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Database COVID

Com o objetivo de frear o número de infecções por COVID-19, vários países adotaram regulamentações de distanciamento social, incluindo o fechamento de estabelecimentos não essenciais e restrições de mobilidade. Este artigo estima o impacto dessas regulamentações sobre os crimes contra a propriedade no estado de São Paulo. O impacto é estimado usando um modelo espaço-temporal Bayesiano e é desagregado por microrregiões. A variabilidade espacial do impacto é usada para inferir quais variáveis observadas caracterizam os locais com maior probabilidade de terem sido impactados. Os resultados indicam que a maioria das microrregiões sofreu uma redução nos crimes contra a propriedade e que esses locais são caracterizados por índices de isolamento mais altos e menos recebimento de transferências emergenciais de dinheiro. With the goal of stopping COVID-19 infections, several countries adopted social distancing regulations, including closure of non essential establishments and mobility restrictions. This paper estimates the impact of these regulations on property crime in the state of São Paulo. The impact is estimated using a Bayesian spatiotemporal model, and is disaggregated by microregions. The space variability of the impact is used to infer which observed variables characterize places with higher probability of impact. We found that most microregions experienced a decrease in property crime, and that these places are characterized by higher isolation indices and less receipt of emergency cash transfers.

This viewpoint note discusses the covid-19 pandemic from the lens of complexity thinking and resilience engineering (RE). It intends to raise questions and encourage critical thinking on the underlying theoretical foundations of the responses adopted so far to cope with the pandemic. Insights arising from this analysis can be useful for the refinement of complexity and RE theory and practice, as well as for the further development of non-medical practices to address the pandemic and its effects.

There is a growing demand for unmanned aerial vehicles (UAV) in the industry since they are being used on a large scale in various fields, such as health, security, military missions, agriculture, etc. These vehicles have the potential to increase productivity and savings. However, the increase in production and use of unmanned aerial vehicles requires improving sound decision-making principles, physical, computational security, and relevant technologies by the computing community. They must continually adapt to carry out missions where they face unpredictable problems. The increase in advanced functionality and the demand for computing capacity exposes these vehicles to different security vulnerabilities. These vulnerabilities are growing not only in number but also in sophistication. Researchers and companies have been developing multidisciplinary methodologies and approaches in recent years, trying to solve the most varied protection and safety problems around unmanned aerial vehicles. As in the past, concerns about unmanned aerial vehicles safety were with aggressors and hijackers, now with the domain of civilian application and insertion into the network, circumstances have changed. In this context, this dissertations objective was to advance state-of-the-art through the definition and development of a resilient architecture for unmanned aerial vehicles (STUART - reSilient archiTecture to dynamically manage Unmanned aeriAl vehicle networksundeR atTack) that dynamically manages the network, even when subjected to an attack during a mission, integrating safety and security methods. The architecture is composed of four parts: (1) Anomaly-based detection system, (2) Triage module, (3) Decision-making module, and (4) Resilience module. This dissertation also investigated the incorporation of safety and security as a unified concept in UAV development. The developed architecture was validated by applying specific techniques in each of the parts that compose the architecture. Three tests were performed: one with a real drone and two tests using computer simulation, composed of a base station and a UAV during a vaccine transport mission for COVID-19. The results allow us to conclude that STUART is effective in detecting GPS spoofing. Unlike other architectures found in the literature that focus on specific missions or vulnerabilities, STUART proposes an organizational structure of metrics aggregated for different contexts, ranging from vulnerability detection, filtering false positives, decision-making, and mitigation. Its efficiency has been proven through computational validation using real data from a drone. Na indústria, há uma crescente demanda por veículos aéreos não tripulados, visto que, eles estão sendo usados em larga escala em vários campos, como saúde, segurança, missões militares, agricultura, etc. Esses veículos possuem potencial para aumentar a produtividade e a economia. No entanto, o aumento da produção e utilização desses veículos exigem o aprimoramento de princípios sólidos de tomada de decisão, segurança física, computacional e tecnologias relevantes pela comunidade de computação, pois eles devem continuamente se adaptar para realizar missões onde enfrentam problemas imprevisíveis. O aumento das funcionalidades avançadas e a demanda por capacidade computacional expõe esses veículos a diferentes vulnerabilidades de segurança. Essas vulnerabilidades estão crescendo não apenas em número, mas também em sofisticação. Como no passado as preocupações com a segurança dos VANTs estavam com agressores e sequestradores, agora com o domínio da aplicação civil e inserção na rede, as circunstâncias mudaram. Neste contexto, o objetivo desta dissertação foi avançar o estado da arte através da definição e desenvolvimento de uma arquitetura resiliente para veículos aéreos não tripulados (STUART - reSilient archiTecture to dynamically manage Unmanned aeriAl vehicle networksundeR atTack) que gerencie dinamicamente a rede, mesmo quando submetida a um ataque, integrando métodos de segurança computacional e física. A arquitetura é composta por quadro partes: (1) Sistema de detecção baseado em anomalias, (2) Módulo de triagem, (3) Módulo de tomada de decisão e (4) (5) Módulo de Resiliência. Esta dissertação também investigou a incorporação de segurança física e computacional como um conceito unificado no desenvolvimento de UAVs. A arquitetura desenvolvida foi validada aplicando técnicas específicas em cada uma das partes que compõem a arquitetura. Três testes foram realizados: um com um drone real e dois testes por meio de simulação computacional, compostas por uma estação base e um UAV durante uma missão de transporte de vacinas da COVID-19. Os resultados permitem concluir que a STUART é efetiva na detecção de GPS spoofing e diferentemente de outras arquiteturas encontradas na literatura que focam em missões ou vulnerabilidades específicas, a STUART propõe uma estrutura organizacional de métricas que podem ser agregadas para diferentes contextos e vão desde a detecção de vulnerabilidades, filtragem de falsos-positivos, até a tomada de decisão e mitigação. A sua eficiência foi comprovada através da validação computacional utilizando dados reais de um drone.

