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Agrivoltaic systems, which integrate solar energy production with agricultural practices, present a sustainable solution to optimize land use. However, specific configurations, such as overhead solar panels, can obstruct Global Navigation Satellite System (GNSS) signals, which are vital for precision agriculture technologies. This study investigates the impact of an agrivoltaic system on GNSS signal performance compared to a conventional orchard in Kressbronn am Bodensee, Germany, using data from multiple GNSS constellations (GPS, GLONASS, Galileo, BeiDou). Key metrics such as carrier-to-noise density ratio (C/N₀), positional accuracy, time to first fix (TTFF), and dilution of precision (DOP) were analyzed. The results showed significant signal degradation under the overhead agrivoltaic system, particularly in C/N₀ and positional accuracy. While there were no significant differences in DOP metrics or TTFF, the average C/N₀ decreased from 30.62 dB-Hz in the conventional orchard to 26.92 dB-Hz in the agrivoltaic system. Moreover, the average of satellites with C/N₀ values below 24 dB-Hz was notably higher in the agrivoltaic area, indicating critical signal degradation. In addition, relying on a single constellation, like GPS, may not provide enough satellites for reliable GNSS performance.Despite the lower signal quality, the average number of satellites with C/N₀ above 24 dB-Hz remained over 22 in both areas. Combined with slight or no significant differences in other metrics compared to the conventional orchard, this suggests GNSS-based systems can support precision agriculture tasks within the agrivoltaic environment. The study highlights the importance of addressing signal interference to avoid hindering the integration of drones, robots, and other advanced technologies in agrivoltaics management, and suggests alternative solutions, such as Real-Time Kinematic (RTK) correction or Simultaneous Localization and Mapping (SLAM) techniques, to enhance performance. These findings are critical as agrivoltaic adoption grows, requiring a balance between solar energy production and the operational needs of precision agriculture.

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production and cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles and standardizing processes across platforms to maximize interoperability. The lack of drone technology standardisation, communication barriers, high costs, and post-processing requirements sometimes hinder their widespread use in agriculture. This research introduces a standardized data fusion framework for creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) for Site-Specific Crop Management (SSM). Two spatial interpolation methods were used (Inverse Distance Weight, IDW, and Triangulated Irregular Networks, TIN), selected for their computational efficiency and input flexibility. The proposed framework can use different UAV image sources and offers versatility, speed, and efficiency, consuming up to 98 % less time, energy, and computing requirements than standard photogrammetry techniques, providing rapid field information, allowing edge computing incorporation into the UAV data acquisition phase. Experiments conducted in Spain, Serbia, and Finland in 2022 under the H2020 FlexiGroBots project demonstrated a strong correlation between results from this method and those from standard photogrammetry techniques (up to r = 0.93). In addition, the correlation with Sentinel 2 satellite images was as strong as that obtained with photogrammetry-based orthomosaics (up to r = 0.8). The proposed approach could support irrigation leak detection, soil parameter estimation, weed management, and satellite integration for agriculture.

Abstract Background Volleyball, with its unique calendar structure, presents distinct challenges in training and competition scheduling. Like many team sports, volleyball features an unconventional schedule with brief off-season and pre-season phases, juxtaposed against an extensive in-season phase characterized by a high density of matches and training. This compact calendar necessitates careful management of training loads and recovery periods. The effectiveness of this management is a critical factor, influencing the overall performance and success of volleyball teams. In this review, we explore the associations between training stress measures, fatigue, and well-being assessments within this context, to better inform future research and practice. Methods A systematic literature search was conducted in databases including PsycINFO, MEDLINE/PubMed, SPORTDiscus, Web of Science, and Scopus. Inclusion criteria were original research papers published in peer-reviewed journals involving volleyball athletes. Results Of the 2535 studies identified, 31 were thoroughly analysed. From these 31 articles, 22 included professional athletes, seven included collegiate-level volleyball athletes, and two included young athletes. Nine studies had female volleyball players, while the remaining 22 had male volleyball athletes. Conclusions Internal training load should be collected daily after training sessions and matches with the session rating of perceived exertion method. External training load should also be measured daily according to the methods based on jump height, jump count, and kinetic energy. If force platforms are available, neuromuscular fatigue can be assessed weekly using the FT:CT ratio of a countermovement jump or, in cases where force platforms are not available, the average jump height can also be used. Finally, the Hooper Index has been shown to be a measure of overall wellness, fatigue, stress, muscle soreness, mood, and sleep quality in volleyball when used daily.

The “EscaYard” dataset comprises multimodal data collected from vineyards to support agricultural research, specifically focusing on vine health and productivity. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing multispectral images and 3D point clouds, and (2) smartphones for detailed ground-level photography. The UAV used was DJI Matrice 210 V2 RTK, equipped with a Micasense Altum sensor, flying at 30 m above ground level to ensure detailed coverage. Ground-level data were collected using smartphones (iPhone X and Xiaomi Poco X3 Pro), which provided high-resolution images of individual plants. These images were geotagged, enabling location mapping, and included data on the phytosanitary status and number of grape clusters per plant. Additionally, the dataset contains RTK GNSS data, offering high-precision location information for each vine, enhancing the dataset's value for spatial analysis. Moreover, the dataset is structured to support various research applications, including agronomy, remote sensing, and machine learning. It is particularly suited for studying disease detection, yield estimation, and vineyard management strategies. The high-resolution and multispectral nature of the data allows for a detailed analysis of vineyard conditions. Potential reuse of the dataset spans multiple disciplines, enabling studies on environmental monitoring, geographic information systems (GIS), and precision agriculture. Its comprehensive nature makes it a valuable resource for developing and testing algorithms for disease classification, yield prediction, and plant phenotyping. For instance, the images of bunches and grape leaves can be used to train object detection algorithms for accurate disease detection and consequent precise spraying. Moreover, yield prediction algorithms can be trained by extracting the phenotypic traits of the grape bunches. The “EscaYard” dataset provides a foundation for advancing research in sustainable farming practices, optimising crop health, and improving productivity through precise agricultural technologies.

Object Detection and Tracking have provided a valuable tool for many tasks, mostly time-consuming and prone-to-error jobs, including fruit counting while in the field, among others. Fruit counting can be a challenging assignment for humans due to the large quantity of fruit available, which turns it into a mentally-taxing operation. Hence, it is relevant to use technology to ease the task of farmers by implementing Object Detection and Tracking algorithms to facilitate fruit counting. However, those algorithms suffer undercounting due to occlusion, which means that the fruit is hidden behind a leaf or a branch, complicating the detection task. Consequently, gathering the datasets from multiple viewing angles is essential to boost the likelihood of recording the images and videos from the most visible point of view. Furthermore, the most critical open-source datasets do not include labels for certain fruits, such as grape bunches. This study aims to unravel the scarcity of public datasets, including labels, to train algorithms for grape bunch Detection and Tracking by considering multiple angles acquired with a UAV to overcome fruit occlusion challenges.

Abstract Background The purpose of this scoping review was to evaluate the current use of technologies in sports settings for training adaptation and injury prevention. The review aimed to map the existing literature, identify key concepts and themes, and highlight gaps in research, thus offering guidance for future studies. Methods This study followed the guidelines of the PRISMA extension for scoping reviews and a search in four major databases was conducted. Results A total of 21 studies were included. The findings highlighted the widespread use of various technologies, including wearable devices and force plates, to monitor athletes’ performance and inform evidence-based decision-making in training and injury prevention. Variables such as Player Load, changes of direction, and acute chronic workload ratio were identified as key metrics in injury prediction. Conclusions This review uncovers a dynamic field of research in athlete injury prevention, emphasizing the extensive use of varied technologies. A key finding is the pivotal role of Player Load data, which offers nuanced insights for customizing training loads according to sport-specific demands, player positions, and the physical requirements of various activities. Additionally, the review sheds light on the utility of tools like force plates in assessing fatigue, aiding recovery, and steering injury rehabilitation, particularly in sports prone to knee and ankle injuries. These insights not only enhance our understanding of injury prevention but also provide a strategic direction for future research, aiming to boost athlete safety, performance, and career longevity.

Memory-related impairments in type 2 diabetes may be mediated by insulin resistance and hyperglycemia. Previous cross-sectional studies have controversially suggested a relationship between metabolic control and a decrease in hippocampal volumes, but only longitudinal studies can test this hypothesis directly. We performed a longitudinal morphometric study to provide a direct test of a possible role of higher levels of glycated hemoglobin with long term brain structural integrity in key regions of the memory system – hippocampus, parahippocampal gyrus and fusiform gyrus. Grey matter volume was measured at two different times – baseline and after ~7 years. We found an association between higher initial levels of HbA1C and grey matter volume loss in all three core memory regions, even in the absence of mild cognitive impairment. Importantly, these neural effects persisted in spite of the fact that patients had significantly improved their glycemic control. This suggests that early high levels of HbA1c might be irreversibly associated with subsequent long-term atrophy in the medial temporal cortex and that early intensive management is critical.

This study aimed to verify whether there are differences in the body composition, functionality, lower-limb power, agility, and cardiorespiratory capacity in futsal players, comparing futsal athletes by competitive level. The athletes (N = 84) were divided into three groups: group Elite (N = 29), group Sub-Elite (N = 29), and group Non-Elite (N = 26). Anthropometric variables were analyzed through a bioimpedance scale (Inbody 270), and functionality was analyzed through a functional movement screen battery. The power of the lower limbs was tested with the Abalakov jump, the agility with the zigzag agility test, and the cardiorespiratory capacity through the futsal intermittent endurance test. Anthropometric data from futsal athletes revealed a homogeneity in relation to the variables analyzed, regardless of the level of competition in which they operate. In performance variables, the power of the members and functionality was considered a discriminating factor of the level of competitiveness of the athletes, with the Elite group athletes presenting the best values. We concluded that there were no differences in relation to the body composition of the athletes. However, the athletes of higher levels, as a rule, present better performances in physiological aspects, results that can be explained by the fact that there is a better periodization in terms of training, with more intense loads and more complex competitive calendars, thus resulting in a greater specialization of these athletes.

The advent of robotics in schools and universities curricula are preparing students to encompass new didactic fields. This article presents ReFiBot which is an education robot that has been used to increase the technical literacy on robotics and bring more awareness to open science at Wageningen University. The ReFiBot combines open-source hardware and software, integrated with a chassis made from recycled plastic from fishnets. The ReFiBot was carefully designed to be easily assembled with off-the-shelf electronic parts and programmed using the Arduino IDE. Moreover, a software library is facilitated to ease its adoption in educational activities from any curricula level. The ReFiBot has been mainly used for education but can also be used for research on swarm robotics. The CAD files, components list, software files, and tutorial within this contribution will guide the reader through the assemblage and best practices of this circular robotics kit.

Assessing crop diseases at an early stage allows farmers to apply chemicals at the most efficient moment to control the pest and focus on the plants with the highest risk of developing the disease. Moreover, the earlier the assessment the lower the consequences, such as yield reduction, which is crucial to feed the increasing world population. BBR is the first open-source software based on physical and phenological crop parameters that grant the early assessment of botrytis bunch rot disease, caused by Botrytis cinerea, in vineyards using UAV images. This software has been released to improve the current procedure for early botrytis bunch rot assessment, which consists of laborious and time-consuming field inspections. In addition, BBR is easily modifiable by users to implement the workflow in their fields. For future work, the software can be extended to other crops and/or other fungal diseases.

LiDAR (Light Detection and Ranging) technology's precision in data collection has gained immense traction in the field of remote sensing, particularly in Precision Agriculture using Unmanned Aerial Vehicles (UAVs). To fulfill the pressing need for public UAV LiDAR datasets in the domain of Agricultural Sciences, especially for woody crops such as vineyards, this study presents an extensive dataset of LiDAR data collected from vineyards in northern Spain. The DJI M300 multi-rotor platform, equipped with a DJI Zenmuse L1 LiDAR sensor, conducted UAV flights at 20, 30, and 50 meters above ground level (AGL) across two vineyards during three development stages in 2021 and 2022. This dataset is composed of ten high-density 3D LiDAR point clouds stored in .laz format with embedded RGB information in each point. It provides insights into vineyard morphology and development, thereby aiding in the optimization of vineyard management strategies. Furthermore, it serves as a valuable tool for agricultural robotics, offering comprehensive terrain information for developing efficient flight paths and navigation algorithms. Finally, it serves as a reliable “ground truth” dataset to validate satellite-derived models, facilitating the creation of highly accurate digital elevation models (DEMs) and other derived models.

BackgroundTracking devices, such as global (GPS) and local (LPS) positioning systems, combined with physiological measurements, have become reliable tools to characterize movement patterns, assessing the external load (EL), internal load (IL), fatigue, and performance of athletes in team sports. This scoping review aims to provide a comprehensive understanding of the applicability of tracking systems in physical performance analysis within team sports and the wellbeing of athletes based on research strategies and combined variables.MethodsA systematic search was conducted in PubMed, Web of Knowledge, and Scopus databases according to PRISMA guidelines. The 79 studies that were reviewed met the following criteria: (1) contained relevant data regarding elite athletes′ performance; (2) athletes' EL and IL; (3) were written in the English language; (4) were related only to team sports.ResultsThe findings indicate that tracking technology has been engaged in several research areas, including performance analysis, training vs. match load management, injuries, and nutrition, through characterization and correlational studies. Metrics, primarily focused on kinematic and mechanical EL aspects, have been employed in combination with IL data to analyze the performance of athletes. However, the lack of an integrative model for the analysis and integration of EL and IL metrics within each team sport suggests an interesting direction for further research.ConclusionThere is a need for coherence between the methods and the research goals on performance analysis. The development of a framework that guides experimental studies is highly recommended, particularly on manipulating metrics analyzed between training and match sessions, injury prevention, and nutrition. This will lead to the development of the most applied sports science research to improve the preparation and decision-making of athletes based on reliable data.Systematic Review Registrationhttps://inplasy.com/?s=2022120039, identifier 2022120039.

There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled “Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard”. The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.

Abstract Background Puberty is a period of intense changes in human body and, additionally, participation in sports is viewed as prominent form of physical activity among male adolescent athletes. The current study was aimed to examine the intra-individual changes in body composition and bone tissue during years of maximal growth and the effect of 12-month participation in sports contrasting in mechanical impact. Methods The sample included 40 male adolescent athletes (soccer: n = 20; swimming: n = 20) aged 12.57 ± 0.37 years who were followed for 12 months. Stature and body mass were measured, bone mineral content (BMC), areal bone mineral density (aBMD), lean soft and fat tissues assessed using DXA. Food intake was estimated using a questionnaires and training sessions individually monitored. Repeated measures ANOVA tested the differences between sports and 12-month intra-individual variation (time moments: TM1, TM2). The analyses on aBMD for total body and total body less head were repeated controlling for variation in stature at baseline. Results Soccer players completed 63 ± 31 sessions (95 ± 47 h). Respective values for swimmers were 248 ± 28 sessions and 390 ± 56 h. In general, the analysis of aBMD as dependent variable evidenced significant effect of sport-associated variation (F = 5.254, p

The use of satellite data in precision agriculture, especially for woody crops, has gained prominence. Such data aid in forecasting yield, predicting crop quality, and irrigation management by providing insights into crop conditions. However, the accuracy of parameters such as the Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) derived from satellites is questioned. This research compares Sentinel-2 satellite imagery with LiDAR data and multispectral imagery gathered over a vineyard in northern Spain using Unmanned Aerial Vehicles (UAVs) in July, August, and September 2022, focusing on veraison, a key stage in precision viticulture. The findings reveal a moderate correlation between satellite and UAV NDVI values (up to R2 = 0.6) but a discrepancy in leaf area estimations from satellite imagery, suggesting its limited use for such applications in hedgerow systems in agriculture. Nonetheless, satellite data’s ability to detect crop spatial variability remains useful for field management. The study emphasizes the potential benefits and drawbacks of diversifying remote sensing techniques for effective agricultural management.

Precision agriculture has seen significant advancements with the integration of remote-sensing technologies. However, challenges such as real-time data availability and computing limitations persist. This study aimed to develop a standardized method for generating spatial variability maps for vineyard management using UAV (unmanned aerial vehicle) imagery. Using IDW (inverse distance weight), nadir images with geotagged locations were processed to extract spectral information. The results were analyzed using the NGRDI (normalized green-red difference index) and demonstrated that geo-interpolation methods are effective compared to traditional photogrammetry-based methods but 90% faster, highlighting their potential in real-time applications and edge computing. In addition, IDW correlation with Sentinel-2 imagery reached values as high as r = 0.8. This method offers a faster, less resource-intensive alternative to existing techniques for crop mapping, addressing the current challenges in precision agriculture.

This study presents a novel approach to address challenges regarding data acquisition for object detection and tracking purposes by enhancing UAV path planning specifically designed for fruit detection in woody crops trained on vertical trellises, considering the biophysical environment of the field. The proposed method implements the Ant Colony Optimization (ACO) algorithm and enables single and multiple UAVs to fly synchronously while ensuring a safe distance between platforms. The results highlight that ACO is able to generate optimal and safe routes, considering the vegetation and covering the whole agricultural area. Moreover, it shows potential to solve partial leaf occlusion for fruit identification.

Unmanned Aerial Vehicle (UAV) thermal imagery is rapidly becoming an essential tool in precision agriculture. Its ability to enable widespread crop status assessment is increasingly critical, given escalating water demands and limited resources, which drive the need for optimizing water use and crop yield through well-planned irrigation and vegetation management. Despite advancements in crop assessment methodologies, including the use of vegetation indices, 2D mapping, and 3D point cloud technologies, some aspects remain less understood. For instance, mission plans often capture nadir and oblique images simultaneously, which can be time- and resource-intensive, without a clear understanding of each image type’s impact. This issue is particularly critical for crops with specific growth patterns, such as woody crops, which grow vertically. This research aims to investigate the role of nadir and oblique images in the generation of CWSI (Crop Water Stress Index) maps and CWSI point clouds, that is 2D and 3D products, in woody crops for precision agriculture. To this end, products were generated using Agisoft Metashape, ArcGIS Pro, and CloudCompare to explore the effects of various flight configurations on the final outcome, seeking to identify the most efficient workflow for each remote sensing product. A linear regression analysis reveals that, for generating 2D products (orthomosaics), combining flight angles is redundant, while 3D products (point clouds) are generated equally from nadir and oblique images. Volume calculations show that combining nadir and oblique flights yields the most accurate results for CWSI point clouds compared to LiDAR in terms of geometric representation (R2 = 0.72), followed by the nadir flight (R2 = 0.68), and, finally, the oblique flight (R2 = 0.54). Thus, point clouds offer a fuller perspective of the canopy. To our knowledge, this is the first time that CWSI point clouds have been used for precision viticulture, and this knowledge can aid farm managers, technicians, or UAV pilots in optimizing the capture of UAV image datasets in line with their specific goals.

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.

Abstract Machado-Joseph disease (MJD) or Spinocerebellar ataxia type 3 (SCA3) is the most common form of dominant SCA worldwide. Magnetic Resonance Imaging (MRI) and Proton Magnetic Resonance Spectroscopy (1H-MRS) provide promising non-invasive diagnostic and follow-up tools, also serving to evaluate therapies efficacy. However, pre-clinical studies showing relationship between MRI-MRS based biomarkers and functional performance are missing, which hampers an efficient clinical translation of therapeutics. This study assessed motor behaviour, neurochemical profiles, and morphometry of the cerebellum of MJD transgenic mice and patients aiming at establishing magnetic-resonance-based biomarkers. 1H-MRS and structural MRI measurements of MJD transgenic mice were performed with a 9.4 Tesla scanner, correlated with motor performance on rotarod and compared with data collected from human patients. We found decreased cerebellar white and grey matter and enlargement of the fourth ventricle in both MJD mice and human patients as compared to controls. N-acetylaspartate (NAA), NAA + N-acetylaspartylglutamate (NAA + NAAG), Glutamate, and Taurine, were significantly decreased in MJD mouse cerebellum regardless of age, whereas myo-Inositol (Ins) was increased at early time-points. Lower neurochemical ratios levels (NAA/Ins and NAA/total Choline), previously correlated with worse clinical status in SCAs, were also observed in MJD mice cerebella. NAA, NAA + NAAG, Glutamate, and Taurine were also positively correlated with MJD mice motor performance. Importantly, these 1H-MRS results were largely analogous to those found for MJD in human studies and in our pilot data in human patients. We have established a magnetic resonance-based biomarker approach to monitor novel therapies in preclinical studies and human clinical trials.

The purpose of this paper was to review the available literature on match analysis in rink hockey. To be included in this review, the publications should have been written in English or Portuguese, have contained relevant data about match analysis in rink hockey, and have been published as original papers, reviews, book chapters, or academic works. A systematic review of Web of Science, SPORTDiscus, PubMed, Scopus, b-on, and Open Access Scientific Portuguese Repository (RCAAP) was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines on July 24, 2020. A risk-of-bias quality form was adapted to evaluate the publications. To make a fair comparison between studies of different designs, a percentage score was calculated as a final measure of methodological quality. From the 20 selected studies, 8 were classified as having excellent methodological quality and 12 had good methodological quality. To organize the results, the material was categorized in 2 analysis levels: (i) dependent on the type of analysis performed; (ii) based on the type of variables analysed. This review highlights that rink hockey could be characterized as a sport where half of the offensive actions end with a shot at goal, but only a small number of these shots end with a goal. Furthermore, there are differences in technical demands between player positions and reductions in movement intensity in the second halves of matches. Future research should provide more scientific knowledge on the sport, using new technologies and new approaches to study tactical sequential events.

Remote sensing makes it possible to gather data rapidly, precisely, accurately, and non-destructively, allowing it to assess grapevines accurately in near real-time. In addition, multispectral cameras capture information in different bands, which can be combined to generate vegetation indices useful in precision agriculture. This dataset contains 16,504 multispectral images from a 1.06 ha vineyard affected by Botrytis cinerea, in the north of Spain. The photos were taken throughout four UAV flights at 30 m height with varying camera angles on 16 September 2021, the same date as the grape harvest. The first flight took place with the camera tilted at 0° (nadir angle), the second flight at 30°, the third flight at 45°, and the fourth flight was also performed at 0° but was scheduled in the afternoon to capture the shadows of the plants projected on the ground. This dataset was created to support researchers interested in disease detection and, in general, UAV remote sensing in vineyards and other woody crops. Moreover, it allows digital photogrammetry and 3D reconstruction in the context of precision agriculture, enabling the study of the effect of different tilt angles on the 3D reconstruction of the vineyard and the generation of orthomosaics.

Counting the number of grape bunches at an early stage of development offers relevant information to the winegrower about the potential yield to be harvested. However, manual counting on the fields is laborious and time-consuming. Remote sensing, and more precisely unmanned aerial vehicles mounted with RGB or multispectral cameras, facilitate this task rapidly and accurately. This dataset contains 40 RGB videos from a 1.06-ha vineyard located in northern Spain. Moreover, the dataset includes mask labels of visible grape bunches. The videos were acquired throughout four UAV flights with an RGB camera tilted at 60 degrees. Each flight recorded one side of a row of the vineyard. The grape berries were between pea-size (BBCH75) and bunch closure (BBCH79) stage, which is two months before harvesting. No operations other than those usual in a commercial vineyard, such as pruning, cane tying, fertilization, and pest treatment, have been carried out, hence, the dataset presents leaf occlusion. The dataset was gathered and labelled to train object detection and tracking algorithms for grape bunch counting. Furthermore, it eases the work of winegrowers to check the sanitary status of the vineyard.

Precision agriculture has drawn much attention in the last few years because of the benefits it has on reducing farming costs while maximizing the harvest obtained. Yield prediction is of importance for farmers to fertilize accordingly to reach the potential yield. However, this task is still relying on manual work, which is expensive and time-consuming. Instance segmentation has been implemented in the last years for fruit detection and yield estimation, obtaining state-of-the-art metrics, and reducing the labor required. This research presents a novel approach for spinach seed yield estimation for seed production purposes, that consists of correlating the number of plants and two phenotyping variables (plant area and canopy cover percentage) with the number of harvested seeds and the thousand seed weight. Mask R-CNN is applied to count the number of detections of spinach plants and obtain the object mask from which the plant area is derived. The results show that there is a high linear correlation between a multivariate linear mixed model of the three variables and the number of seeds, with an R2adj of 0.80. Furthermore, 77.42% of the variation in the weight of thousand seeds can be explained by the number of plants. For future studies, the algorithm should be trained with more spinach images from different locations and under varying weather conditions to allow it to generalize for the crop worldwide. It can be concluded, until further research, that Mask R-CNN can be applied for spinach counting and the computation of its individual plant area, with promising results.

Type 2 Diabetes Mellitus (T2DM) is a metabolic disease that leads to multiple vascular complications with concomitant changes in human neurophysiology, which may lead to long-term cognitive impairment, and dementia. Early impairments of neurovascular coupling can be studied using event-related functional magnetic resonance imaging (fMRI) designs. Here, we aimed to characterize the changes in the hemodynamic response function (HRF) in T2DM to probe components from the initial dip to late undershoot. We investigated whether the HRF morphology is altered throughout the brain in T2DM, by extracting several parameters of the fMRI response profiles in 141 participants (64 patients with T2DM and 77 healthy controls) performing a visual motion discrimination task. Overall, the patients revealed significantly different HRFs, which extended to all brain regions, suggesting that this is a general phenomenon. The HRF in T2DM was found to be more sluggish, with a higher peak latency and lower peak amplitude, relative slope to peak, and area under the curve. It also showed a pronounced initial dip, suggesting that the initial avidity for oxygen is not compensated for, and an absent or less prominent but longer undershoot. Most HRF parameters showed a higher dispersion and variability in T2DM. In sum, we provide a definite demonstration of an impaired hemodynamic response function in the early stages of T2DM, following a previous suggestion of impaired neurovascular coupling. The quantitative demonstration of a significantly altered HRF morphology in separate response phases suggests an alteration of distinct physiological mechanisms related to neurovascular coupling, which should be considered in the future to potentially halt the deterioration of the brain function in T2DM.

