dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP density plotting To plot SNP distribution across chromosomes, SNP density was computed over 40 kbp intervals with the R 117 package karyo-ploteR. 98 Additional tracks included repetitive regions, GC content, raw reference reads coverage and assembly gaps. Repeats were annotated by Windowmasker 1. 0. 0 85 and Repeatmasker 4. 1. 0. 86, 126 GC content was calculated using bedtools v2. 29. 2  87  with the -nuc option on 1 kbp windows. Per base coverage of raw reference reads was calculated by aligning reads back to the bHir-Rus1 assembly and using bedtools v2. 29. 2  87  with the genomecov -d option. Values were then averaged every 500 bp (https://github. com/SwallowGenomics/BarnSwallow/blob/main/Analyses/coverage_analysis/avg_coverage. bash). Standardised values were attributed to specific coverage intervals: 0 for low coverage (between 0 and 10), 100 for regions showing twice the average coverage value (95), or higher, and intermediate fixed values for coverage between 10 and 95.
Linkage disequilibrium and haplotype statistics analysis Genome-wide LD decay LD decay was evaluated in all Illumina WGS datasets using r 2 from Freebayes v1. 3. 1 77 variant calls. r 2 values were calculated using Plink v1. 9. 99 To estimate LD decay trend across the whole genome in filtered ds2 and ds3. 1, we considered marker pairs within a 55 kbp distance with the option -bcf file. bcf -r2 dprime yes-really -ld-window 999,999 -ld-window-kb 55 -ld-window-r2 0 -allow-extrachr -out LD55kb. Option -ld-window 999,999 is required to consider variant pairs more than 9 lines apart from each other. 132 To calculate average r 2 , SNP pairs were grouped according to their distance in bins of 1 kbp (range 1-55 kbp) using a custom perl script (https://github. com/SwallowGenomics/BarnSwallow/blob/main/Analyses/LD-scripts/LDaverage.
Relationship between LD and distance from chromosome ends A potential correlation between LD and distance from chromosome ends was evaluated in ds2. 1, 2. 2, 3. 1. 1, 3. 1. 2 combining chromosomes together according to their type (macrochromosomes, intermediate and microchromosomes; Data S1). Plink v1. 9  99  was used to estimate r 2 values from each dataset with the option -bcf file. bcf -r2 dprime yes-really -ld-window 10,000 -ld-window-kb 20 -ld-window-r2 0 -allow-extra-chr -out LD_20kb. Then, to calculate average LD values for every marker pair having a certain distance bin from chromosome end, a custom perl script was used (https://github. com/SwallowGenomics/BarnSwallow/blob/main/ Analyses/LD-scripts). Marker pairs were grouped using 10kb as distance bin value from chromosome ends. The correlation between distance and LD values was tested in R computing the Spearman nonparametric rank test  124  with the R function cor.
LD scans Before performing the LD scans, variants were filtered with bedtools v2. 29. 2 87 using as maximum coverage (95x) twice the average PacBio reads coverage genome wide (47. 7x) and 10x as the minimum, so to ensure the exclusion of SNPs falling within collapsed or ambiguous regions of the genome. For the first LD scan, we ran Plink v1. 9  99  on Illumina WGS data from American and Egyptian samples (ds3. 1) considering marker pairs within a 15 kbp distance maximum, with the options -bcf file. bcf -r2 dprime yes-really -ld-window 10,000 -ld-window-kb 15 -ld-window-r2 0 -allow-extra-chr -out LD15kb. To scan for genes showing high LD values, r 2 was chosen as it is generally more informative for small datasets and also more consistent with allele frequency variation,  133  whereas D 0 can be more prone to inflation.
To compute the average LD, each scaffold was divided in sliding non-overlapping 5 kbp windows with a custom perl script (https://github. com/SwallowGenomics/BarnSwallow/blob/main/Analyses/LD-scripts/chr_ld. pl), requiring a minimum of 100 markers per window. Only genomic windows with average r 2 > 0. 3 were extracted (Table  S22 ). The threshold was chosen based on similar studies. 133, 134 Coordinates were intersected with the NCBI annotation to find genes potentially carrying alleles with high LD using bedtools v2. 29. 2. 87 For further analysis, two 5 kbp intervals were joined into the same ROI if the distance between them was lower than 100 kbp. Intervals showing high LD values were excluded if in proximity (within $5 kbp) of potentially collapsed or low-confidence assembly regions (considering a PacBio reads coverage value higher than twice the average genomewide coverage or lower than 10, respectively) or if not carrying any annotated gene.
For the average LD computation of chr6 in the H. r. savignii (ds3. 1. 1) and H. r. erythrogaster (ds3. 1. 2) populations separately we used the procedure described above but requiring a minimum of 10 markers per window. The bdnf gene region (belonging to ROI 45) was then analyzed in more details, and LD heatmaps were generated using LDBlockShow v1. 36  100  (https://github. com/BGI-shenzhen/LDBlockShow) with the options -InVCF file. vcf -OutPut Scaffold_name -Region Scaffold:start-end -OutPng -SeleVar 2. CpG islands along the bdnf sequence were identified with cpgiscan v1. 0 101 (https://github. com/jzuoyi/cpgiscan), combining neighboring CpG islands when their distance was lower than 100 bp (Data S1 and Figure  S9 ).
Generation of the pangenome For the generation of the pangenome, we used our newly generated HiFi data from the five H. r. rustica barn swallow individuals (ds1). HiFi reads were checked for adapter contamination and trimmed accordingly with cutadapt v3. 2. 94 Genomescope2. 0 21 was used to predict assembly statistics from HiFi raw data (Table  S11D ). Hifiasm v0. 13-r307 52 was used to assemble both primary and alternate assemblies which were then purged using purge_dups  74  with the minimap2 option -xasm20 and custom cutoffs (Table  S11E ). 136 The two cutoffs were calculated starting from the k-mer coverage (kcov) computed by Genomescope2. 0 21 (value1 = kcov*1. 5, value2 = value1*3). The assemblies were masked with WindowMasker 1. 0. 0 85 and RepeatMasker 4. 1. 0 86 to reduce the alignment computational time.
32 The Cactus Pangenome Pipeline included in Cactus 32 v1. 3. 0 was run as described in the software documentation (https://github. com/ComparativeGenomicsToolkit/cactus/blob/master/doc/pangenome. md). Briefly, Minigraph 108 v0. 14-r415 was used to generate a GFA graph starting from the purged HiFi primary and alternate assemblies (Table  S11F ) and bHirRus1 primary and alternate assemblies with the -xggs preset. Then, cactus-graphmap was used to align the input fasta sequences to the minigraph. Cactus-align was then used to run Cactus in pangenome mode to generate both a HAL alignment and a vg graph starting from the previous alignment. The vg file was modified using vg mod -O for a better visualisation of paths. The commands used for the assembly of the pangenome and subsequent ortholog analysis can be found on the project GitHub page (https://github.
com/SwallowGenomics/BarnSwallow/tree/main/Analyses/Pangenome). Pangenome ortholog analysis Orthologous genes were found running HALPER  109  following the steps described on GitHub (https://github. com/pfenninglab/ halLiftover-postprocessing). Briefly, from the HAL alignment, the coverage of bHirRus1 was calculated with halAlignmentDepth. 89 hen, a file for the ortholog extension was generated from the coverage file and halLiftover  89  and used to lift bHirRus1 gene coordinates on the alternate assembly and the HiFi assemblies aligned in the pangenome graph. Orthologs were then found using the lifted genes. The resulting lists of orthologs were manually evaluated to find genes shared between individuals. The 234 genes that were found only in the bHirRus1 assembly were searched in the HiFi raw reads with BLAST 2.
Pangenome variant calling The pooled Illumina WGS data for 16 barn swallow individuals 2 (ds3. 1) were aligned against the pangenome graph using vg map,  59  after some steps of pre-processing with vg mod -X 256 and vg prune -k 45. The samples were not separated ($5x) to simulate the alignment of an individual with high coverage. The subgraph representing camk2n2 coordinates was extracted with vg chunk (pg, packed-graph format) and the aligned reads (gam format) were embedded in the subgraph using vg augment, generating augmented pg and gam files. Snarls were computed separately with vg snarls from the augmented vg, while the read support was computed from the augmented gam with vg pack. Variants were called with vg call. The commands used can be found on GitHub (https://github. com/SwallowGenomics/BarnSwallow/blob/main/Analyses/Pangenome/Pangenome_variant_calling/Variant_calling.
Graphical representations The R 117 package ggplot2  110  was used to generate correlation plots (Figures  2B  and S2 ), histograms (Figures  5B  and S3B-S3D ) and the gene presence-absence matrix (Figure  5B ). The R package circlize  111  was used to generate Circos plots and the figure legend was generated using the ComplexHeatmap 112 package (Figures 2C, 5A and S1). SequenceTubeMap  113  was used to graphically represent pangenome regions (Figures  5F  and S7 ). MEGA X software  92  was used to generate the phylogenetic trees (Figure  3A  and STAR Methods). The Hi-C contact heatmaps were visualised with PretextView (https://github. com/wtsi-hpag/PretextView, Figures  1D-1F ). The k-mer profiles were generated with Genomescope2. 0 21 (http://qb. cshl. edu/genomescope/genomescope2. 0/) and Merqury  25  (Figures  1B  and 1C ).
Snail plots were generated with BloobToolKit 114 (Figure  1G ). Alignment dot plot was generated with D-genies  115  (Figure  1H ). Manhattan plots were generated with the R package CMplot  116  (Figures  3E  and 3F ). IGV  97  was used to visualise aligned features to the genome (Figure  S4 ). R 117 package karyoploteR  98  was used to plot SNP density visualisation across all chromosomes (Figures 3B, S5 and S6). SNP density was computed using the internal function kpPlotDensity using 40 kbp as window size, for the three types of sequencing technologies considered. To plot SNPs distribution across all chromosomes for the 5x downsampled HiFi dataset (Figure  S6 ), the 20x read coverage of each sample (except for the A2 sample, starting from 15x) was used as the truth set (variants from the 5x reads set were intersected with variants from the 20x reads set before plotting).
IGV  97  was used to visualise bdnf region containing previously annotated methylation sites from the Cactus multialignment (Figure  S9D ). The map showing sampling locations from all datasets was generated in R using the packages ggplot2,  110  rnaturalearth, sf and rnaturalearthdata (Figure  S3A ). Average LD values at increasing distance from chromosome ends were plotted with the ggplot2 110 package using the geom_point function and combined together with the ggarrange function (Figure  S11 ). iHS values were plotted using the manhattanplot function of the Rehh 103 package (Figure  S12 ). Histograms of the HiFi reads coverage were generated with the ggplot2 110 package using the geom_bar function (Figures  S13A-S3E ). To plot recall rate values after HiFi titration experiments, the functions geom_line and geom_point of the ggplot2 package were used.
For the second titration experiment, the legend was generated using the ComplexHeatmap 112 package and plots were arranged together with the packages grid and gridExtra (Figures  S13G-S3I ). Before plotting phased blocks length, the WhatsHap development version v. 1. 2. dev2+g3dffe4a 102 command stats -gtf was used to generate a. gtf file with the size and position of the phased blocks. Phased blocks computed from HiFi reads were plotted with the KaryoploteR 98 package using the kpRegions function (Figure  S14A ). The percentage of phased chromosomes, colored by type, averaged across samples, was plotted with the ggplot2 110 function geom_boxplot (Figure  S14B ). See this github section (https://github. com/SwallowGenomics/BarnSwallow/tree/main/Plots%20and%20figures) to retrieve the lists of commands used for all figures and plots.
ACKNOWLEDGMENTS This work would have not been possible without the dedication of the late  Prof. Nicola Saino. We thank the  INDACO Platform team  (a project of  High Performance Computing at the University of Milan , https://www. indaco. unimi. it/), in particular  Dr. Alessio Alessi , as well as  Prof. Aureliano Bombarely  for providing computational resources and technical assistance. We thank  Prof. Guido Grilli  ( Department of Veterinary, University of Milan, Milan, Italy ) and  Dr. Alessandra Costanzo  for their help in obtaining barn swallow samples. We received support from the  Italian Ministry of Education, University and Research (MIUR)  for the project  PRIN2017 2017CWHLHY  (L. G. and A. T. );  Dipartimenti di Eccellenza Program  ( 2018-2022 ) - Department of Biology and Biotechnology ''L.
Software and algorithms All scripts written and used for this study This study https://doi. org/10. 5281/zenodo. 7474288 VGP genome assembly pipeline 1. 6 Rhie et al. 12 https://vertebrategenomesproject. org/ bowtie2 v2. 4. 1 Langmead and Salzberg 68 https://github. com/BenLangmead/bowtie2 NOVOplasty Dierckxsens et al. 69 https://github. com/ndierckx/NOVOPlasty MITOS2 Donath et al. 70 http://mitos2. bioinf. uni-leipzig. de/index. py.
0 Pacific Biosciences PN 101-685-400; PN: 100-938-900 Iso-seq sequencing kit 3. 0 Pacific Biosciences #101-597-800 TruSeq Stranded mRNA LT Sample Prep Kit Illumina N/A QIAGEN Genomic-tip Qiagen cat# 10223 Deposited data de novo assembly for Hirundo rustica This study RefSeq: GCF_015227805. 1. Genbank: GCA_015227805. 3, GCA_015227815. 3. NCBI BioProject: PRJNA909772 10x and Hi-C genomic data for This study SRA: SRR22566724, SRR22566725, SRR22566726, bHirRus1 reference assembly SRR22566727 (10x). SRA: SRR22566728, SRR22566729 (Hi-C). PacBio CLR reads and Bionano reused from Formenti et al. 16 SRA: SRR7589801 and SRR7589802 (PacBio CLR reads). DLS optical maps for bHirRus1 Bionano optical maps are available in the GigaScience reference assembly GigaDB repository associated to Formenti et al. 16 Hifi sequencing reads This study SRA: SRR22588214, SRR22588215, SRR22588216, SRR22588217, SRR2258821.
Isoseq data This study SRA: SRR9184408 and SRR9184409. RNAseq data This study SRA: SRR13516425, SRR13516426, SRR13516427, and SRR10853074. Raw fastq reads Safran et al. 14 NCBI BioProject: PRJNA323498. Raw fastq reads von Ro ¨nn et al. 40 NCBI BioProject: PRJNA296600. Raw fastq reads Scordato et al. 41 NCBI BioProject: PRJNA323498. Raw fastq reads Smith et al. 2 NCBI BioProject: PRJNA323498. Raw fastq reads Schield et al. 39 NCBI BioProject: PRJNA323498. Newly generated genomic resources This study (variants catalog, pangenome, Cactus alignment) Dataverse: https://doi. org/10. 13130/RD_UNIMI/IDALZG Hirundo r. rustica mitochondrial Reference Sequence Lombardo et al. 3 GenBank: MZ905359 (Continued on next page) 16 Cell Reports 42, 111992, January 31, 2023.
Biometric systems are more and more used for many applications (physical access control, e-payment, etc. ). Digital fingerprint is an interesting biometric modality as it can easily be used for embedded systems (smartcard, smartphone, and smartwatch). A fingerprint template is composed of a set of minutiae used for their comparison. In embedded systems, a secure element is in general used to store and compare fingerprint templates to meet security and privacy requirements. Nevertheless, it is necessary to select a subset of minutiae from a template due to storage and computation constraints. In this study, we present, a comparative study of the main minutiae selection methods from the literature. The considered methods require no further information like the raw image. Experimental results show their relative performance when using di erent matching algorithms and datasets.
An optimal template reduction method should be able to limit the decrease in performance when using less minutiae. Testing all combinations (selection of a subset of minutiae) is not possible for computation limitations. Few algorithms for minutiae selection have been proposed in the literature  (ISO, 2007; Vibert et al. , 2015 Vibert et al. , , 2018)) , and the scope of the proposed study is to compare the main methods. The contributions of this study are as follows: • Identification of main methods in the literature for the minutiae selection from digital fingerprint templates. • Study of the performance (degradation of recognition) and efficiency (computation time) when selecting a subset of minutiae from the initial template. • Use of three fingerprint datasets from different sensors/resolutions and three matching algorithms to draw general conclusions.
Databases In this study, we used three datasets of digital fingerprints composed of 800 images from 100 individuals with eight samples per user. These datasets were used during previous Fingerprint Verification Competitions (FVC): 1. FVC2002 DB2 dataset  (Maio et al. , 2002) : The image resolution is 296 × 560 pixels with an optical sensor "FX2000" by Biometrika. 2. FVC2004 DB1 dataset  (Maio et al. , 2004) : The image resolution is 640 × 480 pixels with a "V300" optical sensor by CrossMatch. 3. FVC2004 DB2 dataset  (Maio et al. , 2004) : The image resolution is 328 × 364 pixels with an optical pickup "U. are. U 4000" by Digital Persona. Figure  2  shows a fingerprint sample from each database. This also shows the diversity of fingerprint quality in the considered databases. Figure  3  shows the distribution of the number of minutiae for each sample in the biometric datasets.
Notably, the number of minutiae is not similar for each dataset, even if they are close on average. The average number of minutiae is 54 for the FVC2002DB2 database, 48 for FVC2004DB1, and 43 for FVC2004DB2. Moreover, the number of minutiae can be greater than 80 or even 100, which is greater than the maximum size accepted by secured elements. This is the reason why we need to reduce the number of minutiae in digital fingerprint templates.
Ground truth Table  1  shows the AUC value for the three used datasets considering the initial template as a reference (with all minutiae). The performance of MCC is lower than expected. We believe that the association between the MINDTCT extractor with MCC is suboptimal. We are not interested in maximizing the performance of user verification with the matching algorithms but to estimate the impact of template reduction methods on performance. These AUC values are considered in this study as the ground truth, i. e. , the best performance when having all minutiae. The application of a template reduction algorithm will decrease the performance. A good reduction method should minimize this decrease in performance, meaning having the lowest AUC value.
Computation time for minutiae selection The processing time to generate a reduced template for each method is analyzed. All computations have been realized under Matlab running on a PC with an Intel Core I7 4-core processor with a frequency of 2. 8GHz and 16 GB of RAM. Table  2  shows the average reduction times for each method considering all datasets. This time criterion is of importance to draw a trade-off between the performance and the computation time in order to formulate recommendations to select a biometric template reduction method for both commercial systems and uses in research. The most simple methods (Truncation, Random truncation, and Media Y) are very fast with a low impact of the maximal number of minutia to keep. Others are much more slower, especially the K-means and MRGA.
Impact of template reduction In this section, we analyze how template reduction methods perform and their impacts on performance. Figures  5 6 7 present the evolution of the performance for each reduction template method with the three matching algorithms on the FVC 2004 DB1 dataset as illustration. As a reference, we considered the performance when using the initial template (no reduction). From these curves, We can draw the following conclusions. First, we can see that the reduction of minutiae has the consequence of a decrease in performance. This is not a surprise to have this result, it was expected. For example, for the most efficient matching algorithm (commercial one), with N max = 30 (where we keep only 30 minutiae from the template), we have an AUC value between 5. 5% and 13. 5% with template reduction methods, while using the initial template, we have an AUC value of 3.
Reduction vs. performance We showed in the previous section that applying a reduction method usually decreases performance. In our experiments, we found a counter-example with the Bozorth3 matching algorithm on the FVC 2002 DB2 dataset (see Figure  8 ). We can see that all reduction methods improved greatly the performance. To better understand these results, we analyzed images from which minutiae templates are extracted. We observed that the images show capture artifacts (minutiae from a latent fingerprint), which mislead the minutiae extraction algorithm. It shows that the minutiae extractor detects false minutiae. Figure  9  illustrates this remark. We note that many minutiae are falsely detected due to capture artifacts. To validate our assumption, we took a sample of the FVC 2002 DB2 database, and we removed the minutiae from artifacts in the initial template (suppression of detected minutiae in the background of the fingerprint image).
Conclusion and perspectives This study has first presented the context of template reduction of digital fingerprints. We realized a literature review of reduction methods. All methods have been tested on three well-known biometric datasets often used in biometric competitions and on two academic comparison algorithms -Bozorth3 and MCC-and a commercial one. We have to mention that no code optimization was carried out in our study, and the proposed methods have been developed under Matlab; hence, we may hope to consequently reduce the computation time. We have shown that the best methods for reducing fingerprint templates are mostly those offering a good spatial distribution of the minutiae in the reduced template. We proposed to use the MRGA method for enrollment and the barycenter one for user verification for a real and efficient implementation.
Results Training of deep learning models for cell segmentation with sparse annotations. A dataset with sparse cell annotations, containing 447 training data and 50 validation data, was created by the procedure described in Methods (Fig. 2 ). Briefly, there are three annotation approaches: (i) dim nuclei preferentially, (ii) bright nuclei preferentially, and (iii) both mixed. I also included conditions with additional background labels for each of them. The number of annotations was incrementally increased to train and evaluate the deep-learning models (Fig. 2 ). StarDist, Cellpose, and ELEPHANT models were trained using the training data under these conditions, and their performance was evaluated using the validation data. In StarDist, Sugawara | bioRχiv | June 13, 2023 | 1-7 D R A F T cell nuclei were well identified with sparse annotations, but many false positives were observed in conditions that did not include background annotations (Fig.
Training of a deep learning model from scratch in a few minutes. To demonstrate that training deep learning models based on sparse annotation is practical, I implemented extensions on the widely used image analysis tool QuPath  (9). To facilitate the annotation process, I have implemented an extension to perform SAM  (8) , which enables the generation of a cell nucleus annotation from a bounding box. In addition, I implemented an extension that calls sparse annotationbased training and inference with a StarDist model. The training was performed from scratch with no prior training. I confirmed that an effective StarDist model can be trained from a few sparse annotations created by SAM in a few minutes. (Supplementary video 1: https://doi. org/10. 5281/zenodo. 8020156).