SARS‐CoV‐2 pandemic and recurrent dengue epidemics in tropical countries have turned into a global health threat. While both virus‐caused infections may only reveal light symptoms, they can also cause severe diseases. Here, we review the possible antibody‐dependent enhancement (ADE) occurrence, known for dengue infections, when there is a second infection with a different virus strain. Consequently, preexisting antibodies do not neutralize infection, but enhance it, possibly by triggering Fcg receptor‐mediated virus uptake. No clinical data exist indicating such mechanism for SARS‐CoV‐2, but previous coronavirus infections or infection of SARS‐CoV‐2 convalescent with different SARS‐CoV‐2 strains could promote ADE, as experimentally shown for antibodies against the MERS‐CoV or SARS‐CoV spike S protein.

The covidBR R package was built to extract data from the Brazilian COVID-19 Portal (Portal do COVID-19) and exported as CSV files.

Study Participants and Samples The whole blood samples were obtained from the Health, Well-being and Aging (Saúde, Ben-estar e Envelhecimento, SABE) study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed up every five years since the year 2000, with DNA first collected in 2010. Samples from 24 elderly adults were collected at two time points for a total of 48 samples. The first time point is the 2010 collection wave, performed from 2010 to 2012, and the second time point was set in 2020 in a COVID-19 monitoring project (9±0.71 years apart). The 24 individuals were 67.41±5.52 years of age (mean ± standard deviation) at time point one; and 76.41±6.17 at time point two and comprised 13 men and 11 women. All individuals enrolled in the SABE cohort provided written consent, and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263-10, University of Toronto RIS 39685. Blood Collection and Processing Genomic DNA was extracted from whole peripheral blood samples collected in EDTA tubes. DNA extraction and purification followed manufacturer’s recommended protocols, using Qiagen AutoPure LS kit with Gentra automated extraction (first time point) or manual extraction (second time point), due to discontinuation of the equipment but using the same commercial reagents. DNA was quantified using Nanodrop spectrometer and diluted to 50ng/uL. To assess the reproducibility of the EPIC array, we also obtained technical replicates for 16 out of the 48 samples, for a total of 64 samples submitted for further analyses. Whole Genome Sequencing data is also available for the samples described above. Characterization of DNA Methylation using the EPIC array Approximately 1,000ng of human genomic DNA was used for bisulphite conversion. Methylation status was evaluated using the MethylationEPIC array at The Centre for Applied Genomics (TCAG, Hospital for Sick Children, Toronto, Ontario, Canada), following protocols recommended by Illumina (San Diego, California, USA). Processing and Analysis of DNA Methylation Data The R/Bioconductor packages Meffil (version 1.1.0), RnBeads (version 2.6.0), minfi (version 1.34.0) and wateRmelon (version 1.32.0) were used to import, process and perform quality control (QC) analyses on the methylation data. Starting with the 64 samples, we first used Meffil to infer the sex of the 64 samples and compared the inferred sex to reported sex. Utilizing the 59 SNP probes that are available as part of the EPIC array, we calculated concordance between the methylation intensities of the samples and the corresponding genotype calls extracted from their WGS data. We then performed comprehensive sample-level and probe-level QC using the RnBeads QC pipeline. Specifically, we (1) removed probes if their target sequences overlap with a SNP at any base, (2) removed known cross-reactive probes (3) used the