dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Means and standard deviations were calculated using GraphPad Prism 8 software (GraphPad Prism 8; GraphPad Software, USA). Three independent repeats were tested for every sample in ELISA. The cutoff value is calculated based on OD450 nm value of samples tested negative in CFT (excluding inconclusive results). Samples were considered positive when the absorbance reading exceeds the cutoff value computed by two standard deviations above the mean of the negative control. The specificity and sensitivity of the ELISA and western blot tests were determined using the CFT as a standard according to the following formulas: Specificity % ð Þ ¼ Reference negative Test positiveþReference negative � 100% Inconclusive CFT results were not considered for these calculations.
Identification of a protein-glycan glycoconjugate antigen for antibody detection In a previous study characterizing the general protein glycosylation system in B. cenocepacia, one of the most abundant and highly glycosylated glycopeptides found, designated A0K9U9, could not be ascribed to any annotated protein in the database of the type strain J2315  [21]. To localize the gene encoding this peptide, its 71-amino acid sequence was used as query in Artemis  [33]  to search the translations in all frames for B. cenocepacia type strain J2315. This resulted in the identification of a short open reading frame in chromosome 1 (at base pairs 3,008,284-3,008,496) located in the intergenic region between BCAL2737 (encoding a putative pseudouridine synthase) and BCAL2738 (encoding a putative exported protein), which was preceded by a ribosomal binding site sequence (Figure  1 ); we designated this open reading frame BCAL2737a.
The predicted BCAL2737a gene encoded a 71-amino acid polypeptide; it also contained a 21-amino acid signal peptide, predicted by SignalP v. 5. 0  [34] , as expected for a glycosylated protein since glycosylation takes place in the periplasm. The BCAL2737a protein did not show any homology with known proteins in the database. The mature polypeptide contained three serine residues (Figure  1 ) that become O-glycosylated based on experimental evidence by mass spectrometry  [21]. Therefore, the BCAL2737a polypeptide was selected for further analysis since because of its small mass, combined with three glycosylation sites, it could be an appropriate antigen to detect antibodies against Burkholderia O-glycans.
Western blot analysis revealed that purified CtxB-BCAL2737a expressed in ΔpglL exhibited decreased electrophoretic mobility, suggesting it corresponded to an unglycosylated form (Figure  3a ), in agreement with the results found with crude lysates (Figure  2b, D ). Similarly, only the protein expressed in K56-2 was detected with PNA, indicating the correct trisaccharide glycan residues were present. Glycosylation was also verified by mass spectrometry confirming at least three serine residues within BCAL2737a are glycosylated (Figure  3b , Supplementary Dataset). To further support the degradation of unglycosylated BCAL2737a peptidome datasets from Oppy et al. [32]  were reanalyzed against a K56-2 proteome containing the BCAL2737a protein  The band corresponding to glycosylated BCAL2737a (purified from strain K56-2) was extracted from the gel, processed as indicated in the "Material"s and Methods" section, and the glycopeptides analyzed by MS-MS in an Orbitrap Fourier transform mass spectrometer (FTMS) with electron transfer dissociation (ETD) and higher energy collision-induced dissociation (HCD).
The data confirm all three serine residues previously reported  [21] , also indicated in Figure  1 , are modified in CtxB-BCAL2737a. The modified serine residues are shown in red. (c) Heatmap of BCAL2737a peptides observed in peptidome preparations of B. cenocepacia K56-2. Peptidome datasets from Oppy et al. [32]  were reanalyzed against a K56-2 proteome containing the BCAL2737a protein sequence, resulting in the identification of multiple peptide breakdown products previously not assigned to known proteins, which are clearly both more numerous and more intense in the glycosylation null strain ΔpglL compared to K56-2 and the pglL complemented strain  [32]. B, Biological replicate. sequence. The new analysis identified multiple peptide breakdown products previously not assigned to known proteins, which were more abundant and intense in the glycosylation null strain ΔpglL compared to K56-2 and the pglL complemented strain (Figure  3c ).
(c) Heatmap of BCAL2737a peptides observed in peptidome preparations of B. cenocepacia K56-2. Peptidome datasets from Oppy et al. [32]  were reanalyzed against a K56-2 proteome containing the BCAL2737a protein sequence, resulting in the identification of multiple peptide breakdown products previously not assigned to known proteins, which are clearly both more numerous and more intense in the glycosylation null strain ΔpglL compared to K56-2 and the pglL complemented strain [32]. B, Biological replicate.
Acknowledgments We thank  Dr Nora Madani  for supplying equine sera from the experimental trial with B. mallei and  Dr Keren Turton  for a critical review of the manuscript. We also thank  Dr Pat Lenihan  (Ireland),  Dr. Vania Lucia Santana  (Brazil), and  Prof. Mohammed Sami   A (Kuwait)  for providing field negative and positive horse sera. We thank the  Melbourne Mass Spectrometry and Proteomics Facility of The Bio21 Molecular Science and Biotechnology Institute at The University of Melbourne  for access to mass spectrometry infrastructure.
The modified serine residues are shown in red. (c) Heatmap of BCAL2737a peptides observed in peptidome preparations of B. cenocepacia K56-2. Peptidome datasets from Oppy et al. [32]  were reanalyzed against a K56-2 proteome containing the BCAL2737a protein sequence, resulting in the identification of multiple peptide breakdown products previously not assigned to known proteins, which are clearly both more numerous and more intense in the glycosylation null strain ΔpglL compared to K56-2 and the pglL complemented strain [32]. B, Biological replicate.
Despite the large body of knowledge recognising the impact of biological invasions on biodiversity, their economic impact has been less evaluated. However, the associated economic costs ought to provide useful information on many different aspects to prevent and manage invasions. Here, we describe the economic costs of biological invasions in Japan using InvaCost, a recently-published global database on monetary costs extracted from English and non-English sources, as well as a complementary search, thereby filling a gap in regional knowledge. We focused on the following four dimensions when analysing the economic costs of biological invasions: damage to biodiversity, damage to human livelihood, management for biodiversity and management for human livelihood. Interestingly, there was no information about biological invasion costs for Japan in English, but the Japanese search and our additional survey provided a total of 630 cost entries, with a total economic cost of 728 million USD (2017 value, equivalent to 62 billion JPY).
Although the Global Invasive Species Database (2020) lists 263 invasive species for Japan, the Ecological Society of Japan identified 2,230 species in its list of alien species for the country almost 20 years ago (Ecological Society of Japan 2002). Amongst these species, many invasive species are known to induce severe ecological impacts and agricultural damage. Examples of ecological impacts include predation on endangered species by invasive predators, such as the small Indian mongoose Urva auropunctata  (Watari et al. 2008) , the black rat Rattus rattus  (Chiba 2010) , free-ranging cats Felis silvestris catus  (Shionosaki et al. 2015; Kobayashi et al. 2019; Maeda et al. 2019; Azumi et al. 2021 ) and the green anole Anolis carolinensis  (Abe et al. 2008). Examples of agricultural damage include damage to agricultural products by common raccoons Procyon lotor  (Suzuki and Ikeda 2019)  and alien invasive insects  (Kiritani 1998 ).
However, this has not yet been done. the economic damage and expenditure associated with invasive species around the world, with an extension that focuses on the entries of economic costs in non-Englishspeaking countries. Using this database and the results of a complementary search performed for this study, we aimed to describe the outline and details of the recorded economic costs of biological invasions for Japan. In particular, we described the economic costs of invasive species in Japan following two approaches. First, we focused on the difference between the economic damage caused by invasive species and the cost of their management. Invasive species damage represents a substantial economic loss (reactive) that requires scientific knowledge and administrative systems to evaluate the damage, whereas invasive species management is an expense (proactive) that allows us to calculate the cost incurred directly from the management budget.
Data acquisition and categorisation To analyse the economic cost of biological invasions in Japan, we used InvaCost (version 3. 0, openly available at https://doi. org/10. 6084/m9. figshare. 12668570), a recently-compiled database which compiles the monetary impacts of invasive species reported in English and non-English documents worldwide  (Diagne et al. 2020b; Angulo et al. 2021). InvaCost was developed following a systematic and standardised methodology to collect information from scientific articles, grey literature, stakeholders and expert elicitation. The most up-to-date version (v3. 0) of InvaCost contains 9,823 cost entries; each entry refers to a unique cost value with specific descriptors (columns) that describe the spatial and temporal information of the cost, the taxonomy of the species causing the cost, the typology of the cost and the document reporting the cost.
