Heterogeneous graphs can represent many network structures in the real world, and research on heterogeneous graph data has attracted more attention. Most existing approaches require additional label information to obtain meaningful node representations. However, labeling heterogeneous graphs is tedious and time-consuming, and low-quality labels will harm the efficiency of models. In addition, the number of nodes increases exponentially with the distance from the root node, but the linearly expanding Euclidean space is difficult to match this growth rate. In this paper, we propose a unified framework that leverages the label irrelevance of contrastive learning and the unique expressive ability of hyperbolic space to encode heterogeneous graphs. Specifically, we use the contrast mechanism to obtain semantic information in an unsupervised way. Meanwhile, we design hyperbolic encoders that are more suitable for graph structure to learn the latent information from heterogeneous graphs efficiently. Experiments on four real-world heterogeneous graph data sets demonstrate the competitive efficacy of the proposed method.

MOTIVATION: Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (i) matching uncertainty between data features and metabolites; (ii) lacking of method to analyze positive mode and negative mode liquid chromatography-mass spectrometry (LC/MS) data simultaneously on the same set of subjects; (iii) the incompleteness of pathways in individual software packages. RESULTS: We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration-(i) a weighted gene set enrichment analysis-type test and (ii) a permutation-based weighted hypergeometric test. The package is capable of combining positive- and negative-ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: Kyoto Encyclopedia of Genes and Genomes, Mummichog and The Small Molecule Pathway Database. AVAILABILITY AND IMPLEMENTATION: The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Most robots currently being sold or developed are either stylized with white material or have a metallic appearance. In this research we used the shooter bias paradigm and several questionnaires to investigate if people automatically identify robots as being racialized, such that we might say that some robots are 'White' while others are 'Asian', or 'Black'. To do so, we conducted an extended replication of the classic social psychological shooter bias paradigm using robot stimuli to explore whether effects known from human-human intergroup experiments would generalize to robots that were racialized as Black and White. Reaction-time based measures revealed that participants demonstrated 'shooter-bias' toward both Black people and robot racialized as Black. Participants were also willing to attribute a race to the robots depending on their racialization and demonstrated a high degree of inter-subject agreement when it came to these attributions.

Solar-induced chlorophyll fluorescence (SIF) has been considered a direct proxy in monitoring vegetation actual photosynthesis. However, applications in the Earth science have been greatly limited due to challenges in use of remotely sensed SIF. This is because the linkage between vegetation actual photosynthesis and SIF retrieved from the canopy reflected radiance exists numerous uncertainties. It is reported that ground-based long-term SIF measurement system can promote the understanding of diurnal and seasonal dynamics of vegetation SIF. This study investigates the interpretation and implication of remotely retrieved canopy diurnal SIF using a newly developed automated SIF field measurement system (Auto-SIF system) in China, involving different vegetation types, data acquisition frequencies and weather conditions. Results may improve the understanding of both vegetation SIF and photosynthesis, thus further boosting the applications of SIF.

Solar-induced chlorophyll fluorescence (SIF) is a direct indicator of vegetation photosynthesis. Time-series in-situ SIF data enable better understanding of SIF-photosynthesis relationship. However, continuous seasonal SIF data is extremely lack due to several technical problems for ground-based long-term SIF observation systems. One of the major problems is it is difficult to measure radiance and irradiance simultaneously, both of which are inputs of traditional SIF retrieval methods. Statistical approaches for SIF retrieval is independent of synchronous irradiance data and therefore provides opportunities for improving long-term SIF observation systems. This report reviewed the state of the art of the automated SIF observation systems and evaluated the potential of using statistical approaches to simplify the design and data processing of these systems. Results showed that reliable SIF could be retrieved using statistical method without irradiance data once good train set was provided. Opportunities and challenges for improving ground-based long-term SIF measurement systems were discussed.

Remote sensing of top-of-canopy (TOC) long-term sun-induced chlorophyll fluorescence (SIF) is necessary to better understand the SIF-photosynthesis relationship. Statistical methods provide an alternative to TOC SIF retrieval, as they are independent of synchronous irradiance measurements and may better describe actual irradiance. This letter aims to evaluate the feasibility of using statistical methods for time series TOC SIF retrieval in the absence of synchronous irradiance measurements. Results show that the training set should include nonfluorescent radiance spectra under a variety of solar zenith angles, and that water vapor is an important contributor of spectral variation within 717-745 nm. On the diurnal scale, atmospheric features trained from irradiance spectra can be used to retrieve SIF values from high-frequency upwelling radiance spectra. Features independently trained from nonfluorescent radiance spectra measured on one day can be used for SIF retrieval on a different day within a relatively short period. Our results show that statistical methods have the potential to simplify ground-based SIF measurements and data processing.

Hyperspectral imaging have become a critical tool for the vegetation remote sensing since it contains abundant spectral information and can detect the subtle features to accurately monitor the vegetation status. Regarding to the number of bands been used, numerous hyperspectal vegetation indices (VIs) can be roughly divided into two categories: VIs derived from band geometric and VIs derived from band transformation. This study used the Compact Airborne Spectrographic Imager (CASI) data to compare the VI derived from band transformation, taking the Vegetation Index based on the Universal Pattern Decomposition Method (VIUPD) as an example, with four widely used VIs derived from band geometric. The results show that VIUPD can reflect more abundant information than others VIs qualitatively and quantitatively.