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The extraction of buildings from high-resolution remote sensing imagery (HRSI) is crucial across various applications and stands as a pivotal task in the field of remote sensing. While recent methods based on convolutional neural networks exhibit superior performance in building extraction from HRSI, there are still challenges, such as incomplete and missing extractions of buildings especially the building boundaries and the small buildings. To address these issues, we propose a Supervised Attention U-Net (SAU-Net), which combines a well-designed encoder and decoder. In the encoder, we incorporate a novel residual channel attention block and a densely connected multidilated convolutional block to enhance semantic features in the channel and spatial dimensions, respectively. In addition, in the decoder, we design a supervised attention block, which reconstructs fine-grained semantic features by systematically refining features in a supervised way and efficiently integrating feature maps from both the encoding and decoding stages. The efficacy of SAU-Net is evaluated using four HRSI datasets encompassing varying scenarios. The experimental results highlight that SAU-Net exhibits superior performance in building extraction, particularly excelling in the extraction of building boundaries and small buildings.

Compared to traditional vegetation indices (VIs), Sun-induced chlorophyll fluorescence (SIF) has the advantage of rapid response to drought and early drought detection. However, the response of red SIF (SIF687) to drought at different growth stages of winter wheat remains unclear. Therefore, this study focuses on winter wheat and sets up three irrigation treatments with different drought intensities. Spectral data collected by an automated spectral observation system are used to retrieve SIF687, far-red SIF (SIF760), the Normalized Difference Vegetation Index (NDVI), the Near-Infrared Reflectance Index (NIRv), as well as meteorological data, soil moisture (SM), and vegetation growth parameters. The goal is to investigate the response of SIF687 to drought at different growth stages of winter wheat. The results indicated that SIF687 showed a positive response to drought, compared to the condition without drought, SIF687’ change rate (31.19% and 47.38%) exhibited a superior monitoring effect for moderate and severe drought than that of SIF760 (13.10% and 10.63%) and VIs (−4.84% and −4.02%), with the jointing stage showing the highest sensitivity to changes in SM. Under different scenarios, the effects of photosynthetically active radiation (PAR), vegetation physiology and canopy structure on SIF687 were different. With the increase of drought intensity and the development of growing season, the impact of PAR on SIF687 gradually decreased. The response of SIF687 to drought was more affected by physiological changes (chlorophyll content, Chl) than by vegetation structure changes (leaf area index, LAI). In summary, SIF687 possesses the potential for early drought monitoring in winter wheat. It can provide timely responses to water stress at different growth stages, thus contributing to the mitigation of agricultural drought risks.

ABSTRACT: The type VI secretion system (T6SS) of avian pathogenic Escherichia coli (APEC) can affect the functions of eukaryotic cells by secreting or injecting effectors. Hemolysin co-regulatory protein (Hcp), one of the markers of the T6SS, is both a structural protein and an effector protein of the T6SS. According to previous studies, mitochondria in eukaryotic cells are targeted by pathogenic bacteria. However, little is known about the regulation of mitochondria in eukaryotic host cells by the T6SS effector protein Hcp of APEC. In our study, DF-1 cells co-incubated with Hcp2a protein for 6 h showed decreased mitochondrial membrane potential, increased Ca2+ concentration, and increased cellular reactive oxygen species (ROS) levels. We therefore conclude that Hcp2a protein causes dysfunction to mitochondria in DF-1 cells. To explain the mechanism that causes mitochondrial dysfunction, we reanalyzed the Hcp2a interaction protein dataset in DF-1 cells, and the Leucine zipper EF-hand-containing transmembrane protein 1 (LETM1), which is associated with mitochondria, was screened. The protein and molecular docking results showed that Hcp2a protein and LETM1 protein have better binding. Finally, subcellular localization results showed that Hcp2a was localized to mitochondria. In summary, Hcp2a effector proteins caused dysfunction to DF-1 cellular mitochondria, and we hypothesize that the interaction of Hcp2a protein with LETM1 protein induces mitochondrial dysfunction and promotes mitochondrial localization of Hcp2a in DF-1 cells.

Surface water mapping based on historical, neighbourhood, and other priori information has shown improved accuracy. However, the accuracy can be compromised due to the lack of consideration for water dynamics in the proximity period and the limited utilization of quantitative methods for integrating multiple types of priori information. In this study, an unsupervised surface water extraction method that integrates historical, proximity, and neighbourhood priori information from remote sensing is proposed to enhance water extraction accuracy. The experiments were conducted in Poyang Lake, a region characterized by active hydrological phenomena. Coarsely extracted water extents using the OTSU method from all available Sentinel-1/2 images within a one-month period priori to the current moment were utilized as proximity priori information to estimate water probability (WP). The optimized WP was then obtained by combining the estimated probabilities from historical, proximity, and neighbourhood priori information using the Bayesian Model Averaging (BMA) method. The experimental results demonstrate that the proposed method outperforms traditional unsupervised water body extraction methods such as K-Means, IsoData, and OTSU in terms of accuracy. Moreover, the integration of historical, proximity, and neighbourhood priori information based on the BMA method results in higher accuracy compared to using each priori information separately. Specifically, the incorporation of proximity priori information significantly enhances the estimation accuracy of WP. In summary, the proposed water extraction method offers high accuracy, automation, and strong robustness, making it applicable to areas characterized by active and complex hydrological changes.

Abstract The bath industry has multiple attributes, such as economic, health, and cultural communication. Therefore, exploring this industry's spatial pattern evolution is crucial to forming a healthy and balanced development model. Based on POI (Points of Interest) and population migration data, this paper uses spatial statistics and radial basis function neural network to explore the spatial pattern evolution and influencing factors of the bath industry in mainland China. The results show that: (1) The bath industry presents a strong development pattern in the north, south-northeast, and east-northwest regions and weak development in the rest of the country. As a result, the spatial development of new bath space is more malleable. (2) The input of bathing culture has a guiding role in developing the bath industry. The growth of market demand and related industries has a specific influence on the development of the bath industry. (3) Improving the bath industry's adaptability, integration, and service level are feasible to ensure healthy and balanced development. (4) Bathhouses should improve their service system and risk management control during the pandemic.

