Knowledge of and attitudes toward sexual and reproductive health among college students

Abstract Introduction: Knowledge provides the foundation for values, attitudes and behavior. Knowledge about sexual and reproductive health (SRH) and positive attitudes are essential for implementing protective behaviors. Objectives: The aim of this study was to evaluate SRH knowledge and attitudes in college students and their association with sexual and reproductive behaviors. Material and methods: A cross-sectional study was conducted in a sample of 1946 college students. The data were collected using a self-report questionnaire on the sociodemographics characteristics of the sample, an inventory on SRH knowledge and an attitude scale, and were analyzed with descriptive and inferential statistics (ANOVA and Pearson’s correlation). Results: The sample was 64% female and 36% male, with a mean age of 21 years. The majority were sexually active and used contraception. The SRH knowledge was moderate (22.27 ± 5.79; maximum score = 44), while the average SRH attitude score was more favorable (118.29 ± 13.92; maximum score = 140). Female and younger students studying life and health sciences had higher (P < .05) SRH knowledge and attitude scores. The consistent use of condom and health care surveillance were highly dependent on the students’ SRH knowledge and attitudes. Engagement in sexual risk behaviors was associated with lower scores for these variables. Conclusions: Strategies to increase SRH knowledge and attitudes are important tools for improving protective behaviors, especially with respect to contraception, health care surveillance and exposure to sexual risk. Older males studying topics other than life sciences should be a priority target for interventions due to their higher sexual risk

Nurses knowledge of health effects of dietary fibre

Introduction: Nurses accompany patients throughout their health care to prevent and treat disease, so their knowledge about diet and dietary fibre is key to successful diet therapy, which is an essential part of a patient's non-pharmacological treatment. It is known from the literature that a high proportion of nurses have limited knowledge about diet therapy and about sources of soluble fibre and other foods that can prevent or treat certain diseases. Given the position of nurses as key providers of dietary guidance, and given the health benefits of dietary fibre, we wanted to assess the level of fibre-related knowledge among nurses in Croatia. Material and Methods: Cross-sectional study based on data collected between October 2014 and March 2015 using a survey developed by the CI&DETS Polytechnic Institute in Viseu, Portugal. The survey contains questions about demographic characteristics as well as about knowledge of sources of dietary fibre, recommended daily intake and effects of fibre intake on particular diseases. The study included a total of 369 nurses from two health institutions and one nursing school from Croatia older than 18 years. Differences in knowledge were assessed for significance using the non-parametric Mann-Whitney U test. Possible associations among variables were explored using Spearman's rank correlation. For all statistical analyses, the threshold of significance was defined as P<0.05. Results: The level of knowledge among nurses in Croatia about dietary fibre varied from «undecided» to «partial knowledge». The median for level of knowledge ranged from 3 to 4 with low variability ranging from 0.11 to 0.33. Average levels of knowledge in percentages varied from 57.6% to 82.1%. Nurses with higher education levels showed significantly higher knowledge levels about the influence of dietary fiber intake on the risk of certain diseases (p = 0.007), constipation (p = 0.016), bowel cancer (p = 0.005) and breast cancer (p = 0.039). Conclusion: The level of nurses’ knowledge about dietary fiber is suboptimal. This indicates the need to strengthen nurse education in the areas of diet and diet therapy. Increase the level of knowledge of nurses about nutrition can positively influence the quality of care.

Resilience at Work - Extending knowledge of resilience in the work context

Diese Dissertation beschäftigt sich mit dem Konzept der Resilienz im Arbeitsumfeld. Drei Forschungsarbeiten liefern einen Beitrag zur Literatur im Bereich der Arbeits- und Organisationspsychologie. In der ersten Studie werden Resilienzskalen miteinander verglichen und im Arbeitskontext validiert. Es wird eine Empfehlung ausgesprochen, welches Messinstrument sich am besten zur Erfassung von Resilienz im Arbeitskontext eignet. Die zweite Studie stellt die Unterschiede zwischen den beiden ähnlichen Konzepten Resilienz und Core Self-Evaluations dar. Zusätzlich wird die kognitive Bewertung von Situationen (Cognitive Appraisal) als Resilienzmechanismus in einem Tagebuchdesign betrachtet. In der dritten Studie wird der regulatorische Fokus von Personen als weiterer Resilienzmechanismus analysiert, um die Zusammenhänge zwischen Resilienz und positiven arbeitsbezogenen Ergebnissen zu erklären. Die Gesamtdiskussion baut auf den Erkenntnissen aus den drei Studien auf und fasst die theoretischen und praktischen Implikationen zusammen. Zusätzlich werden Ideen für die zukünftige Erforschung von Resilienz im Arbeitskontext diskutiert.

“Biomarkers in a One Health perspective: current knowledge of their ability to merge human and animal health

Biomarkers are nowadays essential tools to be one step ahead for fighting disease, enabling an enhanced focus on disease prevention and on the probability of its occurrence. Research in a multidisciplinary approach has been an important step towards the repeated discovery of new biomarkers. Biomarkers are defined as biochemical measurable indicators of the presence of disease or as indicators for monitoring disease progression. Currently, biomarkers have been used in several domains such as oncology, neurology, cardiovascular, inflammatory and respiratory disease, and several endocrinopathies. Bridging biomarkers in a One Health perspective has been proven useful in almost all of these domains. In oncology, humans and animals are found to be subject to the same environmental and genetic predisposing factors: examples include the existence of mutations in BR-CA1 gene predisposing to breast cancer, both in human and dogs, with increased prevalence in certain dog breeds and human ethnic groups. Also, breast feeding frequency and duration has been related to a decreased risk of breast cancer in women and bitches. When it comes to infectious diseases, this parallelism is prone to be even more important, for as much as 75% of all emerging diseases are believed to be zoonotic. Examples of successful use of biomarkers have been found in several zoonotic diseases such as Ebola, dengue, leptospirosis or West Nile virus infections. Acute Phase Proteins (APPs) have been used for quite some time as biomarkers of inflammatory conditions. These have been used in human health but also in the veterinary field such as in mastitis evaluation and PRRS (porcine respiratory and reproductive syndrome) diagnosis. Advantages rely on the fact that these biomarkers can be much easier to assess than other conventional disease diagnostic approaches (example: measured in easy to collect saliva samples). Another domain in which biomarkers have been essential is food safety: the possibility to measure exposure to chemical contaminants or other biohazards present in the food chain, which are sometimes analytical challenges due to their low bioavailability in body fluids, is nowadays a major breakthrough. Finally, biomarkers are considered the key to provide more personalized therapies, with more efficient outcomes and fewer side effects. This approach is expected to be the correct path to follow also in veterinary medicine, in the near future.

Challenges and Opportunities of Machine Learning for Clinical and Omics Data

Clinical and omics data are a promising field of application for machine learning techniques even though these methods are not yet systematically adopted in healthcare institutions. Despite artificial intelligence has proved successful in terms of prediction of pathologies or identification of their causes, the systematic adoption of these techniques still presents challenging issues due to the peculiarities of the analysed data. The aim of this thesis is to apply machine learning algorithms to both clinical and omics data sets in order to predict a patient's state of health and get better insights on the possible causes of the analysed diseases. In doing so, many of the arising issues when working with medical data will be discussed while possible solutions will be proposed to make machine learning provide feasible results and possibly become an effective and reliable support tool for healthcare systems.

The role of networked learning in academics’ writing

This article explores academics’ writing practices, focusing on the ways in which they use digital platforms in their processes of collaborative learning. It draws on interview data from a research project that has involved working closely with academics across different disciplines and institutions to explore their writing practices, understanding academic literacies as situated social practices. The article outlines the characteristics of academics’ ongoing professional learning, demonstrating the importance of collaborations on specific projects in generating learning in relation to using digital platforms and for sharing and collaborating on scholarly writing. A very wide range of digital platforms have been identified by these academics, enabling new kinds of collaboration across time and space on writing and research; but challenges around online learning are also identified, particularly the dangers of engaging in learning in public, the pressures of ‘always-on’-ness and the different values systems around publishing in different forums.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

The transformative potential of community engagement in higher education: an analysis of service-learning effects on students, communities, and institutions.

