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Soil analysis makes for developing precision agriculture and monitoring land quality, while the models available for spectroscopy-based chemometrics are constrained by limited samples from small areas. The paper proposed sample expansion and model construction based on spectral difference and content difference, realizing data augmentation and deep learning applied to original samples with limited numbers. The spectral subtraction based on maximum or minimum values exploited the maximum or minimum values to acquire the spectral difference and content difference, which provided a new data form for model construction. Keeping enhanced samples whose spectral difference and content difference were all zero was useful for improving model performance. Augmentation of all data or training data based on maximum or minimum values-based spectral subtraction, which sorted the contents and made them the maximum or minimum values in sequence, achieved sample expansion by the spectral difference and content difference. The model utilized the random vector functional link (RVFL) network, extreme learning machine (ELM), and one-dimensional convolutional neural network (1D CNN), which could predict the content of new samples through ensemble averaging when predicting content difference. The experimental result showed the model of the spectral subtraction based on maximum or minimum values had a similar performance to that of the original samples. Augmentation of all data improved model performance by only RVFL and ELM. Augmentation of training data verified 1D CNN was better than RVFL and ELM. The paper implements a new data augmentation method and applies CNN to original samples with inadequate numbers, which lays the foundation for an improved model and applying spectral preprocessing., Competing Interests: Declaration of competing interest Lushan Wan, Dong Xiao, Zhizhong Mao, Zhenni Li has patent #202311576119.9 pending to Northeastern University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Surface defects on steel, arising from factors like steel composition and manufacturing techniques, pose significant challenges to industrial production. Efficient and precise detection of these defects is crucial for enhancing production efficiency and product quality. In accordance with these requisites, this paper elects to undertake the detection task predicated on the you only look once (YOLO) algorithm. In this study, we propose a novel approach for surface flaw identification based on the YOLOv5 algorithm, called YOLOv5-KBS. This method integrates attention mechanism and weighted Bidirectional Feature Pyramid Network (BiFPN) into YOLOv5 architecture. Our method addresses issues of background interference and defect size variability in images. Experimental results show that the YOLOv5-KBS model achieves a notable 4.2% increase in mean Average Precision (mAP) and reaches a detection speed of 70 Frames Per Second (FPS), outperforming the baseline model. These findings underscore the effectiveness and potential applications of our proposed method in industrial settings., (© 2024. The Author(s).)

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multiview clustering because of gaps between views. This article proposes an efficient multiview DL algorithm for multiview clustering, which uses the partially shared DL model with a flexible ratio of shared sparse coefficients to excavate both consistency and complementarity in the multiview data. In particular, a differentiable scale-invariant function is used as the sparsity regularizer, which considers the absolute sparsity of coefficients as the l 0 norm regularizer but is continuous and differentiable almost everywhere. The corresponding optimization problem is solved by the proximal splitting method with extrapolation technology; moreover, the proximal operator of the differentiable scale-invariant regularizer can be derived. The synthetic experiment results demonstrate that the proposed algorithm can recover the synthetic dictionary well with reasonable convergence time costs. Multiview clustering experiments include six real-world multiview datasets, and the performances show that the proposed algorithm is not sensitive to the regularizer parameter as the other algorithms. Furthermore, an appropriate coefficient sharing ratio can help to exploit consistent information while keeping complementary information from multiview data and thus enhance performances in multiview clustering. In addition, the convergence performances show that the proposed algorithm can obtain the best performances in multiview clustering among compared algorithms and can converge faster than compared multiview algorithms mostly.

