5G Technology Analysis in Relation to Electromagnetic Waves

Electromagnetic waves are present in most of the main equipment used by humans. The advancement of 5G mobile network technology has been gaining ground in the telecommunications market and with it both positive and potentially negative consequences as it is used. Fundamental research has been conducted to gain knowledge and familiarity with 5G technology, how it works and its millimeter waves, which is a new range of the electromagnetic spectrum, which works with this new very high frequency and the first time used in technology. mobile network, as well as exploratory research through techniques such as bibliographic surveys in search of data such as frequency, related to 5G and the equipment transmitting electromagnetic waves, and conducting a comparative study to determine through the data collected from both , pointing out studies that present the evils that may cause the human being due to the use of high frequency. According to the results obtained on the 5G, the use of mobile network frequency presents no risks compared to other equipment that humans have been using. Therefore, despite the research results, it is still too early to point out possible damage over the years with the use of this technology, not ruling out possible consequences

Aplicação do índice Relação Declividade-Extensão na bacia hidrográfica do rio Guruji para detecção de deformações neotectônicas sobre os sedimentos do Grupo Barreiras, litoral sul do estado da Paraíba, Brasil.

Este documento tiene por objeto analizar morphostrutural y morfométricos cuenca del río Guruji, situado en la costa sur del estado de Paraiba. Este análisis se aplicó a través de la integración de la información cualitativa, basada en los modelos y anomalías de la red hidrográfica verificados, y cuantitativos fundado en índice Relación Inclinación - Extensión para comprobar si la evolución de drenaje y el relieve de la zona de estudio se produjo debido a la influencia tectónica. Entre los valores encontrados en el más inusual de el riachuelo es el Caboclo, que también se encuentra, en la margen derecha de la cuenca, donde el mayor número de canales fluviales disecciones muy fuerte, con valles muy esculpido formando y altitudes más altas, es evidente que esta anomalía está vinculada a Alto Estrutural Coqueirinho. Analizando del punto de vista morfológico la cuencas hidrográficas del río Guruji. Demostrando una inflexión de 90 º a 275 m de la costa. En esta sección, el río cambia bruscamente su dirección de oeste-este a sur-norte, que cubren más de 900 m de la desembocadura. La dirección Sur-Norte del Río Guruji, en su curso inferior parece seguir la tendencia general de los bajos llanuras costeras talladas en los sedimentos del Grupo de Barreiras. La dirección de los afluentes riachuelo del Caboclo y riachuelo Pau Ferro no obedece esa inclinación y son también los grandes tributarios del río Guruji

Kinin B(1) receptor deficiency leads to leptin hypersensitivity and resistance to obesity

OBJECTIVE-Kinins mediate pathophysiological processes related to hypertension, pain, and inflammation through the activation of two G-protein-coupled receptors, named B(1) and B(2). Although these peptides have been related to glucose homeostasis, their effects on energy balance are still unknown.RESEARCH DESIGN and METHODS-Using genetic and pharmacological strategies to abrogate the kinin B(1) receptor in different animal models of obesity, here we present evidence of a novel role for kinins in the regulation of satiety and adiposity.RESULTS-Kinin B(1) receptor deficiency in mice (B(1)(-/-)) resulted in less fat content, hypoleptinemia, increased leptin sensitivity, and robust protection against high-fat diet-induced weight gain. Under high-fat diet, B(1)(-/-) also exhibited reduced food intake, improved lipid oxidation, and increased energy expenditure. Surprisingly, B(1) receptor deficiency was not able to decrease food intake and adiposity in obese mice lacking leptin (ob/ob-B(1)(-/-)). However, ob/ob-B(1)(-/-) mice were more responsive to the effects of exogenous leptin on body weight and food intake, suggesting that B(1) receptors may be dependent on leptin to display their metabolic roles. Finally, inhibition of weight gain and food intake by B(1) receptor ablation was pharmacologically confirmed by long-term administration of the kinin B(1) receptor antagonist SSR240612 to mice under high-fat diet.CONCLUSIONS-Our data suggest that kinin B(1) receptors participate in the regulation of the energy balance via a mechanism that could involve the modulation of leptin sensitivity.Universidade Federal de São Paulo, Dept Biophys, BR-04023062 São Paulo, BrazilUniv Mogi das Cruzes, Mogi Das Cruzes, BrazilUniversidade Federal de São Paulo, Dept Physiol, BR-04023062 São Paulo, BrazilSanofi Aventis, Montpellier, FranceUniversidade Federal de São Paulo, Dept Med, BR-04023062 São Paulo, BrazilInst Natl Sante & Rech Med, Dept Renal & Cardiac Remodeling, U858 I2MR, Toulouse, FranceUniv Toulouse 3, Inst Med Mol Rangueil, F-31062 Toulouse, FranceInst Natl Rech Agron AgroParisTech, UMR914 Nutr Physiol & Ingest Behav, Paris, FranceMax Delbruck Ctr Mol Med, Berlin, GermanyUniversidade Federal de São Paulo, Dept Biophys, BR-04023062 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Physiol, BR-04023062 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Med, BR-04023062 São Paulo, BrazilWeb of Scienc

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Estimating the global conservation status of more than 15,000 Amazonian tree species

Estimates of extinction risk for Amazonian plant and animal species are rare and not often incorporated into land-use policy and conservation planning. We overlay spatial distribution models with historical and projected deforestation to show that at least 36% and up to 57% of all Amazonian tree species are likely to qualify as globally threatened under International Union for Conservation of Nature (IUCN) Red List criteria. If confirmed, these results would increase the number of threatened plant species on Earth by 22%. We show that the trends observed in Amazonia apply to trees throughout the tropics, and we predict thatmost of the world’s >40,000 tropical tree species now qualify as globally threatened. A gap analysis suggests that existing Amazonian protected areas and indigenous territories will protect viable populations of most threatened species if these areas suffer no further degradation, highlighting the key roles that protected areas, indigenous peoples, and improved governance can play in preventing large-scale extinctions in the tropics in this century

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

Aim: Amazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. Location: Amazonia. Taxon: Angiosperms (Magnoliids; Monocots; Eudicots). Methods: Data for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran\u27s eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. Results: In the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R$^{2}$ = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R$^{2}$ = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main Conclusion: Numerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions

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