Article
Biochemical Research Methods
Huimin Xu, Yuanyuan Zhao, Yuanzhen Suo, Yayu Guo, Yi Man, Yanping Jing, Xinqiang He, Jinxing Lin
Summary: This study introduces a label-free imaging technology, coherent Raman scattering (CRS) microscopy, for visualizing the major structures and chemical composition of plant cell walls. This rapid approach can help researchers understand the highly heterogeneous structures and organization of plant cell walls.
Article
Agriculture, Multidisciplinary
Zhi Jin, Weili Cui, Jianfeng Ma, Qian Chen, Yuejin Fu
Summary: Confocal Raman microspectroscopy was used to observe the dissolution of hydroxycinnamates (HCMs) incorporated into lignin-carbohydrate complexes (LCCs) in energy crops Miscanthus sinensis cv. during NaOH treatment. The results showed that mild NaOH treatment led to a higher proportion of HCM depolymerization in highly lignified middle lamella areas compared to carbohydrate-abundant secondary walls. Raman imaging revealed that lignin depolymerization was more pronounced in the sclerenchyma fiber and parenchyma secondary wall with increasing treatment time, while the depolymerization of HCMs was closely related to lignin depolymerization. A better understanding of these processes is important for efficient breaking of LCC bonds in herbaceous biomass.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
(2023)
Article
Chemistry, Applied
Rosita Diana, Ugo Caruso, Francesco Silvio Gentile, Luigi Di Costanzo, David Turra, Stefania Vitale, Barbara Panunzi
Summary: In this study, we designed three novel fluorescent brightening agents based on a benzodifuran skeleton, which have improved water solubility and the ability to interact with living cells. A complete understanding of the emission mechanisms was achieved through Density Functional Theory study and X-ray crystallographic analysis. The fluorescence quantum yields were measured in different solvents and at different pH values. Through fluorescence microscopy technique, two probes with different functionalized branching chains were found to be efficient stains for plant root cell wall.
Review
Plant Sciences
Heather E. McFarlane
Summary: Plant cell walls, composed of polysaccharides, provide support and enable growth. Recent research has made significant progress in understanding cell wall synthesis, including the identification and study of enzymes, the development of tools, and structural information generation. However, many questions about plant cell wall polysaccharide synthesis remain unanswered. This article discusses these questions, reviews supporting data, and explores potential technological advancements for future answers.
JOURNAL OF EXPERIMENTAL BOTANY
(2023)
Review
Cell Biology
Natalie Hoffmann, Samuel King, A. Lacey Samuels, Heather E. McFarlane
Summary: The composition and synthesis of the cell wall vary among different plant species, cell types, and regions within a cell wall, but are underpinned by common cellular mechanisms.
DEVELOPMENTAL CELL
(2021)
Review
Biochemistry & Molecular Biology
Youssef Chebli, Amir J. Bidhendi, Karuna Kapoor, Anja Geitmann
Summary: The plant cell wall serves as an important extracellular matrix that envelops cells, maintains their shape and structure, interacts with symbionts, and protects against external stresses. The assembly of this matrix is regulated by the cytoskeleton, which also plays a key role in perceiving mechanical cues and mediating intracellular responses related to cell wall structure changes. Delivery processes of cell wall precursors and their structural continuity are crucial for cell wall assembly, with various morphogenetic processes relying on cell wall assembly as a critical element.
Article
Plant Sciences
David Stuart Thompson, Azharul Islam
Summary: The extensibility of synthetic polymers and plant cell walls can be modulated by plasticizers and water, affecting their hydration and behavior, potentially leading to physiological consequences and strategies to improve crop resilience. Expansins play a role in facilitating rehydration and swelling of these materials, showing that the relationship between water potential and hydration is influenced by composition.
Review
Biochemistry & Molecular Biology
Luis Alonso Baez, Tereza Ticha, Thorsten Hamann
Summary: Plant cell walls are complex structures that surround all plant cells and play important roles in providing support, protection, and maintaining integrity. Recent studies have shown that plants have a dedicated mechanism to monitor and repair cell wall damage caused by growth, development, and various stresses. This mechanism involves mechano-perception, reactive oxygen species, and phytohormone-based signaling processes. Additionally, there is crosstalk between cell wall integrity maintenance and pattern triggered immunity, which modulates adaptive responses to biotic stress. The review focuses on Arabidopsis thaliana and discusses the conservation of these mechanisms in other plant species as well as the transcriptional machinery responsible for controlling adaptive responses.
PLANT MOLECULAR BIOLOGY
(2022)
Review
Agronomy
Marcia Maria de O. Buanafina, Phillip Morris
Summary: This article summarizes the evolving concepts and scientific findings on cell wall feruloylation and ferulate oxidative coupling processes in grasses. It also explores the effects of these processes on cell wall properties, plant responses to stress, and tissue degradability. Different strategies for genetically modifying cell wall feruloylation are discussed, with a focus on the heterologous expression of cell wall ferulic acid esterase. The article highlights emerging feruloyl transferase candidate genes involved in ferulate incorporation into grass arabinoxylans.
Review
Plant Sciences
Dengying Qiu, Shouling Xu, Yi Wang, Ming Zhou, Lilan Hong
Summary: Plant morphogenesis is influenced by multiple biochemical and physical processes within the cell wall, where the extensibility of the cell wall is a main limiting factor for cell expansion. The control of cell wall mechanical properties largely determines the morphology of plant cells.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Plant Sciences
Konan Ishida, Yoshiteru Noutoshi
Summary: The plant cell wall plays important roles in plant-microbe interactions, including physical defense, storage of antimicrobial compounds, production of cell wall-derived elicitors, and provision of carbon sources. Additionally, we discuss four families of cell surface receptors associated with plant cell walls that have been the subject of important studies in recent years, providing valuable insights into plant cell wall and immunity.
PLANT PHYSIOLOGY AND BIOCHEMISTRY
(2022)
Review
Biochemistry & Molecular Biology
Susanne Dora, Oliver M. Terrett, Clara Sanchez-Rodriguez
Summary: The apoplast is a continuous compartment in plants that connects cells between tissues and organs, serving as an important site for interaction between plants and microbes. The plant cell wall, occupying most of the apoplast, consists of polysaccharides, proteins, and ions. It acts as a physical barrier and nutrient source for the microbe, while also playing crucial roles in interkingdom detection, recognition, and response to other organisms. Both plant and microbe modify the cell wall and its environment to benefit from the interaction, and understanding these dynamic changes is essential for comprehending plant-microbe interactions.
Article
Chemistry, Multidisciplinary
Clemence Simon, Cedric Lion, Hania Ahouari, Herve Vezin, Simon Hawkins, Christophe Biot
Summary: This study demonstrates the ligation of TEMPO-based probes with monolignol reporters in plant cell walls through Diels-Alder chemistry, enabling the study of lignification using EPR spectroscopy and imaging.
CHEMICAL COMMUNICATIONS
(2021)
Review
Biochemistry & Molecular Biology
Baocai Zhang, Yihong Gao, Lanjun Zhang, Yihua Zhou
Summary: The plant cell wall, composed of multiple biopolymers, is one of the most complex structural networks in nature. Through advancements in plant functional genomics, significant progress has been made in understanding cell wall biosynthesis, construction, and functions, as well as utilization of cell wall materials. Cutting-edge technologies have provided new insights into the intricate nanoscale network of the plant cell wall, opening up unprecedented possibilities for research.
JOURNAL OF INTEGRATIVE PLANT BIOLOGY
(2021)
Article
Chemistry, Analytical
Michael E. Hickey, Lili He
Summary: This study introduces a chemical imaging method for mass surveillance of bacteria cells among plant tissues, utilizing pre-labeled bacteria cells with gold nanoparticles. By collating surface-enhanced Raman spectra, panoramic chemical images of bacteria populations are generated, demonstrating the potential of SERS imaging in studying bacterial cells among complex matrices. This approach shows superiority over electron and fluorescent microscopies in certain aspects.
