dataesr/scientific-paragraphs-categorization huggingface dataset:
Corals and numerical models: tools to look back in time Past seasonal data on SST and SSS can be obtained from archives such as massive coral skeletons. The aragonitic skeleton of coral is secreted by polyps over several decades or centuries, at a rate of around 1 cm year -1 for massive forms. The chemical composition of skeletal aragonite reflects the properties of the water in which the coral has lived. In tropical areas, studies focus on the massive Porites sp. corals. The Strontium/Calcium (Sr/Ca) ratio (Corrège 2006) is a robust proxy for SST in these corals. The stable oxygen isotopic ratio (δ 18 O) is used, combined with Sr/Ca, to reconstruct the isotopic composition of surface seawater (δ 18 O sw ), which is in turn closely related to SSS (via the evaporation vs. precipitation budget).
(e. g. different orbital forcings, ice sheets, greenhouse gas concentrations) can be used to simulate past climatic changes. Many model simulations exist for the mid-Holocene (e. g. 19 in the Paleoclimate Modelling Intercomparison Project [PMIP2] database; http://pmip2. lsce. ipsl. fr/; Braconnot et al. 2007). Their main forcing is a millennial-scale change in the seasonality of insolation. For the SW Pacific region, the insolation for January to March was lower than at present while it was higher for August to October.
gambiae transcripts, which may not fully cover the mosquito genome due to incomplete genome annotation. Raw data were deposited in the Array Express archive and are available under the E-MEXP-3677 code. Intensity data for each probe set were calculated from images generated with the GeneChip scanner and were analyzed with Bioconductor software, which implements packages from the R language. Intensities were normalized according to the robust multiarray analysis method (28) using the Affy package  (29). GeneChip array data were quality-controlled using established bioinformatic methods (30) using the Affycore tools package  (31). Probe sets with an absolute fold change greater than 2 were filtered, and significantly regulated probe sets (p Ͻ 0. 05) were identified by conducting a moderated t test, which enhances statistical power compared with the classical t test by borrowing variance information from independent probe sets, 5  followed by p value adjustment according to the method of Westfall and Young (33) using the Multtest package  (34).
Results were visualized in TreeView software after running a hierarchical clustering analysis with Cluster version 3. 0 software  (35). To highlight gene ontology (GO) terms that were significantly enriched among the genes regulated by dsLacZ injection, we conducted a GO enrichment test using the DAVID Bioinformatics Resources 6. 7 Web site. To identify genes potentially regulated by the same pathway, we conducted a k-means clustering analysis that groups genes with respect to similarity of their expression profiles. Affymetrix probe set expression data for the genes significantly regulated after dsLacZ injection and for Fos and Jun were retrieved from the A. gambiae Gene Expression Database at the University of California Irvine for the following conditions: BF3 (3 h post-blood feeding), BF24 (24 h post-blood feeding), and NBF (non-fed).
Well-fed mosquitoes were maintained in the insectary at 28 °C. After 7 days, midguts were dissected, stained with a 0. 4% mercurochrome solution, and examined under light microscopy (ϫ200) for oocyst counts. Significance of differences in oocyst numbers (only for samples with at least one oocyst per midgut) and in prevalence of infection was assessed by computing the standardized mean differences and the odds ratio, respectively, under random effect models using comprehensive meta-analysis software  (38). Genotyping of P. falciparum Gametocytes-Gametocyte filtration and DNA extraction for the samples collected in 2010 were performed as described  (12). Purified gametocytes were genotyped at seven microsatellite loci  (39) : POLY␣ (chromosome 4, GenBank TM ID G37809), TA87 (chromosome 6, G38838), TA109 (chromosome 6, G38842), ARA2 (chromosome 11, G37848), Pfg377 (chromosome 12, G37851), PfPK2 (chromosome 12, G37852), and TA60 (chromosome 13, G38876).
