dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion This work was devoted to compare the efficiency and the robustness of several ML methods. The first part was devoted to the inspection of several technical aspects of data pre-processing which can affect the results of ML algorithms. As for statistical methods, QC constraints, imputing methods for missing genotypes and coding strategies for data analyses may affect the results of ML. For LR methods, the very high AUC values obtained with the less stringent QC argue for a rigorous management of the raw datasets. Nonetheless, imputing the missing genotypes is also a key factor and in our analysis random imputation according to HWE corresponds to the most conservative case. Differences in these criteria may explain the discrepancies between this study and the previous report by Wei et al.
who found a maximum AUC of 0. 86 with almost the same dataset but putatively with a less conservative constraints  15. Similarly, using methods based on random forest algorithm, Botta et al. also analyzed the impact of QC on the results  20. They obtained an AUC of 0. 95 with weak QC pre-processing but only 0. 76 with a more stringent pre-processing. Even if their impact appears less pronounced, coding strategies may also affect the results of ML algorithms, the summation of the number of minor alleles being the most efficient in our analysis. As an added proof for these results, we initially used a different dataset, with less stringent choices for the QC and imputation strategy. The AUC for Lasso LR was artificially inflated to 0. 85 but interestingly, GBT methods outperformed these results, by 2% on this dataset.
In regards to the results obtained later with more rigorous QC choices, we made the hypothesis that GBT better exploit the biased information hidden in missing values. In conclusion and not surprisingly, this analysis confirms that QC, imputing and coding methods have to be taken into account when comparing the results obtained by different groups and different methods. For this study, we used the Immunochip dataset built through the international consortium for IBD genetics. Our choice was motivated by the fact that this dataset contains three fold more cases than the largest GWAS dataset available for CD. However, the Immunochip panel of markers is a less homogeneous representation of the genome than a GWAS panel. Chen et al. estimated that about 25% of the SNP heritability that is tagged in the GWAS data is lost using the ImmunoChip  52.
It can thus be imagined that some key SNPs, somewhere in the genome, common or rare, may have not been tested. However, the Immunochip panel has been built to include all the common genetic variants with nominal p-values < - p 10 4 in previous GWAS analyses. Thus, it is supposed to contain the majority of common CD-associated SNPs. Considering that there is no example of a genetic variant with no nominal associations and with a strong effect discovered by ML algorithms, it seems unlikely that a larger panel would increase a lot the maximum AUC values. The results obtained with variable preselection thresholds on p-values and MAF also argue against putative common or rare alleles unavailable in the dataset but playing a key role in the genetic architecture of CD. Nonetheless the co-occurrence of numerous small epistatic effects cannot be excluded a priori and an in-depth analysis on GWAS-like datasets will be compelling in the future to confirm or exclude this possibility.
Even if we explored a large panel of ML methods, a limitation of the study is that it is based on a finite number of algorithms. Indeed, by definition, not all possible algorithms can be tested by a group alone and one could always wonder if an alternative approach could be more efficient. To tackle this question, we explored the wisdom of the crowd idea. 73 graduate students worked independently on the partial and biased dataset mentioned above, where names and position of SNPs and disease trait were hidden to obfuscate the data. Top performers used GBT methods, obtaining our same score values. Overall, this exercise can be seen as an external confirmation of the robustness of the results presented in this paper. Consistently with GWAS analyses, the different ML methods recognized the CD-associated loci with the best nominal p-values among their best predictors.
10 Figure 2. 102 Figure 2. ROC AUC scores for Linear Regression model under different conditions on the dataset and on the penalty terms. Black dots and error bars refer to mean values and 2 standard deviation confidence intervals for 10 fold cross-validated models on the train dataset. Red diamonds refer to AUC scores obtained on the test dataset with the model trained on the entire train dataset, using the corresponding cross validated hyperparameters. The numbers on top of the error bars refer to the number of features used by the model, and, in parenthesis, to the number of original features in the dataset. We show the AUC scores for: (A) different values of the upper bound on p-values for the SNP preselection phase, with >. MAF 0 01; (B) different values of the lower bound on MAF for the SNP preselection phase, with p-value < - p 10 4 ; (C) different values of the case/ control ratio; (D) different types of regularization.