A set of columns reports the cost value in both local currencies and in USD, i. e. converted by dividing the cost estimate by the official market exchange rate corresponding to the year of the cost estimation and then to 2017 USD using inflation factors  (Diagne et al. 2020b). Given the importance of the search in Japanese, we summarise here how this search was performed (for more details, see  Angulo et al. 2021). First, in the Web of Knowledge, the same search strings were used as in English, i. e. a combination of terms related to the economic costs and invasive species, but setting the Language to Japanese; 64 articles were retrieved but none with costs. Second, we used a similar search string in Google Scholar with Japanese terms: 205 articles were retrieved, including eight with economic costs.
Finally, in the Google search engine, the Japanese terms for "budget" AND "exotic organisms" were used, directing the search to the webpage JUDGIT! (JUDGIT! 2019), a volunteer organisation that compiles the budgets of the Japanese government, which provided most of the Japanese-language entries in InvaCost_v3. 0. The search of the Japanese entries was conducted for economic costs incurred through 2017. A total of 329 cost entries was obtained from InvaCost_v3. 0. Only one source came from the English database; the remaining 328 entries were Japanese and came from the non-English database (Suppl. material 1: Table  S1 ). However, the English cost entry, based on the description of  Armstrong and Ball (2005)  citing  Kiritani (1998) , was excluded from our analyses, because  Kiritani (1998)  did not present a cost description and, therefore, may be considered misquoted.
Moreover, this English entry represented the cost of the eradication project of melon flies Bactrocera cucurbitae and oriental flies Bactrocera dorsalis, while the Japanese entries (Reference ID: JP_6 in Suppl. material 1: Table  S1 ) described these costs in more detail. We also conducted additional surveys to avoid the omission of cost data from important invasive alien species in Japan, because the searches conducted in English and non-English emphasised the uniformity of the search methods across countries, which may lead to the omission of locally-important invasive species. Thus, we conducted searches in the JUDGIT! database (JUDGIT! 2019) using the common names (in Japanese) of all species listed in the "100 worst invasive alien species in Japan" (Ecological Society of Japan 2002) as search strings.
The JUDGIT! database mostly compiles budgets since 2014 with the exception of a few in 2013. Moreover, JUDGIT! not only extracts each entry, but also shows the budget subjects in which the entries are located, which, in turn, allows us to search for other projects located in each budget subject. Using this function, we also extracted cost entries related to invasive species other than the 100 worst species. Similar to the InvaCost, the search was conducted for economic costs incurred through 2017. The cost entries obtained from this additional search were finally combined with the entries obtained from InvaCost_v3. 0 (Suppl. material 1: Table  S1 ). We re-classified the type of costs associated with the entries by dividing them into two categories: "Damage" and "Management". "Damage" includes the economic loss caused by the invasive alien species and the expenses incurred to repair its impacts, while "Management" includes the expenses associated with managing invasive alien species itself, including prevention, eradication, control, research, buying equipment and environmental education to promote a better understanding of invasive species management.
Data analyses We first compared the economic costs reported for each taxonomic group using the Class and species taxonomic classification. Second, to examine the annual change in economic costs, we plotted the costs against the year. Most of the Japanese entries are based on projects conducted by the government, in which the temporal unit of entries is the Fiscal Year (e. g. FY2017 = 2017 April -2018 March). Here, for the sake of convenience, we treated the fiscal year as the year of the beginning period (FY2017 = 2017). Some of the entries in the database described total costs over multiple years. To determine the annual costs, we used the function "expand-YearlyCosts" from the invacost package  (Leroy et al. 2020). In this case, the total cost was divided by the number of years and converted to a cost per year.
Results We obtained a total of 630 cost entries, of which 328 were from the non-English database and 302 from the search performed for this study (there were no recorded costs in English). Based on these entries, invasive species cost a total of 728 million USD (2017 value) to the Japanese economy from 1965 to 2017. These entries came from 33 documents and 16 authors (Suppl. material 1: Table  S1 ). The author with the most entries was JUDGIT!, with 17 data sources (budget subjects of the Japanese government), 499 entries and 86 million USD. Of this information, the budgets of the Ministry of the Environment had the largest number of entries, with 318 entries, followed by 177 entries from the Ministry of Agriculture, Forestry and Fisheries and four entries from the Ministry of Economy, Trade and Industry.
