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About

Bruno M. P. M. Oliveira is an auxiliary professor at the University of Porto (Portugal) and a researcher of the LIAAD, INESC TEC. He has a degree in Astronomy, a MSc in Applied Mathematics, a PhD in Applied Mathematics and an Habilitation in Clinical Nutrition - Basic Sciences.
His research reaches several Mathematical areas that include Dynamical Systems, Game Theory and Statistics.
Of his particular interest are applications to Nutrition and Food Sciences (in particular to Clinical Nutrition and Food and Nutrition Consumption Sciences), Immunology (in particular modelling T cell and Tregs), Epidemiology and Economy (in particular in Cournot competition and random pairing markets).
He has participated in research projects funded by EU and the Portuguese FCT covering Nutrition and Food Sciences and Mathematics.

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Details

Details

  • Name

    Bruno Mendes Oliveira
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st April 2012
001
Publications

2022

Co-W Barrier Layers for Metallization of Copper Interconnects: Thermal Performance Analysis

Authors
Oliveira, BMC; Santos, RF; Piedade, AP; Ferreira, PJ; Vieira, MF;

Publication
NANOMATERIALS

Abstract
The back-end-of-line (BEOL) copper interconnect structure has been subjected to downscaling for the last two decades, while the materials used for conforming and assuring its physical integrity during processing have faced significant obstacles as the single-digit nanometer process node is implemented. In particular, the diffusion barrier layer system comprised of Ta/TaN has faced major constraints when it comes to the electrical performance of the smaller Cu lines, and thus alternative formulations have been investigated in recent years, such as Ru-Ta or Co-W alloys. In this work, we assess how PVD (physical vapor deposition) deposited equimolar Co-W films perform when exposed to different vacuum annealing temperatures and how these films compare with the Ta adhesion layer used for Cu seeding in terms of dewetting resistance. The stacks were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) coupled with energy dispersive X-ray spectroscopy (EDX) mapping. The Cu film at the surface of the Cu/Co-W system exhibited grain growth starting at 300 degrees C, with the formation of abnormally large Cu grains starting at 450 degrees C. Sheet resistance reached a minimum value of 7.07 x 10(-6) omega/sq for the Cu/Co-W stack and 6.03 x 10(-6) omega/sq for the Cu/Ta stack, both for the samples annealed at 450 degrees C.

2022

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity

Authors
Robles-Loaiza, AA; Pinos-Tamayo, EA; Mendes, B; Ortega-Pila, JA; Proano-Bolanos, C; Plisson, F; Teixeira, C; Gomes, P; Almeida, JR;

Publication
PHARMACEUTICALS

Abstract
Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7-35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew's correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

2021

FOREWORD: SPECIAL ISSUE "EURO 2019: GAMES IN ECONOMICS, FINANCE AND BIOLOGY"

Authors
Daniele, P; Fulga, C; Martn Herran, G; Mazalov, V; Petrosyan, L; Oliveira, BMPM; Ramos, C; Weber, GW; Zenkevich, N;

Publication
JOURNAL OF DYNAMICS AND GAMES

Abstract

2021

Prevalence of Nutritional Risk at Admission in Internal Medicine Wards in Portugal: The Multicentre Cross-Sectional ANUMEDI Study

Authors
Marinho, R; Pessoa, A; Lopes, M; Rosinhas, J; Pinho, J; Silveira, J; Amado, A; Silva, S; Oliveira, B; Marinho, A; Jager Wittenaar, H;

