dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The statistical evaluation of the data of insecticide toxicity on pupae and adults was performed through the one-way analysis of variance (ANOVA), and the differences between the means were determined by an LSD multiple range test (P ≤ 0. 05). If the assumptions of ANOVA were not met-i. e. , the data did not fall into a normal distribution-either the raw datasets were transformed to [log (x + 1) or arcsine√x] or a nonparametric test was performed for the data analysis (Kruskal-Wallis test). The statistical analyses were calculated by the Statgraphic V4. 0 program  (STSC 1987). The adult survival was estimated by the Kaplan-Meier method along with the log-rank test for treatment comparisons, while the Bonferroni correction was used for paired comparisons between treatment methods.
2) with age indexed from 0  (Goodman 1982). The TWOSEX-MS Chart computer program was used to estimate the demographic parameters  (Chi 2008). This program includes a procedure for the estimation of standard error of population parameters through the Bootstrap technique (explained in  Yu et al. 2013). Survival, fecundity, and reproductive-value curves were constructed. Differences in life history traits and demographic parameters between E. mundus exposed and unexposed to spirotetramat at MFRC were compared with t or the Kolmogorov-Smirnov tests  (Zar 1996).
Acknowledgements This research was funded by a PICT 2011 No. 1752  and  PIP 0205  projects from the  Argentine National Agency for the Promotion of Science and Technology (ANPCyT)  and  CONICET , respectively granted to  M. I. Schneider. The authors wish to thank  Bayer Crop Science S. A. for providing Movento® used in the bioassays. N. Francesena is grateful to CONICET for the doctoral fellowship granted. We thank  Laura Marote  and  Graciela Minardi  for their kind assistance with the artworks and statistical analyses, respectively. Dr. Donald F. Haggerty , a retired academic career investigator and native English speaker, edited the final version of the manuscript. We are grateful to the two anonymous reviewers whose contributions have helped to improve the manuscript.
Overall, these studies led to the identification of PIH1D3 molecular defects in affected males from nine independent families, which were all family-unique (Fig. 1a ; Supplementary Figs  1  and 2 ): three nonsense mutations (c. 127G4T, p. Glu43* in PCD12 II:1; c. 266G4A, p. Trp89* in PCD392 II:1; c. 511C4T, p. Gln171* in DCP1218), two frameshift mutations (c. 263_268delinsG, p. Ile88Argfs*12 in DCP894; c. 489_492del; p. Ile164Leufs*11 in GVA30 II:1), one missense change (c. 397G4T, p. Asp133Tyr in DCP68), as well as genomic deletions containing the PIH1D3 gene (1. 93-Mb, 3. 27-Mb and 3. 73-Mb deletions in DCP603/DCP1747, DCP1337 and DCP855, respectively). The complement of genes contained within the latter, larger deletions are listed in Supplementary Table  1. In the NGS screening no other variants of interest were detected, and all the nine identified PIH1D3 variants were unique, having never been previously reported and being absent from all available databases of normal human variation (ExAC, 1000G, EVS).
In the case of PCD12 II:1 (p. Glu43*), in spite of the PIH1D3 nonsense mutation there appears to be less severe loss of ODAs than that of the other patients, suggesting a less severe mutation. The involvement of modifying genetic factors cannot be excluded in this individual; alternatively, the resulting PIH1D3 transcripts may escape, at least in part, degradation via the nonsensemediated mRNA decay (NMD) pathway and the protein may retain some degree of normal function. In keeping with this hypothesis, the Human Splicing Finder V3. 0 tool The open reading frame is contained in exon 3-8 (blue boxes). The PIH1D3 mutations identified are shown within the 214 amino acid protein, in relation to its sole identifiable functional domain, a CS (named after CHORD-containing proteins and SGT1) domain-spanning residue 128-214 (ScanProsite).
In cells from PIH1D3-mutated individuals GVA30 II:1 and PCD12 II:1 (middle and lower panels) there is marked reduction of PIH1D3 protein staining with undetectable levels in GVA30 II:1. Differential interference contrast (DIC) microscopy shows the outline of the cells. Scale bars, 10 mm. (http://www. umd. be/HSF3/index. html) predicts that the c. 127G4T mutation identified in PCD12 II:1 causes the loss of a potential exonic splice enhancer (ESE) at the 3 0 end of exon 3 (Supplementary Fig. 7 ). This mutation is expected to alter splicing, especially since the MaxEntScan application  31  predicts the strength of the physiological intron 3 donor splice site to be very low (that is, score of 1. 07, as compared with 7. 97 to 11 for the other PIH1D3 donor splice sites). On the basis of this hypothesis, we performed RT-PCR on total RNA from a limited amount of nasal biopsy material obtained from PCD12 II:1, which confirmed the absence of exon 3 from PIH1D3 transcripts compared with those amplified from nasal biopsy of a healthy control (Supplementary Fig.
CPP07729 and CPP02748). Next-generation sequence analysis. DNA was extracted from whole blood of affected individuals and family members by standard methods, with automated platforms such as Qiagen Autopure LS and NucliSENS used for the Geneva cohort. In the UK, the PCD12 sample was included in a cohort of 43 affected individuals subject to whole-exome sequencing as part of the UK10K project  49. PCD392 was one of 48 affected individuals screened by targeted sequence capture. This used the SureSelectXT system (Agilent) and sequence capture libraries designed using the Agilent SureDesign wizard, to capture the full transcript of 640 target genes plus 25 basepairs (bp) at each intron-exon boundaries. A total of 25 ng of genomic DNA was fragmented with QXT enzyme and tagged with adaptors for initial amplification.
The entire coding sequence corresponding to RefSeq (http://www. ncbi. nlm. nih. gov/refseq/) coding genes was used as the reference for the calculation of coverage and reads on target. In both centres, for subsequent filtering of variants, only exonic and splicing variants (±10 bp of the intron-exon boundary) were retained for further analysis. These were filtered to exclude synonymous variants, variants with a minimum allele frequency Z0. 01 in dbSNP version 137, 1000Genomes (http:// 1000genomes. org/), local databases and variants found within segmental duplications of the genome. Minor allele frequency was then checked on Exome Variant Server (http://evs. gs. washington. edu/EVS/) and ExAC (http:// exac. broadinstitute. org/) databases, as well as in VariantMaster  54  in Geneva, to further filter for variants common among the affected individuals.
Final filtered variants were evaluated individually based on the predicted pathogenicity scores provided by a number of softwares including SIFT, PolyPhen-2 and Mutation Taster, their presence in HGMD (http://www. hgmd. cf. ac. uk/ac/index. php) and the literature, focusing on functional data. Evolutionary conservation scores were provided by PhyloP and GERP þ þ. Whole-genome SNP-array analysis. Patients were genotyped for copy number variations with the HumanOmniExpress 24 BeadChip from Illumina and the data were analysed with the Genome Studio and CNV partition 3. 1. 6 softwares (Illumina). Sanger sequencing. Sanger sequencing of custom-designed PIH1D3 amplicons (NM_001169154) confirmed potentially disease-causing variants in probands and allowed segregation analysis in their families. Primers were designed using Primer 3 (http://primer3.
ut. ee/) and are listed in Supplementary Table  3  with PCR conditions available on request. Sequences were analysed and compared with controls using Sequencher software (Gene Codes Corporation, Ann Arbor, MI, USA) in UK patients, Mutation Surveyor (SoftGenetics, LLC) and Staden (http:// staden. sourceforge. net/) in the Geneva patients and Seqscape software (Applied Biosystems, Foster City, USA) in the French patients. Immunofluorescence analysis. All antibodies used are listed in Supplementary Tables  4  and 5. Respiratory epithelial cells obtained by nasal brush biopsy and suspended in cell culture media were air dried onto glass slides and stored at À 80 °C until use. Immunostaining on the slides was performed as described  55. The cells were incubated with primary antibodies overnight at room temperature at the dilutions listed in Supplementary Table  4 , then with secondary antibodies diluted at 1:1,000 dilution for an hour.
Cells were finally washed five times with PBS, mounted in ProLong Gold Antifade Mountant with DAPI (ThermoFisher Scientific) and confocal images were taken using a Zeiss LSM 710 (Zeiss, Cambridge, UK). Immunofluoresence analysis in Supplementary Fig. 3 verified a  Sigma PIH1D3 antibody by specific detection of PIH1D3 at the expected molecular weight. cDNA cloning and site-directed mutagenesis. Fully sequenced human PIH1D3 (clone 5173807; Genbank NM_001169154) and DNAI2 (clone 5752153; Genbank NM_023036) cDNA clones were purchased from Mammalian Gene Collection (GE Healthcare) for Gateway Cloning. PCR products were amplified using PWO high-fidelity Taq Polymerse (Roche), subcloned into Gateway entry vector (ThermoFisher) and recombined with pcDNA-DEST53 (ThermoFisher) and pCMV-Myc (Clontech) Gateway Vector via LR Clonase II encoding GFP and Myc N-terminal epitope tags, respectively.
For immunoblotting, 20 mg of lysate was resuspended in 2 Â Laemmli sample buffer (BioRad), 1 ml of 2-mercaptoethanol (Sigma) and heated for 5 min at 95 °C and the protocol continued similarly to the IP assay. Membranes were washed four times (10 min each) then incubated with anti-mouse HRP or anti-sheep HRP secondary antibody for 45 min at room temperature. Membranes were then washed five times and developed by Pierce ECL Western Blotting Substrate (ThermoFisher Scientific). Images were developed on X-ray films (GE Healthcare). The following antibodies were used: mouse monoclonal anti-GFP (clones 7. 1 and 13. 1, Roche), mouse monoclonal anti-Myc (1:1,000, clone 9E10, Merck Millipore), rabbit polyclonal anti-PIH1D3 (1:400, HPA051099, Sigma), mouse anti-GAPDH (1:1,500, clone 6C5, Merck Millipore), donkey anti-rabbit HRP antibody (1:5,000, NA934, GE Healthcare) and sheep anti-mouse HRP antibody (1:5,000, NXA931, GE Healthcare).
Quantitative RT-PCR analysis. Total RNA was isolated from ALI culture pellets using Trizol Reagent (Invitrogen) and used to produce complementary DNA with Omniscript (Invitrogen). RT-qPCR primers were designed using Primer-BLAST (http://www. ncbi. nlm. nih. gov/tools/primer-blast/) to ensure specificity and primer sets were optimized across a range of cDNA concentrations by generating standard and melt curves per gene. All real-time qPCR reactions were carried out with a CFX96 Touch Real-Time PCR Detection instrument (Biorad) with samples set up in triplicates in 20 ml reactions containing 2 Â iQ SYBR Green Supermix (Biorad), 3 picomolar of 100 mM primer mix (forward and reverse primers) and 500 ng of cDNA. GAPDH was used as an endogenous control for normalization between target gene expression levels.
Data acquisition of the fluorescent signal was performed at the end of the run. Double delta CT calculations were measured as logarithm and then converted to fold change of 100, after untreated control levels were subtracted from target levels. Primers used in RT-qPCR experiments are listed in Supplementary Table  3. Zebrafish analysis. All zebrafish (Danio rerio) experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Yale University. Husbandry was carried out according to standard protocols  57. A human rescue expression vector was created by cloning human PIH1D3 (PCR primers 5 0 -CACCAGGATCCATGGAATCTGAAAATATGGAT-3 0 and 5 0 -AGGCTCGAG TCAGAAGAAATTAGCAATATC-3 0 ) into the pCS2 þ vector between BamHI and XhoI restriction endonuclease sites. For zebrafish pih1d3, total RNA was isolated from wild-type TuAB embryos, followed by cDNA synthesis with High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems).
Epithelial strips were bound with 2% agar and then dehydrated and infiltrated with araldite resin and embedded in araldite. Ciliated areas were selected in survey sections before cutting dark gold-blue ultra thin sections. Ultra thin sections (B150 nm) were stained with methanolic uranyl acetate and lead citrate and 10 nm gold fidicuals were added to aid correlation of the captured images for tomogram generation. Images of cilia in longitudinal section were acquired at Â 20,000 magnification using a Jeol 1400 þ transmission electron microscope with a mid-mount digital camera (AMT16X). Automated Recorder software package v2. 7 (Jeol Technology Systems) was used to acquire dual axis tilt sequences between 70°and À 70°in 1°i ncrements. Tomograms were then generated using IMOD software created by the University of Colorado  59, 60.
Particle Estimation for Electron Tomography (PEET) software was used to average and align particles along manually selected microtubules  61. The outcome of the PEET process is a density map, representing an average 96 nm repeat of the selected tubule in which regular, repeated features are emphasized. The density maps were opened within the UCSF Chimera graphics package  62. A data threshold was set for each individual map based on achieving a balance between structural detail and background noise. The Segger tool was used to split the volume map into distinct segments which were then isolated and coloured to aid visualization.
