TweeProfiles4: a weighted multidimensional stream clustering algorithm

O aparecimento das redes sociais abriu aos utilizadores a possibilidade de facilmente partilharem as suas ideias a respeito de diferentes temas, o que constitui uma fonte de informação enriquecedora para diversos campos. As plataformas de microblogging sofreram um grande crescimento e de forma constante nos últimos anos. O Twitter é o site de microblogging mais popular, tornando-se uma fonte de dados interessante para extração de conhecimento. Um dos principais desafios na análise de dados provenientes de redes sociais é o seu fluxo, o que dificulta a aplicação de processos tradicionais de data mining. Neste sentido, a extração de conhecimento sobre fluxos de dados tem recebido um foco significativo recentemente. O TweeProfiles é a uma ferramenta de data mining para análise e visualização de dados do Twitter sobre quatro dimensões: espacial (a localização geográfica do tweet), temporal (a data de publicação do tweet), de conteúdo (o texto do tweet) e social (o grafo dos relacionamentos). Este é um projeto em desenvolvimento que ainda possui muitos aspetos que podem ser melhorados. Uma das recentes melhorias inclui a substituição do algoritmo de clustering original, o qual não suportava o fluxo contínuo dos dados, por um método de streaming. O objetivo desta dissertação passa pela continuação do desenvolvimento do TweeProfiles. Em primeiro lugar, será proposto um novo algoritmo de clustering para fluxos de dados com o objetivo de melhorar o existente. Para esse efeito será desenvolvido um algoritmo incremental com suporte para fluxos de dados multi-dimensionais. Esta abordagem deve permitir ao utilizador alterar dinamicamente a importância relativa de cada dimensão do processo de clustering. Adicionalmente, a avaliação empírica dos resultados será alvo de melhoramento através da identificação e implementação de medidas adequadas de avaliação dos padrões extraídos. O estudo empírico será realizado através de tweets georreferenciados obtidos pelo SocialBus.The emergence of social media made it possible for users to easily share their thoughts on different topics, which constitutes a rich source of information for many fields. Microblogging platforms experienced a large and steady growth over the last few years. Twitter is the most popular microblogging site, making it an interesting source of data for pattern extraction. One of the main challenges of analyzing social media data is its continuous nature, which makes it hard to use traditional data mining. Therefore, mining stream data has also received a lot of attention recently.TweeProfiles is a data mining tool for analyzing and visualizing Twitter data over four dimensions: spatial (the location of the tweet), temporal (the timestamp of the tweet), content (the text of the tweet) and social (relationship graph). This is an ongoing project which still has many aspects that can be improved. For instance, it was recently improved by replacing the original clustering algorithm which could not handle the continuous flow of data with a streaming method. The goal of this dissertation is to continue the development of TweeProfiles. First, the stream clustering process will be improved by proposing a new algorithm. This will be achieved by developing an incremental algorithm with support for multi-dimensional streaming data. Moreover, it should make it possible for the user to dynamically change the relative importance of each dimension in the clustering. Additionally, the empirical evaluation of the results will also be improved.Suitable measures to evaluate the extracted patterns will be identified and implemented. An empirical study will be done using data consisting of georeferenced tweets from SocialBus

Geothermal resources research in a granitic basement - the Braga area case study (NW Portugal)

Geothermal resources are increasingly being considered as a strategic alternative in energy production, especially with the latest geopolitical developments. The densely populated Braga region, in NW Portugal, is endowed with a geostructural setting that enables the existence of several thermal water occurrences, spatially associated with a deep-rooted structure – the Vigo-Régua shear zone, set in a granite context. Given the latest advances in geothermal energy production, it is possible to predict a mid- to long-term implementation of geothermal energy production in the vicinity of that deep rooted structure. Although strongly encouraging, the exploratory geophysical, geochemical and geological data are still insufficient to deliver a definitive frame of the potential energy associated with the estimated reservoirs. Ongoing work combining gravimetric, radiometric and geochemical data will provide a better understanding of the deeply concealed structures.Les ressources géothermiques sont de plus en plus considérées comme une alternative stratégique dans la production d'énergie, en particulier compte tenu du contexte géopolitique récent. La région densément peuplée de Braga, au nord-ouest du Portugal, est située dans un contexte granitique spatialement associé à une structure enracinée - la zone de cisaillement Vigo-Régua. Ce contexte géologique et structural a permis le développement de plusieurs occurrences d'eau thermale. Compte tenu des dernières avancées en matière de production d'énergie géothermique, il est possible de prévoir une mise en œuvre à moyen et long terme de la production d'énergie géothermique à proximité de cette structure profonde. Bien que fortement encourageantes, les données géophysiques, géochimiques et géologiques exploratoires sont encore insuffisantes pour fournir un cadre définitif de l'énergie potentielle associée aux réservoirs estimés. Des travaux en cours combinant des données gravimétriques, radiométriques et géochimiques permettront de mieux comprendre les structures profondément enfouies.Los recursos geotérmicos se consideran cada vez más como una alternativa estratégica en la producción de energía, especialmente con los últimos desarrollos geopolíticos. La región densamente poblada de Braga, en el noroeste de Portugal, está dotada de un entorno estructural que permite la presencia de aguas termales, asociadas espacialmente con una estructura profunda en un contexto granítico: la zona de falla Vigo-Régua. Dados los últimos avances en la producción de energía geotérmica, es posible predecir una implementación a mediano y largo plazo de la producción de energía geotérmica en las cercanías de esa estructura de raíces profundas. Aún cuando los datos geofísicos, geoquímicos y geológicos exploratorios son muy alentadores, son insuficientes para brindar un marco definitivo del potencial geotérmico asociada con los yacimientos. El trabajo en curso que integra datos gravimétricos, radiométricos y geoquímicos proporcionará una mejor comprensión de las estructuras profundas por explorar

