Aportaciones al diagnóstico de cáncer asistido por ordenador

Para diagnosticar un cáncer se realiza, entre otras pruebas, algún test de imagen, como puede ser una radiografía, ecografía o resonancia magnética. Mediante estos tests pueden detectarse zonas con alta sospecha tumoral, cuyo diagnóstico debe confirmase finalmente mediante la realización de una biopsia. Este tipo de imágenes, sin embargo, no son fáciles de interpretar, lo que provoca que el profesional encargado de analizarlas, a pesar de su experiencia, no sea capaz de detectar en ellas un porcentaje importante de tumores (falsos negativos). Una posibilidad para mejorar el diagnóstico y disminuir el número de falsos negativos consiste en utilizar sistemas de diagnóstico asistido por ordenador o computer-aided diagnosis (CAD). Un sistema de CAD analiza la imagen médica y trata de detectar zonas sospechosas de contener alguna anomalía. Estas zonas son marcadas sobre la propia imagen con un doble objetivo: llamar la atención del profesional encargado de analizarla hacia la zona sospechosa y aportar una segunda opinión respecto al diagnóstico. En esta tesis se presentan y evaluan diversas técnicas de visión por computador y reconocimiento de formas orientadas a la detección de tumores en imágenes médicas, con el objetivo de diseñar sistemas de CAD que permitan un mejor diagnóstico. El trabajo se ha centrado en el diagnóstico de cáncer de próstata a partir de imágenes de ecografía, y en el diagnóstico de cáncer de mama a partir de imágenes de radiografía. Se han evaluado diversos métodos de extracción de características basados en la intensidad, frecuencia, texturas o en gradientes. En la etapa de clasificación se ha utilizado un clasificador no paramétrico basado en distancias (k-vecinos más cercanos) y otro paramétrico basado en modelos de Markov. A lo largo del trabajo se evidencian las distintas problemáticas que surgen en este tipode tareas y se proponen soluciones a cada una de ellas. El diagnóstico de cáncer de próstata asistido por ordenador es una tarea extremaLlobet Azpitarte, R. (2006). Aportaciones al diagnóstico de cáncer asistido por ordenador [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1862Palanci

Catalytic decomposition of biogas to produce H2-rich fuel gas and carbon nanofibers. Parametric study and characterization

El pdf del artículo es la versión post-print.- Available online December 2, 2011One of the main problems that our society must deal with in a near future is the progressive substitution of traditional fossil fuels by different energy sources, such as renewable energies. In this context, biogas will play a vital role in the future. Nowadays, one of the most important uses of biogas is the production of heat and electricity from its direct combustion in co-generation plants. An interesting alternative consists on its direct valorisation to produce a syn-gas that can be further processed to produce chemicals, liquid fuels, or hydrogen. Results showed in this work evidenced that catalytic decomposition of biogas (CH4/CO2 mixtures) can be carried out with a Ni/Al2O3 catalyst obtaining simultaneously a syn-gas with high H2 content together with carbonaceous nanostructured materials with high added value. The parametric study revealed that temperature, WHSV (Weight Hourly Space Velocity, defined here as the total flow rate at normal conditions per gram of catalyst initially loaded) and CH4:CO2 feed ratio influence directly in CH4 and CO2 conversion, H2:CO ratio and carbon generation (gC/gcat). It was also evidenced that carbon structure depends on temperature. At 600ºC, fishbone like nanofibers with no hollow core are obtained while at 700ºC a mixture of fishbone and ribbon like nanofibers with a clear hollow core are formed.The authors acknowledge the Spanish Science and Innovation Ministry for the financial support of the Project ENE2008-06516.Peer reviewe

A qualitative study exploring the patients' perspective from the 'Reserved Therapeutic Space' nursing intervention in acute mental health units

This study aimed to explore the perspective of people who had experienced treatment as patients at acute mental health units, regarding an intervention model to improve therapeutic relationships in the units, which had been previously designed by the nurses. The study participants were people linked to collectives for social activism in mental health. Six focus groups were held. The results were classified into three themes: (a) the meaning of a space to enable the establishment of a therapeutic relationship, (b) the procedures to implement the space, and (c) the difficulties to overcome to establish the space. For the participants, the Reserved Therapeutic Space intervention was perceived as a space where they could share expectations and needs with the nurses, considering it as both valid and useful to improve the therapeutic relationship in acute units. For the participants, the intervention should be structured in three stages: orientation, follow-up, and discharge. The content of the intervention should be proposed by the patients based on their needs and concerns. The barriers identified for carrying out the intervention were the lack of relational competence, the violation of rights, and the lack of accessibility of nurses. The facilitating elements were the availability of nurses, active listening, and empathy. The resulting intervention model includes realities of both groups, providing insights for nurses to initiate a space with patients and improve their therapeutic relationship. This intervention model could be used by managers to test its effectiveness