IntroductionFunctional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes.MethodsWe acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS.ResultsNo differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation.DiscussionVolume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.

A reproducibility crisis has been reported across many research fields, including neuroimaging, reaching up to 70% of studies. Neuroimaging data, such as magnetic resonance imaging (MRI), requires pre-processing to allow for inter-subject comparison, increase signal contrast and noise reduction. As manual MRI pre-processing is time consuming and requires expertise, multiple automatic pre-processing frameworks have been proposed. However, neuroimaging studies often report divergent results, even for similar populations, thus it is important to determine whether this occurs as a result of different processing tools. Two of the most used tools are FreeSurfer and the Computational Anatomy Toolbox (CAT12). In this study we assessed the reproducibility between these two automatic pre-processing frameworks for structural MRI and test-retest reliability within framework on estimation of cortical thickness. Our results show that the reproducibility between the frameworks is lower at the region-of-interest (ROI) level than at individual level. Furthermore, we found that the reproducibility was lower in paediatric samples than in adults. Finally, an acquisition site effect was also identified. Given the widespread use of these frameworks in basic and clinical neuroscience, the results of multicentric cross-sectional studies must be interpreted with caution, particularly with paediatric samples. The observed reproducibility issue might be one of the sources of discrepancies reported in neuroimaging studies. On a positive note, framework test-retest reliability within subject is high, suggesting that inconsistency of results may be less concerning in longitudinal studies. The code is available at: https://cibit-uc.github.io/fs-cat12-cortical-thickness-reproducibility.

It remains to be understood how biological motion is hierarchically computed, from discrimination of local biological motion animacy to global dynamic body perception. Here, we addressed this functional separation of the correlates of the perception of local biological motion from perception of global motion of a body. We hypothesized that local biological motion processing can be isolated, by using a single dot motion perceptual decision paradigm featuring the biomechanical details of local realistic motion of a single joint. To ensure that we were indeed tackling processing of biological motion properties we used discrimination instead of detection task. We discovered using representational similarity analysis that two key early dorsal and two ventral stream regions (visual motion selective hMT+ and V3A, extrastriate body area EBA and a region within fusiform gyrus FFG) showed robust and separable signals related to encoding of local biological motion and global motion-mediated shape. These signals reflected two independent processing stages, as revealed by representational similarity analysis and deconvolution of fMRI responses to each motion pattern. This study showed that higher level pSTS encodes both classes of biological motion in a similar way, revealing a higher-level integrative stage, reflecting scale independent biological motion perception. Our results reveal a two-stage framework for neural computation of biological motion, with an independent contribution of dorsal and ventral regions for the initial stage.

Rumex obtusifolius (Rumex or broad leaved dock) is one of the most common weeds in grasslands. It spreads quickly, lowers the nutritional value of the grass, and is poisonous for livestock due to its oxalic acid content. Mapping it is important before any control treatment is applied. Current methods for mapping Rumex either involve manual work or the utilization of ground robots, which are not efficient in large fields. This study investigated the feasibility of using aerial images from unmanned aerial vehicles (UAV) and deep learning to map Rumex in grasslands. Seven pre-trained CNN models were tested using transfer learning on UAV images acquired at 10 m, 15 m, and 30 m height. Based on Cross Validation results, MobileNet performed the best in detecting Rumex, with an F1-Score of 78.36% and an AUROC of 93.74%, at 10 m height. At 15 m, the detection performance was relatively lower (F1-score = 72.00%, AUROC = 88.67%), but the results showed that the performance can increase with more data. Experiments also showed that Rumex detection was dependent on the flight height since the algorithm was unable to detect the plants at 30 m height. The code and the datasets used in this work were released in an open access repository to contribute to the advances in grassland management using UAV technology.

Abstract Background Skeletal age (SA) is considered the best method of assessing biological maturation. The aim of this study was to determine intra-observer (reproducibility) and inter-observer agreement of SA values obtained via the Greulich-Pyle (GP) method. In addition, the variation in calculated SAs by alternative GP protocols was examined. Methods The sample was composed of 100 Portuguese female soccer players aged 12.0–16.7 years. SAs were determined using the GP method by two observers (OB1: experience 2000 exams using several methods). The radiographs were examined using alternative GP protocols: (wholeGP) the plate was matched to the atlas as an overall approach; (30-boneGP) bone-by-bone inspections of 30-bones; (GPpmb) bone-by-bone inspections of the pre-mature bones only. For the 30-boneGP and GPpmb approaches, SA was calculated via the mean (M) and the median (Md). Results Reproducibility ranged 82–100% and 88–100% for OB1 and OB2, respectively. Inter-observer agreement (100 participants multiplied by 30 bones) was 92.1%. For specific bones, agreement rates less than 90% were found for scaphoid (81%), medial phalange V (83%), trapezium (84%) and metacarpal V (87%). Differences in wholeGP SAs obtained by the two observers were moderate (d-cohen was 0.79). Mean differences between observers when using bone-by bone SAs were trivial (30-boneGP: d-cohen less than 0.05; GPpmb: d-cohen less than 0.10). The impact of using the mean or the median was negligible, particularly when analyses did not include bones scored as mature. Conclusion The GP appeared to be a reasonably reproducible method to assess SA and inter-observer agreement was acceptable. There is evidence to support a recommendation of only scoring pre-mature bones during later adolescence. Further research is required to examine whether these findings are consistent in younger girls and in boys.

