dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole-mount mRNA in situ hybridization was performed as described previously  9. The probes were detected using alkaline phosphatase (AP)-coupled anti-digoxigenin Fab fragment antibody (Roche) with BCIP/NBT staining (Vector Laboratories). Time-lapse confocal fluorescence imaging. Olympus FV 1000 confocal microscopy was used for the four-dimensional time-lapse fluorescence imaging  15, 16. Z stacks were taken every 2 min from 30 to 50 hpf. Videos were created after processing with the FV10-ASW version3 software. Bromodeoxyuridine (BrdU) labeling assay, TUNEL assay and double immunostaining. Briefly, Tg(cmyb:EGFP) embryos were incubated with 10 mM BrdU (Sigma) for 30 minutes and incubated with egg water for another 2 hours, then fixed in 4% paraformaldehyde (PFA) at 3 dpf. After dehydration and rehydration, the embryos were treated with Proteinase K (10 mg/ml, Finnzyme) for 30 minutes and re-fixed in 4% PFA for 30 minutes.
After blocking with blocking buffer (2 mg/ml BSA, 10% FBS, 0. 3% Triton-X100 and 1% DMSO in PBST), the embryos were stained with rabbit anti-GFP (Invitrogen) primary antibody and Alexa Fluor 488-conjugated anti-rabbit (Invitrogen) secondary antibodies. The embryos were fixed again, treated with Proteinase K for the second time, and re-fixed in 4% PFA. The embryos were then incubated with 2 N HCl for 1 hour and stained with mouse anti-BrdU (Roche) and rabbit anti-GFP (Invitrogen) antibodies. Finally, Alexa Fluor 594-conjugated anti-mouse and Alexa Fluor 488-conjugated anti-rabbit (Invitrogen) secondary antibodies were used. Images were taken using Olympus FV 1000 confocal microscopy equipped with the FV10-ASW version3 software. Terminal transferase UTP nick end labeling (TUNEL) was performed using the In Situ Cell Death Detection Kit, TMR red (Roche) according to the manufacturer's recommendations.
Luciferase reporter assay. 293 T cells were transfected with indicated plasmids using Effectene Transfection Reagent (QIAGEN). Cells were harvested 36 hours after transfection and luciferase activities were analyzed using the Dual Luciferase Reporter Assay Kit (Promega) according to the manufacturer's protocols. Luciferase activity was normalized to Renilla activity. Generation of TALEN-mediated cebpa mutant zebrafish. TAL Effector Nucleotide Targeter software was used to design candidate TALEN target site for cebpa gene  41. TALEN expression vectors were constructed as previously described  42. The corresponding mRNAs were prepared using SP6 mMESSAGE mMACHINE Kit (Ambion) and were injected in pairs into one-cell stage zebrafish embryos at the dosage of 150 pg (for each TALEN, 300 pg in total).
Acknowledgments We appreciate  Dr. Gavine  for proofreading this manuscript. The authors would also like to thank  Dr. Bo Zhang  and  Dr. Marella F. T. R. de Bruijn  for kindly providing TALEN plasmids and Runx1 enhancer plasmids, respectively. We are grateful for  Mei Dong ,  Juan Chen ,  Yi Chen  and  Yi Jin  for their excellent technical support. This work was supported by grants from the  National Basic Research Program of China  ( 973 Program ) (2012CB910300), the  National Natural Science Foundation of China  ( 31371479 ), the  Natural Science Foundation of Shanghai City  ( 13ZR1425800 ) and the  Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning.
[23] , we have carried out axisymmetric simulations of wave propagation in the steel bar using the Autodyn R code. The contact pressure history derived from the Fortran code is applied on the end of the bar and we analyse the evolution of the stress pulse along the bar. Figure  4  shows a comparison between the pressure pulse applied on the end of the bar and the axial stress history observed at the position of the strain gauges (285 mm of the end). It appears that wave dispersion induces significant fluctuations of the stress. All the results presented hereafter take wave dispersion in the steel bar into account (using axisymmetric Autodyn R simulations). Let us now discuss the influence of micro-inertia. Figure  5  shows the experimental stress history and the corresponding numerical results, obtained with (R c = 2 mm) and without (R c = 0) micro-inertia effects, for the impact of Alporas R foam samples of lengths 50 and 100 mm at 380 m s -1.
Autodyn simulations For comparison purpose, we also carried some simulations of the impact tests using the dynamic code ANSYS Autodyn R. In these simulations, both the foam projectile and the Kolsky bar are modelled. The foam behaviour is described by the "crushable foam" model (the version of the "crushable foam model" available in Autodyn R is the same than the one in LS-Dyna, that has been discussed by  Pattofatto & al. [15] ). This model requires the axial stress-volumetric strain curve and the initial bulk and shear moduli. The Autodyn R computations do not take microscale inertia into account. The numerical prediction obtained with Autodyn R for the impact of an Alporas R foam sample (length: DYMAT 2015 50 mm -impact velocity: 380 m s -1 ) is presented in Fig. 6 , together with the experimental stress history and the result of a simulation performed with the 1D fortran code, neglecting micro-inertia.
Simulations were also conducted using ANSYS AUTODYN R. The foam is modelled by using the "crushable foam" model. Micro-inertia effects are not taken into account in these simulations (it might be possible to implement the inertia the proposed modelling in Autodyn R with the use of user-subroutines). The Autodyn R results are close to those obtained by the Fortran code when no micro-inertia is included. Future works will include the implementation of micro-inertia effects into ANSYS AUTODYN R. To check and extend the validity range of the model presented in this paper, impact experiments will be conducted at velocities ranging from 500 to 2000 m s -1. Results of these tests will be the scope of another paper.
Visual reading or manual regions of interest (ROIs) tracing induces interobserver variability. FreeSurfer is a free automated structural MRI image processing software providing many anatomical measures, including gray matter volume (http://surfer. nmr. mgh. harvard. edu/) [8] , and already used to measure atrophy in patients with AD or mild cognitive impairment (MCI)  [9, 10]. It has also been used to analyze and quantify PET data  [11, 12]. PET and MRI biomarkers of neuronal injury focus on the medial temporal region. Both precuneus and cingulate cortex show prominent reduced metabolism on 18 F-FDG PET and atrophy on MRI and could also be considered as one of the biomarkers  [13]. The present study aims to analyze regional CMRglc (as assessed by 18 F-FDG PET) with atrophy (as assessed by MRI) in precuneus and cingulate cortex of patients with AD or prodromal AD (MCI) and healthy controls (HC) from the elderly population, using the same automated software, FreeSurfer.
Image Analysis. Volumetric segmentation was performed with the FreeSurfer image analysis suite (http://surfer. nmr. mgh. harvard. edu/) [8]. Briefly, this processing includes the following: removal of nonbrain tissue using a hybrid watershed/surface deformation procedure; automated Talairach transformation; segmentation of the subcortical white matter and deep gray matter volumetric structures; intensity normalization; tessellation of the gray matter white matter boundary; automated topology correction; and surface deformation using intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the position of the greatest shift in intensity, which defines the transition between tissue classes. This software generated many cortical volumes, surface areas, and cortical thicknesses, as well as other values.
Discussion The main objective of this study was to compare regional hypometabolism with atrophy in precuneus and cingulate cortex of patients with AD or prodromal AD, using the same automated software, FreeSurfer. Several morphological studies have analyzed MRI data using this software for the purpose of classifying AD or to predict the conversion of MCI to AD  [9, 10, 23, 28]. Few recent studies used FreeSurfer ROIs to quantify the buildup of amyloid  [11, 29]. However, few studies focused on the use of FreeSurfer to analyze 18 F-FDG data  [30]. In our study, hippocampal and posterior cingulate volumes were lower in the MCI and AD groups than in HC. Regarding CMRglc, there was significant hypometabolism in the precuneus and posterior cingulate cortex of AD patients compared to MCI subjects and HC and also in MCI patients compared to HC.
Many studies report that FreeSurfer is a reliable method to determine segmentation from MRI in patients with AD or MCI  [9] [10] [11]   and is substantially more reliable than manual segmentation. Variability does however exist in the automated segmentation procedure  [36] [37] [38] , and manual correction of FreeSurfer derived boundaries is sometimes necessary, especially in marked atrophic brains. All our analyses were performed on the same computer and visually assessed by one physician, using the same version of the software, as Gronenschild et al. showed that variability could exist between different FreeSurfer versions or workstation types  [39]. Nonetheless, Su et al. used FreeSurfer to quantify C11-PiB and found that this method was highly reliable for the estimation of regional measurements, despite variability in ROI volumes  [11].
5 T  [46]. MRI data were acquired at 1. 5 T in one of the three centers; this could have introduced bias in favor of PET. Our study has some limitations. Firstly, we included a small number of patients and controls; secondly, there was no histopathological verification of disease status; thirdly we did not apply partial volume correction, but we registered PET data to MRI and used intensities ratio to cerebellum; finally, MCI and AD patients were younger than controls. Nonetheless, FreeSurfer has several advantages: the analysis is a fully automated, accurate, and simple process and offers the option of only analyzing grey matter, thus excluding white matter and its nonspecific uptake (shown, e. g. , by the uptake of florbetapir in amyloid density studies)  [47].
Conclusion In summary, the present study, using the same automated software, FreeSurfer, to analyze both 18 F-FDG and MRI images, confirmed hippocampal atrophy in both AD and MCI patients and hypometabolism in the hippocampus, the cingulate cortex, and the precuneus of AD and MCI subjects. Moreover, our results showed a gradation in the decrease of CMRglc in the posterior cingulate cortex and the precuneus in prodromal AD (or MCI) and AD, suggesting the use of 18 F-FDG in these two regions as a neurodegenerative biomarker, which could be positive earlier than MRI atrophy in those areas.
Acknowledgments This work was funded by the  French Ministry of Health  with Grant no. PHRC-N 2008-1004  and in part by the  EC-FP6-Project   DiMI ,  LSHB-CT-2005-512146 , the  Région Centre , and FEDER: "Radex" programs. This work was conducted with the support of the  GIS Radiopharmaceutiques Caen, Toulouse , and Tours centers with Laboratoires Cyclopharma. The authors are grateful to  JB Deloye  ( Laboratoires Cyclopharma ),  Drs. N. Daluzeau  and  F. Bouvier  ( CH Lisieux ),  Dr. B. Dupuy  ( CH Cherbourg ),  A. Abbas  and  A. Pèlerin  ( INSERM U923 ),  Dr. A. Manrique  ( GIP Cyceron ),  C. Roussel, Ph. D. ,  C. Baringthon, Ph. D. ,  A. Matysiack ,  H. Bansard , and  F. Teasdale  ( CIC/CIC-IT 202 ) for their contributions to the clinical investigation.
Genome Sequencing, Assembly, and Annotation Thirty-six O. oeni and three O. kitaharae genomes were sequenced and assembled either by using Illumina sequencing technology and SOAPdenovo assembler (Macrogen, Seoul, Korea) or 454 sequencing technology and Newbler assembler (GeT-PlaGe Genotoul, Castanet Tolosan, France). Contigs shorter than 200 bp were discarded and final genomes were deposed on NCBI under the accession numbers listed in table 1. All genomes were annotated by RAST  (Aziz et al. 2008) , curated manually and possible pseudogenes were indicated. Curated genes were resubmitted to KAAS annotation server  (Moriya et al. 2007 ) of the KEGG project to get an extra reference. Coding sequences (CDS) annotated by RAST and KAAS were classified according to their ortholog groups using OrthoMCL  (Li 2003).
Detection, Analysis, and Distribution of Single Nucleotide Polymorphisms Raw reads were mapped against the reference genome of strain PSU-1 with the program BWA bwasw  (Li and Durbin 2010). Single nucleotide polymorphism (SNP) were extracted with SAMtools and BCFtools  (Li et al. 2009 ). An independent mapping and extraction of the SNP was carried out with MUMmer nucmer  (Kurtz et al. 2004 ), both for the already assembled public genomes and for the final assemblies of the genomes of this study. The 47,621 resulting SNP positions were parsed into a matrix containing the allele carried by each strain. The distribution of SNP among different groups of strains was determined by measuring the Shannon Entropy for each SNP with the formula H = À P p(xi) log2p(xi), where p(xi) represents the probability of finding the allele xi in an arbitrarily defined group of strains.
Distribution of Orthologs All the CDS from all the strains were assigned to ortholog groups according to orthoMCL v2. 0. 9. The output was parsed to a matrix containing the number of CDS assigned to each ortholog group for each strain. The distribution of CDS among the groups of strains was determined by measuring the Shannon Entropy of each ortholog group from a matrix, exactly in the same way as for SNPs, except that rows represent each group of orthologs, and every cell contains the number of CDS assigned to each ortholog group, as if it were an allele. The distance between genomes was measured by Canberra method from the same matrix used to calculate the entropy. Pheatmap R package (R Core Team 2013) was used to calculate the distance and visualize the results.
000, 2. 000 Markov chain Monte Carlo repetitions, and each step was iterated ten times. The k value that best fitted the model was selected for the definitive analysis. Distances between genomes were calculated by ANIm, ANIb, and Tetra algorithms with JSpecies v1. 1  (Richter and Rossello-Mora 2009). The difference between ANIm and ANIb is that the latter works by cutting the genomes in 1,020 bp pieces and averages the best matches of an allversus-all BLAST, whereas the former does not cut the genomes and searches the matches by MUMmer. The resulting similarity matrices were transformed into distance matrices and used to reconstruct trees by the neighbor joining method with MEGA v5. 2. 2. All trees were further processed and plotted with APE R Package  (Paradis et al. 2004 ).
Acknowledgments This work was supported in parts by the  European commission  ( FP7-SME project Wildwine , grant agreement no. 315065 ) and the  Regional Council of Aquitaine  (project  SAGESSE   2010 ). Authors thank  Andres Aravena  for providing us with the script for calculating the  Shannon Entropy. H. C. S. was recipient of a GIRACT bursary award for promoting flavor research amongst PhD students in Europe.
DNA concentration and quality was determined with NanoDrop (ND-1000 spectrophotometer and software; NanoDrop Technologies, Berlin, Germany). DNA copy number was detected with array-Comparative Genomic Hybridization (aCGH) following manufacturer's recommendations using 60 K oligo probes approximately spaced at 40-100 kb intervals across the genome (Human Genome CGH microarray 60B kit, Agilent™). Sex matched genomic DNA (Promega Corporation, Madison, WI, USA) was used as a reference. Relative fluorescence intensity data was analyzed with the aCGH analysis software v3. 4 (Agilent Technologies Inc. , Santa Clara, CA, USA) by applying Z-score segmentation algorithm with a window size of 10 points to identify chromosome aberrations. We identified a 627-971 kb heterozygous duplication of 6q22. 31 region in the proband [Figure  3 ].
Each child participated in a neuropsychological assessment session in which they were tested on the Wechsler Abbreviated Scale of Intelligence (WASI)  57  , the Wechsler Individual Achievement Test (WIAT-II)  58  and the Working Memory Test Battery for Children (WMTB-C)  59. Full-scale IQ was determined using the WASI; academic achievement in reading and mathematics was assessed using the WIAT-II; working memory was assessed using the WMTB-C. These standardized measures were acquired before tutoring and were not readministered because of their limited validity when repeated within a year. The Scale for Early Mathematics Anxiety was also administered before and after tutoring in both groups  60, 61. Parametric tests were used for all analyses (twosided t-tests), as data were normally distributed in both samples (all K-S tests for normality P values were 40.
155 for the MLD group and P40. 625 for the TD group). Tutoring sessions. All children took part in an 8-week math tutoring program adapted from MathWise  17, 19, 40. The tutoring combined conceptual instruction with speeded retrieval of arithmetical facts. The focus of the tutoring included strengthening of number knowledge (for example, cardinality) and relations within and between operations (for example, commutativity and inverse relation between addition and subtraction) that facilitate the use of sophisticated counting procedures and retrieval-based processes. The tutoring also incorporated a strategic practice component, that is important for building automaticity  40  , and decreasing load on cognitive resources (for example, working memory and non-verbal reasoning)  62. This practice was designed to promote quick responding and use of efficient counting procedures to generate as many correct responses as possible, which in turn supports the formation and strengthening of representations in longterm memory  63, 64.
The larger operand was equally likely to appear in the first or second position, as in the arithmetic verification task performed inside the scanner. The child was required to accurately solve each problem without the use of paper and pencil and to verbally state the answer out loud. For each problem, the experimenter recorded the child's response verbatim on paper. Proportion of correctly solved problems was computed as the outcome measure of interest. Three children (one in the MLD group, and two in the TD group) did not complete this task due to time constraints. Parametric tests were used for this analysis (two-sided t-tests), as data were normally distributed in both samples (all K-S tests for normality P values were 40. 628 for the MLD group and 40. 320 for the TD group for both pre-and post-tutoring session).
The task design also included a total of six rest periods-10 s each -, which occurred at jittered intervals during each run to achieve an optimal event-related fMRI design  66. The rest periods were not explicitly modelled. Accuracy and mean of median reaction times of correctly solved problems were computed separately for each participant for each task-based condition (that is, arithmetic verification and number identity verification). Parametric tests were used for these analyses (two-sided t-tests), as data were normally distributed in both samples (all K-S tests for normality P values were 40. 620 for the MLD group and 40. 657 for the TD group). Structural MRI data acquisition. High-resolution T1-weighted images were acquired in each child at both scan sessions (that is, pre-and post-tutoring), to facilitate anatomical co-registration of fMRI maps.
We used the Matlab package libSVM (http://www. csie. ntu. edu. tw/Bcjlin/libsvm) to fit the classifier. LOOCV was used to measure the performance of the classifier in distinguishing MLD from TD children, based on patterns of brain activity. In LOOCV, data are divided into N folds (here, n ¼ 30). A classifier is built using n À 1 folds, leaving out one sample. The left out sample is then classified using this classifier, and the Classification Accuracy (CA) is noted. The above procedure is repeated N times by leaving out one sample each time, and finally an average CA is computed. Permutation tests (10,000 permutations of class labels) were conducted to arrive at P values associated with CA. Multivariate distance analysis. We used a multivariate distance analysis to assess the behavioral significance of tutoring-induced functional brain plasticity (Fig.
4, 18 ome studies have analysed the classifi cation of individuals as having diabetes or compared prevalence estimates based on diff erent defi nitions in specifi c cohorts, especially for HbA 1c compared with either FPG or 2hOGTT. Most f these analyses were based on a single cohort and very few covered diff erent geographical regions. 3] [64] [65] [66]  There is no pooling study for HbA 1c , which can be measured easily in population-based surveys without the need for overnight fasting and has been approved by the American Diabetes Association and WHO as a diagnostic test for diabetes. 11, 14 However, a review of data from six countries reported that the sensitivity of diabetes diagnosis based on HbA 1c compared with FPG ranged from 17% to 78%,  67  raising concerns about ethnic variation of HbA 1c -based defi nition.
Data sources We used population-based data collated by the NCD Risk Factor Collaboration (NCD-RisC), a worldwide network of health researchers and practitioners who, together with WHO, have collated a large database of populationbased health examination surveys and epidemiological studies of cardiometabolic risk factors. All data sources were checked by at least two independent reviewers as being representative of a national, subnational, or community population, and for study quality indicators such as fasting duration and the protocol for OGTT. We excluded surveys that had not used a standard glucose load for OGTT. Within each survey, we included participants aged 18 years and older who were not pregnant and had fasted at least for 6 h before measurement as a part of the survey instructions.
We excluded HbA 1c data from before the year 2000 to minimise the use of non-standard assays. 71 We also excluded surveys that had measured a biomarker only among participants with a high value of another-eg, studies in which FPG was only measured in participants with HbA 1c above a prespecifi ed value, because the relation between the two measurements might be  We restricted the analysis of sensitivity and specifi city to people without a history of diabetes diagnosis, because previous diagnosis and the use of drug treatments probably aff ect the concentrations of biomarkers used to diagnose diabetes. History of diabetes diagnosis was established with survey-specifi c questions, such as "have you ever been told by a doctor or other health professional that you have diabetes?" or the combination of "do you now have, or have you ever had diabetes?" and "were you told by a doctor that you had diabetes?".