dataesr/scientific-paragraphs-categorization huggingface dataset:
(  5 ). d MPH (Voxet 1 , Voxet 2 ) = N c i=1 1 N c 1 + d(C i 1 , C i 2 ) × 1 + |p i 1 -p i 2 | p i 1 + p i 2 -1 , (5) where p i 1 and p i 2 are the proportions of the paired clusters C i 1 and C i 2 with respect to voxets 1 & 2, respectively. One advantage of selecting cluster representatives independently between two voxets is to lower computing requirements over large ensembles of voxets, performing the analysis for N v voxets instead of N v (N v -1) 2 pairs. However, performing R pattern clustering on two datasets might provide a more accurate and precise way to compute a distance between histograms with the same support of cluster representatives, thus allowing the use of Jensen-Shannon divergence for instance. One can note that we accounted for the size of the clusters but we could also consider the density spread or concentration around cluster representatives.
Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21. 3% of patients were underexposed (AUC 24h < 666 mg.
TDM may be especially useful in patients with BJI who often receive high doses of daptomycin in order to control drug exposure. The use of PK models implemented into Bayesian dosing software is a relevant approach to analyze TDM results, estimate PK quantities such as the AUC, and compute individualized dosages for achieving PK/PD targets with maximum precision  [17, 18]. A number of programs are available, including BestDose and Tucuxi  [19]. To our knowledge, no pharmacometric tool has been presented for dosage individualization of daptomycin. The objectives of this work were: (i) To implement and validate a nonparametric PK model of daptomycin for patients with BJI within the BestDose software; (ii) To compare the performance of this model to that of a published parametric model implemented in the Tucuxi software.
Population Data We report a retrospective analysis of data from patients who were treated by daptomycin for BJI between 2012 and 2016 and had TDM in our reference center for bone and joint infection in Lyon, France. We used two data sets: a learning set used to derive the nonparametric population model of daptomycin and a validation dataset. The learning dataset was described in a previous publication from our group, which focused on the influence of P-gp gene polymorphism on daptomycin PK (n = 81 patients)  [20]. Unpublished data obtained in patients who did not undergo P-gp genotyping were used as the validation dataset (n = 94 patients). All patients were enrolled in a cohort study after information. This cohort study was subject to a declaration to the local Commission for Data Protection and Liberties (number 17-057) and is registered on ClinicalTrial.
Building of Daptomycin Nonparametric Population PK Model for BestDose In our previous work, the final population PK model of daptomycin was built by using a parametric approach (SAEM algorithm in the Monolix software)  [20]. The final model was a two-compartment model with body weight and P-gp genotype as covariates influencing the central volume of distribution, while daptomycin total body clearance was influenced by creatinine clearance and sex. We reanalyzed the original dataset in order to implement a nonparametric model into the BestDose software  [22]. The NPAG algorithm embedded into the Pmetrics package for R was used to develop the nonparametric model  [23, 24]. The final nonparametric model was a two-compartment model parameterized with a central volume of distribution (V 1 , in L/70 kg), non-renal elimination rate constant (K i ), renal elimination rate constant (K s , to be multiplied by creatinine clearance), and constants of rate transfer from the central to the peripheral compartment (K cp ) and backward (K pc ).
Implementation of A Parametric Model in Tucuxi As we aimed at comparing our nonparametric model to a reference model for TDM and dose adjustment, we implemented the seminal daptomycin parametric model developed by daptomycin's manufacturer  [25]  into the Tucuxi software. Tucuxi is a new software dedicated to the analysis of TDM results and individual dose adjustment (http://www. tucuxi. ch/, accessed 27 December 2021)  [26]. It was developed by the School of management and engineering of Vaud (HEIG-VD) and the University Hospital of Lausanne (CHUV), Switzerland. Tucuxi only runs parametric models. It offers a collection of population PK models and also allows new models to be imported. The two-compartment reference model from Dvorchik et al. [25]  was imported into Tucuxi, including its error model and all its covariates except body temperature, which influenced daptomycin clearance in the original model.
External Validation and Comparison of the Two Models We used an external dataset from 94 patients who received daptomycin for BJI and had TDM to validate the predictive performance of our nonparametric model in BestDose and that of the parametric model in Tucuxi. In this evaluation, we only used the daptomycin concentrations collected during the first TDM occasion in each patient. The dosage history, covariate values, times and values of measured concentrations in each patient were entered into each program. The population model was fit to individual data by using Bayesian regression in both tools. After estimation of Bayesian posterior individual PK parameters, the estimated PK profile of each patient was obtained, including predictions of measured daptomycin concentrations. Bias and precision of model-based individual predictions were assessed by computing the mean error (ME) and mean absolute percent error (MAPE) of prediction, respectively, as follows: ME = ∑ n i=1 Cpred i -Cobs i n (1) MAPE = ∑ n i=1 Cpred i -Cobs i Cobs i n (2) where Cpred i are the concentrations predicted by the model, Cobs i are the observed concentrations, and n is the total number of observations.
Population Data A total of 841 plasma concentrations of daptomycin collected in 175 patients were available for analysis. The learning and validation datasets included 577 observations from 81 patients and 264 observations from 94 patients, respectively. The characteristics of the two populations are summarized in Table  1. In the validation dataset, the mean daptomycin dosage was 7. 6 ± 1. 3 mg/kg per day, which is consistent with guidelines. The mean AUC 24h estimated with BestDose was 975 ± 395 mg. h/L on the first TDM occasion, which is higher than the efficacy threshold (AUC 24h ≥ 666 mg. h/L), for a putative MIC of 1 mg/L. However, 21. 3% of patients were underexposed (AUC 24h < 666 mg. h/L). On the other hand, 31. 9% of patients had C min > 24. 3 mg/L and may be considered as overexposed.
Population Modeling in the Learning Dataset The parameter values of the nonparametric population model built with Pmetrics in the learning dataset are shown in Table  2. Of note, as the real parameter distributions are discrete, the means and standard deviations of parameters cannot provide a complete description of the distributions. The final nonparametric model described the data very well in the learning dataset with ME of -0. 72 ± 4. 3 mg/L and MAPE of 6. 3 ± 8. 1%. The plot of observed concentrations versus population and individual predictions is shown in Figure  1.
Comparison of BestDose and Tucuxi Models in the Validation Dataset The model predictions computed with BestDose in the validation dataset were as good as in the learning set, with ME = -0. 087 ± 5. 1 mg/L and MAPE = 7. 8 ± 34. 7%. The parametric model implemented in Tucuxi also described the validation data very well with ME = -2. 3 ± 6. 0 mg/L and MAPE = 9. 9 ± 13. 3%. The ME and MAPE were slightly higher than that from the BestDose model, and the differences were significant (p < 0. 001 and p = 0. 0022 for ME and MAPE, respectively). Figure  2  shows the plot of observations versus individual predictions from both models, which look very similar. The Bayesian estimations of daptomycin concentrations, AUC 24h , D min , D max , as well as the estimates of the main PK parameters (V 1 , half-life and total body clearance) calculated with both programs are summarized in Table  3.
Comparison of BestDose and Tucuxi Models in the Validation Dataset The model predictions computed with BestDose in the validation dataset were good as in the learning set, with ME = -0. 087 ± 5. 1 mg/L and MAPE = 7. 8 ± 34. 7%. parametric model implemented in Tucuxi also described the validation data very w with ME = -2. 3 ± 6. 0 mg/L and MAPE = 9. 9 ± 13. 3%. The ME and MAPE were sligh higher than that from the BestDose model, and the differences were significant (p < 0. and p = 0. 0022 for ME and MAPE, respectively). Figure  2  shows the plot of observati versus individual predictions from both models, which look very similar. The Bayesian estimations of daptomycin concentrations, AUC24 h, Dmin, Dmax, as well as the estimates of the main PK parameters (V1, half-life and total body clearance) calculated with both programs are summarized in Table  3.
3% of patients did not achieve the AUC 24h target on the first TDM occasion after initial dosing, and 31. 9% had C min values above the safety target. Urakami et al. reported a low rate of efficacy target attainment (40%) in patients who received 6 mg/kg  [31]. In another prospective TDM study in patients who received daptomycin for bacteremia, 9. 5% of individuals showed C min > 24. 3 mg/L. Half of those presented an elevation of CPK  [30]. Those published results and ours confirm that exposure to daptomycin is often suboptimal and that dosage individualization based on TDM is desirable. We developed a nonparametric PK model of daptomycin for the BestDose software. Compared with the original model previously published by our group, the model implemented with BestDose was simpler, with only two covariates.
Despite this simplification, the model performance was very good, with low bias and imprecision in individual predictions of daptomycin concentrations in both the learning and validation datasets. This illustrates the relative importance of concentration measurements and covariates in the Bayesian estimation of PK parameters. As shown by Sheiner and colleagues, measured drug concentrations are much more informative about PK parameters than covariates in the Bayesian estimation  [32]. Therefore, models to be used for TDM and individual Bayesian estimation do not necessarily require many covariates. By contrast, covariates are more important for population predictions and model-based initial dosing. Our nonparametric model implemented into BestDose compared well with the reference model from Dvorchik et al.
implemented in Tucuxi  [25]. Indeed, the BestDose model performed slightly better than the Tucuxi model in estimating daptomycin concentration in the validation group. This may be due to the population used in model building, as our model was built in a similar population of patients with BJI, while the model from Dvorchik et al. was based on data from 15 clinical studies conducted in healthy volunteers and patients without BJI. In addition, the assay method was the same in the learning and validation dataset for the model implemented in BestDose, which was not the case for the model in Tucuxi. The predicted concentrations, D min and estimated AUC 24h , were similar between the two programs and showed a good agreement. The dose suggested is probably the most relevant parameter to be compared between two dosing programs, as it directly influences dosing decisions and patient care.
With a nonparametric model, the entire discrete Bayesian posterior distribution is used in model fitting and dosage calculation  [24, 33]. With a parametric model, only a single value of the continuous Bayesian posterior distribution is used in such calculation, which is the maximum a posteriori (MAP)  [34]. Despite those technical differences, the two models provided overall comparable performance in the estimation of daptomycin concentrations and AUC 24h and in the calculation of the dose required to achieve the efficacy target. This study has several limitations. Data were collected during routine patient care, so errors may have occurred in the collection. The population only included patients with BJI, so the nonparametric model developed for BestDose may not perform as well in other patients' groups.
Numerical setup The numerical simulations presented in this article have been accomplished using ONERA's CFD software elsA  [17] [18]  [19] , which has been developed since 1997 and is co-owned by ONERA and SAFRAN. elsA is a multi-application aerodynamic code based on a cell-centered finite volume method for structured and unstructured meshes. Solving the compressible, three-dimensional RANS equations, elsA allows simulating a wide range of aerospace configurations such as aircrafts, space launchers, missiles, helicopters and turbomachines. Therefore a wide range of numerical tools, turbulence models and boundary conditions are available. In the past few years, an important work has allowed the extension of the multi-block structured solver in elsA to an hybrid grid solver, in which structured (ijk-based) and unstructured blocks may coexist within the same computational domain  [20].
Introduction The anthozoan cnidarian Nematostella vectensis (Fig. 1 ) has initially been developed as a research model organism to gain insights into the evolution of developmental mechanisms and novel cell types/ biological features as it is easily cultivable under laboratory conditions  [4]. The first cnidarian genome to be sequenced was the one from Nematostella that has revealed astonishing similarities with the ones from mammalians  [5, 6]. Since then, a wealth of resources and tools have been developed ranging from embryonic RNAseq datasets  [1, 2]  to meganuclease-induced transgenesis  [7] , as well as functional approaches for gene know-downs  [8] [9] [10] [11]  and CRISPR/CAS9 mediated knock-outs and knock-ins  [11] [12] [13]. More recently, Nematostella is emerging as a powerful and complementary whole-body regeneration model, as it is able to regrow missing body parts within days after amputation  [14] [15] [16] [17] [18].
In combination with its historical use as an embryogenesis research model, Nematostella is thus very well suited to compare embryonic development and whole-body regeneration within the same organism  [3, 17, 19]. This is particularly convenient not only to determine to what extent regeneration recapitulates the gene regulatory program deployed during embryonic developmentaddressing one of the long-lasting questions in regeneration biology  [20] , but importantly to highlight if regeneration is controlled by specific toolkits only present in differentiated tissues. Several studies have performed global gene expression analysis during embryonic development  [1-3, 6, 21]  or regeneration  [3, 22]. In the present chapter, we describe the methods we used to create NvERTx, a gene expression database for comparing embryonic development and regeneration gene expression data in Nematostella vectensis  [3, 23].
This database features transcriptomic data combining 22 time points during embryonic development and 16 time points during regeneration (Fig. 1c ). In addition, we have developed a web interface to facilitate the manipulation of the data contained in this database. This website simplifies data mining and can generate some figures such as graphs to compare groups of sequences. This interface also integrates various common online tools like Blast to retrieve all transcripts homologous for a given sequence entered by the user. Building such in silico tool is a mean to make RNAseq data accessible to the larger scientific community and enable additional usage of the vast amount of data that were initially produced for a given scientific question. The workflow presented in this protocol is only a general framework for developing an open-access scientific database.
Materials The protocol and material presented here is the one that was used to develop NvERTx, a website to access and mine quantitative transcriptomic data from the sea anemone Nematostella vectensis  [3]. The protocol will need to be adapted to develop websites presenting other types of data as well as to take into account the unavoidable evolution of software and data format. Yet, this protocol provides a generic and flexible framework to make data accessible and usable to the larger scientific community. In addition to programming skills, we here detail the basic material/data/tools required for the development of a user-friendly transcriptome data mining website. Data implemented in the developed database and website is assumed existent. Refer to  [23]  for a recent protocol describing a standard workflow for quantifying and performing initial analysis of next-generation sequencing (NGS) reads from an RNA-seq analysis.
Genome annotations (see Note 1): Annotations should be provided as a single file in GTF or GFF3 format. These files can be downloaded from each genome-related repository (Genbank or specific website, e. g. , Nemve1, Table  1 ) and do not intend to be generated (or re-generated) by the user. However, it is also possible to perform a gene prediction (using AUGUST, Prodigal, or common ORFing prediction tools) if no annotation is available in public databases. 3. Assembled reference transcriptome (e. g. , NvERTx, Table  1 ): Preferably download and use a transcriptome from a public repository (see Note 2).
Technical Requirements 1. Web server: We used the Apache web server under a Linux computer. A valid domain name and an openly accessible internet access are mandatory. We recommend integrating the website and the database into a Docker container for a reliable security and for more flexibility. 2. Database management system: Database management should follow a relational model. The model organizes data into many tables with a unique key identifying each row. Queries can be performed using the SQL that is the reference language since many decades. We encourage to use MySQL (see Note 4).
Web framework: A modern web development requires to use a robust and reusable syntax. We used Django which is a highlevel Python web framework that encourages rapid development and clean, pragmatic design (https://www. djangoproject. com/). Moreover, Django facilitates the communication with the database directly or through additional NvERTx Transcript models, annotations 1. 0 http://nvertx. kahikai. org or nvertx. ircan. org [3]  libraries such as SQL-Alchemy (https://www. sqlalchemy. org) (see Note 5). For the design of the site, we have used "Bootstrap" (https://getbootstrap. com) which is an additional framework. It facilitates the creation of web interfaces (HTML + JavaScript) and avoids display problems that can occur between web browsers. 4. Data mining software suite: The web interface of NvERTx includes online software to perform basic manipulations of the RNA-seq sequences (Fig.
2 ). We added the BLAST suite to facilitate the search of all transcripts of the NvERTx database homologous to a given sequence entered by the user. The aligner MUSCLE was also added to visually compare a group of similar sequences (SNPs, indels, etc. ). The integration of these tools in the Django code is facilitated by some specific API such as PyBlast (https://pypi. org/project/pyblast/) or django-blastplus (https://github. com/michalstuglik/djangoblastplus). An important aspect for developing a database for internal use or for the scientific community is to define the scope and intended use of the tool. Some preliminaries are therefore required to be clearly defined such as: 1. List the scientific goals of the tool (e. g. , Mining transcriptomic data). 2. Define the community for which the tool is intended (e.
Database Design Prior to starting the building of the database and the website per se, the probably most important aspect of the process is to take time to conceptualize the database regarding its intended usage. Taken this into account will make this online tool user-friendly and as useful as possible for the scientific community. To do so, we encourage you to make an exhaustive inventory of all information and meta-information that will have to be integrated into the database (see  Note 7). This information consists of the biological resources acquired from experiments such as RNA-seq results, but also include every data or metadata that could be related to the numerical values: annotations (genes, go-terms, pathways, etc. ), information of the experiments protocol itself (samples sources, extraction method, library preparation, sequencer device used), general or user comments, image descriptions.
The data inventory should also include the external sources of information that could be useful to associate: GSEA, ontologies, PubMed, websites of collaborators, etc. It is also necessary to consider other information that are not directly related to the biological data, but which will be useful for the management of the database and the website: e. g. , a table grouping the users with their respective access permissions (guest, administrator, permissions to read or modify part of the data in the database, etc. ) (see Note 8).
Build the Database Structure A popular method to build databases is based on a relational model that uses linked tables. Depending on the size of the expected database, it is advisable to create a model before directly building the Tables. A database model is a way to set a representation of the data and its relations understandable by a non-expert and independent from any further technical choice of development. There are several tools or methods to make this model. For example, the Merise method is a general-purpose modeling methodology in the field of information systems development. Merise proceeds to separate treatment of data and processes, where the data-oriented view is modeled in three stages, from conceptual, logical through to physical. The UML (Unified Modeling Language) is also a general-purpose, developmental, modeling language in the field of software engineering that is intended to provide a standard way to visualize the design of a system.
A model built using UML proposes a diagram structure representation that can be easily interpreted. An intermediate solution is to use a graphical table construction and visualization. This type of software allows the biologist to visually verify that all the information is present in the database and in the right place. Also, the relationships between tables can be represented with this type of tools. Once the database schema is finalized, the tool can automatically generate the tables in SQL format for import into a MySQL system for instance. Among these tools, we can mention "MySQL Workbench" which is a free tool and both simple and powerful (https://www. mysql. com/ products/workbench/). Finally, the design of the database must prioritize the independence from the technical solutions that will be used to make the GUI.