dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources and collection methods The structured interview included questions about sociodemographic and clinical characteristics, and consumption of out-of-pocket resources using a recall period of 12 months prior to inclusion in the study, except for medicines that considered a recall period of three months. Information about medical resources utilization, comorbidities and complications were abstracted from medical charts. Outpatient and hospital resources related to HCV and its complications were collected for a recall period up to 24 months prior to study entry. If the FibroScan test could not be performed at the same day as the interview, a second study visit with a maximum interval of eight weeks was scheduled. To assess quality-of-life and function and well-being of patients, the following instruments were used, respectively: EQ-5D-3L and HCV-PRO.
These domains are converted into a 0-100% scale where high percentages represent more impact on productivity  (18). Costs were estimated by multiplying the amount of consumed resources (as self-reported by patients or abstracted from medical charts) for their unit costs. The unit costs sources of tests, outpatient visits, non-drug treatments, and emergency room visits were SIGTAP (System List of Procedures Management, Medicines, Prosthetics and Orthotics, and Specialty Materials of SUS; competency -December, 2015)  (20). The unit cost of hospitalizations/surgical treatments were obtained in SIH (Hospital Information System; competency -2014) of SUS -Unified Health System  (21). The unit costs of drugs were firstly searched in BPS (Health Prices Database)  (22)  by the lowest and latest purchase price accessible; if the unit cost was not available in BPS, the maximum selling price allowed for Government purchases (PMVG ICMS 0%) in the CMED (Medication Market Regulation Chamber -December 18, 2015)  (23)  list was used.
The reasons for emergency room visits, hospitalizations, and surgical treatments were classified according to International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10)  (24). Tests were classified according to Clinical Protocol and Therapeutic Guidelines for Hepatitis C and Coinfections (PCDT)  (25)  , and drugs according to Anatomical Therapeutic Chemical (ATC) code  (26). The unit costs were obtained between December, 2015 and January, 2016 and all data prices were collected in Brazilian Real (BRL) and then converted into American dollar values (USD) using the exchange rate of the date of consultation to the price list (16/Jan/2016), where 1. 00 BRL corresponded to 0. 2479 USD.
Sample size calculation This analysis is a part of a larger study designed to estimate the frequency of any specific HCV therapy in the sample (treatment pattern). The original sample size calculation had the purpose to detect a frequency of at least 10% of the triple therapy (pegylated interferon, ribavirin, telaprevir or boceprevir) recommended for HCV patients with METAVIR F3-F4 (that represents 33% of the total population of HCV patients, according to Poynard et al. (27)  ). Thus, considering an assumed distribution of triple therapy among all HCV patients of 3% (10% of 33%), a margin of error of 3. 0%, and α=0. 05, a sample of 318 HCV patients was required. However, 313 patients were included in the study, which provided a 95% confidence interval (CI) with a margin of error of 3. 3%. The data presented here are related to secondary outcomes of the aforementioned study.
9% of total HCV costs; accordingly, a Brazilian study conducted prior to the incorporation of protease inhibitors have shown that drug treatment represented 88. 2% of the total costs  (47). We acknowledge some limitations of our study, mainly the possibility of missing data in the patients' medical records. Because it applies to all participants, it is unlikely to result in systematic bias among the different treatments or groups of patients. Secondly, some data were cross-sectionally obtained, which does not allow asserting a temporality between exposure and outcome. In addition, the study patients were from tertiary care centers; consequently, there are limitations regarding the understanding of the disease features in other levels of care. Thirdly, an important caveat is that new DAA prices decreased recently in the country, mainly within the Brazilian public healthcare system, what could lead to lower final costs in future analysis.
ACKNOWLEDGEMENT AbbVie participated in the study design, planning and conduct, interpretation of data, review and approval of the content. All the authors had access to all relevant data and participated in data collection, interpretation, writing, review, and approval of this manuscript. Medical writing support and editorial support was provided by  Leticia Dias ,  Pamela Santana , and  Maíra Takemoto  of  ANOVA -Consultoria em Saúde Ltda  and was funded by  AbbVie.
