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[Display omitted] QED atoms are composed of unstructured and point-like lepton pairs bound together by the electromagnetic force. The smallest and heaviest QED atom is formed by a τ + τ - pair. Currently, the only known atoms of this type are the e + e - and μ + e - atoms, which were discovered 64 years ago and remain the sole examples found thus far. We demonstrate that the J τ ( τ + τ - atom with J PC = 1 - - ) atom signal can be observed with a significance larger than 5 σ including both statistical and systematic uncertainties, via. the process e + e - → X + Y - Ɇ (X , Y = e , μ , π , K , or ρ , and Ɇ is the missing energy due to unobserved neutrinos) with 1.5 ab - 1 data taken around the τ pair production threshold. The τ lepton mass can be measured with a precision of 1 keV with the same data sample. This is within one year's running time of the proposed super tau-charm facility in China or super charm-tau factory in Russia. [ABSTRACT FROM AUTHOR]

In a monocropping pattern for the crop domestication process, the interspecific and intraspecific interaction modes can be changed. In domesticated crops, the intraspecific interactions may have detrimental effects. Moreover, soil‐borne diseases in consecutive monocropped medicinal crops can be a critical challenge that has been difficult to solve. This study has been conducted to evaluate the effect of monoculture and intercropped cultivation on soil‐borne diseases caused by root secretions in American ginseng. The results of the correlation analysis showed that the polyethylenes in the roots had a more negative correlation with the occurrence of soil‐borne diseases than ginsenosides. Therefore, polyethylenes can be considered as an important factor in replanting failure of American ginseng. Furthermore, polyethylenes were able to remain in the soil and have a greater impact on the incidence of soil‐borne diseases than ginsenosides. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Fusing hyperspectral images (HSIs) and multispectral images (MSIs) is an economic and feasible way to obtain images with both high spectral resolution and spatial resolution. Due to the limited receptive field of convolution kernels, fusion methods based on convolutional neural networks (CNNs) fail to take advantage of the global relationship in a feature map. In this paper, to exploit the powerful capability of Transformer to extract global information from the whole feature map for fusion, we propose a novel Multiscale Spatial–spectral Transformer Network (MSST-Net). The proposed network is a two-branch network that integrates the self-attention mechanism of the Transformer to extract spectral features from HSI and spatial features from MSI, respectively. Before feature extraction, cross-modality concatenations are performed to achieve cross-modality information interaction between the two branches. Then, we propose a spectral Transformer (SpeT) to extract spectral features and introduce multiscale band/patch embeddings to obtain multiscale features through SpeTs and spatial Transformers (SpaTs). To further improve the network's performance and generalization, we proposed a self-supervised pre-training strategy, in which a masked bands autoencoder (MBAE) and a masked patches autoencoder (MPAE) are specially designed for self-supervised pre-training of the SpeTs and SpaTs. Extensive experiments on simulated and real datasets illustrate that the proposed network can achieve better performance when compared to other state-of-the-art fusion methods. The code of MSST-Net will be available at http://www.jiasen.tech/papers/ for the sake of reproducibility. [Display omitted] • A multiscale spatial–spectral Transformer network is proposed. • Spectral multi-head self-attention is designed to extract spectral features. • Multiscale band/patch embeddings are introduced to extract Multiscale features. • A self-supervised pre-training strategy is developed. [ABSTRACT FROM AUTHOR]

Background: Neutrophil extracellular traps (NETs) have been found to play a role in the development of autoimmune diseases. In the past two years, studies have demonstrated a significantly increase of NETs in skin tissues during the early stages of IgAV, indicating their involvement in disease activity among children with IgAV. However, the presence of NETs in IgAV animal models has not yet been reported. The objective of this study is to investigate whether NETs are involved in the pathogenesis of IgA vasculitis (IgAV) rats. Methods: Twenty-four SD rats were randomly divided into three groups: the ovalbumin group, the gliadin group, and the control group. The IgAV rat models were established administering Indian ink with ovalbumin (ovalbumin group) or gliadin (gliadin group) with Freund's complete adjuvant. The cell-free DNA (cf-DNA) was quantified by using dsDNA quantification kit, while the levels of Immunoglobulins, complement C3 and myeloperoxidase-DNA (MPO-DNA) in serum were tested using enzyme linked immunosorbent assay (ELISA). The IgA, complement C3 and NETs in tissues were detected through multiple immunofluorescences. Results: Both the ovalbumin group and gliadin group showed IgA and C3 deposition in various tissues, including the glomerular mesangial region, skin, and digestive tract, while the control group showed no such deposition. The levels of circulatory cf-DNA and MPO-DNA, which are components of NETs, were significantly elevated in both ovalbumin and gliadin groups compared with the control group. Furthermore, the presence of NETs were found in gastrointestinal and renal tissues of the ovalbumin and gliadin groups, but not in the control group. Conclusions: IgAV model rat can be established through the combination of ovalbumin and gliadin with Indian ink and Freund's complete adjuvant. This study provides the first confirmation that NETs are involved in the pathogenesis of IgAV rat. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost of acquisition equipment, thereby providing a feasible way to improve the quality of remote sensing images. Clearly, image SR is a severe ill-posed problem. With the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. Since the texture information of various remote sensing images are totally different from each other, in this article, we proposed a network based on generative adversarial network (GAN) to achieve high-resolution remote sensing images, named multiattention GAN (MA-GAN). The main body of the generator in MA-GAN contains three blocks: pyramid convolutional residual dense (PCRD) block, attention-based upsampling (AUP) block, and attention-based fusion (AF) block. Specifically, the developed attention pyramid convolutional (AttPConv) operator in the PCRD block combines multiscale convolution and channel attention (CA) to automatically learn and adjust the scale of residuals for better representation. The established AUP block uses pixel attention (PA) to perform arbitrary scales of upsampling. The AF block uses branch attention (BA) to integrate upsampled low-resolution images with high-level features. Besides, the loss function takes both adversarial loss and feature loss into consideration to guide the learning procedure of the generator. We have compared our MA-GAN approach with several state-of-the-art methods on a number of remote sensing scenes, and the experimental results consistently demonstrate the effectiveness of the proposed MA-GAN. For study replication, the source code will be released at: https://github.com/ZhihaoWang1997/MA-GAN. [ABSTRACT FROM AUTHOR]

