Motion-induced sound level control for socially-aware robot navigation

With the growing presence of robots in human populated environments, it becomes necessary to render the relation between robots and humans natural, rather than invasive. For that purpose, robots need to make sure the acoustic noise induced by their motion does not disturb people that are in the same environment. This dissertation proposes a method that allows a robot to learn how to control the amount of noise it produces, taking into account the environmental context and the robot’s mechanical characteristics. The robot performs its task while adapting its speed, so it produces less acoustic noise than the environment’s, and thus, not disturbing nearby people. Before the robot can execute a task in a given environment, it needs to learn how much noise it induces while moving at different speeds on that environment. For that, a microphone was installed on a wheeled robot to gather information about it’s acoustic noise. To validate the proposed solution, tests in different environments with different background sounds were performed. After that, a PIR sensor was installed on the robot to verify the ability of the robot to use the motion controller when someone enters the sensor’s field of vision. This second test demonstrated the possibility of using the proposed solution in another systems.Com a crescente presença dos robôs no espaço ocupado pelos seres humanos, é necessário que a relação entre robôs e humans seja natural e não invasiva. Para alcançar este objectivo, é preciso que os robôs tenham formas de assegurar que o ruído causado pelos seus movimentos não incomodam as pessoas inseridas no meio envolvente. Esta dissertação propôe uma solução que permite que um robô aprenda a controlar a quantidade de ruído que produz, tendo em conta as suas caracteristicas e o meio ambiente onde é colocado. O robô concretiza as suas tarefas adaptando a sua velocidade de forma a produzir menos ruído que o presente no meio ambiente e, assim, não incomodar as pessoas. Para que o robô possa executar alguma tarefa num determinado ambiente, é necessário aprender a quantidade de ruído que faz ao movimentar-se a diferentes velocidades nesse ambiente. Para tal, um microfone foi instalado num robô com rodas para obter informação sobre a sua acústica. Para validar a solução proposta, foram realizados testes, com sucesso, em diferentes ambientes com diferentes ruídos de fundo. Posteriormente, foi instalado no robô um sensor PIR para analizar a capacidade do robô executar o controlador de velocidade quando alguém entra no campo de visão do sensor. Este segundo teste demonstrou a possibilidade de incluir a solução proposta em outros sistemas

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Sugarcane (Saccharum X officinarum): A Reference Study for the Regulation of Genetically Modified Cultivars in Brazil

Global interest in sugarcane has increased significantly in recent years due to its economic impact on sustainable energy production. Sugarcane breeding and better agronomic practices have contributed to a huge increase in sugarcane yield in the last 30 years. Additional increases in sugarcane yield are expected to result from the use of biotechnology tools in the near future. Genetically modified (GM) sugarcane that incorporates genes to increase resistance to biotic and abiotic stresses could play a major role in achieving this goal. However, to bring GM sugarcane to the market, it is necessary to follow a regulatory process that will evaluate the environmental and health impacts of this crop. The regulatory review process is usually accomplished through a comparison of the biology and composition of the GM cultivar and a non-GM counterpart. This review intends to provide information on non-GM sugarcane biology, genetics, breeding, agronomic management, processing, products and byproducts, as well as the current technologies used to develop GM sugarcane, with the aim of assisting regulators in the decision-making process regarding the commercial release of GM sugarcane cultivars

Genomics and epidemiology for gastric adenocarcinomas (GE4GAC): a Brazilian initiative to study gastric cancer

Abstract Gastric cancer (GC) is the fifth most common type of cancer worldwide with high incidences in Asia, Central, and South American countries. This patchy distribution means that GC studies are neglected by large research centers from developed countries. The need for further understanding of this complex disease, including the local importance of epidemiological factors and the rich ancestral admixture found in Brazil, stimulated the implementation of the GE4GAC project. GE4GAC aims to embrace epidemiological, clinical, molecular and microbiological data from Brazilian controls and patients with malignant and pre-malignant gastric disease. In this letter, we summarize the main goals of the project, including subject and sample accrual and current findings

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

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

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

Anti-Diabetic Effects of the Ethyl-Acetate Fraction of Trichilia catigua in Streptozo-tocin-Induced Type 1 Diabetic Rats

Background/Aims: Trichilia catigua A. Juss., known as “catuaba” in Brazil, has been popularly used as a tonic for fatigue, impotence and memory deficits. Previously, our group demonstrated that the ethyl-acetate fraction (EAF) of T. catigua has antioxidant and anti-inflammatory effects. The present study evaluated the anti-diabetic activity of EAF in type 1 diabetic rats. Methods: Male Wistar rats were divided into four groups (N: non-diabetic group, D: type 1 diabetic group, NC: non-diabetic + EAF group and DC: type 1 diabetic + EAF group). The latter two groups were treated with 200 mg/kg EAF. Type 1 diabetes was induced by intravenous streptozotocin (STZ) injection (35 mg/kg). Starting two days after STZ injection, EAF was administered daily by gavage for 8 weeks. Results: EAF attenuated body mass loss and reduced food and water intake. EAF improved hyperglycaemia and other biochemical parameters, such as alkaline phosphatase (ALP), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Furthermore, the number of pancreatic β-cells and the size of the islets had increased by β-cell proliferation in the DC group. EAF promoted reduction in kidney tissue damage in STZ-induced diabetic rats by reduction of renal fibrosis. Conclusion: The present study showed that EAF improves glucose homeostasis and endocrine pancreas morphology and inhibits the development of diabetic nephropathy in STZ-induced diabetic rats