Whereas modern digital cameras use a pixelated detector array to capture images, single-pixel imaging reconstructs images by sampling a scene with a series of masks and associating the knowledge of ...these masks with the corresponding intensity measured with a single-pixel detector. Though not performing as well as digital cameras in conventional visible imaging, single-pixel imaging has been demonstrated to be advantageous in unconventional applications, such as multi-wavelength imaging, terahertz imaging, X-ray imaging, and three-dimensional imaging. The developments and working principles of single-pixel imaging are reviewed, a mathematical interpretation is given, and the key elements are analyzed. The research works of three-dimensional single-pixel imaging and their potential applications are further reviewed and discussed.
In this paper, we discuss the properties for commutators and iterated commutators generated by the multilinear
ω
-CZO and weighted Lipschitz functions on Lebesgue space.
Summary
Heat stress induces misfolded protein accumulation in endoplasmic reticulum (ER), which initiates the unfolded protein response (UPR) in plants. Previous work has demonstrated the important ...role of a rice ER membrane‐associated transcription factor OsbZIP74 (also known as OsbZIP50) in UPR. However, how OsbZIP74 and other membrane‐associated transcription factors are involved in heat stress tolerance in rice is not reported. In the current study, we discovered that OsNTL3 is required for heat stress tolerance in rice. OsNTL3 is constitutively expressed and up‐regulated by heat and ER stresses. OsNTL3 encodes a NAC transcription factor with a predicted C‐terminal transmembrane domain. GFP‐OsNTL3 relocates from plasma membrane to nucleus in response to heat stress and ER stress inducers. Loss‐of‐function mutation of OsNTL3 confers heat sensitivity while inducible expression of the truncated form of OsNTL3 without the transmembrane domain increases heat tolerance in rice seedlings. RNA‐Seq analysis revealed that OsNTL3 regulates the expression of genes involved in ER protein folding and other processes. Interestingly, OsNTL3 directly binds to OsbZIP74 promoter and regulates its expression in response to heat stress. In turn, up‐regulation of OsNTL3 by heat stress is dependent on OsbZIP74. Thus, our work reveals the important role of OsNTL3 in thermotolerance, and a regulatory circuit mediated by OsbZIP74 and OsNTL3 in communications among ER, plasma membrane and nucleus under heat stress conditions.
Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling. However, the single-pixel technique ...requires sequential measurements resulting in a trade-off between spatial resolution and acquisition time, limiting real-time video applications to relatively low resolutions. Compressed sensing techniques can be used to improve this trade-off. However, in this low resolution regime, conventional compressed sensing techniques have limited impact due to lack of sparsity in the datasets. Here we present an alternative compressed sensing method in which we optimize the measurement order of the Hadamard basis, such that at discretized increments we obtain complete sampling for different spatial resolutions. In addition, this method uses deterministic acquisition, rather than the randomized sampling used in conventional compressed sensing. This so-called 'Russian Dolls' ordering also benefits from minimal computational overhead for image reconstruction. We find that this compressive approach performs as well as other compressive sensing techniques with greatly simplified post processing, resulting in significantly faster image reconstruction. Therefore, the proposed method may be useful for single-pixel imaging in the low resolution, high-frame rate regime, or video-rate acquisition.
Deep learning provides an effective way for automatic classification of cardiac arrhythmias, but in clinical decision-making, pure data-driven methods working as black-boxes may lead to ...unsatisfactory results. A promising solution is combining domain knowledge with deep learning. This paper develops a flexible and extensible framework for integrating domain knowledge with a deep neural network. The model consists of a deep neural network to capture the statistical pattern between input data and the ground-truth label, and a knowledge module to guarantee consistency with the domain knowledge. These two components are trained interactively to bring the best of both worlds. The experiments show that the domain knowledge is valuable in refining the neural network prediction and thus improves accuracy.
Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. ...Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.
C-type lectins (CTLs) are characterized by the presence of a C-type carbohydrate recognition domain (CTLD) that by recognizing microbial glycans, is responsible for their roles as pattern recognition ...receptors in the immune response to bacterial infection. In addition to the CTLD, however, some CTLs display additional domains that can carry out effector functions, such as the collagenous domain of the mannose-binding lectin. While in vertebrates, the mechanisms involved in these effector functions have been characterized in considerable detail, in invertebrates they remain poorly understood. In this study, we identified in the kuruma shrimp (Marsupenaeus japonicus) a structurally novel CTL (MjCC-CL) that in addition to the canonical CTLD, contains a coiled-coil domain (CCD) responsible for the effector functions that are key to the shrimp's antibacterial response mediated by antimicrobial peptides (AMPs). By the use of in vitro and in vivo experimental approaches we elucidated the mechanism by which the recognition of bacterial glycans by the CTLD of MjCC-CL leads to activation of the JAK/STAT pathway via interaction of the CCD with the surface receptor Domeless, and upregulation of AMP expression. Thus, our study of the shrimp MjCC-CL revealed a striking functional difference with vertebrates, in which the JAK/STAT pathway is indirectly activated by cell death and stress signals through cytokines or growth factors. Instead, by cross-linking microbial pathogens with the cell surface receptor Domeless, a lectin directly activates the JAK/STAT pathway, which plays a central role in the shrimp antibacterial immune responses by upregulating expression of selected AMPs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Mixed matrix membranes (MMMs), conjugating the advantages of flexible processing‐ability of polymers and high‐speed mass transfer of porous fillers, are recognized as the next‐generation ...high‐performance CO2 capture membranes for solving the current global climate challenge. However, controlling the crystallization of porous metal‐organic frameworks (MOFs) and thus the close stacking of MOF nanocrystals in the confined polymer matrix is still undoable, which thus cannot fully utilize the superior transport attribute of MOF channels. In this study, the “confined swelling coupled solvent‐controlled crystallization” strategy is employed for well‐tailoring the in‐situ crystallization of MOF nanocrystals, realizing rapid (<5 min) construction of defect‐free freeway channels for CO2 transportation in MMMs due to the close stacking of MOF nanocrystals. Consequently, the fabricated MMMs exhibit approximately fourfold enhancement in CO2 permeability, i.e., 2490 Barrer with a CO2/N2 selectivity of 37, distinctive antiplasticization merit, as well as long‐term running stability, which is at top‐tier level, enabling the large‐scale manufacture of high‐performance MMMs for gas separation.
A facile and efficient “confined swelling coupled solvent‐controlled crystallization” strategy is demonstrated to control the in situ crystallization of metal‐organic frameworks nanocrystals in polyethylene oxide membranes, realizing rapid construction of defect‐free freeway channels for CO2 transportation in mixed matrix membranes (MMMs). The optimized MMM showed fourfold enhancement in CO2 permeability, i.e., 2705 Barrer, with a CO2/N2 selectivity of 37.
•A long short-term memory (LSTM) neural networks based car-following model is proposed.•Three characteristics of the asymmetric driving behavior are investigated using LSTM.•The LSTM model ...outperforms other models in capturing the asymmetric driving behavior.
Asymmetric driving behavior is a critical characteristic of human driving behaviors and has a significant impact on traffic flow. In consideration of the asymmetric driving behavior, this paper proposes a long short-term memory (LSTM) neural networks (NN) based car-following (CF) model to capture realistic traffic flow characteristics by incorporating the driving memory. The NGSIM data are used to calibrate and validate the proposed CF model. Meanwhile, three characteristics closely related to the asymmetric driving behavior are investigated: hysteresis, discrete driving, and intensity difference. The simulation results show the good performance of the proposed CF model on reproducing realistic traffic flow features. Moreover, to further demonstrate the superiority of the proposed CF model, two other CF models including recurrent neural network based CF model and asymmetric full velocity difference model, are compared with LSTM-NN model. The results reveal that LSTM-NN model can capture the asymmetric driving behavior well and outperforms other models.
A robust and conductive LiF-rich solid electrolyte interface layer was generated at the phosphorus surface through salt-additive chemistry, and it then served as a high-performance fast-charging ...lithium ion battery anode, delivering a high reversible capacity of 450 mA h g
−1
after 450 cycles with a high charging current density of 8 A g
−1
(about 3 min).
A robust and conductive LiF-rich solid electrolyte interface layer was generated at the phosphorus surface through salt-additive chemistry, and it then served as a high-performance fast-charging lithium ion battery anode.
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