dataesr/scientific-paragraphs-categorization huggingface dataset:
Background An increasing number of transcriptomic analyses are available for scientific community with a growing number of information. Since each experiment has been generated independently with a dedicated purpose, it is somewhere complex to compare experiment to one another. One of the perspectives resulting of the numerous available data is the analysis of expression profile per genes and the comparison between genes in order to produce expression gene cluster. Different web tools such as ATTED II  (Aoki, et al. , 2016) , STRING database  (Szklarczyk, et al. , 2015) , PlaNet  (Ruprecht, et al. , 2017) , Planex  (Yim, et al. , 2013) , CoP  (Ogata et al. , 2010)  PlantExpress  (Kudo, et al. , 2017)  ou COEXPRESSdb  (Obayashi, et al. , 2013)  are available. The purpose of these programs is to represent expression gene cluster mainly for Arabidopsis thaliana.
By selecting the identifier of one or more genes, we can access to the network of genes co-expressed with the gene(s) of interest. These tools are useful for characterizing genes but do not allow the grouping of genes according to their expression profile and do not propose any statistical analysis. On the other hand, softwares used to cluster on large datasets without having to type code like HOMER Moreover, these resources display clustering based on already analysed data sets and it is not possible to include new expression data set in the co-expression analysis. In the particular case of model organisms such as A. thaliana the huge number of data available will create artefact clustering. The Taylor clustering proposed with selected data sets will allow bypassing this bias. In order to perform efficient gene clustering on new data set together with gene annotation, we developed a flexible tool for analysis.
ExpressWeb: computational pipeline Several languages have been used to develop ExpressWeb: HTML5 and CSS3 for the web interface, javascript for the dynamic tools (Highchart library to create dynamic Heatmap and the vis. js library to create network from the data), JSON  (Kobayashi, et al. , 2011)  and JQuery for the use of DOM and Ajax in order to manage the data in dynamic manner. The language of the main script is PHP. Finally, once you have uploaded your values in the database, two scripts using R and Python languages will process it. CC-BY-NC 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted May 2, 2019.
; https://doi. org/10. 1101/625939 doi: bioRxiv preprint First, the R Script is used to compute hierarchical clustering on the up loaded values in order to build genes groups. Before computing the clustering, the values are scaled in order to have better results. The Varclus function is used, computing Hoeffding's Distance between each gene and retrieving it into a similarity matrix. Then the Hclust function computes hierarchical clustering following the Ward's algorithm creating a dendogramm. This dendogramm is cut to produce two types of groups, one with the threshold chosen by the user (Fig. 1A ) and one with a lower threshold. The group definition and the scaled values are uploaded as MySQL database in order to optimize the re-use of the clustering results and to improve the performances during the analysis.
The Python script will read the similarity matrix and the groups definition in order to create nodes and edges that will be used to build the network visualisation (Fig. 1D ). Nodes are created for each gene. Each node contains information about the gene such as its name and its cluster. Then the nodes are connected by creating edges. Inside a group (low level clusters) the clusters are connected by computing average distance between each clusters and creating an edge between a cluster and the one having the best average distance. Inside a cluster, each gene is connected to its closest according to the similarity matrix except if those two are already connected and then the gene is connected to the second closest etc. Nodes and Edges are written in two JSON files that will be read using the vis.
Web-server and standalone package The idea of developing ExpressWeb came from our specific needs in analyzing selected sets of expression data. First ExpressWeb has been developed to be used in local with our own data set or with data set publically available, but rapidly a standalone package has been developed and userfriendly outputs such as heatmap, expression profile and network (Fig. 1 ). A demo version provides a user-friendly interface to ExpressWeb (http://polebio. lrsv. upstlse. fr/ExpressWeb).
Positive and negative controls provided in the FTD Enteric Fever Kit were performed in the same run. Polymerase chain reaction signals were analyzed using CFX Manager software version 3. 1 (BioRad) and expressed as cycle threshold (Ct) values. To validate a result, a serovar-specific signal (either S. Typhi or S. Paratyphi A) must be associated with the detection of enterica species (Salmonella spp. ), except for non-typhoid Salmonella (NTS) isolates which are only positive for the Salmonella spp. signal. The comparative PCR test, hereafter designated "Nga-PCR" assay, has been already described by Nga et al. 28  Briefly, the assay targets STY0201 and SSPA2308 genes and was slightly modified as follows: 25-μL reaction volume containing 15 μL of Takyon kit for probe qPCR assays (Eurogentec, Li ège, Belgium) with the same primer (0.
Blood collection and culture. The recommended blood withdrawal volume for testing was stratified into three different groups based on age (2-5, 5-17, and > 17 years). Three milliliters of blood was collected to perform PCR in all age groups, whereas 3 mL (2-to 4-year-old group) to 5 mL was withdrawn for culture. An additional volume (1 mL) was collected to carry out a secondary endpoint test (TPTest, data not included). Moreover, 205 patients consented to participate in an additional study to evaluate an alternative test, and for these individuals, an additional volume of blood (5 mL) was collected. Blood culture was performed with either the BACTEC (Becton Dickinson and Company) or 3D BacT/Alert (bioM érieux, Marcy-l'Etoile, France) automated culture system at DSH and icddr,b, respectively.
The tubes were then incubated for 5 hours at 37°C with shaking before -80°C storage and shipment. DNA extraction and multiplex PCR amplification were performed at the Fondation M érieux laboratory following the protocol previously described. Ethics. The study was approved by ethical committees of the Bangladesh Institute of Child Health (BICH-ERC-2/6/ 2012) and icddr,b (PR#13014-27/02/2013). Statistical analysis. Categorical variables were described as number, and continuous variables were described as median and interquartile range (IQR); they were compared, respectively, by the Mann-Whitney U-test or Kruskal-Wallis test in one-way analysis of variance, as appropriate. R (http:// www. R-project. org) version 3. 4. 4 software was used for data computation and analysis. R software with epiR package was used to calculate accuracy and odds ratio of the PCRs with blood culture as gold standard.
As molecular diagnostics are becoming increasingly accessible in developing country clinical laboratories, this new tool may be useful alone or in combination with other assays, particularly in the context of high disease burden, and for use in epidemiological studies and as a secondary endpoint test for future vaccine trials. LR+ = positive likelihood ratio; LR-= negative likelihood ratio; PCR = polymerase chain reaction; Se = sensitivity; Spe = specificity. Estimates (CI = 95%) were calculated with MPlus software version 7. 11. Without PCR prioritization means difference in volume (volume BCvolume PCR) ³ -0. 1mL. For computational reason, we selected only samples between 3 mL and 4 mL of blood for culture, eliminating, therefore, 36 samples.
Received December 17, 2018. Accepted for publication May 13, 2019. Published online July 8, 2019. Note: Supplemental tables appear at www. ajtmh. org. Financial support: This work was supported by the Bill & Melinda Gates Foundation (grant number OPP1022554). Disclosure: Dr. Weill reported a patent (US8673568B2) issued, and Dr. Carman is a shareholder of Fast Track Diagnostics company.
Results of latent class model estimates (95% CI) = polymerase chain reaction; LR+ = positive likelihood ratio; LR-= negative likelihood; Se = sensitivity; Spe = specificity. Estimates (CI= 95%) were calculated with MPlus software version 7. 11. Without PCR prioritization means difference in volume (volume BC-volume PCR) ³ -0. 1mL. For computational reason, we selected only samples between 3 mL and 4 mL of blood for culture, eliminating, therefore, 36 samples.
Methods We studied 11 years of surveillance data between 1 January 2007 and 31 December 2017. In FP, all probable and confirmed leptospirosis cases are collected by the local health authority (Bureau de veille sanitaire), based on notification forms filled in by general practitioners and laboratory results provided by laboratories of the Institut Louis Malardé and of the Centre Hospitalier de la Polynésie française. A confirmed case was defined as leptospirosis symptoms with either isolation of bacteria by culture or of its DNA by PCR or seroconversion of specific-class IgM. A probable case was defined as leptospirosis symptoms and IgM detection ELISA on a single serum sample collected more than 1 week after onset of symptoms. An excluded case was defined as leptospirosis symptoms with PCR-negative findings on sera collected within the first week after onset of symptoms, or ELISA IgM negative on sera collected more than 1 week after onset of symptoms.
Demographic data were provided by the FP statistical institute (http://www. ispf. pf/). Population estimation for the duration of the study was calculated using an exponential increase during two consecutive censuses. All confirmed and probable cases were included in the statistical analysis. Qualitative variables were described using frequency and percentages. Quantitative variables were described using mean ± standard deviation (minimum-maximum). Annual incidence rates (AIRs)-that is, number of cases per 100 000 person-years (PY)-were presented with 95% confidence intervals (CI), based on the Poisson model. We used a Z test for comparing AIRs between sexes and among archipelagos. Relative risk (RR) with corresponding 95% CI were computed. A p level of <0. 05 was considered statistically significant.
, 2013). Habitat requirements of this species may dramatically constrain the opportunity offered by a changing climate to its predicted northward expansion  (Rebelo et al. , 2010). In this study, we tested the hypothesis that important environmental variables known from individual radio-tracking studies or presence surveys also influence the demography of the species. We used count data obtained from a fifteen years monitoring scheme at 94 colonies situated in Brittany (France), to investigate the population-habitat relationship in R. hipposideros. We used these data to infer variables that are related to the population dynamics of the colonies before testing their relationship with environmental variables.
Demographic data for R. hipposideros colonies From 2000 to 2014, 94 R. hipposideros maternity colonies were monitored in Brittany, France (Fig. 1 ). Monitoring were performed by volunteers from two NGO, Bretagne Vivante and Le Groupe Mammalogique Breton, who followed the same protocol for all colonies and performed visual counts with minimum disturbance during late June or early July, that is, during the period when new-borns are easily distinguished from adults in Brittany. One or two counts could be performed each year, depending on colony accessibility and volunteer availability, and when multiple counts were carried out in a given year, only the largest one was considered. Adults and juveniles were counted separately: each year, the census size of the colony was estimated by the number of adults, and the fecundity by the number of juveniles divided by the number of adults.
Temporal variation of colony size and fecundity in this dataset, as well as their dependency on environmental covariates, were already investigated in  Jan et al. (2017). In this paper, we focused on intercolony variation instead and their dependency to environmental covariates that were constant over time, the landscape structure and configuration. Hence, the following demographic variables were compiled for each colony: mean colony size (the number of adults averaged over years), fecundity (the juveniles/adults ratio averaged over years), and growth rate. The last variable was estimated with the density-independent Ricker model using the package "PVAclone"  (Nadeem and Solymos, 2016). Five clones in the data-cloning algorithm and 20 000 iterations were performed. The density independent Ricker model is a parsimonious model that considers that the observed colony size variation through time is only the result of growth rate and observation error, and allows to estimate the growth with a known precision.
Explanatory variables were centred and scaled before model computation. Weighted least square regressions were performed to take into account the uncertainty around growth rate estimates by weighting each data point by the inverse of the standard deviation given by the estimation  (Ryan, 2008). These models were then used in the glmulti R package to obtain every possible combination of explanatory variables (without interaction) and order them by AICc criterion  (Calcagno and de Mazancourt, 2010). We then performed model averaging by calculating the Akaike weight of each model within 2ΔAICc of the best model  (Burnham and Anderson, 2003). The modelaveraged regression coefficients of the predictors and their 85% confidence intervals (CI) were calculated based on the cumulative weights of the models including the variable  (Calcagno and de Mazancourt, 2010).
The description of the complete models which were the basis of model averaging are presented in Supp. Inf. C. Our data set provides population dynamic parameters on a large spatio-temporal scale, and a sample size large enough to investigate numerous environmental variables. Complete models included the whole 94 colony dataset to test the significance of no more than 14 environmental variables (climatic variables being present in the model but not tested), ensuring than the ratio sample size (N) / tested predictor (k) was high enough to avoid overparametrisation (N/k = 6. 71, Forstmeier and Schielzeth, 2011). To determine which radius (500 or 2500m) best explained variations in our response variables, we compared the AICc and R² of the models explaining the population dynamics parameters with the significant predictors detected in the previous model averaging.
Acknowledgements We are very grateful to all volunteers from  Bretagne Vivante  and  Le Groupe Mammalogique Breton  for providing visual count data of maternity colonies in Brittany that allowed us to perform this study as well as the companion study published in Oecologia (Jan et al. 2017). We thank  Benoît Persyn  and  Météo-France  for providing us with meteorological data through the research convention  Météo-France/INRA. This work benefited from funding by the  French National Forest Office (ONF).
In their curative intent model, Hettle et al. [11]  evaluated the cost-effectiveness of tisagenlecleucel, based on 15 months of survival data from a phase I/II single-center trial CHP959 (NCT01626495) based in the United States  [10]. Their analysis highlighted the dramatic differences in the expected survival (i. e. area under the curve) depending on the statistical model used for extrapolation of the empirical survival data. Following the evaluation by Hettle et al. , a global multicenter ELIANA trial (NCT02435849) for tisagenlecleucel was published  [12] , reporting survival data out to 1. 5 years. This pivotal trial provides a larger and more representative patient sample than the phase I/II study CHP959. However, the uncertainty regarding long-term survival remains. In the context of relapsed or refractory (r/r) pALL, the challenge of extrapolating survival data is compounded by the young age of patients, differences among patients in terms of relapse or refractory disease and treatment history, limited evidence regarding best supportive care, the absence of randomized controlled trials for tisagenlecleucel (i.
Experts in pALL with experience in tisagenlecleucel and other CART-T cell therapies were invited to participate via email and were sent an information statement prior to enrollment, which outlined the purpose of the study, potential risks, and their rights and responsibilities. The study was double-blinded, meaning neither the experts nor the study sponsor were identified to each other. Experts were assured their identity would remain anonymous throughout the process and in any publication. All experts were identified from the ELIANA trial network, except for one case where a known expert suggested another expert with extensive CAR-T treatment experience in the target population. Information about the seven experts who participated is provided in Table  1. Elicitation of survival estimates beyond available ELIANA data at 2, 3, 4, and 5 years of follow-up During the interviews (May 12-30, 2017), an evidence dossier was reviewed to provide a common basis for expert judgments, which summarized the study purpose, tisagenlecleucel data for r/r pALL  [12, 16, 17]  (Fig.
1 ) and historical data for first-line pALL data and FDA approved interventions for r/r pALL (including stem cell transplant, clofarabine, and blinatumomab). The elicitation process and a practice exercise were also reviewed. Experts were guided through the elicitation using a webbased application (Fig. 2 ), which illustrated the survival from the ELIANA trial and the 99% confidence intervals  [12]. SHELF guidance suggests that experts should be 'almost certain that the quantity of interest lies within the plausible range (i. e. not physically impossible but extremely unlikely)'. In our study, the plausible limit was operationalized as the 99% confidence interval. For each time point, experts were asked to first estimate the upper plausible limit (UPL), followed by the lower plausible limit (LPL) and finally the most likely values (MLV) of survival.
Estimation of extrapolated survival curves without expert information The reported Kaplan-Meier (KM) curve for ELIANA was digitized (DigitizeIt; http://www. digitizeit. de/) and alternative parametric survival models were fitted to the corresponding discrete hazard data  [18] [19] [20]. The following competing survival distributions were used: Weibull, Gompertz, and second-order fractional polynomials with power p 1 = 0 or 1 and power p 2 = -1, - pALL pediatric acute lymphoblastic leukemia, CAR chimeric antigen receptor, r/r relapsed/refractory 0. 5, 0, 0. 5, or 1. These second-order fractional polynomial models can be considered extensions of the Weibull and Gompertz models, and allow arc-and bathtub shaped hazard functions. All analyses were performed in the Bayesian framework with non-informative prior distributions.
Based on the obtained parameters for each of the survival models the corresponding survival curves were plotted up to 5 years of follow-up along including the 95% credible intervals. Additional detail is provided in Additional file 1. The deviance information criteria (DIC) was used to compare the goodness-of-fit of the competing survival models  [21]. DIC provides a measure of model fit to the data that penalizes model complexity. The model with the better trade-off between fit and parsimony has a lower DIC, where a difference of 3 to 7 points was considered meaningful  [22]. All analyses were performed using a Markov Chain Monte Carlo (MCMC) method as implemented in the software package Just Another Gibbs Sampler (JAGS) (version 4. 2. 0), which were summarized in R (version 3. 4. 0). A first series of 20,000 iterations from the JAGS sampler were discarded as 'burn-in' and the inferences were based on 50,000 additional iterations using two chains.
Discussion Given the increasing number of accelerated regulatory approvals for oncology treatments, health technology assessment agencies will be faced with the challenge of evaluating the value of new treatments with limited or immature overall survival data. To obtain useful extrapolated survival estimates, needed for cost-effectiveness evaluations, the available trial data needs to be supported with available external evidence, which may include expert opinion. In this paper, we presented an evidence synthesis method to integrate empirical survival data from a clinical trial with long-term estimates from a formal expert elicitation study. The expert elicitation was performed using an established framework, i. e. SHELF, which is transparent and provides estimates of the most likely value for the parameters of interest along with estimates of uncertainty.
Even in the updated data cut, there are still 54 censored patients. Examining the optimistic and pessimistic curves for this dataset shows there is less uncertainty as compared to the initial cut of data; however it is still possible for long-term survival to be to be less optimistic than that predicted by the experts. This underscores the need to update analyses based on further follow-up before definitive conclusions are drawn. Ultimately, a validation cohort was not evaluated within the current study, which reflects a limitation of the study. There is no standard methodology to combine expert opinion with clinical data, though this is an area of active research  [2]. Our methods, which consider expert opinion as additional data, are similar to that employed by  Guyot et al. 2017 , who included external evidence from an observational database  [5].
The model we have developed improves on existing survival models by systematically integrating external evidence from experts, which could be generalized to any cost-effectiveness analysis. The current synthesis used elicited survival proportions from the experts, which were analyzed using the observed trial data augmented with the additional expert information included as an artificial data set. This approach ensured that expert estimates were grounded in relation to the observed data in the ELIANA trial. Adding expert information to the model validated the model choice and reduced the amount of uncertainty when compared to the model without expert information. It is important to highlight that the estimates from each expert were modelled separately, and that the overall estimate reflects a combined overall distribution.
, Lincoln, USA) and a threeaxis sonic anemometer (Gill R3 ultrasonic anemometer, Hampshire, UK)  (Tagesson et al. , 2015a). The sensors were mounted 9 m above the ground and data were collected at a 20 Hz rate. The post-processing was performed with the Ed-dyPro 4. 2. 1 software (LI-COR  Biosciences, 2012)  and statistics were calculated for 30 min periods. For a thorough de-scription of the post-processing of sensible and latent heat fluxes, see Supplement of  Tagesson et al. (2015b).
(2017) , adapted from  Davidson et al. (1991). The increase rate of NO and NH 3 mixing ratios used in the flux calculation equation was estimated by a linear regression fitted to data measured for 180 to 300 s for NO (120 s for NH 3 ) following the installation of the chamber on the soil, as detailed in  Delon et al. (2017). Close to the Teflon chamber, soil CO 2 respiration was measured with a manual closed dynamic chamber (SRC-1 from PP Systems, 150 mm height × 100 mm diameter) coupled to a non-dispersive infrared CO 2 /H 2 O analyzer EGM-4 (PP Systems, Hitchin, Hertfordshire, UK). Soil CO 2 respiration was measured within 30 cm of the location of the NO and NH 3 fluxes. Measurements were performed on bare soil to ensure only root and microbe respiration. Results of NO, NH 3 , and CO 2 fluxes are presented as daily means with daily standard deviations.