Dr. Campelo's current research focuses on the development of integrated solution frameworks for prescriptive data analysis, developing and adapting optimisation and machine learning techniques for the solution of complex problems in several areas of science and engineering, with a particular interest for bioinformatics and health informatics.
He is also involved with the development of methodologically and statistically sound protocols for the experimental comparison of optimisation and machine learning algorithms.
He is currently a Senior Lecturer and Deputy Head of Computer Science at Aston University. He received his BSc in Electrical Engineer from Universidade Federal de Minas Gerais (UFMG, Brazil) in 2003. He was then awarded the prestigious Monbusho scholarship from the Japanese government to pursue his postgraduate studies in Hokkaido University, Japan, where he obtained first a Research MSc (Information Science and Technology, 2006) and then a PhD (Systems Science and Informatics, 2009). After returning to Brazil in 2009, he held a postdoctoral position at UFMG between 2009-10, before joining their Department of Electrical Engineering as an Assistant (later Associate) Professor in August 2010. While at UFMG he supervised over 15 postgraduate students and acted as deputy head of department between 2013 and 2017.
Areas of Expertise (4)
Hokkaido University: PhD, Systems Science and Informatics 2009
Hokkaido University: MS, Information Science and Technology 2006
Universidade Federal de Minas Gerais: BEng, Electrical Engineering 2003
- Foundation for Open Access Statistics (FOAS) - since 2019
- Association for Computing Machinery (ACM) - since 2013
- Institute of Electrical and Electronics Engineers (IEEE) - since 2004
New approach to estimate macro and micronutrients in potato plants based on foliar spectral reflectanceComputers and Electronics in Agriculture
2022 Tissue testing used to assess the chemical contents in potato plants is considered laborious, time-consuming, destructive, and expensive. Ground-based sensors have been assessed to provide efficient information on nitrogen using leaf canopy reflectance. In potatoes, however, the main organ required for tissue testing is the petiole to estimate the elements of all nutrients. This research aims to assess whether there is a correlation between the chemical contents of potato petioles and leaf spectrum, and to examine whether the spectrum of dried or fresh leaves have higher correlation values. Petiole chemical contents of all elements were tested as a reference point. Leaves were split equally into dried and fresh groups for spectral analysis (400–2500 nm). Lasso Regression models were built to estimate concentrations in comparison to actual values. The performances of the model were tested using the Ratio of (standard error of) Prediction to (standard) Deviation (RPD). All elements showed reasonable to excellent RPD values except for sodium. All elements showed higher correlation in the dried testing mode except for nitrogen and potassium. The models showed that the most significant wavebands were in the visible and very near infrared range (400–1100 nm) for all macronutrients except magnesium and sulfur, while all micronutrients had the most significant wavebands in full range (400–2500 nm) with a common significant waveband at 1932 nm. The results show high potentials of a new approach to estimate potato plant elements based on foliar spectral reflectance.
SARS-CoV-2 IgG Seroprevalence among Blood Donors as a Monitor of the COVID-19 Epidemic, BrazilEmerging Infectious Diseases
2022 During epidemics, data from different sources can provide information on varying aspects of the epidemic process. Serology-based epidemiologic surveys could be used to compose a consistent epidemic scenario. We assessed the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG in serum samples collected from 7,837 blood donors in 7 cities of Brazil during March–December 2020. Based on our results, we propose a modification in a compartmental model that uses reported number of SARS-CoV-2 cases and serology results from blood donors as inputs and delivers estimates of hidden variables, such as daily values of SARS-CoV-2 transmission rates and cumulative incidence rate of reported and unreported SARS-CoV-2 cases. We concluded that the information about cumulative incidence of a disease in a city’s population can be obtained by testing serum samples collected from blood donors. Our proposed method also can be extended to surveillance of other infectious diseases.
Phylogeny-aware linear B-cell epitope predictor detects candidate targets for specific immune responses to Monkeypox virusbioRxiv
2022 Monkeypox is a disease caused by the Monkeypox virus (MPXV), a double-stranded DNA virus from genus Orthopoxvirus under family Poxviridae, that has recently emerged as a global health threat after decades of local outbreaks in Central and Western Africa. Effective epidemiological control against this disease requires the development of cheaper, faster diagnostic tools to monitor its spread, including antigen and serological testing. There is, however, little available information about MPXV epitopes, particularly those that would be effective in discriminating between MPXV infections and those by other virus from the same family. We used the available data from the Immune Epitope Database (IEDB) to generate and validate a predictive model optimised for detecting linear B-cell epitopes (LBCEs) from Orthopoxvirus, based on a phylogeny-aware data selection strategy. By coupling this predictive approach with conservation and similarity analyses, we identified nine specific peptides from MPXV that are likely to represent distinctive LBCEs for the diagnostic of Monkeypox infections, including the independent detection of a known epitope experimentally characterised as a potential specific diagnostic target for MPXV. The results obtained indicate ability of the proposed pipeline to uncover promising targets for the development of cheaper, more specific diagnostic tests for this emerging viral disease. A full reproducibility package (including code, data, and outputs) is available at https://doi.org/10.5281/zenodo.7057489.