Abstract Background The aim of the study was to examine the contribution of chronological age (CA), skeletal maturation, training experience and concurrent body size descriptors, to inter-individual variance in left ventricular mass (LVM) among female adolescent soccer players. Methods The sample included 228 female soccer players 11.8–17.1 years. Training experience defined as years of participation in competitive soccer (range 2–9 years), was obtained by interview. Stature, body mass and skinfolds (triceps, medial calf) were measured. Fat mass was estimated; Fat-free mass was derived. LVM was assessed by echocardiography. Skeletal maturity status was as the difference of skeletal age (SA, Fels method) minus CA. Results Fat-free mass was the most prominent single predictor of LVM (R2 = 36.6%). It was associated with an allometric coefficient close to linearity (k = 0.924, 95%CI: 0.737 to 1.112). A significant multiplicative allometric model including body mass, fat-free mass, CA, training experience and skeletal maturity status was also obtained (R = 0.684; R2 = 46.2%). Conclusion Stature has limitations as a valid size descriptor of LVM. Body mass, fat-free mass, training experience, CA, body mass and skeletal maturity status were relevant factors contributing to inter-individual variability in LVM.

Rink hockey is a highly specialized and physiological demanding sport with sparse research regarding the game and athletes' characteristics. A cross-sectional study was developed to characterize the body composition and grip strength of elite male rink hockey players and to establish the relationship between ethnicity on body composition and grip strength. A sample of 100 elite rink-hockey athletes aged 26.59 ± 6.02 participated in the study, comprised of 69 Caucasian male adults aged 27.58 ± 6.44 years and 31 Black African male adults aged 24.39 ± 4.27. Body composition was assessed by anthropometric measurements. Static grip strength was assessed with an adjustable dynamometer. Multiple regression analysis was applied to understand which variables constraints body fat percentage (BF) and grip strength. Body mass showed an average of 76.36 ± 9.18 kg for 175.80 ± 5.87 cm of height and BF% of 10.82 ± 5.07%. Maximal right grip strength was 50.91 ± 6.26 kg and 50.27 ± 6.23 kg for left grip strength. Four predictors accounted for 70.01% of the variance of BF%: abdominal circumference (p < 0.001), right thigh circumference (p < 0.001), right calf circumference (p = 0.001) and ethnicity (p = 0.016). Three predictors accounted for 13.1% of the variance of right grip strength: ethnicity (p = 0.013), chronological age (p = 0.024) and right distal thigh circumference (p = 0.014). Results suggest that elite rink hockey athletes have a specific anthropometric identity, which at the elite level may lead to reduced body fat and greater handgrip strength. Ethnicity seems to predict body fat and grip strength in elite rink hockey athletes.

In Ultimate Frisbee, players are responsible for administering and reinforcing adherence to the rules of the game. As a self-refereed sport, UF relies upon the Spirit of the Game (SOTG). This study aimed to profile the experience, to ascertain the training history of the sport, the participation and goal orientations of international Ultimate Frisbee players in the European Beach Ultimate Championship, and to evaluate the contribution of these variables in a discriminant function to classify players according to SOTG level and competitive level. The sample consisted of 160 players [females 33.8% (29.3 ± 7.2 years); males 66.2% (37.6 ± 9.7 years)] who competed in the European Beach Ultimate Championship, 2019. Factorial ANOVA was used to test the effect of sex, competitive level and SOTG level (measured by the sum of the scores obtained in five questions addressing the following domains: (1) Knowledge and use of the rules; (2) Fouls and body contact; (3) Fair-mindedness; (4) Positive attitude and self-control; (5) Communication. The results revealed that sex was not a consistent source of SOTG level variation among players. In each competitive level, those with high SOTG levels obtained lower European Beach Ultimate Championship classification (t = 5.73 to 6.55, p < 0.001, d = 1.28 to 2.06), higher SOTG classification (t = −13.21 to −7.04, p < 0.001, d = 1.28 to 2.85), and better evaluation for fouls and body contact (t = 2.76 to 9.23, p < 0.05, d = 0.86 to 1.99) and for positive attitude (t = 5.88 to 7.43, p < 0.001, d = 2.06 to 2.57), than regular SOTG level players. Players of different competitive levels demonstrated similar SOTG results. These findings provide important information to coaches, physical education teachers and sport consultants.

This study aims to explore the communication capabilities for video crucial applications of two commercial drones—the Parrot AR.Drone 2.0 and the Parrot Anafi—in a greenhouse environment. Experiments were conducted on Received Signal Strength (RSS), Round-Trip Time (RTT) and the throughput on 802.11n at the 2.4 GHz network. From the experiments, it was found that none of the UAVs have an isotropic radiation pattern. Indoor measurements close to the roof and the ground were more prone to signal degradation. Even though the RTT of the Parrot Anafi was higher than that of the AR.Drone 2.0, the Anafi in almost all cases managed to achieve higher throughput and lower path loss, proving its superiority for video application. In addition, the maximum distance that the Parrot Anafi could fly in the greenhouse without any video quality loss was 110 m, while the AR.Drone 2.0 was hardly able to reach 30 m. Finally, the effect of the propellers has an insignificant impact on the UAV connection characteristics in all tested scenarios.

Introduction: Ultimate Frisbee (UF) is a non-contact, challenging, and self-promoted team sport. Some factors such as the game environment and rules seem to influence athletes' behavior. Goals: Provide a robust systematic review (SR) of the psychological domains associated with UF.Methods: A SR according to Cochrane guidelines was completed. A reproducible search strategy was conducted by two independent reviewers in thirteen online databases: the Cochrane Central Register of Controlled Trials, Web of Science, SCOPUS, B-On, SportDiscus, Scielo; APA PsycINFO, Psychology and Behavioral Sciences; Academic Search Complete; Medline (PubMed); ERIC; Google Scholar; Open Acess Thesis and Dissertations. The search occurred from 1st to 30th June 2020, and there were no limitations regarding the year of publication. Original papers that contained relevant data regarding psychological domains in the context of UF in English, Portuguese and Spanish were selected. The combination of the main terms “ultimate frisbee” and “sport psychology” was used in all databases. A total of 464 studies were identified and selected in the last phase of selection. After the Screening (n = 301) and Eligibility (n = 71) phases, a total of 30 potential papers were selected and classified. Finally, only four papers were qualified to be included in the final version of SR.Results: The psychological dimensions revealed in the present study were: leadership; basic psychological needs; behaviors; task cohesion and performance; intrateam communication; performance-avoidance goals; friendship goals; sportsmanship associated with goal-directed self-talk and self-regulated learning.Discussion: To our knowledge, this is the first SR about UF. In reviewing all the findings in the studies, there is evidence that UF can promote teamwork, task cohesion, leadership, and increase friendship-approach goals.Conclusion: The results revealed that group goals and promoting teamwork significantly predicted social cohesion and that teamwork and task cohesion was mediated by communication. UF is characterized by communication between all players, whether they are from the same team or the opposing team. In summary, the current study revealed real-time information about the game and its rules. This is important because UF is one of the few team sports worldwide that are self-referred by participants.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=169294, identifier: CRD42020169294.

