dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, this protein was not detected in our proteomic datasets, unlike LGALS3BP, which was also described in the same study  (Intasqui et al. , 2013)  as playing a similar role. The gene, Pdilt (protein disulfide isomerase-like, testis expressed), was modulated by CBZ and CCK at all doses and time points, but only at D21 with IPR (FC 11). PDILT is a testis-specific ER chaperone protein (along with calmegin and calreticulin3) and is widely conserved in mammals, including humans. Sperm-fertilizing proteins (ADAM family proteins) require the action of PDILT as a unique quality control system for their maturation. Testis-specific quality control for nascent proteins in the ER is crucial in equipping spermatozoa with their fertilizing ability in humans  (Tokuhiro et al. , 2012). Consequently Pdilt plays a role in male infertility, and its downregulation in our data suggests that the tested substances as well as their mixture might impair sperm function.
Availability of supporting data section The data sets supporting the results of this article are available as supplemental files (five links are indicated in the text). The transcriptomic raw data discussed in this publication have been deposited in the NCBI Gene Expression Omnibus (GEO) repository and are accessible via GEO Series accession number (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE75446).
49 ± 0. 29 -80. 22 ± 10. 67 (50 nM) Uniprot accession number Day 7 Day 14 Day 21 APOE P02650 3. 36 ± 0. 44 -2. 92 ± 0. 36 2. 43 ± 0. 06 CLUS P05371 5. 35 ± 0. 16 -5. 96 ± 0. 55* -1. 08 ± 0. 04 FUCO P17164 6. 16 ± 0. 93 -1. 29 ± 0. 13* ND LG3BP O70513 5. 07 ± 0. 51 -2. 07 ± 0. 17 6. 82 ± 0. 08 PEBP1 P31044 2. 61 ± 0. 28 ND 3. 80 ± 0. 49 Iprodione (50 nM) Uniprot accession number Day 7 Day 14 Day 21 APOE P02650 ND -6. 20 ± 0. 33 -1. 20 ± 0. 09 CLUS P05371 -2. 02 ± 0. 11 -3. 80 ± 0. 11 -1. 21 ± 0. 05 FUCO P17164 ND ND 2. 64 ± 0. 30* LG3BP O70513 ND -2. 73 ± 0. 23 3. 66 ± 0. 17 PEBP1 P31044 -2. 38 ± 0. 23 -2. 14 ± 0. 09 1. 95 ± 0. 13* Cocktail (50 nM each) Uniprot accession number Day 7 Day 14 Day 21 APOE P02650 -3. 56 ± 0. 36* -3. 46 ± 0. 06 -6. 19 ± 0. 66 CLUS P05371 -1. 21 ± 0. 05 -4. 22 ± 0.
The daytime-only algorithm (referred to hereafter as the MOD06 cloud optical property (COP) phase algorithm) that provides the phase decisions for the MOD06 cloud optical and microphysical property retrievals (e. g. , COT, CER, cloud water path) has undergone an extensive overhaul in the latest MOD06 C6 reprocessing efforts. The primary motivation for the C6 changes was to overcome some well-known shortcomings in Collection 5 (C5). In particular, the C5 phase decision logic was somewhat opaque to end users, and because the algorithm relied on SWIR channel ratio thresholds specific to MODIS, was inadequate for achieving climate data record continuity from multiple passive sensors such as MODIS, VIIRS, and beyond. In addition, the algorithm underperformed in certain situations, such as broken liquid cloud scenes that were often misidentified as ice and thin ice cloud edges that were often misidentified as liquid.
Data The active lidar observations from CALIOP provide an excellent benchmark for developing and evaluating the C6 MOD06 COP phase algorithm. This study uses the CALIOP cloud phase discrimination  (Hu et al. , 2009)  reported in the 1 and 5 km cloud layer products for two selected months  (July 2008 and November 2012). First the CALIOP 1 km layer products are collocated with MODIS by finding the MODIS pixel with the minimum great circle distance with respect to each CALIOP profile. Because some optically thin clouds such as cirrus require lidar horizontal averaging scales longer than 1 km for detection and are only reported in the CALIOP 5 km layer products, the 5 km layer products are also collocated with MODIS by over-sampling the 5 km profiles to 1 km resolution and concatenating with the 1 km layer products.
Thus a complete CALIOP phase data set is created to screen for single-phase ice or liquid profiles only. The importance of this merged data set is illustrated in Fig. 1. Here the CALIOP 1 (panel b) and 5 km (panel d) layer cloud phase, with dark and light blue denoting liquid and ice phases, respectively, is plotted for an example Aqua MODIS granule observed on 3 July 2008 at 08:30 UTC (panel a). Also shown in Fig. 1b , d is a horizontal bar near 20 km altitude indicating the collocated MOD06 C6 cloud phase classification (panel c). It is evident here that the CALIOP 1 and 5 km cloud layer sampling can be quite different, with more lowaltitude, broken liquid clouds found in the 1 km layer product and more high-altitude ice clouds found in the 5 km layer product. Note the CALIOP 333 m layer products were also evaluated, though only minor differences were found with respect to the 1 km products.
Spectral cloud CER tests In C5, the primary COP cloud phase tests were a series of thresholds applied to SWIR reflectance ratios. The rationale for these tests is the fact that ice and liquid particles have different imaginary indexes of refraction at 1. 6 and 2. 1 µm  (Kou et al. , 1993) ; i. e. , ice particles are more absorptive than liquid droplets at these wavelengths and thus have smaller TOA SWIR reflectances. Figure  3a  shows a scatter plot of 2. 1 µm (y axis) vs. 0. 85 µm (x axis) cloud reflectances over ocean, randomly sampled from the MODIS-CALIOP collocated data set. The scatter point color indicates the collocated CALIOP cloud phase (ice phase in light blue and liquid phase in burgundy). The corresponding C5 SWIR ratio thresholds are plotted as dashed red lines, such that all points above the upper dashed red line are considered liquid and all points below the lower dashed red line are considered ice; points between the two lines are considered undetermined.
Forced ice Re Minimum Maximum thresholds Re 1. 6 micron 20 micron 30 micron Re 2. 1 micron 20 micron 30 micron Re 3. 7 micron 15 micron 25 micron vations, as well as a measure of the relative distance to the LUT (note these parameters are reported for the final solution phase in the RFM Scientific data sets (SDS). Thus for pixels for which any ice phase spectral CER retrieval fails, the C6 COP phase algorithm instead uses the nearest LUT CER information from the alternate solution logic. Note also that, because Aqua MODIS has non-functioning detectors at 1. 6 µm, the 2. 1 µm CER test is used as a proxy when 1. 6 µm is not available, and therefore votes twice in such instances. Finally, there are two distinct disadvantages to using spectral CER retrievals in the phase logic. First, computational efficiency is greatly reduced since it is necessary to perform two CER retrievals, i.
Evaluation against CALIOP Contingency tables comparing the MOD06 COP phase algorithm to the collocated CALIOP v3 cloud layer product are shown in Fig. 7  for C6 (panel a) and C5 (panel b). The data used here are from November 2012 for the entire globe (all surface types), and are limited to cases where the MOD06 CSR algorithm identified an overcast scene (CSR = 0) and CALIOP identified only a single phase in the column, regard- less of the success/failure status of the various spectral CER retrievals; the CSR = 0 constraint is applied such that the C6 pixel population is consistent with C5. The abscissa denotes CALIOP phase, and the ordinate denotes MODIS phase. The numerical values in each table can be interpreted as the percent of total collocated cloudy scenes for which the given phase condition is observed.
8 to 2. 6 %), and the liquid phase agreement increased (43. 0 to 54. 4 %). In addition, the portion of pixels identified as undetermined phase decreased by a factor of 2. 5 in C6 (5. 4 to 2. 1 %). The overall increase in liquid phase clouds and decrease in ice phase clouds (i. e. , the decrease in misidentified ice phase by MODIS) in the C6 algorithm is consistent with what is shown previously from the MODIS C5 and C6 comparisons. On the other hand the fraction of misclassified liquid clouds by MODIS remains roughly constant between C5 and C6 (3. 0 to 3. 2 %). These misclassified pixels are in part due to optically thin ice clouds over warm or bright surfaces  but may also be due to insufficiently screening out all multilayer cloud cases from the MODIS-CALIOP collocated data set. In some cases where ice clouds overlap optically thick liquid clouds, CALIOP might detect only the overlying ice cloud, while MODIS may identify the scene as liquid.
which CALIOP observed a single cloud phase in the column, the extent to which the results presented here hold for multilayer clouds is still an open question. Limiting the analysis to the CALIPSO ground track also limits the viewing and scattering angle space such that it is unclear whether the C6 improvements are consistent across the entire MODIS swath; the impacts of potential view angle dependencies are at present unknown. Moreover, because spectral channels sets can vary between satellite sensors (e. g. , MODIS 2. 1 µm vs. VIIRS 2. 25 µm), it is uncertain whether the spectral effective radius tests, as used here, can be applied uniformly across multiple platforms for climate data record continuity, though work to this end is ongoing. Nevertheless, the C6 COP phase algorithm represents a vast improvement over C5, and future work will focus on the remaining challenges such as multilayer clouds and view and scattering angle dependencies.
Data availability MODIS data are available through the LAADS (Level 1 and Atmosphere Archive and Distribution System) web http://modis-atmos. gsfc. nasa. gov/_docs/ C6MOD06OPUserGuide. pdf. Availability: from 2000 (Terra) and 2002 (Aqua) to today. Edited by: B. Mayer The Supplement related to this article is available online at doi:10. 5194/amt-9-1587-2016-supplement.
Material and methods We collated data on the non-breeding movements of eight seabird species; Cory's shearwaters (Calonectris borealis); Scopoli's shearwaters (C. diomedea); lesser black-backed gulls (Larus fuscus); northern gannets (Morus bassanus); great skuas (Stercorarius skua); south polar skuas (S. maccormicki); common terns (Sterna hirundo) and Sabine's gulls (Xema sabini). While these species have been recorded previously in the CCLME, the true importance of this region for specific populations is unknown. Between 2000 and 2011, 123 birds were tracked using miniaturized light loggers from 12 breeding colonies from the north (758 N) to the south (628 S) of the Atlantic (see the electronic supplementary material). To quantify the extent to which each species uses the CCLME, we calculated the proportion of time each individual spent in this region  [16].
To identify areas of high species richness we constructed spatial density maps by binning location data into 200 km diameter tessellated hexagons spanning the Atlantic. We calculated relative richness by summing the number of species occurring in each hexagon during the non-breeding period. To characterize the marine environment, we extracted winter seasonal climatology composites (December -March, 2002 -2010) of sea surface temperature (SST, 8C) and chlorophyll a concentration (CHL, mg m 23 ) from the MODIS instrument onboard the Aqua (EOS PM) satellite (http://oceancolor. gsfc. nasa. gov/) and calculated mean SST and CHL values for each hexagon. We also included a measure of null usage that incorporated both habitat availability and sampling effort, as this was not uniform across species or colonies  [17]  (see the electronic supplementary material).
These data are available via Dryad  [18]. We examined correlations between the observed patterns in relative richness and these covariates using generalized additive models fitted with the packages mgcv  [19]  and MuMIn  [20]  in R v. 3. 1. 0 [21]. We log 10 transformed CHL prior to use. We included SST, CHL and null usage as covariates in the global model with thin plate regression splines fitted with a maximum of 10 knots; superfluous knots were penalized during model fitting. Variance inflation factors revealed no multicollinearity between covariates (VIF , 3). We also included the central X and Y coordinates of each hexagon as a spatial smooth term implemented with a soap film boundary  [22]. The soap film specifies the extent of the predicted surface, preventing smoothing across boundary features such as the Iberian Peninsula.
Plant materials, growth conditions, and hypocotyl measurements The Arabidopsis strains used were Col-0 and axr3-1  (Leyser et al. 1996). The B. rapa strain was FPsc. Seeds were sterilized, stratified, and germinated in constant white light on 0. 7% agar plates with 0. 5× Linsmaier and Skoog salts and vitamins. The temperature used was 22°C. To generate cell-specific pro>>axr3-1::mCit plants, a GAL4-based transactivation expression approach was used, as some of the promoters driving axr3-1 used in this study caused severe defects in adult plants  (Swarup et al. 2005 ; data not shown). Plants homozygous for UAS::axr3-1::mCit (T3 generation) were crossed to lines homozygous for cell-specific pro:: GAL4::VP16 (T3), and F1 seedlings hemizygous for both transgenes were scored. Two of three randomly selected UAS::axr3-1::mCit lines gave similar results when crossed to a single cellspecific pro::GAL4::VP16 line, and one of these was chosen for all subsequent crosses.
Samples were collected in triplicate, with collection times staggered randomly across a 24-h period. One of the shade-treated epidermal peel samples was removed from downstream analysis at the Cuffdiff step due to contamination with an unacceptable number of cells from the inner tissues (Supplemental Fig. S7E ). For RNAseq of Arabidopsis seedlings, plants were grown for 5 d in white light and then treated for 4 h with white light or shade. Seeds from a given silique were divided equally between conditions. Four replicates for each genotype and condition were collected. Ten to 15 whole seedlings were collected per replicate, and replicates were collected over two different days. We noted possible batch effects on gene expression related to silique of origin and day of sample collection (data not shown).
