dataesr/scientific-paragraphs-categorization huggingface dataset:
intestinalis. GO classifications for C. intestinalis were extracted from the Aniseed annotation database (see 'Material and Methods' section). Figure  4C  shows the GO classes resulting specifically enriched in Ciona oCNEs: multicellular organismal development (adjusted P = 6. 58E-06), sequence-specific DNA binding (adjusted P = 1. 17E-05), transcription (adjusted P = 0. 0007), cell differentiation (adjusted P = 0. 0008), transcription factor activity (adjusted P = 0. 008) and calcium ion binding (adjusted P = 0. 017). Taken together, these results clearly indicate that the genes surrounding oCNEs as well as the transcription factors potentially binding oCNEs are significantly associated with genes involved in development and, more specifically, to morphogenesis and differentiation and these enrichments are significantly more specific than the ones related to rCNEs.
This search yielded nine conserved elements in the Oikopleura and 13 in the amphioxus. Genes flanking and overlapping the elements thus discovered were annotated using Blast2GO (42) and considered as putative target genes. Annotations were manually checked against Ciona and mouse overlapping and flanking genes for each respective element (see Supplementary Table  S6 ). Again, these elements resulted not to be located in the vicinity of evolutionarily related genes, although they appear to be associated to genes functionally related. Indeed, according to the Blast2GO classification, the top scoring biological processes represented in the associated genes are related to development and regulation (Supplementary Table  S9 ). The number of conserved elements is small, and therefore, we cannot test for significance; however, the biological functions annotated by Blast2GO are remarkably similar to those enriched in the 183 original oCNE dataset.
Panel B shows, for each Jaspar fam motif, the percentage of C. intestinalis oCNEs (light grey) and C. intestinalis rCNEs (dark grey) containing at least one binding site for the specific motif. Adjusted P-values of the differences between the two groups are reported only if significant. C shows GO enrichments for each GO class associated to genes flanking tunicate oCNEs (light grey) and tunicate rCNEs (dark grey). Only oCNEs-associated significantly enriched classes are reported with the respective adjusted P-values. reported the discovery of >1000 CNEs [defined as phylogenetically conserved non-coding elements (PCNEs)] among vertebrates or between vertebrates and amphioxus. Out of 183 oCNEs, 122 overlap the published set of vertebrate PCNEs, and 42 of them overlap the set conserved between vertebrates and amphioxus (data not shown).
With constant refinements in the technologies capable to detect non-abundant transcripts, the observations that a large number of enhancers are also transcribed are tangibly increasing  (49, 54, 70, 71) , suggesting that, at least in mammals, thousands of enhancers are transcribed. Interestingly, the oCNE dataset also shows significant overlap with the eRNA dataset. This enrichment is not a bias determined by the composition of vCNEs, indicating that oCNEs probably belong to a specific class of enhancers, which can also be transcribed. Furthermore, we indicate, by analyzing a large number of publicly available ENCODE datasets, that they are unlikely to transcribe short RNAs. It should be noted that for most eRNAs and UCEs analyzed, the full length and nature of the RNA molecules transcribed by these regions remains a largely unresolved question.
Similarly, our HMM search of oCNEs in other species such as amphioxus was performed stringently and might thus miss related and relevant CNEs, which could have diverged beyond the stringency of our approach. Manual curations of results and the significant overlaps with other relevant datasets such as eRNAs, UCEs, ENCODE data and the experimental evidence we produced are further proof of oCNEs' biological relevance. A different and altogether more complex issue is to what extent oCNE-like elements could arise by convergent evolution. We do not have sufficient data to tackle appropriately this issue but we speculate that it could be unlikely if we consider a parsimonious scenario for the evolution of such elements. Finally, assembly errors could have generated some of the extensive non-orthologous shuffling we have observed.
ACKNOWLEDGEMENTS The authors would like to acknowledge  John Mattick  for providing the extended set of UCEs data;  Michael Greenberg ,  Tae-Kyung Kim  and  Jesse Gray  for clarifications about the eRNAs dataset;  Paolo Sordino  and  Salvatore D'Aniello  for critical reading of the manuscript;  Michael Brudno ,  Stefano Gustincich ,  Piero Carninci ,  Mariella Ferrante ,  Graziano Fiorito ,  Fiona McNish ,  Daniel Zerbino ,  Alison Meynert ,  Benedict Paten  and  Wolfgang Huber  for helpful discussions and support. A special acknowledgement also goes to the late  Parvesh Mahtani , who shared our enthusiasm for this project. We would like to thank two anonymous referees for their invaluable suggestions.
Given this unexpected result, one cannot exclude that some of the discrepancies found across relevant studies  [4] [5] [6]  may stem from inconstant recording methods of metabolic rates in both embryos and hatchlings. For instance, while metabolic rates were in all cases recorded at similar temperatures in this study (between 28 and 30uC), Angilletta et al. 5  estimated metabolic rates in the lizard Sceloporus undulatus using a flow-through system rather than via heart rate readings. Moreover, Radder & Shine 6 used, as in the current study, the BuddyH digital egg monitor, but anaesthetized weekold lizards, Bassiana duperreyi, with gaseous (Fluothane) inhalation prior to recording heart rates. On the other hand, the way Du et al. 4  collected their data on emydid turtles was fully consistent with the method applied to the current study.
maura) and from the 7 th of July to the 24 th of July 2012 (N. natrix). A total of 231 N. maura eggs (mean clutch size 5 13. 3 6 5. 7) and 156 N. natrix eggs (mean clutch size 5 22. 3 6 6. 8) were obtained. For the purpose of the current experiment, we randomly selected a number of eggs from each N. maura clutch (50 eggs in total; 3. 1 6 2. 4 eggs per clutch) and each N. natrix clutch (28 eggs in total; N 5 4. 0 6 4. 5 per clutch). All females were returned to their exact site of capture within two weeks of egg-laying. From capture to release, females were offered small dead trouts once a week (frozen trouts were purchased from a local fish farm: Pisciculture des Chutes d'Aston, Les cabannes 09310). Data collection. Eggs were collected within 12 hours of laying, placed on a 2 cm layer of wet vermiculite and transferred into an Aqualytic H incubation chamber set at a constant 28uC.
Each bag was then placed onto the sensor pad of a BuddyH digital egg monitor (MK2, Avitronics) for heart rate reading (a stable reading was obtained after approximately 30 seconds). Statistical analysis. All numerical data were Log-transformed prior to analysis. Data were checked for normality using Shapiro-Wilk W tests. Data analysis was performed using two factor repeated measures ANOVAs, post-hoc Tukey HSD and Linear regressions. Means 6 standard deviations are given unless otherwise stated.
Acknowledgments I wish to thank numerous field and laboratory helpers: Melodie Tort,  Tom Sarraude ,  Thomas Achkar ,  Alice Thiney  and  Gae ¨lle Blanvillain. I also wish to thank the  CNRS , the  UniversitePaul Sabatier  for funding (Project  EXUVIE ), and the  Agence Nationale pour la Recherche. This work is part of the  ANR INDHET. There is no conflict of interest associated with this manuscript. This work is part of the  ''Laboratoire d'Excellence'' (LABEX)  entitled  TULIP  ( ANR -10-LABX-41 ).
III. DATA ANALYSIS AND RESULTS The total live-time of data taking for the 214 Po measurement is 10. 2 d. For the two 212 Po sources, the live-times are 6. 3 d and 15. 5 d, respectively. The correspondent overall statistics are ≈ 3. 9 × 10 5 214 Po and ≈ (1. 1 + 1. 7) × 10 5 212 Po candidates for the first and second source, respectively. The two 212 Po candidate samples from the two sources are analyzed independently. The energy response of the detector is calibrated run-by-run, using the light yield obtained by fitting the 14 C energy spectrum: on average 3. 8 p. e. per PMT are detected for a 1 MeV recoiling electron at a random position within the detector volume  [22]. In these measurements, the yields are 240 p. e. /MeV and 220 p. e. /MeV with 70 PMTs for the 214 Po and 212 Po analyses, respectively.
ACKNOWLEDGMENTS We acknowledge the generous support of the  Laboratori Nazionali del Gran Sasso  and we thank the funding agencies:  INFN (Italy) ,  NSF (USA) ,  BMBF ,  DFG  and  MPG (Germany) ,  Rosnauka (Russia) , the  Ministry of Education and Science (Russia) , and  MNiSW (Poland). We are grateful for enlightening discussions with and the valuable comments of  E. Bellotti  and  B. Ricci.