Carbon nanofibres coated with Ni decorated MoS2 nanosheets as catalyst for vacuum residue hydroprocessing

9 págs.,7 figures, 3 tablesCatalysts based on functionalised carbon nanofibers (FCNF) coated with Ni-decorated MoS2 nanosheets were obtained by direct decomposition of ammonium thiomolybdate and nickel nitrate impregnated on the FCNF under controlled temperature conditions in inert atmosphere. The catalysts were characterised by X-ray diffraction (XRD), N2 adsorption, Raman spectroscopy, temperature programmed reduction of sulfur species (TPR-S), NH3 temperature programmed desorption (NH3-TPD) and transmission electron microscopy (TEM). Decomposition temperature was found to have a paramount importance in the formation of uniform MoS2 slabs, as revealed by the TEM study: at 600°C, non-uniform covering of the carbon nanofiber (CNF) was observed together with the presence of small round-shaped metal particles (ca. 20nm). On the other hand, at 450°C CNF appeared homogeneously covered by amorphous MoS2 slabs decorated with Ni, resulting in higher amount of coordinated unsaturated sites (CUS), as determined by TPR-S. Catalysts were tested in the hydroprocessing of a vacuum residue and the results were compared against a benchmark alumina-supported NiMo catalyst. Higher asphaltene conversions were obtained for the CNF-supported catalysts prepared at 450°C, which overperformed the Al2O3-supported benchmark catalyst. However, the catalytic performance in hydrodesulfurisation and hydrodemetallisation of the CNF-based catalysts was slightly lower than that of the benchmark catalyst.J.L.P. thanks the Spanish MEC for a personal grant (Spanish Scientists Mobility Program, ref. EX2009-0822). H.P., D.T and S.d.LL. thank CSIC for the funding of the short stays at ICB-CSIC or Imperial College (project I-LINK ref.0439). H.P. and S.d.LL. thank the support of CONACYT Mexico and DGA (Spain), respectively, for the award of their PhD grant. DT thanks the support of FEDER and Spanish Ministery of Economy and Competitiveness for the award of his PhD grant under the frame of the research project ENE2011-28318-C03-01.Peer Reviewe

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

[EN] The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm's accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.This work was supported in part by the Generalitat Valenciana through the Valencian Institute of Business Competitiveness (IVACE) Distributed Nominatively to Valencian Technological Innovation Centers under Project IMAMCN/2021/1, in part by the Cervera Network of Excellence Project in Data-Based Enabling Technologies (AI4ES) Co-Funded by the Centre for Industrial and Technological Development¿E. P. E. (CDTI), and in part by the European Union through the Next Generation EU Fund within the Cervera Aids Program for Technological Centers under Project CER-20211030.Ortiz, V.; Pérez-Benito, FJ.; Del Tejo Catalá, O.; Salvador Igual, I.; Llobet Azpitarte, R.; Perez-Cortes, J. (2022). Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks. IEEE Access. 10:120074-120085. https://doi.org/10.1109/ACCESS.2022.32219361200741200851

Composition of Constraint, Hypothesis and Error Models to improve interaction in Human-Machine Interfaces

We use Weighted Finite-State Transducers (WFSTs) to represent the different sources of information available: the initial hypotheses, the possible errors, the constraints imposed by the task (interaction language) and the user input. The fusion of these models to find the most probable output string can be performed efficiently by using carefully selected transducer operations. The proposed system initially suggests an output based on the set of hypotheses, possible errors and Constraint Models. Then, if human intervention is needed, a multimodal approach, where the user input is combined with the aforementioned models, is applied to produce, with a minimum user effort, the desired output. This approach offers the practical advantages of a de-coupled model (e.g. input-system + parameterized rules + post-processor), keeping at the same time the error-recovery power of an integrated approach, where all the steps of the process are performed in the same formal machine (as in a typical HMM in speech recognition) to avoid that an error at a given step remains unrecoverable in the subsequent steps. After a presentation of the theoretical basis of the proposed multi-source information system, its application to two real world problems, as an example of the possibilities of this architecture, is addressed. The experimental results obtained demonstrate that a significant user effort can be saved when using the proposed procedure. A simple demonstration, to better understand and evaluate the proposed system, is available on the web https://demos.iti.upv.es/hi/. (C) 2015 Elsevier B.V. All rights reserved.Navarro Cerdan, JR.; Llobet Azpitarte, R.; Arlandis, J.; Perez-Cortes, J. (2016). Composition of Constraint, Hypothesis and Error Models to improve interaction in Human-Machine Interfaces. Information Fusion. 29:1-13. doi:10.1016/j.inffus.2015.09.001S1132

Impact of the 'reserved therapeutic space' nursing intervention on patient health outcomes: An intervention study in acute mental health units.