ABSTRACTQuick and accurate extraction of un-collapsed buildings from post-disaster High-resolution Remote Sensing Images (HRSIs) is imperative for emergency response. Pre-disaster HRSIs could serve as auxiliary data for training models to expedite this extraction process. However, the effectiveness of models trained directly on pre-disaster HRSIs tends to diminish when applied to post-disaster scenarios, mainly due to the notable discrepancies between these datasets. The current popular approach to mitigate this issue involves aligning features from pre- and post-disaster images using an unsupervised domain adversarial learning framework. However, conventional methods often fall short in reducing the substantial disparity between pre- and post-disaster images, due to a lack of comprehensive alignment of category-level and multi-scale features. To overcome these limitations, we propose the Multi-scale Global and Category-attention Features Alignment Network (MGCAN). This novel approach further refines feature alignment strategies by concurrently aligning both multi-scale global and category-attention features, thus effectively narrowing the gap between pre- and post-disaster HRSIs. Extensive experiments have demonstrated that MGCAN significantly improves the accuracy of un-collapsed building extraction from post-disaster HRSIs. Moreover, compared to other state-of-the-art domain adversarial networks, MGCAN exhibits superior performance in different disaster scenarios.

With urban residents’ increasing reliance on metro systems for commuting and other daily activities, extreme weather events such as heavy rainfall and flooding impacting the metro system services are becoming increasingly of concern. Plans for such emergency interruptions require a thorough understanding of the potential outcomes on both the system and individual component scales. However, due to the complex dynamics, constraints, and interactions of the elements involved (e.g., disaster, infrastructure, service operation, and travel behavior), there is still no framework that comprehensively evaluates the system performance across different spatiotemporal scales and is flexible enough to handle increasingly detailed travel behavior, transit service, and disaster information data. Built on an agent-based model (ABM) framework, this study adopts a data-driven ABM simulation approach informed by actual metro operation and travel demand data to investigate the impact of flood-induced station closures on travelers as well as the overall system response. A before-after comparison is conducted where the traveler behaviors in disaster scenarios are obtained from a discrete choice model of alternative stations and routes. A case study of the Shanghai Metro is used to demonstrate the ability of the proposed approach in evaluating the impacts of flood-induced station closures on individual traveler behavior under normal operation and a series of water level rise scenarios of up to 5m. It was found that, when the flood-induced station closures only affect a few river-side stations in the city center, the travelers experience only minor disruptions to their trips due to the availability of unaffected stations nearby as a backup. However, as the water level increases and more stations (mainly in the suburban area) are affected, up to 25% of trips are no longer being fulfilled due to the loss of entrances, exits, or transfer links. The system experiences overall less crowdedness in terms of passenger volume and platform waiting time with a few exceptions of increased passenger load due to concentrations of passenger flows to alternative stations under flooding-induced station closures. The proposed approach can be adapted to other disaster scenarios to reveal the disaster impacts on both aggregated and disaggregated levels and guide the design of more spatio- and temporally-targeted emergency plans for metro systems.

The digestibility of soybean meal can be severely impacted by trypsin inhibitor (TI), one of the most abundant anti-nutritional factors present in soybean seeds. TI can restrain the function of trypsin, a critical enzyme that breaks down proteins in the digestive tract. Soybean accessions with low TI content have been identified. However, it is challenging to breed the low TI trait into elite cultivars due to a lack of molecular markers associated with low TI traits. We identified Kunitz trypsin inhibitor 1 (KTI1, Gm01g095000) and KTI3 (Gm08g341500) as two seed-specific TI genes. Mutant kti1 and kti3 alleles carrying small deletions or insertions within the gene open reading frames were created in the soybean cultivar Glycine max cv. Williams 82 (WM82) using the CRISPR/Cas9-mediated genome editing approach. The KTI content and TI activity both remarkably reduced in kti1/3 mutants compared to the WM82 seeds. There was no significant difference in terms of plant growth or maturity days of kti1/3 transgenic and WM82 plants in greenhouse condition. We further identified a T1 line, #5-26, that carried double homozygous kti1/3 mutant alleles, but not the Cas9 transgene. Based on the sequences of kti1/3 mutant alleles in #5-26, we developed markers to co-select for these mutant alleles by using a gel-electrophoresis-free method. The kti1/3 mutant soybean line and associated selection markers will assist in accelerating the introduction of low TI trait into elite soybean cultivars in the future.

The plastic elongation of mesocotyl (MES) and coleoptile (COL), which can be repressed by light exposure, plays a vital role in maize seedling emergence and establishment under adverse environmental conditions. Understanding the molecular mechanisms of light-mediated repression of MES and COL elongation in maize will allow us to develop new strategies for genetic improvement of these two crucial traits in maize. A maize variety, Zheng58, was used to monitor the transcriptome and physiological changes in MES and COL in response to darkness, as well as red, blue, and white light. The elongation of MES and COL was significantly inhibited by light spectral quality in this order: blue light > red light > white light. Physiological analyses revealed that light-mediated inhibition of maize MES and COL elongation was closely related to the dynamics of phytohormones accumulation and lignin deposition in these tissues. In response to light exposure, the levels of indole-3-acetic acid, trans-zeatin, gibberellin 3, and abscisic acid levels significantly decreased in MES and COL; by contrast, the levels of jasmonic acid, salicylic acid, lignin, phenylalanine ammonia-lyase, and peroxidase enzyme activity significantly increased. Transcriptome analysis revealed multiple differentially expressed genes (DEGs) involved in circadian rhythm, phytohormone biosynthesis and signal transduction, cytoskeleton and cell wall organization, lignin biosynthesis, and starch and sucrose metabolism. These DEGs exhibited synergistic and antagonistic interactions, forming a complex network that regulated the light-mediated inhibition of MES and COL elongation. Additionally, gene co-expression network analysis revealed that 49 hub genes in one and 19 hub genes in two modules were significantly associated with the elongation plasticity of COL and MES, respectively. These findings enhance our knowledge of the light-regulated elongation mechanisms of MES and COL, and provide a theoretical foundation for developing elite maize varieties with improved abiotic stress resistance.

