dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance for different system loads The next results illustrate the performance of the proposed receiver for different system loads. From now on, the ITU channel B is considered in all the simulations. We assume M t = 2, K = 2, P = 16, and N = 20 while the number of users is varied (Q = 4, 6, and 8). Each user transmits two data streams (R (q) = 2, q = 1,. , Q). We assume M r = 1 or 2. Note that these configurations are challenging in terms of receiver spatial diversity, since M r is always smaller than Q. Our aim is, therefore, to show that joint symbol-channel-code estimation is still possible in this situation thanks to the joint use of SFSM and PARAFAC modeling. Note that the sufficient uniqueness http://asp. eurasipjournals. com/content/2013/1/48 condition  (38)  is satisfied in the chosen configurations.
Here, we assume M r = 2, P = 8, N = 50, and Q = 8, each user transmitting a single data stream (i. e. , R (q) = 1, q = 1,. , 8). Figure  6  shows the substantial performance gain obtained with the proposed system, which corroborates the advantages of space and frequency spreadings. We can also note that the gap between ALS and ZF receivers is smaller when SFSM MIMO is used. M t =2, K=2, P=16, N=20 Q=8 (M r =1) Q=6 (M r =1) Q=4 (M r =1) Q=8 (M r =2) Q=6 (M r =2) Q=4 (M r =2) Figure  5  BER versus SNR with semi-blind ALS receiver(different system loads).
Comparison with the SSSMA system In  [10] , an MC-SSSMA system was proposed to provide space and frequency diversities in the forward link of a MIMO wireless system. The space-frequency spreading model proposed therein is a generalization of  [45]  to frequency-selective channels. The multicarrier SSSMA system has some similarity with the proposed SFSM MIMO system in the sense that space and frequencydomain spreadings are performed. In  [10] , a joint spacetime spreading is used by means of Hadamard codes (its structure is detailed in  [11] ), while our approach uses separate space and frequency spreadings using Vandermonde codes. In Figure  7 , the performances of SSSMA and SFSM MIMO are compared. We assume M t = 2 transmit antennas, M r = 1 or 2, F = 64 and K = 2. For a fair comparison, we adjust the transmit parameters and the modulation to keep the same data rate for both systems.
The SSSMA scheme assumes R = 8, P = 2, and BPSK. For the proposed SFSM scheme we have R = 4, P = 4, and 16-QAM. In this case, both schemes have a rate of 2 bits per channel use. For the SSSMA system, a ZF receiver with perfect channel knowledge is used. For the proposed SFSM MIMO system, a semi-blind estimation without channel knowledge is used. The spreading codes are assumed to be known at the receiver for both systems. Note that for M r = 1, SSSMA exhibits a poor performance. This is due to the fact that multiuser detection in the SSSMA system requires M r ≥ M t. This constraint is not necessary in the SFSM MIMO system that makes an efficient use of the frequency diversity to separate the transmitted data streams when spatial diversity is not available at the receiver. For M r = 2, SSSMA outperforms SFSM MIMO over the low-to-medium SNR range.
Conclusion We have proposed a unified tensor model for MIMO communication systems with SFSM. The proposed model unifies several existing multiple-access/multiple-antenna communication systems. We have shown that the received signal can be formulated as a trilinear PARAFAC model, and capitalizing on its uniqueness property we have put in evidence lower bounds on the design parameters (number of transmit/receive antennas, subcarriers, symbols per data stream, and spreading length) for a joint symbolcode-channel recovery. The obtained conditions help the understanding of the existing tradeoffs involving space, frequency, and code diversities that are inherent to the SFSM MIMO system. The performance of the proposed receiver using a semi-blind ALS algorithm has been illustrated by means of computer simulations under realistic channel models and system parameters, and a comparison with other multiple-antenna CDMA-based systems has been made.
Finally, some new peaks are present in the XRD traces. They were verified to be from the -Fe phase. The peak intensity of the -Fe phase increases with the accelerating voltage, indicating that the volume fraction of the  phase detected by XRD increases  [44, 45]. The corrosion resistance of the D2 steels samples was measured in the 3. 5% NaCl water solution. The corrosion data calculated from potentiodynamic polarization tests are summarized in Table  2. It can be found that the corrosion potential ( corr ) becomes nobler after the LEHCPEB treatment, while the corrosion current ( corr ) decreases. As a result, the calculated polarization resistance (  ) increases after the LEHCPEB treatment. The best corrosion resistance is found in the D2 steel sample treated with the highest accelerating voltage (D2-3).
Acknowledgments Professor K. M. Zhang  would like to acknowledge the support of  Top Discipline Plan for Mechanical Engineering of Shanghai Municipal Education Commission  ( YLJX12-2 ). This work is supported by the  Special Foundation of the Shanghai Science and Technology Committee for Nanomaterials Research  (no. 1052nm05000 ) and  National Natural Science Foundation  (nos. 51101096 ,  51271121 ). Professor T. Grosdidier  would also like to acknowledge the " Sea-Sky " professorship position provided by the  Dalian University of Technology. Finally,  Professor T. Grosdidier  and  J. X. Zou  would like to acknowledge the framework of the Labex DAMAS within which this work was carried out.
9%), C2 modestly genic (49. 8%) and C3 (39. 5%) and C4 (29. 3%) mostly intergenic. To investigate gene expression in chromatin states, we used a data set of 17872 genes with a valid expression value in K562 (Materials and Methods). Of those genes, 15869 belong to one of the chromatin states. We found that a vast majority of expressed genes with a RPKMw1 (Equation  7 ) are in the early replicating euchromatin state C1 (Fig. 7B ), which confirms the link between MRT and expressed gene density previously reported in mammals  [55, 58, 59, 61]. As expected, most of the genes in the facultative Pc repressed heterochromatin state C2 are non expressed. Interestingly, we found that the density of non expressed genes in C1 is equivalent to the one in C2, indicating that it is more the predominance of active genes that characterizes early replicating regions than the absence of repressed genes.
Mean replication timing data and replication U-domain coordinates Timing profiles for the immature myeloid cell line K562 and the lymphoblastoid cell line GM06990 were obtained from the authors  [80]. The mean replication timing (MRT) is given for 27656 100 kb non-overlapping windows in hg18 coordinates. We also retrieved the coordinates of the 876 U-domains in K562 and 882 U-domains in GM06990 from the authors  [80]. Histone marks, H2AZ, CTCF, RNAP II, Sin3A and CBX3 ChIP-seq data For all ChIP-seq data, we downloaded data in the Encode standard format ''broadpeaks'' (http://genome. ucsc. edu/FAQ/ FAQformat. html). Broadpeaks format is a table of significantly enriched genomic intervals. Most of the data correspond to the release 3 (August 2012) of the Broad histone track. We downloaded the tables from: http://hgdownload.
cse. ucsc. edu/ goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/. The CBX3 and Sin3A data corresponds to the release 3 (September 2012) of the HAIB TFBS track. Tables were downloaded from the UCSC from: http://hgdownload. cse. ucsc. edu/goldenPath/hg19/ encodeDCC/wgEncodeHaibTfbs/ For the K562 cell line, we downloaded the broadpeak tables for the following antibodies: CTCF, H3K27ac, H3K27me3, H3K36me3, H3K4me3, H3K9me3, RNAP ll, H2AZ, H3K79me2, H3K9me1, H4K20me1, CBX3, Sin3A. For the GM12878 cell line, we downloaded: CTCF, H3K27ac,  H3K27me3, H3K36me3, H3K4me3, H3K9me3. For the Mono-cd14ro1746 cell line we downloaded: CTCF, H2AZ, H3K27ac, H3K27me3, H3K36me3, H3K4me3, H3K79me2, H3K9ac, H3K9me3. Genomic intervals were then mapped back to hg18 using LiftOver.
Rank transformation and Spearman correlation matrix All statistical computations were performed using the R software (http://www. r-project. org/). In order to compute the Spearman correlation matrix, the epigenetic profiles at 100 kb resolution were transformed with the R function rank with option ties. method = max. Then we computed the Pearson correlation matrix on the transformed dataset. To reorder the matrix in Fig. 1 , we computed the Spearman correlation distance dSCor as: dSCor(X ,Y )~1{SCor(X ,Y ), ð1Þ where SCor is the spearman correlation. Then, a dendrogram was computed using the R function hclust with option method = average and with dSCor as dissimilarity.
Principal component analysis Principal component analysis was performed on the rank transformed dataset using the function dudi. pca from the R package ade4 (see http://pbil. univ-lyon1. fr/ADE-4 and Ref. [107] ) with the option scale = TRUE (i. e. each variable is centered and normalized before the PCA computation). The first three components were retained which accounts for 76% of the dataset variance (see Supplementary Fig. S1 ), and clustering was performed in this 3D space.
e. a high value of Gap n (k)). Four clusters is also a reasonable choice according to the gap statistic index computed with R package clusterSim (see Fig. 3C ). Poorly clustered data points were removed from the set of chromatin states. The silhouette value  [108]  is a way to quantify how well a point is clustered. Definition 1. Given a particular clustering, C 1 ,C 2 ,. ,C k , of the data in k clusters, let i be a data point and d(i,C j ) the average distance of the data point i to the members of the cluster C j. Let i be a member of cluster C c and a i ~d(i,C c ), b i ~min j=c (d(i,C j )): ð5Þ The silhouette value of the data point i is defined as: s i ~bi {a i max(a i ,b i ) : ð6Þ A silhouette value below 0 means that the data point is actually closer in average to the points from another cluster than to the one it has been assigned to.
