dataesr/scientific-paragraphs-categorization huggingface dataset:
All images shown correspond to one focal plane (0. 5 µm) and were imported into Gimp Pack Mode 2. 6 software to adjust brightness and contrasts. To determine the development of the newborn neuroblasts in the different groups, the number of nodes and the length of the dendritic arbor were measured in 16-18 BrdU+/DCX+ cells per ewe in the GCL of the MOB and of the DG. Each BrdU+/DCX+ cell was analyzed with confocal laser scanning microscope (LSM 700, Zeiss, Germany), in its entire Z-axis with 0. 5 µm step interval, using x63 oil immersion objective to measure the length between the cell body to the end of the longest process. Total length of the dendritic tree was obtained by summing the length of all processes of each BrdU+/DCX+ cell. The number of nodes was obtained by counting the number of occurrences of branch points in the dendritic arbor.
STATISTICAL ANALYSIS As densities of BrdU+ and Ki67+ cells, proportions of BrdU+/NeuN+ and BrdU+/DCX+ cells, and dendritic lengths of BrdU+/DCX+ cells were not normally distributed, the data were analyzed with nonparametric tests  (Siegel, 1956). Intergroup comparisons were analyzed using two-tailed Kruskal-Wallis and Mann-Whitney tests. As the percentage of BrdU+/DCX+ cells which displayed one or more nodes was normally distributed, the data were analyzed with a one-way analysis of variance (ANOVA), and significance was probed by the Newman-Keuls test. Statistical analyses were performed using the statistical package SPSS 10 (Chicago, IL, USA) and the level of statistical significance was set at p ≤ 0. 05. All data were represented as median and interquartile ranges except the number of nodes which were represented as mean ± SEM.
ACKNOWLEDGMENTS The authors would like to acknowledge the financial support of the " Programme transversal INRA/Institut Pasteur No. 319 ", the  ANR  programme blanc (2012-2015) PLASTMATBEHAV and the  INRA/Région  for the grant to  M. Brus. We particularly thank (1)  N. Jouaneau  and  M. Chauvet  for histological preparation; (2)  J. Cognié  for veterinary assistance; (3)  C. Moussu ,  E. Archer  and  F. Cornilleau  for their assistance at lambing; (4)  J. -P. Dubois  and  A. Arnould  for sacrifice of animals; (5)  D. Capo  and its staff for animal breeding; (6)  G. Le Pape  for statistical analysis; (7) L. Szymanski for English corrections; and (8) the cellular imaging platform (PIC) of  UMR PRC, Nouzilly.
The AGP sample set is a collection of families comprising an affected proband and two parents, as previously described in Pinto et al. 6  and Anney et al. 20, 21  Many of the subjects at the recruiting sites were tested for fragile X syndrome (FXS [MIM 300624]) and assessed for chromosomal rearrangements with karyotype, fluorescence in situ hybridization, or multiplexligation-dependent probe amplification (MLPA); subjects with known karyotypic abnormalities, FXS, or other genetic disorders were typically excluded. The main analyses presented here were restricted to subjects of European ancestry. 21 All diagnostic, clinical, and cognitive assessments were carried out at each contributing site. All data were gathered at a central coordination site for standardization of data formatting and data quality assurance.
Control Subjects Unrelated control subjects were assembled from three studies in which individuals had no obvious psychiatric history: the Study of Addiction Genetics and Environment (SAGE),  26  the Ontario Colorectal Cancer Case-Control Study,  27, 28  and Health, Aging, and Body Composition (HABC) (Table  S1B ). 29 Samples were genotyped on the same array platforms (Illumina 1M single or duo arrays) as those of ASD subjects and parents and were analyzed with the same quality-control (QC) procedures and CNV analysis pipeline. The control data set used in the primary CNV analysis was composed of 2,640 control individuals of European ancestry (1,241 males and 1,399 females) who passed QC (Table  S1B ). Secondary analyses included 1,843 subjects from other ancestries (SAGE and HABC non-European control individuals), giving a total of 4,768 control subjects of all ancestries.
CNV Analysis CNVs were detected with our analytical pipeline of Illumina 1M arrays (v. 1 and v. 3)  6, 30  and analyzed for case-control differences The American Journal of Human Genetics 94, 677-694, May 1, 2014 679 in burden with PLINK v. 1. 0730, R stats, and custom scripts. The p values associated with odds ratios (ORs) were calculated with Fisher's exact test. Rare de novo CNVs, clinically relevant CNVs, and other selected rare CNVs were validated by at least one method (quantitative PCR, MLPA, and/or long-range PCR). Table  S4  shows all validated de novo CNVs. A list of CNV calls passing QC in affected subjects, including all experimentally validated CNVs, is available in Tables S17A, S17B, and S17C. Secondary analyses included comparisons of CNV number, length, and intersected gene number between our 102 de novo CNVs identified in affected subjects and the 76 de novo CNVs in control subjects of two published data sets: (1) 17 de novo CNVs identified in 15 unaffected siblings from 872 families with a single ASD-affected offspring and an unaffected sibling from the Simons Simplex Collection 4 and (2) 59 de novo CNVs detected in 57 out of 2,623 Icelandic control trios.
One-Gene-and Multiple-Gene-Hit Burden Analysis One-gene-hit burden analyses were performed with Fisher's exact test. When considering the possibility that multiple genes within a CNV event or across events in the same subject act in concert to increase risk (i. e. , multiple-gene-hit burden), we fit a series of logit models to the data. For the logit model, which is a special case of generalized linear model, log odds of case status (logit) was fit to predictor variables, namely the number of brain-expressed genes (BrainSpan) covered by the CNV and the level of gene expression. To analyze the expression data, we transformed the normalized RPKM value of each gene in the neocortex to log(1þRPKM). All analyses were performed in the statistical package R with the function ''glm'' and the logit link.
Ancestry was inferred by analysis of SNP genotype data (Table  S1B ). The rate, size, and number of genes affected by rare (<1% frequency) CNVs were assessed. Consistent with our previous data, we observed that compared to control subjects, affected subjects had an increased burden in the number of genes affected by rare CNVs (1. 41-fold increase, empirical p ¼ 1 3 10 À5 ; Table  1 ). This enrichment was apparent for both deletions and duplications and remained after we controlled for potential case-control differences (Table  1 ). Similar findings were obtained when each stage was considered separately (Tables  S3A-S3C ). Array-and exome-based studies have revealed a substantial contribution of de novo variation to ASD risk,  19  prompting us to assess this further. After screening 2,096 trios (of all ancestries), we found 102 rare de novo CNVs in 99 affected subjects (three of whom had two events; Table  S4 ).
For the DAPPLE analysis, we compiled the following lists of genes: (1) 113 genes identified from our de novo CNVs by NETBAG; (2) 122 genes with de novo LoF SNVs from four published exome sequencing studies;  [7] [8] [9] [10]  (3) 31 genes with hemizygous LoF SNVs on the X chromosome of male ASD subjects and not observed in male control subjects;  16  and (4) 92 ASD-implicated genes previously described as autosomal dominant, X-linked dominant, or X-linked recessive in males. 18 B) A DAPPLE network of 151 genes (Table  S15 ) from the genes in (A) shows direct interactions between associated proteins according to the InWeb database. Nodes represent genes and are colored according to gene-set membership depicted in (A): genes identified from our (legend continued on next page) Of the 11 genes, only TM4SF19 (3q29) belongs to a known locus associated with a recurrent genomic disorder.
Edges represent significant direct protein-protein interactions (as defined by a common interactor binding degree of 2) in the InWeb database. Shaded ovals represent enriched biological functions common among 10% or more genes in the network, and their colors represent functional themes shared among Figures  4A, 4B. Functional Metrics for Various Gene Sets Derived from CNV and SNV Studies, as well as HI Scores for Genic Deletions (A) Box plots of pHIs for various genes sets. Boxes correspond to the spread between the upper and lower quartiles; medians are indicated by a solid horizontal line, and whiskers extend up to 1. 53 the interquartile range. ''Genome'' indicates all 16,781 genes with an available pHI from an imputed data set, excluding seed genes. Only genes implicated in dominant, recessive, or X-linked disorders with neurological phenotypes in the HPO database (''HPO het,'' ''HPO hom,'' ''HPO X,'' respectively) and mouse genes whose homozygous, heterozygous, or X-linked knockout (''MPO het,'' ''MPO hom,'' ''MPO X,'' respectively) causes various abnormal phenotypes were considered.
(B) LOD scores of the probability that at least one gene within a rare deletion will cause haploinsufficiency were calculated for affected and control subjects. Deletion-based LOD scores are plotted as a function of the number of genes in each event for rare genic deletions in affected and control subjects. The p value for the difference in the slope of the two regression lines is indicated. (C) Box plots with the distribution of predicted functional indispensability scores for gene categories from Khurana et al. 55  (LoF-tolerant genes, neutral genes, genes with known mutations as listed in the Human Genome Mutation Database, and essential genes [i. e. , genes in which LoF mutations result in infertility or death before puberty]) and CNV or SNV genes from our DAPPLE analysis (185 genes excluded by DAPPLE, 151 genes selected by DAPPLE, 54 known ASD-implicated genes selected by DAPPLE, and 97 genes selected by DAPPLE after exclusion of the 54 ASD-implicated genes).
Parameter estimation The first, and most significant, contribution of Lewis Sheiner in the field of pharmacometrics was the introduction of NLMEM in pharmacokinetics for drug dosage individualization. 1, 2 He introduced the concept of the "population approach" and developed the NONMEM software in 1980, implementing the FO estimation method. He later improved the methodology and expanded its scope, for instance, to discrete data. I have contributed to this area through collaboration with colleagues for development and/or dissemination of several new estimation methods, specifically nonparametric maximum likelihood, iterative two-stage and SAEM. My involvement in stochastic EM approaches for NLMEM was triggered by Lewis Sheiner who asked me in 2001 to investigate the potential of MCPEM developed by Serge Guzy.
Impact of the projection of the data onto finite-dimensional spaces We have seen in Section 4. 2 that, when X lies in an infinite dimensional space (assumptions H X,M and H X,L ), the rates of convergence are slow. This "curse of dimensionality" phenomenon is well known in kernel estimation for high or infinite dimensional datasets. The introduction of the projection semi-metrics d p , leading to the estimators (4), has thus been proposed in order to circumvent this problem. Defining such estimators amounts to project the data into a p-dimensional space. Indeed, this permits to address the problem of variance reduction since ϕ p (h) := P (d p (x, 0) ≤ h) ∼ h→0 C(p)h p and then the variance is of order 1/(nh p ). Notice that, even if the variance orders of magnitude are the same, the situation here is different from Assumption H X,F with γ = p: H X,F amounts to suppose that the curve X lies a.
Numerical study We briefly present an illustration of the theoretical results. We first describe the parameters we use and the way the risks are computed. Then we illustrate the performances of our method with some figures and tables, for both simulated samples and a real dataset. 6. 1. Implementation of the estimators. We implement the two types of estimators (  2 ) and (  4 ) with the uniform kernel K = 1 (0;1). We choose the bandwidth h in a collection H n with H n = {C/k, 1 ≤ k ≤ k max }. Thanks to the definition of the kernel K, it is useless to consider bandwidths h which are larger than max{ X ix 0 , i = 1,. , n}. Indeed, for any h > max{ X ix 0 , i = 1,. , n} we have K h ( X i -x 0 ) = 1/h and F h (y) = 1 n n i=1 1 {Y i ≤y}. Then we set C = max{ X ix 0 , i = 1,. , n}. To choose the other parameter k max , keep in mind that ϕ(h) should not be too small: the aim is to have an empirical counterpart for Assumption H b2 and also to avoid instabilities in the calculation of V (h).
The boxplots for the corresponding risks (over 50 estimators) are also plotted (outliers are not drawn). The mean integrated risks for all the studied models are presented in Table  2. Both the boxplots of Figure  4  and the risks of Table 2 confirm that the risks generally becomes quite smaller when p increases. 6. 3. Application to a spectrometric dataset. We propose to study a classical dataset, widely studied in regression contexts (see  Ferraty and Vieu (2002, 2006) ;  Ferraty et al. (2007) ). The data are available on line 1  , and have been recorded on Tecator Infratec Food and Feed Analyzer. For each unit i (i = 1,. , n, with n = 215), we observe one spectrometric curve X i which corresponds to the absorbances of a piece of chopped meat measured at 100 wavelengths ranging between 850 and 1050 nm.
Time-lapse microscopy The 6cm diameter Petri dish was imaged on an automated inverted microscope setup duplicated from a previous study  18. The setup was made of: OlympusIX70 inverted microscope, Photometrics CoolS-NAP HQ 2 CCD camera, Zeiss HBO 100 microscope illuminating system, Thorlabs SH05 shutter, Thorlabs TSC001 shutter controller, and 2. 5×-5×-10×-20× objectives (5× was used for all experiments shown here). Images were acquired in WinView/32 and the whole setup was controlled by custom-made Visual Basic software. The setup allows Petri dish scanning at regular time intervals (typically 1h), with phase contrast and fluorescence image acquisition at all time points (at 100ms and 1s exposure times respectively). A mosaic image is reconstructed by combining all the images of contiguous areas of the Petri dish at a given time point by a custommade macro using ImageJ software (http://rsbweb.
Images were acquired by time-lapse fluorescence microscopy. All the images were analyzed using ImageJ software (http://rsbweb. nih. gov/ij/) using custommade macros (see Data Set). The analysis consisted in counting fluorescent dots before and after aggregation. For each experiment 1000-10 000 dots/cells were monitored. Dead cells were excluded from counting by looking at cell displacement as an indicator of cell viability. Two fluorescent images taken 1-2h apart were overlapped and cells that showed no displacement were counted and subtracted from the overall non-aggregating population. The density of red dots (RFP-expressing cells) was used to estimate cell density at the onset of starvation in all experiments. Cell density was comparable at the onset of starvation for all starvation protocols used.
Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. 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.
Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Reviewer Report 04 July 2014 https://doi. org/10. 5256/f1000research. 4516. r5221 © 2014 Gomer R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Acknowledgements The carA and pdsA mutants were kindly provided by  Kerry Ann Sheppard  at the  Dicty Stock Center of Northwestern University in Chicago , and the phg2 mutant by  Anna Marchetti  and  Pierre Cosson  at the  CMU in Geneva. We thank  Vidyanand Nanjundiah ,  Santosh Sathe ,  Bahram Houchmandzadeh ,  Silvia de Monte ,  Sandrine Adiba ,  Madhu Priya ,  Pierre Cosson ,  Pierre Golstein ,  Dominique Schneider , and  Nicolas Desprat  for many helpful comments during the course of this work. We are indebted to  Natale Scaramozzino  for technical assistance, and to  Shalmali Kamat  during her internship at LIPhy Grenoble as well as  Zak Frentz  and  Juliette BenArous at the Laboratory of Living Matter of Stanislas Leibler at Rockefeller University  for their contributions during pilot experiments.
○ Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Reviewer Report 04 July 2014 https://doi. org/10. 5256/f1000research. 4516. r5221 © 2014 Gomer R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The pressure correction scheme for the barotropic equations The implicit scheme which we studied in the previous section is easy to write, but difficult to implement in practice, because of the large nonlinear systems to be solved at the algebraic level. Pressure correction methods are based on the idea that one may compute the velocity and the pressure in a sequential way, thus yielding a more practical scheme. The algorithm presented in this section is implemented in the open-source software component library for fluid flows simulation CALIF 3 S  [5] , developed at IRSN on the basis of the software components library PELICANS  [55] ; in this context, it is routinely used for industrial applications.
The computations are performed with the open-source software CALIF 3 S  [5]. The density fields obtained with h = 1/2000 (or a number of cells n = 2000) at t = 0. 035, with and without assembling the corrective source term in the internal energy balance (S K ) K∈M , together with the analytical solution, are shown on Figure  2. The density and the pressure obtained, still with and without corrective terms, for various meshes, are plotted on Figures  3  and 4  respectively. For these computations, we take δt = h/20, which yields a cfl number, with respect to the material velocity only, close to one. The first conclusion is that both schemes seem to converge, but the corrective term is necessary to obtain the right solution. In this case, for instance, we obtain the correct intermediate state for the pressure and velocity up to four digits in the essential part of the corresponding zone: Without a corrective term, one can check that the obtained solution is not a weak solution to the Euler system: indeed, the Rankine-Hugoniot condition applied to the total energy balance, with the states obtained numerically, yields a right shock speed slightly greater than the analytical solution one, while the same shock speed obtained numerically is clearly lower.
In this paper, we consider endometrial tissues imaged using magnetic resonance imaging (MRI). Such models provide information about the observed variability in disease groups. Furthermore, due to data scarcity, these models can be used for extensive simulation studies, where valid realizations of diseased endometrial tissues are randomly generated. While in this paper we only consider modeling endometrial tissue surfaces, the general method described here has many other applications. In medical imaging, researchers are often interested in modeling various anatomies, including subcortical structures in the brain. Studying shapes of 3D structures in the brain is of particular interest because many diseases can potentially be linked to altering these shapes, in conjunction with other physiological symptoms.
Data Description The data analyzed in this paper are ten endometrial tissue surfaces coming from MRI images. These surfaces are naturally cylindrical, which motivates our statistical model of surfaces with a cylinder parameterization. Figure  1  displays all surfaces in our data set. Note that there is a lot of variation in this data, and thus, parsimonious shape models are very important in this application. We are able to achieve a natural shape model through elastic shape analysis of surfaces. This methodology uses a special Riemannian metric to perform surface registration by achieving invariance to re-parameterizations of surfaces.
Reporting of cases with severe influenza In addition to the primary healthcare surveillance, all severe influenza cases defined as patients with a laboratory-confirmed influenza infection (positive RT-PCR for influenza virus) admitted for more than 24 hours to an intensive care unit (ICU) are reported by clinicians through a standardised form. Demographic and clinical data are collected as well as risk factors/comorbidities and vaccination status. All the ICUs (adults and paediatrics) present on the island (n=4), which cover the total population, participate in this surveillance and swab all patients with severe ARI.
Analysis of epidemics relative to prior influenza seasons Epidemic periods are defined as an increase of the number of ARI consultations above the mean of such consultations during the same periods in the previous years (the 2010-2013 mean was taken for comparison to 2014) and an increase of the positive rate for influenza viruses (proportion of persons testing positive for influenza virus among all persons tested for influenza) superior to 50%. Historical data from 2010 to 2014, which had been collected by the influenza surveillance system annually, including during epidemic periods, were compared. For each year, including epidemic periods, the number of cases with severe influenza relative to the numbers of ARI consultations was calculated both for the year in question and for the year's respective epidemic periods.
Numerical simulations of the classical flow The following simulations have been realized with Matlab. The isolevel sets Γ(t) = {x : u(x, t) = 1 2 } are computed and drawn using the Matlab functions contour in 2D and isosurface in 3D. We use the double-well potential W (s) = 1 2 s 2 (1 -s) 2 , and we consider the following PDE system with periodic boundary conditions: ∂ t u = ∆µ -1 ε 2 W (u)µ µ = 1 ε 2 W (u) -∆u. and with initial conditions u(x, 0) = γ d(x,E) ε , µ(x, 0) = -1 ε ∆d(x, E)γ d(x,E) ε. We plot on Figure  2  the graph of t → u ε (•, t) computed for different values of ε. We choose for the other parameters: P = 2 7 , δ t = 1/P -4. In the first experiment, obtained with ε = 5/P, the two circles merge. In contrast, a crossing of interfaces appear for the case ε = 1. 5/P. This corresponds to a solution of the Allen-Cahn equation with unsmooth nodal set.
0   The second and third authors are supported by the program "Project  ANR-07-BLAN-0361 ,  Hamilton-Jacobi et théorie KAM  faible". AF is also supported by  NSF  Grants  DMS-0969962  and  DSM-1262411. GC is supported by  CONACYT  Grant  178838. LR is also supported by the  ANR  project  GCM, program  " Blanche ", project number  NT09-504490. Large part of this work was done while AF was visiting the  Mathematics Department at the University of Nice , whose warm hospitality is gratefully acknowledged.