TFM: programació didàctica semipresencial en l'assignatura de Física i Química de 4t d'ESO

Treball Final de Màster Universitari en Professor/a d'Educació Secundària Obligatòria i Batxillerat, Formació Professional i Ensenyaments d'Idiomes. Codi SAP119. Curs: 2019/2020Aquest document correspon al TFM (Treball Fi Màster), del Màster Universitari en Professor/a d'Educació Secundària Obligatòria i Batxillerat, Formació Professional i Ensenyaments d'Idiomes en l'especialitat de Ciències Experimentals i Tecnologia. Es tracta d’una programació didàctica de l’assignatura de Física i Química per al curs de 4r d’ESO en modalitat semipresencial per als blocs 4 i 5. La programació inclou unitats didàctiques: 4 del bloc 4 i altres 2 del bloc 5, segons mostra el RD 1105/2014. A més, inclou un cronograma amb la temporització de les sessions i les activitats descrites segons les metodologies especificades, alguns materials es troben als enllaços i d’altres accessibles amb enllaços. És objecte d’aquest TFM apropar la docència de la part de física de 4t de l’ESO per a la promoció de les STEM amb una modalitat semipresencial per reduir grups, garantir distanciament social i, a més a més, aplicable a casuístiques de despoblament rural. Aquest model inclourà activitats d’autoaprenentatge, sessions síncrones i asíncrones de teleformació per desenvolupar metodologies com la flipped classroom, activitats amb temàtica de videojocs, activitats grupals per fomentar l’aprenentatge cooperatiu i tot això per assolir les competències clau

Programming Skeletal Muscle Metabolic Flexibility in Offspring of Male Rats in Response to Maternal Consumption of Slow Digesting Carbohydrates during Pregnancy

Skeletal muscle plays a relevant role in metabolic flexibility and fuel usage and the associated muscle metabolic inflexibility due to high-fat diets contributing to obesity and type 2 diabetes. Previous research from our group indicates that a high-fat and rapid-digesting carbohydrate diet during pregnancy promotes an excessive adipogenesis and also increases the risk of non-alcoholic fatty liver disease in the offspring. This effect can be counteracted by diets containing carbohydrates with similar glycemic load but lower digestion rates. To address the role of the skeletal muscle in these experimental settings, pregnant rats were fed high-fat diets containing carbohydrates with similar glycemic load but different digestion rates, a high fat containing rapid-digesting carbohydrates diet (HF/RD diet) or a high fat containing slow-digesting carbohydrates diet (HF/SD diet). After weaning, male offspring were fed a standard diet for 3 weeks (weaning) or 10 weeks (adolescence) and the impact of the maternal HF/RD and HF/SD diets on the metabolism, signaling pathways and muscle transcriptome was analyzed. The HF/SD offspring displayed better muscle features compared with the HF/RD group, showing a higher muscle mass, myosin content and differentiation markers that translated into a greater grip strength. In the HF/SD group, metabolic changes such as a higher expression of fatty acids (FAT/CD36) and glucose (GLUT4) transporters, an enhanced glycogen content, as well as changes in regulatory enzymes such as muscle pyruvate kinase and pyruvate dehydrogenase kinase 4 were found, supporting an increased muscle metabolic flexibility and improved muscle performance. The analysis of signaling pathways was consistent with a better insulin sensitivity in the muscle of the HF/SD group. Furthermore, increased expression of genes involved in pathways leading to muscle differentiation, muscle mass regulation, extracellular matrix content and insulin sensitivity were detected in the HF/SD group when compared with HF/RD animals. In the HF/SD group, the upregulation of the ElaV1/HuR gene could be one of the main regulators in the positive effects of the diet in early programming on the offspring. The long-lasting programming effects of the HF/SD diet during pregnancy may depend on a coordinated gene regulation, modulation of signaling pathways and metabolic flexibility that lead to an improved muscle functionality. The dietary early programming associated to HF/SD diet has synergic and positive crosstalk effects in several tissues, mainly muscle, liver and adipose tissue, contributing to maintain the whole body homeostasis in the offspring.European Union’s Seventh Framework Programme (FP7/2007–2013

