dataesr/scientific-paragraphs-categorization huggingface dataset:
2. Discuss the vertical distribution of aerosol. 3. Investigate the origin of air masses over the selected sites. The paper is organized as follows: The datasets used in this study are presented in Section 2 along with a description of the selected sites and the methodology. The spatiotemporal optical properties and types of aerosols in Southern Africa and Reunion are presented in Section 3. 1, wherein we use the 3D distribution of aerosol type based on CALIOP to obtain the vertical distribution of each type as a function of altitude. The findings are provided in Section 3. 2 and used to initiate HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) back-trajectory simulations in the study area. These back-trajectories are used to highlight the origin, main pathways, and height of transported air masses during the biomass burning season (Section 3.
Satellite Observations 2. 3. 1. MODIS Aerosol Data Set The Moderate Resolution Imaging Spectroradiometer (MODIS) is carried onboard NASA's Aqua (EOS PM-1) and Terra (EOS AM-1) satellites, which have heliosynchronous orbits and have been operational since 2002 and 2000, respectively. MODIS collects data in 36 discrete spectral bands ranging from 0. 4 to 14. 4 µm, with spatial resolution varying from 250 m to 1 km depending on the channel. MODIS products provide several quantities derived from the measure of radiance. These quantities are the distribution of cloud cover, the amount of water vapor in an atmosphere column, the distribution of temperature, and aerosol properties. The data used are the "Combined Dark Target and Deep Blue AOD at 0. 55 micron for land and ocean (MOD08_D3_v6. 1)", collected from the Terra platform on a 1°× 1°grid with daily temporal resolution.
The Giovanni website was used to download aerosol observations (https://giovanni. gsfc. nasa. gov/giovanni/, last accessed on 7 May 2022). AOD from MODIS observations data (called AOD MODIS , hereafter) were taken to the closest point to each sun photometer station (see Figure  1 ). For each site, the time period starts when the sun photometer becomes active and ends on 31 December 2021. Because the study period from AOD p at Skukuza was set to begin in January 2008, we considered the same for MODIS.
The dataset used is the Level-3 tropospheric aerosol profile product (Tropospheric_APro_V4. 20 and V4. 21, downloaded from the EarthData website: https: //search. earthdata. nasa. gov/, last access 18 March 2022) for "cloud-free" conditions during both daytime and nighttime from 2008 to 2021; at the time of conducting this work, data were available until October 2021. The advantage of this dataset is that it corresponds to a combination of qualityscreened Level-2 aerosol data and includes aerosol extinction profiles at 532 nm as well as layer classification information (i. e. , aerosol, cloud, and "clear air"-meaning air without aerosols). The CALIOP Level-3 aerosol profile product is based on monthly statistics, with all Level-2 aerosol profiles reported separately each month at a near-global scale (180°W to 180°E, 85°N to 85°S) on a uniform 2.
0°× 5. 0°(latitude × longitude) grid box with a vertical resolution of 60. 0 m from 0. 5 to 12. 0 km (total of 208 layers) above mean sea level. Data were selected on a latitude × longitude grid box of 2. 0°× 5. 0°to obtain the vertical distribution of each aerosol type for each site. Each collection grid is shown in Figure  A1 , Appendix A. Due to CALIOP L3 data resolution, Skukuza and Pretoria were merged into a larger grid box. According to Gui et al. [51] , the South Atlantic Anomaly degrades the quality of the Level 3 data in 2017 and 2019. As a consequence, there are no data for these three years at Windpoort and Gobabeb.
Based on the results of the vertical distribution of aerosol type obtained with CALIOP, we chose 9 height levels ranging from 2 to 6 km with a resolution of 500 m. To cover the biomass burning season, we ran simulations of 120 h per height every 10 days from 2008 to 2021 between August and November. As mentioned by Hernández-Ceballos et al. [53] , 120 h is considered sufficient to represent synoptic air flows. There are 9 simulations per height level and 13 per year for a total of 1638 simulations per site. The simulation results were then clustered and analyzed for each site to determine the origin of the aerosol particles observed during biomass burning seasons. We clustered the trajectories using TrajStat, an algorithm developed by Wang et al. [54]  as a GIS-based program to view, query, and group the trajectories.
