and try to reduce possible deficiencies in the diagnosis, treatment and follow-up of the ophthalmic diseases. With this update (5th) the Spanish Society of Ophthalmology is placed as one of the major ophthalmology societies providing periodic and systematized recommendations for ophthalmic care during the COVID-19 pandemic

Multiple mechanisms mediating carbon monoxide inhibition of the voltage-gated K+ channel Kv1.5

The voltage-gated K+ channel plays key roles in the vasculature and in atrial excitability, and contributes to apoptosis in various tissues. In this study, we have explored its regulation by carbon monoxide (CO), a product of the cytoprotective heme oxygenase enzymes, and a recognized toxin. CO inhibited recombinant Kv1.5 expressed in HEK293 cells in a concentration-dependent manner which involved multiple signalling pathways. CO inhibition was partially reversed by superoxide dismutase mimetics, and by suppression of mitochondrial reactive oxygen species. CO also elevated intracellular nitric oxide (NO) levels. Prevention of NO formation also partially reversed CO inhibition of Kv1.5, as did inhibition of soluble guanylyl cyclase. CO also elevated intracellular peroxynitrite levels, and a peroxynitrite scavenger markedly attenuated the ability of CO to inhibit Kv1.5. CO caused nitrosylation of Kv1.5, an effect which was also observed in C331A and C346A mutant forms of the channel, which had previously been suggested as nitrosylation sites within Kv1.5. Augmentation of Kv1.5 via exposure to hydrogen peroxide was fully reversed by CO. Native Kv1.5 recorded in HL-1 murine atrial cells was also inhibited by CO. Action potentials recorded in HL-1 cells were increased in amplitude and duration by CO, an effect mimicked and occluded by pharmacological inhibition of Kv1.5. Our data indicate that Kv1.5 is a target for modulation by CO via multiple mechanisms. This regulation has important implications for diverse cellular functions, including excitability, contractility and apoptosis

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). 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Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

Contains fulltext : 75361.pdf (publisher's version ) (Open Access)14 p

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. Journal of Clinical Oncology, 28(11), 1963-1972. doi:10.1200/jco.2009.26.3541Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58(13), R97-R129. doi:10.1088/0031-9155/58/13/r97Dolecek, T. A., Propp, J. M., Stroup, N. E., & Kruchko, C. (2012). CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005-2009. Neuro-Oncology, 14(suppl 5), v1-v49. doi:10.1093/neuonc/nos218Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438. doi:10.1016/j.mri.2013.05.002Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S.-K., … Davatzikos, C. (2008). Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images. Academic Radiology, 15(8), 966-977. doi:10.1016/j.acra.2008.01.029Jensen, T. R., & Schmainda, K. M. (2009). Computer-aided detection of brain tumor invasion using multiparametric MRI. Journal of Magnetic Resonance Imaging, 30(3), 481-489. doi:10.1002/jmri.21878Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Wagstaff KL. Intelligent Clustering with instance-level constraints. PhD Thesis, Cornell University. 2002.Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., & Murtagh, F. R. (2001). Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligence in Medicine, 21(1-3), 43-63. doi:10.1016/s0933-3657(00)00073-7Nie, J., Xue, Z., Liu, T., Young, G. S., Setayesh, K., Guo, L., & Wong, S. T. C. (2009). Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Computerized Medical Imaging and Graphics, 33(6), 431-441. doi:10.1016/j.compmedimag.2009.04.006Zhu, Y., Young, G. 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