dataesr/scientific-paragraphs-categorization huggingface dataset:
This resulted in 8% of all records missing; therefore, we did not use these records during model calibration and verified that missing data did not affect our analyses (see 'Missing fish data' section). Environmental and anthropogenic variables. We selected environmental correlates related to major hypotheses attempting to explain variation in fish diversity in previous studies and based on general ecological theory. Considering the spatially aggregated nature of our data, we focused on large-scale environmental correlates that were mostly relevant to our analysis of occurrence patterns across locations (typically 10-100 km apart), instead of finer-scale environmental correlates (for example, benthic cover) that were inconsistently available across all sites. We considered the following large-scale environmental correlates: (i) biogeography, because it is related to dispersal rates through local connectivity; (ii) habitat area, because it is related to the probability of colonization from neighbouring reefs or patches within reefs and export to other reefs; and (iii) energy, because its availability can constrain species occurrence based on their physiological tolerances and because greater energy availability can sustain larger populations.
We also considered a range of proxies for (iv) human pressure (past and present threat  7  , human impact  8  and ocean health index  9  ) to account for potential pressures on coral reefs resulting from fisheries exploitation, pollution, urban development, aquaculture and past thermal stress (Supplementary Table  6 ), which can affect coral reef ecosystems at a global scale  2, 54. We extracted these environmental data from global data sets (Supplementary Table  6 ) and matched them to the locations where fishes were sampled. Details for individual correlates and data sources are provided below. (i) We used biogeographic correlates as proxies for connectivity and relative position within a species' geographical range, and included the shortest distance (km) to the nearest landmass 54 410 5 km 2 , the shortest distances to the edge of the continental shelf (km) and to the nearest species range margin (km).
This allowed us to keep a consistent definition of the spatial extent and resolution corresponding to each qualifier across data sets. Within each location, a sample typically corresponded to a site or a station where several replicates (transects or stationary point counts) were collected, across which we pooled the fish data for analysis. For the analysis, location was used as the sampling unit (corresponding to an average total sampled area of 2,760 m 2 , range 1,200-4,000 m 2 ), which allowed us to minimize issues of spatial autocorrelation and random sampling error. We tested for potential data source effects using countries and species that were sampled in multiple data sets. These countries included, for example, French Polynesia, New Caledonia, Tonga, Samoa. We compared the probability of presence of each species in each country, according to each data set, and tested for potential differences among data sets by using a permutational multivariate analysis of variance using distance matrices  58  (permanova; function 'adonis' in R package vegan).
Similarly, for data sets with temporal replicates (for example, New Caledonia, Solomon Islands, Lord Howe Island), we tested for both seasonal and interannual differences in species' probabilities of occurrence using permanova. Missing fish data. The PROCFish and WCS data sets included unavailable records for 91 species and 1,650 samples, and 175 species and 247 samples respectively, out of 241 species and 9,828 samples in the entire data set. For several species with incomplete sampling, some PROCFish and WCS locations fell beyond their geographic range, which thus limited the impact of missing data for such species. This resulted in 973 locations with all (potentially present) species sampled, and 103 locations with 15-59% species sampled. For such species, missing data represented on average 13% of all records (median 2%), which in some cases prevented model convergence; such species were thus not considered in further analyses (i.
We tested this hypothesis on a subset of our data for which distance-sampling observations were available. That is, fishes were also recorded beyond the 5-m wide transect on 3,630 of the GASPAR transects spanning a wide range of fishing pressure in New Caledonia, Fiji, Tonga and French Polynesiathese data have been published elsewhere  60. Our hypothesis was that fishes recorded beyond 5 m are little affected by the presence of the diver and, in case of behavioural avoidance under fishing pressure, would only (or mostly) be recorded at such distances. We thus calculated the probability of recording false absences within 5 m as the proportion of transects where a species was only recorded beyond 5 m and, therefore, considered absent within 5 m. We compared the probability of recorded false absences along a gradient of fishing intensity (from 1: no or weak fishing to 5: intense fishing), both for all species and targeted/large ones.
Acknowledgements We thank  C. Mora ,  B. Halpern  and  A. MacNeil  for sharing ideas, and many staff from the authors' institutions for contributing to data collection, in particular  R. Galzin  and  M. Harmelin-Vivien. CM was funded by an  ARC  Grant ( DE140100701 ). AMMS was supported by a  Collaborative Post-doctoral Fellowship  (AIMS,  CSIRO  and  UWA ) from the  Indian Ocean Marine Research Centre. This work was done within the  Marine Biodiversity Hub , a collaborative partnership supported through funding from the  Australian Government's National Environmental Research Program (NERP)  (www. nerpmarine. edu. au). The PROCFish data set was collected under the  COFish and PROCFish-C programs  implemented by the  Secretariat of the Pacific Community (SPC)  through funding by  European Development Funds (EDF).
For immunoPET imaging, mice were injected with 20 µg of either the [ 64 Cu]NODAGA-labeled anti-YadA antibody or the [ 64 Cu]NODAGA-labeled Aspergillusspecific JF5 control mAb, which corresponded to 12-14 MBq. For the blocking studies, 500 µg of the nonradiolabeled YadA antibody was injected 3 h prior to the injection of the tracer. Ten-minute static PET scans were acquired after the uptake time of the F-18 labeled tracer (Figure  2A ) and 3, 24 and 48 h after the injection of the [ 64 Cu]NODAGA-labeled tracers (Figure  3A ). During the PET and magnetic resonance (MR) imaging, the animals were anesthetized with 1. 5 % isoflurane mixed with 100 % oxygen. PET data were acquired in list-mode, histogrammed in one 10-min time frame and reconstructed using an iterative ordered subset expectation maximization (OSEM) algorithm.
No attenuation corrections were applied. MR imaging was performed on a 7 T small animal MR tomography system (Clinscan, Bruker Biospin MRI, Ettlingen, Germany) to obtain anatomical information. A T2-weighted 3D-space sequence (TE / TR 202 / 2500 ms, image matrix of 137 x 320, slice thickness 0. 27 mm) was used for whole-body imaging. PET images were normalized to each other and subsequently fused to their respective MR images and analyzed using the Inveon Research Workplace software (Siemens Preclinical Solutions). Regions of interest (ROIs) were drawn around the respective tissues based on the anatomical information from the MR images. Volumetric data from the spleens were assessed based on the PET/MR data using these ROIs. Absolute quantification of the PET data are expressed as a percentage of the injected dose (%ID/ cc).
Real-time PCR Total RNA was isolated from mucosa using an RNeasy mini kit (Qiagen), and 0. 5 μg of total RNA was reverse transcribed using a high capacity cDNA reverse transcriptase kit (Applied Biosystems). Then, this cDNA served as a template for real-time qPCR, which was performed using an Applied Biosystems StepOnePlus TM thermocycler. The 36B4 / RLP0 gene was used as a housekeeping gene. Primers and probes for all genes were obtained from Applied Biosystems. The data were quantified using the 2 -ΔΔCt method.
Production of triglycerides rich lipoproteins (TRL) is altered in MetS mice Because postprandial TG is largely due to intestinal TG secretion, we measured blood TG secretion during 4 h after the lipid load using a lipoprotein lipase (LPL) inhibitor, Tyloxapol. TG secretion was similar to control mice until 3 h, but became significantly higher 4 h after the lipid load  (Fig 1C). This data indicated that postprandial lipemia observed in MetS mice from 1 to 3 h after the lipid load was not due to hyper TG secretion. The TG secretion measured without a fat challenge is similar in either mice group. This result indicated that the TG secretion coming mainly from liver could not explain the differences of TG secretion observed during the postprandial state in either mice group (Fig  1C). Because the TRL size was also able to modify their clearance, large TRL exhibiting a greater affinity for LPL than smaller ones  [25] , we estimated secreted TRL size by DLS analysis 1 h, 2 h and 4 h after the lipid load.
Acknowledgments Drs. C. Loupiac  and  A. Assifaoui  for assistance with the DLS analysis,  E. Ksiazek  for statistical analysis of lipoprotein distribution. The authors thank  G. Maquart ,  JF. Merlin ,  MC. Monnot ,  C. Wacrenier  for technical assistance and  Jean-Christophe Blanchard  for animal management. A. Bouchot  from  CellimaP (Burgundy University ) for cellular imagery.