Due to the detailed spectral information through hundreds of narrow spectral bands provided by hyperspectral image (HSI) data, it can be employed to accurately classify diverse materials of interest, which is one of the core applications of hyperspectral remote sensing technology. In recent years, with the rapid development of deep learning, convolutional neural networks (CNNs) have been successfully applied in many fields, including HSI classification. However, the random gradient descent-based parameter updating scheme is too general and leading to the inefficiency of CNN models. Moreover, the high dimensionality and limited training samples of HSI data also exacerbate the overfitting problem. To tackle these issues, in this article, a novel deep network with multilayer and multibranch architecture, named 3-D Gabor CNN (3DG-CNN), is proposed for HSI classification. More precisely, since the predefined 3-D Gabor filters in multiple scales and orientations could well characterize the internal spatial–spectral structure of HSI data from various perspectives, the 3-D Gabor-modulated kernels (3-D GMKs) are employed to replace the random initialization kernels. Moreover, the specially designed multibranch architecture enables the network to better integrating the scalable property of 3-D Gabor filters; thus, the representative ability and robustness of the extracted features can be greatly improved. Alternatively, the number of network parameters is substantially reduced due to the incorporation of 3-D Gabor modulation, relieving the training complexity and also alleviating the training process from overfitting. Experimental results on four real HSI datasets (including two newly released ones in the literature) have demonstrated that the proposed 3DG-CNN model can achieve better performance than several widely used machine-learning-based and deep-learning-based approaches. For the sake of reproducibility, the codes of the proposed 3DG-CNN model are available at http://jiasen.tech/papers/. [ABSTRACT FROM AUTHOR]

Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG-Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG-Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG-Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. [ABSTRACT FROM AUTHOR]

Due to the importance in many military and civilian applications, hyperspectral anomaly detection has attracted remarkable interest. Low-rank representation (LRR)-based anomaly detectors use the low-rank property to represent background pixels, and pixels that cannot be well represented are detected as anomalies. The ability of an LRR-based detector to separate background pixels and anomalous pixels depends on the dictionary representation ability, which usually can be enhanced by designing a proper prior for dictionary representation coefficients and constructing a better dictionary. However, it is not easy to handcraft effective and meaningful regularizers for dictionary coefficients. In this article, we propose a novel anomaly detection algorithm that uses a plug-and-play prior for representation coefficients and constructs a new dictionary based on clustering. Instead of cumbersomely handcrafting a regularizer for representation coefficients, we propose solving the anomaly detection problem using the plug-and-play framework, which enables us to plug state-of-the-art priors for representation coefficients. An effective convolutional neural network (CNN) denoiser is plugged into our framework to fully exploit the spatial correlation of representation coefficients. We also propose a modified background dictionary construction method, which carefully includes background pixels and excludes anomalous pixels from clustering results. We refer to the proposed anomaly detection method as plug-and-play denoising CNN regularized anomaly detection (DeCNN-AD) method. Extensive experiments were performed on five data sets in a comparison with eight state-of-the-art anomaly detection methods. The experimental results suggest that the proposed method is effective in anomaly detection and can produce better anomaly detection results than that of the comparison methods. The codes of this work will be available at https://github.com/FxyPd for the sake of reproducibility. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-the-art approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git. [ABSTRACT FROM AUTHOR]

In the hyperspectral image, each pixel corresponds to a small area on the Earth’s surface and represents the intrinsic characteristic of objects, which can be applied for recognition of land covers. Nevertheless, hyperspectral image processing should face some critical issues, and a small sample set problem may be the most challenging one in the research. Deep learning (DL), which has successfully been applied in many fields, has also been introduced for hyperspectral image classification. However, the large gap between the massive parameters to be tuned and limited labeled samples can lead to overfitting scenario, inevitably deteriorating the generalization ability of the DL model. In this article, a lightweight convolutional neural network (LWCNN) is proposed for hyperspectral image classification to mainly tackle the small sample set problem. Especially, spatial–spectral Schroedinger eigenmaps (SSSE) feature extraction is first adopted to obtain the joint spatial–spectral information, and the compressed dimensionality could significantly reduce the number of parameters in the following DL model. Second, a dual-scale convolution (DSC) module is carefully designed to address the SSSE features from a 1-D vector viewpoint (the number of parameters is further decreased), and the DSC procedure is successively employed to obtain the hierarchical structure description that could represent data distribution from different aspects. Subsequently, the feature vectors from all DSC layers are separately filtered by a new bichannel fusion (BCF) module, which could well encode both the intrinsic and contextual information inside DSC features. Finally, the filtered features are concatenated together and imported into a global average pooling classifier to achieve the predicted probability of each category. Experimental results on three famous hyperspectral image data sets illustrate that the developed LWCNN approach is advantageous in both the efficiency and robustness sides for hyperspectral image classification tasks and outperforms other state-of-the-art methods (both traditional-based and DL-based) with very limited labeled samples. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of remote sensing sensors makes it possible to develop multisource feature extraction and fusion techniques to improve the classification accuracy of surface materials. It has been reported that light detection and ranging (LiDAR) data can contribute complementary information to hyperspectral images (HSIs). In this article, a multiple feature-based superpixel-level decision fusion (MFSuDF) method is proposed for HSIs and LiDAR data classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is first designed and applied to HSIs to both reduce the dimensions and compress the noise impact. Next, 2-D and 3-D Gabor filters are, respectively, employed on the KPCA-reduced HSIs and LiDAR data to obtain discriminative Gabor features, and the magnitude and phase information are both taken into account. Three different modules, including the raw data-based feature cube (concatenated KPCA-reduced HSIs and LiDAR data), the Gabor magnitude feature cube, and the Gabor phase feature cube (concatenation of the corresponding Gabor features extracted from the KPCA-reduced HSIs and LiDAR data), can be, thus, achieved. After that, random forest (RF) classifier and quadrant bit coding (QBC) are introduced to separately accomplish the classification task on the aforementioned three extracted feature cubes. Alternatively, two superpixel maps are generated by utilizing the multichannel simple noniterative clustering (SNIC) and entropy rate superpixel segmentation (ERS) algorithms on the combined HSIs and LiDAR data, which are then used to regularize the three classification maps. Finally, a weighted majority voting-based decision fusion strategy is incorporated to effectively enhance the joint use of the multisource data. The proposed approach is, thus, named MFSuDF. A series of experiments are conducted on three real-world data sets to demonstrate the effectiveness of the proposed MFSuDF approach. The experimental results show that our MFSuDF can achieve the overall accuracy of 73.64%, 93.88%, and 74.11% for Houston, Trento, and Missouri University and University of Florida (MUUFL) Gulport data sets, respectively, when there are only three samples per class for training. [ABSTRACT FROM AUTHOR]

As a typical graph-based semisupervised learning technique, the label propagation (LP) approach has gained much attention in recent years. The key to LP algorithms is the propagation capability and efficiency of the similarity matrix, which describes the similarity between two data points. Concerning hyperspectral image which often contains hundreds of thousands of pixels, the corresponding similarity matrix is particularly huge and thus the LP procedure is intractable. Fortunately, superpixel, which can effectively characterize the spatial semantic information of surface objects, provides a reasonable way to solve this problem. In this article, we propose an elaborate superpixel-based weighted LP approach, abbreviated as SuWLP, for hyperspectral image classification. First, the hyperspectral image is oversegmented by the entropy rate segmentation (ERS) method, and the internal consistency of each superpixel can be achieved. Second, a new similarity measure is designed to estimate the similarity between two superpixels, and a superpixel-based similarity matrix can be thus established. Third, after the training samples have been expanded based on the superpixel distribution, a weighted LP technique is designed to propagate the sample label at the superpixel level without any parameter tuning. Finally, the label of each superpixel maps back to the contained pixels. We compared our proposed SuWLP method with several state-of-the-art ones, and experimental results on three real hyperspectral data sets certify the effectiveness and efficiency of the superpixel-level LP strategy. [ABSTRACT FROM AUTHOR]

The aim of this work is to implement real-time 3D MR thermometry for high intensity focused ultrasound (HIFU) monitoring. Volumetric MR thermometry was implemented based on a 3D echo-shifted sequence with short TR to improve temperature sensitivity. The 3D acquisition was accelerated in two phase encoding directions with controlled aliasing in volumetric parallel imaging (CAIPIRINHA). Image reconstruction was run in an open source reconstruction platform (Gadgetron). Phantom experiments showed the proposed volumetric thermometry was comparable to the fiber optical thermometer. In-vivo animal experiments in rabbit thigh showed that the temperature error before and after 4× acceleration was less than 0.65 °C. Finally, real-time 3D thermometry with temporal resolution ~3 s and spatial resolution 2 × 2 × 5 mm3 (spatial coverage 192 × 192 × 80 mm3) was achieved with Gadgetron reconstruction. Real-time temperature monitoring was achieved in-vivo by using parallel imaging accelerated 3D echo-shifted sequence with Gadgetron reconstruction. [ABSTRACT FROM AUTHOR]

Learning from a limited number of labeled samples (pixels) remains a key challenge in the hyperspectral image (HSI) classification. To address this issue, we propose a deep metric learning-based feature embedding model, which can meet the tasks both for same- and cross-scene HSI classifications. In the first task, when only a few labeled samples are available, we employ ideas from metric learning based on deep embedding features and make a similarity learning between pairs of samples. In this case, the proposed model can learn well to compare whether two samples belong to the same class. In another task, when an HSI image (target scene) that needs to be classified is not labeled at all, the embedding model can learn from another similar HSI image (source scene) with sufficient labeled samples and then transfer to the target model by using an unsupervised domain adaptation technique, which not only employs the adversarial approach to make the embedding features from the source and target samples indistinguishable but also encourages the target scene’s embeddings to form similar clusters with the source scene one. After the domain adaptation between the HSIs of the two scenes is finished, any traditional HSI classifier can be used. In a simple manner, the nearest neighbor (NN) algorithm is selected as the classifier for the classification tasks throughout this article. The experimental results from a series of popular HSIs demonstrate the advantages of the proposed model both in the same- and cross-scene classification tasks. [ABSTRACT FROM AUTHOR]

Objectives: To compare visualization of joint intracranial and carotid vessel walls between 5× compressed sensing accelerated three-dimensional DANTE-SPACE sequence (CS VWI) acquired in 5 min and the same sequence accelerated by 2.7× parallel imaging (PI VWI) which takes 9–10 min currently. Methods: Following institutional review board approval and informed consent, 28 subjects including 20 stroke patients underwent PI and CS VWI examinations with an acquired spatial resolution of isotropic 0.55 mm and joint coverage of intracranial and carotid arteries. Quantitative wall thickness measurements of CS VWI and PI VWI were compared on healthy volunteers and patients with wall thickening respectively. Subjective wall visualizations of the two VWI methods on patients were scored by two radiologists blindly and independently using a 4-point scale followed by inter-rater reproducibility analysis. Results: Linear regression analysis of wall thickness measurements showed excellent agreement between CS VWI and PI VWI in both healthy volunteers (r = 0.99) and stroke patients with wall thickening (r = 0.99). Subjective wall visualization score of CS VWI was slightly lower than PI VWI (3.13 ± 0.41 vs. 3.31 ± 0.79) but still had good diagnostic quality (> 3 based on a 4-point scale). The two radiologists' scores agreed excellently, evidenced by the intraclass correlation coefficient (ICC) values being higher than 0.75 (p < 0.001). Conclusions: Compressed sensing expedients joint intracranial and carotid VWI acquired at an isotropic resolution of 0.55 mm in 5 min without compromising quantitative vessel wall thickness measurement or diagnostic wall visualization. Key Points: • CS VWI facilitates comprehensive visualization of intracranial and carotid vessel walls at an acquired isotropic resolution of 0.55 mm in a single 5-min scan. • CS VWI affords comparable vessel wall visualization and morphology measurement as PI VWI with a shortened acquisition time by 45%. • CS VWI alleviates the intensive trade-off between imaging resolution and scan time, and benefits the scan efficiency, motion robustness, and patient tolerance of high-resolution joint intracranial and carotid VWI. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian–Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian–Gabor filters are convolved with the concatenated data of both extended multi-attribute profile (EMAP) features and raw hyperspectral data. Second, an improved fast density peak clustering (IFDPC) method is introduced to select the most representative features from each extracted 3-D Gaussian–Gabor feature cube. Finally, the retained features are combined together to accomplish the classification task. The proposed method is thus named as GG-IFDPC. Three real hyperspectral imagery data sets have been utilized, and the experiments demonstrate the advantages of the proposed GG-IFDPC approach over the compared ones. [ABSTRACT FROM AUTHOR]

In virtue of the spatial structural characteristic of surface materials, the performance of the hyperspectral image classification can be boosted by incorporating texture information. Normally, the spatial structure can be extracted by predefined operators, including the popular extended multiattribute profiles (EMAPs) and the Gabor filters. Recently, superpixel segmentation, which reflects the homogeneous regularity of objects, has drawn much attention in the field. In this paper, a collaborative representation-based multiscale superpixel fusion (CRMSF) approach has been proposed for the hyperspectral image classification. First, after obtaining the EMAPs from the raw hyperspectral image, a group of predesigned 3-D Gabor wavelet filters is convolved with the EMAP features, and the EMAP-Gabor features can, thus, be achieved. Second, the collaborative representation-based classification (CRC) is employed to fully and efficiently make use of the huge amount of extracted EMAP-Gabor features. Third, multiscale superpixel maps are generated from the EMAP features that are utilized to regularize the classification map obtained by CRC. A heuristic strategy has been specially devised to automatically decide the number of extracted superpixels in multiple scales, which can be perfectly compatible with hyperspectral images having various spatial sizes and spatial resolutions. This is the most important contribution of the developed CRMSF approach. Finally, the classification task is accomplished by fusing the multiple regularized classification maps. The CRMSF approach has been evaluated on four popular hyperspectral image data sets, and the experimental results show the advantages of CRMSF, particularly for a hyperspectral image with high spatial resolution. [ABSTRACT FROM AUTHOR]

The present study evaluated the effects of calcitonin gene-related peptide (CGRP) on bone marrow mesenchymal stem cells (BMMSCs) in vitro and in a rat model of mandibular distraction osteogenesis (MDO). Rat BMMSCs were isolated then treated with CGRP or CGRP antagonist (CGRP8-37). The proliferation and migration ability of BMMSCs was determined using 5-bromo-2′-deoxyuridine and Transwell assays, respectively. Osteogenic-related gene expression was analyzed with reverse transcription-quantitative polymerase chain reaction. For the in vivo analysis, thirty MDO rats were randomly assigned to control, CGRP or CGRP8-37 groups. To evaluate the mobilization of BMMSCs, nestin and stromal cell-derived factor 1 (SDF-1) were detected by immunohistochemistry and ELISA. Rats were sacrificed following 14 days and new bone formation was assessed by histological and micro-computed tomography analysis. In the in vitro results, the CGRP group demonstrated significantly higher migration and proliferation, as well as enhanced alkaline phosphatase and runt-related transcription factor 2 expression compared with the control. In the in vivo experiments, bone mineral density of the newly formed bone in the CGRP group was significantly higher than controls. The nestin and SDF-1 expression in the CGRP group was also significantly upregulated. In conclusion, the present study demonstrated that CGRP administration increased new bone formation, possibly via enhancing BMMSC migration and differentiation in MDO rats. [ABSTRACT FROM AUTHOR]

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier (PF), regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k -space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k -space data), including transform sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k -space data accuracy. Finally, ablation experiments show that the proposed method can characterize the physical priors of MR images well. Additionally, experiments show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and is even competitive against supervised-trained deep learning methods in PF and regular undersampling reconstruction. • A physical prior-driven untrained neural network was proposed for MRI reconstruction. • The accuracy of the proposed model's MRI reconstruction was analyzed. • Experimental results confirmed model's superiority in MRI reconstruction. [ABSTRACT FROM AUTHOR]

Since the spatial distribution of surface materials is usually regular and locally continuous, it is reasonable to utilize the spectral and spatial information for the hyperspectral image classification. In this paper, a spectral–spatial Gabor surface feature (GSF) fusion approach has been proposed for hyperspectral image classification. First, Gabor magnitude pictures (GMPs) are extracted by applying a set of predefined 2-D Gabor filters to hyperspectral images. Second, the GSF has been extended to the spectral–spatial domains to comply with the 3-D structure of hyperspectral imagery, called 3-DGSF, which utilizes the first-order derivative of GMPs. Meanwhile, a classic superpixel segmentation method, called simple linear iterative clustering (SLIC), is adopted to divide the original hyperspectral image into disjoint superpixels. Third, principal component analysis is adopted to reduce the dimensionality of each extracted 3-DGSF feature cube. Next, a support vector machine classifier is applied on each reduced 3-DGSF features, and the majority voting strategy is used to obtain the classification results. Finally, the superpixel map obtained by SLIC is used to regularize the classification map, and thus, the proposed approach is named as S3-DGSF. Extensive experiments on three real hyperspectral data sets have demonstrated the higher performance of the proposed S3-DGSF approach over several state-of-the-art methods in the literature. [ABSTRACT FROM AUTHOR]

