Integração e análise de dados aerogeofísicos por meio da aplicação de técnicas de processamento digital de imagens e classificação não supervisionada : o exemplo do Greenstone Belt Rio das Velhas, quadrilátero ferrífero, MG

Afloramentos escassos e um intemperismo acentuado caracterizam o Greenstone Belt Rio das Velhas no sudeste do Brasil. Este artigo sumariza a utilização de dados aerogeofísicos de alta densidade de amostragem aplicados à exploração mineral baseado no realce e na interpretação de dados magnéticos, radiométricos e eletromagnéticos no domínio da freqüência através de métodos de processamento digital de imagens e classificação não supervionada. Os produtos gerados forneceram novos insights e uma excelente ferramenta para mapeamentos dos diferentes litotipos, melhorando o conteúdo da informação presente nos canais individuais. As imagens geofísicas foram processadas utilizando diferentes combinações. O melhor produto obtido foi a integração via IHS da amplitude e da inclinação do sinal analítico. Informações extraídas desta imagem mapeiam tanto a geologia quanto lineamentos na escalas regionais e locais. Aplicou-se a técnica de classificação não supervisionada conhecida com média K aos dados aerogeofísicos. O resultado realça litologias mapeadas por geólogos de campo na escala 1:100.000. Ela mapeia também rochas hospedeiras e diferentes domínios associados com a mineralização aurífera. Tais domínios são conhecidos por hospedar a mineralização aurífera, ilustrando a utilidade destas técnicas para enriquecer o conhecimento geológico da área estudada.Poor outcrop and deep weathering characterize the Rio das Velhas Greenstone Belt in the southeastern from Brazil. This paper summarizes the use of high-density airborne survey for mineral exploration studies based on interpretation enhancements of magnetic, radiometric and frequency domain electromagnetic data using image-processed methods and an unsupervised classification. The generated products provide new insights and an excellent tool for mapping and trace individual lithological units, improving the information content of the single geophysical channels. The geophysical images were processed using different combinations. The best product was the analytical signal amplitude and inclination integrated by IHS transformation. Information extracted from this image maps the geology and lineament patterns at both regional and local scales. The K-means technique using ten classes was also applied to the geophysical data. These results enhance the lithologies mapped by the field geologists at the 1:100.000. Also shows important host rocks and different gold mineralized geological domains. Such domains host the known gold mineralization, illustrating the utility of these techniques to improve the geological knowledge in the study area

Exercise Reduces the Resumption of Tumor Growth and Proteolytic Pathways in the Skeletal Muscle of Mice Following Chemotherapy

The pathogenesis of muscle atrophy plays a central role in cancer cachexia, and chemotherapy contributes to this condition. Therefore, the present study aimed to evaluate the effects of endurance exercise on time-dependent muscle atrophy caused by doxorubicin. For this, C57 BL/6 mice were subcutaneously inoculated with Lewis lung carcinoma cells (LLC group). One week after the tumor establishment, a group of these animals initiated the doxorubicin chemotherapy alone (LLC + DOX group) or combined with endurance exercise (LLC + DOX + EXER group). One group of animals was euthanized after the chemotherapy cycle, whereas the remaining animals were euthanized one week after the last administration of doxorubicin. The practice of exercise combined with chemotherapy showed beneficial effects such as a decrease in tumor growth rate after chemotherapy interruption and amelioration of premature death due to doxorubicin toxicity. Moreover, the protein degradation levels in mice undergoing exercise returned to basal levels after chemotherapy; in contrast, the mice treated with doxorubicin alone experienced an increase in the mRNA expression levels of the proteolytic pathways in gastrocnemius muscle (Trim63, Fbxo32, Myostatin, FoxO). Collectively, our results suggest that endurance exercise could be utilized during and after chemotherapy for mitigating muscle atrophy promoted by doxorubicin and avoid the resumption of tumor growth

Serum sodium disorders in patients with traumatic brain injury

Sodium disorders are the most common and most poorly understood electrolyte disorders in neurological patients. The aim of this study was to determine the incidence of sodium disorders and its association with different traumatic brain injuries. This prospective study was conducted in 80 patients diagnosed with moderate and severe traumatic brain injuries. All patients underwent cerebral computed tomography. Incidence of sodium disorders, presence of injuries in the first computed tomography after traumatic brain injury, and level of consciousness were analyzed. Patients that presented other potential causes of sodium disorders and systemic trauma were excluded from the study. The incidence of sodium disturbances was 45%: 20 patients presented hypernatremia and 16 hyponatremia. Refers to all patients with sodium disturbances 53% were detected in the first sample. We recorded at least one measurement <125 mEq/L in 50% of the patients with hyponatremia. A greater incidence of sodium disorders was found in patients with subdural, intracerebral hematoma and with diffuse axonal injury. The incidence of sodium disorders among the patients with diffuse lesions was greater than in the group of patients with brain contusion (P = 0.022). The incidence of sodium disorders is higher in patients with diffuse traumatic brain injuries. No association was found between focal lesions and proportion of sodium disorders

