dataesr/scientific-paragraphs-categorization huggingface dataset:
18S, Actb, Eif4a2, Sdha and Mrlp32 were tested as reference genes in the Genorm Software  [78]. The qPCR were normalized with the Genorm normalization factor obtained with Actb, Eif4a2 and Sdha. The differences in mRNA levels between CD and either OP or OR mice for each maternal effect (lean versus obese mother) were calculated and are expressed relative to CD levels. The complete list of primers used is provided in Table  S4. Luminometric Methylation Assay (LUMA): Global DNA methylation was analyzed as previously described by Karimi et al. [79]. Restriction enzymes (HpaII, MspI, and EcoRI) were purchased from New England Biolabs. Briefly, 500 ng of genomic DNA was subjected to double digestion with either HpaII + EcoRI in buffer 1 (10 mM bis-Tris propane-HCl, 10 mM MgCl 2 , 1mM DTT) or MspI + EcoRI in buffer 4 (50 mM potassium acetate, 20 mM Tris-acetate, 10 mM magnesium acetate, 1 mM DTT) for four hours at 37uC.
2 mm; Millipore). The membrane was blocked by incubation with 5% nonfat milk in PBS-Tween. After washing in PBS-Tween, membranes were incubated overnight with the primary antibody in 2% nonfat milk (anti-H3K9me3 1:1000, anti-H3K4me3 1:1000, anti-H3K9ac 1:10000, anti-H3K9me1 1:1000; Millipore, 07-442, 07-473, 07-450, 07-352, respectively) and then for 1 hour with secondary antibody conjugated with horseradish peroxidase (anti-rabbit IgG 1:10000, Sigma A6667). Signals were detected with ECL Plus (Amersham) and LAS-1000 (Fujifilm). The membranes were stripped and incubated for one hour with anti-pan H3 antibody (Abcam ab1791, 1:5000). The secondary antibody and signal detection were as previously described. Signals were quantified with AIDA software and the signal for each specific modification was normalized to the amount of total H3.
The whole western blotting procedure for each histone mark and total H3 was carried out twice. We then averaged the signals for the two experiments. Statistical analyses: All statistical analyses were carried out with GraphPad Prism 2. 0 Software. Phenotypic parameters (body weight, caloric intake, hormone levels) with a normal distribution were analyzed by one-way ANOVA, to test for differences between mice with lean and obese mothers and differences between diets. Bonferroni post hoc tests were carried out for pairwise comparisons. Fatty acid levels, IDEXA results, organ weights, RT-qPCR data and levels of DNA methylation and histone marks were analyzed by Kruskal-Wallis tests followed by Dunn's post hoc tests. Differences were considered to be significant if p,0. 05.
This work was supported by  Nestle , the  Fondation pour la Recherche Medicale  and an  Association Francaise d'Etudes et de Recherche sur l'Obesite śtudentship  awarded to AV, a  Fournier-Pharma studentship awarded  to  CGK , a grant from the  Swedish Cancer Society  awarded to TJE, and by grants from  INRA ,  INSERM  ( ATC-Nutrition, PRNH ),  Association Francaise des Diabetiques, Institut Benjamin Delessert , the  Fondation Coeur et Arte  `res (FCA Nu  05-T4 ), the  ANR-Agence Nationale de la Recherche  (The  French National Research Agency)  under the  Programme National de Recherche en Alimentation et Nutrition Humaine , project  ANR-06-PNRA-022-01 , and  Contrat Cadre d'Aide au Projet d'Innovation Strategique Industrielle ''IT-Diab''OSEO-ISI  ( ISI IT-DIAB -18/12/2008 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
No. StV-18/02). Gene expression profile of 88 primary MB tumour samples was performed in Dr. Taylor's Lab (The Hospital for Sick Children, Division of Neurosurgery, Toronto, ON, Canada) and used for gene expression and survival analysis. Normal fetal cerebella were used as control for the mRNA expression studies (n ¼ 5; gestational age: 16-26 weeks). Publicly available microarray data from 285 primary MB samples was used to determine the correlation between the expression of selected genes  (Northcott et al, 2012b). MB expression profiles were generated on Affymetrix Human Gene 1. 1 ST Array (Santa Clara, CA, USA). Data are accessible through the open access database R2 for visualisation and analysis of microarray data (http://r2. amc. nl). MB cell lines were cultured as previously published  (von Bueren et al, 2007)  and maintained at 37 1C in a humidified atmosphere with 5% CO 2.
