Firefighter of the future

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Discriminação contra jovens lésbicas em contexto escolar = Discrimination against young lesbian women in the school context

O presente artigo teve como objetivo elaborar uma resenha teórica sobre a forma como as escolas e seus/suas profissionais se posicionam relativamente à cidadania de jovens lésbicas. Em primeiro lugar, apresentámos as características do ambiente escolar para os/as jovens lésbicas, gays, bissexuais e transgénero. Depois, mostrámos como as pessoas com orientações sexuais e identidades de género não normativas são vistas e reconhecidas pelos seus pares e pelos/as professores/as, auscultando, em particular, as experiências de jovens lésbicas no contexto escolar. Por fim, discutimos sobre o modo como podemos contribuir para uma escola mais inclusiva que fomente a diversidade e a cidadania ativa de jovens lésbicas.info:eu-repo/semantics/publishedVersio

Codificação digital de áudio baseada em retroadaptação perceptual

Doutoramento em Engenharia ElectrónicaFaz-se uma análise do problema da codificação digital de sinais áudio de alta qualidade e identifica-se o princípio de codificação perceptual como a solução mais satisfatória. Apresenta-se uma síntese dos sistemas de codificação perceptual encontrados na literatura, e identificam-se, comparam-se e relacionam-se as técnicas usadas em cada um. Pela sua relevância para a codificação de áudio, faz-se um estudo mais aprofundado das transformadas e bancos de filtros multifrequência, da quantização, dos códigos reversíveis e dos modelos matemáticos da percepção auditiva. Propõe-se um sistema de codificação composto por um banco de filtros multi-resolução, quantizadores logarítmicos adaptativos, codificação aritmética, e um modelo psicoacústico explícito para adaptar os quantizadores de acordo com critérios perceptuais. Ao contrário de outros codificadores perceptuais, o sistema proposto é retroadaptativo, isto é: a adaptação depende exclusivamente de amostras já quantizadas, e não do sinal original. Discutimos as vantagens do uso de retroadaptação e mostramos que esta técnica pode ser aplicada com sucesso à codificação perceptual.The problem of digital coding of high quality audio signals is analised, and the principles of perceptual coding are identified as the most satisfactory approach. We present a synthesis of the perceptual coding systems found in the literature, and we identify, compare and relate the techniques used in each one. Given their relevance for audio coding, transforms and multifrequency filter banks as well as quantization, lossless coding, and mathematical models of auditory perception are subject to a more thorough study. We propose a coding system consisting of a multirate filter bank, logarithmic quantizers, arithmetic entropy coding and an explicit psychoacoustic model to adapt the quantization according to perceptual considerations. Unlike other perceptual coders, the proposed system is backward-adaptive, that is: adaptation depends exclusively on already quantized samples, not on the original signal. We discuss the advantages of backward-adaptation and show that it can be successfully applied to perceptual coding

A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard

This work was supported by the Associate Laboratory for Green Chemistry—LAQV which is financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020). This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 870292 (BioICEP project). J.P. acknowledges a PhD grant (SFRD/BD14610472019), Fundação para a Ciência e Tecnologia (FCT) and R.S.C. the contract CEECIND/01399/2017In this paper, a computational framework is proposed that merges mechanistic modeling with deep neural networks obeying the Systems Biology Markup Language (SBML) standard. Over the last 20 years, the systems biology community has developed a large number of mechanistic models that are currently stored in public databases in SBML. With the proposed framework, existing SBML models may be redesigned into hybrid systems through the incorporation of deep neural networks into the model core, using a freely available python tool. The so-formed hybrid mechanistic/neural network models are trained with a deep learning algorithm based on the adaptive moment estimation method (ADAM), stochastic regularization and semidirect sensitivity equations. The trained hybrid models are encoded in SBML and uploaded in model databases, where they may be further analyzed as regular SBML models. This approach is illustrated with three well-known case studies: the Escherichia coli threonine synthesis model, the P58IPK signal transduction model, and the Yeast glycolytic oscillations model. The proposed framework is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.publishersversionpublishe

Advances and Future Perspectives

Agharafeie , R., Ramos, J. R. C., Mendes, J. M., & Oliveira, R. M. F. (2023). From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives. Fermentation, 9(10), 1-22. [922]. https://doi.org/10.20944/preprints202310.0107.v1, https://doi.org/10.3390/fermentation9100922--- This work was supported by the Associate Laboratory for Green Chemistry - LAQV which is financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020). This work received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement no. 101099487- BioLaMer-HORIZON-EIC-2022-PATHFINDEROPEN-01 (BioLaMer)Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.publishersversionpublishe

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

© The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.Introduction: We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported. Methods: Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0 -100 points) - previously developed and published - were measured. Results: 123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09-0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87-96), similar to the previously obtained value in the training dataset. Conclusion: the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.Open access funding provided by FCT|FCCN (b-on). Cardiovascular Center of the University of Lisbon, INESC-ID / Instituto Superior Técnico, University of Lisbon.info:eu-repo/semantics/publishedVersio

