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A group of temperate grassland plant species termed the "Mansen elements" occurs in Japan and is widely distributed in the grasslands of continental East Asia. It has been hypothesized that these species are continental grassland relicts in Japan that stretch back to a colder age, but their migration history has not been elucidated. To assess the migration history of the Mansen elements, we performed phylogeographic analyses of Tephroseris kirilowii, a member of this group, using single-nucleotide polymorphisms (SNPs) obtained from multiplexed inter-simple sequence repeat genotyping by sequencing (MIG-seq). It was estimated that the Japanese populations of T. kirilowii were divided from those of continental East Asia at 25.2 thousand years ago (ka) with 95% highest probability density interval (HPD) of 15.3–40.0 ka and that Japanese clades first diverged at 20.2 ka with 95% HPD of 10.4–30.1 ka. As the climatically suitable range during the last glacial maximum (LGM) estimated using ecological niche modeling (ENM) was limited in Japan and there was a slight genetic differentiation among Japanese populations, a post-glacial expansion of T. kirilowii in the Japanese Archipelago was indicated. [ABSTRACT FROM AUTHOR]

Recently, the fine‐grained geolocalization of User‐Generated Short Text (UGST) has been increasingly attracting much attention. Accurate geolocation can benefit many applications, especially for the location‐based services. However, since the majority of UGSTs are short, noisy and not geotagged, existing methods still suffer from two issues. One is the heavy reliance on the manual features not fully exploiting the semantic information. Another is the free writing style of social media resulting in extremely few useful geo‐indicative information. To address these issues, we propose a novel Fine‐grained Geolocalization method for UGSTs with Preprocessing, location‐entity consistency Replacing, Filtering, Convolutional neural network (FG‐PRFC), which only relies on UGST itself. Compared to existing methods, FG‐PRFC has four unique characteristics: (1) We present a UGST‐oriented preprocessing method to obtain more semantic information. (2) To tackle the abbreviation issue, we develop a replacing method to allow geo‐indicative words behaving in the same way. (3) Following the idea of TFIDF, we weight the words in UGST and then develop a location‐free UGST filtering method. (4) We employ convolutional neural network to model the relationship between words and locations. Extensive experiments on three ground‐truth datasets demonstrate that our method has a significant improvement compared to state‐of‐art methods. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

City-level carbon footprint has been recognized as a useful measure of anthropogenic impact on climate change associated with citizens' activities within the administrative boundary. Although the promotion of consumer responsibility suggests rethinking urban indirect emissions, the detailed methodology is far from satisfactory for realistic applications. Due to the lack of multi-regional input-output tables for most cities, there is a wide application of single regional input-output tables. However, there still lacks further discussion on if there will be an obvious evaluation bias by applying city-level single-regional tables rather than multi-regional ones. To visualize the table coverage on its application consequence, both single- and multi-regional input-output tables were employed to compare disparities in the carbon footprint accounting in the case of Tokyo, Japan. Our analysis shows that the gap of emissions driven by Tokyo's final demand between single- and multi-regional input-output tables was considerably large. Furthermore, the results of multi-regional table were found to be 8.11 MtC higher for coal-generated emissions, 7.83 MtC for crude oil-generated emissions and 2.90 MtC for natural gas-generated emissions than those of the single-regional table. The largest deviation in emissions accounting was observed in the power, gas and heating supply sector, the construction sector and the private service sector. The gap between these two input-output tables was notable for all three types of fossil fuels (coal, crude oil and natural gas). These indicated that coal-generated emissions have been largely ignored by single-regional input-output table. The study highlighted the difference of carbon footprint accounting between these two types of input-output tables. Our findings are intended to assist policymakers and scholars in pinpointing and reallocating sectors that are likely to yield severely biased evaluation of emissions embodied in trade when a multi-regional table is not available., Competing Interests: Declaration of competing interest There is no conflict of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)