In terms of economic costs, the Ministry of Agriculture, Forestry and Fisheries had the largest budget at 48 million USD, followed by the Ministry of the Environment at 37 million USD and the Ministry of Economy, Trade and Industry at 0. 7 million USD. The next author with most entries was the Ministry of Agriculture, Forestry and Fisheries with two data sources, which resulted in 93 entries and corresponded to 235 million USD. These were statistics on the economic damage caused by invasive mammals and birds recorded in each area of the country since 1999. The third largest entries were taken from a report from Okinawa Prefecture amounting to 10. 55 million USD, which assessed the damage caused by invasive alien species in Okinawa Prefecture; this entry had not been included in the above summary of the Ministry of Agriculture, Forestry and Fisheries.
These costs were followed by the report of invasive insect eradications (JP_6 in Suppl. material 1: Table  S1 ) with five entries, with the largest costs being 333 million USD for the eradication project of invasive insects in the Nansei Islands and Ogasawara Islands, which accounted for almost half of Japan's total costs in our dataset. The number of entries (Fig. 2a ) and the amount of economic costs (Fig. 2b ) aggregated for the four categories of Damage_Biodiversity, Damage_Human, Manage-ment_Biodiversity and Management_Human show that the largest number of entries was recorded for Management_Biodiversity, accounting for 47% of the total number of entries. However, these entries only constituted 6% of the total economic costs. By contrast, both Damage_Human and Management_Human had a high proportion of the total economic costs compared to the number of entries.
This correlation between recorded economic costs and research efforts implies that many costs actually incurred remain unassessed  (Bradshaw et al. 2016; Kourantidou et al. 2021). In addition, it was only recently that damage to biodiversity began to be reported, although the costs recorded as damage to biodiversity in our dataset corresponded to the budget for in situ and ex situ conservation of threatened insects greatly damaged by the green anole (Karube 2019), which did not require additional efforts to evaluate the economic costs of the impact of the invasive species. This suggests that the economic costs of the invasion damage might be greatly underestimated and the degree of underestimation may be more pronounced in the case of damage to the biodiversity. Another indication of this underestimation is that only 54 species were evaluated for the economic costs out of 2,230 known to be present in Japan (Ecological Society of Japan 2002).
2019 ). However, the entries in the dataset were biased towards those implemented by the national government, which were accessible on the internet. For example, the relatively large-scale eradication measures such as those implemented against the small Indian mongoose by the Okinawa Prefecture  (Watari 2011; Yagihashi et al. 2021)  and against Reeves's muntjac (Muntiacus reevesi) by Tokyo metropolitan government (Tokyo Metropolitan Government 2016) are not included in the present dataset. In addition, introduced common raccoons and masked palm civets are widespread in Japan  (Ohdachi et al. 2015; Suzuki and Ikeda 2019)  and many cities, towns and villages capture them to reduce crop damage  (Ohdachi et al. 2015) , but their economic costs are not included in the entries of the dataset. Moreover, even the national measures, conducted outside of the period and reported on the internet, are not included in the dataset.
For example, the mongoose eradication project on Amami-Oshima Island started in 2000  (Watari et al. 2008) , but its costs have only been available on the internet since 2014. Even if the costs are published on the internet, they are not always listed in the dataset. For example, a document in the database summarising the contents of the budget for biological invasion management, based on "Act on the Prevention of Adverse Ecological Impacts Caused by Designated Invasive Alien Species", included a total of 2,512,000,000 JPY (2012-2017), whereas the amount of costs listed in our dataset (Suppl. material 1: Table  S1 ) was 1,615,000,000 JPY (14,766,378 USD in 2017), meaning 38% of the total budget was omitted from the dataset of source documents. This gap is due to the difficulties in identifying the target invasive species from the title of each project listed in the source documents, most of which target multiple invasive species.