Background: Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto‐calibration signals (ACS) signal acquisition and is time‐consuming, while the latter requires a large amount of training data. Purpose: To tackle the above issues, an untrained neural network (UNN) model incorporating wave‐encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS‐ and training data‐free. Methods: Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k‐space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k‐space linear predictability. And then, the iterative paradigm to recover the full k‐space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six‐fold acceleration, respectively. Results: In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice. Conclusions: The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time‐consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency. [ABSTRACT FROM AUTHOR]

As a powerful classifier, sparse representation-based classification (SRC) has successfully been applied in various visual recognition problems. However, due to the highly correlated bands and insufficient training samples of hyperspectral image (HSI) data, it still remains a challenging problem to effectively apply SRC in HSI. Considering the rich information of spatial structure of materials in HSI, that means the adjacent pixels belong to the same class with a high probability, in this paper, we propose an efficient superpixel-based sparse representation framework for HSI classification. Each superpixel can be regarded as a small region consisting of a number of pixels with similar spectral characteristics. The proposed framework utilizes superpixel to exploit spatial information which can greatly improve classification accuracy. Specifically, SRC is firstly used to classify the HSI data. Meanwhile, an efficient segmentation algorithm is applied to divide the HSI into many disjoint superpixels. Then, each superpixel is used to fuse the SRC classification results in superpixel level. Experimental results on two real-world HSI data sets have shown that the proposed superpixel-based SRC (SP-SRC) framework has a significant improvement over the pixel-based SRC method. [ABSTRACT FROM AUTHOR]

Due to the high spectral dimensionality of hyperspectral images as well as the difficult and time-consuming process of collecting sufficient labeled samples in practice, the small sample size scenario is one crucial problem and a challenging issue for hyperspectral image classification. Fortunately, the structure information of materials, reflecting region of homogeneity in the spatial domain, offers an invaluable complement to the spectral information. Assuming some spatial regularity and locality of surface materials, it is reasonable to segment the image into different homogeneous parts in advance, called superpixel, which can be used to improve the classification performance. In this paper, a superpixel-based multitask learning framework has been proposed for hyperspectral image classification. Specifically, a set of 2-D Gabor filters are first applied to hyperspectral images to extract discriminative features. Meanwhile, a superpixel map is generated from the hyperspectral images. Second, a superpixel-based spatial–spectral Schroedinger eigenmaps (S4E) method is adopted to effectively reduce the dimensions of each extracted Gabor cube. Finally, the classification is carried out by a support vector machine (SVM)-based multitask learning framework. The proposed approach is thus termed Gabor S4E and SVM-based multitask learning (GS4E-MTLSVM). A series of experiments is conducted on three real hyperspectral image data sets to demonstrate the effectiveness of the proposed GS4E-MTLSVM approach. The experimental results show that the performance of the proposed GS4E-MTLSVM is better than those of several state-of-the-art methods, while the computational complexity has been greatly reduced, compared with the pixel-based spatial–spectral Schroedinger eigenmaps method. [ABSTRACT FROM PUBLISHER]

The local binary pattern (LBP) is a simple and efficient texture descriptor for image processing. Recently, LBP has been introduced for feature extraction of hyperspectral imagery. Specifically, the LBP codes are extracted from the 2-D band images to capture the spatial correlation among neighboring pixels, and then the statistical histogram features from all bands, which could estimate the underlying distribution in local area, are concatenated together for pixel-wise classification. However, since hyperspectral imagery contains rich spectral and spatial information, which is actually a 3-D data cube, the 2-D LBP (2-DLBP) model cannot fully exploit the joint spectral–spatial structure. In this paper, the 2-DLBP has been extended into 3-D LBP (3-DLBP) model through forming a 3-D regular octahedral frame to characterize the spectral–spatial relationship. In order to reflect the local continuous property of hyperspectral data in both the spectral and spatial domains, while ensuring the rotational invariance of the 3-DLBP model, the code patterns of 3-DLBP model have been divided into eight groups (including seven groups of “dense” patterns and one group of “nondense” patterns) based on the consistency of spectral–spatial topology structure. Specifically, the patterns in seven “dense” groups correspond to the microstructures in the 3-D domains (such as spots, edges, and flat areas), which has a high percentage in all the 3-DLBP patterns, while the rest patterns are aggregated and treated as the “nondense” patterns. The proposed method is thus called 3-D dense LBP (3-D2LBP) model. Moreover, instead of taking zero as the hard threshold, a slack variable has been introduced to enable the difference between the central pixel and the neighboring ones varying in a small interval, which could greatly decrease the impact of spectral variability and noise, and the discriminative power of the features has been further boosted. The slack threshold-based 3-D2LBP model is named ST-3-D2LBP. A series of experiments is conducted on three real hyperspectral imageries to demonstrate the effectiveness of the proposed two 3-D2LBP-based methods. The experimental results show that the performance of the proposed ST-3-D2LBP is significantly superior to that of 2-DLBP, which is also better than the 3-D2LBP model and several state-of-the-art hyperspectral classification methods. [ABSTRACT FROM PUBLISHER]