Monthly Evolution Figure  3  shows the time series of the monthly average and ±1 standard deviation value of AOD at 550 nm from sun photometer and MODIS observations at eight sites from 2008 to 2021. For ease of reading and comparison, the figures in this part are organized by the latitude and longitude of sun photometer sites. Based on the 13-year period (2008-2021), Skukuza, Pretoria, and Reunion have the longest time series (corresponding to 69. 2, 61. 5, and 100% of data availability, see Table  1 ). Simonstown, Gobabeb, and Durban present time series with 53. 8% of data availability for seven years of measurements. Windpoort and Maun Tower show shortened time series with 46. 1 and 30. 7% of data availability and more than four years of measurements. Despite the difference in the length of the sun photometer dataset, time series analysis reveals an annual variation that coincides with the maxima during the spring season.
This season corresponds to the biomass burning season, which lasts from August to November in Southern Africa. In September, AOD peaks exceed 0. 4 in Windpoort, Maun Tower, Skukuza, Pretoria, and Durban. As mentioned in the methodology section, MODIS data were taken closest to each AERONET station and only time-coincident measurements were considered. For both instruments (sun photometer and MODIS), the lowest and highest annual average AOD values are found in Simonstown (0. 07 ± 0. 02 and 0. 12 ± 0. 02) and Skukuza (0. 19 ± 0. 09 and 0. 15 ± 0. 05), respectively. Both Reunion and Simonstown have similar AOD evolution, with a low multiyear annual average value of 0. 07. Similarly to with AOD, the time series of AE are in Figure  4  to give an idea of the predominant aerosol mode from sun photometers.
Based on previous work, the most studied sites in the last decades are Skukuza and Pretoria, followed by Cape Town, Durban, and Reunion Island. Other study locations, such as Windpoort, are only used for brief periods of time during measurement campaigns (such as during the AEROCLO-sA field campaign  [17] ). The slight difference in the multiyear average values of AOD and AE compared with these previous works can be due to different periods, the shift in wavelength, and the different datasets, particularly for MODIS observations. Unlike the AOD with sun photometers, the AOD MODIS is only high in the spring season at Reunion, Maun Tower, Gobabeb, Simonstown, and Durban, with AOD average values ranging between 0. 12 and 0. 16. The autumn season at Windpoort and Pretoria has the highest values, with average values of 0.
Intercomparison For the purpose of this study, the relationship between sun photometer and MODIS observations is investigated. Figure  6  shows the scatter plot that depicts the intercomparison between daily data from sun photometer and MODIS observations over the same time period for each dataset and each site. The statistical parameters summarized in each panel of the figure were calculated using Equations (  5 )-(  7 ) and are reported in Figure  6. In general, the correlation coefficient (r) ranges between 0. 50 to 0. 78, with lower values over coastal/maritime sites (e. g. , Reunion, Gobabeb, and Simonstown) and the inland site Windpoort. Higher correlation coefficient values are reported over rural or urban sites such as Durban, Maun Tower, Pretoria, and Skukuza. Maun Tower and Durban have the highest correlation coefficients of 0.
Acknowledgments: We thank the  PIs  and their staff for establishing and maintaining the Windpoort,  Maun Tower ,  Reunion, Gobabeb, Pretoria, Skukuza, Simonstown , and Durban sites used in this investigation, with special thanks to  Brent Holben  for his commitment in maintaining the stations in Southern Africa. We thank the  PHOTONS National Observation Service of the ACTRIS-France  infrastructure for the AERONET data and products. We would like to thank the  CALIPSO program  for making data available on their website. We would also like to thank the  NOAA-ARL  for supplying the HYSPLIT transport and dispersion model. Finally, thanks to  Yaqiang Wang  to maintaining the code of TrajStat for air mass trajectory statistics.
Data Availability Statement: AOD and AE data Version 3 Level 2. 0 are available on https://aeronet. gsfc. nasa. gov/ (last access on 12 February 2022). Aerosol MODIS observations "Combined Dark Target and Deep Blue AOD at 0. 55 micron for land and ocean, (MOD08_D3_v6. 1)", from Terra platform were downloaded on Giovanni website https://giovanni. gsfc. nasa. gov/giovanni/(last accessed on 7 May 2022). CALIOP Level-3 tropospheric aerosol profile product (Tropospheric_APro_V4. 20 and V4. 21), were downloaded from the EarthData website: https://search. earthdata. nasa. gov/ (last access 18 March 2022). HYSPLIT Global Data Assimilation System (GDAS) data are available on HYSPLIT website: https://www. ready. noaa. gov/HYSPLIT. php (last accessed on 11 July 2022). The GIS-based software TrajStat is available on http://meteothink.
