dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Individual geo-location and migration data were used to attribute source and destination locations for each source-recipient pair. The geo-location of each recipient in a pair was set to the community in which the recipient was found to be infected. For the phylogenetically likely source partner, the location was set to the community of residence at or shortly before the recipient was identified as HIV-positive. If the source partner had migrated within the past 2 years, the location was set as the community before migration. To estimate transmission flows in the cohort, we first counted the proportion of source-recipient pairs by source and destination locations (unadjusted estimates). Next, we adjusted for differential RCCS participation and sequence sampling  rates by individual-level characteristics with Bayesian multilevel models as detailed in the appendix (p 12) using the phyloflows R package version 1.
Deep-sequencing yielded output of  Participants who in-migrated within 2 years before study visit were stratified by the origin of migration, from inland communities (green), from fishing communities (purple), from outside the Rakai area (light blue), and from unknown location (grey). (C) Estimates of HIV prevalence among in-migrants to inland communities to that among in-migrants to fishing communities. HIV prevalence was higher among those individuals migrating to fishing communities than those migrating to inland communities. Sex specific estimates in panels A and C were obtained with Bayesian logistic regression models using the Stan software, version 2. 19. moderate quality. 24 Analysis was restricted to samples from 2652 individuals who satisfied minimum criteria on read length and read depth,  20  which implied a sampling fraction of 68•4% among participants who were infected and self-reported being ART-naive, and an estimated sampling fraction of 45•1% among censuseligible individuals who were infected with unsuppressed virus.
Acknowledgments This study was presented in part at the  International AIDS Society Meeting in Amsterdam, Netherlands , July 26, 2018. This study was supported by the  Bill & Melinda Gates Foundation  ( OPP1175094 ,  OPP1084362 ), the  National Institute of Allergy and Infectious Diseases  ( R01AI110324 ,  U01AI100031 ,  U01AI075115 ,  R01AI110324 ,  R01AI102939 ,  K01AI125086-01 ),  National Institute of Mental Health  ( R01MH107275 ), the  National Institute of Child Health and Development  ( RO1HD070769 ,  R01HD050180 ), the  Division of Intramural Research of the National Institute for Allergy and Infectious Diseases , the  World Bank , the  Doris Duke Charitable Foundation , the  Johns Hopkins University Center for AIDS Research  ( P30AI094189 ), and the  President's Emergency Plan for AIDS Relief  through the  Centers for Disease Control and Prevention  ( NU2GGH000817 ).
An intact unilateral multi-fiber splanchnic or BAT nerve bundle was carefully isolated and placed on a bipolar platinum-iridium wire (36-gauge, A-M Systems, Sequim, WA) and sealed with silicone gel (Kwik-Sil, World Precision Instruments, Sarasota, FL). The nerve recording electrode was attached to a high impedance probe (HIP511, Grass Instruments, West Warwick, RI) leading to an AC preamplifier (P5, Grass Instruments) that magnified the nerve signal at 10 5 power with a low and high frequency filter cutoff at 100 and 1Khz, respectively. The signal from the nerve was routed to a data acquisition system (MacLab Model 8 s), which counted the number of nerve spikes that exceeded a threshold level set just above the noise level each second (spikes/sec) and to a resetting voltage integrator (RVI: Model B600c, University of Iowa Bioengineering) that measures the total voltage.
Surface Body Temperature Measurement by Infrared Camera Recorder The surface body temperature of RYGB and sham-operated mice was recorded during postoperative week 3 using a high-resolution infrared camera (A655sc Thermal Imager; FLIR Systems, Wilsonville, OR). Quantitative analysis of infrared images was performed using FLIR Research IR software version 3. 4. 13039. 1003. Mice were imaged on a multilane treadmill next to their age-matched littermate controls at baseline and after 6 minutes of treadmill exercise at 7 m/min and 15 incline. Images were obtained every 1 s. The side-by-side imaging of mice in the same camera frame allows simultaneous assessment and direct comparison of the body surface heating in the different mouse models, providing an advantage over other methods for assessing body temperature that would be limited by the accuracy and concordance of two separate temperature probes.
Mice were then placed onto the imaging bed under 2% isoflurane anesthesia for the duration of imaging. The CT image was acquired at 80 kV and 500 mA, focal spot of 58 mm. After the CT scan, the mouse was injected intravenously with $37 MBq (100 mCi) of FDG and a 0-60 min dynamic PET was immediately performed. Reconstructed CT and PET images were fused and analyzed using ORS Visual software. One week later, mice underwent either RYGB or sham surgery as mentioned above. Mice were maintained on a post-operative feeding protocol during which liquid diet was provided from post-operative days two through seven. On post-operative day 6, 0. 25 g of HFD was provided on a daily basis until consumed in its entirety. After surgical recovery, RYGB mice were given ad libitum access to HFD, while weight-matched sham mice were provided HFD once a day at the onset of the dark phase.
Data and Code Availability The datasets generated and analyzed during the study are included with the published manuscript (and Supplemental Information). The microbiota 16 s sequencing analysis data are available to public at the following link: https://www. ncbi. nlm. nih. gov/bioproject/ PRJNA630080 under the name "Roux-en-Y Gastric Bypass (RYGB) and Sham dataset" with the corresponding SRA accession code: PRJNA630080. All other data are available from the corresponding author upon request.
ACKNOWLEDGMENTS We would like to thank the technical director (Jamie Soto) and staff members of the Metabolic Phenotyping Core facility at the University of Iowa for all the services provided. We would also like to thank Teresa Ruggle for designing our graphic pictures, as well as Kristina Greiner and Paul Casella for their critical review of the manuscript. M. M. is supported by funds from the VA Merit Review Program (I01 BX004774-01), the American Gastroenterological Association Gut Microbiome Pilot Research Program (18185800 BR01), the Department of Internal Medicine at University of Iowa Carver College of Medicine, and the University of Iowa Fraternal Order of Eagles Diabetes Research Center. K. R. is supported by funds from the NIH (HL084207), the American Heart Association (14EIA18860041), and the University of Iowa Fraternal Order of Eagles Diabetes Research Center.
Mitochondrial DNA variants calling We then performed variants calling on all sequencing data sets obtained from the three different horses DNA, i. e. from the seven sequencing libraries (three from the first sequencing experiment and four from the second sequencing experiment). For this purpose, we used sequence reads of 2000 nt or longer and satisfying a minimal quality threshold of 10, since we obtained a deep coverage and we wanted to avoid reads potentially derived from NUMTs. We used the medaka variant calling method and identified a total of 88 variable positions based on the same Arabian breed mtDNA reference as before: 77 for H25 samples, 10 for H26 and 83 for H27 (Fig. 5a ). All variants were transitions, and all were found in at least one known horse mtDNA sequence available in GenBank.
According to Quail and colleagues, heterozygous variant calling with a 15x coverage using Ion Torrent or various Illumina datasets was comparable to a coverage of 190x for PacBio datasets  [42]. We can deduce conservatively that a similar coverage using Nanopore datasets would be enough to call mitochondrial variants in an efficient manner. This means that we could multiplex four or five times more samples, i. e. a hundred, in a single run. With such a capacity, this method could become very competitive in terms of rapidity, scalability and cost-effectiveness. We estimated at 99. 9% the precision of the obtained sequence, which is satisfactory given that the read median identity was estimated at ~89% for 1D sequencing using R9. 0 chemistry. The process of MDA is known to be highly accurate and can not be considered as the limiting factor when it comes to long-read sequencing.
Whole mitochondrial genome amplification (WMGA) We used the REPLI-g mitochondrial DNA kit (Qiagen) to perform WMGA according to the manufacturer's instruction with the following modifications. We designed 18 primers using CLC Genomic Workbench v6 (CLC bio) based on a horse mitochondrial genome (GenBank KX669268. 1; see Table  1 ). We adopted the following recommendations for primer design: a length of 10-14 bases, the same number of primers hybridize to each strand; primer hybridization sites are evenly spaced and primers include phosphorothioate modifications between the last three bases of their 3′ end. All primers were purchased from Eurofins Genomics. We tested two primer concentrations: 1 μM and 0. 2 μM of each oligonucleotide. The reaction set-up was as follows: 4 μl of DNA treated with Exonuclease V was loaded into a 0.
The qPCR data were analysed using the QuantStudio 12 K Flex software (Applied Biosystems). Cq values were obtained using the auto baseline option and the threshold was manually set to the same value of ΔRn = 0. 1 for all amplicons. The Cq values were considered valid if the duplicate measures differed by less than 0. 5 cycles, undetermined values were set equivalent to a Ct value of 40. We computed the geometric mean of the Cq values of the three mtDNA and nuDNA amplicons, and then we computed the corresponding fold changes. Formation of primer-dimer and amplification specificity were assessed by melting curve analysis.
For the S2 sequencing run, we used DNA amplified with 15 primers, excluding three that were found to match NUMTs loci, and multiplexed 12 samples using barcodes (barcodes one to twelve). We loaded 439 ng of the sequencing library. In this S2 run, we performed each exonuclease treatment in duplicate and each amplification in duplicate, so that each horse's DNA was independently sequenced four times. Altogether, each horse's DNA was sequenced across seven libraries. We performed the sequencing using R9. 4 flowcells from two different batches on a MinION sequencer (ONT) operated using MinION Release v18. 05. 5 for the S1 run and 19. 05. 0 for the S2 run. The MinION sequencing device was connected to a computer running the linux operating system Ubuntu 16. 04 LTS.
Data analysis We used guppy for basecalling with the flip-flop model, trimming and homopolymer correction (guppy-cpu-v2. 3. 1). We then used guppy_barcoder for demultiplexing. For variant calling, we selected reads longer than 2000 bp with a mean quality higher than Q10 using NanoFilt  [61]. The filtered sequences were then aligned using minimap2 on the Equus caballus genome reference assembly (GCF_002863925. 1_EquCab3. 0) [62]. We substituted the mitochondrial genome present in this assembly by a duplicated, pseudo-circularized, mitochondrial genome (GenBank JN398434) to increase the recovery of mapping to the circular mitochondrial genome. We used bioawk and samtools to extract mitochondrial reads and compute their quality scores  [63]. These mtDNA reads were aligned with minimap2 on an Arabian breed reference horse mtDNA (JN398434), a Thoroughbred reference mtDNA (KC202971) or on the mtDNA from the reference Thoroughbred horse genome Twilight (MH586816).
, 2015) , and we make detailed estimates for the years 1997 (GTAP version 5) and 2001 (GTAP6) as well as  2004 , 2007 , and 2011 (GTAP9. (GTAP9. 2), covering 57 sectors and 141 countries and regions. The detailed results are then extended into an annual time series from 1990 to the latest year of the gross domestic product (GDP) data (2018 in this budget), using GDP data by expenditure in the current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregates Database;  UN, 2019)  and time series of trade data from GTAP (based on the methodology in  Peters et al. , 2011b). We estimate the sectorlevel CO 2 emissions using the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match the emission estimates from the carbon budget.
Each method is presented in the order it was published. UEA: Le  Quéré et al. (2020)  YTD. Le  Quéré et al. (2020)  estimated the effect of COVID-19 on emissions using observed changes in activity using proxy data (such as electricity use, coal use, steel production, road traffic, aircraft departures, etc. ), for six sectors of the economy as a function of confinement levels, scaled to the globe based on policy data in response to the pandemic. The analyses employed baseline emissions by country for the latest year available (2018 or 2019) from the Global Carbon Budget 2019 to estimate absolute daily emission changes and covered 67 countries representing 97 % of global emissions. Here we use an update through to 13 November. The parameters for the changes in activity by sector were updated for the industry and aviation sectors, to account for the slow recovery in these sectors observed since the first peak of the pandemic.
Projection. A projection is used to fill the data from 14 November to the end of December, assuming countries where confinement measures were at level 1 (targeted measures) on 13 November remain at that level until the end of 2020. For countries where confinement measures were at more stringent levels of 2 and 3 (see  Le Quéré et al. , 2020)  on 13 November, we assume that the measures ease by one level after their announced end date and then remain at that level until the end of 2020. Priestley Centre:  Forster et al. (2020)  YTD. Forster et al. (2020)  estimated YTD emissions based primarily on Google mobility data. The mobility data were used to estimate daily fractional changes in emissions from power, surface transport, industry, residential, and public and commercial sectors. The analyses employed baseline emissions for 2019 from the Global Carbon Project to estimate absolute emission changes and covered 123 countries representing over 99 % of global emissions.
