dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). Thus, it performs a correlation between these alterations and each receptor/transporter map included in the toolbox. Confounding effects of age, gender and site were regressed out from all images prior to these analyses  (Dukart et al. , 2021). We considered serotonin transmission, i. e. the 5-hydroxytryptamine 1a (5-HT1a) receptor, the 5-HT1b receptor, the 5-HT2a receptor, and the serotonin transporter SERT; dopamine transmission, i. e. the D1 receptor, the D2 receptor, the dopamine transporter (DAT), and the FluoroDOPA, the GABAa receptors, the vesicular acetylcholine transporter (VAChT), the metabotropic glutamate receptor type 5 (mGLUR5), and the noradrenaline transporter (NAT). Each map included in Juspace toolbox was derived by PET data with the exception of DAT, which was derived from SPECT data, and each map was built up with specific numbers of healthy volunteers (Supplementary Table  1  for details).
Statistical analysis Comparisons of demographic and clinical characteristics were performed by the Student's t-test for continuous variables and the χ 2 test for categorical variables. Spearman correlation was used to assess the relationship between each neurotransmitter output obtained with Juspace (i. e. , the GMVneurotransmitters correlation, Fisher's Z transformed) and clinical or behavioral data. Statistical significance was set at p < 0. 05, corrected for multiple comparisons (Family Wise Error-FWE) (SPSS Statistics 22. 0, Chicago, USA).
Neurotransmitter impairment and social cognition in monogenic FTD We assessed the relationship between GMV-neurotransmitters correlation coefficients and social cognition/loss of empathy data in monogenic FTD patients (CDR > 0). We considered only GMVneurotransmitters correlation coefficients significantly impaired in FTD and we excluded those highly correlated to each other (Spearman correlations coefficients>0. 80), namely FDOPA. Thus, we included in the present analyses 5-HT1a receptors, D1 receptors, DAT, VAchT, and mGLUR5. We considered a) Ekman facial emotion recognition task and Fauxpas recognition test (mini-SEA) scores (the lower the scores the worse the performances)  (Funkiewiez et al. , 2012) , and b) loss of empathy and c) poor response to social/emotional cues, as reported by caregiver (which were rated on a 5-point scale: 0 = absent, 0.
We also provide guidance on post-GWAS strategy and interpretation of results. We discuss common limitations and challenges that have been frequently overlooked in the analysis and interpretation of GWAS in dairy cattle. This review is not intended as a detailed guide for performing GWAS in dairy cattle. In addition to the principles, it is meant to provide a good practice so that common mistakes in analysis, interpretation, and reporting can be avoided. For tutorials, protocols, methods and tools, see  (Teo, 2008; Weale, 2010; Turner et al. , 2011; Reed et al. , 2015; Coleman et al. , 2016, Ellingson and Fardo, 2016; Marees et al. , 2018; Agler et al. , 2019; Uffelmann et al. , 2021). The common software tools at different stages of genome-wide as-sociation analysis and useful databases are presented in Table  1  and Table 2 , respectively.
When older data are accompanied with a map file based on an older reference genome, a lift-over to the new reference genome must be performed. The University of Missouri-Columbia has created lift-over files for 25 commonly used cattle genotyping arrays to the latest reference genome ARS-UCD1. 2 (Rosen et al. , 2020)  available at https: / / www. animalgenome. org/ repository/ cattle/ UMC _bovine _coordinates/. SNPchiMp v3, available at https: / / webserver. ibba. cnr. it/ SNPchimp/ , is a very convenient program that can combine data from different commercial SNP arrays and can also switch between independent reference genomes  (Nicolazzi et al. , 2014). Variants deposited at the NCBI/EVA dbSNP databases (Table  2 ) are assigned a nonredundant accession number (rs). This number is a unique identifier (ID), independent of the reference genome.
The 1000 Bull Genomes Project was established to ensure a large reference population for imputation to enhance downstream analyses such as GWAS. For all reference individuals, WGS is aligned against the same reference genome; population-wide SNPs and indels (short insertions/deletions) can be called using variant-calling software such as GATK (Van der  Auwera et al. , 2013)  or Freebayes (https: / / github. com/ freebayes/ freebayes). This results in genotypes of millions of variants for each individual which are efficiently stored in variant call format (VCF) files, a specific format storing large-scale genotype variants, containing meta-data (e. g. , reference genome), variant description (e. g. , position, alleles), and, possibly, genotypes of individuals  (Danecek et al. , 2011). To limit errors, it is required to filter variants based on their quality scores or the sequencing depth; guidelines implemented in the 1000 Bull Genomes Project can be followed  (Daetwyler et al.
Software and Pipeline Several software packages can process the phasing and imputation of large WGS data sets. Some software packages manage both phasing and imputation (e. g. , Beagle;  Browning and Browning, 2007). Others are combined in pipelines that first phase the data with one software package (e. g. , Eagle;  Loh et al. , 2016a)  or SHAPEIT  (O'Connell et al. , 2014)  and then impute the data with another program (e. g. , Minimac;  Fuchsberger et al. , 2015; Das et al. , 2016)  or IMPUTE  (Howie et al. , 2012). Based on the information source used, imputation programs can be classified into 2 types as follows: (1) population-based, which looks at LD between SNP and haplotype frequencies (e. g. , Beagle, IMPUTE, Minimac); and (2) pedigree-based, which looks at cross-generational inheritance of haplotypes [e.
Quality Control After Imputation Poorly imputed variants should be removed from GWAS to avoid spurious associations. This can be accomplished by using estimated imputation accuracies provided by programs such as Beagle R 2 and Minimac R 2 , which are accuracy estimates as the true genotypes are unknown; however, estimated and actual imputation accuracies are highly correlated  (van Binsbergen et al. , 2014; Pook et al. , 2020). Notably, imputation accuracy estimates may strongly vary across imputation software, because of differences in calculation of information metrics at imputed SNPs, and thresholds have to be defined within software (see Supplementary Information S3 in  Marchini and Howie, 2010). The VCF files provide sequence-level genotypes with reference/alternative (REF/ALT) allele coding.
Single-SNP analysis using an LMM is computationally intractable for datasets derived from thousands of individuals, owing to the heavy computational burden of estimating variance parameters  (Kang et al. , 2010). Computation loads can be split into 3 steps: (1) building the genomic relationship matrix [GRM; O(MN 2 )], (2) estimating variance components [O(MN 2 )], and (3) computing association statistics for each SNP [O(MN 3 )], where M is the number of markers and N is number of individuals  (Yang et al. , 2014). In the EMMAX approach  (Kang et al. , 2010) , variance components are estimated only once with a model without SNP effect, and are assumed to be known, with the presumption that the individual SNP contribution to phenotypic variance of a polygenic trait is very small. This is implemented in several software packages, for example EMMAX  (Kang et al.
, 2010; Cai et al. , 2018). Yang et al. (2012)  proposed an approximate conditional and joint analysis (COJO) using summary-level statistics and LD corrections between SNPs estimated from a reference sample; the method is available on GCTA software  (Yang et al. , 2011a). COJO can be performed on a data set with individual-level genotype data or by using GWAS summary statistics (e. g. , from meta-analysis). The latter requires an individual-level genotype data set representative of the LD pattern in the investigated population. The 1000 Bull Genomes data set can serve as a reference for LD in cattle, preferably including only individuals from the same breed, as LD patterns differ across breeds  (de Roos et al. , 2008). The SNPs selected by COJO are assumed to be independently associated with the studied trait, and maximize the captured total variance.
The APY approach  (Misztal et al. , 2014)  would be an interesting alternative but is not yet implemented in common GWAS software at sequence level. Alternatively, SNP effects may be estimated by using the SNP-BLUP model instead of estimating breeding values (GBLUP model). Both models are fully equivalent  (Strandén and Garrick, 2009 ). The equation system is large (M × M) but of constant size, and can therefore accommodate very large populations in GWAS. Reynolds et al. (2021)  recently applied this approach in a GWAS with more than 130K cows. Using SNP information to account for the relatedness of individuals can reduce power due to double-fitting the candidate marker in the model, both as a fixed effect tested for association and as a random effect as part of the GRM  (Yang et al. , 2014 ).
The top SNPs are often not causal  (Cai et al. , 2019). Consequently, many fine mapping methods to account for LD have been proposed recently. The CAVIAR method takes an arbitrary number of causal variants when estimating the posterior probability of a variant being causal  (Hormozdiari et al. , 2016). Another approach, named SuSIE, is based on iterative Bayesian stepwise selection and may account for LD  (Wang et al. , 2020). FINEMAP software uses GWAS summary statistics to calculate effect sizes and heritability of likely causal SNPs  (Benner et al. , 2016). FINEMAP can also use LD matrices to account for LD in the mapping population. The PAINTOR uses a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants  (Kichaev et al.
Confirmation in QTL, GWAS, and Disease Databases The Animal QTL Database  (Hu et al. , 2022)  can provide a starting point to determine if detected QTL regions confirm earlier findings curated in a particular database. In December 2021, the cattle QTL database contained 177,199 QTL from 1,090 publications, representing 689 traits. QTL regions can be prioritized by studying the genes underlying the regions or located close to the QTL. The GWAS results in other species, available in the GWAS Catalog  (MacArthur et al. , 2017)  and GWAS Atlas  (Tian et al. , 2020) , enable the determination of overlap between identified QTL or genes and those reported to encode similar traits. Genes with functions known to be related to the studied phenotype, especially genes included in the Online Mendelian Inheritance in Animals (https: / / omia.
Functional Annotation and Genomic Features The GWAS results are interpreted through the annotation of variants within association signals. The Ensembl variant effect predictor (VEP) is a commonly used tool and database for variant annotation  (McLaren et al. , 2016). By processing a simple input of variant location and nucleotide changes, VEP will output genes and transcripts that harbor the variants, describe the types of variants, predict their consequences, and provide conservation scores of missense variants (i. e. , mutations that alter the amino acid composition of proteins). The VEP conservation score is built under the assumption that important AA sequences will be conserved in a protein family and across species; consequently, changes of conserved AA sequences should affect protein function  (Ng and Henikoff, 2002).
The variants in regulatory regions are poorly annotated by VEP. An alternative strategy can be to check if candidate variants alter transcription factor binding sites. This can be achieved with multispecies transcription factor binding sites model databases, such as JASPAR  (JASPAR CORE 2018 collection; Sandelin et al. , 2004) ,  HOCOMOCO (version v10; Khamis et al. , 2018) , or TRANSFAC (version v3. 2 public;  Knüppel et al. , 1994). An application on dairy cattle is presented in  Sanchez et al. (2021). Genomic features indicate genomic regions that have identified functions. Genomic features include genes (including 5′ UTR, 3′ UTR, intron and exon), rRNA, tRNA, noncoding RNA, pseudogenes, repeat sequences, and regulatory elements. The annotation of protein-encoding genes, rRNA, and tRNA are well defined.
Functional Analysis Analyses of databases that include new information or newly designed experiments may facilitate the prioritization of variant or gene candidates. The most straightforward method is to detect eQTL (i. e. , genomic regions that affect gene expression levels). There are 2 advantages of combining eQTL with GWAS results from the same population: (1) confirmation of causal variant segregation, and (2) without any prior knowledge, eQTL can map variants affecting gene expression by any possible mechanism  (Gilad et al. , 2008). This application can also be extended to summary statistics and to individuals for GWAS mapping and eQTL mapping in the same population  (Zhu et al. , 2016). The combination of GWAS and eQTL is further formalized as a transcriptome-wide association study  (Gamazon et al.
, 2015)  that integrates individual-level genotype data or GWAS summary statistics with externally estimated eQTLs. However, major drawbacks of eQTL include the need to select the appropriate tissue, and costs of sampling and sequencing. The challenge of choosing and sampling a suitable tissue may be partially abrogated by using blood, although with some loss of tissue-specific eQTL  (Qi et al. , 2018). This field of research is expanding very fast in bovine and the recent FarmGTEx database  (Liu et al. , 2022)  compiles the main results on gene expression in cattle. Because regulatory elements of cattle have been mapped only recently, variant effects on gene expression are still poorly understood. Hi-C (Lieberman-Aiden et al. , 2009) , based on chromosome conformation capture, can detect comprehensive nuclear chromatin interactions  (Belton et al.
