Abstract
The varied phenotypes of obstructive sleep apnea (OSA) poses critical challenges, resulting in missed or delayed diagnosis. In this work, we applied k-modes, aiming to identify groups of OSA patients, based on demographic, physical examination, clinical history, and comorbidities characterization variables (n=41) collected from 318 patients. Missing values were imputed with k-nearest neighbours (k-NN) and chi-square test was held. Thirteen variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 were middle-aged men, while Cluster 3 were the oldest men and Cluster 2 mainly middle-aged women. Cluster 3 weighted the most, whereas Cluster 1 weighted the least. The same effect was described in increased neck circumference. The percentages of variables driving sleepiness, congestive heart failure, arrhythmias and pulmonary hypertension were very low (<20%) and OSA severity was more common in mild level. Our results suggest that it is possible to phenotype OSA patients in an objective way, as also, different (although not considered innovative) visualizations improve the recognition of this common sleep pathology.

Abstract
In pharmacovigilance, reported cases are considered suspected adverse drug reactions (ADR). Health authorities have thus adopted structured causality assessment methods, allowing the evaluation of the likelihood that a drug was the causal agent of an adverse reaction. The aim of this work was to develop and validate a new causality assessment support system used in a regional pharmacovigilance centre. A Bayesian network was developed, for which the structure was defined by experts while the parameters were learnt from 593 completely filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre medical expert between 2000 and 2012. Precision, recall and time to causality assessment (TTA) was evaluated, according to the WHO causality assessment guidelines, in a retrospective cohort of 466 reports (April-September 2014) and a prospective cohort of 1041 reports (January-December 2015). Additionally, a simplified assessment matrix was derived from the model, enabling its preliminary direct use by notifiers. Results show that the network was able to easily identify the higher levels of causality (recall above 80%), although struggling to assess reports with a lower level of causality. Nonetheless, the median (Q1:Q3) ITA was 4 (2:8) days using the network and 8 (5:14) days using global introspection, meaning the network allowed a faster time to assessment, which has a procedural deadline of 30 days, improving daily activities in the centre. The matrix expressed similar validity, allowing an immediate feedback to the notifiers, which may result in better future engagement of patients and health professionals in the pharmacovigilance system.

Abstract
The early classification of university students according to their potential academic performance can be a useful strategy to mitigate failure, to promote the achievement of better results and to better manage resources in higher education institutions. This paper proposes a two-stage model, supported by data mining techniques, that uses the information available at the end of the first year of students' academic career (path) to predict their overall academic performance. Unlike most literature on educational data mining, academic success is inferred from both the average grade achieved and the time taken to conclude the degree. Furthermore, this study proposes to segment students based on the dichotomy between the evidence of failure or high performance at the beginning of the degree program, and the students' performance levels predicted by the model. A data set of 2459 students, spanning the years from 2003 to 2015, from a European Engineering School of a public research University, is used to validate the proposed methodology. The empirical results demonstrate the ability of the proposed model to predict the students' performance level with an accuracy above 95%, in an early stage of the students' academic path. It is found that random forests are superior to the other classification techniques that were considered (decision trees, support vector machines, naive Bayes, bagged trees and boosted trees). Together with the prediction model, the suggested segmentation framework represents a useful tool to delineate the optimum strategies to apply, in order to promote higher performance levels and mitigate academic failure, overall increasing the quality of the academic experience provided by a higher education institution.

Abstract
Doctoral programmes are facing several challenges in modern societies. The societal role of the University, funded by the state, requires it to: a) increase the offer and admission of third cycle students; b) to reach industry/companies expectations; c) to ensure reasonable employability prospects for the PhD candidates. With the current demography, most candidates can only find a job in industry/companies. Therefore, significant pressure is being put on doctoral programmes to include transferable skills in their curriculum. This paper presents a course "Fit for Industry?" aiming at filling this need. The course design methodology is presented in detail. It includes: a) the involvement of industry since its inception; b) the joint identification of a small number of key competencies to be addressed; c) the inclusion of assessment and feedback mechanisms in its design; d) an immersive and international dimension. It was found that the course had a profound impact on the candidates' perceptions of industry and valued by industry participants. Other stakeholders, such as PhD supervisors, also had a positive perception. The paper concludes with recommendations for those willing to replicate the course locally.

Abstract
The highly elliptical, 16-year-period orbit of the star S2 around the massive black hole candidate Sgr A* is a sensitive probe of the gravitational field in the Galactic centre. Near pericentre at 120 AU approximate to 1400 Schwarzschild radii, the star has an orbital speed of approximate to 7650 km s(-1), such that the first-order effects of Special and General Relativity have now become detectable with current capabilities. Over the past 26 years, we have monitored the radial velocity and motion on the sky of S2, mainly with the SINFONI and NACO adaptive optics instruments on the ESO Very Large Telescope, and since 2016 and leading up to the pericentre approach in May 2018, with the four-telescope interferometric beam-combiner instrument GRAVITY. From data up to and including pericentre, we robustly detect the combined gravitational redshift and relativistic transverse Doppler effect for S2 of z = Delta lambda/lambda approximate to 200 km s(-1)/c with different statistical analysis methods. When parameterising the post-Newtonian contribution from these effects by a factor f, with f = 0 and f = 1 corresponding to the Newtonian and general relativistic limits, respectively, we find from posterior fitting with different weighting schemes f = 0.90 +/- 0.09 vertical bar(stat) +/- 0.151 vertical bar(sys). The S2 data are inconsistent with pure Newtonian dynamics.

Abstract
Context. eta Car is one of the most intriguing luminous blue variables in the Galaxy. Observations and models of the X-ray, ultraviolet, optical, and infrared emission suggest a central binary in a highly eccentric orbit with a 5.54 yr period residing in its core. 2D and 3D radiative transfer and hydrodynamic simulations predict a primary with a dense and slow stellar wind that interacts with the faster and lower density wind of the secondary. The wind-wind collision scenario suggests that the secondary's wind penetrates the primary's wind creating a low-density cavity in it, with dense walls where the two winds interact. However, the morphology of the cavity and its physical properties are not yet fully constrained. Aims. We aim to trace the inner similar to 5-50 au structure of eta Car's wind-wind interaction, as seen through Br gamma and, for the first time, through the He i 2s-2p line. Methods. We have used spectro-interferometric observations with the K-band beam-combiner GRAVITY at the VLTI. The analyses of the data include (i) parametrical model-fitting to the interferometric observables, (ii) a CMFGEN model of the source's spectrum, and (iii) interferometric image reconstruction. Results. Our geometrical modeling of the continuum data allows us to estimate its FWHM angular size close to 2 mas and an elongation ratio epsilon = 1.06 +/- 0.05 over a PA = 130 degrees +/- 20 degrees. Our CMFGEN modeling of the spectrum helped us to confirm that the role of the secondary should be taken into account to properly reproduce the observed Br gamma and He i lines. Chromatic images across the Br gamma line reveal a southeast arc-like feature, possibly associated to the hot post-shocked winds flowing along the cavity wall. The images of the He i 2s-2p line served to constrain the 20 mas (similar to 50 au) structure of the line-emitting region. The observed morphology of He i suggests that the secondary is responsible for the ionized material that produces the line profile. Both the Br gamma and the He i 2s-2p maps are consistent with previous hydrodynamical models of the colliding wind scenario. Future dedicated simulations together with an extensive interferometric campaign are necessary to refine our constraints on the wind and stellar parameters of the binary, which finally will help us predict the evolutionary path of eta Car.