Microinfarcts, the "invisible lesions", are prevalent in aged and injured brains and associated with cognitive impairments, yet their neurophysiological impact remains largely unknown. Using a multimodal chr...
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Silver nanowires (AgNWs) hold great promise for applications in wearable electronics, flexible solar cells, chemical and biological sensors, photonic/plasmonic circuits, and scanning probe microscopy (SPM) due to thei...
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Silver nanowires (AgNWs) hold great promise for applications in wearable electronics, flexible solar cells, chemical and biological sensors, photonic/plasmonic circuits, and scanning probe microscopy (SPM) due to their unique plasmonic, mechanical, and electronic properties. However, the lifetime, reliability, and operating conditions of AgNW-based devices are significantly restricted by their poor chemical stability, limiting their commercial potentials. Therefore, it is crucial to create a reliable oxidation barrier on AgNWs that provides long-term chemical stability to various optical, electrical, and mechanical devices while maintaining their high performance. Here we report a room-temperature solution-phase approach to grow an ultra-thin, epitaxial gold coating on AgNWs to effectively shield the Ag surface from environmental oxidation. The Ag@Au core-shell nanowires (Ag@Au NWs) remain stable in air for over six months, under elevated temperature and humidity (80 °C and 100% humidity) for twelve weeks, in physiological buffer solutions for three weeks, and can survive overnight treatment of an oxidative solution (2% H2O2). The Ag@Au core-shell NWs demonstrated comparable performance as pristine AgNWs in various electronic, optical, and mechanical devices, such as transparent mesh electrodes, surface-enhanced Raman spectroscopy (SERS) substrates, plasmonic waveguides, plasmonic nanofocusing probes, and high-aspect-ratio, high-resolution atomic force microscopy (AFM) probes. These Au@Ag core-shell NWs offer a universal solution towards chemically-stable AgNW-based devices without compromising material property or device performance.
Kidney cancer is one of the most common cancers worldwide. The major types of malignant renal tumors are renal cell carcinoma and urothelial carcinoma. Although the majority of renal tumors are malignant, up to $20\%$...
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ISBN:
(数字)9798350363043
ISBN:
(纸本)9798350363050
Kidney cancer is one of the most common cancers worldwide. The major types of malignant renal tumors are renal cell carcinoma and urothelial carcinoma. Although the majority of renal tumors are malignant, up to $20\%$ are benign, most commonly renal cyst and angiomyolipomas. Ultrasound is the most accessible imaging tool in medical practice, but it highly depends on operator skill, which may lead to high false-negative rate in diagnosis. The purpose of this study was to develop a predictive model for the automated classification of renal tumors on ultrasound images using deep neural network. A total of 880 kidney ultrasound images were used for training and testing. Transfer learning was used to the ten Convolution neural network models. The kidney ultrasound images were classified as benign or malignant tumors. The classification performance of the model was evaluated by sensitivity and specificity. The research results show that VGG18 yielded the best performance, with a sensitivity of $79 \%$, and a specificity of $86 \%$.
We conducted wavelet coherence analysis to quantify/image the cerebral neurovascular coupling at infra-slow frequencies during four different awake-to-sleep vigilance states using whole-head, simultaneous EEG and func...
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Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the *** EEG headset is a wearable device that records electrophysiological data from the *** paper presents the design and fab-rica...
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Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the *** EEG headset is a wearable device that records electrophysiological data from the *** paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV *** electrode placement locations are modified under a 10–20 standard *** fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and ***,the data is recorded from 20 subjects who used the EEG Headset,and signals were ***,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide *** raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,thesefindings have been compared to previously reported *** concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 *** also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.
Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to...
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In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of ...
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Due to the ready availability of tree leaves in many geographies, the alternative food of leaf concentrate currently has the potential to alleviate hunger in over 800 million people. It is therefore potentially highly...
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Spatially aligning two computed tomography (CT) scans of the lung using automated image registration techniques is a challenging task due to the deformable nature of the lung. However, existing deep-learning-based lun...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
Spatially aligning two computed tomography (CT) scans of the lung using automated image registration techniques is a challenging task due to the deformable nature of the lung. However, existing deep-learning-based lung CT registration models are not trained with explicit anatomical knowledge. We propose the deformable anatomy-aware registration toolkit (DART), a masked autoencoder (MAE)-based approach, to improve the keypoint-supervised registration of lung CTs. Our method incorporates features from multiple decoders of networks trained to segment anatomical structures, including the lung, ribs, vertebrae, lobes, vessels, and airways, to ensure that the MAE learns relevant features corresponding to the anatomy of the lung. The pretrained weights of the transformer encoder and patch embeddings are then used as the initialization for the training of downstream registration. We compare DART to existing state-of-the-art registration models. Our experiments show that DART outperforms the baseline models (Voxelmorph, ViT-V-Net, and MAE-TransRNet) in terms of target registration error of both corrField-generated keypoints with 17%, 13%, and 9% relative improvement, respectively, and bounding box centers of nodules with 27%, 10%, and 4% relative improvement, respectively. Our implementation is available at https://***/yunzhengzhu/DART.
The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic image...
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