Supervised deep networks have achieved promising performance on image denoising, by learning image priors and noise statistics on plenty pairs of noisy and clean images. Unsupervised denoising ...networks are trained with only noisy images. However, for an unseen corrupted image, both supervised and unsupervised networks ignore either its particular image prior, the noise statistics, or both. That is, the networks learned from external images inherently suffer from a domain gap problem: the image priors and noise statistics are very different between the training and test images. This problem becomes more clear when dealing with the signal dependent realistic noise. To circumvent this problem, in this work, we propose a novel "Noisy-As-Clean" (NAC) strategy of training self-supervised denoising networks. Specifically, the corrupted test image is directly taken as the "clean" target, while the inputs are synthetic images consisted of this corrupted image and a second yet similar corruption. A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images. Experiments on synthetic and realistic noise removal demonstrate that, the DnCNN and ResNet networks trained with our self-supervised NAC strategy achieve comparable or better performance than the original ones and previous supervised/unsupervised/self-supervised networks. The code is publicly available at https://github.com/csjunxu/Noisy-As-Clean .
A dipolarizing flux bundle (DFB) is a small magnetotail flux tube (typically < ~3 RE in XGSM and YGSM) with a significantly more dipolar magnetic field than its background. Dipolarizing flux bundles ...typically propagate earthward at a high speed from the near‐Earth reconnection region. Knowledge of a DFB's flux transport properties leads to better understanding of near‐Earth (X = −6 to −30 RE) magnetotail flux transport and thus conversion of magnetic energy to kinetic and thermal plasma energy following magnetic reconnection. We explore DFB properties with a statistical study using data from the Time History of Events and Macroscale Interactions during Substorms mission. To establish the importance of DFB flux transport, we compare it with transport by bursty bulk flows (BBFs) that typically envelop DFBs. Because DFBs coexist with flow bursts inside BBFs, they contribute >65% of BBF flux transport, even though they last only ~30% as long as BBFs. The rate of DFB flux transport increases with proximity to Earth and to the premidnight sector, as well as with geomagnetic activity and distance from the neutral sheet. Under the latter two conditions, the total flux transport by a typical DFB also increases. Dipolarizing flux bundles appear more often during increased geomagnetic activity. Since BBFs have been previously shown to be the major flux transporters in the tail, we conclude that DFBs are the dominant drivers of this transport. The occurrence rate of DFBs as a function of location and geomagnetic activity informs us about processes that shape global convection and energy conversion.
Key Points
Dipolarizing flux bundles are the major flux carrier of bursty bulk flows
DFBs transport flux faster closer to Earth and in tail's premidnight sector
DFBs transport more flux during higher substorm activity
Autophagy is a highly conserved process that degrades certain intracellular contents in both physiological and pathological conditions. Autophagy-related proteins (
) are key players in this pathway, ...among which
is indispensable in both canonical and non-canonical autophagy. Recent studies demonstrate that
modulates the immune system and crosstalks with apoptosis. However, our knowledge of the pathogenesis and regulatory mechanisms of autophagy in various immune related diseases is lacking. Thus, a deeper understanding of
's role in the autophagy mechanism may shed light on the link between autophagy and the immune response, and lead to the development of new therapies for autoimmune diseases and autoinflammatory diseases. In this focused review, we discuss the latest insights into the role of
in autoimmunity. Although these studies are at a relatively early stage,
may eventually come to be regarded as a "guardian of immune integrity." Notably, accumulating evidence indicates that other
genes may have similar functions.
Constitutive and closure models play important roles in computational mechanics and computational physics in general. Classical constitutive models for solid and fluid materials are typically local, ...algebraic equations or flow rules describing the dependence of stress on the local strain and/or strain-rate. Closure models such as those describing Reynolds stress in turbulent flows and laminar–turbulent transition can involve transport PDEs (partial differential equations). Such models play similar roles to constitutive relation, but they are often more challenging to develop and calibrate as they describe nonlocal mappings and often contain many submodels. Inspired by the structure of the exact solutions to linear transport PDEs, we propose a neural network representing a region-to-point mapping to describe such nonlocal constitutive models. The range of nonlocal dependence and the convolution structure are derived from the formal solution to transport equations. The neural network-based nonlocal constitutive model is trained with data. Numerical experiments demonstrate the predictive capability of the proposed method. Moreover, the proposed network learned the embedded submodel without using data from that level, thanks to its interpretable mathematical structure, which makes it a promising alternative to traditional nonlocal constitutive models.