Article
Environmental Sciences
Anand Babu Perumal, Reshma B. Nambiar, Periyar Selvam Sellamuthu, Emmanuel Rotimi Sadiku, Xiaoli Li, Yong He
Summary: Areca nut husk fibers were used to extract cellulose nanocrystals (CNCs) for the reinforcement of polyvinyl alcohol (PVA) and chitosan (CS) films. The CNC showed good thermal stability, enhanced the tensile strength of the bionanocomposite film, and exhibited antimicrobial activity against foodborne pathogens and postharvest pathogenic fungi, suggesting its potential for food packaging applications.
Article
Environmental Sciences
Alireza Sanaeifar, Wenkai Zhang, Haitian Chen, Dongyi Zhang, Xiaoli Li, Yong He
Summary: The study proposed an innovative approach using spectroscopy and chemometrics to monitor heavy metal accumulation in tea and evaluated two machine learning techniques for building quantitative models. By analyzing chemical indicators and leaf structure changes, a new method for monitoring tea quality and safety under airborne heavy metal stress was developed.
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
(2022)
Article
Environmental Sciences
Alireza Sanaeifar, Fengle Zhu, Junjing Sha, Xiaoli Li, Yong He, Zhihao Zhan
Summary: This study investigated the absorption of lead by tea plants and its influencing factors using spectroscopic methods, and established predictive models for rapid monitoring of physiological and biochemical indicators. The results will contribute to the development of more effective and reliable monitoring methods for atmospheric deposition.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Computer Science, Cybernetics
Guanjie Cheng, Yan Chen, Shuiguang Deng, Honghao Gao, Jianwei Yin
Summary: This paper explores the trend of incorporating edge computing in IoT and the security challenges it poses, proposing a blockchain-based mutual authentication scheme to meet the authentication needs between edge servers and IoT devices, including both static and dynamic conditions.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Chemistry, Analytical
Kai Chen, Xiaoshuai Wu, Zhuo Zou, Yulun Dong, Shuai Zhang, Xiaofen Li, Mostafa Gouda, Bingquan Chu, Chang Ming Li, Xiaoli Li, Yong He
Summary: This study systematically investigated the oxidative stress of aquatic microorganisms under heavy metal stress using multiple techniques. For the first time, an electrochemical approach was combined with Raman spectroscopy imaging to study the temporal-spatial variations of oxidative stress and its effects on cell metabolism. This work provides insights into the mechanism of cellular oxidative stress under harsh conditions and holds promise for the development of heavy metal biosensors.
ANALYTICA CHIMICA ACTA
(2022)
Article
Automation & Control Systems
Fengle Zhu, Jianping Cai, Mengzhu He, Xiaoli Li
Summary: Recently, there has been a rising trend in employing 3D CNN for modeling complex high-dimensional hyperspectral images in object-scale analysis. This study investigates an improved 3D CNN architecture with embedded attention modules for adaptive feature refinement in object-scale hyperspectral image modeling. By systematically exploring various modifications on the arrangement and structure of channel and band attention modules in the 3D ResNet architecture, the study shows performance improvement in modeling hyperspectral images.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Review
Chemistry, Applied
Anand Babu Perumal, Lingxia Huang, Reshma B. Nambiar, Yong He, Xiaoli Li, Periyar Selvam Sellamuthu
Summary: Fruits and vegetables are perishable and packaging materials with essential oils can extend shelf life while potentially affecting sensory properties. Combining multiple essential oils or non-thermal techniques can effectively prevent food spoilage.
Article
Biotechnology & Applied Microbiology
Chengfeng Li, Zhiwen Hu, Yi Gao, Yuchen Ma, Xiaoxiao Pan, Xiaoli Li, Shiwang Liu, Bingquan Chu
Summary: This study found that static magnetic fields (SMF) have significant effects on the biomass and metabolites of C. pyrenoidosa and T. obliquus. SMF can promote the growth of microalgae, increase protein synthesis, but decrease the content of carbohydrates and lipids. Under SMF, the carbohydrate content of T. obliquus also significantly decreased, while there were no significant changes in protein and lipid. Additionally, SMF also had negative effects on the fatty acids of microalgae.