iterative Greedycut algorithm to filter out samples and probes, using a detection p-value threshold of 0.01 and (4) removed probes if more than 5% of the samples having a missing value. Since RnBeads does not have a function to perform probe filtering based on bead number, we used the wateRmelon package to extract bead numbers from the IDAT files and calculated the proportion of samples with bead number < 3. Probes with more than 5% of samples having low bead number (< 3) were removed. For the comparison of normalization methods, we also computed detection p-values using out-of-band probes empirical distribution with the pOOBAH() function in the SeSAMe (version 1.14.2) R package, with a p-value threshold of 0.05, and the combine.neg parameter set to TRUE. In the scenario where pOOBAH filtering was carried out, it was done in parallel with the previously mentioned QC steps, and the resulting probes flagged in both analyses were combined and removed from the data. Normalization Methods Evaluated The normalization methods compared in this study were implemented using different R/Bioconductor packages and are summarized in Figure 1. All data was read into R workspace as RG Channel Sets using minfi’s read.metharray.exp() function. One sample that was flagged during QC was removed, and further normalization steps were carried out in the remaining set of 63 samples. Prior to all normalizations with minfi, probes that did not pass QC were removed. Noob, SWAN, Quantile, Funnorm and Illumina normalizations were implemented using minfi. BMIQ normalization was implemented with ChAMP (version 2.26.0), using as input Raw data produced by minfi’s preprocessRaw() function. In the combination of Noob with BMIQ (Noob+BMIQ), BMIQ normalization was carried out using as input minfi’s Noob normalized data. Noob normalization was also implemented with SeSAMe, using a nonlinear dye bias correction. For SeSAMe normalization, two scenarios were tested. For both, the inputs were unmasked SigDF Sets converted from minfi’s RG Channel Sets. In the first, which we call “SeSAMe 1”, SeSAMe’s pOOBAH masking was not executed, and the only probes filtered out of the dataset prior to normalization were the ones that did not pass QC in the previous analyses. In the second scenario, which we call “SeSAMe 2”, pOOBAH masking was carried out in the unfiltered dataset, and masked probes were removed. This removal was followed by further removal of probes that did not pass previous QC, and that had not been removed by pOOBAH. Therefore, SeSAMe 2 has two rounds of probe removal. Noob normalization with nonlinear dye bias correction was then carried out in the filtered dataset. Methods were then compared by subsetting the 16 replicated samples and evaluating the effects that the different normalization methods had in the absolute difference of beta values (|β|) between replicated samples. Background The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias. Methods This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data. Results The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best-performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson’s correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor-performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2). We provide data on an Excel file, with absolute differences in beta values between replicate samples for each probe provided in different tabs for raw data and different normalization methods.

Open peer-reviews on 'National Economic and Health Recovery in the Disruptive Pandemic: A Proposal for Indonesia' (Abraham, Buhaerah, Aji, Onie, & Dalimunthe, 2021; Academia Letters, Article 1345, June 2021). The full article can be accessed via https://ssrn.com/abstract=3899576 and https://doi.org/10.20935/AL1345