dataesr/scientific-paragraphs-categorization huggingface dataset:
Despite the limits of onshore and sparse offshore data to resolve subduction zone earthquakes, there are nonetheless several interesting features of this earthquake rupture which are well constrained. For example, although we cannot distinguish whether the initial phase of the rupture propagated as a slip pulse rather than an expanding crack, the later evolution of the rupture was definitely pulse-like. But perhaps the single most interesting feature of our rupture model is that the location of peak slip was located at depth and not at the trench. We can only draw conclusions in the context of the data, error structure, and model parametrization we utilized. We would expect any other inversion which either neglects the data from the DART stations located updip of the source or which strongly regularizes the slip distribution (especially through the use of Laplacian smoothing) would tend to overestimate the shallow slip relative to our results.
, Beverly, MA, USA), total and phospho-IRE-1 (Thermo Fisher Scientific, Illkirch, France), eNOS and iNOS (Transduction Laboratories, Lexington, KY, USA), albumin and transferrin (Bethyl Laboratories, Inc. , Euromedex, Souffelweyersheim, France). Protein expression was visualized using an enhanced chemiluminescence kit (ECF; Amersham Biosciences, Orsay, France) and signals were quantified with scanning densitometry using the Quantity One software program (Bio-Rad Laboratories, Hercules, CA, USA). Representative western blots represent one of three independent experiments with similar results. All signals were normalized with the loading control HSC70, for PCNA, GRP78, GRP94, CHOP, ATF4, p-50ATF6a, XBP-1(S), TRAF2, caspase 12, eNOS, albumin and transferrin and with total PERK, eIF2a, IER-1a, JNK1/2, p38, ERK1/ 2, caspases 3 and 9 and cytochrome c for p-PERK, p-eIF2a, p-IER-1a, p-JNK1/2, p-p38, p-ERK1/2, cleaved caspases 3 and 9 and (cytosolic and mitochondrial), respectively.
Histology, immunohistology and TUNEL assay. For standard histology evaluation, hematoxylin/eosin staining was performed and the morphological liver integrity was graded on a scale as previously described. 30, 33 mmunohistological studies were performed to evaluate GRP78-and CHOPpositive cells. Apoptosis was assessed by TUNEL as described elsewhere. 30 amage score, ratio of GRP78-and CHOP-positive cells as well as TUNELpositive nuclei were evaluated in a blind manner by two different investigators who counted at least 10 random high-power fields on each section. Statistical analysis. Data were expressed as means ± S. D. Statistical analysis was performed by variance analysis (Kruskal-Wallis test), followed by Mann-Whitney U test. Po0. 05 was considered as significant.
Acknowledgements. We thank  Dr. Bruno Turlin  ( CHU Rennes ) and  Mrs. Pascale Bellaud  ( Histo-pathological platform, Rennes, UMS biosit, Biogenouest ) for histological analysis and  Dr. Remy Le Guevel  for image analyses ( ImPACcell platform, Rennes, UMS Biosit, Biogenouest ). We thank  Dr. Pascal Loyer  for helpful comments and critical reading of the article. This work was supported by the  Institut National de la Santeet de la Recherche Medicale , the  Centre National de la Recherche Scientifique , the  Fonds Europeen de Developpement Regional (FEDER)  and the 'Contrat projets etat region' (axe biotherapie). IBM was funded by the 'Contrat plan etat region' (axe biotherapie) and the  'Fondation pour la Recherche Medicale '. JP was funded by the  'Conseil Regional de Bretagne' and the 'Ligue Contre le Cancer-Comite  ´d' Ille-et-Vilaine '.
Case 2: If Nb i j packet < P th , the node is compensated and r n i, j = 1. Where Nb i j packet is the number of captured packets by CH i and generated by neighbor j. When the reputation related to a node j is computed, a trust value (T i j ) varying between 0 and 1 is attributed to j based on Eq. 2 where n is the number of evaluations. T i j = Max{R i j /n, 0} (2) B. Periodic Gathering of Traffic Data & Routing Data, in the network, is routed based on a geographic routing using a greedy forwarding mechanism. In fact and as stated above, the actual work is based on a previous routing mechanism namely GyTAR where the packet is forwarded intersection by intersection until it reaches its final destination. The selection of the next road segment where the packet should be sent is done based on a real time collection of information about roads densities.
Data about traffic is gathered based on a cluster organization proposed in GyTAR. In this work, we propose to secure this mechanism as follows. Each segment is divided into small cells and the trustworthiest and nearest node to the center of a cell is chosen as a cluster-head to collect information about traffic. The trustworthy node is identified using a pre-processing phase at the beginning of the network organization where all nodes in a cluster are mutually monitoring for a period of time to get an initial idea about the trust level of each vehicle. This pre-processing phase leads, therefore, to the choice of the node with the highest trust level to monitor nodes within the cell. After the choice of the first CH the maintenance of the architecture and the swapping of CHs would be simple as this one knows about all the trusts of its neighbors, it will directly choose the trustworthiest node to be a CH before getting out from the cell.
This method yields to a very efficient way of incorporating modified nucleotides in an RNA sequence without size limitation. This enables the study of large size RNA molecules using spectroscopic tools. The high incorporation efficiency of the paramagnetic probe will be of interest to measure distances with EPR methods. For NMR, it offers the possibility to combine segmental and selective isotopic labeling with PRE measurements, opening a novel perspective to study the dynamics and the structure of RNA molecules. The efficiency of the site-specific spin-labeling reaction and the quality of the spin-labeled RNA were monitored using NMR, mass spectrometry (MS) and EPR. This sitedirected spin labeling method is demonstrated on the 55nt (17. 7 kDa) RNA corresponding to K-turn and Specifier Loop domains from the Bacillus subtilis tyrS T-Box leader RNA, whose structure was solved by NMR by Wang and Nikonowicz (PDB id: 2KZL; 39).
MS experiments Prior to MS analysis, RNA samples were dialyzed against 200 mM ammonium acetate on Amicon Ultra-4 centrifugal filter units-10000NMWL and concentrated to 30 M final. RNA intact mass detections were performed using an ESI-TOF mass spectrometer (Q-TOF micro, Waters, MA, USA). Calibration was achieved in the negative ion mode, using phosphoric acid. Samples were diluted to about 1 M in 1:1 water-acetonitrile containing 1% triethylamine. Max-Ent (maximum entropy deconvolution software) was used to provide a first approximate of the masses whereas exact masses were calculated manually from the multiple charged species. The RNA modification by the spin label was characterized after RNaseA treatment on a MALDI-TOF/TOF mass spectrometer (Autoflex III Smart Beam, Bruker, Billerica, MA, USA).
NMR experiments NMR experiments were recorded at 500, 600 and 700 MHz on Avance Bruker spectrometers equipped with a z-gradient TBI probe (at 500 MHz) and cryoprobes. NMR data were processed using TopSpin (Bruker) and NMRpipe  (44) , and analyzed with Sparky software  (45). NMR experiments were recorded in 70 mM sodium phosphate buffer at pH 6. 5. The concentration of RNA samples was 200 M in 150 l, in 3 mm NMR tubes. Resonance assignments were performed on the basis of previously reported NMR data (BMRB entry: 17316; 39). All NMR spectra were acquired at 293 K. Solvent suppression was either achieved using a 'Jump and Return' combined with a WATERGATE or  single WATERGATE pulse sequence for 1D and 2D experiments, respectively  (46). 2D TOCSY (total correlation spectroscopy) spectra were acquired using a MLEV (Malcolm Levitt composite-pulse decoupling sequence) mixing sequence and a mixing time of 60 ms.
The cross-peak intensities in the homonuclear TOCSY and heteronuclear 1 H- 13 C HSQC (Heteronuclear Single Quantum Coherence) experiments were measured using the Sparky software. Intensities were normalized using the intensity corresponding to the H5-H6 correlation peak of C54 on the TOCSY spectra and the H8-C8 correlation peak of A50 on the HSQC. Both C54 and A50 residues are located more than 50 Å away from the paramagnetic center, according to the 3D structure, and therefore are not affected by the paramagnetic relaxation. PRE was then quantified using the ratio between the intensities of the spin-labeled RNA and those of the unlabeled RNA. Observed perturbations upon the attachment of the spin-label were reported on the structure (PDB id: 2KZL; 39) visualized using the Pymol software  (47).
FUNDING Agence Nationale de la Recherche  [Grant  RNASPIN ANR-2010-JCJC-1507-01  to I. L. ];  French Infrastructure for Integrated Structural Biology (FRISBI)  [ ANR-10-INSB-05-01 ]; INSTRUCT, part of the  European Strategy Forum on Research Infrastructures (ESFRI)  and through national member agreements;  CNRS, University of Strasbourg  and INSERM. Funding for open access charge:  Agence Nationale de la Recherche.
SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23. 1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations.
5. Calculate weight for each SNP: d i = û2 i 2p i (1-p i ), where i is the i-th SNP. 6. Normalize SNP weights to remain the total genetic variance constant. 7. Loop to 2. (ssGWAS1) or 4. (ssGWAS2). SNP weights were calculated iteratively either looping through steps 4-6 (ssGWAS1) or through steps 2-6 (ssGWAS2). Iterations with both scenarios increase weights of SNP with large effects and decrease those with small effects, essentially regressing them to the mean. Experiences with simulated data using ssGBLUP  (Wang et al. , 2012)  and of a similar method based on GBLUP  (Sun et al. , 2012; Zhang et al. , 2012)  indicated that ssGWAS1 was more suitable for identification of SNPs with the largest effects while ssGWAS2 was superior for more accurate GEBVs. Percentage of genetic variance explained by i-th region has been calculated as below: Var (a i ) σ 2 a × 100% = Var( 20 j = 1 Z j ûj ) σ 2 a × 100% Where a i is genetic value of the i-th region that consists of contiguous 20 SNPs, σ 2 a is the total genetic variance, Z j is vector of gene content of the j-th SNP for all individuals, and ûj is marker effect of the j-th SNP within the i-th region.
Computations For analyses, we applied an animal model with fixed effects of sex and contemporary group and random effects for additive animal and maternal permanent environment. Variance components were estimated by REML based on all the individuals in the pedigree. All analyses for REML, BLUP and ssGWAS were run using the BLUPF90 software  (Misztal et al. , 2002; Aguilar et al. , 2011). For BayesB and CGWAS methods, DP were created from BLUP estimates of EBVs as pseudo-observations, following  Garrick et al. (2009)  assuming that 0. 1 of the genetic variance was not accounted for by SNPs, as in  Ostersen et al. (2011). GWAS was then performed using three alternative methods. The first method of ssGWAS was run for five iterations with both ssG-WAS1 and ssGWAS2 options. The second method of CGWAS was implemented in WOMBAT  (Meyer and Tier, 2012).
CONCLUSION This study compares genomic evaluation and association results between different methods: ssGWAS1, ssGWAS2, CGWAS, and BayesB. Because this was real data and the true values are unknown, it is not possible to conclude which method was most accurate for GWAS, but similarity between BayesB and ssGWAS1 was shown in various aspects. CGWAS was the most different but also found the greatest number of signals. The latter could be due to false positives. Advantages of using ssGWAS includes: (1) no pseudo values are required, (2) complex modeling and multipletraits are possible, and (3) computing is fast and implementation is simple.
Real datasets To provide a comprehensive assessment of the accuracy of methods we analysed eight different cohorts that vary in the extent of the relatedness between individuals. The cohorts are summarised in Table  S1 , six of these are considered to be from isolated populations. The Orkney Complex Disease Study (ORCADES) is an ongoing study in the isolated Scottish archipelago of Orkney  [27]. The CROATIA-VIS (Vis) and CROATIA-KORCULA (Korcula) studies contain individuals recruited from the Dalmation islands of Vis and Korcula  [27, 28]. The INGI-Val Borbera population is a collection of 1,664 genotyped individuals collected in the Val Borbera region, a geographically isolated valley located within the Appennine Mountains in Northwest Italy. The valley is inhabited by about 3,000 descendants from the original population, living in seven villages along the valley and in the mountains  [29].
Firstly, we ran Pedstats  [33]  to detect any genotypes that violated Mendelian constraints, and these loci were marked as missing for all individuals in a pedigree where violations were found. Loci that produced Mendel violations for w5% of samples were filtered for all individuals in a cohort. Secondly, Merlin's error detection algorithm was used on all pedigrees, and genotypes which were unlikely were also flagged as missing. This final set of genotypes were used as input in all subsequent analyses. All software was run as per instructions in their respective manuals. The computation times for each method for the largest experiment conducted on European cohorts are summarised in Figure  S1 , a more comprehensive study of running times was conducted in the original SHAPEIT2 paper  [2]. Creating validation haplotypes.
We investigated a simple method where we enumerate every possible partitioning of a pedigree into duos/trios and choose the partition that minimises the number of individuals that are not included in a duo/trio (many partitions often share the same minimum in which case one is picked at random). We applied this partitioning to the datasets and then ran Beagle (since it was the next most competitive method) taking the implied duo and trio information into account. We refer to this as the Beagle duo/trio method. Beagle was found to use substantially more memory in this setting (over 150 GB for the GPC cohort) which may be problematic for some researchers. This issue is noted in the Beagle manual and relates to missing data in parentoffspring duos and trios. On duos and trios this method will agree perfectly with Merlin at sites that Merlin can phase.
Any discordance between these crossover callsets may also be due (in part) to Merlin errors. We also wanted to investigate the ability of our method to call crossover events in duos and trios which cannot be done with the Lander-Green algorithm. For these reasons we created several simulated datasets to investigate these issues. We utilised male chromosome X haplotypes as the basis for these simulated datasets. Since males only have one copy of chromosome X, phase is unambiguously known. As in previous phasing studies  [2, 43, 44] , two male X chromosomes were combined to create a pseudo autosomal diploid founder individual where the true underlying haplotypes are known. We then randomly mate these new diploid individuals to produce offspring with recombined haplotypes. Crossover events were simulated as a Poisson process on the genetic lengths from the HapMap Chromosome X genetic map for females and the same map scaled by 0.
605 for males (difference in rates estimated from 2002 deCODE Map). In all experiments, we applied a simple rejection sampling scheme to avoid large amounts of consanguinity in our new diploid individuals and their offspring. The X chromosomes used to create pedigree founders were sampled such that no pair of chromosomes came from pairs of males with genome wide relatedness rw0:01  [45]. We conducted these experiments using the 1071 (607 females and 464 males) nominally unrelated individuals from the Val Borbera cohort. This allowed us to create up 232 to diploid individuals with known haplotypes. A simulated dataset with extended pedigrees. We wished to investigate accuracy on pedigrees that are collected as part of a larger cohort, hence we simulated pedigrees with the same structures as those observed in the Val Borbera cohort.
5S chip on 1000 Genomes individuals. There were many cases where founders were missing in practice. We also removed the 91 ungenotyped founders leaving 108 genotyped founders and a total of 314 individuals in pedigrees. Finally these extended pedigrees were merged with the 607 female chromosome X data (and the remaining 33 pseudo-autosomal males not in a pedigree) to create a cohort of 954 individuals containing 314 individuals in pedigrees and 640 unrelated individuals. We created 10 versions of this simulated dataset. In the ideal scenario we do not add any genotyping error and do not remove founders (leaving all 199 founders in the data giving us a data set with 1045 individuals (405 within a pedigree). We use the realistic dataset to compare three different versions of our method for phasing extended pedigrees.
First we applied SHAPEIT2 ignoring all pedigree information. Secondly, we applied our duoHMM method to this set of haplotypes to correct SEs. We also used the duoHMM output to identify positions where there was strong evidence of genotyping error, and we investigate the effect of excluding these sites for accuracy comparisons. Finally, we applied our method of partitioning the extended pedigrees into trios and duos and then ran Beagle using this level of family information. We also evaluate Merlin's haplotype accuracy on this simulated data. We also ran Merlin and our duoHMM method on these datasets to detect recombination events on all informative meioses which allowed us to investigate the sensitivity and specificity of the methods in both a realistic and ideal scenario. In the realistic and ideal simulations, there were 183 and 212 informative meioses containing a total of 2422 and 3131 crossover events (across all simulations) on which to evaluate accuracy.
A simulated dataset of uninformative duos. The ability to detect recombination events is enhanced if an individual is part of a large pedigree. Such pedigrees will contain parent-child relationships where the parent's heterozygous sites can be phased independently of that child's loci (such as from a grandparent or another child) this allows changes in the pattern of inheritance (recombination events) to be detected. Previous work on detecting recombination events in pedigrees have used such informative pedigrees  [37, 39, 41, 46]. We wanted to assess the power of our method to detect recombination events in uninformative duos. We created a simulated dataset of 116 mother-father-child trios using the 464 chromosome X haplotypes from the Val Borbera cohort. These trios were merged with the diploid female chromosome X data to increase sample size.
