Institution
University of the Algarve
Education•Faro, Portugal•
About: University of the Algarve is a education organization based out in Faro, Portugal. It is known for research contribution in the topics: Population & Tourism. The organization has 3649 authors who have published 10303 publications receiving 233536 citations.
Topics: Population, Tourism, Context (language use), Gene, Fishing
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors define the universal sl3-link homology, which depends on 3 parameters, following Khovanov's approach with foams, and show that this 3-parameter link homology can be divided into three isomorphism classes.
Abstract: We define the universal sl3 –link homology, which depends on 3 parameters, following Khovanov’s approach with foams. We show that this 3–parameter link homology, when taken with complex coefficients, can be divided into 3 isomorphism classes. The first class is the one to which Khovanov’s original sl3 –link homology belongs, the second is the one studied by Gornik in the context of matrix factorizations and the last one is new. Following an approach similar to Gornik’s we show that this new link homology can be described in terms of Khovanov’s original sl2 –link homology.
81 citations
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TL;DR: Metal binding characteristics and metallothionein induction differ markedly among the gills of the bivalve molluscs Mytilus galloprovincialis and Ruditapes decussatus exposed to sublethal cadmium concentrations.
81 citations
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VU University Amsterdam1, Maastricht University2, University College London3, Heidelberg University4, University of Erlangen-Nuremberg5, German Center for Neurodegenerative Diseases6, University of Aveiro7, University of Göttingen8, Humboldt University of Berlin9, University of Genoa10, University of Geneva11, Carol Davila University of Medicine and Pharmacy12, Örebro University13, King's College London14, Karolinska Institutet15, Lund University16, Eisai17, University of Paris18, University of Eastern Finland19, Aristotle University of Thessaloniki20, Medical University of Łódź21, University of Perugia22, French Institute of Health and Medical Research23, University of Oxford24, University of Coimbra25, University of Lisbon26, University of the Algarve27, University of Caen Lower Normandy28
TL;DR: The models generated could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease.
Abstract: Summary Background Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour. Funding ZonMW-Memorabel.
81 citations
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TL;DR: Freeze-dried chia mucilage adsorption isotherms were determined at 25, 35 and 40°C and fitted with the Guggenheim-Anderson-de Boer model, and enthalpy-entropy compensation for the mucilage showed two isokinetic temperatures: one occurring at low moisture contents and another controlled by changes in water entropy.
81 citations
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TL;DR: Comparison experiments indicate that the dimension reduction techniques have capacity for improving the SVM modeling performance indeed, and the Isomap–SVM model with the nonlinear global dimension reduction outperforms all the candidate models in terms of qualitative and quantitative analysis.
Abstract: Manufacturing quality prediction model, as an effective measure to monitor the quality in advance, has been developed using various data-driven techniques. However, multi-parameter in multi-stage of the modern manufacturing industry brings about the curse of dimensionality, leading to the difficulties for feature extraction, learning and quality modeling. To address this issue, three dimension reduction techniques are investigated in this paper, i.e., principal component analysis (PCA), locally linear embedding (LLE), and isometric mapping (Isomap). Specifically, the PCA is a linear dimension reduction technique, the LLE is a nonlinear reduction technique with local perspective, and the Isomap is a nonlinear reduction technique from global perspective. After getting the low-dimensional information from the PCA, the LLE, and the Isomap methods respectively, a support vector machine (SVM) is utilized for modeling. To reveal the effectiveness of the dimension reduction techniques and compare the difference of the three dimension reduction techniques, two experimental manufacturing data are collected from a competition about manufacturing quality control in Tianchi Data Lab of China. The comparison experiments indicate that the dimension reduction techniques have capacity for improving the SVM modeling performance indeed, and the Isomap–SVM model with the nonlinear global dimension reduction outperforms all the candidate models in terms of qualitative and quantitative analysis.
81 citations
Authors
Showing all 3723 results
Name | H-index | Papers | Citations |
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Shuzhi Sam Ge | 97 | 883 | 40865 |
Martin Ingvar | 79 | 315 | 21363 |
Fernando Albericio | 76 | 965 | 26146 |
Paul Goldberg | 68 | 385 | 17238 |
Anders Björkman | 64 | 282 | 13174 |
José J. G. Moura | 63 | 465 | 15490 |
Karl Magnus Petersson | 63 | 185 | 14441 |
Paulo P. Freitas | 59 | 667 | 13777 |
Maria João Bebianno | 58 | 215 | 10445 |
Ester A. Serrão | 55 | 292 | 9751 |
Rui Filipe Oliveira | 54 | 239 | 10225 |
Deborah M. Power | 53 | 300 | 10130 |
Rui Santos | 52 | 357 | 9020 |
Adelino V.M. Canario | 52 | 289 | 9912 |
Martyn Pillinger | 51 | 257 | 8556 |