dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods. We performed preliminary experiments on the effect of training data augmentation by simulating image defects on the publicly available SNEMI3D challenge dataset ( http://brainiac2. mit. edu/SNEMI3D ). We partitioned the SNEMI3D training volume of 1024 x 1024 x 100 voxels into the center crop of 512 x 512 x 100 voxels for validation, and the rest for training. Then we trained three convolutional nets to detect neuronal boundaries, one without any data augmentation ("baseline"), and the other two with simulated missing section ("missing section") and simulated misalignment ("misalignment") data augmentation, respectively. After training the three nets, we measured the robustness of each net to varying degrees of simulated image defects on the validation set. In the first measurement, we simulated a misalignment at the middle of the validation volume with varying numbers of pixel displacement.
This study developed a new bioinformatics pipeline to acquire all the different copies of multi-copy gene families based on Oxford Nanopore Technologies sequencing of PCR products. We used this pipeline to acquire the sequences of highly similar copies of the cidA and cidB genes present in the genomes of Wolbachia pipientis (wPip) bacteria infecting the cells of Culex pipiens mosquitoes. The approach is based on read mapping, SNP calling and haplotyping, using our already wide existing reference database for the cid genes obtained by cloning and Sanger sequencing. We addressed problems commonly faced when using mapping approaches for multi-copy gene families with highly similar variants (or haplotypes). In addition, we confirmed that PCR amplification causes frequent chimeras which have to be carefully considered when working on families of recombinant genes.
Nanopore sequencing has also recently been used for multiplex amplicon sequencing, decreasing the financial cost by 200x as compared to Sanger sequencing  [10]. A persistent problem for identification of new genetic variants is the non-random distribution of long read sequencing errors, these errors being more frequent in homopolymer regions (representing approximately half of sequencing errors) and in GC-rich regions  [4] , making it hard to discriminate true mutations from sequencing errors even when increasing the depth of sequencing coverage. This can be mitigated by a previous knowledge of the within-gene polymorphism distribution, e. g. with a good pre-existing database. A further issue arises after having identified SNPs, when haplotype phasing is required to obtain the haplotypes. Indeed, common haplotype phasing tools (e.
g. GATK, WhatsHap) require a previous knowledge on the expected number of different gene copies (copy number for multi-copy gene families, ploidy in most of the cases). This is an issue in the case of multi-copy gene families, since many of them display among-individual copy number variations, making the prior specification of copy numbers impossible. Here, we developed a method to sequence gene families based on Nanopore sequencing of PCR products for which a database of existing variants is available. This was done in order to work on two cytoplasmic incompatibility factors, the cidA and cidB genes (altogether named cid), present in the endosymbiotic bacteria Wolbachia pipientis (wPip) genome in Culex pipiens mosquitoes. These genes encode key proteins involved in cytoplasmic incompatibility (CI)  [11, 12] , a well-studied conditional sterility induced by Wolbachia in its arthropod hosts which is currently envisaged or implemented as a means of mosquito vectors and agricultural pests control  [13] [14] [15] [16].
The PCR products were then cloned and bacterial clones were sequenced by Sanger sequencing. This method is extremely time consuming to set up and thus inappropriate for large scale studies. Furthermore, it has limited descriptive power: due to time and money, a maximum of 48 clones were sequenced per individual PCR, making it likely to miss a rare variant in Wolbachia having numerous and unequally distributed variants. Despite this short coming, analysis of the variants led to observe polymorphism hotspots, and to restrict variation analyses to two specific gene regions  [17] [18] [19] [20]. Based on this existing reference database, we developed a bioinformatics pipeline to acquire repertoires using Nanopore sequencing of PCR products. Overall, this pipeline assigns each read to its closest known reference through mapping, then uses a combination of SNP calling and haplotype phasing to identify potential new variants.
We validated and fine-tuned the pipeline and its efficiency to accurately find new variants in spite of Nanopore sequencing errors using a combination of read simulations and molecular biology approaches. Thresholds to discriminate true new variants from sequencing and/or PCR errors were established through PCRs targeting specific regions in order to test for their actual presence/absence. Read simulations were used to (i) confirm that the pipeline could recover reads in spite of Nanopore sequencing errors and (ii) ensure that it was possible to identify distant new variants which were absent in the existing database. The repertoires obtained were further verified through variant specific PCRs, along with cloning and Sanger sequencing, highlighting highly frequent PCR-induced recombination, causing numerous artifactual variants in both Sanger and Nanopore sequencings.
2. 1. Pipeline overview Our goal was to create a bioinformatics pipeline to reconstruct multi-copy gene families with variable copy numbers through Nanopore sequencing. To this aim, we worked on the cidA and cidB genes of wPip Wolbachia infecting various Culex pipiens isofemales lines. The pipeline is based on long reads obtained from Nanopore sequencing of PCR products of cidA and cidB genes. Different PCR products can be pooled for sequencing and separated for downstream analyses (here, we pooled cidA and cidB PCR products). Since we knew that cid genes were made of two variable parts (one upstream and one downstream), we created artificial references by combining known upstream and downstream regions to complete the existing database (Fig  1 ). The reads are then mapped on the full set of references (true and artificial) to identify the references covered above a pre-determined threshold (detailed below).
Mapping is done using minimap2  [21]  at all steps. References above the coverage threshold are then further filtered by removing highly similar references. We remove references having less than a user-defined number X of SNPs to any other above-threshold reference, thus solving downstream SNP calling issues leading to low coverage and spurious indels (X threshold is a pipeline parameter, default 3, samtools 1. 15 is used in the pipeline,  [22] ). Then, reads that mapped to at least one of the references in the pre-existing database are re-mapped to our selected reference subset, and SNP calling is performed using samtools mpileup (samtools 1. 15). Finally, if SNPs are called, gene variants are identified through either consensus assembly or haplotype calling using either samtools consensus  [22]  or WhatsHap  [23]  (Fig  1 ).
Simulations confirm pipeline's ability to recover variants Although Nanopore sequencing quality has drastically improved in the last years, the rate of sequencing errors is still estimated to be around 7%  [4]. We tested, using simulated reads, the ability of the pipeline to recover all variants present in spite of Nanopore errors. To our knowledge, Nanopore read simulators such as NanoSim  [24] , DeepSimulator  [25] , SimLord  [26] , and SNaReSim  [27]  are designed for genomic data and do not mimic targeted sequencing where the majority of reads covers the whole amplicon. We therefore wrote our own read simulator for targeted data based on the full-length transcriptome read simulation pipeline in  [9]  (isONcorrect) (details of the simulation script in the method section Nanopore read simulation).
SNP calling and haplotype calling enable to properly recover new variants We also tested the ability of the pipeline to recover true new variants (i. e. not yet present in the complete database). To that extent, we used our Nanopore read simulator, which enables to create simulated mutants with a user-set mutation rate. We created new variants by introducing a fixed number of SNPs in the existing ones. Nanopore reads were then simulated from (i) simulated variants only and (ii) a mixture of simulated and existing variants. For this experiment, reads were simulated with a 7% error rate, as we showed above that the pipeline correctly recovered the references with error rates up to 12%, and because 7% is close to recent estimates of the realistic Nanopore sequencing error rates  [4]. Simulated SNPs were introduced in the existing variants in such a way that the new simulated variants differed from the existing ones by a distance equal to the median pairwise raw (Hamming) distance among true references (the database being already wide, finding a new variant differing from all others by more than this distance was highly unlikely).
No INDELs were introduced since previous work showed they were unlikely in cid genes  [17, 18, 20] , even if the read simulation script used enables to introduce INDELs. Simulating sequences enabled us to know exactly where the SNPs were located and thus to check our pipeline's performance in a controlled scenario. Using 100 different simulations, we computed the true number of SNPs (ranging from 41 to 71), along with the number of SNPs recovered by SNP calling. We found that on average, 3. 4 ± 3. 1 SNPs were missed (mean ± standard deviation, ranging from 0 to 18). There were two distinct cases for missed SNPs: (i) SNPs located in repeated regions (e. g. AATTAATA -> AATTTAAT, likely considered by the caller as a spurious SNP resulting from a sequencing error); (ii) SNPs not called in spite of a high coverage of the alternative base (e.
Discussion Within a genome, there can be multiple highly similar copies of a multi-copy gene family. For cid genes, our focal example here, we called these different copies 'variants', and the full set of copies per genome is called a 'repertoire'. Sequencing and assembling such families, with highly similar copies and copy number variations is still an issue. Indeed, long reads are highly error-prone, making it difficult to differentiate true polymorphism from sequencing errors, and short reads fail to enable haplotype reconstruction. Here, we developed a bioinformatics pipeline enabling to reconstruct the full repertoire of a gene family within a single genome through long read Nanopore sequencing of PCR products, based on an existing reference database of Sanger sequences. Using numerous specific PCRs, we established the correct coverage threshold to discriminate true variants from artifactual ones.
This bias can be counterbalanced by ensuring that all amplified fragments have approximately the same size, but has to be carefully taken into account when using ddPCR. While the method presented here requires a good pre-existing reference database, the recent development of methods to work on multigene families based on less error-prone PacBio sequencing data  [5] , along with the improvement of both Nanopore basecalling  [7, 38]  and error-correction  [9]  suggest that de novo assembly of multigene families will be possible with Nanopore sequencing in the coming years. Overall, in spite of some limitations due to the PCR step itself, this method enables a quick acquisition of numerous variants, which could be further increased by acquiring more than two genes at the same time. This paves the way to wide scale multigene family acquisitions and, with the present study, to a better understanding of links between cid genes and cytoplasmic incompatibility phenotypes.
References above that threshold were extracted, along with reads mapping on these references. Since highly similar references artificially introduce INDELs, and decrease coverage in SNP calling when using bcftools mpileup, we looked for pairs of references differing by less than 3 SNPs and kept a single representative for each pair (Fig  1 ). Exclusion was done by computing the raw distance among sequences using the dist. dna function in the R package ape  [42]. Reads which had mapped on at least a reference were then mapped on the reference subset, and SNPs were called using bcftools mpileup (with the ont config, a minimum mapping quality of 10, a disabled BAQ and a max depth of 75,000) and bcftools call (multiallelic caller, with an expected substitution rate of 0. 5). If no SNPs were called, variants present and their respective coverage were extracted to assemble the repertoire.
Nanopore read simulation Our read simulation script is based on the simulation of full-length transcripts used in  [9]  with modifications to fit our targeted sequencing scenario. We simulate reads as follows. The script takes as input a fasta file with a number of N starting reference sequences, an integer C (C>N) of targeted simulated references, a fraction S corresponding the mutation rate of the references, and the mean error rate X of the reads. Furthermore, the script has two settings regarding mutation type. It can simulate substitutions only, or both SNPs and indels. The outputs of the script are a fasta file with the references (original and simulated) from which the reads were simulated, and a fastq file with the simulated reads. We now describe the workflow of the script. First, C references are simulated from the N starting references as follows.
The reaction mixtures were loaded into wells of Sapphire chip and were subsequently emulsified (20,000 to 30,000 droplets/sample) and amplified in a geode thermocycler (Stilla technologies). The ddPCR conditions used were 10 min initial denaturation at 95°C, followed by 45 cycles of 95°C for 30s, 58°C for 15s and 72°C for 30s. After template amplification, the chips were transferred to the reader. Extracted fluorescence values for each droplet were analyzed using the Crystal Miner software (Stilla Technologies).
Acknowledgements We thank the  MBB platform  for their help on code-related issues. Di91al PCRs were run on the ISEM qPCR platform. Qubit were run using GenSeq platform facilities. This project was funded by the  MUSE  project with the reference  ANR-16-IDEX-0006. Kristoffer Sahlin  was supported by the  Swedish Research Council (SRC, Vetenskapsrådet)  under Grant No. 2021-04000. We thank  Nicole Pasteur  for thorough reading and helpful comments on previous versions of this manuscript.
Code availability All scripts used for simulating datasets, to run the pipeline and its evaluation are found at https://github. com/alnam3/nano_seq. Specifically, the source code of the pipeline are found under https://github. com/alnam3/nano_seq/Main_pipeline, the code to simulate Nanopore reads under https://github. com/alnam3/nano_seq/Nano-read-simulator, and the suggested awk script to test for missed SNPs under https://github. com/alnam3/nano_seq/Missed_SNPs.
In such characterizations, electron backscatter diffraction (EBSD) has seen a tremendous surge of interest from the 1990s onwards, with the development of digital cameras allowing for fast acquisitions of diffraction patterns and increased computer power driving scanning electron microscopy (SEM) systems. EBSD offers noteworthy properties, such as complete characterization of each phase, very high resolution images for strain analyses  (Wilkinson et al. , 2006; Britton & Hickey, 2018; Shi et al. , 2019; Wang et al. , 2021) , recent and forthcoming promising advances (e. g. transmission Kikuchi diffraction;  Sneddon et al. , 2016) , and ultra-fast characterization  (Wang et al. , 2021). Remarkable progress has been achieved in serial sectioning alongside collection and assembly of 3D EBSD data  (Groeber et al.
, 2006; Burnett et al. , 2016; Guyon et al. , 2016; Echlin et al. , 2020; DeMott et al. , 2021)  and dedicated software  (Bachmann et al. , 2011; Groeber & Jackson, 2014). However, handling and interpreting such results may become very complicated in the case of complex microstructures. Besides, such experimental data feed models of growing complexity to account for realistic microstructures  (Bugat et al. , 1999; Mcirdi, 2000; Panicaud et al. , 2011). Most of the time, quantitative parameter extraction requires the processing of images coming from different acquisition techniques where EBSD plays a significant part. With this feature in mind, numerous imageprocessing toolboxes have been automated and made user friendly  (Schneider et al. , 2012). Image processing based on any scalar information extracted from crystallographic orientations cannot be objective, in the sense that it depends on the chosen reference frame, whereas the frame-indifference property should be a prerequisite to all crystallographic data processing routes.
Generalization of necessary tools used in basic image processing for segmenting phases to standard EBSD data is therefore of great interest. Dedicated post-processing procedures have been developed to significantly improve the accuracy of orientation measurements by applying appropriate filters to crystallographic data  (Humphreys, 2001; Chen & Thomson, 2010; Hielscher et al. , 2019)  or directly to EBSD patterns  (Godfrey, 2004; Wright et al. , 2015). The quaternion orientation formalism adopted in many EBSD processing codes is well suited for this development and will be used here along with some plotting functions available in the MTEX Matlab toolbox  (Bachmann et al. , 2010). Quaternions have been used extensively in monitoring of systems involving orientations (e. g. aerospace, robotics) for their computer-friendly encoding of rotations.
Classification of EBSD data Phase identification from optical or SEM images in metals has been a major task for a long time  (Tamura et al. , 1978; Fischmeister, 1981; Osmont et al. , 2000) , and still benefits from the latest advances in computer power and classification algorithms  (Ducato et al. , 2013; Azimi et al. , 2018)  to exploit the relationships between quantitative phase morphology and mechanical behavior. Such images are acquired from a wide range of imaging systems including EBSD. However, scalar fields of such images do not integrate crystal symmetries and consequently do not do justice to the physical meaning of crystallographic orientations. Hence, suited extensions of classification and segmentation operators in the crystallographic orientation space need to be introduced.
However, for these different processing operations (dilation, K-means, connected components), one must properly extend their formulation to account for the fact that, rather than 'gray levels' (i. e. scalars), crystal orientations are to be considered, and hence a fair definition of distance between two orientations, for instance, is essential. It is the purpose of the following sections to provide such an extension. All orientation data used in this article were acquired on a TESCAN Mira3 600 scanning electron microscope with a 3 mm pixel size (in accordance with the large microstructure) at a working distance of 17 mm and an acceleration voltage of 30 kV.
Conclusion The use of an image-processing-inspired workflow accounting for crystal-orientation data proved to be useful in reading the complex microstructure of a CF8M dual-phase steel. The chosen hierarchical approach using different classification and segmentation steps emphasized different specific features of the studied microstructure. The parent ferritic grain structure was fully retrieved from combined dilation-erosion and classification using a connected-components algorithm. The structure of austenitic laths was determined from similar techniques. However, the K-means algorithm intended for clustering orientations connected to distinct orientation relationships with the parent phase turned out to be insufficient. Regrouping the obtained clusters on the basis of their minimum distance allowed for the restoration of continuity in austenite orientations.
Acknowledgments We thank the participants involved in this study and their families. We thank  NIHR BioResource  volunteers for their participation, and gratefully acknowledge  NIHR BioResource centers, NHS Trusts , and staff for their contribution. We thank the  National Institute for Health Research ,  NHS Blood and Transplant , and  Health Data Research UK  as part of the  Digital Innovation Hub Program. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , or the  Department of Health and Social Care. L. S. received support from the  British Heart Foundation. ( RE/18/1/34212 ) The laboratory of WHO is supported by grants from the  Addenbrooke's Charitable Trust ,  International Society on Thrombosis and Haemostasis, Medical Research Council , the  National Institute for Health Research England, NHS Blood and Transplant and Thermo Fisher Scientific.
TELEMAC-2D is based on a finite element technique. Both use unstructured meshes. Implicit numerical schemes used in TELEMAC-2D have the 1st accuracy order in time and space and allow the use of a high Courant number, i. e. , a large time step. Using MIKE21 FM, the users have the option of 1st or higher accuracy order in space. Explicit numerical schemes used in MIKE21-FM restrict the Courant number to less than 1, i. e. , the time step should be relatively small. MIKE21-FM is coupled with MIKE21 SW to compute wave propagation, and with MIKE21 MT for mud transport. TELEMAC-2D is coupled with the module TOMAWAC for wave computations and with SISYPHE for sediment transport simulations. It is needed to keep in mind that both TOMAWAC and MIKE21 SW cannot take into consideration the reflection of waves.
Models' Calibration and Validation Two field surveys, lasting 15 days each, were performed in 2016 s southwest (SW) from 16 October to 1 November, and 2017's northeast (NE) monsoons from 24 February to 12 March (see Figure  3  for the location of 183 mobile samplings and two fixed in situ stations in Go-Cong and U-Minh Coastal Zones). Data obtained from these field surveys are used to calibrate and validate the models. Water 2022, 14, 3850 7 of 26.
Models' Calibration and Validation Two field surveys, lasting 15 days each, were performed in 2016′s southwest (SW) from 16 October to 1 November, and 2017′s northeast (NE) monsoons from 24 February to 12 March (see Figure  3  for the location of 183 mobile samplings and two fixed in situ stations in Go-Cong and U-Minh Coastal Zones). Data obtained from these field surveys are used to calibrate and validate the models. In the Regional Model, tidal elevations are imposed at open boundaries, such as Taiwan, Luzon (Philippines), and Malacca Straits (Malaysia-Indonesia) (Figure  2 ). The 71 evenly distributed point data of the FES-2014 global tidal model and height, direction, and period of Wave Watch III data (6 points) are used for calibrating the Regional Model. In the Regional Model, tidal elevations are imposed at open boundaries, such as Taiwan, Luzon (Philippines), and Malacca Straits (Malaysia-Indonesia) (Figure  2 ).
The 71 evenly distributed point data of the FES-2014 global tidal model and height, direction, and period of Wave Watch III data (6 points) are used for calibrating the Regional Model. Atmospheric pressure, wind speeds, and direction data of NOAA (National Oceanic and Atmospheric Administration) Global Forecast System are used as surface forcing. These forcing are also imposed on the Local and Study models. The Local Model is set up using a computational domain with My Thuan on the Tien River, and Can Tho on the Hau Rivers, as upstream open boundaries at about 100 km from the sea. It covers over 774 km of the VMD coastline, from Soai-Rap Estuary (on the east coast) to Ha-Tien (on the west coast). The sea open boundaries are about 130 km far from the coastlines (Figure  2 ). We extract the tide and wave boundaries from the Regional Model.