C4 – Centro de Competências em Cloud Computing
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O C4 - Centro de Competências em Cloud Computing conta ainda com o contributo de investigadores afetos à Universidade da Beira Interior (UBI) e aos institutos politécnicos de Castelo Branco e da Guarda.
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- CD45RA, CD8β, and IFNγ Are Potential Immune Biomarkers of Human Cognitive FunctionPublication . Esgalhado, AJ; Reste-Ferreira, Débora; Albino, Stephanie; Sousa, Adriana; Amaral, Ana Paula; Martinho, António; Oliveira, Isabel Tomás; Verde, Ignacio; Lourenço, Olga; Fonseca, Ana M; Cardoso, Elsa M.; Arosa, FAThere is increasing evidence that in humans the adaptive immunological system can influence cognitive functions of the brain. We have undertaken a comprehensive immunological analysis of lymphocyte and monocyte populations as well as of HLA molecules expression in a cohort of elderly volunteers (age range, 64-101) differing in their cognitive status. Hereby, we report on the identification of a novel signature in cognitively impaired elderly characterized by: (1) elevated percentages of CD8+ T effector-memory cells expressing high levels of the CD45RA phosphate receptor (Temra hi); (2) high percentages of CD8+ T cells expressing high levels of the CD8β chain (CD8βhi); (3) augmented production of IFNγ by in vitro activated CD4+ T cells. Noteworthy, CD3+CD8+ Temra hi and CD3+CD8βhi cells were associated with impaired cognition. Cytomegalovirus seroprevalence showed that all volunteers studied but one were CMV positive. Finally, we show that some of these phenotypic and functional features are associated with an increased frequency of the HLA-B8 serotype, which belongs to the ancestral haplotype HLA-A1, Cw7, B8, DR3, DQ2, among cognitively impaired volunteers. To our knowledge, this is the first proof in humans linking the amount of cell surface CD45RA and CD8β chain expressed by CD8+ Temra cells, and the amount of IFNγ produced by in vitro activated CD4+ T cells, with impaired cognitive function in the elderly.
- Irrigation optimization with a deep reinforcement learning model: Case study on a site in PortugalPublication . Alibabaei, Khadijeh; Gaspar, Pedro Dinis; Assunção, Eduardo Timóteo; Alirezazadeh, Saeid; Lima, Tânia M.In the field of agriculture, the water used for irrigation should be given special treatment, as it is responsible for a large proportion of total water consumption. Irrigation scheduling is critical to food production because it guarantees producers a consistent harvest and minimizes the risk of losses due to water shortages. Therefore, the creation of an automatic irrigation method using new technologies is essential. New methods such as deep learning algorithms have attracted a lot of attention in agriculture and are already being used successfully. In this work, a Deep Q-Network was trained for irrigation scheduling. The agent was trained to schedule irrigation for a tomato field in Portugal. Two Long Short Term Memory models were used as the agent environment. One predicts the total water in the soil profile on the next day. The other one was employed to estimate the yield based on the environmental condition during a season and then measure the net return. The agent uses this information to decide the following irrigation amount. An Artificial Neural Network, a Long Short Term Memory, and a Convolutional Neural Network were used to estimating the Q-table during training. Unlike the Long-Short Terms Memory model, the Artificial Neural Network and the Convolutional Neural Network could not estimate the Q-table, and the agent’s reward decreased during training. The comparison of the performance of the model was done with fixed base irrigation and threshold based irrigation. The trained model increased productivity by 11% and decreased water consumption by 20–30% compared to the fixed method.
- Molecular Beacon Assay Development for Severe Acute Respiratory Syndrome Coronavirus 2 DetectionPublication . Carvalho, Josué; Nunes, J. Lopes; Figueiredo, Joana; Santos, Tiago; Miranda, André; Riscado, Micaela; Sousa, Fani; Duarte, A. P.; Socorro, Sílvia; Tomaz, Cândida; Felgueiras, Mafalda; Teixeira, Rui; Faria, Conceição; Cruz, CarlaThe fast spread of SARS-CoV-2 has led to a global pandemic, calling for fast and accurate assays to allow infection diagnosis and prevention of transmission. We aimed to develop a molecular beacon (MB)-based detection assay for SARS-CoV-2, designed to detect the ORF1ab and S genes, proposing a two-stage COVID-19 testing strategy. The novelty of this work lies in the design and optimization of two MBs for detection of SARS-CoV-2, namely, concentration, fluorescence plateaus of hybridization, reaction temperature and real-time results. We also identify putative G-quadruplex (G4) regions in the genome of SARS-CoV-2. A total of 458 nasopharyngeal and throat swab samples (426 positive and 32 negative) were tested with the MB assay and the fluorescence levels compared with the cycle threshold (Ct) values obtained from a commercial RT-PCR test in terms of test duration, sensitivity, and specificity. Our results show that the samples with higher fluorescence levels correspond to those with low Ct values, suggesting a correlation between viral load and increased MB fluorescence. The proposed assay represents a fast (total duration of 2 h 20 min including amplification and fluorescence reading stages) and simple way of detecting SARS-CoV-2 in clinical samples from the upper respiratory tract.
- Natural Transformation as a Mechanism of Horizontal Gene Transfer in Aliarcobacter butzleriPublication . Bonifácio, Marina; Mateus, Cristiana; Alves, Ana R.; Maldonado, Emanuel; Duarte, Ana Paula; Domingues, Fernanda; Oleastro, Mónica; Ferreira, SusanaAliarcobacter butzleri is an emergent enteropathogen, showing high genetic diversity, which likely contributes to its adaptive capacity to different environments. Whether natural transformation can be a mechanism that generates genetic diversity in A. butzleri is still unknown. In the present study, we aimed to establish if A. butzleri is naturally competent for transformation and to investigate the factors influencing this process. Two different transformation procedures were tested using exogenous and isogenic DNA containing antibiotic resistance markers, and different external conditions influencing the process were evaluated. The highest number of transformable A. butzleri strains were obtained with the agar transformation method when compared to the biphasic system (65% versus 47%). A. butzleri was able to uptake isogenic chromosomal DNA at different growth phases, and the competence state was maintained from the exponential to the stationary phases. Overall, the optimal conditions for transformation with the biphasic system were the use of 1 µg of isogenic DNA and incubation at 30 ◦C under a microaerobic atmosphere, resulting in a transformation frequency ~8 × 10−6 transformants/CFU. We also observed that A. butzleri favored the transformation with the genetic material of its own strain/species, with the DNA incorporation process occurring promptly after the addition of genomic material. In addition, we observed that A. butzleri strains could exchange genetic material in co-culture assays. The presence of homologs of well-known genes involved in the competence in the A. butzleri genome corroborates the natural competence of this species. In conclusion, our results show that A. butzleri is a naturally transformable species, suggesting that horizontal gene transfer mediated by natural transformation is one of the processes contributing to its genetic diversity. In addition, natural transformation can be used as a tool for genetic studies of this species.
- On the Modelling of Species Distribution: Logistic Regression Versus Density Probability FunctionPublication . Bioco, João; Prata, Paula; Canovas, Fernando; Fazendeiro, PauloThe concerns related to climate changes have been gaining attention in the last few years due to the negative impacts on the environment, economy, and society. To better understand and anticipate the effects of climate changes in the distribution of species, several techniques have been adopted comprising models of different complexity. In general, these models apply algorithms and statistical methods capable of predicting in a particular study area, the locations considered suitable for a species to survive and reproduce, given a set of eco-geographical variables that influence species behavior. Logistic regression algorithm and Probability density function are two common methods that can be used to model the species suitability. The former is a representative of a class of models that requires the availability (or imputation) of presence-absence data whereas the latter represents the models that only require presence data. Both approaches are compared regarding the capability to accurately predict the environmental suitability for species. On a different way, the behaviour of the species in the projected environments are analysed by simulating its potential distribution in the projected environment. A case study reporting results from two types of species with economical interest is presented: the strawberry tree (Arbutus unedo) in mainland Portugal, and the Apis mellifera (African Lineage) in the Iberian Peninsula.
