Large Quantum Anomalous Hall Effect in Spin-Orbit Proximitized Rhombohedral Graphene

The quantum anomalous Hall effect (QAHE) is a robust topological phenomenon that features quantized Hall resistance at zero magnetic field. Here we report the observation of the QAHE in a rhombohedral pentalayer graphene/monolayer WS2 heterostructure. Distinct from all existing QAHE systems, this system has neither magnetic element nor moir\'e superlattice effect. The QAH states emerge at charge neutrality and feature Chern numbers C = +-5 at temperatures up to about 1.5 K. This large QAHE in our system arises from the synergy of the electron correlation effect in intrinsic flat bands of pentalayer graphene, the gate-tuning effect that breaks the layer/spin-degeneracy, and the proximity-induced Ising spin-orbit-coupling (SOC) effect that further lifts the valley-degeneracy. Our experiment demonstrates the great potential of crystalline two-dimensional materials for intertwined electron correlation and band topology physics, and points to engineering chiral Majorana edge states towards topological quantum computation

Self-assembly of Ni–Fe layered double hydroxide at room temperature for oxygen evolution reaction

Active and stable electrocatalysts are the key to water electrolysis for hydrogen production. This paper reports a facile direct growth method to synthesize NiFe-layered double hydroxides (LDHs) on nickel foil as an electrocatalyst for the oxygen evolution reaction. The NiFe-LDH is synthesized by a galvanic process at room temperature without any additional energy for synthesis. The synthesized NiFe-LDH is a karst landform with abundant active sites and efficient mass diffusion. The NiFe-LDH with an oxygen defect show excellent electrocatalytic performance for the OER, with a low overpotential (272 mV at 10 mA/cm2), a small Tafel slope (43 mV/dec), and superior durability. Direct growth synthesis provide excellent electrical conductivity as well as strong bonding between the catalyst layer and the substrate. In addition, this synthesis process is simple to apply in the fabrication of a large size electrode and is believed to be applicable to commercialized alkaline water electrolysis

Motion Compensation for 3D Multispectral Handheld Photoacoustic Imaging

Three-dimensional (3D) handheld photoacoustic (PA) and ultrasound (US) imaging performed using mechanical scanning are more useful than conventional 2D PA/US imaging for obtaining local volumetric information and reducing operator dependence. In particular, 3D multispectral PA imaging can capture vital functional information, such as hemoglobin concentrations and hemoglobin oxygen saturation (sO2), of epidermal, hemorrhagic, ischemic, and cancerous diseases. However, the accuracy of PA morphology and physiological parameters is hampered by motion artifacts during image acquisition. The aim of this paper is to apply appropriate correction to remove the effect of such motion artifacts. We propose a new motion compensation method that corrects PA images in both axial and lateral directions based on structural US information. 3D PA/US imaging experiments are performed on a tissue-mimicking phantom and a human wrist to verify the effects of the proposed motion compensation mechanism and the consequent spectral unmixing results. The structural motions and sO2 values are confirmed to be successfully corrected by comparing the motion-compensated images with the original images. The proposed method is expected to be useful in various clinical PA imaging applications (e.g., breast cancer, thyroid cancer, and carotid artery disease) that are susceptible to motion contamination during multispectral PA image analysis

Motion Compensation for 3D Multispectral Handheld Photoacoustic Imaging

© 2022 by the authors.Three-dimensional (3D) handheld photoacoustic (PA) and ultrasound (US) imaging performed using mechanical scanning are more useful than conventional 2D PA/US imaging for obtaining local volumetric information and reducing operator dependence. In particular, 3D multispectral PA imaging can capture vital functional information, such as hemoglobin concentrations and hemoglobin oxygen saturation (sO2), of epidermal, hemorrhagic, ischemic, and cancerous diseases. However, the accuracy of PA morphology and physiological parameters is hampered by motion artifacts during image acquisition. The aim of this paper is to apply appropriate correction to remove the effect of such motion artifacts. We propose a new motion compensation method that corrects PA images in both axial and lateral directions based on structural US information. 3D PA/US imaging experiments are performed on a tissue-mimicking phantom and a human wrist to verify the effects of the proposed motion compensation mechanism and the consequent spectral unmixing results. The structural motions and sO2 values are confirmed to be successfully corrected by comparing the motion-compensated images with the original images. The proposed method is expected to be useful in various clinical PA imaging applications (e.g., breast cancer, thyroid cancer, and carotid artery disease) that are susceptible to motion contamination during multispectral PA image analysis.11Nsciescopu

Using bimodal kernel for inference in nonparametric regression with correlated errors

For nonparametric regression model with fixed design, it is well known that obtaining a correct bandwidth is difficult when errors are correlated. Various methods of bandwidth selection have been proposed, but their successful implementation critically depends on a tuning procedure which requires accurate information about error correlation. Unfortunately, such information is usually hard to obtain since errors are not observable. In this article a new bandwidth selector based on the use of a bimodal kernel is proposed and investigated. It is shown that the new bandwidth selector is quite useful for the tuning procedures of various other methods. Furthermore, the proposed bandwidth selector itself proves to be quite effective when the errors are severely correlated.Bimodal kernels Correlated errors Tuning procedure Bandwidth selector

Deep learning‐based multimodal fusion network for segmentation and classification of breast cancers using B‐mode and elastography ultrasound images

Ultrasonography is one of the key medical imaging modalities for evaluating breast lesions. For differentiating benign from malignant lesions, computer-aided diagnosis (CAD) systems have greatly assisted radiologists by automatically segmenting and identifying features of lesions. Here, we present deep learning (DL)-based methods to segment the lesions and then classify benign from malignant, utilizing both B-mode and strain elastography (SE-mode) images. We propose a weighted multimodal U-Net (W-MM-U-Net) model for segmenting lesions where optimum weight is assigned on different imaging modalities using a weighted-skip connection method to emphasize its importance. We design a multimodal fusion framework (MFF) on cropped B-mode and SE-mode ultrasound (US) lesion images to classify benign and malignant lesions. The MFF consists of an integrated feature network (IFN) and a decision network (DN). Unlike other recent fusion methods, the proposed MFF method can simultaneously learn complementary information from convolutional neural networks (CNNs) trained using B-mode and SE-mode US images. The features from the CNNs are ensembled using the multimodal EmbraceNet model and DN classifies the images using those features. The experimental results (sensitivity of 100 ± 0.00% and specificity of 94.28 ± 7.00%) on the real-world clinical data showed that the proposed method outperforms the existing single- and multimodal methods. The proposed method predicts seven benign patients as benign three times out of five trials and six malignant patients as malignant five out of five trials. The proposed method would potentially enhance the classification accuracy of radiologists for breast cancer detection in US images. © 2022 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.11Ysciescopu