dataesr/scientific-paragraphs-categorization huggingface dataset:
This project received funding from the  Marie Curie   International Outgoing Fellowship  within the 7th European  Community Framework Program  under grant agreement No. 235457 -RXOMics (VV),  National Natural Science Foundation of China NSFC  Grant No. 31471492  this pathway represent an important focus for future study to determine how plants evolved to deal with simultaneous abiotic and biotic stresses. (HXL), grants from the  Colorado State University Infectious Diseases Supercluster , the  United States Agency for International Development Linkage Project  (JEL and CVC), and the  Colorado Agricultural Experiment Station  (SPC, JEL and CTA). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background: Joint modelling of longitudinal and time-to-event data is often preferred over separate longitudinal or time-to-event analyses as it can account for study dropout, error in longitudinally measured covariates, and correlation between longitudinal and time-to-event outcomes. The joint modelling literature focuses mainly on the analysis of single studies with no methods currently available for the meta-analysis of joint model estimates from multiple studies. Methods: We propose a 2-stage method for meta-analysis of joint model estimates. These methods are applied to the INDANA dataset to combine joint model estimates of systolic blood pressure with time to death, time to myocardial infarction, and time to stroke. Results are compared to meta-analyses of separate longitudinal or time-to-event models.
A simulation study is conducted to contrast separate versus joint analyses over a range of scenarios. Results: Using the real dataset, similar results were obtained by using the separate and joint analyses. However, the simulation study indicated a benefit of use of joint rather than separate methods in a meta-analytic setting where association exists between the longitudinal and time-to-event outcomes. Conclusions: Where evidence of association between longitudinal and timeto-event outcomes exists, results from joint models over standalone analyses should be pooled in 2-stage meta-analyses.
In this article, we describe a method for pooling results from multiple joint longitudinal and time-to-event models fitted to data from different studies. The approach adopts a 2-stage method, where IPD is modelled separately in each identified study and the model parameters pooled using standard MA techniques. Results of the proposed MA of joint model are compared to MAs of separate longitudinal or time-to-event analyses. The article is organised as follows. We begin with a discussion of the methods used in this investigation. These methods are then applied to an example dataset. A simulation study is then presented, which compares the MA of joint and separate models under a range of situations. The report ends with a discussion of the use of joint models in a 2-stage MA.
| Stage 1 -Modelling of data within studies We fit joint longitudinal and time-to-event models to the data from each study. Usually, a joint model is formulated of 2 submodels; a longitudinal submodel and an event-time submodel, and the 2 components are linked together through some association structure. We considered a joint model where the longitudinal submodel consisted of a linear mixed effect model (Equation  1 ). Y kij ¼ X kij β 1k þ Z kij b ki þ ε kij (1) Throughout this article, k represents study membership, i a particular individual in study k, and j a particular time point at which their longitudinal outcome is measured. In Equation 1, the longitudinal outcome is represented by Y kij , and the design matrix for the fixed effects is represented by X kij with corresponding population coefficients β 1k , which are estimated for each separate study k.
| Software and model fitting During the simulation study, in the first stage, the joint models in this investigation were fitted by using the "joineR"  19  package within the R programming language. 20 Separate models were also fitted for comparison to the joint models; these were fitted by using the R package "nlme",  21  and the R package "survival"  22  for the longitudinal and time-to-event data, respectively. For the real data application, joint models, and separate longitudinal and time-to-event models were fitted by using functions in the R package "joineRmeta" (available through GitHub: https://github. com/mesudell/joineRmeta). When pooling study level results in the second stage, the R package "meta" 23 was used. Separate MAs were conducted for each model parameter extracted. In this investigation, MAs were conducted for the longitudinal submodel treatment effect parameter β 12 , the time-to-event submodel treatment effect parameter β 21 , and the association parameter α.
An example dataset and R code file to demonstrate the process of conducting a 2-stage IPD-MA of joint data are provided in the Supporting Information. We should note that the joint models fitted from the "joineR" or the "joineRmeta" packages require bootstrapping procedures to estimate standard errors (Hsieh et al  24  emphasised that use of the profile likelihood to evaluate standard errors in joint models could lead to underestimation of the standard errors when an unspecified baseline hazard is used). As such, the time taken to obtain the precision estimates from the joint model was considerably longer than for separate longitudinal or time-to-event analyses, an issue further examined in section 6.
| Example data To assess the difference between separate longitudinal and time-to-event analyses and joint models in a real setting, we examined a subset of the INDANA dataset  25. The INDANA dataset consists of IPD from multiple studies and was assembled to determine whether the efficacy of pharmacological treatment for high blood pressure depends on the characteristics of the patient themselves. The subset we analysed consisted of data from 7 trials,  [26] [27] [28] [29] [30] [31] [32]  which we henceforward refer to as the INDANA data. We refer to the individual studies in the subset as EWPHE,  26  COOP, 27 STOP,  28  SHEP, 29 SHPP, 30 MRC1,  31  and MRC2,  32  respectively. INDANA contains hypertensive patients assigned to 1 of 2 treatment groups, namely, any treatment for hypertension, versus placebo, no treatment, or usual care.
Tables of the number of measurements recorded for each study at each time point are available in Tables S1-S3 in the Supporting Information. We present a demonstration of methods applied to the longitudinally measured SBP and each time-to-event outcome. For EWPHE, an intention to treat analysis is only possible for fatal endpoints, and so we only utilised data from the study in the SBP and time to death analysis. Additionally, SHPP did not contribute to any analysis as it lacked any longitudinal information. Therefore, the final dataset contained a maximum of 6 studies totalling a maximum of 29 825 individuals. The exact number of individuals involved in each analysis depended on the amount of missing data for each outcome and are shown in Tables  1 2 3. During this investigation we aimed to demonstrate 2-stage MA methods for joint data, not to investigate potential treatment modifiers in this dataset.
As such, although the INDANA dataset contained additional patient covariates, models in this investigation only involved treatment assignment and longitudinal measurement time covariates. It was noted that the trajectories of the longitudinal outcome SBP included a change point early on (see Figures S1-S3 in the Supporting Information, which plot the individual and the mean longitudinal trajectories panelled by event type for each study). To account for this in the longitudinal submodel, a range of terms were tested, namely, t 2 ki , exp(-t kij ) and exp(-3 * t kij ). Through comparison of the log-likelihoods and Akaike Information Criterion (AIC) values of the tested models, it was determined that inclusion of the term exp(-3 * t kij ) to account for the changepoint in the trajectory gave the best fit.
As such, the joint models fitted to each study within the INDANA dataset had the following format: Y kij ¼ β 10k þ β 11k t kij þ β 12k treat ki þ β 13k exp -3 * t kij À Á þ b 0i þ b 1i t kij þ ε kij λ ki t ð Þ ¼ λ 0 t ð Þ exp β 21k treat ki þ W 2ki t ð Þ ð Þ W 2ki t ð Þ ¼ α k W 1ki t ð Þ ¼ α k b 0i þ b 1i s ki ð Þ (5) In the set of Equation  5 , t kij represents the measurement time for individual i in study k at the jth measurement time and treat ki is their treatment assignment. Random effects are represented by b terms and the fixed effects by β terms. Fixed effects from the longitudinal and the time-to-event submodels are distinguished by the first element of the parameter's subscript, which takes a 1 or a 2 for the longitudinal or the time-to-event submodel, respectively. When linked to the time-to-event submodel, the t kij covariate in the shared component is replaced with the survival time s ki.
| Results from INDANA Tables 1-3 present results of the analysis of the INDANA dataset from the separate longitudinal and time-to-event analyses, and the joint model analyses, for the investigation of SBP and each of time to death, time to MI, and time to stroke. The results contain the study-specific coefficient estimates, and their confidence intervals, for the longitudinal   and time-to-event submodel treatment effects and the association parameters. The pooled results from both fixed and random MA are also included, along with heterogeneity statistics τ 2 , I 2 , and the P value for the chi squared test for presence of significant heterogeneity. 15 Forest plots for the results are available in Figures  S4-S18. The estimates for the longitudinal treatment effect coefficient were similar between the joint model and the separate longitudinal analysis (Tables  1 2 3 ).
The pooled results for both methods were similar, with the fixed and random MA showing a significant pooled negative treatment effect. This indicated that both separate and joint methods showed that assignment to any hypertensive treatment versus no treatment, placebo, or usual care resulted in a decreased risk of stroke. There was little evidence of heterogeneity across any of the MAs for the time-to-event treatment effect coefficient, for any of time to death, MI, or stroke. The association parameters estimated in each study for each set of analysis (SBP and time to death, SBP and time to MI, and SBP and time to stroke) indicate evidence of heterogeneity between the study estimates. For both time to death and time to MI (Tables  1, 2 ), a significant positive association parameter was estimated in the MRC1, the largest trial in the dataset.
| Methods To further investigate the behaviour of MA of joint model, we conducted a simulation study. Data were simulated under the models shown in Equations 1-3. In each scenario investigated, we simulated joint data consisting of a single continuous normal longitudinal outcome with a single time-to-event outcome for 5 studies, each containing 500 individuals randomised equally to 2 treatment groups. A maximum of 5 longitudinal measurements were recorded for each individual, at time points 0, 1, 2, 3, and 4, capped at their survival time. The time-to-event outcome was simulated by using methods described in Bender  34  and Austin. 35 The event times for individuals were assumed to follow a Gompertz distribution with scale parameter equal to exp(θ 0 + αb 0 ) and shape parameter equal to θ 1 + αb 1.
The extreme value distribution was used to calculate values for these parameters given that the mean value of the random effect terms is 0, and assuming a mean time of 3 and standard deviation of 1. We investigated data with high (~75%) and low (~25%) event rates. Event rates were controlled by using the exponentially distributed censoring times. After testing a range of parameter values, the λ parameter was set to exp(-3. 08) for the high event rate and exp(-0. 58) for the low event rate data. Event times were generated in 2 stages to ensure censored event-times. To begin, a random number U i was generated from the uniform distribution U(0, 1) for each individual i in the simulated dataset. In the first stage, an indicator variable was assigned true if the both conditions below were satisfied and false otherwise: Condition 1: θ 1 þ αb 1i ð Þ <0 Condition 2: U i < exp exp θ 0 þ αb 0i ð Þ θ 1 þ αb 1i If the indicator variable was assigned true, the event time of that individual was set to infinite.
To fully investigate the behaviour of joint models compared to separate analyses, we investigated 5 association levels (α = 0, 0. 25, 0. 5, 0. 75, and 1). We examined only positive associations as behaviour for negative associations is expected to be identical but in the opposite direction. Two sets of data were generated for each scenario, one with homogenous treatment effect and one with heterogeneous treatment effect across studies. For the homogenous treatment effect datasets, the longitudinal treatment effect coefficient was set to 2, whilst the time-to-event treatment effect coefficient was set to 3. For the heterogeneous treatment effect datasets, for each study, the longitudinal treatment effect coefficient was a realisation from N(2, 0. 5), whilst the time-to-event treatment effect coefficient was a realisation from N(3, 0.
However, as shown by the simulation study, significant stronger association with higher risk could lead to more apparent differences between pooled results from separate time-to-event analyses and joint analyses. We noted that fixed and random MA methods generally gave similar results for the homogenous simulated datasets, but that results differed more for the heterogeneous treatment simulated datasets. This suggests that researchers conducting a MA by using joint longitudinal and time-to-event data should, as with other MA, ensure that random effects methods are used if it is known or suspected that heterogeneity exists between the trials included in the MA. This can be achieved through examination of forest plots, and exploring estimates of the τ 2 parameter and I 2 statistic. Throughout the simulation study, the coverage for the joint model methods appeared consistently high across different scenarios and association levels.
Where association is present, ie, when α k ≠ 0, the joint analysis models risk of an event associated with the longitudinal outcome via W 2ki (t). This term (which has an effect on the event risk) is not included in the separate time-to-event model, potentially explaining the observed biased treatment effect estimates. This behaviour was not observed between the separate and joint longitudinal analyses as the model specifications for the longitudinal trajectory are identical in both models. We have noted earlier the overall time taken to fit and extract information from joint model fits being larger than for separate analyses because of the bootstrapping requirement of drawing standard errors in the "joineR" and "joineRmeta" software. This issue is even more apparent for 2-stage MAs as separate model fittings are required for multiple studies.
During our application to real data, model fitting and bootstrapping of 200 samples have taken over 12 hours in a standard computing environment. To improve the situation, we have used the University of Liverpool's HTCondor system (see Litzkow et al,  39  https://research. cs. wisc. edu/htcondor/, and http://condor. liv. ac. uk/ which was also used to run the simulations). Researchers without access to such specialised computing systems would have to experience long running times on standalone computers to fit joint models relying on bootstrapping for estimating the standard errors. Guo and Carlin 12 analysed a single dataset by using separate and joint models. The association parameter in the joint model was significant and negative. The differences observed between the estimated survival times produced by the 2 approaches were attributed to the fact that the joint models accounted for correlation between the longitudinal and time-to-event outcomes, whereas the separate time-to-event model did not.
3 mobility (Fig  1C-E ). We next compared the supra-molecular organization of EOS-REM1. 3 by Voronoï tessellation of live PALM data  31 41   7  and Nicotiana benthamiana  30, 31. As REM1. 3 was originally discovered as a PM-associated phosphorylated protein  33  , we asked whether we could detect kinase activity in N. benthamiana extracts leading to REM1. 3 phosphorylation and whether this activity was induced by PVX presence in the cells. Equal protein amounts of microsomal and soluble extracts from N. benthamiana leaves were used as a kinase source to phosphorylate affinity-purified full-length 6His-REM1. 3 in an in vitro kinase assay in the presence of ATP [γ-  33   In silico analysis predicted phosphorylation sites throughout REM1. 3 sequence (Diphos, DEPP and NETPHOS prediction software).
Pseudo-colored images were obtained using the "Red hot" look-up-table (LUT) of Fiji software (http://www. fiji. sc/). All quantifications were performed for at least 10 cells, at least two plants by condition with at least 3 independent replicates. Images acquisitions and processing were done as previously described  91. In silico analysis of REM1. 3 protein sequence Prediction of putative phosphorylation sites was performed by Diphos, DEPP and NETPHOS coupled with published data. Disordered domains were performed by pDONR VL XT.
Accession Numbers Sequence data from this article can be found in the Arabidopsis Genome Initiative (https://www. arabidopsis. org/index. jsp), and GenBank/EMBL (https://www. ncbi. nlm. nih. gov /genbank/) databases under the following accession numbers: REM1. 3 (NP_001274989), AtREM1. 2 (At3g61260), AtREM1. 3 (At2g45820), AtCPK3 (At4g23650).
Acknowledgments Imaging was performed at the  Bordeaux Imaging Center , member of the national infrastructure France BioImaging. AP was supported by the  Greek fellowship program IKY in France. JG, PG and AFD are/were supported by the  Ministère de l'Enseignement Supérieur et de la Recherche, France  (MERS, doctoral grants). We acknowledge  Dinesh Kumar  for the gift of TMV-GFP clone ,  Alicia Zelada  for the gift of PVXΔTGBp1 and viral protein constructs and  Coralie Chesseron  for greenhouse facilities. The  IPS2  is supported by the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). VG, SM, EEB, VS and VG are supported by the  French ANR  project " Potymove"  ( ANR-16-CE20-008-01 ). EEB, VG and SM are supported by the  French ANR  project "Connect". CZ is supported by the  Gatsby Charitable Foundation  and the  European Research Council  (grant " PHOSPHinnATE ").
Acknowledgements We gratefully acknowledge the management, logistical and communication assistance from the  Velaux Municipality  ( J. -P. Maggi  and  L. Melhi ) with its heritage, culture and technical services ( M. Calvier , and  S. Chauvet ), the environment department from  CD 13  ( M. Bourrelly ,  T. Tortosa ,  G. Michel ,  N. Mouly , and  S. Amico) , the ' Service Départemental d'Incendie et de Secours'  ( SDIS 13 ), and numerous volunteers during the field campaigns in 2009 and 2012. This work was supported by the  Ministry of Education and Communication  (research grant  VR1013  to Palaios association), the  Bouches du Rhône department   CD 13  proposals  MAPADGAC23112010-1  and  MAPADGAC16012014-1-AAPC ), by the  Belspo BRAIN. be proposal 'Coldcase' , and by  FRIA  grants to UL and AC.
85% NaCl, pH 7. 2) was added to the plastic bag containing the cloth, and after a 30-s treatment in a Stomacher, 1-ml samples were plated to blood agar directly and after dilution to obtain single well-separated colonies for identification. The agar plates were incubated at 20°C for 5 days. Up to 20 colonies were picked at random, restreaked for purification, and subjected to 16S rRNA sequencing (V3 to V4 region) for identification using the universal 16S rRNA primers TCCTACGGGAGGCAGCAGT and GGACTACCAGGGT ATCTAATCCTGTT (48), as previously described  (37). The taxonomy of each strain was assigned using the SeqMatch tool of the Ribosomal Database Project (RDP), with database v. 11. 5 (https://rdp. cme. msu. edu). Selection criteria for background microbiota strains included in biofilm experiments.
Three L. monocytogenes strains were selected from so-called sporadic MLVA profiles. These strains were selected based on the criteria that they should be isolated during production and that their MLVA profiles were not commonly found after C&D. All seven selected strains had different dapE alleles, enabling their differentiation by MLST sequencing  (50). Alleles and sequence types for MLST were compared with those available in the Institute Pasteur's L. monocytogenes MLST database (http://bigsdb. web. pasteur. fr/listeria/listeria. html). Whole-genome sequencing. DNA isolation, whole-genome sequencing and de novo genome assembly were performed essentially as previously described  (38) , with 300-bp paired-end sequencing on a MiSeq instrument  (Illumina) , except that genome assembly was performed with v3.
10. 0 of SPAdes  (51)  and six k-mer sizes were included  (21, 33, 55, 77, 99, and 127). Contigs with sizes of Ͻ500 bp and with coverage of Ͻ35 were removed from the assemblies. The sequences were annotated using the NCBI Prokaryotic Genomes Automatic Annotation Pipeline (PGAAP) server (http://www. ncbi. nlm. nih. gov/ genome/annotation_prok/). Sequence search for known biofilm genes. The Pseudomonas and Acinetobacter genome assemblies from this study were analyzed for the presence of genes known to be involved in biofilm formation in these genera (52-54) using BLASTϩ v2. 2. 30  (55). The following genes were used as queries in the analysis: pslA to pslR (PA2231 to PA2246), pelA to pelG (PA3064 to PA3058), the alg operon (PA3540 to PA3551), and cdrA (PA4625) from P. aeruginosa PAO1 (accession number AE004091); wssA to wssJ from P.
fluorescens SBW25 (accession number AY074776); lapA to lapG (PP0168 to PP0164), lapF (PP0806), the bcs operon (PP2629 to PP2638), peaA to peaI (PP3133 to PP3141), and the peb locus (PP1795 to PP1788) from P. putida KT4220 (accession number AE015451); csuA to csuE, encoding the pilus usher-chaperone assembly system from A. baumannii 19606 (accession number AY241696); pgaA to pgaD (A1S_2160 to A1S_2162 and A1S_3792) from A. baumannii ATCC 17978 (accession number CP000521); and the gene encoding Bap from A. baumannii 307-0294 (accession number EU117203). Genome comparisons were performed using Mauve  (56). Phylogenetic analysis. The sequences of single genes or whole genomes from reference strains used in the phylogenetic analyses were downloaded from GenBank, and their accession numbers are listed in Table  S5  in the supplemental material.
Metagenomic analysis using QIIME software. Demultiplexed raw reads from the MiSeq run were processed with the QIIME (Quantitative Insights Into Microbial Ecology) software package v1. 9. 1  (63). After paired-end reads were joined, they were quality filtered on q20. Then, samples amplified with dapE primers were assigned to their respective dapE allele using a closed reference operational taxonomic unit (OTU) picking protocol against a custom reference file containing the dapE allele sequences of the seven L. monocytogenes strains (dapE allele numbers 4, 6, 7, 8, 9, 18, and 21 as listed at the Institute Pasteur's L. monocytogenes MLST database). The OTU picking script was run with default parameters except that the sequence similarity threshold was set to 1. For samples amplified with 16S rRNA primers, samples were analyzed using an open reference OTU picking protocol, in which reads were first matched against a custom reference file containing the 16S rRNA allele sequences of L.