Testing the viability of the interacting holographic dark energy model by using combined observational constraints

Using the data coming from the new 182 Gold type Ia supernova samples, the shift parameter of the Cosmic Microwave Background given by the three-year Wilkinson Microwave Anisotropy Probe observations, and the baryon acoustic oscillation measurement from the Sloan Digital Sky Survey, $H(z)$ and lookback time measurements, we have performed a statistical joint analysis of the interacting holographic dark energy model. Consistent parameter estimations show us that the interacting holographic dark energy model is a viable candidate to explain the observed acceleration of our universe.Comment: 15 pages, 9 figures, accepted for publication in JCA

Growth of Large-Area and Highly Crystalline MoS2 Thin Layers on Insulating Substrates

The two-dimensional layer of molybdenum disulfide (MoS2) has recently attracted much interest due to its direct-gap property and potential applications in optoelectronics and energy harvesting. However, the synthetic approach to obtain high quality and large-area MoS2 atomic thin layers is still rare. Here we report that the high temperature annealing of a thermally decomposed ammonium thiomolybdate layer in the presence of sulfur can produce large-area MoS2 thin layers with superior electrical performance on insulating substrates. Spectroscopic and microscopic results reveal that the synthesized MoS2 sheets are highly crystalline. The electron mobility of the bottom-gate transistor devices made of the synthesized MoS2 layer is comparable with those of the micromechanically exfoliated thin sheets from MoS2 crystals. This synthetic approach is simple, scalable and applicable to other transition metal dichalcogenides. Meanwhile, the obtained MoS2 films are transferable to arbitrary substrates, providing great opportunities to make layered composites by stacking various atomically thin layers.Comment: manuscript submitted on 11-Dec-2011, revision submitted on 16-Feb-201

Testing for an effect of a mindfulness induction on child executive functions

Several sessions of mindfulness practice can exert positive gains for child executive functions (EF); however, the evidence for effects of a mindfulness induction, on EF for adults, is mixed and this effect has not been tested in children. The immediate effect of an age appropriate 3-min mindfulness induction on EF of children aged 4–7 years was tested. Participants (N = 156) were randomly assigned to a mindfulness induction or dot-to-dot activity comparison group before completing four measures of EF. A composite score for EF was calculated from summed z scores of the four EF measures. A difference at baseline in behavioural difficulties between the mindfulness induction and comparison group meant that data was analysed using a hierarchical regression. The mindfulness induction resulted in higher average performance for the composite EF score (M = 0.12) compared to the comparison group (M = − 0.05). Behavioural difficulties significantly predicted 5.3% of the variance in EF performance but participation in the mindfulness or comparison induction did not significantly affect EF. The non-significant effect of a mindfulness induction to exert immediate effects on EF fits within broader evidence reporting mixed effects when similar experimental designs have been used with adults. The findings are discussed with consideration of the extent to which methodological differences may account for these mixed effects and how mindfulness inductions fit within broader theoretical and empirical understanding of the effects of mindfulness on EF

A Moment of Mindfulness: Computer-Mediated Mindfulness Practice Increases State Mindfulness

Three studies investigated the use of a 5-minute, computer-mediated mindfulness practice in increasing levels of state mindfulness. In Study 1, 54 high school students completed the computer-mediated mindfulness practice in a lab setting and Toronto Mindfulness Scale (TMS) scores were measured before and after the practice. In Study 2 (N = 90) and Study 3 (N = 61), the mindfulness practice was tested with an entirely online sample to test the delivery of the 5-minute mindfulness practice via the internet. In Study 2 and 3, we found a significant increase in TMS scores in the mindful condition, but not in the control condition. These findings highlight the impact of a brief, mindfulness practice for single-session, computer-mediated use to increase mindfulness as a state

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Diacylglycerol Kinase β Knockout Mice Exhibit Lithium-Sensitive Behavioral Abnormalities

BACKGROUND: Diacylglycerol kinase (DGK) is an enzyme that phosphorylates diacylglycerol (DG) to produce phosphatidic acid (PA). DGKβ is widely distributed in the central nervous system, such as the olfactory bulb, cerebral cortex, striatum, and hippocampus. Recent studies reported that the splice variant at the COOH-terminal of DGKβ was related to bipolar disorder, but its detailed mechanism is still unknown. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we performed behavioral tests using DGKβ knockout (KO) mice to investigate the effects of DGKβ deficits on psychomotor behavior. DGKβ KO mice exhibited some behavioral abnormalities, such as hyperactivity, reduced anxiety, and reduced depression. Additionally, hyperactivity and reduced anxiety were attenuated by the administration of the mood stabilizer, lithium, but not haloperidol, diazepam, or imipramine. Moreover, DGKβ KO mice showed impairment in Akt-glycogen synthesis kinase (GSK) 3β signaling and cortical spine formation. CONCLUSIONS/SIGNIFICANCE: These findings suggest that DGKβ KO mice exhibit lithium-sensitive behavioral abnormalities that are, at least in part, due to the impairment of Akt-GSK3β signaling and cortical spine formation

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures

MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA–pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA–pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA–pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes (“hubs”), most nodes in the miRNA–pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA–pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available

Screening Estrogenic Activities of Chemicals or Mixtures In Vivo Using Transgenic (cyp19a1b-GFP) Zebrafish Embryos

The tg(cyp19a1b-GFP) transgenic zebrafish expresses GFP (green fluorescent protein) under the control of the cyp19a1b gene, encoding brain aromatase. This gene has two major characteristics: (i) it is only expressed in radial glial progenitors in the brain of fish and (ii) it is exquisitely sensitive to estrogens. Based on these properties, we demonstrate that natural or synthetic hormones (alone or in binary mixture), including androgens or progestagens, and industrial chemicals induce a concentration-dependent GFP expression in radial glial progenitors. As GFP expression can be quantified by in vivo imaging, this model presents a very powerful tool to screen and characterize compounds potentially acting as estrogen mimics either directly or after metabolization by the zebrafish embryo. This study also shows that radial glial cells that act as stem cells are direct targets for a large panel of endocrine disruptors, calling for more attention regarding the impact of environmental estrogens and/or certain pharmaceuticals on brain development. Altogether these data identify this in vivo bioassay as an interesting alternative to detect estrogen mimics in hazard and risk assessment perspective