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Climate neutrality within European Union (EU) boundaries by 2050 is an ambitious goal set by the European Commission, with carbon sequestration in soils a key mechanism identified to achieve this. While row crops predominate, around 1.3 million hectares (ha) of land in the EU are dedicated to orchard fruit trees. Italy is second only to Spain in total citrus production area (about 22 %) with revenues of more than half a billion dollars and a planting area of nearly 90,000 ha in Sicily alone, the main orange-producing area in Italy, accounting for approximately 63 % of the total production of the country. Despite their environmental and economic importance, soil carbon studies in these systems are extremely rare. In our 13-year study, we compared the carbon balance (CB) of orange groves that were managed using either conventional (CON) or regenerative (REG) practices. We adopted the SALUS-crop model to determine soil organic carbon (SOC) sequestration and the Carbon Footprint (CF) methodology set by ISO standards to calculate greenhouse gas (GHG) emissions. The CB in the CON groves was 5.44 Mg CO2-eq ha−1 yr−1 (SOC sequestration lower than GHG emissions) while in the REG groves it was −0.61 Mg CO2-eq ha−1 yr−1 (SOC sequestration greater than GHG emissions). Specifically, CON loses SOC at 1.23 Mg CO2-eq ha−1 yr−1 while REG sequesters SOC at 1.68 Mg CO2-eq ha−1 yr−1. Our study shows that regenerative management in orange groves can convert the soil from a carbon source to a carbon sink, and that the CON and REG systems act as an overall GHG emissions source and GHG emissions sink, respectively. Increased soil carbon sequestration in citrus groves will contribute to the EU climate neutrality goal and can offer opportunities for farmers to benefit financially for their improved land stewardship.

Os clubes de Ciências são espaços dedicados a desenvolverem o conhecimento científico auxiliando os clubistas na compreensão e na interpretação do mundo à sua volta de uma maneira reflexiva, não aceitando as informações como verdade absoluta, mas assimilando-as e formando uma visão crítica sobre elas. O presente artigo objetivou investigar o panorama atual dos clubes de Ciências localizados no território brasileiro, verificando quais estão ativos ou não, bem como analisar as suas propostas de atividades. A metodologia da pesquisa foi qualitativa, caracterizando-se como um estudo exploratório e descritivo. Para a coleta dos dados foram feitas buscas em plataformas digitais (Instagram, Facebook, TikTok, YouTube e Google), utilizando o termo “clube de Ciências”. Os resultados revelaram que, dos 1.085 resultados de busca encontrados e cadastrados com o nome “clubes de Ciências”, apenas 281 referiam-se efetivamente a um clube de Ciências. Dentre os estados com maior representatividade estão São Paulo, Pará e Santa Catarina. As plataformas digitais mais utilizadas são o YouTube, Google e o Instagram. Dos 44 retornos das mensagens enviadas, 30 clubes estão ativos, 7 estão com as atividades suspensas e 7 foram temporariamente para o meio remoto. Além disso, identificou-se que a maioria dos clubes de Ciência desenvolve propostas predominantemente de cunho demonstrativo, não demonstrando a presença de atividades de iniciação científica. Portanto, destaca-se a relevância da implantação de clubes de Ciências e uma melhor exploração dos recursos digitais, buscando maior destaque para a socialização das propostas realizadas, a fim de colaborarem com a propagação do conhecimento científico.

(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients’ age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland–Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1–R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1–R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients.