For a few countriesmost notably China and Iran -Google data were not available and so data were obtained from the high-reduction estimate from Le  Quéré et al. (2020). We use an updated version of  Forster et al. (2020)  in which emission-reduction estimates were extended through 3 November. Projection. The estimates were projected from the start of November to the end of December with the assumption that the declines in emissions from their baselines remain at 66 % of the level over the last 30 d with estimates. Carbon Monitor:  Liu et al. (2020)  YTD. Liu et al. (2020)  estimated YTD emissions using emission data and emission proxy activity data including hourly to daily electrical power generation data and carbon emission factors for each different electricity source from the national electricity operation systems of 31 countries, real-time mobility data (TomTom city congestion index data of 416 cities worldwide calibrated to reproduce vehicle fluxes in Paris and FlightRadar24 individual flight location data), monthly industrial production data (calculated separately by cement production, steel production, chemical production, and other industrial production of 27 industries) or indices (primarily the industrial production index) from the national statistics of 62 countries and regions, and monthly fuel consumption data corrected for the daily population-weighted air temperature in 206 countries using predefined heating and temperature functions from EDGAR for residential, commercial, and public buildings' heating emissions, to finally calculate the global fossil CO 2 emissions, as well as the daily sectoral emissions from power sector, industry sector, transport sector (including ground transport, aviation, and shipping), and residential sector respectively.
Global Carbon Budget estimates Previous editions of the Global Carbon Budget (GCB) have estimated YTD emissions and performed projections, using sub-annual energy consumption data from a variety of sources depending on the country or region. The YTD estimates have then been projected to the full year using specific methods for each country or region. This year we make some adjustments to this approach, as described below, with detailed descriptions provided in Appendix C. China. The YTD estimate is based on monthly data from China's National Bureau of Statistics and Customs, with the projection based on the relationship between previous monthly data and full-year data to extend the 2020 monthly data to estimate full-year emissions. USA. The YTD and projection are taken directly from the US Energy Information Agency.
Bookkeeping models Land-use change CO 2 emissions and uptake fluxes are calculated by three bookkeeping models. These are based on the original bookkeeping approach of  Houghton (2003)  that keeps track of the carbon stored in vegetation and soils before and after a land-use change (transitions between various natural vegetation types, croplands, and pastures). Literaturebased response curves describe decay of vegetation and soil carbon, including transfer to product pools of different lifetimes, as well as carbon uptake due to regrowth. In addition, the bookkeeping models represent long-term degradation of primary forest as lowered standing vegetation and soil carbon stocks in secondary forests and also include forest management practices such as wood harvests. BLUE and HandN2017 exclude land ecosystems' transient response to changes in climate, atmospheric CO 2 , and other environmental factors and base the carbon densities on contemporary data from literature and inventory data.
Bookkeeping models do not directly capture carbon emissions from peat fires, which can create large emissions and interannual variability due to synergies of land-use and climate variability in Southeast Asia, in particular during El-Niño events, nor emissions from the organic layers of drained peat soils. To correct for this, HandN2017 includes carbon emissions from peat burning based on the Global Fire Emission Database (GFED4s; van der  Werf et al. , 2017) , and peat drainage based on estimates by  Hooijer et al. (2010)  for Indonesia and Malaysia. We add GFED4s peat fire emissions to BLUE and OSCAR output but use the newly published global FAO peat drainage emissions 1990-2018 from croplands and grasslands  (Conchedda and Tubiello, 2020). We linearly increase tropical drainage emissions from 0 in 1980, consistent with HandN2017's assumption, and keep emissions from the often old drained areas of the extra-tropics constant pre-1990.
org/10. 22033/ESGF/input4MIPs. 1127,  Hurtt et al. , 2017; Hurtt et al. , 2011 Hurtt et al. , , 2020 ; Table  A1 ). This new data set is of quarter-degree fractional areas of land-use states and all transitions between those states, including a new wood harvest reconstruction and new representation of shifting cultivation, crop rotations, and management information including irrigation and fertilizer application. The land-use states include five different crop types in addition to the pasture-rangeland split discussed before. Wood harvest patterns are constrained with Landsat-based tree cover loss data  (Hansen et al. , 2013). Updates of LUH2-GCB2020 over last year's version (LUH2-GCB2019) are using the most recent HYDE/FAO release (covering the time period up to and including 2015), which also corrects an error in the version used for the 2018 budget in Brazil.