The level of H3K4me3 increases in H2A. B. 3 KO euchromatin Next, we investigated whether the loss of H2A. B. 3 had any impact on the abundance of post-translational modifications associated with heterochromatin (H3K9me2) or euchromatin (H3K4me3) (Additional file 17: Figure  S10 ). Compared to wt round spermatids, there is no change in the abundance of H3K9me2 in euchromatin in H2A. B. 3 -/y round spermatids (Additional file 17: Figure  S10a ). Strikingly, in the absence of H2A. B. 3, there is a marked accumulation of H3K4me3 in euchromatin compared to wt round spermatids (Additional file 17: Figure  S10b ). On the other hand, we did not observe an increase in H3K4me2 or H4K8ac (data not shown). To confirm this, we measured the fluorescence intensity of the H3K9me2 or H3K4me3 signal in the heterochromatic chromocenter (CC) and in the surrounding euchromatin (EU) and calculated their ratio (CC -B)/(EU -B), where B is background autofluorescence.
TALEN design The TALENs were assembled using the method previously described  [5, 22]. The TALEN target sites were searched using a computer script developed in Sangamo. The four most common repeat variable di-residue (RVD), NI, HD, NN, and NG, were used to recognize bases A, C, G, and T, respectively. The NK RVD was sometimes used to specify the 3′ half repeat T base. TALENs were generated by linking several pre-made TALE repeat blocks in the forms of monomer, dimer, trimer, or tetramer. The desired repeat blocks were PCR amplified using position-specific primers. The PCR products were pooled, digested with BsaI, and ligated into the TALEN expression vector, pVAXt-3Flag-NLS-TALE-Bsa3-C63-FokI, which had been linearized with the BsaI restriction enzyme. Ligations were transformed into E. coli competent cells, and single clones were picked and sequence verified.
Exome sequencing and bioinformatic analyses The male mice that were chosen for exome sequencing were genotyped prior to sequencing to confirm that all three generations had the same H2A. B. 3 genotype (H2Afb3 (X Δ5 Y), gm14920 (X Δ10 Y) and H2Afb2 (X Δ64 Y)). The purified gDNA was subjected to exome enrichment using the Agilent SureSelect XT2 All Exon Kit. The exome capture efficiency was uniformly high with approximately 45-50% of all DNA sequenced being exonic. Sequenced mouse exomes were analyzed using our in-house variant detection pipeline described in detail previously  [23, 24]. In short, reads were aligned to the mouse reference genome mm10 using BWA  [25]  and sorted BAM alignment files were generated using SAMTools  [26]. Candidate PCR duplicate reads were annotated using Picard software (http://broadinstitute.
github. io/picard/), specifically the MarkDuplicate command. SNVs and small indels were called using SAMTools Mpileup  [27] , and variants were annotated using the Variant Effect Predictor  [28]. To detect larger structural variants, both Pindel  [29]  and Janda (unpublished; https://sourceforge. net/projects/janda/) were utilized. To reduce the number of total variants called, variants specific to the FVBN mouse strain were downloaded from the Sanger Institute ftp site (ftp://ftp-mouse. sanger. ac. uk/REL-1206-FVBNJ/). As these variants were originally reported relative to reference genome mm9, the UCSC liftOver tool (https://genome. ucsc. edu/ cgi-bin/hgLiftOver) was used to map the FVBN variants to mm10. These FVBN-specific variants were annotated and not considered for further off-target analysis.
Acknowledgements We thank  Tara Davidson  for assistance with microinjections of the TALENs and  Dan Andrews  for bioinformatics support in analyzing the exome sequencing data. We acknowledge the excellent high-throughput DNA sequencing service provided by our in-house Bimolecular Research Service headed by  Stephanie Palmer , and the JCSMR mouse containment suit technicians responsible for the breeding and care of the mice. We especially thank  Saadi Khochbin  for providing us with H2A. L. 2 antibodies.
Analysis of post-progression therapy Subsequent therapy after discontinuation of study medication was the decision of the treating physician. Data regarding subsequent therapy were prospectively collected as study follow-up information. Follow-up visits occurred every 21 ± 7 days after the end of treatment visit until progression or start of subsequent anticancer treatment. After the last follow-up visit, patients entered an observation period; data regarding progression and subsequent therapy was collected every 60 ± 15 days, based on patient notes or by telephone contact with the patient, until death or 5 years after the last follow-up visit, whichever occurred first. Time on treatment with subsequent therapies was collected, but response to treatment, duration of response, and PFS were not collected.
The DNA was obtained by the modified method of Sambrook and Russell  [68]  and  Ausubel et al. (2002)   [69]. PCR amplicons were purified, and DNA was sequenced by MACROGEN (Korea). The search in the Genbank database was performed using the BLAST program at the National Centre for Biotechnology Information website (http: //www. ncbi. nlm. nih. gov/BLAST/), and sequencing data were analyzed by comparing the sequences of nearest relatives found by BLAST searching.
