dataesr/scientific-paragraphs-categorization huggingface dataset:
Pritz et al. 49  have noted an agerelated decline in the number of plasma cells in human bone marrow. Here also, the identifiability of this effect could have been compromised because of a restricted range of participant ages because none of them were older than 65 years. In conclusion, the dynamical model constructed from early Phase I data has demonstrated its predictive capacity, with longer follow-up and updated estimates giving promising results for the duration of the immune response. Nevertheless, the simplicity of this model significantly limits its use to fully describe the immune Fig. 6  Evaluation of the ability of the model to predict unseen data using Monte-Carlo cross-validation. The predictive quality was assessed by the evaluation of two criteria: the RMSE (left side) and the percent coverage (right side).
The x-axis corresponds to the percent of participants randomly selected for the training dataset and the y-axis to the value of criteria calculated on the testing dataset. One hundred replicates were performed for each train-test split percentage. Solid lines display the values of criteria and dashed lines, the 95% confidence intervals. The horizontal red dotted line on the right side displays the threshold of 95%. response induced by a multi-dose vaccine strategy. Indeed, the model focuses exclusively on the decrease in antibody concentrations following vaccination, without modeling the establishment of the immune response triggered by each injection. Thereafter, more complex versions of this model have been proposed to model the establishment, reactivation, and persistence of the humoral immune responses induced by vaccination  50, 51.
These studies were performed in four countries: the United Kingdom (UK), Kenya, Tanzania and Uganda. Results of the trials were previously described by Milligan et al. 53  and Winslow et al. 54  for the UK (study registered at ClinicalTrials. gov, NCT02313077, and labeled EBL1001 here), Mutua et al. 55  for Kenya (study registered at ClinicalTrials. gov, NCT02376426, and labeled EBL1003 here), and Anywaine et al. 56  for Tanzania/Uganda (study registered at ClinicalTrials. gov, NCT02376400, and labeled EBL1004 here). In addition, we considered data from two randomized, observer-blinded placebo-controlled, parallel-group Phase II trials on healthy volunteers aged 18-65 or 75 years. These studies were performed in six countries: the UK, France, Kenya, Uganda, Burkina Faso and Ivory Coast. We refer to Pollard et al.
17  for a detailed description of results in the European trial and to Barry et al. 18  for the African trial (two studies registered at ClinicalTrials. gov, NCT02416453 and NCT02564523, and labeled EBL2001 and EBL2002 here respectively for the European and African studies). The last study is a combined open-label, nonrandomized stage 1, and a randomized, observer-blinded, placebo-controlled stage 2 Phase II trial on healthy adults. This study conducted in Sierra Leone also aimed to evaluate the longterm immunogenicity and the humoral immune memory induced by the vaccine regimen. Results of this trial were described by Ishola et al. 19  (study registered at ClinicalTrials. gov, NCT02509494, and labeled EBL3001 here). In Phase I trials, participants were equally randomized into four vaccination regimens: two with MVA-BN-Filo as the first vaccination on day 1, followed by Ad26.
ZEBOV on day 29 or 57, and two with Ad26. ZEBOV was the prime vaccine on day 1, followed by MVA-BN-Filo on day 29 or 57. Within each regimen, participants received either an active vaccine or placebo in a 5:1 ratio. In the study EBL2001, participants in Cohorts I-III were equally randomized into three parallel groups in which they received Ad26. ZEBOV was the first vaccine on day 1, followed by MVA-BN-Filo on day 29, 57, or 85. This first cohort was excluded from the analysis as participants were enrolled to provide data only on safety and the timing of anti-Ebola virus GP ASCs responses. Within each group, participants received active vaccines or placebo in a 14:1 or 10:3 ratio in cohorts II and III respectively. In the study EBL2002, healthy adults (Cohort I) were equally randomized into the same three parallel groups with an active vaccine: placebo ratio of 5:1.
Statistical analysis A preliminary descriptive analysis was performed on the baseline and demographic characteristics of the 487 participants to describe and summarize the basic features of the data. Statistical differences among groups of participants were evaluated using classic t-tests (Welch's t-test in case of unequal variance, identified by a F-test, and Student t-test otherwise) implemented in R, and pvalues were adjusted for test multiplicity with Benjamini and Hochberg correction  20  using the built-in R function p. adjust. Because of the difference in antibody concentrations measured by the distinct laboratories, comparisons of immunogenicity data between trials were not possible. Finally, Spearman correlations between antibody concentrations measured 21 days after the second vaccination and longer-term humoral responses were evaluated by integrating adjustment for test multiplicity on p-values.
Model estimation was performed by the Monolix ®software versions 2019R1 and 2019R2. This software uses the Stochastic Approximation Expectation-Maximization (SAEM) algorithm  58, 59  to estimate the population parameters with likelihood computed by importance sampling  60  and the Fisher information matrix calculated by stochastic approximation. Once population parameters are re-estimated, individual parameters are computed as empirical Bayes estimates (EBEs) representing the most likely values of the individual parameters, given individual data and population parameters. EBEs are calculated as the mode of the conditional parameter distribution by Markov-Chain Monte-Carlo (MCMC) procedure  61  using the Metropolis-Hasting algorithm  62  to compute the conditional distribution and the Nelder-Mead Simplex algorithm  63  to maximize it.
Evaluation of the model quality of prediction The mechanistic model described by Eqs. (  2 )-(  4 ), initially estimated on Phase I data by Pasin et al. 16  , was validated on data from the six trials according to its quality of prediction. To this end, a two-step approach was applied: first, the robustness of the model was assessed by evaluating its ability to predict antibody dynamics from 7 days post-second vaccination to the peak of the dynamics (i. e. , the first local maximum) for all Phase I and Phase II participants. Then, the ability of the model to forecast short-term (i. e. , from the peak to 1 year after the first vaccination) and long-term antibody concentration (i. e. , beyond 1 year following the first vaccination) was evaluated. Because validation of the mechanistic model estimated on Phase I data is sought here, no modification of the observation model defined in Eq.
This additional analysis was performed to identify whether the estimation of individual parameters on a longer follow-up can improve longterm predictions. K-means clustering for longitudinal data  65  was performed to identify distinct trajectories of the dynamics of the humoral response. Using the kml R package  66  , trajectories of antibody concentration from 7 days after the second vaccination to 2 years after the first vaccination were sequentially clustered into two and more clusters. Thereafter, we evaluated the percent coverage and the RMSE to investigate potential differences in prediction abilities according to underlying trajectories for each resulting partition.
The predictive quality of the newly estimated model was assessed by performing a Monte-Carlo cross-validation  74. Participants from the overall dataset were randomly split into a training and a testing dataset, given a particular train-test split percentage. We ensure that the same ratio of participants in each trial was maintained within each of the two sub-datasets. Once the model was fitted on training data, EBEs resulting from this model were evaluated on test data, followed by the prediction of the individual antibody dynamics. Two criteria were then calculated on the testing dataset to estimate how accurately the predictive model performs: the percent coverage (the higher the better) and the RMSE (the lower the better). For each of the seven train-test split percentages {20%, 30%, 40%, 50%, 60%, 70%, 80%}, the procedure was replicated 1000 times.
ACKNOWLEDGEMENTS We thank the members of the  EBOVAC1, EBOVAC2  and EBOVAC3 consortia. We also thank  Thierry Van Effelterre  ( Janssen Pharmaceutica N. V. , Beerse, Belgium ) and  Laura Solforosi  ( Janssen Vaccines & Prevention B. V. , Leiden, Netherlands ) for contribution in an earlier stage of the modeling project. We thank  Anna Dari  ( Janssen Research  and.
DATA AVAILABILITY Janssen has an agreement with the Yale Open Data Access (YODA) Project to serve as the independent review panel for the evaluation of requests for clinical study reports and participant-level data from investigators and physicians for scientific research that will advance medical knowledge and public health. Data will be made available following publication and approval by YODA of any formal requests with a defined analysis plan. For more information on this process or to make a request, please visit the Yoda Project site at http://yoda. yale. edu. The data sharing policy of Janssen Pharmaceutical Companies of Johnson & Johnson is available at https:// www. janssen. com/clinical-trials/transparency. Development, Beerse, Belgium) for extensive review. We thank Lixoft SAS for their support.
Interesting events are selected by the first-level trigger system implemented in custom hardware, followed by selections made by algorithms implemented in software in the high-level trigger  [28]. The first-level trigger accepts events from the 40 MHz bunch crossings at a rate below 100 kHz, which the high-level trigger further reduces in order to record events to disk at about 1 kHz. An extensive software suite  [29]  is used in data simulation, in the reconstruction and analysis of real and simulated data, in detector operations and in the trigger and data acquisition systems of the experiment. 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the 𝑧-axis along the beam pipe. The 𝑥-axis points from the IP to the centre of the LHC ring, and the 𝑦-axis points upwards.
9  [35]  and the CT14NNLO PDF  [36]  set. The NNLO QCD correction was applied as a multiplicative factor from 1. 44 to 0. 29 for different 𝑍 ′ masses on total cross-section. No mixing of the 𝑍 ′ boson with the 𝑍 and 𝛾 * bosons is included. The 𝑑 d → ν𝜏 → ℓℓ ′ samples were generated at LO with MadGraph5_aMC@NLO v2. 3. 3  [37]  interfaced to the Pythia 8. 186 parton shower model with the NNPDF2. 3LO PDF set and the A14 tune. The signal samples were generated with the same mass values as for the 𝑍 ′ model described above. The cross-section was calculated at LO with the same generator used for simulation and corrected to next-to-leading-order (NLO) using LoopTools v2. 2  [38]. The NLO correction factor ranges from 1. 1 to 1. 4, depending on different ν𝜏 masses. The 𝑝 𝑝 → QBH → ℓℓ ′ samples were generated with the program QBH 3.
The 𝑊+jets contribution was estimated with the aid of the Sherpa 2. 2. 2 simulated samples using NLO MEs for up to two partons, and LO matrix elements for up to four partons calculated with the Comix  [54]  and OpenLoops libraries. They are matched with the Sherpa parton shower using the MEPS@NLO  [55]  prescription using the set of tuned parameters developed by the Sherpa authors  [50]. For all samples used in this analysis, the effects of multiple proton-proton interactions per bunch crossing (pileup) were included by overlaying minimum-bias events simulated with Pythia 8. 186 and reweighting the simulated events to reproduce the distribution of the number of interactions per bunch crossing observed in the data. The generated events were processed with the ATLAS simulation infrastructure  [56] , based on Geant4  [57] , and passed through the trigger simulation and the same reconstruction software used for the data.
Experimental uncertainties affect the event yields of background and the signal cross-section through their effects on the acceptance and the event migration between different analysis regions. Theoretical uncertainties are considered for the renormalisation (𝜇 𝑟 ) and factorisation (𝜇 𝑓 ) scales, the choice of PDF, the choice of the value of the strong coupling constant 𝛼 𝑠 , and the modelling of the 𝑡 t and diboson backgrounds. The strategy used in the same analysis with partial data sample at √ 𝑠 = 13 TeV [9] is followed in this search. Variation of the renormalisation and factorisation scales is used to estimate the uncertainty due to missing higher order corrections. Pairwise variations of 𝜇 𝑟 and 𝜇 𝑓 are considered to find the maximum and minimum variations. The PDF uncertainty consists of the contribution from the PDF set used in the matrix element calculation.
Trial Design This was a prospective, multicentre, single-arm imaging trial listed on clinical trials. gov (NCT02355353). All patients gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the ethics committees of the individual institutions (EORTC 1423). Patients with histologically confirmed clinical stage IV metastatic colorectal cancer (mCRC) with metachronous or synchronous liver metastases who were Cancers 2023, 15, 3580 3 of 15 candidates for neoadjuvant therapy were eligible. Three pre-operative treatment regimens were considered acceptable to facilitate recruitment: chemotherapy alone, chemotherapy + anti-angiogenic agent, or chemotherapy + monoclonal antibody.
Trial Design This was a prospective, multicentre, single-arm imaging trial listed on clinical trials. gov (NCT02355353). All patients gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the ethics committees of the individual institutions (EORTC 1423). Patients with histologically confirmed clinical stage IV metastatic colorectal cancer (mCRC) with metachronous or synchronous liver metastases who were candidates for neoadjuvant therapy were eligible. Three pre-operative treatment regimens were considered acceptable to facilitate recruitment: chemotherapy alone, chemotherapy + anti-angiogenic agent, or chemotherapy + monoclonal antibody. Twenty-six patients (14 male, 12 female) aged 45-82 years (mean 64.
Measurement of ADC Parametric maps of ADC were calculated voxelwise for the whole data volume using a mono-exponential fit model implemented in the ADC plugin in Osirix MD  [10]. All metastatic lesions identified on the pre-treatment scan were delineated at each time point to produce tumour specific volumes of interest (VOI). Manual tumour delineation was undertaken by a single experienced radiologist (AJ). Delineation was performed primarily on b = 1000 DW images but with access to all other imaging sequences. A randomly selected group of 25 lesions was redefined by the same observer with an interval of 4-6 weeks in order to determine reproducibility of the VOI definitions and by a second blinded observer (RP) to determine inter-observer variation. Histogram analysis of repeatability and limits of agreement for individual tumours, were also calculated using a 5% level of significance.
Histological Evaluation At surgery, tissue was collected from all resected lesions. The lesion was identified according to the code attributed on the original pre-surgical scans. Haematoxylin and eosin (H&E) stained slides were used to provide data on tissue cellularity. Pathological response was independently determined on a lesion-by-lesion basis at a central reference laboratory. This was carried out independently by two experienced histopathologists (CJ and MU-N) and discrepancies reconciled by consensus discussion and agreement. The pathological response rate was graded for five tumour response groups using the criteria established by Rubbia-Brandt  [11]. The proposed scoring system is based on the presence of residual tumour cells and the extent of fibrosis (TRG1 = absence of tumour cells replaced by abundant fibrosis; TRG2 = rare residual tumour cells scattered throughout abundant fibrosis; TRG3 = residual tumour cells throughout a predominant fibrosis; TRG4 = large number of tumour cells predominating over fibrosis; TRG5 = almost exclusively tumour cells without fibrosis)  [12].
In addition, both pathologists similarly estimated and agreed on viable tumour cell surface area and expressed it as percent of total surface area of the metastatic lesion. Total surface area included tumour, stroma, immune infiltrates, and necrotic areas. Tumour necrosis was quantified as follows: necrotic area/(necrotic area + non-necrotic solid tumour) Analysis of Ki-67 IHC stained slides provided data on tumour proliferation. The Ki-67 status was expressed as the percentage of Ki-67 positive nuclei to all tumour nuclei.
The relationship between baseline (mean ADC, day 14 ADC, ∆ADC early , and ∆ADC late ) and TRG was also examined using Spearman rank correlation. A sensitivity analysis to include the dependency of lesions from the same patient was performed with a repeated measures model using the standardized ranked variables. The one-sided type I error was fixed at 1% for all secondary and exploratory analyses. When relevant, equivalent two-sided 98% confidence intervals were reported. The statistical package used was SAS 9. 4 (STAT 14. 3).
The research leading to these results received support from the Innovative Medicines Initiative Joint Undertaking (www. imi. europa. eu, accessed on 5 July 2023) under grant agreement number 115151, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. M. U. -N. acknowledges European Organization for Research and Treatment of Cancer (EORTC) and European Society of Pathology (ESP) for a fellowship grant. Bart Jacobs' work as Fellow at EORTC Headquarters was supported by a grant from Sir Ronald Grierson. Institutional Review Board Statement: The individual review boards of the institutions participating in EORTC 1423 provided approval at an institutional level.
Funding: The research leading to these results received support from the Innovative Medicines Initiative Joint Undertaking (www. imi. europa. eu, accessed on 5 July 2023) under grant agreement number 115151, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. M. U. -N. acknowledges European Organization for Research and Treatment of Cancer (EORTC) and European Society of Pathology (ESP) for a fellowship grant. Bart Jacobs' work as Fellow at EORTC Headquarters was supported by a grant from Sir Ronald Grierson. Institutional Review Board Statement: The individual review boards of the institutions participating in EORTC 1423 provided approval at an institutional level.
mothur. org/wiki/ MiSeq_SOP. Sequence analysis for fungi was conducted using the software package PIPITS (version 1. 3. x, [44] ). Data processing was done in R version 3. 4. 2 [45]  with the package phyloseq  [46]. Unassignable sequences and sequences not belonging to the kingdoms of bacteria or fungi were removed. Unique operational taxonomic unit (OTU) tables were rarefied (set. seed(631)) and low abundances (< 0. 1%) removed. DNA samples with number of sequences lower than 50% of the mean number of sequences of all samples per country were removed from the analysis (bacteria: n = 6; fungi: n = 4).
4. 2 [45]. The lme4 package version 1. 1-18-1  [51]  was used for fitting linear mixed effect models (function lmer), using maximum likelihood (ML) to investigate the effect of soil disturbance and soil covariates on microbial diversity (fungal and bacterial richness and Shannon Index) and soil functions (respiration and decomposition). Differences among countries in response to the treatment were tested by including 'country' as fixed factor as well as in a 2-way interaction with all other soil covariates. Whether interaction coefficients differed significantly from 0 was determined by applying the sim_slopes function from the package 'interactions'  [52]. All covariates were standardized by subtracting the mean and dividing by the standard deviation across countries before analyses. The year of sampling and the experimental plot within vineyards were included in the models as nested random factors (1|vineyard/plot/year).
The random factor 'year' was removed for soil parameters which were analyzed only once during the study. We checked for collinearity between soil covariates by calculating Pearson correlation coefficients and found no correlations above > 0. 7, where collinearity of predictor variables distorts model estimations  [53]. No variation of any of the soil covariates was significantly explained by the disturbance gradient (Table  S4  in S1 File). Microbial community analysis. Microbial community analysis was done using the package vegan version 2. 5-4  [54]. Bacterial and fungal data sets were analyzed at the highest taxonomic level that could be resolved with the primers and databases used (mostly at the genus level, although for fungi some OTUs could be assigned at the species level). Analyses were carried out across data sets of all countries (hereafter, the European level) and at the level of each country, considering two consecutive sampling years for Switzerland and Germany but only one for the other countries.
All data sets were log-transformed (log10(x+1)) to downweigh very abundant taxa (which were often unresolved taxa of coarse taxonomic levels). Dissimilarity matrices were calculated for all community data sets using Bray-Curtis dissimilarities, which were square-root transformed to avoid negative eigenvalues resulting from eigenvectorbased methods  [55]. We used permutational multivariate analysis of variance (PERMANOVA)  [56]  to test the effect of soil disturbance, country, vineyard and year on microbial community composition. The variables 'disturbance' and 'vineyard' were included simultaneously in the analyses of country-level data sets, except for Switzerland, where these factors were included separately due to the different set-up. 'Disturbance' and 'country' were included in the analyses of the European level.
Further, 'year' was tested for Germany, Switzerland, as datasets from two years were available. All analyses were performed with 10 000 permutations. To visualize microbial community patterns at the European level, principal coordinate analyses (PCoA  [57] ) were performed on bacterial and fungal community dissimilarity matrices. To constrain the ordination of microbial community patterns by soil covariates, we used distance-based redundancy analysis (db-RDA  [55] ), including all soil covariates as independent terms and the European level dissimilarity matrices as response term. The db-RDA models were tested using permutation-based ANOVA testing the significance of the model as well as of individual independent terms. Results of PCoA and db-RDA were visualized using the ordination plots showing the first and second axis.