dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Features Extraction. Features were extracted from the histogram shape, which is an area of the variable description based on the shape, and provided the frequency of values from different ranges of the variable. These features were applied previously in cervical cancer diagnosis using histogram based analyses of diffusion-weighted MR and its relation to histological features, subtype, and grade of cervical cancer  [15, 16]. We quantified the two GBM phenotypes by nine statistical functions (Table  1 ). All GBM patient data were plotted as histograms showing individual GBM data and their respective frequencies (Figure  1(b) ). Features describing major statistical characteristics of these distributions were extracted according to Table 1  [15]. All features were extracted from histograms of GBM, according to 𝑅 VAT = [ [ [ [ [ 𝑓 1,1 ⋅ ⋅ ⋅ 𝑓 1,9.
() is an impurity-based criterion that measures the divergences between the probability distributions of the attribute's values. It is expressed as 𝐼 𝐺(𝑡) = ∑ 𝑖 𝑝 𝑖 (1 -𝑝 𝑖 ) , (5) where   is the relative frequency of class  at node  and node  represents any node at which a given split is performed. is determined by dividing the total number of observations in the class by the total number of observations. DT was applied on the given dataset using a built-in Matlab function from the decision trees for regression and classification toolbox. Comparative results were reported in Tables  3  and 4.
" Particularly, in utterly devastating disease like GBM, improvements in any medical specialty involved are of the utmost essence. Note that numerous factors may have led to varying results between this study and previously published studies potentially due to the following reasons. In this work, the whole GBM tumor was assessed. Only FLAIR sequence was considered and only two phenotypes were addressed based on the data distribution (Figure  1 ). It would have been preferable to include more patients to strengthen the GBM heterogeneity analysis. However, the number of patients included in this study can provide preliminary information about GBM heterogeneity.
Study design and data collection This was a retrospective multicenter registry analysis. Data were provided and approved for this study by the Acute Leukemia Working Party (ALWP) of the EBMT group registry. The EBMT is a non-profit, scientific society representing more than 600 transplant centers, mainly in Europe. The EBMT promotes all initiatives that aim to improve stem cell transplantation or cellular therapy, which includes registering all activities relating to stem cell transplants. Data are entered, managed, and maintained in a central database that includes all EBMT centers. There are no restrictions regarding centers that can report data except those legal requirements concerning patient consent, data confidentiality and accuracy. Quality control measures include several independent systems: confirmation of validity of the entered data by the reporting team, selective comparison of the survey data with MED-A data sets in the EBMT registry database, cross-checking with the national registries, and regular in-house and external data audits.
Since 1990, patients have provided informed consent authorizing the use of their personal information for research purposes. Eligibility criteria for this analysis included adult patients (age >18 years) with acute leukemia receiving HLA-matched or mis-BMT or PBSCT after RIC regimen for acute leukemia matched related or unrelated donor BM or PB transplants after RIC regimens from 2000 to 2012. Two hundred and ninety-four transplant centers reported data on recipients of BM and PB grafts after related or unrelated donor transplantation. We do not have any information about why patients were allocated to a specific graft (BM vs. PB) in the registry, and it is difficult to distinguish between the role of the conditioning approach adopted and the role of a potential effect of the individual center ("center effect"); however, a center effect was not evident in the analysis.
All unrelated donors were HLA (-A, -B, -C, DRB1, -DQB1) matched (10/10) or mismatched at one loci. Exclusion criteria included previous allogeneic or cord blood transplantation, and recipients of grafts that were either ex vivo T-cell depleted or CD34 selected. Data were collected on recipient and donor characteristics [age, gender, cytomegalovirus (CMV) serostatus], disease status at transplant, transplant-related factors including conditioning regimen, immunosuppression (in vivo T-cell depletion vs. none), stem cell source (BM or PB), graft-versus-host disease (GvHD) prophylaxis, and outcome variables [acute and chronic GvHD,  16, 17.
Cell spots were delimited for immunolabeling with a PAP pen (Z672548-1EA, Sigma-Aldrich®) and then permeabilized with PBS 1X containing 0. 3 % Triton™ X-100 (T8787, Sigma-Aldrich®) for 30 min at RT. Incubation with pure NCL-DYS1 Dy4/6D3 (Novocastra Laboratories) was performed overnight at 4 °C, then slides were washed three times with PBS 1X for 30 min each wash. Incubation with a mix of Dylight 549-conjugated AffiniPure F(ab′)2 fragment Goat Anti-Mouse IgG (H+L) (1:1000; 115-506-003, Jackson ImmunoResearch laboratories Inc. , PA, USA) and DAPI (1:2000; 268298, Calbiochem, La Jolla, CA) was done for 30 min at RT. Slides were finally washed three times with PBS 1X (30 min/wash) and mounted with fluoromount (F4680; Sigma-Aldrich®). Observation and image captures were done on an AxioObserver Z1 Zeiss microscope with Zen Blue software (Carl Zeiss Microscopy GmbH, Jena, Germany).
Exon skipping Two days after induction, BMP4-treated hiPSCs 1 were transfected by electroporation with a phosphorodiamidate morpholino oligomer (PMO) targeting exon 53 of the DMD gene, based on previously published data  [36]  at 1, 10, or 100 μM with 200,000 cells in 20 μL solution from the P3 Primary Cell 4D-Nucleofector® X Kit (V4XP-3032, Lonza, Basel, Switzerland) using the CB150 program on the 4D-Nucleofector™ System (Lonza). RNA extraction was carried on transfected cells 24 h later followed by reverse transcription as described above. PCR was done on 250 ng cDNA using forward and reverse primers (Fw 5′-TTACCGACTGGCTTTCTCTGC-3′ and Rv 5′-GTCTGCCACTGGCGGAGGTC-3′, Thermo Fisher Scientific) and Taq DNA polymerase (10342, Thermo Fisher Scientific) as described by the manufacturer's instructions, for a final reaction volume of 50 μL.
We then decided to use 5′RACE PCR to identify a possible new transcript. Two main sequences were recursively found (Additional file 6: Figure  S2 ). The first one was identified as the 164 last nucleotides of the Dp427c exon 1, spliced to the first nucleotides of exon 2. The second was a new sequence of 643 nucleotides (position in chromosome X (chrX), 33101211-33101853; (GenBank: KT072086)) spliced to the first nucleotides of exon 2. A corresponding transcript was not yet described, but a BLAST-like alignment tool (BLAT) analysis  [40]  showed that this sequence of 643 nucleotides was intronic and located 44. 3 kb downstream of the dystrophin protein On the left side, data represent the mean ± SD from nine hPSCs from days 0 through 4 after BMP4 treatment. On the right are data from expression in cultured normal myoblasts and myotubes, as well as in muscle biopsied from a healthy individual.
Acknowledgements This work was supported by the  Institut national de la santé et de la recherche médicale ; the Centre national de la recherche scientifique; the  Association Française contre les Myopathies ;  Fondagen  and the  Université d'Evry. We wish to acknowledge the technical assistance of A-L. Jaskowiak,  J. Tournois ,  A-L. Egesipe ,  C. Denis ,  M. Schmidt ,  Y. Laabi ,  N. Sigrist , and  E. Ricci. We are also grateful to  B. Foret  and  D. Israeli  for their input along the project the manuscript corrections. Thanks are also due to  O. Albagli-Curiel ,  G. Corre ,  B. Weiss , and  S. Vassilopoulos  for data interpretation and analyses.
MS. Eluate from the HPLC system was introduced alternatively in two different mass spectrometers: a TSQ Quantum Ultra triple quadrupole (QqQ) and a LTQ-Orbitrap XL TM (Thermo Scientifi c). TSQ Quantum Ultra. The QqQ mass spectrometer was equipped with a heated electrospray (HESI-II) probe as the ionization source. The mass spectrometer was operated in negative ion mode and controlled by TSQ Tune version 1. 5 software. Nitrogen was used for both the sheath and the auxiliary gases. The MS signals of GG species were fi rst optimized in the MS full scan mode (range from 500 to 1,500 Da) by continuous infusion (10 µl min Ϫ 1 ) of the standards (10 µg ml Ϫ 1 ) dissolved in the mobile phase. The number of sialic acid residues present on a GG molecule determines the number of possible charge states that may be observed in its mass spectra.
For quantifi cation, data were acquired in selected reaction monitoring (SRM) using Xcalibur 2. 0. 7 software (Thermo Scientifi c) from aliquots of GG extracts equivalent to a tenth of a retina. The precursor and product ion pairs for the SRM analysis were selected based on the precursor ion scanning, but some species were not considered in SRM because they stand below the quantifi cation limit (signal/noise <10). Each sample was injected in triplicate. The proportion of each molecular species of a specifi c GG class was calculated as the ratio of its peak area to the sum of all detected peak areas in this class, every GG class being considered separately. The accuracy and repeatability of the method were evaluated by performing between-day and within-day precision using a GG standard mixture as well as a rat retina GG extract.
Measurement of the GG-bound sialic acid content GG-NANA content is frequently used as an estimate of the total GG content of a biological sample, the presence of sialic acid being a distinctive feature of GG among lipids. It was measured using colorimetry as described in Materials and Methods. Rat retinas contained an average of 3. 0 ± 0. 59 µg GG-NANA/mg protein, which was equivalent to 9. 7 ± 1. 91 nmol GG-NANA/mg protein (n = 9, mean ± SD). Those levels were in accordance with previously published data in different species (chicken, rat, calf, and pig), which stand between 5. 9 and 9. 7 nmol GG-NANA/ mg protein  (26) (27) (28). However, the colorimetric reaction of the sialic acid molecule with resorcinol seems to be partially dependent on the nature of the GG that carries it. Especially, it appeared that the intensity of the signal of a nanomole of sialic acid decreases with the complexity of the oligosaccharidic chain of the GG carrying it, the simplest GG GM3 giving the signal closest to free sialic acid (commercial NANA used as a standard for the assay; supplementary Fig.
2  ) and in LC/MS with the QqQ mass spectrometer ( Fig. 3  , peak ␥ ). Moreover, it revealed the presence of acetylated forms of some GG classes, especially Fig. 4. Molecular species distribution of the major GG classes of rat retina. The percentage of each ceramide species is expressed relatively to the sum of all detected species in its specifi c GG class, every GG class being considered separately. Data were obtained by operating the QqQ mass spectrometer in negative SRM. Among molecular species detected in precursor ion scanning ( Table  3  ), only species with signal/noise >10 were considered in SRM. Results are expressed as mean ± SD of four independent samples injected three times in LC/MS. given in Fig. 7. Except for the GT3 class in which the acetylated form accounted for >30% of all molecular species, acetylated GG remained very minor in our analytical conditions (6% for GQ1b, 1.
Moreover, the advent of MS nowadays offers great possibilities to perform glycolipidomic studies and apprehend more easily the huge diversity of GGs. In particular, it enables us to scan the variety of GG molecular species by characterizing their ceramide moiety. The fi rst reports using MS go back to the 1990s with structural characterization of a single GG  ( 39, 40 ). Profi ling studies really started quite recently  ( 30, 38 ). Previous data on ceramide composition of retinal GGs were obtained in the late 1970s with indirect and less effi cient techniques. Authors identifi ed FAs, on one hand, and LCBs, on the other hand, using gas chromatography techniques  ( 32 ). Thanks to the MS techniques described here, including high resolution, we were able to identify combinations of FAs and LCBs and determine the distribution of the different ceramide types among a GG class.
5' UTRs and alternative splice forms of poik A previous study of transcriptomic data suggested the existence of different splice variants of poik (HM00025) in Hm involving both coding exons and alternative 5' UTR exons  (21). We further investigated this using RT-PCR and 5' RACE on RNA from Hm individuals. This revealed an extensive set of alternative 5' UTRs with the furthest being over 100kb upstream of the poik coding exons (Figure  3A ). Using the mRNA sequence of these we were able to detect possible homologous regions upstream of the He poik gene in the HeCr BAC sequence tilepath (Figure  2C ), although no corresponding transcripts were found in available RNA-sequencing (RNA-seq) data for He. The furthest upstream exon was present in both Hm individuals (Hm aglaope and amaryllis) used for 5' RACE and its presence was confirmed by RT-PCR in 17 additional individuals.
Hm amaryllis/aglaope These races have a hybrid zone in Peru and differ at the HmYb and HmN loci controlling the presence of the yellow hind-wing bar and yellow forewing band respectively. RNA-seq data for hind-wings from three developmental stages had previously been obtained for two individuals of each race at each stage (12 individuals in total) and used in the annotation of the Hm genome  (24). We analysed this data to look for differences in gene expression between races and detected twelve, 95 and 208 genes as being differentially expressed between races at final instar larvae, day 2 and day 3 respectively using multiple analysis methods (Table  S3 ). Only two genes were detected as being differentially expressed within the HmYb mapped region and both were only differentially expressed in the day 2 wings.
Association analyses We measured associations between genotype and phenotype using a score test (qtscore) in the GenABEL package in R  (47). This was corrected for background population structure using a test specific inflation factor, λ, calculated from genomic regions unlinked to the major colour pattern controlling loci, as the colour pattern loci are known to have different population structure to the rest of the genome  (24) (25) (26). Information on the individuals used and ENA accessions for sequence data are given in Table  S1.
He We used an existing He BAC library  (20)  to identify BACs extending the sequenced HeCr region, which were then sequenced and assembled. One gap remained in the reference (between positions 800,387 and 848,446), which was filled using scaffolds from an initial assembly of the He genome. Homology and synteny with the Hm reference were identified by aligning the Hm coding sequences to the He reference with BLAST. The Cr contig is deposited in Genbank with accession KC469893. We used shotgun Illumina sequence reads from 45 He individuals from 7 races that were generated as part of a previous study  (48) (Table  S1 ). Reads were aligned to an He reference containing the HeCr contig and other sequenced He BACs  (20, 48).
Tiling microarray Samples were labelled with Cy3 and each hybridised to a separate array. The HmYb probe array contained 9,979 probes distanced on average at 10bp. The whole-genome expression array contained on average 9 probes per annotated gene in the genome (v1. 1 (24)) as well as any transcripts not annotated but predicted from RNA-seq evidence. The microarray data are deposited in GEO with accessions GSM1563402-GSM1563497. The tiling array and whole-genome data sets were analysed separately. Expression values were extracted and quantile-normalised, log 2 -transformed, quality controlled and analysed for differences in expression between individuals and wing regions. P-values were adjusted for multiple hypotheses testing using the False Discovery Rate (FDR) method  (50).
Acknowledgements We thank  Christopher Saski ,  Clemson University , for assembly of the He BACs. Richard Merrill ,  Moises Abanto  and  Adriana Tapia  assisted with raising butterflies. Anna Morrison ,  Robert Tetley  and  Sarah Carl  assisted with lab work at the  University of Cambridge. We thank the governments of  Colombia, Ecuador, Panama and Peru  for permission to collect butterflies. This work was funded by a  Leverhulme Trust  award and  BBSRC  grant ( H01439X/1 ) to CDJ,  NSF  grants ( DEB 1257689 ,  IOS 1052541 ) to WOM and an  ERC  starting grant to MJ. NJN is funded by a  NERC fellowship  ( NE/K008498/1 ).
5 ' 5 RACE was performed using RNA from hind-wing discs from one Hm aglaope and one Hm amaryllis final instar larvae. We identified isoforms from 5' RACE and RT-PCR products by cutting individual bands from agarose gels and if necessary by cloning products before Sanger sequencing. Sequenced isoforms are deposited in GenBank with accessions XXXX-XXXX. The presence of the furthest 5' UTR exon was confirmed in 17 individuals comprising Hm aglaope and Hm amaryllis of various developmental stages.
Cellular motility is a fundamental biological process. Progress in the fields of gene silencing and high-throughput (HT) microscopy provide us with the tools to study its molecular basis and potential perturbators. The primary contribution of this paper is to present MotIW, a generic workflow for single cell motility study in HT time-lapse screening data. We successfully apply it to a simulated screen, as well as a genome-wide screen. Furthermore, MotIW enables the identification of eigth motility patterns into which all trajectories from this dataset divide up into, without any prior model of cell motion.
However, a systematic study of single cell motility has not yet been performed at a genome-wide scale. Thomas Walter has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements number 258068 (Systems Microscopy). This work was partially funded by grant SMAC-ERC-280032 of the European Research Council. The contribution of this paper is to present MotIW (Motility study Integrated Workflow), and its application to motility gene discovery. A generic methodological framework, MotIW enables to quantitatively study cell motility at single cell resolution in HT data. It consists of cell tracking, cell trajectory mapping to an original feature space, and outlier experiment detection (cf figure  1 ). We show the power of our method by applying MotIW to simulated data, which allows us to estimate recall and precision to be expected on real data.
We then apply this workflow to a previously published genome-wide RNAi 1  screen, the Mitocheck dataset. It is composed of 200,000 videos of human cell populations, each of them showing the outcome of a single gene knockdown  [4]. We thus identify a list of genes whose silencing impacts cell motility and which therefore might play a role in this process. On top of that, MotIW also produces an ontology of cell trajectories in this dataset: without any prior assumption on cell motion, we are able to identify eight types of cell trajectories. The remainder of this paper is organized as follows : Section 2 gives a brief presentation of MotIW, and Section 3 discusses its application to simulated data and the Mitocheck screen.
Application to the Mitocheck dataset When we applied MotIW to the Mitocheck dataset, we were able to identify the experiments which significantly deviate from control movies (5%; 7, 153 out of 144, 909). It amounts to 1, 180 genes (out of 17, 816), some of which are known to Fig. 3. Characterization of our ontology of trajectories. Each column is a single cell trajectory ; trajectories are grouped by cluster label. 1,000 trajectories were randomly selected per trajectory cluster. be involved in cellular motility, such as RhoA (Ras homolog family, member A) or CDK5 (cyclin-dependent kinase 5). Prior to MotIW's design was the question to know whether there exists an ontology of cell trajectories. We could not obtain any valuable clustering from pooling trajectories from the whole dataset, as measured by how the silhouette score and cluster cohesion index vary with respect to the number of clusters  [10].
The approach proved successful when pooling trajectories from MotIW's detected experiments, as this smaller dataset is enriched in rare trajectories. It enabled us to identify eight clusters. Each column of fig. 3  corresponds to one cell trajectory, for which the rows show the standard scores of a subset of features. Interestingly, there is more than speed for differenciating trajectory types. For example, types 2 and 3 present very similar mean squared displacements and space length. However, trajectory curvatures are different: the features mean curvature and straigthness index (another measure of trajectory curvature) are quite distinct between the two clusters. This can be observed in the Supplementary movie, where cells whose trajectory belongs to cluster 2 (green) are much straighter than those belonging to cluster 3 (red).
Context. Solar-like oscillations have been observed by Kepler and CoRoT in many solar-type stars, thereby providing a way to probe stars using asteroseismology. Aims. The derivation of stellar parameters has usually been done with single stars. The aim of the paper is to derive the stellar parameters of a double-star system (HIP 93511), for which an interferometric orbit has been observed along with asteroseismic measurements. Methods. We used a time series of nearly two years of data for the double star to detect the two oscillation-mode envelopes that appear in the power spectrum. Using a new scaling relation based on luminosity, we derived the radius and mass of each star. We derived the age of each star using two proxies: one based upon the large frequency separation and a new one based upon the small frequency separation.
2012a,b)  and of sub-giants  (Deheuvels et al. 2012; Benomar et al. 2013). The large asteroseismic database of Kepler allowed us to estimate the properties of an ensemble of solar-type stars that is large enough to perform statistical studies  (Chaplin et al. 2014). The availability of several mode-frequency sets provided by  Appourchaux et al. (2012)  allowed  Metcalfe et al. (2014)  to infer the radius, mass, and the age of 42 stars: model-dependent stellar parameters that all require a proper calibration. Knowing the effective temperature, the measurements of the frequency of maximum mode power and of the large frequency separation provide a proxy of the stellar radius and masses using scaling relations  (Chaplin et al. 2014). These scaling relations also require independent calibration for being applied to other stars.
2014). If the binary system can be imaged, we can then derive the mass of the binary system from the semi-major axis and the period of the system from Kepler's third law  (Kepler 1619). The seismic detection of such binaries would then provide an independent determination of the total mass of the system, as well as calibrating their common stellar age. Miglio et al. (2014)  predicts that about a handful of solar-like stars are detectable as seismic binaries in the Kepler data set. There have been three pairs of binary stars that were observed by Kepler. The pair 16 Cyg A and 16 Cyg B is a pair for which solar-like p modes were observed  (Metcalfe et al. 2012). Although these two stars are gravitationally bound, the orbital period is unfortunately estimated to be longer than 30 000 years  (Hauser & Marcy 1999) ; in this case, the seismic determination of the system mass would constrain the orbit.