dataesr/scientific-paragraphs-categorization huggingface dataset:
Time spent in VPA was categorised as none (0 h/week), 1 and ≥2 h/week. Alternative categorisations were also used: physically active (>0 h/week of MVPA) vs inactive (0 h/week of MVPA); following physical activity recommendations (≥3 h/week of MVPA or ≥2 h/week of VPA) vs not following recommendations. The cut-off for recommended duration was ≥3 h instead of ≥2. 5 h for MVPA and ≥2 h instead of ≥1. 25 h for VPA  [34]  as the amount of time spent in physical activity was recorded as a whole number in 1985-1988. Interactions of physical activity measures with sex and age were also tested. Date of type 2 diabetes diagnosis corresponded to the first record of type 2 diabetes using data from all sources. As fasting glucose was a key component of diabetes diagnosis, the exact date of diabetes onset was not known as it depended on the dates of clinical examination undertaken every 4-5 years (interval-censored data).
In sensitivity analysis, missing data on physical activity (n = 155) and covariates (n = 66) at baseline were imputed using chained equation modelling and data were reanalysed including these participants. Role of post-diabetes MVPA in subsequent risk of all-cause and CVD mortality These analyses were based on participants who developed type 2 diabetes during the follow-up and had physical activity measurement and other covariates assessed at the first available wave of data after type 2 diabetes diagnosis. A Cox proportional hazard model was used to assess the association of physical activity after diabetes diagnosis with risk of subsequent all-cause and CVD mortality, separately. Inverse probability weighting (IPW) was used to account for missing data  [40]. This involved calculation of the probability of being included in the analytical sample using data on MVPA (four categories: 0, 1-2, 3-4 and ≥5 h of MVPA per week), sociodemographic variables, behavioural variables, BMI, hypertension, total cholesterol, self-reported use of CVD drugs in 1985-1988, chronic conditions (chronic obstructive pulmonary disease, cancer, CVD), and mortality status over the follow-up, including an interaction between mortality status and MVPA categories.
Sampling plots Biodiversity data were collected from 64 rubber 'plots' in Songkhla and Phattalung provinces. Plots were defined as even-aged units ≥1 ha (mean 1. 92 ha, range 1. 0-7. 6 ha) at least 100x100 m in dimensions (measured using GPS and laser rangefinder), termed the 'biodiversity dataset'. Plots were categorised as monocultures (MO, n = 25) or agroforests (AF, n = 39). Plots were chosen by first locating an appropriately sized agroforestry plot with the help of local agroforestry groups, because agroforests are relatively rare in the landscape. Almost all agroforestry plots above the minimum size requirements were surveyed, and we then co-located monoculture plots in the vicinity of each agroforest. Clonal rubber trees were usually planted at 3 m intervals in rows 7 m apart in both systems (476 stems ha -1 ,  Phommexay et al.
, 2011). Agroforests contained cultivated non-rubber tree, shrub or herbaceous plants, or naturally-regenerated wild trees, mostly in the inter-row (mean 162 ± 207 SD stems ha -1 ). Complexity ranged from one or two cultivated species (Fig. 1b ), to multiple native tree species (Fig. 1d ). Three monocultures contained nonrubber crops at densities too low to be considered agroforests (e. g. a single bamboo clump, 17 ± 51 stems ha -1 ). Latitude of biodiversity dataset plots ranged from 6. 641374 to 7. 602200 o N (76 km north-south) and elevation from 35. 0 to 137. 1 m asl. Plots were clustered into 600 x 600 m 'blocks' (n = 23) for which landscape composition data was collected. Each block contained two or three plots with centroids 200-400 m apart (Fig. 2a ). Agroforests and monocultures were represented in each block wherever possible, but five blocks contained only.
Biodiversity data Biodiversity data were collected from up to nine agroforestry and monocultural plots each day, to control for potential weather and seasonal effects. The number of rainy days during sampling was recorded based on field or overnight observations (within 10 km of plot). Strong El Niño conditions during sampling could have influenced sampling by decreasing the suitability of relatively open habitats due to increased heat and decreased humidity (such as monocultures with lower canopy cover), or by forcing some species into cooler, wetter, refugia (e. g. stream beds). Birds were surveyed by one experienced ornithologist (L. N. ) on three consecutive mornings (06:00-09:30) in ten-minute point counts at each plot centre, alternating the order in which plots were visited  (Gilroy et al.
Rubber plot management Rubber plot management (and resulting vegetation structure) data were collected from each plot in the biodiversity dataset. Cultivated plant species names were recorded during a farmer interview. Vegetation structure was quantified through field measurements (Fig. S4  and text ) giving plot-level values for herb height (cm), canopy cover (%), small stem density (ha -1 ), non-rubber tree stem density (ha -1 ), fruit tree stem density (ha -1 ), and the number of agroforestry species.
Accepted Article Land cover was recorded as: rubber agroforestry (AF), rubber monoculture (MO), immature rubber, bare ground, scrub, urban (village, road or town), natural forest, fruit orchard, home garden, cassava, oil palm, rice paddy, timber plantation or coconut grove. Using Google Earth we mapped streams and rivers, and calculated riparian feature length per block. These data were summarised into six variables: percentage of the points (hereafter termed extents) in rubber (total AF and MO), open habitats (total of immature rubber, bare ground, cassava or rice paddy) and natural forest, the ratio of AF to MO, Shannon-Wiener diversity index of land uses (using point-frequency data), and riparian length. This point-based sampling approach was validated by comparison with area-based measures of landscape composition, extracted from manual mapping of management units using Google Earth imagery for a subset of ten blocks, informed by all available GPS points for each block.
Rubber yields Rubber yield estimates were collected in 2016, via questionnaires with farmers, for a separate set of 47 agroforest and 34 monocultural rubber plots in Phattalung province with considerable spatial overlap to the 'biodiversity dataset' (Fig. S2 ). These data were collected as part of a wider study of farmer livelihoods and rubber agroforestry, which also included data on agroforestry species composition, and is termed the 'yield dataset' (full methods in  Stroesser et al. , 2018). Annual rubber yields were either reported for latex (coagulated, reported as dry weight) or as 'cup-lump' values per plot (converted to dry weight assuming dry rubber content of 65%) to give yields in t ha -1 yr -1. Plot characteristics with the potential to affect rubber yield were compared between the yield and biodiversity datasets using generalised linear models and Mann-Whitney U tests.
Rubber yields in Southeast Asia are affected by elevation, cold, and drought  (Ahrends et al. , 2015). Mean latitude of yield plots was 7. 473321 o N (SD 0. 199305 o ), and mean elevation (proxy for temperature and drought stress) was 98. 2 m asl (range 42. 0 -164. 0 m asl), differing trivially from the biodiversity plots (mean latitude difference: 47. 2 km, 95% CI 38. 2-55. 6 km; elevation: 14. 1 m, 4. 3-24. 0 m asl). All plots in both datasets contained high-yielding rubber clones, and mature rubber trees (7-30 years).
Accepted Article This article is protected by copyright. All rights reserved There were no differences in cultivated plant species richness per plot, or the stem densities of rubber, fruit and timber trees respectively (stems ha -1 ; Fig. S6-S7 ). The agroforestry systems are thus similar in both datasets, and any effects of structural complexity or intercropping density on yields and biodiversity are likely to be highly comparable. Soil type may affect rubber yield. Soils for the region are defined as Acrisols (Food and Agriculture Organization of the United Nations, 2012), but according to a national-level soil survey, the plots fell within six soil types, dominated by Udults (freely-drained, humus poor, light soils,  USDA, 1999; Department of Land Development, 2002). A greater proportion of yield plots had marginal "skeletal" soil types than biodiversity plots (53% versus 22%; Table  S1 ).
It is possible that soils in yield plots are generally poorer quality than biodiversity plots. However, within the yield dataset, 47% of plots were skeletal, of which 64% were agroforest, suggesting yield variation due to soil type is likely to be present within both agroforests and monocultures. Support from RAOT during rubber planting/re-planting means farmers tend to follow standard practices for immature rubber, but experienced rubber farmers in southern Thailand have diverse ongoing management practices (e. g. fertilisation rates, tapping intensity) once trees are mature  (Besson, 2002; Chambon et al. , 2017). However, we cannot conceive any reason why management practices would differ systematically between the yield and biodiversity datasets.
Accepted Article This article is protected by copyright. All rights reserved Species richness and number of detections or catches per plot (proxies for abundance) were compared between AF and MO plots using generalised linear mixed models (GLMMs), fitted using maximum likelihood, with a Poisson distribution and log link function, using the lme4 package  (Bates et al. , 2015). Block was included as an intercept-only random effect to account for the nested sampling design, with district and rainfall index additional intercept-only random effects for butterfly models, as these influenced butterfly species richness (Fig. S8-S10 ). The AICc of the full models were compared to a null model containing only random effects  (Burnham and Anderson, 2002). Systematic spatial autocorrelation of model residuals, examined using a Monte-Carlo permutation test for Moran's I using package spdep with 1000 iterations  (Bivand and Wong, 2018) , was not found for any model.
, 2011) , a null (intercept-only) model containing the random effects, and a candidate model set (comprising all possible model subsets with four or fewer variables, ensuring at least 15 observations for each candidate variable) were generated using the MuMIn package  (Grueber et al. , 2011; Bartoń, 2016). The 57 candidate models were ranked according to AICc and weights using the bbmle package  (Bolker and R Development Core Team, 2017). Models with a cumulative weight ≥95% were averaged by the full (zero) averaging method  (Burnham and Anderson, 2002; Grueber et al. , 2011) , using the MuMIn package  (Bartoń, 2016). If the 95% confidence intervals of the averaged parameter estimate excluded zero, it was considered influential  (Grueber et al. , 2011). Influential rubber plot management variables were included in the next step of analysis.
Accepted Article This article is protected by copyright. All rights reserved levels of random effects  (Bartoń, 2016). Predictions made using standardised variables and log link function of the final model were back transformed, to visualise predictions relative to variables in their original units. The SE of predictions from the averaged model were not calculated, as tools to calculate prediction intervals for GLMMs using the lme4 package  (Knowles and Frederick, 2016 ) cannot be applied to averaged models. Species composition responses to rubber plot management and landscape composition were assessed using RDA. A global RDA model, including plot type and all plot management and landscape composition variables, was fitted using the vegan package  (Oksanen et al. , 2017)  without rare species (total detections/sum of presences <3;  Barlow et al.
, 2010). Block was not used as a conditional effect because landscape composition was measured at the block level. Systematic spatial autocorrelation in the global model, examined using an adaptation of the Mantel test in the vegan package  (Wagner, 2004; Oksanen et al. , 2017) , was not found for any taxon. Automatic backward model selection was performed on the global model (9999 permutations) with a threshold of p > 0. 05 (a "pseudo-F " test statistic: ratio of constrained and unconstrained total inertia in the RDA, divided by their respective ranks) to drop terms from the model  (Legendre et al. , 2011; Oksanen et al. , 2017). The significance of each term in the final model was examined using the same method.
Accepted Article This article is protected by copyright. All rights reserved Rubber plot management and structure AF plots in the biodiversity dataset had greater fruit and timber tree species richness (mean 6. 6 ± 0. 6 SE non-rubber tree species, 0. 2 ± 0. 1 in MO), greater density of timber, fruit and wild trees (162 ± 33 stems ha -1 , 17 ± 10 in MO), smaller rubber basal area (16. 8 ± 1. 0 m 2 ha -1 , 21. 5 ± 1. 7 in MO), and greater canopy density (76% ± 0. 8, 71% ± 1. 4 in MO), small stem density (2552 ± 522, 601 ± 224 in MO), and understorey density (index 6. 6 ± 0. 7, 4. 3 ± 0. 7 in MO; Fig. S12 ). However, mean herb height was similar between AF and MO, because understorey management varied among plots of both types. Three tree species recorded in agroforests are globally threatened, and a total of 37 plant species were recorded across all plots grown for a variety of uses including timber, fruit, and resin (Table  S2 ).
Acknowledgements EWT was supported by  NERC  ( NE/L50158X/1 ) and a  Small Ornithological Research Grant  from the  British Ornithologists' Union. Werapun Pichpanus  and staff,  Nutjarin Petkliang ,  Tuanjit Sritongchuay  and  Rinmanat Waiyarat  provided logistical support, and  Natrada Plappleung  provided access to the  Princess Maha Chakri Sirindhorn Natural History Museum butterfly collection. Alain Brauman  provided CIRAD connections. This research was only possible through the exceptional generosity of the rubber farmers, and their families, who participated in the study. We thank  Dr Badrul Azhar  and  Dr Kevin Darras  for constructive comments that improved the manuscript.