JOURNAL OF BIOTECHNOLOGY
(2022)
Editorial Material
Biochemistry & Molecular Biology
Mostafa Gouda, Yong He, Alaa El-Din Bekhit, Xiaoli Li
Article
Chemistry, Multidisciplinary
Jinchai Xu, Fangfang Qu, Bihe Shen, Zhenxiong Huang, Xiaoli Li, Haiyong Weng, Dapeng Ye, Renye Wu
Summary: In this study, a portable rapid non-destructive detection device integrating visible/short-wave and long-wave near-infrared spectroscopy was developed to detect tea polyphenol content in fresh tea leaves. The device achieved better prediction performance by fusing spectral data and extracted sensitive spectral wavebands for tea polyphenols. This device provides effective technical support for tea breeding and quality control.
APPLIED SCIENCES-BASEL
(2023)
Review
Agriculture, Multidisciplinary
Alireza Sanaeifar, Mahamed Lamine Guindo, Adel Bakhshipour, Hassan Fazayeli, Xiaoli Li, Ce Yang
Summary: Deep learning algorithms can be used to precisely and effectively identify cereal plant heads in various agricultural applications. This review provides an overview of recent research on deep learning-based head detection in cereal plants, emphasizing object detection and image segmentation. The paper discusses the benefits and drawbacks of different deep learning architectures and training methods, as well as their application in maize, rice, wheat, and sorghum.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Review
Biochemistry & Molecular Biology
Mostafa Gouda, Hesham S. Ghazzawy, Nashi Alqahtani, Xiaoli Li
Summary: The use of acoustic waves for determining chemical structures of biological tissues and their bioactivities is a significant developed technology. New acoustic techniques for in vivo visualizing and imaging of cellular chemical compositions could pave the way toward advanced analytical technologies. This review focuses on the use of advanced acoustic technologies for tracking composition changes in plant and animal tissues, and discusses key configurations of acoustic wave sensors and their applications in biomedical and microfluidic media progress.
Article
Agricultural Engineering
Xuelun Luo, Chanjun Sun, Yong He, Fengle Zhu, Xiaoli Li
Summary: The development of a fast and reliable detection method for quality indicators of fresh tea leaves is important. This study investigated the feasibility of using visible-near-infrared hyperspectral image (VNHI) combined with stoichiometry to determine tea polyphenols (TP) and crude fiber (CF) in 14 cultivars of tea plants. The results showed that different tea cultivars could be distinguished with high accuracy using VNHI and one-dimensional ResNet18. The quantitative determination coefficient for CF and TP contents reached satisfactory values by integrating VNHI with PLS. The study demonstrated the feasibility of VNHI technique in predicting TP and CF content of multiple tea cultivars and provided methods with higher generalization, perceptual intuition, and speediness for the detection of tea quality indicators.
INDUSTRIAL CROPS AND PRODUCTS
(2023)
Article
Biology
Baishao Zhan, Ming Li, Wei Luo, Peng Li, Xiaoli Li, Hailiang Zhang
Summary: This paper focuses on the classification of tea disease leaves using convolution, iterative module, and transformer. Through experiments, the model's optimal cut size, small sample training ability, anti-interference ability, and generalization ability are demonstrated. The class activation map visualization shows that the model accurately captures the location of leaf diseases, validating its effectiveness. Tea diseases are a major cause of reduced tea yield, and using computer vision for classification and diagnosis is an effective means of tea disease management.
Article
Chemistry, Analytical
Qinghai He, Haowen Zhang, Tianhua Li, Xiaojia Zhang, Xiaoli Li, Chunwang Dong
Summary: Soil fertility is crucial for tea plant growth, and the rapid and accurate detection of soil physicochemical properties plays a significant role in precision agriculture in tea plantations. This study utilized spectral data and support vector regression model to predict and optimize the soil physicochemical properties in tea plantation soils. The findings showed that soil particle size and downscaling strategies had an impact on the prediction accuracy.
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