Annotation and expression data As human gene coordinates, we used the UCSC Known Genes table. When several genes presenting the same orientation overlapped, they were merged into one gene whose coordinates corresponded to the union of all the overlapping gene coordinates, resulting in 23818 distinct genes. Expression data were retrieved from the Genome Browser of the University of California Santa Cruz (UCSC). To construct our expression data set, we used RefSeq Genes track as human gene coordinates. Genes with alternative splicing were merged into one transcript by taking the union of exons. Hence the TSS was placed at the beginning of the first exon. We obtained a table of 23329 genes. We downloaded expression valuess from the release 2 of Caltech RNA-seq track (ENCODE project at UCSC: http:// hgdownload.
GO term enrichment Each gene name of our annotation dataset was associated to several GO terms from GO SLIM (high level GO terms) using the online mapper: http://go. princeton. edu/cgi-bin/ GOTermMapper. Then for each chromatin state (C1 to C4), the number of occurrences of each GO term was determined by the number of promoters belonging to that state and associated to this GO term. The enrichment for each GO term in each cluster was tested using Fisher's exact test. We applied a procedure to control the false discovery rate (FDR) as described in  [137]. The upper limit of the FDR was fixed to 20%. After detecting significant deviation from a random repartition of GO term occurrences, we used the odd ratio value to determine if the deviation was an enrichment (odd ratiow1) or a depletion (odd ratiov1).
Acknowledgments We are very grateful to  C. Thermes  for a careful reading of the manuscript and to  Y. d'Aubenton-Carafa ,  A. Baker ,  JC. Cadoret ,  E. Cascales , CL. Cheng,  L. Duret ,  A. Goldar ,  O. Hyrien ,  F. Picard ,  MN. Prioleau  and  C. Vaillant  for helpful discussions.
Acknowledgments We thank  Duff Neill ,  Gregory Soyez , and  Iain Stewart  for helpful conversations. We thank  David Lopez  for performing preliminary studies of C 1 and  Andreas Hinzmann  for performing preliminary studies of C 2. A. L. and J. T. are supported by the  U. S. Department of Energy (DOE)  under cooperative research agreement  DE-FG02-05ER-41360  and by the  DOE Early Career research program   DE-FG02-11ER-41741. G. P. S. is supported in part by the  French Agence Nationale de la Recherche , under grant  ANR-09-BLAN-0060  and by the  EU ITN  grant LHCPhenoNet, PITN-GA-2010-264564. A. L. , G. P. S. , and J. T. are also supported in part by  MISTI  global seed funds. A. L. and G. P. S. are grateful to  Perimeter Institute for Theoretical Physics  for hospitality and support; research at  Perimeter Institute  is supported by the  Government of Canada through Industry Canada  and by the  Province of Ontario  through the  Ministry of Economic Development & Innovation.
MRNA transcriptome profiling and functional classification of differentially expressed genes Until now, mRNA expression profiling of human myoblasts has been performed exclusively on primary cells  [36]. However, primary skeletal myoblasts have not been enriched for myogenic cells prior to analysis. To avoid analyzing mixed populations of myoblasts and non-myogenic cells, we have used RNA extracted from CD56+ myoblasts purified from muscular tissues. Then, using Agilent transcriptome 44 K microarrays, we identified 6,616 differentially expressed genes of which 3,445 and 3,245 were down-or up-regulated, respectively (Additional file 4). In most cases, a functional class could be assigned to these transcripts using the DAVID Gene Ontology database (Additional file 5)  [37, 38]. In agreement with previous reports, the majority of upregulated genes were found to control metabolism, myogenesis, insulin receptor signaling or to positively regulate transcription (Table  2 ).