- On the stability of point cloud machine learning based codingPublication . Prazeres, João; Rodrigues, Rafael; Pereira, Manuela; Pinheiro, Antonio M. G.This paper analyses the performance of two of the most well known deep learning-based point cloud coding solutions, considering the training conditions. Several works have recently been published on point cloud machine learning-based coding, following the recent tendency on image coding. These codecs are typically seen as a set of predefined trained machines. However, the performance of such models is usually very dependent of their training, and little work has been considered on the stability of the codecs’ performance, as well as the possible influence of the loss function parameters, and the increasing number of training epochs. The evaluation experiments are supported in a generic test set with point clouds representing objects and also more complex scenes, using the point to point metric (PSNR D1), as several studies revealed the good quality representation of this geometry-only point cloud metric.
- Prediction of the Arbutus unedo colonization time via an agent-based distribution modelPublication . Bioco, João; Prata, Paula; Canovas, Fernando; Fazendeiro, PauloSpecies distribution models (SDMs) have been used to predict the distribution of species in an environment. Usually, this prediction is based on previous species occurrence data. Nowadays, several species are facing climatic changes that impact the way species behave. Global warming has implicated species displacement from their natural habitat to new suitable places. SDMs can be used to anticipate possible impacts of the climatic changes in species distribution, preventing species extinction scenarios. New SDMs approaches are needed to give valuable information that better approximates these models to reality. This paper presents a novel approach to the prediction of species distribution. It starts by using an agent-based model (ABM) to establish an approximate mapping between the geological and the computational times. Afterwards, the implemented ABM can be used to predict the species distribution at different time intervals. The presented case study concerns the distribution of the Arbutus unedo in the Iberian peninsula, a species with relevant socio-economic impact in Portugal. The results show that the measurement of the geological time, supported by the approximate correspondence to the number of epochs of an ABM, can be a valuable tool for the prediction of the species distribution in a changing environmental scenario.
- Promoter Demethylation Upregulates STEAP1 Gene Expression in Human Prostate Cancer: In Vitro and In Silico AnalysisPublication . Rocha, Sandra; Sousa, Inês; Gomes, Inês M.; Arinto, Patrícia; Pinheiro, Pedro Costa; Coutinho, Eduarda; Santos, Cecilia; Jerónimo, Carmen; Lemos, Manuel C.; Passarinha, L A; Socorro, Sílvia; Baptista, Cláudio MaiaThe Six Transmembrane Epithelial Antigen of the Prostate (STEAP1) is an oncogene overexpressed in several human tumors, particularly in prostate cancer (PCa). However, the mechanisms involved in its overexpression remain unknown. It is well known that epigenetic modifications may result in abnormal gene expression patterns, contributing to tumor initiation and progression. Therefore, this study aimed to analyze the methylation pattern of the STEAP1 gene in PCa versus non-neoplastic cells. Bisulfite amplicon sequencing of the CpG island at the STEAP1 gene promoter showed a higher methylation level in non-neoplastic PNT1A prostate cells than in human PCa samples. Bioinformatic analysis of the GEO datasets also showed the STEAP1 gene promoter as being demethylated in human PCa, and a negative association with STEAP1 mRNA expression was observed. These results are supported by the treatment of non-neoplastic PNT1A cells with DNMT and HDAC inhibitors, which induced a significant increase in STEAP1 mRNA expression. In addition, the involvement of HDAC in the regulation of STEAP1 mRNA expression was corroborated by a negative association between STEAP1 mRNA expression and HDAC4,5,7 and 9 in human PCa. In conclusion, our work indicates that STEAP1 overexpression in PCa can be driven by the hypomethylation of STEAP1 gene promoter.
- Remarks on the Behavior of an Agent-Based Model of Spatial Distribution of SpeciesPublication . Bioco, João; Prata, Paula; Cánovas, Fernando; Fazendeiro, PauloAgent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species.
- SDSim: A generalized user friendly web ABM system to simulate spatiotemporal distribution of species under environmental scenariosPublication . Bioco, João; Cánovas, Fernando; Prata, Paula; Fazendeiro, PauloThis paper presents the Agent-Based Modelling System of spatial distribution of species SDSim. SDSim is an agent-based modelling system designed to simulate spatial distribution of species and populations for conservation and management purposes. SDSim gives to modellers the ability to simulate movements and colonization patterns of species given locations under study and a set of eco-geographical variables in which species depends on.