ENFERMAGEM DO TRABALHO EM PORTUGAL: CONTEXTO E PERSPETIVAS

Objetivo: conhecer a perceção dos enfermeiros do trabalho no que respeita às áreas deconhecimento e intervenção. Método: estudo quantitativo, descritivo e transversal. Envolveuamostra de 472 enfermeiros do trabalho. Utilizou-se como instrumento de recolha de dados, que decorreu entre maio e setembro de 2017, questionário, alicerçado nas áreas nucleares de conhecimentos e competências do enfermeiro do trabalho e na escala de perceção das áreas de conhecimento e intervenção do enfermeiro do trabalho. Resultados: a Enfermagem do Trabalho foi entendida pela maioria como atividade profissional transitória, pelo facto de ser exercida como complemento financeiro à atividade principal ou porque não conseguiram colocação na área de exercício profissional da sua preferência. Conclusão: o exercício profissional nas áreas de conhecimento e intervenção foi desvalorizado ou não reconhecido e encarado pela maioria dos enfermeiros como transitório, pelo facto de quase metade ambicionar trabalhar no futuro nessa área, mas apenas em regime de tempo parcial. Descritores: Enfermagem do Trabalho. Perceção. Educação Continuada. Assistência Integral àSaúde. Desenvolvimento de Programas

Towards a multivariate analysis of genome-scale metabolic models derived from the BiGG models database

First Online: 28 August 2021Genome-Scale metabolic models (GEMs) are a relevant tool in systems biology for in silico strain optimisation and drug discovery. An easier way to reconstruct a model is to use available GEMs as templates to create the initial draft, which can be curated up until a simulation-ready model is obtained. This approach is implemented in merlin's BiGG Integration Tool, which reconstructs models from existing GEMs present in the BiGG Models database. This study aims to assess draft models generated using models from BiGG as templates for three distinct organisms, namely, Streptococcus thermophilus, Xylella fastidiosa and Mycobacterium tuberculosis. Several draft models were reconstructed using the BiGG Integration Tool and different templates (all, selected and random). The variability of the models was assessed using the reactions and metabolic functions associated with the model's genes. This analysis showed that, even though the models shared a significant portion of reactions and metabolic functions, models from different organisms are still differentiated. Moreover, there also seems to be variability among the templates used to generate the draft models to a lower extent. This study concluded that the BiGG Integration Tool provides a fast and reliable alternative for draft reconstruction for bacteria.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit. A. Oliveira (DFA/BD/10205/2020), E. Cunha (DFA/BD/8076/2020), F. Cruz (SFRH /BD/139198/2018), J. Sequeira (SFRH/BD/147271/2019), and M. Sampaio (SFRH/BD/144643/2019) hold a doctoral fellowship provided by the FCT. Oscar Dias acknowledge FCT for the Assistant Research contract obtained under CEEC Individual 2018.info:eu-repo/semantics/publishedVersio

Biological activities and chemical composition of methanolic extracts of selected Autochthonous microalgae strains from the Red Sea

Four lipid-rich microalgal species from the Red Sea belonging to three different genera (Nannochloris, Picochlorum and Desmochloris), previously isolated as novel biodiesel feedstocks, were bioprospected for high-value, bioactive molecules. Methanol extracts were thus prepared from freeze-dried biomass and screened for different biological activities. Nannochloris sp. SBL1 and Desmochloris sp. SBL3 had the highest radical scavenging activity against 1,1-diphenyl-2-picrylhydrazyl, and the best copper and iron chelating activities. All species had potent butyrylcholinesterase inhibitory activity (>50%) and mildly inhibited tyrosinase. Picochlorum sp. SBL2 and Nannochloris sp. SBL4 extracts significantly reduced the viability of tumoral (HepG2 and HeLa) cells with lower toxicity against the non-tumoral murine stromal (S17) cells. Nannochloris sp. SBL1 significantly reduced the viability of Leishmania infantum down to 62% (250 mu g/mL). Picochlorum sp. SBL2 had the highest total phenolic content, the major phenolic compounds identified being salicylic, coumaric and gallic acids. Neoxanthin, violaxanthin, zeaxanthin, lutein and -carotene were identified in the extracts of all strains, while canthaxanthin was only identified in Picochlorum sp. SBL2. Taken together, these results strongly suggest that the microalgae included in this work could be used as sources of added-value products that could be used to upgrade the final biomass value.National Science, Technology and Innovation Program of King Abdulaziz Medical City for Science and Technology, Riyadh, Saudi Arabia [NPST, 11-ENE 1719-02]; Foundation for Science and Technology (FCT), Portugal [SFRH/BD/78062/2011]; FCT [IF/00049/2012, SFRH/BPD/86071/2012, Pest-OE/QUI/UI0612/2013]info:eu-repo/semantics/publishedVersio