Reverse transcription and real-time PCR For microRNA expression analysis 400 ng of total RNA purified via Trizol (Invitrogen) was converted into cDNA using 8 independent pools of primers (#4384791, Applied Biosystems, AB) and TaqMan microRNA Reverse transcription kit (#4366596, AB). cDNA was quantified using via qPCR using TaqMan 2x Universal PCR Master Mix, No AmpErase UNG (#4324018, AB) and human microRNA panel version 1. 0 TLDA (TaqMan Low Density Array, AB), data were acquired on AB7900HT Real-Time PCR machine. Sequences of microRNAs and corresponding codes of TaqMan assays can be found in Additional file 2. For mRNA expression analysis total RNA was isolated from 2x10 6 proliferating myoblasts, or differentiated myotubes using Trizol (Invitrogen) and reverse transcribed using the High Capacity cDNA Archive kit (Applied Biosystems, AB) according to the manufacturer protocol.
Transcriptome profiling and data processing Human primary myoblasts were sacrificed directly on plates at 30% confluency using Trizol, RNA was prepared using organic extraction and ethanol precipitation as described  [119]  followed by silica column cleanup on silica columns (Nucleospin RNA Extraction kit, Macherey Nagel). RNA extracted from individual myoblast lines was Cy5-labeled, mixed with with a pool of RNA samples labeled with Cy3 and hybridized to Gene Expression microarrays (4x44k #G4112F Agilent) and scanned as instructed by the manufacturer. Scanned images were then analysed using the Feature Extraction Software (Agilent), the subsequent gene expression data treatment was performed using R and Bioconductor. Spots with intensity lower than 50 or lower than background in more than 50% of biological replicates have been removed from further analysis.
The background correction and intensity normalization procedures were applied for the remaining ~30 000 probes using the Bioconductor package vsn  [120]. A background offset and a scaling factor for each array and dye channel were calculated using the least squares regression procedure, then the generalized log-transformation was applied. The ordinary least squares regression approach is based on the assumption that "most genes are not differentially expressed". However, in the case of myogenic differentiation, where many genes are differentially expressed, this assumption does not hold. Therefore, to apply the above mentioned approach to myogenic differentiation, the vsn least squares model has first been applied to a subset of features, and then extended for the whole set of features. To select the subset of features, a pool of samples prepared from five different proliferating myoblast lines before and after myogenic differentiation were hybridized to two additional microarrays.
Then 14. 358 features that did not exceed the cutoff value of 1. 23 fold change between the pools have been selected. To determine the differentially expressed genes, a t-test analysis was conducted using the limma package from Bioconductor  [120]. Using this package, a linear model is fitted to the expression data for each gene. An empirical Bayes moderation of the standard errors were performed. This method borrows information across genes in order to arrive at more stable estimates of each individual gene's variance, even for experiments with small number of arrays. The microarray data related to this paper have been submitted to the Array Express data repository at the European Bioinformatics Institute (http://www. ebi. ac. uk/arrayexpress) under the accession number E-MTAB-1577 (release date 2014-3-22).
Scientists specialising in optical technologies will be able to learn through dialogue about the requirements for documentation and analysis of historic architecture, archaeological sites and valuable heritage objects, such as paintings, sculptures and coins, in order to optimise current techniques and ensure that future developments go in the right direction and meet expectations. Material scientists, physicists and chemists, alongside conservators, will be able to provide the theoretical and scientific expertise in connecting optical data with material properties of artefacts. It should be noted that COSCH is a trans-domain Action (TD1201) of the COST Domain Materials, Physics and Nanosciences (MPNS) which facilitates and promotes innovation in material science. The involvement in COSCH of material scientists is of particular importance owing to a strong connection between the surface material of heritage objects, their visual appearance and techniques suitable for optical documentation.
1989). O'  Malley et al. (2003b)  showed that the addition of PYO has a dramatic impact on the ultrastructure of mitochondria from epithelial lung cells and these authors suggested that the redox cycling of PYO takes place in or near mitochondria. However, despite numerous studies, the subcellular site(s) of PYO redox cycling and the mechanisms underlying PYO toxicity remain unclear. Data published on the toxic effects of PYO in eukaryotic cells were performed under aerobic conditions. The few studies conducted under hypoxia or anaerobiosis were on bacteria  (Hassan and Fridovitch 1980; Baron and Rowe 1981; Baron et al. 1989; Yoon et al. 2006; Price-Whelan et al. 2007; Wang et al. 2010 ). Yet, in vivo, both the producer of PYO (P. aeruginosa) and the eukaryotic target cells are subject to oxygen limitation, at least at certain periods  (Hassett et al.
