dataesr/scientific-paragraphs-categorization huggingface dataset:
Costs The analysis was performed from the collective perspective, where all the resources consumed in the production of the study interventions were valued, whatever the source of funding (patients, compulsory and supplementary health insurance schemes, the central government, etc. ). All costs were expressed in Euros 2019. Drug costs, including dispensing fees, were valued by prices obtained from the National Health Insurance Database (Base des medicaments et informations tarifaires de l'Assurance Maladie  [BdM_IT] ). Costs per year were calculated using doses and frequencies of administration obtained from the literature  (21, 24). For pegvisomant, the cost of 15 mg was applied during the first year of the analyses and then the cost of 20 mg was applied, to reflect the real life use, based on Chanson et al.
The present paper focuses on the modeling of the CROCUS reactor with the French MORET 5 continuous energy code and compares k eff , anti-reactivity effects (variation of k eff normalized to k eff due to the insertion of an absorber), sensitivity coefficients and kinetics parameters with those provided by other reference codes. The aim is mainly to extend the experimental validation/verification database of the MORET 5 code to use reactor physics applications. A suite of about 1400 benchmarks is already available for criticality but only a few are dedicated to reactor physics applications. The CROCUS reactor configurations therefore constitute an opportunity to test and improve the implementation of kinetics parameters and sensitivity coefficients in the code and make a verification of these physical quantities through the comparison with other codes such as MCNP.
Introduction The MORET 5  (Cochet et al. , 2015)  Monte Carlo code developed since 2008 at IRSN targets criticality-safety applications. It has been first involved in an industrial calculation route within the CRISTAL package  (Gomit et al. , 2017)  coupled with the deterministic APOLLO2 code  (Sanchez et al. , 2010)  for the calculation of k eff. Then, a continuous energy version was developed outside the CRIS-TAL package. Up to now four versions of this continuous energy code have been released, the last one being MORET 5. D. 1, incorporating developments such as: probability tables to describe cross sections in the unresolved energy range, nuclear data sensitivity based on the Iterated Fission Probability methodology (IFP)  (Kiedrowski and Brown, 2013)  allowing, among other possibilities, calculation of nuclear data biases through the use of sensitivity/uncertainty analyses using GLLSM  (Jinaphanh et al.
, 2017a (Jinaphanh et al. , , 2017b;; Leal et al. , 2019) , reaction rates calculations for reactor physics, a new model for kinetics parameters calculations based on a new estimate of the adjoint flux and therefore improving the existing one that did not follow the population of fission neutrons over more than one generation  (Jinaphanh et al. , 2010). One of the evolutions of the MORET 5 code deals with reactor physics applications. Thus a more complex capability to better model the calculation of kinetics parameters has been recently developed in the MORET 5. D. 1 code. That is also the reason why IRSN extended its experimental validation/verification database already composed of more than 1400 experimental cases in order to cover such applications, using benchmarks from the IRPhe (NEA/NSC/DOC(95)03, 2017) Handbook.
The aim of the paper is to test and validate the use of the MORET 5 code for reactor physics applications comparing the values obtained for reactivity effects, kinetics parameters according with the new code implementation, sensitivity to nuclear data with the ones obtained with other codes such as the MCNP6 code  (Brown et al. , 2017). The CROCUS reactor offers the opportunity to test in particular the calculation of reactivity values and kinetics parameters. Indeed, in this program, three kinds of experiments are proposed (IRPhe Handbook): an exactly critical experiment, without absorber rods, for which criticality is reached by varying the water level in the reactor; slightly over critical experiments, without absorber rods, for which the level of water was raised until delayed critical; slightly over critical experiments, with absorber rods; in that case, an absorber rod (boron or erbium) was introduced in the core and the level of water was adjusted to be critical; then the reactor was made supercritical by removing the absorber rod and keeping the water level constant.
Comparisons are made mainly on reactivity values with the Monte Carlo MCNP5 and MCNP6 codes using the ENDF/B-VII. 0 evaluation for nuclear data, as well as with TRIPOLI results  (Zoia et al. , 2016)  using also a library based on the same evaluation. The results using the data processed by MCNP are compared with those using data processed with the homemade GAIA processing tool  (Haeck, 2015). The objective is to validate the physical models implemented in the MORET 5 code and identify potential processing issues. Moreover, libraries based on various evaluations and processed identically are tested for the MORET 5 code just to assess the impact of the evaluation on the results. Concerning the kinetics parameters, only global quantities, without distinguishing by precursor group, are estimated by the MORET 5 code.
Codes The MORET 5. D. 1  (Cochet et al. , 2015)  Monte Carlo code is used in its continuous energy version using 3D geometry and various nuclear data libraries. The MCNP5, MCNP6  (Brown et al. , 2017)  and TRIPOLI-4  (Brun et al. , 2015)  codes are used as reference routes for a purpose of comparison with the MORET 5. D. 1 continuous energy route. MORET 5. D. 1 and MCNP6 models were realized by the authors of this paper. The TRIPOLI-4 results are were taken from published references.
Nuclear data libraries The codes and nuclear data libraries are gathered in Table  6. The continuous energy route involves the MORET 5. D. 1 code. It solves the transport equation to calculate fluxes and k eff of the 3D configuration. It uses nuclear data processed in the ACE format coming from various libraries. The MCNP5, MCNP6 and TRIPOLI-4 codes are used to make comparisons with the MORET 5 continuous energy code. All are 3D Monte Carlo codes that solve the neutron transport equation. The MCNP6 and MORET 5 codes use exactly the same nuclear data libraries processed at the ACE format by the same homemade GAIA1. 1 processing code  (Haeck, 2015) , based on the 99. 259 release of NJOY for the ENDF/B-VII. 0 evaluation. TRIPOLI-4 uses nuclear data of the same evaluation from PENDF files generated by another processing tool.
Methodology for verification The verification of the aforementioned codes is done through the comparison of the calculated k eff with the MORET 5 code and the calculated k eff with another code (TRIPOLI-4, MCNP5, MCNP6). If the discrepancy between these two k eff values is lower than the combined standard deviation (see Eq. (  10 )) of the two Monte Carlo standard deviations (r MC ) (see Eq. (  10 )), then no bias can be highlighted. r combined ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi r 2 MC1 þ r 2 MC2 q ð10Þ Various libraries are tested for continuous energy codes to assess the impact of nuclear data. When it is not stated, the Monte Carlo standard deviation is equal to 10 pcm, which leads to a combined standard deviation at 3r of about 45 pcm. Moreover, whenever the reactivity worth of an element replaced with another is tested, the calculated k eff corresponds to the k eff with the replaced element minus the k eff of the reference case.
K eff results with continuous energy codes Six configurations are studied in this section. Only the first one (H1) is critical, the other ones being slightly supercritical. The k eff results using the ENDF/B-VII. 0 based library are reported in Table  7. This library is chosen as a reference since TRI-POLI and MCNP calculations use it. It should be noted that a Monte Carlo standard deviation of calculations of 0. 00005 was targeted. A total of 38,720 neutrons per batch and 4371 batches were necessary to reach good convergence. Statistical tests (Chi2 and Lilliefors) can ascertain this good convergence. The convergence curve for case H1 is reported on Fig. 5. After 1500 batches, the convergence on k eff is obtained. The same type of convergence was obtained for other cases. The same ACE files are used for the nuclear data in the MORET 5 and MCNP6 codes.
TRIPOLI-4 and MCNP5 nuclear data are processed independently. MORET 5, MCNP5 and TRIPOLI-4 results are quite consistent. Moreover, a good agreement is obtained between MCNP5 and MCNP6 using the ACE files delivered with the code. One can derive the reactivity effects corresponding to the supercritical states applying below formula. q k ¼ 10 5 1 k crit eff À 1 k Ã eff , where ''crit" corresponds with the critical state and ''*" with the supercritical states (Table  8 ). A quite good agreement within the uncertainty margins is obtained between the results from MCNP5, MCNP6 (ACE delivered) TRIPOLI-4  (Zoia et al. , 2016). However, discrepancies associated with the processing can be identified when looking at the MCNP6 results using the ACE files processed by IRSN. These discrepancies are not surprising; they have already been identified on other benchmarks and suggest that the processing can be source of significant discrepancies on the k eff results.
Spectral indices Spectral indices such as the flux on the whole geometry and 235 U fission rate as well as 238 U capture rate calculated by the MORET 5. D. 1 code are reproduced in Figs. 6 7 8. The CROCUS reactor being a light water reactor it is logical to find that neutrons are captured in the epithermal and thermal energy ranges and undergoing fissions in the thermal energy range. The flux profile is typical of that kind of reactors.
Sensitivity to nuclear data The 5. D. 1 release of the MORET 5 code offers the opportunity to calculate sensitivity coefficients of k eff to the main reactions (cross sections and angular distributions). Such calculations are based on the Iterated Fission Probability technique (IFP)  (Jinaphanh et al. , 2017a (Jinaphanh et al. , , 2017b ) also implemented in the SCALE  (Rearden, 2004)  and MCNP6. 2  (Kiedrowski and Brown, 2013)  codes. The method used for implementation is the differential operator method. In this method, the estimation of the fission source derivatives is replaced by an estimation of the adjoint flux. For the main reactions ( 235 U fission, 235 U capture, 238 U capture, 1 H capture. ), the sensitivity profiles calculated using the MORET 5. D. 1 and MCNP6. 2 Monte Carlo codes with the ENDF/B-VII.
Conclusion The purpose of the present paper was to make a verification of physical quantities (k eff , sensitivity coefficients, kinetics parameters) of the MORET 5. D. 1 code developed at IRSN on criticality and reactor physics applications. A database of more than 1400 benchmark experiments is already available for criticality safety application. However, the number of cases is far more restricted regarding reactor physics applications. To achieve the goal of verification for reactor physics applications, the CROCUS benchmark was selected as being representative of light water reactors and relatively easy to model. Several capabilities implemented in the MORET 5. D. 1 code were tested using various nuclear data libraries (JEFF-3. 1. 1, JEFF-3. 3, ENDF/B-VII. 0, ENDF/B-VII. 1 and ENDF/B-VIII.
0): The calculation of kinetics parameters, The sensitivity of k eff to nuclear data (cross sections), The production of reaction rates. When comparing k eff and reactivity results between the MORET 5, MCNP5, TRIPOLI-4 and MCNP6 Monte Carlo codes using the same nuclear data libraries, no significant discrepancy can be pointed out, except when the processing of nuclear data differs. Another result of the paper is that the effect of the nuclear data library is insignificant regarding k eff , which is not the case for kinetics parameters. Indeed, results from the JEFF libraries are consistent with one another, as is the case for ENDF libraries. Finally, regarding the calculation of sensitivity coefficients, a quite good agreement is obtained between sensitivity coefficients generated using the MORET 5.
D. 1 and the MCNP6. 2 codes. It therefore contributes to the verification of the sensitivity capability development in the MORET 5. D. 1 code. Here are the main points of the paper: Validation of MORET 5 code for k eff and kinetics parameters, Comparison of sensitivity profiles with those obtained with other codes, Impact of nuclear data libraries on calculated quantities, Impact of nuclear data processing on k eff results.
Acknowledgements The authors would like to thank  Valentin Jouault , subcontractor from the  URANUS company , and  Mariya Brovchenko  from  IRSN  for their support in the modelling of the MCNP configurations and the ICSBEP and IRPhe working groups under the auspices of OECD for their involvement in the production of the Criticality and  Reactor Physics Handbooks.
INTRODUCTION The use of automated literature search tools is often referred to as text or data mining. Text mining was initially defined by Martin Hearst in 1999 as "the discovery by computer of new previously unknown information, by automatically extracting and relating information from different written resources, to reveal otherwise 'hidden' meanings". The automated processing and analysis of text can help researchers evaluate findings in scientific literature. Text mining can be used to answer many research questions, ranging from the discovery of drug targets  (1)  and biomarkers  (2)  to drug repositioning  (3). Text mining has evolved into a sophisticated and specialized field in the biomedical sciences, where text processing and machine learning techniques are combined with the mining of biological pathways and gene expression databases.
In general, text mining processes are comprised of several steps, such as information retrieval (usually performed by querying databases) followed by named entity recognition and finally information extraction. Text mining algorithms can operate using two distinct methods: co-occurrence-based methods or natural language processing. Co-occurrence based algorithms search for associations between information present in the text. Natural language processing algorithms take account of links between words in a text, for example, by finding a v-structure using the ABC principle. In other words, it can identify a relationship between A and C in the text which in turn will indirectly identify a potential link with B that is not explicitly mentioned in the text  (4). The "pubmed. mineR" R library has been developed along these lines, as it analyzes linguistic structure at different levels: sentence tokenization and word tokenization  (5).
In R language, some efforts have been made to process text mining transformation of the corpus into a matrix of word citations including tokenization, stemization and atomization processes of linguistic structure. This was previously applied to the "tm" R library, and more recently the "tidytext" R library was developed with text mining functions to enable the structuring of words found in the corpus  (6). Focused more specifically on extracting textual information in the scientific literature using the PubMed resource, the "RISMed" R library was developed to extract all annotations of the abstracts loaded into the NIH biomedical database. The "RISMed" R library was recently used to analyze COVID-19-related data to develop a world collaboration map by means of the Biblioshiny application  (7).
BIOINFORMATICS TOOLS FOR THE TEXT MINING OF GENE RANKING Many text mining applications have been built on the MEDLINE database because it is freely available, features a rich applied programming interface and supplies annotated abstracts containing Medical Subject Heading (MeSH) Terms  (9). To improve the efficiency of querying, similar keywords such as synonyms could be used to define concepts with different reformulations such as in the case of the ConQuR-bio algorithm  (10). In the area of biomedical texts, mining is widely used in the context of gene expression annotation, such as understanding large lists of regulated genes during transcriptome experiments. Other biomedical applications for text mining could also be investigated, such as drug-target discovery, which enables the search for new drug targets or candidates; it would permit detailed and automated analysis of scientific literature to discover how genes are related to particular diseases and how they are involved in the effects of medicinal products.
However, automatic gene name extraction and normalization methods may wrongly identify a significant proportion of gene mentions in a text, therefore contributing noise and ambiguity to the text mining results  (13, 14). In the context of transcriptome analysis, lists of regulated genes are sometimes composed of large numbers of molecules that are difficult to understand during functional annotation with respect to classic biological functions or cellular pathway databases. The co-occurrence of keywords and gene names when searching in biomedical abstracts could aid in the understanding and discovery of principal relationships between regulated molecules. For transcriptome analysis, the concept of the "next generation" text mining has emerged by combining gene lists that result from text mining with gene set enrichment analysis (GSEA)  (15).
This has been applied to enable understanding of the interactions between genes and chemical components. Using text mining, lists of genes were built for each chemical component, and using this custom database the GSEA method was able to retrieve which molecular component was used during experimental stimulation of the transcriptome under investigation  (16). Conventional text mining approaches tend to process PubMed abstracts rather than full text articles and fail to mine information not present in abstracts, but text mining of full text articles has recently gained interest  (17, 18). Thus, the PubTator central algorithm now enables exploration of the relationships between genes, diseases, chemical components, mutations, cell lines and species in more than six million full text articles uploaded on the PubMed Central website  (19).
A biological topic is taken as the input parameter to review the entire MEDLINE database for relevance to that subject, and then evaluated for all genes included in the user' s requested organism according to the relevance of their associated MEDLINE records. Genie associates machine learning and text mining processes by creating a train corpus based on 1,000 abstracts to build a naïve linear Bayesian classifier model. Secondly, using text mining, abstracts in which genes occur are compared to the train set by the machine learning model  (23).
Partial hydatidiform moles are the result of mild trophoblast proliferation; there is fetal circulation and development of the embryo, but the fetus is abnormal and cannot survive. Hydatidiform moles are subject to severe hypoxia, and the persistent vascular immaturity of the villous stroma can lead to hydropic villi, particularly in the case of complete hydatidiform mole. In about 7-17% of cases, trophoblastic hyperplasia extends to and exceeds the uterine cavity, and is referred to as an invasive mole. An invasive mole can either be premalignant or malignant; malignant mole can transform into a highly aggressive tumor called choriocarcinoma  (24). Because no transcriptome data are publicly available on hydatidiform moles, we performed text mining to search for all genes studied in this context and then looked at their expression in three different transcriptome datasets on normal human placenta, focused on the three biological mechanisms associated with the disease: trophoblast differentiation, trophoblast invasion and hypoxic environment.
The "tm" and "RISMed" R libraries that connect to PubMed were used to perform text mining. "RISMed" library was also used to carry out a meta-analysis on the words used in the scientific literature. Natural Language Processing (NLP) allowed us to communicate information from a variety of biological resources (such as Gene Ontology Terms). We were able to discover semantic relationships with the scientific literature and link them to biological databases. In each case, gene expression matrices were used independently and normalized using our previously developed method  (25). Mathematical matrix dimensional reduction was applied to a normalized transcriptome dataset by merging the genes obtained from text mining with identifiers present in gene expression datasets. These transcriptome data were then re-analyzed with the genes retrieved from text mining, using terms such as HM-linked genes studied in cytotrophoblast differentiation, extravillous trophoblast invasion and hypoxia.
The expression of genes during mildly or severely invasive trophoblast proliferation (respectively, cyto-and extravillous trophoblasts) was determined using the Significance Analysis for Microarray algorithm with a threshold of false discovery rate fixed under five percent of error. For different oxygen concentrations, a supervised analysis of variance with two factors (culture conditions and oxygen concentration) was performed using Fisher' s test (500 permutations) on hydatidiform mole-linked genes. To validate the text mining approach, we performed a manual search in the PubMed database for the genes identified by text mining. This analysis of the literature enabled validation of the relationship between these genes and hydatidiform mole  (26). In conclusion, by using text mining and associated bioinformatics and mathematics methods we were able to identify 72 unique genes linked to hydatidiform mole (Figure  1 ).
More recently, single-cell sequencing made it possible to study the expression of the genetic content of an individual cell without the need for prior cell culture. Thanks to this innovative technology, gene expression in thousands of individual cells can be determined in a single experiment  (30). These technologies have generated large quantities of bioinformatics data that are often stored non-systematically, thus hindering most researchers without extensive expertise in advanced computing. In the field of nephrology, there are some ongoing initiatives such as Nephroseq (http: //www. nephroseq. org [accessed on 14 January 2021]), the Renal Gene Expression Database (RGED; http://rged. walleva. net/ [accessed on 14 January 2021]), KUPKB: from the Kidney and Urinary Pathways Knowledge Base  (31) , CKDdb (Chronic Kidney Disease database; www.
padb. org/ckdbd/index. html [accessed on 14 January 2021]) as well as others. These "omics" platforms enable the exploration of gene expression associated with kidney diseases. However, they lack multi-omic databases that could unify dispersed "omics" repositories. We performed an FSGS text mining study and integrated it into a gene expression database obtained by single cell RNAsequencing in glomerular cells from healthy donor kidneys. Based on the result, we propose a bioinformatics tool that helps visualize the different types of cells associated with a specific gene expression linked to FSGS. Our principal goal was to simply determine which particular renal cells (in glomeruli: podocytes, endothelial and mesangial cells) might be involved in a particular gene expression (and/or function) associated with the pathogenesis of FSGS.