dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial correlation Remotely sensed products are the only means to retrieve large scale observations of water availability in the upper layers of the soil. Thus, they are a unique source of information for validating the ability of models to reproduce the spatial structures of SSM. We compute the spatial correlation (ρ) between remotely sensed and modelled data using 5-day averaged maps. The averaging is needed to obtain maps that are as complete as possible. For ORCHIDEE the average was computed based on the thinned dataset where only the values corresponding to SMOS observations are retained. From the sampling error analysis (Section 2. 3) we know that without this methodology we would introduce a spatial correlation error of at most 0. 08. Fig. 4  provides the time series of spatial correlation between the remotely sensed and the simulated SSM.
Spectral analysis To examine in depth the poor spatial correlation and identify the regions degrading the spatial correlations, we will analyse the steepness of the periodograms and its relation to rainfall at each point. The following analysis is based on the assumption, detailed in Section 3. 1, that the variability of SSM is essentially driven by rainfall and the inertia of the  soil moisture reservoir shifts its spectrum towards lower frequencies. Thus we examine how the ratio (R) of energy between the 6-30 and 90-365 days band changes from the rainfall to SSM estimates. To avoid any biases, this diagnostic is performed on daily means sampled as the SMOS observations. The first column of Fig. 6  provides the ratio R for the rainfall datasets. For E-OBS (an independent rain gauge based estimate) the ratio R is between 0.
05), while for SMOS R is between 0. 05 and 0. 09. It could well be that the model does not drain or evaporate quickly enough the water out of the top 5 cm of soil, thus overestimating the slow component of the SSM variability. It is interesting to note that the spectral differences between the two forcing rainfall datasets result in small differences between the spectra of the simulated SSM and which are significantly smaller than the discrepancies with the remotely sensed data. The possibly larger inertia of the modelled SSM could also explain the relatively low temporal correlation found over the Ebro valley in Section 3. 1. However it is difficult to point at a weakness of either the model or the SMOS data without the availability of in-situ observations that could serve as an independent benchmark.
The spatio-temporal co-variance of SSM and rainfall The co-variance of rainfall and soil moisture can be analysed by means of the singular value decomposition (SVD) method  (Bretherton, Smith, & Wallace, 1992; Wallace, Smith, & Bretherton, 1992). We apply it here to the simulated and remotely sensed daily SSM values using the independent rainfall dataset E-OBS  (Haylock et al. , 2008). Using these precipitation estimates as benchmarks ensures that the model is not favoured in the comparison, as it would be if we used the forcing rainfall, and that the differences found originate in SSM. Furthermore all differences in co-variant patterns can only originate in the soil moisture products. The diagnostics will be focused on the first three co-varying patterns (CPi, i = 1,2,3). They represent at least 97% of the co-variance of surface soil moisture and rainfall in all three SSM estimates (Table  2 ).
e. between 2% and 4%. At the REMEDHUS site (see Section 3. 1), the synoptic rainfall variability is the dominant cause of the co-variance between the SSM datasets. On the other hand, at the regional scale the SVD analysis shows that the annual cycle dominates the co-variability of rainfall and SSM. This is the case for SMOS as well as for ORCHIDEE and is explained by different climatological characteristics of rainfall across the region and their impact on SSM structures. The synoptic co-variability summarized by CP2 and CP3 plays a minor role, probably because of the large spatial variability of this part of the signal. The last column of Table  2  provides the spatial correlation of the three first precipitation and SSM CPs. Generally, the fast varying CPs show higher spatial correlation values than CP1.
Acknowledgements This work has been supported by the  Spanish Ministry of Science and Education  under the projects  AYA2010-11062-C05-04  and  AYA2010-11062-C05-05. The first author is grateful to the  Institut national des sciences de l'Univers-CNRS , and in particular  Jean-Marie Flaud , for allowing his secondment to IC3. This work contributes to the  FP7 Earth2Observe project  under grant agreement No  603608. We would like to thank  N. Sánchez  and  J. Martínez-Fernández  for making the REMEDHUS data available and  G. Balsamo  for providing the ERA-I forcing.
Marker Based Selection in C 1 and C 1 S 1 For each population, the eight selected C 1 families were planted ear-to-row. at planting and 6 wk after emergence. Fields were kept free of weeds by hand weeding. The WS and WW evaluations were conducted as described above for the C 0 populations. Data was collected for GY, plant height (PH), anthesis date (AD), and ASI. Anthesis date was recorded as the number of days from planting to when 50% of the plants had shed pollen, while silking date (SD) was recorded as the number of days from planting to when 50% of the silk emerged. The ASI was calculated as the difference between SD and AD. Plant height was measured as the distance from the base of the plant to the height of the first tassel branch. In WS experiments, all ears were harvested from each plot, shelled, and weighed to determine yield and percentage grain moisture.
In the WW experiments, ears harvested from each plot were weighed, and moisture content was determined on a subsample of grain. For both WS and WW environments, GY was estimated by adjusting to 12. 5% (125 g kg -1 ) moisture content. An analysis of variance for GY, AD, ASI, and PH within WS and WW environments was performed for each location and combined across environments for each water management using the PROC MIXED procedure of SAS 9. 3 (SAS Institute, 2009) considering environments and incomplete blocks as random effects and recovering interblock information. Entries were considered fixed effects. Response to selection was determined by regressing mean values to the breeding cycle means (C 0 , C 1 , C 1 S 1 , and C 1 S 2 ) for each population and across all populations combined over WS and WW environments.
5 d. Overall results of the individual MARS populations revealed differential response, with the majority of the populations showing greater GY for C 1 S 2 -derived hybrids than the other group of entries, but few populations showed better GY either at C 1 S 1 or C 1 (Table  5 ). Such differential response to selection was observed in both WW and WS environments. We have no firm explanation for this puzzling phenomenon but hypothesize that it may be related to the fact that the C 1 individuals selected were highly heterozygous relative to both the F 2:3 (C 0 ) and the C 1 S 1 families. Nevertheless, our study involved a large dataset and provides highly relevant information to maize breeders planning to use MARS and GS for developing stress tolerance in maize. In addition to evaluating genetic gains from different cycles of selection, a total of 840 C 3 S 5 lines and 352 doubled-haploid lines have been developed from each cycle for all 10 biparental populations and tested in multilocation trials.
Acknowledgments This study was implemented under the  Water Efficient Maize for Africa (WEMA)  project, supported by the  Bill and Melinda Gates Foundation , the  Howard G. Buffet Foundation and US Agency for International Agriculture (USAID). The authors would like to thank  CIMMYT  and  KARI  research technicians for data collection at various experimental sites and the  Monsanto Company  for performing the genotyping and MARS data analysis.
Live imaging of oocytes Spinning disk images of live oocytes in M2CBSA medium were acquired at 37 8C using a Plan-APO 40!/1. 25 NA objective on a Leica DMI6000B microscope enclosed in a thermostatic chamber (Life Imaging Service, Basel, Switzerland) equipped with a CoolSnap HQ2/CCD-camera (Princeton Instruments) coupled to a Sutter filter wheel (Roper Scientific, Munich, Germany) and a Yokogawa CSU-X1-M1 spinning disk. The Metamorph software (Universal Imaging, Bedford Hills, New York, USA) was used for data collection. For live chromosome imaging, seven z-planes were acquired, spaced 3 mm, every 30 min starting 0530 h after nuclear envelope breakdown, with a power of 10% of laser K581 nm and 200 ms acquisition time.
Accordingly, we monitored the presence of the SKAP transcript and protein in testes from day 4ppt to day 30ppt (Fig. 1B ). The transcript and the protein were readily detectable before entry into meiosis (day 4ppt), and maintained at all monitored ages. By western blot, we detected a signal at two molecular weights: one at 30kDa, present at all ages, and a larger protein of 40 kDa which was only detected in adult testes, where it represented the major form of SKAP. Both proteins were absent in mice deficient for SKAP (see below). The occurrence of different protein sizes can be explained either by alternatively spliced transcripts, by the usage of alternative start codons or by post-transcriptional modifications. In the case of Knstrn, an alternative transcript (alternative version of exon 8) was indeed referenced in databases (NCBI, XM006499894.
Intercrosses between heterozygous males and females resulted in offspring with an expected Mendelian ratio (43 Knstrn C/C :89 Knstrn C/K :32 Knstrn K/K , PZ0. 263, c 2 -test). Intercrosses involving one or both heterozygous parents led to WT litter sizes. In contrast, even though SKAP-deficient male and female mice were fertile, intercrosses involving one homozygous Knstrn K/K parent led to reduced litter size: 4. 1G2. 5 pups with a Knstrn K/K mother and 4. 8G1. 9 pups with a Knstrn K/K father compared with 6. 9G1. 88 pups in crosses between heterozygous animals (P!0. 0011 and 0. 039 respectively, t-test) (Supplementary Table  ST1 , see section on supplementary data given at the end of this articles). To investigate the reduction in litter size in Knstrn K/K female mice, we analyzed histological ovary sections of juvenile (day 4ppt) and adult female mice at various ages (2, 6 and 11 months).
Apoptosis in SKAP-deficient testes occurs within a specific time window To determine whether Knstrn K/K mice present enhanced apoptosis, we investigated apoptosis by performing a weight is significantly lower in SKAP-deficient compared with WT mice. Body and testes were weighed in Knstrn C/C , Knstrn C/K and Knstrn K/K mice from day 4ppt up to 12 weeks of age. From day 7ppt, the testes-to-body weight ratio of Knstrn K/K mice was significantly lower than that in WT animals (*P!0. 05, at day 22 PZ0. 08, Fisher's exact test; nZ6 animals). TUNEL assay in adult and day 4 and 9ppt testes. Apoptosis was significantly enhanced at day 9ppt, but not at day 4ppt nor in adult Knstrn K/K testes (Table  2  and Figure  S3 , see section on supplementary data given at the end of this article). Closer examination of Knstrn K/K testis sections showed that the reduction of the total number of germ cells per tubule was partially caused by the high percentage of tubule sections containing no germ cells at all (Fig.
Acknowledgements We thank  Iain Cheeseman  for his generosity in sharing antibodies and results before publication, members of the  de Massy lab , specially FB for helpful discussion and critical reading of the manuscript,  C Pescia  and the animal facility Biocampus/RAM in Montpellier for generating and breeding our SKAP mice, and  RIO Imaging  and REM Histology platforms for providing excellent services.
Insulin/IGF-1 signalling This pathway is broadly conserved across all species. Variability of conservation was focused around two heteromeric complexes. In the first complex, AKT-1/AKT-2, homologues to C. elegans akt-1 were found in all species. However, akt-2 was restricted to C. elegans. In the second complex, FTT-2/PAR-5, homologues for at least one of the genes were found in each species. Within both complexes, components shared high sequence similarity, indicating gene duplication events at various points in the phylum. The negative dauer regulator, ist-1, likely arose after the split with Clade I, with subsequent independent loss in other lineages, including that leading to Pristionchus species. The P. pacificus daf-16 had been previously cloned and functionally characterized (GenBank Accession: JX891629)  [41].
Dauer TGF -β signalling Broadly conserved across the phylum were genes that encode the TGF -β ligand, DAF-7, the subunits of its receptor, DAF-1 and DAF-4, and the receptor's regulator BRA-1. Genes encoding the Smad transcription factors were increasingly restricted in their phylogenetic distribution. daf-8, an inducer of transcription, and daf-3, a repressor, were found in Clade IV and V species. Another inducer, daf-14, was found throughout Clade V. Homologues to another repressor, egl-4, were found throughout, while daf-5 was restricted to Clade V species. In the Clade I species, we were unable to find homologues for any of the Smad transcription factors, daf-5, scd-1 or scd-2.
