dataesr/scientific-paragraphs-categorization huggingface dataset:
IV. MODELS SIMULATION RESULTS For a fair comparison between the several models listed in Section III, we created an identical Cadence simulation environment. Moreover, to help the design community selecting the most suitable model for their applications without wasting time reading each model publication separately and writing the corresponding code, a Verilog-A code of all the listed models is presented in Appendix-A. In this implementation, a ''num_model'' parameter should be set to target a specific RRAM model. The TEAM, VTEAM, Simmons Tunnel Barrier Model, Linear Ion Drift Model, and Non-Linear Ion Drift models are implemented in Verilog-A within the same code  [28] ,  [29]. Stanford, IM2NP and SPICE models developed initially in Verilog, Eldo, and Pspice respectively are added.
C. ACCURACY The accuracy of the three models, that best match the experimental results, is evaluated by comparison of the simulation results and the measured data on silicon at cell level, provided by ST-Microelectronics  [31]. The mean error between the simulated I-V curves of each model and measured data is given in Table  3. The smallest error observed is between the Stanford model and the experimental data. Figure  14  shows the simulated I-V curves of three selected models along with the experimental I-V curve  [31]. In conclusion, only two physics-based models (Stanford and IM2NP) fulfill most of the essential evaluation criteria with reasonable accuracy.
B. VARIABILITY According to section IV, Stanford and IM2NP models are the only models that can be used to simulate variability in RRAM cells. Appendix B includes the equations implementing the variability of both models. In this section, we provide an example of application where we studied the two types of RRAM of both models: • Device-to-device variability describes the RRAM cell behavior consistency inside the memory array. • Cycle-to-cycle variability measures the RRAM cell bility over different cycles. A B1500 semiconductor parameter analyzer is used for measurements. Quasi-static experimental measurements are performed to extract the memory cell main characteristics (i. e. , V SET , V RESET , etc. ) by applying a 200ms triangular pulses across the 1T1R cell. Figure  16  shows the cumulative distributions of R ON (LRS) and R OFF (HRS) in different RRAM cells within the 1T1R memory array using the Stanford model and the IM2NP model compared to the experimental data  [31].
For all the models discussed in this study, we provide an implementation in the same Verilog-A code (Appendix B), for easier access and assessment by the designers. The user can specify the model number and compare the performance of the different models at the same time. Finally, we validated our comparative analysis at the circuit level using a 1K-bit 1T1R RRAM array, and the results are consistent with the previously published ones at the cell level.
Data Collection and Analysis For the patient's monitoring, both manually entered and automatically registered data via wearable sensors will be tracked. Manually registered data contain the following parameters:  Subjective pain evaluation (using standardized scales for kids and adolescents)  Mood information  Acute pain detection needed to be immediately suppressed by medication  Type of activity the patient performs during a day and its duration (resting, playing, sleeping, eating, walking)  Medication intake (dose, datetime) Automatically registered data are displayed in Tab 1 together with the explanation of their role in pain management. Based upon the results from the clinical study during which all the parameters are to be measured, the final composition of the sensors needed to be tracked will be proposed.
WP2: Definition of User Requirements, Technical, Clinical and Ergonomic Evaluations Using a user-centred design approach, the user needs would be evaluated using focus groups and creation of personas. This would be done collectively for each of the stakeholders of this application. Since current standards of pain measurement are not well defined, a proof of concept with a software prototype would be designed for stakeholder's feedback. The application would initially be developed for an Android OS to be freely available for download for patients and their families. The application designed for the healthcare professionals would be connected with the hospital systems which would enable a single e-health system.
WP3: Preclinical Study During the analysis of suitable sensors that could be used for monitoring of given parameter, the following factors will be taken into consideration: compatibility of medical device with software application, quality of signal, wireless data transmission, size of the device, it´s price and duration of battery used to charge the device. To get sufficient data for development of algorithm, preclinical study will be performed on 20-30 subjects (children in post-operative state, age of 2-8 years) for 2 days. In these patients heart rate variability and physical activity will be monitored via selected wearable devices. In parallel to the automatic data transfer from the wearables, patient´s manually registered information about the pain and daily activities will be registered using usual tool.
WP4: Development First step of the development stage the software architecture will be performed. Based upon the data obtained from WP2 the design, concrete functionalities and final interface of both the mobile and web application will be made, before conducting the validation phase of clinical study. As a first concept of the telemonitoring system we will implement the software part only, without the data evaluation obtained from connected devices. After the validation of the accuracy of the devices and the final device composition is resolved, the upgrade version of the system, including the hardware part of the project, will be implemented, processed through the regulatory, and marketed in parallel to the software solution already approved. The algorithm used for automatic evaluation of pain level will be developed in cooperation with the company producing the pain management system.
The automatic evaluation of physical activity level will be created in accordance to the data analysis made after the preclinical study. During the whole development period, we will also actively consult particular issues with software experts and regulatory affairs to ensure the final product will meet all the essential requirements. Requirements for quality management system will be followed based upon the ISO 9001 standard. Each update of the software versions will be tested based on the testing processes described within the Risk assessment section. As a final control of the software testing phase, the verification and validation is performed based on the IEC 62304.
Intervention Group: The intervention group will get the sensors to measure HR and physical activity. All the data will be automatically transmitted wirelessly to the connected mobile app, synchronized with the server and displayed to a doctor in a hospital. In addition, parents of the kids will provide manually registered information to the app. Both the parents and the doctor will have an access to the data collected through a mobile/web app. The doctor will be able to send a notification to the patient based upon the data obtained through the connected web app. The doctor will check the data approx. 30 mins before patient ś scheduled medication, and in case of the need of the dosage change, he will send the patient a message with the recommended dosage. The phone call can be used in case of emergency situation.
BUSINESS PERSPECTIVE AND ECONOMIC MODEL The business model involves the software application for patients and doctors, which is connected to the database and also includes information from wearable devices. The market entrance strategy focuses primarily on the promotion of the software to enhance the connection between healthcare professionals and patients based on subjective data. For the market entrance of the business model the end user group will be parents, whose children are under 14 years and underwent surgery. Additionally to subjective information the main hardware information will be an objective pain measurement, which is already used during surgery. For future portfolio-expansion different solutions in different field of surgery and specifically chronic pain monitoring will be considered.
For the expansion of the field of areas additional hardware devices could be implemented and the related information included into the database. The main targeted customer groups will be hospitals and health systems, where the biggest benefit will be generated due to cost savings regarding the reduce of the length of stay in hospitals by around three days. There also exists evidence, showing that 110 clinic spots became available due to monitored patients at home after surgery. Based on the saving of three hospital stays per day a potential savings for Germany (1 billion €), France (450 million €), and UK (3. 5 billion €) could be calculated. The main business core activity will be to develop the software application but also to manage the data transfer and storage. To process and analyse the received data different algorithm will be applied, which will be licensed.
ACKNOWLEDGEMENTS This work was supported by The  EIT Health Summer School CLINMED, INSERM CIC-IT 1403 of Lille, Bourgogne Franche-Comté University, Karolinska Institute and Pierre and Marie Curie University and Charles University. The authors would like to thank the surgery department at the  University Hospital of Lille , and  Mdoloris Medical Systems  based in Lille, France for their collaboration.
Gephebase is a manually-curated database compiling our accumulated knowledge of the genes and mutations that underlie natural, domesticated and experimental phenotypic variation in all Eukaryotes--mostly animals, plants and yeasts. Gephebase aims to compile studies where the genotype-phenotype association (based on linkage mapping, association mapping or a candidate gene approach) is relatively well supported. Human clinical traits and aberrant mutant phenotypes in laboratory organisms are not included and can be found in other databases (e. g. OMIM, OMIA, Monarch Initiative). Gephebase contains more than 1700 entries. Each entry corresponds to an allelic difference at a given gene and its associated phenotypic change(s) between two species or two individuals of the same species, and is enriched with molecular details, taxonomic information, and bibliographic information.
Users can easily browse entries and perform searches at various levels using boolean operators (e. g. transposable elements, snakes, carotenoid content, Doebley). Data is exportable in spreadsheet format. This database allows to perform meta-analyses to extract global trends about the living world and the research fields. Gephebase should also help breeders, conservationists and others to identify promising target genes for crop improvement, parasite/pest control, bioconservation and genetic diagnostic. It is freely available at www. gephebase. org.
INTRODUCTION Mutations form the raw bulk of heritable variation upon which traits evolve. Identifying the DNA sequence modifications that drive phenotypic changes is a primary goal of modern genetics, and could greatly improve our understanding of the mechanisms behind biodiversity and adaptation. However, this research program would be most successful if it reaches comparative capacity, for instance by allowing us to detect trends across the Tree of Life  (1) (2) (3). Advances in genome sequencing and editing are accelerating the rate of discovery of the loci of evolution at a quick pace, making data integration increasingly challenging, and it is now crucial to develop a universal, single resource integrating this body of knowledge. As of today, compilations of genotype-phenotype relationships are available for a limited number of species in taxon-specific databases, for example OMIA for animals  (4) , OMIM for humans  (5) , TAIR for Arabidopsis  (6) , FlyBase for Drosophila  (7) , or the Monarch Initiative across the main laboratory animal model species  (8, 9).
To date, there are no databases that consolidate genotype-phenotype relationships related to natural evolutionary cases across all Eukaryotes. For example, evolutionary changes in tigers, butterflies, monkeyflowers, or any non-traditional model organism are lacking from existing genotype-phenotype databases, preventing comparative insights on the diversity and similarities of sequence modifications that fuel the generation of observable differences in the living world. To fill this gap, we developed Gephebase, a manually curated database that gathers published data about the genes and the mutations responsible for evolutionary changes in all Eukaryotes (mostly animals, yeasts and plants) into a single website. The content of Gephebase was developed over the past 10 years, with previous versions of the dataset published as supplementary spreadsheet files associated to   of  a gene name search for 'oca2' is provided here as an example, with a montage of Gephebase summary outputs, pictures, and annotations in color.
Interestingly, the cavefish studies reveal that the Oca2 null alleles also affect catecholamine metabolism in cavefish. Gephebase can be used as a hypothesis generator: by juxtaposition of these entries, one may expect similar effects in the corn snake Oca2 mutants that remain to be tested. 'N' means that the mutation is null. two review articles, which successively compiled 331 entries (1), and 1008 entries (2). These datasets have been used by various authors to highlight several trends regarding the genetic basis of natural variation. For example, based on these compilations it was found (a) that the mutations responsible for long-term evolution have distinct properties than the mutations responsible for short-term evolution (1,10), (b) that certain types of mutations are more likely to be fixed than others during the course of evolution  (11) , (c) that independent evolution of similar traits in distant lineages often involves mutations in the same orthologous gene (2), (d) that current data are biased towards a limited number of model organisms  (12)  and (e) that the cis-regulatory tinkering of signaling ligand genes is a recurring mode of morphological evolution  (13).
Gephebase also indicates that some Oca2 allelic variants have been identified by Genome-Wide Association Studies of pigment variation. Importantly, the focus of Gephebase is always on genetic variations that emerge naturally -it never includes laboratory variants that were generated by random or directed mutagenesis. Thus the Oca2 CRISPR knockout phenotypes that have been generated in frogs  (16)  do not have a dedicated Gephebase entry; the cavefish Oca2 CRISPR/TALEN knockout phenotypes  (17, 18)  do not have a dedicated entry either, but are used as Additional References to support the functionality of the two natural Oca2 null alleles in Gephebase. This makes Gephebase complementary to the Monarch Initiative database, which compiles gene-to-phenotype relation-Figure  2. Three kinds of experimental strategies for identifying gene-to-phenotype variations.
Association Mapping studies are included only if there is additional experimental support for the given gene. Candidate Gene studies require conclusive functional assays for inclusion in Gephebase. Overall, gene-to-phenotype links identified by Linkage Mapping with resolutions <500 kb have priority in the dataset. There are multiple types of experimental evidence that led to the discovery of a relationship between a genetic mutation and a phenotypic change. For sake of simplicity and efficiency, each gene-phenotype association is attributed only one type of Experimental Evidence among three possibilities: 'Association Mapping', 'Linkage Mapping', or 'Candidate Gene' (Figure  2 ). When several methods were used, the least biased one is chosen by the curator (Table  1 ). And when new evidences emerge, they are added to the entry.
Curation protocol Searches for relevant papers to be included in the database are done manually by our team of curators. We screen major journals in evolutionary genetics, perform keyword searches using online search tools, and we pay particular attention to citations in primary research articles as well as in review papers. The 'Suggest an article' button in the top bar menu allows users to suggest articles to our curation team. Of note, our curations efforts have been maximal until 2013 and then relaxed due to our inability to support a full-time curator. Following our inclusion criteria, we estimate that the database is close to comprehensive for studies Table  1. List of the fields of a Gephebase entry (in order of appearance on the View-Entry page).
Comments Free Text which provides additional information about the genotype-phenotype relationship. The following tags are used: @SexualTrait @Fitness @Pleiotropy @GxE @Parallelism @Splicing @TE @GeneDuplication @GeneConversion @ChimericGene @SuperGene @Inversion @Introgression @ILS @HGT @BalancingSelection @Epistasis @SeveralMutationsWithEffect @SeveralCandidateMutations @TwoNucleotideChangesInSameCodon @SuccessiveMutationsAtSameCodon @AllelicSeries. D700 Nucleic Acids Research, 2020, Vol. 48, Database issue published prior to 2013, and up to 30% complete for the 2014-2019 period.
Technical overview of the database and the web interface Gephebase was developed using the Symfony framework (v2. 8) and PHP (v5. 6 compatible 7). MariaDB (v10) is used to store data. The database consists of 33 tables including users management and logs. The main table links genotypic change, phenotypic change, references and validation information. Most fields of other tables are automatically retrieved from NCBI databases. The import procedure uses the NCBI E-utility interface with XML to fill the corresponding tables. Gephebase entries of the main table can be imported and exported through a csv file. For convenience, fields retrieved automatically can be present in the csv file even though they are fetched and stored in other tables. The project code was put under version control (git) from its inception.
Organization of the data into entries This database currently comprises >1900 entries (Supplementary Table  S1 ). One entry corresponds to a single mutation, or a group of linked mutations within a single gene, either between two closely related species or between two individuals of the same species, and its associated phenotypic change (Figure  3 ). For cases of repeated evolution (2), we use the following conventions. When several mutations are found within the same gene in a given individual, with each mutation affecting the trait of interest--i. e. several causative mutations within a haplotype, intralineage hotspot (2)--all are grouped into a single entry. In contrast, when independent mutations occur in the same gene in distinct individuals of the same species, leading to similar phenotypic changes (intraspecific parallel evolution, convergent evolution), we chose to create different entries for each lineagespecific haplotype.
Exploration tools Gephebase is designed for interactive exploration and analysis of the genotype-phenotype relationships across species and populations. First-time users can find help on the Frequently Asked Questions page, in tutorials available on the Documentation page and via 'contextual tips', small boxes providing information when the cursor hovers over an item. Data can be queried using boolean operators via the Search page, via SQL line or via custom tools after downloading the dataset of interest as a CSV file. The entire dataset can be downloaded as a CSV file by searching for the wild card * in the top bar panel, clicking on 'Select all' in the top left corner of the results table and then clicking on 'Complete Export'. A Browse page, accessible from the main menu, displays all the Trait names, Species names and also compiles the genes with the highest number of mutations reported in Gephebase.
DISCUSSION AND CONCLUSION Gephebase contains data for more than 450 eukaryote species and more than 900 distinct genes (Supplementary Table  S1 , Data S1). In Gephebase, physiological traits represent 67% of the entries, morphological traits 31%, behavioral traits 1% and mixed morphological/physiological/behavioral traits 1% (Figure  4A, C ). The most represented traits are Xenobiotic Resis-tance (21% of the entries) and Coloration (18%). The most represented taxa are Vertebrates (40% of the entries), Green Plants (30%) and Arthropods (17%) (Figure  4D ), and a large contribution from traditional model organisms  (12). Most data correspond to intraspecific changes (48% of the entries) and domesticated cases (30%) whereas interspecific cases correspond to 10% of the entries (Figure  4E ). The three categories of Experimental Evidence are relatively Nucleic Acids Research, 2020, Vol.
48, Database issue D703 well-distributed among entries (Figure  4F ). Gephebase contains a higher number of coding mutations (63% of the entries) compared to cis-regulatory changes (18% of the entries, Figure  4G ). While a significant fraction of Gephebase correspond to cases where the exact mutation has not been identified (23% of entries with Aberration Type 'Unknown', Figure  4H ), most mapped mutations are single nucleotide changes (47%) and indels (26%). Gephebase stands out compared to the other current databases of genotype-phenotype relationships in that it compiles genotype-phenotype data across all Eukaryotes. We consider our dataset to be highly complementary to other available databases, which are more species-specific and which usually include more detailed information about genotype-phenotype relationships.
Ultrasonic techniques are developed at CEA (French Alternative Energies and Nuclear Energy Commission) for in-service inspection of sodium-cooled reactors (SFRs). Among them, an ultrasound imaging system made up of two orthogonal antennas and originally based on an underwater imaging system is studied for long-distance vision in the liquid sodium of the reactor's primary circuit. After a description of the imaging principle of this system, some results of a simulation study performed with the software CIVA in order to optimize the antenna parameters are presented. Then, experimental measurements carried out in a water tank illustrate the system capabilities. Finally, the limitations of the imaging performances and the ongoing search of solutions to address them are discussed.
Introduction Since France considered in 2008 that the sodium-cooled reactor (SFR) concept was the most mature for Generation IV nuclear reactors, an extensive R&D programme was launched. In-service inspection was identified as a difficult task to be performed (as the sodium coolant is opaque, hot, highly chemically reactive, and difficult to drain). Ultrasonic techniques have been extensively studied as they are well adapted to Non Destructive Evaluation (NDE) and telemetry measurement in this harsh environment. Thus, development of ultrasonic transducers to be immersed in sodium at about 200 • C in the reactor block (inspection is performed at shutdown conditions) led to a first phase of specification consolidation; then, a prequalification process involving increasingly more realistic experiments using acoustic techniques and simulations was performed with the patented CIVA code  [1].