Deep Reinforcement Learning (DRL) is one powerful tool for varied control automation problems. Performances of DRL highly depend on the accuracy of value estimation for states from environments. However, the Value Estimation Network (VEN) in DRL can be easily influenced by the phenomenon of catastrophic interference from environments and training. In this paper, we propose a Dynamic Sparse Coding-based (DSC) VEN model to obtain precise sparse representations for accurate value prediction and sparse parameters for efficient training, which is not only applicable in Q-learning structured discrete-action DRL but also in actor-critic structured continuous-action DRL. In detail, to alleviate interference in VEN, we propose to employ DSC to learn sparse representations for accurate value estimation with dynamic gradients beyond the conventional ℓ 1 norm that provides same-value gradients. To avoid influences from redundant parameters, we employ DSC to prune weights with dynamic thresholds more efficiently than static thresholds like ℓ 1 norm. Experiments demonstrate that the proposed algorithms with dynamic sparse coding can obtain higher control performances than existing benchmark DRL algorithms in both discrete-action and continuous-action environments, e.g., over 25% increase in Puddle World and about 10% increase in Hopper. Moreover, the proposed algorithm can reach convergence efficiently with fewer episodes in different environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Direct-optimization-based dictionary learning has attracted increasing attention for improving computational efficiency. However, the existing direct optimization scheme can only be applied to limited dictionary learning problems, and it remains an open problem to prove that the whole sequence obtained by the algorithm converges to a critical point of the objective function. In this article, we propose a novel direct-optimization-based dictionary learning algorithm using the minimax concave penalty (MCP) as a sparsity regularizer that can enforce strong sparsity and obtain accurate estimation. For solving the corresponding optimization problem, we first decompose the nonconvex MCP into two convex components. Then, we employ the difference of the convex functions algorithm and the nonconvex proximal-splitting algorithm to process the resulting subproblems. Thus, the direct optimization approach can be extended to a broader class of dictionary learning problems, even if the sparsity regularizer is nonconvex. In addition, the convergence guarantee for the proposed algorithm can be theoretically proven. Our numerical simulations demonstrate that the proposed algorithm has good convergence performances in different cases and robust dictionary-recovery capabilities. When applied to sparse approximations, the proposed approach can obtain sparser and less error estimation than the different sparsity regularizers in existing methods. In addition, the proposed algorithm has robustness in image denoising and key-frame extraction.

Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interference of the background light. To reduce this kind of interference, we propose a novel method called annular convolution filtering (ACF). In our method, the region of interest (ROI) in the spot image is first searched by using the statistical properties of pixels. Then, the annular convolution strip is constructed based on the energy attenuation property of the laser and the convolution operation is performed in the ROI of the spot image. Finally, a feature similarity index is designed to estimate the parameters of the laser spot. Experiments on three datasets with different kinds of background light show the advantages of our ACF method, with comparison to the theoretical method based on international standard, the practical method used in the market products, and the recent benchmark methods AAMED and ALS.

The Synaptotagmin-like Mitochondrial-lipid-binding Protein (SMP) domain is a newly identified lipid transfer module present in proteins that regulate lipid homeostasis at membrane contact sites (MCSs). However, how the SMP domain associates with the membrane to extract and unload lipids is unclear. Here, we performed in vitro DNA brick-assisted lipid transfer assays and in silico molecular dynamics simulations to investigate the molecular basis of the membrane association by the SMP domain of extended synaptotagmin (E-Syt), which tethers the tubular endoplasmic reticulum (ER) to the plasma membrane (PM). We demonstrate that the SMP domain uses its tip region to recognize the extremely curved subdomain of tubular ER and the acidic-lipid-enriched PM for highly efficient lipid transfer. Supporting these findings, disruption of these mechanisms results in a defect in autophagosome biogenesis contributed by E-Syt. Our results suggest a model that provides a coherent picture of the action of the SMP domain at MCSs., (© 2023. The Author(s).)

Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slow speed and high cost. This paper proposed an approach combining reflectance spectroscopy with deep learning (DL) for rapid proximate analysis of coal. First, 80 sets of coal spectral data are enhanced by data augmentation, outlier detection, and dimensional transformation to improve the number and quality of samples. Then, an analytical model combining dilated convolution, multi-level residual connection, and a two-hidden-layer extreme learning machine (TELM), named DR_TELM, was proposed. The model extracted effective features from coal spectral data by a convolutional neural network (CNN) and utilized TELM as a regressor to achieve feature identification and content prediction. The experimental results showed that DR_TELM achieved coefficients of determination (R 2 ) of 0.981, 0.989, 0.990, 0.985, 0.989 and root mean square errors (RMSE) of 0.533, 1.833, 1.111, 1.808, 0.723 for the content prediction of moisture, ash, volatile matter, fixed carbon and higher heating value (HHV), respectively. And while ensuring high accuracy, the test time is only 0.034 s. It is fully demonstrated that DR_TELM can rapidly and accurately analyze coal., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this article, we propose a double sparse DRL via multilayer sparse coding and nonconvex regularized pruning. To alleviate interference in DRL, we propose a multilayer sparse-coding-structural network to obtain deep sparse representation for control in reinforcement learning. Furthermore, we employ a nonconvex log regularizer to promote strong sparsity, efficiently removing the unnecessary weights with a regularizer-based pruning scheme. Hence, a double sparse DRL algorithm is developed, which can not only learn deep sparse representation to reduce the interference but also remove redundant weights while keeping the robust performance. The experimental results in five benchmark environments of the deep q network (DQN) architecture demonstrate that the proposed method with deep sparse representations from the multilayer sparse-coding structure can outperform existing sparse-coding-based DRL in control, for example, completing Mountain Car with 140.81 steps, achieving near 10% reward increase from the single-layer sparse-coding DRL algorithm, and obtaining 286.08 scores in Catcher, which are over two times the rewards of the other algorithms. Moreover, the proposed algorithm can reduce over 80% parameters while keeping performance improvements from deep sparse representations.

Heavy metal pollution in saline-alkali land has a significant impact on the ecological environment and human health. Rapid and accurate inversion of cadmium (Cd) element content in the saline-alkali land is important for environmental protection, saline-alkali soil improvement and conversion of saline-alkali land to cultivated land. Using traditional chemical detection methods to detect the content of heavy metal elements requires a long testing time and has the drawback of high prices. In this paper, we select the saline-alkali land of Zhenlai County as the study area and combine visible-NIR spectroscopy with machine learning models to invert the Cd content in the saline-alkali land. We preprocess the original reflection spectra using fractional order derivatives (FOD), then construct six three-band spectral indices (TBIs) and obtain the corresponding optimal band combination parameters by the optimal band combination (OBC) algorithm. To address the shortcomings of two-hidden-layer extreme learning machine (TELM), this paper introduces new weight parameters among the nodes of the first hidden layer, further extends it to multiple layers on this basis, and proposes the MSC-ELM model. The improved model is compared with several models, such as random forest (RF), partial least squares (PLS) and extreme learning machine (ELM). And the model performance is analyzed and compared by introducing several performance indicators, such as root mean square error (RMSE) and the ratio of the performance to interquartile (RPIQ). The experimental results show that the FOD transformation can eliminate the baseline drift and reduce the spectral noise. The constructed TBIs can effectively enhance the correlation with Cd content relative to the original single band, reduce redundant information and enhance the spectral features. The MSC-ELM model achieves better performance metrics compared to the other models and obtains the optimal prediction performance. This study provides an accurate and rapid method for the detection of Cd content in saline soil, which is important for the improvement and ecological recovery of saline soil., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Bacterial cellulose (BC), an important category of polysaccharides, was investigated as a texture improver in bakery products. This study focused on the changes in the conformational and thermal properties of gluten in the wheat dough system as affected by BC. Significant reductions in the free-SH content, fluorescence intensity, and surface hydrophobicity index (H 0 ) were observed as a result of the increased BC addition. The electrophoresis profile (SDS-PAGE) and size exclusion (SE-HPLC) revealed the variation in molecular weight distribution, and the increase in the content of the 40-91 kDa molecular weight was at the expense of a decrease in the amount of the corresponding 10-40 kDa. When 0.1 % BC was added, both the α-helix and β-sheet contents increased as a result of enhanced chemical interactions, thereby contributing to the gluten matrix with higher thermal stability. Further supplementation interfered with the current ordered gluten structure, which could be supported by the lower α-helix/β-sheet content ratio and the decreased degradation temperature (T d ) of gluten with 0.2 % BC. However, the observed decrease in the ratio of β-turns to β-sheets and weight loss at 600 °C indicated that a reconstructed gluten matrix induced by extra BC addition was formed to maintain the structural stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss function in an unsupervised way, is becoming very pervasive, resulting from keeping the best of both worlds: the benefit of unsupervised learning and the success of the convolutional neural network. There have been growing interests in developing efficient CTL algorithms. However, developing a convergent and accelerated CTL algorithm with accurate representations simultaneously with proper sparsity is an open problem. This article presents a new CTL framework with a log regularizer that can not only obtain accurate representations but also yield strong sparsity. To efficiently address our nonconvex composite optimization, we propose to employ the proximal difference of the convex algorithm (PDCA) which relies on decomposing the nonconvex regularizer into the difference of two convex parts and then optimizes the convex subproblems. Furthermore, we introduce the extrapolation technology to accelerate the algorithm, leading to a fast and efficient CTL algorithm. In particular, we provide a rigorous convergence analysis for the proposed algorithm under the accelerated PDCA. The experimental results demonstrate that the proposed algorithm can converge more stably to desirable solutions with lower approximation error and simultaneously with stronger sparsity and, thus, learn filters efficiently. Meanwhile, the convergence speed is faster than the existing CTL algorithms.

Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Plant brassinosteroid hormones (BRs) regulate growth in part through altering the properties of the cell wall, the extracellular matrix of plant cells. Conversely, feedback signalling from the wall connects the state of cell wall homeostasis to the BR receptor complex and modulates BR activity. Here, we report that both pectin-triggered cell wall signalling and impaired BR signalling result in altered cell wall orientation in the Arabidopsis root meristem. Furthermore, both depletion of endogenous BRs and exogenous supply of BRs triggered these defects. Cell wall signalling-induced alterations in the orientation of newly placed walls appear to occur late during cytokinesis, after initial positioning of the cortical division zone. Tissue-specific perturbations of BR signalling revealed that the cellular malfunction is unrelated to previously described whole organ growth defects. Thus, tissue type separates the pleiotropic effects of cell wall/BR signals and highlights their importance during cell wall placement., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2021. Published by The Company of Biologists Ltd.)

Objective: This study was conducted to compare the pharmacokinetics, safety and immunogenicity of HOT-1010 with bevacizumab (Avastin®) in Chinese healthy male subjects. Methods: A single-center, randomized, double-blind, single-dose, parallel trial was performed in 84 Chinese healthy male subjects who randomly (1:1) received a single intravenous infusion of 1 mg/kg HOT-1010 or Avastin® for 90 min and followed up for 85 days. Serum concentrations of bevacizumab were analyzed by enzyme-linked immunosorbent assay. Primary pharmacokinetic parameters, C max , AUC 0-t and AUC 0-∞, were calculated and evaluated the bioequivalence between HOT-1010 and Avastin®, the safety and immunogenicity of investigational drugs were also assessed. Results: A total of 82 subjects completed the study. The 90% Confidence Intervals for geometric mean ratios of C max , AUC 0-t and AUC 0-∞ were 91.81-103.64%, 85.19-95.39% and 85.04-95.36%, which were all within the bioequivalence margin. Treatment-emergent adverse events were reported in 27 (65.9%) subjects in HOT-1010 group and 23 (56.1%) subjects in Avastin® group. Most TEAEs were mild or moderate. No TEAEs, Serious Adverse Events or deaths leading to discontinuation was reported. Subjects were all tested negative for Anti-drug Antibody. Conclusion: HOT-1010 exhibited the similar pharmacokinetics, safety and immunogenicity profiles of bevacizumab (Avastin®) in Chinese healthy male subjects. Clinical Trial Registration: http://www.chinadrugtrials.org.cn/index.html, CTR20181610., Competing Interests: XZ, YY and YT were employed by the company Shanghai Huaota Biopharmaceutical Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Huang, Que, Ding, Chu, Qian, Shi, Qin, Li, Chen, Gu, Wang, Zhang, Zhang, Zhu, Yang, Tang and He.)

Multiview data processing has attracted sustained attention as it can provide more information for clustering. To integrate this information, one often utilizes the non-negative matrix factorization (NMF) scheme which can reduce the data from different views into the subspace with the same dimension. Motivated by the clustering performance being affected by the distribution of the data in the learned subspace, a tri-factorization-based NMF model with an embedding matrix is proposed in this article. This model tends to generate decompositions with uniform distribution, such that the learned representations are more discriminative. As a result, the obtained consensus matrix can be a better representative of the multiview data in the subspace, leading to higher clustering performance. Also, a new lemma is proposed to provide the formulas about the partial derivation of the trace function with respect to an inner matrix, together with its theoretical proof. Based on this lemma, a gradient-based algorithm is developed to solve the proposed model, and its convergence and computational complexity are analyzed. Experiments on six real-world datasets are performed to show the advantages of the proposed algorithm, with comparison to the existing baseline methods.

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

Inducible, tissue-specific expression is an important and powerful tool to study the spatio-temporal dynamics of genetic perturbation. Combining the flexible and efficient GreenGate cloning system with the proven and benchmarked LhGR system (here termed GR-LhG4) for the inducible expression, we have generated a set of transgenic Arabidopsis lines that can drive the expression of an effector cassette in a range of specific cell types in the three main plant meristems. To this end, we chose the previously developed GR-LhG4 system based on a chimeric transcription factor and a cognate pOp-type promoter ensuring tight control over a wide range of expression levels. In addition, to visualize the expression domain where the synthetic transcription factor is active, an ER-localized mTurquoise2 fluorescent reporter under control of the pOp4 or pOp6 promoter is encoded in driver lines. Here, we describe the steps necessary to generate a driver or effector line and demonstrate how cell type specific expression can be induced and followed in the shoot apical meristem, the root apical meristem and the cambium of Arabidopsis. By using several or all driver lines, the context specific effect of expressing one or multiple factors (effectors) under control of the synthetic pOp promoter can be assessed rapidly, for example in F1 plants of a cross between one effector and multiple driver lines. This approach is exemplified by the ectopic expression of VND7, a NAC transcription factor capable of inducing ectopic secondary cell wall deposition in a cell autonomous manner.

Impact of covalent and non-covalent binding interactions on physicochemical, structural, and rheological properties of waxy rice flour were investigated using several typical types of chemicals. Dynamic rheometer was used to investigate the viscoelastic properties and resistance to deformation or shearing. Both hydrogen bonds and hydrophobic interactions were necessary for waxy rice flour to form a material with stable structure matrix. Besides this, X-ray diffraction pattern and pasting property measurements showed that the relative crystallinity and pasting viscosity values of waxy rice flour exposed to SDS and l-cysteine treatments increased whereas those of urea decreased compared with the control. Addition of acid, alkali, and NaCl might result in electrostatic forces, ion interactions, and indirectly change hydrogen bonds and hydrophobic interactions between starch and protein, which played a synthetic role on property changes of waxy rice flour., (Copyright © 2018. Published by Elsevier Ltd.)

Understanding the context-specific role of gene function is a key objective of modern biology. To this end, we generated a resource for inducible cell type-specific transactivation in Arabidopsis ( Arabidopsis thaliana ) based on the well-established combination of the chimeric GR-LhG4 transcription factor and the synthetic pOp promoter. Harnessing the flexibility of the GreenGate cloning system, we produced a comprehensive set of transgenic lines termed GR-LhG4 driver lines targeting most tissues in the Arabidopsis shoot and root with a strong focus on the indeterminate meristems. When we combined these transgenic lines with effectors under the control of the pOp promoter, we observed tight temporal and spatial control of gene expression. In particular, inducible expression in F1 plants obtained from crosses of driver and effector lines allows for rapid assessment of the cell type-specific impact of an effector with high temporal resolution. Thus, our comprehensive and flexible method is suitable for overcoming the limitations of ubiquitous genetic approaches, the outputs of which often are difficult to interpret due to the widespread existence of compensatory mechanisms and the integration of diverging effects in different cell types., (© 2018 American Society of Plant Biologists. All rights reserved.)

Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ 1∕2 norm as a regularizer. The very recent study on ℓ 1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ 1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The long-standing Acid Growth Theory of plant cell elongation posits that auxin promotes cell elongation by stimulating cell wall acidification and thus expansin action. To date, the paucity of pertinent genetic materials has precluded thorough analysis of the importance of this concept in roots. The recent isolation of mutants of the model grass species Brachypodium distachyon with dramatically enhanced root cell elongation due to increased cellular auxin levels has allowed us to address this question. We found that the primary transcriptomic effect associated with elevated steady state auxin concentration in elongating root cells is upregulation of cell wall remodeling factors, notably expansins, while plant hormone signaling pathways maintain remarkable homeostasis. These changes are specifically accompanied by reduced cell wall arabinogalactan complexity but not by increased proton excretion. On the contrary, we observed a tendency for decreased rather than increased proton extrusion from root elongation zones with higher cellular auxin levels. Moreover, similar to Brachypodium, root cell elongation is, in general, robustly buffered against external pH fluctuation in Arabidopsis thaliana However, forced acidification through artificial proton pump activation inhibits root cell elongation. Thus, the interplay between auxin, proton pump activation, and expansin action may be more flexible in roots than in shoots., (© 2016 American Society of Plant Biologists. All rights reserved.)