Objective To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP). Materials and Methods We analyzed electronic health records from birth admissions in the Northeast United States in 2017. We annotated 1771 clinical notes to generate the initial gold standard dataset. Annotators labeled for exemplars of 5 stigmatizing and 1 positive/preferred language categories. We used a semantic similarity-based search approach to expand the initial dataset by adding additional exemplars, composing an enhanced dataset. We employed traditional classifiers (Support Vector Machine, Decision Trees, and Random Forest) and a transformer-based model, ClinicalBERT (Bidirectional Encoder Representations from Transformers) and BERT base. Models were trained and validated on initial and enhanced datasets and were tested on enhanced testing dataset. Results In the initial dataset, we annotated 963 exemplars as stigmatizing or positive/preferred. The most frequently identified category was marginalized language/identities (n  = 397, 41%), and the least frequent was questioning patient credibility (n  = 51, 5%). After employing a semantic similarity-based search approach, 502 additional exemplars were added, increasing the number of low-frequency categories. All NLP models also showed improved performance, with Decision Trees demonstrating the greatest improvement (21%). ClinicalBERT outperformed other models, with the highest average F1-score of 0.78. Discussion Clinical BERT seems to most effectively capture the nuanced and context-dependent stigmatizing language found in obstetric clinical notes, demonstrating its potential clinical applications for real-time monitoring and alerts to prevent usages of stigmatizing language use and reduce healthcare bias. Future research should explore stigmatizing language in diverse geographic locations and clinical settings to further contribute to high-quality and equitable perinatal care. Conclusion ClinicalBERT effectively captures the nuanced stigmatizing language in obstetric clinical notes. Our semantic similarity-based search approach to rapidly extract additional exemplars enhanced the performances while reducing the need for labor-intensive annotation. [ABSTRACT FROM AUTHOR]

In anticipation of climate changes, strategic soil management, encompassing reduced tillage and optimized crop residue utilization, emerges as a pivotal strategy for climate impact mitigation. Evaluating the transition from conventional to conservative cropping systems, especially the equilibrium shift in the medium to long term, is essential. ARMOSA, a robust crop simulation model, adeptly responds to varied soil management practices such as no tillage, minimum tillage, and specific straw management options such as chopping and incorporating crop residue into the soil (with or without prior nitrogen and water addition before ploughing). It effectively captures dynamic fluctuations in total organic carbon over an extended period. While challenges persist in precisely predicting grain yield due to climatic intricacies, ARMOSA stands out as a valuable and versatile tool. The model excels in comprehending and simulating wheat cultivar responses in dynamic agricultural ecosystems, shedding light on phenological patterns, biomass accumulation, and soil organic carbon dynamics. This research significantly advances our understanding of the intricate complexities associated with past wheat cultivation in diverse environmental conditions. ARMOSA’s ability to inform decisions on conservation practices positions it as a valuable asset for researchers, agronomists, and policymakers navigating the challenges of sustainable agriculture amidst climate change. Its real-world significance lies in its potential to guide informed decisions, contributing to global efforts in sustainable agriculture and climate resilience.

IntroductionStrokes leave around 40% of survivors dependent in their activities of daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown to be efficiency for improving motor recovery after stroke, but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified (e.g., sensors and signal processing), fully optimized BCIs are pointless if patients and clinicians cannot or do not want to use them. We hypothesize that improving BCI acceptability will reduce patients' anxiety levels, while increasing their motivation and engagement in the procedure, thereby favoring learning, ultimately, and motor recovery. In other terms, acceptability could be used as a lever to improve BCI efficiency. Yet, studies on BCI based on acceptability/acceptance literature are missing. Thus, our goal was to model BCI acceptability in the context of motor rehabilitation after stroke, and to identify its determinants.MethodsThe main outcomes of this paper are the following: i) we designed the first model of acceptability of BCIs for motor rehabilitation after stroke, ii) we created a questionnaire to assess acceptability based on that model and distributed it on a sample representative of the general public in France (N = 753, this high response rate strengthens the reliability of our results), iii) we validated the structure of this model and iv) quantified the impact of the different factors on this population.ResultsResults show that BCIs are associated with high levels of acceptability in the context of motor rehabilitation after stroke and that the intention to use them in that context is mainly driven by the perceived usefulness of the system. In addition, providing people with clear information regarding BCI functioning and scientific relevance had a positive influence on acceptability factors and behavioral intention.DiscussionWith this paper we propose a basis (model) and a methodology that could be adapted in the future in order to study and compare the results obtained with: i) different stakeholders, i.e., patients and caregivers; ii) different populations of different cultures around the world; and iii) different targets, i.e., other clinical and non-clinical BCI applications.

