dataesr/scientific-paragraphs-categorization huggingface dataset:
Spurious noise oscillations near edges are also reduced by the final aggregation process. However, the overall quality in terms of preservation of details is not improved by the patchwise version. " All experiments were carried out by considering, as in Subsection 4. 1, 10 noise realizations per noise-free image. Thus, results on Color and Gray-level databases are averages on 210 different denoised images. Figure  1  displays the corresponding curves for the Color IPOL database while Figure  2  is for the Gray-level IPOL database. In order to get a fair comparison, it is also necessary to run the NLM-P and NLM-Pa algorithms on another database containing a set of images disjoint to the IPOL set, on which parameters estimation has been performed. The Volume 3: Miscellaneous set from the USC-SIPI Image Database  [26]  has been chosen because it includes some standard test images, such as House (record ID 4.
1. 05), Mandrill (4. 2. 03) and Lena (4. 2. 04). In what follows, the Miscellaneous Volume of the USC-SIPI Image Database is shortened by USC-SIPI. It contains 44 images (16 color and 28 monochrome) of very different nature and far away from the IPOL set, such as old photographs and test patterns. The PSNR curves (average on 440 denoised images) for the USC-SIPI database is given in Figure  3. It is remarkable that PSNR curves on the USC-SIPI database are very similar to the ones with IPOL databases. This indicates that the computed optimal parameters are robust to image variation. Another indication confirming the relevance of these optimal parameters is in the convex and regular shape of the NLM-P and NLM-Pa curves. In contrast, the few irregularities that can be noted in NLM-B curves suggest that the parameters given in  [4]  are suboptimal in some standard deviation intervals, with respect to the PSNR criterion.
One reason of the curves regularity is in the large PSNR averaging done on images of the database. However the curves regularity remains quite good on a single image, see Figure  4  where are plotted PSNR curves for the standard Lena image (record # 4. 2. 04 in the USC-SIPI database; remember that this image is not in the database used to compute optimal NLM-P and NLM-Pa parameters). Let us now compare the performance of the three denoising methods. The experiments confirm that the Gaussian Euclidean patch distance  (9)  does not bring much compared to the Euclidean one  (11) , see Figure  5  where average PSNR differences between NLM-Pa and NLM-P are drawn. The difference between NLM-B and NLM-Pa is plotted in Figure  6. In agreement with the text from  [4]  quoted above, NLM-B presents better PSNR than NLM-P/NLM-Pa when the noise standard deviation is greater than 3 (USC-SIPI), 5 (Color IPOL) or 15 (Gray-level IPOL).
Note that this CPU-time does not represent the elapsed real time (wall clock) taken from the start of the process until the end: as all algorithms are implemented using parallel processing, on a multi-core computer the elapsed real time would be approximately the CPU-time divided by the number of used CPU (provided that no other program is running at the same time). Figure  9  gives the CPU-time spent on average on the Color IPOL database while Figure  10  is for the Gray-level IPOL database. Figure  11  displays the ratio of the CPU-time between NLM-B and NLM-Pa with the SIL algorithm. We first remark that on the Color IPOL database, the SIL algorithm is faster than the basic one in case of high noise only. The reason is simply in the optimum value of d s = 1 for σ ≤ 46: for such a tiny patch it is useless to develop fast patch distance computation.
The situation differs for NLM-Pa with grayscale images where d s ≥ 3. Nevertheless, the increase in speed of the SIL algorithm compared to the basic one never exceeds the factor 4. 5. However it should be noted that the speed increase may be much more important for NLM variants where the recommended patch size is greater than the maximum value of d s = 4 obtained here, see for example  [14]. In the experiments the NLM-B algorithm appears much slower than NLM-P/NLM-Pa, with a CPU-time ratio between 6 (Color IPOL database, σ = 25) and 49 (Gray-level IPOL database, σ = 83). This may seem surprising in view of  [1]  (Section 5. 5. 2) where the blockwise NLM version is presented as a way to reduce the complexity of the pixelwise NLM. The reason is in the block overlapping that is not lowered in the NLM-B implementation  [4] : using fewer blocks in the aggregation procedure would reduce the computation time, but probably also the denoising performance.
Another element to explain the difference in speed between NLM-B and NLM-P/NLM-Pa is in the search window size, given by the parameter D s. In the textbook case where σ = 20 and for the Gray-level IPOL database, the speed ratio reaches almost 18 while the patch size is larger for NLM-Pa (d s = 3 corresponds to a patch size of 7 × 7) than for NLM-B (patch size of 3 × 3). On the other hand, the search window size is only 11 × 11 for NLM-Pa where it is 21 × 21 for NLM-B. In fact, whatever the database and the noise standard deviation, the optimal search window size for NLM-P and NLM-Pa is always less than or equal to the one of NLM-B. Though one can not compare them precisely (the algorithms being slightly different), this small optimal size is consistent with that given in  [19]  and the study confirms the common overestimation of the search window size.
