3,385 research outputs found
Investigation of the phase behaviour of the 1: 1 adduct of mesitylene and hexafluorobenzene
Variable temperature X-ray diffraction has been used to probe the structure and dynamics of the solid adducts of 1,3,5-trimethylbenzene (mesitylene) and hexafluorobenzene. PXRD patterns and DSC traces of near equimolar mixtures reveal two solid-state phase-transitions at 179.2 K and 111.0 K. The crystal structures of all three solid phases of this material have been solved by SXD. In contrast to previous studies on the adduct benzene–hexafluorobenzene, there is pairing of the mesitylene and hexafluorobenzene molecules in all three phases, each consisting of close-packed parallel columns of alternating C6H3(CH3)3 and C6F6 molecules packed face to face in a staggered conformation. Differences in structure between the phases illustrate the subtle interplay of quadrupole versus bond-dipole electrostatic interactions
Chaos around Holographic Regge Trajectories
Using methods of Hamiltonian dynamical systems, we show analytically that a
dynamical system connected to the classical spinning string solution
holographically dual to the principal Regge trajectory is non-integrable. The
Regge trajectories themselves form an integrable island in the total phase
space of the dynamical system. Our argument applies to any gravity background
dual to confining field theories and we verify it explicitly in various
supergravity backgrounds: Klebanov-Strassler, Maldacena-Nunez, Witten QCD and
the AdS soliton. Having established non-integrability for this general class of
supergravity backgrounds, we show explicitly by direct computation of the
Poincare sections and the largest Lyapunov exponent, that such strings have
chaotic motion.Comment: 28 pages, 5 figures. V3: Minor changes complying to referee's
suggestions. Typos correcte
Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals
Spiking neural networks (SNNs) enable power-efficient implementations due to
their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN
that uses unsupervised learning to extract discriminative features from speech
signals, which can subsequently be used in a classifier. The architecture
consists of a spiking convolutional/pooling layer followed by a fully connected
spiking layer for feature discovery. The convolutional layer of leaky,
integrate-and-fire (LIF) neurons represents primary acoustic features. The
fully connected layer is equipped with a probabilistic spike-timing-dependent
plasticity learning rule. This layer represents the discriminative features
through probabilistic, LIF neurons. To assess the discriminative power of the
learned features, they are used in a hidden Markov model (HMM) for spoken digit
recognition. The experimental results show performance above 96% that compares
favorably with popular statistical feature extraction methods. Our results
provide a novel demonstration of unsupervised feature acquisition in an SNN
Simulation-based analysis of micro-robots swimming at the center and near the wall of circular mini-channels
Swimming micro robots have great potential in biomedical applications such as targeted drug delivery, medical diagnosis, and destroying blood clots in arteries. Inspired by swimming micro organisms, micro robots can move in biofluids with helical tails attached to their bodies. In order to design and navigate micro robots, hydrodynamic characteristics of the flow field must be understood well. This work presents computational fluid dynamics (CFD) modeling and analysis of the flow due to the motion of micro robots that consist of magnetic heads and helical tails inside fluid-filled channels akin to bodily conduits; special emphasis is on the effects of the radial position of the robot. Time-averaged velocities, forces, torques, and efficiency of the micro robots placed in the channels are analyzed as functions of rotation frequency, helical pitch (wavelength) and helical radius (amplitude) of the tail. Results indicate that robots move faster and more efficiently near the wall than at the center of the channel. Forces acting on micro robots are asymmetrical due to the chirality of the robot’s tail and its motion. Moreover, robots placed near the wall have a different flow pattern around the head when compared to in-center and unbounded swimmers. According to simulation results, time-averaged for-ward velocity of the robot agrees well with the experimental values measured previously for a robot with almost the same dimensions
Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models
<p>Abstract</p> <p>Background</p> <p>Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.</p> <p>Results</p> <p>In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be formulated using a logistic mixed model. Estimation hence can proceed within a mixed model framework using standard statistical software. A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect. The methods are illustrated using a prostate cancer data set and evaluated using simulations. An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed.</p> <p>Conclusion</p> <p>Logistic kernel machine regression and its extension generalized kernel machine regression provide a novel and flexible statistical tool for modeling pathway effects on discrete and continuous outcomes. Their close connection to mixed models and attractive performance make them have promising wide applications in bioinformatics and other biomedical areas.</p
Gray matter injury associated with periventricular leukomalacia in the premature infant
Neuroimaging studies indicate reduced volumes of certain gray matter regions in survivors of prematurity with periventricular leukomalacia (PVL). We hypothesized that subacute and/or chronic gray matter lesions are increased in incidence and severity in PVL cases compared to non-PVL cases at autopsy. Forty-one cases of premature infants were divided based on cerebral white matter histology: PVL (n = 17) with cerebral white matter gliosis and focal periventricular necrosis; diffuse white matter gliosis (DWMG) (n = 17) without necrosis; and
Shallow water marine sediment bacterial community shifts along a natural CO2 gradient in the Mediterranean Sea off Vulcano, Italy.
