dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported in part by the  French Ministry of Higher Education and Research (MENESR) , in part by the  French Region Auvergne-Rhône-Alpes  ( Recherche-Action  project), in part by the  Provincial Science and Technology Pillar Program of HUBEI  under Grant  2017AAA027 , in part by the  Outstanding Young Scientific and Technological Innovation Team Plan for Universities of Hubei  under Grant  T201610 , in part by the  2019 Provincial Science and Technology International Cooperation Research Program of HUBEI  under Grant  2019AHB060 , in part by the  State Leading Local Science and Technology Development Special Fund Project  under Grant  2019ZYYD014 , and in part by the  European H2020   CAPTOR  Project.
INTRODUCTION The development of multi-axis inertial sensors has been rising considerably. The scientific community has shed light on multi-motion sensing exemplified in Inertial Measurement Units (IMUs). Such systems are employed to measure linear and rotational motion data such as gravitational forces and angular rates. Nowadays, IMU systems are becoming important and are largely investigated  [1] [2] [3] [4]  due to the fact that they are among the primary used systems in various fields, e. g. they are the main means of environment interaction for robotic applications and their system development  [5, 6]. Also, IMU devices have shown great contribution in medical rehabilitation fields  [7, 8] , they have been broadly investigated in industrial quality control systems  [9] , navigation systems  [10, 11] , motion sensing and portable positioning systems  [12, 13] , and in smart sensors for Internet of Things (IoT) applications  [14, 15].
Therefore, 6-axis detection for linear and angular motion sensors were proposed using piezoresistive  [23] , and piezoelectric principle sensing  [24] [25]  based on a single inertial mass. In exploiting the Coriolis effect with piezoelectric sensors, a sole work has been carried out to cointegrate these two different inertial measurement types into one device through the same detecting elements  [25]. In the current study, an in-depth analysis of a single-mass piezoelectric IMU structure to be used as a 6-axis inertial sensor was carried out. The output of this Sensor Under Development (SUD) is proportional to each of the 3-axis angular rate components in addition to the 3-axis linear accelerations. The structure of this SUD is inspired from the one presented in  [25, 26]. Using the reported experimental values, a newly developed FEM model, using COMSOL Multiphysics software, has been performed and validated.
Culicoides obsoletus sampling and morphological identification The data set analyzed in the present study is composed of 368 individual female biting midges collected in 46 sites in France in April 2011, using the national surveillance network for Culicoides populations or local complementary collections (Additional file 1: Table  S1 ). Collections were made overnight using Onderstepoort Veterinary Institute light traps set up at farms near cattle or sheep. All insects were stored in 70% ethanol. Morphological identification to species level was carried out in the same way as described in a previous study  [44]  under a binocular microscope using available identification keys  [45].
DNA extraction, amplification, sequencing and sequence analyses DNA was extracted from a total of 368 individuals belonging to the C. obsoletus species complex, and cytochrome c oxidase (COI) was amplified and sequenced following the procedure detailed in Mignotte et al. [44]. To ensure that only C. obsoletus individuals were included in the final data set  [46] , sequence assignation was run according to Mignotte et al. [44]  (the reference COI sequences used for specific assignment of all individuals are available in Additional file 2: Table  S2 ). All COI sequences were then aligned using the multiple sequence comparison by logexpectation algorithm  [47]  implemented in the software package GENEIOUS version 6. 0. 5 (Biomatters, http:// www. genei ous. com). In order to estimate unbiased gene flow of C.
Development of microsatellite markers Microsatellite markers were defined by next-generation sequencing following a similar protocol to the one described by Morillo et al. [48]. Culicoides obsoletus samples from France and the UK (Additional file 3: Table  S3 ) were used as biological material to prepare a paired-end DNA library (Illumina Nextera technology) by using the Nextera DNA Sample Kit (ref. GAO9115; Illumina, San Diego, CA). The sequencing was performed on a MiSeq Sequencer [2 × 300-base pair (bp) read mode]. In order to maximize the length of the sequences, an assembly of the 2,299,075 reads was performed with the ABySS assembler  [49]. The simple sequence repeat (SSR) loci search of di-and trinucleotide motifs was done with the MIcroSAtellite identification tool  [50]. Primer design was done with the Primer3 software  [51].
Microsatellite genotyping and loci filtering Microsatellite makers were amplified by multiplex PCR with the Type-it-Microsatellites kit (Qiagen, Valencia, CA) according to the protocol described in the manufacturer's manual and the annealing temperature given in Additional file 4: Table  S4. Standard conditions for PCR amplification included an initial denaturation step at 95 °C for 5 min, 35 cycles of denaturation for 30 s at 95 °C, annealing for 1 min at variable temperature (Additional file 4: Table  S4 ) and elongation for 1 min at 72 °C, followed by a final elongation of 5 min at 72 °C. Fragments were separated on an Applied Biosystems 3500xL Genetic Analyzer. Allelic size allocations for all individuals and microsatellite markers were performed using the program GeneMapper version 5 (Applied Biosystems, Life Technologies) with double blind reading to limit the potential interpretation bias from the reader.
A first validation of the polymorphisms of these 30 markers was carried out by amplifying and genotyping 48 individuals from various locations within the species geographic range. Then 13 loci showing good genetic profiles and clear allelic size variability were characterized as polymorphic markers and were selected for further analyses (Additional file 4: Table  S4 ). The previously extracted DNA of the 368 C. obsoletus was then amplified and genotyped at these 13 microsatellite loci. Genetic variability parameters, such as the number of alleles per locus, the allele size range, and the observed and expected heterozygosity were estimated per locus, and per population over the entire dataset using the R packages ggplot  [52] , poppr  [53]  and mmod  [54]. Short allele dominance can be a source of heterozygote deficiency in a microsatellites data set  [55, 56].
In order to avoid such bias, Spearman's correlations rank between the size of the alleles and their frequencies were calculated in R  [55]. Null allele frequencies were estimated using the R package PopGenReport  [57] , and a Bonferroni correction was applied to all matched tests to take into account potential biases associated with multiple comparisons  [58]. Correlation between F IS and null alleles was calculated in R, and loci with significant correlations were removed. The presence of an imbalance in genetic binding between each pair of loci was tested by Fisher's exact test performed with the R package poppr  [53]. The same R package was used along with the pegas R package to identify potential gaps in the panmictic matching regime. For this purpose, we performed chi-square tests to compare the observed heterozygosity deficits to the expected heterozygosity under the Hardy-Weinberg equilibrium with a significance threshold of 5%.
Genetic clustering analyses In order to determine the most likely number of genetic clusters (K) and the probability of assignment of each individual to these clusters, we used the Bayesian clustering methods implemented in the program STRU CTU RE (version 2. 3. 4) [60]  and GENELAND (version 4. 5. 0) [61]. The algorithm implemented in STRU CTU RE infers genetic clusters that minimize deviations from Hardy-Weinberg equilibrium. In STRU CTU RE, we specified the following settings: correlated allele frequency, an admixture model, and a locprior model. These options respectively allow one to (i) assume that allele frequencies are similar among populations; (ii) estimate for each individual the probability that it belongs to each K cluster; and (iii) use sampling locations as prior information to assist the clustering, which is particularly suitable when there is weak genetic structuring  [62].
The most probable number of clusters was also determined by running the algorithm with K ranging from 1 to 10. The analysis was based on 1,000,000 MCMC iterations with a thinning of 1000, maximum rate of the Poisson process fixed to 100, maximum number of nuclei in the Poisson-Voronoi tessellation fixed to 300 and a burn-in of 100. We used the R package graphics to produce a distribution map of genetic structure resulting from STRU CTU RE and GENELAND analyses.
Computation of genetic distances and mapping genetic dissimilarity We computed three complementary inter-individual genetic distances. The first inter-individual genetic distance is based on a factorial correspondence analysis (FCA) performed with the program GENETIX version 4. 05. 2  [65]. The first ten FCA axes were used to calculate an Euclidean distance between all individuals using the R package ecodist  [66] , hereafter called the "FCA" distance  [67]. The second inter-individual genetic distance is Rousset's distance (a R ),  [68] , which corresponds to F ST /(1 -F ST ), but for pairs of individuals. We used the SPAGeDi 1. 5 program  [69]  to compute a R genetic distance. The third inter-individual genetic metric is Loiselle's kinship coefficient (LKC)  [70] , which, unlike FCA and a R that are measures of genetic dissimilarity, is an index of similarity  [71].
We used the R package fields  [72]  to investigate isolation by distance by performing linear regressions and Mantel tests between inter-individual genetic distances and log-transformed geographic distances with R package fields  [72]. Mantel tests  [73]  were performed with the R package vegan  [74]. Inter-individual genetic distances were mapped using the program MAPI  [43]. The method implemented in MAPI allows the visualization of spatial variation in genetic dissimilarity. It can also define areas where genetic dissimilarity is significantly lower or higher than expected by chance  [43]. MAPI analyses were based on 1,000 permutations and an α-value of 0. 05.
Impact of environmental factors on genetic differentiation We investigated the potential impact of several environmental factors on the genetic differentiation of C. obsoletus. In practice, we tested the association between matrices of pairwise genetic distances (see above) and matrices of environmental distances. These environmental distances were computed using the program Circuitscape 4. 0. 5  [75, 76]   (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010). Wind U and V components were used to compute prevailing meteorological wind direction (in degrees). Map and arrowheads indicating the mean wind direction over the 10 years at each raster pixel were generated with the R package rWind (Additional file 6: Fig. S2 )  [81]. A so-called null raster was also created and used as a negative control, i.
Anisotropic isolation by distance Anisometric isolation by distance is a method used to identify directional gene flow and orientation at which the accumulation of genetic differentiation is the greatest. Thus the notion of anisotropy applied to isolation by distance means that the intensity and significance of isolation by distance varies according to the direction considered. In order to identify directional gene flows, we must calculate the projected distance between two sampling sites. The angle between sites was calculated using the R package geosphere  [86]  and transformed from degrees to radians. The set of angles between 0 and 360 were then tested as angles that maximize isolation by distance. The great-circle geographic distance between sampling sites was computed with the R package fields and then log transformed  [72].
The projected distance matrix was calculated for each angle between all sampling sites, using the formula below. When calculating the projected geographical distance as a function of the angle between populations, d AB is the geographical distance between population A and B and a AB is the angle between populations A and B. A linear regression of the genetic distances on the projected geographical distances obtained for each angle was then performed. The angle that maximizes the R 2 of this regression with a positive regression coefficient was considered as the angle maximizing the isolation by distance signal  [87]. In order to confirm this analysis with a similar but complementary approach, we evaluated the direction that maximizes the correlation between geographic and genetic distance using the PASSaGE program and a bearing analysis.
However, a west-east transect seemed to emerge, as samples collected on this transect were more frequently assigned to cluster 1, contrary to the north-south transect which was more frequently represented by cluster 2. Genetic clusters inferred by GENELAND and STRU CTU RE (K = 2) were consistent with these results, both clusters being found at all the sampling sites (Fig. 1a, b ). Results for the second optimal K value (K = 4) of the STRU CTU RE program gave a similar impression, reinforcing the conclusion that the populations are genetically weakly structured (Additional file 11: Fig. S6 ). Additional STRU CTU RE analysis was carried out with only loci OBSms 4, 11 and 28, which are at Hardy Weinberg equilibrium, and no additional structure was detected using only these loci (data not shown).
Acknowledgements SD and MG are supported by the  Fonds National de la Recherche Scientifique (Belgium). AM and MJ have been awarded fellowships from the  H2020-727393   PALE-Blu  project funded by the  EU. We are most grateful to  Hélène Vignes ,  Ronan Rivallan  and  Xavier Argout  at the Grand plateau technique regional de génotypage in Montpellier, France for their help in the development of the microsatellite markers and  Aurore Manez  for her help in the genotyping. We would like to thank  Sylvain Piry ,  Karine Berthier  and  Marie-Pierre Chapuis  for constructive exchanges which helped to improve the relevance of our conclusions. The authors are grateful to all the partners who assisted in the Culicoides sampling and shipping. We thank  William Wint  ( University of Oxford, PALE-Blu partner ) for his continued support in extracting and preparing meteorological data.
Funding This study was partially funded by  EU  grant  H2020-727393 PALE-Blu  and by the  VectorNet  project ( OC/EFSA/AHAW/2013/02-FWC1 ) funded by the  European Centre for Disease Prevention and Control (ECDC)  and the  European Food Safety Authority (EFSA). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission , the  ECDC  or the EFSA.
6 Mb and 1. 5 Mb, respectively. A total of 18,743 gene models (transcript models) were predicted in the genome assembly, named as OSKA2016. In addition, 19,277 non-redundant transcripts were assembled using RNA-seq data. The OSKA2016 has global sequence similarity of only 86. 5% when compared with the OikoBase, highlighting the sequence difference between the two far distant O. dioica populations on the globe. The genome assembly, transcript assembly, and transcript models were incorporated into ANISEED (https://www. aniseed. cnrs. fr/) for genome browsing and blast searches. Moreover, screening of the male-specific scaffolds revealed that over 2. 6 Mb of sequence were included in the male-specific Yregion. The genome and transcriptome resources from two distinct populations will be useful datasets for developmental biology, evolutionary biology, and molecular ecology using this model organism.
dioica  (Wang et al. , 2015) : only 91. 0 and 94. 8% of exon sequences were conserved at nucleotide and amino acid levels on average, respectively. Having fully accessible genome information for two distinct geographical populations will contribute to the study of chordate development and to understand genome plasticity in this rapidly evolving tunicate. In the present study, we generated the genome assembly of Japanese O. dioica using nextgeneration sequencing (NGS) and third-generation single molecule real-time (SMRT) sequencing. Accordingly, transcriptome information was also improved from the previous version  (Wang et al. , 2015). This genome assembly, named as OSKA2016, was compared with the OikoBase to estimate into a publicly available genome database of tunicates, ANISEED (https://www.
Ten SMRT cell v3 were run on the PacBio RSII System with P6/C4 chemistry and 360 min movies. Lastly, construction and mate-pair library with inserts of approximately 5 Kb and Illumina sequencing were performed by Beijing Genome Institute (BGI Japan). These sequence data have been deposited to the DNA databank of Japan (DDBJ) (accession number DRA010224).
Gene prediction and annotation Prediction of protein-coding genes was carried out using three different programs. SNAP v2006-07-28  (Korf, 2004)  and Augustus v3. 2. 1  (Stanke and Morgenstern, 2005)  were used to predict genes based on mapping results of the transcript assembly onto the genome assembly, and a self-training program, GeneMark v4. 38  (Lukashin and Borodovsky, 1998) , was also used. These three datasets were combined  by MAKER v2. 31. 10 (Holt and Yandell, 2011) , which was carried out following an opened pipeline (URL: https://reslp. github. io/blog/My-MAKER-Pipeline/). Hereafter, we call the F o r P e e r R e v i e w 6 predicted genes "transcript model" and "protein model".
Genome browser Genome scaffolds, predicted genes, and transcriptome assembly were incorporated into ANISEED, a web-based and publicly available genome database  (Brozovic et al. , 2018; Dardaillon et al. , 2020). It includes the genome information of 13 ascidian species as well as larvacean species (present data). This database enables us to visualize various features of these ascidian species, including genome scaffolds, gene features, annotation, and orthology.
Genome assemblies of Japanese O. dioica The genome assembly of Japanese O. dioica was generated through a hybrid program of Platanus (Illumina reads) and SMRT analysis BridgeMapper (PacBio RS II reads). Illumina HiSeq sequencing generated 217 million reads for the pair-end library (read size 250 bp, data size 54. 2 Gb) and 49. 8 million reads for the mate-pair library (read size 100 bp, insert length 5 Kb, data size 6. 2 Gb). PacBio sequencing generated 1. 2 million reads (data size 13. 2 Gb). Therefore, the sequence coverage of the Japanese O. dioica genome was more than 1000×, considering the genome size (see below). This genome assembly is referred to as "OSKA2016". The OSKA2016 is comprised of 576 scaffolds (length >= 1 Kb). The GC content was 41. 4%. The N50 lengths of the scaffolds and contigs are 1.
5 Mb and 0. 62 Mb, respectively (Table  1  and Table   S1 ). The longest scaffold was 6. 8 Mb in length. The N50 length of the scaffolds and contigs are 3. 8-   1 ). The gap rate (Ns) was 0. 4%. Numbers of scaffolds (576) and gaps  (331,456 bp)  in the present assembly are 46% and 8. 4%, respectively, of the OikoBase assembly (build name: Oikopleura_reference_unmasked_v3. 0) (http://oikoarrays. biology. uiowa. edu/Oiko/, Danks et al. , 2013)  (Table  1 ). These data indicate that OSKA2016 was better assembled than the OikoBase genome assembly in some regards. The total length of the OSKA2016 genome assembly was estimated to be 56. 6 Mb. It is smaller than that in the Oikobase (70 Mb) (Table  1 ). The sequence data in the OikoBase were collected by different techniques, i. e. , shotgun sequencing and bacterial artificial chromosomes  (Seo et al.
Assessment of assembly and gene prediction The quality of the OSKA2016 assembly was verified by making and comparing assemblies using different assembly programs/pipelines. Three representative results are shown in Table  S1. Moreover, the completeness of the genome assemblies was assessed by BUSCO  (Simão et al. , 2015). BUSCO is a dataset of near-universal single copy orthologs, and can be used to assess completeness of newly sequenced genome. As shown in Table  S2 , BUSCO returned 87. 2% completion for OSKA2016. This percentage is higher than other assemblies generated by different programs, namely Falcon or MaSuRCA (Table  S2 ). We thus adopted the OSKA2016 as the best genome assembly for Japanese O. dioica. It is reasonable that less than 90% of BUSCO was recovered, because O. dioica is shown to have the smallest and most highly rearranged genome amongst non-parasitic metazoans  (Seo et al.
Latest version of transcriptome assembly Our previous RNA-seq study identified 12,311 transcripts using samples collected at two developmental stages (unfertilized egg and late organogenesis larvae)  (Wang et al. , 2015). Search of similar sequences showed that these transcripts hit more than 95% of genes predicted in the OikoBase  (Wang et al. , 2015). However, the number, 12,311, is clearly less than that of proteincoding genes, which has been estimated to be 17,000-18,000 in the Oikobase  (Seo et al. , 2001; Denoeud et al. , 2010; Danks et al. , 2013) , implying that there remains unassembled transcripts, probably due to transient expression or low expression levels. To improve this, we collected RNAseq data more comprehensively from the following sources: 13 different developmental stages from egg to functional juveniles, mature males and females, and the animal/vegetal hemisphere of embryos.
Genome browser construction on ANISEED The OSKA2016 genome assembly, transcript models, and transcript assemblies were released as a genome browser in ANISEED (A tunicate database, https://www. aniseed. cnrs. fr/). Here, we briefly introduce several important links: Home (Fig. 1a ): Links to go to the BLAST and genome browser are indicated with blue and red rectangles, respectively. Blast search (Fig. 1b ): People can use a BLAST search to identify sequence information of interest. Blastn, tblastx, and tblastn are available as BLAST programs. The database lists the genome assembly and gene models (Transcript models). To get a link for the BLAST hits in the genome browser, the user must choose "genome assembly" as the database. Genome browser (Fig. 1c ): Choose the "Appendicularia" tab and select "OSKA2016 genome browser" (Fig.
2e ). Blast searches using S039 and S064 hit scaffold8 from the OikoBase. Scaffold8 is reported to be located in the terminus of the Y-specific region  (Denoeud et al. , 2010). To test whether these male-enriched scaffolds can be grouped into a single continuous sequence, they were mapped onto an ultra-long genome draft constructed using Oxford Nanopore sequencing reads from a single male individual. In this dataset, the Y-specific region is haploid, and could be assembled in a single sequence (Contig_4), on which 18 out of the 20 male-enriched scaffolds were grouped (Fig. 2f ) The deduced order of the male-enriched scaffolds of OSKA2016 was summarized in Fig. 2g. Nucleotide sequence of the Contig_4 was approximately 2. 6 Mb in length (Supplemental Material 2). In comparison, rough calculation of the Y-specific region based on the previous report is around 4.