Service-learning in higher education is gaining attention as a reliable tool to support students’ learning and fulfil the mission of higher education institutions (HEIs). This dissertation addresses existing gaps in the literature by examining the effects and perspectives of service-learning in HEIs through three studies. The first study compares the effects of a voluntary semester-long service-learning course with traditional courses. A survey completed by 110 students before and after the lectures found no significant group differences in the psychosocial variables under inspection. Nevertheless, service-learning students showed higher scores concerning the quality of participation. Factors such as students’ perception of competence, duration of service-learning, and self-reported measures may have influenced the results. The second study explores the under-researched perspective of community partners in higher education and European settings. Twelve semi-structured interviews were conducted with community partners from various community organisations across Europe. The results highlight positive effects on community members and organisations, intrinsic motivations, organisational empowerment, different forms of reciprocity, the co-educational role of community partners, and the significant role of a sense of community and belonging. The third study focuses on faculty perspectives on service-learning in the European context. Twenty-two semi-structured interviews were conducted in 14 European countries. The findings confirm the transformative impact of service-learning on the community, students, teachers, and HEIs, emphasising the importance of motivation and institutionalisation processes in sustaining engaged scholarship. The study also identifies the relevance of the community experience, sense of community, and community responsibility with the service-learning experience; relatedness is proposed as the fifth pillar of service-learning. Overall, this dissertation provides new insights into the effects and perspectives of service-learning in higher education. It integrates the 4Rs model with the addition of relatedness, guiding the theoretical and practical implications of the findings. The dissertation also suggests limitations and areas for further research.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.

A Distributed Support based on Kubeflow to Ease MLOps of Federated Learning Algorithms

The main objective of my thesis work is to exploit the Google native and open-source platform Kubeflow, specifically using Kubeflow pipelines, to execute a Federated Learning scalable ML process in a 5G-like and simplified test architecture hosting a Kubernetes cluster and apply the largely adopted FedAVG algorithm and FedProx its optimization empowered by the ML platform ‘s abilities to ease the development and production cycle of this specific FL process. FL algorithms are more are and more promising and adopted both in Cloud application development and 5G communication enhancement through data coming from the monitoring of the underlying telco infrastructure and execution of training and data aggregation at edge nodes to optimize the global model of the algorithm ( that could be used for example for resource provisioning to reach an agreed QoS for the underlying network slice) and after a study and a research over the available papers and scientific articles related to FL with the help of the CTTC that suggests me to study and use Kubeflow to bear the algorithm we found out that this approach for the whole FL cycle deployment was not documented and may be interesting to investigate more in depth. This study may lead to prove the efficiency of the Kubeflow platform itself for this need of development of new FL algorithms that will support new Applications and especially test the FedAVG algorithm performances in a simulated client to cloud communication using a MNIST dataset for FL as benchmark.

Reconceitualização das categorias de skemp de compreensão relacional e compreensão instrumental como critérios globais

The thesis presents a systematic description about the meaning, as Skemp, relational understanding and understanding instrumental, in the context of mathematics learning, being that we had as a guide his understanding of the schema. Especially, we analyze some academic productions, in the area of Mathematics Education, who used the categories of understanding relational and instrumental understanding how evaluative instrument and we see that in most cases the analysis is punctual. Being so, whereas the inherent understanding relational schema has a network of connected ideas and non-insulated, we investigated if the global analysis, where it is the understanding of the diversity of contributory concepts for formation of the concept to be learned, is more appropriate than the punctual, where does the understanding of concepts so isolated. For this, we apply a teaching module, having as main content the Quaternos Pythagoreans using History of Mathematics and the work of Bahier (1916). With the data we obtained the teaching module to use the global analysis and the punctual analysis, using research methodology the Case Study, and consequently we conduct our inferences about the levels of understanding of the subject which has made it possible for us to investigate the ownership of global analysis at the expense of punctual analysis. On the opportunity, we prove the thesis that we espouse in the course of the study and, in addition, we highlight as a contribution of our research evidence of need for a teaching of mathematics that entices the relational understanding and that evaluation should be global, being necessary to consider the notion of schema and therefore know the schematic diagram of the concept that will be evaluated

