Miniature fluorescence microscopes are a standard tool in systems ***,widefield miniature microscopes capture only 2D information,and modifications that enable 3D capabilities increase the size and weight and have poo...
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Miniature fluorescence microscopes are a standard tool in systems ***,widefield miniature microscopes capture only 2D information,and modifications that enable 3D capabilities increase the size and weight and have poor resolution outside a narrow depth ***,we achieve the 3D capability by replacing the tube lens of a conventional 2D Miniscope with an optimized multifocal phase mask at the objective’s aperture *** the phase mask at the aperture stop significantly reduces the size of the device,and varying the focal lengths enables a uniform resolution across a wide depth *** phase mask encodes the 3D fluorescence intensity into a single 2D measurement,and the 3D volume is recovered by solving a sparsity-constrained inverse *** provide methods for designing and fabricating the phase mask and an efficient forward model that accounts for the fieldvarying aberrations in miniature *** demonstrate a prototype that is 17mm tall and weighs 2.5 grams,achieving 2.76μm lateral,and 15μm axial resolution across most of the 900×700×390μm^(3) volume at 40 volumes per *** performance is validated experimentally on resolution targets,dynamic biological samples,and mouse brain *** with existing miniature single-shot volume-capture implementations,our system is smaller and lighter and achieves a more than 2×better lateral and axial resolution throughout a 10×larger usable depth *** microscope design provides single-shot 3D imaging for applications where a compact platform matters,such as volumetric neural imaging in freely moving animals and 3D motion studies of dynamic samples in incubators and lab-on-a-chip devices.
Positron emission tomography (PET) is the most sensitive biomedical imaging modality for noninvasively detecting and visualizing positron-emitting radiopharmaceuticals within a subject. In PET, measuring the time-of-f...
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Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures of samples. In the medical domain, p...
Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures of samples. In the medical domain, previous work applied domain knowledge from human experts to qualitatively assess classification difficulty of medical images to guide curriculum learning, which requires extra annotation efforts, relies on subjective human experience, and may introduce bias. In this work, we propose a new automated curriculum learning technique using the variance of gradients (VoG) to compute an objective difficulty measure of samples and evaluated its effects on elbow fracture classification from X-ray images. Specifically, we used VoG as a metric to rank each sample in terms of the classification difficulty, where high VoG scores indicate more difficult cases for classification, to guide the curriculum training process We compared the proposed technique to a baseline (without curriculum learning), a previous method that used human annotations on classification difficulty, and anti-curriculum learning. Our experiment results showed comparable and higher performance for the binary and multi-class bone fracture classification tasks.
In this study, we investigated the drying properties of Sentang (Azadirachta excelsa) dried at different temperatures. Sentang tree planted in Kelantan, Malaysia, was used in this study. Drying specimens with dimensio...
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Decellularized extracellular matrix (dECM)-based materials possess innate biochemical cues to drive cell recruitment and differentiation and are of interest for cartilage tissue engineering. While 3D-printing (3DP) pr...
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Decellularized extracellular matrix (dECM)-based materials possess innate biochemical cues to drive cell recruitment and differentiation and are of interest for cartilage tissue engineering. While 3D-printing (3DP) provides a means for achieving the precise architecture needed for cartilage tissue engineering, dECM hydrogels have proven difficult to translate to 3DP due to low viscosity and weak mechanical properties. In this study, a cartilage dECM (cdECM, 3 w/v%) was combined with varied amounts of gelatin nanoparticles (GNPs;10, 12.5, 15 w/v%) to form novel hydrogel-colloidal composite materials for 3DP. The addition of GNPs increased the viscosity and rheological properties of the cdECM hydrogel in a dose-dependent manner, directly improving the printability of cdECM 3DP inks. Additionally, functionalization of both materials yielded a UV-crosslinkable material for post-printing crosslinking, and increased GNP content increased post-UV storage moduli with 15 w/v% GNPs yielding a storage modulus 26x greater than that of cdECM alone. 3DP construct swelling and degradation were decreased as a function of increased UV-crosslinking dosage (0, 1.5, and 3 J/cm2). After 14 d of swelling in PBS, construct non-porous area was increased by ∼40 % and pore area was increased by ∼30 % for uncrosslinked (0 J/cm2) constructs versus highly crosslinked (3 J/cm2) constructs. Roughly 40 % higher mass retention was observed across GNP content groups for 3 J/cm2 versus 0 J/cm2 UV exposure after 14 d of enzymatic degradation, showing the potential for tuning physicochemical properties via UV exposure. Likewise, the retention of key biochemical components of cdECM over the course of degradation was evaluated. Sulfated glycosaminoglycans, a key reservoir for tissue-specific growth factors, were found to be retained within scaffolds over 14 d of degradation and to be released relative to construct degradation and UV-crosslinking. The
Biomechanics plays an integral role in cancer development and progression. Accurately quantifying the mechanical properties of cells would enable a more comprehensive understanding of cancer, complementing conventiona...
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Denoising diffusion probabilistic models (DDPM) has shown superior performance in image synthesis for various fields. In this study, we propose an enhanced DDPM framework with the joint probability for synthesizing br...
Denoising diffusion probabilistic models (DDPM) has shown superior performance in image synthesis for various fields. In this study, we propose an enhanced DDPM framework with the joint probability for synthesizing brain MRI from brain PET/CT. Although PET/MRI scans provide high-resolution structural information for partial evaluation and image reconstruction, it is costly and time-consuming compared to PET/CT. Also, synthesizing MRI from PET/CT including anatomical details has not been extensively studied. We achieved significant performance of proposed network compared with U-Net and cGAN in various input.
The isothermal amplification coupled with CRISPR detection has attracted tremendous interest for the sensitive, specific, and qualitative detection of nucleic acids. We report here a Microfluidics Enabled Digital Isot...
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Osmolytes play a critical role in enhancing the stability and activity of enzymes for industrial applications. This study systematically investigated the effects of various osmolytes on the activity, optimal pH, tempe...
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