Acknowledgements We are very grateful to the families with PCD who have participated in this study and to the  UK PCD Family Support Group  for their support. We thank  Celia D. Delozier ,  R. Lazor  and  Jane Hurst  for family recruitment and their present or past involvement in the project. We thank  Andrew Rogers  and  Emily Frost  from the  Royal Brompton Hospital  and  Dale Moulding  ( UCL-GOS-ICH Confocal Microscopy Core Facility ) and  Kim Harrison-Lavoie  ( UCL ) for experimental assistance. We thank  Federico A. Santoni  ( University of Geneva School of Medicine and University Hospitals of Geneva ) for bioinformatics expertise. We thank  Shiaulou Yuan  ( Department of Pediatrics, YSM ) for assistance with high-speed microscopy and  SueAnn Mentone  ( Department of Cellular and Molecular Physiology, YSM ) for advice regarding TEM.
A. B. is an  NIHR Senior Investigator  and additionally was supported by the  NIHR Respiratory Disease Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust and Imperial College London. Work by A. S. is independent research funded by a postdoctoral research fellowship from the  National Institute of Health Research and Health Education England , and the views expressed in this publication are those of the authors and not necessarily those of the  NHS , the  National Institute of Health Research  or the Department of Health. Z. S. and S. K. are supported by  NIH  Grants  R01HL125885-01A1  and  R01 DK092808-01A1. J. -L. B. is supported by a grant from the  Swiss National Science Foundation  (# 32003B_135709 ). J. -L. B. , L. B. , C. O. and S. A. are supported by grants from the  Milena Carvajal Pro-Kartagener Foundation of Geneva ; J.
Similar strategy has been applied to biochemical pathway information  [12]. Co-expression gene networks have also been used in this context  [13] [14] [15] , as they offer two main advantages over proteinprotein interactions and biochemical pathways. First, they can be inferred from transcriptomic datasets, which are more abundant than protein-protein interaction datasets. Second, they allow functional annotation of the various classes of RNA genes. We have previously shown that multi-species information improves gene network reconstruction  [16]. In order to further explore the potential of co-expressed gene networks to identify functional equivalents in complex homologous families, we collected transcriptomic data from mouse and rat liver samples. To minimise technical variation, we collected datasets generated using a single microarray platform in each species, resulting into 920 experiments in mouse and 620 experiments in rat.
Data Collection and Normalisation Microarray data for liver and heart samples in mouse and rat were collected from GEO, where data for mouse was generated using Affymetrix Mouse Genome 430 2. 0 Array, and data for rat was generated using Affymetrix Rat Genome 230 2. 0 Array as they were the platforms with a large number of experiments available for each species. Liver experiments came from 62 (mouse) and 28 (rat) independent studies or GEO series. Heart experiments came from 20 (mouse) and 19 (rat) independent studies or GEO series. The GEO accession numbers for individual studies are provided in Supplementary Table  1. Processed data was not directly comparable between studies, as different studies used different normalisation methods, leading to different distribution of values (Supplementary Fig.
1, A  and B , Supplementary Fig. 3, A  and B ). As some datasets had a trimmed lower quartile for reduction in noise by limiting the variability of lowly expressed genes, we applied lower quartile trimming on all datasets (Supplementary Fig. 1, C  and D , Supplementary Fig. 3, C  and D ). Specifically, we set the expression value of all probes belonging to the lower quartile to the value of the 25 percentile. We then applied quantile normalisation resulting into a uniform distribution of values for each experiment. To facilitate the comparison between mouse and rat data, we used liver mouse data as a target for quantile normalisation of heart mouse data and liver and heart rat data, using preprocess-Core functions normalize. quantiles. determine. target and normalize. quantiles. use. target [18].
Gene homology information was retrieved from the Homologen database  [20] , and probe orthology information was obtained using the R package annotationTools  [21]. Due to one-to-many homologs, rat probes and mouse probes intersections resulted into slightly different numbers for each species. Average of the two numbers was used to obtain Jaccard indexes. Jaccard index significance was obtained using the hypergeometric test, and P-values were corrected for multiple testing using Bonferroni correction. SCHype takes as input a list of conserved interactions which was generated as follows. First Spearman correlation coefficient between each pair of probes was obtained independently for both Mouse and Rat expression data. Pairs of probes with a correlation coefficient ≥ 0. 5 were selected. Then if orthologs of two connected probes were connected in the other species, they were kept as an SCHype input.
