dataesr/scientific-paragraphs-categorization huggingface dataset:
The pyoverdine peptide chain was predicted using the non-ribosomal peptide synthase substrate predictor NRPSsp (http://nrpssp. com/execute. php), NRPSpredictor2 (http://nrps. informatik. uni-tuebingen. de), and PKS/NRPS programs (http://nrps. igs. umaryland. edu/) for pyoverdine synthetases. TonB-dependent transporters were identified by selecting TonB annotated proteins from the genomes of the microorganisms used in this study using the RAST Annotation Server (http://rast. nmpdr. org/rast. cgi) and the set of sequences deposited in Genbank (https://www. ncbi. nlm. nih. gov/genbank/). We selected TonBtransporter proteins bearing the characteristic domains of siderophore transporters by application of InterPro (http://www. ebi. ac. uk/interpro/) and Blastp web sites that provide functional analysis.
The different phylogenetic origins of the pyoverdine locus in the P. putida clinical strains and the peculiarities of some of these strains suggest that either the clinical environment does not represent a high selective pressure in terms of iron scavenge and that the human environment is very diverse and adaptation of the different strains is mainly driven by these different conditions, or that P. putida strains are newcomers in hospital environments and that the ironscavenge systems have had not enough time to evolve common mechanisms. Union through project BIO2010-17227 of the Spanish Ministry of Economy and Competitivity. The work in Abengoa Research was funded by H2020 grant Empowerputida number 65703. We thank Gwenaëlle Graulier and Caroline Choufa for their technical assistance.
d. , solvent tolerant n. d. , solvent tolerant Water, toluene degrader Waste water, solvent tolerant Waste water, naphthalene degrader Strains P. putida HB13667 P. putida HB3267 P. putida JLR11 P. monteilii SB3101 P. taiwanensis SJ9 P. putida S16 P. putida KG-4 P. putida NBRC14164T P. putida GB-1 P. putida HB8234 P. putida SJ3 P. putida OUS82 P. putida JCM18798 P. putida HB4184 P. putida B001 P. putida KT2440 P. putida BIRD-1 P. putida PCL1760 P. putida Idaho P. putida S12 P. putida F1 P. putida DOT-T1E P. putida ND6 Subclade A 8 Subclade B Subclade C Subclade A Subclade B Clade I Clade II Clade III Clade IV PRJNA278654Asp, Aspartic acid; OHAsp, threo-β-hydroxy-aspartic acid; Dab, diamino-butanoic acid; Gly, glycine; Lys, Lysine; εLys, Lys linked by its ε-NH 2 ; Orn, Ornithine; cOHOrn, δN-hydroxy-ornithine; 5hOrn, 4,5-hydroxy-L-proline; Ser, Serine; Thr, Threonine; Xxx, undetermined.
Discussion and Conclusion This case study demonstrated the value of pelvic-spinal analysis for the measurement of sagittal alignment and for the evaluation of the effects of treatment. In this case, the treatment involved onabotulinumtoxinA injection of the hip flexor muscles along with a soft lumbar brace. Pelvic-spinal analysis showed significant improvements in sagittal alignment posttreatment. When carrying out pelvic-spinal analysis, it is important to radiograph the whole spine and pelvis from C2 to the proximal femur using a variety of software (eg, Optispine, Keops), with the patient in standing position. Pelvic angles (pelvic incidence, pelvic tilt, and sacral slope), type of lumbar lordosis (high or low, deep or shallow), and the position of a vertical line from C7 on the sacral end plate should be measured in order to fully characterize the pelvic-spinal balance.
Patients and methods Inclusion criteria In 2012, all ESCADHEM patients consecutively treated with bortezomib injection for multiple myeloma starting in January 2009 and ending in December 2011 were contacted to participate in this retrospective study. Of the 88 patients who received at least one bortezomib injection, 54 patients gave their consent and were able to declare their mode of transport. All patient data were collected from electronic and paper hospital files. The first cycle of bortezomib and the first injection of each cycle were administered via OH care. The decision to administer each subsequent injection at home or at the outpatient care unit was determined by the healthcare professionals. Cost assessment In France, there are several organizations for the administration of anticancer chemotherapy which include OH care and HaH.
