dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). The main differences between the reconstructions are in the shape and volume of individual ice sheets. In particular, the North American Ice Sheet reaches an elevation of 4000 m in ICE-6G_C, but is only 3500 m high in GLAC-1D. Similarly, the shape and thickness of the Barents Sea Ice Sheet are not the same in the two reconstructions. The ICE-6G_C dataset is been provided at 1 • and 10 min horizontal resolution, GLAC-1D is provided at 1 • horizontal resolution. Ice surface elevation (topography) should be implemented as an anomaly from present day topography and added to the model's present day topography after re-   LGM Working Group, 2010 , 2015). Land surface properties will need to be adjusted for changes in ice extent. Where ice retreats, land surface should be initialised as bare soil if a dynamic vegetation model is used, otherwise use prescribed vegetation (see Sect.
End-stage renal disease is a chronic and progressive pathology associated with several comorbidities, particularly diabetes. Indeed, diabetes is the first cause of end-stage renal disease and, in France, 42% of incident patients had diabetes in 2012. In the general population, diabetes is associated with increased cancer risk. The aim of this study was to examine the association between risk of cancer death and diabetes in a large French cohort of patients with end-stage renal disease. Data on all patients with end-stage renal disease who initiated dialysis in France between 2002 and 2009 were extracted from the Renal Epidemiology Information Network registry. The risk of dying by cancer was studied using the Fine and Gray model to take into account the competing risk of death by other causes.
We chose to analyze cancer in terms of mortality because in the French REIN registry, the incidence of cancer is not a mandatory field. Conversely, the cause of death is an item that must be filled in by the nephrologist who followed the patient and can thus be considered highly reliable. In addition, the clinical research associates make sure that this item is properly specified in the case of death and, consequently, missing data for this category are not recorded. This allowed an exhaustive analysis of the cancer mortality rate in patients with ESRD in France.
Participants This was a retrospective study, using data that had been prospectively collected in the REIN registry. We included all patients aged 18 years and over who started dialysis in the French regions contributing to the REIN registry between January 1, 2002 and December 31, 2009. The REIN registry has been progressively covering the entire French territory between 2002 and 2011 and 100% uptake of patients was reached in 2011. On December 31, 2009, the REIN registry covered 88% of the French territory (23 of the 26 French regions)  [23]. Biological and clinical data are recorded in the REIN registry when a patient starts RRT (dialysis or preemptive kidney transplantation). Patients with unknown diabetic status, preemptive kidney transplantation or HIV-positive status were excluded (n = 3 785).
Variable and data collection Four categories of variables were studied: i) demographic data: sex and age; ii) biological and clinical data at dialysis initiation: comorbidities (type 1 and type 2 diabetes, cardiovascular diseases, tumors, cirrhosis, respiratory chronic diseases), smoking status (current smoker, former smoker, and never smoker), Body Mass Index (BMI) categorized as <23, 23-25 and >25kg/ m 2 , hemoglobin (<10, 10-12 and >12g/dl) and serum albumin (<30 and 30g/dl); iii) treatment information: first dialysis modality (hemodialysis, peritoneal dialysis), date of the first dialysis and kidney transplantation during the RTT follow-up; iv) date and causes of death in six categories (heart diseases, renal diseases, cancer, diabetes, infectious diseases and other causes). When missing data were present, they were grouped in an independent category and included in the statistical analysis.
Ethics statement This retrospective study approved by the French Biomedecine Agency included patients' informations which have been anonymized and de-identified directly in the database and before the extraction for analysis. Subjects involved were extracted from the French REIN registry which received agreement from the CNIL (Commission Nationale de l'Information et des Libertés) in 2010. The REIN is registered with the CNIL with 903188 Version 3 number.
To our knowledge, this is the first population-based study with such a large cohort of incident patients with ESRD (n = 39 811). These data were extracted from the REIN registry that includes all patients starting dialysis in most regions of France. No patient was lost during follow-up. Therefore, our data can be considered to be highly reliable and complete. Our study has some limitations. Ideally, the association between diabetes and cancer should be analyzed in terms of tumor incidence and not only of mortality. However, our data were extracted from the REIN registry in which cancer incidence is not a mandatory field, differently from the cause of death. Moreover, in the REIN, there is a lack of information related to the treatment of cancer administered in patients and grades or types of cancer.
In addition, in France there is no national cancer registry that we could use to study tumor incidence. Another limitation might concern the relative heterogeneity in the burden of diabetes-related comorbidities. Indeed in the REIN registry, data relative to the diabetes history before the ESRD stage are missing (duration, severity, complications. ). This study confirms, as previously described in the annual reports of the US Renal Data System  [1]  or in the French REIN registry  [17] , that the mortality rate is higher in ESRD patients with diabetes than in those without diabetes. However, diabetic patients had 30% lower risk of death by cancer compared to the non-diabetic population. In addition, at dialysis initiation, the presence of a cancer was less frequently recorded for patients with diabetes than for those without diabetes after adjustment for age and sex.
85]) and bladder cancer (HR = 0. 60; 95%CI: (0. 60-0. 85)) was significantly reduced among patients with diabetic nephropathy compared with other nephropathies  [11]. Denton et al. , found that diabetes was inversely associated with the presence of renal adenomas in patients with ESRD before kidney transplantation (HR = 0. 1; 95%CI: [0. 1-0. 8]) [10]. Finally, Wong et al. observed that mild to moderate chronic kidney disease did not increase the risk of cancer in patients with type 2 diabetes  [5]. However, these studies have several limitations. Some used data issued from different registries and of variable quality  [11] [12] [13]. Indeed, when data were not available in the national registries, information was extracted from regional registries or from neighboring countries. In other studies, only one type of cancer was specifically studied  [10, 11].
The GHG-CCI project (http://www. esa-ghg-cci. org/) is one of several projects of the European Space Agency's (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The "ECV Greenhouse Gases" (ECV GHG) is the global distribution of important climate relevant gases -namely atmospheric CO 2 and CH 4 -with a quality sufficient to obtain information on regional CO 2 and CH 4 sources and sinks. The main goal of GHG-CCI is to generate long-term highly accurate and precise time series of global near-surface-sensitive satellite observations of CO 2 and CH 4 , i. e. , XCO 2 and XCH 4 , starting with the launch of ESA's ENVISAT satellite.
These products are currently retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode observations in the near-infrared/shortwave-infrared spectral region. In addition, other sensors (e. g. , IASI and MIPAS) and viewing modes (e. g. , SCIAMACHY solar occultation) are also considered and in the future also data from other satellites. The GHG-CCI data products and related documentation are freely available via the GHG-CCI website and yearly updates are foreseen. Here we present an overview about the latest data set (Climate Research Data Package No. 2 (CRDP#2)) and summarize key findings from using satellite CO 2 and CH 4 retrievals to improve our understanding of the natural and anthropogenic sources and sinks of these important atmospheric greenhouse gases. We also shortly mention ongoing activities related to validation and initial user assessment of CRDP#2 and future plans.
Also visible is the good agreement of the different GHG-CCI CRDP#2 XCO 2 data products. Perfect agreement is not expected due to different spatio-temporal sampling and different altitude sensitivities (averaging kernels). Appropriate knowledge about the CO 2 sources and sinks is needed for reliable prediction of the future climate of our planet  (IPCC, 2013). This is also true for methane (CH 4 ; e. g. , IPCC, 2013;  Kirschke et al. , 2013). The goal of the GHG-CCI project  (Buchwitz et al. , 2013a) , which is one of several projects of ESA's Climate Change Initiative  (CCI, Hollmann et al. , 2013) , is to generate global satellite-derived CO 2 and CH 4 data sets as needed to improve our understanding of the regional sources and sinks of these important atmospheric gases. Global near-surface-sensitive satellite observations of CO 2 and CH 4 combined with inverse modeling yields information on the regional sources and sinks of these gases.
One of the first activities within GHG-CCI was to establish the user requirements, e. g. , in terms of required accuracy and precision of the different data products. The result of this activity was the initial version of the GHG-CCI User Requirements Document (URD)  (Buchwitz et al. , 2011) , which has recently been updated  (Chevallier et al. , 2014b). Note that the GHG-CCI URD requirements are more detailed and often also more demanding compared to the GCOS requirements  (GCOS, 2011). The GHG-CCI data products and related documentation are freely available via the GHG-CCI website and yearly updates generated with improved retrieval algorithms and covering (where possible) longer time series are foreseen. Here we present an overview about the latest data set -Climate Research Data Package No.
CLIMATE RESEARCH DATA PACKAGE 2 (CRDP#2) In this section, we present an overview about the Via the GHG-CCI website also the previous data set CRDP#1 and related documentation is available. Note that for CRDP#2 an improved data format has been defined focusing on harmonization of the ECA products  (Buchwitz et al. , 2014). An overview about the various satellite-derived data products stored in the CRDP#2 data base is shown in Tab. 1 (for ECA products) and Tab. 2 (for ACA products). Table  1  lists the GHG-CCI ECV core data products XCO 2 and XCH 4 as retrieved from SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. Note that more details for each product are available on the GHG-CCI website including spatiotemporal coverage, detailed documentation (e. g. , Algorithm Theoretical Basis Documents (ATBDs)), point of contact information, information on data access, figures, etc.
OVERVIEW SCIENTIFIC ACHIEVEMENTS In this section a short overview is presented on scientific publications related to CO 2 and CH 4 satellite retrievals, which have been published during approximately the first four years of the GHG-CCI project (until mid March 2015). Focus is on publications related to GHG-CCI retrieval algorithms and corresponding data products and their use to address important scientific questions related to the natural and anthropogenic sources and sinks of CO 2 and CH 4. In this context also some other (non-GHG-CCI) publications are mentioned (in the References GHG-CCI-related publications are marked with (*); currently the number of peer-reviewed publications with GHG-CCI/CCI funding explicitly acknowledged is 38). The list of all GHG-CCI publications is available via the GHG-CCI website (http://www.
g. ,  Noël et al. , 2011 , Laeng et al. , 2014). EMMA which uses the individual satellite Level 2 XCO 2 data products as input data to generate a new Level 2 data product where essentially outliers have been identified and removed to generate a potentially more robust "median product" exploiting the availability of an ensemble of individual products exists generated with algorithms each having different strengths and weaknesses. The EMMA method is used within GHG-CCI as a comparison tool for the individual global products. The advantage here is that products can be compared with the median product without relying on model simulations and without being limited to (sparse) groundbased validation sites. Note that for EMMA not only the European GHG-CCI data products are being used but also non-European GOSAT products generated in Japan at NIES  (Yoshida et al.
, 2013, used  GOSAT XCO 2 and solar induced chlorophyll fluorescence (SIF) retrievals to better understand the carbon balance of southern Amazonia.   Ross et al. , 2013, used  GOSAT data to obtain information on wildfire CH 4 :CO 2 emission ratios.  For flux inversions not only the retrieved greenhouse gas values are relevant but also their error statistics, in particular the reported uncertainties. Chevallier and O'Dell, 2013, analyzed  this aspect in the context of CO 2 flux inversions using GOSAT XCO 2 retrievals.
ONGOING ACTIVITIES AND FUTURE PLANS Currently (mid March 2015) a preliminary unvalidated version of CRDP#2 is available via the GHG-CCI website (http://www. esa-ghg-cci. org -> CRDP (Data)) as validation and initial user assessments as conducted by the GHG-CCI validation team and Climate Research Group (CRG) are ongoing activities. The validation results will be reported in a document called "Product Validation and Intercomparison Report, version 3. x" (PVIRv3. x) and the user assessments will be reported in the "Climate Assessment Report, version 2. x" (CARv2. x). PVIRv3. x will be ready end of March 2015 and CARv2. x in April 2015 and both documents will be made publicly available along with the final validated CRDP#2 data products via the GHG-CCI website. These documents are updates of the corresponding CRDP#1 documents PVIRv2.