High-throughput whole-transcriptome sequencing and analysis Total RNA was extracted using Qiagen RNeasy microkit and digested with DNase I. Agilent tape station was used to determine RNA integrity numbers prior to library preparation. Stranded mRNA-seq libraries were prepared using Illumina TruSeq stranded mRNA library preparation kit according to the manufacturer's instructions. Libraries were then quantified, pooled, and sequenced at single-end 50-base-pair reads using the Illumina HiSeq 2500 platform at the Salk Next-Generation Sequencing Core. Raw sequencing data were demultiplexed and converted into FASTQ files using CASAVA (version 1. 8. 2). Libraries were sequenced at an average depth of 16. 5 million and 14. 7 million reads per library for Brassica and Arabidopsis samples, respectively.
RNA-seq analysis was performed using the Tuxedo protocol  (Trapnell et al. 2012 ) using iPlant/CyVerse  (Goff et al. 2011). Briefly, raw reads were aligned to Arabidopsis TAIR10 or B. rapa FPsc version 1. 3 (DOE-JGI, http://phytozome. jgi. doe. gov) using TopHat version 2. 0. 9 and Bowtie 2. 1. 0 (strand-specific). Cufflinks, Cuffmerge, and Cuffdiff versions 2. 1. 1 were used for transcriptome assembly and differential expression analysis using default parameters. For reanalysis of SAUR gene expression in the Brassica data set, the analysis was reperformed in the same manner except using multimapped read correction in Cufflinks and Cuffdiff. Test_ids (referred to here as genes) that had FPKM (fragments per kilobase per million mapped fragments) > 0 in all samples tested and a Benjamini and Hochberg-corrected P-value (false discovery rate, q) of <0.
05 between two samples were designated as differentially expressed. Only nuclear genes in Arabidopsis were analyzed. Putative orthologs of Brassica genes were identified from the annotation file accompanying the B. rapa FPsc version 1. 3 genome release (generally, the top result from a BLASTP alignment of the FPsc proteome to the Arabidopsis TAIR10 proteome; determined by Department of Energy Joint Genome Institute). BiNGO 3. 03 was used for GO (biological process only) enrichment analysis with a Benjamini and Hochbergcorrected P-value of <0. 05  (Maere et al. 2005). For Brassica, a custom annotation file was provided to BiNGO (see the Supplemental Material), where each Brassica gene was assigned the same GO terms as its putative ortholog from Arabidopsis (Arabidopsis TAIR10 annotation data downloaded September 2015) on the assumption that the two genes would have conserved function.
Acknowledgments We thank  Scott Woody  and  Rick Amasino  for providing B. rapa FPsc seeds and sharing information. The FPsc genome data were produced by the  US Department of Energy Joint Genome Institute , with additional information provided by  Stephen Fairclough. We also thank  Roger Granbom  for assistance with IAA measurements;  Ullas Pedmale  for the CER6pro plasmid;  Javier Gallego-Bartolome  and  Björn Willige  for reagents and discussion;  Carol Huang  for guidance on statistical analysis;  Adam Seluzicki  and  J. Gallego-Bartolome  for critical reading of the manuscript; and  Leah Baum ,  Cory Chen , and  Kira Bender  for technical assistance. RNA-seq was performed with assistance from  Manching Ku  at the  Next-Generation Sequencing Core of the Salk Institute  with funding from  NIH-National Cancer Institute Cancer Center Support Grants  ( P30 014195 ), the  Chapman Foundation , and the  Helmsley Charitable Trust.
HARPS spectra The HARPS spectrograph, mounted on the ESO 3. 6 m telescope at La Silla was used to observe sunlight scattered from the bright asteroid 4/Vesta (its average magnitude during the run was 7. 6). Two to three measurements per night were made with simultaneous Thorium exposures for a total of 98 observations, spread over 37 nights between 2011 September 29 and December 7. The geometric configuration of the Sun and Vesta relative to the observer is illustrated in Figure  1. At the time of the observations, the Sun was just over three years into its 11-year magnetic cycle; the SDO data confirm that the Sun showed high levels of activity. The spectra were reprocessed using the HARPS DRS pipeline  (Baranne et al. 1996; Lovis & Pepe 2007). Instead  of applying a conventional barycentric correction, the wavelength scale of the calibrated spectra was adjusted to correct for the Doppler shifts due to the relative motion of the Sun and Vesta, and the relative motion of Vesta and the observer (see Section 2.
