dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmark datasets In order to evaluate our abundance and compositionbased binning modules, we generated synthetic datasets of increasing complexity, ranging from 5 to 700 different genotypes, at 2 different read lengths (150 and 600 bp) and under different abundance distributions for the sampled organisms. We assumed that species levels follow a power-law distribution, i. e. , the abundance of the x th species is defined by: A(x) = βx -α , where α is the powerlaw parameter and β a normalization parameter such that ∑ x ∈ S A(x) = 1, and S designates the set of organisms. The mean coverage of the datasets was fixed to 10X and 1X for two distinct series. Two types of datasets, embodying different abundance patterns were created: the first with fixed parameter α = 0. 2, and the second where this parameter was a function of the dataset's complexity (richness) |S|.
Datasets for the biomarker discovery use case For the biomarker discovery use case, we generated a dataset consisting of reads from 50 different samples, e. g. , 50 stool microbiomes originating from 50 different individuals. Each microbiome contains sequences sampled from 100 different bacteria randomly selected from a pool of 700 distinct bacterial genomes (Additional file 1: Table  S2 ). The average coverage for each sample is fixed to 10X (note that this means that the bulk of species populating the long tail of the abundance distribution are sampled well below this level) and read length is set to 400 nucleotides, a length that can be achieved nowadays on several Illumina platforms (e. g. , using overlapping paired-end sequencing schemes on the MiSeq platform or HiSeq 2500 v2 reagents for rapid run mode).
In addition to the genome from the "pathogen" strain (arbitrarily chosen as Haemophilus parasuis ZJ0906 in this study), the synthetic microbiomes also included the genome of a closely related strain (Haemophilus parasuis SH0165), considered "harmless" in our experiments, as well as two further Haemophilus genomes: Haemophilus parainfluenzae T3T1 and Haemophilus somnus 2336 (see Additional file 1: Table  S2 ). For all the synthetic datasets, the read generation process was performed using the mason software  [10]. This program can sample reads of the Illumina type from reference genomes under various abundance distributions, while also inserting position specific sequence modifications according to empirically calibrated and sequencing platform dependent error models. Figure  3  shows abundance levels of the 700 bacterial genomes sampled across the 50 microbiomes.
Class definitions To evaluate the performance of the various clustering modules on the benchmark datasets, we compared class memberships of elements (reads) in each dataset to the memberships induced by the clustering. Class membership of elements is trivial to define for datasets used in the composition-based clustering experiments, simply consisting in the genomes the reads were sampled from, and the cardinality of the class set equating the sample's richness. Reference classes are more tricky to define in the coverage-based binning experiments, as genomes from organisms of similar abundance need to be evaluated together. To achieve this, we devised a greedy heuristic relying on the assumption that the abundance difference between any two genomes being separable by abundance-based methods has to be at least two-fold  [8].
Evaluation metrics In order to evaluate the quality of the clustering produced by the various modules, we use the V-measure  [11] , an external entropy-based clustering evaluation metric which evaluates clustering solutions independently of the clustering algorithm or dataset size, and measures how successfully the clustering satisfies the criteria of homogeneity and completeness (V-measure is the harmonic mean of these two values). A perfect clustering satisfies the homogeneity criteria when each of the clusters is made up of reads that belong to a single class. On the other hand, a perfect clustering satisfies the completeness criteria when, for each class, all reads of the class are assigned to a single cluster. The known read to genome memberships provide the ground truth for these evaluations.
1 , which displays the logarithm of the number of distinct k-mers as a function of their coverage), which serves two roles. First, it is the input for the EM engine that will fit a mixture of Poisson distributions to it. Second, it enables a first pass filtering on the data, e. g. , by removing unique or rare k-mers. In addition to dramatically reducing the dimensionality of the k-mer space and RAM memory requirements, this can filter out erroneous k-mers provoked by sequencing artefacts (deliberately injected at the dataset generation step to mimic real sequencing processes). Multiple worker threads analyze the content of reads in long k-mers and store their counts in a large hash-table supporting lock-free, concurrent modifications by multiple threads. Classes from the java. util. concurrent package (http:// docs.
Composition-based clustering Our composition-based clustering implementation is compared to MetaCluster  [14] , a well recognized and state-of-the-art family of programs that share the same rationale. As different flavors of MetaCluster have been developed over time, differing in various pre and postprocessing steps, we performed our comparisons against MetaCluster's core algorithm, which is shared by all its versions and roughly corresponds to MetaCluster 3. 0. The compositional module geometrizes the reads by measuring their content in short k-mers (typically of length 4 or 5), and performs a k-means clustering of the sequences in k-mer space using Spearman's footrule distance, which operates on rank vectors and is defined as: F r m ! ; S i ! ¼ X K j j j¼1 R j r m ð Þ-R j Si ð Þ : Our k-means implementation uses multiple worker threads (one for each cluster prototype) to calculate in parallel the distances between read vectors and cluster centroids, while others re-compute centroids and assign reads to clusters under the supervision of a coordinating thread.
Relevance to biomarker discovery use cases We assessed the usefulness of the binning scheme with respect to a simulated biomarker discovery use case consisting in the "de novo" identification of low abundance sequences correlated to disease status from metagenomic (e. g. , microbiome) datasets derived from a cohort comprising healthy and sick individuals (see Methods and Fig. 3 ). The unassembled reads from 50 metagenomic datasets were processed by the three level binning pipeline, resulting in 6523 two-level (intra-sample) clusters, which in turn yielded 1664 third-level (crosssample) clusters. Using the known reads to genome assignments as ground truth yielded homogeneity, completeness and V-measure of 0. 61, 0. 62 and 0. 61 respectively, coherent with the evaluation metrics from the two-level pipeline (Fig.
2  Abundance distributions of synthetic bacterial communities. Abundance distribution of sampled species for various datasets of increasing complexity, ranging from 5 to 700 distinct genomes (see main Text and Methods). A four order of magnitude difference between the number of cells from the most abundant versus less abundant organisms is used to determine power law parameters for each dataset (see Methods) Fig. 3  Heat map showing sampling levels for the 700 distinct genomes (rows) in each of the 50 samples (columns). Abundance levels of bacterial genomes across the 50 microbiome samples used to investigate the biomarker discovery use case data in an unsupervised way, we analyzed microbiome data generated during the 2011 STEC outbreak in Germany. Starting with the roughly 150,000 contigs resulting from a cross-assembly of the 53 microbiomes described in  [5] , we tested the ability of our compositional module to accurately cluster contigs and enable the reliable identification of clusters derived from the STEC causing strain by using meta-data pertaining to the health status of microbiome donors.
Conclusions We have presented a set of sequence clustering modules and their application to biomarker (e. g. , genomes of pathogenic strains) discovery from complex synthetic and real metagenomics datasets. Although initially designed for "assembly-free" analyses of single samples, we have demonstrated their relevance to experimental setups involving multiple samples through the use of the d 2 S "alignment-free statistic" to map clusters across samples, and have further illustrated how the binning modules can be used for de novo "pre-assembly" by segregating unassembled reads into biologically relevant partitions. By combining read-based with assemblybased analyses, this approach exemplifies the strategy advocated in  [4]  for moving towards genome-centric metagenomics. In the future, we intend to extend the presented methods in order to fully exploit the coverage covariation signal that is embedded in the larger cohorts that are increasingly becoming available for investigating the biomedical impacts of variation in the human microbiome.
Subjects and study design The study protocol was approved by the local ethics committee and written informed consent was obtained from all subjects prior to starting the trial. This work was carried out in accordance with The Code of Ethics of theWorld Medical Association (Declaration of Helsinki) and is registered at ClinicalTrials. gov (identifier: NCT02273622). Samples of this research were obtained from a previous intervention study, whose objective was to evaluate the effect of HT on the gene expression of Phase II enzymes  (Crespo et al. , 2015). Briefly, twenty-one volunteers were recruited for a randomized, crossover, placebo-controlled, and double-blind intervention study. The design of this study is shown in Fig. 1. We performed a Latin square design: after one-week washout, i. e.
