Temperature dependence of surface stress across an order-disorder transition: p(1x2)O/W(110)

Strain relaxations of a p(1x2) ordered oxygen layer on W(110) are measured as a function of temperature across the disordering transition using low-energy electron diffraction. The measured strains approach values of 0.027 in the [1-10] and -0.053 in the [001] direction. On the basis of the measured strain relaxations, we give quantitative information on temperature-dependent surface stress using the results of ab initio calculations. From the surface formation energy for different strains, determined by first-principles calculations, we estimate that surface stress changes from -1.1 for the ordered phase to -0.2N/m for the disordered one along [1-10], and from 5.1 to 3.4 N/m along [001]. Moreover, our observation that the strains scale inversely with domain size confirms that the strain relaxation takes place at the domain boundaries.Comment: 8 pages, 5 figure

Amyloid and tau in the brain in sporadic Alzheimer's disease: defining the chicken and the egg

In the October 2013 issue of Acta Neuropathologica there were three very interesting articles on: Amyloid or tau: the chicken or the egg? In the first article, David Mann and John Hardy argued that the deposition of aggregated amyloid β (Aβ) protein in the brain is a primary driving force behind the pathogenesis of Alzheimer’s disease with tau pathology following as a consequential or at least a secondary event. In the communication that followed, Braak and Del Tredici presented the contrary argument with accumulation of tau protein as the primary event in sporadic Alzheimer’s disease. Attems and Jellinger questioned the concept of a chicken and egg and suggested that the majority of cases of age-associated dementia are not caused by one single primary pathological mechanism

Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC), and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use shape-from-shading (SfS) to recover depth and provide a richer representation of the tissue’s structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation interception over union (IU) of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state-of-the-art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance

Preventing type 2 diabetes mellitus in Qatar by reducing obesity, smoking, and physical inactivity: mathematical modeling analyses.

BACKGROUND: The aim of this study was to estimate the impact of reducing the prevalence of obesity, smoking, and physical inactivity, and introducing physical activity as an explicit intervention, on the burden of type 2 diabetes mellitus (T2DM), using Qatar as an example. METHODS: A population-level mathematical model was adapted and expanded. The model was stratified by sex, age group, risk factor status, T2DM status, and intervention status, and parameterized by nationally representative data. Modeled interventions were introduced in 2016, reached targeted level by 2031, and then maintained up to 2050. Diverse intervention scenarios were assessed and compared with a counter-factual no intervention baseline scenario. RESULTS: T2DM prevalence increased from 16.7% in 2016 to 24.0% in 2050 in the baseline scenario. By 2050, through halting the rise or reducing obesity prevalence by 10-50%, T2DM prevalence was reduced by 7.8-33.7%, incidence by 8.4-38.9%, and related deaths by 2.1-13.2%. For smoking, through halting the rise or reducing smoking prevalence by 10-50%, T2DM prevalence was reduced by 0.5-2.8%, incidence by 0.5-3.2%, and related deaths by 0.1-0.7%. For physical inactivity, through halting the rise or reducing physical inactivity prevalence by 10-50%, T2DM prevalence was reduced by 0.5-6.9%, incidence by 0.5-7.9%, and related deaths by 0.2-2.8%. Introduction of physical activity with varying intensity at 25% coverage reduced T2DM prevalence by 3.3-9.2%, incidence by 4.2-11.5%, and related deaths by 1.9-5.2%. CONCLUSIONS: Major reductions in T2DM incidence could be accomplished by reducing obesity, while modest reductions could be accomplished by reducing smoking and physical inactivity, or by introducing physical activity as an intervention

A {\mu}-TPC detector for the characterization of low energy neutron fields

The AMANDE facility produces monoenergetic neutron fields from 2 keV to 20 MeV for metrological purposes. To be considered as a reference facility, fluence and energy distributions of neutron fields have to be determined by primary measurement standards. For this purpose, a micro Time Projection Chamber is being developed to be dedicated to measure neutron fields with energy ranging from 8 keV up to 1 MeV. In this work we present simulations showing that such a detector, which allows the measurement of the ionization energy and the 3D reconstruction of the recoil nucleus, provides the determination of neutron energy and fluence of these neutron fields

Phase Transitions on Nonamenable Graphs

We survey known results about phase transitions in various models of statistical physics when the underlying space is a nonamenable graph. Most attention is devoted to transitive graphs and trees

Constructing a Stochastic Model of Bumblebee Flights from Experimental Data

PMCID: PMC3592844This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

α-Dystrobrevin knockout mice have increased motivation for appetitive reward and altered brain cannabinoid receptor 1 expression

Abstract: α-Dystrobrevin (α-DB) is a major component of the dystrophin-associated protein complex (DAPC). Knockout (KO) of α-DB in the brain is associated with astrocytic abnormalities and loss of neuronal GABA receptor clustering. Mutations in DAPC proteins are associated with altered dopamine signaling and cognitive and psychiatric disorders, including schizophrenia. This study tested the hypothesis that motivation and associated underlying biological pathways are altered in the absence of α-DB expression. Male wildtype and α-DB KO mice were tested for measures of motivation, executive function and extinction in the rodent touchscreen apparatus. Subsequently, brain tissues were evaluated for mRNA and/or protein levels of dysbindin-1, dopamine transporter and receptor 1 and 2, mu opioid receptor 1 (mOR1) and cannabinoid receptor 1 (CB1). α-DB KO mice had significantly increased motivation for the appetitive reward, while measures of executive function and extinction were unaffected. No differences were observed between wildtype and KO animals on mRNA levels of dysbindin-1 or any of the dopamine markers. mRNA levels of mOR1were significantly decreased in the caudate-putamen and nucleus accumbens of α-DB KO compared to WT animals, but protein levels were unaltered. However, CB1 protein levels were significantly increased in the prefrontal cortex and decreased in the nucleus accumbens of α-DB KO mice. Triple-labelling immunohistochemistry confirmed that changes in CB1 were not specific to astrocytes. These results highlight a novel role for α-DB in the regulation of appetitive motivation that may have implications for other behaviours that involve the dopaminergic and endocannabinoid systems