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

Noise induced hearing loss: record of distortion product otoacoustic emissions as a method for the early diagnosis

Introdução. A Perda Auditiva Induzida pelo Ruído (PAIR) tem sido objeto de estudos crescentes no campo da saúde publica devido às suas repercussões à audição, afetando a qualidade de vida dos trabalhadores. A exposição ao ruído ocupacional pode provocar lesões em nível da orelha interna, sendo que os registros das Emissões Otoacústicas por Produtos de Distorção (EOAPD) podem identificar alterações auditivas iniciais relacionadas a tais lesões, auxiliando no diagnóstico precoce da PAIR. Objetivos. Avaliar as EOAPD como método de diagnóstico de alterações fisiopatológicas iniciais provocadas por exposição ao ruído ocupacional. Métodos. Foram avaliados 74 trabalhadores do sexo masculino, lotados no Campus Universitário da Universidade de São Paulo na capital, divididos em dois grupos pareados por idade e com exame de audiometria tonal dentro de limites aceitáveis: 37 indivíduos expostos ao ruído ocupacional e 37 não expostos. Realizou-se anamnese clínica e ocupacional, otoscopia e registro das EOAPD. Resultados. A estimativa do risco (Odds Ratio) de ausência de resposta no registro das EOAPD foi 12 vezes maior para o grupo de expostos ao ruído ocupacional (IC 95% 3,1 - 45,9), nas freqüências de 3, 4 e 6 kHz agrupadas. A prevalência da ausência de respostas foi de 37,5% para aqueles trabalhadores expostos a doses de ruído de 1 a 1,5 e de 77,0% para os expostos a doses de ruído maiores que 1,5. Conclusões. Os resultados sugerem que a exposição ao ruído ocupacional pode provocar alterações nos registros das EOAPD, mesmo em indivíduos com exame de audiometria tonal dentro de limites aceitáveis, indicando que este exame pode ser importante como método de diagnóstico precoce da PAIR.Introduction. The noise induced hearing loss (NIHL) has been subject of increasing research on public health due to its repercussions to hearing, affecting the life quality of workers. The exposition to occupational noise may cause injuries to the internal ear and the record of the distortion product otoacoustic emissions (DPOAE) may identify initial auditory alterations, assisting the early diagnosis of the NIHL. Objective. The goal of this research is to evaluate the DPOAE as a method of diagnosis of initial fisipatho!ogical alterations caused by exposition to occupational noise. Methods. Underwent the study 74 workers of the University of São Paulo, in the capital city of the State. They were divided in two groups matched by age and with tonal audiometry inside the acceptable limits: 37 exposed to occupational noise and 37 non exposed. Data were collected through occupational and clinical interviews, otoscopy and register of the DPOAE. Results. The risk estimates (Odds Ratio) of absent responses in the record of the EOAPD was 12 times higer for the group exposed to occupational noise (IC 95% 3,1 - 45,9), in frequencies of 3, 4 and 6 kHz. The prevalence of absent responses was of 37,5% for those workers exposed to doses of noise of 1 to 1,5 and 77,0% for those exposed to doses of noise higer than 1,5. Conclusions. the record of the DPOAE may be useful in the identification of fisiopathological alterations of hearing caused by exposition to occupational noise, even in individuals with examination of tonal audiometry inside of acceptable limits

Thermodynamic Signatures of Structural Transitions and Dissociation of Charged Colloidal Clusters: A Parallel Tempering Monte Carlo Study

Computational simulation of colloidal systems make use of empirical interaction potentials that are founded in well-established theory. In this work, we have performed parallel tempering Monte Carlo (PTMC) simulations to calculate heat capacity and to assess structural transitions, which may occur in charged colloidal clusters whose effective interactions are described by a sum of pair potentials with attractive short-range and repulsive long-range components. Previous studies on these systems have shown that the global minimum structure varies from spherical-type shapes for small-size clusters to Bernal spiral and "beaded-necklace" shapes at intermediate and larger sizes, respectively. In order to study both structural transitions and dissociation, we have organized the structures appearing in the PTMC calculations by three sets according to their energy: (i) low-energy structures, including the global minimum; (ii) intermediate-energy "beaded-necklace" motifs; (iii) high-energy linear and branched structures that characterize the dissociative clusters. We observe that, depending on the cluster, either peaks or shoulders on the heat-capacity curve constitute thermodynamics signatures of dissociation and structural transitions. The dissociation occurs at T=0.20 for all studied clusters and it is characterized by the appearance of a significant number of linear structures, while the structural transitions corresponding to unrolling the Bernal spiral are quite dependent on the size of the colloidal system

Structure and Thermodynamics of Li⁺Ar<i>_n</i> Clusters beyond the Second Solvation Shell

Small Li+Arn clusters are employed in this work as model systems to study microsolvation. Although first and second solvation shells are expected to be the most relevant ones for this type of atomic solvents, it is also interesting to explore larger clusters in order to identify the influence of external atoms on structural and thermodynamic properties. In this work, we perform a global geometry optimization for Li+Arn clusters (with n = 41–100) and parallel tempering Monte Carlo (PTMC) simulations for some selected sizes. The results show that global minimum structures of large clusters always have 6 argon atoms in the first solvation shell while maintaining the number of 14 or 16 argon atoms in the second one. By contrast, third and fourth solvation shells vary significantly the number of argon atoms with the cluster size, and other shells can hardly be assigned due to the reduced influence of Li+ on the external argon atoms for large clusters. In turn, PTMC calculations show that the melting of the most external solvation shells of large microsolvation clusters occurs at T∼50K, which is independent of cluster size. Structural transitions can be observed between quasi-degenerated structures at low temperatures. Moreover, the present results highlight the fluxional character of the external solvation shells of these large Li+Arn clusters, which may be seen as typical “snowball” structures

Intermolecular Forces: From Atoms and Molecules to Nanostructures

Intermolecular forces, determined by the critical balance of interacting components having physical and chemical natures, control most of the static and dynamic properties of matter such as their existence in solid, liquid and gaseous phases, with their relative stability, and their chemical reactivity [...]