dataesr/scientific-paragraphs-categorization huggingface dataset:
b. Oscillation The calculation of the νe survival probability P ee (E ν ; L core ) for a given νe energy E ν and oscillation baseline L core is performed using the Prob3++ code  [47]. We include Earth matter effects, whose impact on the survival probability is at the 1 % level for JUNO baselines  [48]. The oscillated νe flux impinging on the JUNO detector from one reactor core at the distance L core follows from the unoscillated flux Φ 0 as dΦ osc dE ν (E ν ; L core ) = P ee (E ν ; L core ) • dΦ 0 dE ν (E ν ; L core ). (2) c. Exposure The rate of detected νe events per unit energy is obtained as the sum over the contributions from all ten reactor cores, d ṄIBD dE ν (E ν ) = cores dΦ osc dE ν (E ν ; L core ) • A eff (E ν ) , (3) where we have dropped the explicit dependence on the set of baselines {L core } on the left.
Starting from two-dimensionalazimuth-and season-averaged-tables, we evaluate the flux of each atmospheric neutrino species on a (200×390) grid in (cos(θ Z ), E ν ) via an integral-preserving interpolation  [40]. Systematic flux uncertainties which we consider include the neutrino energy scale, to which we assign an uncertainty of 10 %, the ratio between electron and muon neutrino fluxes, assuming an uncertainty of 3 %, the ratio between neutrino and anti-neutrino fluxes, with an uncertainty of 10 %, as well as a shift of the spectral index with respect to the nominal flux model, assuming an uncertainty of 5 %. b. Oscillation The NMO signature in IceCube depends on the Earth matter density distribution through its impact on the neutrino oscillation probabilities  [14] [15] [16] [17]. In order to calculate the latter we again use the Prob3++ code together with the Preliminary Reference Earth Model (PREM)  [50] , with a division of the Earth into 12 constant-density layers.
III. EXPECTED NMO SENSITIVITIES IN A COMBINED ANALYSIS A. Statistical approach a. Sensitivity proxy In the following, all NMO sensitivities are obtained by producing toy data under the ordering assumed to be true (TO)-generated from the oscillation parameter values in Table  III  (unless stated otherwise) and with all systematic uncertainties at their nominal values-and fitting the toy data by numerically minimizing a χ 2 function over all free parameters while restricting the minimizer to the wrong ∆m 2  31 half-plane (wrong ordering, WO). Since no statistical fluctuations are applied to the toy data, we refer to it as the "Asimov dataset"  [54]. We employ the test statistic ∆χ 2 ≡ |χ 2 NO -χ 2 IO | , ( 8 ) where χ 2 NO(IO) ≡ min {pi}∈NO(IO) χ 2 corresponds to the minimum when the fit hypothesis (with free parameters {p i }) is NO (IO).
B. Synergy effects Why is it to be expected that the NMO sensitivity of the combined analysis exceeds the simple sum of the two individual experiments' sensitivities? Here, we discuss PINGU in the combined analysis with JUNO with the aim of clarity in illustrating the synergy effects and to be able to compare our results to  [33]. The upper part of Fig. 4  shows the JUNO and PINGU ∆χ 2 profiles as function of the tested value of ∆m 2   31   within the true ordering (NO on the left, IO on the right) for an exposure time of 6 years, minimized over all other parameters at each point. As the scan is performed on the Asimov dataset, the minimal ∆χ 2 value is found at the true value of ∆m 2  31 and vanishes exactly for both JUNO and PINGU; the widths of the minima give the precision to which the two experiments are able to constrain ∆m 2  31 when either the NO or the IO are correctly identified.
V. CONCLUSION In this paper we investigate the potential of a combined neutrino mass ordering analysis of the data from the fu-ture reactor neutrino experiment JUNO and the future atmospheric neutrino experiments IceCube Upgrade and PINGU. Owing to the different positions of the minima in the oscillation parameter space within the wrong-ordering hypothesis, the combined analysis of JUNO and the Ice-Cube Upgrade or PINGU achieves an NMO sensitivity which exceeds the purely statistical combination of their stand-alone sensitivities. This synergy effect is based on the distinct experimental setups-for which the dominant neutrino oscillation channels differ, as well as neutrino energies, baselines, and the relevance of Earth matter effects-and is most prominent in ∆m 2  31. Both JUNO and PINGU will have sensitivity to this mass-squared difference much beyond that of the existing generation of oscillation experiments or their combination.
ACKNOWLEDGMENTS The IceCube collaboration gratefully acknowledges the support from the following agencies and institutions:  USA -U. S. National Science Foundation-Office of Polar Programs, U. S. National Science Foundation-Physics Division ,  Wisconsin Alumni Research Foundation ,  Center for High Throughput Computing (CHTC)  at the  University of Wisconsin-Madison ,  Open Science Grid (OSG) ,  Extreme Science and Engineering Discovery Environment (XSEDE) ,  U. S. Department of Energy-National Energy Research Scientific Computing Center , Particle astrophysics research computing center at the  University of Maryland, Institute for Cyber-Enabled Research at Michigan State University , and Astroparticle physics computational facility at  Marquette University ;  Belgium -Funds for Scientific Research (FRS-FNRS and FWO) ,  FWO Odysseus and Big Science pro-grammes , and  Belgian Federal Science Policy Office (Belspo) ;  Germany -Bundesministerium für Bildung und Forschung (BMBF) ,  Deutsche Forschungsgemeinschaft (DFG) ,  Helmholtz Alliance for Astroparticle Physics (HAP) ,  Initiative and Networking Fund of the Helmholtz Association, Deutsches Elektronen Synchrotron (DESY) , and  High Performance Computing cluster of the RWTH Aachen; Sweden -Swedish Research Council, Swedish Polar Research Secretariat, Swedish National Infrastructure for Computing (SNIC) , and  Knut and Alice Wallenberg Foundation; Australia -Australian Research Council ;  Canada -Natural Sciences and Engineering Research Council of Canada ,  Calcul Québec ,  Compute Ontario ,  Canada Foundation for Innovation ,  WestGrid , and  Com-pute Canada; Denmark -Villum Fonden ,  Danish National Research Foundation (DNRF) ,  Carlsberg Foundation ;  New Zealand -Marsden Fund ;  Japan -Japan Society for Promotion of Science (JSPS) and Institute for Global Prominent Research (IGPR) of Chiba University ;  Korea -National Research Foundation of Korea (NRF) ;  Switzerland -Swiss National Science Foundation (SNSF) ;  United Kingdom -Department of Physics, University of Oxford.
Data Collection The paper-based questionnaire had 46 questions with multiple choice, Likert scale, and open-ended options. The questionnaire was adapted from a previously published study, the objectives of which were to better understand access and use of technology and preferences of using technology for a range of mental health services  (6, 7). The sample from that study was recruited from a different clinical program for FEP, within the same city. We adapted the previous questionnaire as follows: 1) updated sections on access and use of technology and demographic questions, 2) replaced the rest of the questionnaire with sections on obstacles to attending appointments (e. g. , finding time, financial, public transportation, physical limitations, etc. ), satisfaction with services, attitudes towards technology, and perspectives on telepsychiatry.
FUNDING This research was partially funded by a  Junior Research Scholar establishment grant  from the  Fonds de recherche du Québec-Santé (FRQS)  awarded to SL and an  undergraduate scholarship award  through the " PRogramme d'Excellence en Médecine pour l'Initiation En Recherche" (PREMIER)  awarded to FB. SL is supported by the  Canada  Research Chairs Program and was previously supported by a  New Investigator Salary Award  from the  Canadian Institutes of Health Research  and a  Junior Research Scholar Salary Award  from the  Fonds de recherche du Québec-Santé (FRQS).
Sensitivity (Se) and specificity (Sp) were calculated for the most predictive dimensions, and the best cutoff points were estimated through receiver operating characteristic curves. SPSS 22 was used for all analyses except Factor 10. 9. 02  (Ferrando & Lorenzo-Seva, 2017)  for EFA, and R 3. 6. 0 (R Core Team, 2018) for IRT analysis (package "ltm";  Rizopoulos, 2006) , CFA, and ESEM (package "lavaan";  Rosseel, 2012). We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by project PI15/00536, part of the Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016, financed by the ISCIII Subdirección General de Evaluación and the cofinanced by the European Regional Development Fund (ERDF, "A way to build Europe"; PI: F. Gutiérrez).
The geographic association between dengue cases and mosquito intrinsic growth rate and abundance is evaluated for a Dengue endemic area in Brazil. The advantage in the use of intrinsic growth rate is that its estimation requires abundance data only when compared to other indexes (reproductive number, R0, and vectorial capacity  (Reiner, Perkins et al. 2013 )). Dengue is the second most important vector-borne disease worldwide with 2. 5 billion people at risk  (World Health Organization 2014). Dengue and other arboviral diseases (Zika and chikungunya) lack efficient vaccines, are transmitted by the same Aedes vectors, and their control still relies on preventing contact between vector and humans  (World Health Organization 2009). The dengue cycle involves mosquitoes and humans, although limited virus circulation in other vertebrate hosts has also been reported  (Diallo, Ba et al.
Dengue cases During the mosquito sampling campaign, 44 dengue cases were confirmed with onset of symptoms between November 2010 and July 2011. DENV1 and DENV3 were the most common dengue serotypes, while DENV2 was confirmed in only one patient. Month of symptom onset and geographic coordinates were used for this analysis, i. e. IGR and mosquito abundance were extracted at the month of onset of symptoms and within 100 m from the home address (see methods). Home address reported to health authorities were anonymized. Unlinked anonymous testing of human blood samples (where the term unlinked refer to blood samples obtained from tests not exclusively for dengue) was approved by the ethics committee in research (COEP) of Universidade Federal de Minas Gerais (number 415/04). Use of this secondary data was also approved by the Faculty of Health and Medicine Research Ethics Committee at Lancaster University (FHMREC18067).
Development of Statistical Methods The statistical analysis was performed on the monthly sum of catches of female adult Ae. aegypti for each trap. AT, RH, WB and AP were monthly averaged for a month before the day of trap inspection (i. e. for the 14th October inspection, AT, RH, WB and AP were individually averaged from 14th September to 13th of October). Other monthly temporal lags (i. e. 2 and 3 months before trap inspection) were tested but their coefficients were not statistically significant (see supplementary table  ST2 ). Analyses were carried out in R-cran software  (Chambers 2008).
Results The majority of captured Aedes mosquitoes were female (95. 9%) and belonging to Ae. aegypti (88%). The Ae. aegypti mosquito surveillance data was characterised by a large amount of zero catches (83% of the entire dataset). The non-zero range spans from 1 to 16 Ae. aegypti. The data is characterised by a strong seasonality with largest mosquito catches in the wet season (December to May with up to 110 Ae. aegypti total area catches) and lowest mosquito catches in the dry season (June to November, with maximum of 25 Ae. aegypti total area catches). See supplementary table  ST1  for catches in individual traps. Finally 66% of the traps caught at least one Ae. aegypti mosquito during the surveillance campaign (traps indicated in yellow in Figure  1 ). Ae. aegypti population dynamic in terms of Moran curve parameters are presented in Table  1  and visualized in Figure  3.
Data analytic strategy Spearman's rank-order was used to calculate correlations between the variables. Taking into account the non-normal distribution of variables, the Mann-Whitney two-sample test was used to verify whether there were differences between male and female adolescents in the analyzed variables. Additionally, descriptive statistics were presented in the form of means (M), standard deviations (SD), median (Me) and quartile deviation (Q). Multivariate The theoretical model of relations between five dimensions of childhood maltreatment, shame, guilt, depression and suicidal ideation intensity and severity was tested using path analysis. The Stata 14 software with ado-file: swain_gof  (Antonakis and Bastardoz, 2013; Langer, 2017)  was used to estimate the structural equation model.
05. Values of standardized root mean square residual (SRMR) lower than 0. 08 suggest that the model is a good fit to data. The comparative fit index (CFI) should be higher than 0. 90 for an acceptable model and equal to 1 for a perfect model. Values of Tucker-Lewis Index (TLI) higher than 0. 95 suggest that the model fits the dataset well  (Byrne 2010; Kline, 2011). The Swain correction of the Sattora-Bentler adjusted likelihood ratio test, CFI, TLI, and RMSEA were used (see  Antonakis and Bastardoz, 2013; Langer, 2017). In order to analyze the indirect effects of childhood maltreatment on suicidal ideation intensity and severity via generalized shame, generalized guilt, and depressive symptoms, we calculated indirect effects for these relations.
Swift operates autonomously in response to BAT triggers on new GRBs, automatically slewing to point the XRT and the UVOT at a new source within 1-2 minutes. Data are promptly downloaded, and localizations are made available from the narrow-field instruments within minutes (if detected). Swift then continues to follow-up GRBs as they are viewable within the observing constraints and if the observatory is not in the South Atlantic Anomaly (SAA), for at least several hours after each burst, sometimes continuing for days, weeks, or even months if the burst is bright and of particular interest for follow-up.
A. (J2000), decl. (J2000) = 03 h 38 m 01 s. 16, -26 • 56 46. 9 with an uncertainty of 0. 42 arcsec  (Osborne et al. 2019; Siegel & Gropp 2019) , which is also consistent with the LAT position. Both the XRT and the UVOT continued observing the burst location throughout the following two weeks, with the last observation occuring 13. 86 days post trigger. The XRT light curve is taken from the XRT GRB light curve repository  (Evans et al. 2007 (Evans et al. , 2009)). However, the lower energy limit was raised from the default of 0. 3 keV to 0. 7 keV in order to avoid an apparent increase in the low-energy background caused by additional events created by the effects of trailing charge on the Windowed Timing (WT) readout mode data (see Section 4. 2. 2 and www. swift. ac. uk/analysis/xrt/digest cal.
GBM-LAT Joint Spectral Analysis We examined the underlying spectral characteristics of the prompt emission from GRB 190114C by performing joint time-resolved spectral analysis using the GBM and LAT data from T 0 to the start of the settled XRT observations at T 0 + 68. 27 s. For GBM, we used the Time-Tagged Event data for two NaI detectors (n4 and n7) from 10 keV -1 MeV, and one BGO detector (b0) from 250 keV -40 MeV, after considering the spacecraft geometry and viewing angles of the instruments to the burst location. We also include the LLE data, covering an energy range of 30 MeV -100 MeV. For both the GBM and LLE data, the background rate for each energy channel was estimated by fitting a second-order polynomial to data before and after GRB 190114C, taking care to exclude a weak soft precursor emission and any extended emission during the power-law decay observed in the GBM.
The LAT instrument response for the each analysis interval was computed using gtrspgen. The spectral fits were performed using the XSPEC software package (version 12. 9. 1u) (Arnaud 1996) , in which we minimize the P G stat statistic for Poisson data with Gaussian background  (Arnaud et al. 2011). The best-fit model is selected by minimizing the Bayesian information criterion (BIC;  Schwarz 1978). For each time interval, we test a variety of spectral models, including a power law (PL), a power law with an exponential cutoff (CPL), the Band function  (Band; Band et al. 1993 ), a black body (BB), and combinations thereof. The time interval from T 0 to T 0 + 25 s was subdivided into 7 intervals after considering the temporal characteristics shown in Figure  2. Figure 2 also shows the best-fit model for each time interval.
The rest of the process is the same as described in Section 4. 2. 1. We retrieve Swift data from the HEASARC archive. The BAT spectra are generated using the event-by-event data collected from T 0, BAT -239 s to T 0, BAT + 963 s, with the standard BAT software (HEASOFT 6. 25 5  ) and the latest calibration database (CALDB 6  ). The burst left the BAT FoV at ∼ T 0, BAT + 720 s, and was not re-observed until ∼ T 0, BAT + 3800 s. For the intervals that include spacecraft slews, an average response file is generated by summing several short-interval (5 s) response files, weighted by the counts in each interval (see  Lien et al. 2016 , for a more detailed description). The XRT acquired the source at T 0 + 64. 63 s, and started taking WT data at T 0 + 68. 27 s. In the analysis that follows the XRT data were initially processed by the XRT data analysis software tools available in HEASOFT version 6.
25, using the gain calibration files released on 2018-Jul-10. Prior to extracting spectra, we processed the WT event data using an updated, but as yet unreleased, version of the XRT science data analysis task xrtwtcorr (version 0. 2. 4), which includes a new algorithm for identifying unwanted events caused by the delayed emission of charge from deep charge traps that have accumulated in the CCD due to radiation damage from the harsh environment of space. Such trailing charge appears as additional low-energy events and can cause a significant spectral distortion at low energies, especially for a relatively absorbed extragalactic X-ray source, like GRB 190114C. Once identified, the trailing charge events were removed from the event list, resulting in clean WT spectra that are usable below 0.
7 keV. The XRT spectral extraction then proceeded using standard Swift analysis software included in HEASOFT software (version 6. 25). Grade 0 events were selected to help mitigate pile-up and appropriately sized annular extraction regions were used, when necessary, to exclude pile-up from the core of the WT point spread function (PSF) profile when the source count rate was greater than ∼ 100 cts s -1. PSF and exposure-corrected ancillary response files were created to ensure correct recovery of the source flux during spectral fitting. We tested three models in the joint spectral fits, a PL, a broken power law (BKNPL), and a smoothly broken power law (SBKNPL). Each model was multiplied by two photoelectric absorption models, one for Galactic absorption ("TBabs") and another for the intrinsic host absorption ("zTBabs").
Sampling and recruitment No formal sample size calculation was done for the interview study as data were collected until saturation was reached. Using a purposive sampling strategy, 27 physicians were recruited through the ARena study team at the Department of General Practice and Health Services Research, University Hospital Heidelberg between March and May, 2018. The strategy supported the identification of individuals who were especially experienced in the phenomenon of interest and enabled detailed exploration and understanding of central themes and relations to specific experiences, roles and behaviors  [17]. Eligible were all physicians participating in ARena. Written informed consent had to be signed prior to the interview. Recruitment followed a structured procedure aiming for even distribution regarding gender and intervention groups.
Data collection Data were collected by open-ended, semi-structured telephone interviews with physicians and a one-time sociodemographic survey. All interviews were conducted and digitally audio recorded in the first months (April to June 2018) after start of the ARena intervention. Based on a literature review and defined research questions, an interview guide was developed (see Additional file 2 for a translated version). The first two interviews served as a pilot; after this, minor adjustments were made where considered appropriate. All interviews were conducted by three researchers of the study team at the Department of General Practice and Health Services Research, University Hospital Heidelberg. During and after the interviews, additional notes were taken to document participant suggestions with regard to aspects of intervention delivery.
Trustworthiness of analysis and findings was ensured by following methodological strategies such as seeking for similarities and differences across and within accounts to ensure representation of different perspectives. Charting the participants' views with regards to identified themes facilitated comparisons within and across interviews  [20]  and enhanced the transparency of the analysis. MAXQDA Analytics PRO 18 (Release 18. 1. 0) was used to organize and manage the data. Socio-demographic characteristics (see Results Table  2 ) were analyzed descriptively using IBM SPSS Statistics Version 24 to describe the study population and facilitate assessment of generalizability. Subsequently, the approach of the Dual Process Theory  [11-13, 21, 22]  was applied to enable a profound understanding of the scope of individual health professional factors that induce dysrational prescribing decisions, or promote rational ones respectively.
Overview Within the TICD domain of 'Individual health professional factors', results outlined below are structured with a focus on individual perceptions, emotions and images and domain-specific context. This reflects the identified scope of factors relevant to primary care physicians' decision-making process regarding antibiotics prescribing for acute non-complicated self-limiting infections. The code system matrix extracted from MAXQDA (Table  1 ) reflects the overall thematic framework for the analysis and indicates the proportional distribution of key themes by symbol size. Findings are presented with an indication of how frequent aspects were brought up. Extracted quotations are included for illustration. A supplementary table provides additional quotations [see Additional file 3]. All cited quotes were translated into English with due diligence and are referenced with participant number and transcript position.
A total of 27 primary care physicians (9 female, 18 male) participated in the interviews, ranging in age from 43 to 66 years. They were general practitioners (n = 16), internists (n = 6), ear-nose-throat specialists (ENT) (n = 3) and pediatricians (n = 2) with a mean of 26 years working experience. The sample represented the study population of physicians participating in the ARena study and reflected their overall demographics. All interviews were conducted as scheduled and no interview had to be aborted. When data saturation was reached, no further recruitment efforts were made. The response rate of 22. 5% was to be expected in qualitative research involving physicians in primary care and considered satisfactory. Interview duration varied between 7:54 min and 62:50 min, with a mean duration of 28:14 min.