TGF-β phylogenetic reconstruction In addition to the putative homologues to DAF-7, searches were run using C. elegans DBL-1 (Wormbase: T25F10. 2), TIG-2 (F39G3. 8), TIG-3 (Y46E12BL. 1) and UNC-129 (C53D6. 2) as the seeds. Individually cloned sequences were identified from the literature. The alignment was built with MAFFT, using the linsi options  [71]. The most appropriate phylogenetic model was LG + G  [72]. The phylogenetic tree was reconstructed with PhyML using the following command line options: '-c 4 -m LG -v 0. 0 -a e -o tlr -f d -d aa -b 1000 -s BEST -rand_start -n_rand_starts 101'  [73]. Nodes with less than 50 % bootstrap support were left unresolved. The final tree figure was generated using TreeGraph 2  [74]  and FigTree (http://tree. bio. ed. ac. uk/ software/figtree/).
Very High Energy gamma-ray astronomy with the Cherenkov Telescope Array (CTA) is evolving towards the model of a public observatory. Handling, processing and archiving the large amount of data generated by the CTA instruments and delivering scientific products are some of the challenges in designing the CTA Data Management. The participation of scientists from within CTA Consortium and from the greater worldwide scientific community necessitates a sophisticated scientific analysis system capable of providing unified and efficient user access to data, software and computing resources. Data Management is designed to respond to three main issues: (i) the treatment and flow of data from remote telescopes; (ii) "big-data" archiving and processing;  (iii)  and open data access. In this communication the overall technical design of the CTA Data Management, current major developments and prototypes are presented.
Overall Concept CTA is a new observatory for very high-energy (VHE) gamma rays  [1]. The Data Management (DATA) project of CTA concerns all major components for both data-flow administration and the scientific data production and analysis for CTA. The main scope of the project is the design of the CTA Science Data Centre (SDC), which is in charge of the off-site handling of data reduction, Monte Carlo (MC) simulations, data archiving and data dissemination. The remote (e. g. intercontinental) transmission of data from CTA sites to the CTA archive is one of the key services that the SDC administers at both ends: off and on the CTA site. The development and provision of software and middle-ware services for dissemination including observation proposal handling is a task that DATA guarantees to be interfaced with the Operation Centre.
38 Gb/s and 0. 65 Gb/s, well manageable (with respect to the latency requirements) with a local temporary storage unit on the CTA sites, and a 1 Gb/s effective network for off-site data export. Current perspectives for the availability of 10 Gb/s network from candidate CTA sites exist. The exported data are received by the ingestion unit of the CTA Archive system, which is operated (together with all main work-flow management services) by a dedicated Observatory Data Management Centre (CTAO-DMC). The proposed CTAO computing model is built upon a Distributed Computing Infrastructure (DCI) approach, in which a limited number of first-class dataand-computing centres share the workload of archiving and processing the CTA data. The baseline of DCI model adopted to estimate the work and investment distribution is made of four centres (from DC1 to DC4), equally sharing the CTAO data workload (Figure  1 ).
One out of these four centres also hosts the CTAO-DMC. The CTAO-DMC plus the four DCs correspond together to the proposed implementation of the CTA Science Data Centre. The CTAO-DMC simultaneously guarantees: (i) the orchestration of the relay-mode activities among the four centres, while centrally managing the database of the CTAO archive; (ii) all interface services with the users, providing tools to the Observatory User Support group and to advanced users for archive queries and data processing or to access technical data for devices monitoring purposes as well as User Support; (iii) the integration of the Scientific Analysis System (SAS) with the CTAO and the CTA Consortium (CTAC) infrastructures, which are also DCIs and are used currently for MC simulation production (through the existing CTA Computing Grid infrastructure -CTACG).
Each data-centre will permanently archive one half of the full CTA data: it receives 25% of raw data to be processed and will play the role of back-up centre archiving the replica of another 25% of the data, coming form another centre. The geographical location of these centres is not specified, and from the technical point of view any place where a qualified (such as WLCG Tier1-like) data centre is willing to contribute to the implementation of the model (including in the CTA array host countries) may be considered. The total volume to be managed by the CTAO Archive is of the order of 27 PB/year, when all data-set versions and backup replicas are considered. This will correspond to a permanent.
