dataesr/scientific-paragraphs-categorization huggingface dataset:
Alignments were visually inspected to remove any obvious poor-quality data, and the data were realigned as described above. OH-SNAP: Automation of CodeML using Snakemake An automated workflow called OH-SNAP was constructed to run CodeML as implemented in the PAML package  (63). The workflow was designed using the Snakemake workflow management system  (64)  and was executed on a high-performance compute cluster. The workflow requires a species tree (described below), an alignment, a list of taxa contained within the branch to be labeled, and a template control file specifying the model(s) to run (fig. S7 ). To account for heterogeneity in missing data across the data set, the workflow begins by pruning the species tree to match the taxa present in the alignment. Using the aforementioned taxa list, the target branch to be tested is then labeled.
Data selection To determine whether our data set of 225 telomere maintenance genes was differentially expressed in bats compared to other mammals, we carried out a comparative transcriptomic analysis. Data were chosen under two criteria: (i) The study individual must be an adult and (ii) the tissue must be derived from a healthy individual. Using M. brandtii and M. myotis as representatives of the long-lived bat genus Myotis, we compared the expression of these genes with their homologous counterparts in diverse mammalian taxa-cow (Bos taurus), human (Homo sapiens), mouse (M. musculus), naked mole rat (H. glaber), pig (Sus scrofa), rat (Rattus norvegicus) and bowhead whale (Balaena mysticetus) across four tissue types: blood, kidney, liver and brain (table  S3 ). 1:1 ortholog identification For genome-wide, cross-species comparison of gene expression, 1:1 orthologs were identified between Myotis and other species for quantification.
CDSs were obtained from their respective genomes (table  S3 ). The M. lucifugus genome was used as the common reference to standardize the analysis for both M. myotis and M. brandtii because it is better annotated. Where multiple transcripts were present, the longest transcript was chosen for analysis. The genomic telomere maintenance gene data set was added to the CDS data sets for each species. Reciprocal BLAST was performed on the filtered CDS data set between Myotis and other species, with the e value cutoff of 1 × 10 -10. Resulting pairwise orthologs were extracted for differential gene expression analysis.
Analysis of differentially expressed genes Adaptor trimming and base quality filtering of raw RNA-seq data were carried out following Huang et al. (49). Using Sailfish v. 0. 7. 6  (68) , clean reads were mapped to 1:1 orthologous transcript sets that were identified between Myotis and other species respectively and the orthologs were quantified. Sailfish k-mer parameter settings were determined on the basis of the length of RNA-seq reads. k-mer values were set to 31 when RNA-seq reads were longer than 75 bp and set to 25 when reads were shorter than 75 bp. Because library type varied across data sets (single-end or paired-end), the single-end mode (-l SR) was applied to all libraries for transcript quantification. For each tissue type, a pairwise differential gene expression analysis was carried out between Myotis and other species using the Bioconductor package DESeq2 in R v.
3. 0. 3  (69). Expression values for our target 225 telomere maintenance genes were extracted from this data set for further investigation. After FDR correction, genes with a significance value of <0. 05 were considered significantly differentially expressed. To investigate whether differentially expressed genes (DEGs) were consistently differentially expressed across multiple tissues, we selected DEGs that showed upor down-regulation in Myotis compared to at least three other species, for further analysis. Given that our data set was chosen a priori to explore telomere maintenance genes, a protein-protein interaction network was constructed to determine whether Myotis DEGs showed signs of secondary enrichment for any other process besides telomere maintenance. To explore this, the STRING database v.
Acknowledgments: For assistance in the field, we would like to thank the members of  Bretagne Vivante ,  S. Dool , local volunteers in Bristol, students from  University College Dublin , and all owners/local authorities in each country for allowing access to the sites. We thank  L. A. J. O'Neill  for providing the mouse tissue for the fibroblast analysis. We would also like to thank  J. A. Finarelli  for helpful discussions of the analyses. Funding: This project was funded by a  European Research Council   Research Grant  ( ERC-2012-StG311000 ) awarded to E. C. T. The French field study is supported by a  Contrat Nature  grant awarded to  Bretagne Vivante , G. K. was supported by the  German (Deutsche Forschungsgemeinschaft ;  KE 746/2-1/3-1/4-1/5-1/6-1  and  GRK 2010 ) and the  Swiss (Schweizerische Nationalfonds ;  31-59556.
We last investigated a natural intronic ATCOPIA78 insertion that is present in the reference Col-0 genome but absent from other accessions (Figure  S6 ). Analysis of publicly available RNA-seq data (Pietzenuk et al. 2016) indicated that the insertion is epigenetically reactivated by heat stress and that reactivation is associated with the production of a truncated transcript from the target gene (Figure  6E ). To demonstrate causality, we compared the splicing level of the second intron of the target gene (AT1G11270) in Col-0 plants subjected or not to heat stress as well as in plants of the Bl-1 accession, which does not contain any ATCOPIA78 insertion within this gene. While the second intron was spliced in heat-stressed and control Bl-1 plants, this was not the case for Col-0 plants, which showed splicing only under non-stress conditions (Figure  6F ).
Mapping and detection of TE insertions using WGS Reads were mapped with BWA v. 0. 6. 1 using the parameters -R 10000 -l 35 -O 11, and the parameters n 10000 N 10000 -s for sampe, onto the TAIR10 reference sequence. Reads hanging over chromosome ends were removed using picard CleanSam and duplicate pairs were removed using picard MarkDuplicates. TE insertions were detected by implementing TE-Tracker software (available at http://www. genoscope. cns. fr/TE-Tracker) exactly as described before  (Gilly et al. , 2014). WGS did not produce sufficient coverage (<10X) for 16 of the 123 epiRILs analyzed at generation F8, and these 16 epiRILs were not considered further (Table  S1 ). TE-Tracker is a computational method that we have previously developed for accurately detecting both the identity and destination of newly mobilized TEs in genomes re-sequenced using mate-pair libraries  (Gilly et al, 2014).
Importantly, TE-Tracker does not rely on prior annotation, yet is able to integrate it, making the results easily interpretable. Briefly, TE-Tracker uses paired reads mapping information to identify discordant reads that mapped partially over TE-sequences to detect the position of TE insertions. Insertion site positions were refined at the single nucleotide resolution by exploiting sequence information contained in split-reads. To this end, we implemented the software SPLITREADER (available at https://github. com/LeanQ/SPLITREADER; Quadrana et al. , 2016). Homozygous and heterozygous insertions were defined based on the normalized number of reads supporting each insertion event (Table  S2 ). In addition, this approach enabled us also to identify insertions that were likely present in only one of the 10-20 seedlings used to extract DNA and which reflect transposition during the reproductive phase of the parent.
Libraries were then amplified through 7 cycles of ligation-mediated PCR using the KAPA HiFi Hot Start Ready Mix and primers AATGATACGGCGACCACCGAGA and CAAGCAGAAGACGGCATACGAG at a final concentration of 2µM. 1µg of multiplexed libraries were then subjected to TE-sequence capture exactly as previously reported  (Quadrana et al. 2016). Enrichment for captured TE sequences was confirmed by qPCR and estimated to be higher than 1000 fold. Pair-end sequencing was performed using one lane of Illumina NextSeq500 and 75bp reads. About 42 million pairs were sequenced per library and mapped to the TAIR10 reference genome using Bowtie2 v2. 3. 2  (Langmead and Salzberg 2012)  with the arguments --mp 13 --rdg 8,5 --rfg 8,5 --very-sensitive. An improved version of SPLITREADER (available at https://github.
Counts were normalized and annotations were declared differentially expressed between samples using DESeq2  (Love, Huber, and Anders 2014). When specified, uniquely mapped reads (mapping quality > 10) were selected with samtools (H. Li et al. 2009). RNAseq data from heat-stressed plants were obtained from (Pietzenuk et al. 2016) and analyzed as described above. Splicing of intronic TE insertions was calculated as described previously  (Teixeira et al. 2017). Briefly, the number of split-reads (SR) and non-splitreads (NSR, which should be fully and uniquely contained within the interval surrounding the same exon-intron junction) mapping to an exon-intron junction was extracted and the ratio SR/(SR+NSR)*100 was then calculated.