As a powerful visual model, convolutional neural networks (CNNs) have demonstrated remarkable performance in various visual recognition problems, and attracted considerable attention in recent years. However, due to the highly correlated bands and insufficient training samples of hyperspectral image data, it still remains a challenging problem to effectively apply the CNN models on hyperspectral images. In this paper, an efficient CNN architecture has been proposed to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final CNN outputs are the predicted class-related results. The proposed CNN infrastructure has several distinct advantages. Firstly, different from traditional classification methods those need hand-crafted features, the CNN model used here is designed to deal with the problem of hyperspectral image analysis in an end-to-end way. Secondly, the parameters of the CNN model are optimized from a small training set, while the over-fitting problem of the neural network has been alleviated to some extent. Finally, in order to better deal with the hyperspectral image information, 1 × 1 convolutional layers have been adopted, and an average pooling layer and larger dropout rates have also been employed in the whole CNN procedure. The experiments on three benchmark data sets have demonstrated that the proposed CNN architecture considerably outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or $$\mathrm{S}^{3}\mathrm{RC}$$ , to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of $$\mathrm{S}^{3}\mathrm{RC}$$ , named $$\mathrm{FS}^{3}\mathrm{RC}$$ . Experimental results have shown that both the proposed SRC-based approaches, $$\mathrm{S}^{3}\mathrm{RC}$$ and $$\mathrm{FS}^{3}\mathrm{RC}$$ , could achieve better performance than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The large amount of spectral and spatial information contained in hyperspectral imagery has provided great opportunity to effectively characterize and identify the surface materials of interest. As a novel feature extraction technique, a series of Gabor wavelet filters with different scales and frequencies was applied on hyperspectral data to extract spectral–spatial-combined features, which produced impressive performance on pixel-oriented classification. However, the incredibly large number of Gabor features could cause too much burden for onboard computation, limiting the efficiency of the method. To make matters worse, due to the nonhomogeneous spatial distribution of materials as well as the different characteristics of the constructed Gabor filters, some Gabor features could have a smaller or even negative impact on material representation, deteriorating the classification accuracy eventually. In this paper, a Gabor cube selection based multitask joint sparse representation approach, abbreviated as GS-MTJSRC, was proposed for hyperspectral image classification. First, based on the Fisher discrimination criterion, the most representative Gabor cubes for each class were picked out. Next, under multitask joint sparse representation framework, a coefficient vector could be obtained for each test sample with the selected Gabor cube features, which could be directly used for the following residual-based classification. Experimental results on three real hyperspectral data sets with different characteristics and spatial resolutions demonstrated the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Feminist news media researchers have long contended that masculine news values shape journalists’ quotidian decisions about what is newsworthy. As a result, it is argued, topics and issues traditionally regarded as primarily of interest and relevance to women are routinely marginalised in the news, while men’s views and voices are given privileged space. When women do show up in the news, it is often as “eye candy,” thus reinforcing women’s value as sources of visual pleasure rather than residing in the content of their views. To date, evidence to support such claims has tended to be based on small-scale, manual analyses of news content. In this article, we report on findings from our large-scale, data-driven study of gender representation in online English language news media. We analysed both words and images so as to give a broader picture of how gender is represented in online news. The corpus of news content examined consists of 2,353,652 articles collected over a period of six months from more than 950 different news outlets. From this initial dataset, we extracted 2,171,239 references to named persons and 1,376,824 images resolving the gender of names and faces using automated computational methods. We found that males were represented more often than females in both images and text, but in proportions that changed across topics, news outlets and mode. Moreover, the proportion of females was consistently higher in images than in text, for virtually all topics and news outlets; women were more likely to be represented visually than they were mentioned as a news actor or source. Our large-scale, data-driven analysis offers important empirical evidence of macroscopic patterns in news content concerning the way men and women are represented. [ABSTRACT FROM AUTHOR]

Through imaging the same spatial area by hyperspectral sensors at different spectral wavelengths simultaneously, the acquired hyperspectral imagery often contains hundreds of band images, which provide the possibility to accurately analyze and identify a ground object. However, due to the difficulty of obtaining sufficient labeled training samples in practice, the high number of spectral bands unavoidably leads to the problem of a “dimensionality disaster” (also called the Hughes phenomenon), and dimensionality reduction should be applied. Concerning band (or feature) selection, conventional methods choose the representative bands by ranking the bands with defined metrics (such as non-Gaussianity) or by formulating the band selection problem as a clustering procedure. Because of the different but complementary advantages of the two kinds of methods, it can be beneficial to use both methods together to accomplish the band selection task. Recently, a fast density-peak-based clustering (FDPC) algorithm has been proposed. Based on the computation of the local density and the intracluster distance of each point, the product of the two factors is sorted in decreasing order, and cluster centers are recognized as points with anomalously large values; hence, the FDPC algorithm can be considered a ranking-based clustering method. In this paper, the FDPC algorithm has been enhanced to make it suitable for hyperspectral band selection. First, the ranking score of each band is computed by weighting the normalized local density and the intracluster distance rather than equally taking them into account. Second, an exponential-based learning rule is employed to adjust the cutoff threshold for a different number of selected bands, where it is fixed in the FDPC. The proposed approach is thus named the enhanced FDPC (E-FDPC). Furthermore, an effective strategy, which is called the isolated-point-stopping criterion, is developed to automatically determine the appropriate number of bands to be selected. That is, the clustering process will be stopped by the emergence of an isolated point (the only point in one cluster). Experimental results on three real hyperspectral data demonstrate that the bands selected by our E-FDPC approach could achieve higher classification accuracy than the FDPC and other state-of-the-art band selection techniques, whereas the isolated-point-stopping criterion is a reasonable way to determine the preferable number of bands to be selected. [ABSTRACT FROM AUTHOR]

A compact plasmonic structure is proposed to actualize the subwavelength beam focusing, through a metal slit array arranged along a triangular or trapezium surface profile. The incident light passes through the metal slits in the form of surface plasmon polaritons (SPPs) and then scattered into radiation fields. The constructive interference of radiation fields from individual slits with different depths and widths gives rise to beam focusing. The advantages of the proposed plasmonic lens are having a much smaller lateral dimension and broad working wavelength range. This is of importance for realizing densely integrated photonic circuits. The finite-difference time-domain (FDTD) method is employed to verify the proposed design. The simulation results indicate that the focal spot is beyond the diffraction limit. [ABSTRACT FROM AUTHOR]

The application of learning algorithms to big datasets has been identified for a long time as an effective way to attack important tasks in pattern recognition, but the generation of large annotated datasets has a significant cost. We present a simple and effective method to generate a classifier of face images, by training a linear classification algorithm on a massive dataset entirely assembled and labelled by automated means. In doing so, we perform the largest experiment on face gender recognition so far published, reporting the highest performance yet. Four million images and more than 60,000 features are used to train online classifiers. By using an ensemble of linear classifiers, we achieve an accuracy of 96.86% on the most challenging public database, labelled faces in the wild (LFW), 2.05% higher than the previous best result on the same dataset (Shan, 2012). This result is relevant both for the machine learning community, addressing the role of large datasets, and the computer vision community, providing a way to make high quality face gender classifiers. Furthermore, we propose a general way to generate and exploit massive data without human annotation. Finally, we demonstrate a simple and effective adaptation of the Pegasos that makes it more robust. [ABSTRACT FROM AUTHOR]