Variabilidade do armazenamento de água no Brasil

Brazil hosts a large amount of freshwater. Knowing how this stored water is partitioned in space and time between surface and subsurface components is a crucial step towards a more correct depiction of the country’s water cycle, which has major implications for decision making related to water resources management. Here, we extracted monthly water storage (WS) variability, from 2003 to 2020, based on multiple state-of-the-art datasets representing different WS components – groundwater (GW), soil moisture (SM), surface waters (SW), and artificial reservoirs (RS) – in all Brazilian Hydrographic Regions (BHRs), and computed each component’s contribution to the total variability. Most of the variability can be attributed to SM (40-68%), followed by GW (18-40%). SW has great influence in the north-western BHRs (humid monsoon influenced) with 18-40% and the southern BHRs (subtropical system influenced) with 5-10%. RS has important contributions in the Paraná with 12.1%, São Francisco with 3.5%, and Tocantins-Araguaia with 2.1%. In terms of long-term variability, water storages have been generally decreasing in the eastern and increasing in north-western and southern BHRs, with GW and RS being the most affected, although it can also be observed in SW peaks. Comparisons made with previous studies show that the approach and datasets used can have a considerable impact in the results. Such analysis can have broad implications in identifying the nature of amplitude and phase variability across regions in order to better characterize them and to obtain better evaluations of hydrological trends under a changing environment.O Brasil abriga uma grande quantidade de água doce. Saber como essa água armazenada é repartida no espaço e no tempo entre os componentes superficiais e subsuperficiais é crucial para uma representação mais correta do ciclo hídrico do país, o que tem grandes implicações para a tomada de decisões relacionadas à gestão dos recursos hídricos. Neste estudo, extraímos a variabilidade mensal do armazenamento de água, de 2003 a 2020, com base em diferentes fontes que representam o estado da arte da informação sobre diferentes componentes de armazenamento águas subterrâneas, umidade do solo, águas superficiais, e reservatórios artificiais – em todas as regiões hidrográficas brasileiras, e computamos a contribuição de cada componente em relação a variabilidade total. A maior parte da variabilidade pode ser atribuída a umidade do solo (4068%), seguida por águas subterrâneas (18-40%). Águas superficiais tem grande influência nas regiões hidrográficas do noroeste (influência de sistemas de monção) com 18-40% e nas BHRs do sul (influência de sistemas subtropicais) com 5-10%. O estoque em reservatórios artificiais tem contribuições importantes nas regiões do Paraná com 12,1%, do São Francisco com 3,5% e do Tocantins-Araguaia com 2,1%. Em termos de variabilidade de longo prazo, os estoques de água têm geralmente diminuído nas regiões leste e aumentado no noroeste e no sul, sendo os estoques de águas subterrâneas e reservatórios os mais afetados, embora essa tendência também possa ser observada nos picos de água superficial. Comparações feitas com estudos anteriores mostram que a abordagem e os conjuntos de dados utilizados podem ter um impacto considerável nos resultados. Tal análise pode ter amplas implicações na identificação da natureza da variabilidade de amplitude e fase entre as regiões, a fim de melhor caracterizá-las e obter melhores avaliações das tendências hidrológicas

Segmentação Distribuída de Imagem de Sensoriamento Remoto a partir de Banco de Dados PostgreSQL/InterIMAGE

A abordagem de classificação baseada em objetos representa um novo paradigma no processamento de imagens de altas resoluções espaciais, espectrais e temporais, e a construção de objetos baseia-se na segmentação das imagens. A análise de imagens baseada em objetos (GEOBIA - Geographic Object-Based Image Analysis) apresenta métodos capazes de explorar, além de atributos espectrais, elementos como textura, forma ou contexto. Existem aplicações que buscam melhorar o desempenho computacional com soluções sequenciais e distribuídas, ou programas como TerraView que abordam o uso de sistemas gerenciadores de banco de dados. Este trabalho propõe explorar especificações de aplicações para integrar o Sistema Gerenciador de Banco de Dados (SGBD) PostgreSQL/PostGIS e o classificador Object-Based Image Analysis (OBIA) do InterIMAGE Desktop para processamento de grandes imagens orbitais. O método apresentado é expansível no uso da biblioteca TerraLib 5, com linguagem de programação C++. Os experimentos realizados com as representações matriciais (raster) indicaram a viabilidade das aplicações e podem se consolidar sob a forma de processos de armazenamento e processamento da segmentação no SGBD

Global evapotranspiration datasets assessment using water balance in South America

Evapotranspiration (ET) connects the land to the atmosphere, linking water, energy, and carbon cycles. ET is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in ET are needed from regional to continental scales. This study compared eight global actual ET datasets (ETgl) and the average actual ET ensemble (ETens) based on remote sensing, climate reanalysis, land-surface, and biophysical models to ET computed from basin-scale water balance (ETwb) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of ET, comparing ETgl and ETens with ETwb. Global ET datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using ETwb as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (ETens), and RMSE ranged from 35.6 (ETens) to 40.5 mm·month⁻¹(MOD16). Overall, ETgl estimates ranged from 0 to 150 mm·month−1 in most basins in South America, while ETwb estimates showed maximum rates up to 250 mm·month⁻¹. Tgl varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly ETgl and ETwb, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between ETgl and ETwb in the humid tropics (in the Amazon), with ETgl showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between ETgl and ETwb for interannual variability. Assessing ET datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of ET estimation and monitoring for water management