Transforming YouTube into a valid source of knowledge for Anatomy students

[EN] YouTube is a free and easily accessible tool, with growing importance in the teaching field due to the content of the videos and their interaction options through comments, responses and insertion in social networks. However, some limitations can reduce the value of this tool in University teaching if institutional control is not carried out. Our project consists of the search for experiences based on learning Anatomy on YouTube to be able to incorporate this tool in our department. Almost all researchers found that most of students use YouTube as a source of anatomical knowledge, despite limitations and criticism based on ethical and privacy issues, the video experience itself, the YouTube search algorithm, lack of quality control, advertising purposes or excessive video offer. Researchers experienced that most of the available videos had a poor quality and many mistakes, so professors must be involved in the search and selection of the best appropriate videos. We conclude that YouTube can be used as a source of knowledge for anatomical learning. However it is necessary to inform students of the inconveniences and risks, and make a critical selection by the professors of the videos that best fit in the teaching program.Alegre-Martínez, A.; Martínez-Martínez, MI.; Alfonso-Sánchez, JL. (2020). Transforming YouTube into a valid source of knowledge for Anatomy students. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):293-300. https://doi.org/10.4995/HEAd20.2020.11044OCS29330030-05-202

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires

Computational models; Data acquisitionModelos computacionales; Adquisición de datosModels computacionals; Adquisició de dadesArtificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression. The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education. The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69–0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment

Budget Transparency in Local Governments: An Empirical Analysis

The aim of this paper is to shed additional light on the determinants of budget transparency in local governments. Our work is based on a Likert-type survey questionnaire specifically designed to measure budget transparency in small municipalities. The questionnaire is based on the IMF’s revised Code of Good Practices on Fiscal Transparency (2007). Results from 33 Galician municipalities are used to assess its internal consistency and to test a battery of hypotheses on the determinants of budget transparency. While several previous findings of the literature are confirmed, some new results are also obtained

Hierarchical Cluster Analysis Based on Clinical and Neuropsychological Symptoms Reveals Distinct Subgroups in Fibromyalgia: A Population-Based Cohort Study

Cluster analysis; Fibromyalgia; Neuropsychological symptomsAnálisis de clústers; Fibromialgia; Síntomas neuropsicológicosAnàlisi de clústers; Fibromiàlgia; Símptomes neuropsicològicsFibromyalgia (FM) is a condition characterized by musculoskeletal pain and multiple comorbidities. Our study aimed to identify four clusters of FM patients according to their core clinical symptoms and neuropsychological comorbidities to identify possible therapeutic targets in the condition. We performed a population-based cohort study on 251 adult FM patients referred to primary care according to the 2010 ACR case criteria. Patients were aggregated in clusters by a K-medians hierarchical cluster analysis based on physical and emotional symptoms and neuropsychological variables. Four different clusters were identified in the FM population. Global cluster analysis reported a four-cluster profile (cluster 1: pain, fatigue, poorer sleep quality, stiffness, anxiety/depression and disability at work; cluster 2: injustice, catastrophizing, positive affect and negative affect; cluster 3: mindfulness and acceptance; and cluster 4: surrender). The second analysis on clinical symptoms revealed three distinct subgroups (cluster 1: fatigue, poorer sleep quality, stiffness and difficulties at work; cluster 2: pain; and cluster 3: anxiety and depression). The third analysis of neuropsychological variables provided two opposed subgroups (cluster 1: those with high scores in surrender, injustice, catastrophizing and negative affect, and cluster 2: those with high scores in acceptance, positive affect and mindfulness). These empirical results support models that assume an interaction between neurobiological, psychological and social factors beyond the classical biomedical model. A detailed assessment of such risk and protective factors is critical to differentiate FM subtypes, allowing for further identification of their specific needs and designing tailored personalized therapeutic interventions.This work was partially supported by the National Institute of Health “Carlos III” in Madrid, Spain through the grant (reference number: PI09/90301)

Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics

Predicting mobile apps spread: An epidemiological random network modeling approach

