dataesr/scientific-paragraphs-categorization huggingface dataset:
59). The strong correlation between Mendeley bookmarks and citations from citation databases is not surprising, since researchers, in particular graduate students, bookmark titles in order to cite them. Mendeley bookmarks are also moderately correlated with the number of OAPEN downloads, as well as with the number of domains mentioning a title and socialnetworks mentions. The average monthly downloads from the OAPEN repository and the number of citations on Wikipedia are not strongly correlated with other metrics. The following correlations had p-values above 0. 05: OAPEN average downloads, Twitter, Wikipedia, and Facebook with all data sources. The only exception for Facebook was the correlation between Facebook mentions and Mendeley bookmarks, which had a correlation coefficient of 0. 29 with p = 0.
03. Only p-values below 0. 05 were accepted as significant for the correlations between Mendeley bookmarks, Goodreads ratings, OAPEN downloads, number of domains mentioning titles, and citation numbers from Google Scholar and WoS. In summary, strong correlations are observed between citations on Google Scholar, citations on WoS, Mendeley bookmarks and OAPEN downloads. In addition, OAPEN downloads correlated with the number of domains mentioning titles, and number of Goodreads ratings. Among the book review and rating sites Goodreads had the most ratings of the titles (49), covering 16 books. These data displayed a weak correlation with citation databases (0. 19 with WoS and 0. 29 with Google Scholar, both having p < 0. 01) and the Mendeley data (0. 37, p < 0. 01). A similar correlation was obtained by  Zuccala and colleagues (0.
Conclusion This study demonstrates the potential for altmetrics approaches to provide useful insights into the ways in which OA monographs are being used by communities. However, in order to access these benefits, altmetric studies of books need to engage with book specific platforms; adopt a title and author name based, rather than identifier or URL based, approach to searching for social media mentions related to titles. Currently, most social media platforms make their usage data publicly available. Since these platforms use either DOI or ISBN as identifiers, there is a need to have both identifiers to collect data. Crossref metadata API (http://api. crossref. org/) can be used to find and convert ISBN and DOI identifiers. Using the DOI identifier it is possible to collect social media and citation data from Crossref or altmetric.
com platforms. Open source tools are also available to download data from these platforms, such as the tools provided by rOpenSci initiative (https://ropensci. org/). A key challenge in engaging with repository and platform data related to inconsistent approaches to classifying the subject of individual titles. Subject classification approaches range from the very general to the very specific, making comparisons between platforms difficult. Using OCLC 7 subject classifications provides a practical strategy for overcoming this issue. OCLC provides Dewey Decimal Classification (DDC) and Library of Congress Classification (LCC) numbers for books identified via ISBN. The platform offers these classifications from their Classify API service 8 based on bibliographic records they collect from libraries.
(Li et al. 2012) 2012)made for 20 titles. Most bookmarks for these titles were made by graduate students (159) which is in accordance withHaustein and Larivière's work  (2014)on readership counts on Mendeley, where they examined 1. 2 million documents published in journals from four disciplines (biomedical research, clinical medicine, health and psychology). They found that approximately two thirds of these documents were bookmarked by at least one user on Mendeley. The majority of these users were PhD students, postgraduate students and postdoctoral researchers. The citation database Google Scholar and Web of Science showed high coverage of the 28 titles, and high coverage with each other. This indicates that Google Scholar, as a free citation database, is a viable data source in monograph research.
This study also showed a significant correlation between Mendeley and citation databases (0. 59 with Google Scholar and 0. 51 with WoS, both having p < 0. 01). These findings are in accordance with a study conducted by Bar-Ilan and colleagues, where they sampled 1,136 unique papers authored by 57 presenters who attended the 2010 Leiden Science and Technology Indicators (STI) conference(Bar-Ilan et al. 2012). Of these 1,136 documents indexed in Scopus, they found a significant correlation (r=0. 45) between an article's number of bookmarks in Mendeley and number of citations in Scopus. In another study, which involved 1,613 papers published in Nature and Science in 2007(Li et al. 2012), positive correlations.
1d, e ). All variants were predicted pathogenic by commonly used in silico software (Polyphen-2, Mutation Taster, SIFT and CADD; Supplementary Fig. 1f ) and co-segregated with the phenotype in each pedigree (Fig. 1a-d ). Of note, we found two other de novo variants of unknown significance in patients: one hemizygous variant in ARHGAP4 (chromosome X) in P1 that also segregated in his healthy brother and one de novo in UBR3 (NM_172070. 3, c. 5023G>C; p. Glu1675Gln) in P4, that showed a weak pathogenic score based on in silico predictions. None of the four KIF21B variants is reported in public databases, including dbSNP, 1000 Genomes and gnomAD. Overall, we identified variants in KIF21B gene in four patients presenting with mild to severe neurodevelopmental delay associated with heterogeneous brain malformations (Table  1 , Supplementary Note 1).
Discussion Our findings highlight the critical role of KIF21B in the regulation of processes involved in cortical development and implicate variants in KIF21B in ID and brain malformation. We identified three missense variants and one duplication of four nucleotides. The duplication leads to a frameshift introducing a premature termination codon in exon 20. The resulting mutant mRNA is likely degraded by nonsense-mediated mRNA decay. Although we demonstrated in mice that Kif21b haploinsufficiency leads to an impaired neuronal positioning (Supplementary Fig. 3d-f ), the p. Asn988Serfs*4 protein truncated variant is possibly not pathogenic. Indeed, hKIF21B gene might partially tolerate loss-offunction variants: in the gnomAD (Genome Aggregation Database, v2. 1. 1 "non-neuro") populations that is supposed to be depleted in severe pediatric conditions, 28 loss-of-function variants have been reported.
Exome libraries were sequenced on an Illumina HiSeq 2500 instrument and the following sites are used to search for previously described gene pathogenic variants and polymorphisms: the Human Gene Mutation Database (HGMD), the single Nucleotide Polymorphism database (dbSNP), 1000 genomes, HapMap data. Patient 3: Exome capture was done using the Nimblegen SeqCa-p_EZ_Exome_v3 (Nimblegen). Exome libraries were sequenced on an Illumina HiSeq instrument (Illumina, San Diego, USA) with 150 bp paired-end reads at a median coverage of 100×. Sequence reads were aligned to the hg19 reference genome using BWA. Variants were subsequently called by the GATK unified genotyper, and annotated using a custom diagnostic annotation pipeline. Patient 4: Exome was captured using the Clinical Research Exome kit (Agilent Technologies, Santa Clara, CA).
Primary and secondary coupled HRP antibodies used for western blot are described in Supplementary Table  1. Relative protein expression was quantified using ImageJ software (Java 1. 8. 0_112). Cycloheximide (CHX) treatment. To assess the protein half-life of WT-and mutated hKIF21B proteins, treatments using the translational inhibitor cycloheximide (CHX) were performed. N2A cells were cultured on 6-well plates and transfected with the adequate NeuroD-hKIF21B constructs using Lipofectamine 2000 (Invitrogen) according to the manufacturer's protocol. The day after, cells were treated with CHX (Sigma) diluted in media at 10 µg/mL for either 2, 4, 6, 8 or Human AGGCGAACCTCTCGGCTTTC TCATCATCACTAATCACGACGCC 10 h. Cells were lysed as described above. For analysis, 10 µg of protein of each sample were loaded on a SDS-gel followed by western blotting analysis as described above.
Experiments consisted of at least three independent replicates. Relative protein expression was quantified using ImageJ software (Java 1. 8. 0_112). Zebrafish manipulation. Zebrafish (D. rerio) embryos (AB strain) maintenance and experiments were performed as described here https://zfin. org/zf_info/zfbook/ cont. html#cont1. Human WT and mutant full-length cDNA were cloned into pCS2 vector and transcribed using the SP6 Message Machine kit (Ambion). We injected 1 nL of diluted RNAs (WT or mutants) at 100 ng/µL into wild-type zebrafish eggs at 1-to 2-cell stage. Length of the head was measured at 5 dpf as shown by the double arrow headed red lines (Fig. 5a ). All the experiments were repeated at least three times and an unpaired two-tailed Student's t-test or a Welch's two samples t-test (when the variances are unequal) was performed to determine significance.
All images were taken using a macroscope (Leica M420) dedicated to brightfield acquisitions equipped with a Coolsnap CF camera controlled by Coolsnap software. Image acquisition and analysis. Cell counting and in vivo branching analyses were done in at least three different brain slices of at least three different embryos or pups for each condition. After histological examination, only brains with comparative electroporated regions and efficiencies were conserved for quantification. For neuronal migration analyses, proliferation analyses and brain immunofluorescence experiments, a z stack of 1. 55 μm was acquired in 1024 × 1024 mode using a confocal microscope (Leica TCS SP5 equipped with an hybrid camera and a HC PL APO ×20/0. 70 objective) controlled by Leica Las X software v3. 7 and analyzed using ImageJ software (Java 1.
For in vivo branching analyses, a z stack of 3 μm was acquired in 1024 × 1024 mode using confocal microscope (Leica TCS SP5 equipped with an hybrid camera and a PL FL ×10/0. 30 objective) and analyzed using ImageJ software (Java 1. 8. 0_112). For axonal midline crossing analyses, the fluorescence within a box of fixed size placed at the contralateral side of the midline of the brain section was measured and divided by the fluorescence at the ipsilateral side of the midline area (Fig. 6c ). This value was normalized to empty vector (control) value. For projecting neurons analyses, the fluorescence intensity within a box of fixed size placed at the ipsilateral side of the midline of the brain section was measured using ImageJ (Java 1. 8. 0_112) and this value was divided by the number of Scarlet-positive neurons within a box of fixed size placed at the upper cortical plate (Fig.
6d ). For quantification of layer V (ipsilateral) collateral branching, a box of fixed size was drawn encompassing layer V and the fluorescence area within this box was measured. This value was then divided by the fluorescence at the ipsilateral side of the midline area (Fig. 7c ). We quantified contralateral branching 66 as follows: fluorescence within a box of fixed size placed contralaterally is measured on a vertical axis (from ventricular boundaries to pial surface) and converted from pixel size to percentile (0%: ventricular zone, 100%: pial surface) using the ProfilePlot plugin from FIJI software. Signal intensity of 100% is set as the maximum signal intensity in migration percentile 0 to 20 (considered as the white matter). For in vitro branching analyses, primary neuronal cultures were done independently at least two (DIV5) or three (DIV2) times.
DIV2 and DIV5 axonal length measures were performed in 125-146 and 30-66 independent cells respectively. For DIV5 axon collateral length distribution analyses, measures were performed in 187-385 collaterals from 30 to 66 independent cells. Images were acquired using upright fluorescence (DM 4000B; Leica) equipped with a HC PLAN APO20/×0. 70 objective and a CoolSnap FX monochrome camera (Photometrics) controlled by Leica Las X software v3. 7. To measure the axonal length (longest neurite) at DIV2 and DIV5, the longest Scarlet-positive-labeled neurite was traced and the length was measured using Simple Neurite Tracer plugin (https://imagej. net/Simple_Neurite_Tracer) from FIJI software. Branch number and length was measured at DIV5 using Simple Neurite Tracer plugin. For variant subcellular localization analysis (Fig.
1+/N-HA-hKIF21B constructs or empty vector with a DNA ratio of 1:2 as described above. Live-cell imaging was done 24 h after transfection. Live videomicroscopy was performed on an inverted microscope Leica CSU W1 DMI8 (Leica) with an Adaptative Focus Control (AFC) controlled by Metamorph software v 7. 6, using an HCX PL APO Lambda blue ×63/1. 40 oil objective or an HC PL APO 100 × 1. 47 oil objective. The microscope and the chamber were kept at 37 °C and 5% CO 2. Images were collected using an Orca Flash 4. 0 camera with an exposure time of 80-150 ms. Images were acquired every 250 ms for 30 s to 1 min. Single GFP-positive kinesin were tracked using Manual Tracking plugin (https:// imagej. nih. gov/ij/plugins/track/track. html) from Fiji software. For each condition, 182-203 kinesin particles in 25-32 independent cells were analyzed from at least three independent transfections.
BDNF vesicles velocities and RFP-positive mitochondria were obtained from kymograph analysis, using FIJI plugin Kymo Tool-Box v. 1. 01 (https://github. com/fabricecordelieres/IJ-Plugin_KymoToolBox/ releases). For each condition, 250-322 BDNF particles in 10-19 independent cells were analyzed from at least three independent transfections. For each condition, 173-256 Mito-RFP particles in 19-31 independent cells were analyzed from four independent transfections. Statistics and reproducibility. Immunofluorescence, in situ hybridization, FACS-sorting and western blot experiments (Figs. 2c-e and 3e; Supplementary Figs. 2a-d, 3c , 4c, 5b) were repeated three times independently and gave similar results. Experiments on Kif21b WT and knockout mouse brain (Supplementary Fig. 2e, f ) were performed on n = 3 and n = 5 different brains per genotype, respectively, and gave similar results.