ACKNOWLEDGEMENTS This study was supported by a grant from the  Ministerio de Ciencia e Innovación  ( AGL2011-28995 ) and co-funded by the " Marie Curie Amarout European Programme ",  European FEDER Funds ,  Programa de  actividades en tecnologías  ALIBIRD-CM S2013/ABU-2728  de la  Comunidad de Madrid. The postdoctoral contract fellowship from the  Spanish government  awarded by the  Ministry of Innovation and Science ("Juan de la Cierva" program)  to  Olha Khymenets  is acknowledged. The authors wish to thank  ACADELION Scientific  and  Medical Communications  for critically reading and carefully editing this manuscript.
Data Sets The altimeter data used in this study are from a merged and calibrated altimeter wave height database processed by the French ERS Processing and Archiving Facility (CERSAT). This data set merges the observations from a series of altimeter missions including ERS-1/2, ENVISAT, TOPEX/Poseidon, Jason-1/2, GEOSAT Follow-on and Cryosat-2 and is carefully validated against buoy measurements and jointly calibrated by cross altimeter comparisons. It can be regarded as a homogeneous and consistent data set of altimeter SWH measurements. More detailed information about this database can be found at URL http://tinyurl. com/ kg7kofg. The model output of wave parameters used here is spectra computed by WW3 versions 4. 11 with physical parameterizations of  Ardhuin et al. [2010]. The accuracy of wave parameters of the model output has been verified against measurements from buoy measurements, showing a good agreement  [Delpey et al.
com/yetsofy. Nearly 10 years of data of altimeter observation and model output from the period January 2003 to March 2012 are selected in the present study to track the source of swells. The wave parameters of WW3 output are interpolated into the spatiotemporal positions of altimeter measurements. Using this way, every record of altimeter-observed SWH can be collocated with a set of wave parameters from the WW3 output. Because altimeter cannot separate wind-sea and swell or distinguish different swell components, its measurements can be selected as representative measurements of swell height only when all of the following criteria are met: 1. The SWH measured by the altimeter is larger than 0. 7 m and less than 5. 0 m. The error model of SWH measured by the altimeter can be expressed as: r ALT 50:1010:05H s;a (1) where H s;a is the SWH measured by the altimeter.
Swell Tracking After collocating altimeter data and model output, each record has the necessary information of swell SWH, peak wave period, and peak wave direction, required to estimate the dissipation rate. Based on these data, the analysis of swells can be conducted using a method similar to  Ardhuin et al. [2009]. Following their methodology, the first step is to identify swell sources. This step has been done in the analysis SAR data set provided by CLS. Using the algorithm developed by  Husson [2012] , the approximated diameter and duration of each storm can be estimated from the size of the convergence area. Only the storms with the diameters d 1600 km and durations s 36 h are retained, and the identified swell sources are also verified by back propagating the collocating altimeter-WW3 data set.
To demonstrate the effectiveness of this simple scheme, an example of organizing a swell field using model output information is presented. Figure  2  shows the distribution of wind speed from CFSR on 15 May 2011. It is clear from the figure that a well-defined storm region with the highest wind speed more than 20 m s 21 can be observed at approximately 608S, 1308W, validating the swell source information from the SAR data set. Although there is a slight deviation between the geometrical centers of the storm defined by the wind and of the swell source, it is noted that the storm ''center'' itself is an ambiguous definition and varies under different circumstances. Figure  3  represents the wave partition associated with this swell source in the WW3 output, with results given at 2 days intervals from 19 May 2011.
Only the swell tracks with observations spanning more than 3000 km along the great circle and with more than 4 altimeter tracks intersected are retained. These criteria are made to be strict to ensure the quality of measurements as well as the robustness of the estimation of swell attenuation and dissipation rates. After applying all these selection criteria, the final database consists of 202 swell events with wavelengths ranging from 250 to 500 m, which could extend the data amount of related field experiments.
More advanced tools and methods and more related data are needed for further study. The dissipation rate of the present study can be applied to wave prediction and employed as references to other studies of swell dissipation or air-sea and wind-wave-turbulence interactions. Future research can be conducted with the accumulation of more data, to enlarge the data amount of field experiments and improve the significance of the results. The Sentinel-1A, and future Sentinel-1B, Jason-3, Sentinel-3, and CFOSAT are all potential good-quality data source for this aim. Meanwhile, improving the swell source model and developing other schemes of measuring the energy of the swell system are also promising for improving the accuracy of field experiments. Such efforts on the observation of swell dissipation are helpful for a better parameterization in numerical wave modeling and a better understanding in air-sea interactions.