CRISPR/Cas9-based genome editing system is a powerful tool for plant genetic improvement. However, the variable efficiency of guide RNA(s) (gRNA) represents a key limiting factor that hampers the broad application of the CRISPR/Cas9 system in crop improvement. Here, we employed the Agrobacterium-mediated transient assays to evaluate the effectiveness of gRNAs for editing genes in Nicotiana benthamiana and soybean. We designed a facile screening system based on indels that can be introduced by CRISPR/Cas9-mediated gene editing. A gRNA binding sequence (23 nucleotides) was inserted into the open reading frame of yellow fluorescent protein (YFP) gene (gRNA-YFP), which disrupted the YFP reading frame and results in no fluorescent signal when it was expressed in plant cells. Transiently co-expression of Cas9 and a gRNA targeting the gRNA-YFP gene in plant cells could restore the YFP reading frame and recover the YFP signals. We evaluated five gRNAs targeting Nicotiana benthamiana and soybean genes and confirmed the reliability of the gRNA screening system. The effective gRNAs targeting NbEDS1, NbWRKY70, GmKTI1, and GmKTI3 had been used to generate transgenic plants and resulted in expected mutations on each gene. While a gRNA targeting NbNDR1 was confirmed to be ineffective in transient assays. This gRNA indeed failed to trigger target gene mutations in stable transgenic plants. Thus, this new transient assay system can be used to validate the effectiveness of gRNAs before generating stable transgenic plants.

Abstract Background Intercropping is often used in the tea producing areas where land resources are not so abundant, and the produced green tea is tasted more delicious through a tea-Chinese chestnut intercropping system according to the experience of indigenous farmers. The length and weight of tea leaf increase under this intercropping system and their root systems are stratified vertically and coordinate symbiosis. However, the delicacy mechanism under the intercropping is not fully understood. Results Green tea from the Chinese chestnut–tea intercropping system established in the 1980s ranked highest compared with a pure tea plantation from the same region. Based on the non-targeted metabolomics, 100 differential metabolites were upregulated in the tea leaves from intercropping system relative to monoculture system. Twenty-one amino acids were upregulated and three downregulated in response to the intercropping based on the targeted metabolomics; half of the upregulated amino acids had positive effects on the tea taste. Levels of allantoic acid, sugars, sugar alcohols, and oleic acid were higher and less bitter flavonoids in the intercropping system than those in monoculture system. The upregulated metabolites could promote the quality of tea and its health-beneficial health effects. Flavone and flavonol biosynthesis and phenylalanine metabolism showed the greatest difference. Numerous pathways associated with amino acid metabolism altered, suggesting that the intercropping of Chinese chestnut–tea could greatly influence amino acid metabolism in tea plants. Conclusions These results enhance our understanding of the metabolic mechanisms by which tea quality is improved in the Chinese chestnut–tea intercropping system and demonstrate that there is great potential to improve tea quality at the metabolomic level by adopting such an intercropping system.

Accurate knowledge of urban forest patterns contributes to well-managed urbanization, but accurate urban tree canopy mapping is still a challenging task because of the complexity of the urban structure. In this paper, a new method that combines double-branch U-NET with multi-temporal satellite images containing phenological information is introduced to accurately map urban tree canopies. Based on the constructed GF-2 image dataset, we developed a double-branch U-NET based on the feature fusion strategy using multi-temporal images to obtain an accuracy improvement with an IOU (intersection over union) of 2.3% and an F1-Score of 1.3% at the pixel level compared to the U-NET using mono-temporal images which performs best in existing studies for urban tree canopy mapping. We also found that the double-branch U-NET based on the feature fusion strategy has better accuracy than the early fusion strategy and decision fusion strategy in processing multi-temporal images for urban tree canopy mapping. We compared the impact of image combinations of different seasons on the urban tree canopy mapping task and found that the combination of summer and autumn images had the highest accuracy in the study area. Our research not only provides a high-precision urban tree canopy mapping method but also provides a direction to improve the accuracy both from the model structure and data potential when using deep learning for urban tree canopy mapping.

It is very important to obtain continuous regional crop parameters efficiently in the agricultural field. However, remote sensing data can provide spatial-continuous / temporal-disperse crop information while crop growth model can provide temporal-continuous / spatial-disperse crop information. Therefore, the assimilation between crop growth model and remote sensing data is an efficient way for obtaining continuous vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images, were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model.

Ethanol has great effects on the adsorption of phthalate acid esters (PAEs) on activated carbon (AC), which are usually overlooked and hardly studied. This study investigated the overlooked effects of ethanol on the adsorption of PAEs in alcoholic solutions. The adsorption capacities of dibutyl phthalate (DBP) on AC in solutions with ethanol contents of 30, 50, 70, and 100 v% were only 59%, 43%, 19%, and 10% of that (16.39 mg/g) in water, respectively. The ethanol content increase from 50 v% to 100 v% worsened the adsorption performances significantly with the formation of water–ethanol–DBP clusters (decreasing from 13.99 mg/g to 2.34 mg/g). The molecular dynamics simulation showed that the DBP tended to be distributed farther away from the AC when the ethanol content increased from 0 v% to 100 v% (the average distribution distance increased from 5.25 Å to 15.3 Å). The PAEs with shorter chains were more affected by the presence of ethanol than those with longer chains. Taking DBP as an example, the adsorption capacity of AC in ethanol (0.41 mg/g) is only 2.2% of that in water (18.21 mg/g). The application results in actual Baijiu samples showed that the adsorption of PAEs on AC had important effects on the Baijiu flavors.

How does the economy of China and Japan interact with each other? Through building vector autoregression (VAR) model, this study examines the transmission mechanism of economic synchronization among the two nations from three perspectives, namely foreign direct investment (FDI), bilateral trade, and political relation. Based on this, relevant stabilizing measures for China’s economy are proposed. The findings of this study are as follows: (a) There exists business cycle co-movement between China and Japan. (b) The transmission mechanism of this co-movement contains bilateral trade and FDI. Therein, import contributes the most to China’s economic fluctuation, which is followed by FDI and export. (c) Bilateral political relation exerts indirect influence on China-Japan economic synchronization. (d) The impact of Japan’s economic fluctuations on the economy of China would be mitigated by expanding international trade markets, reducing financial market access restrictions, strengthening the political mutual trust, and encouraging communications between China and Japan in science, technology, and education, etc.

With the rise of the strategy of Coordinated Development for the Beijing-Tianjin-Hebei Region, it is necessary to evaluate the trade-offs/synergies of the survival environment and human well-being in Hebei, the capital region of China. However, existing methods cannot analyze and express trade-offs/synergies of two or more variables simultaneously. Therefore, this paper proposes a new framework to express the trade-offs/synergies among land-use intensity, ecosystem services, and human well-being. In this paper, we first identified the land-use intensity change and land-use transformation and evaluated ecosystem services and human well-being in Hebei from 2000–2015 under the Millennium Ecosystem Assessment framework. Then, the trade-offs/synergies of the three indicators were determined by GIS-based methods and MATLAB. The results show that land-use intensity and human well-being mainly present a synergistic relationship, while ecosystem services and land-use intensity mainly present a trade-off relationship, and ecosystem services and human well-being also present a trade-off relationship in Hebei during 2000–2015. In addition, some regional solutions to achieve sustainable development were proposed: region 1 needs to adjust land-use structure, region 2 needs to protect the ecological environment to improve the supply of ecosystem services, and region 3 needs to commit to improving the regional comprehensive human well-being. This study not only proposes a new framework for analyzing trade-offs/synergies of land use intensity, ecosystem services, and human well-being, but it also provides regional solutions for Hebei to achieve sustainable development.

The quantitative and qualitative assessment of post-disaster vegetation damage and recovery in the core area of the Wenchuan earthquake is of great significance for the restoration and reconstruction of natural ecosystems and the construction of human settlements in China. This study used time series analysis to determine the time of MODIS data and used the data to study the vegetation damage and restoration in the core area of the Wenchuan earthquake. The determined MODIS images were used to quantitatively analyze a series of vegetation damage changes and the vegetation recovery rate in the core area of the Wenchuan earthquake before and after the earthquake. By applying the topographic factors, we analyzed the spatial and temporal characteristics of the dynamic changes of vegetation damage and the recovery rate in the disaster area. The results show that the study area’s vegetation damage was correlated to topographic factors and distance from towns. Besides, the overall vegetation restoration after the disaster was relatively optimistic. In some areas, the vegetation restoration level even exceeded the vegetation coverage level before the disaster. The recovery study of MODIS-NDVI showed a specific lag delay effect on the image of vegetation cover. The vegetation damage and the recovery rate of vegetation cover were significantly correlated with the distance from towns and the topographic factor. Overall, the results contribute to the theoretical support for the damage and recovery of vegetation in the core area affected by the earthquake.

The rational allocation of functional areas is the foundation for addressing the sustainable development of cities. Efficient and accurate identification methods of urban functional areas are of great significance to the adjustment and testing of urban planning and industrial layout optimization. Firstly, by employing multisource geographic data, an identification method of urban functional areas was developed. A quantitative measurement approach of the urban functional area was then established considering the comprehensive effects of human-land, space-time, and thematic information to present the covering area of ground objects, public awareness, and empirical research. Finally, the Zhengzhou city, which locates in Henan province of central China, was used to test the method. The results show that the developed method is efficient, accurate, and universal and can identify urban functional areas quickly and accurately. We found that the overall distribution of Zhengzhou’s functional areas presents a spatial pattern of single and multimixed coordinated development. The city’s commercial functional areas and commercial-based mixed functional areas are located in the city’s central area. The green square’s function area occupies relatively low and is mainly distributed in the city’s fringe.

Traffic congestion across the world has reached chronic levels. Despite many technological disruptions, one of the most fundamental and widely used functions within traffic modeling, the volume–delay function has seen little in the way of change since it was developed in the 1960s. Traditionally macroscopic methods have been employed to relate traffic volume to vehicular journey time. The general nature of these functions enables their ease of use and gives widespread applicability. However, they lack the ability to consider individual road characteristics (i.e., geometry, presence of traffic furniture, road quality, and surrounding environment). This research investigates the feasibility to reconstruct the model using two different data sources, namely the traffic speed from Google Maps’ Directions Application Programming Interface (API) and traffic volume data from automated traffic counters (ATC). Google’s traffic speed data are crowd-sourced from the smartphone Global Positioning System (GPS) of road users, able to reflect real-time, context-specific traffic condition of a road. On the other hand, the ATCs enable the harvesting of the vehicle volume data over equally fine temporal resolutions (hourly or less). By combining them for different road types in London, new context-specific volume–delay functions can be generated. This method shows promise in selected locations with the generation of robust functions. In other locations, it highlights the need to better understand other influencing factors, such as the presence of on-road parking or weather events.

Abstract Background Understanding the DNA methylome and its relationship with non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), is essential for elucidating the molecular mechanisms underlying key biological processes in plants. Few studies have examined the functional roles of the DNA methylome in grass species with highly heterozygous polyploid genomes. Results We performed genome-wide DNA methylation profiling in the tetraploid switchgrass (Panicum virgatum L.) cultivar ‘Alamo’ using bisulfite sequencing. Single-base-resolution methylation patterns were observed in switchgrass leaf and root tissues, which allowed for characterization of the relationship between DNA methylation and mRNA, miRNA, and lncRNA populations. The results of this study revealed that siRNAs positively regulate DNA methylation of the mCHH sites surrounding genes, and that DNA methylation interferes with gene and lncRNA expression in switchgrass. Ninety-six genes covered by differentially methylated regions (DMRs) were annotated by GO analysis as being involved in stimulus-related processes. Functionally, 82% (79/96) of these genes were found to be hypomethylated in switchgrass root tissue. Sequencing analysis of lncRNAs identified two lncRNAs that are potential precursors of miRNAs, which are predicted to target genes that function in cellulose biosynthesis, stress regulation, and stem and root development. Conclusions This study characterized the DNA methylome in switchgrass and elucidated its relevance to gene and non-coding RNAs. These results provide valuable genomic resources and references that will aid further epigenetic research in this important biofuel crop.