) are forested. There, human impact depends on local forest policy and atmospheric deposition. Very few observatories are located in areas impacted by other human activities, such as urban development (Baltimore Ecosystem Study, Maryland, USA) or intensive agriculture (Kervidy-Naizin, France). Water quality issues differ according to the ecosystem and its land use. Long-term water quality monitoring is generally performed to capture temporal trends and potential differences. However, with the exception of the Plynlimon observatory  (Neal et al. , 2011) , very little effort has been devoted to the analysis and publication of the obtained longterm databases. Long-term water quality data exhibit high variability patterns depending on the timescales at which they are analysed  (Reynolds et al.
An outstanding study site The Kervidy-Naizin catchment is located in western France (Central Brittany: 48 • N, 3 • W) (Fig. 1 ). It is a 4. 82 km 2 headwater catchment, drained by a 2nd order stream, that can occasionally fall dry in summer. The catchment's observatory (ORE-AgrHys, for AgroHydrosystem) belongs to the French network of observatories SOERE-RBV (Long-term Monitoring and Experimental System for Research in the Environment-Network of Basin Catchments). Data, metadata and scientific papers about the catchment are available on the ORE-AgrHys website (http://www. inra. fr/ore agrhys). Many soil, hydrological and biogeochemical studies have been performed. As of January 2012, 21 journal articles based on the catchment observations are referenced in the Web of Knowledge. The catchment lies on upper Proterozoic schist covered by a layer of weathered material up to 30 m thick.
Data collection This study analyses data collected from September 2000 to August 2010 (10 hydrological years). Measurements of stream discharge at the Kervidy-Naizin outlet are available since autumn 2000 at a frequency of one measurement per 15 min. Discharge is measured at a gauging station including a float-operated sensor and a data logger (Thalimèdes OTT). The weather station at Kervidy (Cimel Enerco 516i) is located approximately 1 km from the outlet. It records hourly rainfall, air and soil temperatures, air humidity, global radiation and wind direction and speed, which allows the calculation of Penman evapotranspiration. Solute concentrations, i. e. NO 3 , SO 4 , Cl, dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC), are measured at the outlet at a frequency of one measurement per day, except during the hydrological year 2002-2003 when the sampling frequency was reduced to once every 2-4 days for solute concentrations.
Deep shallow-groundwater (GW) data were collected from a 4 m-deep downslope piezometer and from an 8 mdeep upland piezometer, set along a topographic transect (Gueriniec transect; see Fig. 1 ) (respectively referred to as deep downslope GW and upland GW throughout the rest of this paper). Shallow GW data were also collected from eight shallow (20-40 cm), zero-tension lysimeters set in the wetland domain (Mercy wetland; see Fig. 1 ) (referred to as wetland GW). Water-table depth in this catchment is measured every 15 min by pressure probes (Orpheus OTT) since 2000. Water-table chemistry is measured at different frequencies depending on water-table depth. In the deep piezometers, measurements are performed roughly every three months, providing 24 analyses for each piezometer since 2000. This is justified by the relative chemical inertness of the GW at that depth.
Data treatment Based on the concentrations measured in the four studied water compartments (upland, deep downslope and wetland GW, and stream water) and on the mean annual pattern and year to year variability of solute concentrations in the stream, we identify what the hydro-chemical signature of a typical livestock farming catchment is. While the mean concentrations of the four hydrological compartments depict what is called a "static signature", the annual and interannual variations and variability of the 10 yr-long daily dataset allow us to identify what is called a "temporal signature". The annual patterns are observed at two scales: the hydrological year and the floods. As no specific sampling strategy was implemented to account for floods, we used the following decision rule (adapted from  Morel, 2009, and Molenat et al.
, 2008)  to distinguish between base-flow and flood-flow periods: if daily rainfall on the sampling day exceeded 1. 5 mm or daily rainfall from the previous day exceeded 5 mm or daily discharge on the sampling day exceeded 200 L s -1 , then the sample was considered as taken during a flood; otherwise it was considered as taken during base flow. The interannual patterns of  Considering the whole dataset, descriptive statistics and boxplots were computed using R software. Temporal variograms were plotted using "gstat" and "variogram" functions in R  (Zhang and Schilling, 2005). Temporal distance was given as a number of days. The chosen lag was 30 days, and the span 2000 days.