Materials and Methods Data analysis pipelines and R scripts are available at: https://github. com/ nonnohasegawa/varroa-transcriptomics. Mite Collection, Sequencing, and Bioinformatic Analysis. Varroa mites were collected from 1989 to 2019 from 43 countries and islands spanning six continents (SI Appendix, Tables  S1  and S2 , Dataset S1, and Fig. 1A ). Mite RNA was extracted from single adult female mites using TRIzol and prepared for sequencing using the previously described methodology  (25). Sequencing was performed on an Illumina NovaSeq S2 at 150 cycles with paired-end reads. Raw sequence data were first mapped to V. destructor reference genome Vdes_3. 0 [GCF_002443255. 1] (26) using Bowtie2  (27). The unmapped reads from the previous step were mapped to a selection of viruses that are known to be associated with varroa mites  (21).
To address this issue, we performed an additive log-ratio transform on the count data  (33) , using the number of reads mapped to the varroa genome as a common denominator. This procedure makes the viral titers independent of one another, so that increasing the titer of one virus does not lead to a corresponding decrease in the others, given a fixed number of reads, i. e. , the resulting ratios should be independent of each other. We then used the ratios to perform a principal component analysis and to quantitatively assess the effects of honey bee host species, mite species, and geographic region using analysis of matrix distances analysis using Euclidean distances  (34). Statistical analyses were conducted on a total of 166 libraries using the R programming environment using the vegan package  (35).
To this dataset, we added complete viral genomes of DWV-A and DWV-B from NCBI and consensus sequences from a published dataset  (36) , excluding those identified as recombinants by other studies (Dataset S2). While recombinant viruses play increasingly important roles in honey bee pathology  (37, 38) , their sequences do not follow the tree-like topology and excluding them is generally desirable for clock-based and phylgeographic analyses. To improve alignment quality, all genomes were trimmed to remove the untranslated regions and the polyprotein was codon aligned. All viral genomes were screened for possible recombination events using RDP4  (39). Sequences were considered likely recombinants if most detection methods implemented in RDP4 showed significant evidence for recombination. Overall, we identified one DWV-A sequence (out of 56) and two DWV-B sequences (out of 32) that were likely to be recombinants and excluded them from further analysis that relied upon a tree-like sequence topology.
The geographic origin of each DWV strain was then reconstructed based on the (posterior) ancestral state/location probabilities computed at the node representing the MRCA of each strain. Inferred origins, therefore, take into account uncertainty both in the phylogenetic (tree) and reconstructed ancestral states. We provide the input BEAST XML files for further details on the phylogenetic analysis (Dataset S1). We also reconstructed the demographic history of each DWV strain using a Bayesian Skyline analysis in BEAST 2  (42). To account for phylogenetic uncertainty, phylogenetic trees were jointly reconstructed along with demographic parameters using the same substitution and clock models as above. Historical changes in effective population sizes (N e ) were reconstructed under a Skyline coalescent tree prior 10 coalescent time intervals, each with a Jeffrey's prior on N e.
Results Each library generated an average of 9,216,119 reads (SD 3,057,629), of which an average of 57. 7% (SD 12. 8%) mapped to viruses (ranging between 21. 7 to 93. 3%). We extracted 83 viral genome sequences, including 54 from our study, adding a further 29 reference sequences for phylogenetic analysis (Datasets S1 and S2). Host Effects on the Virome. Our dataset consisted of RNA-seq data that captured viruses from two species of mites collected from either original or novel bee hosts (A. cerana and A. mellifera). We used these data to determine the relative importance of host and vector species for the composition of the varroa virome, which showed that the host bee species played a greater role than the species of the mite (Fig. 1 ). Viromes of V. destructor mites which were collected from A.
DWV-A titer was shown to be strongly correlated with changes in mite gene expression, whereas DWV-B titer was not. However, relatively few viruses replicate in varroa, and therefore must be taken up from the bee hosts. Thus, the biology of the host bee likely remains the primary driver of viral community composition. An important caveat to the virome analysis is that short-read data cannot accurately distinguish recombinant genotypes which are increasing in frequency worldwide  (37, 38). They were relatively rare in our phylogeographic dataset, much of which are historic, but are likely more prevalent in more modern collections. A larger database of recombinant sequences could allow for a more detailed study of how recombination affects DWV genomes, which is expected to play a major role in viral adaptation by generating sequence diversity and reducing genetic linkage  (47).
g. , a subsequent in-depth study of Australian viruses, where libraries were prepared and sequenced in Australia, without the possibility of crosscontamination, failed to find DWV  (48). To further illustrate this point, a cursory examination of sequenced negative controls in the NCBI SRA database (search terms negative control AND "A. mellifera"[orgn] in RNA data, 19 January 2023), yielded just two sequencing runs of library blanks (50), and both have DWV contamination present (accession numbers DRR333172 and DRR333191). We recommend that future studies aiming to ascertain the presence of DWV rule out contamination by sequencing negative controls. This is particularly important for studies aiming to detect viral spillover into non-Apis taxa, which are usually prepared and sequenced alongside honey bee samples.
However, the reconstructed location of the most ancestral lineages in our dataset appears in the Middle East, and an East Asian origin is unlikely. Additional sampling of DWV-B could resolve this uncertainty as well as potentially provide more details into temporal changes in effective population size. Our study illustrates the dynamic interaction between virus, vector, and host. Although the original host switch of V. destructor took place in the mid-20th century, (co-)evolutionary dynamics continue in this system with the acquisition of novel viruses and novel vectors  (14, 15). Viral fitness depends on successfully exploiting both vectors and hosts, and how viruses evolve to do that remains an open question. For example, DWV-A appears to affect varroa gene expression more than DWV-B  (21) , suggesting that even relatively closely related viruses may have significantly different ways of interacting with their vector.
ACKNOWLEDGMENTS. We are grateful to all our generous material providers who supported this work by collecting fresh mites in their apiary or digging in their freezers:  Lilia I. De Guzman  and  Mandy Frake (Brazil and Louisiana, U. S. A) ;  Alison McAfee ,  Leonard Foster ,  Heather Higo  ( British Columbia, Canada );  Alina Varaldi  and the ROMAPIS beekeepers (Romania);  Jevrosima Stevanovic  (Serbia);  Vivian Wepfer  (Switzerland);  Peter Rosenkranz  (Germany);  USDA APHIS (U. S. A) ;  Henriette Rasolofoarivao  (Madagascar);  Tjeerd Blacquiere (Netherlands) ;  Junichi Takahashi  ( Japan and New Zealand ); and finally the Onna Village Office with the  Honey Coral Project (Okinawa). We warmly thank  Miyuki Suenaga  for technical help with the RNA extraction and library preparation. Library sequencing was performed with the support of the OIST sequencing center.
Reference databases with wide taxonomic coverage are greatly needed in many fields of biology, most particularly for the taxonomic assignment of metabarcoding sequences. Therefore, it is fundamental to be able to access and pool data from different primary databases. The COInr database is a freely available, easy-to-access database of COI reference sequences extracted from the BOLD and NCBI nucleotide databases. It is a comprehensive database: not limited to a taxon, a gene region or a taxonomic rank; therefore, it is a good starting point for creating custom databases. Sequences are dereplicated between databases and within taxa. Each taxon has a unique taxonomic identifier (taxID), fundamental to avoid ambiguous associations of homonyms and synonyms in the source database. TaxIDs form a coherent hierarchical system fully compatible with the NCBI taxIDs, allowing their full or ranked lineages to be created.
The mkcoinr tool is a series of Perl scripts designed to download sequences from BOLD and NCBI, to build the COInr database and to customize it according to the users' needs. It is possible to select or eliminate sequences for a list of taxa, select a specific gene region, select for minimum taxonomic resolution, add new custom sequences, and format the database for blast, vtam, qiime and rdp classifier. This is a semiautomated pipeline using command lines in a Linux environment. The COInr database can be downloaded from https://doi. org/10.
MEGLÉCZ technological advances, and the continuous reduction in sequencing costs has made it accessible for a wide range of studies  (Slatko et al. , 2018). Metabarcoding is applied mainly for biodiversity assessment, but it can be used in other fields such as studying interaction networks or understanding animal diets  (Compson et al. , 2020)  One of the difficulties of metabarcoding lies in the taxonomic assignment of sequences and the completeness of the underlying reference databases. Methods of taxonomic assignment can be alignment-based, relying of sequence similarities detected by blast  (Altschul et al. , 1997)  or vsearch  (Rognes et al. , 2016)  implemented in different software  (Bokulich et al. , 2018; Huson et al. , 2007)  or based on machine learning  (Murali et al. , 2018; Pedregosa et al.