Consistent with our previous study  (12) , no effect of wounding or of gene silencing was observed in polyclonal infections (COI Ͼ 2) (Fig. 1A ). Levels of TGase2 transcripts increased within the first 3 h after dsLacZ-injection. How does this transient TGase2 up-regulation impacts P. falciparum development 3-4 days later? We believe that wounding, similar to Cactus depletion, boosts the efficiency of parasite killing  (6). However, better understanding of the TGase2 role, direct or indirect, in parasite killing will be required to answer this question. Transcript abundance was quantified using microarray data for the following conditions: non-injection, injection (3 h after dsLacZ injection), BF3 (3 h after blood feeding), BF24 (24 h after blood feeding), and NBF (nonfed). Data for the last three conditions were retrieved from the A.
Acknowledgments-We are grateful to the children of Mfou primary schools and to their parents and guardians for participating in this study. We thank  Dr. E. Manga ,  C. Efemba , and  M. Biloa  of the Mfou hospital for assistance in the field;  S. Kemleu ,  J. -P. Agbor , and  R. Nyambam  for blood smear readings; and  E. Onana  and  I. Tchikangwa  for mosquito rearing in OCEAC. We thank  J. Soichot  for help with the mosquito colonies. Drs. F. Catteruccia  and  S. Mitchell  are gratefully acknowledged for critical reading of the manuscript.
* This work was supported by funds from  CNRS  ( UPR 9022 ),  INSERM  ( U963 ), and l' Institut de Recherche pour le Développement (IRD) ; by grants from  EC FP7 MALVECBLOK  (grant agreement number  223601 ) and  EVIMalar  (grant agreement number  242095 ); by the  IRD-CNRS collaboration  ( Convention 6774. 00 ); by a fellowship from the  Fondation pour la Recherche Médicale (FRM)  (to J. P. ); by a  DSFIRD scholarship  (to S. E. N. ); and by a  BIOMALPAR Ph. D. fellowship  (to A. R. ). contains supplemental Tables  S1-S3  and Figs. S1-S7.
TRFLP, CLONING-SEQUENCING, AND STATISTICAL ANALYSIS Bacterial 16S rRNA sequences were amplified for terminal restriction fragment length polymorphism (TRFLP) analysis with primers 27f  (Vergin et al. , 1998)  labeled with hexachlorofluorescein (HEX) at the 5 end, and 519r  (Lane, 1991)  as described earlier  (Lymer et al. , 2008). Triplicate PCR products were digested using three restriction enzymes in parallel: HaeIII, HhaI, and HinfI. Fragments were separated and detected with an ABI 3730 capillary sequencer together with internal size standards. TRFLP peaks were determined with GeneMarker 1. 51 (SoftGenetics) following thresholds and criterions published earlier  (Lymer et al. , 2008). The different data sets from the three different restriction enzymes were grouped together and a matrix based on peak intensity was built to compare community composition between samples.
, 2010) , and environmental TRFLP peaks were identified by comparison with theoretical TRFLP Cloned sequences were taxonomically identified using BLAST and best matching sequences were selected to build a phylogenetic tree. The ca. 800-base-pair sequences were aligned using MUSCLE  (Edgar, 2004 ) and manually checked. DNADIST from the program PHYLIP version 3. 68  (Felsenstein, 2008)  was used to calculate genetic distances with Kimura-2 model and the distance tree was estimated with FITCH. Sequence data have been archived in the GenBank database under accession numbers JN399060-JN399069. A similarity percentage analysis (SIMPER;  Clarke, 1993)  was conducted to identify the phylotypes contributing the most to the dissimilarity between BrdU labeled and total community. SIMPER calculates the average Bray-Curtis dissimilarity for all phylotypes between groups of samples and is expressed as average contribution for each phylotype.
, 2010). Our results thus show that even though some Arctic microorganisms may have been inhibited, a large diversity of them take up BrdU at the concentration added. The major components of the bacterial communities were identified by cloning and sequencing bacterial 16S rRNA genes from the three experiments, and we used these data to identify the TRFLP peaks by using the program TRiFLe. We obtained 41, 57, and 61 sequences (ca. 950 bp long) from the libraries Tot_Nov_6d, Tot Jan_10d, and Tot_Feb_13d, respectively. Diversity was low in all three libraries, which were dominated by the genus Colwellia that represented >88% of the sequences. Colwellia is a psychrophilic marine bacteria, which is well adapted to life in cold environments  (Methé et al. , 2005). Members of Colwellia have been detected in a wide range of marine habitats, including polar regions  (Deming and Junge, 2005) , and appear to be important to carbon and nutrient cycling in the cold marine environments  (Methé et al.
The three Colwellia clusters were detected in clone libraries from seawater dilution cultures with Arctic winter waters in an earlier study (11 sequences from clus-ter1, 1 from cluster 3 and 1 from cluster 2, Alonso-Sáez et al. , 2010), and were successfully resolved based on their TRFLP profiles using the software TRiFle Additional analyses identified other abundant TRFLP peaks, including the Gammaproteobacteria Arctic 96B-16 cluster, Polaribacter, and SAR11 (Figure  2 ). Colwellia cluster 1, Polaribacter, and Arctic 96B-16 were more abundant in the active community represented by the BrdU-labeled DNA, indicative of a high potential for growth in Arctic winter waters. In contrast, Colwellia cluster 3, SAR11, and Colwellia cluster 2 were underrepresented in the BrdU-labeled community compared to total communities (significant difference between means between total and active communities (p < 0.
(Fernandez-Guerra et al. , 2010))tic Microbiology data. Peak identities were verified with T-RFPred(Fernandez- Guerra et al. , 2010)using 6370 sequences (>1000 bp) from the Global Ocean Sampling (GOS) database digested in silico with the three enzymes we used, as in(Fernandez-Guerra et al. , 2010).
ACKNOWLEDGMENTS We thank  Ramon Terrado  for carrying out a portion of the experimental sampling,  Carmen Gutierrez  for laboratory assistance,  Maria Hellman  for her advice with the BrdU protocol and  José M González  for help with the program T-RFPred. We also thank our fellow scientists, officers and crew of the  CCCG Amundsen  for their support during the cruise. This work is a contribution to the  International Polar Year -Circumpolar Flaw Lead system study  ( IPY-CFL 2007/2008 ) and  ArcticNet. Pierre E. Galand  and  Laura Alonso-Sáez  were supported by  Marie Curie Intra-European Fellowship  grants  CRENARC MEIF-CT-2007-040247  and  CHEMOARC PIEF-GA-2008-221121 , respectively. Connie Lovejoy was supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC) SRO  and Discovery grants and  ArcticNet.
M. Verma et al. : Remote sensing of annual terrestrial gross primary productivity from MODIS how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10-80 %). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited.
Our analysis builds upon and extends previous efforts in three important ways. First, we examine spatial (across sites) and temporal (across years) variation separately, focusing on GPP at annual scale. Distinguishing between spatial and interannual variation is important because the drivers and magnitudes of geographic and interannual variation in GPP are different  (Burke et al. , 1997; Richardson et al. , 2010a). Second, previous studies have examined results from only one or two models. The analysis we present here encompasses 10 different proxies and models that have not previously been systematically assessed and compared. Third, we use a data set that encompasses a much larger number of sites and siteyears than previous studies. Our analysis is therefore much more comprehensive than previous studies.
FLUXNET data Our analysis is based on measurements included in the FLUXNET La Thuile data set (http://www. fluxdata. org/ SitePages/AboutFLUXNET. aspx). This data set contains measurements of net ecosystem exchange (NEE) and near surface meteorology for 247 sites encompassing approximately 850 site-years of data since 2000. The data set uses an empirical temperature response function to model ecosystem respiration  (Reichstein et al. , 2005) , and estimates GPP as the residual of measured NEE and modeled respiration. The temperature response function is calibrated using nighttime data when winds are usually low and assumes that the calibrated relationship holds during daytime. It is worth noting that some site investigators are not in full agreement regarding the method used to model respiration in La Thuile data set.
To address these concerns, efforts to refine and improve respiration estimates are underway. Until these revised data are available, however, the La Thuile data set is the only reference data set available for this type of study. Most importantly, the La Thuile data set has been widely used, including in a number of high-profile synthesis studies (e. g. ,  Beer et al. , 2010). We therefore believe that the GPP estimates used here are of sufficient quality to meet the needs of this study, although we recognize that it includes errors and uncertainties associated with modeled respiration, which can propagate to GPP estimates. To minimize these errors, we only included sites with high quality data and identified a subset of 176 sites with 515 site-years of data where each site-year satisfied two conditions: (i) more than 95 % of the days had daily GPP data, and (ii) the mean daily quality flag was more than 0.
