Datos en tiempos de pandemia: la urgencia de un nuevo pacto. Reflexiones desde América Latina y el Caribe

Los avances de las Tecnologías de la Información y la Comunicación (TIC) permiten acceder en tiempo real a una cantidad ingente de datos, a través de los cuales es posible conocer el comportamiento de hechos sociales. En este escenario, la actual pandemia por SARS-CoV-2 ha permitido, bajo cuestionables criterios de inmediatez y urgencia, circular información que genera realidad e impacta en la toma de decisiones; y, además, ha favorecido la apropiación del dato, exponiendo a las personas a violaciones de sus derechos fundamentales. Ambos asuntos son sensibles para América Latina y el Caribe, región que hoy se presenta no sólo como el epicentro de la pandemia sino también de las desigualdades. La contribución que desde la reflexión y deliberación bioética puede realizarse en esta materia, adquiere especial relevancia con vistas a generar un nuevo pacto para el tratamiento de los datos.

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Preeclampsia and COVID-19: results from the INTERCOVID prospective longitudinal study

Background: It is unclear whether the suggested link between COVID-19 during pregnancy and preeclampsia is an independent association or if these are caused by common risk factors. Objective: This study aimed to quantify any independent association between COVID-19 during pregnancy and preeclampsia and to determine the effect of these variables on maternal and neonatal morbidity and mortality. Study Design: This was a large, longitudinal, prospective, unmatched diagnosed and not-diagnosed observational study assessing the effect of COVID-19 during pregnancy on mothers and neonates. Two consecutive not-diagnosed women were concomitantly enrolled immediately after each diagnosed woman was identified, at any stage during pregnancy or delivery, and at the same level of care to minimize bias. Women and neonates were followed until hospital discharge using the standardized INTERGROWTH-21st protocols and electronic data management system. A total of 43 institutions in 18 countries contributed to the study sample. The independent association between the 2 entities was quantified with the risk factors known to be associated with preeclampsia analyzed in each group. The outcomes were compared among women with COVID-19 alone, preeclampsia alone, both conditions, and those without either of the 2 conditions. Results: We enrolled 2184 pregnant women; of these, 725 (33.2%) were enrolled in the COVID-19 diagnosed and 1459 (66.8%) in the COVID-19 not-diagnosed groups. Of these women, 123 had preeclampsia of which 59 of 725 (8.1%) were in the COVID-19 diagnosed group and 64 of 1459 (4.4%) were in the not-diagnosed group (risk ratio, 1.86; 95% confidence interval, 1.32–2.61). After adjustment for sociodemographic factors and conditions associated with both COVID-19 and preeclampsia, the risk ratio for preeclampsia remained significant among all women (risk ratio, 1.77; 95% confidence interval, 1.25–2.52) and nulliparous women specifically (risk ratio, 1.89; 95% confidence interval, 1.17–3.05). There was a trend but no statistical significance among parous women (risk ratio, 1.64; 95% confidence interval, 0.99–2.73). The risk ratio for preterm birth for all women diagnosed with COVID-19 and preeclampsia was 4.05 (95% confidence interval, 2.99–5.49) and 6.26 (95% confidence interval, 4.35–9.00) for nulliparous women. Compared with women with neither condition diagnosed, the composite adverse perinatal outcome showed a stepwise increase in the risk ratio for COVID-19 without preeclampsia, preeclampsia without COVID-19, and COVID-19 with preeclampsia (risk ratio, 2.16; 95% confidence interval, 1.63–2.86; risk ratio, 2.53; 95% confidence interval, 1.44–4.45; and risk ratio, 2.84; 95% confidence interval, 1.67–4.82, respectively). Similar findings were found for the composite adverse maternal outcome with risk ratios of 1.76 (95% confidence interval, 1.32–2.35), 2.07 (95% confidence interval, 1.20–3.57), and 2.77 (95% confidence interval, 1.66–4.63). The association between COVID-19 and gestational hypertension and the direction of the effects on preterm birth and adverse perinatal and maternal outcomes, were similar to preeclampsia, but confined to nulliparous women with lower risk ratios. Conclusion: COVID-19 during pregnancy is strongly associated with preeclampsia, especially among nulliparous women. This association is independent of any risk factors and preexisting conditions. COVID-19 severity does not seem to be a factor in this association. Both conditions are associated independently of and in an additive fashion with preterm birth, severe perinatal morbidity and mortality, and adverse maternal outcomes. Women with preeclampsia should be considered a particularly vulnerable group with regard to the risks posed by COVID-19