3% (w/v). Oxygen concentrations were measured by high-resolution respirometry with the Oroboros Oxygraph-2k  (Hutter et al. 2006 ) in standard configuration, with a 2 mL volume for the two chambers at 30°C and 500 rpm stirrer speed  (Dufour et al. 2013). Data were recorded at 1 sec intervals using the Datlab 4 Acquisition software (Oroboros, Innsbruck, Austria). Standardized calibration procedures of the oxygen signal were carried out using the mannitol/Tris-maleate buffer. Respiration was automatically corrected for contributions of the polarographic oxygen sensor and of oxygen diffusion to total apparent respiration as a continuous function of oxygen concentration. P/O ratios (ATP formed vs. oxygen atoms consumed) were assessed by measuring the decrease in [O 2 ] during the rapid burst of state 3 respiration after adding 0.
We therefore studied the impact of H 2 O 2 and PYO uniquely in oxidative conditions, using glycerol as growth substrate (YPGly medium). Under both YPD and YPgly conditions, PYO inhibited growth kinetics without affecting cell survival at any growth stage. Examples of growth kinetics are shown in Figure  S1. S1  and Saccharomyces genome database at www. yeastgenome. org/): Wild-type W303 and BY-4742 are commonly used lab strains. WT-BY4742 is partially impaired in respiration as it is mutated in the HAP1 locus which encodes a major heme-responsive transcription factor that regulates O 2 -responsive genes. The RWT-BY4742 strain is restored for Hap1p  (Gaisne et al. 1999). The yap1D and skn7D strains are hypersensitive to oxidative stress due to deletions of major transcription factors required for oxidative stress tolerance.
Antioxidants only slightly protect against PYO toxicity The addition of L-ascorbate  (Muller 2009)  or NAC can attenuate PYO toxicity in epithelial lung cells  (Gloyne et al. 2011). We examined the effects of several cell-permeable antioxidants on PYO toxicity under oxidative conditions in YPGly medium. The antioxidants, L-ascorbate (10 mmol/L), resveratrol (200 lmol/L), and tiron (2 mmol/L) had no effect or even slightly increased the inhibitory effect of PYO (data not shown). This suggests that the use of such antioxidants to counteract PYO toxicity needs to be reexamined. As shown in Figure  3 , a protective effect was, however, observed in WT strains with NAC, a precursor of glutathione biosynthesis and a radical scavenger. A similar protective effect of NAC was found for the yap1D mutant that is hypersensitive to oxidative stress.
PYO appears to interact with S. cerevisiae respiratory chain We were unable to detect any effect of PYO (100-500 lmol/L) on the instant respiration of YPGly-grown cells (data not shown). We, therefore, used isolated yeast mitochondria and examined whether PYO at 100 lmol/L concentration interferes with the respiratory chain and whether a redox cycling occurs at this level. Succinate was added to intact mitochondria (Fig. 4 ). Our data suggest that the addition of PYO has no apparent effect on "state 4" respiration, that is, in the absence of ADP or after the conversion of ADP (100 lmol/L) to ATP (Fig. 4A  and B ). It is possible that PYO alters the efficiency of ATP synthesis, therefore, we also measured the P/O ratios (ATP formed vs. oxygen atoms consumed). Similar P/O values were obtained with ADP (0.
As shown in Figure  4E , PYO did not induce this form of respiration in HQNO-poisoned mitochondria. Although no significant effect was detected on states 4 and 3 respirations, these results suggest that PYO can accept electrons from the respiratory chain through a complex III-dependent process. In the actively respiring state 3, we assessed the release of ROS from mitochondria by adding CAT and/or SOD (both at 500 U/mL) after the consumption of 200 lmol/L O 2 and when the [O 2 ] reached zero in the oxygraph chambers. We found that the addition of PYO (AE cyanide) did not cause the release of detectable amounts of O 2 originating from H 2 O 2 or O 2 ÁÀ (data not shown).
PYO can, therefore, be considered as another mitochondria-targeted agent, together with the well-known poisons, cyanide and HQNO, also produced by P. aeruginosa under hypoxia  (Machan et al. 1992; Williams et al. 2007 ). Despite the apparent absence of a short-term effect of PYO on cellular and "state 3" O 2 fluxes, it is still unknown whether a futile redox cycling of PYO occurs in vivo in mitochondria. In this study, we obtained a panel of data showing a role for mitochondrial respiration in triggering aerobic PYO toxicity. PYO might also target specific steps in the oxidative metabolism of glycerol (e. g. , glycerol-3-phosphaste dehydrogenase). Despite the inhibition of PYO toxicity by NAC, the role of oxidative stress remains unclear. For instance, the yap1D and skn7D mutants, both of whom are hypersensitive to oxidative stress, were not PYO-hypersensitive.