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Few-shot hyperspectral image classification aims to develop the ability of classifying image pixels by using relatively few labeled pixels per class. However, due to the inaccuracy of the localization system and the bias of the ground survey, the potential noisy labels in the training data pose a very significant challenge to few-shot hyperspectral image classification. To solve this problem, this paper proposes a weighted contrastive prototype network (WCPN) for few-shot hyperspectral image classification with noisy labels. WCPN first utilizes a similarity metric to generate the weights of the samples from the same classes, and applies them to calibrate the class prototypes of support and query sets. Then the weighted prototype network will minimize the distance between features and prototypes to train the network. WCPN also incorporates a weighted contrastive regularization function that uses the sample weights as gates to filter the fake positive samples whose labels are incorrect to further improve the discriminative power of the prototypes. We conduct experiments on multiple hyperspectral image datasets with artificially generated noisy labels, and the results show that the WCPN has excellent performance that can sufficiently mitigate the impact of noisy labels. [ABSTRACT FROM AUTHOR]

Enhancing crop nitrogen use efficiency (NUE) in agricultural sciences is a pivotal challenge, particularly for high-demand crops like potatoes (Solanum tuberosum L.), the world's third most significant food crop. This study delves into the molecular responses of potatoes to low nitrogen (LN) stress, employing an integrative approach that combines transcriptomics and metabolomics to compare two cultivars with divergent NUE traits: XS6, known for its high NUE, and NS7, characterized by lower NUE. Our research unveils that XS6 exhibits higher chlorophyll and N content, increased tuber yield, and elevated N assimilation capacity under LN stress conditions compared to NS7. Through transcriptome analysis, we identified critical genes involved in C and N metabolism that had higher expression in XS6. A significant discovery was the high-affinity nitrate transporter 2.7 gene, which showed elevated expression in XS6, suggesting its key role in enhancing NUE. Metabolomics analysis further complemented these findings, revealing a sophisticated alteration of 1252 metabolites under LN stress, highlighting the dynamic interplay between carbon and N metabolism in coping with N scarcity. The integration of transcriptomic and metabolomic data underscored the crucial role of trehalose in mitigating N deficiency and enhancing NUE. This study provides novel insights into the molecular mechanisms governing NUE in potatoes, offering valuable perspectives for molecular breeding to enhance NUE in potatoes and potentially other crops. [ABSTRACT FROM AUTHOR]

The United Nations Sustainable Development Goals (SDGs) and the rise of global sustainability science have led to the increasing recognition of basins as the key natural geographical units for human–land system coupling and spatial coordinated development. The effective measurement of spatiotemporal patterns of urban connectivity within a basin has become a key issue in achieving basin-related SDGs. Meanwhile, China has been actively working toward co-ordinated regional development through in-depth implementation of the Yellow River Basin's ecological protection and high-quality development. Urban connectivity has been trending in urban planning, and significant progress has been made on different scales according to the flow space theory. Nevertheless, few studies have been conducted on the multiscale spatiotemporal patterns of urban agglomeration connectivity. In this study, the urban network in the Yellow River Basin was constructed using Tencent population migration data from 2015 and 2019. It was then divided into seven distinct communities to enable analysis at both the basin and community scales. Centrality, symmetry, and polycentricity indices were employed, and the multiscale spatiotemporal patterns of urban agglomerations in the Yellow River Basin were identified using community detection, complex networks, and the migration kaleidoscope method. Community connectivity was notably concentrated at the basin scale with a centripetal pattern and spatial heterogeneity. Additionally, there was a symmetrical and co-ordinated relationship in population migration between the eastern and western regions of the basin, as well as between the internal and external parts of the basin. At the community scale, there was significant variation in the extent of central agglomeration among different communities, with few instances of similar-level, long-distance, and interregional bilateral links. The utilization of multiscale spatiotemporal patterns has the potential to enhance the comprehension of economic cooperation between various cities and urban agglomerations. This understanding can aid decision-makers in formulating sustainable development policies that foster the spatial integration of the basin. [ABSTRACT FROM AUTHOR]

Dendrobium catenatum is a classical and precious dual-use plant for both medicine and food in China. It was first recorded in Shen Nong's Herbal Classic, and has the traditional functions of nourishing yin, antipyresis, tonifying the stomach, and promoting fluid production. The stem is its medicinal part and is rich in active polysaccharide glucomannan. As an excellent dietary fiber, glucomannan has been experimentally confirmed to be involved in anti-cancer, enhancing immunity, lowering blood sugar and blood lipids, etc. Here, the status quo of the D. catenatum industry, the structure, bioactivities, biosynthesis pathway and key genes of glucomannan are systematically described to provide a crucial foundation and theoretical basis for understanding the value of D. catenatum and the potential application of glucomannan in crop biofortification. [ABSTRACT FROM AUTHOR]

Plant pathogens are one of the main factors hindering the breeding of cash crops. Pathogens, including oomycetes, fungus, and bacteria, secrete effectors as invasion weapons to successfully invade and propagate in host plants. Here, we review recent advances made in the field of plant-pathogen interaction models and the action mechanisms of phytopathogenic effectors. The review illustrates how effectors from different species use similar and distinct strategies to infect host plants. We classify the main action mechanisms of effectors in plant-pathogen interactions according to the infestation process: targeting physical barriers for disruption, creating conditions conducive to infestation, protecting or masking themselves, interfering with host cell physiological activity, and manipulating plant downstream immune responses. The investigation of the functioning of plant pathogen effectors contributes to improved understanding of the molecular mechanisms of plant-pathogen interactions. This understanding has important theoretical value and is of practical significance in plant pathology and disease resistance genetics and breeding. [ABSTRACT FROM AUTHOR]

Land use is used to reflect the expression of human activities in space, and land use classification is a way to obtain accurate land use information. Obtaining high-precision land use classification from remote sensing images remains a significant challenge. Traditional machine learning methods and image semantic segmentation models are unable to make full use of the spatial and contextual information of images. This results in land use classification that does not meet high-precision requirements. In order to improve the accuracy of land use classification, we propose a land use classification model, called DADNet-CRFs, that integrates an attention mechanism and conditional random fields (CRFs). The model is divided into two modules: the Dual Attention Dense Network (DADNet) and CRFs. First, the convolution method in the UNet network is modified to Dense Convolution, and the band-hole pyramid pooling module, spatial location attention mechanism module, and channel attention mechanism module are fused at appropriate locations in the network, which together form DADNet. Second, the DADNet segmentation results are used as a priori conditions to guide the training of CRFs. The model is tested with the GID dataset, and the results show that the overall accuracy of land use classification obtained with this model is 7.36% and 1.61% higher than FCN-8s and BiSeNet in classification accuracy, 11.95% and 1.81% higher in MIoU accuracy, and with a 9.35% and 2.07% higher kappa coefficient, respectively. The proposed DADNet-CRFs model can fully use the spatial and contextual semantic information of high-resolution remote sensing images, and it effectively improves the accuracy of land use classification. The model can serve as a highly accurate automatic classification tool for land use classification and mapping high-resolution images. [ABSTRACT FROM AUTHOR]

Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing the shapes of building footprints in modern electronic maps. Furthermore, their performance depends on enough labeled samples for each class of building footprints. However, it is impractical to label enough samples for each type of building footprint shapes. Therefore, the deep learning methods using few labeled samples are more preferable to recognize and classify the building footprint shapes. In this paper, we propose a relation network based method for the recognization of building footprint shapes with few labeled samples. Relation network, composed of embedding module and relation module, is a metric based few-shot method which aims to learn a generalized metric function and predict the types of the new samples according to their relation with the prototypes of these few labeled samples. To better extract the shape features of the building footprints in the form of vector polygons, we have taken the TriangleConv embedding module to act as the embedding module of the relation network. We validate the effectiveness of our method based on a building footprint dataset with 10 typical shapes and compare it with three classical few-shot learning methods in accuracy. The results show that our method performs better for the classification of building footprint shapes with few labeled samples. For example, the accuracy reached 89.40% for the 2-way 5-shot classification task where there are only two classes of samples in the task and five labeled samples for each class. [ABSTRACT FROM AUTHOR]

Lipid-derived jasmonates (JAs) play a crucial role in a variety of plant development and defense mechanisms. In recent years, significant progress has been made toward understanding the JA signaling pathway. In this review, we discuss JA biosynthesis, as well as its core signaling pathway, termination mechanisms, and the evolutionary origin of JA signaling. JA regulates not only plant regeneration, reproductive growth, and vegetative growth but also the responses of plants to stresses, including pathogen as well as virus infection, herbivore attack, and abiotic stresses. We also focus on the JA signaling pathway, considering its crosstalk with the gibberellin (GA), auxin, and phytochrome signaling pathways for mediation of the trade-offs between growth and defense. In summary, JA signals regulate multiple outputs of plant defense and growth and act to balance growth and defense in order to adapt to complex environments. [ABSTRACT FROM AUTHOR]

In barley (Hordeum vulgare L.) breeding, heading date is one of the most important agricultural traits that is essential for the completion of its life cycle. Certain endogenous and environmental factors regulate floral transition, morphing the complex genetic mechanism of the heading phase, which could serve as a premise of orchestration for improved yields. To elaborate the network of genetic and environmental signals, a hydroponic experiment was carried out using two spring (i.e., DM65 and DM70) and two winter barley genotypes (i.e., DM269 and DM385). Our results confirmed that the vernalized environment produced a substantial reduction in plant height, biomass and photosynthetic activity compared with the control plants. A noticeable increase in oxidative stress was exhibited by DM65 and DM70 plants compared with their respective controls at 20 °C, while no significant difference was observed for any genotype grown in the greenhouse (25 °C). Simultaneously, increased antioxidant enzyme activity in winter barley genotypes showed a defensive mechanism under vernalized conditions (4 °C). Furthermore, the expression of key regulatory flowering genes revealed that the vernalization gene (HvVRN1) is the key regulator of floral induction after cold exposure, whereas Photoperiod Response Locus 1 (HvPpd-H1) had significantly higher expression under greenhouse conditions, along with Phytochrome C (HvPHY-C), validating their involvement as upstream heading time regulators. These findings contribute to enriching the study of environmental signals that substantially modulate the complex mechanism of barley heading date. [ABSTRACT FROM AUTHOR]

Urban vitality is a key indicator for measuring urban development. This topic has been trending in urban planning and sustainable development, and significant progress has been made in measuring single indicators of urban vitality based on parcel or block units. With the continuous development of smart sensing technology, multisource urban data are becoming increasingly abundant. The application of such data to measure the multidimensional urban vitality of street space, reflecting multiple functions of an urban space, can significantly improve the accuracy of urban vitality analyses and promote the construction of people-oriented healthy cities. In this study, streets were taken as the analysis unit, and multisource data such as the trajectories of taxies and shared bicycles, user reviews and cultural facility points of interest (POIs) in Chengdu, a city in southwestern China, were used to identify spatial patterns of urban vitality on streets across social, economic and cultural dimensions. The correlation between the built environment factors and the multidimensional urban vitality on the street was analyzed using a multiple regression model. The spatial distribution of the different dimensions of urban vitality of the street space in Chengdu varies to a certain extent. It is common for areas with high social vitality to have production and life centers nearby. High economic vitality centers are typically found along busy streets with a high concentration of businesses. Areas with high cultural vitality centers tend to be concentrated on the city's central streets. Land use, transportation, external environment, population and employment are all closely linked to urban vitality on streets. The crowd counting and POI density have the greatest impact on multidimensional urban vitality. The crowd and the level of service facilities profoundly affect social interaction, trade activities and cultural communication. The goodness of fit (R2) of the regression models for social, economic and cultural vitality are 0.590, 0.423 and 0.409, respectively. Using multisource urban data, our findings can help stakeholders better understand the spatial patterns and influencing factors of multidimensional urban vitality on streets and provide sustainable urban planning and development strategies for the future. [ABSTRACT FROM AUTHOR]

The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a graph to represent the adjacency relationships between the points (i.e., the vertices of the polygons shaping the buildings), and extract a list of geometric features for each point. This paper proposes a deep point convolutional network to recognize building shapes, which executes the convolution directly on the points of the buildings without constructing the graphs and extracting the geometric features of the points. A new convolution operator named TriangleConv was designed to learn the feature representations of each point by aggregating the features of the point and the local triangle constructed by the point and its two adjacency points. The proposed method was evaluated and compared with related methods based on a dataset consisting of 5010 vector buildings. In terms of accuracy, macro-precision, macro-recall, and macro-F1, the results show that the proposed method has comparable performance with typical graph neural networks of GCN, GAT, and GraphSAGE, and point cloud neural networks of PointNet, PointNet++, and DGCNN in the task of recognizing and classifying building shapes in map space. [ABSTRACT FROM AUTHOR]

Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and guide the expansion of urban spaces in the future. At present, the concept and delineation of urban boundaries do not follow a unified method or standard. However, many scholars have made use of multi-source remote sensing images of various scales and social auxiliary data such as point of interest (POI) data to achieve large-scale, high-resolution, and high-precision land cover mapping and impermeable water surface mapping. The accuracy of small- and medium-scale urban boundary mapping has not been improved to an obvious extent. This study uses multi-temporal Sentinel-2 high-resolution images and POI data that can reflect detailed features of human activities to extract multi-dimensional features and use random forests and mathematical morphology to map the urban boundaries of the city of Zhengzhou. The research results show that: (1) the urban construction land extraction model established with multi-dimensional features has a great improvement in accuracy; (2) when the training sample accounts for 65% of the sample data set, the urban construction land extraction model has the highest accuracy, reaching 96.25%, and the Kappa coefficient is 0.93; (3) the optimized boundary of structural elements with a size of 13 × 13 is selected, which is in good agreement in terms of scope and location with the boundary of FROM-GLC10 (Zhengzhou) and visual interpretations. The results from the urban boundary delineation in this paper can be used as an important database for detailed basic land use mapping within cities. Moreover, the method in this paper has some reference value for other cities in terms of delineating urban boundaries. [ABSTRACT FROM AUTHOR]

In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system's stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method. [ABSTRACT FROM AUTHOR]

In this work, the effect of drought on K+ uptake in root and its translocation from root to shoot was investigated using six barley genotypes contrasting in drought tolerance. Results showed that drought conditions caused significant changes in K+ uptake and translocation in a time- and genotype-specific manner, which consequently resulted in a significant difference in tissue K+ contents and drought tolerance levels between the contrasting barley genotypes. The role of K+ transporters and channels and plasma membrane (PM) H+-ATPase in barley's adaptive response to drought stress was further investigated at the transcript level. The expression of genes conferring K+ uptake (HvHAK1, HvHAK5, HvKUP1, HvKUP2 and HvAKT1) and xylem loading (HvSKOR) in roots were all affected by drought stress in a time- and genotype-specific manner, indicating that the regulation of these K+ transporters and channels is critical for root K+ uptake and root to shoot K+ translocation in barley under drought stress. Furthermore, the barley genotypes showed a strong correlation between H+ efflux and K+ influx under drought stress, which was further confirmed by the significant up-regulation of HvHA1 and HvHA2. These results suggested an important role of plasma membrane H+-ATPase activity and/or expression in regulating the activity of K+ transporters and channels under drought stress. Taken together, it may be concluded that the genotypic difference in drought stress tolerance in barley is conferred by the difference in the ability to regulate K+ transporters and channels in root epidermis and stele. [ABSTRACT FROM AUTHOR]

Understanding the spatial patterns of retail stores in urban areas contributes to effective urban planning and business administration. A variety of methods have been proposed in the scientific literature to identify the spatial patterns of retail stores. These methods invariably employ arbitrary grid cells or administrative units (e.g., census tracts) as the fundamental analysis units. As most urban retail stores are distributed along street networks, using area-based analysis units is subject to statistical biases and may obfuscate the spatial pattern to some extent. Using the street segment as the analysis unit, this paper derives the spatial patterns of retail stores by crawling points of interest (POI) data in Zhengzhou, a city in central China. Then, the paper performs the network-based kernel density estimation (NKDE) and employs several network metrics, including the global, local, and weighted closeness centrality. Additionally, the paper discusses the correlation between the NKDE value and the closeness centrality across different store types. Further analysis indicates that stores with a high correlation tend to be distributed in city centers and subnetwork centers. The comparison between NKDE and cell-based KDE shows that our proposed method can address potential statistical issues induced by the area-based unit analysis. Our finding can help stakeholders better understand the spatial patterns and trends of small business expansion in urban areas and provide strategies for sustainable planning and development. [ABSTRACT FROM AUTHOR]

A soil erosion model is used to evaluate the conditions of soil erosion and guide agricultural production. Recently, high spatial resolution data have been collected in new ways, such as three-dimensional laser scanning, providing the foundation for refined soil erosion modelling. However, serial computing cannot fully meet the computational requirements of massive data sets. Therefore, it is necessary to perform soil erosion modelling under a parallel computing framework. This paper focuses on a parallel computing framework for soil erosion modelling based on the Hadoop platform. The framework includes three layers: the methodology, algorithm, and application layers. In the methodology layer, two types of parallel strategies for data splitting are defined as row-oriented and sub-basin-oriented methods. The algorithms for six parallel calculation operators for local, focal and zonal computing tasks are designed in detail. These operators can be called to calculate the model factors and perform model calculations. We defined the key-value data structure of GeoCSV format for vector, row-based and cell-based rasters as the inputs for the algorithms. A geoprocessing toolbox is developed and integrated with the geographic information system (GIS) platform in the application layer. The performance of the framework is examined by taking the Gushanchuan basin as an example. The results show that the framework can perform calculations involving large data sets with high computational efficiency and GIS integration. This approach is easy to extend and use and provides essential support for applying high-precision data to refine soil erosion modelling. [ABSTRACT FROM AUTHOR]