dataesr/scientific-paragraphs-categorization huggingface dataset:
Google apps are also used for data handling (Google Sheets) and presentation (Google Slides). Students may also use Microsoft Office's Excel and PowerPoint software for the same purpose. All of the databases and analysis tools are open access. The core set of databases and web tools used in the internship are: The Allen Brain Atlas (gene expression data based on donor brains and correlation analysis; http://brain-map. org/), Venny (Venn diagram generator; http://bioinfogp. cnb. csic. es/tools/venny/), DAVID (Database for Annotation, Visualization and Integrated Discovery; functional annotation, pathway information, and clustering; https:// david. ncifcrf. gov/; Huang et al. , 2009) , PythonAnywhere (statistics, graphing; https://www. pythonanywhere. com) and STRING (network analysis; http://string-db.
2. The authors need to include more information about the project itself (i. e. a discussion of the modules used for teaching, the mentoring, etc. ). The reader is left wondering if the students are doing actually self-initiated projects, or if they are being led by the modules. It was helpful to read about the databases that the students use, but the reader does not gain any knowledge about the actual e-internship program through which the students progress (or are those the modules? It is unclear. ). In other words, how were they mentored? Is the mentoring effective for student growth? How are the modules designed? Is there any research to show that the module design is effective? Do they present and defend their research in a scientific forum (even online)? It would also help to put the Project Design section first (before the database discussion).
Reviewer's comment 4) In other words, how were they mentored? Is the mentoring effective for student growth? Author's response Student mentoring primarily occurs through video conferencing and includes a walkthrough of each database using screen sharing, project/topic discussion, and troubleshooting. Mentoring sessions occur weekly and last anywhere from 30 min -90 min. Students also email us with questions. The effectiveness of the mentoring sessions on student growth has not been assessed formally but we believe that mentoring is necessary for the students to perform their research and complete their projects.
Author Response 21 Feb 2017 Anna Delprato, BioScience Project, Wakefield, USA The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000. com.
05; Table  2 ; Additional file 7: Table  S6 ), though our sample size is underpowered for genome-wide statistical tests (Additional file 2: Figure  S6 ). However, crosschecking these 21 genes in another in-parallel HSCR WES (190 cases, 740 controls) revealed that only RET was significantly overrepresented with deleterious variants (p < 0. 001; A. Chakravarti, manuscript in preparation). Besides the eight LOF mutations, six out of twelve missense mutations were consistently predicted to be deleterious (Additional file 8: Table  S7 ). As for the seven synonymous DNMs, we found no in silico evidence indicating that those changes interfered with splicing and/or RNA structure (Additional file 8: Table  S7 ). After checking those genes with DNMs against the ATGU's gene look-up server  [13]  and the Exome Aggregation Consortium (ExAC) Browser  [17] , in total 11 genes (Table  2 ) were identified as evolutionarily constrained where variants are more likely to be deleterious  [13].
1k ), suggesting the phenotype observed initially was an off-target effect. On the contrary, coinjection of p53 morpholino with dennd3a and b, ncl1, nup98, or tbata SBMOs resulted in the same phenotype Genes evolutionarily constrained as per AGTU's server and ExAC database: "No" for not constrained, "Yes" for constrained, "NA" for not available. b Data from in-house human induced pluripotent stem cell-derived neural crest, mouse expression data, and RT-PCR and in situ hybridization in zebrafish; ND not done. NT not tested (gene carries synonymous mutation and/or has no ortholog in zebrafish) (Fig. 1l-o ), indicating the phenotype was not caused by unspecific apoptosis. qPCR analysis showed that the expression of dennd3a, dennd3b, nup98, and tbata was markedly reduced in the SBMO-injected embryos (Additional file 2: Figure  S8 ).
Noteworthy, the SBMOs targeting three of the orthologs (aff3, scube3, and vezf1a) did not knockdown the target transcripts as expected, highlighting the limitation of morpholinos  [23]. To strength our morpholino data we opted for CRISPR knockout, which allowed phenotype analysis in F0 larvae. The HSCR-like phenotype was reproduced in the CRISPR knockout of dennd3a and b, ncl1, nup98, and tbata. Although the presence of indel mutation was confirmed by T7E1 assay in all larvae injected with gRNA, the number of larvae displaying the phenotype was relatively low. We suspected this was mainly due to the varying CRISPR efficiency (number of indels present in the target gene over the total number of the target gene copy in a larva) between larvae. For those four mutations from the newly validated genes (DENND3, NCLN, NUP98, and TBATA), two of them (NCLN:Q166* and DENND3:K640fs) are loss-offunction mutations that disrupt gene translation; the other two (NUP98: N1662S and TBATA:R53C) are predicted as highly deleterious by Polyphen2 and a logistic regression model that combine different predicted scores  [24].