Acknowledgments: I. S. L. is grateful to  CONACYT and BEIFI-IPN  for student grant. C. J. H. -G. and M. M. -O. are grateful to  Instituto Politécnico Nacional (IPN)  for the support " Beca de exclusividad (COFAA)" and "Estímulo al Desempeño de los Investigadores (EDI) ". C. H. acknowledges  Biogenouest  for the support. Thanks for the support in molecular analysis to  Dra. Bárbara González-Acosta , and  César Guadarrama.
High-Throughput Sequencing High-throughput sequencing was performed on total RNA extracted from all the three samples (UKMa K4D, UKMa1 and L232) using the Illumina HiSeq platform at Source Bioscience, Nottingham, UK. Genomic DNA (gDNA) was depleted using DNaseI, and ribosomal RNA (rRNA) was removed using NEBNext ® rRNA Depletion Kit (Human/Mouse/Rat) with RNA Sample Purification Beads (New England Biolabs, Ipswich, MA, USA) prior to the library construction. Each read length was 2 × 150 bp, and the insert size was 200 bp on average. All the sequence data generated were analyzed using the Geneious Prime 2019. 0. 4 software. The reads were assembled into contiguous sequence (contigs) with de novo assembly performed with Geneious Prime. By using BlastX, the assembled contigs were compared against the RefSeq sequence database of all the virus proteins downloaded from GenBank.
Analysis of Virus Abundance The number of viral reads in each data set was measured by mapping the raw total reads generated from the high-throughput sequencing to the identified viral contigs. The host ribosomal protein L4 (RPL4) gene was used as a host gene reference marker. The Oryctolagus cuniculus RPL4 gene (NM_001195817. 1) was used for the rabbit, while the Microtus ochrogaster RPL4 gene.
Analysis of Virus Abundance The number of viral reads in each data set was measured by mapping the raw total reads generated from the high-throughput sequencing to the identified viral contigs. The host ribosomal protein L4 (RPL4) gene was used as a host gene reference marker. The Oryctolagus cuniculus RPL4 gene (NM_001195817. 1) was used for the rabbit, while the Microtus ochrogaster RPL4 gene (XM_005347781. 3) was employed for the field vole. The abundance of viral reads was calculated by dividing the number of mapped reads by the total number of reads in each library. The graphs were generated with GraphPad Prism 8 (v8. 1. 2, GraphPad Software, San Diego, California USA).
Contig Confirmation PCR Specific primers were designed for contig confirmation, and for gap-filling between contigs, based on the known sequences of the contigs. All the primers were evaluated in silico with the Primer3 online tool. The Geneious Prime software was used for mapping and annotating. PCR reactions were carried out in a PTC-200 Peltier Thermal Cycler (MJ Research, Waltham, Massachusetts, USA). The PCR reactions were performed with HotStarTaq Polymerase (Qiagen, Hilden, Germany) according to manufacturer's instructions. The PCR products were subjected to Sanger sequencing at Source Bioscience, Nottingham. All the virus sequences generated in this study have been deposited on GenBank under the accession numbers MN626413-MN626440.
Phylogenetic Analysis To facilitate phylogenetic analysis, 57 reference paramyxovirus sequences were downloaded from GenBank and the F, H, L, M, N and P genes extracted. Similarly, 50 rotavirus A reference sequences were downloaded for the VP1, VP2, VP3, VP6, NSP2, NSP3 segments, as were 47 reference sequences of astroviruses for ORF1a, ORF1b and capsid genes. Amino acid and nucleotide sequences were aligned using ClustalW  [29]  and phylogenetic analyses were performed using the maximum likelihood (ML) method within the Molecular Evolutionary Genetics Analysis version 7 (MEGA7)  [30]  package. Analyses of aligned amino acid sequences utilized a Jones-Taylor-Thornton (JTT) amino acid substitution model with uniform rates of variation, and complete deletion of gaps, with statistical robustness assessed using bootstrap resampling (500 pseudo-replicates).
While most core eudicots have one gene in each clade, Solanaceae have two: FUL1 and FUL2 in the former, and MBP10 and MBP20 in the latter. We characterized the evolution of the euFUL genes to identify changes that might be correlated with the origin of fleshy fruit in Solanaceae. Our analyses revealed that the Solanaceae FUL1 and FUL2 clades probably originated through an early whole genome multiplication event. By contrast, the data suggest that the MBP10 and MBP20 clades are the result of a later tandem duplication event. MBP10 is expressed at weak to moderate levels, and its atypical short first intron lacks putative transcription factor binding sites, indicating possible pseudogenization. Consistent with this, our analyses show that MBP10 is evolving at a faster rate compared to MBP20.
Plant Material for Sequencing Sources of plant and tissue material for sequencing are listed in Table  S1  in Supplementary Data Sheet S1. Plants were grown in temperature controlled glasshouses at University of California, Riverside (UCR), The New York Botanical Garden, NY (NYBG), and The University of Antioquia, Colombia (UdeA) or collected from the grounds at UCR and the Universidad de Antioquia or the field at Parque Arvi, Vereda Santa Elena, El Tambo, Colombia. For ease of reference and to simplify language, throughout the paper, members of Solanoideae, including the dry-fruited Datura, will be referred to as "fleshy-fruited species" (rather than "fleshy-fruited species and Datura"). Likewise non-Solanoideae, including the fleshy-fruited Cestrum and Duboisia, will be referred to as "dry-fruited species" (rather than "dry-fruited species and Cestrum and Duboisia").
, 2018)  were generated at UdeA and sequenced at Macrogen (Korea). All resulting euFUL sequences from both degenerate primer PCRs and transcriptomes are listed in Table  S2  in Supplementary Data Sheet S1. Individual sequences from PCR-based methods have been deposited in the GenBank (for accession numbers, see Table  S1  in Supplementary Data Sheet S1) and transcriptome data for N. obtusifolia, C. diurnum, C. nocturnum, D. spinosa, F. viscosa, G. glauca, and S. grahamii have been deposited on the SolGenomics network 1.
Mining euFUL Sequences From de novo Transcriptome Assembly and Databases For transcriptome assembly, raw paired-end reads and singleend reads from Illumina sequencing were first quality trimmed using Trimmomatic v0. 36  (Bolger et al. , 2014)  or TrimGalore  (Krueger, 2017)  and de novo assembled on the UCR High Performance Computing Cluster (HPCC) using the default settings of Trinity v2. 4. 0  (Grabherr et al. , 2011). Dunalia spinosa, Fabiana viscosa, Grabowskia glauca, and Salpiglossis sinuata libraries were assembled by combining both 75 bp paired-end and 150 bp single-end reads. Each assembled transcriptome was then used to create a custom Basic Local Alignment Tool (BLAST)  (Altschul et al. , 1990)  database. The BLAST database for each species was queried on the HPCC with both blastn and tblastx using all available sequences in our euFUL sequence file using a UNIX command line that sequentially matched each sequence in our query file against the database (BLAST R Command Line Applications User  Manual, 2008).
BLAST analyses were also conducted on the NCBI 2  (NCBI Resource Coordinators, 2017) and oneKP 3   (Matasci et al. , 2014)  databases using A. thaliana FUL and various Solanaceae FUL homologs as query. Matching output sequences (Table  S1  in Supplementary Data Sheet S1) from both transcriptomes assemblies and database mining were further confirmed by compiling a gene tree as described below. We confirmed the accuracy of our sequences using gene specific primers and Sanger sequencing. Unless specified otherwise, all sequences referred to in this manuscript are the full or partial mRNA sequences.
Gene-Tree Generation The Multiple Sequence Comparison by Log-Expectation (MUSCLE)  (Edgar, 2004)  tool was used to align euFUL sequences (Supplementary Data Sheet S2). The appropriate model for tree building, GTR+G, was determined with jModelTest 2. 0  (Darriba et al. , 2012). Ten independent maximum likelihood (ML) analyses starting with random trees were performed using GARLI v2. 1 (Genetic Algorithm for Rapid Likelihood Inference)  (Bazinet et al. , 2014). euFUL genes from Convolvulaceae (Convolvulus, Cuscuta and Ipomoea species), which were retrieved from the oneKP database 4  , were designated as the outgroup in each analysis, which meant these sequences were automatically excluded from the ingroup clades. Each ML run was set to terminate when there was no significantly better scoring topology for 20,000 consecutive generations.