This work was supported, in whole or in part, by: the  Fonds Unique Interministériel  ( FUI SYMPPA ); a  French Government  grant managed by the  National Research Agency (ANR)  under the program " SensoFAT 2"  ( ANR-12-BSV1-0027 ). M. B. was financed by the  Fonds Unique Interministériel  ( FUI SYMPPA ), and V. T. and T. T. T. were financed by the  Ministère de l'Enseignement Supérieur et de la Recherche.
All patients were asked to consent for an optional collection of tumour samples ( paraffin embedded) and blood samples as part of the trial. Blood samples (for genomic and proteomic analysis) were collected at three time points: at baseline prior to any treatment, on day 22 of treatment and on completion of treatment. The sample collection schedule is shown in table  1. Copies of all CRFs continue to be returned to the trials centre for statistical analysis. All forms are tracked and entered into a study-defined database for which consistency checking is programmed in. Data managers check for missing and invalid data using SQL queries and statistical programmes. Any queries raised are returned to the centres for correction or clarification.
Data collection and management On completion of the study, the data will be written onto compact disk and archived in a safe and secure location within the MAHSC-CTU. Paper copies of the CRF's will be retained at sites for at least 15 years following the last, patient entered or, if all are deceased may be archived off site. All paper data will be destroyed after 15 years on the approval of the chief investigator. The trials centre staff are in regular contact with local centre personnel to check on progress and to help with any queries that may arise. Incoming forms are checked for completeness, consistency, timeliness and compliance with the protocol.
Statistical analysis plan The analysis will be based on intention-to-treat principles (ITT). Patient data will be grouped by treatment arm according to the treatment assignments made via the MAHSC-CTU randomisation line. The primary data analysis is planned for January 2015. The stratification factors include centre, PS (0-1 vs 2) and biochemical factors (eg, LDH, sodium, alkaline phosphatase). Comparison of data by centre will be scrutinised to identify any data inconsistencies, it will also be used to identify those centres planning to give either four or six cycles of chemotherapy. Analysis will be carried out to identify any differences between the two schedules. Other factors have been identified in previous studies to be prognostic factors and will be used to calculate the Manchester score, which gives three groupings of good, intermediate and poor prognosis; these scores will be used to compare OS and response rate.
The only planned interim analyses have been performed for the Independent Data Monitoring Committee (IDMC). Reports have been submitted to the IDMC on an annual basis starting 12 months after the first patient was randomised. The following are the qualifications for analysis of time-to-event efficacy parameters: ▸ All randomised patients will be included in the analysis of overall survival (OS) and local progression free survival (ITT). OS is the time between date of randomisation and date of death of any cause. Survivors will be censored on the last date known to be alive. Local progression-free survival (local control) will be calculated from the date of randomisation to the date of first clinical evidence of progressive disease at the primary site, or death. Kaplan-Meier curves will be drawn for each treatment group.
Trial monitoring and oversight No formal on site data monitoring activities were performed as part of the CONVERT trial. Data are reviewed annually by an IDMC, consisting of three clinicians not entering patients into the trial, and an independent statistician. Throughout the duration of the trial, the IDMC has recommended whether the accumulated data from the trial, together with results from other relevant trials, justified continuing recruitment of further patients. The IDMC has made confidential recommendations to the Trial Steering Committee (TSC). The role of the TSC has been to act on behalf of the funder, to provide overall supervision for the trial, to ensure that it is conducted in accordance with GCP and to provide advice through its independent Chairman. This independent committee reviews the recommendations from the IDMC and decides on continuing or stopping the trial or modifying the protocol.
Acknowledgements The authors would like to acknowledge the support of The  NCRI Radiotherapy Trials quality assurance team , the  Manchester Academic Health Science Centre Clinical Trials Unit ,  Sally Falk  (CONVERT trial coordinator),  Priscilla EsehSumbele  (CONVERT data manager),  Dr David Girling  (IDMC),  Dr Steve Roberts  (IDMC),  Professor Christian Manegold  (IDMC),  Dr Robert Huddart  (IDMC) and  Alice Taylor  (editorial assistance).
Estimates of the prevalence of workers with occupational exposures were obtained from data available in the 2000 Brazilian Census  [37]  and in the 2003 National Household Sample Survey (PNAD, 2003)  [38]. The occupations for each field based on IBGE's occupational classification with exposure to carcinogenic agents according to IARC (Group 1)  [2] , as listed in Table  1 , were included in the study. Sources of environmental exposure data included surveys and studies supporting approximate estimates of background population exposures. The population exposed to particulate matter (PM10) pollution was estimated as being the proportion of individuals living in metropolitan areas in Brazil in 2008  [39]. The average annual exposure to PM10 in these areas was considered similar to the weighted average exposure in 21 industrialized cities obtained by measurements of the Brazilian Environment Protection Agency's pollution monitoring programs between 2000 and 2008  [39].
The mortality for 2020 was projected using a linear regression model with its naperian logarithm as the response variable. Lastly, the incidence for each type of cancer was obtained by multiplying mortality by the average incidence: mortality (I/M) ratio estimated from 11 pooled PBCR with the best data quality in the period from 2000 to 2010. The criteria to select these PBCR were: population size greater than 200,000; reporting data from at least three consecutive years; information with death-certificate-only registrations less than 20%; more than 70% morphologically-verified cases (MV > 70%) and no unexpected fluctuations in the number of incident cases (stable coverage). This study was approved by the Research and Ethics Committee of the Institute of Social Medicine of the Rio de Janeiro State University in 20 August 2013 (Process Number: CAAE 18415713.
As many of these reproductive factors are not subject to preventive intervention, only breast-feeding and the use of oral contraceptives were included in the present study. Although the use of oral contraceptives and not breast-feeding are prevalent conditions among Brazilian women, small attributable fractions were estimated (2. 1% and 1. 4%, respectively, as shown in S2 Table ), due to the small relative risks related to these factors. The occupational agents analyzed in the present study, despite high relative risks for some conditions, show a lower impact on total cancer in the Brazilian population (2. 3% in men and 0. 3% in women) when compared to data from other countries where a range from 3 to 14% in male adults and 1 to 2% in female adults was observed  [57]. The low impact of the occupational agents studied is noteworthy, possibly suggesting an underestimation of the real prevalence.
This fact may be explained by the use of a specific criterion for determining occupations highly exposed to agents that are classified as definitively carcinogenic. The number of workers included in this criterion was estimated based on data drawn from official statistics. Since many individuals in occupations with a high potential for carcinogenic-agent exposure work in the informal market, underreporting is likely to occur  [58]. This fact may have affected, in particular, the data on workers' exposure to asbestos. The relative risk estimates used in this study were drawn from meta-analyses and large international studies. Although this option helps external comparison, some of these estimates may not accurately represent the exposure effects in our population. Another limitation refers to the suboptimal quality of mortality and incidence data.
Indeed, ERA-Interim analyses  28  show that the region received about twice lower rainfall beween January and May in 2011 compared to other studied years, which likely caused decreased crop yields and agricultural waste. The top-down emissions, expressed as burnt biomass (in TgC), are compared with four independent inventories for biomass burning in Fig. S6. Two versions of the Global Fire Emissions Database are used, version 3  5  and the GFED4s which includes burned area from small fires based on active fire detections  29  ; the Global Fire Assimilation System (GFAS) based on assimilation of fire radiative power observed from MODIS aboard the Terra and Aqua satellites  30  , and the Fire Inventory from NCAR (FINN) version 1. 5, an updated version of the FINN inventory  31. On average over the eight years the top-down emissions are about 14, 7 and 4 times larger than in GFAS, GFED4s, and FINN, respectively, whereas the GFED3 emissions are even lower.
