dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients who were identified as high probability of having sleep apnea underwent an overnight polysomnogram. The sleep studies were scored using standard AASM scoring criteria. CSF and blood which is obtained as part of routine care was tested for leptin levels in all subjects. Independent variables Opening pressure, CSF leptin, plasma leptin, apnea-hypopnea index, oxygen desaturations, arousals, and carbon dioxide recordings where available. Covariates We recorded demographic and anthropometric measurements, medical comorbidities and medications. Results: Data from a total of 18 patients was collected. Higher opening pressures were found in subjects with higher BMI (p = 0. 03) and SA (p = 0. 05). Leptin concentrations in CSF were not correlated to the plasma level (r = 0. 5; p = 0. 216). CSF leptin levels were not correlated to body mass index (r = 0.
Note also that, as a consequence of the lower dynamic range of GCaMP3 compared to GCaMP6s  (Chen et al. , 2013) , we are likely to overestimate the concentration-invariance of mitral cell responses, so that the difference in the fraction of concentration-invariant cells in piriform cortex compared to olfactory bulb may be greater than this analysis suggests. Responses in both piriform and mitral cells are highly heterogeneous. We therefore used a bootstrapping analysis to ensure that the population of concentration-invariant neurons, we observed are indeed over-represented and are not merely a consequence of the inherent response variability across population within each dataset. We repeated the analysis of variance on 10,000 shuffled cellodor pairs, in which cell identities were scrambled across stimuli but the population statistics for each odorant response were preserved (see Materials and methods).
Shuffling mitral cell identities across stimuli indeed resulted in the identification of the same percentage of concentration-invariant neurons observed experimentally (bootstrap mean 3. 9 ± 0. 8%; observed value 5. 5%, p=0. 073). By contrast, shuffling cell identities from the piriform dataset yielded significantly fewer concentrationinvariant neurons than observed experimentally (bootstrap mean 5. 3 ± 0. 41%; observed value 11. 7%, p<0. 001, Figure  7e ). Finally, we computed the significance of these findings for each individual imaging site. We considered the observed number of concentration-invariant neurons per imaging site to be significantly above chance if it was higher than 99% of the values calculated from the bootstrap samples. We found that the observed number of concentration-invariant neurons in piriform was above chance in 8 out of 13 imaging sites.
Functional imaging A typical piriform imaging experiment lasted between 2 to 3 hr, and a maximum of three different fields of view were imaged per mouse, at a position between 0 to 1. 5 mm posterior to Bregma. Body temperature was maintained at 37 ˚C using a feedback-controlled heating pad (FST). Piriform imaging data were acquired on two different microscopes: a Leica SP5 with a 25x Olympus objective, 256 Â 256 pixels for a 347 Â 347 mm field of view, and a Scientifica Multiphoton VivoScope with a 20x Olympus objective, 256 Â 160 pixels for a 357 Â 220 mm field of view. A Mai Tai DeepSee laser (Spectra-Physics, Santa Clara, CA) was tuned to 910 nm. Densely packed piriform layer II neurons at a depth of ~250 mM below the pial surface were imaged. 30 s movie sequences were acquired at a frame rate of ~15 Hz.
Mitral cell imaging data were acquired on two different microscopes: Ultima, Prairie Technologies with a 16x objective at 2x zoom or Leica SP5 with a 25x Olympus objective. 25 s movie sequences at 256 Â 256 pixels were acquired at a frame rate of 2. 53 Hz (Ultima) or 2. 9 Hz (Leica SP5). An odor trial lasted 30 s (8 s of pre-stimulus baseline, 2 s of stimulation, 20 s of post-stimulus acquisition). Odors were delivered at a flow rate of 1 L/min with inter-trial intervals of ~35 s. Odor stimuli for a given experiment consisted of one of two odor sets, delivered through a 16-channel olfactometer (Automate Scientific, Berkley, CA): 13 monomolecular odorants (purchased from Sigma Aldrich at the highest purity available), diluted at 1:10,000 vol. /vol. in mineral oil (Sigma Aldrich), and a 'concentration series' consisting of acetophenone, ethyl acetate, and hexanone, at 10-fold increasing concentrations (1:100, 1:1000 and 1:10,000 vol.
Data analysis Signal extraction Data analysis was conducted in Matlab. Motion artifacts were first corrected by using a subpixel translational-based discrete Fourier analysis. Regions of interest (ROIs) were then manually drawn for mitral cell data. For piriform data, ROIs were selected using a semi-automated hierarchical clustering algorithm based on pixel covariance over time (see detailed method below), and the weighted pixel gray value average inside each ROI was used to estimate the fluorescence of single cells at each time frame. The raw fluorescence trace was then upsampled to match the highest sampling rate of each set of experiments (i. e. ~15 Hz for piriform datasets, ~3 Hz for mitral cell datasets). When needed, we corrected for piriform neuropil contamination using a published method  (Kerlin et al.
To obtain a first neuronal segmentation, we therefore selected the clustering output obtained at a particular iteration step. This parameter is user selectable, but the final result is weakly sensitive to it. A value of I = 25,000 was typically chosen for these datasets. We then extract neurons from this first segmentation step by identifying merged pixel ensembles that have typical neuronal size and shapes (Figure  1 -figure supplement 1d). To do so, we compute three measures from each aggregated region and retain only regions falling within a particular range: 1. Size of the region defined as regsize ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi number of pixels p must be included between a minsize and maxsize parameters.
Importantly, the correlations used in the algorithm are calculated on the full duration of the dataset, but to reduce computational costs, the data is temporally binned into bins of 30 s. Also, two additional preprocessing procedures are applied to remove correlations with large spatio-temporal scales: 1. Global slow drifts are removed by high-pass filtering all time courses with a cutoff period of 100 s. 2. The average signal over all pixels is calculated, and its contribution in each individual pixel (i. e. the projection of the pixel time courses onto this average signal) is subtracted. To compute the 'average' signals of the regions obtained at the end of the first neuronal segmentation step, we assign weights to each individual pixel in order to obtain the best estimate of the signal common to all these pixels.
Analysis of variance of single neurons and bootstrap methods To investigate the source of modulation of individual neural responses, we used a linear regression approach by fitting an analysis of variance (ANOVA) with concentration, odorant identity, and trial number as fixed effects, using type II sum of square. We defined concentration-invariant cells as cells that were only significantly modulated by odorant identity (Test 1, p<0. 01); containing information that enables them to identify at least one of the three odorants, but were not significantly modulated by concentration (Test 2, p>0. 01) or by interactions between identity and concentration (Test 3, p>0. 01). 'Generic neurons' are all other cells. It is important to note that in our dataset, only 39. 9% of all recorded piriform neurons were significantly modulated by odorant identity.
We generated 1000 bootstrapped datasets in which we randomly shuffled the cell identities for each odor-concentration pair. In other words, if the data set is described as a 3D array A[Cell_list,Odor_Conc_list,Trials_list], one surrogate dataset AS is generated by performing a randomization of 'Cell_list' for each of the nine items of the Odor_Conc_list. Note that individual trials of a given cell-odor-concentration triplet are kept together. We then submitted the surrogate datasets to the statistical analysis described above for detecting concentration-invariant cells. This enabled us to compute the expected distribution of the fraction of concentration-invariant cells for the 'random coding scheme' hypothesis. We found that the fraction of cells observed in cortex is incompatible with this hypothesis (Figure  6d , p<0.
Acknowledgements We thank  Ludovic Cacheux  for help with imaging experiments and data analysis,  Dmitri Bryzgalov  and  Simon Daste  for help with data analysis,  Yves Dupraz  for his work on the in vivo imaging set-up, and  Jeremie Teillon  and  Philippe Mailly  for help with imaging and imaging data pre-processing. We thank  Kevin Bolding ,  Thomas Preat ,  Andreas Schaefer ,  German Sumbre  and  Jonathan Touboul  for critical comments of the manuscript. This work was supported by a  Marie Curie International Reintegration grant  ( IRG 276869 ), and the  'Amorcage de jeunes equipes' program  ( AJE201106 ) of the  Fondation pour la Recherche Medicale  (to AF), an  EMBO  short term fellowship ( ASTF 395-2014 ) and a postdoctoral fellowship by the  LabEx 'MemoLife'  (to BR), by grants from the  NIDCD  ( DC009839  and  DC015525 ) to KMF), grants from the  Agence Nationale pour la Recherche (ANR  ' SENSEMAKER '), the  Marie Curie Program  ( CIG 334581 ), and The  International Human Frontier Science Program Organization  ( CDA-0064-2015 ) (to BB).
Most subgroup D strains possessed antigen 43, while subgroup C and E strains did not. (iv) Antibiotic resistance determinants. None of the 200 CC95 isolates had the chromosomal mutations in gyrA or parC associated with fluoroquinolone resistance. To investigate the extent to which the presence of resistant determinants that are typically plasmid associated varied among CC95 subgroups, the analysis was restricted to the 83 Australian strains from humans for which whole-genome sequence (WGS) data were FIG  4  Similarity among E. coli CC95 isolates based on the variable genome. Genes present in all genomes or in a single strain were eliminated. Among-strain similarity was quantified using a Jaccard metric, and the dimensions of the matrix were reduced using principal-coordinate (PCO) analysis as implemented in PAST.
Among the blood isolates, those from France were predominantly from subgroup D, whereas none of those from Australia were from subgroup D. Among the fecal isolates, those from France were mostly from subgroup D, but subgroup B isolates were also quite common, whereas those from Australia were mostly from subgroup A. Virulence factor data obtained through PCR screening were available for many of the non-Australian CC95 strains as well as the Australian strains. However, given the very nonrandom distribution of CC95 subgroups among countries, there were insufficient sample sizes to compare strains belonging to all subgroups among all countries. Therefore, a comparison of the distributions of virulence factors was restricted to CC95 subgroup B (Table  5 ). These analyses revealed that the virulence trait profile of subgroup B CC95 isolates varied with the country of origin.
MATERIALS AND METHODS Determining the CC95 pangenome. The nature of the pangenome of CC95 was investigated using WGS data for 200 CC95 strains. These included a collection of strains from Australia (n ϭ 114) and France (n ϭ 17) for which WGS data (Roche 454 GS FLXϩ system, Illumina MiSeq, and Illumina HiSeq2000) were available plus a collection of CC95 strains (n ϭ 66) as identified by Deshpande et al. (55)  for which WGS data were available from NCBI, as well as three CC95 reference strains (APECO1, S88, and UTI89) (Table  S1 ). The assemblies and annotations for all non-NCBI strains are available in Enterobase (http:// enterobase. warwick. ac. uk/). The assembled genomes were annotated using Prokka  (41) , and ROARY (42) was used for the pangenome analysis. The Harvest suite of tools  (43)  was used to align the strains and visualize the inferred phylogenetic relationships of these 200 CC95 strains.
Statistical analysis. Factors influencing the relative frequency of CC95 subgroups were investigated using either contingency table analysis or nominal logistic regression where appropriate. For analyses involving multiple factors, nonsignificant terms were dropped using backwards elimination, and only significant terms were reported. Accession number(s). The raw sequence read files have been deposited in NCBI and are associated with BioProject PRJNA385370, accession no. SRX2786117 to SRX2786219.
However, there was no correlation between myotube width and the size of the AChR clusters it expressed (Figure  7F ), indicating that the mechanism of AChR clustering is independent of the extent of myotube fusion. (Gaudel et al. , 2008; Zhu et al. , 2011; Lawlor et al. , 2014; Gibbons et al. , 2016). (D) mRNA expression of MyoD and myogenin normalized to day 0 for each condition plotted for the indicated number of days after induction of differentiation (n = 6). Data are shown for both substrates of myogenic stiffness, that is, 12 kPa (closed triangles), and with a mechanical pattern (open squares); expression levels are time dependent (p < 0. 05). (E, F) MHC was assessed by qPCR, and data were normalized to GAPDH and plotted for glass, myogenic substrates, and mechanically patterned hydrogels (n = 5).
Mechanical characterization Substrate stiffness was measured via indentation using an MFP-3D-Bio (Asylum Research, Santa Barbara, CA) atomic force microscope (AFM). We used chromium/gold-coated, silicone nitride (SiN) cantilevers with pyramid tips (PNP-TR; NanoWorld) with a nominal spring constant of ∼30 pN/nm, as determined from the MFP-3Ds built-in thermal calibration function. Samples were mounted on glass slides using vacuum grease and immersed in PBS. The probe was indented into the sample with an approach velocity of 2 μm/s and a force trigger of 2 nN. AFM data were analyzed using custom code in Igor Pro (Wavemetrics); the substrate spring constant, that is, Young's modulus, was determined using a linearized Sneddon model  (Kaushik et al. , 2011). Cell line and human embryonic stem cell-derived myotube culture C2C12 mouse myoblast cell line, which was validated by the American Type Culture Collection (CRL-1772), was seeded on the surface of mechanically patterned and myogenic hydrogels or glass coverslips coated with collagen IV (100 μg/ml) at a density of 4000 cells/ cm 2 and maintained in growth medium consisting of DMEM (with 4.
Myoblast fusion index was calculated as the percentage of Hoechst-positive cell nuclei residing within MF20-positive myotubes. Average myotube width was calculated by measuring the width at the widest point on the myotube using the ImageJ measure function. For each metric, data were collected from 20 representative 20× images. BTX-immunoreactivity on 5 (mouse) or 20 (human) representative 60× images was used to evaluate AChR clustering. Average cluster size was quantified by manual tracing using ImageJ software. The width of each myotube that expressed AChR clusters was recorded and plotted against AChR cluster area to evaluate the correlation between cluster size and myotube width.
Immunofluorescence staining, fusion characterization, and AChR characterization Cultures were fixed with 3. 7% formaldehyde for 25 min at room temperature, permeabilized with 1% Triton-X for 5 min, and then stimulation with 1 mM ACh chloride (Sigma-Aldrich) in a 10-μl volume. Kymographs of myotube contraction were generated using ImageJ software.
Data are shown for both substrates of myogenic stiffness, that is, 12 kPa (closed triangles), and with a mechanical pattern (open squares); expression levels are time dependent (p < 0. 05). (E, F) MHC was assessed by qPCR, and data were normalized to GAPDH and plotted for glass, myogenic substrates, and mechanically patterned hydrogels (n = 5). *p < 0. 05, **p < 0. 01, and ***p < 0. 001 based on ANOVA comparisons of the indicated groups.
ACKNOWLEDGMENTS We thank  Heather Main  and  Jamshid Arjomand  ( Genea Biocells ) for use of hESC-derived myoblasts. We acknowledge funding support from the  National Institutes of Health  ( DP2OD006460  to A. J. E. and  F32HL131424  to C. L. H. ) and the  National Science Foundation  ( 1463689  to A. J. E. ), as well as support via the  National Science Foundation Graduate Research Fellowship Program  (to A. K. G. ).
Current treatments are based on data from small cell lung cancer, such as cisplatin-etoposide or carboplatin-etoposide  (14). First-line treatment for GI-NECs has remained unchanged since the early 1990s, when high tumour response rates were reported with etoposide-platinum combination (41-67%)  (15). In addition, a number of small retrospective series have published results of other chemotherapy regimens (temozolomide-based  (12, 16) , taxane-based  (12) , 5-FU-based  (17, 18)  or topotecan  (19) ) after failure of platinum-etoposide therapy in patients with NECs  (12, (16) (17) (18) (19) (20). Results from ongoing studies such as the ECOG-ACRIN 2142 trial (NCT02595424), which plans to randomise 126 patients diagnosed with NEC-G3 to first-line cisplatin/etoposide or capecitabine/temozolomide may clarify the most suitable treatment for this population of patients.
Patients in cohort 2: External Validation Cohort Patients diagnosed with GI-NECs who were seen between April 2000 and December 2015 were identified retrospectively in five different European Countries (France, Belgium, Germany, Spain and UK) from centres with expertise in neuroendocrine malignancies. The same inclusion criteria as the Training Cohort were used and the same baseline and demographic characteristics were collected. Patients with missing data in any one of the items included in the score were excluded and considered ineligible. The primary end-point was OS. A sample size for the external validation was estimated to replicate the hazard ratio achieved by the score (as a discrete variable) in the multivariable analysis from the Training Cohort (HR 1. 9); a minimum of 82 patients was required to externally validate the score (power: 80% and twotailed α-error: 0.
Patients in cohort 3: Prospective Validation Cohort The Prospective Validation Cohort included all consecutive patients diagnosed with GI-NECs seen at The Christie NHS Foundation Trust between July 2014 and November 2015. Patients were identified and data was collected prospectively. The same inclusion criteria and data collection items as in the External Validation Cohort were applied; patients with missing data for any of the score items were excluded. The primary end-point was OS.
Human Subjects and Phenotype Data We assembled a cohort of subjects with 22q11. 2DS (Tables  I  and II  in the Data Supplement). Subjects were previously recruited by the International Chromosome 22q11. 2DS Consortium, the International 22q11. 2 Brain Behavior Consortium (http://22q11-ibbc. org), and clinical groups that specialize in the treatment of individuals with 22q11. 2DS. All subjects within the cohort had a clinical diagnosis of 22q11. 2DS that was confirmed by the presence of a 22q11. 2 deletion using fluorescence in situ hybridization or multiplex ligation-dependent probe amplification (SALSA MLPA kit P250 DiGeorge; MRC Holland, The Netherlands). Informed consent was obtained for all participants, and this study was conducted under an Internal Review Board-approved protocol at the Albert Einstein College of Medicine (CCI 1999-201).
SNP Array Genotype and Data Quality Control Genomic DNA from 1244 study subjects was array genotyped using Affymetrix GeneChip Genome-Wide SNP 6. 0 array. The majority of samples were genotyped was at the Genomics Facility core laboratory of Albert Einstein College of Medicine. However, 37 samples were genotyped in the Advanced Genomics laboratory core at the Children's Research Institute (Milwaukee, WI) for clinical purposes,  16  and 191 Chilean samples were genotyped in the Center for Human Genetics, Clínica Alemana Universidad del Desarrollo, Santiago, Chile. 15 he raw data from all arrays were processed through the same pipeline using the same criteria. Genotype data from arrays with contrast quality control scores ≤0. 4 per sample, contrast quality control <1. 7 per batch, and Median Absolute Pairwise Difference metric >0.
35, were excluded. Genotypes were called using the Birdseed V2 Genotyping Algorithm (call rate: 99. 02±0. 02%). To account for batch effects, BEAGLECALL Version 1. 0. 1 software was used to rescore genotypes. 17 SNPs with call rates <95%, minor allele frequency <1%, or Hardy-Weinberg equilibrium P value <10 -5 were excluded. In addition, samples that showed second-degree relatedness or closer, based on identity by state, were removed. For each subject, deletion size was determined using the log 2 intensity ratio as estimated by the Copy Number Analysis Module of Golden Helix Powerseat Package. The CEL files and genotype data are being deposited to National Center for Biotechnology Information database of Genotypes and Phenotypes phs001339. v1. p1. We performed imputation to increase the number of SNPs available for analysis.
Only genotyped SNPs with minor allele frequency >1% were used for imputation. Haplotypes were prephased using SHAPEIT software,  18, 19  and imputation was performed using IMPUTE2 with the 1000 Genomes Phase I data set as the reference panel. 20 Imputed SNPs with minor allele frequency ≤1% or imputation quality (INFO) scores ≤0. 8 were excluded from the GWAS.
Statistical Methods We used a case-control approach, in which individuals with 22q11. 2DS and TOF were considered cases and individuals with 22q11. 2DS without CHD were considered controls. We conducted principal component analyses to identify the PCs of race/ethnicity. Potential associations between TOF, sex, and deletion size were assessed using logistic regression adjusted for the first 4 PCs. A P value <0. 05 was considered significant. The association between TOF and each SNP was assessed by logistic regression analysis under an additive genetic model using data from all study subjects. These analyses were performed using SNPTEST v2. 5. 2 https://mathgen. stats. ox. ac. uk/genetics_software/ snptest/snptest. html) and accounted for the genotyping accuracy and first 4 PCs of race/ethnicity.
21, 22 A P value of 5×10 -8 was used as the genome-wide significance cutoff for single association tests. For a meta-analysis, the cohort was split into groups determined by principal component analyses. Each group was analyzed separately using logistic regression, and the results were meta-analyzed using the inverse-variance method. Power for these analyses was assessed using QUANTO (http://biostats. usc. edu/Quanto. html). Manhattan plots and quantile-quantile (Q-Q) plots were generated using Golden Helix Powerseat. For regions of interest identified in the GWAS, regional association plots were generated using LocusZoom software (http://locuszoom. sph. umich. edu/locuszoom/). 23 onditional logistic regression analyses were performed to determine whether multiple variants within a region are independently associated with TOF in individuals with 22q11.
Linkage Disequilibrium Analysis of Whole-Genome Sequence to Identify Variants in Linkage Disequilibrium With GWAS Findings Whole-genome sequencing of a subset (n=397) of our 22q11. 2DS samples was performed using the Illumina HiSeq2000 and HiSeq X Ten platform at Hudson Alpha Institute for Biotechnology (Huntsville, Alabama) as part of the International 22q11. 2 Brain and Behavior Consortium to find genes for schizophrenia. Variant calling was performed using PEMapper software for read mapping to the hg38 (GRCh38) reference genome and PECaller software for variant calling. 24 CrossMap (http://crossmap. sourceforge. net/) was used to convert genome coordinates between hg38(GRCh38) and hg19 (GRCh37). 25 To follow-up on the top result from the GWAS (Results), the genomic region (chromosome 5 [chr5]:88 703 723-91 409 593) from MEF2C through ARRDC3 was extracted from the whole-genome sequencing data.