Quando as professoras se encontram para estudar matemática: saberes em movimento

This text results of a research in an Education Doctorate about teachers, professional background, formation, teaching knowledge and abilities. In this text, it s described the history of a study group in mathematics education composed by teachers who teach mathematics in the 2nd cycle of Ensino Fundamental (5th year of schooling), all belonging to the same school of the municipal public schools network. It presents the trajectory of the collaborative group, in all particularities, singularities, and the constant search to become collaborative. This trajectory was marked by the stories of it s participants in the ceaseless path to constitute teachers, by the sharing of knowledge, by the process of collaboration, by the thinking about the teaching practice, and by the personal and professional improvement of the teachers that form the group. The interpretative and qualitative research had as its investigation field the study group. The data supplied by the collect instruments indicate us that the collaboration between the teachers, the access to specific knowledge of mathematics area, the reflections about the teaching practice in a given context, are paths that lead to and make possible the re-elaboration of the teaching skills by teachers that teach mathematics to the first years

As concepções de professores formadores em relação ao uso da história da matemática no processo ensino aprendizagem nos cursos de licenciatura em matemática

O objetivo desta pesquisa é investigar as concepções de professores formadores de professores de Matemática com relação ao uso da história da Matemática no processo ensino aprendizagem, com a finalidade de compreender que idéias e metodologias esses professores formadores utilizam ao tratar de abordagens históricas ou ao ministrar as disciplinas de História da Matemática. Para isso foi realizada uma pesquisa qualitativa com o uso de entrevistas semi-estruturada com um grupo de nove professores que ministram aulas em instituições de Ensino Superior, em particular em cursos de Licenciatura Plena em Matemática. Ao analisar as falas desses professores, nossos sujeitos de pesquisa, buscamos compreender suas concepções e práticas ao tratar a história da Matemática. Elegemos três categorias de análise tendo como parâmetro as análises das entrevistas que foram: Primeiros Contatos com História da Matemática; Estratégias de Ensino e Potencialidades Pedagógicas; e Obstáculos ao uso da História da Matemática. Na primeira categoria aconteceram diferenças significativas, como o fato de cinco entrevistados argumentarem não ter mantido nenhum contato com história da Matemática enquanto estudantes de graduação e os outros quatro tiveram contato apenas em uma disciplina acompanhada apenas de um determinado livro-texto. Na segunda categoria percebemos que a estratégia de ensino utilizada pela maioria dos professores ao abordar a história da Matemática é unicamente através de seminários. Na terceira categoria cinco entrevistados argumentaram haver alguns obstáculos para o uso da história da Matemática no processo ensino aprendizagem destacando alguns desses obstáculos. O estudo das concepções dos professores pesquisados possibilitou destacar o papel da história da Matemática na formação do professor, como também reflexões sobre a aplicabilidade ou dificuldade do uso da história da Matemática no ensino aprendizagem e a contribuição da história da Matemática no desenvolvimento matemático e crítico do aluno.

Caracterización de la “mirada profesional” de los estudiantes para maestro sobre la comprensión de la generalización de patrones

El objetivo de esta comunicación es caracterizar la mirada profesional de los estudiantes para maestro (EPM) cuando describen e interpretan respuestas de alumnos de Primaria a problemas de identificación de patrones. El estudio conjunto de los grados de evidencia de la identificación de los elementos matemáticos relevantes en el proceso de generalización de patrones y de la interpretación de la comprensión de los estudiantes, nos permitió identificar cinco perfiles de EPM con una gradación entre ellos. Esto nos ha llevado a caracterizar cinco perfiles de EPM que muestran que el conocimiento de matemáticas es necesario pero no suficiente para el desarrollo de una “mirada profesional”.