VQGraph: Graph Vector-Quantization for Bridging GNNs and MLPs

Graph Neural Networks (GNNs) conduct message passing which aggregates local neighbors to update node representations. Such message passing leads to scalability issues in practical latency-constrained applications. To address this issue, recent methods adopt knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (MLP) by mimicking the output of GNN. However, the existing GNN representation space may not be expressive enough for representing diverse local structures of the underlying graph, which limits the knowledge transfer from GNN to MLP. Here we present a novel framework VQGraph to learn a powerful graph representation space for bridging GNNs and MLPs. We adopt the encoder of a variant of a vector-quantized variational autoencoder (VQ-VAE) as a structure-aware graph tokenizer, which explicitly represents the nodes of diverse local structures as numerous discrete tokens and constitutes a meaningful codebook. Equipped with the learned codebook, we propose a new token-based distillation objective based on soft token assignments to sufficiently transfer the structural knowledge from GNN to MLP. Extensive experiments and analyses demonstrate the strong performance of VQGraph, where we achieve new state-of-the-art performance on GNN-MLP distillation in both transductive and inductive settings across seven graph datasets. We show that VQGraph with better performance infers faster than GNNs by 828x, and also achieves accuracy improvement over GNNs and stand-alone MLPs by 3.90% and 28.05% on average, respectively. Code: https://github.com/YangLing0818/VQGraph.Comment: arXiv admin note: text overlap with arXiv:1906.00446 by other author

Construction of a cross-species cell landscape at single-cell level.

Individual cells are basic units of life. Despite extensive efforts to characterize the cellular heterogeneity of different organisms, cross-species comparisons of landscape dynamics have not been achieved. Here, we applied single-cell RNA sequencing (scRNA-seq) to map organism-level cell landscapes at multiple life stages for mice, zebrafish and Drosophila. By integrating the comprehensive dataset of > 2.6 million single cells, we constructed a cross-species cell landscape and identified signatures and common pathways that changed throughout the life span. We identified structural inflammation and mitochondrial dysfunction as the most common hallmarks of organism aging, and found that pharmacological activation of mitochondrial metabolism alleviated aging phenotypes in mice. The cross-species cell landscape with other published datasets were stored in an integrated online portal-Cell Landscape. Our work provides a valuable resource for studying lineage development, maturation and aging

Comparative Metaproteomic Analysis on Consecutively Rehmannia glutinosa-Monocultured Rhizosphere Soil

National Natural Science Foundation of China [30772729, 30671220, 31070403]; Natural Science Foundation of Fujian province, China [2008J0051]Background: The consecutive monoculture for most of medicinal plants, such as Rehmannia glutinosa, results in a significant reduction in the yield and quality. There is an urgent need to study for the sustainable development of Chinese herbaceous medicine. Methodology/Principal Findings: Comparative metaproteomics of rhizosphere soil was developed and used to analyze the underlying mechanism of the consecutive monoculture problems of R. glutinosa. The 2D-gel patterns of protein spots for the soil samples showed a strong matrix dependency. Among the spots, 103 spots with high resolution and repeatability were randomly selected and successfully identified by MALDI TOF-TOF MS for a rhizosphere soil metaproteomic profile analysis. These proteins originating from plants and microorganisms play important roles in nutrient cycles and energy flow in rhizospheric soil ecosystem. They function in protein, nucleotide and secondary metabolisms, signal transduction and resistance. Comparative metaproteomics analysis revealed 33 differentially expressed protein spots in rhizosphere soil in response to increasing years of monoculture. Among them, plant proteins related to carbon and nitrogen metabolism and stress response, were mostly up-regulated except a down-regulated protein (glutathione S-transferase) involving detoxification. The phenylalanine ammonia-lyase was believed to participate in the phenylpropanoid metabolism as shown with a considerable increase in total phenolic acid content with increasing years of monoculture. Microbial proteins related to protein metabolism and cell wall biosynthesis, were up-regulated except a down-regulated protein (geranylgeranyl pyrophosphate synthase) functioning in diterpenoid synthesis. The results suggest that the consecutive monoculture of R. glutinosa changes the soil microbial ecology due to the exudates accumulation, as a result, the nutrient cycles are affected, leading to the retardation of plant growth and development. Conclusions/Significance: Our results demonstrated the interactions among plant, soil and microflora in the proteomic level are crucial for the productivity and quality of R. glutinosa in consecutive monoculture system

Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method

Understanding the Role of Shallow Groundwater in Improving Field Water Productivity in Arid Areas

Soil water and salt transport in soil profiles and capillary rise from shallow groundwater are significant seasonal responses that help determine irrigation schedules and agricultural development in arid areas. In this study the Agricultural Water Productivity Model for Shallow Groundwater (AWPM-SG) was modified by adding a soil salinity simulation to precisely describe the soil water and salt cycle, calculating capillary fluxes from shallow groundwater using readily available data, and simulating the effect of soil salinity on crop growth. The model combines an analytical solution of upward flux from groundwater using the Environmental Policy Integrated Climate (EPIC) crop growth model. The modified AWPM-SG was calibrated and validated with a maize field experiment run in 2016 in which predicted soil moisture, soil salinity, groundwater depth, and leaf area index were in agreement with the observations. To investigate the response of the model, various scenarios with varying groundwater depth and groundwater salinity were run. The inhibition of groundwater salinity on crop yield was slightly less than that on crop water use, while the water consumption of maize with a groundwater depth of 1 m is 3% less than that of 2 m, and the yield of maize with groundwater depth of 1 m is only 1% less than that of 2 m, under the groundwater salinity of 2.0 g/L. At the same groundwater depth, the higher the salinity, the greater the corn water productivity, and the smaller the corn irrigation water productivity. Consequently, using modified AWPM-SG in irrigation scheduling will be beneficial to save more water in areas with shallow groundwater

Effect of Planting Density on the Growth and Yield of Sunflower under Mulched Drip Irrigation

A field experiment was conducted to test the suitability of growing sunflower under mulched drip irrigation with saline water in the HID (Hetao Irrigation District), North China. The experiment included five planting densities in which the plant spacing was 30, 35, 40, 45, and 50 cm with the same spacing (50 cm) between rows. The results indicated that mulched drip irrigation with saline water was more water-saving than traditional ground irrigation using fresh water, while the irrigation quota increased with the increase of planting density. Little difference of soil salinization was found for the treatments in the 50–100 cm soil layer, which indicated that additional measures should be taken for salt balance with saline water irrigation. The height and leaf area index (LAI) of sunflower increased in response to the increase of plant density, and the head dry matter transferred to the stem at plant densities higher than 47,619 plants/hm2. Though the grain weight and 1000-seed weight decreased with increasing plant density, the achene yield and biomass production increased. This research suggests that a plant spacing of 35 cm with 50 cm of row spacing is more suitable for sunflower mulched drip irrigation with saline water at concentrations of 3.0 g∙L−1

Spatial and Temporal Distribution Characteristics of Water Requirements for Maize in Inner Mongolia from 1959 to 2018

Crop water requirements are crucial for agricultural water management and redistribution. Based on meteorological and agricultural observation data, the effective precipitation (Pe), water requirements (ETc), and irrigation water requirements (Ir) in the maize growing areas of Inner Mongolia were calculated. Furthermore, climatic trends of these variables were analysed to reveal their temporal and spatial distributions. The research results are as follows: the average Pe of maize in Inner Mongolia during the entire growth period was 125.9 mm, with an increasing trend from west to east. The Pe in the middle growth period of maize was the highest and was small in the early and late growth stages. The Pe climate exhibited a negative slope with a decreasing trend. The average ETc of maize during the entire growth period was 480.6 mm. The high-value areas are mainly distributed in the Wulatzhongqi and Linhe areas. The average Ir of maize during the entire growth period was 402.9 mm, and the spatial distribution is similar to that of ETc. In each growth period, Ir showed an increasing trend. Supplemental irrigation should be added appropriately during each growth period to ensure the normal growth of maize. This study can provide an effective basis for the optimisation of irrigation and regional water conservation in the maize cultivation area of Inner Mongolia