dataesr/scientific-paragraphs-categorization huggingface dataset:
This was not the case, and the GL model had equivalent performance to the models that make separate predictions for each ME. The location by genotype interaction within ME was at least ten times greater than the genotype by ME interaction, which may help explain why ME accuracies were lower despite high correlations among global and ME-specific estimated yields. This means that correlations between the individual trials and the global or ME-specific estimated yields are likely to be variable. This has been shown to be the case in previous analyses of this dataset  (Trethowan et al. , 2002; Manès et al. , 2012). These studies examined locational clusters based on phenotypic correlations among locations that shared genotypes over a subset of the years present in the complete dataset  (Trethowan et al.
, 2002). In an analysis of phenotypic data for grain yield from the 17 years with SAWYT data available,  Manès et al. (2012)  found that a few locations in Argentina and India were significantly correlated over 50% of the time with the global mean for the lowest yielding 50% of trials. They proposed that low yielding locations should be grouped into more uniform and meaningful clusters in order to improve the correlation of locations within clusters and the gain from selection for stressed environments. The present study attempted to overcome the limitations of phenotypic correlations by using the realized relationship matrix to make genomic predictions for each location in each year. This made it possible to compare all locations rather than only the subset that were present in any particular year, but we still did not find explanatory groupings of locations.
, 2012 ). This appears to be the case in the SAWYT. However, the international breeding programs do have preliminary yield trial data in selection environments for the lines that are candidates for the international trials, and the use of genomic predictions for lines with some phenotypic data available may be a strategy to improve selection of candidates for inclusion in these trials. Using this preliminary yield trial data could be the focus of future work. In our study, clustering methods based on genomic predictions within locations did not identify better groupings of locations for the purpose of making genomic predictions in subsequent years. In addition, these clustering methods did not explain a large proportion of the G×E within individual years of the SAWYT, or identify groups of locations that were known to be similar in terms of climate or geography.
In the CIMMYT East-African Maize programs, grouping trials into high and low-yielding environments produced a better characterization of G×E for the maize breeding programs than the existing ME, and selecting for performance within ME did not improve predicted gains compared to selecting for performance across ME  (Windhausen et al. , 2012). Heslot et al. (2013)  used a European barley multi-locational dataset and identified four clusters but no predictive environmental factors for the assignment of locations to clusters. The use of these methods with unbalanced data may be unsatisfactory because generating genomic predictions within locations based on a limited number of genotypes and years may be sensitive to the particular environmental conditions of the years and sets of genotypes evaluated in that location.
Predicting broad and specific adaptation for mega-environments Plant breeders seek to develop varieties with the best performance for their target environment or environments. Most research on genomic selection has focused on accurately predicting the genotypic mean performance over all environments, assuming that all trial locations belong to the same target population of environments. Accuracies of global genomic predictions for global estimated yields were relatively high for such a diverse and unbalanced dataset, and these predictions provide valuable information on potential broad adaptation of breeding lines. However, these accuracies varied significantly over years. This may make it difficult for breeders to decide how much weight to give these predictions in selection decisions. The forward prediction scenario is realistic for breeding programs, which would use data before the selection year to train the model and then predict breeding values for new lines.
Interestingly, global forward prediction accuracies with either training set (using all the data before the validation year or a sliding window of training years before the validation year) produced approximately the same accuracy. This implies that the additional data in the training set including years farther back in time from the validation set did not improve predictions. Further research on potential factors impacting accuracy with empirical datasets is needed. The strategy CIMMYT uses to identify lines with broad adaptation has led to genotypes that perform similarly on average across ME globally. This leads to a higher global mean value of new lines, which is important to broad adaptation. However, including only lines with high global mean values may be less suited to identifying lines with superior performance in any particular ME.
Conclusion The purpose of this research was to explore the possibility of using genomic selection strategies to improve targeting of lines to different target environments in a global breeding program. This does not appear to be effective in this particular dataset, which was produced by a breeding program designed for phenotypic selection. While the SAWYT represents the most extensive global database of wheat yield trials for stress environments, it remains a relatively limited sample of genotypes used in global wheat breeding programs. It may be more difficult to detect consistent ME effects in this dataset, where growing conditions are strongly affected by year-to-year variation. An analysis of patterns of environmental adaptation should include lines selected in many different environments, rather than lines selected in a limited number of environments and then distributed for global evaluation.
There is still germplasm exchange among the breeding programs that feed into these trials and some lines may do well under irrigated and drought conditions but selection nurseries are managed separately and lines included in the SAWYT are selected for drought tolerance. Previous authors have analyzed the different CIMMYT wheat yield trials in order to define patterns of environmental variation and identify similar locations in terms of genotypic performance  (Trethowan et al. , 2001 (Trethowan et al. , , 2002 (Trethowan et al. , , 2003;; Lillemo et al. , 2004; Singh et al. , 2007; Manès et al. , 2012). This has proved challenging due to the highly unbalanced representation of locations and genotypes across years. Even when the dataset is restricted to locations with data from multiple years, patterns of genotypic adaptation are difficult to interpret in a way that is useful for breeders making selection decisions, as an individual location may cluster with different groups of locations in different years  (Trethowan et al.
, 2012). CIMMYT breeders in Ciudad Obregon have recently merged the high rainfall and drought stress target ME and modified the irrigation regimes in Ciudad Obregon to mimic different patterns of drought stress that correspond to different global environmental conditions  (Trethowan and Crossa, 2007). There is also generally a delay of up to two years between trial distribution and receipt of international data by CIMMYT. This means that the trials are constructed of new lines each year rather than repeating high yielding lines from the previous year  (Trethowan et al. , 2002). This practice has recently changed, with the inclusion of the best performing lines in trials after SAWYT 18. While international collaborators use the best lines in their own breeding programs, this information is generally not included in the CIMMYT databases and the percent of lines from one of the international yield trials that might be adapted to any particular location is estimated at 5-10%  (Trethowan et al.
FA models use principal component analysis on the G×E term to estimate a heterogenous variance-covariance matrix for the target environments that captures the majority of the G×E interaction variance using only a few principal components  (Meyer, 2009; Smith et al. , 2005; Piepho, 1998). This makes it possible to use a more complex variance-covariance structure for the G×E interaction for large datasets, which is particularly important when correlations among pairs of environments differ significantly  (Piepho and Mohring, 2005). The use of mixed model approaches to multi-locational trials has been reviewed by  van Eeuwijk et al. (2001) ,  Smith et al. (2005) ,  Meyer (2009)  who concluded that the mixed model framework improved the estimation of effects in multi-locational trials. In a mixed model framework, it is also possible to include a genetic covariance matrix calculated from pedigree or molecular marker data rather than assuming genotypes are independent, which may improve the estimation of genetic effects if a good estimate of this covariance matrix is available  (Piepho and Mohring, 2005; Beeck et al.
, 2010; Burgue ño et al. , 2012). This covariance matrix can be used regardless of whether the genetic covariance among environments is assumed to be heterogeneous or uniform. The use of high-density genetic marker data to calculate a realized relationship matrix as an estimate of the genetic covariance matrix can allow the analysis of datasets that are unbalanced for genotypes and locations over time, by providing a genetic covariance structure that relates genotypes across locations even when lines are not present in all locations. Where there are different target regions in a breeding program, models that account for G×E should be superior to models that only use data from the particular target region or that use a global average value for genotypic performance  (Piepho and Mohring, 2005).
A. 2. 1. Data curation Population structure can have a significant effect on the results of genome-wide association analysis if it is not identified. While population structure is less of an issue in terms of genotypic ranking in genomic selection, the presence of unidentified population structure in the training and validation populations can inflate the observed accuracies of genomic predictions. Population structure for the SAWYT genotypes was examined using singular value decomposition (SVD) of the relationship matrix. The presence of population structure was tested using linear regression of yield on the first five principal components from the SVD using the lm function in R. Based on an F-test (Table  A. 4), none of the PC were found to be significant, so the relationship matrix alone was considered sufficient to control for relatedness among genotypes in subsequent analyses.
The algorithm searches for a local optimum by moving points from one cluster to another and recalculating the within and between cluster sums of squares. The algorithm repeats the steps of assigning observations to clusters with the closest mean value (the centroid of the cluster) and calculating the new means and dispersal of clusters until individual observations no longer change clusters. Model based clustering uses a Bayesian Information Criterion to select the optimal number of clusters for the data and then selects membership in clusters based on minimizing the resulting within-cluster variability  (Fraley et al. , 2012). The optimal number of clusters was selected using the BIC using the mclust function, and this number of clusters was used for the kmeans, Ward's and complete linkage methods.
, 2012) , assuming all other effects random. Plot values were adjusted for these year effects. For each validation dataset in the comparison of the four different G×E models, global or cluster-specific yield estimates were calculated in ASReml assuming independent genotypes and all effects random, using the model: yield = blocks(rep) + rep(loc) + cluster + loc(cluster) + GID(cluster) + error A. 2. 4. Simulations Simulations were done in R (R Development Core Team, 2012). Genotypic data from SAWYT GIDs was used to calculate the relationship matrix using the package rrBLUP  Endelman (2011)  for the most recent 200 GIDs. Phenotypic data were simulated as follows: The first parameter in the simulation was the number of years (N year ) of phenotypic data available for each genotype. This parameter was varied from N year = 1, which was completely unbalanced (like the SAWYT), up to N year = 4 (completely balanced).
The ratio of each variance component to the genetic variance component was used in the simulation of phenotypic data as described below. Random variates were added to each true breeding value (within ME) to simulate phenotypes across locations and years. The phenotype for genotype i in year l(k) and location j(k) of ME k was y ijkl = g ik + c j(k) + d l(k) + ε ijkl , where c j(k) ∼ N(0, V loc ), d j(k) ∼ N(0, V year ), and ε ijkl ∼ N(0, V err ). The levels of the variance components were modeled as (1) ME variance V me = 32V g , (2) location variance V loc = 94V g , (3) year variance V year = 94V g , and (4) error variance V err = 12V g. The number of locations per year depended on the genotypic balance so as to maintain the same number of observations per genotype. In the simulations where each genotype was evaluated in only one year, there were 12 locations per year; where each genotype was evaluated 2 years there were 6 locations per year; where each genotype was evaluated 3 years there were 4 locations per year; and where each genotype was evaluated 4 years there were 3 locations per year.
For instance an inflation of the type I error has been reported in another clinical context where data were sparse  [15]. With the new potent triple therapies against HCV the amount of information available may also be limited by the fact that a large proportion of VL data are below LOD. Here our goal was to evaluate the capacity of NLMEM to precisely estimate the parameters of viral kinetic models when there is a large proportion of BLD data and a limited number of data per patient. In particular we aimed to evaluate by simulation the type I errors and the power of the Wald test to compare the antiviral effectiveness of two groups receiving different triple therapies (noted PI-A and PI-B in the following). Parameter estimation and Wald test property were evaluated according to the number of patients, the number of samples per patient and the expected difference in antiviral effectiveness between the two treatment groups.
Clinical trial simulation We considered different designs in real-life setting, i. e. , with a limited number of VL measurements per patient. Two schedules for the VL assessments were considered, called "7 VL" and "5 VL" in the following. "7 VL" had seven VL measurements at days 0, 0. 33, 1, 2, 3, 7 and 14 whereas "5 VL" was sparser and did not have the early measurements at days 0. 33 and 1 that are often difficult to obtain in clinical practice. Then different scenarios were considered according to the number of VL measurements (n) and the number of patients per group of treatment (N). In order to have designs that could be easily compared, we considered different designs with 5 VL or 7 VL but constant total numbers of observations per group n tot = N×n. We considered small sample size with: n tot = 50 (N = 10 and n = 5 Examples of simulated dataset with the design N = 30 and n = 7 and the different levels of antiviral effectiveness considered with the percentage of patients below the LOD at day 3, 7 and 14 is shown in Figure  1.
Parameter estimation Data of each simulated trial were analyzed using MONO LIX version 4. 2 (http://www. lixoft. eu/monolix/productmonolix-overview/) [21] , a software devoted to maximum likelihood estimation of parameters in NLMEM using an extension of the stochastic approximation expectationapproximation (SAEM) algorithm  [22, 23]. Of note one advantage of maximum likelihood estimation (noted "ML" in the following) is that it takes into account the information brought by BLD data  [10]. We compared accuracy and precision of parameter estimations obtained with those that would be obtained if all data were observed with no BLD data at all (referred as "all data"). For each scenario, relative estimation errors REE θk ; k ¼ 1; …; K were computed as shown in equation (  5 ),  where θk is the parameter estimated for the k th replicate and θ * the true parameter value used to generate the data.
Detection of a difference in antiviral effectiveness We analyzed for each scenario the ability to detect a difference of antiviral effectiveness between the two PIs. Given that treatment antiviral effectiveness was estimated on a logistic scale we defined accordingly the difference of effectiveness between the two PIs, β, as β = logit(ε A )logit (ε B ). For each simulated dataset, the estimated value of β; β , was obtained along with its (estimated) standard error, SE β. Then the Wald test statistics given by β SE β was calculated and the null hypothesis "H 0 : β = 0" was rejected at the level of 5% if β SE β > 1:96. Thus in scenarios where ε A = ε B = 0. 999 and therefore β = 0, the type I error was given by the proportion of dataset among the 500 simulated that led to reject H 0. Similarly the power to detect a difference in treatment antiviral effectiveness was calculated in scenarios where β ≠ 0 and was given by the proportion of datasets among the 500 simulated that led to reject H 0.
With ε A = 0. 999 and with values of ε B equal to 0. 998, 0. 995 and 0. 990, β were equal to 0. 7, 1. 6 and 2. 3, respectively. Type I error and power were evaluated with all designs describe above and consistent with previous analysis  [15, 24] , we expect an inflation of the type I error with the Wald test. To ensure a type I error of 5%, we define for each design a correction threshold as the 5 th percentile of the distribution of the p-values of the test under H 0 for the 500 simulated dataset. Then we used that corrected threshold as a limit of significance in the evaluation of the tests under H 1 to compute the corrected power  [15, 25]. Furthermore we evaluated type I error with 2 larger sample size to approach asymptotic conditions with: n tot = 350 (N = 50 and n = 7) and n tot = 700 (N = 100 and n = 7).