The ten resulting trees were checked for agreement by calculating the pairwise Robinson-Foulds distance using 'ape' and 'phangorn' packages on R  (Robinson and Foulds, 1981; Paradis et al. , 2004; Schliep, 2010; R Core Team, 2018). The tree with the largest ML value was chosen as the starting tree in a bootstrap analysis involving 1,000 replicates. The results of the replicates were summarized and bootstrap values were calculated using SumTrees tool of DendroPy package on Python ver. 2. 7 (Python Language Reference, 2010;  Sukumaran and Holder, 2010)  or Geneious 10. 2  (Darling et al. , 2010; Kearse et al. , 2012). Any sequences that did not group with any of the subclades were aligned with the paralogs to investigate whether these may have been splice isoforms. Any such isoform was expected to have large insertions/deletions at splice junctions.
Selection Pressure Analysis The CODEML program within the Phylogenetic Analysis by Maximum Likelihood (PAML)  (Yang, 1997)  v 1. 3 5  software package was run on the HPCC at UCR to analyze the selection pressure acting on euFUL genes. These analyses were performed to test if different gene lineages as well as sub-groups within those lineages were evolving at significantly different rates. Further scenarios were considered in which each gene, the transition branches from dry to fleshy fruit trait, or specific sites in the sequences were tested for significantly different rates of evolution. Model 0 (M0) was used to estimate a single evolutionary rate for all genes when the clades being analyzed encompassed the entire dataset. Model 2 (M2) was used when two groups encompassing the entire data set have different rates or when two groups that are being compared do not encompass the entire data set.
Solanaceae Have Four Clades of euFUL Genes Our analysis consisted of 106 sequences from 45 species in 26 genera obtained from direct amplification, transcriptomes, and online genomic databases (Table  S1  in Supplementary Data Sheet S1). Of these, 64 sequences belonged to species from the Solanoideae, characterized by the derived fleshy fruit, whereas the other 42 sequences were from species with the ancestral dryfruit trait. We designated euFUL genes from Convolvulaceae, the sister-group of Solanaceae, as the outgroup  (Stefanoviae et al. , 2003). For many species in the analysis, we have an incomplete set of paralogs; however, we had substantial and diverse representation from across the phylogeny, which allows us to test hypotheses regarding the evolution of this gene lineage in Solanaceae.
We performed a maximum likelihood phylogenetic analysis (Garli v2. 1)  (Bazinet et al. , 2014)  on a data set that consisted of 106 Solanaceae members of the euFUL gene lineage  (Litt and Irish, 2003; Shan et al. , 2007) , which we obtained through amplification and sequencing (37 sequences), generating transcriptome sequence data (29 sequences), or mining databases (40 sequences). As outgroup we used 10 euFUL genes from Convolvulaceae, the sister family to Solanaceae (Figure  2  and Supplementary Table  S1 )  (Stefanoviae et al. , 2003). The resulting tree shows the two major clades of core-eudicot euFUL genes, the euFULI and euFULII lineages  (Litt and Irish, 2003; Shan et al. , 2007). Within each of these clades there is evidence of a Solanaceae-specific duplication, resulting in two subclades in each lineage.
The MBP10 and MBP20 Clades Are the Result of a Tandem Duplication Event The FUL1 and FUL2 genes of tomato are located on different chromosomes (6 and 3, respectively), which is consistent with the proposed Solanaceae whole genome duplication  (Blanc and Wolfe, 2004; Schlueter et al. , 2004; Song et al. , 2012)  or triplication  (The Tomato Genome Consortium, 2012; Albert and Chang, 2014; Vanneste et al. , 2014; Bombarely et al. , 2016)  followed by loss of one paralog. The lack of a FUL2 ortholog in our dataset from Goetzia or Schizanthus (Figure  2 ), the two earliest diverging genera that we included in our analyses, raises the possibility that the FUL1/FUL2 clades originated via a duplication that occurred after the diversification of these genera, and not as a result of a whole genome event that preceded the diversification of the family.
AUTHOR CONTRIBUTIONS AL designed and supervised research, and assisted in writing the paper. DM contributed to the design of the study, generated Cestrum diurnum, C. nocturnum, and Schizanthus grahamii transcriptome libraries, retrieved sequences from PCR-based methods and database mining, analyzed the data, and wrote the paper. CE assisted with PAML analysis, contributed suggestions for analyses, and made suggestions on the paper. AR generated Dunalia spinosa, Fabiana viscosa, Grabowskia glauca, and Salpiglossis sinuata transcriptome libraries, contributed suggestions for analyses, contributed in recording the associated protocols, and commented on the paper. JM retrieved sequences from PCR-based methods. MS generated the Nicotiana obtusifolia transcriptome libraries and additional sequences using PCR-based methods.