dataesr/scientific-paragraphs-categorization huggingface dataset:
Although these points are all important to address, their thorough pursuit was beyond the scope of this study. Future studies that aim to inquire into these different questions should greatly benefit from the complete database of dysregulated genes that we report here. The GABA B receptor is involved in the long-lasting analgesic effect of oxycodone The spinal cord is the site of both pro-and anti-nociceptive modulations. Released from primary afferent fibres, Substance P (SP), CGRP and glutamate exacerbate neuronal activity and have a pro-nociceptive action. Conversely, activation of GABA B receptors inhibits voltage-dependent calcium channels via Gprotein-coupled adenylate cyclase  [42, 43]  and reduces the release of excitatory amino acids and SP from primary afferent terminals in the spinal cord  [44, 45].
In conclusion, this study provides evidence that treatment with oxycodone produces a profound anti-nociceptive effect under a neuropathic pain-like state. Furthermore, our results reveal a thorough list of genes that are dysregulated in DRGs at the end of chronic analgesic treatment. These findings will be indispensable to future studies that aim to understand the analgesic effects of oxycodone as compared to morphine in chemotherapy-induced neuropathic pain. Table  S1  Complete list of genes dysregulated at the end of analgesic treatment. Data are illustrated as follows: 1) Gene accession number; 2) Gene symbol; 3) Mean expression of the genes for the six different groups; 4) ident: an identifying z-z-z for the statistical analysis significance (z corresponding to the three tests; z = 1 if the null hypothesis is rejected with a risk of 5% or z = 0); 5) results of variance analysis between vincristine and control: p-value; 6) results of Student's t test between morphine and oxycodone in vincristine-treated rats: p-value and expression difference (morphine -oxycodone); 7) results of Student's t test between morphine and oxycodone in control rats: p-value and expression difference (morphine -oxycodone).
We compared the nutrient levels in selected and non-selected forages to the nutritional requirements of lactating white-tailed deer to evaluate whether deer would be able to meet their dietary nutritional requirements of peak lactation (i. e. support maximum growth of two fawns) if forage availability was not limiting intake. Further, we compared body weights of hunter-harvested yearling males over a high and low population density. Our results would support hypothesis 1 if forage quality did not meet the nutritional requirements of peak lactation and body weight did not respond to changes in density. Our data would support hypothesis 2 if forage quality did meet the nutritional requirements of peak lactation and body weight did respond to changes in density.
025 g g Ϫ 1  (McEwen et al. 1957 , Barnes et al. 1990 ). We used a general linear model in SPSS (  2008 ) to test for diff erences between each nutrient level within selected and non-selected forages and between each nutrient level within selected and non-selected forages by forage classes. We used a conservative Tukey ' s mean separation test when interactions were detected. We used a log transformation to correct for non-normality when necessary.
Body weight collection and analysis We obtained body weight measurements from all harvested yearling males at Fort Bragg during 1984 -1990 (n ϭ 996) and 2000 -2010 (n ϭ 1867). We used yearling males because the tooth wear and replacement technique may not accurately assign older age classes  (Gee et al. 2002) , and yearling males were the best indicators of density as reported in  Keyser et al. (2005). We used a general linear model in  SPSS (2002)  to test for diff erences in body weight between periods. We used a conservative Tamhane ' s T2 mean separation test, which does not assume equal variances, when interactions were detected.
In this study, we use data from the FLUXNET LaThuile data set, a global standardized database of eddy-covariance measurements which includes a large number of sites. Measurements of sensible (H ) and latent (λE) heat fluxes are used to compute EF, while global radiation (i. e. , incoming shortwave, R g ) and potential global radiation (i. e. , extraterrestrial radiation, R pot g ) are used to get a proxy for cloud cover (see Sect. 3. 2). One of the main issues with eddy-covariance measurements is that the energy balance is not closed: the sum of H and λE typically underestimates the available energy by 10-30 % (e. g. ,  Wilson et al. , 2002; Mauder et al. , 2006; Foken, 2008; Hendricks Franssen et al. , 2010). However, as we do not use H and λE directly but only their ratio through EF, we note that the commonly used "fixed Bowen ratio" correction for the energy balance closure (i.
