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The southern Iberian megalithic cemeteries of Panoria and El Barranquete offer an excellent opportunity to explore ancient dietary patterns. Due to the special nature of these funerary contexts as palimpsests, a multi-proxy approach including multi-isotopic determination and a high-resolution dating framework was carried out. As a result, 52 samples were isotopically measured, of which 48 were also radiocarbon dated. With this new isotopic series as a basis, three main conclusions can be drawn: (i) the diet was based on C3 plants and terrestrial animals with no evidence of marine protein consumption; (ii) there is a general tendency for carbon isotope values to increase during the Bronze Age, which is consistent with the intensification of crop farming taking place at the time; and (iii) nitrogen isotope variability is especially remarkable when comparing collective to individual tombs. People buried individually show the highest and the most variable nitrogen ratios in contrast with those buried in collective tombs that show similar nitrogen values over time. These differences support the hypothesis of a conservative megalithic population resisting cultural innovations during the Argaric Bronze Age.

Finding graph indices which are unbiased to network size and density is of high importance both within a given field and across fields for enhancing comparability of modern network science studies. The degree variance is an important metric for characterising network degree heterogeneity. Here, we provide an analytically valid normalisation of degree variance to replace previous normalisations which are either invalid or not applicable to all networks. It is shown that this normalisation provides equal values for graphs and their complements; it is maximal in the star graph (and its complement); and its expected value is constant with respect to density for Erd\"os-R\'enyi (ER) random graphs of the same size. We strengthen these results with model observations in ER random graphs, random geometric graphs, scale-free networks, random hierarchy networks and resting-state brain networks, showing that the proposed normalisation is generally less affected by both network size and density than previous normalisation attempts. The closed form expression proposed also benefits from high computational efficiency and straightforward mathematical analysis. Analysis of 184 real-world binary networks across different disciplines shows that normalised degree variance is not correlated with average degree and is robust to node and edge subsampling. Comparisons across subdomains of biological networks reveals greater degree heterogeneity among brain connectomes and food webs than in protein interaction networks., Comment: 16 pages, 6 figures

This study was designed to analyze the magnetoencephalogram (MEG) background activity from 20 patients with probable Alzheimer's disease (AD) and 21 control subjects by using two nonlinear methods: sample entropy (SampEn), and Lempel-Ziv complexity (LZC). The former quantifies the signal regularity, and the latter is a complexity measure. The signals were acquired with a 148-channel whole-head magnetometer placed in a magnetically shielded room. Our results show that MEG recordings are less complex and more regular in patients with AD than in control subjects. Significant differences between both groups were found in 16 MEG channels with SampEn and in 134 with LZC (p

We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI). ApEn quantifies regularity in data, while AMI detects linear and nonlinear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p

Objectives There is a need for techniques to conduct Clinical Trials (CTs) in Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) more efficiently to reduce their duration and cost. However, large variability in the rating scales increases the number of subjects required to obtain significant results in the CTs [1,2]. Alternatively, Magnetic Resonance Imaging (MRI) and Cerebrospinal Fluid (CSF) are promising AD biomarkers but none is optimal for all disease stages [1,2]. Additionally, Machine Learning can detect biomarker patterns to characterize AD and MCI. In this study, we assessed the usefulness of Machine Learning to select subjects with the clearest signs of the disease for inclusion in more efficient CTs [3,4].