Acknowledgments We thank members of the  Colot group  and especially  Pierre Baduel  for discussions and critical reading of the manuscript and  S. Khouider  for help with plant crosses. We thank the  Genomics and Informatics facilities at IBENS  for their help. Support was from the  Agence National de la Recherche  ( ANR-09-BLAN-0237  to V. C. and P. W. ), the  Investissements d'Avenir   ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02   PSL* Research University  (to V. C. ), the  European Union Seventh   Framework Programme Network of Excellence EpiGeneSys  (Award  257082 , to V. C. ) and the  Centre National de la Recherche Scientifique (MOMENTUM program , to L. Q. ). M. E. was supported by a PhD studentship from the  French Ministry of Research  and by a postdoctoral fellowship from  MEMOLIFE.
These descriptors have been successfully used for 3D scan registration and shape retrieval  [28] [29]. The shape correspondence problem has also been addressed by Deep Learning approaches  [30]  [31]  [32]  when the shapes are represented as meshes. However those approaches require large datasets to train the network to be later used on the test data. Shape resampling and denoising. Many other feature preserving resampling methods have been investigated, even if they do not explicitly take similarity into account. For example Huang et al. [33]   [34]  proposed an algorithm to detect sharp features, and resample shapes starting from surface parts that are distant from the edges and resample gradually toward the edges in order to better recover them. This method, called Edge-Aware Resampling (EAR), is built upon the Locally Optimal Projection  [35]  and its weighted variant the Weighted Locally Optimal Projection  [36] , a relaxation method to resample shapes.
Implementation details Our algorithm was implemented in C++ using the Eigen library for matrix representation. For a non-optimized code and 1. 1 million input point set, covered by 100000 LPFs with a pattern containing 193 points (a pattern of 16 × 16 points intersected with a sphere) and a dictionary of 32 atoms, the initial LPF computation took 30s and the shape learning process took 150s (10 iterations of dictionary computation, pose estimation and recomputation) on a 4-core Intel R Core TM i7 laptop.
Limitation in the on-time shipment assurance Disruptions can initiate a delay to the shipment. Handling disruptions requires visibility of the operation through the logistics chain in real time. Then, the pattern of the normal operations and the operations with disruption can be distinguished. Risks and impact of disruptions should also be monitored in real time. Further, the decision time for disruption response is shortened and the disruption impact such as delay time can also be minimized. However, as mentioned, the supply chain visibility is currently limited by scattered traceability data storage. Traceability data of the logistics operation seems to be a critical resource to evaluate the service performance in terms of the product quality surveillance and the delivery punctuation. Delay time is a significant factor that degrades the goods quality and the customer satisfaction  [17].
A Conceptual Framework for Real-time Management According to the previous study proposed in  [21] , the technology of Smart Container  [22, 23]  offers the capturing of container status parameters through the operation seamlessly in real-time as shown in Fig. 3. The operation of each container is recorded to the historical database. The situation awareness of containers enables the logistic chain analysis in all decision level (operational, tactical, strategical). This research aims to extend the ability of the smart container by improving the service performance of the container logistics operation in disruptions.
The emerging of smart container technology offers traceability data that improve visualization of the logistics operation. The availability of traceability data in container storage condition and its movement can extend the awareness to the logistics situation. Data are recorded in the central knowledge base. With seamless data visibility and quality, the improvement of logistics chain performance can be conducted using the proposed strategies. The central knowledge base provides the classified trajectory patterns to support decisions in a near real time. Along the travelling, each smart container proactively perceives the situation of itself by using data provided by the knowledge base to predict the chance of confronting to disruption and the possible delay time. A reactivity may be required to adjust the operation process in order to improve the performance.
The EU-JASMIN project (7th FP of EURATOM) has been centred on the development and validation of the new severe accident analysis code ASTEC-Na (Accident Source Term Evaluation Code) for Sodium-cooled Fast Reactors (SFR). The development of such computational tool being able to assist safety analysis of innovative reactor concepts is of crucial importance. One of the challenging issues when modelling SFRs is the neutronic reactivity feedbacks. This paper presents the model implemented in ASTEC-Na for representing the reactivity effects in SFR as well as the benchmarking results of a ULOF transient against SAS-SFR code results. It has been verified that the models are correctly implemented and that ASTEC-Na is now able to calculate reactivity feedbacks not only in the sodium single phase, but also after boiling onset and fuel in-pin relocation.