TFS from different data sets The impact of before-noon EF on precipitation occurrence is quantified using the modified triggering feedback strength, TFS * (see Sect. 3). TFS * is computed at FLUXNET sites from three data set combinations: (i) a reanalysis product (NARR), (ii) direct measurements of surface turbulent heat fluxes at FLUXNET sites for EF in combination with radar precipitation from NEXRAD, and (iii) EF estimates from a satellite-data-driven evaporation product (GLEAM) in combination with NEXRAD precipitation. We compare estimates of TFS * from these data sets (Sect. 4. 1) and general characteristics of the EF data sets (Sect. 4. 2).
g. , in Fig. 3 ). Results at other sites have been confirmed by analyses with other precipitation data sets (not shown; e. g. , with CMORPH;  Joyce et al. , 2004). Several reasons might contribute to the observed differences between TFS * estimates from the different data sets, some of which can be discussed with the support of Fig. (TFS * for the different combinations of EF and precipitation data sets for the same subset of days, namely the potentially convective days according to the NARR selection): i. Spatial scale of the EF data: the footprint of FLUXNET measurements is much smaller than the grid cells of NARR and GLEAM (typically 100-2000 m and 25-30 km, respectively; see Sect. 4. 2). Since EFprecipitation coupling is expected to occur at scales of about 20-100 km and NEXRAD data are at such a scale, FLUXNET may be less appropriate for this application.
Although different TFS * cannot be clearly attributed to differences in footprints based on Fig. 4 , EF uncertainties are shown to play a strong role in controlling the convection triggering metric (see also Sect. 4. 2). ii. Time series length and noise: the lengths of the time series considered here range from a few years in FLUXNET to 13 year (with some gaps) in GLEAM-NEXRAD and NARR. Comparing Fig. 2  with the respective panels of Fig. 4  shows that the decreased sample size in Fig. 4  affects TFS * in NARR and in the GLEAM-NEXRAD combination. A relatively large number of days is required to estimate TFS * robustly, as smaller or noisier samples lead to lower and less significant values. Thus, higher noise levels in observationbased data sets and incomplete sampling due to short record length could explain their weaker values of the metric in the eastern US.
e. , after removing the seasonal cycle within JJA) instead of actual values display similar results (not shown). Several reasons might explain these differences. First, the spatial scale over which EF is estimated, or footprint, is dataset-specific, as mentioned above (Sect. 4. 1, point i). Differences might thus arise from contrasting environmental conditions over the respective footprints (e. g. , input of water from rainfall in the case of very local precipitation events), but also from differences in land covers. Indeed, while wet vs. dry periods might be similar in all data sets, some studies have shown that different vegetation might respond differently to given conditions  (Teuling et al. , 2010). Land cover is in fact different at FLUXNET sites compared to the larger scale in NARR, in particular in regions with cultivated land, as FLUXNET sites are often located over natural vegetation.
Discussion and conclusions A recent study  (Findell et al. , 2011)  statistically relates the occurrence of afternoon convective precipitation to beforenoon evaporative fraction (EF) through the TFS metric (triggering feedback strength), based on data from the North American Regional Reanalysis (NARR), and suggests the existence of an extended region of positive land-surfaceprecipitation coupling over the eastern US. Our study extends that analysis with a systematic cross validation of additional, independent, observation-driven data sources and an in-depth investigation of all components contributing to the identified pattern from  Findell et al. (2011). Comparing the relationship patterns from the different data set combinations, the FLUXNET-NEXRAD and GLEAM-NEXRAD combinations do not reproduce positive TFS * in the eastern US found in NARR.
Higher noise levels in these data sets and uneven sampling of different land cover types in the FLUXNET data may contribute to the differences. Nevertheless, our results suggest that land-surface dynamics in NARR and their stronger apparent coupling with precipitation in the eastern US might reflect model artifacts (see also  Ferguson et al. , 2012 , who find that surface soil moisture from NARR correlates poorly with remote-sensing estimates in the eastern US). Conversely, a significant relationship between EF and convection triggering is found for the observation-driven GLEAM-NEXRAD combination in the central US (consistent with, e. g. ,  Koster et al. , 2004) , although no such signal emerges from NARR in this region. The FLUXNET-NEXRAD combination displays low TFS * values there, possibly due to higher noise levels and short samples.
