Nature-based solutions are critical for putting Brazil on track towards net-zero emissions by 2050.

Most of the world's nations (around 130) have committed to reaching net-zero carbon dioxide or greenhouse gas (GHG) emissions by 2050, yet robust policies rarely underpin these ambitions. To investigate whether existing and expected national policies will allow Brazil to meet its net-zero GHG emissions pledge by 2050, we applied a detailed regional integrated assessment modelling approach. This included quantifying the role of nature-based solutions, such as the protection and restoration of ecosystems, and engineered solutions, such as bioenergy with carbon capture and storage. Our results highlight ecosystem protection as the most critical cost-effective climate mitigation measure for Brazil, whereas relying heavily on costly and not-mature-yet engineered solutions will jeopardise Brazil's chances of achieving its net-zero pledge by mid-century. We show that the full implementation of Brazil's Forest Code (FC), a key policy for emission reduction in Brazil, would be enough for the country to achieve its short-term climate targets up to 2030. However, it would reduce the gap to net-zero GHG emissions by 38% by 2050. The FC, combined with zero legal deforestation and additional large-scale ecosystem restoration, would reduce this gap by 62% by mid-century, keeping Brazil on a clear path towards net-zero GHG emissions by around 2040. While some level of deployment of negative emissions technologies will be needed for Brazil to achieve and sustain its net-zero pledge, we show that the more mitigation measures from the land-use sector, the less costly engineered solutions from the energy sector will be required. Our analysis underlines the urgent need for Brazil to go beyond existing policies to help fight climate emergency, to align its short- and long-term climate targets, and to build climate resilience while curbing biodiversity loss.

Computational methods of EEG signals analysis for Alzheimer's disease classification.

Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.

Quantile graphs for EEG-based diagnosis of Alzheimer's disease.

Known as a degenerative and progressive dementia, Alzheimer's disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here-clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index-showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients' scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.

The impact of climate change on Brazil's agriculture.

Brazilian agricultural production provides a significant fraction of the food consumed globally, with the country among the top exporters of soybeans, sugar, and beef. However, current advances in Brazilian agriculture can be directly impacted by climate change and resulting biophysical effects. Here, we quantify these impacts until 2050 using GLOBIOM-Brazil, a global partial equilibrium model of the competition for land use between agriculture, forestry, and bioenergy that includes various refinements reflecting Brazil's specificities. For the first time, projections of future agricultural areas and production are based on future crop yields provided by two Global Gridded Crop Models (EPIC and LPJmL). The climate change forcing is included through changes in climatic variables projected by five Global Climate Models in two emission pathways (RCP2.6 and RCP8.5) participating in the ISIMIP initiative. This ensemble of twenty scenarios permits accessing the robustness of the results. When compared to the baseline scenario, GLOBIOM-Brazil scenarios suggest a decrease in soybeans and corn production, mainly in the Matopiba region in the Northern Cerrado, and southward displacement of agricultural production to near-subtropical and subtropical regions of the Cerrado and the Atlantic Forest biomes.

Expanding the Soy Moratorium to Brazil's Cerrado.

The Cerrado biome in Brazil is a tropical savanna and an important global biodiversity hot spot. Today, only a fraction of its original area remains undisturbed, and this habitat is at risk of conversion to agriculture, especially to soybeans. Here, we present the first quantitative analysis of expanding the Soy Moratorium (SoyM) from the Brazilian Amazon to the Cerrado biome. The SoyM expansion to the Cerrado would prevent the direct conversion of 3.6 million ha of native vegetation to soybeans by 2050. Nationally, this would require a reduction in soybean area of approximately 2%. Relative risk of future native vegetation conversion for soybeans would be driven by the Brazilian domestic market, China, and the European Union. We conclude that, to preserve the Cerrado's biodiversity and ecosystem services, urgent action is required, including a zero native vegetation conversion agreement such as the SoyM.

The q-G method : A q-version of the Steepest Descent method for global optimization.

In this work, the q-Gradient (q-G) method, a q-version of the Steepest Descent method, is presented. The main idea behind the q-G method is the use of the negative of the q-gradient vector of the objective function as the search direction. The q-gradient vector, or simply the q-gradient, is a generalization of the classical gradient vector based on the concept of Jackson's derivative from the q-calculus. Its use provides the algorithm an effective mechanism for escaping from local minima. The q-G method reduces to the Steepest Descent method when the parameter q tends to 1. The algorithm has three free parameters and it is implemented so that the search process gradually shifts from global exploration in the beginning to local exploitation in the end. We evaluated the q-G method on 34 test functions, and compared its performance with 34 optimization algorithms, including derivative-free algorithms and the Steepest Descent method. Our results show that the q-G method is competitive and has a great potential for solving multimodal optimization problems.

Forest structure and species composition along a successional gradient of Lowland Atlantic Forest in Southern Brazil / Estrutura e composição ao longo de um gradiente sucessional de Floresta de Terras Baixas no sul do Brasil

The Lowland Forest is one of the most disturbed and fragile ecosystems in the Atlantic Forest biome, yet little is known regarding its successional trajectory and resilience. We evaluated changes in species assemblages and forest structure of the canopy and understory along a successional gradient (young 21-yrs old forest, immature 34-yrs old forest and late successional 59-yrs old forest) aiming to assess changes in species composition and successional trajectory of different strata of secondary forests. A 0.1 ha plot (ten 10x10 m sub-plots) from each forest stand was surveyed for trees and shrubs with a diameter at breast height (DBH) ≥ 4.8 cm (canopy) and for individuals with heights ≥ 1 m and DBH < 4.8 cm (understory). A total of 3,619 individuals from 82 plant species were sampled. The successional gradient was marked by a unidirectional increase in species richness and a bidirectional pattern of density changes (increasing from young to immature forest and decreasing from immature to late successional forest). Community assemblages were distinct in the three forests and two strata; indicator species were only weakly shared among stands. Thus, each successional forest and stratum was observed to be a unique plant community. Our results suggest slight predictability of community assemblages in secondary forests, but a relatively fast recovery of forest structure.

As Florestas de Terras Baixas constituem um dos ecossistemas mais perturbados e frágeis no bioma Mata Atlântica, mas ainda pouco se sabe sobre sua trajetória sucessional e resiliência. Foram avaliadas alterações na composição de espécies e a estrutura florestal do dossel e sub-bosque ao longo de um gradiente sucessional (floresta jovem-21 anos, floresta imatura-34 anos, floresta madura-59 anos) com o objetivo de verificar as mudanças na composição de espécies e a trajetória sucessional de diferentes estratos destas florestas secundárias. Uma parcela de 0,1 ha (dez sub-parcelas de 10x10 m) foi estabelecida em cada floresta, amostrando-se árvores e arbustos com um diâmetro è altura do peito (DAP) ≥ 4,8 cm (dossel) e para indivíduos com altura > 1 m e DAP < 4,8 cm (sub-bosque). Um total de 3.619 indivíduos de 82 espécies de plantas foram amostrados. O gradiente sucessional foi marcado por um aumento unidirecional na riqueza de espécies com o tempo, e um padrão bidirecional de mudanças de densidade (aumentando da floresta jovem para a imatura e diminuindo da imatura para a madura). As assembléias de plantas eram distintas nas três florestas e nos dois estratos; espécies indicadoras foram pouco compartilhadas entre as florestas. Portanto, cada estádio da cronosequência e cada estrato representam uma comunidade única de plantas. Nossos resultados sugerem pouca previsibilidade das assembleias de plantas destas florestas secundárias, mas uma recuperação relativamente rápida da estrutura da floresta.

Duality between time series and networks.

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways.