Universidade Federal do Oeste do Pará

Advances in connectomics and the characterization of neuronal diversity have been fundamental to understanding how the brain works. Defining a taxonomy is still challenging and requires complex computational methods. In this paper, we present a systematic review of the state-of-the-art methods for neuronal morphology classification, shedding light on trends and future directions. We address three core research questions: (1) What computational methods are used to classify neuronal morphologies? (2) What kind of representation do the classification models work on? (3) How are the classification models evaluated? We applied a structured protocol including peer-reviewed studies published since 2018. We collected 840 papers from five databases, published between 2018 and 2024. We assessed risk of bias using predefined quality criteria on study design, methodology, and reporting; 35 studies met these standards and were included to extract information and consolidate the results. The results show that Artificial Neural Networks are currently the main method for classifying neurons based on morphometric features (21 papers). Representations based on microscopy images for extracting morphometric features (30) are commonly used. Accuracy (29) is the predominant evaluation measure. Our research findings contribute to the body of knowledge by providing an up-to-date perspective on neuronal classification, which can guide future research in neuroscience and related fields.

The Amazon rainforest stores ~150–200 Pg of carbon and plays a central role in the global carbon cycle, yet it is highly vulnerable to land-use change, fire, and climate change, leaving its future carbon balance uncertain. Net Ecosystem Exchange (NEE) quantifies the balance between ecosystem carbon uptake and release, but long-term NEE assessments in Central Amazonia remain scarce due to limited observational coverage from eddy covariance towers and CO₂ profile measurements. The Amazon Tall Tower Observatory (ATTO; https://www.attoproject.org) helps overcome this limitation by providing more than a decade of continuous measurements and by capturing two extreme drought events (2015/2016 and 2023/2024). In this work, we analyzed 11 years of NEE estimates at ATTO (2014-2024) and we propose a methodology for selecting the friction velocity threshold (u*), based on the identification of a plateau in the NEE-u* relationship, where NEE becomes independent of increasing turbulence, and all periods with u below the threshold are filtered due to insufficient turbulence*. We estimate monthly u* thresholds ranging from 0.18 to 0.21 m s⁻¹. We found that, on average, the forest in the ATTO flux footprint generally acted as a net carbon sink. However, in years following severe droughts, such as 2016 and 2024, we detect a temporary reversal, with the ecosystem becoming a CO₂ source during the wet season. We also quantified the effect of environmental drivers modulating NEE across seasons. We find that higher air temperature reduces carbon uptake during the wet season. In contrast, soil moisture shows opposite relationships depending on season: during the dry season, increasing soil moisture (10 cm depth) reduces net carbon uptake, whereas during the wet season, increasing it enhances net carbon uptake. Our findings deliver critical observational evidence to refine model parameterizations of tropical carbon-water interactions and to reduce uncertainty in predictions of the Amazon carbon balance under future climate scenarios.

This study evaluated the effects of biostimulant treatments on the ionic composition and yield of ‘Formosa’ papaya (Carica papaya L.) subjected to varying water replacement levels. The research utilized a randomized complete block design in a split-plot scheme comprising three irrigation depths (100%, 75%, and 50% ETc) and four biological treatments: control, Trichoderma harzianum, Ascophyllum nodosum, and Bacillus aryabhattai. Contrary to initial expectations, water restriction was associated with increased yield and leaf concentrations of zinc (Zn) and nitrogen (N), challenging the hypothesis that water restriction limits production. Quantitative results showed that T. harzianum under 75% ETc attained 93.29 kg plant−1, whereas the control at 50% ETc recorded 19.14 g kg−1 of N. Under 50% ETc, B. aryabhattai increased the bacterial population to 10.46 log10 CFU g−1 soil compared to the control. The T. harzianum-based biostimulant reduced leaf sodium (Na) under 75% ETc and maintained the nutrient accumulation order K > N > Ca > Fe > Mn > Zn > Cu > Na. Conversely, B. aryabhattai and A. nodosum improved yield under 100% ETc through N accumulation. This study confirms that microbial and seaweed-based biostimulants mitigate water stress through rhizosphere modification and nutritional homeostasis, offering a practical strategy for sustainable fruit production in semi-arid regions.

O afilamento do fuste constitui uma variável essencial para o manejo e o aproveitamento racional da madeira, permitindo estimar os diâmetros ao longo do fuste e determinar os respectivos sortimentos. Este estudo teve como objetivo comparar o desempenho de diferentes abordagens estatísticas e de aprendizado de máquina na predição do afilamento de Tectona grandis L.f. em Santarém, Pará. Foram amostradas 30 árvores, totalizando 261 observações de diâmetro (di). Utilizaram-se como variáveis preditoras o diâmetro a 1,3 m de altura (dap), a altura comercial (hc), a altura total (ht) e a altura relativa (hi). As abordagens testadas foram o modelo de Kozak, a árvore de regressão (CART), o Random Forest (RF), a Support Vector Machine (SVM) e a Gradient Boosting Machine (GBM). A avaliação do desempenho foi realizada por meio da raiz do erro quadrático médio (RMSE), erro médio absoluto (MAE) e coeficiente de correlação linear para as etapas de treinamento e generalização. O algoritmo GBM apresentou os melhores resultados, com > 0,99 e RMSE de 0,45 cm, seguido pelo RF. O modelo de Kozak e a árvore de regressão apresentaram maior dispersão dos erros e menor capacidade de generalização. A análise dos erros relativos indicou que o GBM produziu predições mais precisas, sem viés sistemático. Conclui-se que as abordagens baseadas em aprendizado de máquina, especialmente o GBM, apresentam maior eficiência para modelar o afilamento do fuste de teca, superando modelos convencionais de regressão quanto à exatidão e à precisão preditiva. Palavras-chave: aprendizado de máquina; inteligência artificial; mensuração florestal. Predicting taper in Tectona grandis L.f. in the Eastern Amazon ABSTRACT: Stem taper is an essential variable for sustainable forest management and the rational use of timber, as it enables the estimation of diameters along the bole and the determination of log assortments. This study aimed to compare the performance of different statistical and machine learning approaches for predicting the stem taper of Tectona grandis L.f. in Santarém, Pará, Brazil. A total of 30 trees were sampled, yielding 261 diameter observations (di) along the stem. Diameter at breast height (dbh), commercial height (hc), total height (ht), and relative height (hi) were used as predictor variables. The tested approaches included the Kozak taper model, Classification and Regression Tree (CART), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM). Model performance was evaluated using the root mean square error (RMSE), mean absolute error (MAE), and the linear correlation coefficient ( ) for both training and generalization (testing) stages. The GBM algorithm achieved the best results, with > 0.99 and an RMSE of 0.45 cm, followed closely by RF. The Kozak model and CART exhibited greater error dispersion and lower generalization ability. The relative error analysis indicated that GBM provided the most accurate predictions, with no evidence of systematic bias. We conclude that machine learning approaches, particularly GBM, are more effective for modeling teak stem taper, outperforming conventional regression models in predictive accuracy and precision. Keywords: machine learning; artificial intelligence; forest mensuration.

The objective of this study was to evaluate the effects of hydrogel polymer application on the antioxidant activity and physiological performance of colored-fiber cotton cultivars grown under different levels of water restriction. Two experiments were conducted under greenhouse conditions. In the first experiment, the effects of the hydrogel polymer, cultivars, and irrigation replacement levels were evaluated; in the second, the residual effect of the hydrogel polymer applied in the first experiment was assessed using the same cultivars and irrigation depths. Water restriction negatively affected relative water content, gas exchange, chlorophyll a fluorescence, and antioxidant activity, and increased electrolyte leakage in cotton cultivars. Water deficit reduced relative water content, gas exchange, chlorophyll a fluorescence, and antioxidant activity, while increasing electrolyte leakage in the cultivars. However, hydrogel polymer application up to 6.5 g dm-3 of soil and its residual effect in subsequent cycles were beneficial. The polymer increased relative water content and antioxidant activity, in addition to improving gas exchange and chlorophyll fluorescence, suggesting maintenance of plant physiological health. Residual polymer doses also enhanced relative water content, antioxidant activity, gas exchange, and chlorophyll fluorescence in plants during Experiment II.

ABSTRACT Nymphs of several cicada species build clay towers before adult metamorphosis. However, tower function is poorly understood. Here, we show experimentally that towers of the Amazonian cicada Guyalna chlorogena reduce predation risk and display a size‐dependent growth response to gas exchange obstruction. These findings support them as adaptive extended phenotypes.

Noisy environments tend to mask sounds and disrupt animal behaviour. One of the most common responses from many animal species to noise is leaving or avoiding the noisiest parts of their ranges. Individuals may alter their movement patterns, either rapidly moving away or changing routes in response to increased noise levels. Here, we tested whether urban-dwelling pied tamarins (Saguinus bicolor): (1) preferentially used quieter areas within their home range, (2) increase travel speed, and (3) alter routes to avoid noise levels. To achieve this, we followed nine pied tamarin groups in five forest fragments within the city of Manaus, Brazil, recording their locations and levels of associated ambient noise levels. We also estimated fruit availability at random points within the group’s home ranges. We found that pied tamarins use the quieter areas of their ranges more intensively, but only if the fruit availability is high. Furthermore, groups did not increase their travel speed or change turning angles in response to noise alone, but travel speed increase in noisier areas closer to the border of their home ranges. Thus, pied tamarins do seem to use the quieter areas of their home ranges, but do not hurry to reach them unless their location lies closer to their home range borders. This may occur because higher noise levels at home-range borders could diminish intergroup communication effectiveness. In general, we found that pied tamarin’s use of space and movement patterns are soundscape-related but mediated by resource availability and social aspects.

Facing water scarcity and environmental contamination, a sustainable approach combining bioeconomy and circular economy principles has emerged: the use of onshore oil and gas produced water (PW) to irrigate Nopalea cochenillifera. This study evaluated the ability of Nopalea cochenillifera to phytoextract contaminants, focusing on translocation and bioaccumulation factors for the recovery of degraded soils. The experiment was conducted in a randomized block design with five treatments (T1: 100% supply water; T2: 75% supply water + 25% PW; T3: 50% supply water + 50% PW; T4: 25% supply water + 75% treated PW; T5: 100% PW) and five replicates in 20 L pots. After 240 days, plant and soil samples were analyzed for micronutrients (Cu2+, Mn2+, Fe2+, Zn2+ and Na+) and heavy metals (Cr, Ni, Cd and Pb). The highest median TF was observed for Mn in treatment T3 (10.55), while the highest median BF occurred for Cu in treatment T2 (10.852). Nopalea cochenillifera effectively translocated Mn, Zn, Ni, Cd, and Pb from roots to shoots and bioaccumulated all analyzed nutrients, particularly Cu, Mn, Fe, and Zn. PW irrigation altered elemental transport and intensified metals accumulation. Thus, Nopalea cochenillifera demonstrates strong phytoextraction potential for environmental remediation in semi-arid regions.