The effects of increasing atmospheric CO(2) on ocean ecosystems are a major environmental concern, as rapid shoaling of the carbonate saturation horizon is exposing vast areas of marine sediments to corrosive waters worldwide. Natural CO(2) gradients off Vulcano, Italy, have revealed profound ecosystem changes along rocky shore habitats as carbonate saturation levels decrease, but no investigations have yet been made of the sedimentary habitat. Here, we sampled the upper 2 cm of volcanic sand in three zones, ambient (median pCO(2) 419 μatm, minimum Ω(arag) 3.77), moderately CO(2)-enriched (median pCO(2) 592 μatm, minimum Ω(arag) 2.96), and highly CO(2)-enriched (median pCO(2) 1611 μatm, minimum Ω(arag) 0.35). We tested the hypothesis that increasing levels of seawater pCO(2) would cause significant shifts in sediment bacterial community composition, as shown recently in epilithic biofilms at the study site. In this study, 454 pyrosequencing of the V1 to V3 region of the 16S rRNA gene revealed a shift in community composition with increasing pCO(2). The relative abundances of most of the dominant genera were unaffected by the pCO(2) gradient, although there were significant differences for some 5 % of the genera present (viz. Georgenia, Lutibacter, Photobacterium, Acinetobacter, and Paenibacillus), and Shannon Diversity was greatest in sediments subject to long-term acidification (>100 years). Overall, this supports the view that globally increased ocean pCO(2) will be associated with changes in sediment bacterial community composition but that most of these organisms are resilient. However, further work is required to assess whether these results apply to other types of coastal sediments and whether the changes in relative abundance of bacterial taxa that we observed can significantly alter the biogeochemical functions of marine sediments
Clinical trial of laronidase in Hurler syndrome after hematopoietic cell transplantation.
BackgroundMucopolysaccharidosis I (MPS IH) is a lysosomal storage disease treated with hematopoietic cell transplantation (HCT) because it stabilizes cognitive deterioration, but is insufficient to alleviate all somatic manifestations. Intravenous laronidase improves somatic burden in attenuated MPS I. It is unknown whether laronidase can improve somatic disease following HCT in MPS IH. The objective of this study was to evaluate the effects of laronidase on somatic outcomes of patients with MPS IH previously treated with HCT.MethodsThis 2-year open-label pilot study of laronidase included ten patients (age 5-13 years) who were at least 2 years post-HCT and donor engrafted. Outcomes were assessed semi-annually and compared to historic controls.ResultsThe two youngest participants had a statistically significant improvement in growth compared to controls. Development of persistent high-titer anti-drug antibodies (ADA) was associated with poorer 6-min walk test (6MWT) performance; when patients with high ADA titers were excluded, there was a significant improvement in the 6MWT in the remaining seven patients.ConclusionsLaronidase seemed to improve growth in participants <8 years old, and 6MWT performance in participants without ADA. Given the small number of patients treated in this pilot study, additional study is needed before definitive conclusions can be made
Shaping bursting by electrical coupling and noise
Gap-junctional coupling is an important way of communication between neurons
and other excitable cells. Strong electrical coupling synchronizes activity
across cell ensembles. Surprisingly, in the presence of noise synchronous
oscillations generated by an electrically coupled network may differ
qualitatively from the oscillations produced by uncoupled individual cells
forming the network. A prominent example of such behavior is the synchronized
bursting in islets of Langerhans formed by pancreatic \beta-cells, which in
isolation are known to exhibit irregular spiking. At the heart of this
intriguing phenomenon lies denoising, a remarkable ability of electrical
coupling to diminish the effects of noise acting on individual cells.
In this paper, we derive quantitative estimates characterizing denoising in
electrically coupled networks of conductance-based models of square wave
bursting cells. Our analysis reveals the interplay of the intrinsic properties
of the individual cells and network topology and their respective contributions
to this important effect. In particular, we show that networks on graphs with
large algebraic connectivity or small total effective resistance are better
equipped for implementing denoising. As a by-product of the analysis of
denoising, we analytically estimate the rate with which trajectories converge
to the synchronization subspace and the stability of the latter to random
perturbations. These estimates reveal the role of the network topology in
synchronization. The analysis is complemented by numerical simulations of
electrically coupled conductance-based networks. Taken together, these results
explain the mechanisms underlying synchronization and denoising in an important
class of biological models
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