dataesr/scientific-paragraphs-categorization huggingface dataset:
Geophysical results -peat thickness Basin depth, and therefore peat thickness, was found to vary both within and between sites. A summary of the maximum and mean depth to the peat-mineral soil interface recorded for each site are reported in Table  1. The maximum thickness of peat was 5. 48 m, recorded at Gwaun Nant Ddu. The shallowest mean peat thickness was measured at Mawnbwll du Mawr. Here, the peat surface showed a subtle raised or domed area, as often seen with well-developed ombrotrophic bogs. The raised area was not central however, instead located in the northwestern region of the bog (Figure  7 ). Analysis of the GPR depth data confirmed that the dome was located above a shallow basal depression where the thickest peat was recorded. Gwaun Nant Ddu demonstrated a more typical raised bog profile, with an obvious and welldefined lagg, rand and dome.
The GPR data illustrated that the peat had developed in a topographically confined hollow with the dome located relatively centrally over it. Peat thicknesses were highly variable and some of the thickest of all sites were recorded here. The GPR data from Craig y Cilau exhibited considerable small-scale variation in basal topography, illustrated by undulations in the basal reflection. A possible explanation for this is debris and boulders from the limestone escarpment which bounds its northern and western edges, falling into the basin prior to peat formation. Even so, a depression was recorded in the basal topography and a distinct dome formed the surface, confirmed by achieving greatest peat thickness measurements in this location.
Validation of peat thickness from GPR using peat core data At Gwaun Nant Ddu and Craig Y Cilau, cores of 489 cm and 444 cm length were collected, respectively. The measured depth of peat from these two cores were found to be close to the peat depths measured by the GPR (Figure  5 ) and suggests that the assumption of using the same GPR velocity for both sites is acceptable. Additionally, a comparison of the GPR data with the loss-on-ignition data from peat core analysis gave further confidence that GPR is effective at recording the base of peat. At both these sites the coring had sampled fully the ombrotrophic peat and extended into the underlying mineral layer of the bog. This is evidenced by a significant drop in % organic matter in the LOI data. Accordingly, the depth at which a strong reflection was recorded in the radargram was shown to correspond well with the depth at which an increase in the mineral content of the peat was seen.
Acknowledgements: The authors are grateful to the  Brecon Beacons National Park Authority and Natural Resources Wales  who granted access to sites. We would also like to thank  Professor Siwan Davies , for co-supervision of the PhD from which this work has developed and to those who assisted with fieldwork. This research was funded by  KESS  ( Knowledge Economy Skills Scholarships ) which is supported by the  European Social Fund  through the  Welsh Government.
Digital contact tracing and open innovation ecosystem The constantly evolving COVID-19 pandemic involves multidisciplinary research that contributes to a roadmap for business strategy and public policy  (Verma & Gustafsson, 2020). Recent 1 The average number of people who will catch a disease from one contagious person in a population that was previously free of infection and not vaccinated. A reproduction rate below 1 is the necessary condition to contain the viral spread  (Braithwaite et al. , 2020). 2 Among the world's largest economies, China is expected to be the only one to achieve positive GDP growth (around 1. 9%) in 2020 (IMF World Economic Outlook Database, 2020). statistics of OECD countries show that the relationship between protecting people's health and maintaining economic growth is not a 'trade-off' but rather goes hand-in-hand during the COVID-19 pandemic  (Hasell, 2020).
On January 23, the municipal government of Wuhan launched a draconian lockdown that suspended all local and external transportation connections as well as closed down almost all nonessential businesses. To reduce the viral spread in public, the municipal government of Wuhan banned all unauthorized public and private transportation and urged residents to avoid unnecessary transfers in and out of the city. However, due to lacking location-specific information on infections and coordinated crisis management, the scale of infections still escalated sharply (see Figure  3 ). Although some early interventions were undertaken, the infection rates still surged rapidly due to the shortage of personal to enable the big data analysis. In Wuhan, two contact tracing apps were introduced at the municipal and provincial level (see Table  1 ), namely, Wuhan Zhanyi on the WeChat platform and Ehuiban-Hubei Health QR Code on the Alipay platform (see Table  1 ).
While coordinating patient transportation, the community contact tracers further aggregated the local outbreak information in the big data analytics system. The government and supporting ICT firms compared aggregated community-level information with administrative databases to estimate the risk of infection within local communities, and then identify closed contacts of diagnosed cases for quarantine. Based on the real-time contact tracing data, the municipal government could effectively track potential viral spreading paths. Moreover, the government could assess community-level spreading risk and adopt differentiated measures to lift the lockdown. The more complete and accurate digital contact tracing with differentiated lockdown measures contributed to the sharp drop in new infections and an increase in hospital discharge, which laid down the evidential ground for lifting the lockdown.
On the one hand, the communities aggregated real-time data clusters for more complete and accurate big data analytics at the upperground. On the other hand, by implementing the monitoring measures and providing personalized services, the communities ensured the broad underground citizen participation in the digital contact tracing and intervened in cluster infections together with upperground organizations. Finally, the communities served as the social force to ensure the smooth transition in lifting the lockdown including controlling health QR codes for returning workers and coordinating universal nucleic tests. In the underground, the essential contact tracing systems were built on ubitiously used digital platforms, i. e. WeChat and Alipay, so that the citizens encountered little operational hurdles to get adapted to the system.
11, 12 4] [15] [16] [17]  As countries work towards meeting global progress targets for reducing the prevalence of smoking tobacco use and premature mortality from non-communicable diseases, timely data on the prevalence of smoking tobacco use and attributable disease are necessary to guide effective policy and planning. 11, 17 8] [19] [20] [21]  Estimates of the attri butable burden of smoking tobacco use have been included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) since its initial publication in 1997. 22 Previous studies estimating the attributable burden of tobacco smoking have combined indirect estimation using the Smoking Impact Ratio method for cancers and chronic obstructive pulmonary disease, which uses observed lung cancer mortality to indirectly estimate the disease burden attributable to tobacco smoking, with direct estimation using lagged prevalence of daily smoking tobacco use for cardiovascular and circulatory diseases and all other health outcomes.
Prevalence of smoking tobacco use We systematically identified and extracted data from 3625 nationally representative surveys, including both multinational and country-specific surveys, covering 200 of the 204 countries and territories included in the analysis. 171 (86%) of 200 countries had at least five surveys for the period 1980-2019, and 141 (71%) countries had data available from 2015 or later. Data for 1980-89 were used to inform time trends, but are not reported in the results. For countries without data, estimates were entirely based on models. We extracted data for individuals aged 10 years and older, and used data on individuals aged 10-14 years to inform model estimates; however, we report prevalence among individuals aged 15 years and older. Additional infor mation on identification of sources, inclusion criteria, and data extraction are in appendix 1 (pp 11-12).
A complete list of data sources used in our analysis is available through the Global Health Data Exchange (GHDx). To ensure that all data included in the model were comparable, and to prevent compositional bias from affecting our estimates, we used two key data processing steps. First, we used linear regression to adjust data from surveys that only reported non-reference case definitions. Our reference case definitions were current use of any smoked tobacco product on a daily or occasional basis, and former use of any smoked tobacco product. We included all smoked tobacco products-eg, cigarettes, pipes, cigars, shisha, bidis, kreteks, and other local smoked tobacco products. We did not include smokeless tobacco, electronic cigarettes (also known as e-cigarettes), vaping products, or heated tobacco products.
Risks from chewing tobacco and second-hand smoke are included as other risk factors in GBD and are outside the scope of this study. 1, 26 econd, we split data reported in aggregated age groups or as both sexes combined into our standard 5-year age-sex groups. To estimate an age-sex pattern that reflects observed spatial and temporal variation, we estimated a preliminary prevalence model using only data available in our standard age-sex groups. We then used the estimated age-sex pattern, which varied by location and year, to split aggregated data, a process that also allowed us to propagate uncertainty in the agesex pattern. These methods have been previously published,  19, 20, 27  and full details are in appendix 1 (pp 13, 20). We used spatiotemporal Gaussian process regression (ST-GPR) to model prevalence of both current and former smoking tobacco use (appendix 1 p 21).
Estimates of cigarette-equivalents consumed per smoker per day combine two sources of information: self-reported smoking patterns from household surveys and supply-side data on country-level consumption available from the Food and Agriculture Organiza tion of the UN (1961-2013), the US Department of Agriculture (1960-2005), and Euromonitor (2002-17). Details on the modelling process for the supply-side data and approach to integrating the two sources of information are in appendix 1 (pp 22-23). Using estimates of cigarette-equivalents per smoker per day by location, age, and sex from 1960 to 2019, along with estimates of the distribution of initiation age, we reconstructed individual smoking histories on the basis of birth cohort smoking patterns to estimate population-level distributions of pack-years consumed.
Dose-response risk curves Previous estimates of smoking-attributable burden in GBD relied on dichotomous exposures, despite well documented dose-response associations. To address this limitation, we estimated dose-response risk curves for both current and former smokers for 36 health outcomes using meta-regression (appendix 1 p 26). This process involved an extensive systematic review, covering 71 996 total search string hits, from which 902 prospective cohort and case-control studies were found to be eligible and from which data were extracted (Preferred Reporting Items for Systematic reviews and Meta-Analyses [PRISMA] diagrams for each outcome are in appendix 1 [pp 31-66]). For cancers and chronic obstructive pulmonary disease, we used pack-years as the exposure, allowing risk to reflect both duration and dose (cigarette-equivalents per day) of exposure.
We estimated that fewer than 15% of deaths attributable to smoking tobacco use in 2019 occurred among former smokers aged 30 years and older, despite former smokers comprising approximately a third of the ever smoker population aged 30 years and older. 1] [42]  Relative risks of all-cause mortality from long-running cohort studies in the UK  41, 42  and the USA  43  indicate that up to two-thirds of long-term smokers will eventually die of a disease attributable to smoking. These data underscore the importance of adopting policies and interventions that increase rates of cessation. The results of our study must be interpreted in the context of several limitations. First, data on smoking tobacco use are self-reported, which might lead to underestimates in demographic groups with low social acceptability of smoking, particularly among females in Asia and Africa.
SyMA would like to acknowledge the  International Centre for Casemix and Clinical Coding ,  Faculty of Medicine, National University of Malaysia and Department of Health Policy and Management, Faculty of Public Health, Kuwait University  for the approval and support to participate in this research project. MarA, AdP, and CH are partially supported by a grant of the  Romanian National Authority for Scientific Research and Innovation (CNDS-UEFISCDI , project number  PN-III-P4-ID-PCCF-2016-0084 ). TWB was supported by the  Alexander von Humboldt Foundation  through the  Alexander von Humboldt Professor award , funded by the  German Federal Ministry of Education and Research. SB-H was supported by  National Institutes of Health (NIH) National Center for Advancing Translational Science (NCATS) UCLA (CTSI  grant number  UL1TR001881 ).
CH and AdP are partially supported by a grant of the  Romanian National Authority for Scientific Research and Innovation (CNDS-UEFISCDI , project number  PN-III-P2-2. 1-SOL-2020-2-0351 ). JYI is supported by  UNC 's Cancer Care Quality Training  2T32CA116339-11. SMSI is funded by the  National Heart Foundation of Australia  and  NHMRC. MihJ  acknowledges funding through grant  OI 175 014  of the  Ministry of Education Science and Technological Development of the Republic of Serbia. PJ acknowledges the  Wellcome Trust/DBT India Alliance  Fellowship support ( IA/CPHI/14/1/501497 ). HK acknowledges being the  Kornhauser Research Associate at The University of Sydney , Save Sight Institute. SVK acknowledges funding from a  NHS Research Scotland Senior Clinical Fellowship  ( SCAF/15/02 ), the  Medical Research Council  ( MC_UU_00022/2 ) and the  Scottish Government Chief Scientist Office  ( SPHSU13 ).
