dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the assessments at ground stations have the added difficulty of comparing an observed value at a point with a value simulated in a relatively large grid size. based observations, and where satellite sensors have major problems to obtain accurate information due to high ground reflectivity. For this reason dust reanalysis data become unique information in this study domain. The results highlighted in the present study will help not only the climate-weather scientific community but also end-user communities to prevent the impact of severe events over desert source regions where dust is considered to be a harmful air pollutant. Moreover, MACC-II reanalysis could be used in several health-related applications, such as epidemiological studies, or to obtain maps of solar radiation attenuation by mineral dust in suspension, or in ocean research to relate dust deposition with chlorophyll records, among others.
This work has been supported by EU-project Monitoring Atmospheric Composition and Climate (MACC-II) under the European Union Seventh Framework Programme, grant agreement number 283576. Based on a French initiative, AMMA was developed by an international scientific group and funded by a large number of agencies, especially from Africa, European Community, France, UK and USA. More information on the scientific coordination and funding is available on the AMMA International web-site: http://www. amma-international. org. The authors acknowledge B. Chatenet who managed the Sahelian Dust Transect from 2006 to 2012, J. L. Rajot who largely participated to its installation and operation, and the local site managers in Niger (A. Maman and A. Zakou, IRD, Niamey), Mali (M. Coulibaly and I. Koné, IER, Cinzana) and Senegal (A.
Acknowledgements. This work has been supported by EU-project Monitoring Atmospheric Composition and Climate (MACC-II) under the European Union Seventh Framework Programme, grant agreement number 283576. Based on a French initiative, AMMA was developed by an international scientific group and funded by a large number of agencies, especially from Africa, European Community, France, UK and USA. More information on the scientific coordination and funding is available on the AMMA International web-site: http://www. amma-international. org. The authors acknowledge B. Chatenet who managed the Sahelian Dust Transect from 2006 to 2012, J. L. Rajot who largely participated to its installation and operation, and the local site managers in Niger (A. Maman and A. Zakou, IRD, Niamey), Mali (M. Coulibaly and I.
Synthetic and in situ data set Three different data sets are used for the present study. The first one (thereafter referred as DS1) is only used to develop the theoretical background of the algorithm. This synthetic data set has been generated from numerical simulations of the radiative transfer performed for various inherent optical properties (IOPs) and three different sun zenith angles (0°, 30°, and 60°). More details on this data set are provided in the IOCCG report 5  [24]. The second data set (DS2) only gathers in situ data collected in various and mainly coastal environments. For DS2 we use the NOMAD data set  [25]  together with data collected during other oceanographic cruises occurring in European and French Guiana coastal waters  [16, [26] [27] [28]. DS2 is divided into two sub-data sets, one for the algorithm development (DS2-D), the other for validation exercises (DS2-V).
DS2-D and DS2-V encompass 80% (N = 505) and 20% (N = 126) of the data points and presents the same distribution in terms of a cdom (410) values. The last data set (DS3) used in the present study is the NOMAD SeaWiFS match-up data set (N = 109) which is composed by in situ a cdom (410) measurements and nearly simultaneous SeaWiFS derived remote sensing reflectance, R rs , after atmospheric correction of the top of atmosphere signal. The mean, standard deviation, and range values of K d (λ), a cdom (410), and a cdom /a tot (410), where a tot is the sum of the particulate (a p ) and colored dissolved organic matter absorption (a cdom ) coefficients, for the different data sets are provided in Table  1. The a cdom (410) values cover a great range of variability, about three order of magnitude, for both the synthetic and in situ data sets (Table  1 ).
The mean and standard deviations values of a cdom (410) for DS1, DS2-D, DS2-V, and DS3 are 0. 51 ± 0. 74, 0. 23 ± 0. 26, 0. 24 ± 0. 27, and 0. 13 ± 0. 20 m -1 , respectively. The a cdom (410) values are equally distributed in DS2-D and DS2-V (with however some slight differences in the maximum values). The contribution of a cdom (410) to the total absorption process, represented by the a cdom /a tot ratio, are very similar among the three data sets and range between about 16 to 93% (Table  1 ). Besides the a cdom (410), a tot (410), K d (410), and K d (670) parameters gathered in the three considered data sets, DS2-V and DS3 also include the remote sensing reflectance, R rs (λ), values at the standard ocean color visible wavelengths (412, 443, 490, 510, 550, and 670 nm). R rs (λ) is considered for DS2-V and DS3 for validation purposes in the context of remote sensing applications, where R rs is the input parameter used for calculating K d (λ) which represent the input parameter for deriving a cdom (410) from the present algorithm.
Development of the model from the synthetic data set DS1 The theoretical background and general formulation of the model presented in the previous section is now analyzed from radiative transfer simulations data gathered in DS1. While a cdom will be estimated at 412 nm from the in situ database (section 4. 2), the development based on DS1 is performed at 410 nm, the available wavelength in the blue. The pure sea water values of the vertical attenuation coefficient are set from radiative transfer simulations using the Hydrolight radiative transfer code  [48]  for a pure sea water body, different sun zenith angles, and in presence of Raman scattering. The resulting K w (λ) values at 410, 555, and 670 nm for a sun angle at 30° are 0. 0097, 0. 0645, and 0. 4878 m -1 , respectively. These values are only slightly higher from those generally adopted in the literature (K w (410) = 0.
Development of the model from the in situ data set The evaluation of the impact of each term of Eq. (  4 ) is now performed using the in situ data set DS2-D. Considering that the performance of the method established from DS1 is relatively similar for red and green channels and the greater complexity to assess K d from space in the red part of the spectrum, this part will only focus on the green channel. The advantage of each step on the proposed approach is summarized in Fig. 3. As for the synthetic data set, a tight relationship is observed between a cdom (410) and (K d (410) -K w (410)) over the whole range of variability of DS2-D (Fig. 3(a) ). Note that this relationship slightly differs from the one established using the synthetic data set. The inherent uncertainties associated with in situ measurements, but also the relationship between a cdom and absorption by phytoplankton cells used in the synthetic data set  [24] , are at the origin of the observed discrepancies.
The bias values for the first and last quartile significantly differ from the one established from the whole data set. This latter pattern emphasizes that a residual effect of the particulate matter still remains. Such effect, inherent to all empirical approaches  [11] , could however be corrected through classification approaches  [28, 50]. Based on these different findings the following relationship, developed from the in situ data set DS2-D, is adopted: ( ) ( ) 2 Log X Log X cdom a   + +     = (6) with X = (K d (412) -K w (412)) -(K d (555) -K w (555)) -(Δ p (412)-Δ p (555)) and with (Δ p (412)-Δ p (555)) estimated such as: ( ) ( ) 2 Log Y Log Y p p   - + -     Δ -Δ = (7) where Y = (K d (412) -K w (412) -(K d (555) -K w (555)). The slight differences observed in the coefficients appearing in Eqs.
5  (b), (c), and Table  2 ). However, by fitting the empirical formulations used in these a cdom vs. R rs empirical models to the DS2-D data set, their performances evaluated on DS2-V improve. For instance, the RMSD values decrease by a factor of 1. 58, and 1. 06 for the models of Mannino et al. [4] , and D'Sa et al. [17] , respectively. The formulation between R rs and a cdom used in these models are therefore relatively dependent on the data set used for their development. Note that the number of validation data points is reduced for Mannino et al. ,  [4] 's algorithm which, by construction, cannot be applied when the R rs (490)/R rs (555) ratio becomes lower than 0. 4443 (resulting to a logarithmic calculation of a negative expression). The recent semi-analytical approach developed by Dong et al.
According to the ESA (2012) [51] validation report, the relative percentage differences calculated between the estimated and measured reflectance values in coastal waters at 412 and 560 nm are + 1. 6% and -6. 3%, respectively. The impact of these errors on the a cdom (412) retrieval accuracy is assessed using the validation data set DS2-V, by comparing the a cdom (412) values calculated using the true R rs values, a cdom (412)-error-free, with the a cdom (412) values calculated using these R rs values modified by the relative differences provided above, a cdom (412)-error. These atmospheric correction errors only slightly impact the a cdom (412) retrieval accuracy, as the mean relative absolute difference and bias values calculated between these two a cdom (412) values in linear scale are 8.
Acknowledgments The authors would like to thank all participants and voluntary contributors for collecting data that have been assembled in the NOMAD data set. This study has been performed in the frame of the GlobCoast (www. foresea. fr/globcoast)which is funded by the  Agence Nationale de la Recherche  ( ANR-11-BLAN-BS56-018-01 ). The  GlobCoast  project is affiliated to LOICZ and AQUIMER. This work has also been supported by the  French Spatial Agency (CNES)  through the  TOSCA program  in the frame of the  MODOC  project. S. Shang  is deeply thanked for providing us the results of the Dong et al. (2013)'s model. We thank the two anonymous reviewers for their comments and suggestions.
ACKNOWLEDGMENTS We would like to thank two anonymous referees that helped to improve the manuscript. Patrick S. Fitze was supported by funds from  Swiss National Science Foundation  ( PPOOP3_128375 ), funds from  ARAID foundation , and funds from the  Spanish Ministry of Education and Science  ( CGL2005-01187 ,  CGL2008-01522 , and  Programa Ramón y Cajal). Virginia Gonzalez-Jimena  was supported by a PhD grant the  Spanish Ministry of Education and Science (FPU   AP2006-01678 ), and  Luis M. San-Jose  by a PhD grant ( I3P 060501 ) from the  Consejo Superior de Investigaciones Científicas (CSIC)  co financed by the  European Social Fund. Benoit Heulin  was supported by funds from  CNRS   UMR6553. Barry Sinervo  was supported by funds from  NSF , the  National Geographic Society , the  France Berkeley Fund  and  CNRS.
This led to the detection of 49, 36, and 81% of allelically regulated transcripts in LCLs, fibroblasts, and monocytes, respectively. Examples of proximal and distal allelic expression associations in individual transcripts are depicted in Fig 1. In order to include the optimal number of associated SNPs in our analysis, we assessed simulated candidate loci with known "causal" sites (Supplementary Methods). Through the simulation analysis, we observed that the percentage of missing causal SNPs is below 5% when the top 10 ranked SNPs by P-value are included per locus, and thus, we focused our subsequent analyses using this cutoff  (Supplementary Fig S1). A summary of mapped associations and number of tested SNPs are displayed in Table  1  and Supplementary Table  S1 , respectively. All classes of transcripts annotated in GENCODE V15 were included in our analysis.
We explored the relevance of mapped shared associations in full datasets using IPA (Ingenuity Systems, www. ingenuity. com). Networks of genes associated to cis-rSNPs shared across all three studied cellular lineages are involved in basic cellular functions  (Supplementary Fig S2). In contrast, associations shared by the two LCL panels are enriched in cell-to-cell signaling and immune response networks, and associations shared between lymphoblasts and monocytes are enriched in networks related to immunological disease and immune response. Finally, we hypothesized that the variation in the number of mapped loci, which are tissue independent or cell-type selective, could be due to differences in the number of analyzed samples, leading to discovery of weaker effects less likely to be shared across tissues in larger samples.
Down sampling to equally powered datasets (see Supplementary Methods) shows the ratio of cell type-dependent versus all associations is quite stable across comparisons, suggesting that the level of sharing we observed is mainly influenced by (i) tissue-dependent differences, where cultured cells of Caucasian origin (CEU and FB) show lowest level of tissue differentiation and (ii) population genetic variation (YRI versus all others) where sequence divergence predominates  (Supplementary Fig S3). To improve the mapping resolution, we next applied metaanalyses across populations (see Methods). By breaking up blocks of SNPs in high LD, this approach significantly reduced the total number of associated SNPs per loci by ~2. 1-fold (chi-squared test, P < 2 × 10 À32 , Supplementary Fig  S4 )  (Hess & Iyer, 2007).
Intersecting our data with the RegulomeDB (RegDB) database  (Boyle et al, 2012 ) yielded a significant enrichment of mapped SNPs overlapping functional elements (1. 1-fold, chi-squared test, P = 0. 02). A significant increase in the proportion of GWAS hits (1. 9-fold, chi-squared test, P = 2 × 10 À16 ) was also observed, particularly in autoimmune diseases (1. 5-fold, chi-squared test, P = 1. 4 × 10 À32 ). These findings may be partially attributed to the use of two immune cell types (lymphoblasts and monocytes) among the three cell lineages studied. The spatial distribution of the mapped cis-rSNPs follows an expected trend  (Nica et al, 2011) : high density of sites in 5 0 UTRs, gene bodies, and 3 0 UTRs, which rapidly decreases as a function of the distance from the gene in flanking regions.
Motif disruption at cis-rSNPs revealed new regulatory function One mechanism by which cis-rSNPs may act and mediate disease risk is through the disruption of transcription factor binding sites. Databases of TF binding matrices, such as TRANSFAC (http:// www. biobase-international. com), can be used to detect these events. However, they contain only a minority of the human transcription factors with predicted high-quality sequence-specific DNA binding domains  (Vaquerizas et al, 2009). Recently, Neph et al (2012) performed unbiased de novo motif discovery within the footprints left by regulatory factor binding to genomic DNA and protecting the underlying sequence from cleavage by DNase I in 41 cell types. This approach allowed them to discover 683 unique de novo motifs (numbered 1-683), 289 of which showed no match in previous motif databases.
This new genome-wide dataset of transcription factor binding, in conjunction with the major motif database TRANSFAC which contains 721 motifs, provides an opportunity to interrogate both known and unknown DNA-protein interactions that may be affected by cis-rSNPs. To investigate the relationship between cis-rSNPs and TF binding, we used the FIMO motif scanning software (see Methods) to calculate the number of disrupted binding sites per motif at cis-rSNPs based on cell-type specificity (Supplementary Table  S6 ). As expected, sites for general transcription factors such as SP1, AP1/2, and CTCF are frequently disrupted in all cell types (Supplementary Table  S7 ). However, we also observed cell-type-specific activity at TRANSFAC and footprint-derived motifs (Fig 3) (chisquared test, P < 0. 05): For example, binding motifs for NFjB and IRF factors and the de novo motif "616" are frequently disrupted in LCLs in agreement with previous studies  (Ernst et al, 2011; Neph et al, 2012).
Several of the top 10 most significant motifs are bound by factors with published inhibitory activity, such as NRSF (a. k. a. REST) (Chong   et al, 1995; Schoenherr & Anderson, 1995)  with an extensively documented repressor activity, HFH4, a FOX factor  (Hoggatt et al, 2000; Myatt & Lam, 2007) , or PAX5  (Fazio et al, 2008). Unexpectedly, we also identified many "de novo" motifs among the most significant hits: 79% of the "de novo" motifs are bound by factors with repressive function  (Fig 4A). To exclude bias due to the way in which matrices were produced with the "DNase I footprinting" method, we examined footprint-generated motifs which had not been called as "de novo" due to redundancy with other datasets  (Neph et al, 2012). We observed a majority of "activators" (62%), a proportion more similar to the motifs from the TRANSFAC database (90%), suggesting no bias in the footprint-derived motif generation (Fig 4A A The most significant cis-regulatory variant of RP11-221J22.
Genome-wide validation of NFjB allelic regulation in LCLs For confirmation of TF-cis-rSNP interactions in a functional context, we developed a method that perturbs TF followed by monitoring genome-wide allelic expression measurements in living cells. As a model for this novel approach, we chose the factor NFjB. Activation of NFjB is known to regulate the expression of genes that are involved in the pathogenesis of inflammatory pathologies  (Kempe et al, 2005; Sehnert et al, 2013)  and SNPs associated to diseases are enriched in NFjB binding regions  (Karczewski et al, 2013). Three motifs for NFjB binding ("V_NFΚB_C", "V_NFΚB_Q6_01", and "V_NFKAPPAB_01") are associated with significant global enhancer activity (P binomial test < 0. 01) in this dataset (Supplementary Table  S8 ). We observed that cis-rSNPs from 126 loci disrupted one of these NFjB binding sites are preferentially located in NFjB ChIPseq peaks (ENCODE) (6.
5-fold, chi-squared test, P = 2. 6 × 10 À23 ), when compared to other mapped loci in LCLs suggesting true regulation by this TF. Among these cis-rSNPs, 12 are in high LD with GWAS hits for 12 diseases and linked to differential allelic expression of 15 genes. Overall, both the literature and our own data support NFjB as an interesting biological model to test this novel approach. Briefly, we performed TNF-a induction coupled to inhibition of NFjB in LCLs followed by AE analysis on Illumina Human-Omni5-Quad BeadChips (see Methods). Samples used included two HapMap trios: one from the CEU (GM12891, GM12892, and GM12878) and one from the YRI (GM19239, GM19238, and GM19240) population. Validation of NF-jB knockdown was done using RT-PCR of known gene targets for NFjB (Supplementary Fig  S8 )  (Mori & Prager, 1996; Catz & Johnson, 2001; Kang et al, 2007; Son et al, 2008).