Nitrate Months mg NO3-N l-1   Fig. 3. Boxplots of monthly concentrations of nitrate, chloride, sulphate, dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC) based on daily data from the 10 yr dataset. The middle bar represent the median, the lower limit the 1st quartile (q 0. 25 ) and the upper limit the 3rd quartile (q 0. 75 ). The lower and upper dashed lines are, respectively, the 1st quartile minus 1. 5 times interquartile range and the 3rd quartile plus 1. 5 times the interquartile range. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • sep nov • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • sep nov • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • sep nov jan feb apr jun jul aug 0123456 Sulphate Months mg SO4-S l-1 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • sep nov of rainfall and (ii) rainfall water contributes less than 10 % of the flood flow, the major part coming from soil water.
The 10 yr-long dataset complemented earlier studies examining short timescale DOC concentration variations and enabled us to propose a new seasonal conceptualisation for sulphate. The pattern observed for DOC, sulphate and DIC was similar to that of chloride between floods (Fig. 3b-e ). However, flushes occurred during the hydrological year with each rainfall event (Figs. 3c-e  and 2 ) leading to concentration peaks. Mean stream DOC concentration during and between floods was 6. 3 and 3. 3 mg L -1 , respectively (Table  1 ). Stream sulphate concentrations were higher during floods (3. 0 mg SO 2- 4 -S L -1 ) than between floods (2. 3 mg SO 2- 4 -S L -1 ). The intensity of peaks decreased as the year progressed (Fig. 3 ). An increase in interflood concentration was observed during the summer.
Therefore, variation of the recharge period (duration, quantity and intensity of rain) and temperature between years partly explains the high variability in the autumn-winter period. Lastly, the dataset enables the study of variability over the whole time period. The observed interannual patterns reinforced the previously proposed groups. Plotting variograms over 2000 days with a 30 day time lag (Fig. 4 ), three groups were identified. DOC, sulphate and DIC signals were periodic, with a 360 day period, and similar to that of temperature. Chloride constituted a second group. Besides an annual periodicity, it showed a pluriannual variation marked by a break in the 36th month (1080 days). An equivalent break was observed in the discharge variogram. This pointed out the hydrological control on the export of chloride to the stream.
A generic classification of solutes applied to a livestock farming catchment The 10 yr-long daily dataset allowed a representative description of the hydro-chemical signature of the catchment. Its conceptualisation leads to a generic classification of solutes. Many authors agree that element availability is a factor controlling export to streams  (Creed et al. , 1996; Hornberger et al. , 1994; Reynolds and Edwards, 1995). Based on this major factor,  Hornberger et al. (1994, for DOC ) and Creed et al. (1996, for nitrate) proposed the "flushing hypothesis", which consists of the contribution of a productive area in the catchment at a given moment. This contribution leads to a concentration peak in the stream until the supply is depleted, thus leading to a decrease in concentration. The limiting stage is the production.
Unlike nitrate for which export is continuous during the rainy period, DOC and sulphate exports are highly discontinuous: every flood leads to a peak in concentration. For DOC and sulphate, the wetland domain is the main productive compartment close to the stream and contributes mostly during floods. It would be interesting to analyse data from other agricultural catchments to corroborate our classification. A longer dataset would provide longer temporal variograms, which may verify the periodicity identified in this catchment. 1234567 895AB56 CD32A9E6 743FDB56 298B85 724AC62 7234567 85DA2F2689C98F457CA6B569856A8B3CB5CD685 6C677 9262CD69CB3 FA2C67767CA6B569856A8B3FB5CD672 FA2E4C592895D985D6 CB5CD685 298B857FB59B3 724AC6 F3B8E!" 66FE28#732F6!" "653B8E!" 1FB59B3 E975A9$459282 !"C28C685AB59287% F3B8E && "653B8E& 66FE28#732F6 F3B8E'"653B8E& 66F E28#732F6 66FE28#732F6&& F3B8E'"653B8E 66FE28#732F6&& F3B8E "653B8E&& F3B8E'66F E28# 732F6 (9982 C28C68# 5AB5928 98CA6B76) 16B728B3 16B728B3*75322E 16B728B3*322E7 14BA+ABFD 28C685AB5928 28C685AB5928 28C685AB5928 28C685AB5928 28C685AB5928 152C,98F6A92E -B8++6BA7 FB75BCC443B5928 146A 76B728B3FA2E4C5928 856A#322EF6A92E7 285984247FA2E4C5928 (AB87F2A5#39956E FA2C677.
Conclusions From this original dataset, the hydro-chemical signature of a livestock farming catchment dominated by shallow groundwater flow has been defined. This signature was identified by analysing (i) mean concentrations in four water compartments in the catchment (stream water, upland, wetland and deep downslope groundwater), considered as a static signature, and (ii) annual patterns of stream water concentrations and their interannual variability, considered as a temporal signature. Our results broadened some previously proposed conceptual models, based on short-term studies, for nitrate and DOC. The data enabled us to explain annual patterns for chloride and sulphate. Both aspects of the signature, static and temporal, lead to a generic conceptual model of stream water quality. The static signature is defined by average long-term concentrations in the stream and the contributing compartments.
TIR T. Analytical calculations are performed for geophysical variables (atmospheric profiles and surface properties) and finite differences for instrumental parameters (shifts and corrections, see Sect. 2. 2). The radiometric error matrix S ε UV+TIR contains estimations of the radiometric noise variances for each element of y UV+TIR. For the TIR, the noise standard deviation is taken as 20 nW/(cm 2 cm -1 sr)  (Eremenko et al. , 2008). For the UV, noise is estimated for each wavelength using Müller matrix radiance response elements  (Nowlan et al. , 2011; Cai et al. , 2012). Signal-to-noise ratios for GOME-2 are approximately a factor 1/ √ 8 lower than for coarser pixels considered in  Cai et al. (2012). Ozone spectroscopic parameters used by IASI + GOME-2 are TIR atmospheric absorption line parameters taken from HITRAN 2004 (HIgh-resolution TRANsmission molecular absorption database;  Rothman et al.
, 2005)  and UV temperature-dependent cross sections from  Brion et al. (1993, hereafter BRION1993). These spectroscopic parameters are used by validated single-band approaches  (Eremenko et al. , 2008; Cai et al. , 2012)  and also in  Fu et al. (2013). Among several tests with different spectroscopic databases (see Sect. 3. 3), only when using HITRAN 2004 and BRION1993 we obtain good agreement between IASI + GOME-2 retrievals in the troposphere and ozonesondes. Forward calculations consider as well the spectral signatures of H 2 O and CO 2 (SO 2 and NO 2 ) for the TIR (UV). Except for H 2 O (see Sect. 2. 3), climatological profiles are taken for the other trace gases. For all profiles, the vertical grid is set between the surface and 60 km of altitude above sea level (a. s. l. ), with steps of 1 km and 2 km, respectively below and above 40 km of altitude.
5 and 4. 5 DU). Subtracting the variance from ozonesonde errors, we estimate a precision of the IASI + GOME-2 retrievals in the LMT of 21. 4 % for direct comparisons and 16. 2 % when smoothing by the retrieval sensitivity. Table  6  presents additional tests on the potential effects on the multispectral ozone retrieval due to inconsistencies in the UV/TIR spectroscopic parameters. According to laboratory studies, spectral discrepancies between UV crosssection databases and TIR line intensities (e. g. HITRAN 2004) may reach 4-5 %  (Picquet-Varrault et al. , 2005; Gratien et al. , 2010). The consequences of such incompatibilities are not straightforward, since several fitting parameters modify the absolute calibration of the spectra (i. e. TIR spectral offsets, fitting effective UV surface albedos and UV soft recalibration).
Thus, we verify the overall effects by comparing ozonesonde measurements (105 sondes) with the multispectral ozone retrieval either using HITRAN 2000 (with TIR line intensities approximately + 4 % higher than HITRAN 2004) or changing the UV cross sections by -5 %. The results in Table  6  indicate that these changes in the spectroscopic databases induce low (0. 7 % for HITRAN 2000) and positive (5. 7 % with -5 % UV cross sections) mean biases at the UPTO30 partial column (partially including the stratospheric ozone layer). However, aberrant retrievals are obtained in the troposphere for both corrections. Table  6  shows strong negative biases in the LMT and the LT (between -10 and -17 %), higher RMS and much lower linear correlations than in Table  2. Among the tested databases, only HITRAN 2004 and BRION1993 enable a good match between the IASI + GOME-2 approach and ozonesondes (shown in Tables 2 and 3).