[EN] The mobile applications business is a really big market, growing constantly. In app marketing, a key issue is to predict future app installations. The influence of the peers seems to be very relevant when downloading apps. Therefore, the study of the evolution of mobile apps spread may be approached using a proper network model that considers the influence of peers. Influence of peers and other social contagions have been successfully described using models of epidemiological type. Hence, in this paper we propose an epidemiological random network model with realistic parameters to predict the evolution of downloads of apps. With this model, we are able to predict the behavior of an app in the market in the short term looking at its evolution in the early days of its launch. The numerical results provided by the proposed network are compared with data from real apps. This comparison shows that predictions improve as the model is fed back. Marketing researchers and strategy business managers can benefit from the proposed model since it can be helpful to predict app behavior over the time anticipating the spread of an appAlegre-Sanahuja, J.; Cortés, J.; Villanueva Micó, RJ.; Santonja, F. (2017). Predicting mobile apps spread: An epidemiological random network modeling approach. Transactions of the Society for Computer Simulation. 94(2):123-130. https://doi.org/10.1177/0037549717712600S12313094

Pain and depression are associated with more anxiety in ME/CFS: A cross-sectional cohort study between Norway and Spain.

Objectives: Lasting, unexplained and high levels of pain may cause anxiety in patients with chronic fatigue syndrome. The objectives of the current study were to test assumptions of the association between pain and anxiety in patients diagnosed with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and to clarify the role of depression in this relationship. Methods: Data were collected from 664 participants (age 18-65 years) with 133 ME/CFS patients and 201 healthy controls from Norway and 330 CFS patients from Spain. Binary logistic regression model was applied to test relationships between the included variables in the samples. Results: Both pain and depression made significant direct contributions to the level of anxiety. The strongest risk for higher levels of anxiety was the combination of high levels of depression and high levels of pain in the overall sample (OR=49.70; P < 0.001), not so much in the Spanish cohort (OR=11.99; P < 0.0001) and most of all in the Norwegian cohort (OR=88.21; P < 0.001) sample. Conclusions: It was the combination of high pain levels and high levels of depression that to the greatest extent increased the risk of anxiety in patients with CFS/ME. Whatever diagnostic criterion that is applied, anxiety and depression should be mandatory to assess in the clinical assessments performed for diagnosing the ME/CFS. Approaches addressing anxiety-related pain and treatment of depression should be warranted.publishedVersio

Unsupervised Cluster Analysis Reveals Distinct Subtypes of ME/CFS Patients Based on Peak Oxygen Consumption and SF-36 Scores

Biomarker; Cardiopulmonary exercise test; Chronic fatigue syndromeBiomarcador; Prova d'esforç cardiopulmonar; Síndrome de fatiga crònicaBiomarcador; Prueba de esfuerzo cardiopulmonar; Síndrome de fatiga crónicaPurpose Myalgic encephalomyelitis, commonly referred to as chronic fatigue syndrome (ME/CFS), is a severe, disabling chronic disease and an objective assessment of prognosis is crucial to evaluate the efficacy of future drugs. Attempts are ongoing to find a biomarker to objectively assess the health status of (ME/CFS), patients. This study therefore aims to demonstrate that oxygen consumption is a biomarker of ME/CFS provides a method to classify patients diagnosed with ME/CFS based on their responses to the Short Form-36 (SF-36) questionnaire, which can predict oxygen consumption using cardiopulmonary exercise testing (CPET). Methods Two datasets were used in the study. The first contained SF-36 responses from 2,347 validated records of ME/CFS diagnosed participants, and an unsupervised machine learning model was developed to cluster the data. The second dataset was used as a validation set and included the cardiopulmonary exercise test (CPET) results of 239 participants diagnosed with ME/CFS. Participants from this dataset were grouped by peak oxygen consumption according to Weber's classification. The SF-36 questionnaire was correctly completed by only 92 patients, who were clustered using the machine learning model. Two categorical variables were then entered into a contingency table: the cluster with values {0,1} and Weber classification {A, B, C, D} were assigned. Finally, the Chi-square test of independence was used to assess the statistical significance of the relationship between the two parameters. Findings The results indicate that the Weber classification is directly linked to the score on the SF-36 questionnaire. Furthermore, the 36-response matrix in the machine learning model was shown to give more reliable results than the subscale matrix (p − value < 0.05) for classifying patients with ME/CFS. Implications Low oxygen consumption on CPET can be considered a biomarker in patients with ME/CFS. Our analysis showed a close relationship between the cluster based on their SF-36 questionnaire score and the Weber classification, which was based on peak oxygen consumption during CPET. The dataset for the training model comprised raw responses from the SF-36 questionnaire, which is proven to better preserve the original information, thus improving the quality of the model