Analysis of metabolic flux distributions in relation to the extracellular environment in Avian cells

Continuous cell lines that proliferate in chemically defined and simple media have been highly regarded as suitable alternatives for vaccine production. One such cell line is the AG1.CR.pIX avian cell line developed by PROBIOGEN. This cell line can be cultivated in a fully scalable suspension culture and adapted to grow in chemically defined, calf serum free, medium [1]–[5]. The medium composition and cultivation strategy are important factors for reaching high virus titers. In this project, a series of computational methods was used to simulate the cell’s response to different environments. The study is based on the metabolic model of the central metabolism proposed in [1]. In a first step, Metabolic Flux Analysis (MFA) was used along with measured uptake and secretion fluxes to estimate intracellular flux values. The network and data were found to be consistent. In a second step, Flux Balance Analysis (FBA) was performed to access the cell’s biological objective. The objective that resulted in the best predicted results fit to the experimental data was the minimization of oxidative phosphorylation. Employing this objective, in the next step Flux Variability Analysis (FVA) was used to characterize the flux solution space. Furthermore, various scenarios, where a reaction deletion (elimination of the compound from the media) was simulated, were performed and the flux solution space for each scenario was calculated. Growth restrictions caused by essential and non-essential amino acids were accurately predicted. Fluxes related to the essential amino acids uptake and catabolism, the lipid synthesis and ATP production via TCA were found to be essential to exponential growth. Finally, the data gathered during the previous steps were analyzed using principal component analysis (PCA), in order to assess potential changes in the physiological state of the cell. Three metabolic states were found, which correspond to zero, partial and maximum biomass growth rate. Elimination of non-essential amino acids or pyruvate from the media showed no impact on the cell’s assumed normal metabolic state

A Batalha do Buçaco enquanto destino turístico estruturante da região centro

A história e legado cultural de um povo/região é dos fatores determinantes para a sua identidade coletiva e é um recurso imaterial único e diferenciador. Assim, esse legado é o ponto de partida para a estruturação de um destino turístico, pois não só, os recursos base são únicos, como também, a identificação dos agentes envolvidos com o território e herança cultural, torna mais espontâneo e fácil a aglutinação dos meios e recursos necessários em torno dessa marca chapéu, conferindo-lhe estrutura, coerência e identidade, fundamentais num mundo global e competitivo. Neste sentido, alinhado com um dos eixos estratégicos consagrados na Estratégia Turismo 2027 e com as tendências turísticas globais, nesta dissertação, partiu-se do elemento comum, ao território em estudo, a batalha do Buçaco e a 3ª invasão francesa e, tendo por base o Turismo Militar, fez-se uma análise do potencial turístico da região Centro, para a construção de um destino turístico e desenvolvimento de um cluster turístico, assente nos recursos endógenos, no envolvimento das comunidades locais e dos agentes do território, sempre tendo por base princípios de uma sustentabilidade 360º, inovação com valor e desenvolvimento de um turismo emocional e autêntico. É objetivo deste estudo criar uma proposta de oferta turística integrada assente no manancial histórico da 3ª Invasão Francesa e uma oferta de Turismo Militar e Cultural. Para isso, utilizou-se uma metodologia qualitativa, através da técnica de entrevistas semiestruturadas, aplicada a stakeholders estratégicos. Os resultados apurados permitiram realizar uma proposta de valor, tendo em vista o planeamento e gestão de um destino turístico competitivo e diferenciado

Construção de websites com ferramentas open-source: duas experiências de implementação em bibliotecas de ensino superior

As Tecnologias da Informação e da Comunicação (TIC) têm influenciado de forma inequívoca o desenvolvimento das bibliotecas à escala global. Nas últimas décadas, as TIC mudaram a dinâmica das bibliotecas permitindo a sua modernização (pelo desenvolvimento da eficiência das tarefas já realizadas), favorecendo a inovação (pela utilização das tecnologias como base para o desenvolvimento de novos serviços/técnicas) e promovendo a sua transformação (ao nível do paradigma funcional, da disponibilização de conteúdos, etc.) – criando, em suma, uma nova relação com os seus público

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

Framework for a Hospitality Big Data Warehouse: The Implementation of an Efficient Hospitality Business Intelligence System

order to increase the hotel's competitiveness, to maximize its revenue, to meliorate its online reputation and improve customer relationship, the information about the hotel's business has to be managed by adequate information systems (IS). Those IS should be capable of returning knowledge from a necessarily large quantity of information, anticipating and influencing the consumer's behaviour. One way to manage the information is to develop a Big Data Warehouse (BDW), which includes information from internal sources (e.g., Data Warehouse) and external sources (e.g., competitive set and customers' opinions). This paper presents a framework for a Hospitality Big Data Warehouse (HBDW). The framework includes a (1) Web crawler that periodically accesses targeted websites to automatically extract information from them, and a (2) data model to organize and consolidate the collected data into a HBDW. Additionally, the usefulness of this HBDW to the development of the business analytical tools is discussed, keeping in mind the implementation of the business intelligence (BI) concepts.SRM QREN IDT [38962]FCT projects LARSyS [UID/EEA/50009/2013]CIAC [PEstOE/EAT/UI4019/2013]CEFAGE [PEst-C/EGE/UI4007/2013]CEG-IST - Universidade de Lisboainfo:eu-repo/semantics/publishedVersio

Patient-physician discordance in assessment of adherence to inhaled controller medication: a cross-sectional analysis of two cohorts

We aimed to compare patient's and physician's ratings of inhaled medication adherence and to identify predictors of patient-physician discordance.(SFRH/BPD/115169/2016) funded by Fundação para a Ciência e Tecnologia (FCT); ERDF (European Regional Development Fund) through the operations: POCI-01-0145-FEDER-029130 ('mINSPIRERS—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases—generalisation and evaluation of gamification, peer support and advanced image processing technologies') cofunded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia).info:eu-repo/semantics/publishedVersio

Influenza severe cases in hospitals, between 2014 and 2016 in Portugal

Rede Portuguesa de Laboratórios para o Diagnóstico da GripeBackground: Since 2009, the Portuguese Laboratory Network (PLNID) for Influenza Diagnosis has integrated 15 Laboratories in mainland and Atlantic Islands of Azores and Madeira. This PLNID added an important contribute to the National Influenza Surveillance Program regarding severe and hospitalized influenza cases. The present study aims to describe influenza viruses detected in influenza like illness (ILI) cases: outpatients (Outp), hospitalized (Hosp), and intensive care units (ICU), between 2014 and 2016. Methods: The PLNID performs influenza virus diagnosis by biomolecular methodologies. Weekly reports to the National Influenza Reference Laboratory ILI cases tested for influenza. Reports include data on detecting viruses, hospital assistance, antiviral therapeutics, and information on death outcome. Were reported during two winter seasons 8059 ILI cases,being 3560 cases in 2014/15 (1024 in Outp, 1750 Hosp, and 606 in ICU) and 4499 cases in 2015/2016 (1933 in Outp, 1826 Hosp, and 740 in ICU). Results: The higher percentage of influenza positive cases were detected in Outp in both seasons, 18% during 2014/15 and 20% in 2015/16. In 2014/15,influenza cases were more frequent in individuals older than 65 years old and these required more hospitalizations,even in ICU. In 2015/16,the influenza cases were mainly detected in individuals between 15-64 years old. A higher proportion of influenza positive cases with hospitalization in ICU were observed in adults between 45-64 years old.During the study period,the predominant circulating influenza viruses were different in the two seasons: influenza B and A(H3) co-circulated in 2014/15,and influenza A(H1)pdm09 was predominant during 2015/16. Even when influenza A is notthe dominant virus, A(H3) and A(H1)pdm09 subtypes correlate with higher detection rate in hospitalized cases (Hosp and UCI), with higher frequencies in adults older than 45. Influenza B,detected in higher proportion in outpatients, was frequently relatedwith influenza cases in younger age groups: 0-4 and 5-14 years old. Conclusions: This study highlights the correlation of theinfluenza virus type/subtype that circulates in each season with the possible need for hospitalization and intensive care in special groups of the population. Circulation of influenza A subtypes can cause more frequentdisease in individuals older than 45, with need of hospitalization including intensive care. On the other hand, influenza B is more frequently associated with less severe cases and with infection in children and younger adults. Influenza B circulation might predict lower number of hospitalizations.The identification of influenza type in circulation,byPLNID ineach season, could guide action planning measures in population health care.info:eu-repo/semantics/publishedVersio

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio