dataesr/scientific-paragraphs-categorization huggingface dataset:
7 GeV, pC collisions at √ s NN = 41. 5 GeV and pp collisions at √ s = 200 GeV. The differential yields of J/ψ as functions of y * and p T , obtained from pAr data, are also shown in Fig. 1. Since the luminosity measurement is not available, only differential distributions with arbitrary normalisation are shown. The D 0 differential cross-sections per target nucleon obtained for the pHe dataset, as functions of y * and p T , are shown in Fig. 2   measured total cross-section, they are rescaled by a factor 1. 44 in Fig. 2  to compare the shape of the distributions. Differential yields, with arbitrary normalisation, of D 0 as functions of y * and p T obtained from pAr data are also shown. In fixed-target configuration, the LHCb acceptance gives access to the large Bjorken-x region of the target nucleon.
Search terms Preliminary individual analyses were performed on several key researchers in hypertension in order to identify the most commonly used MeSH terms  [11]. These terms were then used in the MeSH tree to conduct a thorough search. Only documents with these MeSH terms declared as major topics  [12]  were extracted. However, MeSH terms are available only for articles already indexed in MedLine; therefore, we also searched the PubMed database based on the article title and a list of journals specializing in hypertension. Exclusion terms were added in order to exclude disorders not related to hypertension. Only documents defined as 'journal article' in PubMed were included in the analysis; other documents, such as reviews, letters or editorials were excluded. All guidelines were also excluded.
Analysis After extraction, data were transferred into InCites for bibliometric analysis. InCites allows analysis of several bibliometric indicators relating to researchers, institutions, countries and regions. It also allows investigators to focus on thematic fields, with different levels of analysis. For example, the focus can be on specific fields (Web of Science categories), such as 'peripheral vascular disease' or 'pharmacology and pharmacy'  [13]  or on a larger area, such as 'medical and health sciences', which groups all Web of Sciences categories related to the biomedical domain. We first compared the total global output relating to hypertension with that for 'peripheral vascular disease' and 'medical and health sciences'. We then looked at the contributions from major countries to the global output, assessed on the basis of number of articles with authors from each country.
All summaries and, when necessary, full articles were read in order to identify the subfields most cited. The abstract of each article was read by one of the authors (J. M. ) in order to exclude those not directly relating to hypertension, as well as some guidelines and nonsystematic reviews that had not been excluded by the initial search strategy. Finally, we focused on the output from the European Union countries over four 5-year periods in order to identify trends in publication. VosViewer software developed by the Centre for Science and Technology Studies (bibliometric department of Leiden University, Leiden, Netherlands) was used to enable collaborations between European Union countries to be visualized  [14, 15]. This software analyses the addresses provided in bibliographic files. It allows computation of the collaborations to produce a graphical representation that indicates clustering and numbers of publications.
European Union collaborations Finally, we looked at collaborations between European Union countries in the final 5-year period. In order to analyse only articles with an important scientific impact, we extracted the 1363 articles that were among the 10% most cited articles and that had also been authored by at least one researcher in the European Union. Analysis was then performed using VosViewer software (Fig. 4 ). The VosViewer results show the importance of the UK, Germany, Italy, Netherlands and France in the European Union collaboration network relating to the top 10% most cited articles. It positions USA (which is present because of co-authoring with European Union authors) at the centre, with UK very close. Countries are divided into three clusters: Italy, France, Poland, Spain, Belgium, Greece and Romania are grouped in the blue cluster.
DISCUSSION This bibliometric analysis of the global research output in the field of hypertension shows an increase in the number of publications over the 20-year analysis period, although the rate of increase is less than that seen for all medical and health sciences articles during the same period. We chose PubMed for the extraction as it is the largest database in the biomedical field and it provides a precise and specific search engine. PubMed also provides wider coverage of the biomedical field than Web of Science. However, not all journals indexed in PubMed are indexed in the Science Citation Index (used to compute impact factors), so PubMed articles published in journals without impact factors are not indexed in Web of Science and InCites. Results depend not only on the database used but are also strongly dependent on the search strategy.
Use of VosViewer to visualize the main collaborations identified countries whose predominant language is English as being in a central position; these countries were also the most productive in terms of publications relating to hypertension. This cluster visualization may help to stimulate more collaborations between countries, either to reinforce existing ones because of their productivity, or to encourage new ones. In conclusion, about 80% of the burden of hypertension occurs in low-and middle-income economies, so it will be important to undertake research specific to these countries in the near future. The problems that will be encountered in hypertension management and research in these regions are likely to be very similar to those that have been seen in highincome countries over the past 50 years.
ACKNOWLEDGEMENTS The authors thank  Oxford PharmaGenesis ,  Oxford, UK  for assistance with the editing and formatting of this article. Part of this work was presented at the 28th European Meeting on Hypertension and Cardiovascular Protection, Barcelona, Spain in June 2018 Contributions: J. M. and P. D. developed the study concept, P. D. conducted the extractions and analyses, J. M. checked the content of all top 1% publications. J. M. and P. D. interpreted the results and prepared the manuscript for publication.
Acknowledgments We thank the participants of the 3C study, for their important contributions. The 3-City Study is conducted under a partnership agreement among  INSERM ,  Univ. Bordeaux , and  Sanofi-Aventis. The  Fondation pour la Recherche Médicale  funded the preparation and initiation of the study. The 3C Study is supported by the  Caisse Nationale Maladie des Travailleurs Salariés ,  Direction Générale de la Santé ,  Mutuelle Générale de l'Education Nationale ,  Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne ,  Fondation de France , and  Ministry of Research-INSERM Programme "Cohortes et collections de données biologiques. " Stéphanie Debette  is supported by the  European Research Council , and a grant from the  Joint Programme of Neurodegenerative Disease research , from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements (No  643417 & No 640643 ), by the  Initiative of Excellence of Bordeaux University , and the  Agence Nationale pour le Recherche.
Data processing: design of correlation models by a statistical approach The aim of the data processing was to design algorithms to correlate the biological data (fluorescence values) obtained from the selected strains with the BOD5 reference values; the final goal was to propose a mathematical model to predict the BOD5 value from only the data provided by these strains. All models were designed via a dedicated software (Neuro One 6. 13. 0. 5, Netral, France). 2. 8. 1. Architecture of the model (number of biological descriptors and nature of the model) First, the data were analysed using a mono-parametric approach. The information (fluorescence values) from each strain was individually correlated with the reference values by linear regression. Because the results were insufficient (low correlation rates), the data processing focused on a multi-component strategy (based on several strains), which was also based on a linear regression algorithm.
This step allowed the determination of the number of biological descriptors (selected strain) required to assess the BOD5. The predictive abilities of these models are limited. To improve the performance of the models for the assessment of the BOD5, non-linear algorithms by neural networks were deployed. These models are defined according to architectural characteristics, such as the number of hidden neurons, the activation modalities of the neurons, and the data standardization conditions. Several architectures were compared for the overall database to identify a priori the most relevant algorithms.
Model validation The last data processing step consisted of comparing the preselected architectures based on their predictive abilities with new samples. For this purpose, the overall database was randomly separated into two groups: a learning set (n= 148 assays) and a validation set (n= 60 assays). The learning set was used to design new algorithms based on the preselected architectures (number of hidden neurons, activation mode, and data standardization). To assess the robustness of these new models and choose the most relevant model, the estimated BOD5 values obtained from the validation set were compared to the reference values. This step was also performed with Neuro One 6. 13. 0. 5 (Netral, France).
coli. Figure  5B  depicts the biodegradation kinetics of each strain for the synthetic wastewater (SWW). From these data that were calculated from the maximal measured slope of oxygen consumption, we noted significant differences between strains. For example, the biodegradation kinetics reached 0. 33 mg BOD. L - 1. h -  for B. vertrisii, whereas the kinetics achieved by P. putida (DSM 1868) were approximately 12 mg BOD. L - The final selection process of representative strains among each cluster was based on these last two criteria in addition to some criteria relative to the utilization of the strains, such as the flocculation of cells during growth (complexity of the preparation step). For example, group A consists of 6 strains  (B. funiculus, B. vertrisii, Z. resiniphila, P. pantrophus, B.
The final database consisted of 208 assays. Each sample is defined by 8 fluorescence values (one per strain) and a BOD5 reference value obtained using the standard method. The data analysis was performed from this database. The main objective of this supplementary step was to propose a reliable correlation model that allowed prediction of the BOD5 after only 3 hours. Nevertheless, we were focused on the number of strains required to assess the BOD5 before improving the correlation algorithm a second time (numerical model allowing calculation of the BOD5 from fluorescence values provided by the selected strains).
Determination of the number of biological descriptors needed to assess the BOD5 The data provided by each selected bacterial strain were individually correlated by linear regression with the BOD5 reference values (Fig. 6 ). The average correlation rate was approximately 0. 11, with a standard deviation of 0. 08. Among the 8 selected strains, the best correlation rate obtained with B. aquatica (r²=0. 2638) was too low to be used as a reliable bio-indicator of the BOD5. Nevertheless, each strain provided a portion of the overall information. Consequently, increasing the number of strains used to assess the BOD5 should improve the relevance of the approach. However, the number of strains required to ensure a reliable measurement is unknown. Correlation models (linear regression) were designed based on the use of 1 to 8 strains.
The obtained models are presented on Figure  7. The correlation rate between the predicted (calculated from fluorescence data provided by the selected strains) and reference values (standard method) tended to increase with the number of strains used. Indeed, the average rate obtained from a single strain was approximately 0. 11, whereas this rate reached approximately 0. 6 when the 8 strains were used. Increasing the number of strains used to assess the BOD5 was associated with a significant increase in the correlation rate of the models. However, the predictive capabilities of the best model based on the 8 strains was not sufficient (r²=0. 58). In view of these results, a second step was required to improve the predictive capabilities of the data analysis model. overlined in grey). Ten models were generated based on the architecture.
The robustness of these models was compared from unused data in the validation set based on the correlation rate between the predicted and reference values. Among the 100 models, the most relevant was able to estimate the BOD5 of the validation samples (data not included during the learning process) with strong reliability (r²=0. 851) (Fig. 8 ). This model consists of a hidden layer of three neurons activated by a hyperbolic tangent function. The input and output data are standardized with respect to a centred, reduced, normal distribution. From a global perspective, the BOD5 values calculated from the bacterial set are relatively consistent with the reference values. The model seems to be particularly reliable between 25 and 300 mg/L -1 BOD5 (according to the reference method) for wastewaters (collective or non-collective sanitation).
The validation was only performed on artificial samples (synthetic wastewater spiked with phenol, milk, fat or cellulose) and not on real wastewater samples. Nevertheless, the correlation rate between the calculated and reference values was close to 1 (r²=0. 85 in our study). Opposition to real samples seems to decrease the correlation rate (calculated values versus reference values), because mimicking a real sample with synthetic effluents is difficult. The reference values own an inherent variability close to 20%  (Jouanneau et al. , 2014)  notably due to the environmental variability of used inoculum. Conversely, the mean variability of data provided by the selected strains is 6. 62% (results not showed). During the learning process, the model adjusts the model variables to match the data provided by the bacterial set with the reference data integrating at the same time its inherent variability (20%).
Ethics and trial registration The study will be conducted in accordance with the principles of the Declaration of Helsinki and the Medical Research Involving Human Subjects Act  [29]. This study has been approved by a national ethical committee (Comité de Protection des Personnes Ile de France 1) under number CPPIDF1-2018-ND16. The experiment has also been approved by the Agence Nationale de Sécurité du Médicament (N°EUDRACT 2017-004198-15). Finally, the study protocol was registered on ClinicalTrials. gov (NCT03362775). All patients will give written informed consent before study inclusion and randomization. Patient participation is voluntary; the participant can request to stop participation in the study at any time.
EEG data acquisition EEG signals will be acquired using the OpenViBE platform  [30]  with a Biosemi Active Two 128-channel EEG system, arranged in the Biosemi's ABC system covering the entire scalp at 2048 Hz. Among all recorded sites, some of the electrodes will be localized around the primary motor cortex, the motor cortex, the somatosensory cortex, and the occipital cortex, which will allow us to observe the physiological changes due to the RM, the kinesthetic MI, and the MNS  [25, 26, 31, 32 ]. An external electromyogram (EMG) electrode will be added in order to verify that there was no movement during the MI task. All offline analyses will be performed using the EEGLAB toolbox  [33]  and Matlab2016a (The MathWorks Inc. , Natick, MA, USA). Considering the large number of electrodes used in this study (128) and the purpose of this research (motor patterns over the motor cortex), we chose to use a common average referencing (CAR) performed using EEGLAB  [34, 35].
05). The ERD and ERS will also be visualized as event-related spectral perturbations (ERSPs). ERSPs allow one to visual-ize event-related changes in the average power spectrum relative to a baseline of 1. 5 s taken 2 s before the auditory cue for all motor tasks and different propofol concentrations  [39]. A surrogate permutation test (p <0. 05; 2000 permutations) from the EEGLAB toolbox will be used to validate differences in terms of time-frequency of this ERSPs. In addition to this analysis, we will apply a false discovery rate (FDR) correction test in order to clarify how the FDR will be controlled for multiple comparisons. This test consists of repetitively shuffling values between conditions and recomputing the measure of interest using the shuffled data. It will be performed by drawing data samples without replacement and is considered suitable to show the difference for all motor tasks during different concentrations of propofol  [40].
Data collection Data will be collected with the use of the case report form (CRF), which will be prospectively maintained from the time the patient signs the informed consent until the completion of the study. Patient data will be anonymized; each individual will be given a unique study number (first letter of the first name and first letter of the last name, supplemented by a number assigned to the inclusion, in accordance with Reference Methodology MR001). The data monitoring committee is the Direction de la Recherche et de l'Innovation (sponsor) of the University Hospital of Nancy. An independent data monitoring committee is also nominated to assess serious adverse reactions. The CRF will contain: • Demographic data for each patient; • Adverse events that may occur during the study; • The monitoring data (blood pressure, heart rate, saturation); • EEG data, which will be recorded electronically and stored on a computer remaining in the hospital under the supervision of the investigating anesthetist.
Measuring longitudinally polarized vector boson scattering in the ZZ channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible new physics. We investigated several deep neural network structures and compared their ability to improve the measurement of the longitudinal fraction ZLZL. Using fast simulation with the Delphes framework, a clear improvement is found using a previously investigated 'particle-based' deep neural network on a preprocessed dataset and applying principle component analysis to the outputs. A significance of around 1. 7 standard deviations can be achieved with the integrated luminosity of 3000 f b -1 that will be recorded at the High-Luminosity LHC. The technique developed in this article is also useful to other LHC analyses involving helicity fraction measurement.
The particle-based DNN model has the best discrimination power among all of the discriminants and will be the main focus of this paper. Next, we re-organize the particle-based DNN output structure such that it has five output nodes instead of two output nodes, with the five output nodes corresponding to LL, TL, TT, qqZZ, and ggZZ scores, respectively. We also studied the impact of several data pre-processing methods on the DNN performance, including standardization (STD), Yeo-Johnson power transformation  [20]  together with standardization (YJ&STD), and no preprocessing. The STD uses the LL train dataset as a base dataset to construct a transformation which transforms each variable distribution such that its mean and standard deviation are 0 and 1. The standardization of the base dataset is applied to all of the remaining training and test datasets for the signal and all of the backgrounds.
Yeo-Johnson power transformation makes the distribution of the LL train dataset a normal distribution. The transformation is an extension of Box-Cox power transformation  [27]  onto negative values, and thus is well suited to our input variables. Fig. 2  shows a comparison of the distribution of P j1 T when different data preprocessing methods are applied, where the signal is LL and the background is TL, TT, qqZZ and ggZZ merged together. The LL distribution for the DNN output in the YJ&STD case is shown in Fig. 3. Fig. 4  shows the impact of the different preprocessing methods on the ROC curves for the particle-based DNN. This result shows that the best discriminating power comes from YJ&STD. We thus adopt this preprocessing method. The signal significance is calculated based on the Asimov dataset  [28].
It is very important to investigate the chaotic systems for such a phenomenon  [34] [35] [36]. By transforming the parallel resistor and capacitor (RC) feedback network to a series RC feedback network a simple third-order Wien-bridge oscillator has been developed in  [37]. The investigation confirms the existence of bistability phenomenon in this system. Many researches have been carried out in the last decade on fractional order systems and their applications  [38] [39] [40]. Numerical methods to simulate fractional-order nonlinear system have been proposed in  [41] , and Matlab solutions for fractional-order chaotic systems have been discussed in  [42]. Field Programmable Gate Array (FPGA) implementation of chaotic systems has been a hot topic recently  [43] [44] [45]. Motivated by the above discussions, we are proposing a modified third order Wien bridge oscillator with fractional order memristor.
1. In this table x i = x, x j = z, A i 0 = x(0), A j 0 = z(0). We implement the discrete FOW system (12) in FPGA using the Adomian polynomials and the Kintex 7 (Device=7k160t and Package=fbg484 S) processors. All the necessary static components such as h q ,     2 2 2 1 , 1 q q h q     ,       3 3 1 , 2 1 1 q q h q q       ,       4 4 1 , 3 1 1 q q h q q       ,       5 5 1 , 4 1 1 q q h q q       are calculated before the iteration to increase the processing speed. The step size h = 0. 001 and the commensurate fractional order for implementing the FOW in FPGA is taken as q = 0. 95. The entire discretized FWO system is implemented in System Generator tool in Simulink-Matlab. Figure  8  shows the FWO system (  16 ) ( ) 1 Nonlinear Component Adomian polynomials 2 ( ) i x                       1 02 2 0 1 3 0 2 12 2 4 0 3 2 1 5 0 4 3 1 22 6 0 5 3 2 4 1 ( ) 2 2 1 2 ( ) 1 3 1 2 2 2 1 1 4 1 2 2 ( ) 3 1 1 5 1 2 2 2 4 1 1 i i i i i i i i i i i i i i i i i i i i i i i i i i i A A A A A q A A A A q q A A A A A q q q A A A A A A q q q A A A A A A A q q                                           2 i j x x                   3 1 2 2 2 1 1 4 1 2 2 ( ) 3 1 1 5 1 2 2 2 4 k i j k i i j k i i j i j k i i j i i j k i i j i i j i j k i i j i i j i i j A A A A A AA q A A A A A A q q A A A A A AA q q q A A A A A AA A A q q q A A A A A A A A AA                                            1 1 q q    Tab.
9 This plan focussed on the following four areas: informing patients of their rights and ensuring person-centered care and decision-making; developing palliative care in the community; addressing regional disparities of access to palliative care services; and improving health-care professionals' palliative care skills through education and clinical placements, and supporting research in palliative care. Nevertheless, few individual data were available and analyzed together. ] [12] [13] [14] [15] [16] [17]  The incidence of CRC in France was estimated to be 23 200 cases in 2018 with 9200 deaths (46% of women each). 18 The net 1-year, 3-year, and 5-year survivals for individuals diagnosed with CRC between 2005 and 2010 were 83%, 70%, and 64%, respectively. 19 The annual cost for national health insurance of patients managed for CRC in France in 2014 was €1477 million, that is 11% of all cancers and 1.