The southern Alpine regions were affected by several magmatic and volcanic events between the Paleozoic and the Tertiary. This activity undoubtedly had an important effect on the density distribution and structural setting at lithosphere scale. Here the gravity field has been used to create a 3D lithosphere density model on the basis of a high-resolution seismic tomography model. The results of the gravity modeling demonstrate a highly complex density distribution in good agreement with the different geological domains of the Alpine area represented by the European Plate, the Adriatic Plate and the Tyrrhenian basin. The Adriatic-derived terrains (Southalpine and Austroalpine) of the Alps are typically denser (2850 kg m−3), whilst the Alpine zone, composed of terrains of European provenance (Helvetic and Tauern Window), presents lower density values (2750 kg m−3). Inside the Southalpine, south of the Dolomites, a well-known positive gravity anomaly is present, and one of the aims of this work was to investigate the source of this anomaly that has not yet been explained. The modeled density suggests that the anomaly is related to two different sources; the first involves the middle crust below the gravity anomaly and is represented by localized mushroom-shaped bodies interpreted as magmatic intrusions, while a second wider density anomaly affects the lower crust of the southern Alpine realm and could correspond to a mafic and ultramafic magmatic underplating (gabbros and related cumulates) developed during Paleozoic extension.

The ripening of climacteric fruits, such as apple, is represented by a series of genetically programmed events orchestrated by the action of several hormones. In this study, we investigated the existence of a hormonal crosstalk between ethylene and auxin during the post-harvest ripening of three internationally known apple cultivars: 'Golden Delicious', 'Granny Smith' and 'Fuji'. The normal climacteric ripening was impaired by the exogenous application of 1-methylcyclopropene (1-MCP) that affected the production of ethylene and the physiological behaviour of specific ethylene-related quality traits, such as fruit texture and the production of volatile organic compounds. The application of 1-MCP induced, moreover, a de-novo accumulation of auxin. The RNA-Seq wide-transcriptome analysis evidenced as the competition at the level of the ethylene receptors induced a cultivar-dependent transcriptional re-programming. The DEGs annotation carried out through the KEGG database identified as most genes were assigned to the plant hormone signaling transduction category, and specifically related to auxin and ethylene. The interplay between these two hormones was further assessed through a candidate gene analysis that highlighted a specific activation of GH3 and ILL genes, encoding key steps in the process of the auxin homeostasis mechanism. Our results showed that a compromised ethylene metabolism at the onset of the climacteric ripening in apple can stimulate, in a cultivar-dependent fashion, an initial de-novo synthesis and de-conjugation of auxin as a tentative to restore a normal ripening progression.

To assess carbon sequestration in the agricultural and natural systems, it is usually required to report soil organic carbon (SOC) as mass per unit area (Mg ha-1) for a single soil layer (e.g., the 0-0.3 m ploughing layer). However, if the SOC data are reported as relative concentration (g kg-1 or %), it is required to compute the SOC stock and its standard deviation (SD) for a given layer as the product of SOC concentration and bulk density (BD). For a proper computation, it is required to consider that these two variables are correlated. Moreover, if the data are already reported as SOC stock for multiple sub-layers (e.g., 0-0.15 m, 0.15-0.3 m) it is necessary to compute the SOC stock and its SD for a single soil layer (e.g., 0-0.3 m). The correlation between stocks values from adjacent and non-adjacent soil sub-layers must be accounted to compute the SD of the single soil layer.The present work illustrates the methodology to compute SOC stock and its SD for a single soil layer based on SOC concentration and BD also from multiple sub-layers. An Excel workbook automatically computes the means of stocks and SD saving the results in a ready-to-use database. • Computation of a carbon (SOC) stock and its standard deviation (SD) from the product between SOC concentration and bulk density (BD), being correlated variables. • Computation of a SOC stock and its SD from the sum of SOC stocks of multiple correlated sub-layers. • An Excel workbook automatically computes the means of SOC stocks and SD and saves the results in a ready-to-use database.

Pears: Genes linked to fruit maturation and ripening Numerous genes, including many involved in the production and signaling of key growth hormones, regulate the on-tree maturation and postharvest ripening of pear fruit. Fabrizio Costa from the Edmund Mach Foundation in San Michele All’adige, Italy, and colleagues identified nearly 9,000 genes that were differentially expressed across four maturation stages of the Abate Fetel cultivar of European pear (Pyrus communis). From this long list of genes, the researchers selected 12 involved in hormone signaling, cell wall metabolism or aromatic volatile compound production, and evaluated their expression pattern with and without application of a chemical added to delay ripening during postharvest storage. Six of the genes, five of which controlled the activity of the hormones auxin and ethylene, showed differential expression, and could be informative as molecular biomarkers for improving fruit quality in pears.