Abstract Background Mechanical properties of transgenic switchgrass have practical implications for biorefinery technologies. Presented are fundamentals for simple (thermo)mechanical measurements of genetically transformed switchgrass. Experimental basics are provided for the novice, where the intention is to promote collaboration between plant biologists and materials scientists. Results Stem sections were subjected to two stress modes: (1) torsional oscillation in the linear response region, and (2) unidirectional torsion to failure. Specimens were analyzed while submerged/saturated in ethylene glycol, simulating natural hydration and allowing experimental temperatures above 100 °C for an improved view of the lignin glass transition. Down-regulation of the 4-Coumarate:coenzyme A ligase gene (reduced lignin content and altered monomer composition) generally resulted in less stiff and weaker stems. These observations were associated with a reduction in the temperature and activation energy of the lignin glass transition, but surprisingly with no difference in the breadth and intensity of the tan δ signal. The results showed promise in further investigations of how rheological methods relate to stem lignin content, composition, and functional properties in the field and in bioprocessing. Conclusions Measurements such as these are complicated by small specimen size; however, torsional rheometers (relatively common in polymer laboratories) are well suited for this task. As opposed to the expense and complication of relative humidity control, solvent-submersion rheological methods effectively reveal fundamental structure/property relationships in plant tissues. Demonstrated are low-strain linear methods, and also nonlinear yield and failure analysis; the latter is very uncommon for typical rheological equipment.

High-accuracy retrieval of the crop leaf area index (LAI) in farmlands via remote sensing is the premise of reflecting the true growth condition of the crop. This paper aimed at scaling correction of LAI retrieval and developed an LAI scaling transfer model for farmland landscape pattern with multitype spatial heterogeneities according to the multiple types of farmland underlying surfaces in China. The interclass heterogeneity (caused by the alternate distribution of different cover types) and intraclass heterogeneity (caused by the difference in growth conditions within the same crop) both exist in the farmland landscape. The contextural parameters (fractions of components) and fractal dimension of the up-scaling pixel were used to quantitatively describe and correct the scaling effect caused by the two types of spatial heterogeneity, respectively. A scaling transfer model of inversed LAI was built by comprehensively considering intraclass and interclass heterogeneities. Results indicated that the LAI scaling bias of the up-scaling mixed pixel was mainly caused by the interclass heterogeneity even when the areal proportion of the noncrop component was low. The scaling transfer model corrected the scaling effect of LAI, with the root-mean-square error and mean absolute percentage error decreasing from 0.599 and 10.00% to 0.077 and 1.11%, respectively. The developed method based on fractal theory and contextural parameters effectively weakened the influence of the scaling effect on the accuracy of LAI retrieval.

Blending and heat-treatment play significant roles in adjusting gas separation performances of membranes, especially for incorporating thermally labile polymers into carbon molecular sieve membranes (CMSMs). In this work, cellulose acetate (CA) is introduced into polyimide (PI) as a sacrificial phase to adjust the structure and gas separation performance from polymer to carbon. A novel result is observed that the gas permeability is reduced, even when the immiscible CA phase decomposes and forms pores after heat treatment at 350 °C. After carbonization at 600 °C, the miscible CA has changed without contribution, while the role of the immiscible CA phase has changed from original hindrance to facilitation, the composite-based CMSM at a CA content of 10 wt.% shows highest performances, a H2 permeability of ~5300 Barrer (56% enhancement) with a similar H2/N2 permselectivity of 42. The structural analyses reveal that the chain interactions and phase separation behaviors between CA and PI play critical roles on membrane structures and gas diffusion, and the corresponding phase structural evolutions during heat treatment and carbonization determine gas separation properties.

Abstract Background Perennial ryegrass (Lolium perenne L.) is an important temperate grass used for turf and forage purposes. With the increasing accumulation of genomic and transcriptomic data of perennial ryegrass, an efficient protoplast and transient gene expression protocol is highly desirable for in vivo gene functional studies in its homologous system. Results In this report, a highly efficient protoplast isolation (5.6 × 107 protoplasts per gram of leaf material) and transient expression (plasmid transformation efficiency at 55.2%) was developed and the detailed protocol presented. Using this protocol, the subcellular locations of two ryegrass proteins were visualized in chloroplasts and nuclei, respectively, and protein–protein interaction between two chlorophyll catabolic enzymes (LpNOL and LpNYC1) was recorded in its homologous system for the first time. Conclusion This efficient protoplast isolation and transformation protocol is sufficient for studies on protein subcellular localization and protein–protein interaction, and shall be suitable for many other molecular biology applications where the mesophyll protoplast system is desirable in perennial ryegrass.

At present, convolutional neural networks (CNN) have been widely used in building extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the one hand, there are so many parameters in the previous network with complex structure, which will occupy lots of memories and consume much time during training process. On the other hand, low-level features extracted by shallow layers and abstract features extracted by deep layers of artificial neural network cannot be fully fused, which leads to an inaccurate building extraction from RSI. To alleviate these disadvantages, a dense residual neural network (DR-Net) was proposed in this paper. DR-Net uses a deeplabv3+Net encoder/decoder backbone, in combination with densely connected convolution neural network (DCNN) and residual network (ResNet) structure. Compared with deeplabv3+net (containing about 41 million parameters) and BRRNet (containing about 17 million parameters), DR-Net contains about 9 million parameters; So, the number of parameters reduced a lot. The experimental results for both the WHU Building Dataset and Massachusetts Building Dataset, DR-Net show better performance in building extraction than other two state-of-the-art methods. Experiments on WHU building data set showed that Intersection over Union (IoU) increased by 2.4% and F1 score increased by 1.4%; in terms of Massachusetts Building Dataset, IoU increased by 3.8% and F1 score increased by 2.9%.

The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as it is widely used to generate synthetic data. However, the ESTARFM algorithm uses moving windows with a fixed size to get the information around the central pixel, which hampers the efficiency and precision of spatiotemporal data fusion. In this paper, a modified ESTARFM data fusion algorithm that integrated the surface spatial information via a statistical method was developed. In the modified algorithm, the local variance of pixels around the central one was used as an index to adaptively determine the window size. Satellite images from two regions were acquired by employing the ESTARFM and modified algorithm. Results showed that the images predicted using the modified algorithm obtained more details than ESTARFM, as the frequency of pixels with the absolute difference of mean value of six bands’ reflectance between true observed image and predicted between 0 and 0.04 were 78% by ESTARFM and 85% by modified algorithm, respectively. In addition, the efficiency of the modified algorithm improved and the verification test showed the robustness of the modified algorithm. These promising results demonstrated the superiority of the modified algorithm to provide synthetic images compared with ESTARFM. Our research enriches the spatiotemporal data fusion method, and the automatic selection of moving window strategy lays the foundation of automatic processing of spatiotemporal data fusion on a large scale.

Switchgrass is an important bioenergy crop typically grown in marginal lands, where the plants must often deal with abiotic stresses such as drought and salt. Alamo is known to be more tolerant to both stress types than Dacotah, two ecotypes of switchgrass. Understanding of their stress response and adaptation programs can have important implications to engineering more stress tolerant plants. We present here a computational study by analyzing time-course transcriptomic data of the two ecotypes to elucidate and compare their regulatory systems in response to drought and salt stresses. A total of 1,693 genes (target genes or TGs) are found to be differentially expressed and possibly regulated by 143 transcription factors (TFs) in response to drought stress together in the two ecotypes. Similarly, 1,535 TGs regulated by 110 TFs are identified to be involved in response to salt stress. Two regulatory networks are constructed to predict their regulatory relationships. In addition, a time-dependent hidden Markov model is derived for each ecotype responding to each stress type, to provide a dynamic view of how each regulatory network changes its behavior over time. A few new insights about the response mechanisms are predicted from the regulatory networks and the time-dependent models. Comparative analyses between the network models of the two ecotypes reveal key commonalities and main differences between the two regulatory systems. Overall, our results provide new information about the complex regulatory mechanisms of switchgrass responding to drought and salt stresses.

Soil salinization is a major factor limiting crop growth and development in many areas. Switchgrass (Panicum virgatum L.) is an important warm-season grass species used for biofuel production. The objective of this study was to investigate antioxidant metabolism, proline,and protein variation associated with alkali-salt tolerance among 30 switchgrass lines and identify metabolic markers for evaluating alkali-salt tolerance of switchgrass lines. The grass lines were transplanted into plastic pots containing fine sand. When the plants reached E5 developmental stage, they were subjected to either alkali-salt stress treatment (150 mM Na+ and pH of 9.5) or control (no alkali-salt stress) for 20 d. The 30 switchgrass lines differed in alkali-salt tolerance as determined by the level of leaf malondialdehyde (MDA), antioxidant enzyme activity [(superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX)], proline and protein. Alkali-salt stress increased MDA, proline, SOD, reduced CAT activity, but its effect on protein and APX varied depending on lines. Wide variations in the five parameters existed among the 30 lines. In general, the lines with higher CAT activity and lower proline content under alkali-salt stress had less MDA, exhibiting better alkali-salt tolerance. Among the five parameters, CAT can be considered as valuable metabolic markers for assessment of switchgrass tolerance to alkali-salt stress.

Gram-negative bacterial pathogens of plants and animals employ type III secreted effectors to suppress innate immunity. Most characterized effectors work through modification of host proteins or transcriptional regulators, although a few are known to modify small molecule targets. The Xanthomonas type III secreted avirulence factor AvrRxo1 is a structural homolog of the zeta toxin family of sugar-nucleotide kinases that suppresses bacterial growth. AvrRxo1 was recently reported to phosphorylate the central metabolite and signaling molecule NAD in vitro, suggesting that the effector might enhance bacterial virulence on plants through manipulation of primary metabolic pathways. In this study, we determine that AvrRxo1 phosphorylates NAD in planta, and that its kinase catalytic sites are necessary for its toxic and resistance-triggering phenotypes. A global metabolomics approach was used to independently identify 3'-NADP as the sole detectable product of AvrRxo1 expression in yeast and bacteria, and NAD kinase activity was confirmed in vitro. 3'-NADP accumulated upon transient expression of AvrRxo1 in Nicotiana benthamiana and in rice leaves infected with avrRxo1-expressing strains of X. oryzae. Mutation of the catalytic aspartic acid residue D193 abolished AvrRxo1 kinase activity and several phenotypes of AvrRxo1, including toxicity in yeast, bacteria, and plants, suppression of the flg22-triggered ROS burst, and ability to trigger an R gene-mediated hypersensitive response. A mutation in the Walker A ATP-binding motif abolished the toxicity of AvrRxo1, but did not abolish the 3'-NADP production, virulence enhancement, ROS suppression, or HR-triggering phenotypes of AvrRxo1. These results demonstrate that a type III effector targets the central metabolite and redox carrier NAD in planta, and that this catalytic activity is required for toxicity and suppression of the ROS burst.