mHealth in urology

Introduction: Smartphones are increasingly playing a role in healthcare and previous studies assessing medical applications (apps) have raised concerns about lack of expert involvement and low content accuracy. However, there are no such studies in Urology. We reviewed Urology apps with the aim of assessing the level of participation of healthcare professionals (HCP) and scientific Urology associations in their development. Material and Methods: A systematic search was performed on PubMed, Apple's App Store and Google's Play Store, for Urology apps, available in English. Apps were reviewed by three graders to determine the app's platform, target customer, developer, app type, app category, price and the participation of a HCP or a scientific Urology association in the development. Results: The search yielded 372 apps, of which 150 were specific for Urology. A fifth of all apps had no HCP involvement (20.7%) and only a third had been developed with a scientific Urology association (34.7%). The lowest percentage of HCP (13.4%) and urological association (1.9%) involvement was in apps designed for the general population. Furthermore, there was no contribution from an Urology society in "Electronic Medical Record" nor in "Patient Information" apps. A limitation of the study is that only Android and iOS apps were reviewed. Conclusions: Despite the increasing Mobile Health (mHealth) market, this is the first study that demonstrates the lack of expert participation in the design of Urology apps, particularly in apps designed for the general public. Until clear regulation is enforced, the urological community should help regulate app development. Maintaining a register of certified apps or issuing an official scientific seal of approval could improve overall app quality. We propose that urologists become stakeholders in mHealth, shaping future app design and promoting peer-review app validation

Pervasive hybridization with local wild relatives in Western European grapevine varieties

Grapevine (Vitis vinifera L.) diversity richness results from a complex domestication history over multiple historical periods. Here, we used whole-genome resequencing to elucidate different aspects of its recent evolutionary history. Our results support a model in which a central domestication event in grapevine was followed by postdomestication hybridization with local wild genotypes, leading to the presence of an introgression signature in modern wine varieties across Western Europe. The strongest signal was associated with a subset of Iberian grapevine varieties showing large introgression tracts. We targeted this study group for further analysis, demonstrating how regions under selection in wild populations from the Iberian Peninsula were preferentially passed on to the cultivated varieties by gene flow. Examination of underlying genes suggests that environmental adaptation played a fundamental role in both the evolution of wild genotypes and the outcome of hybridization with cultivated varieties, supporting a case of adaptive introgression in grapevine.info:eu-repo/semantics/publishedVersio

Nationwide access to endovascular treatment for acute ischemic stroke in portugal

Publisher Copyright: Copyright Ordem dos M dicos 2021.Introduction: Since the publication of endovascular treatment trials and European Stroke Guidelines, Portugal has re-organized stroke healthcare. The nine centers performing endovascular treatment are not equally distributed within the country, which may lead to differential access to endovascular treatment. Our main aim was to perform a descriptive analysis of the main treatment metrics regarding endovascular treatment in mainland Portugal and its administrative districts. Material and Methods: A retrospective national multicentric cohort study was conducted, including all ischemic stroke patients treated with endovascular treatment in mainland Portugal over two years (July 2015 to June 2017). All endovascular treatment centers contributed to an anonymized database. Demographic, stroke-related and procedure-related variables were collected. Crude endovascular treatment rates were calculated per 100 000 inhabitants for mainland Portugal, and each district and endovascular treatment standardized ratios (indirect age-sex standardization) were also calculated. Patient time metrics were computed as the median time between stroke onset, first-door, and puncture. Results: A total of 1625 endovascular treatment procedures were registered. The endovascular treatment rate was 8.27/100 000 inhabitants/year. We found regional heterogeneity in endovascular treatment rates (1.58 to 16.53/100 000/year), with higher rates in districts closer to endovascular treatment centers. When analyzed by district, the median time from stroke onset to puncture ranged from 212 to 432 minutes, reflecting regional heterogeneity. Discussion: Overall endovascular treatment rates and procedural times in Portugal are comparable to other international registries. We found geographic heterogeneity, with lower endovascular treatment rates and longer onset-to-puncture time in southern and inner regions. Conclusion: The overall national rate of EVT in the first two years after the organization of EVT-capable centers is one of the highest among European countries, however, significant regional disparities were documented. Moreover, stroke-onset-to-first-door times and in-hospital procedural times in the EVT centers were comparable to those reported in the randomized controlled trials performed in high-volume tertiary hospitalspublishersversionpublishe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost