dataesr/scientific-paragraphs-categorization huggingface dataset:
Example data To investigate the behavior of the proposed methods in a real world scenario, the methods were applied to a subset of the INDANA dataset. 26 This is a multi-study dataset compiled to investigate the effect of patient characteristics on the efficacy of pharmacological treatment for high blood pressure. The subset analyzed here (henceforward referred to as the INDANA dataset) contains any study identified by the INDANA collaboration 26 that supplied both longitudinal and time-to-event data and contains 6 studies (EWPHE,  27  COOP,  28  STOP,  29  SHEP, 30 MRC1,  31  and MRC2  32  ). The INDANA dataset concerns hypertensive patients assigned to one of two treatment groups, that is, any treatment for hypertension versus placebo, no treatment, or usual care. Longitudinally measured Systolic and Diastolic Blood Pressure were available, referred to as SBP and DBP.
Three time-to-event outcomes were measured, namely, time to death, time to myocardial infarction (MI), and time to stroke. The data contained 9 possible longitudinal time-points at baseline, 6 months, 1 year, and annually thereafter to a maximum of 7 years. The SHEP study recorded individuals at only 6 measurement times, while STOP and MRC1 presented 7 measurement times, with the remaining studies presenting data at each of the 9 possible measurement times. Only longitudinal data recorded prior to an individual's survival time contributed to the analyses. Tables of the number of measurements provided by each study at each time point are available in the supplemental information (supplemental tables S1-S3). Analyses of SBP and each time-to-event outcome are presented in Tables  2 to 4. For EWPHE, an intention to treat analysis was only possible for fatal endpoints, and so the study only contributes to the analysis of SBP and time to death.
As such, the final dataset examined contained a maximum of 6 studies totalling at most 29825 individuals. The exact number of individuals involved in each analysis is stated in the captions of Tables  2 to 4. The aim of this investigation was to illustrate the proposed one-stage joint meta-analytic models, rather than to investigate potential treatment modifiers. As such, while the INDANA dataset contained a range of patient covariates that could influence the outcomes, models in this investigation included only treatment assignment, study membership, and the longitudinal time covariate. The models of specification shown in Table  1  were fitted to the data for each combination of outcomes (SBP and each of time to death, time to MI, and time to stroke, with longitudinal outcome Y kij = SBP kij ).
Results from the INDANA dataset meta-analyses Tables 2-4 present the results of application of model groups 0-5 (as stated in Supplemental Table  S4 ) to the INDANA dataset. Graphical representations of these results are shown in Supplemental Figures  S4-S12. Across all pairwise combinations of outcomes investigated, the estimated treatment effect from the separate longitudinal one-stage IPD-MA and the joint one-stage IPD-MA longitudinal sub-model was significant and negative, indicating that assignment to treatment for hypertension significantly reduced SBP compared to placebo, no treatment, or usual care. The estimated treatment effect from the separate and joint analyses agreed well across model groups examined, apart from model group 3 (which solely accounted for between study heterogeneity using study level random effects).
Data simulation Data for each set of simulations were simulated under the same model structure, but with different model parameter values, which we will now describe. For each set of simulations, for each scenario, 1000 datasets were simulated. For each dataset within each set of simulations, multi-study joint data were generated containing a single continuous normally distributed longitudinal outcome and a single censored time-to-event outcome. The number of included studies varies between simulation sets; however, each simulated study contained 500 individuals randomized equally to two treatment groups. A maximum of 10 longitudinal measurements at times 0, 0. 25, 0. 5, 1, 1. 5, 2, 2. 5, 3, 3. 5, and 4 were permitted, with measurements recorded only up to the individual's survival time (T Ski ).
0𝑘𝑖 ) 𝜃 1 + 𝛼 (2) b (2) 1𝑘𝑖 ⎞ ⎟ ⎟ ⎟ ⎠. If the conditions are both true, the individual is automatically assigned an event time of infinity; otherwise, their event time is generated under Equation (  4 ). The censoring times were simulated under an exponential distribution with parameter  cens. As such, individual censoring times T Cki are generated using equation T 𝐶𝑘𝑖 = -log(U 𝑘𝑖 ) 𝜆 cens. ( 6 ) The event rate of the simulated data was controlled through the censoring process. Due to the volume of planned simulations, only datasets with a "low" (∼25%) event rate were generated. A range of censoring parameters were tested to obtain datasets with mean event rate at 25%, leading to setting  cens = exp (-0. 426). The survival time for each individual was the minimum of their censoring and event times (T Ski = min (T Eki , T Cki )).
All data used in the simulation studies were simulated under the models shown in Equations (  2 ) and (3), although certain parameter values were altered between different sets of simulations. Parameter values in the simulation sets were chosen such that deviations of different methods from the true parameter values would be clearly discernible. A summary of the values used for the different sets of simulations is given in Table  5. All simulation groups utilized the same fixed effect and error term variance values ( 10 = 1,  11 = 3,  12 = 2,  21 = 3,  2 e = 0. 01). Additionally, throughout different sets of simulations, the individual level random effects covariance matrix D remained constant (defined in Table  5 ). However, the remaining aspects of the datasets (association parameters, number of included studies, level of between study heterogeneity) varied between simulation sets.
Simulation set 1: Varying levels of association In practice, the magnitude of the association between the longitudinal and time-to-event outcomes at the individual and the study level of the data could impact the performance of the model groups defined in Section 2. Consequently, we performed a simulation investigation to assess the effect of varying magnitudes of association at different levels. The individual level association parameter   (2)  and the study level association parameter   (3)  were permitted to take values 0, 0. 5, and 1, giving a total of 9 unique scenarios. The number of included studies in each dataset equalled 5, whereas the study level random effects covariance matrix A (Table  5 ) remained constant across scenarios.
Simulation set 2: Varying numbers of studies included in the meta-analysis The models introduced in Section 2 that include study level random effects may not reliably estimate the distribution of the study level random effects unless the number of studies included in the meta-analysis is large. In addition, models including fixed interaction terms between study membership and treatment group may become unwieldy or difficult to estimate as the number of included studies increases. To investigate this, simulations were conducted comparing one-stage analyses of joint data for datasets containing 5, 10, or 15 studies. During this set of simulations, the association parameters were held constant across scenarios (with   (2)  =   (3)  = 0. 5). Additionally, the study level random effects covariance matrix A (Table  5 ) remained constant across scenarios.
Simulation set 3: Varying levels of between study heterogeneity Finally, the level of between study heterogeneity could affect the behavior of the different one-stage models described in Section 2. As such, the third set of simulations alters the study level random effects covariance matrix A across different scenarios, to increase or reduce between study heterogeneity. Values taken for A, labeled A 1 , A 2 , and A 3 are specified in Table  5 , representing cases for no between study heterogeneity and, then, two increasing levels of between study heterogeneity. During this simulation set, across all scenarios, 5 studies were simulated for each dataset, with association parameters held constant across scenarios at   (2)  =   (3)  = 0. 5.
The models investigated utilized an unspecified baseline hazard in the time-to-event sub-model. Hseih et al  40  noted that when unspecified baseline hazards are used in a joint model, standard errors should be obtained through bootstrapping procedures to avoid their underestimation. As such, the time commitment to perform bootstrapping procedures on large meta-datasets was considerable. Performing bootstrapping procedures on a standard computing environment took several days for the real dataset. Consequently, bootstraps were performed in parallel using the University of Liverpool's HTCondor system (see the work of Litzkow et al,  41  https://research. cs. wisc. edu/htcondor/, and http://condor. liv. ac. uk/), which was also used to run the simulations, with the results compiled using purpose written code rather than relying on single computer bootstrapping procedures.
Researchers using large datasets without coding experience or access to such computer systems may experience issues conducting large scale joint data meta-analyses. In our clinical example, we assume common association parameters across treatment groups. However, in reality, the association between the longitudinal blood pressure and risk of an event could differ between those assigned to any treatment for hypertension versus those assigned to placebo, no treatment, or usual care. 42 In single study cases, association structures involving interactions between baseline covariates and the association parameters have been presented  2, 4  ; however, this association structure has not yet been investigated in meta-analytic joint models. The research presented here prompts a range of future areas of research.
The user then submits the task in a sequential way to the StarPU system. StarPU is able to construct at runtime a task graph from the data dependencies. The task graph is analyzed and the tasks are scheduled automatically to the available workers (CPU cores, GPUs, etc. ). If possible, they are executed in parallel into concurrent threads. The data transfer tasks between the threads are automatically generated and managed by StarPU, which greatly simplifies the programming. When a StarPU program is executed, it is possible to choose among several schedulers. The simplest eager scheduler adopts a very simple strategy, where the tasks are executed in the order of submission by the free workers, without optimization. More sophisticated schedulers, such as the dmda scheduler, are able to measure the efficiency of the different codelets and the data transfer times, in order to apply a more efficient allocation of tasks.
Recently a new data access mode has been added to StarPU: the commute mode. In a task, a buffer of data can now be accessed in commute mode, in addition to the write or read-write modes. A commute access tells to StarPU that the execution of the corresponding task may be executed before or after other tasks containing commutative access. This allows StarPU to perform additional optimizations. There exists also a MPI version of StarPU. In the MPI version, the user has to decide an initial distribution of data among the MPI nodes. Then the tasks are submitted as usual (using the function starpu_mpi_insert_task instead of starpu_insert_task). Required MPI communications are automatically generated by StarPU. For the moment, this approach does not guarantee a good load balancing. It is the responsibility of the user to migrate data from one MPI node to another for improving the load balancing, if necessary.
Macrocell approach. StarPU is quite efficient, but there is an unavoidable overhead due to the task submissions and to the on-the-fly construction and analysis of the task graph. Therefore it is important to ensure that the computational tasks are not too small, in which case the overhead is not amortized, or not too big, in which case some workers are idle. For achieving the right balance, we have decided not to apply directly the above task submission algorithm to the cells but to groups of cells instead. The implementation of the whole kinetic scheme has been made into the schnaps software 2. schnaps is a C99 software dedicated to the numerical simulation of conservation laws. In schnaps we construct first a macromesh of the computational domain. Then each macrocell of the macromesh is split into subcells.
The workload distribution across nodes was made using a standard domain decomposition approach: the mesh was partitioned statically into as many sub-domains as computing nodes, ranging from 1to 4 for our experiment on the IRMA-ATLAS cluster. From an implementation point of view, the transition from a multithreaded code to a hybrid MPI/multithread one is made fairly easy by StarPU. When declaring data to StarPU, one simply has to specify the MPI process owning the data. At runtime, each MPI process hosts a local scheduler instance which acts only on data relevant to the local execution graph. All MPI communications are handled transparently by the local scheduler when inter-node data transfers are necessary. In table 1 we show the wall time for a hundred iterations of the full scheme for the D3Q15 model.
Long-term trends in growth caused by environmental change Comparison of growth between subsequent rotations of the same species grown at the same site allows for diagnoses of long-term changes in the growth behaviour over multiple generations  (Kenk et al. 1991; Pretzsch et al. 2014; Röhle 1994 Röhle , 1997;; Wiedemann 1923). Obviously, the analysis over such a large time span requires an exceptionally extensive database with consistent surveys of the present and previous generation, which might easily date back 200 years. Few long-term experimental plots provide such valuable data, so that stem analysis combined with statistical analysis to filter out influencing factors (e. g. ontogeny) has also been proposed as an approach  (Bontemps et al. 2012 ). However, if appropriate long-term plots are available, the comparison can be carried out for all covered stand characteristics, of which stand height is especially useful as it remains almost unaffected by stand silvicultural treatment.
(1996) ,  Pretzsch et al. (2014)  and  Bontemps et al. (2012)  confirmed that forest stand growth dynamics in Central and Western Europe have accelerated since 1870. For a close analysis of the growth trends in this review, we compiled a data set of several hundred long-term experiments from Austria, Denmark, England, France, Germany, Poland, Spain, Sweden, and Switzerland. For illustration in Fig. 14  and evaluation in Fig. 15 , we selected 577 fully stocked and unthinned or just moderately thinned longterm experiments of Norway spruce (n = 202), Scots pine (n = 189), European beech (n = 97), and common and sessile oak (n = 89). On many plots, the total stand volume production develops more rapidly than suggested by the reference yield tables and finally even exceeds the maximum values of the yield tables with as much as 500-1000 m 3 ha -1.
