Reactivity of dolomitizing fluids and Mg source evaluation of fault-controlled dolomitization at the Benicàssim outcrop analogue (Maestrat Basin, E Spain)

Peer reviewedPostprin

Serum sex hormone-binding globulin levels are reduced and inversely associated with intrahepatic lipid content and saturated fatty acid fraction in adult patients with glycogen storage disease type 1a

PURPOSE: De novo lipogenesis has been inversely associated with serum sex hormone-binding globulin (SHBG) levels. However, the directionality of this association has remained uncertain. We, therefore, studied individuals with glycogen storage disease type 1a (GSD1a), who are characterized by a genetic defect in glucose-6-phosphatase resulting in increased rates of de novo lipogenesis, to assess the downstream effect on serum SHBG levels. METHODS: A case-control study comparing serum SHBG levels in patients with GSD1a (n = 10) and controls matched for age, sex, and BMI (n = 10). Intrahepatic lipid content and saturated fatty acid fraction were quantified by proton magnetic resonance spectroscopy. RESULTS: Serum SHBG levels were statistically significantly lower in patients with GSD1a compared to the controls (p = 0.041), while intrahepatic lipid content and intrahepatic saturated fatty acid fraction-a marker of de novo lipogenesis-were significantly higher in patients with GSD1a (p = 0.001 and p = 0.019, respectively). In addition, there was a statistically significant, inverse association of intrahepatic lipid content and saturated fatty acid fraction with serum SHBG levels in patients and controls combined (β: - 0.28, 95% CI: - 0.47;- 0.09 and β: - 0.02, 95% CI: - 0.04;- 0.01, respectively). CONCLUSION: Patients with GSD1a, who are characterized by genetically determined higher rates of de novo lipogenesis, have lower serum SHBG levels than controls

Automated discrete electron tomography – Towards routine high-fidelity reconstruction of nanomaterials

Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials

Long term geological record of a global deep subsurface microbial habitat in sand injection complexes

Peer reviewedPublisher PD

Crystallographic preferred orientations of ice deformed in direct-shear experiments at low temperatures

Synthetic polycrystalline ice was sheared at temperatures of −5, −20 and −30&thinsp;∘C, to different shear strains, up to γ=2.6, equivalent to a maximum stretch of 2.94 (final line length is 2.94 times the original length). Cryo-electron backscatter diffraction (EBSD) analysis shows that basal intracrystalline slip planes become preferentially oriented parallel to the shear plane in all experiments, with a primary cluster of crystal c axes (the c axis is perpendicular to the basal plane) perpendicular to the shear plane. In all except the two highest-strain experiments at −30&thinsp;∘C, a secondary cluster of c axes is observed, at an angle to the primary cluster. With increasing strain, the primary c-axis cluster strengthens. With increasing temperature, both clusters strengthen. In the −5&thinsp;∘C experiments, the angle between the two clusters reduces with strain. The c-axis clusters are elongated perpendicular to the shear direction. This elongation increases with increasing shear strain and with decreasing temperature. Highly curved grain boundaries are more prevalent in samples sheared at higher temperatures. At each temperature, the proportion of curved boundaries decreases with increasing shear strain. Subgrains are observed in all samples. Microstructural interpretations and comparisons of the data from experimentally sheared samples with numerical models suggest that the observed crystallographic orientation patterns result from a balance of the rates of lattice rotation (during dislocation creep) and growth of grains by strain-induced grain boundary migration (GBM). GBM is faster at higher temperatures and becomes less important as shear strain increases. These observations and interpretations provide a hypothesis to be tested in further experiments and using numerical models, with the ultimate goal of aiding the interpretation of crystallographic preferred orientations in naturally deformed ice.</p

Socially impaired robots: Human social disorders and robots’ socio-emotional intelligence

© Springer International Publishing Switzerland 2014. Social robots need intelligence in order to safely coexist and interact with humans. Robots without functional abilities in understanding others and unable to empathise might be a societal risk and they may lead to a society of socially impaired robots. In this work we provide a survey of three relevant human social disorders, namely autism, psychopathy and schizophrenia, as a means to gain a better understanding of social robots’ future capability requirements.We provide evidence supporting the idea that social robots will require a combination of emotional intelligence and social intelligence, namely socio-emotional intelligence. We argue that a robot with a simple socio-emotional process requires a simulation-driven model of intelligence. Finally, we provide some critical guidelines for designing future socio-emotional robots

Relationship between de novo lipogenesis and serum sex hormone binding globulin in humans

Objective Obesity and liver fat are associated with decreased levels of serum sex hormone binding globulin (SHBG). Laboratory studies suggest that hepatic de novo lipogenesis (DNL) is involved in the downregulation of SHBG synthesis. The aim of the present study was to address the role of DNL on serum SHBG in humans. Design A cross-sectional study examining the association between DNL, measured by stable isotopes, and serum SHBG, stratified by sex. Participants Healthy men (n = 34) and women (n = 21) were combined from two cross-sectional studies. Forty-two per cent of participants had hepatic steatosis, and the majority were overweight (62%) or obese (27%). Results DNL was inversely associated with SHBG in women (beta: -0.015, 95% CI: -0.030; 0.000), but not in men (beta: 0.007, 95% CI: -0.005; 0.019) (p for interaction = .068). Adjustment for study population, age and body mass index did not materially change these results, although statistical significance was lost after adjustment for serum insulin. Conclusions An inverse association between DNL and SHBG may explain the decreased SHBG levels that are observed in obesity, at least in women.Peer reviewe

Distinct Transcriptome Expression of the Temporal Cortex of the Primate Microcebus murinus during Brain Aging versus Alzheimer's Disease-Like Pathology

Aging is the primary risk factor of neurodegenerative disorders such as Alzheimer's disease (AD). However, the molecular events occurring during brain aging are extremely complex and still largely unknown. For a better understanding of these age-associated modifications, animal models as close as possible to humans are needed. We thus analyzed the transcriptome of the temporal cortex of the primate Microcebus murinus using human oligonucleotide microarrays (Affymetrix). Gene expression profiles were assessed in the temporal cortex of 6 young adults, 10 healthy old animals and 2 old, “AD-like” animals that presented ß-amyloid plaques and cortical atrophy, which are pathognomonic signs of AD in humans. Gene expression data of the 14,911 genes that were detected in at least 3 samples were analyzed. By SAM (significance analysis of microarrays), we identified 47 genes that discriminated young from healthy old and “AD-like” animals. These findings were confirmed by principal component analysis (PCA). ANOVA of the expression data from the three groups identified 695 genes (including the 47 genes previously identified by SAM and PCA) with significant changes of expression in old and “AD-like” in comparison to young animals. About one third of these genes showed similar changes of expression in healthy aging and in “AD-like” animals, whereas more than two thirds showed opposite changes in these two groups in comparison to young animals. Hierarchical clustering analysis of the 695 markers indicated that each group had distinct expression profiles which characterized each group, especially the “AD-like” group. Functional categorization showed that most of the genes that were up-regulated in healthy old animals and down-regulated in “AD-like” animals belonged to metabolic pathways, particularly protein synthesis. These data suggest the existence of compensatory mechanisms during physiological brain aging that disappear in “AD-like” animals. These results open the way to new exploration of physiological and “AD-like” aging in primates

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology