dataesr/scientific-paragraphs-categorization huggingface dataset:
Random Forest predictive model The predictive model was built on the matrix of methylation percentages at DMCs, where samples were in rows and DMCs in columns. The DMCs with missing values were either filtered out before model construction or conserved if they contained less than 10% missing values; the latter were then imputed using the R package missMDA with default options  [101]. Random Forest models were built using the 'rf ' option from the R package caret (v6. 0-84)  [102]. The "mtry" parameter was estimated by the square root of the number of features in the model, and the number of trees was set at 500. Three different strategies were applied to assess the performance of the predictive model (Fig. 7 ), and the predicted fertility status was compared with the actual fertility status of samples included in each type of testing set.
Literature mining A systematic review of the literature was performed for all genes differentially methylated with the Biomart annotation available ("Gene name" column in Additional file 4: Table  S3 ; 139 genes out of 170). The NCBI Pubmed database was interrogated using the gene names successively associated with each of the following terms: "embryo*", "sperm*" and "fertility", and all the relevant references were collected (Additional file 7: Table  S6 ). The comparison with human case studies was performed as follows: among all the listed references, those focusing on the comparison of genome-wide DNA methylation patterns between fertile and infertile/subfertile human sperm samples, and with accessible supplementary data available, were pointed out. This led to the selection of four studies highlighting 1843  [27] , 31  [26] , 3  [28]  and 384  [52]  differentially methylated genes.
Funding This study was funded by the  French National Research Agency  (Grant  ANR-13-LAB3-0008-01  SeQuaMol) and  APIS-GENE  ( AP-2018-44 ). VC was a CIFRE fellow of the  French National Agency for Research and Technology (ANRT). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. APIS-GENE  has read and approved the final version of the manuscript.
Genome assembly GridION data were trimmed using Porechop v0. 2. 1 (https://github. com/rrwick/Porechop) and filtered using NanoFilt v2. 2. 0  (De Coster, D'Hert, Schultz, Cruts, & Van Broeckhoven, 2018)  with the parameters -l 3000 and -q 7. A de novo assembly was constructed with SmartDeNovo  (Ruan, 2015 (Ruan, /2019 ), Wtdbg2 v2. 1  (Ruan & Li, 2020 ) and flye v2. 3. 7  (Kolmogorov, Yuan, Lin, & Pevzner, 2019) , each with default parameters. The resulting assembly metrics were compared, and the draft assembly with the best metrics generated by SmartDeNovo was kept and used as reference. This assembly was then further corrected using long reads. After mapping the trimmed and filtered GridION reads with minimap2 v2. 11 (H. Li, 2018)  with parameter -x map-ont, the assembly was polished using Racon  (Vaser, Sović, Nagarajan, & Šikić, 2017)  v1.
3. 1 with default parameters for three rounds. The assembly was then corrected using short reads. After mapping 10X short reads with Long Ranger v2. 1. 1, Pilon  (Walker et al. , 2014)  v1. 22 was run with parameters --fix bases,gaps -changes. Again, three rounds of these short reads polishing were performed. The final polished genome assembly was then scaffolded using Hi-C information. Reads were aligned to the draft genome using Juicer  (Durand, Shamim, et al. , 2016)  with default parameters. A candidate assembly was then generated with 3D de novo assembly (3D-DNA) pipeline  (Dudchenko et al. , 2017)  with the -r 0 parameter. The candidate assembly was manually reviewed using Juicebox  (Durand, Robinson, et al. , 2016)  assembly tools. Gaps in this chromosome scaled assembly were filled using GapCloser (https://github.
RNA-Seq reads of P. hypophthalmus (NCBI BioProject PRJNA256973) from the PhyloFish project  (Pasquier et al. , 2016)  were used for genome annotation and aligned to the chromosomal assembly using STAR v2. 5. 1b  (Dobin et al. , 2013)  with outWigType and outWigStrand options to output signal wiggle files. Cufflinks v2. 2. 1  (Trapnell et al. , 2010)  was used to assemble the transcripts that were used as RNA-seq evidence. Braker v2. 0. 4  (Hoff, Lange, Lomsadze, Borodovsky, & Stanke, 2016)  provided de novo gene models with wiggle files provided by STAR as hint files for GeneMark  (Hoff et al. , 2016)  and Augustus  (Stanke et al. , 2006)  training. The best supported transcript for each gene was chosen using the quality metric called Annotation Edit Distance (AED)  (Eilbeck, Moore, Holt, & Yandell, 2009).
