Despite great developments in inertial microfluidics, there is still a lack of knowledge to precisely define the particles' behavior in the microchannels. In the present study, as a prerequisite to experimental studies, numerical simulations have been used to study the capture efficiency of target particles in the contraction-expansion microchannel, aiming to provide an estimation of the conditions at which the channel performs best. Fluid analysis based on Navier-Stokes equations is conducted using the finite element method to determine the streamlines and vortices. The highest capture efficiency for 10, 15, and 19-micron particles occurs when the center of the vortex is approximately in the middle of the wide section (at the flow rate of 0.35 ml/min). In addition to investigating the effect of particle diameter and input flow rate, the effect of channel geometry parameters (channel height and initial length of the channel) on particle trapping has also been studied. Also, to consider great interest in separating different-sized bioparticles from a sample, a three-stage platform has been designed to separate four types of bone marrow cells and evaluate the possibility of using contraction-expansion channels in this application.
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http://dx.doi.org/10.1007/s10544-021-00577-w | DOI Listing |
J Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFNat Mater
January 2025
Condensed Matter Physics and Materials Science Division, Brookhaven National Laboratory, Upton, NY, USA.
Spin waves, or magnons, are essential for next-generation energy-efficient spintronics and magnonics. Yet, visualizing spin-wave dynamics at nanoscale and microwave frequencies remains a formidable challenge due to the lack of spin-sensitive, time-resolved microscopy. Here we report a breakthrough in imaging dipole-exchange spin waves in a ferromagnetic film owing to the development of laser-free ultrafast Lorentz electron microscopy, which is equipped with a microwave-mediated electron pulser for high spatiotemporal resolution.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
View Article and Find Full Text PDFSci Rep
January 2025
College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.
To address the problems of complex cloud features in satellite cloud maps, inaccurate typhoon localization, and poor target detection accuracy, this paper proposes a new typhoon localization algorithm, named TGE-YOLO. It is based on the YOLOv8n model with excellent high-low feature fusion capability and innovatively achieves the organic combination of feature fusion, computational efficiency, and localization accuracy. Firstly, the TFAM_Concat module is creatively designed in the neck network, which comprehensively utilizes the detailed information of shallow features and the semantic information of deeper features, enhancing the fusion ability of features at each layer.
View Article and Find Full Text PDFMethods Enzymol
January 2025
Department of Biology, Indiana University, Bloomington, Indiana, United States. Electronic address:
Exactly two decades ago, the ability to use high-throughput RNA sequencing technology to identify sites of editing by ADARs was employed for the first time. Since that time, RNA sequencing has become a standard tool for researchers studying RNA biology and led to the discovery of RNA editing sites present in a multitude of organisms, across tissue types, and in disease. However, transcriptome-wide sequencing is not without limitations.
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