Yet, the precision of both the fixed effect and inter-individual variance parameters were very close to those found with the ML uncensored data (Table  2 ), showing the relevance of maximum likelihood in the handling of censored data. Similar results were obtained when comparing the distribution of the REE from the 500 simulated datasets (Figure  2 ). Equally good performance was obtained when considering sparser sampling design with n = 5 VL measurements per patient except for the viral clearance rate, c (Table  2  and Figure  2 )  [11].
For instance the software PFIM uses a first order approximation of the likelihood and, under this approximation, an analytical form of the Fisher matrix can be obtained  [27]. Thus the expected variance of viral kinetic parameters could be obtained without the intensive simulations done here. Although such approximations worked well even with limited number of patients  [11] , it does not take into account BLD data and hence could underestimate the standard error when a large proportion of data are BLD. It should be noted that optimal design theory predicts that an increase of variances in random effect may deteriorate the precision of parameter estimates and the power of the Wald test. However this possibility was not investigated in this study where the inter-individual variance parameters were fixed.
) and n tot = 70 (N = 10 and n = 7 or N = 14 and n = 5), middle sample size with: n tot = 100 (N = 20 and n = 5) and n tot = 140 (N = 20 and n = 7 or N = 28 and n = 5) and larger sample size with: n tot = 150 (N = 30 and n = 5) and n tot = 210 (N = 30 and n = 7 or N = 42 and n = 5). For each scenario, K = 500 dataset were generated using R software version 2. 15. 0 (R foundation for Statistical Computing, Vienna, Austria).
Using this particular signature, a dedicated analysis for the SM Higgs boson invisible decay was performed in  [64]. The upper limit on µ Hj ≡ σ gg→Hj × B(H → inv)/σ SM gg→Hj at 95% C. L. was found to be µ Hj < 25 (50) for 200 GeV (300 GeV) Higgs boson using just 1 fb -1 of data at 7 TeV. It might be possible for 14 TeV LHC to probe the prediction of this model, µ Hj = R 2 u = 2. 7. Finally, we assess the quantitative impact of existing Higgs measurements on the model's parameter space by performing a fit to the latest LHC Higgs data assuming that h is the newly discovered Higgs resonance (for details see appendix B). The partial decay widths normalized to the SM ones are given by.
ACKNOWLEDGMENTS We thank the  Australian Astronomical Observatory , who have made this project possible. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation. This work has been supported by the  RSAA and Australian Research Council  grant  DP0988751. E. A. gratefully acknowledges financial support by the  CNES  and by the  European Commission  through the  DAGAL Network  ( PITN-GA-2011-289313 ). J. B-H is supported by an  ARC   Federation Fellowship. G. F. L thanks the Australian research council for support through his  Future Fellowship  ( FT100100268 ) and  Discovery Project  ( DP110100678 ).
However, this claim has not been supported by blind tests on independent data sets provided by other experiments. The HiRes experiment has found no significant clustering signal at any angular scale up to 5 • for any energy above 10 EeV  [2]. Below, we report in more details on autocorrelation analyses performed on TA and Auger data. The autocorrelation analysis extended to all angular scales is illustrated in Figure  1 , using three energy thresholds of 10 EeV, 40 EeV, and 57 EeV with the TA data  [3]. For each energy threshold, the plot shows the number of pairs with the angular separations δ binned in 2 • bins (data points). The shaded region represents the average number of pairs expected in the case of the uniform distribution. Both the data and the uniform expectation are normalized bin-by-bin to the area of the bin, so that in the case of a uniform full-sky exposure the expectation would be flat.
Correlation with the VCV catalogue The Pierre Auger Collaboration reported in  [12, 13]  a correlation of events with the AGNs in the Veron-Cetty & Veron (VCV) catalogue  [14]. The first 14 events were used for an exploratory scan that yielded the following search parameters : energy threshold E th = 55 EeV, angular separation Ψ ≤ 3. 1 • , and redshift z ≤ 0. 018. Those parameters minimize the probability that the correlation with AGN could result from a background fluctuation if the flux were isotropic. The subsequent 13 events established a 99% confidence level for rejecting the hypothesis of isotropic cosmic ray flux. The reported fraction of correlation events was 69 +11 -13 %. Fig. 5  (left panel) presents the updated high energy sky as measured at the Auger Observatory  [15] , where the data with E th = 55 EeV up to the end of 2009, 69 events in total, are shown by black dots.