Assembly and annotation The P. gigas and P. djambal genomes were assembled from 2x250 bp short reads using the DiscovarDeNovo assembler (https://github. com/bayolau/discovardenovo/) with default parameters. For P. sanitwongsei, P. conchophilus, P. bocourti, P. larnaudii, P. mekongensis, and P. krempfi, 2x150 bp reads were assembled using SPADes v. 3. 11. 1  (Bankevich et al. , 2012)  and then purged using purge_dups  (Guan et al. , 2020). The P. elongatus, P. macronema and P. siamensis 2x150 bp short reads were assembled with SPADes v. 3. 14. 1 instead of v. 3. 11. 1 because of a higher individual genome heterozygosity (> 1%), followed by a more stringent purge with Redundans v0. 14a  (Pryszcz & Gabaldón, 2016). All these species were then assembled into pseudo-chromosomes using a reference-guided strategy and the "query assembled as reference" function from DGenies v1.
Fulllength CDS were predicted based on their genomic and protein sequence annotation or retrieved from GenBank (see Table  S2  and multi-fasta files of these sequences are publicly available at https://doi. org/10. 15454/M3HYAX). To verify the tree topology of amhr2/Amhr2 homologs, besides complete protein and cDNA sequences, we also constructed phylogenetic trees with only the first and second codons of the coding sequences  (Lemey, 2009). All putative CDS and protein sequences were then aligned using MAFFT (version 7. 450)  (Katoh & Standley, 2013). Residue-wise confidence scores were computed with GUIDANCE 2  (Sela, Ashkenazy, Katoh, & Pupko, 2015) , and only wellaligned residues with confidence scores above 0. 99 were retained. Phylogenetic relationships among the amhr2 sequences were inferred with both maximum-likelihood implemented in IQ-TREE (version 1.
Transcriptome analyses Reads from P. hypophthalmus adult organs and embryos  (Pasquier et al. , 2016)  were mapped on the complete P. hypophthalmus reference transcriptome using bwa mem version 0. 7. 17 (H. Li, 2013). Unique mapped reads were then filtered and a raw count matrix was generated with htseq-count  (Anders, Pyl, & Huber, 2015)  and normalized using DESeq2  (Love, Huber, & Anders, 2014). Genes of interest were extracted from this complete transcriptome dataset and missing values were replaced by a minimal value (0. 1) in the normalized raw count matrix. Hierarchical classification was carried out after log transformation and gene median centering using the cluster 3. 0 software (de Hoon, Imoto, Nolan, & Miyano, 2004) with an uncentered correlation similarity metric and an average linkage clustering method.
Subsequently, a sync file containing the nucleotide composition for each position in the reference was created from the pileup file using popoolation mpileup2sync version 1. 201 with a min quality of 20 (-min-qual 20)  (Kofler, Pandey, & Schlötterer, 2011). Read depth was then calculated in a 10 kb non-overlapping window using PSASS (version 2. 0. 0, doi:10. 5281/zenodo. 2615936).
Expression of amhr2by and amhr2a in P. hypophthalmus adult tissues Using P. hypophthalmus RNA-Seq from the PhyloFish database  (Pasquier et al. , 2016) , we examined the organ expression of amhr2a and amhr2by along with a series of SD genes previously identified in other teleosts, i. e. , amh, dmrt1, gsdf, gdf6a, gdf6b and sox3. Among these genes, amh, dmrt1, and gsdf display predominant expression in the adult testis and / or ovary with a much lower expression in the eight additional somatic organs examined or in embryos (Fig. 3A ). The two amhr2 genes also have a gonadal-predominant expression pattern with amhr2a being expressed in both ovary and testis while amhr2by being strictly expressed in the testis as expected for a Y chromosome sex determination gene (Fig. 3B ). The two gdf6 paralogs (gdf6a, gdf6b) and sox3 have no expression or a low expression in gonads and are more expressed in embryos for sox3 and gdf6a or in bones and brain for sox3.
