The purpose of this investigation was to determine lipid peroxidation markers, physiological stress and muscle damage in elite kayakers in response to a maximum 4-min kayak ergometer test (KE test), ...and possible correlations with individual 1000m kayaking performances. The sample consisted of twenty-three adult male and nine adult female elite kayakers, with more than three years' experience in international events, who voluntarily took part in this study. The subjects performed a 10-min warm-up, followed by a 2-min passive interval, before starting the test itself, which consisted of a maximum 4-min work paddling on an ergometer; right after the end of the test, an 8 ml blood sample was collected for analysis. 72 hours after the test, all athletes took part in an official race, when then it was possible to check their performance in the on site K1 1000m test (P1000m). The results showed that all lipoproteins and hematological parameters tested presented a significant difference (p≤0.05) after exercise for both genders. In addition, parameters related to muscle damage such as lactate dehydrogenase (LDH) and creatine kinase (CK) presented significant differences after stress. Uric acid presented an inverse correlation with the performance (r = -0.76), while CK presented a positive correlation (r = 0.46) with it. Based on these results, it was possible to verify muscle damage and the level of oxidative stress caused by indoor training with specific ergometers for speed kayaking, highlighting the importance of analyzing and getting to know the physiological responses to this type of training, in order to provide information to coaches and optimize athletic performance.
•Parent material is the main forming factor for soil differentiation at Vega Island.•Vega Island is a pedological transition zone between Maritime and Continental Antarctica.•Salinization, desert ...pavement formation and cryoturbation mark the transition.•Sulfurization is the main pedogenic process responsible for chemical changes.•Despite the semiarid climate, pedogenesis is very active in the soils of Vega Island.
Vega Island in the Weddell Sea is characterized by a semiarid climate and peculiar soil formation due to its transitional location between warmer and wetter Maritime Antarctica (MA) and drier colder Continental Antarctica (CA). We investigated the main factors and processes involved in soil genesis at Cape Lamb on Vega Island. Thirty pedons were sampled, described and analyzed for morphological, physical, geochemical and mineralogical properties. Twenty-two pedons were classified as Gelisols/Cryosols. Three soil groups were identified: (1) mixed substrates soils (2), acid sulfate soils and (3) basaltic soils. Acid sulfate soils showed the greatest degree of weathering with pronounced acidity due to sulfurization. Basaltic soils were the least developed and presented shallow pedons of skeletic character and high alkalinity. Parent material was the main factor in the differentiation of the studied soils. Climate and landforms were also important. Desert pavement, salinization and ahumic features induced by the semiarid climate, oppose acidification and cryoturbation, characterizing the transitional location of Vega Island. Patterned ground dominates on periglacial highlands, while desert pavements are more common on paraglacial surfaces. Despite the semiarid climate, pedogenesis is active in the studied soils, with clay minerals, sulfate minerals and pedogenic Fe-oxides formation.
The aim of the present study was to analyze the effect of creatine (Cr) supplementation on peak torque (PT) and fatigue rate in Paralympic weightlifting athletes. Eight Paralympic powerlifting ...athletes participated in the study, with 25.40 ± 3.30 years and 70.30 ± 12.15 kg. The measurements of muscle strength, fatigue index (FI), peak torque (PT), force (kgf), force (N), rate of force development (RFD), and time to maximum isometric force (time) were determined by a Musclelab load cell. The study was performed in a single-blind manner, with subjects conducting the experiments first with placebo supplementation and then, following a 7-day washout period, beginning the same protocol with creatine supplementation for 7 days. This sequence was chosen because of the lengthy washout of creatine. Regarding the comparison between conditions, Cr supplementation did not show effects on the variables of muscle force, peak torque, RFD, and time to maximum isometric force (
> 0.05). However, when comparing the results of the moments with the use of Cr and placebo, a difference was observed for the FI after seven days (U
: 1.12; 95% CI: (0.03, 2.27);
= 0.02); therefore, the FI was higher for placebo. Creatine supplementation has a positive effect on the performance of Paralympic powerlifting athletes, reducing fatigue index, and keeping the force levels as well as PT.
•The chemical weathering intensity in Maritime Antarctica is modeled.•A geophysical survey and lithological characteristics as input data.•Proximal geophysical sensors and terrain attributes are ...combined for modeling.•Use of leave-one-out-cross-validation method in modeling.•Periglacial processes control the distribution of geophysical variables.
