dataesr/scientific-paragraphs-categorization huggingface dataset:
Image Acquisition and Analysis The hybridized fluorescent slides were viewed under a Leica DM6000B microscope. Images were acquired under identical conditions at objective X63. For each sample, at least one hundred cells were counted. Analyses and quantifications of these samples were performed using Image J software. Figure  S4  JMJD6/ERa interaction in human breast cancer cells. ZR75-1 (A), and Cama-1 (B) cells were analyzed for ERa methylation and JMJD6/ERa interaction. Immunoprecipitation of JMJD6 from extracts of estrogen-deprived cells (t = 0) stimulated with 10 28 M E 2 for the indicated times was performed followed by western blotting with antibody against ERa and JMJD6. On the same extract metERa was analyzed by immunoprecipitation with the anti metERa revealed with an anti-ERa. PRMT1 expression was also analyzed by western blotting.
Sample Preparation Crystalline l-histidine hydrochloride monohydrate-d 7 and a-oxalic acid dihydrate-d 6 (see Figure  1 ) were dissolved in 2 H 2 O (Eurisotop, 99 % deuteration) at room temperature. Recrystallisation was conducted in a dessicator to avoid absorbing light water from the atmosphere. Adding silica gel accelerated crystallisation. After two days, the dry crystals had grown to a fine powder and were transferred to a 4 mm rotor (ZrO 2 ). Recrystallisation with 2 H 2 O led to the replacement of exchangeable protons by deuterons in l-histidine and a-oxalic acid, including those of the carboxylic and amine groups. Samples were stored at room temperature in an airtight box to avoid exchange with atmospheric water. For both compounds, atomic coordinates obtained from the Cambridge Structural Database (CSD) were used to determine the internuclear distances.
Data Analysis All raw experimental data were processed by using the TopSpin program (Bruker) and further analysed with a home-written program based on Mathematica 8. This program was developed for the deconvolution of the different 2 H sites for each spinning sideband and to analyse the relaxation times T 1Z and T 1Q by using a global fitting approach. The Arrhenius-type studies of the relaxation rates and of the SQ and DQ linewidths have also been performed with a home-written Mathematica program. All programs are available upon request.
The deconvolution was performed with a home-written Mathematica program and the signal was normalised with respect to the most intense signal for each compound (i. e. ND 3 + for l-histidine and COOD for a-oxalic acid). The shape of the envelope of the spinning sidebands thus obtained is preserved throughout the full temperature range exploited in this work. The spinning sideband envelope has also been analysed with the SOLA routine of the TopSpin program (Bruker) to extract the quadrupolar tensor parameters for each deuterium site as a function of temperature; the tensor parameters thus obtained are tabulated in the Supporting Information, SI. In principle, the spinning sideband patterns contain the complete information on the anisotropy and asymmetry of the quadrupolar interaction. In an exhaustive study comparing the reliability of the determination of tensor parameters in solid-state NMR experiments under static and spinning conditions,  [34]  the best spinning frequency for the accurate determination of the quadrupolar interaction has been found to be near n rot /C Q = 0.
4 MHz. Given that the absence of a well-defined minimum renders the interpretation of the relaxation data challenging, the activation energies derived from the T 1Z and T 1Q (Table  5 ) should be taken with caution. The relaxation rate constants of SQ-and DQ-coherences can be further combined with the relaxation rate constants of the Zeeman and quadrupolar orders. The four relaxation rate con-stants measured at a given static magnetic field depend on the spectral density function evaluated at three distinct frequencies. Although of debatable applicability to solids, it is interesting to check the validity of the consistency relationship [Eq. (  2 )]  [17, 32]  originally derived for liquids in the fast motion limit (Redfield): R fast DQ ¼ 1 2 R 1Z þ 1 6 R 1Q ð2Þ If we assume that this relationship holds, we can calculate the relaxation rate constants for DQ-transitions induced by fast motions, R fast DQ , and compare them to the experimental values R DQ measured at 61.
Acknowledgements The authors thank  Quentin Llopis  for his contributions in the early stages of this work,  Nicole FaurØ  and  Smita Odedra  for evaluating some Jeener-Broekaert experiments,  Philippe Pelupessy ,  Daniel Abergel ,  Piotr Tekely ,  Christian Bonhomme ,  Joanna R. Long  and  Robert Graf  for stimulating discussions. NMR spectroscopic calculations were performed at the IDRIS supercomputer centre of the CNRS (Project  091461 ). M. C. -H. , G. B. and L. D. acknowledge financial support from the  Centre National de la Recherche Scientifique (CNRS, France) , the  Agence Nationale pour la Recherche (ANR, France)  (grant  ANR-09-BLAN-0111 ) and the  European Research Council (ERC). Financial support from the  IR-RMN-THC   FR3050  CNRS for conducting preliminary experiments is gratefully acknowledged.
We compared the powers of the likelihood ratio test (LRT) and the Pearson correlation test (CT) from empirical Bayes estimates (EBEs) for various designs and shrinkages in the context of nonlinear mixed-effect modeling. Clinical trial simulation was performed with a simple pharmacokinetic model with various weight (WT) effects on volume (V). Data sets were analyzed with NONMEM 7. 2 using first-order conditional estimation with interaction and stochastic approximation expectation maximization algorithms. The powers of LRT and CT in detecting the link between individual WT and V or clearance were computed to explore hidden or induced correlations, respectively. Although the different designs and variabilities could be related to the large shrinkage of the EBEs, type 1 errors and powers were similar in LRT and CT in all cases.
Population pharmacokinetics (PK) is increasingly used in drug development and is based on nonlinear mixed-effect models (NLMEMs). 1 Several software algorithms developed to estimate the parameters of these models have been compared, as by Plan et al. 2  The first and most popular software is NONMEM and, since version 7, several estimation algorithms have been available, such as first-order conditional estimation with interaction (FOCEI) and stochastic approximation expectation maximization (SAEM). Gibiansky et al. 3  compared the performance of NONMEM estimation methods in simulated examples of a target-mediated drug disposition model using either default options or by designing the options to get the best possible results for each algorithm ("expert" options). They demonstrated the importance of the estimation options in the algorithm performance by comparing standard errors of NONMEM estimates with those predicted using PFIM 3.
2 optimal design software. 4 The SAEM algorithm provided estimates similar to those of FOCEI. For a given model, when population parameters are estimated, individual parameters from individual observations can be estimated by a Bayesian approach using the maximum a posteriori method. If the data are from the individuals used to estimate population parameters, the same approach is used, but it is an empirical Bayes estimation. These individual parameter estimates are useful for investigating the influence of the individual's baseline characteristics on the individual parameters. 5 opulation analysis allows the use of sparse sampling, but the sampling design influences the precision of population estimates. 6 The informativeness of a data set in parameter estimation is a function of the number of subjects and of the number and timing of the samples.
However, we note that the slight differences in experimental conditions between genotyping RH clones on the BovineSNP50K array and on the OvineSNP50K array. It could bring certain systematic errors when merging the two datasets, which we cannot assess, since our RH vectors have not been validated by replicated genotyping. However, the availability of negative control SNPs within each clone offers a replication across clones. For increased robustness, we applied three approaches to remove possible genotyping errors before constructing the integrated robust maps.
Genome map construction and characterization Briefly, for each chromosome, the analysis of RH mapping was done in three steps: 1) We portioned 53,075 mapped SNPs (mSNPs) according to their assigned chromosome on CHIR_1. 0. For each of the 30 chromosomes, a linkage group was established using RH data alone, which removed 13 unlinked markers. 2) We then built comparative RH maps with all remaining markers, using prior information on their ordering from assembly CHIR_1. 0. We further excluded 1,918 markers, which possibly have errors or have poor information in RH vector using three criteria (see Methods for details). This resulted in 51,144 SNPs being positioned on 30 chromosomal LKH maps. 3) We extracted a subset of markers from the LKH maps for which the ordering was strongly supported by RH data.
This fragmentation does not affect gene discovery, polymorphism analysis and sequence comparison between closely related species but it greatly limits the study of genome structure and chromosomal evolution. The comparative RH approach incorporated the prior knowledge of marker orders in CHIR_1. 0 into the statistical model. It has the key property that a different ordering than that of the reference order will be accepted as an alternative order only  when the RH data is informative enough. As a consequence, the robust maps allow pinpointing the regions where the assembly order disagrees with an RH map order that is strongly supported by the RH data, so this approach allowed improvements over classical a framework-based mapping strategy  [19]. The RH maps presented here offer a powerful complement to the available genetic/cytogenetic maps with very limited resolutions  [2, 6, 20]  and the genome sequence map  [7].
Finally, the p-value that a (non-negative) SNP is not retained was calculated as the proportion of Imax in the negative SNPs that exceeds the observed Imax. If none was found, we set the p-value at 1/Nneg. After calculating p-values for all observations, we estimated their corresponding q-values using the Q-value R package  [18]. This approach allows controlling the false positive and false negative rates and provides an estimate of the proportion of true null hypotheses in the data, which is in our case an estimate of one minus the panel retention fraction. We called "retained" data points at an FDR threshold of 1% and removed SNPs which retention fraction was either greater than 50% or lower than 10%. Furthermore, one RH clone (#73) showed a very high rate of retention (>90%) and was therefore removed from the analysis.
Map construction Our pipeline of RH mapping is very similar to that recently used for recently building porcine RH maps  [13]. The Carthagene software  [12, 14, 54]  was used to compute efficiently marker ordering for candidate maps with reference maps. This approach is based on a probabilistic Bayesian model integrating the usual RH probabilistic model with a probabilistic model of breakpoint occurrences with the reference order. In this probabilistic model, breakpoints induced by chromosomal rearrangements are considered as rare events, following a Poisson law. Meaning, the model considers that genome assembly errors create rare spurious breakpoints between the RH map order and the current goat genome assembly order  [12]. The map construction process was conducted in parallel to the goat genome assembly.
As our first goal to build RH maps was to help ordering scaffolds of the goat genome, only SNPs that could be mapped unambiguously to a working draft of the goat genome were used to build RH maps. These summed up to 54,318 SNPs, with 35,203 SNPs from the Ovine array and 19,115 SNPs from the bovine array. We portioned 53,075 mapped SNPs (mSNPs) according to their assigned chromosome and 1243 unassigned SNPs (uSNPs) remained on assembly CHIR_1. 0. For each autosome, we determined linkage groups among mSNPs, based on RH data alone. Of the 53,075 SNPs that generated RH vectors, 53,062were assigned to 30 single linkage groups based on two-point analysis at a LOD threshold of 10. In these linkage groups 13 markers did not show enough evidence for linkage. Each RH linkage group was assigned to a chromosome based on homologous comparison between RH markers and the goat genome.
(1)  We manually increased the LOD threshold in a two-point analysis to remove minor marker groups (<5 markers) to remove possible error vector. (2) A primary map was first generated with Carthagene by converting the RH data into a TSP and solved using a LKH heuristic method (lkh1 1). The initial RH maps were compared to the assembly to observe the general discrepancies, and were used to inspect inter-markers intervals with extremely long distances (>25 cR). In this step, we manually removed possible questionable markers, most of which were located on map ends. (3) The command of find_errors in RHMAPPER  [19]  was used to flags marker/hybrid assay results that are likely to be the result of laboratory error, where the order for these results set to the primary map. A threshold of 3 was specified in the command i.
e. reporting that this result is 1000:1 more likely to represent a laboratory error than a true result. Once again, we converted the RH data into a TSP and solved using the LKH heuristic method, and constructed the LKH map. A novel approach  [14]  was applied next to build maps with a highly reliable ordering, called robust maps. Briefly, the principle of the method is to estimate a posterior distribution of marker ordering using an MCMC approach and then to extract from this distribution a subset of markers that show the same ordering across maps of high probability. We performed 5000 MCMC iterations and discarded the first 1000 as burning iterations in the command of Carthagene software, mcmc, using the LKH maps obtained above as starting orderings. We extracted robust maps from the posterior distribution using the metamap software  [14].
Genome map comparisons and gene comparisons Map discrepancies were determined by comparing the RH map with target genome data including goat scaffold sequence, goat assemblies (CHIR_1. 0 and CHIR_1. 1) and cattle genomes (BTA4. 2 and UMD3. 1). The probe sequences of markers in RH maps aligned with these target genome maps using BLASTN (version 2. 2. 21) with E-values < 1 × 10 -10. We also conducted synteny-based comparisons between the goat genome sequence and other mammalian sequences. The comparisons were performed using NUCmer  [55]  with parameter "-c 500" (with cattle) or "-c 150" (with other mammals). The assignment of assembled sequences to chromosomes was done based on a best alignment and majority rule. The Interquartile Range method was used to filter out suspicious markers and compute the scaffold positions on chromosomes.
The Least Square method was used to compute the orientation of the scaffold that had multiple markers aligned to them. Orientation was considered forward if the slope value was positive, reverse if the slope value was negative and unknown if the slope was close to be 0. For the reconstruction of the evolutionary breakpoints, the Evolution Highway program  [33, 39, 40]  was used to compare more mammalian genomes including human, mouse, dog, horse, cattle, rat and goat RH maps. The data of orthologous gene pairs between cattle genome and other mammalian genome for generating homologous synteny block was downloaded from the Biomart database. We used lastz program and lastz-based Cassis program  [56]  to precisely detect sequence homology and genomic rearrangement breakpoints. RepeatMasker was run against both libraries separately to identify homologous repeats, which were classified into known classes of repeats  [57].
SDs was identified using the identical whole-genome sequence comparison method  [58]. The characterized elements of sequence were graphed by circus  [59]. For phylogenetic analysis of gene family, the sequence of goat ANKRD26 was blasted (BlastP) against the NCBI-nr database. In the results, genes with less than 40% homology or 40% coverage were filtered out. The annotated genes of ANKRD26 in public protein databases also were retrieved. Multiple sequence alignment was performed and the Neighbor-joining tree were constructed based on Dayhoff PAM matrix method with a bootstrap value of 1000  [60]. and 6 locate in homologous synteny (blue cross). Most of the members expanded specifically in species and lineage specific evolutionary breakpoints or in centromeres. The duplication of ANKRD26 gene follows the patterns of convergent breakpoint reuse through chromosome evolution.
Acknowledgements We thank  DNA LandMarks ,  Inc. for its contributions of experimental supplement for genotyping the RH panel and its technical assistance form  Dr. Tunping Yu. We thank  Ruheena Javed  for reviewing the manuscript. This work has been supported by  National High-Tech Research and Development Program of China  (No. 2013AA102506 ), the International collaborating grants from  National ministry of science and technology projects of China  ( NO. 1111 ), the  973 program  ( 2013CB835200 ) of China, the  IAEA's Coordinated Research Project  (Contract No. 16087/R0 ), the  FP7-3SR  ( FP7-KBBE-2009-3 : No. 245140 ), and the  Fundamental Research Funds for the Central Universities.
SOPE Genome Annotation The assembled genome sequence of SOPE was submitted to the National Center for Biotechnology Information (NCBI) Prokaryotic Genomes Automatic Annotation Pipeline (PGAAP) for annotation. The resulting candidate open reading frames (ORFs) were then aligned to the HAMAP database  (Lima et al. 2009 ) and classified according to their percent protein identity and length. ORFs that had more than 90% protein identity and more than 80% of the length of the database match and did not contain frameshifts or premature stop codons were classified as intact ORFs. The remaining candidate ORFs were then classified as intact or pseudogenes by generating a Blast database from the top HAMAP result for each candidate ORF, then two nucleotide query files were generated: one based on the PGAAP annotation and another including 2,500 nucleotides on either end of the candidate ORF.
BlastX searches against the database generated from the top HAMAP result were then performed on each query file, and the output was parsed to search for extended protein matches that indicated either truncated candidate ORFs or possible frameshifted candidate ORFs. The annotation was then aligned to the draft genome sequence and annotation of strain HS using the Smith-Waterman algorithm implemented in cross_match  (Gordon et al. 1998). Custom Perl scripts were used to identify any ORFs not identified by PGAAP as well as refine and classify ORFs as intact or pseudogenized. ORFs not identified by PGAPP but spanning more than 99% of the orthologous strain HS ORF or more than 90% of ORFs smaller than 300 nucleotides in size were annotated as intact only if no inactivating mutation were present.
Additional manual curation was preformed with extensive use of the Bacterial Annotation System (BASys)  (van Domselaar et al. 2005)  and EcoCyc databases  (Keseler et al. 2013). IS elements were identified and annotated using ISSaga  (Varani et al. 2011). The resulting annotation was also manually curated and adjusted in Artemis  (Rutherford et al. 2000).