Acknowledgments We acknowledge the contribution of the following institutions and associations for their help in this study:  Centre Hospitalier Universitaire (CHU) de Limoges, Agence Régionale de Santé (ARS) Aquitaine Limousin Poitou-Charentes, Unité de Recherche Clinique en Hématologie (URC-H du CHU de Limoges) , and  Limousin Association pour le Développement et la Recherche en HEmatologie Clinique (LADRHEC). We would like to thank  Nicole Fort  for her help in writing the earlier version of the manuscript and  Rachel Tipton  for English proofreading and light editing. We also thank the patients and their families for their contributions to this work.
Dating of the arc yield a Late Cretaceous Age (84-74 Myr), and petrographic and geochemical evidence suggests it is comprised of igneous bodies of felsic to intermediate composition, which intrude the LMV continental crust, originated in a subduction setting. The origin of the LMV continental crust seems to be related to the continent-continent collision (consolidation of Pangaea) during the Permian (300 Ma ago), and to the post-Alleganian extension event of Triassic age (232 Ma ago).
INTRODUCTION The tectonic evolution of NW South America, between the Permian and Late Cretaceous-Paleogene, was related to the complex interaction of South American, Caribbean (Plateau and proto-Caribbean) and Farallones plates. The Permian recorded the Pangaea consolidation and the Allaganian Mountains generation  (Condie & Sloan, 1997; Yañez, Ruiz, Patchett, Ortega-Gutierrez, & Gehrels, 1991). During the Triassic, the post-Alleganian event marked the rifting onset between Gondwana-Laurentia. Meanwhile, for the Jurassic-Late Cretaceous intra-continental rift (E. g. Cediel, Shaw, & Caceres, 2003 Pindell & Kennan, 2009)  or extensional subduction scenarios (E. g. Aspden et al. , 1987; Villagómez et al. , 2011; Cochrane et al. , 2014)  have been set out. Two tectonic models explain the geological evolution in the area after these events.
fissures. Other locations where magmatic crystallization has been recorded from the Late Permian to the Early Triassic are: the SNSM (288. 1 ± 4. 5 Myr and 276. 5 ± 5. 1 Myr; Cardona et al. , 2010), the North of the CC (248 ± 17 Myr; Ordoñez and Pimentel, 2001a, b), and the Puquí Complex (Tarazá, station 30325), which tonalite sample exhibits an age of 231. 8 ± 4 Myr (Figure 6d; Annex 3). This first tectono-thermal event occur in the frame of the Pangaea´s collision and Allaganian.
ACKNOWLEDGEMENTS This research was funded by  Instituto Colombiano del Petróleo ICP-Ecopetrol S. A  on the framework of the project " Estudio de rocas pre-Neógenas en el Caribe  Colombiano a través de cronoestratigrafía, geoquímica, análisis de proveniencia y termocronología". We thank  Gabriel Mendoza ,  Carlos Acuña ,  Edward Salazar ,  Gyna Rincón , and some other Colombian geoscientist for sample collecting, field observations and beneficial discussions. Additional support during the data synthesis phase of this research was provided by  Mauricio Pinzón  and  Davis Guerra , whom organized this valuable information in a very practical georeferenced database. L. F. Peña  and staff of  Geologic Samples Laboratory at the Instituto Colombiano del Petróleo  are also acknowledged for their help with sample preparation.
The calorimeter isolation energy is corrected for pileup by the subtraction of the average energy per unit area of (η, φ), computed for each event using the FASTJET package  [32]. For the two electrons from the Z boson decay (in the ee * → eeZ → 4e and µµ * → µµZ → 2µ2e channels), the tracker isolation and calorimeter isolation for each electron are modified to remove the contribution of the other electron  [33].
Jets and Z → jj tagging Hadronic jets are reconstructed from the list of particle flow (PF) candidates that are obtained with the PF algorithm  [34] , which reconstructs and identifies single particles by combining information from all sub-detectors. Charged PF constituents not associated to the primary vertex are not used in the jet clustering procedure. Good PF candidates are clustered into jets using the Cambridge-Aachen (CA) algorithm  [35]  with a distance parameter R = 0. 8. An areabased correction is applied, to take into account the extra energy clustered in jets from neutral particles in pileup interactions, using the FASTJET software package  [32]. Jet energy corrections are derived from the simulation, and are validated with in-situ measurements using the energy balance of dijet, photon+jet, and Z+jets events  [36].
Sources of background Several SM processes contribute to the expected background for the various channels. Those contributions are discussed in the following. • * → γ channels: Drell-Yan (DY) production is the most important background for the * → γ channels, mostly originating from production of a photon in association with a Z, which has a very similar signature to the signal. It is simulated using SHERPA1. 4 [48]  and its production cross section is normalized using a NLO cross section calculated with the Monte Carlo (MC) program MCFM6. 1&6. 6  [49, 50]. Subleading contributions to the background arise from diboson events with an additional high energy photon or events in which an electron is misidentified as a photon. Such events are simulated using PYTHIA6. 4 [23]. Background contributions also arise from events in which an additional prompt photon is produced together with a top pair (tt+γ).
The numerator sample can have a contribution from isolated true photons. The contamination is estimated using the distribution of energy-weighted shower width computed in units of crystal lateral dimension. The shower shape for isolated true photons is obtained from a simulated sample. The shape of non-isolated photons is obtained from data by considering a background dominated region (side-band region) of the photon isolation variable. The true photon fraction in the numerator is estimated by fitting these two different shower shapes (signal and background templates) to the shower shape distribution of the numerator sample. The photon misidentification rate is calculated in photon E T bins. It decreases with increasing photon E T and is at most of the order of a few percent. As an example, for photons of E T = 100 GeV the jets-to-photon misidentification rate is about 1.
5%. In order to estimate the contribution of misidentified photons to the selected events, the misidentification rate is applied to a subsample of data events that satisfy all selection criteria listed in Section 4. 3 except that the photon candidate must pass a looser set of shower shape requirements and fail one of the photon isolation criteria. There are two main sources of uncertainties in the determination of jet to photon misidentification rate. First, the shower shape of non-isolated photons is obtained from data in the side band region: changing the side band region results in some change in the template for non-isolated photons. Second, the probability for a jet to fake a photon is different for quark and gluon jets and the fraction of jets due to quarks may not be the same in the sample used to obtain the fake rate and in the sample where this fake rate is applied.
Management All procedures involving animals were approved by the Institutional Animal Care and Use Committee of Adnan Menderes University. The birds were obtained from commercial hatchery, Ege Tav, İzmir, Turkey. Bird sexing and vaccinating were carried out at the hatchery without administration of any coccidia vaccine. The chicks were vaccinated against infectious bursal disease virus and Newcastle disease virus with Gumbopest (Merial SAS R , Lyon-France). The experimental house was divided into 36 pens (replicate) of equal size, each having an area of 3. 00 m 2 , with fresh wood shavings as bedding with a thickness of approximately 6 cm on a concrete floor. Each replicate pen was equipped with three nipple drinkers, two tube-type feeders and electrical heater. From d 1 until 5, feed was also supplied on trays, directly placed on the litter.
Statistical Analysis All data were analyzed using SAS (SAS Institute Inc. , Cary, NC). Data were subjected to ANOVA by using the GLM procedure. The pen was the experimental unit and a completely randomized statistical design was followed. Duncan's multiple-range test was carried out to detect differences among treatments. All differences were considered significant at P < 0. 05. Since the oocyst yields and lesion scores were not distributed normally, the Kruskal-Wallis non-parametric analysis (SAS, 2001) was employed. Arcsin transformation was applied to the percentage values (i. e. , mortality rate) before testing for differences.
, 2008; Franks et al. , 2014). Numerous dendroclimatic studies report that growth-climate relationships are modulated by environmental variables such as regional precipitation or temperature regimes, soil variables and forest stands characteristics (e. g. ,  Babst et al. , 2013; Gewehr et al. , 2014; Housset et al. , 2015; Brienen et al. , 2016). The effect of those ecological gradients could be confounded with a genetic effect on the growth responses to climate and need to be controlled. In this study, we test for the correlation between population genetic structure and tree-rings responses to climate, while controlling for ecological gradients through an approach combining model selection and variance partitioning. To test this approach, we took advantage of two existing datasets on the boreal tree Thuja occidentalis L.