Agrobacterium overgrowth is a common problem in Agrobacterium-mediated plant transfor-mation. To suppress the Agrobacterium overgrowth, various antibiotics have been used during plant tissue culture steps. The antibiotics are expensive and may adversely affect plant cell differentiation and reduce plant transformation efficiency. The SacB-SacR proteins are toxic to most Agrobacterium tumefaciens strains when they are grown on culture medium sup¬plemented with sucrose. Therefore, SacB-SacR genes can be used as negative selection markers to suppress the overgrowth of Agrobacterium tumefaciens in the plant tissue culture process. We generated a mutant Agrobacterium tumefaciens strain GV2260 (recA-SacB/R) that has the SacB-SacR cassette inserted into the bacterial genome at the recA gene locus. The mutant Agrobacterium strain is sensitive to sucrose but maintains its ability to transform plant cells in both transient and stable transformation assays. We demonstrated that the mutant strain GV2260 (recA-SacB/R) can be inhibited by sucrose that reduces the overgrowth of Agrobacterium and therefore improves the plant transformation efficiency. We employed GV2260 (recA-SacB/R) to generate stable transgenic N. benthamiana plants expressing a CRISPR-Cas9 for knocking out a WRKY transcrip¬tion factor.

Switchgrass is one of the most promising energy crops and only recently has been employed for biofuel production. The draft genome of switchgrass was recently released; however, relatively few switchgrass genes have been functionally characterized. CENH3, the major histone protein found in centromeres, along with canonical H3 and other histones, plays an important role in maintaining genome stability and integrity. Despite their importance, the histone H3 genes of switchgrass have remained largely uninvestigated. In this study, we identified 17 putative switchgrass histone H3 genes in silico. Of these genes, 15 showed strong homology to histone H3 genes including six H3.1 genes, three H3.3 genes, four H3.3-like genes and two H3.1-like genes. The remaining two genes were found to be homologous to CENH3. RNA-seq data derived from lowland cultivar Alamo and upland cultivar Dacotah allowed us to identify SNPs in the histone H3 genes and compare their differential gene expression. Interestingly, we also found that overexpression of switchgrass histone H3 and CENH3 genes in N. benthamiana could trigger cell death of the transformed plant cells. Localization and deletion analyses of the histone H3 and CENH3 genes revealed that nuclear localization of the N-terminal tail is essential and sufficient for triggering the cell death phenotype. Our results deliver insight into the mechanisms underlying the histone-triggered cell death phenotype and provide a foundation for further studying the variations of the histone H3 and CENH3 genes in switchgrass.

Toxin-antitoxin (TA) systems are ubiquitous bacterial systems that may function in genome maintenance and metabolic stress management, but are also thought to play a role in virulence by helping pathogens survive stress. We previously demonstrated that the Xanthomonas oryzae pv. oryzicola protein AvrRxo1 is a type III-secreted virulence factor that has structural similarities to the zeta family of TA toxins, and is toxic to plants and bacteria in the absence of its predicted chaperone Arc1. In this work, we confirm that AvrRxo1 and its binding partner Arc1 function as a TA system when expressed in Escherichia coli. Sequences of avrRxo1 homologs were culled from published and newly generated phytopathogen genomes, revealing that avrRxo1:arc1 modules are rare or frequently inactivated in some species and highly conserved in others. Cloning and functional analysis of avrRxo1 from Acidovorax avenae, A. citrulli, Burkholderia andropogonis, Xanthomonas translucens, and Xanthomonas euvesicatoria showed that some AvrRxo1 homologs share the bacteriostatic and Rxo1-mediated cell death triggering activities of AvrRxo1 from X. oryzae. Additional distant putative homologs of avrRxo1 and arc1 were identified in genomic or metagenomic sequence of environmental bacteria with no known pathogenic role. One of these distant homologs was cloned from the filamentous soil bacterium Cystobacter fuscus. avrRxo1 from C. fuscus caused watersoaking and triggered Rxo1-dependent cell collapse in Nicotiana benthamiana, but no growth suppression in E. coli was observed. This work confirms that a type III effector can function as a TA system toxin, and illustrates the potential of microbiome data to reveal new environmental origins or reservoirs of pathogen virulence factors.

Satellite data for studying surface dynamics in heterogeneous landscapes are missing due to frequent cloud contamination, low temporal resolution, and technological difficulties in developing satellites. A modified spatiotemporal fusion algorithm for predicting the reflectance of paddy rice is presented in this paper. The algorithm uses phenological information extracted from a moderate-resolution imaging spectroradiometer enhanced vegetation index time series to improve the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). The algorithm is tested with satellite data on Yueyang City, China. The main contribution of the modified algorithm is the selection of similar neighborhood pixels by using phenological information to improve accuracy. Results show that the modified algorithm performs better than ESTARFM in visual inspection and quantitative metrics, especially for paddy rice. This modified algorithm provides not only new ideas for the improvement of spatiotemporal data fusion method, but also technical support for the generation of remote sensing data with high spatial and temporal resolution.

Soil salt-alkalization is a major limiting factor for crop production in many regions. Switchgrass (Panicum virgatum L.) is a warm-season C4 perennial rhizomatous bunchgrass and a target lignocellulosic biofuel species. The objective of this study was to evaluate relative alkali-salt tolerance among 30 switchgrass lines. Tillers of each switchgrass line were transplanted into pots filled with fine sand. Two months after transplanting, plants at E5 developmental stage were grown in either half strength Hoagland's nutrient solution with 0 mM Na+ (control) or half strength Hoagland's nutrient solution with 150 mM Na+ and pH of 9.5 (alkali-salt stress treatment) for 20 d. Alkali-salt stress damaged cell membranes [higher electrolyte leakage (EL)], reduced leaf relative water content (RWC), net photosynthetic rate (Pn), stomatal conductance (gs), and transpiration rate (Tr). An alkali-salt stress tolerance trait index (ASTTI) for each parameter was calculated based on the ratio of the value under alkali-salt stress and the value under non-stress conditions for each parameter of each line. Relative alkali-salt tolerance was determined based on principal components analysis and cluster analysis of the physiological parameters and their ASTTI values. Significant differences in alkali-salt stress tolerance were found among the 30 lines. Lowland lines TEM-SEC, Alamo, TEM-SLC and Kanlow were classified as alkali-salt tolerant. In contrast, three lowland lines (AM-314/MS-155, BN-13645-64) and two upland lines (Caddo and Blackwell-1) were classified as alkali-salt sensitive. The results suggest wide variations exist in alkali-salt stress tolerance among the 30 switchgrass lines. The approach of using a combination of principal components and cluster analysis of the physiological parameters and related ASTTI is feasible for evaluating alkali-salt tolerance in switchgrass.

