We study intact and bulging Escherichia coli cells using atomic force microscopy to separate the contributions of the cell wall and turgor pressure to the overall cell stiffness. We find strong ...evidence of power-law stress stiffening in the E. coli cell wall, with an exponent of 1.22±0.12, such that the wall is significantly stiffer in intact cells (E=23±8 MPa and 49±20 MPa in the axial and circumferential directions) than in unpressurized sacculi. These measurements also indicate that the turgor pressure in living cells E. coli is 29±3 kPa.
Protein-directed intracellular transport has not been observed in bacteria despite the existence of dynamic protein localization and a complex cytoskeleton. However, protein trafficking has clear ...potential uses for important cellular processes such as growth, development, chromosome segregation, and motility. Conflicting models have been proposed to explain Myxococcus xanthus motility on solid surfaces, some favoring secretion engines at the rear of cells and others evoking an unknown class of molecular motors distributed along the cell body. Through a combination of fluorescence imaging, force microscopy, and genetic manipulation, we show that membrane-bound cytoplasmic complexes consisting of motor and regulatory proteins are directionally transported down the axis of a cell at constant velocity. This intracellular motion is transmitted to the exterior of the cell and converted to traction forces on the substrate. Thus, this study demonstrates the existence of a conserved class of processive intracellular motors in bacteria and shows how these motors have been adapted to produce cell motility.
Dropout is an effective regularization method for deep learning tasks. Several variants of dropout based on sampling with different distributions have been proposed individually and have shown good ...generalization performance on various learning tasks. Among these variants, the canonical Bernoulli dropout is a discrete method, while the uniform dropout and the Gaussian dropout are continuous dropout methods. When facing a new learning task, one must make a decision on which method is more suitable, which is somehow unnatural and inconvenient. In this paper, we attempt to change the selection problem to a parameter tuning problem by proposing a general form of dropout, <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-dropout, to unify the discrete dropout with continuous dropout. We show that by adjusting the shape parameter <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>, the <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-dropout can yield the Bernoulli dropout, uniform dropout, and approximate Gaussian dropout. Furthermore, it can obtain continuous regularization strength, which paves the way for self-adaptive dropout regularization. As a first attempt, we propose a self-adaptive <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-dropout, in which the parameter <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> is tuned automatically following a pre-designed strategy. The <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-dropout is tested extensively on the MNIST, CIFAR-10, SVHN, NORB, and ILSVRC-12 datasets to investigate its superior performance. The results show that the <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>-dropout can conduct finer control of its regularization strength, therefore obtaining better performance.
Whereas recent studies suggest that cholesterol plays important role in the regulation of membrane proteins, its effect on the interaction of the cell membrane with the underlying cytoskeleton is not ...well understood. Here, we investigated this by measuring the forces needed to extract nanotubes (tethers) from the plasma membrane, using atomic force microscopy. The magnitude of these forces provided a direct measure of cell stiffness, cell membrane effective surface viscosity and association with the underlying cytoskeleton. Furthermore, we measured the lateral diffusion constant of a lipid analog DiIC₁₂, using fluorescence recovery after photobleaching, which offers additional information on the organization of the membrane. We found that cholesterol depletion significantly increased the adhesion energy between the membrane and the cytoskeleton and decreased the membrane diffusion constant. An increase in cellular cholesterol to a level higher than that in control cells led to a decrease in the adhesion energy and the membrane surface viscosity. Disassembly of the actin network abrogated all the observed effects, suggesting that cholesterol affects the mechanical properties of a cell through the underlying cytoskeleton. The results of these quantitative studies may help to better understand the biomechanical processes accompanying the development of atherosclerosis.
Optical coherence tomography (OCT) has been extensively utilized in the field of biomedical imaging due to its non-invasive nature and its ability to provide high-resolution, in-depth imaging of ...biological tissues. However, the use of low-coherence light can lead to unintended interference phenomena within the sample, which inevitably introduces speckle noise into the imaging results. This type of noise often obscures key features in the image, thereby reducing the accuracy of medical diagnoses. Existing denoising algorithms, while removing noise, tend to also damage the structural details of the image, affecting the quality of diagnosis. To overcome this challenge, we have proposed a speckle noise (PSN) framework. The core of this framework is an innovative dual-module noise generator that can decompose the noise in OCT images into speckle noise and equipment noise, addressing each type independently. By integrating the physical properties of noise into the design of the noise generator and training it with unpaired data, we are able to synthesize realistic noise images that match clear images. These synthesized paired images are then used to train a denoiser to effectively denoise real OCT images. Our method has demonstrated its superiority in both private and public datasets, particularly in maintaining the integrity of the image structure. This study emphasizes the importance of considering the physical information of noise in denoising tasks, providing a new perspective and solution for enhancing OCT image denoising technology.
Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are ...established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R2 is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m−3). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions.
Single molecule based superresolution techniques (STORM/PALM) achieve nanometer spatial resolution by integrating the temporal information of the switching dynamics of fluorophores (emitters). When ...emitter density is low for each frame, they are located to the nanometer resolution. However, when the emitter density rises, causing significant overlapping, it becomes increasingly difficult to accurately locate individual emitters. This is particularly apparent in three dimensional (3D) localization because of the large effective volume of the 3D point spread function (PSF). The inability to precisely locate the emitters at a high density causes poor temporal resolution of localization-based superresolution technique and significantly limits its application in 3D live cell imaging. To address this problem, we developed a 3D high-density superresolution imaging platform that allows us to precisely locate the positions of emitters, even when they are significantly overlapped in three dimensional space. Our platform involves a multi-focus system in combination with astigmatic optics and an ℓ 1-Homotopy optimization procedure. To reduce the intrinsic bias introduced by the discrete formulation of compressed sensing, we introduced a debiasing step followed by a 3D weighted centroid procedure, which not only increases the localization accuracy, but also increases the computation speed of image reconstruction. We implemented our algorithms on a graphic processing unit (GPU), which speeds up processing 10 times compared with central processing unit (CPU) implementation. We tested our method with both simulated data and experimental data of fluorescently labeled microtubules and were able to reconstruct a 3D microtubule image with 1000 frames (512×512) acquired within 20 seconds.
Single-molecule localization based super-resolution microscopy, by localizing a sparse subset of stochastically activated emitters in each frame, achieves subdiffraction-limit spatial resolution. Its ...temporal resolution, however, is constrained by the maximal density of activated emitters that can be successfully reconstructed. The state-of-the-art three-dimensional (3-D) reconstruction algorithm based on compressed sensing suffers from high computational complexity and gridding error due to model mismatch. In this paper, we propose a novel super-resolution algorithm for 3-D image reconstruction, dubbed TVSTORM, which promotes the sparsity of activated emitters without discretizing their locations. Several strategies are pursued to improve the reconstruction quality under the Poisson noise model, and reduce the computational time by an order-of-magnitude. Numerical results on both simulated and cell imaging data are provided to validate the favorable performance of the proposed algorithm.
Differentiating arterioles and venules in the fundus image is important for not only various eye diseases but also systemic diseases such as hypertension and ischemic stroke. In this paper, we use ...dual-modal fundus images and develop a cascade refined U-net (CRU-net) to improve the arteriovenous segmentation. In this paper, dual-modal fundus images include not only a regular color fundus image (RGB image) but also another two monochromic images acquired using two different wavelengths, 570 and 610 nm. The choice of these two wavelengths is based on the absorption spectra of hemoglobin. The two monochromic images provide much richer information on the arteriole and venule. Our proposed CRU-net can fully utilize the information and achieves the state-of-the-art performance on our dual-modal dataset (DualModal2019). The arteriovenous classification accuracy evaluated on the automatically detected vessels is 97.27%, significantly surpassed previous methods. The F1-scores are 77.69% and 79.53% for the arteriole and venule segmentation, respectively. We also test our CRU-net on the public DRIVE dataset with only the color fundus images. We achieve the accuracy of 93.97%, F1-scores of 73.50%, and 75.54% for the arteriole and venule, all of which significantly surpassed previously published methods. Our DualModal2019 dataset with manually annotated arterioles and venules is publicly available.
Familial exudative vitreoretinopathy (FEVR) is a hereditary disorder that can damage the retina. This retinal damage can lead to vision loss and even blindness in the late stages. Thus, early ...diagnosis and prevention of the disease's progression are critical. The purpose of this study was to develop an automated diagnosis system for FEVR based on combining deep learning and domain knowledge. A transfer learning scheme was designed to train a deep convolutional neural network (DCNN) to provide segmentation of the retinal vessels. Based on this vessel segmentation and prior clinical knowledge, the vascular characteristics, including the retinal avascular area, vessel angle, fractal dimension, branching and density of blood vessels, were automatically evaluated. Finally, the diagnosis of FEVR was achieved by a feature fusion neural network. Our method was evaluated on 300 images with 168 healthy and 132 FEVR images. By combining deep features and handcrafted features (extracted vascular characteristics), the proposed method achieved an average F1-score of 0.95, with excellent accuracy (94.34%) and sensitivity (91.43%); the quadratic weighted κ was 0.88 for the diagnosis of FEVR. We demonstrated the effectiveness and robustness of the proposed method using five-fold cross-validation. The proposed automatic diagnosis system can assist doctors for better judgment and make sense of early diagnosis and prevention of the disease's progression.
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