Vulnerability and marine resource-dependence in coastal and marine social-ecological systems

Vulnerability research in coastal and marine social-ecological systems (CM-SES) to date has focused primarily on conceptual analyses of exposure, sensitivity and adaptive capacity. Meanwhile the Sustainable Livelihoods Approach (SLA) has been utilized mainly in natural resource management or poverty alleviation strategies. The present thesis combines these two frameworks in order to investigate the relationships between marine resource dependence and vulnerability within the CM-SES, using the SLA as a point of departure for analysis. Using a case study approach, questionnaires, key informant interviews, focus groups and participant observation were carried out in two regions, Zanzibar, Tanzania and the Spermonde Archipelago, Indonesia

Analyzing Vision Transformers for Image Classification in Class Embedding Space

Despite the growing use of transformer models in computer vision, a mechanistic understanding of these networks is still needed. This work introduces a method to reverse-engineer Vision Transformers trained to solve image classification tasks. Inspired by previous research in NLP, we demonstrate how the inner representations at any level of the hierarchy can be projected onto the learned class embedding space to uncover how these networks build categorical representations for their predictions. We use our framework to show how image tokens develop class-specific representations that depend on attention mechanisms and contextual information, and give insights on how self-attention and MLP layers differentially contribute to this categorical composition. We additionally demonstrate that this method (1) can be used to determine the parts of an image that would be important for detecting the class of interest, and (2) exhibits significant advantages over traditional linear probing approaches. Taken together, our results position our proposed framework as a powerful tool for mechanistic interpretability and explainability research.Comment: NeurIPS 202

Opções de investimentos via mercado de capitais em cenário de redução de taxa de juros

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Uso de voltametria de pulso diferencial combinada com quimiometria para determinação simultânea de antioxidantes em amostras de biodiesel

O biodiesel vem se consolidando como combustível alternativo devido às suas vantagens e importância econômica. Conforme seu percentual no diesel comercial cresce, o monitoramento da qualidade se torna cada vez mais importante para uma comercialização segura. No entanto, a estabilidade oxidativa do biodiesel é inferior à do diesel fóssil, assim antioxidantes sintéticos, tais como BHA, BHT, PG e TBHQ, são adicionados para prevenir a degradação do mesmo, evitando danos ao sistema de combustão automotivo. Os métodos para avaliar a estabilidade oxidativa e a quantidade de antioxidantes são em geral demorados, requerem preparação amostral ou equipamentos de alto custo. Com o objetivo de contornar tais problemas, neste trabalho foi aplicada uma metodologia para análise direta de antioxidantes em amostras de biodiesel por meio de voltametria de pulso diferencial. Conjuntamente, foi estudada a viabilidade da associação dessa metodologia com técnicas quimiométricas para a determinação simultânea desses antioxidantes em misturas de antioxidantes em biodiesel. Para permitir uma análise direta, sem extração e pré-concentração dos analitos, o biodiesel foi diluído em meio etanólico. Medidas de voltametria de pulso diferencial para cada antioxidante individualmente mostraram relação linear entre as concentrações dos antioxidantes e a corrente de oxidação. Os limites de detecção individuais obtidos foram de 20,5 mg L-1 para BHA, 32,4 mg L-1 para BHT, 35,5 mg L-1 para PG e 26,5 mg L-1 para TBHQ. A modelagem quimiométrica foi aplicada por meio das ferramentas Mínimos Quadrados Clássico (CLS), Mínimos Quadrados Parciais (PLS), Redes Neuronais Artificiais (ANN), Componentes Principais-Redes Neuronais Artificiais (PC-ANN) e Árvore de Decisão-Redes Neuronais Artificiais (DT-ANN). O modelo construído por PLS se mostrou melhor quando comparado à modelagem por CLS. O modelo construído por ANN’s sem seleção de dados de entrada apresentou erros semelhantes ao PLS. Quando a redução da quantidade de dados de entrada foi aplicada em conjunto com ANN’s através de Análise por Componentes Principais (PCA) e DT, a aplicação de PCA levou a aumento de 10,2% no erro de predição, enquanto que na seleção por DT os erros de predição foram reduzidos em 8,5%. A determinação simultânea dos quatro compostos pelo modelo DT-ANN apresentou precisão satisfatória, com recuperação de 98% para BHA, 97% para BHT, 103% para PG e 100% para TBHQ, o que indica que a técnica analítica e a modelagem quimiométrica são viáveis e promissoras para aplicação no controle de qualidade do biodiesel, bem como em análises de monitoramento nas plantas industriais.Biodiesel is becoming established as an alternative fuel because its advantages and economic importance. As the levels of biodiesel in commercial diesel grows, quality monitoring becomes increasingly important for safe marketing. However, the oxidative stability of biodiesel is smaller than that of fossil diesel and synthetic antioxidants such as BHA, BHT, PG and TBHQ are added to it in order to prevent its degradation, avoiding damage to the automotive combustion system. Methods for evaluating oxidative stability and the amount of antioxidants are usually time-consuming, require sample preparation or expensive equipment. In order to overcome such problems, in this work a methodology was applied for direct analysis of antioxidants in biodiesel samples by means of differential pulse voltammetry. The viability of associating this methodology with chemometric techniques was studied for the simultaneous determination of these antioxidants in biodiesel. To allow a direct analysis, without extraction and preconcentration of analytes, biodiesel was diluted in ethanolic medium. Differential pulse voltammetric measurements for each antioxidant individually showed a linear relationship between antioxidant concentrations and oxidation current. The individual detection limits were 20,5 mg L-1 for BHA, 32,4 mg L-1 for BHT, 35,5 mg L-1 for PG and 26,5 mg L-1 for TBHQ. The chemometric modeling was applied using the Classical Least Squares (CLS), Partial Least Squares (PLS), Artificial Neural Networks (ANN), Principal Component-Artificial Neural Networks (PC-ANN) and Decision Tree-Artificial Neural Networks (DT-ANN) techniques. The model constructed by PLS was better than that obtained with CLS. The model constructed by ANN’s without input selection presented similar deviations in comparison to PLS. When amount of input data reduction was applied together with ANNs through Principal Component Analysis (PCA) and DT, the PCA application led to a 10.2% increase in prediction error, whereas in the selection by DT prediction errors were reduced by 8,5%. The simultaneous determination of the four compounds by the DT-ANN model presented satisfactory accuracy with 98% recovery for BHA, 97% for BHT, 103% for PG and 100% for TBHQ, indicating that the analytical technique and the chemometric modeling are feasible and promising for application in biodiesel quality control, as well as in monitoring analyzes in the industrial plants