Whole Genome Shotgun project of P. hypophthalmus, is available in the Sequence Read Archive P. hypophthalmus small RNA-Seq sequences are available in SRA under Bioproject PRJNA256963. P. gigas and P. djambal genomes assembled with a P. hypophthalmus reference-guided strategy have been submitted to SRA under the respective BioProjects PRJNA593917 and PRJNA605300. All other Pangasiidae genomes assembled with a P. hypophthalmus reference-guided strategy without their genome annotations are available in SRA under BioProject PRJNA795327, and their genome assemblies plus their annotations are available in the omics dataverse (Open source research data repository) server with the following DOI (https://doi. org/10. 15454/M3HYAX). Pangasius siamensis has been considered by NCBI curators as a P. macronema synonym and its genome is then recorded in NCBI with P.
ACKNOWLEDGEMENTS This project was supported by funds from the  "Agence Nationale de la Recherche " ( PhyloSex  project, grant No. ANR-13-ISV7-0005 ;  GenoFish  project, grant No. ANR-16-CE12-0035 ) and  R35 GM139635  and  R01 OD011116  from the  National Institutes of Health, USA. The  GeT-PlaGe, Gentyane  and  Montpellier GenomiX  (MGX) facilities were supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (grant No. ANR-10-INBS-09 ). MRR was supported by the Swiss.
macronema as a Biosample species name, with sample name PaSia (for Pangasius siamensis) under accession (SRA), under BioProject reference PRJNA547555 with 10X genomics and Hi-C Illumina sequencing data is available in SRA under accession number SRX6071341 and SRX6071345 and Oxford Nanopore long reads data under SRA accession numbers SRX6071342 to SRX6071344 and SRX6071346 to SRX6071355. BioSample number SAMN24707637.
Introduction In the data fusion community, it is now commonly accepted that a same phenomenon can be partially contained in observations from several measurement devices, with different resolutions and noise contaminations  [1] [2] [3]. The observations are often complementary, meaning that given dataset-specific information can be enriched from information contained in other datasets, and vice-versa. Hence data fusion was proposed to exploit the complementarity of available measurements  [4]. In some engineering fields such as remote sensing or biomedical imaging, observed data often possess more than two dimensions, thus they can be represented as tensors. Several low-rank factorizations can be considered for approximating such data. A popular one is the canonical polyadic (CP) decomposition (CPD), due to its powerful uniqueness conditions.
Link between uniqueness and identifiability First, we explain how uniqueness of the coupled CP model (  6 ) and  (7)  in the noiseless case is related to the calculation of the CRB. In estimation theory, the notion of identifiability lacks a unified definition. In the literature, it is also called "observability"  [40, 41]. In this paper, we propose to define it as the uniqueness of the proposed model. Let us consider the probability density function (PDF) f Y ;ω of the random real dataset Y ∈ R n parameterized by the unknown real deterministic parameter ω ∈ ⊆ R m. We assume that Y is a random real Gaussian dataset parameterized by its mean, that is, Y ∼ N ( μ( ω ) , ) with a known, non-singular covariance matrix. We say that the statistical model  [42]  , i. e. , any distribution f Y ;ω corresponds to a single parameter ω.
For the case of our Gaussian dataset, the following holds true: F = { f Y ;ω : ω ∈ } is identi- fiable if the mapping ω → f Y ;ω is injective f Y ;ω 1 = f Y ;ω 2 ⇔ μ( ω 1 ) = μ( ω 2 ). ( 14 ) Thus, identifiability of the distributions is equivalent to identifiability of the means, i. e. , identifiability in the noiseless case.
