dataesr/scientific-paragraphs-categorization huggingface dataset:
4%-88. 6%; Table  S5 ). In Africa, access, convenience and cost were also commonly selected reasons for choosing a second-line therapy (17. 4%, 14. 0% and 11. 8%, respectively), whereas tolerability, weight and hypoglycaemia were common factors in Europe (28. 9%, 28. 9% and 26. 9%, respectively). The results of the Firth logistic regression models assessing the factors associated with second-line treatment choices are shown in   F I G U R E 1 Likelihood of receiving (A) MET and a DPP-4i, (B) MET and an SGLT-2i, (C) MET and insulin, and (D) MET and a GLP-1 RA, compared with receiving MET and an SU in DISCOVER patients who received MET monotherapy as first-line treatment. Data are presented as odds ratios (95% CI), estimated using Firth multinomial logistic regression models adjusted for all variables in the table.
Datalink databases in the UK, and the Disease Analyzer database in Germany)  [30] [31] [32] [33]  and have been used to assess treatment patterns in patients with type 2 diabetes in these countries. 13, 34, 35 The substantial efforts made before the start of the study to survey the types and locations of medical practices treating patients with type 2 diabetes have resulted in a heterogeneous patient population from diverse clinical settings. However, as for any observational study of this type, a fully representative patient sample was not possible to achieve. 9 Factors limiting the ability of the study to be completely representative of the treatment of patients with type 2 diabetes in each country include infrastructure and other practical constraints, which prevented the participation of some sites in rural locations and some primary care sites.
-year, noninterventional, prospective studies conducted in parallel in 38 countries, and including a total of 15 992 patients; DISCOVER (NCT02322762) in 37 countries and J-DISCOVER (NCT02226822) in Japan. Countries are grouped into regions according to World Health Organization (WHO) categories: Africa (Algeria and South Africa); Americas (Argentina, Brazil, Canada, Colombia, Costa Rica, Mexico and Panama); South-East Asia (India and Indonesia); Europe (Austria, Czech Republic, Denmark, France, Italy, Netherlands, Norway, Poland, Russia, Spain, Sweden and Turkey); Eastern Mediterranean (Bahrain, Egypt, Jordan, Kuwait, Lebanon, Oman, Saudi Arabia, Tunisia and the United Arab Emirates); and Western Pacific (Australia, China, Japan, Malaysia, South Korea and Taiwan). The study protocols were approved by the relevant clinical research ethics committees in each country and institutional review boards at each site, and complied with the Declaration of Helsinki, the International Conference on Harmonization of Good Clinical Practice, and the local regulations for clinical research.
Strengths and limitations DISCOVER is a large and global study, which provides valuable and unique insights into treatment patterns in patients with type 2 diabetes globally, and also reflects variation in local practice because of differences in healthcare systems, physicians' demographics and training, site organization and recommendations from local clinical guidelines across included countries. However, only a limited number of countries could be included for practical reasons, and the focus was placed on lower-middle and upper-middle income countries for which no alternative data sources are currently available. The USA and Western European countries, such as Germany and the UK, were not included because established and comprehensive databases already exist (for example, the Diabetes Collaborative Registry in the USA, The Health Improvement Network and Clinical Practice Research.
ACKNOWLEDGMENTS The authors would like to thank all the investigators and patients participating in the DISCOVER study. Medical writing support was provided by  Stéphane Pintat, PhD , of  Oxford PharmaGenesis, Oxford, UK , and was funded by  AstraZeneca. The DISCOVER study programme is funded by  AstraZeneca. DIS-COVER is a non-interventional study programme and no drugs were supplied or funded. A team of  AstraZeneca  employees contributed to the design of the study. All authors had full access to the data. H. C. , P. F. and F. S. are employees of  AstraZeneca ; they contributed to the analysis and interpretation of the data, and to the development of the manuscript. The corresponding author had final responsibility for the decision to submit the manuscript. All statistical analyses were funded by  AstraZeneca  and conducted independently of the study sponsor, by the statistical group at  Saint Luke's Mid America Heart Institute, Kansas City, Missouri.
4. 1 | 1 Strengths and limitations DISCOVER is a large and global study, which provides valuable and unique insights into treatment patterns in patients with type 2 diabetes globally, and also reflects variation in local practice because of differences in healthcare systems, physicians' demographics and training, site organization and recommendations from local clinical guidelines across included countries. However, only a limited number of countries could be included for practical reasons, and the focus was placed on lower-middle and upper-middle income countries for which no alternative data sources are currently available. The USA and Western European countries, such as Germany and the UK, were not included because established and comprehensive databases already exist (for example, the Diabetes Collaborative Registry in the USA, The Health Improvement Network and Clinical Practice Research.
New calibration of the EACF In order to update the calibration of E, we took advantage of the large sets of stars observed by CoRoT and Kepler, either in the short-cadence mode (e. g. ,  Michel et al. 2008; Chaplin et al. 2011c Chaplin et al. , 2014)) , or in the long-cadence mode (e. g. ,  Mosser et al. 2010; Stello et al. 2013 ), all reprocessed with the EACF in an identical way. We con- Table  1. Instrumental noise (with m ref = 8). Project B 1/2 sys B 1/2 pho B 1/2 add a (ppm hr 1/2 ) (ppm hr 1/2 ) (ppm hr 1/2 ) CoRoT (a) 3. 2 15. 5 Kepler (b)  2. 8 8. 0 0. 4 0. 7 PLATO (c)  9 9 2 0. 66 TESS (d)  0 -60 60 10 0. 68 (a)  Appourchaux et al. (2008)  and  Auvergne et al. (2009) ; as only bright stars were considered, we did not model the additional component. sidered the data available on the KASOC 5 database  (Handberg & Lund 2014) , avoiding stars close to the red edge of the instability strip since their oscillations are significantly damped  (Chaplin et al.
9 previously found. The calibration of E with Kepler data is shown in Fig. 2  where, in order to make the comparison possible, we neutralized the duration dependence by considering a uniform observation duration and corrected E accordingly. All stars follow the calibration reported by Eq. 7 except in three cases: 1. Very high-luminosity red giants with ν max ≤ 1. 5 µHz show E in excess with the fit. This is in line with the fact that these semi-regular variables do not oscillate purely in the solar-like mode, but evolve toward the Mira stage  (Mosser et al. 2013a ). Therefore, they require a specific treatment beyond the scope of this work. Moreover, their observation with future space-borne missions requires a long duration (at least as long as the four-year length provided by Kepler ) and is, unfortunately, unlikely.
5. 4. This simplification, used to consider ν max as the single variable instead of ν max and T eff , does not hamper the conclusion of the paper. [3] The definition of the thresholds is an important ingredient of the method. The predicted E and the threshold values are all in line with the EACF method exposed by MA09; the calibration with CoRoT and Kepler data ensures the consistency of the scalings, regardless of the exact recipe used in MA09. However, as stated earlier, the performance index E defined by Eq. 7 is no longer related to the EACF computation and is now used independently. [4]  A positive detection requires E κ ≥ E det. The seismic performance then depends on the adopted criterion. The seismic estimate of the stellar mass with a relative precision of 10 % is ensured for all unevolved stars showing solar-like oscillations (with the caveat that scaling relations are not yet fully calibrated, which adds some systematic uncertainty upon the statistical uncertainties).
We therefore managed to test how the method can discriminate between the different evolutionary stages with shorter data sets, and noted that it requires E ≥ 60. According to Eq. 22, this threshold corresponds to an observation duration longer than six months on the RGB or 14 months in the RC. Therefore, the ability to distinguish the differ-ent evolutionary stages is not as efficient as determination with the mixed modes. However, the discrimination of the evolutionary stage using E is independent of the determination of the mixed modes, which can be an advantage, for instance, when mixed modes are depressed  (Mosser et al. 2012a; García et al. 2014; Mosser et al. 2017). We note that the ratio η keeps its two-component distribution at low frequency, in the domain corresponding to evolved RGB or AGB stars (asymptotic giant branch).
We may expect the study of η to help us to distinguish AGB from RGB stars, if we assume that the structural properties that explain low values of η in RC stars hold in AGB stars. Three significant features make us confident that this is the case: stars identified as AGB by  Mosser et al. (2014)  according to their period spacings have a low η; stars identified on the AGB with our new method are hotter than the ones identified on the RGB; and more than 3/4 of the stars with a mass below 0. 9 M ⊙ are identified on the AGB (Fig. 6 ). The last two tests made use of the APOKASC data  (Majewski et al. 2017). A fourth test, performed with the method proposed by  Kallinger et al. (2012) , provides similar results  (Kallinger, private communication). A dedicated study of the stars identified on the AGB can now be performed, but is beyond the scope of this paper.
