Deep Spatial Domain Generalization

Spatial autocorrelation and spatial heterogeneity widely exist in spatial data, which make the traditional machine learning model perform badly. Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space. Specifically, it learns a model under varying data distributions that generalizes to unseen domains. Although tremendous success has been achieved in domain generalization, there exist very few works on spatial domain generalization. The advancement of this area is challenged by: 1) Difficulty in characterizing spatial heterogeneity, and 2) Difficulty in obtaining predictive models for unseen locations without training data. To address these challenges, this paper proposes a generic framework for spatial domain generalization. Specifically, We develop the spatial interpolation graph neural network that handles spatial data as a graph and learns the spatial embedding on each node and their relationships. The spatial interpolation graph neural network infers the spatial embedding of an unseen location during the test phase. Then the spatial embedding of the target location is used to decode the parameters of the downstream-task model directly on the target location. Finally, extensive experiments on thirteen real-world datasets demonstrate the proposed method's strength

Graph Neural Network for spatiotemporal data: methods and applications

In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies. There is a large amount of existing work that focuses on addressing the complex spatial and temporal dependencies in spatiotemporal data using GNNs. However, the strong interdisciplinary nature of spatiotemporal data has created numerous GNNs variants specifically designed for distinct application domains. Although the techniques are generally applicable across various domains, cross-referencing these methods remains essential yet challenging due to the absence of a comprehensive literature review on GNNs for spatiotemporal data. This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data. Then, a systematic categorization and summary of existing spatiotemporal GNNs are presented to enable domain experts to identify suitable techniques and to support model developers in advancing their research. Moreover, a comprehensive overview of significant applications in the spatiotemporal domain is offered to introduce a broader range of applications to model developers and domain experts, assisting them in exploring potential research topics and enhancing the impact of their work. Finally, open challenges and future directions are discussed

Nitrogen, manganese, iron, and carbon resource acquisition are potential functions of the wild rice Oryza rufipogon core rhizomicrobiome

Background: The assembly of the rhizomicrobiome, i.e., the microbiome in the soil adhering to the root, is influenced by soil conditions. Here, we investigated the core rhizomicrobiome of a wild plant species transplanted to an identical soil type with small differences in chemical factors and the impact of these soil chemistry differences on the core microbiome after long-term cultivation. We sampled three natural reserve populations of wild rice (i.e., in situ) and three populations of transplanted in situ wild rice grown ex situ for more than 40 years to determine the core wild rice rhizomicrobiome. Results: Generalized joint attribute modeling (GJAM) identified a total of 44 amplicon sequence variants (ASVs) composing the core wild rice rhizomicrobiome, including 35 bacterial ASVs belonging to the phyla Actinobacteria, Chloroflexi, Firmicutes, and Nitrospirae and 9 fungal ASVs belonging to the phyla Ascomycota, Basidiomycota, and Rozellomycota. Nine core bacterial ASVs belonging to the genera Haliangium, Anaeromyxobacter, Bradyrhizobium, and Bacillus were more abundant in the rhizosphere of ex situ wild rice than in the rhizosphere of in situ wild rice. The main ecological functions of the core microbiome were nitrogen fixation, manganese oxidation, aerobic chemoheterotrophy, chemoheterotrophy, and iron respiration, suggesting roles of the core rhizomicrobiome in improving nutrient resource acquisition for rice growth. The function of the core rhizosphere bacterial community was significantly (p < 0.05) shaped by electrical conductivity, total nitrogen, and available phosphorus present in the soil adhering to the roots. Conclusion: We discovered that nitrogen, manganese, iron, and carbon resource acquisition are potential functions of the core rhizomicrobiome of the wild rice Oryza rufipogon. Our findings suggest that further potential utilization of the core rhizomicrobiome should consider the effects of soil properties on the abundances of different genera. [MediaObject not available: see fulltext.]

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte

Comparison of methane metabolism in the rhizomicrobiomes of wild and related cultivated rice accessions reveals a strong impact of crop domestication

Microbial communities from rhizosphere (rhizomicrobiomes) have been significantly impacted by domestication as evidenced by a comparison of the rhizomicrobiomes of wild and related cultivated rice accessions. While there have been many published studies focusing on the structure of the rhizomicrobiome, studies comparing the functional traits of the microbial communities in the rhizospheres of wild rice and cultivated rice accessions are not yet available. In this study, we used metagenomic data from experimental rice plots to analyze the potential functional traits of the microbial communities in the rhizospheres of wild rice accessions originated from Africa and Asia in comparison with their related cultivated rice accessions. The functional potential of rhizosphere microbial communities involved in alanine, aspartate and glutamate metabolism, methane metabolism, carbon fixation pathways, citrate cycle (TCA cycle), pyruvate metabolism and lipopolysaccharide biosynthesis pathways were found to be enriched in the rhizomicrobiomes of wild rice accessions. Notably, methane metabolism in the rhizomicrobiomes of wild and cultivated rice accessions clearly differed. Key enzymes involved in methane production and utilization were overrepresented in the rhizomicrobiome samples obtained from wild rice accessions, suggesting that the rhizomicrobiomes of wild rice maintain a different ecological balance for methane production and utilization compared with those of the related cultivated rice accessions. A novel assessment of the impact of rice domestication on the primary metabolic pathways associated with microbial taxa in the rhizomicrobiomes was performed. Results indicated a strong impact of rice domestication on methane metabolism; a process that represents a critical function of the rhizosphere microbial community of rice. The findings of this study provide important information for future breeding of rice varieties with reduced methane emission during cultivation for sustainable agriculture