Low-dimensional space modeling-based differential evolution for large scale global optimization problems

Large-Scale Global Optimization (LSGO) has been an active research field. Part of this interest is supported by its application to cutting-edge research such as Deep Learning, Big Data, and complex real-world problems such as image encryption, real-time traffic management, and more. However, the high dimensionality makes solving LSGO a significant challenge. Some recent research deal with the high dimensionality by mapping the optimization process to a reduced alternative space. Nonetheless, these works suffer from the changes in the search space topology and the loss of information caused by the dimensionality reduction. This paper proposes a hybrid metaheuristic, so-called LSMDE (Low-dimensional Space Modeling-based Differential Evolution), that uses the Singular Value Decomposition to build a low-dimensional search space from the features of candidate solutions generated by a new SHADE-based algorithm (GM-SHADE). GM-SHADE combines a Gaussian Mixture Model (GMM) and two specialized local algorithms: MTS-LS1 and L-BFGS-B, to promote a better exploration of the reduced search space. GMM mitigates the loss of information in mapping high-dimensional individuals to low-dimensional individuals. Furthermore, the proposal does not require prior knowledge of the search space topology, which makes it more flexible and adaptable to different LSGO problems. The results indicate that LSMDE is the most efficient method to deal with partially separable functions compared to other state-of-the-art algorithms and has the best overall performance in two of the three proposed experiments. Experimental results also show that the new approach achieves competitive results for non-separable and overlapping functions on the most recent test suite for LSGO problems

TNF-α mediated upregulation of Na1.7 currents in rat dorsal root ganglion neurons is independent of CRMP2 SUMOylation

Clinical and preclinical studies have shown that patients with Diabetic Neuropathy Pain (DNP) present with increased tumor necrosis factor alpha (TNF-α) serum concentration, whereas studies with diabetic animals have shown that TNF-α induces an increase in NaV1.7 sodium channel expression. This is expected to result in sensitization of nociceptor neuron terminals, and therefore the development of DNP. For further study of this mechanism, dissociated dorsal root ganglion (DRG) neurons were exposed to TNF-α for 6 h, at a concentration equivalent to that measured in STZ-induced diabetic rats that developed hyperalgesia. Tetrodotoxin sensitive (TTXs), resistant (TTXr) and total sodium current was studied in these DRG neurons. Total sodium current was also studied in DRG neurons expressing the collapsin response mediator protein 2 (CRMP2) SUMO-incompetent mutant protein (CRMP2-K374A), which causes a significant reduction in NaV1.7 membrane cell expression levels. Our results show that TNF-α exposure increased the density of the total, TTXs and TTXr sodium current in DRG neurons. Furthermore, TNF-α shifted the steady state activation and inactivation curves of the total and TTXs sodium current. DRG neurons expressing the CRMP2-K374A mutant also exhibited total sodium current increases after exposure to TNF-α, indicating that these effects were independent of SUMOylation of CRMP2. In conclusion, TNF-α sensitizes DRG neurons via augmentation of whole cell sodium current. This may underlie the pronociceptive effects of TNF-α and suggests a molecular mechanism responsible for pain hypersensitivity in diabetic neuropathy patients.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

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

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

Metodo evolutivo baseado em espacos de busca de baixa dimensionalidade para problemas de otimização contínua em larga escala

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2023.A Otimização Global em Larga Escala (LSGO) tem sido um campo ativo de pesquisa, em parte devido às suas aplicações em áreas de ponta, como Deep Learning, Big Data e problemas complexos do mundo real, como criptografia de imagens e gerenciamento de tráfego em tempo real. A alta dimensionalidade apresenta um desafio significativo na resolução de problemas de otimização. A maldição da dimensionalidade refere-se à crescente dificuldade de encontrar soluções ótimas à medida que o número de dimensões do problema aumenta, tornando a questão desafiadora. Para abordar isso, esta tese propõe um método evolutivo denominado LSMDE (Evolução Diferencial baseada em Modelagem de Espaço de Baixa Dimensionalidade). O método LSMDE utiliza redução de dimensionalidade por meio da Decomposição em Valor Singular para construir um espaço de busca de baixa dimensionalidade a partir das soluções candidatas geradas por um algoritmo de evolução diferencial híbrido denominado GM-SHADE. Esse algoritmo incorpora um modelo de mistura gaussiana para melhor explorar o espaço de busca reduzido e mitigar a perda de informação ao mapear soluções de alta dimensionalidade para soluções de baixa dimensionalidade. Adicionalmente, o método proposto não exige conhecimento prévio da topologia do espaço de busca, tornando-o adaptável a diferentes problemas de LSGO. Esta tese buscou comparar o método proposto com as principais abordagens da literatura nos benchmarks mais reconhecidos para alta dimensionalidade, usando os critérios definidos pelo IEEE CEC Special Sessions and Competitions on Large-Scale Global Optimization. Para analisar a significância das diferenças observadas, foi utilizado o teste estatístico de Kruskal-Wallis com a igual 0,05. Experimentos realizados indicam a superioridade do LSMDE em diferentes características de espaços de busca, especialmente para funções parcialmente separáveis. Também se observou uma melhor performance do LSMDE em condições em que o número de avaliações da função objetivo é restrito, facilitando sua utilização em ambientes onde os recursos computacionais são limitados. Além disso, esta tese também realizou um teste de escalabilidade ao comparar o desempenho do LSMDE e seus principais competidores à medida que a dimensionalidade do problema aumenta. Para essa tarefa, utilizou-se a suíte de teste bbob-largescale. Os resultados demonstram a robustez do LSMDE ao aumento da dimensionalidade, alcançando uma taxa de acerto ao valor-alvo entre 40% e 80%.Abstract: Large-Scale Global Optimization (LSGO) has been an active research field, partly due to its applications in cutting-edge areas such as Deep Learning, Big Data, and real-world complex problems like image encryption and real-time traffic management. High dimensionality presents a significant challenge in solving optimization problems. The curse of dimensionality refers to the increasing difficulty of finding optimal solutions as the number of problem dimensions increases, making the issue challenging. In response to this challenge, this thesis proposes an evolutionary method named LSMDE (Low-Dimensional Space Modeling-based Differential Evolution). LSMDE uses dimensionality reduction through Singular Value Decomposition to construct a low dimensionality search space from the candidate solutions generated by a hybrid differential evolution algorithm called GM-SHADE. This algorithm incorporates a Gaussian mixture model to better explore the reduced search space and mitigate information loss when mapping high dimensionality solutions to low-dimensionality ones. Additionally, the proposed method does not require prior knowledge of the search space topology, making it adaptable to various LSGO problems. This thesis sought to compare the proposed method with the main approaches in the literature on the most recognized benchmarks for high dimensionality, using the criteria defined by the IEEE CEC Special Sessions and Competitions on Large-Scale Global Optimization. To analyze the significance of the observed differences, the Kruskal-Wallis statistical test was used with a set at 0.05. Experiments conducted indicate LSMDE?s superiority in different search space characteristics, especially for partially separable functions. A better performance of LSMDE was also observed in conditions where the number of objective function evaluations is limited, facilitating its use in environments where computational resources are constrained. Furthermore, this thesis also conducted a scalability test by comparing LSMDE?s performance and its main competitors as the problem?s dimensionality increases. For this task, the bbob-largescale test suite was used. The results demonstrate LSMDE?s robustness to increasing dimensionality, achieving a success rate to the target value between 40% and 80%