The follow-up consists in questionnaires assessments only, including the self-reported pain and clinical measures associated with PET and fMRI meausres. The questionnaires will be presented through an online survey that is suitable for RCT's (Research Electronic Data Capture, redcap) and available at the Universities Fribourg,and that can be lled at home. The measures that are not self-reported will be performed by trained assessors, who will be blind to the treatment condition to minimize expectation bias of the assessors. The wait-list control will undergo the same measures at pre-test and post-test, with a post-test planned on average after 8 weeks. They will not participate in any speci c intervention, but will be given the opportunity to participate in MORE after the follow-up assessment.
Socio-demographic data Data to be collected at baseline include age, sex, marital status, education level, professional status. Measures of functioning includes a measure of quality of life with the World Health Organization quality of life questionnaire WHOQOL Brief questionnaire  (Skevington, 2004 ) and the Fibromyalgia Impact Questionnaire-Revised (FIQ-R;  Bennett, 2009)  that also includes measure of fatigue. Measures of sleep quality include the medical outcomes study sleep scale (MOS;  (Stewart, 1988 Hays, 1992 ) that has been widely used in rheumatology research and is recommended by OMERACT-10.
Mood states The PET Data acquisition PET images will be acquired at the Department of Nuclear Medicine at the University Hospital Zurich. MRI overlay images will be acquired at 3 Tesla at the Department of Neuroradiology at the University Hospital Zurich, using T1 weighted sequence (MP-RAGE) to provide an anatomical framework for image analysis. 18-F DOPA is a well-validated measure of presynaptic DA function (Heiss and Hilker, 2004); and previous studies showed differences in 18-F DOPA binding between FM and healthy participants at rest  (Wood, 2007). PET-measures will be performed before and after the MORE intervention (after 8 weeks for the wait-list control group). One hour before scanning they will receive as routinely an oral dose of Carbidopa 100mg, a peripheral DOPA decarboxylase blocker and Entacopone 400mg, a peripheral catechol Omethyltransferase antagonist, and an additional 50mg dose of Carbidopa 30 minutes before 18 F-DOPA injection in order to provide increased availability of 18F-DOPA for striatal uptake.
The purpose of the premedication regimen is to limit the metabolism of the 18 F-DOPA tracer by peripheral enzymes, i. e. The fMRI data acquisition will take place at the Neuroradiology Department at the University Hospital Zurich. To measure brain structure and function, we use a 3. 0 Tesla whole-body scanner. The measures include an anatomical scan, a diffusion MRI and a resting state functional MRI and a task based functional MRI. The total time in the scanner will be about 75 minutes. The subjects will perform the Fribourg reward task, an event related fMRI task adapted from the spatial delayed response task to measure neural responses to monetary reward  (Gaillard et al. 2019 ). DOPA.
Analysis plan Primary outcome measures State of the art image processing techniques will be used to analyse the PET and the fMRI images. PET image pre-processing will be done using PMOD Version 4. 1 or higher (PMOD software, PMOD technologies Zurich) and SPM (Wellcome Department of Imaging Neuroscience, London, UK) for the 18-F DOPA PET study. The most often used method to quantify biochemical function from 18-F DOPA PET image is the multiple time graphical approach (MTGA) that provides rate constants (Ki) for the storage of 18F-DOPA within regions of interest (ROIs) placed over the striatum (Patlak and Blaberg, 1995). Images from each dynamic DOPA PET dataset will be aligned and parametric images of 18F-DOPA in ux (Ki) will be created for each subject. The Ki images will be transformed into standard stereotactic space.
Regional Ki values of striatal regions (putamen, caudate, nucleus accumbens) will be analyzed with statistical parametric mapping for comparison of regional Ki values on a voxel by voxel basis. Group comparisons will be tested using independent samples T-test; treatment group x time comparisons will be analysed with two factorial ANOVA's including Ki values obtained in the nucleus accumbens and the caudate to test the primary outcomes. The reward task will be performed in the fMRI scanner and we will correlate 18-FDopa in ux with the striatal activation obtained with the fMRI task. In order to measure behavioural differences in the fMRI task results, in the ambulatory assessment data and in the intervention program's e cacy between the groups tested, we plan to use different kind of variance analyses and multilevel analyses.