Publication
ACTA MEDICA PORTUGUESA

Abstract
Introduction: Disease-related undernutrition is highly prevalent and requires timely intervention. However, identifying undernutrition often relies on physician judgment. As Internal Medicine wards are the backbone of the hospital setting, insight into the prevalence of nutritional risk in this population is essential. We aimed to determine the prevalence of nutritional risk in Internal Medicine wards, to identify its correlates, and to assess the agreement between the physicians' impression of nutritional risk and evaluation by Nutritional Risk Screening 2002. Material and Methods: A cross-sectional multicentre study was performed in Internal Medicine wards of 24 Portuguese hospitals during 2017. Data on demographics, previous hospital admissions, primary diagnosis, and Charlson comorbidity index score were collected. Nutritional risk at admission was assessed using Nutritional Risk Screening 2002. Agreement between physicians' impression of nutritional risk and Nutritional Risk Screening 2002 was tested by Cohen's kappa. Results: The study included 729 participants (mean age 74 +/- 14.6 years, 51% male). The main reason for admission was respiratory disease. Mean Charlson comorbidity index score was 5.8 +/- 2.8. Prevalence of nutritional risk was 51%. Nutritional risk was associated with admission during the previous year (odds ratio = 1.65, 95% confidence interval: 1.22 - 2.24), solid tumour with metastasis (odds ratio = 4.73, 95% confidence interval: 2.06 - 10.87), any tumour without metastasis (odds ratio = 2.04, 95% confidence interval:1.24 - 3.34), kidney disease (odds ratio = 1.83, 95% confidence interval: 1.21 - 2.75), peptic ulcer (odds ratio = 2.17, 95% confidence interval: 1.10 - 4.25), heart failure (odds ratio = 1.51, 95% confidence interval: 1.11 - 2.04), dementia (odds ratio = 3.02, 95% confidence interval: 1.96 - 4.64), and cerebrovascular disease (odds ratio = 1.62, 95% confidence interval: 1.12 - 2.35). Agreement between physicians' evaluation of nutritional status and Nutritional Risk Screening 2002 was weak (Cohen's kappa = 0.415, p < 0.001). Discussion: Prevalence of nutritional risk in the Internal Medicine population is very high. Admission during the previous year and multiple comorbidities increase the odds of being at-risk. Subjective physician evaluation is not appropriate for nutritional screening. Conclusion: The high prevalence of at-risk patients and poor subjective physician evaluation suggest the need to implement mandatory nutritional screening.

2021

Firms, technology, training and government fiscal policies: An evolutionary approach

Authors
Accinelli, E; Martins, F; Muniz, H; Oliveira, BMPM; Pinto, AA;

Publication
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B

Abstract
<p style='text-indent:20px;'>In this paper we propose and analyze a game theoretical model regarding the dynamical interaction between government fiscal policy choices toward innovation and training (I&amp;T), firm's innovation, and worker's levels of training and education. We discuss four economic scenarios corresponding to strict pure Nash equilibria: the government and I&amp;T poverty trap, the I&amp;T poverty trap, the I&amp;T high premium niche, and the I&amp;T ideal growth. The main novelty of this model is to consider the government as one of the three interacting players in the game that also allow us to analyse the I&amp;T mixed economic scenarios with a unique strictly mixed Nash equilibrium and with I&amp;T evolutionary dynamical cycles.</p>

Supervised
thesis

2021

Digital Polar Transmitter for Emerging Wireless Communications

Author
Rui Filipe de Pinho Gomes

Institution
UP-FEUP

2020

Avaliação do impacto da ingestão de proteína na glicemia pós-prandial em indivíduos adultos com Diabetes Mellitus tipo I com insulinoterapia funcional

Author
Lisandra Marina Baptista Ribeiro

Institution
UP-FCNAUP

2020

Valor preditivo dos marcadores nutricionais na mortalidade e morbilidade dos doentes em Hemodiálise.

Author
Rosária Maria Afonso Rodrigues de Melo

Institution
UP-FCNAUP

2020

Hábitos alimentares e estado de saúde numa população idosa da Região Autónoma da Madeira.

Author
Sara Gomes Müller Pereira

Institution
UP-FCNAUP

2019

Evolução da densidade mineral óssea em doentes submetidos a cirurgia bariátrica

Author
Beatriz Isabel Guimarães Pereira

Institution
UP-FCNAUP