MODIS proxies of GPP Remotely sensed data such as the growing season mean and integral of NDVI have been used as proxies for GPP in several previous studies  (Tucker et al. , 1981 (Tucker et al. , , 2001;; Myneni et al. , 1998; Zhou et al. , 2001). In this work we examined four different MODIS-based proxies of GPP (Table  1 ): (i) the growing period length (GPL), (ii) the growing season integral of EVI (EVI-area), (iii) the growing season mean NDVI, and (iv) the growing season mean EVI. GPL and EVI-area were obtained from the MODIS Collection 5 Land Cover Dynamics product  (Ganguly et al. , 2010). We included the GPL in our analysis because several studies have suggested that GPL is an important control on annual GPP  (White et al. , 1999; Barr et al. , 2004; Churkina et al. , 2005). GPL and EVI-area estimates were not extracted for evergreen broadleaf forest (EBF) sites because we assume that GPL is not a significant control on annual GPP in this biome.
GPP models based on MODIS data We examined six remote sensing-based models in this study (Table  1 ): the MODIS GPP product (MOD17;  Running et al. , 2004) , the temperature and greenness (TG) model  (Sims et al. , 2008) , the vegetation photosynthesis and respiration model (VPRM)  (Mahadevan et al. , 2008) , a non-parametric neural network model (e. g. ,  Beer et al. , 2010; Moffat et al. , 2010) , the MOD17 algorithm calibrated to tower GPP (e. g. ,  Heinsch et al. , 2006;  hereafter referred to as "MOD17-Tower"), and regression models that use one of the four proxies and mean annual temperature or mean annual precipitation as predictors. Below we provide a brief description of each model (also see Table  1 ). (i) MOD17. We obtained modeled 8-day estimates of GPP at each of the selected FLUXNET sites for the MODIS GPP product (MOD17A2;  Running et al.
, 2004). The algorithm used to generate this product is based on light use efficiency and combines 8-day MODIS FPAR data with daily coarse resolution meteorological data and five biome-specific parameters to produce daily GPP estimates at 1 km spatial resolution (Table  1 ). (ii) MOD17-Tower. The standard MOD17 product uses coarse resolution (1 • by 1 • ) PAR, temperature and vapor pressure deficit data. However, due to land surface heterogeneity and atmospheric variability, the three meteorological variables show a great deal of fine scale variability. Heinsch et al. (2006)  showed that GPP estimated by the MOD17 algorithm is sensitive to the quality of meteorological forcing data. To minimize the errors due to the coarse resolution and low quality of meteorological data, we calibrated the MOD17 algorithm using fine spatial and temporal resolution, high quality meteorological data from the FLUXNET data set.
We then treated the half saturation point (PAR 0 ) and maximum light use efficiency (ε max ) as biomespecific parameters, and optimized them for each biome by minimizing the sum of the squared errors between daily modeled and observed GPP: SSE(ε max , PAR 0 ) = (GPP VPRM -GP P T OW ER ) 2 (1) where GPP VPRM is daily modeled GPP and GP P TOWER corresponds to measured GPP. We then randomly sampled the parameter space 1000 times and used the "trust-region" algorithm in MATLAB (MathWorks, 2009b) to find the vector [ε max , PAR 0 ] that minimized the cost function. To account for noise and missing data, we used quality assurance flags from the MODIS NBAR product to remove poor quality EVI and LSWI data and applied a locally weighted least squares algorithm  (Mahadevan et al. , 2008)  to smooth the resulting MODIS time series.
Machine learning models that use meteorological and remote sensing data to predict carbon fluxes have been used in many recent studies (e. g. ,  Xiao et al. , 2010; Moffat et al. , 2010). These models include no explicit biophysical structure, but are based on the assumption that functional relationships exist between the response (i. e. , GPP) and predictor variables. We used a feed-forward neural network model with a single hidden layer and a sigmoid transfer function  (MATLAB, 2009a). The model was estimated by minimizing the difference between predicted and observed GPP at daily time steps using the same input variables that were used to calibrate the MOD17-Tower model. (vi) Regression models combining remote sensing proxies and climate predictors. We estimated regression models at annual time steps for each biome using two predictors: (1) a remotely sensed proxy (from Sect.
Analysis Our analysis uses estimates of annual GPP from the La Thuile database. Despite the size of this database, five biomes had fewer than 10 tower sites (savannas, woody savannas, open shrublands, closed shrublands, and mixed forest); we therefore pooled these into one group, labeled as SSMF. Savannas, woody savannas, open shrublands, and closed shrublands are all arid or semi-arid, where precipitation is a dominant control on primary productivity. Our analysis revealed that annual GPP at the mixed forests sites was more highly correlated with annual precipitation than with temperature. Thus, while the SSMF group includes sites with different plant functional types, variability in GPP at all of the sites in this group is largely controlled by water. Our analysis explores both spatial and interannual covariance between in situ measurements and remotely sensed proxies and model-based estimates of annual GPP.
We calibrated four models (the MOD17-Tower, neural network, Proxy+Met, and VPRM model) with tower data at biome level. To do this, we estimated separate models for each biome, where the parameters of each of the four models were optimized using tower GPP and meteorological data specific to each biome. In evaluating these four models we used a leave-one-site-out cross-validation method. This method allows the most efficient and objective use of available data while enabling independent calibration and evaluation of a model. Thus, in a biome with a total of n sites, we successively used n -1 sites to calibrate model parameters and employed these parameters to predict GPP at the left-out nth site. The first part of our analysis examines spatial covariance between remotely sensed estimates (or proxies) and in situ measurements of annual GPP.
Conclusions We draw two main conclusions from this work. First, the remote sensing models and proxies that we examined provide statistically significant and useful information related to spatial variation in annual GPP. Second, the remotely sensed proxies and modeled estimates of annual GPP only explained relatively modest amounts of variance in annual GPP across years. These conclusions are important for two main reasons. First, no previous study has explored these issues using a database as large and comprehensive as the FLUXNET La Thuile data set. Second, and more importantly, a large number of recent studies have used remote sensing to infer regional-to-global changes in GPP or net primary productivity. Many of these papers justify their conclusions based on previous studies that use models or proxies to explain spatial variance in annual GPP (or NPP) across large spatial scales or at seasonal timescales.
Acknowledgements. This research was partially supported by  NASA  grant number  NNX11AE75G , the  National Science Foundation Macrosystem Biology program  (award  EF-1065029 ), and  AmeriFlux  [the  Office of Science (BER) ,  US Department of Energy (DOE ;  DE-FG02-04ER63917  and  DE-FG02-04ER63911 )]. MV and MAF gratefully acknowledge the efforts of the FLUXNET community to compile and make available the La Thuile data set. This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the following networks:  Amer-iFlux  ( US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program  ( DE-FG02-04ER63917  and  DE-FG02-04ER63911 )),  AfriFlux ,  AsiaFlux, CarboAfrica, Car-boEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada  (supported by  CFCAS ,  NSERC ,  BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC.
We acknowledge the financial support for the eddy covariance data harmonization provided by  CarboEuropeIP ,  FAO-GTOS-TCO, iLEAPS ,  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, Université Laval, Environment Canada  and  US Department of Energy  and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California -Berkeley , and the  University of Virginia. M. Verma ,  M. A. Friedl , and  A. D. Richardson  designed the analysis. M. Verma conducted the analysis. A. D. Richardson ,  G. Kiely ,  A. Cescatti ,  B. E. Law ,  G. Wohlfahrt ,  B. Gielen ,  O. Roupsard ,  E. J. Moors ,  P. Toscano ,  F. P. Vaccari ,  D.
6) (0. 018) = 0. 0108 x B PCR6 = x B,2 PCR6 + x B,3 PCR6 = 0. 0054 + 0. 0018 = 0. 0072 From the implementation point of view, PCR6 is simpler to implement than PCR5. For convenience, Matlab codes of PCR5 and PCR6 fusion rules can be found in  [76]. It is worth noting that there is a strong relationship between PCR6 rule and the averaging fusion rule which is commonly used to estimate probabilities in the classical frequentist interpretation of probabilities. Such a probability estimate cannot be obtained using DS rule, nor the PCR5 rule and that is why we recommend to use PCR6 when combining more than two basic belief masses altogether  [92].