An assessment of measurement uncertainty is a task, which has to be the final step of every chemical assay. Apart from a commonly applied typical assessment method, Monte Carlo (MC) simulations may be used. The simulations are frequently performed by a computer program, which has to be written, and therefore some programming skills are required. It is also possible to use a commonly known spreadsheet and perform such simulations without writing any code. Commercial programs dedicated for the purpose are also available. In order to show the advantages and disadvantages of the ways of uncertainty evaluation, i. e. , the typical method, the MC method implemented in a program and in a spreadsheet, and commercial programs, a case of pH measurement after two-point calibration is considered in this article.
Stage 3. Derivation of sensitivity coefficients formulae It is necessary to derive the sensitivity coefficients according to Equation (  13 ), symbolically, by making the derivation of Equation  (26)  with respect to all sources. The task does not have to be simple, in general. It is possible to apply specialized software, like Mathcad (MathSoft, Cambridge, MA, USA) or MATLAB (Mathworks, Sherborn, MA, USA). Numerical calculations are also allowed  [11] , but the values of the coefficients change with the change of sources and it is necessary to recalculate them for each example. Here, the sensitivity coefficient formula for the potential indications of the pH-meter when the electrode is immersed in the first buffer is: c E 1 = ∂pH X ∂E 1 = (E 2 -E X )(pH 1 -pH 2 ) (E 1 -E 2 ) 2 (27) and in the second buffer: c E 2 = ∂pH X ∂E 2 = (E X -E 1 )(pH 1 -pH 2 ) (E 1 -E 2 ) 2.
Such program may look like that written in C++ and presented in Appendix A. After initialization and model definition, a loop is performed, in which random variables are generated and added to the parameters of the model. Then, the pH value is calculated, compared with the true value and the error is squared and cumulated. When the loop is finished, the uncertainty is calculated as the square root of cumulated squared errors divided by the number of loops performed. Such approach engages a small amount of memory and works quite fast. Such simulations may also be performed writing code with very compact matrix notations. The code presented in Appendix B is written in the MathWorks R MATLAB language, which is very often used by scientists in developing the MC method  [25]. The script works well also in the freeware implementation-GNU Octave.
It consists of initialization of variables with measurement values and uncertainties, the definition of the model function, the definition of vectors with disturbed quantities, calculation of the vector with disturbed measured pH, and finally the calculation and presentation of standard deviation. A similar code presented in Appendix C is written in the R language. The language is very popular among statisticians and is distributed as freeware. The script is very similar that written in Matlab. It is also possible to write code using matrix notation in a general-purpose programming language, such as Python. The example is included in Appendix D. Application of the Numpy library makes the code very similar to the Matlab and R ones. The outputs of the programs may look like that presented in Figure  2.
The simulation times performed using the enumerated languages differs a bit-cf. Table  3. The differences are caused rather by the complexity of the algorithm of the random number generator applied in the scripts than the optimisation of the code. The MC simulations are performed very fast. Uncertainties obtained by simulations are different from simulation to simulation. This is an inherent feature of the method. However, if the number of trials is in the order of 10 6 , the results differ among themselves by about 0. 1%. If the number of trials is higher, the uncertainty is predicted better, but the program takes longer to calculate. The probable error varies inversely with the square root of the number of trials  [29]. The use of the MC method makes it possible to analyze the shape and asymmetry of the output PDF resulting from the propagation through a function, which can be nonlinear.
The typical propagation method uses linearization, and therefore the effect is invisible  [25]. During the evaluation of measurement uncertainty by applying the MC simulations implemented in a program, it is not required to solve derivatives. Instead, programming skills are needed. The obtained uncertainty is not exact, but it is very close. In some complicated and nonlinear cases, the MC result can be considered as more reliable than the result obtained from the typical propagation method  [25]. A computer program that executes calculations, like GNU Octave, has to be installed or its online implementation may be applied.