The number of participants for each part of the study has been determined in order to assure a good statistical power according with this speci c research domain (e. g. including potential dropout, outliers). fMRI data analysis will be performed with state-of-the art software freely available online. T1 weighted scans will be analysed with regard to cortical thickness and subcortical volumes using surface based morphometry implemented in FreeSurfer software suite 5. 0. 1. (http://surfer. nmr. mgh. harvard. edu). This includes a fully automated method. Brie y, this processing includes 1) motion correction, 2) removal of non-brain tissue, 3) automated Talairach transformation, 4) segmentation of the subcortical white matter and deep gray matter volumetric structures (amygdala, hippocampus, thalamus, caudate, putamen, pallidum, nucleus accumbens, ventricles), 5) intensity normalization, 6) tessellation of the gray matter/white matter boundary, 7) automated topology correction, and 8) surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal uid borders at the location where the greatest shift in intensity de nes the transition to the other tissue class.
Freesurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across eld strengths. Obtained subcortical volumes will be normalized by individual intra-cranial volumes for further statistical analyses. Exploratory approaches will involve a vertex-based analysis across the whole brain. Diffusion MRI scans will be analysed for structural connectivity with FMRIB software library 4. 1. 9 (FSL, http://www. fmrib. ox. ac. uk/fsl). Functional MRI scans will be analysed for activity and functional connectivity with the latest version of Statistical Parametric Mapping (SPM, http://www. l. ion. ucl. ac. uk/spm). Functional MRI data will be pre-processed according to the following steps: 1) slice timing correction, 2) realignment, 3) linear and non-linear normalization on to a standard EPI template, 4) voxel re-sampling to 2x2x2 mm 3 5) smoothing with a Gaussian kernel of 6 mm full width at half maximum, 6) detrending, 7) ltering (such that frequencies 0.
, 2014)  as well as one ANCOVA controlling for depressive symptoms at pre-test. Measures related to the primary outcomes of the clinical effects of MORE, including measures of pain intensity and severity, measures of functioning, measures of sleep, and measures of mood will be performed with ANOVA's. In case of not normally distributed data, we will use a transformation in Z-scores, rather than using nonparametric tests. We will perform per protocol analyses to test our hypotheses. Intent-to-treat analyses will be used for sensitivity analyses  (Garland et al. , 2014). To account for missing data we will use multiple imputation procedures. Analyses of the secondary outcomes related to the treatment mechanisms will be performed using twofactorial (treatment group x time) ANOVAs to test the pre-post-test differences.
9; ηpartial 2 =. 17 (MORE. N = 26, Support Group: N = 29) using multilevel analyses. In order to be able to do group comparisons with the AA measures, we will add 15 healthy participants who will only do the AA part. On the basis of these analyses, we consider that an estimation of 30 participant in each condition (waitinglist versus MORE) after treatment should be su cient to obtain signi cant results for our aims. In their randomized clinical trial of the MORE intervention in chronic pain patients with opioid abuse, Garland et al. 2014 found 20% drop-out between beginning and completion of the intervention. We will therefore postulate 20% drop-out in total, giving a starting sample of 38 participants in each group, that we will round up to 40 to account for data loss related to technical problems, giving a total sample of 80.
In addition, we have created a large network of outpatient clinics and practices with our project partners at the different centers, who will directly support the study with recruitment. With regard to the medication, opioids should not be too much of a concern, given low e cacy in these patients, and relatively restrictive prescription practices in Switzerland for this population. Another barrier of recruitment is going to be the requirement of randomization into immediate or delayed groups. As such, participants will have to make themselves available for program dates over a period of 5 months, in case they are randomized to the waitlist groups. Logistically managing simultaneous baseline and follow-up measures can be challenging. Another issue is drop-outs or missing data. By using an online format for the questionnaires, we aim to eliminate incomplete datasets.