In order to explore the application effect of different light quality as the light source of tobacco growth, by taking Nicotiana benthamiana as the experimental material under the condition of artificial climate chamber, three light sources with different light quality, including LED-v1 (R1.2/B1, R3.4/FR1), LED-v2 (R7.4/B1, R2.9/FR1), and LED-v1/v2 (R2.3/B1, R3.1/FR1), were set up to analyze the response of tobacco growth indicators to different light quality. The results showed that the plant height, stem thick, leaf area, blade number and biomass of Nicotiana benthamiana treated with LED-v1/v2 were significantly better than those of other treatments. Low R/FR and low R/B light quality could increase the N content in the leaves of tobacco, help to synthesize the organic matters and improve the stress resistance, and also improve the economic quality of tobacco. Appropriately reducing the R/B and R/FR values of LED-v1/v2 light quality was more conducive to the growth of f tobacco and its quality improvement. There was no significant difference in the chlorophyll fluorescence parameters such as photosynthetic reaction centre II (ΦPSII)> apparent electron transport rate (ETR), PS II maximal photochemical efficiency (Fv/Fm) among different treatments. In summary, the most suitable artificial light source for the growth of tobacco was LED-v1/v2 (R2.3/B1, R3.1/FR1). [ABSTRACT FROM AUTHOR]

Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-supervised collaborative learning framework (SelfCoLearn) for accurate dynamic MR image reconstruction from undersampled k-space data directly. The proposed SelfCoLearn is equipped with three important components, namely, dual-network collaborative learning, reunderampling data augmentation and a special-designed co-training loss. The framework is flexible and can be integrated into various model-based iterative un-rolled networks. The proposed method has been evaluated on an in vivo dataset and was compared to four state-of-the-art methods. The results show that the proposed method possesses strong capabilities in capturing essential and inherent representations for direct reconstructions from the undersampled k-space data and thus enables high-quality and fast dynamic MR imaging. [ABSTRACT FROM AUTHOR]

Abstract: We propose a scheme for the non-destructive detection of an unknown Werner state by detecting the steady-state output intensity of the probe field. Moreover, with a similar mechanism, a Werner state can be generated as a stationary state by pumping the cavity field. The numerical simulations indicate that both the state detection and generation schemes are effective and efficient. [Copyright &y& Elsevier]

Abstract: We design a novel plasmonic lens consisting of multiple nano-metal slits surrounded by dielectric air slot waveguides. The surface plasmon polaritons (SPPs) are excited from the metal slits. The slot waveguides provide desired phase retardations of light focusing. Numerical simulations with finite-difference time-domain (FDTD) method show that the transmitted fields through the design example can generate light focusing and deflection by altering the width or length of slot waveguides. The focal spot size is far narrower than the incident wavelength and the focal length can be controlled in several micrometers, which agree with our theoretical analysis. [Copyright &y& Elsevier]

We investigate the optical Kerr effect of SrTiO3 (STO) crystal, of which the nonlinear response time was measured to be less than 200 fs, while the nonlinear refractive index is estimated to be 2.16 × 10-15 cm2/W. Using the optical Kerr gate (OKG) technique with an STO crystal as the Kerr medium, we obtain narrow-bandwidth and symmetric gated spectra from a supercontinuum generated in distilled water by a femtosecond laser. The experimental results show superiority compared with the gated spectra obtained using OKG with CS2 as the Kerr medium, demonstrating that STO crystal is a promising OKG medium. [ABSTRACT FROM AUTHOR]

Abstract: We performed the femtosecond nondegenerate optical Kerr shutter (OKS) measurements in a 10-mm-thick optical glass. The time-resolved OKS signals showed an obvious asymmetry and a large broadening with respect to that for thin samples. The time-resolved OKS signals in samples with different thicknesses showed that, the temporal broadening of the OKS signals was attributed to the group velocity mismatch between pump and probe beams of different wavelengths. By imaging the filament induced inside the glass, we attributed the asymmetry of the time-resolved OKS signals to the focusing position change of pump beam caused by self-focusing and re-focusing effect. [Copyright &y& Elsevier]

We propose a scheme to directly measure the exact value of the geometric quantum discord of an arbitrary two-qubit state. We need only to perform the projective measurement in all the anti-symmetric subspace and our scheme is parametrically efficient in contrast to the widely adopted quantum state tomography scheme in the sense of less parameter estimations and projectors. Moreover, the present scheme can be easily realized with the current experimental techniques. [ABSTRACT FROM AUTHOR]

Abstract: A novel method is proposed to actualize the beam focusing through multiple subwavelength metal slits surrounded by chirped dielectric surface gratings. The slits have equal widths, depths, and interspaces. The electro-magnetic energy is transported by the slits in the form of surface plasmon polaritons (SPPs). The dielectric gratings converge surface plasmon wave on a single point. Numerical simulation of the design example is performed through finite-difference time-domain (FDTD) method. The results show that the focal length is just several times of the incident wavelength and the cross section of focus spot is far narrower than the incident wavelength. [Copyright &y& Elsevier]

Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Unfortunately, the L1 regularizer cannot enforce further sparsity when the full additivity constraint of material abundances is used, hence limiting the practical efficacy of NMF methods in hyperspectral unmixing. In this paper, we extend the NMF method by incorporating the L1/2 sparsity constraint, which we name L1/2-NMF. The L1/2 regularizer not only induces sparsity but is also a better choice among Lq(0 < q < 1) regularizers. We propose an iterative estimation algorithm for L1/2-NMF, which provides sparser and more accurate results than those delivered using the L1 norm. We illustrate the utility of our method on synthetic and real hyperspectral data and compare our results to those yielded by other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: We performed the femtosecond two-color collinear optical Kerr shutter (OKS) measurements in a thick Kerr medium. The time-resolved OKS signals showed a multi-peak structure and a large broadening with respect to that for non-collinear. By measuring the time-resolved OKS signals in the sample at different powers, we demonstrated that the abnormal profile of the OKS signals was mainly attributed to self-focusing and re-focusing of the pump beam in the Kerr medium and the group velocity difference between the pump and the probe beams. [ABSTRACT FROM AUTHOR]