The chemical weathering intensity in Antarctica is underestimated. As the chemical weathering intensity increases, hydrological, geochemical and geophysical changes occur in the different environmental spheres and at their interfaces through reactions and energy flows. Thus, once chemical weathering rates are understood and estimated, they can be used to predict and assess changes and trends in different environmental spheres. Few studies on the chemical weathering intensity have been performed in Antarctica. We used radiometric and magnetic properties associated with terrain attributes and the chemical degree of alteration of the igneous rock to model the chemical weathering intensity in Maritime Antarctica by using machine learning. Then, we related the chemical weathering intensity and geophysical variables with periglacial processes. To do this, gamma-spectrometric and magnetic readings were carried out using proximal-field sensors at 91 points located on different lithologies in a representative area of Maritime Antarctica. A qualitative analysis of chemical alteration for the different lithologies was carried out based on field observations and rock properties, and the levels of the chemical weathering degree were established. The geophysical data associated with terrain attributes were used as input data in the modeling of the weathering intensity. Then, the levels of the rock weathering degree were used as the “y” variable in the models. The results indicated that the C5.0 algorithm had the best performance in predicting the weathering intensity, and the most important variables were eTh, 40K, 40K/eTh, 40K/eU, the magnetic susceptibility and terrain attributes. The contents of radionuclides and ferrimagnetic minerals in different lithologies, concomitantly with the intensity at which chemical weathering occurs, determine the contents of these elements. However, the stability and distribution of these elements in a cold periglacial environment are controlled by periglacial processes. The chemical weathering intensity prediction model using gamma-spectrometric and magnetic data matched the in situ estimate of the chemical degree of alteration of the rock. The pyritized andesites showed the highest intensities of weathering, followed by tuffites, diorites, andesitic basalts and basaltic andesites, and the lowest weathering intensity was shown by undifferentiated marine sediments. This work highlighted the suitability of using machine learning techniques and proximal-field sensor data to study the chemical weathering process on different rocks in these important and inhospitable areas of the cryosphere system.
Despite optical remote sensing (and the spectral vegetation indices) contributions to digital soil-mapping studies of soil organic carbon (SOC), few studies have used active radar remote sensing ...mission data like that from synthetic aperture radar (SAR) sensors to predict SOC. Bearing in mind the importance of SOC mapping for agricultural, ecological, and climate interests and also the recently developed methods for vegetation monitoring using Sentinel-1 SAR data, in this work, we aimed to take advantage of the high operationality of Sentinel-1 imaging to test the accuracy of SOC prediction at different soil depths using machine learning systems. Using linear, nonlinear, and tree regression-based methods, it was possible to predict the SOC content of soils from western Bahia, Brazil, a region with predominantly sandy soils, using as explanatory variables the SAR vegetation indices. The models fed with SAR sensor polarizations and vegetation indices produced more accurate results for the topsoil layers (0–5 cm and 5–10 cm in depth). In these superficial layers, the models achieved an RMSE in the order of 5.0 g kg−1 and an R2 ranging from 0.16 to 0.24, therefore explaining about 20% of SOC variability using only Sentinel-1 predictors.
Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through ...adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was “weed control + plant seedlings” (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations.
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•Methods of forest restoration monitoring using LIDAR and UAV were elaborated.•ML and RF algorithms were used to classify images in restoration indicators.•Kappa Index and Overall ...Accuracy were considered excellent in all methods.•RF presented better performance than ML, in both imaging methods.•These methods can be considered promising to monitor large-scale restoration.
