dataesr/scientific-paragraphs-categorization huggingface dataset:
Seroprevalence and seropositivity Of the included 11,626 KiGGS Baseline participants, 511 were seropositive (4. 4%, unweighted), and 131 among the 2,891 KiGGS Wave 2 participants (4. 5%, unweighted). The overall weighted seroprevalence in children and adolescents aged 3-17 years in Germany was 4. 4% (95% CI: 3. 9-4. 9%) in the period 2003 to 2006 and 4. 1% (95% CI: 3. 2-5. 1%) in 2014 to 2017 (Table  1 , Table  2 ). The age-standardised seroprevalence for KiGGS Wave 2, given the same population distribution as in the KiGGS Baseline, was 4. 3%. In both surveys, seroprevalence increased by age (Figure, Table  1  and 2 ), more in males than in females. More detailed data differentiating between males and females are provided in Supplementary Table  S3. The chance of being seropositive was higher in all older age groups compared with the reference group of 3-6-year-olds and odds ratios were highest in the oldest age category of 14-17 years (Table  1  and 2 ).
It is sometimes difficult to build a consensus around the novelty of a product, but the authors thought that biofuels was an emblematic example of innovation in the agricultural world and the energy market. A bibliometric study was performed for a period of 59 years. This period covers the emergence of biofuels, from the very beginning to their current status. We searched for articles on biofuels in the ISI Web of Science and Scopus databases with multiple keywords, due to the variable nature of the vocabulary used in this domain: bio(-)fuel, bio(-)ethanol, bio(-)diesel. We also searched for articles coupling these keywords to (1) life cycle assessment or LCA, (2) second generation or 2G, (3) stakeholder(s), and (4) design process. We coupled the biofuel keywords with Life Cycle Assessment or LCA only for sustainability assessment because we tried to determine when sustainability assessments for biofuels first appeared and sustainability assessment was initially limited to environmental impact when it was first introduced into design processes, and LCA was the preferred method for assessing this impact at the time  (Bockstaller et al.
Environmental parameters A weather station including a photosynthetic active radiation (PAR) sensor (S-LIA-M003), temperature/RH smart sensor (S-THB-MOO8), rain gauge sensor (S-RGx-M002) and HOBO data logger (H21-USB) (Onset Computer Corporation, USA) was installed in the nearby village, two kilometres away from the experimental site, and readings registered every 10 min. One hygrochron ibutton ((DS1923-F5 hygrochron, ibutton Link, United States) per block was hung below the cocoa canopy in the control plots 1. 5 m above the soil surface, monitoring below canopy temperature and relative humidity every 10 min. Vapour pressure deficit (VPD) was calculated from temperature and relative humidity readings according to  Howell and Donald (1995). The ibuttons were shielded under plastic bowls covered with aluminium foil.
Data analyses The statistical analyses fall into 3 groups depending on the sampling schemes. The first group investigated the influence of shade (2 levels: full sun, 40 % shade) and rainwater suppression (3 levels, full rainwater, 1/3 rainwater suppression, 2/3 rainwater suppression) on pods and beans physical characteristics. The second group investigated the influence of shade, rainwater suppression and day with either 6 levels  on gas exchange and chlorophyll fluorescence; or 26 levels (Nov-2018, Dec-2018 up to Dec-2020) on water potential, stem expansion and litter fall. The third group investigated the influence of shade, rainwater suppression, and soil depth (13 levels, 0-10 cm, up to 120-130 cm) on soil moisture. The effects stated above were included as fixed effects together with their interactions in a linear normal model.
In addition to the fixed effects, the statistical models also included random effects of plant id as well as temporal correlated residuals (for the second group) and spatialtemporal correlated residuals (for the third group) to model potential correlations between the observations. Thus, this resulted in linear mixed effects models, which were estimated using the nlme-package  (Lindstrom and Bates, 1990)  in the R statistical software (version 4. 1. 2, R Core Team, 2021). Conditions of homoscedasticity were verified by plotting residuals against predicted values, and the normality of residuals were validated by normal quantile plots. When necessary, response variables were transformed to verify these conditions. Particularly, data from diurnal measurements of water potential, transpiration, sub-stomatal CO 2 concentration, stomatal conductance, water use efficiency, stem expansion and litter fall were log-transformed while soil moisture values were square-root transformed.
The genitalia were indeed absent. Instead, the closeup of the ventral area between the third leg pair shows two pairs of genital papillae  [3] , as demonstrated in Fig. 1b. For illustrative purposes, we have added closeups of the head, the first and second pairs of legs, the posterior margin, and a pore in the posterior margin, all shown in Fig. 2. In summary, Bergmann (2022)  [1]  may have inadvertently given imaging physicists an additional reason to avoid cleaning their facilities: to image most interesting microfauna. Funding The scanning electron microscope data were obtained with the aid of the IBiSA Electron Microscopy Facility of the University of Tours and the University Hospital of Tours. Funding Open access funding provided by Tampere University including Tampere University Hospital, Tampere University of Applied Sciences (TUNI).
Several studies have highlighted the presence of contaminated entries in public sequence repositories, calling for special attention to the associated metadata. Here, we propose and evaluate a fast and efficient k-mer-based approach to assess the degree of mislabeling or contamination. We applied it to high-throughput whole-genome raw sequence data for 236 Ind-Seq and 22 Pool-Seq samples of the invasive species Drosophila suzukii. We first used Clark software to build a dictionary of species-discriminating k-mers from the curated assemblies of 29 target drosophilid species (including D. melanogaster, D. simulans, D. subpulchrella, or D. biarmipes)  and 12 common drosophila pathogens and commensals (including Wolbachia). Counting the number of k-mers composing each query sample sequence that matched a discriminating k-mer from the dictionary provided a simple criterion for assignment to target species and evaluation of the entire sample.
Analyses of a wide range of samples, representative of both target and other drosophilid species, demonstrated very good performance of the proposed approach, both in terms of run time and accuracy of sequence assignment. Of the 236 D. suzukii individuals, five were reassigned to D. simulans and eleven to D. subpulchrella. Another four showed moderate to substantial microbial contamination. Similarly, among the 22 Pool-Seq samples analyzed, two from the native range were found to be contaminated with 1 and 7 D. subpulchrella individuals, respectively (out of 50), and one from Europe was found to be contaminated with 5 to 6 D. immigrans individuals (out of 100). Overall, the present analysis allowed the definition of a large curated dataset consisting of > 60 population samples representative of the worldwide genetic diversity, which may be valuable for further population genetics studies on D.
Introduction With the democratization of sequencing technologies, the availability of genomic sequence in public repositories is increasing at an unprecedented rate. This is enabling the construction of large and highly informative combined datasets for an increasing number of model and nonmodel species, which in turn is refining the power and resolution of population genomics inference (e. g. Kapun et al. , 2021). However, this increased availability of data comes at the cost of increased heterogeneity in the resulting combined dataset. For example, data sets may combine different sequencing library preparation protocols or technologies that are rapidly evolving with variable sequence quality or coverage. Similarly, for a given species, publicly available data may refer to original studies based on different sampling strategies consisting of either sequencing individuals (aka Ind-Seq) or pools of individuals (aka Pool-Seq) representative of some populations, the latter approach being quite popular due to its cost-effectiveness  (Schlötterer et al.
simulans individuals was not uncommon  (Kapun et al. , 2021; Machado et al. , 2021). In addition to biological sources, contamination may be of experimental (e. g. , sample contamination or mislabeling) and/or computational (e. g. , during data processing) origin  (Cornet and Baurain, 2022). It should also be noted that these contamination problems are obviously not specific to publicly available data and may be even more pronounced in newly generated data that have not yet been analyzed. In recent years, several software packages have been developed to assess the level of contamination in genomic datasets, which has been greatly facilitated by the active field of metagenomics. As recently reviewed by  Cornet and Baurain (2022) , the available approaches can be classified into either database-free or reference-based methods.
Database-free methods roughly consist of partitioning sequences based on their DNA composition (e. g. GC content or frequencies in short DNA sequences of a few nt), but they are not well suited for the analysis of large amounts of samples as they require a case-by-case inspection of the results  (Cornet and Baurain, 2022). Reference-based methods consist of aligning sequences to a set of tagged sequences representative of all or part (e. g. , genes) of the genomes of candidate species. In practice, this may allow either negative and/or positive filtering (i. e. , removal of contaminating sequences or identification of sequences from some species of interest) of the sequencing data  (Cornet and Baurain, 2022). To accomplish this task, approaches based on the exact matching of k-mers (i. e. , k nt long DNA words) constituting the query sequences to a dictionary of labeled k-mers (built from target species genomes) have proven highly efficient and are now very popular for sequence taxonomic classification in the metagenomic field  (Ounit and Lonardi, 2016; Ounit et al.
