dataesr/scientific-paragraphs-categorization huggingface dataset:
Virtualization: One-to-one correspondence between applications and hardware can no longer be applied to HPC systems, especially if looking towards Exascale. This is mainly due to resilience needs, and to management of a computing system that has to adapt to the changing conditions and needs of applications. Increased needs for resilience and improved manageability make virtualization the turnkey solution to achieve at the same time maximum performance and high flexibility. However, virtualization of compute accelerators and efficient check-pointing of virtual machines are still active research topics. In ExaNoDe, virtualization will be at the basis of each compute element with a resident KVM hypervisor  [21] , and applications wrapped within virtual machines. KVM has been chosen as target hypervisor because of its ease of use and high availability, being it part of any upstream Linux kernel.
The following programming models are considered in the ExaNoDe project: MPI  [23] , GPI  [24] , OmpSs  [25]  and OpenStream  [26]. OmpSs and OpenStream are extensions of OpenMP with new directives for offloading tasks. OmpSs uses directionality clauses on tasks and address-based tracking of data dependencies in the runtime system, and it supports heterogeneous devices such as GPUs and FPGAs. OpenStream has explicit dependencies in the source program marked using streams. Together, OmpSs and OpenStream explore two different trade-offs relating to performance and overheads vs. ease of programming. GPI is an open-source communication library that implements the GASPI PGAS API. It provides a portable and lightweight API that leverages remote completion and one-sided RDMAdriven communication, both being efficiently supported by the UNIMEM architecture.
IV. EXPECTED RESULTS ExaNoDe touches many research areas where promising technological results are expected. These all together will contribute to solve some limitations of the current available technologies related to HPC, advancing the state of the art in some specific domains and research topics that find applications also outside the HPC domain. The major results expected by the project will be now listed. From the integration point of view, ExaNoDe is acting at both physical device level and software programmatic level. In this context, FPGA has been chosen not only for its widelyexplored acceleration possibilities, but also as a mechanism that can enable the integration of the UNIMEM technology introduced in Subsection III-B. ExaNoDe will focus its effort in innovating new technologies regarding the integration of FPGAs, providing as well the reference software API to fully exploits the tight integration between the FPGA and the CPU.
5D results will be deployed. This will be realized as a printed-circuit board (PCB) which is designed to be aligned and inter-operable with the PCB compute element being developed within the associated HPC ExaNeSt project, which in turn is inter-operable with the new form factor being used commercially by some industrial players. Regarding the software innovations, as introduced in Subsection III-C, virtualization extensions play a key role to drastically improve resiliency and manageability of the computing system. Open source solutions for virtualization, such as KVM, offer already a solid infrastructure, very extensible and based on available open source code, which however has not been designed specifically for HPC use-cases. In this context, the project aims at enhancing open source solutions to improve snapshot and check-pointing support, reducing at a minimum the down-time of the virtual machine and, as a consequence, improving the overall efficiency of the system.
The virtualization solution will be extended accordingly to reduce the gap between the virtual machines and the underlying hardware as, for instance, the FPGA. This will require not only to extend device-assignment solutions, but also to design and implement the needed infrastructure to expose the ExaNoDe hardware to the guest systems. All the novelty regarding hardware components and software infrastructure is coupled with programming models (GPI-2, MPI, OmpSs and OpenStream) that will be extended to use UNIMEM. In this context, ExaNoDe will ease the integration of the UNIMEM concept to existing HPC applications by extending the programming models mentioned in the previous Section. This expected result will have a huge impact on how the ExaNoDe concepts will be received by the HPC software developers: providing a variety of programming models to test and exploit the ExaNoDe technology will ease the spread and dissemination of the ExaNoDe results.
More specifically, the MPICH  [27]  model which has been chosen as target implementation of the MPI standard, will be extended with a network module plugin that extends the base runtime with UNIMEM based communication optimizations, allowing unmodified HPC application to exploit this new communication back-end. OpenStream will be also target of the ExaNoDe research activities as it will be enhanced to operate over both the traditional SMP shared memory model, as well as the UNIMEM memory-scalability model adopted by this project. This programming environment, together with OmpSs, will be extended to leverage the UNIMEM architecture, with specific optimizations in the compiler (OpenStream) and runtime system (OmpSs and OpenStream). UNIMEM, on the other hand, introduced some challenges in regard to remote atomic operations over the global address space that the ExaNoDe partners were asked to solve.
Firstly, an atomic service has been envisioned to handle atomic requests that target the global address space. Furthermore, an additional challenge came from the way the interconnection works, since the atomic operations issued by different CPUs might not reach the destination node in the correct order. The consortium expects to resolve this challenge as well, developing specific IPs for the final prototype. Besides the ExaNoDe commitment to lower the power consumption drawn by the cores thanks to the improved compute density, additional results are expected oriented towards a self-adaptive management of the CPU power consumption. This will be achieved by designing and deploying a power and thermal management runtime that harmonize the power supply among the cores according to the software requirements.
Material and Methods Potential participants for RAVES were identified by their urologist or radiation oncologist (RO) and invited to participate in the DA study. Participants received a prerandomised package containing the standard RAVES participant information sheet and either the custom developed DA or a blank notebook. The packages were identical in appearance and both participant and recruiting clinician were blinded to the intervention. Questionnaire measures of decisional conflict and knowledge (including RAVES knowledge) were administered at baseline, one and six months. The primary outcome measure was decisional conflict. Secondary outcomes measured included knowledge about clinical trials and RAVES as well as recruitment to RAVES. Results 127 men (median age = 63 years) were recruited through urologists (n = 91) and radiation oncologists (n = 36).
2015) , and immediately killed for DNA and RNA collection. RNA preparation, microarray design, and data normalization RNA preparation, gene expression microarrays, hybridization, and normalization procedures are described in detail in  Leder et al. (2009 Leder et al. ( , 2015)). Briefly, total RNA was isolated from offspring liver tissue using standard protocols. RNA that passed quality thresholds was labeled (Cy3 or Cy5) using the Agilent QuickAmp Kit, with equal numbers of individuals within family groups (control and temperature-treated; males and females) assigned to each dye. Labeled RNA was hybridized to a custom 8 • 15K microarray, with sample order randomized (Agilent Hi-RPM kit). Labeling, hybridization, and scanning was performed at the University Health Network in Toronto, Canada.
Images of the arrays were acquired, image analysis was performed, and array quality was assessed as detailed in  Leder et al. (2015). Postprocessed signals were standardized across arrays using a supervised normalization approach, implemented in the package "snm" for R/Bioconductor  (Mecham et al. 2010; R Core Team 2015). Dye, array, and batch (i. e. , slide) were defined as "adjustment variables"; sex, family, and temperature treatment were defined as "biological variables. " Following normalization, individual intensity values more than two SD from their family-by-treatment mean, and probes with missing values for an entire family or. 10% of individuals were removed. The final dataset contained 10,527 expression traits (10,495 genes plus 32 additional splice variants) and 563 individuals (158 control females, 125 control males, 152 treated females, and 128 treated males).
Variant calling Reads were split by barcode, and barcodes removed, using a custom perl script. Low-quality bases were removed from the reads via window adaptive trimming using Trim. pl (available: https://github. com/LJI-Bioinformatics/HLATyphon/blob/master/01. Pre_Processing/trim. pl, Illumina quality score # 20). Paired-end reads for each of these individuals were aligned to the BROAD S1 stickleback genome using BWA aln/sampe (v 0. 6. 2) with default parameters  (Li and Durbin 2009). The threespine stickleback genome comprises 21 assembled chromosomes plus 1823 unplaced genomic scaffolds. Unmapped reads, and reads with nonunique optimal alignments, pair-rescued alignments, or any alternative suboptimal alignments, were discarded from the resulting SAM files. SAM files were converted to sorted BAM files using samtools 0.
Genotype quality control Vcftools  (Danecek et al. 2011 ) was used to recode genotypes with a genotype quality phred score (GQ) , 25 or a sequencing depth (DP) , 8 or. 1000 to missing. Vcf files for all families were merged and the merged file converted to the input format for Plink 1. 07  (Purcell et al. 2007). For SNPs on all autosomal chromosomes and the pseudoautosomal region of chromosome 19 (see below), the following filters were applied in Plink: hwe (based on founders only) , 0. 01, maximum missing genotypes = 0. 25, minor allele frequency. 0. 05, and offspring with. 70% missing data removed. Adjacent SNPs in complete linkage disequilibrium were manually consolidated into a single locus, with combined SNP information used to call genotypes. Several approaches were used check for sample contamination or errors in barcode splitting and family assignment: in Plink, the mendel option was used to screen families for Mendelian errors, and sample relatedness was examined by graphically visualizing genome-wide IBDsharing coefficients generated by genome; the program SNPPIT  (Anderson 2012 ) was used to assign individuals to parents, based on five independent datasets of 100 SNPs; and 220 SNPs on Stratum II of chromosome 19 (see below) were examined for their expected pattern in males and females (all heterozygous in males vs.
Linkage map construction We constructed a linkage map using the improved version of Crimap  (Green et al. 1990 , available: http://www. animalgenome. org/tools/ share/crimap/). Remaining Mendelian errors in the dataset were removed using the set-me-missing option in Plink. For each SNP, the number of informative meioses were examined using Crimap, and markers with , 150 informative meioses or within 500 bp of one another were discarded. The initial map build included 6448 markers. Where applicable, SNPs were ordered according to the modified genome build of  Roesti et al. (2013). We attempted to position all previously unplaced scaffolds containing at least two genotyped SNPs on to the map. Scaffolds were assigned to chromosome on the basis of LOD score using the Crimap function two-point, and then positioned using a combination of information from pilot Crimap builds, chrompic, and fixed together with known start and end points of previously assembled scaffolds  (Roesti et al.
Ortholog identification In order to maximize the functional information available, we identified human orthologs for G. aculeatus genes. As a first attempt, we used BioMart  (Durinck et al. 2005; Smedley et al. 2009)  to identify human orthologs and obtain the HGNC symbols for the human genes. When BioMart failed to return a human ortholog, protein BLAST searches were used to identify orthologs using the Ensembl human protein database. The identifier conversion tool, db2db, from bioDBnet (https://biodbnet-abcc. ncifcrf. gov/db/db2db. php) was used to convert between Ensembl identifiers and HGNC gene symbols when needed  (Mudunuri et al. 2009).
Upstream regulator and functional interaction analyses To search for regulatory genes that may be responsible for the expression variation in genes with identified trans-eQTL, we used the upstream regulator analysis in the Ingenuity Pathway Analysis (IPA) software (QIA-GEN). This analysis uses a Fisher's Exact Test to determine whether genes in a test dataset are enriched for known targets of a specific transcription factor. We used the human HGNC symbols as identifiers in IPA. First, we examined all genes that had a significant trans-eQTL mapping to any location at a genome-wide P , 0. 021 (chromosome-wide P , 0. 001). To investigate the upstream regulators potentially involved in generating eQTL hotspots in more detail, we lowered our stringency and also examined all genes with trans-eQTL mapping to the hotspot locations at genome-wide P , 0.
057 (chromosome-wide P , 0. 0027). Since transcription is typically initiated by a complex of genes rather than a single transcription factor, we examined functional relationships among the identified upstream regulators for each hotspot (Supplemental Material, Table  S8 ), the genes located within a hotspot, and the genes with significant eQTL mapping to that hotspot (Table  S4 ; cis-eQTL significant at chromosome-wide P , 0. 01, trans-eQTL significant at genome-wide P , 0. 021), using STRING v10 (Jensen et al. 2009, http://string-db. org/). We searched for evidence of functional relationships from experiments, databases, and gene coexpression, and applied a minimum required interaction score of 0. 4.
Data availability QTLMap input files are provided as Files Files S1-S5. Raw and normalized microarray data, in addition to R scripts describing the normalization procedure, are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-3098. RAD sequence reads for each individual have been deposited in the NCBI Sequence Read Archive under BioProject ID PRJNA340327. Further information about archived data is provided in File S6.
Identification of cis-and trans-eQTL Expression data were available for 500 of the 517 genotyped offspring. Twenty-six of these offspring had. 60% missing genotype data and were removed from the analysis. As we found that missing values in the expression trait file caused QTLMap to overestimate the LRT statistic, we eliminated these from the dataset by removing one additional individual and 195 expression traits. Eighty-eight genotyped parents, 473 genotyped and phenotyped offspring (mean no. offspring per family = 15. 8, mean proportion of missing genotypes in offspring = 0. 11; maximum = 0. 56), and 10,332 expression traits were retained for the analysis. At chromosome-wide P , 0. 01, we identified 5366 eQTL associated with 4507 expression traits (43. 7% of the 10,322 expression traits examined, Table  S4 ).
(2015)  to exhibit a significant change in expression in response to a temperature treatment. We identified significant eQTL underlying 1131 of these genes (Table  S4 ; eQTL type: 79. 8% cis, 12. 7% trans, 2. 6% both, and 4. 9% unknown). The distribution of the 177 significant trans-eQTL across 5 cM bins indicated four hotspots (five or more eQTL, P , 0. 01, Figure  S3 ), all of which had been previously observed in the full dataset. The chromosome 16 hotspot was greatly increased in relative importance (Chr4b: 6 eQTL; Chr 6: 12 eQTL; Chr12a: 9 eQTL; and Chr16: 7 eQTL).
Upstream regulator and functional interaction analyses When examining all 405 genes with trans-eQTL significant at genomewide P , 0. 021, 79 significantly enriched upstream regulators were identified using IPA (Table  S8 ). In total, these regulators had 208 of the genes in the dataset as known targets. Hepatocyte nuclear factor 4a (HNF4A) was identified as a particularly important regulator (P = 9. 3 • 10 28 ), with 70 (33. 7%) of these genes as downstream targets. Other highly enriched regulatory factors included one cut homeobox 1 (ONE-CUT1; P = 3. 2 • 10 25 ; 16 target genes), Nuclear Receptor Subfamily 4 Group A Member 1 (NR4A1; P = 2. 0 • 10 24 ; 11 genes), Signal Transducer And Activator Of Transcription 5B (STAT5B; P = 5. 5 • 10 24 ; 10 genes), Krüppel-like factor 3 (KLF3; P = 8. 7 • 10 24 ; 15 genes), estrogen receptor 1 (ESR1; P = 1.
ACKNOWLEDGMENTS Tiina Sävilammi  wrote the script for barcode splitting and we thank her for bioinformatics advice. We also thank the numerous people who helped in obtaining and maintaining sticklebacks. Generous computing resources were provided by the  Finnish Centre for Scientific Computing. Research was conducted under an ethical license from the  University of Helsinki  ( HY121-06 ). Comments from two anonymous reviewers greatly improved the manuscript. This work was funded by grants from the  Academy of Finland  to C. R. P. , E. H. L. , M. N. , and  J. M. (grant numbers  129662 ,  134728 ,  133875 ,  136464 ,  141231 ,  250435 , and  265211 ).
Observational signatures of Kinetic Matter Mixing In this section we discuss the effects of KMM on the power spectrum of the matter density contrast and on the CMB. In particular, we compute the comoving matter density contrast, defined as ∆ m ≡ δ m -3Hv m , (4. 1) where δ m and v m are in Newtonian gauge. For the CMB we focus on the lensing potential and the temperature fluctuations. The observables are computed using COOP  [47] , which solves linear perturbations in Newtonian gauge and in the Jordan frame, assuming minimal coupling of all matter species. In the ΛCDM case, COOP evolves Ψ, Ψ Ne ≡ dΨ/dN e and matter perturbations, where N e ≡ ln a is the program time variable. The detailed algorithm and equations can be found in Ref. [71]. To describe deviations from ΛCDM using the Effective Theory of Dark Energy, COOP evolves two additional variables, µ ≡ Hπ and µ Ne ≡ dµ/dN e.
Acknowledgements: It is a pleasure to thank  Jérôme Gleyzes ,  Martin Kunz ,  David Langlois ,  Jean-Baptiste Melin ,  Federico Piazza ,  Ignacy Sawicki  and  Jeremy Tinker  for useful discussions. M. M. and F. V. also thank the  Theoretical Physics Departments of CERN  and  Université de Genève  for hospitality during large part of this work. G. D'A. thanks the  CCPP at New York University  for hospitality during the final stages of this work.
The inventory of biodiversity in the Comoros archipelago is still in progress. We propose here to contribute to this effort by assessing the diversity of the Hymenophyllaceae family, which forms one of the most diverse and emblematic fern communities in rainforests of the region. Data were extracted from floras, literature, recent collects and observations, and from an exhaustive investigation of the Paris herbarium collection. We also completed an online knowledge database including a computeraided identification (CAI) tool by using the Xper platform. Our checklist recognizes 21 taxa involving 1 endemic and 7 species newly reported for the archipelago. The taxonomy and discrimination of each taxon is discussed and a dichotomous key is provided. We also defined 80 morphological characters and their corresponding states into the Xper database and CAI that could be easily enriched for neighbouring areas and additional taxa.
However, recent taxonomic studies in the Indian Ocean  (Pynee et al. 2012; Dubuisson et al. 2013b Dubuisson et al. , 2014 Dubuisson et al. , 2016;; Bauret et al. 2015)  show that the family still needs additional taxonomic investigations, with potential new taxa for the region. Consequently, the diversity of Hymenophyllaceae in Comoros also needs a revision that can take advantage of recent taxonomic studies (as reported hereabove), recent collects (mostly by F. Rakotondrainibe, M. Pignal and G. Rouhan, MNHN, Paris) and exhaustive investigations of herbarium collections. This paper proposes therefore to: 1) evaluate the diversity of Hymenophyllaceae in Comoros with the associated and updated taxonomic revision; and 2) provide with taxonomic tools via identification keys and an online knowledge database on Comorian Hymenophyllaceae in the context of biological inventories.
Floras and databases Data were first extracted from floras and checklists that include information on ferns in the western Indian Ocean and neighbouring areas:  Kuhn (1868) ;  Baker (1877) ;  Cordemoy (1895) ;  Sim (1915) ;  Bonaparte (1920a Bonaparte ( , 1920b Bonaparte ( , 1923 Bonaparte ( , 1925)) ;  Christensen (1920) ;  Tardieu-Blot (1951 , 1960 , 1977 , 2008) ;  Schelpe (1966) ;  Lorence (1978) ;  Schelpe & Anthony (1986) ;  Burrows (1990) ;  Kornaś (1993 Kornaś ( , 1994)) ;  Beentje (2008) ;  Roux (2009)  and  Grangaud (2010). They were then confronted to online databases: Sonnerat (MNHN, Paris herbarium P, https://science. mnhn. fr/institution/mnhn/collection/p/item/search/ form), the Checklist of ferns and lycophytes of the world (http://worldplants. webarchiv. kit. edu/ferns/), the Index of the vascular flora of La Réunion (http://flore.
cbnm. org), and Mayotte (http://floremaore. cbnm. org), The Plant List (http://www. theplantlist. org) and the Catalogue of the Vascular Plants of Madagascar included in Tropicos (http://www. tropicos. org/Project/MADA). The crossed data obtained from floras and databases were then updated according to recent taxonomy. This update was carried out via IPNI (http://www. ipni. org), Tropicos (http://www. tropicos. org), Roux (2009) ,  Grangaud (2010)  and especially by following the recent phylogenetic studies  (Ebihara et al. 2006; Pynee et al. 2012; Dubuisson et al. 2013b Dubuisson et al. , 2014 Dubuisson et al. , 2016;; Bauret et al. 2015). The aim of these taxonomic and nomenclatural investigations was to check and to obtain, for every potential distinct species, a scientific valid and updated name.
Knowledge database and identification tools using Xper2 and Xper3 Dichotomous keys are the traditional tool used to make taxon identification. But more powerful tools exist since the development of softwares dedicated to taxonomic description and Identification Assisted by Computer (CAI). This is the case of Xper2  (Ung et al. 2010 ) and its updated Xper3 version (http://www. xper3. fr/?language=en) that we used in this study. Xper2 is a classical software usable only offline while Xper3 is the online collaborative version. Xper platforms propose automatic edition of identification keys. Compared to a traditional dichotomous key where the path is imposed and identification can be difficult or impossible if the selected characters are lacking or not well observable, Xper users can select any character.
Furthermore, inapplicability or dependencies (i. e. , the absence of a descriptor does not allow the presence of some others and inversely) and uncertainty are taken into account and users can integrate continuous/quantitative data (e. g. , accurate leaf length). The tools are therefore more flexible and interactive. However, the construction of the knowledge database that allows the identification also requires that a list of appropriate descriptors (characters) and states was carefully defined, completed and coded for every taxon. These descriptors and states were extracted from previous studies  (Dubuisson 1997; Hennequin 2003; Pynee et al. 2012; Dubuisson et al. 2013b Dubuisson et al. , 2014;; Bauret et al. 2015)  and from present morphological investigations, and more especially adapted to the Comorian taxa and specimens.