(eastern white-cedar, hereafter "cedar"). The first dataset results from a study of stem annual radial growth carried out at its northern boreal margin evidencing an effect of tree size, soil, regional precipitation, and regional temperature  (Housset et al. , 2015). The second is a genetic study conducted in the same study area that investigated the cedar neutral genetic structure based on neutral markers  (Xu et al. , 2012). The objective is to test for a correlation between neutral population genetic structure and tree-rings, while accounting for environmental variables, at both the intrapopulation and the inter-population levels.
Genetic Data Genetic structure variables were inferred from a recent study  (Xu et al. , 2012) , which analyzed the cedar neutral genetic structure of our 24 populations. For each population, 15-30 trees (Table  1 ) were randomly selected and used for microsatellite genotyping. The genetic structure was assessed by genotyping at four polymorphic microsatellite loci derived from the phylogenetically closed species Thuja plicata Donn ex D. Don (TP9, TP10, TP11, and TP12). Xu et al. (2012)  selected the optimal combination of markers (available at that time) delivering accurate estimates of cedar genetic diversity. The four microsatellite loci showed high discriminating power, with a combined probability of exclusion (PE) greater than 99% and a joint probability of genetic identity (PI) smaller than 10 -5  (Peakall and Smouse, 2012).
Several indicators of intrapopulation genetic variability were used in this study. The expected heterozygoty (H e ) and the average within-population pairwise relatedness between each pair of trees in a population (Gen relatedness ) statistics were computed with the software Genalex 6. 5  (Peakall and Smouse, 2012). H e is a proxy of gene diversity, with values ranging from 0 to 1 for low to high diversity. The allelic richness (AR), representing the alleles number corrected for sampled population size effect, was calculated using the software FSTAT2. 9. 3. The inbreeding coefficient (F is ) was calculated for each population using the software FSTAT2. 9. 3. Genetic variability among populations at the landscape scale was analyzed through a clustering approach using the STRUCTURE v. 2. 3. 2 software  (Pritchard et al.
Tree-Ring Data and Growth-Climate Relationships The present study focused on the variability in growth sensitivity to climate among populations, but not on the growth itself. As response variables, we used the correlation matrix between climate and growth relationships described in  Housset et al. (2015). This matrix was established on the correlations between monthly climatic variability and annual radial growth of cedar from 27 sampling sites, including the aforementioned 24 populations. Annual radial growth increments were obtained and analytical approaches were conducted using classical dendroclimatological procedures. The following paragraph summarizes Housset et al. 's approach used to derive the correlation matrix, and the main variables selected. During summer 2010, up to 30 cedars were sampled in each site for dendroclimatic analysis.
The genetic and dendrochronological samplings were conducted separately but on the exact same locations. Only dominant and codominant trees were sampled to lower the biases due to competition on tree-ring series. Two cores per tree were collected at 1. 3 m above ground, sanded, measured, and cross-dated. To maximize the high-frequency growth responses associated with yearto-year climatic variability, each raw ring-width time-series was detrended using 60-year splines with a 50% frequency response  (Cook and Kairiūkštis, 1990). After removing the first order autocorrelation, a mean Tree Growth Index (TGI) chronology was computed for each population using the R package "dplR"  (Bunn, 2008). These residual chronologies were statistically tested against climatic time-series using bootstrapped correlation and response function analyses over the 1953-2010 period covered by meteorological observations  (Biondi and Waikul, 2004).
Correlation coefficients are computed between a site residual chronology and the matrix of monthly climatic data in order to represent the average relationship for the studied period. On the other hand, PCR (also known as response function analysis) is a multiple regression technique that uses the principal components of monthly climatic data to assess growth-climate relationships. Climatic time-series were monthly mean temperatures and total monthly precipitation, interpolated from Environment Canada meteorological records at each of the sampling sites using the software BioSIM for 1953-2010  (Régnière and Bolstad, 1994). This analysis revealed that cedar interannual growth variability was positively correlated with May temperature of the current year, but negatively correlated with June temperatures of the current year and with August temperatures of the year preceding ring formation.
