Publicações
Autores
Financiador
Organizações

Pesquisar

Explore também...
 

Entradas recentes

  • Item type: Item , Access status: Acesso aberto ,
    Gold- or silver-nanoparticle SERS platforms for plasma-based diagnostics and AI-driven analysis
    (2026) Elizur, G. L.; Canhoto, A.; Soares, G.; Ferreira, L. S.; Pereira, E.; Franco, R.
    Surface-enhanced Raman spectroscopy (SERS) has emerged as a highly promising analytical technique for disease diagnostics due to its exceptional sensitivity, molecular specificity, and ability to detect a broad range of biomarkers in complex biological matrices. This review provides a comprehensive overview of gold- and silver-nanoparticle-based SERS platforms for plasma disease diagnostics, covering advances in plasmonic nanostructures, biological sample analysis, biomarker detection, and AI-driven spectral data processing. Particular emphasis is placed on the application of SERS to clinically relevant biofluids, especially plasma, where the technique has demonstrated considerable potential for detecting diseases such as cancer, inflammatory disorders, and neurological conditions. The review also critically examines the major challenges currently limiting the clinical translation of SERS technologies. These include variability associated with substrate fabrication, matrix-induced signal fluctuations, limited interlaboratory reproducibility, and the lack of standardized protocols for spectral preprocessing and data analysis. Strategies proposed to address these issues are discussed, including comprehensive post-synthesis substrate characterization, optimization of biological sample preparation, advanced spectral preprocessing workflows, and the integration of machine learning and artificial intelligence algorithms to improve diagnostic robustness and reproducibility. Collectively, the advances summarized in this review indicate that SERS-based diagnostic technologies are rapidly progressing beyond proof-of-concept studies toward clinically applicable systems. Continued interdisciplinary collaboration and standardization efforts will be essential to bridge the remaining gap between experimental SERS methodologies and routine clinical implementation.
  • Item type: Item , Access status: Acesso aberto ,
    Do strategic research agendas change over time?
    (2026) Horta, H.; Santos, J. M.
    Strategic Research Agendas (SRAs) play a crucial role in shaping research behaviors, conceptions of science, and scientific productivity, yet their dynamic nature over time remains underexplored. This study investigates whether SRAs are static or dynamic and the extent to which institutional and individual factors may affect these orientations. Drawing on a longitudinal dataset of survey responses from 2,337 researchers across all fields of science, we explore whether these researchers’ SRAs shifted over a 6-year span. Using cluster analysis and logistic regression, we analyzed the data and found that SRAs are dynamic, changing over time, albeit slowly. Two broad archetypal shifts were identified: from Cohesive (consolidative) to Trailblazing (innovative) agendas, and vice-versa. Key drivers of these shifts include increased social satisfaction, positively associated with Trailblazing agendas, and higher levels of institutional unconstraint (i.e., the freedom to focus solely on research), associated with a reduced likelihood of adopting Trailblazing agendas. Disciplinary differences also emerged, with researchers in the Natural, Agricultural, Medical, and Health Sciences more likely to shift toward Trailblazing agendas than those in the Social Sciences and Humanities. Female researchers were also less likely to transition to Trailblazing agendas than their male counterparts, suggesting persistent gendered patterns in science.
  • Item type: Item , Access status: Acesso aberto ,
    Counter hate speech detection in youtube conversations
    (2025) Fialho, P.; Ribeiro, R.; Batista, F.; Ramos, G.; Fonseca, A.; Moro, S.; Guerra, R.; Carvalho, P.; Marques, C.; Silva, C.; Lesot,Marie-Jeanne; Vieira, Susana; Reformat, Marek Z.; Carvalho, João Paulo; Batista, Fernando; Bouchon-Meunier, Bernadette; Yager,Ronald R.
    One of the forms to fight the spread of hate speech is to reply to such utterances with counter speech. In this paper, we present models to identify counter speech, based on an annotated set of comments to Youtube videos spoken in Portuguese. We leverage the sequence of replies to comments, to form a corpus of pairs of comments, where a target is labelled as neutral, hate speech or counter speech, relative to a context, which corresponds to a preceding comment. To the best of our knowledge, this is the first corpus with counter speech examples in Portuguese. Using such corpus, we compute models by fine-tuning pre-trained models based on Transformers, and experiment with both multilingual and Portuguese pre-trained models. Our approach follows a recent work for English, both in corpus design and experimental setup, and we obtain similar performance results in Portuguese.