Acknowledgments The altimeter data are downloaded from the database of CERSAT, available at http://tinyurl. com/kg7kofg. The model output used for the analysis is part of the IOWAGA database, available at http://www. tinyurl. com/yetsofy. Original L2 SAR data were provided by the  European Space Agency (ESA)  and they were analyzed and qualified using the swell tracking methodology developed by  CLS. This work is supported by the  National Natural Science Foundation of China  ( 41331172 ,  U1406404 , and  61361136001 ). The anonymous reviewers are greatly appreciated for their helpful comments and suggestions on this study.
Monthly net ecosystem productivity (NEP) was calculated from model outputs as the difference between net primary production and heterotrophic respiration (NPP-Rh)  [Chapin et al. , 2006]. This neglects fire and other disturbances that are often absent or poorly represented in models and poorly constrained by observations  [Huntzinger et al. , 2013]. The CO 2 concentration resulting from the NEP fluxes was calculated for each no nitrogen cycle, no dynamic vegetation (circles), no nitrogen cycle, dynamic vegetation (triangles), and nitrogen cycle, no dynamic vegetation (crosses). Grey shading is observational uncertainty from  [Graven et al. , 2013]. model and simulation using the TM3 atmospheric transport model at 5 ∘ × 3. 83 ∘ resolution, forced by National Centers for Environmental Prediction reanalysis data specific for each year  [Heimann and Korner, 2003].
at the IPSL institute in France. The simulations were performed with the support of the GhG Europe FP7 grant with computing facilities provided by "LSCE" or "TGCC. " SiB3 research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. TEM6 research is supported in part by the U. S. Department of Energy (DOE), Office of Science, Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-BATTELLE for DOE under contract DE-AC05-00OR22725. VISIT was developed at the National Institute of Environmental Studies, Japan. This work was mostly conducted during a visiting stay at Oak Ridge National Laboratory. This study was supported by KAKENHI Grand No. 26281014 by the Japan Society of Promotion of Science.
Statistical Analysis Analysis of variance was performed to assess the effect of rhizobial inoculation on gum arabic production, soil total microbial biomass, microbial CO 2 respiration, N content, FDA, and acid phosphatase activities. Principal component analysis was performed to evaluate the correlation between gum arabic production and soil fertility indicators. These tests were performed with the XLSTAT TM software package  (version 2009, Addinsoft, Paris, France). Means of these parameters were compared using the Student-Newman-Keuls rang test (P < 0. 05).
THE EXPERIMENT The 106 CdWO 4 crystal scintillator was viewed through a lead tungstate (PbWO 4 ) crystal lightguide (⊘40 × 83 mm) by 3 inches low radioactive photomultiplier tube (PMT) Hamamatsu R6233MOD (see Fig. 2 ). The PbWO 4 crystal was developed from deeply purified  [33]  archaeological lead  [34]. The detector was installed in an ultra-low background GeMulti HPGe γ spectrometer of the STELLA (SubTErranean Low Level Assay) facility  [35]  at the Gran Sasso underground laboratory of the INFN (Italy) at the depth of 3600 m of water equivalent. Four HPGe detectors of the GeMulti set-up are mounted in one cryostat with a well in the centre. The volumes of the HPGe detectors are approximately 225 cm 3 each. An event-by-event data acquisition system is based on two four-channel digital spectrometers (DGF Pixie-4, XIA, LLC).
ACKNOWLEDGMENTS The authors from the  Institute for Nuclear Research (Kyiv, Ukraine ) were supported in part by the project  CO-1-2/2015  of the Program of collaboration with the  Joint Institute for Nuclear Research (Dubna)  " Promising basic research on High Energy and Nuclear Physics " for 2014-2015 of the  National Academy of Sciences of Ukraine. We are grateful to  S. R. Elliott  for his note on presence of 116 Cd 2ν2β -decay in 106 CdWO 4 data. We would like to thank  Prof. H. Ejiri  for useful discussions.
MATERIALS AND METHODS Two datasets were used for different parts of the study. Both datasets consisted of sequence data on chromosome 1. The first set (SEQ, Supplemental Material, File S1) contained nonimputed sequence data of 122 Holstein (HOL), 27 Jersey (JER), 28 Montbéliarde (MON), 23 Normande (NOR), and 45 Danish Red (RDC) bulls. These bulls were the first bulls to be sequenced in France and Denmark, and were major ancestors contributing to each breed. While all JER and RDC bulls were Danish bulls, and all MON and NOR were French, HOL bulls originated from different populations. There were 41 French, 22 Danish, 22 American, eight Canadian, eight German, three Italian, two Swedish, and one Finnish HOL bulls. Variant calling was done using GATK  (DePristo et al. 2011). There were 1,475,541 biallelic SNP and indels on chromosome (BTA) 1 segregating in at least one of the five breeds.
Causative mutations were simulated by randomly sampling 100 or 250 variants, either out of all variants (C 100 and C 250 ) or out of all variants with a MAF below 0. 10 (C 100LM and C 250LM ). The second set (IMP, File S2) consisted of imputed sequences of 1230 JER and 961 RDC bulls. Imputation was done in two steps, using IMPUTE2  (Howie et al. 2012) , first from 50K to HD, and subsequently to full sequence. More details on the imputation can be found in  Höglund et al. (2014). After filtering out variants with an IMPUTE2 INFO score below 0. 9, there were 1,150,905 variants segregating on chromosome 1. The majority of these variants were segregating in both JER and RDC, while this was not the case for the variants in the SEQ data. To make the dataset comparable, only a subset of the data were used for analyses, containing 247,141 variants for which the percentages of variants segregating in both JER and RDC, in JER but not in RDC, in RDC but not in JER, and neither in JER nor in RDC, were equal to those in SEQ.
For the individuals in IMP, phenotypes were simulated as following. First, 50, 100, or 250 variants were randomly selected from all 247,141 variants as causative mutations for scenarios C 50 , C 100 , and C 250 , respectively. The effects were then drawn from a normal distribution $ Nð0; 1Þ. Subsequently, true breeding values (TBV) were obtained by summing the effects of the causative mutations, and a random error was added to the TBV to obtain a phenotype, so that the heritability = 0. 8. For the SEQ dataset, markers were selected according to one of the following scenarios. In scenarios P 50K and P HD , markers were either all variants on BTA1 present on the Illumina BovineSNP50 Beadchip, or on the Illumina BovineHD Beadchip. In scenarios P 50KC and P HDC , only the closest variant of each causative mutation present on the 50K or the HD SNP chip were selected.
RF was either obtained (RF O ) by estimating R 2 and R 2 , or predicted as RF P = 1 2 ð12bÞ 2. The b in the RF P is the regression coefficient of the genomic relationships at markers on the genomic relationship markers at the causative mutations: G n þ 1;i ¼ b n þ 1 G n þ 1;i þ j n þ 1;i ; ði ¼1;. ; nÞ; where G n þ 1;i and G n þ 1;i are the genomic relationships between individual n+1 and individuals 1 to n at the markers and the causative mutations, respectively, and j n þ 1;i a random residual. First, b was computed for each individual. Subsequently, RF P was computed within replicate, using the b averaged across individuals. Finally, RF P was averaged across replicates. To estimate RF O , the IMP dataset was split into training and validation sets, using either JER to predict genomically estimated breeding values (GEBVs) of RDC, or prediction of JER GEBVs using phenotypic information of RDC.
Prediction of RF For some scenarios, we compared the RF estimated by cross-validation with the RF predicted by the formula of de los  Campos et al. (2013). While the differences between RF P and RF O are in the same range as those observed by de los Campos et al. (2013), both RF P and RF O were influenced by the number of causative mutations, but in opposite direction. When the number of causative mutations increased, RF P increased, while RF O decreased. Consequently, RF P overestimated the loss in prediction reliabilities in the scenarios with 50 and 100 causative mutations, while it is supposed to be a measure of the minimum reduction in prediction reliability. Therefore, with the SEQ data set, we focused on the scenarios with 250 causative mutations, where RF P could be used as an approximation for the minimum loss in reliability.