SHARING DATA AND SUMMARY STATISTICS A common practice in human GWAS is to deposit summary statistics in publicly accessible databases such as GWAS Catalog  (MacArthur et al. , 2017)  and GWAS Atlas  (Tian et al. , 2020)  to facilitate validation and cross-study joint analyses. However, this approach may be impeded in livestock research because of proprietary interests of breeding companies. Consequently, the advantages of sharing data and information within the breeding industry must be highlighted. Such cooperation will expand access to large population maps of phenotype and genotype, because these data are routinely recorded for management and breeding purposes. GWAS findings should be published in detail to facilitate future research and implementation. Therefore, we discuss in the following paragraphs what type of reporting could be informative in such a setup.
The phenotypes and genotypes of dairy cattle are often owned by breeding companies or associations, and hence cannot be shared easily. In contrast, GWAS summary statistics may be more accessible. The GWAS summary statistics typically include chromosome, position, effect allele, effect size, its standard error, and P-value. The minimal requirements to match SNPs across different data sets include chromosome, position, and the reference genome. Reporting the allele on which the effect was estimated (the counted allele) is required to ensure that it was identified as the same effect allele across data sets. Generally, information regarding GWAS accuracy is not shared. Hence, we advise sharing additional SNP information by including imputation R 2 (and imputation software), allele frequency of the SNP in the studied population, as well as phenotypic information including trait heritability, genetic variance, trait definition, and units.
FUTURE CONSIDERATIONS Methodologies and software for large-scale GWAS are developed primarily by scientists working with human data. There are distinct differences between human and livestock studies; samples are less related in human studies, whereas dairy cattle are characterized by large half-sib families. The LD in dairy cattle is spread over a longer distance due to small effective population size, emphasizing the need for large designs, use of metaanalyses across populations, and confirmation studies. Owing to routine genotyping for genomic prediction, hundreds of thousands of cows are genotyped; consequently, their data could be used for GWAS. However, computer capacities for existing LMM-based tools are exceeded, especially if the number of traits is large. Alternative approaches to processing large data have been suggested  (Jiang et al.
, 2020). Isolated pyramidal neurons were selected based on morphology and imaged with a confocal microscope. The dendrites were manually traced using the "ruler" tool, and dendritic spines were quantified using the "counter" tool from DIVA. Only primary dendrites were considered, and on these dendrites all spines were taken into account. We did not discriminate between spines based on their morphology. We measured a significant increase in the density of spines/µm of dendrites between 1 mpi (0. 00028 ± 0. 00055) and 6 mpi (0. 11 ± 0. 027) (Figure  4E  i) as well as changes in the average distance from the soma, which were all not significant. We therefore wanted to measure the maturity of synapses formed by the human cells at 7 mpi. We quantified the density of signal for Homer 1 and Synapsin markers using the Icy software  (De Chaumont et al.
3. 0)  (Hao et al. , 2021)  RunPCA with 15 components and 200 variable genes, using centering and scaling was followed Seurat's clustering function (15 PCAs, resolution = 0. 5, K = 10) to produce 8 clusters that were manually renamed (0-5. 8 = NPC, cluster 6 = mature). Cells expressing at least one UMI of either NANOG or POU5F1 were assigned "stem-like. " All analyses and visualizations were performed using SCHNAPPs. A docker version of SCHNAPPs with the data is available on docker-hub (schnapps. Rais). Raw sequences and scripts to produce input data for SCHNAPPs and figures are available upon request.
2P data analysis Image analysis was performed offline with ImageJ software. Images were stabilized using the "moco" software  (Dubbs et al. , 2016). Then, a mean-normalized maximum intensity projection of each movie was obtained using Fiji. This biased detection toward active cells. ROIs were manually selected using the ROI manager in Fiji, and the mean intensity of fluorescence was measured for each ROI using the multi measure tool. Detection of Ca 2+ transients of individual neurons was performed automatically by using a custom-written toolbox in MATLAB (v2018b, The MathWorks, USA, RRID:SCR_001622). Calcium traces obtained from 2P microscope movies were also analyzed with MATLAB. Typically, 50 calcium traces are generated from a film, each corresponding to the calcium signal of a single cell followed for about 5 min.
These traces typically have 0 to 2 prominent peaks due to cell activation, with a rapid onset and a slower descent rate extending over several seconds. Occasionally, they are mixed with other peaks, typically 10 to 50, equally large and long, due to the animal movements during acquisition. These artefactual peaks occur at the same time for all cells in the same visual field and can be of positive or negative amplitude, which can be exploited to filter them out of the analysis. First, all the cellular traces of a film are loaded into MATLAB. Each of them reports the fluorescence of the calcium reporter intensity in a single cell over time with an image interval of 32 ms. For each trace, we calculate the end signal F/F0: dF/F0 = (F(t) -F0(t))/F0(t) With: -F(t) the raw fluorescence signal; -F0(t) is the average of the raw fluorescence in the 50% lowest intensity over a sliding window of 30 s before the time t.
Synaptic marker quantification Slices were imaged on a ZEISS LSM 700 inverted confocal microscope. For each brain, 3 slices were imaged (when the graft was large enough) corresponding to the most rostral, thickest, and more caudal part of the graft. For each slice, 3 images were taken. Images were tile scans from the surface of the brain to the deepest region of the brain containing human projections and z-stacks spanning 5 µm separated by 1 µm depth. The quantification of synaptic marker expression was performed using the Icy software  (De Chaumont et al. , 2012). Regions of Interest were defined as follows: The graft region is defined as the region which includes all the GFP-positive somas. This ROI is then reduced by 50 µm is then split into 2 regions: the Below 50 µm ROI corresponds to the tissue between the 50 µm of graft closest to the mouse cortex, while the region closest to the brain surface is the Above 50 µm ROI.
Dendrites and spine tracing For each mouse, brain slices stained with anti-GFP antibodies were first imaged at low resolution to identify regions where isolated cells were visible within the graft. Then a selection of up to 5 slices per mouse was imaged with a confocal microscope (Leica SP8) at 63X magnification. Z-stacks with a step of 0. 3 µm containing the entire depth of the slice stained with GFP were taken to follow the dendrites as far as possible. The images were then treated in Fiji (image J) with a Gaussian blur filter to remove some background noise. The images were then uploaded in the DIVA virtual reality software  (El-Beheiry et al. , 2020). In the software, pyramidal neurons were identified based on morphology. Primary dendrites were traced using the Ruler tool. We focused on dendrites that remained within the plane of the section and were clearly differentiated from neighboring projections throughout the analyzed image.
Acknowledgments We would like to acknowledge the support of the  CRT2T of Institut Pasteur for Scientific and Technological Support. In particular,  Jean-Yves Tinevez  of the  Image Analysis, Bioinformatics and Biostatistics Hub  for his work designing the Matlab program used for the calcium transient analysis,  Martin Sachse  from the  UTechS UBI  for performing the EM preparation of the sample and imaging, and finally the whole UTechs PBI structure for support with image acquisition. We would also like to thank  Jean-Baptiste Masson  and  Mohamed El Beheiry  for providing the DIVA software and their support in using it, and  Isabelle Cloez-Tayarani ,  Maria Llach Pou , and  Camille Thiberge  for valuable comments on the manuscript.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. CR and DG were Ph. D. students at  Sorbonne Université, Ecole  doctorale 3C. CR was funded by the  Laboratory of Excellence LABEX BIOPSY  and the  Fondation pour la Recherche Médicale FRM  (contrat  FDT201805005198 ). Work in the NISC laboratory was supported by  PasteurInnov ,  FRM Equipe 2019 ,  Laboratory of Excellence LABEX BIOPSY ,  Fondation Alzheimer ,  INCa TABAC 2022 , and  SPA 2020 , all to UM, and by  Programme Explore Institut Pasteur  and  CNRS.
Applications of minimal perfect hashing range from computer networks  [27]  to databases  [10] , as well as language models  [38, 39, 45] , compilers, and operating systems. MPHFs have been also used recently in Bioinformatics to implement fast and compact dictionaries for DNA strings  [34, 33, 1, 31]. In its simplicity and versatility, the minimal perfect hashing problem does not take into account specific types of inputs, nor the intrinsic relationships between the input keys. Each key x ∈ S is considered independently from any other key in the set and, as such, P[f (x) = i] ≈ 1 n for any fixed i ∈ [n]. In practice, however, the input keys often present some regularities that we could exploit to let f act "less randomly" on S. This, in turn, would permit to achieve a lower space complexity for f.