dataesr/scientific-paragraphs-categorization huggingface dataset:
(DOCX) Table  S2  Thiopurine S-methyltransferase activity (TPMTa) for patients with multiple assays. RBC: red blood cells. TPMTa: TPMT activity. Over the 51 patients that underwent more than one TPMTa assay, only one patient had results that could induce a change in groups. He was assigned to the normal TPMTa group (group from his first TPMTa assessment). For all the other patients, we considered that the TPMTa was stable over time. (DOCX) Table  S3  Results of the preliminary phenome-wide association study on patients from TPMT cohort versus randomly selected patients from hospital clinical data warehouse. The ICD codes aggregation used was based on the 3-digit ICD-10 codes (2040 groups). Only the statistically significant results are reported here. (DOCX)   S6  Results of the Phenome-wide association study (PheWAS) between very high TPMT activity patients and other TPMT activity patients for the ICD-9-CM mapping aggregation.
Changes in seasonal variation and the mean values of LAI, mostly due to an increase in temperature at the beginning of the growing season, have important implications on the global carbon cycle. However, considerable uncertainty remains with regard to greening trends and the ability of models to reproduce satellite-derived trends. With new and improved LAI data now available  [25] [26] [27] [28] , a more precise validation of model output is imperative. The objective of this paper is to compare LAI from satellite-derived measurements with modeled output from a set of 8 LSMs over the Northern Hemisphere. We ask three questions to fulfill this objective:  Do uncoupled (LSMs) models correctly reproduce the spatial variability of LAI shown by satellite data over the Northern Hemisphere?  How does the length of the growing season in the different models compare with the satellite data? And where are the main discrepancies (onset or dormancy)?  What are the trends in LAI and the growing season over this period?.
Satellite Data The LAI data set used in this study was generated using an Artificial Neural Network (ANN) from the latest version (third generation) of the GIMMS AVHRR NDVI data for the period July 1981 to December 2010 at a 15-day frequency  (Zhu et al. this issue  [36] ). The ANN was trained with best-quality Collection 5 MODIS LAI product and corresponding GIMMS NDVI data for an overlapping period of 5 years (2000 to 2004) and then tested for its predictive capability over another five year period  (2005) (2006) (2007) (2008) (2009). The average uncertainty of the MODIS LAI product is estimated to be 0. 66 LAI units  [24] , though it varies depending on the mean LAI, and the data is for 1-sided LAI; further details are provided in Zhu et al. [36]. The 10 years of MODIS LAI/FPAR (2000-2009) was further processed to generate climatology.
Mean LAI All of the models overestimate mean LAI, LAI trend and interannual variability (IAV) over the high-latitude Northern Hemisphere compared to the satellite observations (Figure  2 ). In general, models with the highest average LAI also have strong positive trends. This occurs regardless of whether the models use imposed or dynamic vegetation, or the number of PFTs implemented. Interestingly, models with a trend and average LAI closest to the satellite records, such as ORCHIDEE, OCN and TRIFFID have very different values of IAV, ranging from values similar to the satellite data up to 4 times higher. On the contrary, the most dissimilar models to the observations, such as LPJ and CLM4CN, have larger IAV. Looking at the spatial distribution of LAI, most of the models simulate the observed spatial distribution pattern (Figure  3 ).
Peaks in LAI are evident over the boreal forest (55°-65°N) and the North American temperate forest (30°-55°N). The lowest values are found over the cold Gobi plateau and the Siberian Tundra. As noted above, there is a general overestimation of mean LAI in the models, relative to observations. LAI values range from 0 to 2. 5 in the satellite data, while for the models they are as high as 5. Models and observations agree on values over the deserts and low-LAI regions but the differences are higher (3-4) over the boreal region. As shown by spatial correlations, differences between satellite data and models are higher in VEGAS and TRIFFID, and smaller in LPJ and LPJ-GUESS (Figure  3 ). It is noteworthy that much of the discrepancies occur over evergreen vegetation, suggesting that the lack of regenerative vegetative states, fire and gap dynamics over this region lead to an overestimation of the number of fully grown trees on models, which ultimately means a much higher LAI than observed.
However, satellite signal saturation-this is the inability of the satellite to distinguish between areas with high LAI-could be leading to an underestimation of LAI in dense forested areas such as the boreal forest, which might also account for the lower LAI over this area. The seasonal amplitude patterns show large disagreements between the models and the satellite data (Figure  4 ). Most models overestimate the mean amplitude (RSME = 1. 02-2. 21), which is particularly evident over Europe and Eastern North America. The exception here is SDGVM, which displays little seasonality and performs better than the rest of the models in reproducing the satellite-derived observations. The RSME show that models using dynamic vegetation are less similar to observations than those using imposed vegetation.
Growing Season The growing season onset derived from LAI is broadly consistent across the models, with high correlations compared to the satellite data (>0. 5) (Figure  5 ). In general the satellite observations show a later onset as latitude increases, remarkably similar to the thermal gradient. CLM, LPJ-GUESS, LPJ, SDGVM and, to a lesser extent, OCN, ORCHIDEE and VEGAS correctly reproduce this spatial pattern, as shown by the RSME and spatial correlations. This is not surprising as those models include a thermal limitation to photosynthesis and a snow scheme. TRIFFID shows no detectable onset above 50°N but has later values compared to the satellite below that threshold, likely due to the distribution of the evergreen PFT over the whole NH. Models that have the highest correlations with the satellite on the SA also show very similar values to the satellite on the onset, as shown by RSME (Figure  5 ).
The discrepancies between the models and satellite observations are larger when considering the end of the growing season or dormancy (Figure  6 ). While the satellite data shows a latitudinal gradient, with the dormancy occurring earlier at higher latitudes, most models overestimate the dormancy day (RSME = 31-63). Out of the eight models, LPJ-GUESS, LPJ, ORCHIDEE and VEGAS have a similar dormancy distribution with minor discrepancies over the taiga and boreal forest, as shown by the spatial correlations. CLM, OCN and TRIFFID have patchy areas of agreement, while SDGVM has a much later dormancy than the satellite data. In some regions, particularly boreal deciduous forest, modeled dormancy can happen after the end of the year (DOY higher than 365). However, over these months the snow corrupts the satellite signal, leading to an underestimation of LAI.
When looking at the hemispheric mean values it is clear that all of the models overestimate the LAI, dormancy and length of the growing season (Table  2 ). Satellite LAI average for the Northern Hemisphere was 0. 83, while LAI from the models varies between 0. 98-2. 16. Both growing season onset and dormancy were later in all of the models, in some cases by more than a month. The effect of the late offset translates as an increased GSL, with values 9 to 180 days higher than the satellite data (Table  2 ). However, when the dormancy period is calculated based on GPP the modeled values become much closer to the observations, with an average GSL of 144 ± 15 days, compared to 184 days in the satellite data. This again suggests a decoupling between the active period of photosynthesis and leaves in the models.
Discussion The first important point to address is the validity of the satellite data. Satellite data does not represent true observations per se, but rather a model in itself. However, it is the closest product to observations, and available globally. It has been widely validated, but nevertheless there are some important issues that need to be considered. The satellite LAI product may have some problems detecting LAI in wintertime, since there is little sunlight in high latitudes. Sun angles are low and the satellite signals are heavily corrupted. Additionally snow cover affects reflection in winter and early spring. Hence, in the processing of any satellite data, there is a sun-angle cut-off. In these regions in the winter period there is little or no data. This partially explains the difference with modeled dormancy dates.
Conclusion We compare LAI from eight different uncoupled LSMs against satellite data over the Northern Hemisphere, during the 1986-2005 period. This was achieved by calculating the mean LAI, seasonal amplitude and growing season variables (onset, dormancy an length). Our results show that all models overestimate LAI by 2-3 units, particularly over the boreal forest, relative to the satellite data and literature estimates. Models that include a high number of plant functional types (10-16) compare more favorably to the satellite data than those that only have a few  (4) (5). Models that calculate their phenology based on temperature perform better than those with complex photosynthetic modules. Likewise, models with prescribed vegetation more closely match observations than those that simulate it dynamically.
Acknowledgments We acknowledge the  TRENDY-DGVM  project that is responsible for all the data on DGVMs used in this work. We also thank  Ranga Myneni  for his valuable contribution and comments on the development of the paper and  Xuhui Wang  for is help on the methodology. The corresponding author also thanks the  CONACYT-CECTI  and the  University of Exeter  for their funding during the PhD studies. The  National Center for Atmospheric Research  is sponsored by the  National Science Foundation.
To model phytoplankton primary production from remotely sensed data, a method to estimate photosynthetic parameters describing the photosynthetic rates per unit biomass is required. Variability in these parameters must be related to environmental variables that are measurable remotely. In the Arctic, a limited number of measurements of photosynthetic parameters have been carried out with the concurrent environmental variables needed. Such measurements and their relationship to environmental variables will be required to improve the accuracy of remotely sensed estimates of phytoplankton primary production and our ability to predict future changes. During the MALINA cruise, a large dataset of these parameters was obtained. Together with previously published datasets, we use environmental and trophic variables to provide functional relationships for these parameters.
The photosynthetic curves computed using the retrieved parameters can be normalized to a proxy of biomass (or photosynthetic absorption), generally the chlorophyll a concentration  ([chl] , mg m -3 ), to obtain a biomass-specific rate of photosynthesis (P chl , mg C mg chl -1 h -1 ). These biomassnormalized PvsE curves can be applied to remote sensing or in situ measurements of biomass, and the primary productivity can be computed using modeled or measured light profiles (e. g. ,  Morel, 1991; Longhurst et al. , 1995; Antoine et al. , 1996). Photosynthetic parameters have been documented in the Arctic Ocean, but only to a limited extent because of its remoteness. A dataset of PvsE collected over several years by the Marine Ecology Laboratory (MEL) at the Bedford Institute of Oceanography is presently the largest available.
This dataset allowed the first estimates of primary production in Arctic waters  (Subba Rao and Platt, 1984). However, the variability in the MEL dataset could only be partly explained based on relationships with depth (light) and temperature. While the dataset contains information about nutrients, it does not contain information about species composition to assess the potential link between community structure and photosynthetic parameters. A study by  Rey (1991) , conducted over several years and seasons in the Barents Sea, arrived at similar conclusions: the PvsE parameters appear to be controlled by light and temperature. They furthermore observed important seasonal differences. No information was available about the community structure. Reviewing the literature regarding polar waters,  Sakshaug and Slagstad (1991)  highlighted the lower chlorophyll-normalized rates of photosynthesis in the Arctic compared to more temperate regions.
More recently, a study comprising 15 PvsE curves from the Beaufort Sea, the same region as in our study, was published  (Palmer et al. , 2011) ; however, the small size of the dataset limits the significance of the relationships with in situ parameters. In this study, we examine data from the Beaufort Sea during the period of the MALINA cruise (30 July to 27 August 2009). We describe the photosynthetic parameters and provide functions of environmental variables that can be used to estimate them. These functions could, in turn, be used to estimate primary production in the Beaufort Sea during oligotrophic periods similar to those encountered during the MALINA cruise. By comparing our dataset with the MEL dataset, we extend our functional relationships to more eutrophic conditions.
(2002) , we find that the multiplication factor is on average 0. 35 with a standard deviation of 0. 04 (N = 231). However, using the extreme phytoplankton absorption spectrum (S f = 1 or 0) proposed by  Ciotti et al. (2002)  with our lamp would change the multiplication factor from 0. 26 to 0. 89 for the picoplankton (S f = 1) and the macroplankton (S f = 0) spectra, respectively. Using absorption spectra that have shapes that are similar to those measured in situ is thus important in these computations; in our case this was possible using an S f of 0. 5. For comparison with our own dataset, data from a series of reports from the Marine Ecology Laboratory at the Bedford Institute of Oceanography  (Irwin et al. , 1978a (Irwin et al. , , b, 1980 (Irwin et al. , , 1982 (Irwin et al. , , 1983a (Irwin et al.
, , b, 1984a (Irwin et al. , , b, 1985) )  were retrieved for all stations north of 60 • N. This provided a total of 350 PvsE curves. Most of this dataset has been analyzed thoroughly by  Harrison and Platt (1986). This dataset mostly covers regions of the eastern Canadian Arctic including the Labrador Sea and Shelf, Hudson Bay and Strait, Lancaster Sound, Foxe Basin, Davis Strait, Baffin Bay, and Jones Sound. Community size and taxonomy: to describe the fraction of the chlorophyll-a corresponding to different size fractions we use the method developed by  Uitz et al. (2006)  based on marker pigments from the HPLC dataset. As noted by  Uitz et al. (2006) , the size classes are in reality taxonomic classes. For example, some diatoms species whose physical size would place them as nanoplankton would be classified by the method as microplankton since fucoxanthin is used as a marker pigment for microplankton.
Results and discussion The P chl max values measured during the MALINA cruise were generally lower than those reported in the MEL reports (Fig. 1a ). Indeed, the region we studied is different from those in the MEL reports, which mostly include data from the eastern Canadian Arctic. We also note that the trophic state of the waters encountered during the MALINA cruise and during the MEL cruises were quite different. The Beaufort Sea had very low  [chl]  during the MALINA cruise. The cruise median for the samples from the top 10 m was 0. 09 mg m -3 with a 25th and 75th percentile of 0. 06 and 0. 11 mg m -3. On the other hand, in the MEL dataset only 5 out of 187 samples had a surface chlorophyll concentration below 0. 13 mg m -3 for the top 10 m. The median of the MEL dataset for the top 10 m is 0.
7 mg m -3 with the 25th and 75th percentiles at 0. 36 and 1. 68 mg m -3 , respectively. The oligotrophic state may have led to the lower P chl max values for the MALINA dataset. The frequency distributions of α chl and E k for the PvsE curves are presented in Fig. 1b  and c , respectively. A comparison with the MEL data is more difficult for these parameters as no spectra for the incubator irradiance sources used in the MEL data are available, the type of lamps were changed between years, and the absorption spectra are not available. To avoid misrepresenting the MEL data, we did not compare these data. For similar reasons, a comparison with any other dataset is difficult. This is the reason why we provide (see Methods) the relationship with PUR using a standard phytoplankton absorption spectrum, which should allow comparisons with other PUR-based measurements.
The phytoplankton community composition as described using the percent of chlorophyll a attributed to different phytoplankton groups derived from pigment composition varied during the cruise depending on the station and depth (Fig. 2 ). Waters in the upper 50 m were typically dominated by picoplankton (similar to what was found by  Lovejoy et al. , 2007) , or occasionally by microplankton, while deeper waters were typically dominated by nanoplankton. For all depths and samples, the microplankton fraction amounted to less than 40 % of the phytoplankton community biomass for 82 % of the 231 PvsE samples. As previously observed in many datasets of PvsE curves (e. g. ,  Harrison and Platt, 1986 ; see Discussion  Sakshaug and Slagstad, 1991 , and a review in  Behrenfeld et al. , 2004) , we observed a correlation between P chl max and α chl (Fig.
In any case, below we parameterize these changes as a function of depth rather than the optical depth. Besides depth, very little of the remaining variability could be explained by the variables tested: nutrient concentrations, temperature (see Fig. 5 , described later), previous day photon dose, etc. However, the dataset could be well separated using the microplankton fraction (color scale in Fig. 4a ), which was derived using the pigment composition by the method described in  Uitz et al. (2006). Samples that had a microplankton fraction greater than 0. 65 had significantly higher P chl max. This is highlighted in Fig. 4b  for depths shallower than 50 m, where all samples with a high microplankton fraction were located. This observation is consistent with the parameterization in  Uitz et al.
(2008)  where microplankton (HPLC-based size classes) shows a higher P chl max than other phytoplankton size classes. Palmer et al. (2011)  also made a similar observation for the Beaufort Sea based on five samples from the subsurface chlorophyll maximum: the size fraction greater than 5 µm had a higher P chl max than that for the whole water sample. For our dataset, three parameterizations with depth were derived. The first, P chl max = 0. 85 • 10 -7. 2×10 -5 z 2 -4. 5×10 -3 z , (2) is for samples where the fraction of microplankton was lower than 0. 65, where z(m) is the depth. The second, P chl max = 2. 0 • 10 -8. 7×10 -3 z , (3) is for samples where the fraction of microplankton was greater than 0. 65. While the last, P chl max = 0. 88 • 10 -1. 2×10 -4 z 2 -1. 1×10 -3 z , (4) is for all of the MALINA data pooled.
The fit for the samples with a fraction of microplankton greater than 0. 65 is very close to the fit for the whole MEL dataset (Fig. 4a ). While this suggests that the differences between the MEL dataset and the present dataset can be attributed to different community structures linked to the different trophic states of the waters sampled, it is impossible to verify this interpretation. A closer look at the two datasets (Fig. 5a ), however, provide some support to this hypothesis. Consistent with the results shown in Fig. 4a , points with a higher fraction of microplankton and higher chlorophyll concentration fit well within the cloud of points for the MEL dataset. However, without more overlap between the datasets and more information on the communities encountered during the MEL cruises, it is not clear whether the difference can be attributed to the ecology or to the methods used.
The effect of temperature on P chl max over the range observed during the MALINA cruise is not significant; a temperature effect is not seen in the MEL dataset either (Fig. 5b ). Notably, there is no relationship (not shown) between temperature and the microplankton fraction for the whole dataset. Nevertheless, with the limited information available to date, and assuming that the differences between the datasets are not methodological, it appears reasonable to propose the high macroplankton relationship (Eq. 2) for [chl] above ∼ 0. 1 mg m -3. This provides results that are in line with those of  Harrison and Platt (1986). For lower [chl] concentrations, for which there does not appear to be other Arctic data available in the literature besides our study, Eq. (  1 ) should be used. Using this parameterization with two size classes, the mean absolute percent error for the estimate of P chl max is 31.
7 ), we conducted the same analysis as  Arrigo (1994, his Eqs. 10-13)  to provide a fit to E kPUR , and we plotted the trends as a function of the mean in situ PUR for the last 24 h at the depth of sampling. We compare our results with the relationship provided by  Arrigo (1994). Our dataset shows E kPUR that are lower by a factor of about three. This is consistent with the lower P chl max observed (and a more-or-less-constant α chl ) leading to reduced values of the E kPUR parameter. Although other explanations are possible, such as a different phytoplankton absorption spectrum, it is doubtful that such secondary effects could lead to the factor of three observed here. While both datasets are from polar waters, the dataset underlying Arrigo's relationship is mostly from Antarctic waters and certainly reflects different growth conditions, communities, and photophysiological states.
65, we used α chl = 0. 041 ± 0. 02; and (3) for the whole dataset, we used α chl = 0. 021 ± 0. 01. These relationships could be used in a similar fashion to that proposed for P chl max (e. g. , Fig. 8c ). Dividing by 0. 35 provides the PUR values. Using the relationships described above for P chl max and E k , Fig. 9  illustrates the main changes observed in the PvsE curves in these waters as a function of depth and trophic status. In particular, it highlights the important difference in P chl max between the samples with a larger fraction of microplankton and those with a smaller fraction. It also  Relationship between E kPUR and the average daily PUR irradiance within the water column (see  Arrigo, 1994 , for details of the calculations). Of the two regressions (N = 70) for our dataset, one is consistent with the sigmoidal functional form of  Arrigo (1994)  and the other is a loglinear regression: E kPUR = 11.
Conclusions The large number of PvsE curves collected in the Beaufort Sea as part of the MALINA cruise provided a dataset to characterize the photosynthetic parameters of the phytoplanktonic community. It highlighted three key aspects: (1) the maximum photosynthetic rates and saturation irradiance for the phytoplankton were lower than in other regions in polar waters; (2) strong gradients with depth were observed for P chl max and E k , while α chl remained constant; and (3) the community structure was the most important parameter influencing the remaining variability in this dataset. The very oligotrophic conditions encountered during the sampling conditions make this dataset unique in Arctic research; however, they also limit the applicability of the relationships to conditions similar to those encountered during the cruise.
Acknowledgements. This work was supported by the  Canada   Research Chairs program  and a grant from the  NSERC Discovery Program  to Y. H. We thank the captain and crew of the  CCGS Amundsen. We are also thankful to  M. Gosselin  for sharing facilities and advices and to  Joannie Ferland  for compiling a large dataset of cruise data. Edited by:  E. Boss.