Over Troubled Water: plataformas de e-health e protecção de dados pessoais: o caso de Portugal

ABSTRACT - How healthcare is being administered is nowadays one of the distinctive traits expressing the progress of a given society. The steadfast implementation of e-health services has become an indispensable tool in order to bring the provision of healthcare to the next level. Notwithstanding e-health’s actual and promising applications, e-health hinges on highly sensitive information on patients’ personal lives and even intimacy, which, in Member States of the European Union (EU), must comply with the pertinent personal data protection legislation. In effect, health data have been classified as a special category of personal data by Directive 95/46/EC, the Data Protection Directive (DPD). The DPD subjects the processing of personal health data to a specific, stronger protection compared to less sensitive personal data in the form of a prohibition, which can only be excepted when the data subjects grant their explicit consent to the processing or if such consent is overridden by a superior interest provided by the law. Aware of the major changes brought about by technological progresses in this field, the EU initiated in January 2012 a revision of the DPD. Eventually, Regulation (EU) 2016/679 of the European Parliament and the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) were published in May 2016, to be applicable as of spring 2018. Regulation 2016/679 displays an even greater carefulness with the safeguard of health data than the DPD. Yet, it is unclear whether this legal reform is up to the challenge of current technological developments, particularly, as so-called big data technologies advance. Notwithstanding the impulse that the EU is placing on e-health and cross-border cooperation, e-health systems are developing primarily at the domestic level. In this article, we will seek to review and compare different e-health platforms now operating under the public health system of a EU member state, Portugal, with a specific focus on how the legal protection of personal data is being configured for each of them. Given the growing importance of big data in the field of health, we extend our comparative endeavour to this emerging phenomenon.RESUMO - No modo como os cuidados de saúde são ministrados reside um traço distintivo do nível de progresso de uma dada sociedade. A rápida implementação de serviços de e-saúde converteu-se num instrumento indispensável do progresso na prestação de serviços de saúde. Não obstante as promessas que acompanham as atuais e futuras aplicações no domínio da e-saúde, estas implicam a recolha e utilização de informação de elevado grau de sensibilidade sobre a vida pessoal e mesmo a intimidade dos pacientes, a qual, nos Estados-membros da União Europeia (UE), deve respeitar a legislação pertinente sobre a proteção de dados pessoais. Na realidade, a Diretiva 95/46/ CE, Diretiva Proteção de Dados (DPD), classifica os dados de saúde como uma categoria especial de dados. A DPD sujeita o processamento de dados de saúde a uma proteção específica mais forte se comparada com a proteção conferida a dados pessoais menos sensíveis sob a forma de uma proibição que apenas pode ser exceptuada em caso de consentimento explícito dos titulares dos dados ou se esse consentimento for superado por um interesse superior contemplado pela lei. Consciente das mudanças decorrentes dos progressos tecnológicos neste domínio, a UE iniciou em 2012 o processo de revisão da DPD. O Regulamento (UE) 2016/679 do Parlamento Europeu e do Conselho de 27 de abril de 2016 sobre a proteção das pessoas naturais no que respeita ao tratamento de dados pessoais e a livre circulação desses dados (Regulamento Geral de Proteção de Dados) foi publicado em maio de 2016, para entrar em vigor na Primavera de 2018. Este Regulamento revela uma preocupação ainda maior do que a DPD no que se refere à salvaguarda dos dados de saúde. No entanto, não é claro se este regime está à altura dos desafios suscitados pelo desenvolvimento tecnológico, particularmente, em face dos avanços das tecnologias de “big data”. Apesar do impulso dado pela UE à cooperação internacional no domínio da e-saúde, os sistemas de saúde vêm sendo desenvolvidos antes de mais no plano nacional. Neste artigo, procuramos examinar e comparar diferentes plataformas de e-saúde que operam hoje em dia no quadro do sistema nacional de saúde de um Estado- membro da UE, Portugal, focando a atenção no modo como é configurada a proteção legal dos dados pessoais no âmbito de cada uma dessas plataformas. Dada a importância crescente das aplicações de “big data” na área da saúde, estendemos a nossa análise comparativa a este fenómeno emergente.info:eu-repo/semantics/publishedVersio