Summary: A known arabidopsis cDNA clone, the CRT/DRE binding factor 1 (CBF1), was isolated and introduced into tobacco plants. It has been reported that CBF1 is one member of CBF gene family related to low temperature and enhancing low temperature tolerance of plants. In the present work, the transcripts could be detected in the transgenic lines. The photochemical efficiency of PSII (F v/F m) and the photo-oxidizable P700 in the transgenic lines overexpressing CBF1 were higher than that in the wild type plants during the chilling stress under low irradiance. Similarly, the higher NPQ, higher qf, lower Φ NF, higher activity of SOD, and lower content of MDA were also detected in the transgenic tobacco lines. Additionally, higher expression levels of Nicotiana tabacum copper/zinc superoxide dismutase (Cu/Zn SOD) were also detected in the transgenic lines. These results suggest that CBF1 protein plays an important role in protection of PSII and PSI during the chilling stress under low irradiance. [Copyright &y& Elsevier]

Abstract: We investigate the entanglement dynamics and decoherence of a three-qubit system under a quantum spin environment at a finite temperature in the thermodynamics limit. For the case under study, we find the evolution of pairwise entanglement depends not only on the initial states but also on the parameters related to the system and the spin environment. In addition, an undesirable entanglement sudden death occurs in the process of entanglement evolution, and this effect can be controlled by the coupling constant between two qubits, external magnetic field, and the interaction between the system and the environment. [Copyright &y& Elsevier]

Abstract: Three isomeric nitronyl nitroxide radical compounds, 2-[n-(N-benzyl)pyridinium]-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide bromide (n =2, 3 and 4 for 1, 2 and 3, respectively), have been synthesized and structurally characterized. The influence of steric hindrance on the molecular packing structures and physical properties has been observed. In the radical 1, such steric hindrance leads to a folding conformation of the imidazoline and benzene rings and the intramolecular C–H…π interaction between the methyl group and the benzene ring. There is no such effect in 2 and 3. In crystal of 2, there are the intermolecular C–H…π between methyl groups and benzene ring and intermolecular π…π stacking interaction between pyridine and benzene rings. Crystal of 2 with a chiral space group P212121 shows the SHG response about 0.4 times as that of urea. In crystal of 3, there are three symmetry-independent radical molecules, which form an unusually six-membered supramolecular ring via intermolecular O…π interactions. For the solid sample of 3, the X-band EPR exhibits an axially symmetric signal and magnetic susceptibility data suggest intermolecular antiferromagnetic (AFM) coupling interactions and very weak intermolecular ferromagnetic (FM) coupling interactions which is more likely caused by magnetic anisotropy, while measurements of both 1 and 2 show isotropic X-band EPR signals and simple Currie–Weiss magnetic behavior. [Copyright &y& Elsevier]

We propose a scheme for long-distance teleportation of an unknown atomic state between two atoms that are trapped separately in optical cavities. Our scheme is based on the effects of quantum statistics of indistinguishable photons emitted from the trapped atoms. In this scheme, the probability of success is independent of the state to be teleported. Moreover, in the Lamb-Dicke limit, the requirement of the simultaneous clicks of the detectors is not necessary. [ABSTRACT FROM AUTHOR]

Abstract: Five ion-pair complexes, consisting of R-benzylidene-1-aminopyridinium derivatives and [Ni(mnt)2]2− (R= p-nitro (1), p-methyl (2), p-bromo (3), p-chloro (4) and m-nitro (5); mnt2− =maleonitriledithiolate), were synthesized and structurally characterized. As for 1, it is interesting to observe a large deviation from square-planar coordination geometry for the Ni atom, while no deviation is observed in the other four complexes. In the solid state, UV–Vis–NIR spectra of 2–5 show similar properties with intense absorption in the 200–750nm and moderate near IR absorption in the 750–1000nm region, whereas 1 exhibits an intense absorption from UV/Visible to near-IR region (200–1100nm). This unique spectral feature of 1 is attributed to its distinctive structural differences from 2 to 5, namely the strong intermolecular packing interactions between anions and cations, as well as a significant deviation from the planarity of the anion. Based on DFT and TDDFT calculations, near-IR absorbance bands in 1–5 were assigned to combined transitions of d–d, MLCT and π–π∗ in the [Ni(mnt)2]2− anion as well as the ion-pair charge transfer (IPCT) from the anionic HOMO to the cationic LUMO. The IPCT band position in acetonitrile is independent of the substituent group feature in benzene ring of cations for 1–5, which could be interpreted that the substituent group in benzene ring only has a minor contribution to the cationic LUMO. [Copyright &y& Elsevier]

Recently, independent component analysis (ICA) has been used in hyperspectral imagery (HSI) unmixing. It considers the hyperspectral signal from a given pixel as a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources). This subpixel-based ICA model has two disadvantages. One is that it is difficult to choose the "mixed" pixels automatically, and the other is that the sources are not statistically independent. As the spatial resolution of HSI data grows higher, we introduce a new substance-based ICA model; that is, distinct substances are regarded as the original sources, and every band image is a linear mixture of them. However, ICA is a very general-purpose statistical technique — it does not take the spatial information into account while unmixing HSI. In this paper, the Markov random field (MRF) model is adopted to incorporate the spatial information into ICA. It is thought of as a powerful tool to model the joint probability distribution of the image pixels based on local spatial interactions. The experimental results demonstrate that the proposed MRF-ICA mixture model provides an effective approach for HSI unmixing. [ABSTRACT FROM AUTHOR]