, 2011; Wang et al. , 2007). However, for all methods, the quality of the reference database is crucial  (Hleap et al. , 2021). Many methods are sensitive to gaps in the taxonomic coverage of the reference database  (Hleap et al. , 2021) , and thus the creation of a reference database with the best coverage available is greatly needed. Several different markers can be used for metabarcoding, since each of them are subject to different taxonomic biases and provide different taxonomic resolution  (Ruppert et al. , 2019). The most widespread markers are the ribosomal RNA (rRNA) genes (18 S, 28 S, 16 S), the Cytochrome Oxidase C subunit I (COI) gene and internal transcribed spacer sequences (ITS)  (Creer et al. , 2016; Porter & Hajibabaei, 2020). rRNA genes allow amplification from a wide range of taxa, and are the most widely used markers for microorganisms  (Creer et al.
, 2016). The choice of the ideal marker is more difficult when dealing with Eukaryotes. Plants and fungal studies most often use ITS markers or rbcL, since the COI gene often contains indels of variable size and location and is not sufficiently variable in these groups. In addition, the taxonomic resolution of plant and fungal rRNA genes is relatively low  (Bruns et al. , 1991; Yao et al. , 2010). For animals, the use of both rRNA genes and COI sequences is widespread  (Creer et al. , 2016). The COI marker has been proposed as the marker of choice for animals and it is one of the most widely sequenced genes  (Porter & Hajibabaei, 2018b) , since it is the main maker of the Barcode of Life database  (Hebert, Cywinska, et al. , 2003; Hebert, Ratnasingham, & deWaard, 2003). Although it has become clear that the COI gene or any of its fragment is not sufficient to differentiate species in some groups, most particularly Diptera  (Meier et al.
, 2006; Roe & Sperling, 2007; Rubinoff et al. , 2006) , it is still a frequently used marker, mostly because more animal taxa have been barcoded with COI than with any other marker  (Andújar et al. , 2018). Regularly updated, curated and marker-specific databases are available for ITS-UNITE (Rolf Henrik  Nilsson et al. , 2019) , PLANTiTS  (Banchi et al. , 2020) -and for rRNA genes-Greengenes  (DeSantis et al. , 2006) , SILVA  (Pruesse et al. , 2007). Conversely, COI sequences are deposited in two different major databases, which are not COIspecific: (i) the nucleotide database of NCBI (hereafter NCBI-nt database;  Sayers et al. , 2022)  and their European (ENA) and Japanese equivalents (DDBJ) are generalist databases without focusing on a taxon or a gene; and (ii) the Barcoding of Life Data System (BOLD;  Ratnasingham & Hebert, 2007)  contains barcoding sequences of several markers, but most of the sequences are from the barcoding fragment of the COI gene.
Although the overlap of data between these databases is considerable, each of them has sequences that are not found in the other database. Therefore, creating a merged database with sequences from both sources is highly desirable. Most existing COI databases are sourced from NCBI-nt (e. g. ,  Bengtsson-Palme et al. , 2018; Curd et al. , 2019; Keller et al. , 2020; Richardson et al. , 2020)  and only a few of them combine sequences from BOLD and NCBI  (Arranz et al. , 2020; Balech et al. , 2022; Macher et al. , 2017; Porter & Hajibabaei, 2018a). A major challenge of pooling sequences from different sources into a single database is to reconcile their taxonomic lineages. This step is not trivial due to the presence of homonyms (e. g. , Plecoptera is both an insect order and a moth genus), synonyms and misspellings.
Therefore, the only clean solution to deal with taxon names is the use of unique taxonomic identifiers (taxID) that are connected to a nonambiguous, hierarchical system and allow the identification of the lineage for each taxon. Both the NCBI-nt and the BOLD databases use taxIDs, but the two systems are independent of each other, and thus they cannot be simply merged. Finding the equivalent taxon names and taxIDs between the two databases calls for a careful comparison of taxon names and their lineages in order to match them. However, a further complication arises from occasional inconsistencies of taxonomic lineages from different databases (e. g. , the genus Vexillata is a nematode belonging to the family Ornithostrongylidae according to BOLD, but to the family Trichostrongylidae according to NCBI taxonomy), which further complicates pooling of taxonomic information into a single coherent system.