Big data analytics in healthcare : are end-users ready?

This dissertation aims to understand if end-users are aware of big data analytics, and given this, if the perceived value of healthcare products that use big data techniques is sufficient to surpass their concerns for sharing personal data. Additionally, it is tested whether they are interested in purchasing such products. In order to address this topic, the theoretical foundations are based on the Theory of Reasoned Model, which studies human’s decision-making process. Based on the data from a questionnaire directed to end-users, a Chi-square test studies if it exists an association between the different variables and Simple Linear Regressions evaluate the strength of the associations. The results obtained from both type of tests prove that a higher perception of value from health products that require the use of big data technologies (PV) is positively correlated with a superior willingness to share personal data (WTS), as well as a higher willingness to buy (WTB), a positive word of mouth for both sharing data (WoM_sd) and purchasing such devices (WoM_d). Finally, three Multiple Regression models are created. The first model explains WTB as a positive influence of PV, WTS, WoM_d and WoM_sd. The second regression tests the WoM_d dimension as a result of PV, WTB and WTS. The third model shows that WoM_sd is explained by PV, WTB and WTS. These three models are in line with the previous conclusions obtained from both the Chi-Square test and the Simple Linear Regressions.Esta dissertação tem como objetivo compreender se os consumidores finais estão cientes das técnicas analiticas de big data e, em caso afirmativo, se o valor percepcionado de produtos na área da saúde que usem tecnologias big data é suficiente para ultrapassar os receios de partilha de dados pessoais. Adicionalmente é testado se estes estão interessados na compra de tais produtos. Para tal, a abordagem teórica é baseada no modelo Theory of Reasoned Action, o qual estuda o processo de tomada de decisão do ser humano. Com base nos dados obtidos através de um questionário destinado aos consumidores finais, um teste de tabelas de contingência de Qui-quadrado testa se existe associação entre as diferentes variáveis, enquanto regressões lineares simples avaliam a força destas associações. Os resultados obtidos comprovam que uma maior percepção de valor dos produtos de saúde que exigem o uso de tecnologias big data (PV) está positivamente corelacionada com uma maior predisposição para a partilha de dados pessoais (WTS), bem como uma maior intenção para a aquisição deste tipo de produtos (WTB) e, finalmente, com uma positiva recomendação, tanto para a partilha de dados pessoais (WoM_sd) como para a compra de tais dispositivos (WoM_d). Finalmente, são criados três modelos de regressões lineares múltiplas. O primeiro modelo relaciona a dimensão de WTB com uma influência positiva de PV, WTS, WoM_d e WoM_sd. A segunda regressão testa a dimensão de WoM_d associada a PV, WTB e WTS. O terceiro modelo mostra que WoM_sd é explicado por PV, WTB e WTS. Estes três modelos estão em linha com as conclusões anteriormente obtidas no teste Qui-quadrado e nas regressões lineares simples

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

Anais do V Encontro Brasileiro de Educomunicação: Educação midiática e políticas públicas

A presente coletânea, que chega ao público através de um suporte digital, tem como objetivo disponibilizar os papers, bem como os relatos de experiências educomunicativas apresentados durante o V ENCONTRO BRASILEIRO DE EDUCOMUNICAÇÃO, que teve como tema central: “Educação Midiática e Políticas Públicas”. O evento foi realizado em São Paulo, entre 19 e 21 de setembro de 2013, a partir de uma parceria entre o NCE/USP - Núcleo de Comunicação e Educação da USP, a Licenciatura em Educomunicação da ECA/USP, a ABPEducom – Associação Brasileira de Pesquisadores e Profissionais da Educomunicação e a FAPCOM – Faculdade Paulus de Tecnologia e Comunicação, que ofereceu seu campus, na Vila Mariana, para os atos do evento. Os presentes anais disponibilizam o texto de abertura, de autoria do coordenador geral do evento, denominado “Educação midiática e políticas públicas: vertentes históricas da emergência da Educomunicação na América Latina”. Na sequência, apresentam 61 papers sobre aspectos específicos da temática geral, resultantes de pesquisas na área, seguidos de 27 relatos de práticas educomunicativas, em nível nacional

