917 research outputs found
Gut microbe-derived extracellular vesicles induce insulin resistance, thereby impairing glucose metabolism in skeletal muscle
Gut microbes might influence host metabolic homeostasis and contribute to the pathogenesis of type 2 diabetes (T2D), which is characterized by insulin resistance. Bacteria-derived extracellular vesicles (EVs) have been suggested to be important in the pathogenesis of diseases once believed to be noninfectious. Here, we hypothesize that gut microbe-derived EVs are important in the pathogenesis of T2D. In vivo administration of stool EVs from high fat diet (HFD)-fed mice induced insulin resistance and glucose intolerance compared to regular diet (RD)-fed mice. Metagenomic profiling of stool EVs by 16S ribosomal DNA sequencing revealed an increased amount of EVs derived from Pseudomonas panacis (phylum Proteobacteria) in HFD mice compared to RD mice. Interestingly, P. panacis EVs blocked the insulin signaling pathway in both skeletal muscle and adipose tissue. Moreover, isolated P. panacis EVs induced typical diabetic phenotypes, such as glucose intolerance after glucose administration or systemic insulin injection. Thus, gut microbe-derived EVs might be key players in the development of insulin resistance and impairment of glucose metabolism promoted by HFD.11148Ysciescopu
Observation of spin Coulomb drag in a two-dimensional electron gas
An electron propagating through a solid carries spin angular momentum in
addition to its mass and charge. Of late there has been considerable interest
in developing electronic devices based on the transport of spin, which offer
potential advantages in dissipation, size, and speed over charge-based devices.
However, these advantages bring with them additional complexity. Because each
electron carries a single, fixed value (-e) of charge, the electrical current
carried by a gas of electrons is simply proportional to its total momentum. A
fundamental consequence is that the charge current is not affected by
interactions that conserve total momentum, notably collisions among the
electrons themselves. In contrast, the electron's spin along a given spatial
direction can take on two values, "up" and "down", so that the spin current and
momentum need not be proportional. Although the transport of spin polarization
is not protected by momentum conservation, it has been widely assumed that,
like the charge current, spin current is unaffected by electron-electron (e-e)
interactions. Here we demonstrate experimentally not only that this assumption
is invalid, but that over a broad range of temperature and electron density,
the flow of spin polarization in a two-dimensional gas of electrons is
controlled by the rate of e-e collisions
Effect of Peierls transition in armchair carbon nanotube on dynamical behaviour of encapsulated fullerene
The changes of dynamical behaviour of a single fullerene molecule inside an
armchair carbon nanotube caused by the structural Peierls transition in the
nanotube are considered. The structures of the smallest C20 and Fe@C20
fullerenes are computed using the spin-polarized density functional theory.
Significant changes of the barriers for motion along the nanotube axis and
rotation of these fullerenes inside the (8,8) nanotube are found at the Peierls
transition. It is shown that the coefficients of translational and rotational
diffusions of these fullerenes inside the nanotube change by several orders of
magnitude. The possibility of inverse orientational melting, i.e. with a
decrease of temperature, for the systems under consideration is predicted.Comment: 9 pages, 6 figures, 1 tabl
Ripple Texturing of Suspended Graphene Atomic Membranes
Graphene is the nature's thinnest elastic membrane, with exceptional
mechanical and electrical properties. We report the direct observation and
creation of one-dimensional (1D) and 2D periodic ripples in suspended graphene
sheets, using spontaneously and thermally induced longitudinal strains on
patterned substrates, with control over their orientations and wavelengths. We
also provide the first measurement of graphene's thermal expansion coefficient,
which is anomalously large and negative, ~ -7x10^-6 K^-1 at 300K. Our work
enables novel strain-based engineering of graphene devices.Comment: 15 pages, 4 figure
Multifunctional semi-interpenetrating polymer network-nanoencapsulated cathode materials for high-performance lithium-ion batteries
As a promising power source to boost up advent of next-generation ubiquitous era, high-energy density lithium-ion batteries with reliable electrochemical properties are urgently requested. Development of the advanced lithium ion-batteries, however, is staggering with thorny problems of performance deterioration and safety failures. This formidable challenge is highly concerned with electrochemical/thermal instability at electrode material-liquid electrolyte interface, in addition to structural/chemical deficiency of major cell components. Herein, as a new concept of surface engineering to address the abovementioned interfacial issue, multifunctional conformal nanoencapsulating layer based on semi-interpenetrating polymer network (semi-IPN) is presented. This unusual semi-IPN nanoencapsulating layer is composed of thermally-cured polyimide (PI) and polyvinyl pyrrolidone (PVP) bearing Lewis basic site. Owing to the combined effects of morphological uniqueness and chemical functionality (scavenging hydrofluoric acid that poses as a critical threat to trigger unwanted side reactions), the PI/PVP semi-IPN nanoencapsulated-cathode materials enable significant improvement in electrochemical performance and thermal stability of lithium-ion batteries.open
Rumour Veracity Estimation with Deep Learning for Twitter
Part 4: Security, Privacy, Ethics and MisinformationInternational audienceTwitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models
Attention deficit hyperactivity symptoms predict problematic mobile phone use
Attention-deficit-hyperactivity disorder (ADHD) is the most commonly diagnosed childhood disorder characterised by inattention, hyperactivity/impulsivity, or both. Some of the key traits of ADHD have previously been linked to addictive and problematic behaviours. The aim of the present study was to examine the relationship between problematic mobile phone use, smartphone
addiction risk and ADHD symptoms in an adult population. A sample of 273 healthy adult volunteers completed the Adult
ADHD Self-Report Scale (ASRS), the Mobile Phone Problem Usage Scale (MPPUS), and the Smartphone Addiction Scale
(SAS). A significant positive correlation was found between the ASRS and both scales. More specifically, inattention symptoms
and age predicted smartphone addiction risk and problematic mobile phone use. Our results suggest that there is a positive
relationship between ADHD traits and problematic mobile phone use. In particular, younger adults with higher level of inattention symptoms could be at higher risk of developing smartphone addiction. The implication of our findings for theoretical
frameworks of problematic mobile phone use and clinical practice are discussed
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