0  (Notholt et al. , 2013)  and CARv1. 1  (Chevallier et al. , 2013). Based on the outcome of the quality assessments the retrieval algorithms will be further improved and the satellite data will be reprocessed (if necessary) and, where possible, the time series will be extended. Yearly updates are foreseen and it is planned to release CRDP#3 in April 2016. GHG-CCI retrieval experts are also members of the OCO-2 Science Team and involved in the development of retrieval algorithms for Sentinel-5-Precursor and the data products of these sensors will also be considered by GHG-CCI. For OCO-2 it is initially planned to perform detailed comparisons to determine the consistency of the XCO 2 data products and to perform initial retrievals. GHG-CCI team members are also involved in the specification of future GHG satellites, in particular CarbonSat  (Bovensmann et al.
, 2010 , Buchwitz et al. , 2013b). CarbonSat, if selected for ESA's Earth Explorer 8 satellite, will continue the time series of greenhouse gas observations from space presented in this manuscript but will also address many important new aspects which cannot (or only with severe limitations) be addressed with other existing or planned satellites in particular the detection of localized CO 2 and CH 4 sources and the quantification of their emissions. Like SCIAMACHY, GOSAT and OCO-2, sun induced chlorophyll fluorescence, SIF, will be a secondary data product from CarbonSat  (Buchwitz et al. , 2013b)  suitable to obtain Gross Primary Production (GPP; e. g. ,  Parazoo et al. , 2013 , and references given therein) and for investigating the impact of stress on vegetation and the CO 2 uptake at the few km 2 spatial resolution scale of CarbonSat.
ACKNOWLEDGEMENTS We thank  JAXA  and  NIES  for support and for providing us with GOSAT Level 1 data. We thank  NIES  and the  NASA/ACOS team  for supporting us and for providing us with GOSAT Level 2 data products. We thank  ESA  and  DLR  for providing us with SCIAMACHY Level 1 data products and  ESA  for funding this project.
The analysis of structural data obtained by X-ray crystallography benefits from information obtained from complementary techniques, especially as applied to the crystals themselves. As a consequence, optical spectroscopies in structural biology have become instrumental in assessing the relevance and context of many crystallographic results. Since the year 2000, it has been possible to record such data adjacent to, or directly on, the Structural Biology Group beamlines of the ESRF. A core laboratory featuring various spectrometers, named the Cryobench, is now in its third version and houses portable devices that can be directly mounted on beamlines. This paper reports the current status of the Cryobench, which is now located on the MAD beamline ID29 and is thus called the ID29S-Cryobench (where S stands for 'spectroscopy').
, 2009)  for room-temperature measurements. Several technical choices have been made in order to maximize the compatibility between the environments of the Cryobench and the ESRF macromolecular crystallography beamlines. In particular, the analogue camera previously used for sample visualization has been replaced by the same digital Prosilica camera as used on the beamlines. New control modules for video acquisition, goniometer operation, laser triggering and cryosystem temperature control have been written with the BLISS FrameWork, an ESRF softwaredevelopment platform, and run on Linux OS. In this way, experimentcontrol modules can be taken from, or ported to, the beamline-control software MxCuBE  (Gabadinho et al. , 2010). All Cryobench experiment-control modules have been grouped together in a main control software called SpeCuBE (Fig.
Absorption and fluorescence emission spectra are measured with the commercial SpectraSuite software (Ocean Optics) running on Linux. In order to measure an absorption spectrum A(), three different spectra need to be recorded: the dark reference I dark () (the spectrum in the absence of light), the light reference I ref () (the lamp spectrum in the absence of a sample) and the sample spectrum I sample () (the light transmitted through the sample): AðÞ ¼ À log I sample ðÞ À I dark ðÞ I ref ðÞ À I dark ðÞ : ð1Þ Fluorescence emission spectra  (Bourgeois et al. , 2002)  and fluorescence lifetime histograms  (Royant et al. , 2007)  are measured using laser lines as excitation light with two objectives at 90 from each other in order to minimize excitation laser scattering to the CCD detector (Fig.
1e ). Because fluorophores are usually photoactivatable or photobleachable, laser irradiation needs to be kept to a minimum by synchronizing laser triggering pulses (typically of milliseconds to hundreds of milliseconds in duration) with the recording of spectra via TTL pulses generated by an ESRF-developed board (OPIOM type). Laser lines can be chosen from more than ten typical wavelengths ranging from 266 nm (deep UV) to 671 nm (far red), with a maximum power of 10-100 mW at the sample position. Raman spectra are measured in backscattering mode using a Renishaw InVia spectrometer  (Carpentier et al. , 2007) , the probe of which has been modified by the addition of a 45 mirror inserted before the microscope objective (Fig. 1f  ). Spectra are recorded with the commercial software Wire3 running on a Windows PC.
, 2013). The Cryobench is a platform of the Grenoble Instruct centre (ISBG; UMS 3518 CNRS-CEA-UJF-EMBL) with support from ESRF, FRISBI (ANR-10-INSB-05-02) and GRAL (ANR-10-LABX-49-01) within the Grenoble Partnership for Structural Biology (PSB). AR acknowledges financial support from the French National Research Agency (ANR-11-JSV5-0009-01). We would like to thank Dominique Bourgeois, Philippe Charrault, Philippe Duru, Guillaume Gotthard, John McGeehan, Sean McSweeney, Jeremy Ohana, Thomas Ursby, Xavier Vernede and Martin Weik for past or present contributions and discussions about this work. We are grateful to all users of the Cryobench, especially Tiago  Barros, Isabel Bento, Hans-Petter Hersleth, Yvain Nicolet, Roberto Orru and Patrick Scheerer for providing spectroscopic data presented in this work.
Background: MicroRNAs (miRNA) are small endogenous non-coding RNA involved in the post-transcriptional regulation of specific mRNA targets. The first whole goat genome sequence became available in 2013, with few annotations. Our goal was to establish a list of the miRNA expressed in the mammary gland of lactating goats, thus enabling implementation of the goat miRNA repertoire and considerably enriching annotation of the goat genome. Results: Here, we performed high throughput RNA sequencing on 10 lactating goat mammary glands. The bioinformatic detection of miRNA was carried out using miRDeep2 software. Three different methods were used to predict, quantify and annotate the sequenced reads. The first was a de novo approach based on the prediction of miRNA from the goat genome only. The second approach used bovine miRNA as an external reference whereas the last one used recently available goat miRNA.
The three methods enabled the prediction and annotation of hundreds of miRNA, more than 95% were commonly identified. Using bovine miRNA, 1,178 distinct miRNA were detected, together with the annotation of 88 miRNA for which corresponding precursors could not be retrieved in the goat genome, and which were not detected using the de novo approach or with the use of goat miRNA. Each chromosomal coordinate of the precursors determined here were generated and depicted on a reference localisation map. Forty six goat miRNA clusters were also reported. The study revealed 263 precursors located in goat protein-coding genes, amongst which the location of 43 precursors was conserved between human, mouse and bovine, revealing potential new gene regulations in the goat mammary gland. Using the publicly available cattle QTL database, and cow precursors conserved in the goat and expressed in lactating mammary gland, 114 precursors were located within known QTL regions for milk production and composition.
Once the goat genome had been released, it started to be used for miRNA identification. Dong et al. [18]  also identified 487 miRNA in their assembly using predictions by the INFERNAL software  [26]  against the Rfam database. Among the 487 miRNA found, 157 were located in 44 genomic clusters containing between two and 46 miRNA. Comparing the miRNA sequences from human, cattle, dog, chimpanzee, mouse and rat, they determined six goat-specific miRNA  [18]. The goat genome was also utilized for the mapping of sequencing reads from goat testis, urine and skeletal muscle, and annotation of the miRNA thus characterized was performed by sequence homology with sheep, cow or human miRNA  [27, 28]. Finally, in order to map sequencing reads to the goat genome, miRDeep2 software combined with sequences from miRBase were used to detect 205 known and nine putative miRNA in goat hair follicles  [29].
The final step was acrylamide gel purification of the 140-150 nt amplified cDNA (corresponding to cDNA obtained from small RNA + 120 nt from the adapters). The libraries were checked for quality and then quantified using the 2100 Bioanalyzer Instrument (Agilent). Libraries were loaded in the flowcell at an 8 pM concentration and clusters were generated using Cbot and sequenced on HiSeq 2500 (Illumina) as single-end 50 base reads, according to the manufacturer's instructions. The quantity and quality of reads for each library are shown in Additional file 1: Table  S1. RNA sequencing data were deposited in the Gene Expression Omnibus (GEO): GSE61025.