The Martian Gas-Analytic Package for the Landing Platform Experiments of the ExoMars 2018

The paper describes the architecture of the Martian Gas Analytic Package, which is proposed for the Russian ExoMars Lander 2018.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Analytic validation of a gas chromatography-mass spectrometry method for quantification of six amino acids in canine serum samples.

OBJECTIVE To analytically validate a gas concentration of chromatography-mass spectrometry (GC-MS) method for measurement of 6 amino acids in canine serum samples and to assess the stability of each amino acid after sample storage. SAMPLES Surplus serum from 80 canine samples submitted to the Gastrointestinal Laboratory at Texas A&M University and serum samples from 12 healthy dogs. PROCEDURES GC-MS was validated to determine precision, reproducibility, limit of detection, and percentage recovery of known added concentrations of 6 amino acids in surplus serum samples. Amino acid concentrations in serum samples from healthy dogs were measured before (baseline) and after storage in various conditions. RESULTS Intra- and interassay coefficients of variation (10 replicates involving 12 pooled serum samples) were 13.4% and 16.6% for glycine, 9.3% and 12.4% for glutamic acid, 5.1% and 6.3% for methionine, 14.0% and 15.1% for tryptophan, 6.2% and 11.0% for tyrosine, and 7.4% and 12.4% for lysine, respectively. Observed-to-expected concentration ratios in dilutional parallelism tests (6 replicates involving 6 pooled serum samples) were 79.5% to 111.5% for glycine, 80.9% to 123.0% for glutamic acid, 77.8% to 111.0% for methionine, 85.2% to 98.0% for tryptophan, 79.4% to 115.0% for tyrosine, and 79.4% to 110.0% for lysine. No amino acid concentration changed significantly from baseline after serum sample storage at -80°C for ≤ 7 days. CONCLUSIONS AND CLINICAL RELEVANCE GC-MS measurement of concentration of 6 amino acids in canine serum samples yielded precise, accurate, and reproducible results. Sample storage at -80°C for 1 week had no effect on GC-MS results.

Vitamin D Deficiency and Depressive Symptomatology in Psychiatric Patients Hospitalized with a Current Depressive Episode: A Factor Analytic Study.

BACKGROUND Low vitamin D levels have been associated with depressive symptoms in population-based studies and non-clinical samples as well as with clinical depression. This study aimed to examine the association of vitamin D levels with the severity and dimensions of depressive symptoms in hospitalized patients with a current episode of depression taking into account confounding variables. METHODS We investigated 380 patients (mean age 47 ± 12 years, 70% women) who were consecutively hospitalized with a main diagnosis of an ICD-10 depressive episode. All patients self-rated depressive symptom severity with the Hospital Anxiety and Depression Scale (HADS-D), the Beck Depression Inventory-II (BDI-II), and the Brief Symptom Inventory. A principal component analysis was performed with all 34 items of these questionnaires and serum levels of 25-hydroxyvitamin D3 (25-OH D) were measured. RESULTS Vitamin D deficiency (< 50 nmol/l), insufficiency (50-75 nmol/l), and sufficiency (> 75 nmol/l) were present in 55.5%, 31.8% and 12.6%, respectively, of patients. Patients with vitamin D deficiency scored higher on the HADS-D scale and on an anhedonia symptom factor than those with insufficient (p-values ≤ 0.023) or sufficient (p-values ≤ 0.008) vitamin D. Vitamin D deficient patients also scored higher on the BDI-II scale than those with sufficient vitamin D (p = 0.007); BDI-II cognitive/affective symptoms, but not somatic/affective symptoms, were higher in patients with vitamin D deficiency (p = 0.005) and insufficiency (p = 0.041) relative to those with sufficient vitamin D. Effect sizes suggested clinically relevant findings. CONCLUSIONS Low vitamin D levels are frequent in hospitalized patients with a current episode of depression. Especially 25-OH D levels < 50 nmol/l were associated with cognitive/affective depressive symptoms, and anhedonia symptoms in particular.

Blue bananas: development of a task to investigate the representation of synaesthetic experiences

Research has mainly focussed on the perceptual nature of synaesthesia. However, synaesthetic experiences are also semantically represented. It was our aim to develop a task to investigate the semantic representation of the concurrent and its relation to the inducer in grapheme-colour synaesthesia. Non-synaesthetes were either tested with a lexical-decision (i.e., word / non-word) or a semantic-classification (i.e., edibility decision) task. Targets consisted of words which were strongly associated with a specific colour (e.g., banana - yellow) and words which were neutral and not associated with a specific colour (e.g., aunt). Target words were primed with colours: the prime target relationship was either intramodal (i.e., word - word) or crossmodal (colour patch - word). Each of the four task versions consisted of three conditions: congruent (same colour for prime and target), incongruent (different colour), and unrelated (neutral target). For both tasks (i.e., lexical and semantic) and both versions of the task (i.e., intramodal and crossmodal), we expected faster reaction times (RTs) in the congruent condition than in the neutral condition and slower RTs in the incongruent condition than the neutral condition. Stronger effects were expected in the intramodal condition due to the overlap in the prime target modality. The results suggest that the hypotheses were partly confirmed. We conclude that the tasks and hypotheses can be readily adopted to investigate the nature of the representation of the synaesthetic experiences.

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths

The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.