Abstract. This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of f 79.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by fc-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both fc-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators.

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2–, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

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<p class="Body"><span lang="EN-US">Paleogeographic reconstructions of the Western Mediterranean are often based on the present location of sedimentary outcrops. However, most geodynamic and biogeographic models for the region have highlighted the importance of up-to-hundreds of km of horizontal displacements of the terrains forming the western Mediterranean orogenic arcs since the early Miocene until the Pliocene. Here we update the known paleogeographic evolution for the westernmost Mediterranean, considering published biogeographic and recent new geological constraints, including paleontological, stratigraphic, tectonic kinematic data, seismic reflection lines, low-temperature thermochronological dating, detrital zircon age populations, among others. During the Burdigalian to Langhian the rocks of the Betic hinterland, corresponding to the Alboran domain, where exhumed in a forearc setting as far East as Mallorca, now located 450 to 700 km of their present outcrops. Those exhuming rocks floored sedimentary basins among an island archipelago. The land connection between Mallorca and Alboran domains continued until the Serravallian as attested by the shared fossils of vertebrate insular fauna and biogeographic data of different taxa including trap-door spiders, beetles and fresh-water planarians. The westward migration of the Alboran forearc archipelago and its overlying basins (currently forming the Betic intramontane and western Alboran basins) was concomitant to the Langhian to Tortonian opening of the Algero-Balearic back-arc basin and the retreat of the Betic-Rif subducted slab. At a smaller scale, the Granada supra-detachment intramontane basin moved > 100 km between the Tortonian and Present, implying that previously interpreted, emerged domains, like the Sierra Nevada island where either inexistent or in a different location during the Tortonian. Sediment interpreted to represent marine gateways around and through the Alboran archipelago in the westernmost Mediterranean, may have being partially deposited as far East as Mallorca, and probably migrated westwards from the Langhian to the late Pliocene in the Gibraltar straits. Of particular interest, is the Late Messinian to Late Pliocene westward migration of the Gibraltar straits documented by sedimentary onlap over erosive channels in the Western Alboran basin and marine terraces along its Betic and Rif shores. The above proposals are evolving questions concerning the Neogene paleogeographic evolution of the Western Mediterranean that may be tested by future work and drilling.</span></p>