The FAO wood harvest data have changed for the years 2015 onwards and so those are now being used in this year's LUH-GCB2020 data set. This means the LUH-GCB2020 data are identical to LUH-GCB2019 for all years up to 2015 and differ slightly in terms of wood harvest and resulting secondary area, age, and biomass for years after 2015. DGVMs implement land-use change differently (e. g. an increased cropland fraction in a grid cell can either be at the expense of grassland or shrubs, or forest, the latter resulting in deforestation; land cover fractions of the non-agricultural land differ between models). Similarly, model-specific assumptions are applied to convert deforested biomass or de-forested areas and other forest product pools into carbon, and different choices are made regarding the allocation of rangelands as natural vegetation or pastures.
, 2013). These estimates differ in many respects: they use different maps of surface pCO 2 , different atmospheric CO 2 concentrations, wind products, and different gas-exchange formulations as specified in Table  A3. We refer to them as pCO 2 -based flux estimates. The measurements underlying the surface pCO 2 maps are from the Surface Ocean CO 2 Atlas version 2020 (SOCATv2020;  Bakker et al. , 2020) , which is an update of version 3  (Bakker et al. , 2016)  and contains qualitycontrolled data through 2019 (see data attribution Table  A5 ). Each of the estimates uses a different method to then map the SOCAT v2020 data to the global ocean. The methods include a data-driven diagnostic method  (Rödenbeck et al. , 2013;  referred to here as Jena-MLS), a combined self-organizing map and feed-forward neural network  (Landschützer et al.
GOBM evaluation and uncertainty assessment for S OCEAN The mean ocean CO 2 sink for all GOBMs and the ensemble mean falls within 90 % confidence of the observed range, or 1. 5 to 2. 9 GtC yr -1 for the 1990s  (Ciais et al. , 2013)  and within the derived constraints for the 2000s and 2002-2011 (see Sect. 2. 4. 1) before and after applying corrections. The GOBMs and flux products have been further evaluated using the fugacity of sea surface CO 2 (f CO 2 ) from the SOCAT v2020 database  (Bakker et al. , 2016 (Bakker et al. , , 2020)). The fugacity of CO 2 is 3 ‰-4 ‰ smaller than the partial pressure of CO 2  (Zeebe and Wolf-Gladrow, 2001). We focused this evaluation on the root mean squared error (RMSE) between observed f CO 2 and modelled pCO 2 and on a measure of the amplitude of the interannual variability of the flux (modified after  Rödenbeck et al.
ILAMB variables cover key processes that are relevant for the quantification of S LAND and resulting aggregated outcomes. The selected variables are vegetation biomass, gross primary productivity, leaf area index, net ecosystem exchange, ecosystem respiration, evapotranspiration, soil carbon, and runoff (see Fig. B2  for the results and for the list of observed databases). Results are shown in Fig. B2  and discussed in Sect. 3. 1. 3. For the uncertainty for S LAND , we use the standard deviation of the annual CO 2 sink across the DGVMs, averaging to about ±0. 6 GtC yr -1 for the period 1959 to 2019. We attach a medium confidence level to the annual land CO 2 sink and its uncertainty because the estimates from the residual budget and averaged DGVMs match well within their respective uncertainties (Table  5 ).
The atmospheric inversion perspective The worldwide network of in situ atmospheric measurements and satellite-derived atmospheric CO 2 column (xCO 2 ) observations can be used with atmospheric inversion methods to constrain the location of the combined total surface CO 2 fluxes from all sources, including fossil and land-use change emissions and land and ocean CO 2 fluxes. The inversions assume E FOS to be well known, and they solve for the spatial and temporal distribution of land and ocean fluxes from the residual gradients of CO 2 between stations that are not explained by fossil fuel emissions. Six atmospheric inversions (Table  A4 ) used atmospheric CO 2 data to the end of 2019 (including preliminary values in some cases) to infer the spatio-temporal distribution of the CO 2 flux exchanged between the atmosphere and the land or oceans.