•Proposed a framework for learning nonlocal constitutive models with neural networks.•Neural network structure is inspired by the PDE solution and thus interpretable.•Derived and validated rule-of-thumb guideline for the size of nonlocality.•Demonstrated predictive capability of the proposed constitutive neural network.•The network can learn embedded submodels without using data from that level.
Organic semiconductor gas sensor is one of the promising candidates of room temperature operated gas sensors with high selectivity. However, for a long time the performance of organic semiconductor ...sensors, especially for the detection of oxidizing gases, is far behind that of the traditional metal oxide gas sensors. Although intensive attempts have been made to address the problem, the performance and the understanding of the sensing mechanism are still far from sufficient. Herein, an ultrasensitive organic semiconductor NO2 sensor based on 6,13‐bis(triisopropylsilylethynyl)pentacene (TIPS‐petacene) is reported. The device achieves a sensitivity over 1000%/ppm and fast response/recovery, together with a low limit of detection (LOD) of 20 ppb, all of which reach the level of metal oxide sensors. After a comprehensive analysis on the morphology and electrical properties of the organic films, it is revealed that the ultrahigh performance is largely related to the film charge transport ability, which was less concerned in the studies previously. And the combination of efficient charge transport and low original charge carrier concentration is demonstrated to be an effective access to obtain high performance organic semiconductor gas sensors.
An ultrasensitive organic semiconductor NO2 sensor based on crystalline 6,13‐bis(triisopropylsilylethynyl)pentacene films is achieved with a sensitivity over 1000% ppm–1 and fast response/recovery within 200 s/400 s. The relationship between sensor performance and film charge transport is studied. The low original carrier concentration and efficient charge transport are demonstrated to be key factors for the ultrahigh performance.
Cells of eukaryotic multicellular organisms have inherent heterogeneity. Recent advances in single-cell gene expression studies enable us to explore transcriptional regulation in dynamic development ...processes and highly heterogeneous cell populations. In this study, using a high-throughput single-cell RNA-sequencing assay, we found that the cells in Arabidopsis root are highly heterogeneous in their transcriptomes. A total of 24 putative cell clusters and the cluster-specific marker genes were identified. The spatial distribution and temporal ordering of the individual cells at different developmental stages illustrate their hierarchical structures and enable the reconstruction of continuous differentiation trajectory of root development. Moreover, we found that each root cell cluster exhibits distinct patterns of ion assimilation and hormonal responses. Collectively, our study reveals a high degree of heterogeneity of root cells and identifies the expression signatures of intermediate states during root cell differentiation at single-cell resolution. We also established a web server (http://wanglab.sippe.ac.cn/rootatlas/) to facilitate the use of the datasets generated in this study.
Using high-throughput single-cell RNA sequencing (scRNA-seq) assay, this study reveals high degree of heterogeneity of Arabidopsis root cells and identifies the expression signatures of intermediate states during root cell differentiation at the single-cell resolution. The spatial distribution and temporal ordering of the individual cell at different developmental stages illustrate their hierarchical structure and reconstructs continuous differentiation trajectory of root development.
Systemic lupus erythematosus (SLE) is a complex autoimmune disease, in which immune defects can occur at multiple points of the cascading auto‐aggressive immune reactions, resulting in a striking ...heterogeneity of clinical presentations. The clinical manifestations of such autoimmune response can be severe: common manifestations symptoms include rash and renal inflammation progressing to kidney failure. Autophagy, the cellular “self‐digestion” process, is a key factor in the interplay between innate and adaptive immunity. Dysregulation of autophagy has been implicated in numerous autoimmune diseases. Several lines of evidence from genomic studies, cell culture systems, animal models, and human patients are emerging to support the role of autophagy in progression and pathogenesis of SLE. In this review, we summarize recent key findings on the aberrations of autophagy in SLE, with a special focus on how deregulated autophagy promotes autoimmunity and renal damage. We will also discuss how the observed findings may be translated into therapeutic settings.
