dataesr/scientific-paragraphs-categorization huggingface dataset:
Across six recent publications reporting on de novo variation in large cohorts with DD and/or ID or autism,  [27] [28] [29] [30] [31] [32]  two protein-altering BRSK2 variants were reported: GenBank: NM_001256627. 1 (c. 992_994del, [p. Lys331del]) was found in a cohort of 2,500 probands with autism,  30  and GenBank: NM_001256627. 1 (c. 770G>A, [p. Arg257His]) was found in a cohort of 4,293 DD-and/or ID-affected probands. 28 Interestingly, this second variant has been observed as a heterozygote seven times in gnomAD, suggesting it is not a highly penetrant allele contributing to DD and/or ID. These data raise an interesting question, namely as to why the frequency of observed BRSK2 variation in this study is markedly higher than that found in previous studies. This is particularly true for the HudsonAlpha CSER study,  6  in which four variants were found among 581 affected probands (461 of whom were sequenced as trios).
Additionally, considering only exomes, we compared coverage of BRSK2 exons to exons in other disease-associated genes that are annotated in the Development Disorder Genotype-Phenotype Database (DDG2P). Although, again, only 76% of samples have at least half of BRSK2 bases covered at 203 in gnomAD exomes, 99% of samples have half or more of the bases in previously reported DDG2P genes covered in gnomAD exomes (Figure  3B ). Thus, we find it likely that the lower rates of BRSK2 variation found in other DD and/or ID studies reflects, at least in part, reduced variant sensitivity of exome sequencing in BRSK2. We have identified nine individuals harboring rare, heterozygous BRSK2 variants that are likely to be deleterious, and we provide detailed clinical descriptions of the.
Computational Modeling of BRSK2 Missense Variants A full model of BRSK2 was created with I-TASSER modeling using PDB: 4YOM, 4YNZ, and 4IW0. This model of BRSK2 was combined via ConSurf mapping with sequences for BRSK2 from 99 species. Amino acid coloring is as follows: gray ¼ not conserved, yellow ¼ conserved hydrophobic, green ¼ conserved hydrophilic, red ¼ conserved polar acidic, blue ¼ conserved polar basic, and magenta ¼ conserved human variants of interest. Zoomed-in views of the three locations are shown, along with codon usage throughout evolution. The conservation score is defined as an additive metric of amino acid conservation and codon selection as previously defined. 12 For example, a conservation score of 2 indicates 100% conservation with >2 standard deviations above the mean for codon selection.
Acknowledgments We thank all of the families who participated in the study. This study was supported by: a grant from the  National Human Genome Research Institute (NHGRI) ,  UM1HG007301  (S. M. H. , M. L. T. , J. M. J. L. , D. E. G. , E. M. B. , and G. M. C. ); the  Radiz-Rare Disease Initiative Zurich, Clinical Research Priority Program University of Zurich  (A. R. ); the  French Foundation for Rare Diseases  (C. N. ); the  National Institutes of Health (NIH) Office  of the Director, K01ES025435 (J. W. P. ); and the  Fondation Maladies Rares  (C. N. , C. D. , and C. M. ).
RNA purity and yield (A260/A280 N 1. 8, A260/A230 N 2, yield N100 ng/μL) was determined using NanoDrop-1000 Spectrophotometer (NanoDrop Technology, Wilmington, DE) and quality (RIN N 7) was assessed with Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA) using RNA Nano LabChip Kit (Agilent Technologies). Photometric parameters and RNA integrity number determined the quality of the RNA sequenced samples. Strand-specific TruSeq™ RNA-seq pair-end libraries with 350 bp fragment size were prepared for each treatment (three biological replicates). For each sample ca 30 × 10 6 reads (read length 150 bp) were sequenced using two lanes of Illumina HiSeq 4000 (Norwegian High Throughput Sequencing Centre in Oslo, Norway), and made available on ArrayExpress (accession E-MTAB-8004). Sequenced reads were mapped to the Ensemble reference genome WBcel235 using STAR  (Dobin et al.
Pathway and network analysis For predicted pathway and biological function analyses of DEGs, SimpleMine  (Lee et al. , 2017) , Reactome Knowledgbase  (Fabregat et al. , 2017)  and KEGG Pathways (Kyoto Encyclopedia of Genes and Genomes)  (Kanehisa et al. , 2018 ) tools were used. The analysis was performed on the total number of DEGs for each of the exposure groups and the most significant categories found in each of the databases were compiled and subsequently manually curated in order to obtain annotations of the cellular and molecular processes affected by exposure to gamma radiation. Gene interaction analysis was performed using GeneMANIA 3. 5. 1  (Warde-Farley et al. , 2010; Franz et al. , 2018)  within Cytoscape 3. 7. 1 to identify predicted networks based on the total DEGs resulting from the 100 mGy•h -1 exposure.
Forty randomly chosen cells per replicate (160 cells per biological replicate, total of 480 cells per dose rate) were scored using the Comet IV analysis software (Perceptive Instruments Ltd. , Bury St. Edmunds, UK). Tail intensity (% Tail DNA), defined as the percentage of DNA migrated from the head of the comet into the tail, was used as a measure of DNA damage induced by gamma radiation. Mean percentage (%) of DNA in the tail per exposure group was calculated using the median values of % Tail DNA from the 40 comets from each technical replicate (total of 12 median values per exposure group).
Statistical analysis Statistical analysis was performed using Minitab® 18 (Minitab Statistical Software (2010). [Computer software]. State College, PA: Minitab, Inc. (www. minitab. com)), JMP Pro v14 (SAS institute, Cary, NC, USA) and SigmaPlot 10. 0 (Systat Software, San Jose, CA). Significant differences between different treatments were calculated using oneway analysis of variance (ANOVA) and, when significance was found, the Tukey pairwise comparisons method was applied. For ANOVA analysis, normality and homogeneity assumption were assessed on residuals by using Anderson-Darling normality test and visually on residuals vs. fitted value plot, respectively. Statistical significance was considered when p-value was lower than 0. 05, unless differently stated. The Effective Dose-Rate estimations were obtained on 10 and 50% of the population (EDR10 and EDR50) for reproduction and DNA damage on embryonic cells, by using the free software RegTox developed by Eric Vindimian (http://www.
Functional enrichment analysis of DEGs Gene function analysis of DEGs assessed by Gene Ontology (GOTERM) enrichment showed distinct differences in functionally enriched categories between the 10 and 100 mGy•h -1 exposures. A total of 21 significantly over-represented Biological Functions were identified for the 10 mGy•h -1 group (Fig. S. 4). Integrated pathway analysis combining the outputs from Simplemine, Reactome and KEGG databases corroborated the enrichment analysis from the 10 mGy•h -1 exposed group with respect to cuticle-collagen, protein and lipid metabolism (Table  S. 4). In addition, we found 10 genes with functions related to biological oxidation and Glutathione metabolism and 45 genes related to Immune system, Signal transduction, Peroxisome and Response to pathogens. A total of 18 GOTERMs were significantly over-represented among the down-regulated genes in the 100 mGy•h -1 exposure group (Fig.
PMA binds to DNA, making the DNA unavailable for amplification during PCR steps  [22]. Due to its higher molecular weight and/or charge, PMA cannot penetrate into cells that have an intact cell membrane (i. e. , viable) but can bind to free floating DNA or DNA inside cells with a compromised cell membrane (i. e. , dead cells)  [22, 23]. It is in this way that many studies have utilized PMA to distinguish between intact/viable cells and compromised/dead cells  [2, [24] [25] [26]. This comprehensive analysis of the ISS microbiome was used to assess how microbial communities change over time (temporal distribution) and throughout the ISS (spatial distribution). In addition, the ISS environmental microbiome data were compared with other Earth built environmental microbiome data such as the Earth Microbiome Project  [27] , Hospital (  [28] , Qiita study 10,172), and Office microbiome (  [28] , Qiita study  10, 423).
Results Twenty-four surface wipes were collected from eight locations across the ISS during three flight missions over the course of 14 months. In addition to these 24 wipes, wipes that were taken out of the kits and exposed to the ISS environment, but not used for sampling, were designated as controls and processed in parallel with the sample wipes. A summary of the sampled locations and the associated metadata is presented in Fig. 1  and Table  1.
Bacteriome analysis After processing the raw data from 48 samples (24 PMA samples and 24 non-PMA-treated samples) and various wipe and reagent controls, amplicon sequence variants ("ASVs") (a higher resolution analogue of the ubiquitous "OTU")  [33]  that had a cumulative sum of more reads in the controls than the cumulative sum of all the samples, were removed from the dataset. Next, ASVs that were found to be statistically significantly higher (ALDEx2 test, P < 0. 05) in the control group than in the sample group were further removed from the dataset. Additional file 2: Dataset 1A summarizes the ASV read count in each sample and in each control after the above quality control measures were implemented. Next, the program "SourceTracker" was used to predict what percent of reads in the samples were unique to the samples and what percent were from "contaminating" ASVs (i.
e. , those were represented in a Dirichlet model trained from the control samples). Additional file 1: Figure  S2B  summarizes the results from SourceTracker and shows that contamination was negligible in 32 out of 48 samples and for the remaining 16 samples; the contamination was less than 6% of the total sequences. A canonical correspondence analysis (CCA) plot verifies that the bacterial communities of the controls were indeed different than those of the samples (Fig. 4 ). The ASV table used for downstream analysis, after the above quality control measures were implemented and after verification that the samples represented a unique microbiome, different than that of the controls, is presented in Additional file 2: Dataset S1A. A summary of read counts, number of ASVs, and most abundant taxa in the samples are presented in Additional file 3: Table  S1.
Mycobiome analysis Amplicon sequencing of the fungal ITS region was performed on samples collected during F1 and F2. Since F3 exhibited low abundance of cultivable fungal counts, it was not possible to generate amplicons for further sequencing. Similar to what was done with bacterial sequences, OTU counts that were higher across controls compared to samples were removed from the dataset. For one OTU, even though the cumulative read count of the controls was 33 times lower than the samples, since the count was 300,000, it was removed from the dataset. Additional file 2: Dataset S1B shows the fungal OTU table that was used for analyses after OTUs associated with controls were removed. The SourceTracker results for the mycobiome are shown in Additional file 1: Figure  S6A  and the total OTU read count for the sample and control wipes is presented in Additional file 1: Figure  S6B.
Using the unrarefied data, the unique sub-operational taxonomic units (sOTUs) present in the ISS data were also compared with other built environment datasets collected on various locations of Earth to determine whether any sOTUs appear to be unique to the ISS. For this analysis, only Flight 3 data were included since all the other built environment datasets used the same primer set. The sOTUs observed in the ISS controls were removed. There were four sOTUs that appear to be unique relative to all the built environment datasets analyzed (see Additional file 4: Table  S4 ), although they accounted for a very small total amount of the sequence mass (~0. 0005% of the reads). These unique sOTUs exhibited high identity to Bacteroides sp. , Gottschalkia acidurici, Paenibacillus thailandensis, and Thermus thermophilus based on BLAST to nr/nt  [34].
In total, 68 different family level taxa were detected but only the most relatively abundant taxa are listed in the legend. A full list of organisms detected can be found in Dataset S1. Those sequences that could not be resolved to the family level are prefixed with either "o" for Order or "c" for Class A B Fig. 6  Temporal and spatial distribution of the ISS microbiome over 14 months and across eight locations. Boxplots show the temporal (a, b) and spatial (c, d) distribution of the most relatively abundant family level taxa (as presented in Fig. 4 ). The box in each graph signifies the 75% (upper) and 25% (lower) quartiles and thus shows the percent abundances for 50% of the samples (N = 8). The black line inside the box represents the median. The bottom whisker represents the lowest datum still within the 1.
(ii) A metagenomics analysis was performed using the same DNA samples as for amplicon sequencing and all three flights were sequenced simultaneously and without multiple displacement amplification prior to sequencing. The family level barplot for this metagenomics data in Additional file 1: Figure  S11  shows the same pattern distribution of taxa, as presented in Additional file 1: Figure  S3  for the amplicon sequencing. (iii) Lastly, all statistical analyses were performed with ALDEx2 which estimates per-feature technical variation within each sample using Monte-Carlo instances drawn from Dirichlet distributions. ALDEx2 uses the centered log-ratio transformation that ensures that data are scale invariant and sub-compositionally coherent meaning that all samples are numerical consistent with each other, regardless of the total sequencing read capacity at the time of sequencing  [70, 71].
Illumina sequencing -Bacteria Flight sampling 1 and 2 Bacterial diversity was assessed by analyzing the V4 hypervariable region of the 16S rRNA gene coding sequence. Amplification was performed with the following primer pair: forward primer, A519F (new nomenclature: S-D-Arch-0519-a-S-15), 5′-CAG CMG CCG CGG TAA-3′, and the reverse primer 802R (new nomenclature: S-D-Bact-0785-b-A-18,) 5′-TAC NVG GGT ATC TAA TCC-3′  [83]. Expected amplicon size is 283 for Bacteria as estimated for 16S rRNA gene sequences deposited in the Silva SEED Reference Database  [84]. Fungal diversity was assessed by analyzing the ITS1 region between 18S and 5. 8S rRNA coding sequences. Amplification primers were ITS1-F_KYO2 (5′-TAG AGG AAG TAA AAG TCG TAA-3′) and ITS2_KYO2 (5′-TTY RCT RCG TTC TTC ATC-3′)  [85]. Expected amplicon length distribution is 271 ± 90 bp for Ascomycota, 284 ± 42 bp for Basidiomycota, and 216 ± 94 bp for non-Dikarya species  [86].
Final cleaned pools were then sequenced on a HiSeq 2500 2 × 150 bp Rapid Run. Illumina sequence processing-Bacteria (flight 1, 2, and 3) For F1 and F2 samples, the forward reads were de-multiplexed by using fastq-multx v. 1. 02. 772, a tool from ea-utils software package  [88] , with the forward amplification primers for prokaryotes as search targets. The reads were further processed to remove all remaining sequences of the amplification primers and the Illumina TruSeq adapters from their 3′-ends using consecutively fastq-mcf v. 1. 04. 807 program  [88]  for exact sequence search, and agrep (http://www. tgries. de/agrep/) and treagrep (0. 8. 0: https://github. com/laurikari/tre/) programs for search allowing up to three mismatches between the primers/adapters and the reads to accommodate for sequencing errors.
The F3 reads were demultiplexed and adaptors removed using Qiita (http://qiita. ucsd. edu) using the parameters max_barcode_errors: 1. 5; barcode_type: golay_12; and phred_quality_threshold: 3. The demultiplexed reads for F1, F2, and F3 were then processed using the DADA2 pipeline, trimming the 3′ end of the forward reads to a length of 130 bp, and setting the filter parameters to maxN = 0, maxEE = 2, trunQ = 2, and rm. phix = True. The DADA2 pipeline (https://benjjneb. github. io/dada2/index. html) w a s followed to obtain an amplicon sequence variant table ("ASV" table), a "higher resolution analogue of the ubiquitous OTU table". Taxonomy was assigned used the SILVA reference database.
Illumina sequence processing-Fungi (Flight sampling 1 and 2) The forward reads were de-multiplexed by using fastq-multx v. 1. 02. 772, a tool from ea-utils software package  [88] , with the forward amplification primers fungi as search targets. The 5′-ends of the sorted reads were trimmed for a predetermined length based on the length of the corresponding amplification primer for each dataset. The reads were further processed to remove all remaining sequences of the amplification primers and the Illumina TruSeq adapters from their 3′-ends using consecutively fastq-mcf v. 1. 04. 807 program  [88]  for exact sequence search, and agrep (http://www. tgries. de/agrep/) and treagrep (0. 8. 0: https:// github. com/laurikari/tre/) programs for search allowing up to three mismatches between the primers/adapters and the reads to accommodate for sequencing errors.
After primers/adapters were removed, the processed reads exhibited multimodal length distribution. The reads from the fungal datasets formed three groups of 184-223 bp, 224-246 bp, and 246-282 bp length. This correlates well with known length variability of ITS sequences from different fungal phyla  [89]. Each of the three groups was separately subjected to the OTU clustering and taxonomy assignment procedures, and the results were merged together for further statistical treatment and visualization. ITS1 sequence clustering and taxonomy assignment were performed using USEARCH version 8. 1. 1756 [90]. For each collection of the related datasets, the OTUs were established by selecting high-quality reads with an expected error rate not exceeding 0. 5%. The selected reads were further de-replicated, sorted, clustered at the default 3% difference, and de-chimerized against the UCHIME reference dataset distributed by UNITE  [79].
Then, the reads from individual samples were filtered to exclude those with the expected error rate above 6%, and mapped to the OTUs. Taxonomy was assigned using the Warcup training dataset V1 (http:// drive5. com/utax/data/utax_warcup_trainset1. tar. gz), with a bootstrap threshold of 50%. The ITS targeted amplicon sequencing for Flight 3 samples did not yield any product to move forward in generating sequences and this might be due to the low fungal biomass of the samples.
Amplicon sequence analysis Bacterial ASV sequences and Fungal OTUs were summarized to the family and/or genus level using QIIME  [91]. The ALDEx R package version 2  [70]  was used to statistically compare the relative abundances of bacterial family level taxa between the different flights and locations based on the expected values of 128 Dirichlet Monte Carlo instances of centered log ratio (clr) transformed data  [71]. A value of zero indicates that organism abundance was equal to the geometric mean abundance. Thus, organisms more abundant than the mean would have positive values, and those less abundant than the mean would have negative values. Significance was based on the Benjamini-Hochberg corrected P value of the Kruskal-Wallis statistical test (significance threshold P < 0. 05). ALDEx2 was also used to compare fungal genus level taxa between flights and differential ASVs and OTUs between samples and controls.
The R script of SourceTracker (version 0. 9. 1), the contamination predictor tool, was used to assess contamination of the samples  [92]. ISS surface wipes were designated as sink and the field and Maxwell negative controls as sources. Samples were rarified to 1000 reads. QIIME was also used to calculate Shannon's diversity and taxa richness. Statistical analysis of Shannon's diversity and taxa richness was performed in Prism using the non-parametric Kruskal-Wallis test with the Benjamini Hochberg FDR multiple test correction. Genus level counts were clr transformed using the "compositions" package in R  [93]  and visualized with a heat-map created with the "gplot" package in R. Barplots, boxplots, CCA plots, and pie charts were all created in R.
Comparison of ISS environmental microbiome with Earth microbiome The ISS environmental microbiome data were processed by Deblur 1. 0. 4 [94]  trimming at 90 nt with defaults except for setting -min-reads 1 to avoid filtering sequences across samples prior to merging sample sets. The published Earth Microbiome Project 90 nt BIOM table  [27]  was obtained from ftp://ftp. microbo. me. Deblur 1. 0. 4 90nt BIOM tables of Hospital Microbiome Project (Qiita study 10,172) and Office Succession Study (Qiita study 10,423) were obtained from Gonzalez et al. [95]  using redbiom analysis (https://github. com/biocore/redbiom). Only the reference-hit sOTUs were used across all studies including ISS microbiome datasets. All studies were merged using the BIOM Table Python application programming interface (API).
Acknowledgements Part of the research described in this publication was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with  NASA. We would like to thank astronauts  Captain Terry Virts  ( F1  and  F2 ) and  Commander Jeffrey Williams  ( F3 ) for collecting samples aboard the  ISS , and the  Implementation Team at NASA Ames Research Center  for coordinating this effort. We would like to thank  Adriana Blachowicz  for helping in processing the samples at  Jet Propulsion Laboratory  and  Dr. Douglas Botkin  for coordinating the arrival of first sample set. © 2019  California Institute of Technology. Government sponsorship acknowledged.
Funding This research was funded by a 2012 Space Biology NNH12ZTT001N grant nos. 19-12829-26  under Task Order  NNN13D111T  award to KV, which also funded post-doctoral fellowship for ACS and GBMM. Subcontracts to GF and RK were given through grant no. 19-12829-26. Funding for CU was supported by the  NASA   Postdoctoral Program.
Availability of data and materials The data presented in this manuscript are available in the NCBI Sequence Read Archive under the accession no. SRP091672 that is under the BioProject no. PRJNA319355. The 16S rRNA and ITS sequences of the isolates were deposited in the NCBI GenBank under accession no. : KY218802-KY218904 (16S rRNA) and KY218664-KY218730 (ITS).
A small number of studies designed benchmarks in order to explore the reliability of diffusion MRI tractography  (Schilling et al. , 2018). For example, using a phantom dataset composed of known tracts reconstructed by diffusion MRI tractography as ground truth, the accuracy of a large number of state-of-the-art tractography algorithms was assessed in humans  (Maier-Hein et al. , 2017). The results showed, for all the algorithms, their ability to recover most of the existing bundles, but also revealed a variable, but substantial, number of false positives. Similarly  Sarwar, Ramamohanarao, & Zalesky (2018)  compared deterministic and probabilistic tractography algorithms with a numerically generated phantom and concluded on a trade-off to be made between sensitivity and specificity depending on the type of tractography algorithm.
Diffusion MRI Data High-resolution MRI were acquired ex vivo using a small animal 7 Tesla Bruker MRI scanner (Neurospin, Saclay, France). The acquisitions were performed postmortem in order to reduce movement artefacts and improve sensitivity  (Holmes et al. , 2017). The brain was obtained from a 2-month-old ferret. The ferret was euthanized by an overdose of pentobarbital and perfused transcardially with 0. 9% saline solution and post-fixed with phosphate-buffered 4% paraformaldehyde (PFA). After extraction, the brain was stored at 4 • C in a 4% PFA solution until the MRI acquisition. All procedures were approved by the Institutional Animal Care and Use Committee of the Universidad Miguel Hernández and CSIC (Consejo Superior de Investigaciones Científicas), Alicante, Spain. The brain was transferred to a 0.