Maize lines that contain the single dominant gene Rxo1 exhibit a rapid hypersensitive response (HR) after infiltration with the rice bacterial streak pathogen Xanthomonas oryzae pv. oryzicola, but not with the rice bacterial blight pathogen X. oryzae pv. oryzae. The avirulence effector gene that corresponds to Rxo1, designated avrRxo1, was identified in an X. oryzae pv. oryzicola genomic library. When introduced into X. oryzae pv. oryzae, clones containing avrRxo1 induced an HR on maize with Rxo1, but not on maize without Rxo1. The avrRxo1 gene is 1,266 bp long and shows no significant homology to any database sequences. When expressed in an X. oryzae pv. oryzae hrpC mutant that is deficient in the type III secretion system, avrRxo1 did not elicit the HR, indicating that the avrRxo1-Rxo1 interaction is dependent on type III secretion. Transient expression of avrRxo1 in onion cells after biolistic delivery revealed that the protein product was associated with the plasma membrane. Transient expression in maize lines carrying Rxo1 resulted in cell death, suggesting that AvrRxo1 functions from inside maize cells to elicit Rxo1-dependent pathogen recognition.

Decreasing lignin content of plant biomass by genetic engineering is believed to mitigate biomass recalcitrance and improve saccharification efficiency of plant biomass. In this study, we compared two different pretreatment methods (i.e., dilute acid and cellulose solvent) on transgenic plant biomass samples having different lignin contents and investigated biomass saccharification efficiency. Without pretreatment, no correlation was observed between lignin contents of plant biomass and saccharification efficiency. After dilute acid pretreatment, a strong negative correlation between lignin content of plant samples and overall glucose release was observed, wherein the highest overall enzymatic glucan digestibility was 70% for the low-lignin sample. After cellulose solvent- and organic solvent-based lignocellulose fractionation pretreatment, there was no strong correlation between lignin contents and high saccharification efficiencies obtained (i.e., 80-90%). These results suggest that the importance of decreasing lignin content in plant biomass to saccharification was largely dependent on pretreatment choice and conditions.

BACKGROUND: Switchgrass (Panicum virgatum L.) is a prime candidate crop for biofuel feedstock production in the United States. As it is a self-incompatible polyploid perennial species, breeding elite and stable switchgrass cultivars with traditional breeding methods is very challenging. Translational genomics may contribute significantly to the genetic improvement of switchgrass, especially for the incorporation of elite traits that are absent in natural switchgrass populations. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we constitutively expressed an Arabidopsis NAC transcriptional factor gene, LONG VEGETATIVE PHASE ONE (AtLOV1), in switchgrass. Overexpression of AtLOV1 in switchgrass caused the plants to have a smaller leaf angle by changing the morphology and organization of epidermal cells in the leaf collar region. Also, overexpression of AtLOV1 altered the lignin content and the monolignol composition of cell walls, and caused delayed flowering time. Global gene-expression analysis of the transgenic plants revealed an array of responding genes with predicted functions in plant development, cell wall biosynthesis, and flowering. CONCLUSIONS/SIGNIFICANCE: To our knowledge, this is the first report of a single ectopically expressed transcription factor altering the leaf angle, cell wall composition, and flowering time of switchgrass, therefore demonstrating the potential advantage of translational genomics for the genetic improvement of this crop.

Effectors of the bacterial type III secretion system provide invaluable molecular probes to elucidate the molecular mechanisms of plant immunity and pathogen virulence. In this report, we focus on the AvrBs2 effector protein from the bacterial pathogen Xanthomonas euvesicatoria (Xe), the causal agent of bacterial spot disease of tomato and pepper. Employing homology-based structural analysis, we generate a three-dimensional structural model for the AvrBs2 protein and identify catalytic sites in its putative glycerolphosphodiesterase domain (GDE). We demonstrate that the identified catalytic region of AvrBs2 was able to functionally replace the GDE catalytic site of the bacterial glycerophosphodiesterase BhGlpQ cloned from Borrelia hermsii and is required for AvrBs2 virulence. Mutations in the GDE catalytic domain did not disrupt the recognition of AvrBs2 by the cognate plant resistance gene Bs2. In addition, AvrBs2 activation of Bs2 suppressed subsequent delivery of other Xanthomonas type III effectors into the host plant cells. Investigation of the mechanism underlying this modulation of the type III secretion system may offer new strategies to generate broad-spectrum resistance to bacterial pathogens.

Quick and accurate extraction of un-collapsed buildings from post-disaster High-resolution Remote Sensing Images (HRSIs) is imperative for emergency response. Pre-disaster HRSIs could serve as auxiliary data for training models to expedite this extraction process. However, the effectiveness of models trained directly on pre-disaster HRSIs tends to diminish when applied to post-disaster scenarios, mainly due to the notable discrepancies between these datasets. The current popular approach to mitigate this issue involves aligning features from pre- and post-disaster images using an unsupervised domain adversarial learning framework. However, conventional methods often fall short in reducing the substantial disparity between pre- and post-disaster images, due to a lack of comprehensive alignment of category-level and multi-scale features. To overcome these limitations, we propose the Multi-scale Global and Category-attention Features Alignment Network (MGCAN). This novel approach further refines feature alignment strategies by concurrently aligning both multi-scale global and category-attention features, thus effectively narrowing the gap between pre- and post-disaster HRSIs. Extensive experiments have demonstrated that MGCAN significantly improves the accuracy of uncollapsed building extraction from post-disaster HRSIs. Moreover, compared to other state-of-the-art domain adversarial networks, MGCAN exhibits superior performance in different disaster scenarios. [ABSTRACT FROM AUTHOR]