Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Lecture Notes in Computer Science, 9273

Predicting type of delivery by identification of obstetric risk factors through data mining

In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries.

Rockburst laboratory tests database - Application of data mining techniques

Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.

Modelling Amazonian forest eddy covariance data: a comparison of big leaf versus sun/shade models for the C-14 tower at Manaus I. Canopy photosynthesis

In this study, we concentrate on modelling gross primary productivity using two simple approaches to simulate canopy photosynthesis: "big leaf" and "sun/shade" models. Two approaches for calibration are used: scaling up of canopy photosynthetic parameters from the leaf to the canopy level and fitting canopy biochemistry to eddy covariance fluxes. Validation of the models is achieved by using eddy covariance data from the LBA site C14. Comparing the performance of both models we conclude that numerically (in terms of goodness of fit) and qualitatively, (in terms of residual response to different environmental variables) sun/shade does a better job. Compared to the sun/shade model, the big leaf model shows a lower goodness of fit and fails to respond to variations in the diffuse fraction, also having skewed responses to temperature and VPD. The separate treatment of sun and shade leaves in combination with the separation of the incoming light into direct beam and diffuse make sun/shade a strong modelling tool that catches more of the observed variability in canopy fluxes as measured by eddy covariance. In conclusion, the sun/shade approach is a relatively simple and effective tool for modelling photosynthetic carbon uptake that could be easily included in many terrestrial carbon models.

Search for H→γγ produced in association with top quarks and constraints on the Yukawa coupling between the top quark and the Higgs boson using data taken at 7 TeV and 8 TeV with the ATLAS detector

A search is performed for Higgs bosons produced in association with top quarks using the diphoton decay mode of the Higgs boson. Selection requirements are optimized separately for leptonic and fully hadronic final states from the top quark decays. The dataset used corresponds to an integrated luminosity of 4.5 fb−1 of proton--proton collisions at a center-of-mass energy of 7 TeV and 20.3 fb−1 at 8 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No significant excess over the background prediction is observed and upper limits are set on the tt¯H production cross section. The observed exclusion upper limit at 95% confidence level is 6.7 times the predicted Standard Model cross section value. In addition, limits are set on the strength of the Yukawa coupling between the top quark and the Higgs boson, taking into account the dependence of the tt¯H and tH cross sections as well as the H→γγ branching fraction on the Yukawa coupling. Lower and upper limits at 95% confidence level are set at −1.3 and +8.0 times the Yukawa coupling strength in the Standard Model.

Search for Higgs Boson Pair Production in the γγbb¯ Final State using pp Collision Data at s√=8TeV from the ATLAS Detector

Searches are performed for resonant and non-resonant Higgs boson pair production in the hh→γγbb¯ final state using 20 fb−1 of proton--proton collisions at a center-of-mass energy of 8TeV recorded with the ATLAS detector at the CERN Large Hadron Collider. A 95% confidence level upper limit on the cross section times branching ratio of non--resonant production is set at 2.2 pb, while the expected limit is 1.0 pb. The corresponding limit observed for a narrow resonance ranges between 0.8 and 3.5 pb as a function of its mass.

Development of computational tools for the integrated analysis of DNA microarray data with applications in cancer research

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

The effects of solvent composition on the affinity of a peptide towards hair keratin: experimental and molecular dynamics data

The study of the interaction between hair filaments and formulations or peptides is of utmost importance in fields like cosmetic research. Keratin intermediate filaments structure is not fully described, limiting the molecular dynamics (MD) studies in this field although its high potential to improve the area. We developed a computational model of a truncated protofibril, simulated its behavior in alcoholic based formulations and with one peptide. The simulations showed a strong interaction between the benzyl alcohol molecules of the formulations and the model, leading to the disorganization of the keratin chains, which regress with the removal of the alcohol molecules. This behavior can explain the increase of peptide uptake in hair shafts evidenced in fluorescence microscopy pictures. The model developed is valid to computationally reproduce the interaction between hair and alcoholic formulations and provide a robust base for new MD studies about hair properties. It is shown that the MD simulations can improve hair cosmetic research, improving the uptake of a compound of interest.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.

Search for new phenomena in the dijet mass distribution using pp collision data at s√=8 TeV with the ATLAS detector

Dijet events produced in LHC proton--proton collisions at a center-of-mass energy s√=8 TeV are studied with the ATLAS detector using the full 2012 data set, with an integrated luminosity of 20.3 fb−1. Dijet masses up to about 4.5 TeV are probed. No resonance-like features are observed in the dijet mass spectrum. Limits on the cross section times acceptance are set at the 95% credibility level for various hypotheses of new phenomena in terms of mass or energy scale, as appropriate. This analysis excludes excited quarks with a mass below 4.09 TeV, color-octet scalars with a mass below 2.72 TeV, heavy W′ bosons with a mass below 2.45 TeV, chiral W∗ bosons with a mass below 1.75 TeV, and quantum black holes with six extra space-time dimensions with threshold mass below 5.82 TeV.

Lideranças curriculares intermédias no contexto da avaliação externa das escolas em Portugal

Tese de Doutoramento em Ciências da Educação (área de especialização em Desenvolvimento Curricular).

Spatio-temporal modelling of environmental data

Programa Doutoral em Matemática e Aplicações.

Search for charged Higgs bosons decaying via H± → τ±ν in fully hadronic final states using pp collision data at √s = 8 TeV with the ATLAS detector

The results of a search for charged Higgs bosons decaying to a τ lepton and a neutrino, H±→τ±ν, are presented. The analysis is based on 19.5 fb−1 of proton--proton collision data at s√=8 TeV collected by the ATLAS experiment at the Large Hadron Collider. Charged Higgs bosons are searched for in events consistent with top-quark pair production or in associated production with a top quark. The final state is characterised by the presence of a hadronic τ decay, missing transverse momentum, b-tagged jets, a hadronically decaying W boson, and the absence of any isolated electrons or muons with high transverse momenta. The data are consistent with the expected background from Standard Model processes. A statistical analysis leads to 95% confidence-level upper limits on the product of branching ratios B(t→bH±)×B(H±→τ±ν), between 0.23% and 1.3% for charged Higgs boson masses in the range 80--160 GeV. It also leads to 95% confidence-level upper limits on the production cross section times branching ratio, σ(pp→tH±+X)×B(H±→τ±ν), between 0.76 pb and 4.5 fb, for charged Higgs boson masses ranging from 180 GeV to 1000 GeV. In the context of different scenarios of the Minimal Supersymmetric Standard Model, these results exclude nearly all values of tanβ above one for charged Higgs boson masses between 80 GeV and 160 GeV, and exclude a region of parameter space with high tanβ for H± masses between 200 GeV and 250 GeV.