Importance of including non-English information Given the current situation where information from non-English sources has been ignored, becoming a barrier to the advancement of ecological scientific knowledge biodiversity conservation  (Amano et al. 2016; Nuñez et al. 2019; Konno et al. 2020) , using the InvaCost database will help to ensure that information will be shared more equitably, leading to better global and domestic evaluation of economic costs of invasive species. Japan is one of the countries with a lower proportion of English speakers (Amano and Sutherland 2013) and, hence, information is more prone to be transmitted/published in Japanese, rather than in English  (Konno et al. 2020). Indeed, only one entry was found for Japan in the English database  (Diagne et al. 2020b ). As a result, the English-based scientific community, has significantly overlooked evidence at least in terms of the economic cost evaluation of invasive species from non-English speaking countries.
Acknowledgements We would like to thank  Gauthier Dobigny  for the translation of the abstract into French. YW was funded by the  Environment Research and Technology Development Fund  ( JPMEERF20184004 ) of the  Environmental Restoration and Conservation Agency of Japan. The  French National Research Agency  ( ANR-14-CE02-0021 ) and  BNP-Paribas Foundation Climate Initiative  funded the  InvaCost  project, which allowed the development of the InvaCost database, including the non-English version. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  Call 2018 on biodiversity scenarios. Funding for the contracts of EA and LBM contracts come from the  AXA Research Fund Chair of Invasion Biology.
HTLV-1 serologic test and proviral load (PVL) determination HTLV-1 serologic diagnosis was made by ELISA (Ortho Diagnostics, USA) and positive samples were confirmed by western blot (HTLV Blot 2. 4 test, DBL, Singapore). All patients whose serum sample was reactive with either test was submitted to a nested-PCR using HTLV-1 generic primers and amplified products were digested with restriction enzymes  17. In order to determine HTLV-1 proviral load, peripheral blood mononuclear cells (PBMC) were isolated from an acid-citrate-dextrose solution and separated by Ficoll density gradient centrifugation (Pharmacia, Uppsala, Sweden). DNA was extracted using a commercial kit (GFX Pharmacia, Uppsala, Sweden). The forward and reverse primers used for HTLV-1 DNA quantitation were SK110 (5'-CCCTACAATCCAACCAGCTCAG-3', HTLV-1 nucleotide 4758-4779 (GenBank accession No.
J02029)), and SK111 (5'-GTGGTGAAGCTGCCATCGGGTTTT-3', HTLV-1 nucleotide 4943-4920). The internal HTLV-1 TaqMan probe (5'-CTTTAC TGACAAACCCGACCTACCCATGGA-3') was selected using the Oligo (version 4, National Biosciences, Plymouth, MI, USA) and Primer Express (Perkin-Elmer Applied Biosystems, Boston, MA, USA) software programs. The probe was located between positions 4829 and 4858 of the HTLV-1 genome and carried a 5' reporter dye FAM (6-carboxy fluorescein) and a 3' quencher dye TAMRA (6-carboxy tetramethyl rhodamine). Albumin DNA quantification was used to normalize variations due to differences of DNA extraction or PBMCs counts as described previously  18. The normalized value of HTLV-1 proviral load was calculated as the ratio of (HTLV-1 DNA average copy number/albumin DNA average copy number) x 2 x 10 6 and is reported as the number of HTLV-1 copies/10 5 PBMC  19.
Gating strategy All data were analyzed using FlowJo TM Software Version 10. 5. 3 for Mac OS X. Major lineage subsets were selected from forward and side scatter properties followed by single live cells (Supplementary Figure  1 ). Doublet discrimination was achieved by plotting FSC-H vs. SSC-A. For innate immunity live, single, HLA-DR+ cells were selected. Hierarchical gating allows then the discrimination of the following innate cell subsets: cDC1 (CD11c + , BDCA2/3 + ), cDC2 (CD11c + , BDCA2/3 -, BDCA1 + ), pDC (CD11c -, BDCA2/3 + ), monocytes (CD11c+, BDCA2/3 -, BDCA1 -). CD16 and CD14 expression further defined the following subsets of monocytes: classical (CD14 + CD16 -), intermediate (CD14 + CD16 + ) and nonclassical monocytes (CD14 -CD16 + ). NK populations were defined as HLA-DR -, Lin -, and subdivided into CD56 dim CD16 + and CD56 high CD16 -NK cell subsets.