Juan Manuel Muñoz Pichardo

This paper applies three supervised classification techniques to distinguish between tsunami-generated and non-tsunami-generated layers in a sediment core obtained from Doñana National Park (south-west Spain), based on their mineralogical and rare-earth element content. The tsunami-generated layers are characterized by high rare-earth element contents and, in most cases, increased levels of quartz, feldspars and dolomite. This differentiation, combined with palaeontological and sedimentological criteria, reveals that a tsunami occurred in this area during the Upper Pleistocene, between 44 and 50 kyr ago, during the MIS-3 isotopic stage. These statistical techniques could be applied to define tsunami-generated layers in sediment cores more precisely, in conjunction with multidisciplinary geological analyses.

Textural and geochemical analysis of intertidal sediments in the southern Atacama region makes it possible to identify sites primarily affected by mining-related pollution, based on a multivariate statistical analysis of the concentrations of five elements (Fe, Cu, Zn, As, Pb) and their geoaccumulation indices. These concentrations are not correlated with grain size, which is dominated by the sandy fractions. Spearman’s matrix and principal component analysis make it possible to distinguish between two groups of elements (group A: Fe-Cu-As; group B: Zn-Pb), with a strong correlation between them (ρ ≥ 0.51; p < 0.01) and the first two components explain 96.3% of the variance. Three heavily polluted sites (Playa Blanca, Bahía Sarco and Chañaral de Aceituno; I_geo Cu > 8) have been identified linked to waste from the washing of tailings, a copper smelter and frequent boat trips. In addition, four moderately polluted sites (Playa Grande, Balneario Caldera, the mouth of the Copiapó River and Carrizal Bajo; 1.7 < I_geo Cu < 8; 1.2 < I_geo Pb < 2.6), mainly due to activities associated with mining and oil refineries, have been identified.

This communication analyzes the distribution of benthic foraminifera in the infralittoral zone of northern Chile located between 26° and 29°S at depths between 7 and 12 m. Twenty-six taxa have been identified, whose density is mainly conditioned by grain size and hydrodynamics of the environment. A comparison with other coastal sectors of the Pacific coast of South America allows us to identify the most representative species of this area from southern Chile to Colombia. The results obtained are very interesting for their possible application in the reconstruction of Holocene paleoenvironments in the Atacama Desert.

Currently, most diagnostic algorithms for alpha-1 antitrypsin deficiency (AATD) commence with the measurement of serum alpha-1 antitrypsin (AAT) levels, followed by genotyping or phenotyping to confirm the deficiency [1]. However, there is no consensus on the threshold level at which further diagnostic procedures should be pursued [2–5]. To make matters more complicated, it has been shown that elevated acute phase reactants, such as C-reactive protein (CRP), can modify AAT values, further complicating diagnosis [6]. Of note, there no agreement on the AAT threshold levels when a pro-inflammatory situation with increased CRP is present. By examining a real-life prospective cohort encompassing a variety of mutations, the current analysis seeks to establish the optimal cut-off value for serum AAT levels in diagnosing AATD adjusting the estimation with the value of CRP.

Shellfishing was a common activity among prehistoric hunter-gatherers living in coastal areas in Iberia. Interpreting shellfish collection strategies is crucial to understand the lifeways of these coastal hunter-gatherers. Among collection strategies, the estimation of collection areas is essential for interpretation of mobility and subsistence strategies. In this paper we present a new methodological procedure to identify mollusc collection areas using a Technical Scoring Matrix (TSM). A TSM is a qualitative tool that infers the origin of one or more objects based on probability categories that can be quantified using a scoring system. First, a TSM must be built for a given area, including the range of mollusc species identified in archaeological sites, and considering the type of coastline, substrate and the littoral zone where they currently live. The scoring system is then applied to archaeological molluscs recovered from shell middens to establish collection areas. The application of a TSM to Upper Palaeolithic, Epipaleolithic and Mesolithic shell middens from northern Iberia showed that mollusc collection was focused on rocky substrates of exposed and sheltered coastlines during the Magdalenian and the Azilian, with an increase in diversification of collection areas through time, including important shellfishing activity in soft-bottom areas, such as estuaries, during the Mesolithic. From the Azilian onwards, the lower mesolittoral replaced the upper mesolittoral as the most heavily harvested zone. Higher diversification in collection areas and larger collection in the lower zones over time suggest that intensification started at least in the Magdalenian and increased in the Mesolithic, which aligns with previous interpretations based on the decrease in shell size.

Dimensionality reduction techniques are highly useful statistical tools in analyzing datasets from various scientific fields. These methods often provide, in addition to reducing the number of variables or cases, interpretable and informative variables or dimensions. This work proposes a new technique for reducing the number of variables in datasets. The procedure is based on combining Variable Cluster Analysis and Canonical Correlation Analysis to determine synthetic variables that are representative of the clusters. The design of the procedure leads to the definition of a homogeneity index based on the statistical dependence within each cluster, and based on this index, a measure of the adequacy of the obtained cluster structure is proposed. Various artificial datasets have been generated in order to illustrate the ability of the statistical technique to detect the dependence structure between variables and reduce the dimensionality. Given that the technique can be applied to datasets with a dimension greater than the sample size, its application is illustrated in a dataset that suffers from this issue: high-dimensional data and small sample size. Furthermore, the application of the technique on two real data sets is illustrated.