3,020 research outputs found
Diamagnetic and paramagnetic shifts in self-assembled InAs lateral quantum dot molecules
We uncover the underlying physics that explains the energy shifts of discrete states of individual InAs lateral
quantum dot molecules (LQDMs) as a function of magnetic fields applied in the Faraday geometry. We observe
that ground states of the LQDM exhibit a diamagnetic shift while excited states exhibit a paramagnetic shift.
We explain the physical origin of the transition between these two behaviors by analyzing the molecular exciton
states with effective mass calculations. We find that charge carriers in delocalized molecular states can become
localized in single QDs with increasing magnetic field. We further show that the net effects of broken symmetry
of the molecule and Coulomb correlation lead to the paramagnetic response.NSF
DMR-0844747
DMR 1309989
GV VALi+d Grant
APOSTD/2013/052
NRF of Korea
2011-C0030821/2013R1A1A1007118
MINECO Project
CTQ2011-2732
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength
The aim of this paper is to design a deep learning-based model to predict
proximal femoral strength using multi-view information fusion. Method: We
developed new models using multi-view variational autoencoder (MVAE) for
feature representation learning and a product of expert (PoE) model for
multi-view information fusion. We applied the proposed models to an in-house
Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345
African Americans and 586 Caucasians. With an analytical solution of the
product of Gaussian distribution, we adopted variational inference to train the
designed MVAE-PoE model to perform common latent feature extraction. We
performed genome-wide association studies (GWAS) to select 256 genetic variants
with the lowest p-values for each proximal femoral strength and integrated
whole genome sequence (WGS) features and DXA-derived imaging features to
predict proximal femoral strength. Results: The best prediction model for fall
fracture load was acquired by integrating WGS features and DXA-derived imaging
features. The designed models achieved the mean absolute percentage error of
18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using
linear models of fall loading, nonlinear models of fall loading, and nonlinear
models of stance loading, respectively. Compared to existing multi-view
information fusion methods, the proposed MVAE-PoE achieved the best
performance. Conclusion: The proposed models are capable of predicting proximal
femoral strength using WGS features and DXA-derived imaging features. Though
this tool is not a substitute for FEA using QCT images, it would make improved
assessment of hip fracture risk more widely available while avoiding the
increased radiation dosage and clinical costs from QCT.Comment: 16 pages, 3 figure
Renormalization group and nonequilibrium action in stochastic field theory
We investigate the renormalization group approach to nonequilibrium field
theory. We show that it is possible to derive nontrivial renormalization group
flow from iterative coarse graining of a closed-time-path action. This
renormalization group is different from the usual in quantum field theory
textbooks, in that it describes nontrivial noise and dissipation. We work out a
specific example where the variation of the closed-time-path action leads to
the so-called Kardar-Parisi-Zhang equation, and show that the renormalization
group obtained by coarse graining this action, agrees with the dynamical
renormalization group derived by directly coarse graining the equations of
motion.Comment: 33 pages, 3 figures included in the text. Revised; one reference
adde
Broken symmetry and the variation of critical properties in the phase behaviour of supramolecular rhombus tilings
The degree of randomness, or partial order, present in two-dimensional
supramolecular arrays of isophthalate tetracarboxylic acids is shown to vary
due to subtle chemical changes such as the choice of solvent or small
differences in molecular dimensions. This variation may be quantified using an
order parameter and reveals a novel phase behaviour including random tiling
with varying critical properties as well as ordered phases dominated by either
parallel or non-parallel alignment of neighbouring molecules, consistent with
long-standing theoretical studies. The balance between order and randomness is
driven by small differences in the intermolecular interaction energies, which
we show, using numerical simulations, can be related to the measured order
parameter. Significant variations occur even when the energy difference is much
less than the thermal energy highlighting the delicate balance between entropic
and energetic effects in complex self-assembly processes
Exciton swapping in a twisted graphene bilayer as a solid-state realization of a two-brane model
It is shown that exciton swapping between two graphene sheets may occur under
specific conditions. A magnetically tunable optical filter is described to
demonstrate this new effect. Mathematically, it is shown that two turbostratic
graphene layers can be described as a "noncommutative" two-sheeted
(2+1)-spacetime thanks to a formalism previously introduced for the study of
braneworlds in high energy physics. The Hamiltonian of the model contains a
coupling term connecting the two layers which is similar to the coupling
existing between two braneworlds at a quantum level. In the present case, this
term is related to a K-K' intervalley coupling. In addition, the experimental
observation of this effect could be a way to assess the relevance of some
theoretical concepts of the braneworld hypothesis.Comment: 15 pages, 3 figures, final version published in European Physical
Journal
Analytical methods in wineries: is it time to change?