The rest was subjected to mass spectrometry analysis. Two independent experiments were performed. Candidate endothelial PKA targets were identified as peptides that were at least 2-fold (log ratio(AS/WT)>1) enriched in the AS-PKACa samples compared to WT-PKACa samples in both experiments. The ninety-seven proteins identified in both experiments (Table  1 -source data 1) included several known PKA substrates such as NFATC1  (Niswender et al. , 2002) , VASP  (Anton et al. , 2014; Butt et al. , 1994; Profirovic et al. , 2005) , PRKAR2A and PRKAR2B  (Manni et al. , 2008)  indicating that the chemical genetic screen worked well to identify PKA substrates. Thirty proteins with at least 8-fold enrichment (log ratio(AS/WT)>3) are listed according to their log ratio (AS/WT) value in Table  1 , presenting the most likely direct substrates of PKA in this screen.
25 ml/min and ionized using a Proxeon ion source. Mass spectrometric acquisition was done at a resolution of 60,000 with a scan range of 200-1,700 m/z in FTMS mode selecting the top 20 peaks for collision-induced dissociation fragmentation. Tandem mass spectrometric scans were measured in ion-trap mode with an isolation width of 2 m/z and a normalized collision energy of 40. Dynamic exclusion was set to 60 s. For data analysis, the MaxQuant software package version 1. 5. 2. 8  (Cox and Mann, 2008)  was used. Carbamidomethylation on cysteine was set as a fixed modification and oxidized methionine, acetylated N-termini and phosphorylation as variable modifications. An FDR of 0. 01 was applied for peptides and proteins and the Andromeda search  (Cox et al. , 2011)  was performed using a mouse Uniprot database (August 2014).
The mass spectrometer was operated in data-dependent mode, automatically switching between MS and MS/MS acquisition. Full-scan MS spectra (400-2000 m/z) were acquired at a resolution of 70,000 in the orbitrap analyzer after accumulation to a target value of 3,000,000. The five most intense ions above a threshold value of 17,500 were isolated (window of 2. 0 Th) for fragmentation at a normalized collision energy of 25% after filling the trap at a target value of 50,000 for maximum 80 ms. MS/ MS spectra (200-2000 m/z) were acquired at a resolution of 17,500 in the orbitrap analyzer. Raw LC-MS/MS data files were searched against the human proteins in the Uniprot/Swiss-Prot database (database version of September 2017 containing 20,237 human sequences, downloaded from www. uniprot. org). The mass tolerance for precursor and fragment ions were set to 4.
Acknowledgements We are grateful to  Evy Timmerman  and  Francis Impens  for their help to identify the phosphorylation sites of ATG16L1 in MS analysis at the  VIB proteomics core facility. We are grateful to professor  Chantal Boulanger  for providing the ATG5 flox/flox mice. This work was supported by grants from the  Fonds voor Wetenschappelijk Onderzoek (FWO)  [ G. 0742. 15N  to HG]; the  Vlaams Instituut voor Biotechnologie (VIB) Tech  watch grant [ Q3 2015  to PIN]; the  Elsa-Kroener-Stiftung  [ 2014_A26  to HG and PIN]; the  European Research Consortil [ERC Consolidator  grant  REshape 311719  to HG]; This work was supported by grants from the  German Centre for Cardiovascular Research (DZHK ) partner site Berlin ( 81XZ100146 ), the  Deutsche Forschungsgemeinschaft  ( DFG KL1415/7-1  and  394046635-SFB 1365 ) and the  Bundesministerium fu€r Bildung und Forschung (BMBF ;  16GW0179K ) to EK.
g. , Ka-band altimetry and SAR mode), have enabled the development of new coastal altimetry datasets  (Birol et al. , 2017; Passaro et al. , 2018). However, these products remain inhomogeneous in space and time, and efforts are needed to construct a consistent global gridded coastal altimetry database. A detailed discussion of coastal sea level monitoring by satellite altimetry is provided in Section "Coastal Zone Altimetry Processing and Exploitation: Progress and Outlook for the Next Decade. ".