Western blot expression profile of NeuroD-IRES-GFP constructs (Supplementary Fig. 3a ), HA-tagged, Myc-tagged (Supplementary Fig. 4a ) and GFP-tagged constructs (Supplementary Fig. 4b ) were performed once. For in utero electroporation experiments, only brains with comparative electroporated regions and efficiencies were conserved for quantification and statistical analyzes. Analysis of IUE experiments were performed blinded. For one embryo or pup brain, cell counting and in vivo branching analyses were performed in three different slices. The exact numbers (n) of samples, animals, cells, particles, axons or branches used to derive statistics are mentioned in figure legends along with the respective data and are also reported in Supplementary Data 1. The number of times each experiment was repeated independently (i.
Acknowledgements This work was funded by grants from  INSERM (ATIP-Avenir program , J. D. G. ), the  Fyssen foundation  (J. D. G. ), the  French  state funds through the  Agence Nationale de la Recherche  under the project  JCJC CREDO ANR-14-CE13-0008-01  (J. D. G. ), CILAXCAL (C. D. ) and AXYON  ANR-18-CE16-0009-01  (F. S. ), and the  program Investissements d'Avenir labeled  ( ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT , to J. D. G. and C. G. ), the Fondation pour la recherche sur le cerveau (F. S. ),  INSERM/CNRS  and  University of Strasbourg. N. A. H. and G. Z. are supported by  USDA  Grant Number  3092-51000-057-04S. L. A. and J. R. A. are funded through the  IGBMC PhD program  ( ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ). L. A is currently supported by  Fondation pour la recherche médicale  ( FDT201805005184 ).
16, P = 0. 66; Table  S17 ). The relative abundance of the Aerococcaceae was very similar in the immune-challenged and non-immunechallenged workers, pointing to a possible coevolution between these bacteria and their ant host. Four OTUs were assigned to the Aerococcaceae, but only one was common. An online BLAST search of this OTU sequence against the 16S bacterial/archaeal NCBI database gave the highest identity (100%) with a lactic acid bacterium, Dolosigranulum pigrum; so it is possibly this bacterium or a related species. Lactic acid bacteria are regularly found in insect guts, though most often from the family Lactobacillaceae  [39]. One operational taxonomic unit (OTU) of the genus Pseudomonas (Pseudomonaceae) was present in every single gut community. The genus Pseudomonas is known for its metabolic diversity and ability to colonize many niches, including guts.
We sequenced the fat body transcriptomes of eight workers from each of our four treatments, resulting in 32 sequenced samples of 45 million read pairs each (Fig. 1 ). The raw reads were trimmed using Trimmomatic-v0. 36 [78]  and quality checked using FastQC-v0. 11. 5. Subsequently, all paired reads were assembled de novo using Trinity (trinityrnaseq-Trinity-v2. 4. 0)  [79]. The final assembly contained 150. 423 contigs, a mean length of 1128 bp, and a mean back mapping rate of 70. 65% (details in supplement, Table  S3a ). We conducted a blastX search against the nonredundant invertebrate protein database (state September 2017) to annotate the contigs. About 50. 42% were annotated in Blast with 24. 15% having a unique Blast hit (contig list and Blast annotation Table  S3b ). After translation of nucleotide sequences into amino-acid sequences with Transdecoder-v3.
0. 1 (https://github. com/TransDecoder), the gene ontology (GO) and the Kyoto encyclopaedia of genes and genomes (KEGG) term annotation were performed using InterProScan-v5. 25-64. 0 [80]. Read count per contig and sample was obtained by using RSEM-v1. 3. 0  [81] , and we completed a differential gene expression analysis with the R package of Deseq2-v1. 2. 10  [82]. We separately tested the effects of fertility induction and immune challenge, and their interaction, while controlling for colony identity. Additionally, among the contigs that were significantly influenced by the fertility x immunity interaction, we compared the different subgroups (any combination of two factors): i) fertile versus infertile workers, separately and successively, within the immune-challenged workers, and within the non-immune-challenged ones; and ii) immunechallenged versus non-immune-challenged workers, separately and successively, within the fertile workers, and within the non-fertile ones.
The enrichment analysis was done for all contigs affected by the interaction through completing separate tests for each factor level. The contigs that were upregulated in response to one treatment irrespective of the other treatment (e. g. upregulated in fertile workers compared to infertile ones irrespective of the immune challenge). We used the R package TopGo  [83]  for the enrichment analysis, and the p-values for each GO term were obtained with a Fisher's exact test. In order to identify gene networks, a gene co-expression analysis was performed with WGCNA (Zhao et al. 2010) on the top 20,000 contigs with the highest across sample variance (details see supplement). Finally, GO terms enrichment analyses were performed on the contig list of each of the co-expression modules, as described above.
We used an industrial kit (MasterPure™, from EpiCentre, Wisconsin, USA) for DNA extraction (details in supplement). The amplification and sequencing of the 16S V4 region was performed by StarSEQ GmbH, Mainz, Germany. Amplification was done with the 515f-806rB primer pair. Overlapping, paired-end reads of 250 bp were generated with Illumina MiSeq. De-multiplexing, adapter trimming and quality filtering was done following the normal Illumina MiSeq workflow by StarSEQ. The initial data set contained 505. 050 read pairs (available online see data accessibility section). We mainly used the Uparse 9. 2 pipeline  [84]  to determine operational taxonomic units (OTUs). Paired-end reads were merged (on average 86. 2% ± 2. 7 SD could be merged per sample) and subsequently filtered (maxEE cut-off set to 1).
The "-fastx_uniques" step was used to dereplicate filtered reads, after which we used the "-cluster_ otus" step to cluster the sequences into OTUs with a threshold of 97%. This latter step also removes chimeras. Next, the merged, unfiltered reads were mapped to the OTUs with a 97% identity cut-off with the "usearch_global" algorithm (on average 97. 7% ± 1. 1 SD were mapped to the OTUs per sample). Using Qiime 1. 9. 1  [85] , we allocated taxonomic groups to the OTUs, the Greengenes database gg_13_8 as a reference. Unassigned OTUs and OTUs classified as chloroplasts or mitochondria were removed from the OTU table, but archaeal OTUs were left in. If an OTU was not at least five times more abundant in any experimental sample than in a control sample, it was discarded. Additionally, we excluded OTUs that did not make up at least 0.
1% of the community in at least one sample to further eliminate contaminations. The resulting OTU table contained 378 taxonomic units. Total bacterial 16S copy numbers per ng DNA were quantified by quantitative realtime PCR (qrt-PCR) with universal 16S primers (details see supplement). The total DNA concentration of each sample in ng/μl was determined with a Qubit Fluorimeter (Thermofisher). For each replicate, we calculated the amount of bacterial 16S copies per ng of DNA in a sample. The means of the replicates were log transformed and subsequently compared between non-immune-challenged and immunechallenged individuals using linear mixed-effects (LME) models from the R package nlme, including colony as random effect. The OTU table was rarefied to a depth of 1500 reads. Using the rarefied table, we calculated the pairwise Bray-Curtis dissimilarities between the samples in Qiime.
Samples were clustered based on their Bray-Curtis dissimilarities (average linkage method, 1000 bootstraps) with the pvclust package in R  [86]. Clusters assigned AU values higher than 98. 5 are highly supported. The OTUs were binned according to microbial family and the relative abundance of each microbial family was calculated for each sample. This data was used to generate a stacked bar plot, which was combined with the pvclust tree plotted with the R package ggtree  [87] , to visualize the differences in community composition between samples. To compare the bacterial diversity between the treatments for each sample we calculated the Shannon's diversity index with the alpha_diversity. py script of Qiime using the rarefied table. Additionally, we computed Simpson's diversity index, which gives more weight to common species than Shannon's diversity, with the diversity function of the R package vegan.
We tested for an effect of fertility, immune challenge and their interaction on both measures of diversity with LME models: The fragment and the colony of the ant were included as a nested random effect term. Simpson's diversity index was arcsine square roottransformed to improve normality. Model residuals were visually checked for deviation from normality. To identify taxonomic groups that differed in relative abundance between treatments we binned the OTUs into their respective microbial families. To restrict the analysis to the more important taxa, the microbial families that were present in less than 15% of the samples were removed from the OTU table. For the differential abundance testing we performed permutation tests as implemented in the fitLogNormal function of the meta-genomeSeq R package  [88] , using 100 permutations.