Introduction Within the JASMIN Collaborative Project (Joint Advanced Severe accidents Modelling and Integration for Na-cooled fast neutron reactors) supported by the 7th Euratom Framework Program, the new European severe accident analysis code for Sodium-cooled Fast Reactors (SFR), ASTEC-Na, has been further developed and assessed (The JASMIN). The ASTEC-Na code aims at providing capabilities to evaluate the consequences of unprotected severe accidents including the source term evaluation  (Girault and Van Dorsselaere, 2013; Girault and Cloarec, 2015). The development performed so far was focused on the Initiating Phase of SFR severe accident sequence. One of the challenging elements in such analyses is the reactivity feedback model. In order to extend ASTEC-Na capabilities in the SFR analysis domain, one of the work packages of the JASMIN project was devoted to the development and implementation of neutron physics feedback modelling capabilities.
Initially the development of a Point Kinetics model in the ASTEC-Na code was based on the one available in the IRSN SCA-NAIR code  (Moal et al. , 2014). As this model was dedicated to the neutron physics feedback reactivity computation for the LWR fuel pin tests in the CABRI facility, a more generic reactivity feedback model (so called 'LOCAL' model) using coefficients related to temperature variation (pcm/K) had been implemented. Such approach is acceptable only until sodium boiling or material relocation starts. For modelling fuel and cladding relocation reactivity feedbacks, as well as the sodium void reactivity effect, it has been necessary to develop a new approach for the assessment of the reactivity feedbacks. By changing the reactivity feedback model based on temperature variation to mass variation, ASTEC-Na neutron physics model (so called 'LOCALM') is able to cope with post boiling phenomena.
Benchmark exercise The scope of the benchmark exercise is to compare the actually implemented neutron physics capabilities of ASTEC-Na with the capability of other current severe accident code, the SAS-SFR code  (Imke and Struwe, 1994; Kruessmann et al. , 2015; Genshiryoku and Kiban, 2011) , to simulate the initiation phase of an unprotected loss of flow accident (ULOF) in a pool-type sodium-cooled fast reactor. The ULOF accident is one of the enveloping cases for the consequence of postulated severe accidents in SFR and the neutron physics feedback plays a crucial role in the transient. Hence the insights of the benchmark can be usefully extrapolated to other scenarios under the domain of validity of the Point Kinetics model. As a benchmark reactor model serves the ''Reference Oxide core design" concept of the CP-ESFR project under Beginning of Life (BOL) core load conditions  (Blanchet and Buiron, 2009; Rineiski et al.
ASTEC-Na model For the benchmark exercise a model of the ESFR ''Reference Oxide core design" has been developed by using the latest version of ASTEC-Na v2. 1. Following the benchmark specifications, the reactor is described in a simplified manner. The primary circuit is not explicitly modelled. The model was made using two ASTEC-Na modules -CESAR and ICARE. The cold plenum (VBOT) as well as the hot plenum (VTOP) is represented by a single CESAR volume. The SFR core is represented using ICARE. A constant temperature sodium (395 °C) injection is simulated directly into the volume VBOT by a pump at a constant rate and the sodium is removed from the volume VTOP (break in the top volume). The core consists of 453 subassemblies (SA) grouped into 10 channel groups. Each channel group is modelled in ASTEC-Na using CHANNEL structure, with the cross-section corresponding to overall cross-section of the corresponding SA group.
In a reactivity feedback model using only one set of sodium reactivity coefficients, the results would be very different whether the coefficients are computed with a small perturbation of the sodium density or with a complete voiding of the core. The kinetics parameters for beginning-of-life (BOL) were used in accordance with the benchmark specifications. The prompt neutron generation time was set to 0. 495 ls and 6 groups of delayed neutron precursor families have been used (Table  4 ). In ASTEC-Na calculations using LOCALM, the advanced model named RIA has been deployed to simulate the thermomechanical behaviour of fuel and cladding during a reactivityinitiated accident. This RIA model implemented in ICARE for computing the fuel thermo-mechanics is based on the fuel safety analysis code SCANAIR LWR (where cladding post-failure events are not modelled) developed for Reactivity Insertion Accident (RIA) transients and extended to SFR  (Cloarec and Moal, 2012).
