dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Appropriate tools of the MaBoSS tool suite can address a wide variety of questions, in particular related to cancer and immunerelated diseases. A MaBoSS mathematical model is based on discrete levels of entities (genes, proteins, cell types, compartments) and predicts time-dependent state probabilities from a userdefined initial condition. There are a number of documents that facilitate the construction of models with MaBoSS and its extensions, including online presentations of the tools, training materials, dedicated GitHub repositories and tutorials. In addition, all the published models using MaBoSS modeling tools have accompanying Jupyter notebooks to reproduce published results. These materials can all be easily reused with new models and are gathered in a GitHub repository: https://github.
com/sysbio-curie/ MaBoSS-Review. Therefore, MaBoSS modeling framework is particularly suited when signaling pathways are described as a directed influence network (a set of logical influences between entities), with a limited number of cell types. MaBoSS evolved over time to answer new questions that emerged with new data in order to represent more complex models: from a single cell approach with MaBoSS tool, to a more heterogeneous tissue within EnsembleMaBoSS, to a dynamical heterogeneous population of interacting cells, and finally to a full spatial modeling approach with PhysiBoSS. In a given biological context, other frameworks than MaBoSS may be better fit to address a question, or could be included in a more thorough study incorporating different data types. For this purpose, MaBoSS is part of the CoLoMoTo consortium  [39]  that allows the exchange, the composition and the reuse of logical models and tools.
Acknowledgments GK is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR)  -Projets blancs; AMMICa  US23/CNRS UMS3655 ; Association pour la recherche sur le cancer (ARC);  Cancéropôle Ile-de-France ;  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Projects Oncobiome and Crimson ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); a  Cancer Research ASPIRE Award  from the  Mark Foundation ; the  RHU Immunolife; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM).
This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001. LC, EB, VN, and GS are part of the  ModICeD  project from MIC ITMO 2020: Approches interdisciplinaires des processus oncogéniques et perspectives thérapeutiques: apports á l'oncologie des mathématiques et de l'informatique. Part of the research leading to these results has received funding from the  European Union 's  Horizon 2020 Programme  under the  PerMedCoE  Project (www. permedcoe. eu),grant agreement n°951773. The development of PhysiBoSS is partly funded by a grant for promoting emerging research from  Inserm.
Over a batch of ten tested devices, the median quality factor is around 50 (ref. 31 ). This technology has already reached industrial maturity, and it is not bio-inspired per se. Combining the information of different pMUT membranes is a well-known technique to infer angular information from pMUT devices, using for instance beamforming techniques  31, 54. However, the signal processing required to extract the angular information does not suit low-power sensing. The proposed system, coupling a neuromorphic circuit for pre-processing of the pMUT data and a RRAM-based neuromorphic computational map inspired by the Jeffress model (Fig. 2c ), offers an alternative energy-efficient and resource-constrained hardware solution. We conducted an experiment locating two pMUT sensors about 10 cm apart from each other, thus fully taking advantage of the different ToF of sound being sensed by the two receiving membranes.
Input pulses have a width of 1 μs and rise/fall edge of 50 ns. This type of pulse is assumed as the stereotypical spiking event in the spike-based analog circuit. Concerning the outputs, a 1 GHz Teledyne LeCroy oscilloscope was utilized to record the output signals. The acquisition speed of the oscilloscope has been proven not to be a limiting factor analyzing and collecting data from the circuits. Variability in neuron and synapse circuits. Exploiting analog electronics' dynamics to mimic the behavior of neurons and synapses is an elegant and efficient solution to improve computation efficiency. The downside of such a computational substrate is that it is subject to variability, from circuit to circuit. We quantified the variability in neuron and synapse circuits (Supplementary Fig. 2a, b ).
Acknowledgements We acknowledge funding support from the  H2020   MeM-Scales  project (871371). This work is also supported by the  ERC   DIVERSE  project ( 101043854 ) as well as the  French ANR  via Carnot funding. In addition, we thank  J. -F. Nodin ,  F. Andrieu ,  S. Bonnetier ,  T. Hirtzlin  ( CEA-Leti ),  D. Querlioz  ( Université Paris-Saclay, CNRS ), and  A. Valentian  ( CEA-List ) for discussing various aspects of the article.
Busulfan, a drug used in conditioning prior to hematopoietic stem cell transplantation (HSCT) in children, has a narrow therapeutic margin. The model-informed precision dosing (MIPD) of busulfan is desirable, but there is a lack of validated tools. The objective of this study was to implement and cross-validate a population pharmacokinetic (PK) model in the Tucuxi software for busulfan MIPD in HSCT children. A search of the literature was performed to identify candidate population PK models. The goodness of fit of three selected models was assessed in a dataset of 178 children by computing the mean error (ME) and root-mean-squared error of prediction (RMSE). The best model was implemented in Tucuxi. The individual predicted concentrations, the area under the concentration-time curve (AUC), and dosage requirements were compared between the Tucuxi model and a reference model available in the BestDose software in a subset of 61 children.
The model from Paci et al. best fitted the data in the full dataset. In a subset of 61 patients, the predictive performance of Tucuxi and BestDose models was comparable with ME values of 6. 4% and -2. 5% and RMSE values of 11. 4% and 13. 6%, respectively. The agreement between the estimated AUC and the predicted dose was good, with 6. 6% and 4. 9% of the values being out of the 95% limits of agreement, respectively. To conclude, a PK model for busulfan MIPD was cross-validated and is now available in the Tucuxi software.
A number of population PK models of busulfan in HSCT children have been published in the last two decades  [3]. However, only a few models have been implemented in clinical PK programs and evaluated for drug dosing in an external manner. Neely et al. have developed and validated a nonparametric model for the Bayesian dose adjustment of busulfan in children, which is available in the BestDose TM software  [10]. An AUC estimation based on only two blood samples was shown to be accurate compared with the AUC estimated from rich PK profiles. In addition, the dosage adjustments based on this model in an external cohort of patients were consistent with the dosage adjustments calculated using noncompartmental analysis in a reference laboratory in the USA. This model can thus be considered the best standard for the model-guided TDM and dose adjustment of busulfan in children.
BestDose is the latest version of a program formerly known as USC*Pack, MM-USC*Pack, and RightDose. It has been developed for more than four decades by the Laboratory of Applied Pharmacokinetics and Bioinformatics (LAPKB), at the University of Southern California, Los Angeles, USA. It is available from the laboratory website (http://www. lapk. org/ (accessed on 26 September 2022). More details are also available in a book edited by LAPKB directors  [11]. However, BestDose only runs nonparametric PK models, which are scarce in population PK, as most published population models are parametric. Tucuxi is a new standalone program developed for the model-guided TDM of drugs  [12]. It offers a user-friendly interface for clinicians, helping them in the process of drug dosage adjustment, by proposing concentration predictions, percentile calculations, and dosage adjustments.
It has been developed by HEIG-VD in close collaboration with CHUV and is available via http://www. tucuxi. ch (accessed on 26 September 2022). The development team paid particular attention to user-friendliness and performance, to offer the best user experience to non-specialists. Its core computing engine is written in C++14, one of the best programming languages for efficient computation. Genericity is offered through the possibility to add new population PK models written in specific files, without modifying the software itself. Tucuxi only runs parametric PK models, but no busulfan model was available in the program so far. The objective of the present study was to implement and cross-validate a PK model for the AUC estimation and dosage adjustment of busulfan in HSCT children with the Tucuxi software, using BestDose as a reference.
Literature Search for Candidate Population PK Models of Busulfan First, we searched the literature to identify candidate busulfan population PK models for implementation in Tucuxi. We conducted a PubMed search in July 2018, using the keywords "busulfan" and "population pharmacokinetics". The search period was restricted to 10 years  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018). We screened the references of retrieved papers as well. We examined the title and abstract of the identified articles and discarded some of them based on the following exclusion criteria: adult population only, nonparametric model, and model including gene polymorphism as covariates. The latter criterion was justified because the genetic data on glutathione-S-transferase, the main metabolic pathway of busulfan, were not available in our patients' cohort.
Patients' Data We used a previously described dataset  [13]  to assess the predictive performance of the selected models and for subsequent analyses. Those data were collected as part of routine patient care in a hematology and oncology pediatric center in Lyon (Hospices Civils de Lyon, France), from June 2006 to June 2015. Ethics approval was obtained from two committees, including one from Hospices Civils de Lyon, as described previously  [13]. We obtained a waiver of informed consent, as we only used the existing clinical data that were de-identified for this project. Briefly, busulfan was administered over 4 consecutive days as a 2 h infusion administered every 6 h for 4 days (total of 16 doses) except in five patients (four were administered busulfan once daily and one twice daily). Busulfan TDM and dose individualization was performed for all the patients to achieve the conventional AUC target of 3.
7-6. 1 mg•h/L (900-1500 µM•min) per dose, corresponding to an AUC cum of 60 to 100 mg•h/L. Two blood samples were obtained at 0. 5 and 2 h after the end of the first busulfan infusion in each patient. For some patients, TDM was repeated with the same two-sample collection on the subsequent days of therapy on the physician's demand. A validated high-performance liquid chromatography (HPLC) assay was used to measure the busulfan concentrations. More details about the drug assay are available elsewhere  [10]. The data from 178 individuals (85 males and 93 females) were available, with 491 measured busulfan concentrations. The median (min, max) values of age, body weight, and height were as follows: 4. 0 (0. 13-21) years, 16. 7 (3. 2-90. 9) kg, and 103 (46-190) cm, respectively.
Goodness of Fit of Selected Models The three models selected after the literature search were imported into the NONMEM ® software. We created two versions of each model, with and without inter-occasion variability (IOV). Then, the models were used to fit the entire busulfan dataset by using the MAXEVAL = 0 option of NONMEM, which practically computes the individual Bayesian posterior parameter values based on both the prior information and the patients' observations. We assessed the predictive performance of each model by computing the bias and imprecision of each individual posterior model's predicted concentrations. The bias was defined as the mean percent error (MPE) of prediction: MPE = 1 N ∑ N i = 1 Cpred i -Cobs i Cobs i (1) where Cpred i and Cobs i are individual predicted concentrations and observed concentrations, respectively.
Cross-Validation of Busulfan Model in Tucuxi A probe subset of 61 individuals (n = 119 measured concentrations) who received busulfan every 6 h were randomly selected from the full dataset described above and used to compare the exposure and dosages predicted by the two models. For each individual patient, the measured busulfan concentrations after the first dose were fitted with both the Tucuxi and BestDose models. The goodness of fit was assessed by computing the MPE and RMSE characterizing the comparison of observed concentrations and the individual posterior model predictions, as described previously. The AUC after the first dose (i. e. , the first 6 h of therapy), denoted AUC 0-6 , was computed with both models, as well as the predicted future dose required to achieve a cumulative AUC set at 80 mg•h/L for the entire therapy.
4% on the Bland-Altman plot) was observed in a very young patient (female, 2 months old, 3. 2 kg, 45. 5 cm) who exhibited a high concentration 2. 5 h postdose (630 ng/mL), just slightly lower than the concentration 0. 5 h post-dose (800 ng/mL), which suggests a very low clearance, probably due to immature metabolism. Again, this observation was better fitted by the Tucuxi software. In this patient, the dosage requirements predicted by the two programs were not consistent (-41. 2% on the Bland-Altman plot).
Discussion Busulfan population PK in children has been examined in numerous reports. In their recent review, Lawson et al. identified 21 population PK models of busulfan in children published between 2007 and 2019  [3]. Among those, a few models were externally validated in terms of the predicted concentrations. However, very few studies reported the implementation of such models in Bayesian PK programs and their validation for dose prediction, which are key for model-informed prediction dosing (MIPD). To our knowledge, the model from Neely et al. implemented in BestDose  [10]  is the only model that has been externally validated in terms of both drug exposure and dosage requirements. We aimed at implementing a parametric population PK model in the Tucuxi software and cross-validate its results with those from the reference model of Neely et al.
Among the three models selected from the literature, the model from Paci et al. [16]  best fit the entire dataset in the NONMEM run. This model was developed in a large number of patients, including a significant proportion of very young patients (33% of the patients with BW < 9 kg). The median age and weight were the closest to those of our cohort. The model from Booth et al. also performed well. The model from Trame et al. appeared less adequate. Compared with our dataset, this model was developed in older children, and part of them (57%) received oral busulfan. Those characteristics may explain its poorer performance when applied to our data. The model from Paci et al. was then imported into the Tucuxi software and was individually fitted to the data from a random subset of 61 probe patients.
By contrast, the MAP approach is more flexible, because the prior and posterior distributions of PK parameters are continuous. However, this flexibility may also be a weakness in case of a sampling or measurement error, because the estimation may converge, and the apparent good fit will not warn users about the potential error. Strategies to cope with this, using, for example, the software-calculated percentiles to identify the out-of-scope measurements, are under investigation in Tucuxi. Notably, a hybrid fitting method not evaluated in the present study is available in BestDose and can better fit the outlier data  [23]. Busulfan PK models are available in other dosing software tools, such as DoseMeRx, InsightRx, NextDose, and PrecisePK  [24, 25]. In a recent study, Lawson et al. compared the busulfan AUC predicted by models implemented in two different tools (NextDose and InsightRX) to the AUC calculated with the traditional trapezoidal rule, for various sampling strategies  [26].
They showed that the bias and precision of the AUC estimation were not always consistent between software tools. However, they did not examine the agreement between the two PK programs. In addition, they only studied the AUC estimation, not the dosage requirements, which is arguably the most important criterion for MIPD. While the goodness of fit is a classic criterion in the validation of population PK models, we think that dose recommendations should be the primary diagnostic criterion when evaluating the dosing tools in a "fit-for-purpose" validation approach  [27]. The previous studies evaluating dosing software considered this criterion as well  [28, 29]. This study has several limitations. The data were collected in routine patient care, and sampling for the drug's concentration measurements was sparse.
However, the reference model from Neely et al. was based on rich data and was externally validated based on part of this dataset  [10]. The model from Paci et al. was also externally validated in the original study. Therefore, both models have been externally validated, and they are now cross-validated in the present study. We did not examine all the available models in our NONMEM analysis. Our primary goal was not to perform an external evaluation of all the published models but rather to identify a model to be cross-validated with our reference model implemented in BestDose. As our reference model was a one-compartment model, developed in children and adolescents only, including body weight and age as covariates, we preferred to select those models having similar characteristics to avoid confounders in the analysis of discrepancies.
Model selection was based on both the goodness of fit and the characteristics facilitating the comparison with the reference model. Other models might be adequate as well and could be implemented in Tucuxi in the future. Indeed, it may be relevant to have several models available for a given drug in dosing software programs. This allows for a comparison of data fitting and dosage prediction for selecting the most adequate model for the population under consideration. It has been suggested that the best model should be chosen for each individual among a given set of competing models  [28, 30, 31]. We did not evaluate the forecasting of concentration to be observed on subsequent TDM occasions, based on the first occasion data, because TDM was not repeated in all the patients. Although the models including IOV performed better in the NONMEM analysis, they could not be evaluated in the two dosing software programs, as neither currently handles IOV.
By definition, IOV can be quantified a posteriori but is hardly predictable when designing a future dosage regimen. It seems that the best way to handle IOV in MIPD programs is to include IOV in the parameter estimation and data fitting but to ignore it in dose forecasting by considering IOV as an unexplained residual error  [32, 33]. Further research is necessary to confirm these findings. To conclude, we implemented a PK model for busulfan dosing in children in the Tucuxi software. The model performance in terms of the estimated exposure and dosage prediction could be properly validated against a reference model available in BestDose. The results from both tools were consistent and indicated appropriate adequacy for the MIPD of busulfan in routine practice. Our approach may be proposed as a framework for the cross-validation of models used for MIPD in dosing software programs.
NoSQL stores have become ubiquitous since they offer a new cost-effective and schema-free system. Although NoSQL systems are widely accepted today, Business Intelligence & Analytics (BI&A) wields relational data sources. Exploiting schema-free data for analytical purposes is a challenge since it requires reviewing all the BI&A phases, particularly the Extract-Transform-Load (ETL) process, to fit big data sources as document stores. In the ETL process, the join of several collections, with a lack of explicitly known join fields is a significant dare. Detecting these fields manually is time and effort-consuming and infeasible in large-scale datasets. In this paper, we study the problem of discovering join fields automatically. We introduce an algorithm that aims to automatically detect both identifiers and references on several document stores.
Introduction For more than a decade, NoSQL datastore became commonly used to store big data. These systems are schema-free and built upon distributed systems, which makes them easy to scale and shard. However, in a rush to solve the challenges of big data and large numbers of concurrent users, NoSQL abandoned some of the core features of relational databases, which make them highly scalable and easy to use  [1] [2] [3]. Although the use of NoSQL systems is widely accepted today, Business Intelligence & Analytics (BI&A) wields relational data sources  [4]. In fact, from the earliest days of data warehousing, the qualities of the relational model have been highly valued in the quest for data consistency and quality. Exploiting NoSQL stores for analytical purposes requires reviewing all the BI&A phases.
NoSQL systems are based on four different families of models: key-value, column-oriented, document-oriented, and graphoriented  [5]. According to DB-Engines Ranking, 1 MongoDB, which is a document database, is the most well-known among NoSQL databases. In our previous work  [6] , we have proposed a hybrid BI&A approach that considers both schemaless data sources and analytical needs to explore over one document store efficiently. The ETL, i. e. , extract, transform and load, is the cornerstone of our approach. However, carrying out primary ETL operations, especially how to correctly join two different document stores, i. e. , collections, still being a challenge  [7, 8]. Fetching relevant data that meet the decision-makers requirements often needs to access more than one document store using the join operation.
While joining tables in relational data sources is straightforwardly owed to the availability of a precise join key, in document stores, collections are the furthest from having an exact join key because of the absence of integrity constraints. So, identifying the ''joinable'' fields to stick to two document stores is a tricky challenge. Despite its importance, no previous work has paid close heed to detect join key pairs in the context of NoSQL stores, particularly in document-oriented stores. For this, we introduce IRIS-DS (Identifiers and References DIScovery in Document Stores), a new approach that aims to discover the pairs of join keys (identifier, reference) starting from more than two document stores. Thus, the sighting features of our approach are: • to the best of our knowledge, no former approach has been dedicated to joining keys discovery in the context of document stores.