Respostas morfofisiológicas de plantas matrizes de cafeeiro conilon em jardim clonal superadensado

The objective of this work was to evaluate the morphophysiological responses and cutting production of clones of Conilon coffee (Coffea canephora) cultivars in a super-dense clonal garden in the state of Espírito Santo, Brazil. The super-dense clonal garden was built in 2019 using 39 clones: 9, 9, 9, and 12 of cultivars Centenária ES8132, Diamante ES8112, ES8122 (Jequitibá), and Marilândia ES8143, respectively. The experiment was carried out in a randomized complete block design, with three replicates. Cutting production and the following morphophysiological traits were evaluated at 9 and 18 months after planting: chlorophyll index, normalized difference vegetation index, plant height, canopy height, canopy diameter, number of shoots, number of viable cuttings, number of leaves, fresh leaf mass, and plant fresh and dry matter mass. The super-dense clonal garden caused different morphophysiological responses among the studied clones. In general, clones C2, C5, C6, C8, D1, D8, D9, J8, M2, M9, M10, and M12 showed a higher mean cutting production, whereas C4, J1, J4, M4, and M5 were the most sensitive to the super-dense regime. Under these conditions, it is recommended to increase the proportion of matrix plants of the latter clones.O objetivo deste trabalho foi avaliar as respostas morfofisiológicas e a produção de estacas de clones de cultivares de café conilon (Coffea canephora) em jardim clonal superadensado, no estado do Espírito Santo, Brasil. O jardim clonal superadensado foi implantado em 2019, com 39 clones: 9, 9, 9 e 12 das cultivares Centenária ES8132, Diamante ES8112, ES8122 (Jequitibá) e Marilândia ES8143, respectivamente. O experimento foi realizado em delineamento em blocos ao acaso, com três repetições. A produção de estacas e as seguintes características morfofisiológicas foram avaliadas aos 9 e 18 meses após o plantio: índice de clorofila, índice de vegetação por diferença normalizada, altura da planta, altura da copa, diâmetro da copa, número de brotações, número de estacas viáveis, número de folhas, massa fresca de folhas, e massa fresca e seca da planta. O jardim clonal superadensado promoveu diferentes respostas morfofisiológicas entre os clones estudados. Em geral, os clones C2, C5, C6, C8, D1, D8, D9, J8, M2, M9, M10 e M12 apresentaram maior produção média de estacas, enquanto C4, J1, J4, M4 e M5 foram os mais sensíveis ao regime superdenso. Nessas condições, recomenda-se aumentar a proporção de plantas matrizes destes clones

Morphophysiological responses of Conilon coffee matrix plants in a super-dense clonal garden

Abstract The objective of this work was to evaluate the morphophysiological responses and cutting production of clones of Conilon coffee (Coffea canephora) cultivars in a super-dense clonal garden in the state of Espírito Santo, Brazil. The super-dense clonal garden was built in 2019 using 39 clones: 9, 9, 9, and 12 of cultivars Centenária ES8132, Diamante ES8112, ES8122 (Jequitibá), and Marilândia ES8143, respectively. The experiment was carried out in a randomized complete block design, with three replicates. Cutting production and the following morphophysiological traits were evaluated at 9 and 18 months after planting: chlorophyll index, normalized difference vegetation index, plant height, canopy height, canopy diameter, number of shoots, number of viable cuttings, number of leaves, fresh leaf mass, and plant fresh and dry matter mass. The super-dense clonal garden caused different morphophysiological responses among the studied clones. In general, clones C2, C5, C6, C8, D1, D8, D9, J8, M2, M9, M10, and M12 showed a higher mean cutting production, whereas C4, J1, J4, M4, and M5 were the most sensitive to the super-dense regime. Under these conditions, it is recommended to increase the proportion of matrix plants of the latter clones

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