PoS(ICRC2015)947 archive of the order of more than 400 PB in 2031. The computing needs are less critical: peak values of less than 10 4 CPU cores are expected for the annual data processing. The CTA data processing is based on several levels of processing: the MC simulation pipeline, the low-level data processing pipelines, the high-level science software tools and the Virtual Observatory (VO) data access services. The speed and performance requirements of the design of the data processing pipelines necessitate a high level of parallelization. This will imply a large effort to reducing the I/O speed and CPU time required. This is particularly relevant in the case of real-time or on-site analysis, which initially will use similar software to that used off-site, but with more strict requirements on speed and looser requirements on accuracy.
Off-site, in each DC i data center, the data production consists of a series of processing steps that transform archived (reduced) raw data DL0 (Data Level 0) to calibrated camera data (DL1), then to reconstructed shower parameters such as energy, direction, and particle ID (DL2), and finally to high-level observatory products comprised of selected gamma-like events, instrument response tables, and housekeeping data (DL3). DL3 data will have a total volume of about 2% of the DL0 data volume and guaranteed access will be provided in the CTA archive to basic users (e. g. Guest Observers and Archive Users). The science tools are then used either automatically or by users to produce DL4 (e. g. spectra, sky-maps). Finally (DL5) legacy observatory data, such as CTA survey sky maps or the CTA source catalog will be produced.
When all replicas and versions are considered, according to user analysis requirements and archive optimization, the data processing and archiving processes generate 27 PB/year data out of incoming reduced DL0 4 PB data per year (Figure  1 ). The CTAO computing model makes use of disk (∼20%) and tape storage (∼80%) to guarantee effective storage, access and throughput of all data, while trying to reduce the associated costs. Another big challenge for the management of data by the CTAO is the open access to the CTA data. To operate as an open observatory, a minimum set of services and tools are needed to support the scientific users of CTA. These services are intended to be mostly web-oriented and consist of: electronic support services to help Guest Observers in writing and submitting a proposal to CTA in response to an Announcement of Opportunity for observing time; user interfaces to follow the status of an observation, including the scheduling, the data acquisition, the data processing, the data distribution and the ingestion of the data in the public archive after the end of the proprietary period; and finally services for downloading the processed data (DL3) as well as the software tools.
PoS(ICRC2015)947 that are necessary for scientific analysis. Web-based information about the data and the analysis software, including user manuals, cook books, etc. will also be available. Archive Users will browse the archive to access and retrieve CTA data of interest selecting events based on specific criteria (source location, observation time interval, observation condition criteria, energy range etc. ). CTA basic users will analyse the data will be analysed on their own computing. DATA will ensure the integration of CTA high-level data (DL4-to-DL5) within the Virtual Observatory (VO) infrastructure, by adopting and extending the VO data model standards suitable for the description of gamma-ray data. High-energy data at this level have never before been available to the VO community, and this represents a major step toward unifying the data products from all high-energy experiments.
Current astronomical metadata standards and VHE gamma-ray data conventions have been studied for this purpose via a close working relationship between both VHE and VO scientists. The extension of standard VO semantic models will allow compatibility with a large range of existing tools and infrastructures for data discovery, visualization, and processing. Detailed Characterization Data Model fields will be completed for high-level products (images, spectra, light curves) and also for event lists and (Instrument Response Functions) IRFs, allowing scientists from other backgrounds to discover and manipulate high-level CTA data products without requiring specialized CTA tools for all operations. The final goal is to integrate CTAO data in astronomical multi-wavelength data archives where scientist will be able to combine them together in a single analysis with data from other facilities.
The size and the world-wide scope of the CTA consortium, along with the desire of CTAC advanced users to access the full archive and to manage more complex analysis work-flows, demands the implementation of services to operate a common scientific analysis platform. In this respect an important baseline of the IC-infrastructure solution for data access is the CTA Scientific Gateway: a web-based community-specific set of tools, applications, and data collections that are integrated together via a web portal, providing access to resources and services from a distributed computing infrastructure. The Gateway aims at supporting work-flow handling, virtualization of hardware, visualization as well as resource discovery, job execution, access to data collections, and applications and tools for data analysis.