Similarly, NARR might underestimate a possible EF-precipitation coupling in these regions. In the area of the southwestern US close to the Mexican border, all data sets agree on the existence of significant relationships between EF and convective triggering. We find that the choice of the EF data set has a large impact on the relationship between EF and convection triggering, although the patterns of average EF, EF variability, and persistence in the different data sets do not clearly indicate the sources of this discrepancy. This comparison is further hampered by short observational records, uncertainties, and different spatial scales. Furthermore, we find that precipitation of the previous day is a better predictor of afternoon precipitation than beforenoon EF, pointing to a short timescale dominance of the atmosphere over land.
Acknowledgements. We thank  Han Dolman ,  Beverly E. Law ,  Altaf Alan ,  Markus Reichstein ,  Heini Wernli , and  Christopher M. Taylor  for discussions. We are also grateful for comments from the two anonymous reviewers. The authors acknowledge the  Swiss State Secretariat for Education and Research SER  for funding under the framework of the  COST Action   ES0804. NARR data is provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their Web site at http://www. esrl. noaa. gov/psd/. This work used eddy-covariance data acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program -DE - FG02-04ER63917  and  DE-FG02-04ER63911 ) and  Fluxnet-Canada  (supported by  CFCAS ,  NSERC, BIOCAP, Environment Canada , and NRCan).
We acknowledge the financial support of the eddy-covariance data harmonization provided by  CarboEuropeIP ,  FAO-GTOS-TCO, iLEAPS ,  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, University 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. Edited by:  L. Ganzeveld.
Acknowledgements. Confocal image acquisition and analysis were performed on CYMAGES imaging facility. We thank  S Netter ,  B Limbourg-Bouchon ,  B Hay ,  J-A Lepesant  and  ME Fortini  for providing fly stocks and antibodies. We are very grateful to  S Netter  and  I Guenal  for suggestions on the manuscript and helpful discussions throughout this work. We thank  C Pirou ,  P Gandille ,  C Wintz  and our team for their contribution to this project. This article is dedicated to the memory of Didier Contamine who succumbed to a long illness after initiating this project. We acknowledge the  NIG (National Institute of Genetics, Kyoto, Japan), Vienna Drosophila RNAi Center (VDRC, Vienna, Austria) and Bloomington Stock center (Bloomington, IN, USA)  for providing fly stocks.
Observational data The data used in this study are the ozone and NO 2 columns from the ground-based SAOZ/NDACC instruments at the two tropical stations of Bauru and Reunion Island and collocated EP-TOMS, GOME, SCIAMACHY and OMI satellite data. Increased scatter and offsets between SAOZ and satellite are expected in case of significant column geographical gradients or large tropospheric contributions. For limiting their impact,  Lambert et al. (2012)  suggested to use satellite footprints overlapping the SAOZ air mass (a method called optical path matching technique) instead of direct overpass data. The advantage of applying this method for reducing the noise in the differences between data sets is well established in polar and mid-latitude regions where horizontal gradients due to dynamical barriers and vortex intrusions are frequent.
EP-TOMS EP-TOMS, is a six-wavelength-nadir-viewing instrument on the helio-synchronous Earth Probe (EP) satellite, crossing the equator at 11:00 LT which operated between July 1996 and the end of  2005 (McPeters et al. , 1998)). The data used here are those of the version 8 retrieval algorithm available on (ftp://jwocky. gsfc. nasa. gov/pub/eptoms/data/overpass/). The ozone measurements are based on a differential pair method at 317. 5 and 331. 2 nm for a SZA smaller than 70 • always available in the tropics  (Wellemeyer et al. , 2004). The estimated accuracy is about 3 to 5 %. Since 2000, EP-TOMS experienced instrumental problems introducing a 3 % bias in total ozone  (Bramstedt et al. , 2003). For the version 8, an empirical correction was applied by NASA to overcome this bias  (McPeters et al.
