dataesr/scientific-paragraphs-categorization huggingface dataset:
GK alleles on the X chromosome were lost early in the breeding program by two consecutive breedings of male backcross animals to BN females. All animals used in this study were systematically genotyped with markers chosen to accurately monitor retention of GK alleles across the congenic intervals and absence of GK allele contaminants from the genetic background  [47, 48]. All strains were co-housed in order to avoid cage-specific microbiome selection  [14, 49]. Rats were allowed free access to tap water and standard laboratory chow pellets (B&K Universal Ltd, Grimston, Aldbrough, Hull, UK) and were maintained on a 12-h light-dark cycle. All rats were identified using a microchip (identity chip, Animal Care Ltd, York, UK) linked to a database specifically designed to administer the project (husbandry, phenotype scheduling, and data Name and genomic position (Mb) of the genetic markers flanking the GK congenic intervals are given, along with the minimum and maximum genomic length (Mb) of the introgressed GK genomic segment.
0) in 5-mm tubes for NMR acquisition. Standard 1 H NMR spectra were measured on a Bruker spectrometer (Rheinstetten, Germany) operating at 600. 22 MHz 1 H frequency, as described previously  [18, 55]. The 1 H NMR spectra were imported into Matlab and phase-and baseline-corrected at high resolution. The region δ5. 0-4. 5 was removed to eliminate baseline effects of imperfect water signal presaturation. Each spectrum was normalized to a constant intensity sum and each variable was mean centered. Analyses were carried out using R and Matlab.
, San Diego, CA, USA). Resulting data were then preliminarily analyzed using the Illumina® BeadStudio Application software before undergoing comprehensive statistical analysis. Particular attention was given to the following quality control parameters: 0 ≤ G sat ≤ 1; Green 95 percentile (GP95) for consistency between arrays (around 2000); GP5 background level in the range of the low 100 s or below. Microarray experiments were compliant with MIAME (Minimum Information About a Microarray Experiment) and both protocol details and raw data have been deposited in ArrayExpress (http://www. ebi. ac. uk/arrayexpress/) under accession number E-MTAB-1048.
Network representation of genome-metabolome associations In order to explore genome-metabolome associations, a functional association network was derived from metabotype and genotype correlation coefficients using the bipartite graph Rgraphsviz package from R to represent the O-PLS correlation matrix derived from the linkage analysis between NMR variables and genotypes. A cutoff was then applied to the P value of the Pearson's correlation coefficient adjusted using Benjamini and Hochberg's multiple testing correction (P BHadj < 0. 05); significant correlations were set to 1 and non-significant correlations were set to 0, defining the adjacency matrix for the graph. Hence, G = (N, E) specifies a graph G with N denoting the two node sets (two types of nodes: genomic regions and metabolites) and E the edge set (link between nodes, here a correlation between metabotypes and genotypes above the cutoff, i.
Integration of eQTL-responsive genes and mQTLresponsive metabolites on KEGG metabolic networks to identify functional candidates Differentially regulated genes and metabolites that could be associated with GK haplotypes were mapped to KEGG pathways. Metabolic pathways were imported in R using KEGGgraph  [55, 58]. An in-house python script was written to generate an adjacency table. The resulting regionspecific networks were mathematically formalized as a directed multilabeled graph Gm,n = (Vm,E) composed of two types of nodes Vm, where m = ("metabolic reactions", "metabolites"), and functional equally-weighted edges corresponding to network connectivity (i. e. , metabolic reactions) between enzymes and metabolites. To identify pairs of eQTL-associated genes coding for enzymes that are metabolically connected with mQTL-associated metabolites, we computed shortest paths from the eQTL genes mapped on the KEGG network to the target mQTL metabolites using the igraph R package.
H NMR-based metabotyping of adipose tissue in congenic strains To complement pathophysiological phenotypes in the congenics with molecular phenotypes, and to investigate possible relationships between physiological and metabolic variables, we carried out 1 H NMR metabotyping of aqueous extracts of white adipose tissue from rats of the 12 BN. GK congenic strains and the BN control (Additional file 2: Figure  S1 ). A total of 34 metabolites were detected, 31 of which could be assigned using published data  [9, 19, 46]  and in-house databases (Table  2 ). We then built orthogonal partial least squares discriminant analysis (O-PLS-DA) models to compare metabolite patterns in each congenic strain to the BN normoglycemic control (Additional file 2: Figure  S2 ). The robustness of each O-PLS model was tested by sevenfold cross-validation and resampling under the null hypothesis as described previously  [21, 48].
42), inositol polyphosphate phosphatase-like 1 (Inppl1; LOD = 8. 04), phosphoinositide-3-kinase, catalytic subunit type 2 alpha (Pik3c2a; LOD=5. 26), phosphatidylinositol 4-kinase type 2 alpha (Pi4k2a; LOD = 9. 87), and inositol-3-phosphate synthase 1 (Isyna1; LOD = 5. 73), which plays a critical role in myo-inositol biosynthesis. Furthermore, association with the transcript encoding protein phosphatase 2, regulatory subunit B' beta (Ppp2r5b; LOD = 6. 34), regulated by Pik3c2a, supports the proposed role of defective regulation of serine/threonine protein phosphatases on insulin resistance in GK adipocytes  [43]. Integrative analysis of multi-level -omic datasets in QTL mapping contexts remains a challenge  [39] , which has been best addressed in segregating populations in yeast  [44]  and mice  [45]  through the application of network biology tools.
Softwares The last point to address is which software could we use to perform the tree search. The bioinformaticians have worked for long on sophisticated algorithms to find the phylogenetic classification of living organisms. We can simply use them. All softwares presented below are freely available and most are open source, except PAUP that is in the process of becoming so. Undoubtly, the most useful software package to undertake a maximum parsimony search on astrophynomical data is PAUP* (Phylogenetic Analysis Using Parsimony 1 ,  Swofford, 2003). There are many useful tools to analyse, manipulate, and modify trees. But an important point I think is the possibility to use up to 32 or 64 bins for the characters. A very convenient environment to prepare the matrix and analyse the results is the statistical package R which sees a recent and considerable development for phylogenetic studies 2.
Unfortunately, it still lacks the ability to perform the MP search itself, even though the package phangorn allows this with up to eight bins, which I do not find enough. With these two softwares, you have all that is necessary to perform a complete cladistic analysis of astrophysical objects. There are other tools you may find interesting. Mesquite 3  (Maddison & Maddison, 2004 ) is a rich java environment with very convenient graphical visualisation of trees. There are more and more modules allowing many kinds of computations, and it is linkable with PAUP and R to perform other analyses. MacClade is as popular and powerful as PAUP but it is only available for MACs and seems not to work anymore on new versions of the OS. They advise to use Mesquite instead. Phylip 4 is also very powerful but devoted mainly to genetic data which have no equivalent in astrophysics.
The Cladistics Analysis If the populations of globular clusters were formed in different environments, they probably formed during different stages of evolution of the environment in our Galaxy at different stages of development. In other words, the environments in which the globular cluster populations formes, were different and are related by "evolutionary" relationships due to the changes in metallicities and/or physical conditions. As a consequence, the globular cluster populations are also related by some "evolutionary" relationships. This problem has been reconsidered with cladistics by  Fraix-Burnet et al. (2009). The data were retrieved from the literature on 54 globular clusters of our Galaxy, described by the three parameters selected from previous other studies : • metallicity ([Fe/H]), • absolute V magnitude (Mv, more or less indicative of the total stellar mass of the cluster), • maximum effective temperature (Te) on the horizontal branch.
A. 4 Minimum Spanning Tree We first need to compute the pairwise distance matrix, and then compute the MST. We can use the mst command of the ape package. The igraph package is more powerful for graphical representation of networks. We import the distance matrix into igraph and compute the MST. We can get informations on the list of vertices (nodes), edges (branches), their weigths (distances) and the degrees of the vertices. There are many possibilities to plot the tree. In particular there are several "layout" that optimize the visibility of all vertices and edges on the graph. We can also separate the MST by cutting the edges whose length is larger than say 5. gp <-g-E( g ) [ which (E( g ) $ w e i g h t >=5)] plot ( gp , layout=layout. mds ( gp , d i s t=as. matrix ( d i s ) ) , v e r t e x.