2018). Analogous to the LTER, LTE, LTAE, and Rothamsted Research initiatives and networks mentioned in the introduction, the institutions responsible for long-term experiments in forest stands should strive for an even better cooperation, standardised data storage and exchange and common funding platform. With the DESER-Norm  (Johann 1993)  the German Association of Forest Research Institutes, DVFFA, (in German: "Deutscher Verband Forstlicher Forschungsanstalten"), has made an important step towards such a standardisation. Recent works taking into account the DESER standards in new evaluation software  (Biber 2013)  and DVFFA initiatives for defining common XML-based data exchange formats point into the same direction. However, internationalisation is still below its possibilities. The course of forest growth as observed during more than a century reveals site and species-specific reactions on various disturbances and may contribute to a less emotional but more objective discussion of the human influence on tree growth, forest dynamics, and long-living ecosystems in general.
This method, relying on the sorting of specific T cells through the use of HLA/peptide-coated magnetic beads  (3) , is currently used in the MELSORT clinical trial to treat metastatic melanoma patients (NCT02424916, https://clinicaltrials. gov). This standardized procedure allows the production of fully specific, polyclonal and tumor reactive specific T cells. Nonetheless the diversity of these polyclonal populations has been addressed so far through the use of anti-Vß specific antibodies, and we could document that these populations were composed with various Vß subfamilies, but the number of T cell clonotypes present among a given Vß subfamily remained unknown. Furthermore, the available panel of 24 Vß-specific antibodies does not always cover the entire T cell repertoire of all antigen-specific T cell populations.
Read Trimming/Clonotype Calling FASTQ files were analyzed in the QIAGEN GeneGlobe Data Analysis Center (https://www. qiagen. com/us/shop/genesand-pathways/data-analysis-center-overview-page/) using the Immune Repertoire Application The web-based read processing service generates clonotype calls and quantity estimates from reads generated by the QIAseq Immune Repertoire Library Kit. The clonotype calls are generated using the IMSEQ software  (11). The main tasks of IMSEQ are to align the reads to model V-region and J-region sequences, extract the CDR3 region sequence, and cluster together highly similar CDR3 sequences that likely came from the same input sample clones. A detailed description of the IMSEQ algorithm is at the following URL: http://www. imtools. org.
For our analysis, we considered data where we had at least 1 UMI and 3 reads per UMI to be a true clonotype. For Melan-A-specific T cell repertoires, we observed high numbers of CDR3α and CDR3ß clonotypes with the highest amount of starting RNA, consistent with increased sensitivity in detecting rare clonotypes when starting with more sample. Concerning MELOE-1 specific T cell repertoires, the differences between the number of clonotypes detected with 10 or 25 ng of RNA are either null or rather modest, in accordance with the fact that MELOE-1 specific T cell repertoires are less diverse than Melan-A-specific ones. Thus, the majority of MELOE-1 specific-T cell clonotypes are already detected with 10 ng of starting RNA. Figure  1  illustrates the rank of individual CDR3α and ß clonotypes identified for the 6 Melan-A (1A) and MELOE-1 (1B) specific populations, and the relative abundance of each sequence (number of reads of each sequence associated to a unique UMI), for the two starting RNA quantities.
The dominant length of these clonotypes is of 17 amino acids, and in this subgroup, the TRAV19 chain is mainly associated with TRAJ44 and TRAJ22 segments. This study also revealed a preferential usage of the TRBV2 chain with 14/153 clonotypes, nonetheless representing only 3. 5% of expanded clonotypes (Figures  3B, C ). Thus TRBV chain may be less crucial in conferring TCR specificity, also confirmed by the absence of any clear conserved motif in CDR3ß sequences (Figure  5B ). The most significant feature concerning TRB chain for MELOE-1 specific repertoire was the dominant usage of the TRBJ2-1 segment (38/153 clonotypes), associated with 21 different TRBV chains. This suggest, that TRBJ segment, rather than TRBV chain could be involved in TCR-peptide interaction. Overall, these data suggest two different structural hypotheses that could explain the high frequencies of Melan-A and MELOE-1 specific T lymphocytes, based either on a specific role of the germline encoded CDR1α and the somatically rearranged CDR3ß regions for Melan-A T cell repertoire, or based on probable interactions within the somatically rearranged CDR3α region, for MELOE-1 specific T cell repertoire, as suggested by the presence of the highly conserved "GP" motif in TRAV19 chain, and also possibly involving the TRBJ2-1 segment.
FUNDING This work has been carried out thanks to the support of the  LabEx IGO  project (n •  ANR-11-LABX-0016-01 ) funded by the  Investissements d'Avenir French Government program , managed by the  French National Research Agency (ANR) , of the  Ligue contre Le Cancer (committees 44 ,  53 ,  56 ) and of the  Région Pays de la Loire. SS was supported by an allocation from the  LabEx IGO  program  ANR-11-LABX-0016-01.
The pattern of haplotype clustering was similar among the three domestic groups in all such regions (Supplementary Fig. 5 ). Comparisons of nucleotide diversity and haplotype clustering between wild and domestic groups supported directional positive or stabilizing selection for a total of 45 regions in sheep and 30 regions in goats, with the remaining 15 being inferred as having undergone relaxed or diversifying selection. Out of these 90 regions, functional annotations are available for 59, based on overlapping or close genes (Supplementary Note 4 and Supplementary Data 3), which displayed pleiotropic effects. Interestingly the representation of the higher level GO terms for these genes under selection differed from those of the reference build from the Uniprot database (χ 2 -test, p-value ≤ 0.
Libraries were purified with 0. 8x AMPure XP beads (Beckmann Coulter), and analyzed with the Agilent 2100 Bioanalyzer (Agilent ® Technologies) and qPCR quantification. Libraries were sequenced using 100 base-length read chemistry in paired-end flow cell on the Illumina ® HiSeq2000. Illumina paired-end reads for Ovis were mapped to the sheep reference genome (OAR v3. 1, GenBank assembly GCA_000298735. 1  46  ), and for Capra to the goat reference genome (CHIR v1. 0, GenBank assembly GCA_000317765. 1  47  ), using BWA-MEM  48. The BAM file produced for each individual was sorted using Picard SortSam and improved using sequentially Picard MarkDuplicates (http://picard. sourceforge. net), GATK RealignerTargetCreator and GATK IndelRealigner  49  , and SAMtools calmd  50. Variant discovery was carried out using three different algorithms: Samtools mpileup  50  , GATK UnifiedGenotyper 51 , and Freebayes  52.
Variant sites were identified independently for each of six groups, using the multi-sample modes of the calling algorithms: (i) 162 samples from MOOA; (ii) 20 samples from IROA; (iii) 14 samples from IROO; (iv) 162 samples from MOCH; (v) 20 samples from IRCH; (vi) 19 samples from IRCA. For some groups, the WGS of more individuals were available as part of the NextGen project (see above). The samples used in the present study were selected to obtain balanced groups of 20 individuals wherever possible. For IRCA and IROO groups, additional samples became available at a later stage and were added for downstream analyses. Animals with low alignment and calling quality were removed to obtain the final data set (Supplementary Data 5). Within each group, there were two successive rounds of variant site quality filtering.
First, we used the pairwise alignment pipeline from the Ensembl release 69 code base  54  to align the reference genomes of sheep (OARv3. 1) and goat (CHIR1. 0). This pipeline uses LastZ  55  to align at the DNA level, followed by post-processing in which aligned blocks are chained together according to their location in both genomes. The LastZ pairwise alignment pipeline is run routinely by Ensembl for all supported species, but the goat is not yet included in Ensembl. To avoid bias toward either species, we produced two different inter-specific alignments. One used sheep as the reference genome and goat as non-reference while the other used goat as the reference genome and sheep as non-reference. The difference is that genomic regions of the reference species are forced to map uniquely to single loci of the non-reference species, whereas non-reference genomic regions are allowed to map to multiple locations of the reference species.