Abstract Background This study aimed to determine the allometric exponents for concurrent size descriptors (stature, body mass and fat-free mass) and also to examine the contribution of chronological age and pubertal status combined with above mentioned size descriptors to explain inter-individual variability in the peak of oxygen uptake (VO2peak) among girls during circumpubertal years. Methods The final sample included 51 girls (10.7–13.5 years). VO2peak was derived from an incremental progressive maximal protocol using a motorized treadmill. Anthropometry included body mass, stature and skinfolds. Measurements were performed by a single trained observer. Sexual maturation was assessed as self-reported stage of pubic hair (PH) development. Static allometric models were explored as an alternative to physiological output per unit of size descriptors. Allometry also considered chronological age and sexual maturation as dummy variable (PH2 vs. PH3 and PH3 vs. PH4). Results Scaling coefficients for stature, body mass and fat-free mass were 1.463 (95%CI: 0.476 to 2.449), 0.516 (95%CI: 0.367 to 0.666) and 0.723 (95%CI: 0.494 to 0.951), respectively. The inclusion of sexual maturation increased explained variance for VO2peak (55% for PH2 vs. PH3 and 47% for PH3 vs. PH4). Body mass was identified as the most prominent body size descriptor in the PH2 vs. PH3 while fat-free mass was the most relevant predictor combined with PH3 vs. PH4. Conclusions Body mass and fat-free mass seemed to establish a non-linear relationship with VO2peak. Across puberty, inter-individual variability in VO2peak is explained by sexual maturation combined with whole body during early puberty and by sexual maturation and fat-free mass during late puberty. Additional studies need to confirm ontogenetic allometric models during years of maximal growth.

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Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B0 field inhomogeneity. The component derived from in-plane spin dephasing induces pixel intensity variations and, more critically, geometric distortions. Despite the physical mechanisms underlying the susceptibility artifact being well established, a systematic investigation on the impact of the associated geometric distortions, and the direct comparison of different approaches to tackle them, on fMRI data analyses is missing. Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE). We first characterized the geometric distortions and the correction approaches based on the estimated ΔB0 field offset and voxel shift maps, and then conducted three types of analyses on the distorted and corrected fMRI data: (1) registration into structural data, (2) identification of resting-state networks (RSNs), and (3) mapping of task-related brain regions of interest. GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP. fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.

Background: Social cognition impairment is a key phenomenon in serious mental disorders such as schizophrenia (SCZ) and bipolar disorder (BPD). Although genetic and neurobiological studies have suggested common neural correlates, here we hypothesized that a fundamental dissociation of social processing occurs at an early level in these conditions. Methods: Based on the hypothesis that key structures in the social brain, namely the temporoparietal junction, should present distinctive features in SCZ and BPD during low-level social judgment, we conducted a case-control study in SCZ (n = 20) and BPD (n = 20) patients and controls (n = 20), using task-based fMRI during a Theory of Mind (ToM) visual paradigm leading to interpretation of social meaning based on simple geometric figures. Results: We found opposite neural responses in two core ToM regions: SCZ patients showed social content-related deactivation (relative to controls and BPD) of the right supramarginal gyrus, while the opposite pattern was found in BPD; reverse patterns, relative to controls and SCZ, were found in the left posterior superior temporal gyrus, a region involved in inferring other’s intentions. Receiver-operating-characteristic curve analysis showed 88% accuracy in discriminating the two clinical groups based on these neural responses. Conclusions: These contrasting activation patterns of the temporoparietal junction in SCZ and BPD represent mechanistic differences of social cognitive dysfunction that may be explored as biomarkers or therapeutic targets.

Farmers require accurate yield estimates, since they are key to predicting the volume of stock needed at supermarkets and to organizing harvesting operations. In many cases, the yield is visually estimated by the crop producer, but this approach is not accurate or time efficient. This study presents a rapid sensing and yield estimation scheme using off-the-shelf aerial imagery and deep learning. A Region-Convolutional Neural Network was trained to detect and count the number of apple fruit on individual trees located on the orthomosaic built from images taken by the unmanned aerial vehicle (UAV). The results obtained with the proposed approach were compared with apple counts made in situ by an agrotechnician, and an R2 value of 0.86 was acquired (MAE: 10.35 and RMSE: 13.56). As only parts of the tree fruits were visible in the top-view images, linear regression was used to estimate the number of total apples on each tree. An R2 value of 0.80 (MAE: 128.56 and RMSE: 130.56) was obtained. With the number of fruits detected and tree coordinates two shapefile using Python script in Google Colab were generated. With the previous information two yield maps were displayed: one with information per tree and another with information per tree row. We are confident that these results will help to maximize the crop producers' outputs via optimized orchard management.

ABSTRACT Objective To describe and identify the importance of different indicators of the aerobic and anaerobic fitness of male ultra-trail runners according to their level of participation (regional or national). Methods Forty-four male ultra-trail runners were assessed (36.5±7.2 years). They were classified as regional (n=25) and national (n=19). Wingate test was used to assess the anaerobic pathway. A progressive incremental running test was performed and ventilatory thresholds registered, in parallel to heart rate and lactate concentration at the end of the protocol. Comparison between groups was performed using independent samples t-test. Results No significant differences were found between outputs derived from Wingate test. For aerobic fitness, while examining absolute values, differences were uniquely significant for the second ventilatory threshold (ultra-trail regional runners: 3.78±0.32L.min-1; ultra-trail national runners: 4.03±0.40L.min-1 p

Schizophrenia is believed to be a neurodevelopmental disease with high heritability. Differential diagnosis is often challenging, especially in early phases, namely with other psychotic disorders or even mood disorders. such as bipolar disorder with psychotic symptoms. Key pathophysiological changes separating these two classical psychoses remain poorly understood, and current evidence favors a more dimensional than categorical differentiation between schizophrenia and bipolar disorder. While established biomarkers like cortical thickness and grey matter volume are heavily influenced by post-onset changes and thus provide limited possibility of accessing early pathologies, gyrification is assumed to be more specifically determined by genetic and early developmental factors. The aim of our study was to compare both classical and novel morphometric features in these two archetypal psychiatric disorders. We included 20 schizophrenia patients, 20 bipolar disorder patients and 20 age- and gender-matched healthy controls. Data analyses were performed with CAT12/SPM12 applying general linear models for four morphometric measures: gyrification and cortical thickness (surface-based morphometry), and whole-brain grey matter/grey matter volume (voxel-based morphometry - VBM). Group effects were tested using age and gender as covariates (and total intracranial volume for VBM). Voxel-based morphometry analysis revealed a schizophrenia vs. control group effect on regional grey matter volume (p

Background: This study aims to analyze the effects of anti-hypertensive monotherapy and combined therapy on functional status, and cardiovascular risk outcomes in older adults. Methods: This longitudinal non-randomized cohort study, involved hypertensive older adults (n = 440) aged 60 or more years with comorbidities. Participants underwent a community exercise training program and one of the following 2 conditions: i) use of daily mono-dose angiotensin-converting enzyme inhibitors (ACEi; n= 232); ii) combined therapy including ACEi plus other class agent (Combined; n= 208). Baseline and 2-year follow-up evaluations included the functional fitness, health-related quality of life (HRQoL), health history questionnaires, anthropometric and hemodynamic profile. Results: Both experimental groups have significantly improved physical functional status, and have significantly decreased blood pressure and waist circumference. ACEi group has significantly reduced body mass and body mass index, the Combined group significantly reduced the waist-to-hip ratio. Additionally, both groups perceived better physical HRQoL. Conclusions: Functional status has improved with ACEi medication and exercise training, regardless the ACEi medication therapy. Exercise training plus ACEi antihypertensive therapy should be recommended into the standard prescription practice to reduce the rate of physical disability among hypertensive older adults.

The purpose of the study was to model the longitudinal development of knee extension (KE) and flexion (KF) strength in adolescent soccer players. A mixed-longitudinal sample composed of 67 soccer players aged 11.0–13.9 years at baseline was followed on three-to-five occasions over 5 years. Stature, body mass and several skinfold thicknesses were measured. Fat mass was estimated from skinfolds and fat-free mass (FFM) derived. Skeletal age was estimated with the TW2-RUS protocol. An isokinetic dynamometer was used to obtain peak torque of KE and KF from concentric assessments at an angular velocity of 180°/s. Multilevel random effects regression analyses were performed. Among youth soccer players aged 11–16 years, isokinetic strength of the knee muscle groups was reasonably predicted from chronological age (CA), stature and FFM: KE = –66.170 + 5.353 × (CA) + 0.594 × (CA2) + 0.552 × (stature) + 1.414 × (FFM), and KF = –9.356 + 2.708 × (CA) + 1.552 × (FFM). In conclusion, CA per se accounted for annual increments of 5.4 Nm in KE and 2.7 Nm in KF.

Abstract Background Exploring the osteogenic effect of different bone-loading sports is particular relevant to understand the interaction between skeletal muscle and bone health during growth. This study aimed to compare total and regional bone and soft-tissue composition between female adolescent swimmers (n=20, 15.71±0.93 years) and volleyball players (n=26, 16.20±0.77 years). Methods Dietary intake was obtained using food frequency questionnaires. Body size was given by stature, sitting height, and body mass. Six skinfolds were measured. Bone mineral content (BMC) and density (BMD), lean soft tissue, and fat tissue were assessed using dual-energy X-ray absorptiometry. Pearson’s product moment correlation coefficients were calculated to examine the relationships among variables, by type of sport. Comparisons between swimmers and volleyball players were performed using student t-tests for independent samples and multivariate analysis of covariance (controlling for age, training history and body size). Results Swimmers (BMC: 2328±338 g) and volleyball players (BMC: 2656±470 g) exceeded respectively by 2.1 and 2.8 standard deviation scores the average of international standards for whole body BMC of healthy adolescents. Years of training in swimmers were positively related to the upper limbs BMC (r=+0.49, p

The agricultural sector plays a fundamental role in our society, where it is increasingly important to automate processes, which can generate beneficial impacts in the productivity and quality of products. Perception and computer vision approaches can be fundamental in the implementation of robotics in agriculture. In particular, deep learning can be used for image classification or object detection, endowing machines with the capability to perform operations in the agriculture context. In this work, deep learning was used for the detection of grape bunches in vineyards considering different growth stages: the early stage just after the bloom and the medium stage where the grape bunches present an intermediate development. Two state-of-the-art single-shot multibox models were trained, quantized, and deployed in a low-cost and low-power hardware device, a Tensor Processing Unit. The training input was a novel and publicly available dataset proposed in this work. This dataset contains 1929 images and respective annotations of grape bunches at two different growth stages, captured by different cameras in several illumination conditions. The models were benchmarked and characterized considering the variation of two different parameters: the confidence score and the intersection over union threshold. The results showed that the deployed models could detect grape bunches in images with a medium average precision up to 66.96%. Since this approach uses low resources, a low-cost and low-power hardware device that requires simplified models with 8 bit quantization, the obtained performance was satisfactory. Experiments also demonstrated that the models performed better in identifying grape bunches at the medium growth stage, in comparison with grape bunches present in the vineyard after the bloom, since the second class represents smaller grape bunches, with a color and texture more similar to the surrounding foliage, which complicates their detection.

Ultimate Frisbee (UF) is a non-contact, challenging, and self-promoted team sport. Its characteristics, such as the game environment and rules, appear to influence the on-the-pitch behaviour of players. This article examines the content of qualitative studies to determine if and how the unique characteristics of UF may be related to nine sociological themes, that may be identified during gameplay. These themes include the following: (a) competition and performance; (b) enjoyment; (c) communication; (d) cooperation/friendship; (e) behaviors/welfare; (f) teamwork/social skills; (g) environment/lifestyle; (h) rules/self-refereeing and (i) spirit of the Game (SOTG). The review was conducted according to PRISMA guidelines. A comprehensive search protocol was used to identify, screen, and select published research articles under a Qualitative Systematic Review (QSR). The search was occurred from 1 June to 30 December 2020 with no limitations regarding the year of publication. Original English-language papers that contained relevant data regarding sociological themes and UF were selected. As a result, nine papers were qualified to be included in the final version of QSR. The files analyzed were structured with MAXQDA. A total of 521 references were identified and selected for analysis. After the Screening (n = 301) and Eligibility (n = 71) phases, a total of 30 potential papers were selected and classified. Nine studies were included in the final analysis. The three most cited sociological themes in these studies were: communication, teamwork/social skills, and spirit of the game. Research suggests that UF involves patterns of interaction related to communication and the spirit of the game that encourage active lifestyles. Finally, we point out that UF is an appropriate sport to include in physical education classes in which the creation of positive relationships between students is the desired outcome. This topic should be explored further through interventional studies done in different contexts although the evidence suggests that UF offers players unique opportunities to experience a combination of physical activity and enjoyment.