A Customer complaint from a telecommunication company: a Bayesian data analysis

This study considers a customer complaint dataset due to the technical services provided by a telecommunications company collected for 134 consecutive weeks from the first week of January 2018 up to the year 2019. The total count of weekly complaints is the sum of different causes, which characterizes compositional data. The data was analyzed assuming a Poisson regression model for the weekly total complaint count data in presence of a random factor and compositional models both under a Bayesian approach using existing MCMC (Monte Carlo Markov Chain) to get the posterior summaries of interest. The obtained results are of great importance to improve the service quality of the company

A DNA intercalating dye-based RT-qPCR alternative to diagnose SARS-CoV-2

Early detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been proven crucial during the efforts to mitigate the effects of the COVID-19 pandemic. Several diagnostic methods have emerged in the past few months, each with different shortcomings and limitations. The current gold standard, RT-qPCR using fluorescent probes, relies on demanding equipment requirements plus the high costs of the probes and specific reaction mixes. To broaden the possibilities of reagents and thermocyclers that could be allocated towards this task, we have optimized an alternative strategy for RT-qPCR diagnosis. This is based on a widely used DNA-intercalating dye and can be implemented with several different qPCR reagents and instruments. Remarkably, the proposed qPCR method performs similarly to the broadly used TaqMan-based detection, in terms of specificity and sensitivity, thus representing a reliable tool. We think that, through enabling the use of vast range of thermocycler models and laboratory facilities for SARS-CoV-2 diagnosis, the alternative proposed here can increase dramatically the testing capability, especially in countries with limited access to costly technology and reagents.Fil: Fuchs Wightman, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Godoy Herz, Micaela Amalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Muñoz, Juan Cristóbal. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Stigliano, Jose Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Bragado, Laureano Fabian Tomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Nieto Moreno, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Palavecino Ruiz, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Servi, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Cabrerizo, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Clemente, Jose Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Avaro, Martín. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: Pontoriero, Andrea. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: Benedetti, Estefanía. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: Baumeister, Elsa. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; ArgentinaFil: Rudolf, Fabian. Eidgenössische Technische Hochschule Zürich; SuizaFil: Remes Lenicov, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Garcia, Cybele. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Buggiano, Valeria Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Kornblihtt, Alberto Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Srebrow, Anabella. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: de la Mata, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Muñoz, Manuel Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Schor, Ignacio Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Petrillo, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentin

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

Qualidade de vida de indivíduos com HIV: revisão integrativa / Quality of living of individuals with HIV integration review

 A qualidade de  vida de pacientes vivendo com HIV/AIDS é essencial, tendo em vista o caso específico dos adolescentes é ainda mais importante porque o jovem além de lidar com a resistência e a aceitação da doença, deve também enfrentar os conflitos da puberdade.O presente trabalho tem como objetivo realizar uma revisão integrativa em relação a qualidade de vida dos pacientes com HIV. Trata-se de um estudo de revisão integrativa realizado no período de 2016 a 2017, utilizando-se o banco de dados Medline e Lilacs. Apesar do impacto da infecção pelo HIV/AIDS na saúde psicológica e nas relações sociais dos indivíduos infectados, assim como no domínio físico, o uso da TARV provocou consequências positivas na saúde psicológica, proporcionando a desconstrução da ideia de morte advinda ao diagnóstico de portador do HIV/AIDS e a construção de melhores perspectivas de vida. A adesão ao tratamento em HIV/AIDS está relacionada a um aumento da sobrevida e de sua qualidade. Porém deve-se respeitar a posição pessoal do portador para fazer ou não o tratamento

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

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

Denudação química e implicações na composição das águas superficiais da Bacia do Rio Jaú (SP)

This work evaluated the geochemical denudation and implications in the composition of surface waters from Jaú River basin, six sampling points were established and six fi eld campaigns were carried out in 08/14/2009, 09/17/2009, 10/17/2009, 11/20/2009, 12/23/2009 and 01/12/2010, which involved the dry and wet periods. The analyses were executed for discharge, temperature, pH, electric conductivity, calcium, magnesium, sodium, potassium, silicon dioxide, alkalinity, chloride, sulfate, phosphate and nitrate. The results indicated that calcium, magnesium and nitrate have similar behavior, i.e. the amount increases upstream (P1 and P3, sediments of Itaqueri Formation and the Middle Hills relief unit) to downstream (P2 and P4, Hillock Elongated and Spikes associated with igneous rocks of the Serra Geral Formation). P5 and P6 also drain the basalts of Serra Geral Formation in Jaú River, where except for Ca2+, Mg2+ and Cl- all other parameters have a higher concentration at the point P6, near the Tietê River mouth. However, the total cations and anions (t/km2 /year) is greater in point P5, due to the higher concentration of Ca2+ and Mg2+. Because the rocks at Jaú River don´t have Cl- , NO3 - , PO4 3- and SO4 2- bearing minerals as a main constituent, the chemical denudation (t/year) in Jaú River basin was calculated based on the concentration of Ca2+, Mg2+, Na+ , K+ and SiO2 (leached solution of minerals that make up the basin rocks). The results indicated that the Jaú River basin exports about 16,000 tons of weathered material per year to Tietê River.Este trabalho avaliou a denudação química e suas implicações na composição das águas superfi ciais da bacia do Rio Jaú, seis pontos de amostragem foram estabelecidos e realizaram-se seis amostragens (14/08/2009, 17/09/2009, 17/10/2009, 20/11/2009, 23/12/2009 e 12/01/2010), envolvendo os períodos seco e chuvoso. As análises foram executadas para vazão, temperatura, pH, condutividade elétrica, cálcio, magnésio, sódio, potássio, sílica, cloreto, sulfato, fosfato e nitrato. Os resultados indicam que os parâmetros cálcio, magnésio e nitrato possuem um comportamento semelhante, ou seja, aumentam de montante (P1 e P3, sedimentos da Formação Itaqueri e a unidade de relevo Colinas Médias) para jusante (P2 e P4, Morrotes Alongados e Espigões associados às rochas ígneas da Formação Serra Geral). No Rio Jaú (P5 e P6) que também drena os basaltos da Formação Serra Geral, com exceção de Ca2+, Mg2+ e Cl- todos os outros parâmetros analisados possuem uma concentração maior no ponto P6, próximo à foz com o Rio Tietê. No entanto, a soma total de cátions e ânions (t/km2 /ano) é maior no ponto P5, devido à alta concentração de Ca2+ e Mg2+ no local. Como as rochas da bacia do Rio Jaú não possuem minerais portadores de Cl- , NO3 - , PO4 3- e SO4 2- como constituintes principais, a denudação química (t/ano) na bacia do Rio Jaú foi calculada com base na concentração de Ca2+, Mg2+, Na+ , K+ e SiO2 (solução lixiviada dos minerais que compõem as rochas da bacia). Os resultados indicaram que a bacia do Rio Jaú exporta cerca de 16.000 toneladas de material alterado por ano ao Rio Tietê.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq