dataesr/scientific-paragraphs-categorization huggingface dataset:
A de nitive database will be declared that will be registered with signature and date. Two protected copies of the same will be kept, and paper lists of the variables contained in the database will be generated for archiving. The nal database will be used for statistical analysis. Registry and le maintenance. All the essential documentation of the clinical trial will be led in a master le of the study, whose safe and complete conservation will be ensured for the time required according to the legislation in force and at the disposal of the authority that requests it.
In case that an audit of the study is conducted, the auditors who perform it, as well as the health authorities that may require it for regulatory purposes related to the study, may also have access to patient data. All participants in this research project expressly commit themselves not to disclose the identity of the treated patients and to respect the rules of con dentiality regarding the data and information to which they have access when participating in the trial. The personal data collected and stored for the purpose of this study will be treated in accordance with the provisions of the General Data Protection Regulation (GPDR: Regulation EU 2016/679). Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33} In the case a subject agrees to participate in the Proteomics Substudy, after carrying out the planned experiments during the trial, the remaining biological samples will be donated to the biobanks belonging to the corresponding ISCIII Biobank Network of each participating center and the Biobank of the Center Hospitalier Universitaire Grenoble Alpes (France).
Interim analyses {21b} Not applicable. Methods for additional analyses (e. g. subgroup analyses) {20b} Not applicable. Methods in to handle protocol non-adherence and any statistical methods to handle missing data {20c} Analyzes will be carried out by intention to treat (ie, each patient will be considered to belong to the group to which they were randomized, regardless of the intervention they receive). For each patient that leaves the study before its completion, the cause will be collected, which will be classi ed as: Lack of response to biotherapy; occurrence of adverse events; loss of follow-up; withdrawal of consent; the investigator's decision; use or need for medication not allowed; erroneous inclusion in the study; unforeseen circumstances; or cancellation of the study. Following the advice of Little et al (28), for the management of lost data multiple imputation techniques will be carried out, in addition to sensitivity analysis with the object to check that the assumptions made in the imputation and check whether the conclusions of the study are modi ed, or not, according to the analysis strategy adopted.
Discussion This single-blind controlled multicentre clinical trial study is the rst validation study of a bDMARD response prediction software, bringing personalized medicine into the management of in ammatory rheumatism. This interest is also shared by pharmaceutical companies who wish to improve the strati cation of patients and thus offer more targeted treatments. We believe that ndings from this study will bring several bene ts for the patient and for the health system as reducing patient exposure to ineffective and potentially poorly tolerated bDMARDs, control of RA activity. The SinnoTest® will maximize the chances of successful treatment with biotherapy, the patient will bene t from the most appropriate treatment at the right time, which will improve its quality of life. For public health, the short-term bene t will focus on optimizing the management of RA, improve and adjust the SinnoTest® predictive software, thanks to the clinical results obtained.
Data from clinical validation studies will be used to validate and improve the existing algorithm for performance, ergonomics and functionality. Anticipate access to the market by collecting medico-economic data and pursuing the search for partners to allow rapid deployment of the test in clinical practice. The SinnoTest® will allow physician optimize the selection of the biotherapy in real time. The combination of SinnoTest® algorithms will optimize the selection of biotherapy for RA patients and given the minimal risk for patients and the expected collective bene t, the bene t-risk ratio is very favourable.
2019 and is predicted to end in January 2021. The current protocol is version 1. 0, created in March 2019 and approved the Clinical Research Ethics Committees (CREC) of the participating centers, as well as the approval of the Spanish Agency of Medicines and Health Products in December 2019 before starting the study. Registered at Clinicaltrials. gov, identi er: NCT04147026. Registered on 31 October, 2019.
Curriculum vitae of all the personnel participating in the study; Random assignment list and treatment allocation codes; Individual Data Collection Notebooks; Documentation related to the study monitoring procedures; Documentation of the study database and de nitive database; Documentation of data management and clari cation requests; Statistical analysis; Adverse Event Noti cations; Final report; Certi cates of audits; Standardized Work Procedures applied in the study; Study nancing and payments; Correspondence.
Acknowledgements We would like to thank all clinicians and Site Investigators for their invaluable support for this project. This work has been supported by  Spanish Clinical Research Network (SCReN) , funded by  Institute of Health Carlos III-General Subdirectorate for Evaluation and Promotion of Research , through project  PT17/0017/0018  integrated in the State Plan I+D+I 2013-2016 and co nanced by  European Development Regional Plan ("Fondo Europeo de Desarrollo Regional"-FEDER).
Curriculum vitae of all the personnel participating in the study; Random assignment list and treatment allocation codes; Individual Data Collection Notebooks; Documentation related to the study monitoring procedures; Documentation of the study database and de nitive database; Documentation of data management and clari cation requests; Statistical analysis; Adverse Event Noti cations; Final report; Certi cates of audits; Standardized Work Procedures applied in the study; Study nancing and payments; Correspondence. This documentation will include: Work protocol ( nal version) and amendments; Models of all the employed versions of information sheet and informed consent form; CREC permits; Authorizations of the Health Authorities;.
Using only a limited number of computationally expensive functions, we show a way how to construct accurate and computationally efficient approximations of the Colebrook equation for flow friction. The presented approximations are based on the asymptotic series expansion of the Wright ω-function and symbolic regression. The results are verified with 8 million of Quasi-Monte Carlo points covering the domain of interest for engineers. In comparison with the built-in "wrightOmega" feature of Matlab R2016a, the herein introduced related approximations of the Wright ω-function significantly accelerate the computation. With only two logarithms and several basic arithmetic operations used, the presented approximations are not only computationally efficient but also extremely accurate. The maximal relative error of the most promising approximation which is given in the form suitable for engineers' use is limited to 0.
[6, 45]. These approximations are expressed in terms of   () functions from Eqs. (3-7). The coefficients of () series expansion about infinity are unsigned Stirling numbers of the first kind  [46]  as pointed out by  Rollmann and Spindler [38]. The symbol  = ℎ() - represents the "exact" explicit accurate solution of the Colebrook equation given by Eq. (  2 ), in which  is calculated by the Matlab's built-in command wrightOmega (e. g. similar function does not yet exist in MS Excel [47-49] and user-defined Visual Basic for Applications -VBA script should be provided instead  [50] [51] [52] [53] [54] [55] ). However, this wrightOmega solution is extremely slow; it requires more than 11 394 seconds, i. e. 189. 9 minutes(!) to evaluate our benchmark, even the here presented approximations need, depends on the required accuracy, only seconds or even fragments of seconds.
Further, to increase the accuracy of expansions given with Eqs. (8-12), approximations can be written in a form  +  ≈ (  ) -, where  represents a real constant, which minimizes the relative error of the flow friction factor. The constants were obtained in Matlab by the global optimization solver Globalsearch. This optimal constant  is 0. 00056 for Eq. (  8 ) gives Eq. (  13 ), -0. 0014 for Eq. (  9 ) gives Eq. (  14 ), and -0. 000093 for Eq. (  10 ) gives Eq. (  15 ). This strategy also makes the distribution of the error more uniform, and the methodology for the relative error and time measurement is in correlation with Eqs. (8-12). ≈  1 () + 0. 00056 (13)  ≈  1 () +  2 () -0. 0014 (14)  ≈  1 () +  2 () +  3 () -0. 000093 (15) Eq. (  13 ) is with the maximal relative error of no more than {δ % } max =0.
Symbolic regression-based expansions of 𝑦 = 𝜔(𝑥) -𝑥 for Colebrook equation An additional strategy for increasing the accuracy of  = () - for the Colebrook equation is given in Eqs. (17) (18) (19) (20). These approximations were found by symbolic regression as already explained in  [21]  and software Eureqa is used to perform analyses  [40]  (very recently, also novel artificial intelligence -AI software AI Feynman: A physics-inspired method for symbolic regression is available  [62] ). These approximations are optimized for the domain 7. 51<<619, which is of the interest for the Colebrook equation. With the strategy used for Eq. (  16 ), the accuracy of Eq. (  19 ) is significantly improved using a newly constructed error function  1 as shown in Eq. (  20 ). 𝑦 𝑠𝑟 ≈ 𝑌 -𝜉 1 = 𝑌 - 𝑥•𝑌 2 +3. 0636•𝑥•𝑌+18.
129%/0. 000391% ~ 330. Of course, the computational time of novel Eq. (  25 ) is 1. 1 seconds, as it is slower than Eq. (  22 ) by the factor 1. 1/0. 42~ 2. 616, as Eq. (  25 ) is more complex. However, Eq. (  25 ) can be implemented easily in software codes, as  is a simple rational function, in which both the numerator and the denominator are polynomials. Here, only the error function  is developed using symbolic regression, whereas this technique is more thoroughly evaluated also in Section 3. 2. 2. The error function  is defined in Eq. (  16 ). 26 ) and (  27 ) are already given in [21], whereas Eqs. (  28 ) and (  29 ) are novel (they are simple, but very accurate). Although Eureqa allows the only minimization of the absolute error  [40] , we can use it also for minimizing the relative error as  Gholamy and Kreinovich [63]  suggested (they showed how to use existing absolute-error-minimizing software to minimize relative errors).
Finally, Eq. (  30 ) is based on accurate approximation of () - found by symbolic regression and also corrected with the carefully selected function for minimization of the maximal relative error (this corrective function is also found using symbolic regression). We can see from Table  1  that it is really worth to construct approximations, as the exact solution of the Colebrook equation; Eq. 2, given by the builtin wrightOmega function of Matlab is extremely slow (11 394 seconds, i. e. 189. 9 minutes(!),).
Conclusions and future work In this review, we showed very accurate and fast explicit approximations for the implicitly given Colebrook equation for flow friction. They are based on the Lambert W-function and its cognate the Wright ω-function. These explicit approximations are constructed on series expansion and symbolic regression analysis of the Wright ω-function which are much faster compared with the related Matlab built-in function. Moreover, numerical experiments on 8 million of quasi Monte-Carlo pairs show that the approximations based on symbolic regression provide for the fixed complexity more accurate flow friction approximations than the general approximations based on the series expansion. The reason is that the here presented symbolic regression approximations are optimized for the Colebrook equation, whereas the series expansion is more general.
Contributors: Both authors contributed equally to this study. Pavel Praks worked mostly on the mathematical background of the proposed methods and procedures, while Dejan Brkić put them in a form suitable for engineering use. Pavel Praks was responsible for experiments with symbolic regression tools and global optimization. Dejan Brkić made the draft of this paper. Funding: This work has been partially supported by the Technology Agency of the Czech Republic under the project "National Centre for Energy" TN01000007,"Energy System for Grids" TK02030039 and by the Ministry of Education, Youth and Sports of the Czech Republic through the National Programme of Sustainability (NPS II) project "IT4Innovations excellence in science-LQ1602".
Orientation Imaging Microscopy (OIM), usually made from Electron Backscatter Diffraction (EBSD), is now widely used as a characterization technique. Indeed, it is in great interest for investigating the grain morphology and local crystal orientations in crystalline materials. Raw EBSD data can be considered as matrices of measurements of crystallographic data: each dot contains information about the phase and its orientation at the corresponding position. In order to perform Finite Element Analysis (FEA) on a polycrystal, one needs to first generate a mesh based on either EBSD or reconstructed grains. In this mesh, the Grain Boundaries (GBs) must be accurately described since they play an important role in the overall behaviour of aggregates. Indeed, it is known that GBs increase the energy of the materials.
Dancette, Browet, Martin, Willemet, & Delannay (2016)  proposed the following method to generate a conforming mesh with smooth GBs: • computation of the GBs based on a proper criterion; • grain reconstruction using a graph theory-based method; • spline interpolation of the GBs; • meshing. The criterion used by the previous authors for defining the GBs, called weight in the context of graph theory, was specially designed for cubic phases. The geometry was meshed using the Gmsh software  (Geuzaine & Remacle, 2009). As a conclusion, it appears that no existing tool for generating meshes from EBSD data is able to provide a robust grain description (e. g. suitable for any kind of phase and geometry) together with customizable features (e. g. variable element sizes). The proposed software, named MTEX2Gmsh works regardless the number of phases and the symmetries of those phases.
In addition, it provides a smooth and accurate definition of the GBs. It is based on the MTEX toolbox for Matlab  (Bachmann, Hielscher, & Schaeben, 2011)  and the Gmsh software. Figure  1  schematically illustrates the proposed algorithm. MTEX2Gmsh allows to mesh the volume with a couple of options, such as: • increasing element size with increasing distance from the grains boundaries; • element type (tetrahedron, wedge or brick elements); • nesting the Region of Interest (ROI) into a larger medium. This sofware comes with an Abaqus plugin for importing the mesh and allocating the phase and Euler Angles of each grain. (Bachmann et al. , 2011) , the algorithm fetches all triple junctions (TJ) in the whole map; 2) each grain boundary is divided into TJ-to-TJ segments; 3) all those segments are smoothed using Bspline approximation; 3) this decriptions of the grains can be converted into Gmsh-readable files  (Geuzaine & Remacle, 2009) , allowing to mesh the whole region efficiently.
They may serve as scaffolds in the assembly of protein complexes. Du et al. demonstrated that circ-Foxo3 can combine with cyclin dependent kinase 2 (CDK2) and p21 (CDKN 1a) to form a ternary RNA-protein complex that represses the function of CDK2  [31]. Recent studies have shown that several of circRNAs can be translated into small peptides in cells  [32]  and have key roles despite their sometimes-low level of expression  [33, 34]. In this respect, CircCode (freely available at https://github. com/PSSUN/CircCode) was developed recently as a tool to predict circRNAs with translation potential  [35]. Although thousands of circRNAs have been described in humans, only a minor fraction of these shows potentially important biological roles  [12, 13, 17]  and a unified explanation for the potential function of the vast majority of circRNAs is still lacking.
CircRNA Online Resources To facilitate the study of different aspects of circRNAs, several laboratories have developed various algorithms  [14, 27, 36]  to detect circRNA sequences from RNA sequencing data based on different approaches including back-splice junctions. For accurate circRNAs quantification and differential gene expression, Zhang et al. have recently developed a novel algorithm named CIRIquant  [37]. Additionally, Chen and colleagues have lately developed an interesting algorithm; circMeta, that can be used for genomic feature annotation and differential expression analysis of circRNAs  [38]. These tools have helped to construct numerous circRNA databases (Table  1 ) with searching and browsing functions. Such searchable databases are made freely by several web servers and provide valuable information of circRNAs (Table  1 )  [39].
Sample To conduct the empirical study, a large-scale sample was selected from the Austrian company database AURELIA. Hereby, only higherlevel executives and managers were considered in a key informant approach. These managers were invited via personalized e-mails; and, if they chose to participate, they were redirected to an online German language questionnaire. After checking responses for consistency and completeness for the QCA technique, a final sample of 451 key firm executives remained. Next, a possible non-response bias was checked for all measures applied in the QCA on an item-based level using a variant of the procedure by  Armstrong and Overton (1977). Averages from pairwise t-tests for equally large quantiles of early-to-respond informants for each item showed no indication of a systematic non-response pattern (no significant differences).
RWD and RWE RWD are data collected outside of the constraints of an RCT. Sources of RWD include claims databases, medical records, patient registries, and health surveys, as well as supplemental data from RCTs. In addition, an increasing number of outcomes are being assessed using RWD, including quality-of-life measures, clinical outcomes, psychological factors, financial burden, adherence, and persistence. There are distinct differences between RCTs and RWE studies. Whereas RCTs provide internal validity, RWE provides generalizability to larger, more diverse populations. Although there are many differences between the two study types, a key difference is cost. The cost of a cardiovascular outcome trial (CVOT) now averages $150-$160 million. RWE is now becoming important for prescribers, payers, patients, and regulatory agencies.
Improving applicability to RWE from RCTs Confirmation of findings from RCTs in the real-world setting is important. For example, a 2010 RCT by Zoungas et al. showed an association between severe hypoglycemia and the risks of macrovascular or microvascular events and death among 11,140 patients with T2D. 16 This assessment was later repeated in a real-world, retrospective cohort study of 13,682 individuals with T1D and T2D from a large database. Findings from this analysis confirmed the findings from the earlier RCT but also extended the findings to show that there was no difference in risk between diabetes groups. It was also found that the risk of CV events in individuals in both diabetes groups with established CV disease was significantly higher than in those without CV disease; a finding not reported in the earlier RCT.
Assessing the risk for CKD A recent study explored the use of RWD to identify individuals with diabetes who are at risk for developing CKD in the near future. 42 The analysis compared an algorithm derived from patient records from 417,912 individuals with newly diagnosed T1D or T2D from the IBM Explorys database with algorithms derived from four large clinical trials. The Roche/IBM algorithm targeted seven features-age, BMI, glomerular filtration rate, HbA1c, glucose, and concentrations of creatinine and albumin-as important predictors of risk based on data-driven and medical selection. The algorithm was applied to data originating from 104,504 further, independent individuals of the IBM Explorys database as well as from 82,912 individuals with T2D included in the Indiana Network for Patient Care (INPC) database.
Area under the receiver operating characteristic curve (AUC), a common measure of the quality of clinical markers, was used to evaluate the ability of the algorithms to correctly calculate the risk for developing CKD over the next 3 years. Using the seven prioritized measures, the AUC was 76%. In the same population, HbA1c had an AUC of only 57%. Investigators also assessed the prediction capabilities of the algorithm using subcohorts that matched the inclusion and exclusion criteria of the respective clinical studies. In this analysis, the AUC of the Roche/IBM algorithm remained superior to algorithms derived from the clinical trials. Because some data out of the seven predictors might be unavailable in real-world clinical practice, investigators also explored the prediction capabilities of the algorithms when there are gaps in the data compared with the reference studies.
Session E/Lecture 3 Automated Insulin Delivery and Decision Support Systems: From Science to Clinical Practice Jessica Castle, Oregon Health & Science University, Portland, Oregon Overview Data from the T1D Exchange registry show that use of CGM has increased significantly over the past 8 years, from 6% to 38%. The REPLACE-BG trial demonstrated that Dexcom CGM values can be used for making treatment decisions without confirmatory capillary blood glucose. The DIAMOND study, which included individuals with uncontrolled T1D and T2D treated with MDI, showed that the use of CGM improved HbA1c levels. However, despite this improvement, many participants had HbA1c levels above the typical goal of <7%. Can the vast amount of data provided by CGM be better leveraged to improve glucose control using decision support systems or automated insulin delivery (AID) systems?.
Session G/Lecture 2 Noninsulin Drugs for People with Type 1 Diabetes: Is It Time for a Change? Francisco Javier Ampudia-Blasco, University Hospital Valencia, Valencia, Spain Background Data from the T1D Exchange registry show that a large proportion of individuals with T1D are not achieving their glycemic goals. This is particularly true in young adults. In addition, the prevalence of overweight/obesity in T1D is increasing. Since the Diabetes Control and Complications Trial (DCCT), intensive insulin therapy has been the treatment of choice in people with T1D. However, insulin therapy is frequently associated with hypoglycemia and weight gain. In addition, many patients cannot reach individual glycemic targets and/or have excessive glycemic variability despite strict adherence to their treatment.
Telehealth and decision support Telecommunication technologies are also being utilized to provide long-distance health care. The National University Hospital, Singapore, recently launched a telehealth initiative, utilizing four simple home devices: weighing scale, glucometer, blood pressure meter, and patient-international normalized ratio meter, which monitors patient response to warfarin. These devices, via a secure cloud, are connected through a 3G or 4G telecommunication network to the hospital. Data from patient measurements are reviewed by health care providers who either call patients or send emergency assistance, if needed, when a problem is detected. With this program, patients can continue with daily activities in addition to their usual testing, with assurance that a health care provider is monitoring the health and safety.