Acknowledgements. BM, EM, RS, and MJG acknowledge financial support from the  Programme National de Physique Stellaire (CNRS/INSU)  and from the  SPACEInn FP7  project (SPACEInn. eu). AM, BM, GRD, and LG gratefully acknowledge the support of the  International Space Institute (ISSI)  for the program AsteroSTEP (Asteroseismology of STEllar Populations). GRD acknowledges the support of the  UK Science and Technology Facilities Council.
Actigraphy data were scored automatically for sleep/wake using the Actiwatch Activity and Sleep Analysis 7 ® software algorithm, version 7. 23. We used four different sensitivitythreshold settings: automatic, low, medium, and high. The low sensitivity-threshold is defined as a threshold level of 80 counts, i. e. , an activity score of 80 counts or more during one epoch is necessary for that epoch to be scored as wake. The medium sensitivity threshold corresponds to a threshold level of 40 counts, the high sensitivity threshold to a threshold level of 20 counts, and the automatic sensitivity threshold to a variable threshold level derived from the subject's individual activity level. The low sensitivity-threshold setting of actigraphy showed the best fit for the comparison of actigraphy-derived sleep parameters to PSG and was thus reported here (for all other settings, please refer to Supplementary Data).
While activity counts were recorded in 1-min epochs, they were converted into 30-s epochs for the epoch-by-epoch analyses, as done in previous studies  (24, 25) , in order to allow for comparison with PSG data. The conversion has been done using the provided feature in the actigraphy reading software (MotionWare 8 ® ), which consists in dividing a 1-min epoch of wake in two 30-s epochs of wake and similarly so for the sleep epochs.
Polysomnography Polysomnography was conducted with Compumedics Siesta 802a (Compumedics, Abbotsford, Australia). We collected the following channels for sleep staging: 13 electroencephalogram (EEG) channels (FP1, FP2, F3, F4, C3, Cz, C4, T3, T4, M1(A1), M2(A2), O1, O2), bilateral electrodes for electrooculogram (E2, E1) and submental electromyogram (Chin2-Chin1), and electrocardiograms (ECG+, ECG-) and sensors to monitor airflow. Raw data were digitalized at a sampling rate of 1,024 Hz, and a notch filter of 50 Hz was applied. PSG studies were manually double scored by two independent raters using Compumedics Profusion PSG V4. 1 version 445 Software following the American Academy of Sleep Medicine (AASM) guidelines for sleep staging in 30-s epochs  (26).
Data Analyses The comparison of polysomnography-and actigraphy-derived sleep parameters was based on a single concurrent night of recording and included two sets of comparisons as primary analyses: 1) the agreement analysis of the four sleep parameters: TST, SOL, WASO, and SE, and 2) an epoch-by-epoch agreement analysis. For secondary analyses, we compared the ACT sensitivity-threshold settings for each sleep parameter and epochby-epoch variables, as reported in the Supplementary Data. All analyses were restricted to nighttime sleep-wake patterns. All statistical analyses were performed using R Statistics Software Version 3. 4. 3.
Study Participants Among the 29 subjects who initiated the combined recording of actigraphy and PSG, one participant had incomplete data because he pulled the PSG electrodes off during the night, one participant did not tolerate the actigraph, and for one participant, a technical issue with the actigraph compromised the data. Overall, 26 participants completed the concurrent PSG and ACT recordings and were included in the final analysis. The sample included 20 boys and six girls, with a mean age of 5. 36 ± 1. 57 years and an age range [2. 94-8. 1] years. As seen in Table  2 , most of the children exhibited a developmental delay in adaptive behaviors with delays ranging from [0. 58-5. 92] years.
Based on disc diffusion data for gentamicin, tobramycin and kanamycin, and epidemiologic cut-off values (EUCAST ECOFFs of 16 mm for tobramycin and gentamicin  [18]  and the local IMM ECOFF of 15 mm for kanamycin), 3068/3359 non-duplicate E. coli clinical strains were categorized as susceptible to the aminoglycosides tested. All 451 E. coli clinical isolates classified as resistant to at least one aminoglycoside and thirty of the 3068 susceptible strains were included in the study and subjected to WGS. In addition, seven E. coli highly resistant to all aminoglycosides (inhibition zone diameters = 6 mm, collected at the IMM between January 1, 2015 and December 31, 2017), putatively producing RMTases, were added  [19].
Whole genome sequencing Library preparation for whole genome sequencing was performed with the QIAGEN QIASeq FX kit. The quality of the library was assessed using capillary electrophoresis (Fragment Analyzer Automated CE System, Advanced Analytical, Heidelberg, Germany). DNA libraries were pooled in equal concentrations and paired-end sequencing (2 × 150) was performed on an Illumina MiSeq platform (Illumina, San Diego, CA, USA). Sequences were deposited in the European Nucleotide Archive under accession number PRJEB29576.
Detection of resistance genes Raw sequencing reads (FASTQ) were filtered and trimmed using the fastq trimmer tool of the FASTX-Toolkit (Hannon Lab, Cold Spring Harbour Laboratories, New York, NY, USA) applying a threshold PHRED score ≤ 25. To identify antibiotic resistance genes and mutations, trimmed FASTQ reads were analysed using the ARIBA pipeline  [23] , querying the ARG-ANNOT  [24]  and CARD databases  [25].
Discussion The aminoglycoside resistance patterns of 458 E. coli clinical isolates were determined by disc diffusion which, by virtue of its convenience, flexibility, efficiency and low-cost, is probably the most widely used AST method worldwide. The prevalence of AMEs was consistent with data from recent reports from Spain, Poland and Norway, where the most common resistance genes (alone or in combination) are aac(3)-II, aph(3′)-Ia and aac(6′)-Ib-cr  [15, 31, 32]. We established an algorithm (Fig. 1 ) based on gentamicin, tobramycin and kanamycin ECOFFs, and on an amikacin working separator cut-off, which reliably identified aminoglycoside resistance mechanisms in E. coli (sensitivity of 99. 6% and specificity of 97. 1%). ARMIA failed to predict resistance only in two cases (0•4%). WGS-based analysis failed to predict resistance in eight isolates (1•6%).
Acknowledgments We thank the technicians of the IMM and the  University of Zurich  for continuous support. We are grateful to  N Blöchliger  for help in various phases of the study,  J Allgäuer  for performing AST and  V Pereira Pires  for technical assistance. We thank  J Kalinowski  ( CeBiTec, Bielefeld University, Germany ) for WGS data and  S Elmer  for technical support.
VI. CONCLUSIONS CLONETS is a coordination and support action funded by the European Commission. We conducted surveys and subcontracted a market study that confirms the interest of a fiber-baser network for dissemination of time and frequency standard. A first estimate of the necessary funding to start such a network in Europe is in the range from 20 to 30 million euros. Possible partnership with NRENs for a T/F service in parallel to data traffic is possible in some countries, and sounds more difficult to be implemented in other countries. GÉANT acting at the European level is also considering the development of such a service in its infrastructure. Fiber-based technology is nowadays well matured, but we still lack standardized methods for time and frequency dissemination. Some technical challenges need to be faced, such as the question of gateways and interoperability in a heterogeneous network.
DACCIWA (Dynamics Aerosol Chemistry Cloud Interactions in West Africa) project and the associated ground-based field experiment, which took place during the summer 2016, provided a comprehensive dataset on the low-level stratiform clouds (LLC) which develop almost every night over southern West Africa. The LLC, inaccurately represented in the climate and weather forecasts, form in the monsoon flow during the night and break up the day after, affecting considerably the radiation budget. The DACCIWA field experiment dataset supports several published studies which give an overview of the measurements during the campaign, analyze the dynamical features in which the LLC develop, and quantify the processes involved in the LLC formation. Based on the main results of these studies and new analyses, we propose in this paper a conceptual model of the diurnal cycle of the LLC over southern West Africa.
Kalthoff et al. (2018)  and  Bessardon (2019)  offer an overview of the measurements during the DACCIWA field experiment at the three sites, and detail the dataset available in the BAOBAB (Base Afrique de l'Ouest Beyond AMMA Base) data base. The most comprehensive dataset, acquired in Savè (180 km north of the Gulf of Guinea), allowed three complementary studies on the LLC. Using the 41-day period,  Dione et al. (2018)  give a statistical overview and a day-to-day quantification of the LLC appearance and breakup time, and of the MI and NLLJ arrival times at Savè. The statistical characteristic of the monsoon flow and the NLLJ are also addressed. Besides this statistical overview, the days with occurrence of density current, due to convection in the surroundings of Savè, or rain, which are common phenomenon during summer season and likely influence the LLC formation, are listed.
, 2019; Haslett et al. , 2019b). Taylor et al. (2019)  conclude that smoke from biomass burning in Central Africa is transported to West Africa, causing a polluted background which limits the effect of local pollution on cloud properties. The objective of this paper is to draw the most important lessons learned from  Adler et al. (2019) ,  Babić et al. (2019a)  and  Dione et al. (2018)  in order to build a conceptual model of the LLC diurnal cycle observed over southern West Africa. The LLC breakup stage and the LLC impact on radiation and CBL vertical development, not addressed in  Adler et al. (2019) ,  Babić et al. (2019a)  and  Dione et al. (2018) , are also analyzed in this study. Our synthetic analysis is mostly based on the Savè supersite dataset. When it is possible, according to the instrumentation availability, we also apply our approach on Kumasi and Ile-Ife dataset in order to test our conceptual model on different geographical places.
The second Section presents the three instrumented supersites of the DACCIWA campaign. The differences in instrumentation or operations between Savè and the two other supersites are emphasized to explain whether the analyses can also be applied to the Kumasi or Ile-Ife datasets. The third Section introduces the four-phase representation of the diurnal cycle of the LLC. In a first subsection, the two phases prior to the LLC formation are described and the main processes responsible of the LLC formation are addressed and quantified. In the second subsection, the LLC and the lower troposphere characteristics and their interactions are analyzed during nocturnal and turbulent convection conditions, until the LLC breakup. Discussion and conclusion are addressed in Section 4.
As far as possible, we apply the same approach and methodology to the two other site's datasets. The most used instruments, which support the conceptual model, are listed below. -The frequent radiosondes were launched every 1 to 1. 5 hrs, depending on the IOP, between 1700 UTC on day D and 1100 UTC on day D+1 in Savè. The temperature, relative humidity and wind vertical profiles in the low atmosphere (0 to 1. 5 km) were used at Savè site to study the processes involved in the LLC formation  (Adler et al. , 2019; Babić et al. , 2019a)  and dissolution. This methodology will be partly applied to the Kumasi dataset where the radiosondes were released every 1. 5 to 3 hrs during some IOPs. Unfortunately, this approach is not applicable for Ile-Ife site where the soundings with a tethered ballon were sparse (every 3 hrs at the best) and did not reach a sufficiently high level.
In order to quantify the processes responsible for the cooling,  Babić et al. (2019a)  and  Adler et al. (2019)  used the budget equation for the mean potential temperature (θ)  (Stull, 1988) : δθ δt T OT = -u δθ δx -v δθ δy HADV -w δθ δz V ADV + 1 ρc p δQ δz RAD - 1 ρc p δH δz T U RB - 1 ρc p δL v E δz SQ (1) where u, v, w are the wind components, ρ the mean air density, c p the specific heat capacity of the air at constant pressure,   2019 ) for details of the method). At last, the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model  (Ricchiazzi et al. , 1998)  is applied to estimate RAD (see  Babić et al. (2019a)  for details of the method) at Savè site only. Such method could not be applied to the Kumasi dataset because too few radiosoundings were launched during the jet phase.
This means that RES, which is mainly the sensible heat flux divergence term at Savè, is the main contribution to the cooling close to the ground, -0. 4 K h -1 , with a decreasing effect with height. Above Z * = 1. 5 the sum of RAD and HADV is nearly equal to TOT. These three processes and their respective role during the stable and jet phases are indicated in Figure  2. Adler et al. (2019)  conclude, when integrating the vertical profiles with height for Savè site, that the advection is the main cooling process, responsible for 50 % of the cooling of the atmosphere during the stable and jet phases. Each of radiation and heat flux divergences contributes around 22% to the total cooling. Very similar results are obtained with the Kumasi dataset. A slightly lower total cooling is nevertheless observed at Kumasi site.