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

Unraveling the genetic background of individuals with a clinical familial hypercholesterolemia phenotype

Familial hypercholesterolemia (FH) is a common genetic disorder of lipid metabolism caused by pathogenic/likely pathogenic variants in LDLR, APOB, and PCSK9 genes. Variants in FH-phenocopy genes (LDLRAP1, APOE, LIPA, ABCG5, and ABCG8), polygenic hypercholesterolemia, and hyperlipoprotein (a) [Lp(a)] can also mimic a clinical FH phenotype. We aim to present a new diagnostic tool to unravel the genetic background of clinical FH phenotype. Biochemical and genetic study was performed in 1,005 individuals with clinical diagnosis of FH, referred to the Portuguese FH Study. A next-generation sequencing panel, covering eight genes and eight SNPs to determine LDL-C polygenic risk score and LPA genetic score, was validated, and used in this study. FH was genetically confirmed in 417 index cases: 408 heterozygotes and 9 homozygotes. Cascade screening increased the identification to 1,000 FH individuals, including 11 homozygotes. FH-negative individuals (phenotype positive and genotype negative) have Lp(a) >50 mg/dl (30%), high polygenic risk score (16%), other monogenic lipid metabolism disorders (1%), and heterozygous pathogenic variants in FH-phenocopy genes (2%). Heterozygous variants of uncertain significance were identified in primary genes (12%) and phenocopy genes (7%). Overall, 42% of our cohort was genetically confirmed with FH. In the remaining individuals, other causes for high LDL-C were identified in 68%. Hyper-Lp(a) or polygenic hypercholesterolemia may be the cause of the clinical FH phenotype in almost half of FH-negative individuals. A small part has pathogenic variants in ABCG5/ABCG8 in heterozygosity that can cause hypercholesterolemia and should be further investigated. This extended next-generation sequencing panel identifies individuals with FH and FH-phenocopies, allowing to personalize each person’s treatment according to the affected pathway

12,500+ and counting: biodiversity of the Brazilian Pampa

Knowledge on biodiversity is fundamental for conservation strategies. The Brazilian Pampa region, located in subtropical southern Brazil, is neglected in terms of conservation, and knowledge of its biodiversity is fragmented. We aim to answer the question: how many, and which, species occur in the Brazilian Pampa? In a collaborative effort, we built species lists for plants, animals, bacteria, and fungi that occur in the Brazilian Pampa. We included information on distribution patterns, main habitat types, and conservation status. Our study resulted in referenced lists totaling 12,503 species (12,854 taxa, when considering infraspecific taxonomic categories [or units]). Vascular plants amount to 3,642 species (including 165 Pteridophytes), while algae have 2,046 species (2,378 taxa) and bryophytes 316 species (318 taxa). Fungi (incl. lichenized fungi) contains 1,141 species (1,144 taxa). Animals total 5,358 species (5,372 taxa). Among the latter, vertebrates comprise 1,136 species, while invertebrates are represented by 4,222 species. Our data indicate that, according to current knowledge, the Pampa holds approximately 9% of the Brazilian biodiversity in an area of little more than 2% of Brazil’s total land. The proportion of species restricted to the Brazilian Pampa is low (with few groups as exceptions), as it is part of a larger grassland ecoregion and in a transitional climatic setting. Our study yielded considerably higher species numbers than previously known for many species groups; for some, it provides the first published compilation. Further efforts are needed to increase knowledge in the Pampa and other regions of Brazil. Considering the strategic importance of biodiversity and its conservation, appropriate government policies are needed to fund studies on biodiversity, create accessible and constantly updated biodiversity databases, and consider biodiversity in school curricula and other outreach activities

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora