dataesr/scientific-paragraphs-categorization huggingface dataset:
À1. 75 est 3. 16EÀ011 est 20, 30, 45, 67. 5, 101. 25 mg/L C 1 = tested in Phase 1; 2 = tested in Phase 2; NA = not available; MW = molecular weight; HLC = Henry's law constant; db = experimental database match; est = estimated; Note: log Kow and HLC were estimated using EPISUITE 4. 0 (2008) except when measured values were available (cited within EPISUITE); C = industrial chemicals; PPP/ B = plant protection products/biocides; P = pharmaceuticals; FF = flavours/fragrances; All chemicals (at the highest purity available) were purchased from Sigma-Aldrich (same lot number) except dimethyl sulfoxide (Gruessing GmbH). 1-5 = five test concentrations; nC = negative control (dilution water); iC = internal plate control (dilution water); pC = positive control (3,4-dichloroaniline 4mg/L); sC = solvent control.
Acknowledgments The authors wish to thank the participating laboratories for their commitment and most valuable contributions. They further would like to thank the  OECD expert group ,  ILSI-Health and Environmental Sciences Institute (Michelle Embry ) and the  European Centre for Toxicology and Ecotoxicology of Chemicals  ( Malyka Galay-Burgos ) for influential discussions and workshops on fish embryo testing in advance of and during the OECD validation. The support of the analytical team ( Mike Karb  and  Ken Wehmeyer ) of the  Procter & Gamble Group  is acknowledged. UFZ acknowledges technical assistance by  Barbara Klüver ,  Nicole Schweiger  and  Martin Lohde. VITO acknowledges technical assistance by  Ingrid Selderslaghs  and  Francis Boonen. The  German Federal Environment Agency (UBA)  acknowledges technical assistance by  Julia Scheunert  and  Stefan Koch.
, 2002; Kempf et al. , 2010). We tested sex bias dispersal between each unit that appeared significant during the HierFstat analysis. Sex biased dispersal between traps was also tested independently within each subgroup. We then computed the mean over all subgroups of each statistic and combined the tests with the generalized binomial procedure  (Teriokhin et al. , 2007)  with the software MultiTest V1. 2  (De Meeûs et al. , 2009). We also tested sex bias dispersal between subgroups keeping only one male and one female per trap for the sake of effect of trap control (if any) as we know this can have a spectacular effect  (Kempf et al. , 2010). Since we expect females to disperse less  (Koné et al. , 2011) , tests were all unilateral. Isolation by distance was studied with Rousset's method  (Rousset, 1997).
The software also computes 95% confidence of the slope with ABC bootstrap procedure. The minimum distance for the regression was set at 0. 3 km. We also analyzed isolation by distance between individuals with the statistic a and e (equivalent to FST/(1ÀFST) and with similar properties) because neighborhood size N b (86 and 51 respectively) appeared close to the limit (50) of choice between the two statistics  (Watts et al. , 2007). Effective population sizes (N e ) were assessed through the linkage disequilibrium based method of Waples  (Waples, 2006). It was undertaken with LDNe  (Waples and Do, 2008). We also used the within and between locus correlation method  (Vitalis and Couvet, 2001b)  with the software Estim 1. 2  (Vitalis and Couvet, 2001a)  and the mean value of F IS subsamples, the negative values of which can be connected to the effective population size of dioecious populations as N e = [À1/(2F IS )] À [F IS /(1ÀF IS )]  (Balloux, 2004).
Effective population density was then estimated as D e = N e /S. Slope of regression (b) of Rousset's model in two dimension isolation by distance  (Rousset, 1997)  allows estimating mean effective number of immigrants per generation in a subpopulation as N e m = 1/(2pb), the neighbourhood size in the strict sense as N b 0 = 1/(4pb)  (Rousset, 2008)  (note that N b 0 = D e r 2 ) and the mean dispersal distance between reproducing adults and their parents as r = p (pbD e /4). There are three X-linked loci that are thus haploid in males. Consequently, males were coded as missing data for these loci for all F IS based analyses and were coded homozygous otherwise. When using traps as subpopulation units, only traps containing at least five flies were kept. Most procedures used are more detailed in De Meeûs et al.
Acknowledgements The genotyping at CIRDES was supported with funding from  IAEA Technical Cooperation  project  RAF5061. We thank  Mr. Hasim Abdul Mohamed  of the  FAO/IAEA Insect Pest Control Laboratory in Seibersdorf, Austria  for his assistance with the extraction of the DNA of some of the samples. We are thankful to  PATTEC/Ghana  for providing logistics and technical staff and to  Dr. Sadou Maiga  for the field tsetse sampling and to the  Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture  for providing financial support to  Dr. Sadou Maiga. The late  Prof. Peter Van Den Bossche , from  ITM Belgium , is here posthumously acknowledged for his contribution in the initial framework. We thank the management and personnel of  CIRDES laboratory in Burkina Faso  for the use of facilities and conducting the laboratory analysis of the field samples.
Necrotic areas were surrounded by large, irregular, nonseptate, wide-angle branching hyphae on periodic acid-Schiff and Grocott stainings (Fig. 2 ). Cultures of biopsy specimens on Sabouraud chloramphenicol agar slants (BioRad Laboratories, Marnes-La-Coquette, France) incubated at 30 1C and 35 1C were positive (dayþ5) with profuse fluffy whitish colonies presenting a lemon-yellow reverse. The identification was performed by sequencing of the ITS1-5. 8S-ITS2 region of the ribosomal DNA using the universal primers ITS1-ITS4  [5]. The nucleotide sequence (deposited in Genbank under accession number KJ472786) had 100% identity over 451 bp for M. irregularis (formerly Rhizomucor variabilis var. variabilis  [6] ), compared to the nucleotidic sequences of strains CMFCCC B 50 m and CBS 654. 78 published under GenBank accession numbers JX976252 and JX976261  [7].
This approach involves clustering gene/protein sequences from 3 or more organisms based on their multiple pair-wise alignment e. g. (Di Tommaso et al. , 2011)  and can be optimized by adjusting the inclusion parameters for each cluster, including alignment length and/or percent identity, or through the use of more sophisticated orthology prediction algorithms  (Kuzniar et al. , 2010). Once clustered, functional information can be transferred from annotated to unannotated sequences, often with high confidence. In HelmDB, the annotation transfer approach allowed the improved functional characterization of 8-20% of the transcripts available for each of 11 species incorporated into the database. Fig. 5. Utility of high-throughput sequencing (HTS) to explore the development of anthelmintic resistance.
(C) Orange boxes demonstrate the identification of small non-coding RNAs by their mapping to a reference genome and exploration of their potential role in regulating differentially expressed genes. These analyses include the identification of micro-RNAs (miRNAs) by prediction of their hair-pin precursor, and their annotation using data in miRBase (www. miRBase. org). Additional small RNAs can be explored by identifying ncRNA precursor transcripts (represented by large, contiguous clusters of small-RNA reads mapping to a reference genome) and their annotation through comparisons [e. g. , by hidden Markov modeling (HMM)] with the Rfam database (rfam. sanger. ac. uk); a small-RNAs are also identified by their length, sequence and relative mapping position (e. g. , with respect to coding genes, gene regulatory and/or transposable elements).
com, to dedicated protocol-based publications, such as Current Protocols in Bioinformatics. The latter provides step-by-step command-based instructions for everything from extracting information from public databases, to assembling and annotating genomes and transcriptomes, exploring CHIP-seq data, conducting comparative genomic investigations of SNPs and structural variants, modelling regulatory elements, identifying small non-coding RNAs, and processing proteomic, metabolomic and other small molecular data-sets. Although not specifically dedicated to bioinformatics, Nature Protocols also represents an excellent resource for some of the major bioinformatic applications currently available; key examples including excellent protocols for refining/finishing draft genomes using the Post-Assembly Genome Improvement Tool  (Swain et al.
, 2012)  and the reconstruction and differential analysis of transcriptomic data using Tuxedo suite (i. e. , Tophat/Cufflinks:  (Trapnell et al. , 2012) ). Just as guidelines are being established for experimental design (e. g. , MINSEQE), projects are under-way to assess the specificity and sensitivity of these many software applications and establish 'gold-standard' methods for data analysis. A key example is the RNA-seq Genome Annotation Assessment Project (RGASP), which benchmarks leading RNA-seq analysis tools for their ability to accurately reconstruct the transcriptome, identify splice isoforms and quantify transcriptional abundance (see  Engstrom et al. , 2013; Steijger et al. , 2013). The increasing availability of bioinformatic applications, reduced demands for computing power, and development of guidelines and protocols for bioinformatic analyses are vastly improving the accessibility of this field to the global scientific community.
Nonetheless, knowledge of Unix-commands and some access to custom Perl/Python scripting is needed, particularly to take the output from one application and reformat it for another. This remains a significant obstacle for many research groups. The development of bioinformatic pipelines with a Graphical User Interface (GUI) is of significant interest to many wishing to enter this field. An example is the proprietary software Geneious  (Kearse et al. , 2012) , which allows users to perform many standard bioinformatic analyses using menu-based commands on a personal computer. These include pipelines to conduct de novo assemblies, align reads to a reference genome/transcriptome, and conduct/ visualize a variety of comparative analyses. The Galaxy Project (http://galaxyproject. org/) provides arguably the best of GUI-based resources currently available, allowing users free access to a remote server for their analyses and a web-based interface.
Major strengths of the Galaxy Project is that it utilizes a growing variety of leading open source bioinformatic applications, including SAMtools  (Li et al. , 2009 ), GATK (McKenna et al. , 2010)  and TopHat/ Cufflinks  (Trapnell et al. , 2010) , and is supported by a large community of bioinformaticians seeking to expand the available applications accessible to users. Users can build custom pipelines linking these applications to create semi-automated workflows for their analyses and, through the Galaxy Project community, have access to a wealth of presentations, lectures and blog-based articles on conducting bioinformatics research. Collectively, these resources improve accessibility to bioinformatic platforms for non-bioinformaticians. A key message from these platforms is that although experimental design remains critical and data analysis/handling is not trivial, many resources now exist to provide entry into this field for researchers wishing to utilize the power of HTS to explore helminths and the development of anthelmintic resistance.
Acknowledgements Funds to support the CARS2013 meeting were provided by  Zoetis , and  Virbac Animal Health. Alessandro Puoti  is thanked for providing Figs. 4A andB. Support to RJM was via  NIH  grant  R01 AI047194   National Institute of Allergy and Infectious Diseases. The funding agency had no role in the design, execution or publication of these studies. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of Allergy and Infectious Diseases.
In this case, the larger the group, the more interactions were sampled, and therefore the more individuals with similar interactions for each focus animal. For the lognormal group structure, variability of Ûg was also greater than for the other group structures, and decreased as group size increased. We then considered that only a sub-sample of the animals could be observed in each of the simulated data sets used above. We made the sample ratio being either 0 (all individuals are sampled), 0. 1, 0. 3 or 0. 6 and analysed the behaviour of U g according to the number of individuals in the group. Two sampling procedures were considered. In Figure  6b , the same subset of animals for a given simulation did not vary across measurement days. In Figure  6c , the missing animals were randomly sampled at each date.
Grazing activity was defined based on the 'headdown' position of Ethosys collars  (Scheibe et al. , 1998) , and our estimator was computed when at least two-thirds of the recorded animals were grazing. Recording periods were then sub-sampled for 5 min each to match the sampling interval chosen by  Sibbald et al. (2005). All the recordings for a given group were then merged as affinities are stable in familiar cattle herds  (Bouissou et al. , 2001; Gygax et al. , 2010). First, we tested whether affinities were equally spread in each group using a randomization test with 1000 independent simulations. We therefore randomly reallocated animal identity in each group date by date, before computing the estimator in the same way as for original data. The assumption that inter-individual affinities are equal was rejected if the estimator value of original data was out of the range of 0.
Statistical Analysis For Exp. 1, pH, E h , rH and VFA contents determined at T0 and T8 were compared between methods (in vivo vs. in vitro) by ANOVA with GLM of SPSS (SPSS Version 13. 0 for Windows, SPSS Inc. , Chicago, Illinois, USA). For Exp. 2, pH, E h , rH, and VFA contents determined at T0 and T8 were analyzed by ANOVA with GLM of SPSS, according to the following model: ( ) ijk i j k ijk jk Y C M D M D e µ = + + + + × + where Y ijk is the dependent variable, µ, the overall experimental mean, C i , the mean effect of cow, M j , the mean effect of the method, D k , the mean effect of the diet and e ijk the random residual. Differences were then assessed by pairwise comparisons (Tukey's test). Mean values were reported with Standard Error of the Mean (SEM). Differences were considered significant at P ≤ 0.
This is a conservative, non-quantitative method that has a non-constant significance levels and restricts downstream analysis of the data  [10]. With the RR methodology implemented at Wellcome Trust Sanger Institute (WTSI), the probability of observing a significant phenotype by chance is about 6610 26 per sex per tested phenotype, and the chance that either sex is significant is about 1. 2610 25 per phenotype. The disadvantage of the RR is that it does not take into account batch variation, and as we show below, it is thereby possible to make false positive RR calls despite its apparently stringent significance threshold. Recently, we demonstrated that a mixed model (MM) methodology in which temporal variation is modelled gave significantly improvements  [10]. The methodology is implemented in the PhenStat R package  [12].
PhenStat starts with a model that includes fixed effects for sex, genotype, genotype-by-sex interaction and optionally weight, and a random effect for batch. It tests the significance of each of these terms in order to optimise the assessment of the genotype effect. The genotype-by-sex effect is also tested to determine whether there is a sexual dimorphic knockout effect. In this study we investigate the impact of temporal variation and workflow differences on our ability to call phenotypes. To do this, we constructed datasets sampled from real control data from the WTSI MGP Select pipeline to mimic various workflows. To investigate false positive rates (FPR) we relabelled a subset of controls as knockout mice and then compared them to the remaining controls. Thus at a nominal p% significance threshold there should be p% significant calls if the statistical test is accurate when the null hypothesis is true.
We consider data from five assays to ensure the findings are representative. The assays studied included the open field behavioural assay, three blood parameters (peripheral blood leukocytes, haematology and clinical chemistry) and Dual-energy X-ray absorptiometry (DEXA) that assesses body composition. We also investigated data from other phenotyping institutes. We show our ability to call phenotypes accurately and sensitivity depends on the workflow. We observed the FPR was inflated in certain workflows, so we constructed and tested further simulated data that perfectly met the mathematical assumptions underlying the mixed model. Our analysis demonstrates that the inflated FPR is associated with the distribution of control data, combined with the workflow characteristics. We also show that unexpectedly large batch-to-batch fluctuations in the control data, cause false positive significant phenotype calls when mice are phenotyped across only a few batches.
Construction of artificial data Artificial control data were constructed based on the current knowledge of having sex and temporal variation as dominate sources of variation and assumes that the temporal variation arises from a batch effect that is independent normally distributed. As these sources of variation are modelled within the mixed model, this data would meet the assumptions of the mixed model test perfectly. A script (Code S1) generated data by randomly sampling from a population with defined mean and standard deviation. Batch variation was simulated under the assumption it was normally distributed with mean zero and defined variance. Data were generated for fifteen dependent variables with 300 independent batches for different values of the population mean and standard deviation, and batch variance.
Assessing the false positive rate (FPR) Resampling studies were conducted to assess the FPR under the null hypothesis. Mice (7 males and 7 females) were selected from the control dataset without replacement and relabelled as knockout. Prior to combining the knockout mice back with the control mice dataset, the mice selected to be relabelled as knockout were removed from the control dataset. The resulting dataset were then examined statistically for statistically significant differences between control and fake knockout mice (phenodeviants), which would all be false positives. The mice were selected in a manner to mimic the various workflows used in high throughput pipelines, which depend on breeding strategy and pipeline operational issues (Table  1 ). Up to 500 datasets were constructed by selecting different mice based on the workflow rules.
Assessing the statistical power Simulated control data, comprising 300 batches with 7 males and 7 females in each date, were constructed based on the population characteristics estimated for the WTSI DEXA control dataset. The population characteristics were estimated by fitting a mixed model with sex and weight as a fixed effect and assay date as a random effect. From the model fitting, estimates were extracted for the male and female biological mean. The standard deviation estimated on the intercept equating the female data was taken as the biological standard deviation. To assess the impact of batch variability, datasets were constructed with varying amounts of batch variant (from 25 to 400% of the estimated biological standard deviation). Phenodeviant mice were constructed by sampling 7 male and 7 female animals from the simulated dataset, adding a defined signal and relabeling as knockout mice and assessed using PhenStat (model: without Weight).
Identification of significant phenotypes An iterative top down mixed modelling strategy was performed as described in  [10]  using PhenStat an R package  [12]  freely available from Bioconductor  [13]. There are two possible start models, depending on whether weight is included as a factor (see Eq1. and Eq2. ). The complexity of the model is limited by the low number of mice used in high throughput studies as such key fixed effects have been selected. depVariable*GenotypezGender zGenotype Ã GenderzWeight ð1Þ depVariable*GenotypezGenderzGenotype Ã Gender ð2Þ.
Using control data to mimic various workflows To understand the variation to be expected under the null hypothesis that there is no phenotypic effect due to the knockout allele, we simulated a variety of different workflows, the details of which are described in Table  1  and Figure  S1. We sampled phenotype data from control animals on different days to mimic a given workflow. Workflows can be grouped into three main categories based on how many batches of knockout animals are tested per line, which we call traditional, multi-batch, and lowbatch workflows. The traditional workflow is a one batch design where all phenotype data for a given knockout and procedure are collected on one day, and typically with concurrent controls. In a multi-batch workflow, the knockout mice are phenotyped over a minimum of 5 days.
It was also independent of the significance thresholds selected (Figure  S2 ). As would be expected if there were large fluctuations between batches, low-batch and traditional workflows with the MM methodology showed elevated false positive rates for both the genotype and the genotype-by-sex effects (Figure  5 ). We observed similar behaviour when we resampled control data from other institutes (Figure  6 ). However, in contrast to the WTSI data, the multi-batch workflow with ICS dataset also had an inflated FPR. ICS has a lower throughput than WTSI and typically uses a concurrent control design. Consequently data are collected less frequently and the dataset available for resampling were smaller. The proportion of variance attributable to batch was higher in the ICS data relative to the WTSI (paired t-Test, p = 3.
Our simulations show that the mixed model methodology has the expected FPR in workflows with many batches of mice (multibatch workflows) but that the FPR is inflated in low-batch and traditional workflows. This is the case across all five assays tested, selected to represent the breadth of phenotyping tests in different Figure  6. False Positive Rates for other institutes for various workflows. Shown are FPRs under the null hypothesis of no phenotypic effect, estimated by resampling controls from two independent institutes for various workflows. The y-axis's show box-and-whisker plots of the distribution of the FPR, defined as the fraction of resampled datasets significant at the nominal 5% level in a mixed model. A, B: Resampling results using data from the German Mouse Clinic, where A is the FPR for genotype effect and B is the FPR for genotype-by-sex effect.
The work of JKW and AOS was funded by the  Wellcome Trust  (www. wellcome. ac. uk) grant  WT098051. RM was funded by  Wellcome Trust  through grants  083573/Z/07/Z  and  090532/Z/09/Z. NAK was funded by the  National Institutes of Health  (www. nih. govgrant number: 1  U54 HG006370-01 ). MHA was funded by the  German Federal Ministry of Education and Research  (www. bmbf. deInfrafrontier grant:  01KX1012 ). YH was funded by the  French National Agency for Research  (www. cnrs. frPHENOMIN,  ANR-10-INBS-07 ) and by  European Commission  (http://ec. europa. euEUMODIC ( LSHG-CT-2006-037188 )) and  INFRAFRONTIER I3  ( FP7-INFRASTRUCTURES-2012-1. 1. 4/312325 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.