Here, we focused on two recently published and publicly available Pool-Seq and Ind-Seq datasets, consisting of whole-genome sequences (WGS) for i) 22 pools of individual DNA (with n=50 to n=100 individuals per pool) representative of populations sampled both in the Asian native range (n=6) and in the European (n=8) and American invaded ranges (n=8)  (Olazcuaga et al. , 2020) ; and ii) 236 individuals collected mainly in North America but also at several sites in Europe, Brazil and Asia  (Lewald et al. , 2021). A combined analysis of these two datasets using standard descriptive approaches revealed anomalous behavior of some samples (not shown), thus motivating a systematic screening of all samples for putative contamination or (taxonomic) misidentification problems. Indeed, as highlighted by  Piper et al.
Construction of the Clark and C l a r k-l target dictionaries of species-discriminating k-mers Of the 136 reference genome assemblies available for species belonging to the genus Drosophila in the NCBI repository (https://www. ncbi. nlm. nih. gov/datasets/genomes/ accessed in February 2022), 29 were retained based on assembly quality criteria such as contiguity (evaluated with contig N50) and completeness (using BUSCO scores,  Manni et al. , 2021) ; but also and mostly based on phylogenetic criteria (Figure  1 ). Our goal was to obtain a good representation of species closely related to D. suzukii, focusing on those belonging to the two subgenera Sophophora and Drosophila that are not unambiguously resolved (see Discussion). For subgroups or groups represented by multiple species (among those with good quality assemblies available), only one target species was selected, favoring the most cosmopolitan or temperate species  (Jezovit et al.
The high quality D. simulans de novo assembly from  Chang et al. (2022)  was also included for similar reasons. The resulting 32 assemblies, described in Table  1 , were further screened for non-Drosophila contaminating sequences using the program K ra ke n 2 v 2. 1. 2  (Wood et al. , 2019)  by querying a database constructed from the NCBI non-redundant nucleotides (nt) released in February 2020. A contig or scaffold sequence was considered contaminating if it was assigned to a taxonomic identifier unrelated to any drosophilid species. Note that contigs assigned to Wolbachia endosymbionts were also flagged as contaminating, as we chose to consider Wolbachia specifically here (see below). Of the 90,071 sequences (i. e. , contigs or scaffolds) from all 32 assemblies (5. 96 Gb in total), 16,123 sequences (17.
immigrans and D. willistoni with 10. 6% and 8. 1% of their total length contaminated, respectively (Table  1 ). As expected, the completeness of the assemblies (except for the draft assembly for D. suzukii from Ometto et al. , 2013 mentioned above) remained quite good after filtering out contaminating sequences, with more than 98% (resp. 95%) of the 3,285 BUSCO genes of the diptera_odb10 dataset  (Manni et al. , 2021)  identified in 26 (31) of the assemblies (Table  1 ). Finally, in addition to the drosophilid species and following  Kapun et al. (2021) , 13 genome assemblies representing twelve different common drosophilid commensals and pathogens were included in the construction of the k-mer dictionaries (Table  1 ). Note that the two reference assemblies for the Wolbachia endosymbiont of D.
10GHz) took 2h46min with a peak RAM usage of 128G using C l a r k and 55s with a peak RAM usage of 2. 65G using Clark-l. The resulting database consisted of  3,714,249,662 31-mers and 50,311,519 27mers, respectively, and required 47. 8  Gb and 1. 97 Gb of RAM to load when computing the query sequence classification with C l a r k and C l a r k-l, respectively.
(2020). These were all sequenced on an Illumina HiSeq2500 in PE125 mode (Table  S3 ). Raw PE reads were filtered with fastp 0. 23. 1  (Chen et al. , 2018)  with the default options to remove contaminating adaptor sequences and trimmed for poor quality bases (i. e. with a phred Table  1  -Description of the reference genome assemblies for the 29 drosophilid species (n=32 assemblies) and 12 common commensals and pathogens (n=13 assemblies) used to build the target k-mer dictionaries. All genome assemblies were downloaded from the NCBI repository (https://www. ncbi. nlm. nih. gov), except for the two additional assemblies for D. simulans and D. suzukii, which were downloaded from the Dryad (https://datadryad. org) and ENA (https: //www. ebi. ac. uk/ena) repositories, respectively (with accession ID in italics in the third column).
The size and N50 of all assemblies are given in the fourth and fifth columns. For drosophilid species, these correspond to the assemblies after filtering out the identified contaminant contigs (or scaffolds), the percentage of the original assembly retained is given in parentheses in the fourth column. Similarly, the BUSCO scores in parentheses correspond to the percentage of complete genes identified among the 3,285 genes of the diptera_odb10 dataset  (Manni et al. , 2021)  quality score <15). In addition, the --merge and --include_unmerged options were used to merge the detected overlapping PE reads into a single sequence. Finally, the --stdout option was enabled to generate an interleaved fastq output, which was converted to fasta format (losing quality and pairing information) with a simple awk one-liner for assignment analysis.
Both Clark and C l a r k-l were run with the options -n 1 (i. e. , on a single thread) and -m 0 (to compute the confidence score). The resulting csv files were parsed with a custom awk script to count for each sample i) the total number of sequences with no matching k-mer; ii) the total number of sequences with at least one matching k-mer; and iii) the proportion of sequences assigned to each target species. Four different criteria were considered for assigning sequences to their inferred species t, taking into account both the minimum number nk min (t) of matching k-mers and the confidence score c q (t): i) nk min (t) ≥ 1 and c q (t) > 0. 9; ii) nk min (t) ≥ 1 and c q (t) > 0. 95; iii) nk min (t) ≥ 5 and c q (t) > 0. 9; and the most stringent iv) nk min (t) ≥ 5 and c q (t) > 0. 95. All subsequent analyses were performed using the R software (R Core Team, 2017).
After filtering, the total number of sequences per sample ranged from 1. 61×10 6 to 367×10 6 (median of 18. 5×10 6 ) for a total length ranging from 0. 248 Gb (i. e. ∼0. 9X of the D. suzukii genome) to 36. 9 Gb (i. e. ∼137X of the D. suzukii genome). The sequence length was representative of typical short read datasets, with a sample mean length (after merging overlapping reads) ranging from 92. 7 bp to 287 bp (Figure  S1C ). Tables  S2  and S3  show the total C l a r k and C l a r k-l run times t r for each sample, together with the time t l required to load the corresponding k-mer target dictionary and the time t a required to assign all sequences (t r = t l +t a ). As summarized in the Table 2, t l was a few seconds for Clark-l and a few minutes for C l a r k, the C l a r k-l target dictionary containing about 75 times less k-mer than C l a r k's (see M&M).
Proportion of assigned sequences The percentage of sequences with no matching k-mer (i. e. , not assignable) was similar between Clark (ranging from 2. 29% to 85. 5% with a median value of 20. 1%) and C l a r k-l (ranging from 4. 07% to 86. 1% with a median value of 15. 7%) (Figures  2A  and 2B ). Surprisingly, this percentage tended to be slightly lower for the D. suzukii sample (Ind-Seq or Pool-Seq) when analyzed with Clark-l, which may be related to the smaller k-mer size (k=27 for C l a r k-l and k=31 for Clark) leading to lower specificity. However, the proportion of sequences with no matching kmer remained higher for C l a r k-l analyses for samples representative of the other species either represented or not represented in the target dictionaries (Figure  2B ). As expected, and regardless of the program used, the highest percentages were observed for samples belonging to species not represented in the target dictionaries (up to 85.
Dvari To empirically evaluate the extent to which the proportion of assigned sequences from a sample provides an accurate proxy for species assignment, we focused on the results obtained for the 13 short-read datasets derived from strains representative of one of the target species (including Wolbachia), but also on 30 additional samples representative of unrepresented drosophilid species, considering our most stringent filtering threshold for sequence assignment (Figures  3  and S3  for C l a r k and C l a r k-l results, respectively). The results obtained were highly consistent for all 13 samples representing the target reference species. More precisely, with C l a r k, the percentage of sequences assigned to their species of origin was >99% or close to 99% (with 98. 9% for both D. suzukii and D.
Discussion The primary objective of this study was to propose and evaluate a computationally fast and accurate method for assessing contamination levels in publicly available WGS data for the D. suzukii species, which has been increasingly studied over the past decade. The availability of high quality genome assemblies for a wide range of drosophilid species  (Kim et al. , 2021)  made it possible to rely on a k-mer-based approach consisting of constructing and querying dictionaries of species-discriminating k-mers. Such an approach has already proven to be quite valuable and benefits from the availability of optimized software, such as K ra ke n  (Wood et al. , 2019; Wood and Salzberg, 2014)  or C l a r k  (Ounit and Lonardi, 2016; Ounit et al. , 2015) , which were primarily developed for metagenomics applications but have also been proposed for contaminant detection  (Cornet and Baurain, 2022).
, 2019; Wood and Salzberg, 2014) , which was used here to identify contaminating contigs in the assemblies of the target species. Indeed, this program can rely on k-mers shared by several species for sequence assignment, and not only species discriminating k-mers, since all the k-mers of the target dictionary (possibly built from very large databases such as the NCBI nt) are mapped to the nodes of a phylogenetic tree (species discriminating k-mers to terminal nodes and shared k-mers to internal nodes). Overall, the results obtained from the analysis of WGS data for reference samples belonging to different target species and single or pools of D. suzukii individuals demonstrated the high accuracy of the k-mer-based approach. It also allowed the unambiguous identification of 16 mislabeled D.
, 2020)  collected in the Asian native area were also, and unexpectedly, found to be contaminated with D. subpulchrella individuals, namely CN-Nin with 7 D. subpulchrella individuals and to a lesser extent JP-Tok with 1 D. subpulchrella individual (both out of 50 individuals in total). More surprisingly, but confirming a gene-based analysis by D. Obbard (pers. comm. ), the DE-Jen pool collected in Jena (Germany) was found to be contaminated with 5 to 6 D. immigrans individuals (out of 100). These observations may indicate that great care should be taken when analyzing sequencing data from wild-caught samples, and that more attention should probably be paid to species identification prior to sequencing. Highthroughput metabarcoding and non-destructive approaches, such as those recently proposed by  Piper et al.
Hence, in the sample of identified D. subpulchrella individuals, if about 2% of the short-read sequences were assigned to D. suzukii (in a similar but reversed pattern as observed for D. suzukii individuals), one (presumably) D. subpulchrella individual had nearly 10% of its sequences assigned to D. suzukii. The status of this sample may be of special interest for further study as it could represent a previously unreported case supporting some recent (i. e. , only a few generations back) admixture events between D. suzukii and D. subpulchrella. As discussed by  Lalyer et al. (2021) , if no such recent events have been reported to date, several studies suggest that hybridization has occurred between these two sister species  (Conner et al. , 2017). Overall, the present analysis allowed the definition of a large curated dataset consisting of > 60 population samples representative of global genetic diversity, which may be valuable for further D.
Acknowledgements I am grateful to the genotoul bioinformatics platform  Toulouse Occitanie (Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12) for providing computing resources. I also wish to thank  Darren Obbard ,  Alan Bergland ,  Joaquin Nunez  and  Nicolas Rode  for helpful discussion and feedback. Preprint version 2 of this article (Gautier, 2023a, https://doi. org/10. 1101/2023. 04. 18. 537389) has been peer-reviewed and recommended by Peer Community In Genomics (Galtier, 2023, https://doi. org/10. 24072/pci. genomics. 100244 ).
1 GCA_017639315. 1 GCA_008121215. 1 GCA_018148935. 1 GCA_018153845. 1 GCA_018152505. 1 GCA_003286155. 1 GCA_018153835. 1 GCA_018152265. 1 GCA_003285905. 1 GCA_018153375. 1 GCA_004354385. 2 GCA_018152535. 1 GCA_000001215. 4 GCA_003369915. 1 GCA_018153725. 1 GCA_018151105. 1 GCA_009870125. 1 GCA_018152115. 1 GCA_018153105. 1 GCA_002093755. 1 GCA_016746395. 2 dryad. 280gb5mr6 GCA_008121235. 1 GCA_014743375. 2 GCA_018150325. 1 GCA_013340165. 1 CAKG01000000 GCA_018152695. 1 GCA_003285735. 1 GCA_000005925. 1 GCA_016746365. 2 NZ_AEUP00000000. 1 NZ_AGFR00000000. 1 NC_004668. 1 NZ_AGQV00000000. 1 NC_008497. 1 NC_004567. 2 NZ_AKKM01000049. 1 NZ_AKKL00000000. 1 NC_008027. 1 NZ_AJSB00000000. 1 GCF_000146045. 2_R64 NC_002978. 6 NC_012416 Size in Mb (% init. ) N50 in Mb (BUSCO) 165. 85 (100. 0) 33. 43 (96.