AE mapping and normalization of allele ratios in Illumina Beadchips AE mapping was performed as previously described  (Ge et al, 2009)  except for the signal intensity normalization at heterozygous sites, which followed a slightly modified approach  (Grundberg et al, 2011). Briefly, approximately 200 ng of genomic DNA and a 50-300 ng double-stranded cDNA sample was used for the parallel genotyping and AE analysis on the Illumina Infinium Human1M or Human1M-Duo SNP bead microarray according to the manufacturer's instructions. Raw data were processed in the genotyping module (Ver. 3. 3. 7) of BeadStudio software (3. 1. 3. 0), filtered and normalized. For transcript-based AE regression tests, associations were carried out using average intensity signal for SNPs (minimum three per transcript) across any annotation from GENCODE version 15.
We used 1000 Genomes project data as a reference set (release 1000G Phase I v3, updated 26 Aug 2012) for the imputation of genotypes from our panel of HapMap individuals. Untyped markers were inferred using algorithms implemented in IMPUTE2  (Howie et al, 2009). R 2 was used as an imputation quality control metric, which estimates the squared correlation between imputed and true genotypes. We systematically removed all poorly imputed markers with r 2 < 0. 8. For each gene, only the most highly expressed isoform with minimum FPKM value of 0. 01 was retained. In regression tests, we decided to exclude intensity signal from genomic regions overlapped by more than one gene to avoid conflicting data. We called "partial-length transcripts" in cases where only part of the AE data were used due to these low confidence regions.
Shared associations and Fisher combined test For each locus, all primary associations from each population were compared to the first percentile of mapped SNPs in others cell population. A P-value cutoff of 1. 1 × 10 À4 was used in the secondary population. At this threshold, > 95% of mapped associations showed the same direction of effect between two populations. When at least one regulatory variant was found in common, we considered this locus as sharing regulatory activity between these populations. A Fisher's combined approach was then used to improve mapping resolution and generate new associations for shared loci across populations. To allow comparison of our approach to recent works reporting higher level of sharing, we used same approach for comparing P-values between datasets. Specifically, we took lead association in one tissue (based on our definition by FDR) and fetch exactly the data for same SNP-transcript pair in the other tissues.
We used as input these P-value lists in the R package "qvalue" and run the default setting in the two datasets separately. The output p0 in the summary file is used to calculate p1 values (1-p0), which represents the proportion of shared hits among the tested. This approach is more liberal and allows for uncertainties of mapping accuracy in differently powered datasets; consequently, pairwise sharing estimated based on this approach is substantially higher (40-60%). To evaluate the potential functionality of these new associations, we intersected our data with the RegulomeDB (RegDB) database  (Boyle et al, 2012). Categories 1-5 were used to indicate active regulatory regions (a lower score indicating stronger evidence of functionality), whereas categories 6 and 7 are attributed to SNP with low potential of functionality.
Motif over-representation and allelic positional bias Matrices for TRANSFAC  (version 2009. 4 ) and de novo footprintderived motifs  (Neph et al, 2012)  were used in association with the FIMO motif scanning software, version 4. 9. 0, using a P < 1 × 10 À4 threshold, to find all motif instances AE 15 nucleotides from a mapped cis-rSNP sitting on a DHS footprint region  (Grant et al, 2011). All motifs displaying no change in matrix affinity score, according to cis-rSNP genotypes, were discarded. To account for multiple testing, we used a Bonferroni correction (0. 05/1380 = 3. 62e-05). Among the de novo motifs, 58% matched matrices from other databases (TRANS-FAC, JASPAR or UniPROBE). These motifs were not considered as de novo for the analyses. After normalization to a mean value of 0 and variance 1, a heat map with 1 row per motif instance was generated using matrix2png  (Pavlidis & Noble, 2003) , version 1.
RT-PCR Total RNA was annealed to 500 ng of random primers. First-strand cDNA synthesis was performed using SuperScriptII reverse transcriptase (Invitrogen Corporation, Carlsbad, CA, USA) according to the manufacturer's recommendations and as described above. The cycling conditions on the Rotor-Gene TM 6000 real-time rotary analyzer were 4 min at 95°C, 40 cycles ×20 s at 95°C, 30 s at 58°C and 30 s at 72°C, followed by the dissociation protocol at 72°C. Results were analyzed using the comparative CT method. The CT mean and standard deviation of each technical replicate were calculated, and the mean CT values were then normalized to the 18S mean CT value. Primers were designed using the Primer3 v. 0. 4. 0 software (http://frodo. wi. mit. edu/), and all primer sequences used can be found in Supplementary Table  S10.