Why is NanoSIMS elemental imaging of arsenic in seaweed (Laminaria digitata) important for understanding of arsenic biochemistry in addition of speciation information?

Acknowledgements The work was supported by funding from the French “Agence Nationale de la Recherche” via the project ANR-11-EQPX-0027 MARSS. E. E. thanks the EU Erasmus Programme for financial support.Peer reviewedPostprin

Net2Brain: a toolbox to compare artificial vision models with human brain responses

In cognitive neuroscience, the integration of deep neural networks (DNNs) with traditional neuroscientific analyses has significantly advanced our understanding of both biological neural processes and the functioning of DNNs. However, challenges remain in effectively comparing the representational spaces of artificial models and brain data, particularly due to the growing variety of models and the specific demands of neuroimaging research. To address these challenges, we present Net2Brain, a Python-based toolbox that provides an end-to-end pipeline for incorporating DNNs into neuroscience research, encompassing dataset download, a large selection of models, feature extraction, evaluation, and visualization. Net2Brain provides functionalities in four key areas. First, it offers access to over 600 DNNs trained on diverse tasks across multiple modalities, including vision, language, audio, and multimodal data, organized through a carefully structured taxonomy. Second, it provides a streamlined API for downloading and handling popular neuroscience datasets, such as the NSD and THINGS dataset, allowing researchers to easily access corresponding brain data. Third, Net2Brain facilitates a wide range of analysis options, including feature extraction, representational similarity analysis (RSA), and linear encoding, while also supporting advanced techniques like variance partitioning and searchlight analysis. Finally, the toolbox integrates seamlessly with other established open source libraries, enhancing interoperability and promoting collaborative research. By simplifying model selection, data processing, and evaluation, Net2Brain empowers researchers to conduct more robust, flexible, and reproducible investigations of the relationships between artificial and biological neural representations

When subcellular chemical imaging enlightens our understanding on intestinal absorption, intracellular fate and toxicity of PFOA in vitro.

peer reviewedPerfluorooctanoic acid (PFOA) is a persistent organic pollutant that accumulates in the human body, leading to major health issues. Upon oral uptake, the gastrointestinal tract is the first biological barrier against PFOA. However, the localization of PFOA and its impact on the intestinal wall are largely unknown. Here we achieve a breakthrough in the knowledge of intestinal absorption, intracellular fate and toxicity of PFOA using in vitro assays combined with novel analytical imaging techniques. For the first time, we localized PFOA in the cytosol of Caco-2 cells after acute exposure using high spatial resolution mass spectrometry imaging, and we estimated the PFOA cytosolic concentration. Knowing that PFOA enters and accumulates in the intestinal cells, we also performed common toxicity assays assessing cell metabolic activity, membrane integrity, oxidative stress response, and cell respiration. This study integrating powerful analytical techniques with widely used toxicology assays provides insightful information to better understand potential negative impacts of PFOA and opens new opportunities in toxicology and life science in general