Variability within a Population Growth synchronicity was evaluated between the trees of each population. A mean chronology was computed for each tree from the spline-detrended chronologies of all its cores. The average pairwise correlation between single-tree chronologies within a population (r bt ) was calculated  (Cook and Kairiūkštis, 1990). This statistic, representing the average correlation between trees, was used as a proxy of growth synchronicity. It is inversely linked to growth variability. To remove biases due to population size differences, we resampled eight trees in each population and estimated this parameter by averaging it over 1000 repetitions. For each resampling, the r bt was computed using the R package "dplR"  (Bunn, 2008). Growth synchronicity between trees could be influenced by various factors such as differences in DBH or by genetic variability within a population.
The overall comparison of environmental vs. genetic variables was analyzed through the calculation of multivariate variance partitioning  (Legendre and Legendre, 2012)  on the seven monthly climatic variables using the R package "vegan"  (Oksanen et al. , 2013). Significance of the variance components were assessed through redundancy analysis (RDA) and partial redundancy analysis (pRDA) with 99 999 permutations  (Legendre and Legendre, 2012). RDA are multivariate regressions where a set of response variables (here the growth-climate correlation matrix) are regressed against a set of explanatory variables. pRDA are RDA where the effect of the explanatory variables is assessed while controlling for another set of variables. pRDA can be particularly useful to test for the effect of genetic variables while controlling for the effect of local climate or soil conditions for example.
, 2013; Moreira et al. , 2014). Further,  Fan et al. (2008)  reported local adaptation to precipitation for T. plicata, for both height increments and transpiration efficiency traits. Because our genetic dataset only included neutral markers, (as in  Bosela et al. , 2016) , further investigations including adaptive genetic markers are needed to test for causal relations between genetic and growth responses to climates. However, our findings provide interesting hypotheses to be tested in future research on cedar adaptation to climate.
ACKNOWLEDGMENTS We thank  Aurélie Terrier  and  Nathalie Isabel  for their useful comments on preliminary versions of this manuscript, as well as  Cécile Leroy ,  Mélanie Desrochers ,  Danielle Charron ,  Véronique Paul ,  Pamela Cheers ,  Isabelle Lamarre , and  Daniel Lesieur  for their valuable help. This study was conducted with the financial support of the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through its  CREATE program in Forest Modeling Complexity and of the French program Paris Nouveau Monde  of the heSam consortium (Ph. D. grants to JH). This research was also funded by a  NSERC  strategic grant ( STPGP336871 ) to FT. The research was carried out within the framework of the  MONTABOR International Associated Laboratory (LIA France-Canada   CNRS-UM2-EPHE-UQAT-UQAM-UQAC ) and the  European Union   IRSES NewForest program.
g. , genomes of pathogenic organisms) discovery from large synthetic and real metagenomics datasets. Initially designed for the "assembly-free" analysis of individual metagenomic samples, we demonstrate their extension to setups involving multiple samples via the usage of the "alignment-free" d 2 S statistic to relate clusters across samples, and illustrate how the clustering modules can otherwise be leveraged for de novo "pre-assembly" tasks by segregating sequences into biologically meaningful partitions.
To increase the amount of sequence information extractable from raw datasets, we developed and evaluated an "assembly-free" analytical pipeline combining frequency (coverage) and compositional features to partition unassembled sequences in a fully unsupervised way, and evaluated the accuracy and usefulness of the resulting sequence sets in several ways (see Methods): i) via extensive benchmarking against state of the art methods, using synthetic metagenomic datasets made of up to 700 distinct genomes under realistic abundance and sequence error models, ii) by evaluating the newly developed clustering modules on real metagenomics datasets derived from both environmental bioremediation projects and human microbiomes, iii) by correlating the output clusters, treated as variables, with metadata labels (i.
e. , health status) for a virtual cohort of 50 microbiomes of 100 organisms each, originating from 40 healthy and 10 sick individuals, the latter being spiked at low levels with a mock "pathogenic" strain, iv) by complementing this virtual cohort experiment with an analysis of a real-world STEC O104:H4 outbreak dataset  [5]  based on our compositional binning module, in order to evaluate its ability to identify and partition sequences from the diseasecausing genome.