Generation of uncertain input data To generate random variables with standard normal distribution, the =NORM. S. INV(RAND()) formula should be used in the spreadsheet. The GUM allows for treating all uncertainty contribution, as if the distributions were Gaussian  [35]. Creating a spreadsheet causes an inconvenience occurs-a change of commands with time between spreadsheets. Older Excel versions, Gnumeric and Google Sheets use commands without dots, such as NORMSINV(), which are treated as deprecated in newer versions of Excel. For the above reason, the user has to decide whether the code should be universal or up-to-date. Additionally, most of the available spreadsheets are nationalized. This means that the names of the functions are translated to the default language used in the operating system.
Uncertainty Evaluation Using Dedicated Software There are a lot of dedicated software for evaluation of measurement uncertainties. Such list is available on Wikipedia  [37]. GUM Workbench (Professional Version 2. 4, Metrodata, Braunschweig, Germany) is a commercial software program  [38] -the single user licence is 3213 EUR. A demo version that does not allow for data saving is available for free. The application has a friendly and intuitive user interface (UI). It allows for introducing a measurement model (Figure  6a ), and their parameters with values, units, uncertainties and distributions (Figure  6b ). The sensitivity coefficients are calculated symbolically (Figure  6c ). The budget is built and dominant sources are pointed out (Figure  6d ). The measurement result is reported according to the standard.
We investigate the properties of highly confined Love modes in a phononic crystal based on an array of holes made in SiO2 deposited on ST-cut Quartz substrate. An optimal choice of the geometrical parameters of the holes enables us to obtain wide stop-bands frequency for shear wave's modes. The introduction of defect by removing lines of holes leads to nearly flat modes within the band gap and consequently paves the way to implement advanced design of electroacoustic filters and high-performance cavity resonators based on shear wave modes. The calculations are performed using finite element method based on the commercial software (COMSOL-Multiphysics). For transmission spectra, piezoelectric excitations are applied by considering the interdigital transducers, with results corroborating well the band structure predictions and the position of defects modes within the band gap.
Observations and data reduction High-resolution spectra of the CO lines in the GEMS were obtained with the IRAM 30 meter telescope as part of an extended survey of the brightest atomic and molecular emission lines conducted in the 3-mm, 2-mm, 1. 3-mm and 0. 8-mm bands. Observations were carried out with the eight mixer receiver broadband instrument  (EMIR, Carter et al. 2012) , during several programs between September 2012 and June 2014 (PI: N. Nesvadba, see Table  A. 1). The blind redshift search in the 2mm and 3-mm bands described in C15 was performed during programs 082-12, D09-12 and 094-13, while program 223-13 was intended to extend our characterization of the CO ladder to higher-J. EMIR offers a total bandwidth of up to 16-GHz separated into 4-GHz sidebands, in each of the two orthogonal polarizations.
8+54. 9 in Figure  6. The best-fitting values of n H 2 , T k and N CO /dv are taken from the 3 https://github. com/yangcht/radex_emcee maximum of the joint probability distribution and are listed in Table  3  together with 1σ uncertainties. We also plot the modeled CO SLEDs from the best-fitting estimates of the gas densities and kinetic temperatures together with our CO flux measurements with EMIR in Figs. 6, B. 1, and B. 2.
Acknowledgements RC would like to thank  Raphael Gavazzi  for useful discussions. The authors would like to thank the anonymous referee, whose comments were helpful in improving the paper, and the telescope staff at the IRAM 30-m telescope for their excellent support during observations. We are also very grateful to the former director of IRAM,  P. Cox , for his generous attribution of Director's  Discretionary Time early on during the program , and to  Daniel Dicken  for sharing telescope time. This work is based on observations carried out under projects number  082-12 ,  D09-12 ,  065-13 ,  094-13 ,  223-13 ,  108-14 , and  217-14  with the IRAM 30-m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). This work was supported by the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES.
Pile-up events are identified and rejected using the SPD  [47, 59] , and they amount to about 0. 7% of all events. During the pp run at 2. 76 TeV, the information from the SDD was read out only for a fraction of the recorded events to maximize the data acquisition speed. For the current analysis all events have been reconstructed without the SDD information in order to obtain a homogeneous sample over the full statistics. For the Pb-Pb analysis, the same data sample recorded at √ s NN = 2. 76 TeV was used as for previous publications  [28, 42]. The events were collected with a MB interaction trigger using information from the coincidence of signals between the V0A and V0C detectors. Central and semi-central Pb-Pb collisions were selected online by applying different thresholds on the V0 signal amplitudes resulting in central (0-10%) and semi-central (10-50%) trigger classes  [50].
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL) ,  State Committee of Science and World Federation of Scientists  (WFS), Armenia;  Austrian Academy of Sciences and Nationalstiftung für Forschung ,  Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Universidade Federal do Rio Grande do Sul (UFRGS) ,  Financiadora de Estudos e Projetos (Finep)  and  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Brazil; Ministry of Science & Technology of China (MSTC) ,  National Natural Science Foundation of China (NSFC)  and  Ministry of Education of China (MOEC) , China; Ministry of Science and Education, Croatia; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic ; The  Danish Council for Independent Research -Natural Sciences , the  Carlsberg Foundation  and  Danish National Research Foundation (DNRF) ,  Denmark ;  Helsinki Institute of Physics (HIP) ,  Finland ;  Commissariat à l'Energie Atomique (CEA)  and  Institut National de Physique Nucléaire et de Physique des Particules  ( IN2P3 ) and  Centre National de la Recherche Scientifique (CNRS), France ;  Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie (BMBF)  and  GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany ;  General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece ;  National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India  (DAE),  Department of Science and Technology, Government of India (DST) ,  University Grants Commission, Government of India (UGC)  and  Council of Scientific and Industrial Research (CSIR), India ;  Indonesian Institute of Science, Indonesia ;  Centro Fermi -Museo Storico della Fisica e Centro Studi.
Microscope image acquisition and analysis DHE staining and Comet assay images were acquired using a fluorescent microscope (Scanner Microvision instruments) with a 20X objective. To create a full image of the specimen, images of adjacent fields were stitched together using Cartograph software (Microvision). Images of MitoSOXRed and γH2AX staining were acquired using a TCS SP8 confocal microscope (Leica Microsystems) with a 63X oil immersion objective. Z-stacks were acquired using a frame size of 1024×1024, and 0. 5 µm z-steps, with sequential multitrack scanning using the 405, 488 and 543 nm laser wavelengths.
5, Trevigen). The slides were allowed to dry completely at room temperature before observation under the epifluorescence microscope (Microvision instruments, using the 488 nm laser wavelengths, 20X zoom). Images of 100 randomly selected non-overlapping cells were captured and analyzed using Tritek CometScore software. A variety of measurements including the percentage of DNA in the tail (expressed as % of total DNA), the tail length (measured from the leading edge of the comet head) and tail moment (the measure of tail length multiplied by tail intensity) were calculated to evaluate the extent of DNA damage. Mean values from at least 100 nuclei were used in calculations. All steps were conducted in the dark to minimize extraneous sources of DNA damage.
DHE staining Superoxide levels were measured using the Dihydroethidium probe (DHE) (Muse oxidative stress kit, Millipore). In the presence of the superoxide anion, DHE is rapidly oxidized to oxyethidium, which binds DNA and emits light in the 570-580 nm ranges when excited at 488 nm. B-cells after appropriate treatments were rapidly washed in 0. 3X PBS, fixed in 4% paraformaldehyde (Euromedex) in 0. 3X PBS (10 min, RT) and incubated in a DHE-containing ROS staining solution diluted to 1:8000 in a proprietary assay buffer for 15 min in the dark at room temperature while shaking, washed 3 times with 1X PBS, mounted using a DAPI mounting medium (Vector laboratories) and observed under a fluorescent microscope (excitation/emission: 520/610 nm, red fluorescence). Images from adjacent fields were stitched together by using Cartograph software (Microvision) to create one large image of the specimen.
The fluorescence of at least 1000 cells per sample was quantified using ImageJ software, normalized to cells number following DAPI staining. After background subtraction using the "rolling ball" algorithm  [62] , the integrated density value of each cell was measured automatically by the software. In the graphical representations, the value corresponding to untreated B-cells (used as negative control) was set as 1 and the value of the tested conditions was represented as the fold change compared to the negative control.
1% Tween (1 h, RT). After three washes in TBS and 0. 1% Tween (5 min, RT), the membrane was incubated with antibodies against human Phospho-Rel A/NF-κB p65 (R&D Systems, 1:1000), mouse antibodies against α-tubulin (Sigma, 1:1000) or human Topoisomerase II (1:10000, Merck Millipore) for 2 h at RT with gentle rocking. After three washes in 0. 1% TBS with 0. 1% Tween, the membrane was incubated (1 h, RT) with an anti-rabbit secondary antibody conjugated to horseradish peroxidase (Jackson ImmunoResearch). Blots were revealed using Immobilon Western Chemiluminescent HRP substrate (Millipore) and an ImageQuant Las 4000 mini (GE Healthcare). The obtained bands were then analyzed by density measurement using the ImageJ software. For quantification, protein levels in controls were set to 1.
The fluorescence intensity was calculated from a minimum of 1000 cells examined for each condition. The experiments were carried out in duplicate. (D) Detection of ROS in Tat-treated cells with the Amplex red assay. The concentration of H 2 O 2 released in nmol/hour*10 7 cells by B-cells treated with 250 ng/ml Tat and untreated B cells after 6 h is represented on Y axis. The experiments were carried out in duplicate. (E) Tat induces DNA damage in B-cells. The Y axis shows the proportion of Bcells treated with 250 ng/ml Tat for 6 h and exhibiting γH2AX staining compared to the negative control, for which the obtained basal value was set as 1. A minimum of 300 cells were examined per time point. The data are means ± SEM from at least three fields with B-cells from a single donor at every time point except at 6 h, when experiments were repeated on Bcells from three different healthy donors.
The Y axis shows the average number of chromosomal aberrations per cell compared to the untreated LCLs, for which the obtained basal value was set as 1. The experiments were carried out in duplicate. All data are expressed as the mean ± SEM. The statistical analyses were carried out by the one-way ANOVA test. The statistical significance was calculated vs. untreated controls; ***: p value < 0. 001; **: 0. 001 < p < 0. 01; * 0. 01 < p < 0. 5. Fig. 2. HIV-1 induces oxidative DNA damage via ROS production in B-cells from HIV patients. (A) ROS production in B-cells isolated from the blood of healthy donors and HIV-infected individuals detected by DHE staining. The Y axis shows the ROS level in Bcells from HIV-patients compared to that in healthy donors, for which the obtained basal value was set as 1.
A minimum of 1000 cells were examined for each sample. (B) DNA damage in B-cells isolated from the blood of healthy donors and HIV patients immunostained for γH2AX. The Y axis shows the proportion of cells from HIV-infected individuals exhibiting γH2AX staining compared to B-cells from healthy donors (negative control), for which the obtained basal value was set as 1. A minimum of 300 cells were examined for each sample. All data are expressed as the mean ± SEM. The statistical analyses were carried out by the one-way ANOVA test. The statistical significance was calculated vs. untreated controls; ***: p value < 0. 001). Eight different HIV patients were examined in this study (see Table  1 ).