SAS-SFR models The SAS-SFR code is a system code which performs deterministic analysis for steady-state power operation and the initiation phase of accidental transients in Sodium-cooled Fast Reactors. It is a version of the SAS4A code originally developed by Argonne National Laboratory  (Cahalan et al. , 1994; Wider et al. , 1982; Hill, 1985)  that has been jointly modified and maintained by KIT/INR (Germany), CEA, IRSN (France), and JAEA (Japan). Besides the development of SAS4 code, SAS-SFR accumulates the experience of about 400 person years. It has been validated successfully on results of the different CABRI experimental programmes  (Perez-Martin and Pfrang, 2016; Perez-Martin and Pfrang, 2015). As in the case of ASTEC-Na, the reactor is described in a simplified manner. The primary circuit is not explicitly modelled and the steady-state boundary conditions (temperatures, pressures) are set at pin extremities.
No control rod relative axial expansion has been modelled for this benchmark since ASTEC-Na was not able to deal with this feedback at the time of the benchmark. PRIMAR-1 is the simplified module selected for this calculation where the coolant flow is specified as a function of time in the input. The reference value for the coolant mass flow per pin is selected so that the coolant heat-up along the simulated pin is 150 °C. The coolant exit pressure is set to 2. 0 bar constantly during the transient and the coolant inlet temperature is set to 668. 15 K (395 °C). No coolant by-pass is assumed and all sodium passes through the core. The basic equation used in SAS-SFR to compute the mass flow and pressure drop is the following: 1 A dw dt þ dp dz þ 1 A dðwvÞ dz ¼ À dp dz fr À dp dz K À qg ð13Þ where the friction and orifice pressure drops and the gravity head are considered.
This results in a total power excursion of about 50 times the nominal power. The difference in the boiling dynamics of the two SAS SFR calculations most probably is due to the different assumptions on the inlet orificing established at the steady-state representation. Fig. 17  shows the pressure evolution in channel 5 at bottom fertile zone (BFZ) and top fertile zone (TFZ) for the whole transient. Fig. 18  focuses on the pressure behaviour in the second phase of the transient. The temperature evolutions of sodium, cladding and fuel are generally in good agreement among the codes during the first phase of the transient. ASTEC-Na computes a slightly lower sodium outlet temperature at the end of phase I, given the lower power evolution. A difference in the initial value of the peak fuel temperature is observed for SAS-SFR (2) calculation, consistent to what was observed on average core fuel temperature (Table  5 ).
Astronomical objects may be classified into types in many ways, and the evolution of such categorisations changes with new discoveries and progress in astrophysical understanding. The SIMBAD database contains information on astronomical objects that have been studied in the published literature, including a field that specifies astronomical object types. As a record that is derived entirely from the literature, a given astronomical object in SIMBAD may have multiple object types, and the list of object types must be maintained and updated. The SIMBAD object type list currently contains some 200 types, that are organised into a hierarchy based on astrophysical concepts. The hierarchical structure also includes relations between object types, and this facilitates searches of SIMBAD to obtain lists of all of the astronomical objects in a given category independently of the publisher or the year of publication.
Introduction The SIMBAD database  [1]  contains a lot of information on astronomical objects. Among these the object type is of crucial importance. It can be just a wavelength (e. g. infrared or X-ray source), or give the basic nature of the object (e. g. star, galaxy, or molecular cloud), or give a much more detailed description on the nature, geometry, or evolutionary status of the object (e. g. Seyfert 1 or 2 galaxy, or Wolf-Rayet star). In practice, SIMBAD lists a main object type and a list of secondary types for each object. It is fundamental that the CDS team can give the most detailed object type as a main type to the users, which means that we have to rely on a robust algorithm to decide when the main type should be changed and when contradictory classifications should be checked.