Coupled model with constraints Let f Y 1 ;ω and f Y 2 ;ω be the PDFs of the random real datasets Y 1 ∈ R n 1 and Y 2 ∈ R Y 1 ∼ f Y 1 ;ω and Y 2 ∼ f Y 2 ;ω , g ( ω ) = 0 , ( 18 ) with g a non-redundant deterministic vector function differentiable ∀ ω ∈. Non-redundancy means that the system of equations g i ( ω ) = 0 is not reducible  [28]. We assume that i) the PDFs f Y 1 ;ω and f Y 2 ;ω are non-redundant functions differentiable w. r. t. ω , and that their supports do not depend on ω ; and that ii) the variables Y 1 and Y 2 are statistically independent. In some cases, the model parameter ω ∈ corresponds to the stacking of two parameters ψ ∈ ⊆ R m 1 and ξ ∈ ⊆ R m 2 ( m = m 1 + m 2 ) such that ω T = ψ T ξ T , where ξ can be expressed as a function of ψ , i. e. , ξ = h ( ψ ). The function h is a non-redundant, differentiable function ∀ ψ ∈.
Uncoupled CRB We consider that Y 1 and Y 2 are random real Gaussian distributed datasets parameterized by their mean, i. e. , Y 1 ∼ N ( μ 1 ( ω ) , 1 ) and Y 2 ∼ N ( μ 2 ( ω ) , 2 ) where 1 and 2 are known covariance matrices. The parameter ω is unknown real and assumed to be deterministic. The uncoupled FIM for ω is obtained by using the Slepian-Bangs formula  [43]  : F ( ω ) = ⎡ ⎢ ⎣ ∂ μ 1 ( ω ) ∂ ω T ∂ μ 2 ( ω ) ∂ ω T ⎤ ⎥ ⎦ T Diag { 1 , 2 } -1 ⎡ ⎢ ⎣ ∂ μ 1 ( ω ) ∂ ω T ∂ μ 2 ( ω ) ∂ ω T ⎤ ⎥ ⎦. ( 22 ) If the FIM is non-singular, then the uncoupled CRB for ω (namely CRB ( ω ) ) is obtained as CRB ( ω ) = F -1 ( ω ). From Section 3. 1 , we see that invertibility  5  of the FIM implies local identifiability of the whole parameter ω. For uncoupled estimation, the constraint g ( ω ) = 0 is ignored.
We consider that the observations Y 1 ∈ R I d ×J d ×K and Y 2 ∈ R I×J×K d are random real Gaussian datasets. For all models and scenarios, Y 1 and Y 2 are distributed as in  (18). Here, from the relationships between the model parameters, we can express  (19)  as g ( ω ) = g 1 ( θ 1 , θ 2 ) g 2 ( φ 1 , φ 2 ). ( 27 ) For each scenario and model (uncoupled, partially-coupled or fully-coupled), the expression of g 1 ( θ 1 , θ 2 ) and g 2 ( φ 1 , φ 2 ) might change, resulting in different sets of constraints between the parameters. As in Section 3 , the PDFs might only be parameterized by a subset of ω ; in the following subsections, the expressions of these PDFs will be provided for each scenario. Calculation of CRBs often requires inversion of a FIM, as explained in Section 3. For the FIM to be full rank, scaling ambiguities in the CPDs need to be solved  [24]  regarding the parameters we wish to estimate: indeed the manifold of rank-R tensors in R I×J×K has dimension (I + J + K -2) R.
Model and parameterization for the fully-coupled model The particular scaling option  (31)  leads to the following model with additive constraints between the CP factors: Y 1 = [[ A 1 , B 1 , C 1 ]] + E 1 , Y 2 = [[ A 2 , B 2 , C 2 ]] + E 2 , ( 32 ) subject to A 1 = P A 2 • D -1 α , B 1 = Q B 2 • D -1 β , C 2 = R C 1 • ( D α D β ) -1 for the fully coupled case. The datasets are thus distributed according to ⎧ ⎨ ⎩ f Y 1 ; φ 2 , θ1 = 2 πσ 2 1 -I d J d K 2 exp -1 2 σ 2 1 Y 1 -[[ P A 2 • D -1 α , Q B 2 • D -1 β , C 1 ]] 2 F , f Y 2 ; φ 2 , θ1 = 2 πσ 2 2 -IJK d 2 exp -1 2 σ 2 2 Y 2 -[[ A 2 , B 2 , R C 1 • ( D α D β ) -1 ]] 2 F , ( 33 ) which is a parameterization different from  (29). The only case where the PDFs in (  29 ) and (  33 ) are equivalent is the specific case where D α = D β = I R , addressed in Prévost et al.
