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Abstract Building models that allow phenotypic evaluation of complex agronomic traits in crops of global economic interest, such as grain yield (GY) in soybean and maize, is essential for improving the efficiency of breeding programs. In this sense, understanding the relationships between agronomic variables and those obtained by high-throughput phenotyping (HTP) is crucial to this goal. Our hypothesis is that vegetation indices (VIs) obtained from HTP can be used to indirectly measure agronomic variables in annual crops. The objectives were to study the association between agronomic variables in maize and soybean genotypes with VIs obtained from remote sensing and to identify computational intelligence for predicting GY of these crops from VIs as input in the models. Comparative trials were carried out with 30 maize genotypes in the 2020/2021, 2021/2022 and 2022/2023 crop seasons, and with 32 soybean genotypes in the 2021/2022 and 2022/2023 seasons. In all trials, an overflight was performed at R1 stage using the UAV Sensefly eBee equipped with a multispectral sensor for acquiring canopy reflectance in the green (550 nm), red (660 nm), near-infrared (735 nm) and infrared (790 nm) wavelengths, which were used to calculate the VIs assessed. Agronomic traits evaluated in maize crop were: leaf nitrogen content, plant height, first ear insertion height, and GY, while agronomic traits evaluated in soybean were: days to maturity, plant height, first pod insertion height, and GY. The association between the variables were expressed by a correlation network, and to identify which indices are best associated with each of the traits evaluated, a path analysis was performed. Lastly, VIs with a cause-and-effect association on each variable in maize and soybean trials were adopted as independent explanatory variables in multiple regression model (MLR) and artificial neural network (ANN), in which the 10 best topologies able to simultaneously predict all the agronomic variables evaluated in each crop were selected. Our findings reveal that VIs can be used to predict agronomic variables in maize and soybean. Soil-adjusted Vegetation Index (SAVI) and Green Normalized Dif-ference Vegetation Index (GNDVI) have a positive and high direct effect on all agronomic variables evaluated in maize, while Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) have a positive cause-and-effect association with all soybean variables. ANN outperformed MLR, providing higher accuracy when predicting agronomic variables using the VIs select by path analysis as input. Future studies should evaluate other plant traits, such as physiological or nutritional ones, as well as different spectral variables from those evaluated here, with a view to contributing to an in-depth understanding about cause-and-effect relationships between plant traits and spectral variables. Such studies could contribute to more specific HTP at the level of traits of interest in each crop, helping to develop genetic materials that meet the future demands of population growth and climate change.

Abstract Background Understanding how trees develop their root systems is crucial for the comprehension of how wildland and urban forest ecosystems plastically respond to disturbances such as harvest, fire, and climate change. The interplay between the endogenously determined root traits and the response to environmental stimuli results in tree adaptations to biotic and abiotic factors, influencing stability, carbon allocation, and nutrient uptake. Combining the three-dimensional structure of the root system, with root morphological trait information promotes a robust understanding of root function and adaptation plasticity. Low Magnetic Field Digitization coupled with AMAPmod (botAnique et Modelisation de l’Architecture des Plantes) software has been the best-performing method for describing root system architecture and providing reliable measurements of coarse root traits, but the pace and scale of data collection remain difficult. Instrumentation and applications related to Terrestrial Laser Scanning (TLS) have advanced appreciably, and when coupled with Quantitative Structure Models (QSM), have shown some potential toward robust measurements of tree root systems. Here we compare, we believe for the first time, these two methodologies by analyzing the root system of 32-year-old Pinus ponderosa trees. Results In general, at the total root system level and by root-order class, both methods yielded comparable values for the root traits volume, length, and number. QSM for each root trait was highly sensitive to the root size (i.e., input parameter PatchDiam) and models were optimized when discrete PatchDiam ranges were specified for each trait. When examining roots in the four cardinal direction sectors, we observed differences between methodologies for length and number depending on root order but not volume. Conclusions We believe that TLS and QSM could facilitate rapid data collection, perhaps in situ, while providing quantitative accuracy, especially at the total root system level. If more detailed measures of root system architecture are desired, a TLS method would benefit from additional scans at differing perspectives, avoiding gravitational displacement to the extent possible, while subsampling roots by hand to calibrate and validate QSM models. Despite some unresolved logistical challenges, our results suggest that future use of TLS may hold promise for quantifying tree root system architecture in a rapid, replicable manner.

Timed Up and Go (TUG) test is one of the most popular clinical tools aimed at the assessment of functional mobility and fall risk in older adults. The automation of the analysis of TUG movements is of great medical interest not only to speed up the test but also to maximize the information inferred from the subjects under study. In this context, this article describes a dataset collected from a cohort of 69 experimental subjects (including 30 adults over 60 years), during the execution of several repetitions of the TUG test. In particular, the dataset includes the measurements gathered with four wearables devices embedding four sensors (accelerometer, gyroscope magnetometer and barometer) located on four body locations (waist, wrist, ankle and chest). As a particularity, the dataset also includes the same measurements recorded when the young subjects repeat the test while wearing a commercial geriatric simulator, consisting of a set of weighted vests and other elements intended to replicate the limitations caused by aging. Thus, the generated dataset also enables the investigation into the potential of such tools to emulate the actual dynamics of older individuals.

Cities need to improve sustainability levels demanded by climate change mitigation efforts. The use of big data analytics is crucial for understanding its dynamics and deploying solid public policies. Nevertheless, data availability poses great challenges, being difficult to produce reliable analyses. Delivering trustable cross-sectorial energy datasets with high spatial and temporal resolution is thus critical to provide valuable insights for informed policymaking.This paper describes the MetaExplorer, a GIS-platform, which gathers trustable energy-related datasets, at municipal level for Portugal, providing a user-friendly georeferenced visualisation tool that can be used to derive statistical models, and support policymaking. Publicly available data was collected and cleaned, divided on five thematic areas: energy demand, buildings, mobility, waste management, and socio-economic, while a visualisation tool was developed to provide the possibility to further explore relations between indicators and support the energy transition at local level, delivering customised analyses with a global perception.

Stroke is the second most common cause of death in developed countries. Rapid clinical assessment and intervention have a major impact on preventing infarct growth and consequently on patients' quality of life. Clinical interventions aim to restore perfusion deficits via pharmaceutical or mechanical intervention. Regardless of which reperfusion procedure is used, clinicians need to consider the risks and benefits based on multi-modal neuroimaging studies, such as MRI scans, as well as their own clinical experience. This intricate decision-making process would benefit from an automatic prediction of the final infarct, which would provide a estimation of tissue that will probably infarct. This paper introduces a deep learning method to automatically predict ischemic stroke tissue outcome. The authors propose an end-to-end deep learning architecture that combines information from perfusion dynamic susceptibility MRI, alongside perfusion and diffusion parametric maps. We aim to automatically extract features from the raw perfusion DSC-MRI to further complement the information gleaned from standard parametric maps, and to overcome the loss of information that can occur during perfusion postprocessing. Combining both data types in a single architecture, with dedicated paths, we achieve competitive results when predicting the final stroke infarct core lesion in the publicly available ISLES 2017 dataset.

Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

Rooftop photovoltaic generation can help cities become key players in the transition to clean energy. The optimal solar photovoltaic production on rooftops depends on two angles: tilt angle and azimuth angle. It is accepted in all studies that the ideal orientation of photovoltaic modules is toward the south (north) in the northern hemisphere (south). In contrast, the determination of the optimum tilt angle is more complex, and there are different equations for its calculation. Most of these equations do not take albedo into account. In this work, 47 Spanish province capitals representing the most populated cities have been studied with different equations for the calculation of the optimum annual tilt angle (Technical report by the Spanish Institute for the Diversification and Saving of Energy (IDAE), Lorenzo’s and Jacobson’s equation) and different types of albedo. Accounting for the geographical and the meteorological conditions of the cities, we analyzed the optimum tilt angle through a Mathematica© optimization code. The influence that different variables have on optimum tilt angle has been quantified by means of the term relative energy harvested. The use of the equations as a function of latitude increases the annual relative energy harvested by increasing the albedo. When the albedo is 0.2, the annual relative energy harvested is very similar in all equations. Comparing to the method that maximizes the total irradiation incident on a tilted surface, the minimum and maximum value of the percentage of relative energy harvested per year were 0.01 and 2.50% for the IDAE guideline, 0.00 and 2.38% for Lorenzo’s equation, 0.00 and 2.46% for Jacobson’s equation. A simplified polynomial regression model to estimate optimum tilt angle as a function of latitude, altitude and albedo has been proposed as well.

The accurate prediction of forest above-ground biomass is nowadays key to implementing climate change mitigation policies, such as reducing emissions from deforestation and forest degradation. In this context, the coefficient of determination ($${R^2}$$) is widely used as a means of evaluating the proportion of variance in the dependent variable explained by a model. However, the validity of $${R^2}$$ for comparing observed versus predicted values has been challenged in the presence of bias, for instance in remote sensing predictions of forest biomass. We tested suitable alternatives, e.g. the index of agreement ($$d$$) and the maximal information coefficient ($$MIC$$). Our results show that $$d$$ renders systematically higher values than $${R^2}$$, and may easily lead to regarding as reliable models which included an unrealistic amount of predictors. Results seemed better for $$MIC$$, although $$MIC$$ favoured local clustering of predictions, whether or not they corresponded to the observations. Moreover, $${R^2}$$ was more sensitive to the use of cross-validation than $$d$$ or $$MIC$$, and more robust against overfitted models. Therefore, we discourage the use of statistical measures alternative to $${R^2}$$ for evaluating model predictions versus observed values, at least in the context of assessing the reliability of modelled biomass predictions using remote sensing. For those who consider $$d$$ to be conceptually superior to $${R^2}$$, we suggest using its square $${d^2}$$, in order to be more analogous to $${R^2}$$ and hence facilitate comparison across studies.

Standing dead trees (known as snags) are historically difficult to map and model using airborne laser scanning (ALS), or lidar. Specific snag characteristics are important for wildlife; for instance, a larger snag with a broken top can serve as a nesting platform for raptors. The objective of this study was to evaluate whether characteristics such as top intactness could be inferred from discrete-return ALS data. We collected structural information for 198 snags in closed-canopy conifer forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter point clouds to serve as predictor variables in random forest (RF) models to classify snags into four groups by size (small (

Electrified domestic hot water systems, being deferrable loads, are an important demand side management tool and thus have the potential to enhance photovoltaic self-consumption. This study addresses the energy and economic performance of photovoltaic self-consumption by using a typical Portuguese dwelling. Five system configurations were simulated: a gas boiler (with/without battery) and an electric boiler (without demand management and with genetic and heuristic optimization). A sensitivity analysis on photovoltaic capacity shows the optimum photovoltaic sizing to be in the range 1.0 to 2.5 kWp. The gas boiler scenario and the heuristic scenario present the best levelized cost of energy, respectively, for the lower and higher photovoltaic capacities. The use of a battery shows the highest levelized cost of energy and the heuristic scenario shows the highest solar fraction (56.9%). Results also highlight the great potential on increasing photovoltaic size when coupled with electrified domestic hot water systems, to accommodate higher solar fractions and achieve lower costs, through energy management.

Forest canopies exert significant controls over the spatial distribution of snow cover. Canopy snow interception efficiency is controlled by intrinsic processes (e.g., canopy structure), extrinsic processes (e.g., meteorological conditions), and the interaction of intrinsic-extrinsic factors (i.e., air temperature and branch stiffness). In hydrological models, intrinsic processes governing snow interception are typically represented by two-dimensional metrics like the leaf area index (LAI). To improve snow interception estimates and their scalability, new approaches are needed for better characterizing the three-dimensional distribution of canopy elements. Airborne laser scanning (ALS) provides a potential means of achieving this, with recent research focused on using ALS-derived metrics that describe forest spacing to predict interception storage. A wide range of canopy structural metrics that describe individual trees can also be extracted from ALS, although relatively little is known about which of them, and in what combination, best describes intrinsic canopy properties known to affect snow interception. The overarching goal of this study was to identify important ALS-derived canopy structural metrics that could help to further improve our ability to characterize intrinsic factors affecting snow interception. Specifically, we sought to determine how much variance in canopy intercepted snow volume can be explained by ALS-derived crown metrics, and what suite of existing and novel crown metrics most strongly affects canopy intercepted snow volume. To achieve this, we first used terrestrial laser scanning (TLS) to quantify snow interception on 14 trees. We then used these snow interception measurements to fit a random forest model with ALS-derived crown metrics as predictors. Next, we bootstrapped 1000 calculations of variable importance (percent increase in mean squared error when a given explanatory variable is removed), keeping nine canopy metrics for the final model that exceeded a variable importance threshold of 0.2. ALS-derived canopy metrics describing intrinsic tree structure explained approximately two-thirds of the snow interception variability (R2 ≥ 0.65, RMSE ≤ 0.52 m3, relative RMSE ≤ 48%) in our study when extrinsic factors were kept as constant as possible. For comparison, a generalized linear mixed-effects model predicting snow interception volume from LAI alone had a marginal R2 = 0.01. The three most important predictor variables were canopy length, whole-tree volume, and unobstructed returns (a novel metric). These results suggest that a suite of intrinsic variables may be used to map interception potential across larger areas and provide an improvement to interception estimates based on LAI.

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

The addition of alkaline and high-cation exchange capacity (CEC) biochars is a suitable strategy to increase the CEC of weathered soils. The aim of this study was to evaluate the effect of biochar from different feedstocks and pyrolysis temperatures on the CEC of two contrasting Oxisols. Biochars produced from chicken manure (CM), eucalyptus sawdust (ES), coffee husk (CH) and sugarcane bagasse (SB),plus a control (without biochar), at 350, 450, and 750 °C were mixed with the soils at 2; 5; 10 and 20% (w/w) and incubated for 9 months. Feedstock, pyrolysis temperature and addition rate of biochar were key factors controlling the alteration of soil CEC. The CH biochar pyrolyzed at 350 °C was the most effective matrix at increasing soil CEC. In a rate-dependent way, ES and SB biochars increased C contents of both soils without improving soil CEC. The efficiency of high-ash biochars in enhancing soil CEC in both Oxisols was limited by the alkalization caused by high rates of CH and CM biochars. The increase in CEC is soil-dependent and modulated by high-ash biochar CEC and application rate, as well as by the original soil CEC.

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m2). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior.

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.

Aim of study: In this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands. Area of study: Two commercial forest stands were selected, with two plots each. The forest species studied were Eucalyptus spp. and Pinus taeda L. and the trees had an age of 1.5 years. Material and methods: The individual trees were detected based on watershed segmentation and local maxima, using the spectral values stored in the point cloud. After the tree detection, the heights were calculated using two approaches, in the first one using the Digital Surface Model (DSM) and a Digital Terrain Model, and in the second using only the DSM. We used the UAV-derived heights to estimate an uniformity index. Main results: The trees were detected with a maximum 6% of error. However, the height was underestimated in all cases, in an average of 1 and 0.7 m for Pinus and Eucalyptus stands. We proposed to use the models built herein to estimate tree height, but the regression models did not explain the variably within the data satisfactorily. Therefore, the uniformity index calculated using the direct UAV-height values presented results close to the field inventory, reaching better results when using the second height approach (error ranging 2.8-7.8%). Research highlights: The uniformity index using the UAV-derived height from the proposed methods was close to the values obtained in field. We noted the potential for using UAV imagery in forest monitoring.

Summary: Climate change, geopolitical tensions, and decarbonization targets are bringing the resilience of the European electric power system to the forefront of discussion. Among various regulatory and technological solutions, voluntary demand response can help balance generation and demand during periods of energy scarcity or renewable energy generation surplus. This work presents an open data service called Interoperable Recommender that leverages publicly accessible data to calculate a country-specific operational balancing risk, providing actionable recommendations to empower citizens toward adaptive energy consumption, considering interconnections and local grid constraints. Using semantic interoperability, it enables third-party services to enhance energy management and customize applications to consumers. Real-world pilots in Portugal, Greece, and Croatia with over 300 consumers demonstrated the effectiveness of providing signals across diverse contexts. For instance, in Portugal, 7% of the hours included actionable recommendations, and metering data revealed a consumption decrease of 4% during periods when consumers were requested to lower consumption.

ABSTRACT Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

In the present study we investigated the participation of brain-derived neurotropic factor (BDNF) on the activation of the mitogen activated protein kinase (MAPK) protein extracellular signal-regulated kinase-1/2 (ERK1/2) as a mechanism of curcumin (CUR) to provide an antioxidant defense system mediated by the nuclear factor erythroid 2-related factor 2 (Nrf2) in the neurotoxic model induced by quinolinic acid (QUIN). Wistar rats received CUR (400 mg/kg, intragastrically) for 6 days after intrastriatal injection with QUIN (240 nmol). CUR improved the motor deficit and morphological alterations induced by QUIN and restored BDNF, ERK1/2, and Nrf2 levels. CUR treatment avoided the decrease in the protein levels of glutathione peroxidase (GPx), glutathione reductase (GR), γ-glutamylcysteine ligase (γ-GCL), and glutathione (GSH) levels. Only, the QUIN-induced decrease in the GR activity was prevented by CUR treatment. Finally, QUIN increased superoxide dismutase 2 (SOD2) and catalase (CAT) levels, and the γGCL and CAT activities; however, this increase was major in the QUIN+CUR group for γ-GCL, CAT, and SOD activities. These data suggest that the therapeutic effect of CUR could involve BDNF action on the activation of ERK1/2 to induce increased levels of protein and enzyme activity of antioxidant proteins regulated by Nrf2 and GSH levels.

Stroke is a public health problem due to its high mortality and disability rates; despite these, the pharmacological treatments are limited. Oxidative stress plays an important role in cerebral damage in stroke and the activation of the nuclear factor erythroid 2-related factor 2 (Nrf2) confers protection against oxidative stress. Different compounds, such as diallyl trisulfide (DATS), have the ability to activate Nrf2. DATS protects against the damage induced in oxygen-glucose deprivation in neuronal cells; however, in in vivo models of cerebral ischemia, DATS has not been evaluated. Male Wistar rats were subjected to 1 h of ischemia and seven days of reperfusion and the protective effect of DATS was evaluated. DATS administration (IR + DATS) decreased the infarct area and brain damage in the striatum and cortex; improved neurological function; decreased malondialdehyde and metalloproteinase-9 levels; increased Nrf2 activation in the cortex and the expression of superoxide dismutase 1 (SOD1) in the nucleus, SOD2 and glutathione S-transferase (GST) in the striatum and cortex; and increased the activity of catalase (CAT) in the striatum and glutathione peroxidase (GPx) in the cortex. Our results demonstrate the protective effect of DATS in an in vivo model of cerebral ischemia that involves Nrf2 activation.

In light of the potential correlation between hip alterations and the development of patellar luxation, as well as the challenge in selecting the ideal surgical technique for each patient, the aim is to report the treatment of a 17 kg, 1-year-old, female dog of undefined breed, presenting with grade III patellar luxation, patella alta, and hip subluxation. The patient experienced clinical improvement following the implementation of several surgical techniques. An ilio-trochanteric suture was employed to stabilize the hip joint. Due to significant contracture of the rectus femoris muscle observed during the coxal approach, which impeded proper patellar positioning, a rectus femoris tenotomy was also performed. This intervention resulted in an immediate reduction in the degree of patellar luxation from III to II. Additionally, modified trochleoplasty was performed to deepen the groove. We concluded that the ilio-trochanteric suture, in conjunction with rectus femoris tenotomy, contributed to partial alignment of the quadriceps femoris muscles, enhancing stability of the hip joint and resulting in a one-degree reduction in patellar luxation. Modified trochleoplasty stabilized the patella alta. This study reinforces the correlation between hip joint positioning and the probable etiology of patellar dislocation.

This paper presents a joining by plastic deformation process for fabricating hybrid electrical busbars made from copper and aluminum sheets. The process comprises the innovative use of wire-arc additive manufacturing to deposit copper rivets at specific locations on the copper sheets, the machining of slots with the required geometry in the aluminum sheets, and the compression of the copper sheets to force the rivets into the pre-machined slots of the aluminum sheets, creating form-closed mechanical interlocks. The work combines experimentation and finite element modeling to analyze the influence of the most significant process parameters, and the electrical performance of the hybrid busbar joints is evaluated and compared to that of conventional fastened joints at different service temperatures. Results demonstrate the effectiveness and potential advantages of the new joining by plastic deformation process for fabricating hybrid electrical busbars.

Organochlorine pesticides have been widely used in agriculture to control agricultural pests. Although effective in controlling pests, organochlorine pesticides present numerous hazards to ecosystems and human health due to their persistence in the environment, bioaccumulation, and toxicity. Scientific studies have shown that organochlorines may be associated with endocrine and neurological problems. Several strategies have been developed to apply treatment techniques to remove pesticides from various ecosystems, both aquatic and terrestrial. Physicochemical and biological methods have revealed other potentialities for remediation of aqueous environments contaminated with organochlorine pesticides. In addition, combined processes using different approaches have been highlighted as efficient alternatives to mitigate the impacts of agrochemicals on the environment, e.g., physical technique followed by a biological process. However, there are still numerous gaps that need to be explored and elucidated. Therefore, this review addressed the impacts of organochlorine pesticides on ecosystems and some treatment techniques used to remove agrochemicals from water. Furthermore, new findings, technological perspectives, and opportunities on this subject were presented and discussed.

O uso agrícola do composto de lixo (CL) nas lavouras constitui a alternativa econômica e ambiental viável, por ser fonte de nutrientes e matéria orgânica. Este estudo foi conduzido em casa de vegetação e teve o objetivo de avaliar a disponibilidade temporal de micronutrientes (Cu, Fe, Mn e Zn) por Mehlich-1, em cinco solos (Latossolo Vermelho-Amarelo distrófico: LVd; Argissolo Vermelho-Amarelo distrófico: PVd; Chernossolo Haplico Órtico: MXo; Planossolo Hidromórfico distrófico: SGd, e Nitossolo Vermelho Eutroférrico: Nvef) incubados com diferentes doses de CL (0; 25; 50 e 100 Mg ha-1), em períodos de incubação (0; 16; 32; 64 e 150 dias). A maioria dos metais pesados teve sua disponibilidade reduzida nos primeiros períodos de incubação, sendo esse efeito dependente de textura, pH e teor de matéria orgânica do solo. O uso continuado de CL, principalmente nas doses mais elevadas, pode resultar em riscos de contaminação do ambiente pelo efeito cumulativo, em especial para o Mn e Zn nos solos Chernossolo e Nitossolo, cujo tempo de decaimento de sua disponibilidade é acima de seis meses.The agricultural use of urban waste compost (WC) is an alternative for a better environmental-economic viability, because this organic residue is a source of nutrients and organic matter. This study was carried out in a greenhouse at the Brazilian Agricultural Research Corporation/National Soil Research Center. It had the objective of evaluating the effect of WC doses (0; 25; 50 and 100 Mg ha-1) and incubation periods (16; 32; 64 and 150 days) on micronutrients availability Cu, Fe, Mn and Zn by Mehlich-1, in Rio de Janeiro and Sao Paulo soils (Red-yellow Latosol, Red-yellow Podzol, Chernossolo Haplico Órtico, Planosol and Red Dusky Podzol). Most of the micronutrients had their availability reduced at the beginning of incubation, which was dependent of the texture, pH and organic matter content in the soil. Continued use of WC, mainly in the highest doses, and especially for the Mn and Zn, can result in risks of soil contamination, in periods more than 6 months with reduction of availability of the elements.