Genetic and environmental factors can promote the pathogenesis and/or development of SLE. Normal levels of autophagy contribute to the maintenance of the immune homeostasis, whereas up‐ or downregulated autophagy contributes to the loss of tolerance leading to autoantibodies production. We here review how immune cell autophagy is deregulated in lupus.
Constitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensive. For example, in ...fluid dynamics, constitutive models are required to describe nonlocal, unresolved physics such as turbulence and laminar–turbulent transition. However, traditional constitutive models based on partial differential equations (PDEs) often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that can be learned with data. The model predicts the closure variable at a point based on the flow information in its neighborhood. Such nonlocal information is represented by a group of points, each having a feature vector attached to it, and thus the input is referred to as vector cloud. The cloud is mapped to the closure variable through a frame-independent neural network, invariant both to coordinate translation and rotation and to the ordering of points in the cloud. As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations. The merits of the proposed network are demonstrated for scalar transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains.
•Proposed vector-cloud neural network for learning nonlocal constitutive models.•Preserved invariance to coordinate translation and rotation and to the ordering of points.•Suitable for unstructured meshes in practical applications with any number of arbitrarily arranged grid points.•Demonstrated predictive capability for transport PDEs on a parameterized periodic hill geometries.•The learned nonlocal relationship is consistent with physical intuition.
Developing urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. ...We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations.
Plain Language Summary
Predicting the velocity field in the urban area with fine resolution at the meter scale is computationally expensive. Yet a detailed velocity field is necessary for improving the accuracy of urban land surface representation in weather and climate models. We propose using a convolutional neural network to predict the velocity field from the three‐dimensional (3D) building distribution. The similarity between the predicted velocity fields and LES simulations in the testing geometries illustrates the prediction capability of the trained model. We also investigate the aerodynamic drag coefficient, a key parameter for quantifying the land‐atmosphere momentum exchange. The results indicate that the trained model prediction is much closer to values derived from large‐eddy simulation models than those from the default parameterization scheme, showing the promise of using machine learning to improve urban land surface modeling.
Key Points
Machine learning (ML) can help develop city‐specific parameterization that fully utilizes urban form data
It is a first attempt to develop an ML model for high‐Reynolds number urban canopy flow with multiple bluff‐body obstacles
Limitation of the geometry to flow field approach is quantified by accessing the extrapolative capability of the trained model
Benzoxepane derivatives were designed and synthesized, and one hit compound emerged as being effective in vitro with low toxicity. In vivo, this hit compound ameliorated both sickness behavior ...through anti‐inflammation in LPS‐induced neuroinflammatory mice model and cerebral ischemic injury through anti‐neuroinflammation in rats subjected to transient middle cerebral artery occlusion. Target fishing for the hit compound using photoaffinity probes led to identification of PKM2 as the target protein responsible for anti‐inflammatory effect of the hit compound. Furthermore, the hit exhibited an anti‐neuroinflammatory effect in vitro and in vivo by inhibiting PKM2‐mediated glycolysis and NLRP3 activation, indicating PKM2 as a novel target for neuroinflammation and its related brain disorders. This hit compound has a better safety profile compared to shikonin, a reported PKM2 inhibitor, identifying it as a lead compound in targeting PKM2 for the treatment of inflammation‐related diseases.
Fishing around: The benzoxepane derivative A was effective in vivo, ameliorating both sickness behavior through anti‐inflammation in LPS‐induced neuroinflammatory mice model and cerebral ischemic injury through anti‐neuroinflammation in rats subjected to transient middle cerebral artery occlusion. Target fishing identified PKM2 as the target protein for A. Furthermore, A exhibited an anti‐neuroinflammatory effect in vitro and in vivo by inhibiting PKM2‐mediated glycolysis and NLRP3 activation.
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