Instrument failures Three out of 48 DEPAS stations (RR02, RR04, RR15) delivered neither seismometer nor hydrophone data because their data loggers failed (reason unclear). The seismometers in nine DEPAS stations (RR23, RR24, RR32, RR35, RR42, RR49, RR51, RR54, RR57) featured a redesigned sen- sor/casing package with broader band CMG-OBS40T sensors (120 s), which had previously not been deployed in the deep sea. The levelling mechanisms failed (remained stuck) in all nine stations, for reasons that are still under investigation. Automatic, prolonged attempts to level the sensors drained their batteries prematurely so that the functioning hydrophones also ran out of power 8-9 months into the experiment. DEPAS seismometers RR27 and RR45 recorded, but at high noise levels (reason under investigation).
The band-pass filter strongly enhances the P-wave, compared to the wideband traces, where it is lost in the long period noise for most DEPAS stations. though this might not be due to the sensor. The hydrophones in RR33 and RR39 measured, but at a high noise level. The 9 INSU stations (RR28, RR29, RR31, RR34, RR36, RR38, RR40, RR50, RR52) were affected by a bug in the data logger software that activated the level-sensing circuitry every 3620 s (roughly every hour). Each such event caused a "glitch" in the seismograms of roughly 1200 s duration, i. e. a characteristic, complex pulse shape, that is very similar but not identical across events. Pulse amplitudes are between 500-800 counts, corresponding to 1. 5 µm ground displacement after instrument correction and filtering between 20 and 500 s period.
Conclusions From October 2012 to November 2013, the RHUM-RUM experiment deployed and successfully recovered 48 German DEPAS and 9 French INSU broadband ocean-bottom seismometers around La Réunion, western Indian Ocean, making this the largest deployment of either instrument type, and the only joint experiment. Overall network performance was very satisfactory, but a number of technical issues have been described here, including blocked levelling mechanisms, data logger malfunctioning, and loss of clock synchronization. For the first time, we publish instrument response information on the DEPAS OBS, which allows to calculate the true ground displacement in a wide frequency range. This shows that at periods longer than 10 s, the INSU OBS are much quieter than the DEPAS instruments, on all three seismometer components.
The RHUM-RUM data set has been assigned FDSN network code YV and will be freely available by the end of 2017. Data and detailed StationXML meta-data files are hosted and served by the RESIF data centre in Grenoble (http://portal. resif. fr/?RHUM-RUM-experiment&lang=en). G ADC (f ) = S d,ADC. (A3) The sensitivity of this stage is S d,ADC = 3. 62 ⇥ 10 5 counts V 1  , resulting in an overall sensitivity for the LOB-STER seismometers of roughly 7. 4 ⇥ 10 5 counts (m s 1 ) 1 at reference frequency f r = 1 Hz (see Fig. 4 ). The decimation of the digital signal to the recording frequency is described by a series of N FIR FIR decimation filters. The kth digital filter stage has L k coefficients b l,k , decimating an input signal of sampling rate 1t i. The total FIR response is the product of the individual FIR stages:   A1d  and e ).
The capacitance C hyd is instrument-specific. The reference value from the manufacturer HighTechInc is C hyd = 45 nF. Before sale, every hydrophone is calibrated, which showed a mean value C hyd = 56. 3 nF with a sample standard deviation of 3. 5 nF amongst the 60 instruments in the DEPAS pool. The input resistance R of the data logger was either 210 or 500 M, depending on the instrument version. The sensitivity S h is different for each hydrophone, around 185 µV Pa 1 with a sample standard deviation of 8 µV Pa 1 amongst the DEPAS instruments. DEPAS supplied us with values for S d , R and C hyd for each instrument. From those, we calculated poles, zeros and sensitivities, which are listed in the dataless SEED and StationXML files available from the RESIF data centre. Geomar instruments were equipped with a similar hydrophone model, HTI-01-PCA from the same manufacturer.