[30]. In  (32)  , we can see that the relationships linking the CP factors involve the scaling factors D α and D β. Rewriting these relationships in terms of the model parameters gives: ⎧ ⎨ ⎩ g 1 ( θ 1 , θ 2 ) = θ 2 -( D α D β ) -1 R θ 1 , g 2 ( φ 1 , φ 2 ) = φ 1 -M 1 D -1 α P 0 0 D -1 β Q M T 2 φ 2. ( 34 ) Due to the definition of D α and D β in  (31) ,  (34)  are nonlinear constraints on the model parameters. Parameterizations for uncoupled and partially-coupled models In the uncoupled case, the datasets are distributed according to ⎧ ⎨ ⎩ f Y 1 ; φ 1 ,θ 1 = 2 πσ 2 1 -I d J d K 2 exp -1 2 σ 2 1 Y 1 -[[ A 1 , B 1 , C 1 ]] 2 F , f Y 2 ; φ 2 ,θ 2 = 2 πσ 2 2 -IJK d 2 exp -1 2 σ 2 2 Y 2 -[[ A 2 , B 2 , C 2 ]] 2 F , (35) and follow model  (6). For the partially-coupled problem, we have the following model: Y 1 = [[ A 1 , B 1 , C 1 ]] + E 1 , Y 2 = [[ A 2 , B 2 , C 2 ]] + E 2 , ( 36 ) subject to C 2 = R C 1 • ( D α D β ) -1.
The datasets are distributed according to ⎧ ⎨ ⎩ f Y 1 ; φ 1 , θ1 = 2 πσ 2 1 -I d J d K 2 exp -1 2 σ 2 1 Y 1 -[[ A 1 , B 1 , C 1 • ( D α D β ) -1 ]] 2 F , f Y 2 ; φ 2 , θ1 = 2 πσ 2 2 -IJK d 2 exp -1 2 σ 2 2 Y 2 -[[ A 2 , B 2 , R C 1 • ( D α D β ) -1 ]] 2 F. ( 37 ) Here, we only consider the constraint (34). g 1 ( θ 1 , θ 2 ) = θ 2 - ( D α D β ) -1 R θ 1 instead of.
Calculation of performance bounds In this section, we derive performance bounds in the uncoupled, partially-coupled, and fully coupled cases. For the case of fully coupled datasets (i. e. , all degradation matrices are known), we address both scenarios described above. The closed-form of the matrices to be inverted and their submatrices are all available in a supplementary material. The proposed framework for computing the bounds, depending on the estimation scenario and parameter constraints, is summarized below in Fig. 2.
Scenario 1 -linear constraints In the first scenario, the most straightforward approach is to consider the reparameterization change for the CCRB, using model (  6 ) and  (7). We consider the random real Gaussian distributed dataset Y such that Y ∼ N ( μ( ψ ) , ) , with Y = vec { Y 1 } T vec { Y 2 } T T , = Diag { 1 , 2 } , μ( ψ ) = vec { [[ P A 2 , Q B 2 , C 1 ]] } vec { [[ A 2 , B 2 , R C 1 ]] } = I K Q P R I IJ P vec { [[ A 2 , B 2 , C 1 ]] }. Since P is constant, we only have to consider the derivatives of  (38). As a result, we can compute the reparameterized FIM for ψ (namely F c ( ψ ) ) from the Slepian-Bangs formula as vec { [[ A 2 , B 2 , C 1 ]] } w. r. t. ψ from F c ( ψ ) = M 3 S 1 S 2 S 3 T P T -1 P S 1 S 2 S 3 M T 3. ( 40 ) The reparameterized CRB for ψ is obtained as rCRB ( ψ ) = F c -1 ( ψ ).