Mussel-inspired chemistry (polydopamine) offers great opportunities to develop inexpensive and efficient process for many types of materials with complex shapes and functions in a mild and friendly environment. This paper describes a facile, yet green approach to synthesize polydopamine/silver (PDA/Ag) nanocomposite particles with a combination use of polydopamine chemistry and electroless metallization of Ag. In this approach, monodisperse spherical polydopamine particles are first synthesized by the oxidation and self-polymerization of dopamine (monomer) in an alkaline water-ethanol solution at room temperature, which are served as the active templates for secondary reactions due to the abundant catechol and amine groups on the surface. Subsequently, the silver precursor-[Ag(NH3)2](+) ions introduced are easily absorbed onto the surface of the PDA particles, and are immediately in situ reduced to metallic Ag nanoparticles with the help of these active catechol and amine groups. During the preparation, no additional reductants, toxic reagents and intricate instruments are needed. These as-synthesized PDA/Ag nanocomposite particles are ideal candidates for antibacterial application because they do not show significant cytotoxicity against HEK293T human embryonic kidney cells in the in vitro cytotoxicity assay, whereas demonstrate enhanced antibacterial abilities against Escherichia coli (Gram-negative bacteria) and Staphylococcus aureus (Gram-positive bacteria) in the antibacterial assays. Owing to their excellent cytocompatibilities and antibacterial activities, these PDA/Ag nanocomposite particles can be considered as the promising antibacterial materials for future biomedical applications., (Copyright © 2015 Elsevier B.V. All rights reserved.)

We present a fast, efficient algorithm for learning an overcomplete dictionary for sparse representation of signals. The whole problem is considered as a minimization of the approximation error function with a coherence penalty for the dictionary atoms and with the sparsity regularization of the coefficient matrix. Because the problem is nonconvex and nonsmooth, this minimization problem cannot be solved efficiently by an ordinary optimization method. We propose a decomposition scheme and an alternating optimization that can turn the problem into a set of minimizations of piecewise quadratic and univariate subproblems, each of which is a single variable vector problem, of either one dictionary atom or one coefficient vector. Although the subproblems are still nonsmooth, remarkably they become much simpler so that we can find a closed-form solution by introducing a proximal operator. This leads to an efficient algorithm for sparse representation. To our knowledge, applying the proximal operator to the problem with an incoherence term and obtaining the optimal dictionary atoms in closed form with a proximal operator technique have not previously been studied. The main advantages of the proposed algorithm are that, as suggested by our analysis and simulation study, it has lower computational complexity and a higher convergence rate than state-of-the-art algorithms. In addition, for real applications, it shows good performance and significant reductions in computational time.

Maize (Zea mays) is an important C4 plant due to its widespread use as a cereal and energy crop. A second-generation genome-scale metabolic model for the maize leaf was created to capture C4 carbon fixation and investigate nitrogen (N) assimilation by modeling the interactions between the bundle sheath and mesophyll cells. The model contains gene-protein-reaction relationships, elemental and charge-balanced reactions, and incorporates experimental evidence pertaining to the biomass composition, compartmentalization, and flux constraints. Condition-specific biomass descriptions were introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lignocellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of information from the MetaCrop and MaizeCyc databases, this updated model spans 5,824 genes, 8,525 reactions, and 9,153 metabolites, an increase of approximately 4 times the size of the earlier iRS1563 model. Transcriptomic and proteomic data have also been used to introduce regulatory constraints in the model to simulate an N-limited condition and mutants deficient in glutamine synthetase, gln1-3 and gln1-4. Model-predicted results achieved 90% accuracy when comparing the wild type grown under an N-complete condition with the wild type grown under an N-deficient condition., (© 2014 American Society of Plant Biologists. All Rights Reserved.)

Inspired by mussel-adhesion phenomena in nature, we present a simple, mild and green method to prepare polystyrene/Ag (PS/Ag) nanocomposite particles with enhanced antibacterial activities. In this approach, monodisperse polystyrene particles are used as template spheres, which are then coated with polydopamine (PDA) through the self-polymerization of dopamine in a weakly alkaline aqueous environment (pH = 8.5). Silver precursor-[Ag(NH 3 ) 2 ] + ions are added and absorbed onto the surfaces of the PS/PDA composite spheres by the active catechol and amine groups of the polydopamine coating. Meanwhile, these adsorbed [Ag(NH 3 ) 2 ] + ions are in situ reduced into metallic silver nanoparticles by the "bridge" of the polydopamine coating, and the formed Ag nanoparticles are home positioned. As polydopamine is an environmentally friendly reagent with abilities as a universal adhesive to any surface and as a mild reductant for noble metal salts, because of its abundant active catechol and amine groups, neither additional reducing and toxic reagents nor special surface modifications of the template are needed in this procedure. Moreover, preliminary antibacterial assays indicate that these PS/Ag nanocomposite particles show enhanced antibacterial activities against Escherichia coli (Gram-negative bacteria) and Staphylococcus aureus (Gram-positive bacteria), while they do not show significant in vitro cytotoxicity against HEK293T human embryonic kidney cells. These results suggest that these PS/Ag nanocomposite particles could be promising antibacterial materials for future biomedical applications.