This practice is the same as that used by other OBS facilities (e. g. Godin et al. , 2013) , but it leaves a significant uncertainty in the converted signal amplitudes. from laboratory experiments. Hence we re-ran several data recorders after their return to the DEPAS lab at AWI Bremerhaven, in an attempt to measure their clock drifts. Only seven data loggers were available (RR06, RR11, RR41, RR43, RR44, RR45, RR55); the remainder had been redeployed in new experiments. Attached to their original lithium batteries and a seismometer, the recorders were run for 7 days, and then for another 33 days. Table  B1  shows the skews measured after the two runs, linearly extrapolated to a hypothetical run time of 365 days. For 6 out of 7 stations, skew values from the two runs agree to within less than 0.
Black squares indicate permanent GEOSCOPE stations. Small black dots mark earthquake hypocentres above magnitude 4 between 1981 and 2015, as published by the Preliminary Determination of Epicentres (PDE) bulletin of the US National Earthquake Information Center (NEIC). The seismicity is mainly concentrated on the oceanic ridges. Colour-shaded bathymetry is based on the global 30 arcsec merged bathymetry dataset by Becker et al. (2009), available at: http://topex. ucsd. edu/WWW_html/srtm30_plus. html.
Acknowledgements. RHUM-RUM is funded by  Deutsche Forschungsgemeinschaft  (grants  SI1538/2-1  and  SI1538/4-1 ) and  Agence National de la Recherche  (project  ANR-11-BS56-0013 ). Additional support is provided by  Centre National de la Recherche Scientifique-Institut National des Sciences de l'Univers, Terres Australes et Antarctiques Françaises, Institut Polaire Paul Emile Victor ,  Alfred Wegener Institute Bremerhaven , and a  Marie Curie Career Integration Grant  to  K. Sigloch. Instruments were provided by " Deutscher Geräte-Pool für Amphibische Seismologie" at Alfred-Wegener-Institut, Bremerhaven, "Parc Sismomètre fond du mer" at INSU/IPGP , and Geomar, Kiel. We thank  Erik Labahn ,  Henning Kirk , and the crews of research vessels  Marion Dufresne and Meteor  for excellent support during deployment and recovery.
We thank  Carlos Corela  for preparing the initial compilation of the DEPAS metadata. Ulf Gräwe  assisted with downloading HYCOM ocean model data (http://hycom. org). All figures were produced with the ObsPy software, version 0. 10. 2(The ObsPy Development Team, 2015). We thank the RESIF data centre in Grenoble, especially  Catherine Pequegnat  and  Pierre Volcke , for hosting the RHUM-RUM data. RESIF is supported by the  French Ministry of Education and Research , by 18  Research Institutions and Universities in France , by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-11-EQPX-0040 ) and by the  French Ministry of Ecology, Sustainable Development and Energy. Edited by:  D. Pesaresi Reviewed  by:  D. Suetsugu  and three anonymous referees.
Numerical implementation The numerical case study performed in the next section was implemented in the open-source software DuMu x  (Flemisch et al. 2011). Generally DuMu x allows the simulation of flow and transport processes in porous media and is itself based on the Distributed and Unified Numerics Environment (DUNE) toolbox which provides an opensource foundation for the solution of partial differential equations with grid-based methods  (Bastian et al. 2008 ). As a fundamental benefit, DuMu x permits the user to independently advance the simulations by developing new instructions. In the previous paper  (Hagemann et al. 2015) , the simulation studies were focused on structured 2D grids with isotropic and homogenous properties of the porous medium. Such grids have been imported in the format of ''dune-alugrid''  (Dedner et al.
2014). In this publication, the numerical investigations are extended to 3D applications by importing and computing on realistic unstructured grids with heterogeneous reservoir properties. The ''opmparser''  (Flornes et al. 2016 ) and ''dune-corner point''  (Bastian et al. 2016 ) modules were used to import and adapt these grids for the usage within DuMu x. Some fundamental adjustments of the DuMu x code were necessary to ensure the 3D simulations.
Program adjustments DuMu x offers two different spatial discretization schemes. In the previous publications, the default box method was used which is appropriate for conforming grids. For the processing of geological more complex reservoir structures in corner-point format, the more conventional cell-centered finite volume method is recommended. Since DuMu x was initially developed to compute near surface ground water processes, the expected default permeability range does not cover the very small permeability values of deeper hydrocarbon reservoirs. In this case, an automatic request of a DuMu x control function aborts the simulation since a physically incorrect result is misleadingly assumed. By adapting the equivalent permeability threshold value, larger value ranges can be computed. During the adjustments from structured 2D grids to unstructured 3D reservoir applications, further numerical challenges occurred.