Monitoring and evaluating forest restoration projects is a challenge especially in large-scale, but the remote monitoring of indicators with the use of synoptic, multispectral and multitemporal data allows us to gauge the restoration success with more accurately and in small time. The objective of this study was to elaborate and compare methods of remote monitoring of forest restoration using Light Detection and Ranging (LIDAR) data and multispectral imaging from Unmanned Aerial Vehicle (UAV) camera, in addition to comparing the efficiency of supervised classification algorithms Maximum Likelihood (ML) and Random Forest (RF). The study was carried out in a restoration area with about 74 ha and five years of implementation, owned by Fibria Celulose S.A., in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, Near Infrared) on UAV and LIDAR data composition (intensity image, Digital Surface Model, Digital Terrain Model, normalized Digital Surface Model). The monitored restoration indicator was the land cover separated in three classes: canopy cover, bare soil and grass cover. The images were classified using the ML and RF algorithms. To evaluate the accuracy of the classifications, the Overall Accuracy (OA) and the Kappa index were used, and the last was compared by Z test. The area occupied by different land cover classes was calculated using ArcGIS and R. The results of OA, Kappa and visual evaluation of the images were excellent in all combinations of the imaging methods and algorithms analyzed. When Kappa values for the two algorithms were compared, RF presented better performance than ML with significant difference, but when sensors (UAV camera and LIDAR) were compared, there were no significant differences. There was little difference between the area occupied by each land cover classes generated by UAV and LIDAR images. The highest cover was generated for canopy cover followed by grass cover and bare soil in all classified images, indicating the need of adaptive management interventions to correct the area trajectory towards the restoration success. The methods employed in this study are efficient to monitor restoration areas, especially on a large scale, allowing us to save time, fieldwork and invested resources.
Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management ...dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps’ attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation.
Purpose: to identify genetically enhanced physical skills (speed, strength, endurance and motor coordination), provided by the dermatoglyphic method and to analyze the preliminary correlation between ...dermatoglyphic and acoustic data of lyrical and pop singers. Methods: the study was approved by the Ethics and Research Committee. Four male singers were evaluated (two lyrical and two pop singers), 31-53 years old. Data collection and analysis procedures comprised (1) Survey-Self-Perception of Vocal Characteristics in Singers (vocal habits, voice performance and phenotypic characteristics); (2) Dermatoglyphic Profile (fingerprint image digitalization: predominance of digital drawings (Arch, Loop and Whorl); scores of deltas (D10); the Total Ridge Count (TRC); digital formula and dermatoglyphic profile (aerobic, anaerobic and mix)); (3) Acoustic Analysis (the Expression Evaluator script application to the audio recordings: f0, intensity, spectral slope and long-term average spectrum--LTAS values); and (4) Integrated (Statistical) Analysis; cluster analysis. Results: correlations were found between dermatoglyphic variables (Arch, Loop, Whorl, D10, TRC) and acoustic parameters (f0 (median); intensity (asymmetry); spectral slope (mean); and LTAS (SD)). The dermatoglyphic profile did not segregate singing styles. Conclusion: the dermatoglyphic profiles showed a preliminary correlation with the acoustic vocal measures, especially f0 and LTAS measures. Keywords: Linguistics; Phonetics; Dermatoglyphics; Speech Acoustics; Voice Quality
•New information on the frost prediction for forest.•Frost forecasting from spatial data.•Machine learning algorithms to classify frost risk probability.•High prediction performance using Random ...Forest classifier.•Transferability of the predictive approach for other agricultural plantations.
Brazil is one of the leading timber producers in the world. However, in South Brazil, frost events frequently cause damage and reduce yield in forest plantations, a situation predicted to become more common under global change scenarios. This raises the need for low cost and efficient tools, such as machine learning algorithms to improve forecasting of frost risk. This study used machine learning algorithms to create zoning classifications forecasting frost risk for forest plantations located in the south-central region of Rio Grande do Sul State, Brazil. For this, we gathered and processed data from a local geodatabase (i.e. high-spatial-resolution contour lines, hydrography, and forest stands limits) comprising 30 management units with consistent historical data of frost occurrence. Then, we generated possible local-scale predictors of frost occurrence, which included longitude, latitude, elevation, relative altitude, relief orientation, and Euclidean distance from hydrography. We carried out tests of three machine learning classifiers (Random Forest – RF; Support Vector Machine-SVM and Multi-layer Perceptron-MLP) in order to determine which would most accurately predict frost occurrence. We found that RF provided the highest accuracy (> 90%), as well as the smallest percentages of class-specific errors (i.e. commission and omission errors), when compared to SVM and MLP. Latitude was the most important predictor of frost occurrence when using RF. Conversely, MLP performed worst, especially for classifying frost occurrence versus non-occurrence, and therefore had the highest percentage of class-specific errors. Our findings lead us to conclude that RF is the most proficient algorithm for forecasting frost occurrence from local-scale geomorphological data, without the need for high-cost investment in micro-meteorological sensors to monitor climate frost events linking temperature to plant damage. With increasing global climate extreme events, accurate risk zoning is essential for planning strategies of plantation at the landscape scale.
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