Levantamento e comparação entre abordagens de estado da arte para detecção de vivacidade em biometria facial

Relatório técnicoMecanismos para verificação de identidade mediante biometria constituem uma importante categoria de abordagens para a segurança da informação. A detecção de vivacidade adiciona uma verificação nesse processo de forma a garantir que a biometria apresentada seja legítima e não uma cópia ou artefato artificial. Assim sendo, neste relatório apresenta-se um levantamento do estado da arte em abordagens para detecção de vivacidade no contexto de biometria facial. Além disso, também são apresentados os principais conjuntos de dados publicamente disponíveis para treinamento ou avaliação de métodos. Por último, é apresentada uma análise comparativa quanto ao desempenho das principais abordagens.CNP

Duration of post-vaccination immunity against yellow fever in adults

Submitted by Nuzia Santos ([email protected]) on 2015-06-22T17:37:43Z No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2015-06-22T17:37:52Z (GMT) No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2015-06-22T17:58:36Z (GMT) No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5)Made available in DSpace on 2015-06-22T17:58:36Z (GMT). No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5) Previous issue date: 2014Fundação Oswaldo Cruz. Brasilia, DF, BrasilFundação Oswaldo Cruz. Escola Nacional de Saúde Pública. Rio de Janeiro, RJ, BrazilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicosde Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Imunopatologia .Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Esquistossomose. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFood and Drug Administration Center for Biologics Evaluation and Research. Bethesda, USA.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratorio de Fla-vivirus. Rio de JaneiroFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratorio de Fla-vivirus. Rio de JaneiroFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratorio de Fla-vivirus. Rio de JaneiroInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilUniversidade Federal de Alfenas. Alfenas, MG, BrasilUniversidade de Brasília. Faculdade de Medicina. Brasilia, DF, BrasilFundação Oswaldo Cruz. Instituto Evandro Chagas. Ananindeua, PA, BrasilINTRODUCTION: Available scientific evidence to recommend or to advise against booster doses of yellow fever vaccine (YFV) is inconclusive. A study to estimate the seropositivity rate and geometric mean titres (GMT) of adults with varied times of vaccination was aimed to provide elements to revise the need and the timing of revaccination. METHODS: Adults from the cities of Rio de Janeiro and Alfenas located in non-endemic areas in the Southeast of Brazil, who had one dose of YFV, were tested for YF neutralising antibodies and dengue IgG. Time (in years) since vaccination was based on immunisation cards and other reliable records. RESULTS: From 2011 to 2012 we recruited 691 subjects (73% males), aged 18-83 years. Time since vaccination ranged from 30 days to 18 years. Seropositivity rates (95%C.I.) and GMT (International Units/mL; 95%C.I.) decreased with time since vaccination: 93% (88-96%), 8.8 (7.0-10.9) IU/mL for newly vaccinated; 94% (88-97), 3.0 (2.5-3.6) IU/mL after 1-4 years; 83% (74-90), 2.2 (1.7-2.8) IU/mL after 5-9 years; 76% (68-83), 1.7 (1.4-2.0) IU/mL after 10-11 years; and 85% (80-90), 2.1 (1.7-2.5) IU/mL after 12 years or more. YF seropositivity rates were not affected by previous dengue infection. CONCLUSIONS:Eventhough serological correlates of protection for yellow fever are unknown, seronegativity in vaccinated subjects may indicate primary immunisation failure, or waning of immunity to levels below the protection threshold. Immunogenicity of YFV under routine conditions of immunisation services is likely to be lower than in controlled studies. Moreover, infants and toddlers, who comprise the main target group in YF endemic regions, and populations with high HIV infection rates, respond to YFV with lower antibody levels. In those settings one booster dose, preferably sooner than currently recommended, seems to be necessary to ensure longer protection for all vaccinee