A review of the methods for the most common parameters determined in wine—namely, ethanol, sulfur dioxide, reducing sugars, polyphenols, organic acids, total and volatile acidity, iron, soluble solids, pH, and color—reported in the last 10 years is presented here. The definition of the given parameter, official and usual methods in wineries appear at the beginning of each section, followed by the methods reported in the last decade divided into discontinuous and continuous methods, the latter also are grouped in nonchromatographic and chromatographic methods because of the typical characteristics of each subgroup. A critical comparison between continuous and discontinuous methods for the given parameter ends each section. Tables summarizing the features of the methods and a conclusions section may help users to select the most appropriate method and also to know the state-of-the-art of analytical methods in this area
ST-V-Net: Incorporating Shape Prior Into Convolutional Neural Netwoks For Proximal Femur Segmentation
We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance
A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images
Purpose: Proximal femur image analyses based on quantitative computed
tomography (QCT) provide a method to quantify the bone density and evaluate
osteoporosis and risk of fracture. We aim to develop a deep-learning-based
method for automatic proximal femur segmentation. Methods and Materials: We
developed a 3D image segmentation method based on V-Net, an end-to-end fully
convolutional neural network (CNN), to extract the proximal femur QCT images
automatically. The proposed V-net methodology adopts a compound loss function,
which includes a Dice loss and a L2 regularizer. We performed experiments to
evaluate the effectiveness of the proposed segmentation method. In the
experiments, a QCT dataset which included 397 QCT subjects was used. For the
QCT image of each subject, the ground truth for the proximal femur was
delineated by a well-trained scientist. During the experiments for the entire
cohort then for male and female subjects separately, 90% of the subjects were
used in 10-fold cross-validation for training and internal validation, and to
select the optimal parameters of the proposed models; the rest of the subjects
were used to evaluate the performance of models. Results: Visual comparison
demonstrated high agreement between the model prediction and ground truth
contours of the proximal femur portion of the QCT images. In the entire cohort,
the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 and
a specificity of 0.9992. In addition, an R2 score of 0.9956 (p<0.001) was
obtained when comparing the volumes measured by our model prediction with the
ground truth. Conclusion: This method shows a great promise for clinical
application to QCT and QCT-based finite element analysis of the proximal femur
for evaluating osteoporosis and hip fracture risk
Intrinsic connectivity network disruption in progressive supranuclear palsy
Objective Progressive supranuclear palsy (PSP) has been conceptualized as a large-scale network disruption, but the specific network targeted has not been fully characterized. We sought to delineate the affected network in patients with clinical PSP. Methods Using task-free functional magnetic resonance imaging, we mapped intrinsic connectivity to the dorsal midbrain tegmentum (dMT), a region that shows focal atrophy in PSP. Two healthy control groups (1 young, 1 older) were used to define and replicate the normal connectivity pattern, and patients with PSP were compared to an independent matched healthy control group on measures of network connectivity. Results Healthy young and older subjects showed a convergent pattern of connectivity to the dMT, including brainstem, cerebellar, diencephalic, basal ganglia, and cortical regions involved in skeletomotor, oculomotor, and executive control. Patients with PSP showed significant connectivity disruptions within this network, particularly within corticosubcortical and cortico-brainstem interactions. Patients with more severe functional impairment showed lower mean dMT network connectivity scores. Interpretation This study defines a PSP-related intrinsic connectivity network in the healthy brain and demonstrates the sensitivity of network-based imaging methods to PSP-related physiological and clinical changes. Ann Neurol 2013;73:603-61
Prognosis of HIV Patients Receiving Antiretroviral Therapy According to CD4 Counts: A Long-term Follow-up study in Yunnan, China
We aim to evaluate the overall survival and associated risk factors for HIV-infected Chinese patients on antiretroviral therapy (ART). 2517 patients receiving ART between 2006 and 2016 were prospectively enrolled in Yunnan province. Kaplan-Meier analyses and Cox proportional hazard regression analyses were performed. 216/2517 patients died during a median 17.5 (interquartile range [IQR] 6.8-33.2) months of follow-up. 82/216 occurred within 6 months of starting ART. Adjusted hazard ratios were10.69 (95%CI 2.38-48.02, p = 0.002) for old age, 1.94 (95%CI 1.40-2.69, p < 0.0001) for advanced WHO stage, and 0.42 (95%CI 0.27-0.63, p < 0.0001) for heterosexual transmission compared to injecting drug users. Surprisingly, adjusted hazard ratios comparing low CD4 counts group (<50 cells/μl) with high CD4 counts group (≥500 cells/μl) within six months after starting ART was 20.17 (95%CI 4.62-87.95, p < 0.0001) and it declined to 3.57 (95%CI 1.10-11.58, p = 0.034) afterwards. Age, WHO stage, transmission route are significantly independent risk factors for ART treated HIV patients. Importantly, baseline CD4 counts is strongly inversely associated with survival in the first six months; whereas it becomes a weak prognostic factor after six months of starting ART
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