BPMG – Um Modelo Conceitual para Governança em BPM – Aplicação numa Organização Pública

Governança em BPM é um aspecto apontado como fundamental para o sucesso efetivo do gerenciamento de processos de negócio. Isso parece ser especialmente verdade no contexto do setor público, onde a efetividade, prestação de contas e transparência (três princípios básicos da governança) precisam ser coordenadas. No entanto, as abordagens dessa governança têm se caracterizado por conceitos complexos e pouco precisos, e modelos em alto nível que oferecem pouca ajuda sobre como implementá-la. Além disso, há um número reduzido de trabalhos empíricos com relatos práticos neste tema. Este trabalho relata a construção de um Modelo Conceitual para Governança em BPM e sua aplicação numa organização pública brasileira, utilizando uma abordagem de design-science. A avaliação sobre a aplicação do modelo apontou que ele foi percebido como significativamente útil para criar um entendimento comum bem como para apoiar a estruturação da governança em BPM na organização pesquisada

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

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

Problemas em Iniciativas de Melhoria de Processos de Software sob a Ótica de uma Teoria de Intervenção

Iniciativas de melhoria de processos de software (MPS) têm tido uma importância cada vez maior na indústria de software como fator de evolução da qualidade de seus produtos e de combate à alta taxa de insucesso em projetos desta indústria. Pesquisas mostram que as iniciativas de MPS, por sua vez, também têm uma alta taxa de insucesso. Mostram também, que vários dos principais fatores críticos em MPS não são questões técnicas de engenharia de software e sim questões humanas, sociais e organizacionais, relativas à condução das iniciativas de melhoria, e à interação entre seus participantes. Esta dissertação mostra que iniciativas de MPS podem ser vistas como uma intervenção na organização que produz software. A teoria de intervenção e conceitos complementares de teorias de ação e de aprendizagem organizacional do cientista social Chris Argyris e seus colaboradores são usados nesta dissertação para reinterpretar e compreender mais profundamente os problemas sócio-técnicos de iniciativas de MPS. São particularmente apontados como problemas que permeiam iniciativas de MPS: a incongruência das normas internalizadas da organização com os objetivos da intervenção de MPS; a dificuldade dos atores em lidar produtivamente com situações conflitivas; a incongruência da teoria em uso dos atores para com as atividades primárias de intervenção preconizadas por Argyris; e finalmente, as limitações da abordagem predominantemente técnica na condução da intervenção de MPS. A mesma abordagem teórica é utilizada como base para prescrição de estratégias de ação de como tratar os problemas levantados. A interpretação dos problemas tomou por base uma pesquisa de campo sobre fatores críticos (facilitadores e barreiras) em iniciativas de MPS. A pesquisa foi realizada com entrevistas a profissionais desempenhando vários papéis (engenheiros da qualidade, consultores externos, diretor, gerentes de projetos e engenheiros de software) e que estiveram envolvidos em iniciativas de MPS em suas empresas. Esta dissertação contribui, assim, para preencher uma lacuna comumente encontrada na condução de iniciativas MPS, que usam modelos normativos tais como CMMI e MPS.Br, quanto ao entendimento e tratamento dos fenômenos humanos e sociais inerentes a este tipo de iniciativ

Effect of gingival barrier brands on operator perception, cervical adaptation, and patient comfort during in-office tooth bleaching: a randomized clinical trial

Abstract Background Light-cured resins are widely used as gingival barriers to protect the gums from highly concentrated peroxides used in tooth bleaching. The impact of barrier brand on clinical outcomes is typically considered negligible. However, there is limited evidence on the effects of different brands on operator experience, barrier adaptation, and patient comfort. Objective This clinical trial assessed the impact of four commercial gingival barrier brands (Opaldam, Topdam, Lysadam, and Maxdam) on operator perception, adaptation quality, and patient comfort. Methods Twenty-one undergraduate students placed gingival barriers in a randomized sequence using blinded syringes. Photographs of the barriers were taken from frontal and incisal perspectives. After bleaching procedures, operators rated handling features and safety using Likert scale forms. Two experienced evaluators independently assessed barrier adaptation quality on a scale from 1 (perfect) to 5 (unacceptable). The absolute risk of barrier-induced discomfort was recorded. Data were analyzed using Friedman and Chi-square tests (α = 0.05). Results Opaldam and Topdam received the highest scores in most handling features, except for removal, which was similar among all brands. No significant difference was observed in barrier adaptation quality between the evaluated brands. Discomforts were mainly reported in the upper dental arch, with Maxdam having the highest absolute risk (35% for this arch and 24% overall). Conclusions This study suggests that gingival barrier brands can influence operator perception and patient comfort. Opaldam and Topdam were preferred by operators, but all brands demonstrated comparable adaptation quality. Clinical trial registration The study was nested in a randomized clinical trial registered in the Brazilian Clinical Trials Registry under identification number RBR-9gtr9sc

Genomic, epidemiological and digital surveillance of Chikungunya virus in the Brazilian Amazon.