Aims To evaluate the effectiveness of the ‘reserved therapeutic space’ intervention for improving the nurse–patient therapeutic relationship in acute mental health units in Spain. Design Multicentre intervention study with control group. Methods The study will be carried out in 12 mental health units. The ‘reserved therapeutic space’ intervention to be tested has been co-designed and validated by both nurses and patients. The quality of the therapeutic relationship, the care received and perceived coercion among patients will be assessed. An estimated 131 patients per group are expected to participate. Funding was granted by the Instituto de Salud Carlos III. Co-financed by the European Union (European Regional Development Fund (ERDF) (PI21/00605)) and College of Nurses of Barcelona (PR-487/2021). The proposal was approved by all the Research Ethics Committees of participating centres. Results This project will lead to changes in clinical practice, transforming the current models of organization and care management in mental health hospitalization units. No patient or public contribution

Preparation of polymer composites using nanostructured carbon produced at large scale by catalytic decomposition of methane

7 págs, 7 figures, 1 Table.Polymer-based composites were prepared using different concentrations of nanostructured carbons (NCs), produced by catalytic decomposition of methane (CDM). Four carbonaceous nanostructures were produced using different catalysts (with Ni and Fe as active phases) in a rotary bed reactor capable of producing up to 20 g of carbon per hour. The effect of nanostructured carbon on the thermal and electrical behaviour of epoxy-based composites is studied. An increase in the thermal stability and the decrease of electrical resistivity were observed for the composites at carbon contents as low as 1 wt%. The highest reduction of the electrical resistivity was obtained using multi-walled carbon nanotubes obtained with the Fe based catalysts. This effect could be related to the high degree of structural order of these materials. The results were compared with those obtained using a commercial carbon nanofibre, showing that the use of carbon nanostructures from CDM can be a valid alternative to the commercial nanofibres.The authors acknowledge the Spanish Science and Innovation Ministry for the financial support of Project ENE2008-06516-C03-01 Spanish Economy and Competitiveness Ministry under project ENE2011-28318-C03-01.Peer Reviewe

Impact of collaborative nursing care on the recovery process of mental health day hospital users: a mixed-methods study protocol

Introduction: Very few collaborative nursing care interventions have been studied and shown to be effective in the context of the paradigm shift towards recovery in mental health nursing. Understanding the changes produced in the recovery process of people with mental health problems can contribute to the design and implementation of new methodologies to offer effective and person-centred care. Methods and analysis: This is a mixed-methods study, which is structured in three phases. In phase one (baseline) and phase three (follow-up), quantitative data will be collected from patients at a mental health day hospitals based on a two-armed, parallel-design, non-randomised trial. In phase two, two groups will be established: an intervention group in which the intervention based on collaborative nursing care will be carried out through the codesign and implementation of activities through Participatory Action Research, and a control group in which the usual care dynamics will be continued. All the users of three mental health day hospitals who agree to participate in the study will be studied consecutively until the necessary sample size is reached. The outcomes used to evaluate the impact of the intervention will be the stage of the recovery process, the quality of the therapeutic relationship and the patient's level of positive mental health. Ethics and dissemination: This study has been approved by the institutional review board of the reference hospital, FIDMAG Hermanas Hospitalarias (PR-2020-10) in July 2020. All participants will be able to voluntarily withdraw from the study at any time. For this reason, users will be given a sheet with all the precise information about the study to be carried out and written consent will be requested. Preliminary and final results will be published in peer-reviewed journals and presented at national and international congresses

Espacio Terapéutico Reservado: una intervención enfermera para unidades de agudos de salud mental. Guía breve de implementación

Esta Guía breve se centra en describir la intervención Espacio Terapéutico Reservado. Esta intervención ha sido diseñada por enfermeras de salud mental y personas con experiencia propia en unidades de hospitalización de salud mental. La finalidad de la intervención es mejorar el proceso de relación terapéutica enfermera-paciente, proceso que es considerado tanto por las enfermeras como por los pacientes como el eje central de los cuidados en enfermería de salud mental. Además, se sabe que su adecuado establecimiento mejora la efectividad de cualquier intervención enfermera en la práctica clínica