Scripts and Data Availability R scripts used for this analysis are available in a Github repository https://github. com/gdevailly/liver_mouse_rat. Normalised expression matrices, fold change matrices, as well as probe clusters (hierarchical clustering and SCHype clustering) are available through two Zenodo collections: https://zenodo. org/record/439483 (liver data) and https://zenodo. org/record/839015 (heart data).
Identification of Variable Genes Across Datasets We downloaded 920 and 620 experiments for gene expression data in rat and mouse liver from the GEO database. We firstly normalised the data using lower quartile trimming (Supplementary Fig. 1, C  and D ) and quantile normalisation (Supplementary Fig. 1, E  and F ) independently for each species. We then selected the probes with dynamic expression across samples (standard deviation ≥ 1). This resulted into 3777 probes in mouse (8. 4%) and 2116 probes in rat (6. 8%), with a wide range of expression values (Fig. 1 ). 735 mouse variable probes out of 3777 had a homologue in rat variable probes, and 624 rat variable probes out of 2116 had a homologue in mouse variable probes. Variable genes were enriched for pathways and functions related to liver biology (Table  1 ), including metabolism of lipid an protein (rat, adjusted P value ≤10 -4 ), regulation of cholesterol biosynthesis by SREBP (mouse and rat, respectively adjusted P value ≤ 0.
Co-clustering of Mouse and Rat Expression Data To identify clusters of homologous probes between mouse and rat, we used the hyper-graph clustering tool SCHype  [17]. SCHype uses a recursive spectral clustering algorithm to identify sets of  nodes in each species with a greater than expected number of conserved interactions (based on co-expression in this case) between them (Fig. 3A ). Input data for SCHype was built using three graphs: a mouse probe graph built from pairs of probes with a Spearman correlation coefficient ≥ 0. 5 (Supplementary Fig. 2A ), a rat probe graph with pairs of probes with a Spearman correlation coefficient ≥ 0. 5 (Supplementary Fig. 2B ), and a probe to probe homology graph between rat and mouse built using the Homologene database  [20]  and the annotationTools package  [21].
Co-clustering Across Species as Source of Information for Inferring Shared Functionality Amongst Orthologs SCHype clustering successfully identified clusters of homologous genes co-expressed in both mouse and rat datasets. This information adds an independent evidence in support of a functional annotation transfer for pairs of orthologous genes across species found in the same SCHype cluster(s), as functionally equivalent orthologs would be co-expressed with the same set of genes in both species, and therefore would be included in the same SCHype cluster(s). We investigated if SCHype clusters could help identify functionally equivalent orthologs amongst complex homology groups. Eighteen homology groups of three members or more had at least one member of each species in the same SCHype cluster(s).
Functional Annotation Transfer Across Rat and Mouse Using Heart Transcriptomic Data To test whether the approach described above was extendible to other tissues, we collected expression data in heart for mouse and rat. Data from heart samples processed using the same microarray platform were downloaded from GEO, for a total of 248 experiments from 20 studies in mouse and 1202 experiments from 19 studies in rat. Same data processing pipeline as for the liver samples (Supplementary Fig. 3 ) resulted into selection of 7371 (mouse) and 917 (rat) variable genes for clustering (Supplementary Fig. 4 ). The large difference between the selected number of genes might be due to the large difference in the number of samples available (and therefore used in analysis) for each species. Functional enrichment analysis of the variable genes revealed pathways related to the heart functions (Supplementary Table  4 ), confirming that at least part of the gene expression variability was reflecting biological differences.
Hierarchical clustering of the mouse and rat dataset separately revealed clusters of probes and clusters of experiments (Supplementary Fig. 5 ). Notably, experiments from a same series (GEO) were split in distinct clusters. Co-clustering of the mouse and rat co-expression network in heart, using Homologene homology information and the SCHype hypergraph clustering tool, identified clusters of homologous genes co-expressed in both species (Supplementary Fig. 6 ). Notably, this provided an independent evidence in favour or against shared functionality for 12 complex orthology groups (Supplementary Table  5 ). In details, for 6 groups all homologous genes where found in the same SCHype cluster (i. e. were in homologous co-expressed gene networks in both species). For 3 one-to-two homology groups (Ifit3, Ogn and Ppp1r3c), only one of the two paralogs was included in the same SCHype cluster(s) as the ortholog copy, suggesting a loss or change of functionality for the second paralog.