Cytogenetic analysis of baryancistrus xanthellus (Siluriformes: Loricariidae: Ancistrini), an ornamental fish endemic to the Xingu river, Brazil

Baryancistrus xanthellus is a species from the Ancistrini tribe known commonly as “amarelinho” or “golden nugget pleco”. It is one of the most popular and valued ornamental fishes due to its color pattern. Also, it is an endemic species from the Xingu River occurring from Volta Grande do Xingu, region where the Belo Monte Hydropower Dam is being built, to São Félix do Xingu. The current study aimed to cytogenetically characterize B. xanthellus. Results point to the maintenance of 2n=52, which is considered the most common condition for the tribe, and a single nucleolus organizer region (NOR). Mapping of the 18S rDNA confirmed the NOR sites, and the 5S rDNA was mapped in the interstitial position of a single chromosome pair. The 18S and 5S rDNA located in different pairs constitute an apomorphy in Loricariidae. Large blocks of heterochromatin are present in pairs 1 and 10 and in the regions equivalent to NOR and the 5S rDNA. Data obtained in this study corroborated with the currently accepted phylogenetic hypothesis for the Ancistrini and demonstrate evidence that the genus Baryancistrus occupies a basal position in the tribe. © 2016, Sociedade Brasileira de Ictiologia. All rights reserved

Iron Deficiency Generates Oxidative Stress and Activation of the SOS Response in Caulobacter crescentus

In C. crescentus, iron metabolism is mainly controlled by the transcription factor Fur (ferric uptake regulator). Iron-bound Fur represses genes related to iron uptake and can directly activate the expression of genes for iron-containing proteins. In this work, we used total RNA sequencing (RNA-seq) of wild type C. crescentus growing in minimal medium under iron limitation and a fur mutant strain to expand the known Fur regulon, and to identify novel iron-regulated genes. The RNA-seq of cultures treated with the iron chelator 2-2-dypiridyl (DP) allowed identifying 256 upregulated genes and 236 downregulated genes, being 176 and 204 newly identified, respectively. Sixteen transcription factors and seven sRNAs were upregulated in iron limitation, suggesting that the response to low iron triggers a complex regulatory network. Notably, lexA along with most of its target genes were upregulated, suggesting that DP treatment caused DNA damage, and the SOS DNA repair response was activated in a RecA-dependent manner, as confirmed by RT-qPCR. Fluorescence microscopy assays using an oxidation-sensitive dye showed that wild type cells in iron limitation and the fur mutant were under endogenous oxidative stress, and a direct measurement of cellular H2O2 showed that cells in iron-limited media present a higher amount of endogenous H2O2. A mutagenesis assay using the rpoB gene as a reporter showed that iron limitation led to an increase in the mutagenesis rate. These results showed that iron deficiency causes C. crescentus cells to suffer oxidative stress and to activate the SOS response, indicating an increase in DNA damage

Alternative Biodefensive based on the Essential Oil from Allium sativum Encapsulated in PCL/Gelatin Nanoparticles

The goal of this paper was to develop a biodegradable system containing the essential oil from Allium sativum bulbs encapsulated in PCL/gelatin-based nanoparticles, as well as evaluate its efficiency to control Aedes aegypti Linn. larvae and Cerataphis lataniae Bois. aphids. The essential oil was analyzed by GC-FID and GC-MS, and six compounds were identified, representing 93.1% of the total oil. The major compounds were diallyl trisulfide (51.8%), diallyl disulfide (23.2%) and allyl methyl trisulfide (13.6%). The PCL/gelatin-based nanoparticles containing this essential oil exhibited encapsulation efficiency higher than 94%, average particle diameter around 200 nm and zeta potential values about -36 mV. The essential oil presented no antioxidant nor enzymatic activities, so its effectiveness might be explained by the presence of sulfur compounds. The release kinetics of the encapsulated essential oil confirmed the release mechanism by the Fick's Law. About 50% of the encapsulated essential oil was released after 1 h, and about 90% was released after 50 h. This behavior is interesting from the technological point of view since the nanoparticles released as much oil as possible in a short period of time and then the lethal dosages were maintained along the time. Nanoparticles containing the encapsulated essential oil was submitted to in vitro bioassays against A. aegypti and C. lataniae and showed 100% of mortality against larvae and aphids up to 24 h. In conclusion, the essential oil from A. sativum presented effectiveness to be applied in sustainable management of pests in greenhouses, as well as for larvicidal control

Comparison between python and matlab algorithms for the evaluation of the corrosion grade in guyed transmission towers using artificial neural networks and machine committees / Comparação entre algoritmos em python e matlab na avaliação do grau de corrosão em torres de transmissão estaiadas utilização comitês de máquina de redes neurais artificiais

Nesse artigo são descritos dois comitês de máquinas baseados em redes neurais artificiais (RNAs) criados para avaliação de corrosão em estais de torres de transmissão. Os dados utilizados para treinamento das redes neurais foram coletados experimentalmente e descritos em artigo previamente publicado. Aqui foram complementados pela coleta de dados referentes a resistividade do solo. Um dos comitês foi implementado em Python com utilização da biblioteca Keras Tensorflow e foi composto por 3 RNAs. O outro foi elaborado em MATLAB e consistiu em 8 RNAs. Nos diferentes comitês de máquinas foram utilizadas arquiteturas distintas, sendo a diferença mais notável nos números de neurônios e camadas escondidas utilizados em cada modelo. Ambos os comitês apresentaram resultados satisfatórios para classificação quanto ao grau de corrosão dos estais. Um foco especial foi dado para o caso onde graus de corrosão elevados ocorriam, sendo necessário garantir alto grau de precisão, evitando que fossem apontadas classes leves de corrosão quando na realidade o grau é elevado. O comitê desenvolvido em MATLAB apresentou número significativamente menor de neurônios, o que possibilita implementação mais rápida e maior velocidade no processamento dos dados. Além disso, a correlação obtida foi de 96.70% para o comitê elaborado em MATLAB e de 95.17% para o comitê elaborado em Python ao passo que o anterior havia sido de 80%.

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

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