Improving the connectivity of community detection-based hierarchical routing protocols in large-scale wsns

The recent growth in the use of wireless sensor networks (WSNs) in many applications leads to the raise of a core infrastructure for communication and data gathering in Cyber-Physical Systems (CPS). The communication strategy in most of the WSNs relies on hierarchical clustering routing protocols due to their ad hoc nature. In the bulk of the existing approaches some special nodes, named Cluster-Heads (CHs), have the task of assembling clusters and intermediate the communication between the cluster members and a central entity in the network, the Sink. Therefore, the overall efficiency of such protocols is highly dependent on the even distribution of CHs in the network. Recently, a community detection-based approach, named RLP, have shown interesting results with respect to the CH distribution and availability that potentially increases the overall WSN efficiency. Despite the better results of RLP regarding the literature, the adopted CH election algorithm may lead to a CH shortage throughout the network operation. In line with that, in this paper, we introduce an improved version of RLP, named HRLP. Our proposal includes a hybrid CH election algorithm which relies on a computationally cheap and distributed probabilistic-based CH recovery procedure to improve the network connectivity. Additionally, we provide a performance analysis of HRLP and its comparison to other protocols by considering a large-scale WSN scenario. The results evince the improvements achieved by the proposed strategy by means of the network connectivity and lifetime metrics. (C) 2016 The Authors. Published by Elsevier B.V.Federal University of São Paulo, Avenida Cesare Mansueto Giulio Lattes, 1201, Parque Tecnológico, 12247014, São José dos Campos-SP-BrazilFederal University of São Paulo, Avenida Cesare Mansueto Giulio Lattes, 1201, Parque Tecnológico, 12247014, São José dos Campos-SP-BrazilWeb of Scienc

A Formação Ambiental na Visão dos Graduandos de Licenciatura Plena em Ciências Naturais – Habilitação Química na Universidade do Estado Pará - UEPA

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American palm ethnomedicine: A meta-analysis

<p>Abstract</p> <p>Background</p> <p>Many recent papers have documented the phytochemical and pharmacological bases for the use of palms (<it>Arecaceae</it>) in ethnomedicine. Early publications were based almost entirely on interviews that solicited local knowledge. More recently, ethnobotanically guided searches for new medicinal plants have proven more successful than random sampling for identifying plants that contain biodynamic ingredients. However, limited laboratory time and the high cost of clinical trials make it difficult to test all potential medicinal plants in the search for new drug candidates. The purpose of this study was to summarize and analyze previous studies on the medicinal uses of American palms in order to narrow down the search for new palm-derived medicines.</p> <p>Methods</p> <p>Relevant literature was surveyed and data was extracted and organized into medicinal use categories. We focused on more recent literature than that considered in a review published 25 years ago. We included phytochemical and pharmacological research that explored the importance of American palms in ethnomedicine.</p> <p>Results</p> <p>Of 730 species of American palms, we found evidence that 106 species had known medicinal uses, ranging from treatments for diabetes and leishmaniasis to prostatic hyperplasia. Thus, the number of American palm species with known uses had increased from 48 to 106 over the last quarter of a century. Furthermore, the pharmacological bases for many of the effects are now understood.</p> <p>Conclusions</p> <p>Palms are important in American ethnomedicine. Some, like <it>Serenoa repens </it>and <it>Roystonea regia</it>, are the sources of drugs that have been approved for medicinal uses. In contrast, recent ethnopharmacological studies suggested that many of the reported uses of several other palms do not appear to have a strong physiological basis. This study has provided a useful assessment of the ethnobotanical and pharmacological data available on palms.</p

High anti-SARS-CoV-2 antibody seroconversion rates before the second wave in Manaus, Brazil, and the protective effect of social behaviour measures: results from the prospective DETECTCoV-19 cohort

Background: The city of Manaus, Brazil, has seen two collapses of the health system due to the COVID-19 pandemic. We report anti-SARS-CoV-2 nucleocapsid IgG antibody seroconversion rates and associated risk factors in Manaus residents before the second wave of the epidemic in Brazil. Methods: A convenience sample of adult (aged ≥18 years) residents of Manaus was recruited through online and university website advertising into the DETECTCoV-19 study cohort. The current analysis of seroconversion included a subgroup of DETECTCoV-19 participants who had at least two serum sample collections separated by at least 4 weeks between Aug 19 and Oct 2, 2020 (visit 1), and Oct 19 and Nov 27, 2020 (visit 2). Those who reported (or had no data on) having a COVID-19 diagnosis before visit 1, and who were positive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at visit 1 were excluded. Using an in-house ELISA, the reactivity index (RI; calculated as the optical density ratio of the sample to the negative control) for serum anti-SARS-CoV-2 nucleocapsid IgG antibodies was measured at both visits. We calculated the incidence of seroconversion (defined as RI values ≤1·5 at visit 1 and ≥1·5 at visit 2, and a ratio >2 between the visit 2 and visit 1 RI values) during the study period, as well as incidence rate ratios (IRRs) through cluster-corrected and adjusted Poisson regression models to analyse associations between seroconversion and variables related to sociodemographic characteristics, health access, comorbidities, COVID-19 exposure, protective behaviours, and symptoms. Findings: 2496 DETECTCoV-19 cohort participants returned for a follow-up visit between Oct 19 and Nov 27, 2020, of whom 204 reported having COVID-19 before the first visit and 24 had no data regarding previous disease status. 559 participants were seropositive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at baseline. Of the remaining 1709 participants who were seronegative at baseline, 71 did not meet the criteria for seroconversion and were excluded from the analyses. Among the remaining 1638 participants who were seronegative at baseline, 214 showed seroconversion at visit 2. The seroconversion incidence was 13·06% (95% CI 11·52–14·79) overall and 6·78% (5·61–8·10) for symptomatic seroconversion, over a median follow-up period of 57 days (IQR 54–61). 48·1% of seroconversion events were estimated to be asymptomatic. The sample had higher proportions of affluent and higher-educated people than those reported for the Manaus city population. In the fully adjusted and corrected model, risk factors for seroconversion before visit 2 were having a COVID-19 case in the household (IRR 1·49 [95% CI 1·21–1·83]), not wearing a mask during contact with a person with COVID-19 (1·25 [1·09–1·45]), relaxation of physical distancing (1·31 [1·05–1·64]), and having flu-like symptoms (1·79 [1·23–2·59]) or a COVID-19 diagnosis (3·57 [2·27–5·63]) between the first and second visits, whereas working remotely was associated with lower incidence (0·74 [0·56–0·97]). Interpretation: An intense infection transmission period preceded the second wave of COVID-19 in Manaus. Several modifiable behaviours increased the risk of seroconversion, including non-compliance with non-pharmaceutical interventions measures such as not wearing a mask during contact, relaxation of protective measures, and non-remote working. Increased testing in high-transmission areas is needed to provide timely information about ongoing transmission and aid appropriate implementation of transmission mitigation measures. Funding: Ministry of Education, Brazil; Fundação de Amparo à Pesquisa do Estado do Amazonas; Pan American Health Organization (PAHO)/WHO.World Health OrganizationRevisión por pare

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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