Solar rest frame The data reduction pipeline for HARPS assumes that the target observed is a distant point-like star, and returns its RV relative to the solar system barycenter. In order to place the observed RVs of Vesta in the solar rest frame, we perform the following operation: RV = RV bary,Earth + vsv + vve, (1) where RV bary,Earth is the barycentric RV of the Earth, i. e. the component of the observer's velocity relative to the solar system barycentre, toward the apparent position of Vesta. It can be found in the fits header for each observation. The two components vsv and vve, retrieved from the JPL horizons database 2  correspond to: -vsv: the velocity of Vesta relative to the Sun at the instant that light received at Vesta was emitted by the Sun; -vve: the velocity of Vesta relative to Earth at the instant that light received by HARPS was emitted at Vesta.
The lineof-sight velocity varies with sin θ. The asteroid's equatorial velocity veq is given by: veq = 2π Rvesta Prot. (6) Using a mean radius Rvesta = 262. 7 km  (Russell et al. 2012 ) and the rotational period Prot = 5. 34 hours, we obtain veq = 85. 8 m s -1. The maximum RV amplitude of Vesta's rotational modulation, expected at θ = π/4 is thus approximately 4. 7 m s -1. The RV modulation due to surface brightness inhomogeneity should therefore dominate strongly over shape effects. We find that this RV contribution is well modelled as a sum of Fourier components modulated by Vesta's rotation period: ∆RVvesta(t) = C cos(2π -λ(t)) + S sin(2π -λ(t)), (7) where λ(t) is the apparent planetographic longitude of Vesta at the flux-weighted mid-times of the HARPS observations and can be retrieved via the JPL horizons database (the values of λ are listed in Table  A1 ).
We conclude that small airmass-dependent guiding errors provide a plausible explanation. During the latter part of the run, the observations were contiguous and were thus conducted at similar airmass, leading to more consistent guiding and smaller scatter. Other factors such as asymmetric image quality, coma of the telescope varying with elevation (and sky rotation on an equatorial telescope), atmospheric dispersion, etc. may also contribute to this effect. The remaining variations, of order 7-10 m s -1 , are modulated by the Sun's rotation and are caused by the presence of magnetic surface markers, such as sunspots and faculae. These variations are the primary focus of this paper, and we model them using SDO/HMI data in Section 3.
Time lag between Vesta and SDO observations At the time of the observations, the asteroid Vesta was trailing the SDO spacecraft, which orbits the Earth (see Figure  1 ). In order to model the solar hemisphere facing Vesta at time t, we used SDO images recorded at t+∆t, where ∆t is proportional to the difference in the Carrington longitudes of the Earth/SDO and Vesta at the time of the HARPS observation. These longitudes were retrieved from the JPL horizons database. The shortest delay, at the start of the observations was ∼ 2. 8 days, while at the end of the observations it reached just over 6. 5 days (see Table  A1 ). We cannot account for the evolution of the Sun's surface features during this time, and must assume that they remain frozen in this interval. The emergence of sunspots can take place over a few days, but in general large magnetic features (sunspots and networks of faculae) evolve over timescales of weeks rather than days.
We expect these differences to result in a small night-to-night uncorrelated noise contribution. In addition, this variance term will absorb any residual signal of Vesta's axial rotation, which will naturally not be completely sinusoidal. For each value of s, the basis functions are orthogonalised by subtracting their inverse-variance weighted means prior to performing the scaling. We determine the maximum likelihood via a procedure similar to the one described in Collier  Cameron et al. (2006) , whereby we maximise the likelihood L of the solution: ln L = - n 2 ln(2π) - 1 2 χ 2 - 1 2 i=n i=1 ln(σ 2 i + s 2 ) (29) with respect to s, where χ 2 is the chi-squared value of the n HARPS data points, with uncertainties σi. This procedure is applied to the unbinned (not nightly-averaged) HARPS dataset, in order to determine the appropriate scaling coefficients (C and S) for Vesta's axial rotation (see Equation  7 ).
We see good agreement between the data and the model. The Spearman correlation coefficient is 0. 64 for the full dataset and 0. 87 when considering only the second part of the run. This is close to, although not as good as the correlation coefficient of 0. 94 found by  Meunier et al. (2010b)  between simulated velocities of  Meunier et al. (2010a)  and MDI/SOHO Dopplergrams. Panel (e) of Figure  8  shows the residuals remaining after subtracting the total model ∆RV model from the HARPS observations of the Sun as-a-star ∆RVSun. We see that the residuals are within the level of the error bars, when considering an extra variance term s of 2. 7 m s -1. Maximumlikelihood analysis of the additional uncorrelated noise during the first part of the run (nights 0-11) yields an additional variance with rms amplitude 4.