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Ethics, ecosystem and social organisation: the case of Zika virus

What is the level of responsibility humans have towards the deterioration of the ecological environment and the development of re-emerging diseases affecting human reproduction? Is Zika a warning sign of future events for being a devastating pathogenic entity without precedent

A percepção da comunidade sobre a atuação do Serviço de Atenção Farmacêutica em ações de educação em saúde relacionadas à promoção do uso racional de medicamentos Community's perception towards the performance of Pharmaceutical Service Care in the health education actions related to rational medicine use

A educação em saúde visa democratizar o acesso ao conhecimento e tornar os indivíduos da comunidade capazes de atuar como corresponsáveis na promoção da saúde. O objetivo deste trabalho foi apresentar a experiência do Serviço de Atenção Farmacêutica em ações na comunidade com vistas a promover o uso racional de medicamentos. Utilizou-se uma abordagem qualitativa, do tipo pesquisa-ação. Realizou-se a descrição da experiência e o levantamento das percepções dos responsáveis pelos grupos envolvidos nas palestras. Foram realizadas 22 palestras alcançando 565 participantes; os temas pré-selecionados envolviam assuntos como polimedicação, automedicação e adesão terapêutica, e os que surgiram no decorrer das palestras foram classificados como temas emergentes, representados por experiências dos participantes quanto às doenças e suas formas de tratamento, os efeitos colaterais de medicamentos, o manejo não farmacológico de problemas de saúde, qualidade, segurança e eficácia de medicamentos genéricos e o acesso aos medicamentos. Na percepção dos líderes dos grupos foi mencionado que as palestras contribuíram para o uso racional de medicamentos, proporcionando a mudança de postura, segundo alguns relatos. As palestras colaboraram para reforçar que o papel da equipe de saúde não é apenas o de permitir o acesso aos medicamentos, mas também de garantir o seu uso correto. Essa experiência destaca a ação do farmacêutico como profissional da saúde e o papel que desempenha na promoção do uso racional de medicamentos, empregando nesta ação, além do conhecimento científico, habilidades para usar as experiências da população no ato de ensinar, respeitando o indivíduo e tornando-o ativo nesse processo.<br>Health education aims at democratizing the access to knowledge and to make the community people able to act as co-managers in health's promotion. This work's objective was to present pharmaceutical service care experience in action in the community, in order to promote the rational use of medicine. We used a research- action qualitative approach. Experience description and people's perception survey (responsible for groups involved in the current lecture) were accomplished, and observed in this study. 22 lectures were done, reaching as a total 565 participants; pre-selected themes involved subjects as polymedication, self-medication and adhesion therapeutics, and the ones that emerged during each lecture were classified as emergent topics, represented by participant experiences (as for their diseases and treatment solutions, their collateral effects etc; also non-pharmacological health problems handling; quality, security and generic medicine effectiveness and medicine access should be approached here, in these cases). under group leaders perception, and approval,; it was mentioned that the lectures contributed to a more rational use, provided an important posture change, as some reports in the media said, as can be read. The lectures collaborated to reinforce that the health team role is not only to allow an access to medicine, but also to guarantee its correct use. This experience highlights the pharmacist action as a health professional. His role is carried out in rational medicine use promotion, employing in this action - besides scientific knowledge - abilities to use the population experiences in the action of teaching , respecting all individuals and making them active in this process

Computational approaches to explainable artificial intelligence: advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated humanlevel performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.Fil: Górriz, J. M.. Universidad de Granada; España. University of Cambridge; Reino UnidoFil: Álvarez Illán, I.. Universidad de Granada; EspañaFil: Álvarez Marquina, A.. Universidad Politécnica de Madrid; EspañaFil: Arco, J. E.. Universidad de Granada; España. Universidad de Málaga; EspañaFil: Atzmueller, M.. Osnabrück University; Alemania. German Research Center For Artificial Intelligence; AlemaniaFil: Ballarini, Fabricio Matias. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Barakova, E.. Eindhoven University of Technology; Países BajosFil: Bologna, G.. University of Applied Sciences and Arts of Western Switzerland; SuizaFil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Instituto Tecnológico de Buenos Aires; Argentina. Universidad Politécnica de Cartagena; España. Universidad de Buenos Aires; ArgentinaFil: Castellanos Dominguez, G.. Universidad Nacional de Colombia; ColombiaFil: Castillo Barnes, D.. Universidad de Granada; España. Universidad de Málaga; EspañaFil: Cho, S. B.. Yonsei University; Corea del SurFil: Contreras, R.. Universidad de Concepción; ChileFil: Cuadra, J. M.. Universidad Nacional de Educación a Distancia; EspañaFil: Domínguez, E.. Universidad de Málaga; EspañaFil: Domínguez Mateos, F.. Universidad Rey Juan Carlos; EspañaFil: Duro, R. J.. Universidad da Coruña; EspañaFil: Elizondo, D.. de Montfort University; Reino UnidoFil: Fernández Caballero, A.. Universidad de Castilla-La Mancha; España. Instituto de Salud Carlos III; EspañaFil: Fernández Jover, Eduardo. Universidad de Miguel Hernández; EspañaFil: Formoso, M. A.. Universidad de Málaga; España. Universidad de Granada; EspañaFil: Gallego Molina, N. J.. Universidad de Málaga; España. Universidad de Granada; EspañaFil: Gamazo, J.. Universidad Nacional de Educación a Distancia; EspañaFil: García González, J.. Universidad de Málaga; EspañaFil: Garcia Rodriguez, J.. Universidad de Alicante; EspañaFil: Wang, W.. University of Leicester; Reino UnidoFil: Zhang, Y. D.. University of Leicester; Reino UnidoFil: Zhu, H.. University of Leicester; Reino UnidoFil: Zhu, Z.. University of Leicester; Reino UnidoFil: Ferrández Vicente, J. M.. Universidad Politécnica de Cartagena; España. Universidad Politécnica de Madrid; Españ

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.Fil: Górriz, Juan M.. Universidad de Granada; España. University of Cambridge; Estados UnidosFil: Ramírez, Javier. Universidad de Granada; EspañaFil: Ortíz, Andrés. Universidad de Málaga; EspañaFil: Martínez Murcia, Francisco J.. Universidad de Málaga; EspañaFil: Segovia, Fermin. Universidad de Granada; EspañaFil: Suckling, John. University of Cambridge; Estados UnidosFil: Leming, Matthew. University of Cambridge; Estados UnidosFil: Zhang, Yu Dong. University of Leicester; Reino UnidoFil: Álvarez Sánchez, Jose Ramón. Universidad Nacional de Educación a Distancia; EspañaFil: Bologna, Guido. Universidad de Ginebra; SuizaFil: Bonomini, Maria Paula. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Casado, Fernando E.. Universidad de Santiago de Compostela; EspañaFil: Charte, David. Universidad de Jaén; EspañaFil: Charte, Francisco. Universidad de Jaén; EspañaFil: Contreras, Ricardo R. Universidad de Concepción; ChileFil: Cuesta Infante, Alfredo. Universidad Rey Juan Carlos; EspañaFil: Duro, Richard J.. Universidad da Coruña; EspañaFil: Fernández Caballero, Antonio. Universidad de Castilla-La Mancha; EspañaFil: Fernández Jover, Eduardo. Universidad de Miguel Hernández; EspañaFil: Gómez Vilda, Pedro. Universidad Politécnica de Madrid; EspañaFil: Graña, Manuel. Universidad del País Vasco; EspañaFil: Herrera, Francisco. Universidad de Granada; EspañaFil: Iglesias, Roberto. Universidad de Santiago de Compostela; EspañaFil: Lekova, Anna. The Bulgarian Academy Of Sciences; BulgariaFil: de Lope, Javier. Universidad Politécnica de Madrid; EspañaFil: López Rubio, Ezequiel. Universidad de Málaga; EspañaFil: Martínez Tomás, Rafael. Universidad Nacional de Educación a Distancia; EspañaFil: Molina Cabello, Miguel A.. Universidad de Málaga; EspañaFil: Montemayor, Antonio S.. Universidad Rey Juan Carlos; EspañaFil: Novais, Paulo. Universidade do Minho; PortugalFil: Fernández Vicente, José Manuel. Universidad Politécnica de Cartagena; Españ