Data generation Whole exome sequencing DNA samples were sequenced in four centers. The exome-capture kit and sequence platforms used per center are detailed in Table  S2. Appropriate mapping tools (Burrows-Wheeler aligner (BWA) for Illumina data and Bfast for Solid data) were used to align sequence reads to the human reference genome (build 19)  [48]. Sequence quality was re-evaluated using the FastQC toolbox, Picard's metric summary, and the Genome Analysis Toolkit (GATK) Depth-of-Coverage module. After initial quality control (QC) all eligible sequences were pre-processed for local indel realignment, PCR duplicate removal, and base quality recalibration  [49].
Variant calling and prioritization Aligned reads from all sequenced samples were preprocessed according to standard guidelines  [49]. Variant calling was done independently for Illumina reads or Solid reads using the GATK unified Genotyper 2. 0  [50]. To avoid mismatched regions across different capture kits, calling was performed whole genome-wide without limiting to any capture array. A special setting (allow potentially miscoded quality scores) was used to make color-spaced solid reads compatible to the program (Broad institute). Raw variants (including single nucleotide variants and short insertions/deletions) with individual genotypes and their affiliated quality scores were stored in a standard VCF format after calling. Quality assessment (QA) and QC were then adopted on a few sets of variants (raw variants, exonic variants, rare variants) to generate a confident variant set for downstream prioritization (Additional file 1: Supplementary methods).
A clean variant set at exonic regions was produced after variant-level and genotype-level QC. Rare coding sequence variants were then prioritized by filtering out those variants with minor allele frequency >0. 01 in any of these public databases (dbSNP137, 1000 Human Genome project, and NHLBI Exome Sequencing project). An automatic pipeline integrating GATK  [49] , KGGSeq  [51] , Annovar  [52] , and Plink  [53]  was used to generate the final set of qualified variants (Additional file 2: Figure  S1 ).
RET gene inspection To guarantee that no de novo mutations had been missed in the major HSCR gene, the depth of coverage of each of the 21 exons of RET was manually inspected for each patient. All exons with a coverage <10 were Sanger sequenced. Mutation Detector software (Thermo Fisher Scientific) was used to identify rare coding sequencing mutations from raw Sanger sequences; any mutation found in a trio proband was further checked in his/her parents. Besides rare mutations, bi-allelic genotypes for the common risk single nucleotide polymorphisms (IVS1 + 9494, rs2435357T) were extracted from local databases or newly genotyped.
CNV detection The Nexus®software program (Biodiscovery, El Segundo, CA, USA) was used to normalize and analyze the SNP array data as mentioned above. Loss is defined as the loss of a minimum of five probes in a 150-kb region, with a minimum log R ratio of -0. 2. Gain is defined as the gain of a minimum of seven probes in 200-kb region, with a minimum log R ratio of 0. 15. The minimum length of regions of homozygosity analysed was 2 Mb. The identified CNVs were reviewed for pathogenicity using the UCSC genome browser (http://genome. ucsc. edu/), the DGV database (http://dgv. tcag. ca/dgv/app/ home), the Decipher database (https://decipher. sanger. ac. uk/), and our in-house local reference database that consists of 250 healthy controls and 250 individuals of the general population.
Gene-wide burden analysis Genes with DNMs were further scrutinized for the presence of inherited rare damaging variants in the trios as well as in HSCR singletons for whom WES data were available. A detailed analytical protocol was shared before running the association in each center. Briefly, genotypes of rare damaging variants (as previously defined) in genes carrying one or more de novo mutations were extracted from raw sequencing reads. The CMC test in the Rvtest package was used to collapse multiple variants into the same gene (boundary defined using hg19 refgene) and compare overall burden between cases and local matched controls  [55]. P values were estimated by asymptotic chi-square distribution. The gene-wise p value, burden direction, and variant count per gene were exported. Ultimately, the sample size weighted Z-score method was used to conduct meta-analysis on gene-wise summary statistics from three centers using the same protocol.
Bioinformatics analysis Variant-level implication The impact of each DNM to its carrying gene was predicted using several bioinformatics tools or databases. The conservation of missense SNVs was predicted using GERP and PhyloP across 29 different species. The deleteriousness of missense or nonsense SNVs were determined by a logit model incorporating five prediction programs (Polyphen2, Sift, MutationTaster, PhyloP, and likelihood ratio)  [24]. Human Splicing finder was used to predict whether DNMs causing synonymous change or locating at splicing sites (exon ±2 bp) created or disrupted splice sites  [56]. To assess if synonymous DNMs had any effect on the transcripts, we used RNAmute  [57]. Finally, ClinVar and PubMed were searched for the same or similar mutations in the same gene that present in healthy controls or other disease patients.
Gene-level implication Evidence of gene-level implication was collected from two aspects. First, those 24 genes carrying DNMs were searched against databases (the ATGU's server (https:// github. com/michaelchess/gene-lookup) and ExAC browser (http://exac. broadinstitute. org/)) for recurrence and constrained scores  [13, 17]. Second, ENS candidate genes/gene sets were curated (Additional file 1: Supplementary methods)  [58] [59] [60]  and then linked to newly identified genes using pathway or protein-protein interaction network information. Disease Association Protein-Protein Link Evaluator (DAPPLE) was used to test whether the genes carrying DNMs in our study are functionally connected to each other. The significance of observed pathway enrichment and network connectivity was evaluated empirically using randomly selected genes from among genes of the same genomic size as the identified DNM genes.
Availability of data and materials We have de-identified all our samples for patient privacy and deposited raw sequence read data in this analysis to the European Nucleotide Archive (with accession number PRJEB19327). The methods and corresponding results generated during this study are included in this published article and its additional files. The primers for zebrafish experiments are all provided in supplementary tables for further replication study in other groups. The identified de novo mutations in four new Hirschsprung genes have been deposited in the NCBI ClinVar database with accession IDs SCV000392345, SCV000494031, SCV000494032, and SCV000494033.
Acknowledgments The authors would like to thank the patients and families involved in this study. We thank  W. W. Pijnpappel  and  M. Broeders  for providing the Cas9 protein. Funding DS, AB, RC, BE,YS, and RH are supported by research grants from  ZonMW  ( TOP-subsidie 40-00812-98-10042  to BJLE/RMWH) and the Maag Lever Darm stichting ( WO09-62  to RMWH); AC is supported by  NIH  grant  R37 HD28088 ; HG, WC, VL, EN, PS, MS, CT, PT, and MMG-B are supported by  Health and Medical Research Fund  ( HMRF 01121326  to VCHL;  HMRF 02131866  to MMGB; and  01121476  to ESWN);  General Research Fund  ( HKU 777612  M to PKT); HKU seed funding for basic research (201110159001 to PKT) and small project funding (201309176158 to CSMT). The work described was also partially supported by a grant from the  Research Grant Council of the Hong Kong Special Administrative Region, China , project number  T12C-714/14R  to PKT; CH and PG are supported by grants from the  Italian Ministry of Health  through " Cinque per mille " and  Ricerca Corrente  to the  Gaslini Institute ; GA, MB, BL-T, MR-F, and SB are supported by the  Spanish Ministry of Economy and Competitiveness (Institute of Health Carlos III (ISCIII) ,  PI13/01560  and  CDTI, FEDER-Innterconecta   EXP00052887/ITC-20111037 ), and the  Regional Ministry of Innovation, Science and Enterprise of the Autonomous Government of Andalusia  ( CTS-7447 );  NINDS  ( 5R21NS082546 ) awarded to ITS.
, 2004 ) (Figure  7A, B ) and from our transcripts analyses measuring the relative expression levels of Efnas in acutely isolated RGCs (Figure  2C ). We observed graded expression of Efna2 and Efna5 along the nasal-temporal axis of the retina, whereas Efna3 is homogeneously expressed in WT animals (Figure  7A, B ). Curve fitting analysis using MATLAB revealed the equation L A (x) retina WT   sequential mapping of 100 RGCs onto a 1D array of 100 SC neurons along the rostral-caudal axis, followed by 100 V1 cortical neurons innervating the SC. We made two assumptions: (1) endogenous collicular EFNAs are no longer active for incoming V1 axons as they have been engaged previously in RGCs axon guidance by binding to retinal EPHAs (forward signaling) which leads to the cleavage of their extracellular domains  (Janes et al.
Our model is consistent with a retinal-matching model, suggesting that retinal inputs are required for proper cortico-collicular mapping  (Khachab and Bruce, 1999; Triplett et al. , 2009; Cang and Feldheim, 2013). In our model, retinal inputs carry molecular cues, EFNA3 and likely other retinal EFNAs present at the level of the TZs to provide positional information for ingrowing V1 axons (Figure  9 ). EFNA3 could be detected on RGC axons by immunohistochemical staining on proximal and middle parts of the optic nerve but fell below detection limit in the distal part and in the SC. We cannot exclude that a proportion of collicular EFNAs also contributes to the cortico-collicular mapping process. Altogether, our data suggest that transposed retinal EFNAs in the SC instruct cortico-collicular map alignment and act together with correlated neuronal activity pattern shared between RGCs and V1 axons  (Triplett et al.
Projections analysis/mapping Anterograde and retrograde DiI (1,1-dioctadecyl-3,3,3,3-tetramethylindocarbocyanine perchlorate), DiD (1,1'-dioctadecyl-3,3,3',3'-tetramethylindodicarbocyanine, 4-chlorobenzenesulfonate) and/or retrograde CTB-488 (Cholera Toxin B subunit-Alexa 488) labelling were performed as described  (Reber et al. , 2004; Triplett et al. , 2009). Whole-mount SC were processed as described and TZs were plotted along the rostral-caudal axis on Cartesian coordinates (y axis)  (Reber et al. , 2004). For cortico-collicular map analyses, sagittal vibratome sections were performed on P14 SC and TZs were plotted along the rostral-caudal axis on Cartesian coordinates (y axis). Retinas were dissected and imaged using Zeiss Axioscope two and Axiovision software. Retinal coordinates of the DiI injections were calculated using IntactEye algorithm  (Hjorth et al.
, 2015) , confirmed using the projection method  (Reber et al. , 2004)  and plotted on Cartesian coordinates (x axis). V1 cortices were photographed as whole-mount and focal injections plotted along the V1 lateral-medial axis (x axis)  (Triplett et al. , 2009). Retino-collicular and cortico-collicular maps were generated using nonparametric smoothing technique, termed LOESS smoothing  (Efron and Tibshirani, 1991) , to estimate the profile of the one-dimensional mapping either from retina to SC, or from V1 to SC. To estimate the variability in a mapping containing N data points, we repeated the procedure N times with N-1 datapoints, each time dropping a different datapoint. This is termed a 'leave-one-out' method and was used in the R Project for Statistical Computing (RRID:SCR_001905). The script is available in Source code 1 file.
Semi-quantitative in situ hybridization and gradient fitting of retinal Efnas expression Analysis of the expression of the Efnas was performed as previously described  (Reber et al. , 2004)  on nasal-temporal 20 mm thick sections of P1/P2 WT retinas. Probes used were: mouse Efna2 (NM007909. 3, 879 bp, pos. 387-1266), mouse Efna3 (NM010108, 791 bp, pos. 208-999)   and Efna5   (NM207654, 696 bp, pos. 189-885). Experimental values (mean +SD, Efna2/a3/a5, for each Efnas, n = 18 sections total, 3 sections/retina, from 6 retinas À2 left, 2 right-from 3 animals) were plotted along the nasal-temporal axis and fitted using MATLAB (RRID:SCR_001622).
In silico replication of the duplication of the cortico-collicular map The Koulakov model in MATLAB (RRID:SCR_001622)  (Tsigankov and Koulakov, 2006; 2010)  was used to simulate the formation of both the retino-and cortico-collicular maps in the presence of an oscillatory Efna gradients in the target structure. Each brain structure (retina, SC, V1) is modelled as a 1-d array of 100 neurons (N) in each network. Two maps are generated: first, the map from retina to SC; second, the map from V1 to SC. Each map is modelled sequentially in the same way. This model consists in the minimization of affinity potential (E) which is computed as follows: E ¼ E act þ E chem At each step, this potential is minimized by switching two randomly chosen axons probabilistically according to the degree such a switch reduces the energy in the system by Delta E (DE).
Acknowledgements We are grateful to  Dr. Feldheim  for providing biological material from Efna3KO mice. We thank  Dr. Sophie Reibel-Foisset ,  Laurence Huck  and  Noemie Charlery-Adele  ( UMS 3415 CNRS -Chronobiotron, Strasbourg, France ) for mouse colony management,  Dr. Nick Bevins  and  Dr. Alexandre Charlet  for technical help,  Dr. Melinda Owens  for providing the Koulakov/Triplett scripts in MATLAB and  Dr. Alexandra Rebsam  for providing a batch of DiD. ES, MP, AB and MR performed the experiments. SE generated the Leave-One-Out algorithm. GL and MR analyzed the Efnas ISH quantitation. ES, S. E and MR generated the theoretical modelling. ES, SE FWP and MR analyzed the results. MR wrote the manuscript with the active participation of all the authors. Material requests and correspondence should be addressed to MR email: michael.
Data/codes availability Software package IntactEye can be found at: http://github. com/hjorthmedh/IntactEye. 'Leave-oneout' script in R and 3-step Map Alignment Model provided as a source code files (Source code 1 and 2). Model also available at Reber, 2017 (with a copy archived at https://github. com/elifesciences-publications/3-step-Map-Aligment-Model).
(  10 ) published in  Kieffer and Stone (2005) , the solar spectrum given by  Wehrli (1986)  and the Earth-Moon and Sun-Moon distances, with each of the CE318-T filter responses. The result must be multiplied by the solid angle of the Moon (σ M = 6. 4177 × 10 -5 sr) divided by π , according to  Kieffer and Stone (2005). The lunar ephemeris have been extracted using the Navigation and Ancillary Information Facility (NAIF) of the NASA Jet Propulsion Laboratory (https://naif. jpl. nasa. gov/naif/), which uses data of the orbital position of many celestial bodies known as kernels or Spacecraft, Planet, Instrument, C-matrix (pointing) and Events (SPICE) data files. This NAIF SPICE toolkit is freely available at http://naif. jpl. nasa. gov/.
AOD differences at nighttime ( AOD fit ) are defined through the comparison of the nighttime AOD estimated from linear interpolation using AERONET daylight data (AOD night,interp ) and the AOD obtained directly from nocturnal CE318-T measurements using Eq. (1) (AOD night ). Then, AOD fit can be obtained by means the following expression: AOD fit = AOD night,interp. -AOD night (4) These differences are displayed in Fig. 3a  with asterisks. We observe a clear dependence with the phase angle (g), increasing considerably for higher g values. This dependence is more evident for higher wavelengths and seems somewhat asymmetric (higher differences for positive g, namely, after full Moon, especially for the 1020 nm channel), which is in agreement with the results obtained in Sect. 3. 1. We have found the best fit for this dependency (δ g ) to be a second- order polynomial (Eq.
3), as it is displayed in Fig. 3a  with solid lines. δ g (λ) = a 0 (λ) + a 1 (λ) • g + a 2 (λ) • g 2 (5) The coefficients a i in Eq. (  3 ) are calculated for each channel centered at λ. In addition, the presence of a nocturnal cycle on AOD fit is also evident from Fig. 3a. These results are also in agreement with those found by  Barreto et al. (2016) , who found higher AOD discrepancies with respect to daytime AERONET data for higher phase angles, and a faint nocturnal AOD cycle. The increasing uncertainty in AOD with g and the asymmetry of this dependence within the Moon cycle can be attributed, at least partially, to the existence of some residuals on the ROLO model, according to the results presented in Sect. 3. 1. This effect results in a systematic g-dependent AOD error observed in Fig.
14 and 0. 19, respectively, and average Ångström exponent of 1. 4 and 1. 2, respectively). In contrast, the station located in Dakar (M'Bour, Senegal; 14 • 23 N, 16 • 57 W; 0 m a. s. l. ) presents a significant contribution of marine aerosols and biomass burning aerosols during the dry season but also mineral dust with maximum influence in summer  (Léon et al. , 2009). In this case the averaged AOD at 500 nm and Ångström exponent are 0. 45 and 0. 37, respectively, indicating the predominant coarse-mode aerosol at this station. We have used nocturnal CE318-T AOD data at Carpentras in a 9-night period from 15 to 24 February 2016, a 10-night period at Dakar from 18 to 29 April 2016 and a 12-night period at Lille from 13 to 28 April 2016. These three instruments have been calibrated using the lunar Langley cal- ibration technique at Izaña.