Methylated bands were detectable if 425% of the DNA was methylated. Similarly, methylated bands were not detectable when all DNA was unmethylated and, vice versa, confirming the sensitivity and specificity of the method. In silico search for miR-9 target genes. Predictions on miR-9-HES1 interactions were extracted either from databases that predict miRNA target sequences, for example, TargetScan  (Lewis et al, 2003) , Pictar  (Krek et al, 2005) , DIANA-microT 3. 0 algorithm  (Maragkakis et al, 2009)  and microRNA. org (Betel et al, 2008)  or from databases that can predict miRNA binding sites on potential target genes, such as StarBase v2. 0  (Yang et al, 2011) , microPIR  (Piriyapongsa et al, 2012) , and miRWalk  (Dweep et al, 2011). Western blotting and immunofluorescence analysis. Western blotting analyses were performed as previously published  (Fiaschetti et al, 2011).
All stainings were analysed with an Axioskop2 mot plus fluorescence microscope (Zeiss, Jena, Germany). Cell cycle and apoptosis analysis. MB cells were harvested, PBS washed, and immediately fixed in 5 ml ice-cold 70% ethanol. Before flow cytometry analysis, cells were resuspended in 300 ml propidium iodide solution, 2 ml of RNase (100 mg ml À 1 ) was added, and the cells were incubated at 37 1C for 45 min. Flow cytometry analysis was performed on a BD FACS Canto II instrument (BD Biosciences, Franklin Lakes, NJ, USA) following the manufacturer's recommended protocol. Data were collected with DIVA software (BD Biosciences) and analysed with FlowJo software (TreeStar Inc. , Ashland, OR, USA) using appropriate controls and gates. Activation of caspases 3 and 7 was detected using the Caspase-Glo 3/7 Assay (Promega Corporation, Madison, WI, USA).
Indeed, MYCinduced miR-9 repression was counteracted by 5-Aza treatment, suggesting that the oncogene MYC is involved in the reduction of miR-9 level likely by regulating the DNA methylation status of miR-9 promoter. miR-9 regulates the expression of human HES1 gene in MB. To identify putative targets for miR-9 with potential implications in MB tumour biology, we conducted an in silico search using several independent databases. Among potential miR-9 targets, based on target site conservation in humans, distinct programs identified HES1 (hairy and enhancer of split 1) gene as a potential target for miR-9 (Figure  3A  and Supplementary Figure  S2A ). As HES1 has a central role in normal brain development and in MB pathogenesis  (Hallahan et al, 2004; Dakubo et al, 2006; de Bont et al, 2008; Ingram et al, 2008) , we decided to focus our further investigation on miR-9-mediated regulation of HES1.
ACKNOWLEDGEMENTS We thank the  Swiss Pediatric Oncology Group (SPOG)  for providing medulloblastoma samples. This project was supported by the  Swiss Research Foundation  Child and Cancer and by  Krebsliga Zu  ¨rich. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.
2004a ), or were selected from existing databases such as GigasBase (http://public-contigbrowser. sigenae. org:9090/Crassostrea_gigas/index. html, Fleury et al. 2009)  and Genbank. Microsatellite analysis was performed with 8 markers, according to the initial protocols for L48  (Huvet et al. 2000a ), CGE09  (Yu and Li 2007) , AMY  (Sellos et al. 2003) , sili29 and sili44  (Sauvage et al. 2009). A multiplex protocol was used for L10, CG49 and CG108  (Taris et al. 2005). AFLP analyses for 4 primer pairs (Table  S1 ) were performed using a modified version of  Vos et al. (1995). For each sample, 250 ng of genomic DNA was digested with 1. 25 units EcoRI (NEB) and MseI (NEB) and ligated with 100 units T4 DNA ligase (NEB), 0. 1 lM EcoRI adapter and 1 lM MseI adapter [sequences in  Vos et al. (1995) ] in 25 lL 1X NEB buffer 2, 1X BSA and 1X manufacturer's ligase buffer for 16 h at 16°C.
Preselective PCRs were performed on 2. 5 lL diluted ligation (1:9 in 0 X TE) in 25 lL of 0. 5 lM preselective primers, 0. 2 mM of each dNTP, 1. 5 mM MgCl 2 and 0. 5 units GoTaq (Promega) in manufacturer's buffer. Selective PCRs were performed on 5 lL diluted preselective amplification product (1:9 in 0 X TE) in 20 lL of 5 ng selective primers, 0. 2 mM of each dNTP, 1. 5 mM MgCl 2 and 0. 4 units GoTaq (Promega) in manufacturer's buffer. Conditions for preselective and selective PCRs were as given in  Vos et al. (1995). Electrophoresis and data collection were carried out on an ABI3100 sequencer (Applied Biosystems, Foster City, CA, USA) for both AFLP and microsatellite loci.
Diversity and genetic structure After automatic analysis with GeneMapper â software, AFLP genotypes were filtered using AFLPScore  (Whitlock et al. 2008)  to minimize error rate (<10%). A between-class analysis, which is a specific type of correspondence analysis (CA) where each class is a population, was performed for all loci (AFLP, microsatellite and SNP) with ade4 package implemented in R  (Chessel et al. 2004 ). The significance of observed structure was tested using a Monte Carlo test on the between-groups inertia percentage with 1000 randomizations. Nonbiased heterozygosity (Hnb)  (Nei 1978 ) and pairwise F ST (1000 bootstraps) were computed using GENETIX 4. 03  (Belkhir et al. 2001)  for SNPs and microsatellites. Allelic richness was computed using FSTAT2. 9. 3  (Goudet 2001)  for microsatellite loci.
Outlier detection For technical reasons related to the quality and quantity of DNA, AFLP genotyping was only possible on 12 of the 16 available samples. Detection of AFLP outlier loci was performed using a version of FDIST2 software  (Beaumont and Nichols 1996)  modified for dominant markers (DFDIST program; http://www. rubic. rdg. ac. uk/~mab/stuff), BAYE-SCAN v2. 0  (Foll and Gaggiotti 2008)  and the method of  Bonhomme et al. (2010). Frequencies of null alleles were computed using the approach of  Zhivotovsky (1999). In DFDIST, a mean 'neutral' F ST value was calculated after trimming 30% of the highest and lowest F ST values (see  Gagnaire et al. 2009 ). The number of demes was set at 100, and 50 000 loci were generated by coalescent simulations under the finite island model. The maximum frequency of the commonest allele allowed was set at 0.
Acknowledgements This work was funded by the  Agence National de la Recherche  ( Hi-Flo  project  ANR-08-BLAN-0334-01 ). We thank  M. S. Berggren ,  H. T. Christensen ,  L. Meistertzheim ,  P. Norling ,  J. Petersen ,  S. Pien  and  D. B. Toring  for providing samples and  Helen McCombie  for English editing.
(i) Àx þ 1; Ày þ 2; Àz þ 2; (ii) Àx; Ày þ 2; Àz þ 2; (iii) Àx þ 1; Ày þ 1; Àz þ 2. Data collection: COLLECT  (Nonius, 1998) ; cell refinement: DIRAX/LSQ  (Duisenberg, 1992) ; data reduction: EVALCCD  (Duisenberg et al. , 2003)  This publication was supported by a grant from the Ligue Nationale contre le Cancer (ComiteAquitaine-Charentes, Bordeaux, France). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: FF2115).
Refinement Crystallographic data were collected at 293 K on a Brucker nonius k-CCD diffractometer with monochromatic Mo-Kα radiation (λ = 0. 71073 Å). At 293 K, the full sphere data collection was performed using φ scans and ω scans. The unit cell determination and data reduction were performed using DIRAX/LSQ  (Duisenberg, 1992)  and Collect  (Nonius, 1998)  programs on the full set of data. The crystal structure was solved by direct methods and successive Fourier difference supplementary materials sup-2 Acta Cryst. (2013). E69, o1531-o1532 syntheses with SHELXS97 program  (Sheldrick, 2008). The refinements of the crystal structure were performed on F2 by weighted anisotropic full-matrix least squares methods using the SHELXL97 program  (Sheldrick, 2008). Both pieces of software were used within OLEX2 package  (Dolomanov et al.
Computing details Data collection: COLLECT  (Nonius, 1998) ; cell refinement: DIRAX/LSQ  (Duisenberg, 1992) ; data reduction: EVALCCD  (Duisenberg et al. , 2003) ; program(s) used to solve structure: SHELXS97 (Sheldrick, 2008); program(s) used to refine structure: SHELXL97 (Sheldrick, 2008); molecular graphics: OLEX2  (Dolomanov et al. , 2009) ; software used to prepare material for publication: publCIF (Westrip, 2010).
A source of data that has not yet been explored for prospective syndromic surveillance in cattle is milk recording data. Milk recording consists in the measurement of milk quantities and constituents from all the cows of a dairy herd on a regular, mostly monthly, basis. A drop in milk yield can be expected to be a non specific and precocious symptom associated with most diseases, as milk production represents a high metabolic demand for the dairy cow  [11]. Therefore, milk recording data are potentially good candidates to be included in a disease surveillance system, and this should be evaluated. How to carry out this evaluation is however non trivial. Indeed, the next disease to emerge, how it will spread and the extent of its effects on the milk production of individual cows are all, by definition, unknown.
Data Milk recording is the regular collection of milk production data from all lactating cows in a herd. It is usually performed at monthly intervals, on two consecutive milkings. The number of kg of milk given per cow per day (milk yield) is measured and a milk sample is taken to determine milk composition, for all the cows of a tested herd. Farmers participate in order to get individual cow performance data. These data are also used for the genetic evaluation of sires and cows. In France, they are centralised by the Centre de Traitement de l'Information Genetique (CTIG) for this purpose. Milk recording data collected in herds enrolled in the French milk recording scheme between 2003 and 2006 were extracted from the CTIG national database. Records that originated from only one milking (as opposed to the usual 2 consecutive milkings) or from herd-recording dates (test-day) with less than 10 cows recorded were excluded.
In total, 48,713 herds were included. Farmers have some control over lactation length and this can be used to adjust the quantity of milk sold to external factors. In order to limit this source of variation, only recordings from cows between 5 and 305 days in lactation were used. The median number of cows recorded between 5 and 305 days in milk per test-day in these herds was 29. Herd location was available at the municipality level. Three levels of data are used in the remainder of this manuscript. Milk losses were simulated at the individual cow-recording level. Prediction of expected milk quantities were carried out on mean milk productions per test-day. Disease detection was undertaken at the municipality/week level.
Expected Milk Production In order to detect a deviation from an expected milk production, the expected milk production must be predicted. Expected test-day milk productions in 2006, including cows recorded between 5 and 305 days in lactation only, were predicted using herd specific historical data as follows. Mean test-day milk production per cow between 2003 and 2005 was modeled using a linear mixed model. The model specification was: Y ij ~b0 zu j 0 z P 8 k~1 (b k zu j k )T k ze ij u j *MVN(0,S u ) e ij *N(0,s ij ) where Y ij was the mean test-day milk production per cow recorded between 5 and 305 days in lactation on test-day i in herd j, T k was the day of the year linearly interpolated on 2 out of 8 consecutive time intervals, b was a vector of coefficients associated with the effect of day of year on milk production, u j a matrix of herd random effects following a multivariate normal distribution with mean 0 and variance-covariance S u and e was a vector of residual error following a normal distribution with mean 0 and variance s 2.
Cluster Detection The detection of a disease emergence based on its effects on milk production assumes that the emergence will result in a decrease in milk production that is localised in space and time. This type of deviation can be detected using a scan statistic  [17]. Disease emergence detection was carried out with SaTScan TM using a space-time prospective analysis  [18]. The cluster detection algorithm was parametrized assuming a normal distribution  [12]  for the indicator analysed. The model input was either the mean simulated losses per municipality per week or the mean difference between observed and predicted milk production per municipality per week (kg of milk/municipality/week) with each observation weighted by the square root of the number of cows recorded. In order to detect a drop in milk production on a given week, data for this week and the 4 preceding weeks were analyzed.
Association between Random Noise and Cluster Detection Random values with mean 0 and standard deviation 1, 3 and 5 were assigned to each test-day that occurred in 2006. Cluster detection was carried out on the 3 vectors of random values separately. For each week under investigation, data for the 4 preceding weeks were also analysed with SaTScan TM as background information. Therefore, 47 out of 52 weeks were analysed per year. Clusters with p-value smaller than 0. 05 were reported. The numbers of detected clusters per week for these 3 levels of random noise are presented in Figure  6. The maximum number of clusters detected in any week for the entire country was one. Of the 13 detected clusters, only 2 occurred in a week directly following another detection. Increasing the amount of noise did not change the number of detected clusters.
Simulation is a useful tool to explore the strengths and weaknesses of a detection method and to allocate resources towards the improvements of particular components. In the present case, efforts should be devoted to improving the prediction of the expected milk production in order to decrease the number of false alarms. Simulation has been used several times to assess the determinants of detection in surveillance  [19]. For example, Kulldorff et al. [20]  used simulation to evaluate disease outbreak detection methods. They generated 134,977 benchmark datasets with a random number of cases of a hypothetical disease or syndrome. In the present study, with 576 nation wide datasets, it would have been intractable to run the scan statistic on all simulated scenarios. The chosen approach was to define families of scenarios based on space-time patterns of disease induced milk losses.
doi:10. 1371/journal. pone. 0073726. t003 Table  4. Number of weeks between disease emergence and cluster detection for groups of scenarios 1 to 8 for various levels of random noise added to the simulated milk losses. Group None N(0, 1) N(0, 3) N(0, 5) was deemed sufficient to capture the main features of most vector borne diseases without being specific of a particular disease. There were limited data available to choose the parameters from. For bluetongue, Pioz et al. [15]  estimated the speed of the epidemic front-wave to be of 5. 6 km/day. Using a large milk recording dataset, Nusinovici et al. [16]  estimated milk losses in infected herds between 2 and 3%. The input values used for the current work were consistent with this work. Santman-Berends et al. [24]  gave lower estimates for the milk losses associated with BTV, but these were based on seroconversions with no associated clinical signs and were therefore not deemed representative of what happened during the start of the epidemic.
The main limitation of our method's performance was the bias in predicted values. This was because there were predicted values that were systematically lower than the observed values and the differences were clustered in space as well as in time. This type of pattern is what would be expected if there were a vector borne disease to emerge. Therefore, the scan statistic behaved as expected and to improve its performance, the quality of the predictions must be improved. Systematic differences between observed and predicted values were even visible when predicting milk yields in the fitting dataset (2003 to 2005, see Figure  4 ). This was because milk production varies between years depending on factors such as for example climate, feed availability, feed price or quota extensions that are difficult to capture in a model.
This would decrease the power of the surveillance system but would ensure a manageable number of false positives. This will be the next step of our work. Different choices could have been made regarding the level of data aggregation both in time and space. Milk recording data were available at monthly intervals for all the cows of a herd, but in a given area, there would be herds tested every day. Herd locations Table  5. Number of weeks between disease emergence and cluster detection for groups of scenarios 1 to 8 for 2 disease starting locations and 2 starting dates.
It is then sufficient to run the detection method on a subset of simulated disease scenarios that are sufficiently different to determine the strengths and weaknesses of the methods. When monitoring continuous indicators, known factors of variation should be accounted for, especially when they are clustered in space and time. Linear mixed models can be used for this purpose. Milk yield from milk recording can be used for the detection of vector borne emerging diseases. The main limitation of this indicator was the difficulty to predict unbiased expected milk productions. Further work should be undertaken on improving the prediction of milk yield. providing the data. This study was undertaken as part of the EMIDA ERA NET project: http://www. emida-era. net/home. html. A description of this particular project can be found on the EMIDA ERA NET website: http:// database.
Experimental Crystal data  Data collection: CrysAlis CCD (Oxford Diffraction, 2009); cell refinement: CrysAlis RED  (Oxford Diffraction, 2009) ; data reduction: CrysAlis RED; program(s) used to solve structure: SIR92  (Altomare et al. , 1993) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg & Berndt, 1999)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and publCIF  (Westrip, 2010).
Computing details Data collection: CrysAlis CCD (Oxford Diffraction, 2009); cell refinement: CrysAlis RED  (Oxford Diffraction, 2009) ; data reduction: CrysAlis RED (Oxford Diffraction, 2009); program(s) used to solve structure: SIR92  (Altomare et al. , 1993) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg & Berndt, 1999)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and publCIF  (Westrip, 2010).
Titration of viral RNAs by qRT-PCR We used the Power SYBR Green RNA-to-Ct One-Step Kit (Applied Biosystems, Carlsbad, California) according to the manufacturer's protocol. It allowed amplifying a 108 bp sequence located between nucleotide 1485 and nucleotide 1593 of the M segment of RVFV. The primers selected were as follows: upper 59-CATGGATTGGTTGTCCGATCA-39 and lower 59-TGAGTG-TAATCTCGGTGGAAGGA-39. Each sample was analyzed in duplicate against a standard curve produced from a specific concentration range of synthetic RNA. We amplified the samples on an Applied Biosystems 7500 instrument using the following PCR program: a reverse transcriptase (RT) step for 30 min at 50uC; inactivation of the RT enzyme and activation of DNA polymerase for 10 min at 95uC; 40 PCR cycles of 15 sec at 95uC and 1 min at 60uC (annealing temperature of primers), during which fluorescence data is collected; and finally, 20 sec at 95uC with ramping 19 min 59 sec for melting curves.