It turns out sourcing sampling material for a global effort on a single day every year is not a minor undertaking. We had to streamline the entire process for ambient sample collection methods, sequencing protocols, and analysis pipeline. It took a while to standardize everything and define the metadata to collect. For ''We continue to improve logistics, solve challenges, and track down microbial mysteries, but the fantastic group of people in MetaSUB discusses these problems openly to find a solution. For a consortium that is so diverse, we work well together. '' the past several years, however, the consortium has had a well-standardized protocol that ensures the use of identical swab collection protocol and metadata analysis pipeline, using methods from the Genome Standards Consortium. In fact, our coordination has smoothed to the extent that we now ship all the reagents and kits from New York City for global City Sampling Day (gCSD), which happens annually on June 21st, to all cities internationally.
This resulted in the first analysis of the sewage system microbiome in Montevideo  (Fresia et al. , 2019) , which motivated the creation of new projects within MetaSUB like MetaSEW. In 2019, I created the Latinbiota Consortium (http://latinbiota. net), a network of collaborators from 8 countries from Latin America that is aiming to understand the composition and variation of the human microbiome at the continental scale. In collaboration with the Wellcome Sanger Institute (United Kingdom), we have performed shotgun metagenomics on more than 600 fecal samples. Comparison with global datasets revealed a massive expansion of microbial biodiversity associated with the human body. The integration of this data with environmental metagenomes from MetaSUB will enable unprecedented comparisons of the ecological relationships between the human microbiome and its surrounding environment.
It has been interesting how the Meta-SUB consortium has evolved in the last five years. Due to public misconception and administrative challenges, there was initial resistance to global sampling. However, due to the increasing positive press coverage recently, the reception of those microbial studies in public is changing. Besides the scientific aspect, the educational aspect is also important to us, and we have seen an impact here. Hazrin-Chong. Convincing the authorities and railway companies to grant us sampling permits was complicated, which I learned to be the norm for other cities with privately owned transport systems. The most challenging task in expanding globally, I think, is to have the public and authorities on board with what MetaSUB is doing, and how the scientific data derived from this research can benefit the public.
The grocery store and the gas station are where we got more attention. Volunteers reported first getting an inquisitive: ''What are you doing?'', then a somewhat excited ''That's cool!''. ''Comparison with global datasets revealed a massive expansion of microbial biodiversity associated with the human body. The integration of this data with environmental metagenomes from MetaSUB will enable unprecedented comparisons of the ecological relationships between the human microbiome and its surrounding environment. '' Three of our volunteers were homeschooling parents who took this opportunity to educate their children about the microbial diversity present in our environment. One of our volunteers chose to swab high-touch areas at our local police station. The officers welcomed the experience and were actually very excited to learn about MetaSUB and swabbing.
Moreover, now, one of our high school student volunteers started working in our lab after the experience they had swabbing at Los Alamos. Mason. In addition to navigating the complex public and government perceptions, we faced enormous logistical challenges as MetaSUB continued to expand. The projects really required tailoring collection protocols that worked very well anywhere, in marshes, polar regions, or other peculiar environments. For example, the collaboration with the CRG in Spain resulted in an improved and optimized protocol for high-throughput sampling. Even the app we use for data collection needed adjustment to work offline and online, e. g. , in the tunnel and subway transit systems. Ryon. What helped us overcome some of the most significant challenges is that we successfully established philanthropic and corporate partnerships, which are integral to enabling this science.
Emmanuel Dias-Neto (A. C. Camargo Cancer Center, Sao Paulo, Brazil). In 2010, we decided to revisit some data from our Human Cancer Genome Project (sponsored by the Ludwig Institute for Cancer Research and FAPESP, Brazil), which showed the presence of bacterial DNA in human tumors  (Dias Neto et al. , 2000). We needed approaches to evaluate the microbiota in large-scale and the progress was hindered by the consensus on sample collection, standardization of sequencing protocol, and many other logistical challenges for this emerging idea. After association with MetaSUB, though, we have largely resolved these issues. We continue to improve logistics, solve challenges, and track down microbial mysteries, but the fantastic group of people in MetaSUB discusses these problems openly to find a solution.