BackgroundSince its first detection in the Caribbean in late 2013, chikungunya virus (CHIKV) has affected 51 countries in the Americas. The CHIKV epidemic in the Americas was caused by the CHIKV-Asian genotype. In August 2014, local transmission of the CHIKV-Asian genotype was detected in the Brazilian Amazon region. However, a distinct lineage, the CHIKV-East-Central-South-America (ECSA)-genotype, was detected nearly simultaneously in Feira de Santana, Bahia state, northeast Brazil. The genomic diversity and the dynamics of CHIKV in the Brazilian Amazon region remains poorly understood despite its importance to better understand the epidemiological spread and public health impact of CHIKV in the country.Methodology/principal findingsWe report a large CHIKV outbreak (5,928 notified cases between August 2014 and August 2018) in Boa vista municipality, capital city of Roraima's state, located in the Brazilian Amazon region. We generated 20 novel CHIKV-ECSA genomes from the Brazilian Amazon region using MinION portable genome sequencing. Phylogenetic analyses revealed that despite an early introduction of the Asian genotype in 2015 in Roraima, the large CHIKV outbreak in 2017 in Boa Vista was caused by an ECSA-lineage most likely introduced from northeastern Brazil. Epidemiological analyses suggest a basic reproductive number of R0 of 1.66, which translates in an estimated 39 (95% CI: 36 to 45) % of Roraima's population infected with CHIKV-ECSA. Finally, we find a strong association between Google search activity and the local laboratory-confirmed CHIKV cases in Roraima.Conclusions/significanceThis study highlights the potential of combining traditional surveillance with portable genome sequencing technologies and digital epidemiology to inform public health surveillance in the Amazon region. Our data reveal a large CHIKV-ECSA outbreak in Boa Vista, limited potential for future CHIKV outbreaks, and indicate a replacement of the Asian genotype by the ECSA genotype in the Amazon region

Chikungunya virus outbreak in the Amazon region: replacement of the Asian genotype by an ECSA lineage

Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Universidade de São Paulo. Faculdade de Medicina. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Patologia Experimental. Salvador, BA, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Patologia Experimental. Salvador, BA, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil / Fundação Ezequiel Dias. Instituto Octávio Magalhães. Laboratório Central de Saúde Pública. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Universidade Federal do Rio de Janeiro. Instituto de Biologia. Departamento de Genética Laboratório de Virologia Molecular. Rio de Janeiro, RJ, Brazil.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Harvard Medical School. Department of Pediatrics. Boston, MA, USA / Boston Children’s Hospital. Computational Health Informatics Program. Boston, MA, USA.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom / Boston Children’s Hospital. Computational Epidemiology Lab. Boston, MA, USA.University of Birmingham. Institute of Microbiology and Infection. Birmingham, United Kingdom.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Universidade de São Paulo. Faculdade de Medicina. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Centro de Inovações Tecnológicas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Centro de Inovações Tecnológicas. Ananindeua, PA, Brasil.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Laboratório Central de Saúde Pública. Boa Vista, RR, Brazil.Laboratório Central de Saúde Pública. Boa Vista, RR, Brazil.Laboratório Central de Saúde Pública. Boa Vista, RR, Brazil.Secretaria Municipal de Saúde de Boa Vista. Superintendência de Vigilância em Saúde. Boa Vista, RR, Brazil.Fundação de Medicina Tropical Doutor Heitor Vieira. Departamento de Virologia. Manaus, AM, Brazil.Secretaria Municipal de Saúde de Boa Vista. Superintendência de Vigilância em Saúde. Boa Vista, RR, Brazil.Laboratório Central de Saúde Pública do Amazonas. Manaus, AM, Brazil.Organização Pan - Americana da Saúde/Organização Mundial da Saúde. Brasília, DF, BrazilMinistério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.University of Birmingham. Institute of Microbiology and Infection. Birmingham, United Kingdom.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Universidade de São Paulo. Faculdade de Medicina. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Background Since its first detection in the Caribbean in late 2013, chikungunya virus (CHIKV) has affected 51 countries in the Americas. The CHIKV epidemic in the Americas was caused by the CHIKV-Asian genotype. In August 2014, local transmission of the CHIKV-Asian genotype was detected in the Brazilian Amazon region. However, a distinct lineage, the CHIKV-East-Central-South-America (ECSA)-genotype, was detected nearly simultaneously in Feira de Santana, Bahia state, northeast Brazil. The genomic diversity and the dynamics of CHIKV in the Brazilian Amazon region remains poorly understood despite its importance to better understand the epidemiological spread and public health impact of CHIKV in the country. Methodology/Principal Findings We report a large CHIKV outbreak (5,928 notified cases between August 2014 and August 2018) in Boa vista municipality, capital city of Roraima’s state, located in the Brazilian Amazon region. In just 48 hours, we generated 20 novel CHIKV-ECSA genomes from the Brazilian Amazon region using MinION portable genome sequencing. Phylogenetic analyses revealed that despite an early introduction of the Asian genotype in 2015 in Roraima, the large CHIKV outbreak in 2017 in Boa Vista was caused by an ECSA-lineage most likely introduced from northeastern Brazil. Epidemiological analyses suggest a basic reproductive number of R0 of 1.66, which translates in an estimated 39 (95% CI: 36 to 45) % of Roraima’s population infected with CHIKV-ECSA. Finally, we find a strong association between Google search activity and the local laboratory-confirmed CHIKV cases in Roraima. Conclusions/Significance This study highlights the potential of combining traditional surveillance with portable genome sequencing technologies and digital epidemiology to inform public health surveillance in the Amazon region. Our data reveal a large CHIKV-ECSA outbreak in Boa Vista, limited potential for future CHIKV outbreaks, and indicate a replacement of the Asian genotype by the ECSA genotype in the Amazon region