Because of different generation mechanisms, storm surges caused by tropical cyclones are different than those produced by higher-latitude storms. Tropical storm surges tend to be of smaller spatial scale, and shorter duration, but are much larger in amplitude. Valuable sources of information on sea level extreme events come from the data banks of tide gauge records collected by the University of Hawaii Sea Level Center (UHSLC) and the British Oceanographic Data Centre (BODC), for the Global Sea Level Observing System (GLOSS, a program from the Intergovernmental Oceanographic Commission of United Nation Educational, Scientific and Cultural Organization -UNESCO; IOC, 2012). The most complete sea level data set assembled to date, combined from governmental and research sources, is called GESLA-2  (Woodworth et al.
Monitoring seasonal to longer-term changes requires highresolution observations in space and time. However, the actual specifications depend on the coastal site of interest. For example, multi-decadal surveys in highly dynamic deltas can benefit from medium resolution data (e. g. , Landsat data), which can be analyzed using a semi-automatic procedure  (Luijendijk et al. , 2018). However, most shorelines around the world are currently evolving at rates in the order of ±1 m/year or less  (Bird, 1985). With pioneering global coastal evolution datasets being made available today  (Luijendijk et al. , 2018) , meeting local user needs in terms of precision and accuracy (below 1 m/year) has become a next challenge for Earth Observations (EO).
Digital Elevation Models and Bathymetry High resolution Digital Elevation Models (DEMs) and coastal bathymetric data are critical datasets for a number of applications in coastal zones, including accurate modeling of flooding during storm surges, and quantification of coastal morphological changes due to sedimentary processes or human interventions. High-resolution bathymetric and topographic LiDAR techniques have allowed important progresses over the last decade, but still require post-processing to remove trees and cars from the raw data. However, the lack of repetitiveness in current LiDAR acquisitions is presently limiting the applications, especially in highly dynamic coastal zones such as beaches, marshes, and estuaries. Further acquisition of high-resolution topography, near-shore bathymetry and foreshore beach slopes data is an observational priority, especially along densely populated coastlines.
Summary To better understand how coastal zones react to various perturbing factors and change through time, a number of observing systems, either space-based, airborne or in situ should be implemented and combined. We summarize below a (nonexhaustive) series of observational needs related to the topics addressed in this section: -Ensure that the global and regional monitoring networks of extreme sea levels are completed to their full technical requirements. -Ensure that a global sea level database such as GESLA-2 is maintained and extended in the future. -Ensure that extreme water levels observations are acquired in areas unaffected by the wave setup. -Increase the number of GNSS stations co-located with tide gauges for monitoring vertical land motions  (Woodworth et al. , 2017). -Develop precise positioning capabilities in coastal areas combining GNSS and InSAR approaches to monitor local to regional land motions.
-Altimetry can provide wave height, wind speed and sea level at the same time. However, systematic monitoring of the fine-scale variations of these parameters at the coast is still lacking. The high-resolution wave field in the coastal band is also relevant, as it helps developing more realistic wave models that can be used to estimate wave setup. R&D investigations using archived and coming altimeter data sets (in particular with the new SAR mode) need to be conducted. Moreover, multi-sensor approach for wind and waves in the coastal zone using altimetry, SAR and scatterometry where ground-truth is available would be highly useful. -Coherent and homogenized SPM time series over the global coastal region should be collected from past and present space-based ocean-color data. This task requires inter-calibration between the different sensors, as well as the development of common atmospheric and bio-optical algorithms.
Uncertainty maps should also be provided. Fusion of high-spatial-low-spectral and medium-spatialhigh-spectral resolutions sensors should be undertaken to better sample the coastal environment at the relevant scales. -Long-term high-resolution observations of river discharge, ground water discharge, SPM in global coastal zones are highly needed. -The full potential of remote sensing for monitoring shoreline changes has not been fully investigated yet. Use of SAR data to monitor shorelines changes in tropical areas has been explored but the resolution of this data set (about 30 m for ASAR on-board Envisat) remains too limited. More recent high-resolution sensors (e. g. , Cosmo-Skymed) and improvements in orbital parameters control (e. g. , TerraSAR-X) offer useful alternative. Research to automatize the precise georectification of images (beyond what is classically provided by current workflows) and, ultimately, to retrieve shoreline proxies automatically would be much beneficial to the community.