Notes

Fat1 curbs the tumor-initiating ability of nonsmall cell united states cells by promoting Yes-associated protein One nuclear-cytoplasmic translocation.
The proposed algorithm identifies blocks with stretching distortions and subsequently fuses them to predict perceptual quality without reference, achieving improvement in performance compared to existing no-reference QA algorithms that are not trained on the IETR dataset. The proposed algorithm can also identify the blocks with stretching artifacts efficiently, which can further be used in downstream applications to improve the quality of 3D views. Our source code is available online https//github.com/sadbhawnathakur/3D-Image-Quality-Assessment.Lateral motion estimation has been a challenge in ultrasound elastography mainly due to the low resolution, low sampling frequency, and lack of phase information in the lateral direction. Synthetic transmit aperture (STA) can achieve high resolution due to two-way focusing and can beamform high-density image lines for improved lateral motion estimation with the disadvantages of low signal-to-noise ratio (SNR) and limited penetration depth. In this study, Hadamard-encoded STA (Hadamard-STA) is proposed for the improvement of lateral motion estimation in elastography, and it is compared with STA and conventional focused wave (CFW) imaging. Simulations, phantom, and in vivo experiments were conducted to make the comparison. The normalized root mean square error (NRMSE) and the contrast-to-noise ratio (CNR) were calculated as the evaluation criteria in the simulations. The results show that, at a noise level of -10 dB and an applied strain of -1% (compression), Hadamard-STA decreases the NRMSEs of lateral displacemonstrate that Hadamard-STA achieves a substantial improvement in lateral motion estimation and maybe a competitive method for quasi-static elastography.The development of Internet of Things (IoT) requires demanding accurate and low-power indoor localization. In this article, a high-precision 3-D ultrasonic indoor localization system with ultralow power is proposed. learn more A new piezoelectric micromachined ultrasonic transducer (PMUT) chip with a slotted membrane is designed and fabricated as a receiver, breaking the dilemma of low resonant frequency and wide field of view required for indoor localization. The system works based on the time difference of arrival (TDoA), and an improved quantum genetic algorithm (QGA) is used to estimate the location. The results show that the system achieves centimeter-level positioning precision, which is among the best solutions nowadays. Due to the high performance and small size endowed by the PMUT, the receiver footprint reaches as small as 0.25 cm2 and power consumption could reach as low as 0.1 mW, which are far better than that of current indoor localization systems.Transcranial ultrasound imaging (TUI) is a diagnostic modality with numerous applications, but unfortunately, it is hindered by phase aberration caused by the skull. In this article, we propose to reconstruct a transcranial B-mode image with a refraction-corrected synthetic aperture imaging (SAI) scheme. First, the compressional sound velocity of the aberrator (i.e., the skull) is estimated using the bidirectional headwave technique. The medium is described with four layers (i.e., lens, water, skull, and water), and a fast marching method calculates the travel times between individual array elements and image pixels. Finally, a delay-and-sum algorithm is used for image reconstruction with coherent compounding. The point spread function (PSF) in a wire phantom image and reconstructed with the conventional technique (using a constant sound speed throughout the medium), and the proposed method was quantified with numerical synthetic data and experiments with a bone-mimicking plate and a human skull, compared with the PSF achieved in a ground truth image of the medium without the aberrator (i.e., the bone plate or skull). A phased-array transducer (P4-1, ATL/Philips, 2.5 MHz, 96 elements, pitch = 0.295 mm) was used for the experiments. The results with the synthetic signals, the bone-mimicking plate, and the skull indicated that the proposed method reconstructs the scatterers with an average lateral/axial localization error of 0.06/0.14 mm, 0.11/0.13 mm, and 1.0/0.32 mm, respectively. With the human skull, an average contrast ratio (CR) and full-width-half-maximum (FWHM) of 37.1 dB and 1.75 mm were obtained with the proposed approach, respectively. This corresponds to an improvement of CR and FWHM by 7.1 dB and 36% compared with the conventional method, respectively. These numbers were 12.7 dB and 41% with the bone-mimicking plate.In this work, a compact model is presented for a 14-nm CMOS-based fin resonant body transistor (fRBT) operating at a frequency of 11.8 GHz and targeting radio frequency (RF) generation/filtering for next-generation radio communication, clocking, and sensing applications. Analysis of the phononic dispersion characteristics of the device, which informs the model development, shows the presence of displacement component coupling due to the periodic nature of the back-end-of-line (BEOL) metal phononic crystal (PnC). An eigenfrequency-based extraction process, applicable to resonators based on electrostatic force transduction, has been used to model the resonance cavity. Augmented forms of the Berkeley short channel IGFET model (BSIM)-common multigate (CMG) model for FinFETs are used to model the drive and sense transistors in the fRBT. This model framework allows easy integration with the foundry-supplied process design kits (PDKs) and circuit simulators while being flexible toward change in transduction mechanisms and device architecture. Ultimately, the behavior is validated against RF measured data for the fabricated fRBT device under different operating conditions, leading to the demonstration of the first complete model for this class of resonant device integrated seamlessly in the CMOS stack.Limited-angle CT is a challenging problem in real applications. Incomplete projection data will lead to severe artifacts and distortions in reconstruction images. To tackle this problem, we propose a novel reconstruction framework termed Deep Iterative Optimization-based Residual-learning (DIOR) for limited-angle CT. Instead of directly deploying the regularization term on image space, the DIOR combines iterative optimization and deep learning based on the residual domain, significantly improving the convergence property and generalization ability. Specifically, the asymmetric convolutional modules are adopted to strengthen the feature extraction capacity in smooth regions for deep priors. Besides, in our DIOR method, the information contained in low-frequency and high-frequency components is also evaluated by perceptual loss to improve the performance in tissue preservation. Both simulated and clinical datasets are performed to validate the performance of DIOR. Compared with existing competitive algorithms, quantitative and qualitative results show that the proposed method brings a promising improvement in artifact removal, detail restoration and edge preservation.Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are developed to improve the readability of cataract fundus images in order to increase the certainty in diagnosis and treatment for cataract patients. Unfortunately, the requirement of annotation limits the application of these algorithms in clinics. This paper proposes a network to annotation-freely restore cataractous fundus images (ArcNet) so as to boost the clinical practicability of restoration. Annotations are unnecessary in ArcNet, where the high-frequency component is extracted from fundus images to replace segmentation in the preservation of retinal structures. The restoration model is learned from the synthesized images and adapted to real cataract images. Extensive experiments are implemented to verify the performance and effectiveness of ArcNet. Favorable performance is achieved using ArcNet against state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in cataract patients is promoted by ArcNet. The capability of properly restoring cataractous images in the absence of annotated data promises the proposed algorithm outstanding clinical practicability.
Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments.

Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model's decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection.

Making sense of the transformer's self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts.

Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes.

Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings.
Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings.
Tracking changes in hemodynamic congestion and the consequent proactive readjustment of treatment has shown efficacy in reducing hospitalizations for patients with heart failure (HF). However, the cost-prohibitive nature of these invasive sensing systems precludes their usage in the large patient population affected by HF. The objective of this research is to estimate the changes in pulmonary artery mean pressure (PAM) and pulmonary capillary wedge pressure (PCWP) following vasodilator infusion during right heart catheterization (RHC), using changes in simultaneously recorded wearable seismocardiogram (SCG) signals captured with a small wearable patch.

A total of 20 patients with HF (20% women, median age 55 (interquartile range (IQR), 44-64) years, ejection fraction 24 (IQR, 16-43)) were fitted with a wearable sensing patch and underwent RHC with vasodilator challenge. We divided the dataset randomly into a training-testing set (n = 15) and a separate validation set (n = 5). We developed globalized (population) regression models to estimate changes in PAM and PCWP from the changes in simultaneously recorded SCG.

The regression model estimated both pressures with good accuracies root-mean-square-error (RMSE) of 2.5 mmHg and R
of 0.83 for estimating changes in PAM, and RMSE of 1.9 mmHg and R
of 0.93 for estimating changes in PCWP for the training-testing set, and RMSE of 2.7 mmHg and R
of 0.81 for estimating changes in PAM, and RMSE of 2.9 mmHg and R
of 0.95 for estimating changes in PCWP for the validation set respectively.

Changes in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF.

This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.
This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.
Read More: https://www.selleckchem.com/Wnt.html