We report a method for growing rectangular InAs nanofins with deterministic length, width, and height by dielectric-templated selective-area epitaxy. These freestanding nanofins can be transferred to lay flat on a separate substrate for device fabrication. A key goal was to regain a spatial dimension for device design compared to nanowires, while retaining the benefits of bottom-up epitaxial growth. The transferred nanofins were made into devices featuring multiple contacts for Hall effect and four-terminal resistance studies, as well as a global back-gate and nanoscale local top-gates for density control. Hall studies give a 3D electron density 2.5-5 × 10 cm, corresponding to an approximate surface accumulation layer density 3-6 × 10 cm that agrees well with previous studies of InAs nanowires. We obtain Hall mobilities as high as 1200 cm/(V s), field-effect mobilities as high as 4400 cm/(V s), and clear quantum interference structure at temperatures as high as 20 K. Our devices show excellent prospects for fabrication into more complicated devices featuring multiple ohmic contacts, local gates, and possibly other functional elements, for example, patterned superconductor contacts, that may make them attractive options for future quantum information applications.
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Heliyon
July 2024
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge.
View Article and Find Full Text PDFBiomed Opt Express
January 2025
Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, 200093 Shanghai, China.
Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
View Article and Find Full Text PDFCad Saude Publica
January 2025
Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Córdoba, Córdoba, Argentina.
This study aimed to identify latent (unobservable) dimensions representing specific physical activity-related behaviors and explore their potential effects on obesity burden and spatial distribution in Colombia. A cross-sectional study (n = 9,658) was conducted based on the Colombian National Survey of Nutritional Status. A generalized structural equations model was proposed, combining exposure and measurement models to define a disease model.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, 11829, Badr City, Egypt. Electronic address:
Weakly-supervised learning (WSL) methods have gained significant attention in medical image segmentation, but they often face challenges in accurately delineating boundaries due to overfitting to weak annotations such as bounding boxes. This issue is particularly pronounced in thyroid ultrasound images, where low contrast and noisy backgrounds hinder precise segmentation. In this paper, we propose a novel weakly-supervised segmentation framework that addresses these challenges.
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