Acknowledgments This study is part of the  H2STORE  project sponsored by the  German Federal Ministry of Education and Research (BMBF). The authors would like to thank the  DuMu x development group  and especially  Bernd Flemisch  from the  University of Stuttgart  for providing us an interface between  Schlumberger Petrel  ( 2016 ) and DuMu x and his support for optimizing the DuMu x code for cornerpoint grids.
on urban and non-urban geo-classified LULC polygons currently in literature cannot provide pertinent data for local government measles managers. Subsequently, patient population proportional ratio cannot be measured on urban diffusing land cover nor on rural geo-classified, undisturbed pasture grasslands. Previous measles related models have not been objectively incorporated into a measles landscape surveillance data system and instead relied on vaccination coverage data as the primary indicator of local disease burden. However, Poisson probability paradigms can render elucidative explanatory count-variable responses and the residuals from the model output e. g. , (pseudo R 2 values at a 95% confidence interval in PROC REG). Consequently, these models could either consistently capture the effects of large measles mortality where low vaccination coverage is reported, or show periods of low mortality between outbreaks when high vaccination coverage is reported in specific neighborhoods.
Similarly, these frequentist models have revealed over-dispersion due to extreme observations (outliers) and missing interactions in the independent variables  [7]. Over-Poissonian variation is commonly due to violation of the assumption that the variance is equal to the mean  [8, 9]. In statistics, over dispersion is the presence of greater variability in a data set than would be expected based on a given statistical model. A negative binomial regression model with a non-homogenous gamma distributed mean can compensate for over dispersion in PROC GENMOD for a geo-sampled dataset of unvaccinated measles count in LULC forecasting vulnerability model. The regression residual rendered from the compensatory model cannot quantitate clustering tendencies in urban and non -urban geo-classified supervised or unsupervised LULC dataset  [8].
Furthermore, a first-order autocorrelation cluster models in SAS can provide a probabilistic framework to predict the unobserved elements of a dynamic landscape process, such as true measles incidence in an urban commercial environment, when provided with observed elements of the dynamic process, such as reported measles cases and vaccination coverage. Traditional Measles data analyses often study the form, direction, and strength of the relationship exhibited by two quantitative variables measured on a single set of n observations. A scatterplot in SAS can visualize this relationship, with a conventional correlation coefficient describing the direction and strength of a straight-line relationship of the overall landscape measles analyses pattern. Positive spatial autocorrelation means that geographically nearby values of a variable tend to be similar on a measles land covariate map: high values tend to be located near high values, medium values near medium values, and low values near low values.
The eigenvector filtering approach is a non-parametric technique that removes the inherent autocorrelation from generalized linear regression models by treating it as a missing variable  [10]. However, the purpose of a non-parametric spatial filtering technique is to control for autocorrelation with a set of proxy variables rather than to identify a global autocorrelation parameter for a spatial process  [10]. The basis for this procedure is the decomposition of the Moran's I into orthogonal and uncorrelated measles unvaccinated risk related geo-referenced map-able pattern components in SAS (e. g. , AUTOREG, PROC VARIOGRAM). This orthogonal decomposition would make the latent spatial correlation represented by the geographic configuration of important unsupervised local government area stratified clusters ("hot spot") as described by a given spatial weights matrix.
Research on the application of GIS and remote sensing for measles vaccination surveillance is sparse in Nigeria. Additionally, no study had quantitated clustering tendencies for measles non-vaccination in Nigeria. However, Bharti et al. employed spatial techniques in determining the role of epidemiologic connectivity and seasonally variability for measles incidence in Nigeria. Therefore, we intend to apply spatial autocorrelation techniques to qualitatively quantitated the clustering tendencies in our experimental dataset to help local measles managers focus supplementary immunization activities to high risk communities. Our research objectives were: 1. To construct Poisson regression model to determine anthropogenic related covariates associated with each diva-GIS land cover map.