Nucleotide Substitution Rates and Predicted Cryptic Pseudogenes Orthologous genes in SOPE, So. glossinidius, and strain HS were determined using OrthoMCL  (Li 2003)  with recommended parameters  (Fischer et al. 2002). Before input into OrthoMCL all pseudogenes, IS elements, and phage sequences were removed from the sets of SOPE and So. glossinidius genes. The output of OrthoMCL was then screened, and any nonorthologous genes or low-quality matches were discarded, and a total of 1,601 strain HS orthologous genes were obtained for SOPE and 1,734 for So. glossinidius. Sequence alignments for each orthologous gene pair was generated using MUSCLE  (Edgar 2004). Pairwise estimates of the synonymous (dS) and nonsynonymous (dN) substitution rates were obtained from the YN00 program of the PAML 4. 6 package  (Yang 2007).
Functional Analysis The Artemis Comparison Tool  (Carver et al. 2005)  was used to perform a pairwise comparison between the genomes of SOPE, strain HS, and So. glossinidius, to explore conservation of synteny, and to help in the identification of orthologous genes and pseudogenes, to identify similarities and discrepancies in the functional capabilities of these organisms. The reannotated genome of So. glossinidius was used in this comparison  (Belda et al. 2010). Metabolic capabilities were analyzed with Blast2Go  (Conesa et al. 2005)  and KAAS  (Moriya et al. 2007 ) programs and manually curated. Functional information was retrieved from the BioCyc  (Caspi et al. 2010) , KEGG  (Ogata et al. 1999) , and BRENDA  (Scheer et al. 2011)  databases and extensive literature searches.
2012] ), the genome sequence of strain HS provided a unique opportunity to accurately identify all the mutations leading to predicted ORF inactivations in SOPE. The complete genome sequence of strain HS consists of a circular chromosome of 4,709,528 bases and one mega plasmid of 449,897 bases. The average GC content of the chromosome is 57. 47%, and the plasmid GC content is 53. 22%. There are a total of 3,993 CDSs encoded on the chromosome with only 61 pseudogenes and 365 CDSs encoded on the mega-plasmid with 14 predicted pseudogenes. Pseudogenes in strain HS were identified based on alignments of the CDSs with homologs from closely related bacteria in the NCBI database.
Acknowledgments K. F. O. participated in design and coordination of the study, manuscript writing, genome sequencing, assembly, annotation, nucleotide substitution analysis, RNA evolutionary analysis, and comparative analysis. R. G. participated in design and coordination of the study, functional analysis, manuscript writing, genome assembly and annotation, and comparative analysis. A. L. C. participated in genome sequencing, assembly, and annotation as well as the nucleotide substitution analysis and comparative analysis. D. M. D. participated in genome sequencing and assembly. A. C. v. N. participated in genome sequencing and assembly. C. H. participated in genome sequencing and assembly. A. A. participated in genome sequencing and assembly. B. D. participated in genome sequencing and assembly.
This work was supported by a  National Science Foundation  (www. nsf. gov)grant  EF-0523818  and  National Institutes of Health  (www. nih. gov)grant  1R01AI095736  to C. D. and by grant  BFU200912-1289539816-C02-01/BMC  ( Ministerio de Ciencia e InnovacionEconomı ´a y Competitividad, Spain)  to A. L. , Prometeo /2009/092  ( Conselleria d'Educacio, Generalitat Valenciana, Spain ) to A. M. , and  ANR-2010-BLAN-170101  (ImmunSymbArt) to A. H. The authors thank  Maha Mahmoud  for her technical assistance.
MRI Data Analysis We used Osirix software (Pixmeo, Geneva, Switzerland) to measure, according to the Iwaki method,  20  the anteroposterior tibial translation with respect to the femoral condyles (distance ''D'') at all angles of flexion for both ACLdeficient and healthy knees. The distance was recorded on sagittal T1-weighted images scanned at each center of the medial and lateral compartments by measuring the distance between the tangent line of the posterior tibial cortex and the center of a circle materializing the femoral condyle, called the flexion facet center (FFC)  20  (Figure  2 ). To determine the center of each compartment, we used an initial 3dimensional multiplanar reconstruction scan to choose the slice that included the medial edge of the fibular head as a landmark for the center of the lateral compartment and the attachment of the medial head of the gastrocnemius for the medial compartment.