Recent progress in observing and manipulating mechanical oscillators at quantum regime provides new opportunities of studying fundamental physics, for example to search for low energy signatures of ...quantum gravity. For example, it was recently proposed that such devices can be used to test quantum gravity effects, by detecting the change in the x^,p^ commutation relation that could result from quantum gravity corrections. We show that such a correction results in a dependence of a resonant frequency of a mechanical oscillator on its amplitude, which is known as the amplitude-frequency effect. By implementing this new method we measure the amplitude-frequency effect for a 0.3 kg ultra-high-Q sapphire split-bar mechanical resonator and for an ∼10−5 kg quartz bulk acoustic wave resonator. Our experiments with a sapphire resonator have established the upper limit on a quantum gravity correction constant of β0 to not exceed 5.2×106, which is a factor of 6 better than previously measured. The reasonable estimates of β0 from experiments with quartz resonators yields β0<4×104. The datasets of 1936 measurements of a physical pendulum period by Atkinson E. C. Atkinson, Proc. Phys. Soc. London 48, 606 (1936). could potentially lead to significantly stronger limitations on β0≪1. Yet, due to the lack of proper pendulum frequency stability measurement in these experiments the exact upper bound on β0 cannot be reliably established. Moreover, pendulum based systems only allow one to test a specific form of the modified commutator that depends on the mean value of momentum. The electromechanical oscillators to the contrary enable testing of any form of generalized uncertainty principle directly due to a much higher stability and a higher degree of control.
We report the results from a haloscope search for axion dark matter in the 3.3-4.2 μeV mass range. This search excludes the axion-photon coupling predicted by one of the benchmark models of ..."invisible" axion dark matter, the Kim-Shifman-Vainshtein-Zakharov model. This sensitivity is achieved using a large-volume cavity, a superconducting magnet, an ultra low noise Josephson parametric amplifier, and sub-Kelvin temperatures. The validity of our detection procedure is ensured by injecting and detecting blind synthetic axion signals.
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•Two approaches for developing predictive models of MCI-to-AD progression were compared.•These models combined MRI-based markers with standard cognitive measures using a longitudinal ...study.•The approaches predict MCI to AD progression with an accuracy of 78% at baseline.•Adding new patient visits improves the performances of the models (in the 36-month visit accuracy is 85%).•The source code and data are available online.
Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD.
This paper compared two approaches for the construction of longitudinal predictive models: a) two-group comparison between converter and nonconverter MCI subjects and b) longitudinal survival analysis. Predictive models combined MRI-based markers with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effects modeling to capture the longitudinal trajectories of the markers. The two-group comparison approaches used linear discriminant analysis and the survival analysis used risk ratios obtained from the extended Cox model and logistic regression.
The proposed approaches were developed and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 1330 visits from 321 subjects. With both approaches, a very small number of features were selected. These markers are easily interpretable, generating robust, verifiable and reliable predictive models. Our best models predicted conversion with 78% accuracy at baseline (AUC = 0.860, 79% sensitivity, 76% specificity). As more visits were made, longitudinal predictive models improved their predictions with 85% accuracy (AUC = 0.944, 86% sensitivity, 85% specificity).
Unlike the recently published models, there was also an improvement in the prediction accuracy of the conversion to AD when considering the longitudinal trajectory of the patients.
The survival-based predictive models showed a better balance between sensitivity and specificity with respect to the models based on the two-group comparison approach.
Patients with mild cognitive impairment (MCI) have a high risk for conversion to Alzheimer’s disease (AD). Early diagnose of AD in MCI subjects could help to slow or halt the disease progression. ...Selecting a set of relevant markers from multimodal data to predict conversion from MCI to probable AD has become a challenging task. The aim of this paper is to quantify the impact of longitudinal predictive models with single- or multisource data for predicting MCI-to-AD conversion and identifying a very small subset of features that are highly predictive of conversion. We developed predictive models of MCI-to-AD progression that combine magnetic resonance imaging (MRI)-based markers (cortical thickness and volume of subcortical structures) with neuropsychological tests. These models were built with longitudinal data and validated using baseline values. By using a linear mixed effects approach, we modeled the longitudinal trajectories of the markers. A set of longitudinal features potentially discriminating between MCI subjects who convert to dementia and those who remain stable over a period of 3 years was obtained. Classifier were trained using the marginal longitudinal trajectory residues from the selected features. Our best models predicted conversion with 77% accuracy at baseline (AUC = 0.855, 84% sensitivity, 70% specificity). As more visits were available, longitudinal predictive models improved their predictions with 84% accuracy (AUC = 0.912, 83% sensitivity, 84% specificity). The proposed approach was developed, trained and evaluated using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with a total of 2491 visits from 610 subjects.
Progress in realizing the SI second had multiple technological impacts and enabled further constraint of theoretical models in fundamental physics. Caesium microwave fountains, realizing best the ...second according to its current definition with a relative uncertainty of 2-4 × 10(-16), have already been overtaken by atomic clocks referenced to an optical transition, which are both more stable and more accurate. Here we present an important step in the direction of a possible new definition of the second. Our system of five clocks connects with an unprecedented consistency the optical and the microwave worlds. For the first time, two state-of-the-art strontium optical lattice clocks are proven to agree within their accuracy budget, with a total uncertainty of 1.5 × 10(-16). Their comparison with three independent caesium fountains shows a degree of accuracy now only limited by the best realizations of the microwave-defined second, at the level of 3.1 × 10(-16).
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•A patch-based labeling method for the hippocampal segmentation is applied.•Our segmentations show accuracy and robustness using multi-site data.•Two markers from the hippocampus ...segmentation are used for predicting progression in AD.•Validated on predicting AD from elderly controls.•The source code is available online.
We provide and evaluate an open-source software solution for automatically measuring hippocampal volume and hippocampal surface roughness based on T1-weighted MRI, which allows for discriminating between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC) using only one scan.
This solution is based on a fast multiple-atlas segmentation technique, which combines a patch-based labeling method with an atlas-warping using non-rigid registrations.
The classifications are comparable to the best classifications in a large clinical dataset. For AD vs control, we obtain a high degree of accuracy, approximately 90%. For MCI vs control, we obtain accuracies ranging from 70% to 78%. The average time for the hippocampal segmentation from a T1-MRI is less than 17min.
In this study, we investigate a combination of our method with annotations using the Harmonized Hippocampal Protocol (HarP). We compare its capabilities with the FreeSurfer method and verify its impact on segmentation and diagnostic group separation capabilities. Our approach is developed and validated using 134 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with annotations from HarP. Then, this method, tuned with the best parameters, is applied to 162 subjects from a private image database.
Our approach with HarP annotations has a high level of accuracy for segmentation of the hippocampus and is robust to multi-site data. The bio-markers extracted from our proposed method have discriminative power based on a scalar feature, showing robustness in generalization and avoid overfitting. The computational time in our hippocampal segmentation algorithm has decreased considerably compared to other available analysis.
Resonant photon modes of a 5-mm-diameter yttrium iron garnet (YIG) sphere loaded in a cylindrical cavity in the 10-30-GHz frequency range are characterized as a function of applied dc magnetic field ...at millikelvin temperatures. The photon modes are confined mainly to the sphere and exhibited large mode filling factors in comparison to previous experiments, allowing ultrastrong coupling with the magnon spin-wave resonances. The largest observed coupling between photons and magnons is 2g/2pi=7.11 GHz for a 15.5-GHz mode, corresponding to a cooperativity of C=1.51+ or -0.47x10 super(7). Complex modifications, beyond a simple multioscillator model, of the photon mode frequencies were observed between 0 and 0.1 T. Between 0.4 and 1 T, degenerate resonant photon modes were observed to interact with magnon spin-wave resonances with different coupling strengths, indicating time-reversal symmetry breaking due to the gyrotropic permeability of YIG. Bare dielectric resonator mode frequencies were determined by detuning magnon modes to significantly higher frequencies with strong magnetic fields. By comparing measured mode frequencies at 7 T with finite element modeling, a bare dielectric permittivity of 15.96+ or -0.02 of the YIG crystal has been determined at about 20 mK.
Hippocampal atrophy measures from magnetic resonance imaging (MRI) are powerful tools for monitoring Alzheimer’s disease (AD) progression. In this paper, we introduce a longitudinal image analysis ...framework based on robust registration and simultaneous hippocampal segmentation and longitudinal marker classification of brain MRI of an arbitrary number of time points. The framework comprises two innovative parts: a longitudinal segmentation and a longitudinal classification step. The results show that both steps of the longitudinal pipeline improved the reliability and the accuracy of the discrimination between clinical groups. We introduce a novel approach to the joint segmentation of the hippocampus across multiple time points; this approach is based on graph cuts of longitudinal MRI scans with constraints on hippocampal atrophy and supported by atlases. Furthermore, we use linear mixed effect (LME) modeling for differential diagnosis between clinical groups. The classifiers are trained from the average residue between the longitudinal marker of the subjects and the LME model. In our experiments, we analyzed MRI-derived longitudinal hippocampal markers from two publicly available datasets (Alzheimer’s Disease Neuroimaging Initiative, ADNI and Minimal Interval Resonance Imaging in Alzheimer’s Disease, MIRIAD). In test/retest reliability experiments, the proposed method yielded lower volume errors and significantly higher dice overlaps than the cross-sectional approach (volume errors: 1.55% vs 0.8%; dice overlaps: 0.945 vs 0.975). To diagnose AD, the discrimination ability of our proposal gave an area under the receiver operating characteristic (ROC) curve (AUC)
=
0.947 for the control vs AD, AUC
=
0.720 for mild cognitive impairment (MCI) vs AD, and AUC
=
0.805 for the control vs MCI.
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