56% in the standard (STD) optimisation. The better agreement with the observations is realised by even larger emissions, 950 Gg in DE vs. 850 Gg and 690 Gg in STD and HE, respectively. The increase is especially pronounced in the northernmost part of the NCP (ca. factor of 5), apparently reflecting a misrepresentation of the spatial patterns of the a priori emissions, for reasons currently unclear (Table  1 , Fig. S7 ). Another important source of uncertainty lies in the assumed biomass burning emission factors. The standard setup employs the widely used updated (2011) emission factors from ref. 18, whereas a recent compilation of emission factors based on measurements performed in fresh smoke  19  is adopted in two sensitivity inversions, AK and AKU. The agricultural waste emission factors are similar in both databases for most compounds, however large differences exist for individual compounds, e.
g. acetaldehyde (2. 77 g/kg in ref. 18, 1. 24 in ref. 19), glyoxal and methylglyoxal (1. 0 and 0. 73 g/kg in ref. 18, 0 in ref. 19). Comparison with measurements of crop burning emission factors in China  32  indicates that the emission factors adopted in our standard simulation  18  are likely too high for oxygenated compounds (as they are not corrected for chemical production in the fire plumes) and possibly too low for pure hydrocarbons such as alkanes, alkenes and aromatics. A large source of variability and uncertainty might lie in the complex influence of fuel moisture on the emission factors  33. Overall, the a priori biomass burning emission in June is by 20% lower when the newer datasets  19, 32  are used (280 vs. 350 Gg, Table  1 ). The optimisation AKU, also based on ref. 19, acknowledges the fact that a significant mass fraction of the gas-phase VOC emitted by biomass burning is unidentified, as revealed by field and laboratory measurements  34, 35.
Discussion The strong increase in the satellite-derived fluxes is further corroborated by satellite observations of HCHO retrieved by the GOME-2 sensor onboard Metop-A which measures HCHO columns daily with a morning overpass time (9:30 local time). As in the case of OMI (afternoon overpass 13:30), the GOME-2 dataset indicates a pronounced maximum in the NCP in June, by ca. 10-50% higher than the preceding and following months. The GOME-2 peak is by about 10% lower than the corresponding maximum observed by OMI (Figs  3  and 4 ), the difference being primarily due to the diurnal cycle of HCHO columns, which is mostly affected by the diurnal cycle of emissions, as well as by OH and solar radiation levels  15. The current version of the GOME-2 data is briefly described in Section 1 of the Supplement and in detail in ref.
The secondary glyoxal peak observed in October (Fig. 5a ), not reproduced by the a posteriori model, might be partly due to fires set up for clearing fields after the maize harvest season. These fires cause a secondary peak in the seasonal profile of the a priori emissions  6  , but of very weak amplitude, about a factor of 20 lower than the June peak. Furthermore, the OMI HCHO dataset does not show a consistent pattern, with a local maximum in October seen only for two years (2006 and 2012) out of 8. Note that the higher zenith angles and lower magnitude of HCHO columns reduce the signal-to-noise ratio and the ability of the inversion to retrieve reliable fluxes in October. The higher top-down emissions lead to a better agreement with satellite obeservations of NO 2 from the OMI instrument (DOMINOv2 11 ).
Methods Modelling. The emission inversions performed with the IMAGESv2 global chemistry transport model for all years between 2005 and 2012 following a four-month spin-up time. The model resolution is 2° × 2. 5° and is extended vertically from the surface to the lower stratosphere through 40 unevenly spaced hybrid (σ-p) levels. The IMAGESv2 model is also run in regional mode, at 0. 5° × 0. 5° resolution, in a domain delimited as 17-54°N and 73-150°E. The regional model uses boundary conditions at its lateral borders from simulations using the global model. The chemical scheme, emission databases and parameterizations of the regional model and global model are identical. The a priori anthropogenic fluxes in IMAGESv2 are obtained from the REASv2 inventory for Asia until 2008 20 and are set to the 2008 value beyond that year.
Slant column densities of HCHO are fitted in the 328. 5-346 nm spectral window based on the algorithm described in ref. 9, which has been improved (i) to reduce the O 2 -O 2 absorption effects and minimise the interferences with other molecules, (ii) to remove spike residuals, and (iii) to reduce offsets between rows by applying a destriping correction in the background reference sector  10. These modifications eliminate inconsistencies and unphysical results, increase the signal-to-noise ratio, and offer a higher temporal stability to the long-term dataset of OMI HCHO columns. Monthly mean HCHO columns between 2005 and 2012 are used as constraints in a flux inversion scheme based on the discrete adjoint model of the IMAGESv2 global CTM (see below). The target region is Eastern China, and in particular the NCP delimited as 32-40°N, 112.