We created a second version of this simulated dataset by first removing individuals from the Val Borbera dataset such that no pair of individuals had a genome wide relatedness rw0:35 in the remaining data. This reduced the dataset from 1071 to 778 individuals (440 females and 338 males) allowing us to simulate a dataset with 84 mother-father-child trios, merged together with the original 440 females. By removing closely related individuals from the cohort we create a simulated dataset more like a non-isolated population. Detection of recombination events will be harder in this setting due to reduced levels of IBD sharing. The merging, mating and recombination was simulated ten times (each simulation analysed separately) creating 2270 and 3190 recombination events to evaluate sensitivity and specificity of our method.
Results on the simulated dataset with extended pedigrees. The simulated pedigree data allows us to evaluate the confounding effect of genotype errors in the Merlin and duo/ trio phased haplotypes. Figure  S23  (top right) plots the SE of SHAPEIT2+duoHMM versus Merlin on simulated data with realistic levels of genotyping error. SHAPEIT2+duoHMM is generally more accurate (average of 0. 033% versus 0. 215% for Merlin on sites resolved by Merlin). Without any genotyping error (Figure  S23  bottom right) the performance is much improved for Merlin (SHAPEIT2+ duoHMM SE = 0. 005%, Merlin = 0. 021%). Figure  S24  plots the SE rates on the simulated pedigrees for Beagle Duo/Trio SE rates of all individuals versus those of SHAPEIT2 (left) and SHAPEIT2+duoHMM (centre). The plot shows that the most accurate haplotypes are attained by the SHAPEIT2+duoHMM approach, and that the duo/trio constrained phasing can be susceptible to genotyping error.
This method may also find utility in studies that aim to locate IBD segments between individuals. Based on these results we might suggest that a strategy of estimating haplotypes with SHAPEIT2 followed by application of the GERMLINE method  [50]  for IBD inference from haplotype data may provide an accurate and efficient Table  4. Comparison of recombination detection using our method and Merlin for all informative paternal (P) and maternal (M) meioses events in each cohort. solution. As cohorts increase in size, or as cohorts are combined, the chance of any individual sharing a close relative in the cohort increases. Methods such as SHAPEIT2, that can accurately leverage this IBD information when estimating haplotypes may help to extract the most information from such large cohorts. Previous research has demonstrated SHAPEIT2's effectiveness for phasing cohorts of unrelated individuals, in this paper we demonstrate that SHAPEIT2 is in fact effective across the full spectrum of relatedness.
Supporting Information Figure  S1  Computation time in hours for phasing chromosome 10 for each of the full cohorts run as unrelated (second column of Table  4 ). All jobs were run on an Intel Xeon CPU E5-2690 (2. 90 GHz) CPU with 256 GB of RAM. This is the total compute time for three runs of HAPI-UR as specified in the manual. Beagle was ran without the lowmem option for better performance. Whilst no parallelism was employed here, options exists for exploiting multiple processors with varying degrees of difficulty for each piece of software. SHAPEIT2 and SLRP have the ability to run on N threads resulting in an approximately N| reduction in computation time. HAPI-UR 36 can be run as three simultaneous processes rather than sequentially. Most simply, the genome can be partitioned into chunks which are phased separately with the results being ligated.
Left: True positive rate (TPR) versus false discovery rate (FDR) for recombination detection using SHAPEIT2+duoHMM (red) versus Merlin (blue). Merlin detects 90. 57% of crossovers with a substantial FDR of 62. 48% whilst SHAPEIT2+duoHMM detects 92. 40% of events with an FDR of 3. 78% (the red point at P(R~1)w0:5). On ideal data SHAPEIT2+duoHMM achieve a 95. 75% TPR and 0. 71% FDR and Merlin has 95. 66% and 3. 48% respectively. Right: Switch error for SHAPEIT2 (DuoHMM corrected) versus Merlin. The black point is the mean for each method. Merlin had 0. 215% (0. 021% ideal scenario) switch error while SHAPEIT2 had a rate of 0. 033% (0. 005% ideal scenario). (TIFF) Figure  S24  Switch error results on simulated data for extended pedigrees. Points are coloured according to what family information was used by Beagle in duo/trio mode; red meaning an individual could not be included in a duo or trio.