Both products use the same AMF as described in the section on GOME data. The only exception is that for SGP HALOE profiles are used instead of a climatology  (Lambert et al. , 2000). The estimated accuracy for the IUP version is about 5 to 10 %  (Richter et al. , 2004)  and the data can be found at http: //www. iup. uni-bremen. de/doas/scia_no2_data_acve. htm. The estimated accuracy of the SGP version ranges from 5 to 20 % at polar latitudes and in the Northern Hemisphere. The NO 2 columns are low biased by about 5 × 10 14 molec cm -2 at low and middle latitudes of the Southern Hemisphere. This low bias exhibits a seasonal cycle (larger in summer) and a latitudinal dependence. The NO 2 columns are affected by larger errors over polluted areas (large underestimation of total column) and in the South Atlantic Anomaly.
7 %) and present the same variations as EP-TOMS until 2001 where an increase of the difference is observed. The two versions of OMI exhibit relatively the same variations and on average the same different amounts of O 3 (-3. 4 % for OMI TOMS and -3. 1 % for OMI DOAS) relative to SAOZ columns. The difference with SCIAMACHY is similar to EP-TOMS until 2005 then increases and becomes as large as that for the OMI versions. Because each satellite presents different long-term variations, the evaluations of satellite data drifts with respect to the SAOZ have been calculated with a linear regression on the relative differences. From this analysis, only SCIAMACHY exhibits a significant drift with respect to the SAOZ (0. 3 % yr -1 for SCIAMACHY compared to less than 0. 04 % yr -1 for the other). Finally, a seasonal variation of the difference between each satellite and SAOZ is observed.
In order to highlight those biases, Fig. 4  presents the relative difference between satellite and SAOZ seasonal cycle. It should be noted that the data have been normalized in order to make variations comparable. In this figure, all satellite data present a seasonal variation of the difference with a minimum during the austral summer (mean difference of 1 %) and a maximum during the austral winter, except for OMI-DOAS. EP-TOMS presents the smallest bias with a minimum -0. 7 % during austral summer and a maximum of 0. 7 % during austral winter. SCIAMACHY and OMI-TOMS.
A cloud filter has been applied on SCIAMACHY data in Bauru for characterizing the impact the clouds on the comparisons with SAOZ measurements (cf. Appendix A). From this analysis, the application of a cloud filtering results in a noise increase in the summer when clouds are more frequent and the number of satellite data reduced. However, there is no change in the  NO 2 mean value showing that clouds have a limited impact on mean NO 2 column measurements. The best satellite combination covering the full 1995-2011 SAOZ period are thus GOME  GDP5 (1995 GDP5 ( -2002) )  and  SCIAMACHY IUP V2 (2003 -2011)  showing smallest seasonality, requiring bias corrections of 6. 3 % for GOME GDP5 and 18. 8 % for SCIAMACHY IUP in Bauru and 0. 8 and 4. 1 % in Reunion for best matching with SAOZ.
Acknowledgements. The authors thank the personnel of Reunion ( J. -M. Metzger ) and Bauru ( B. Biazon  and  D. Quintao ) stations for running the instruments. The SAOZ network is part of  NDACC (Network for Detection of Atmospheric Composition Changes)  supported by  CNRS/INSU  and  CNES in France. The Reunion station is run in collaboration with the  Observatoire de Physique Atmosphérique de la Réunion (OPAR)  and  University of La Réunion  and that of Bauru with the  Instituto de Pesquisas Meteorologicas of the University of Sao Paulo (IP-MET/UNESP ) in Brazil, which are gratefully acknowledged The satellite data are available through web access:  TOMS (NASA)  via the  World Data Center for Remote Sensing of the Atmosphere (WDC/RSAT) ,  Ozone Monitoring Instrument (NIVR/KNMI ,  FMI, NASA)  via http://avdc.
In practice, this means sequence databases need to be populated with enormous quantities of data for which we have some certainties about the source. Most important is the taxonomic identity of the organism from which a sequence is derived and as much functional identification of the encoded proteins as possible. In an ideal world, such information would be available as a large set of complete, wellcurated, and annotated genomes for all the major organisms from the environment in question. Reality substantially diverges from this ideal, but at least for bacterial molecular ecology, there is a database consisting of thousands of complete genomes from a wide range of taxa, supplemented by a phylogeny-driven approach to diversifying genomics  [2]. For eukaryotes, the number of available genomes is far, far fewer, and we have relied much more heavily on random growth of sequence databases  [3, 4] , raising the question as to whether this is fit for purpose.
The Wrong Biases Compared with those of prokaryotes, nuclear genomes are large and disproportionately difficult to analyze, and this means that eukaryotic genomics have been even more strongly affected by ''prioritization. '' This results in acute taxonomic biases in the nuclear genomes chosen for sequencing, with a large proportion of them being derived from organisms of particular biomedical or biotechnological significance. Specifically, the great majority of nuclear genomes come from animals, fungi, and plants, and from parasites that infect animals  [3, 4]. For marine systems, this makes for a weak reference database, because these organisms are collectively a poor representation of eukaryotic life in the seas. Indeed, the marine organisms that maintain Earth's atmosphere, fuel the world's fisheries, and sustain the historical (pre-anthropogenic) global carbon cycle, as well as major chemical and nutrient cycles in the ocean, fall outside these groups.
The lack of appropriate reference sequences risks erroneous conclusions as we compare marine ecological sequence data to references too phylogenetically distant and, therefore, too biologically different. Each sequenced genome of an aquatic unicellular eukaryote has provided a bevy of new and unexpected insights (e. g. ,  [5] [6] [7] [8] [9] [10] [11] [12] [13] ). However, because nuclear genomes can be difficult to sequence and assemble, and gene modeling is not always straightforward, our immediate needs require an alternative way to generate a reference database, the most obvious being transcriptomics  [1]. Large-scale sequencing of an organism's mRNA allows the rapid and efficient characterization of expressed genes without spending sequencing resources on the large intergenic regions, introns, and repetitive DNA so common to eukaryotes, while at the same time eliminating many problems with assembly as well as gene prediction and modeling.
As a first step, transcriptomes from pure cultures are suitable building blocks to begin to assemble reference databases for eukaryotic microbial ecology. This approach generates a large number of coding sequences (in the form of assembled contigs) from a known organism. The availability of transcriptomic data from an organism should not be viewed, however, as a substitute for sequencing its genome. The two approaches have different strengths and weaknesses and are better viewed as complementary rather than ''either/or. '' Indeed, nuclear genome sequencing generally requires substantial transcript sequencing to inform gene prediction algorithms. As sequencing and computational methods grow increasingly powerful, many of the challenges to genome sequencing are being reduced. Nevertheless, until more genomes are available, transcriptomes from a sufficient number of representative species from a given environment could provide a valuable benchmark against which environmental data can be analyzed.
MMETSP-The Right Stuff The Marine Microbial Eukaryotic Transcriptome Sequencing Project, or MMETSP, aims to provide a significant foothold for integrating microbial eukaryotes into marine ecology by creating over 650 assembled, functionally annotated, and publicly available transcriptomes. These transcriptomes largely come from some of the more abundant and ecologically significant microbial eukaryotes in the oceans. The choice of species, strain, and physiological condition was based on a grassroots nomination process, where researchers working in the field nominated projects based on phylogeny, environmental and ecological importance, physiological impact, and other diverse criteria. The data have been assembled and annotated by homology with existing databases (see Text S1), providing baseline information on gene function.
This is not to say these data solve all our problems: new biases have been introduced (see below), and Illumina-based transcriptomes can be challenging to assemble and work with. In addition, there is an apparently universal problem of low levels of contamination-some from other species living with the target organism in culture, others possibly from the process of library construction and sequencing. Importantly, however, the taxa from which these data are derived on aggregate conform much more closely to our understanding of marine eukaryotic diversity from sequence surveys than do the current reference databases, which are the result of ad hoc sequencing priorities that do not fit   and MALV2 ) were excluded for visualization purposes. Syndiniales are not represented in any complete genome data or the MMETSP, and the vast majority are only known as sequences from uncultivated taxa that often dominate clone libraries  [22, 31].