Source Code The source code associated to this publication is available at the IPOL webpage  6. It corresponds to an ANSI C (C89) implementation of Algorithm 1 and Algorithm 5. If available it uses the OpenMP 3. 0 API to allow parallel computation. The main difference between the pseudo-code and the source code is in the way tables and in particular images are addressed. Indeed, the computer memory is a one-dimensional array of words and multidimensional arrays have to be addressed as one-dimensional ones. This is a very common constraint in image processing and the reader should not be confused by the necessary adaptation, including pointer manipulation to speed up array indexing. In the following is detailed the way the L2 array is addressed in the SIL algorithm, since it raises the question of which sum of lines has to be recorded.
Results and Discussion For each CNS disorder and Cancer type independently, we undertook meta-analyses from a large collection of microarray gene expression datasets to identify the genes that are significantly up-and down-regulated in disease when compared with their corresponding healthy control samples (Differentially Expressed Genes -DEGs -, FDR corrected p-value (q-value),0. 05, see Methods and Table  S1 ). Then, the DEGs of the CNS disorders and Cancer types were compared to each others. There were significant overlaps (Fisher's exact test, corrected p-value (qvalue),0. 05, see Methods) between the DEGs upregulated in CNS disorders and those downregulated in Cancers. Similarly, DEGs downregulated in CNS disorders overlapped significantly with DEGs upregulated in Cancers (Figure  1A ).
These diseases, for which large enough expression datasets were available, included Asthma, HIV, Malaria, Dystrophy and Sarcoidosis (see Methods). Significant overlaps were observed between DEGs of all these diseases and DEGS of CNS disorders or Cancers (Figure  1B ). However, patterns of expression deregulation in opposite directions, which were found to be characteristic of the relation between CNS disorders and Cancers, are in most cases not observed with these other genetic or infectious diseases (Figure  1B ). Indeed, the overlaps are predominantly significant between DEGs deregulated in the same directions, i. e. between upregulated genes of the different diseases (or conversely between down-regulated genes), and could be a signature of putative positive comorbidities. It is to note that Malaria and CNS disorders DEGs present overlaps between DEGs deregulated in opposite directions, contrarily to what is detected for other diseases.
05, see Methods and Table  S3 ) in each of the six diseases independently. Among all the KEGG  [25]  pathways significantly up-and down-regulated in the 6 diseases, 30 are shared by CNS disorders and Cancers (i. e. , significantly deregulated in at least 1 CNS disorder and 1 Cancer type). Strikingly, of these 30 shared pathways, 24 (80%) are deregulated in opposite directions in CNS disorders and Cancers (Figure  2 , 63% and 86% for the Biocarta (http://www. biocarta. com/) and Reactome  [26]  databases, respectively, Figure  S2 ). The p53 signalling pathway is an anticipated candidate for deregulations in these diseases and for a role in inverse comorbidity  [4]. Indeed, deregulations of the p53 signalling pathway are associated with the initiation and progression of Cancers, while recent studies also point to a role for this pathway in CNS disorders  [27].
As such, specific polymorphisms in the TP53 gene are found in SCZ patients  [27]. Although the TP53 gene itself does not appear to be differentially regulated in our analysis, the p53 pathway is upregulated in CRC and LC, while it is downregulated in PD, AD and SCZ (Reactome database; Figure  S2 , Table  S3 ). Similarly, the Wnt pathway may be particularly relevant as mutations in the genes encoding APC and b-catenin, elements of the Wnt pathway, have been described in CRC, while b-amyloid induced neurotoxicity in AD has been associated with impaired Wnt signalling  [4, 18]. Furthermore, alterations in the Wnt signalling pathway are known to be involved in SCZ  [28]. In our meta-analyses, we found the Wnt pathway to be downregulated in.
2, FARSA, MRPL4, API5, RP3-425P12. 4, RFC3, RANBP9, TFCP2, GMDS, CCNB1, TMEM177, GUF1, HSPA13, NMD3, GCFC2, TUBGCP5, TBCE, YKT6, PHF14, BRCC3 doi:10. 1371/journal. pgen. 1004173. t001 AD and PD, and upregulated in CRC (Reactome database; Figure  S2 ). Aside the Wnt and p53 pathways, our analysis reveals other pathways related to protein folding and protein degradation displaying patterns of downregulation in CNS disorders and upregulation in Cancers, and that may be relevant for inverse comorbidity. For instance, the Ubiquitin/Proteasome system is consistently downregulated in CNS disorders and upregulated in Cancers according to the three pathway databases analyzed (Figure  2 , Figure  S2 , Table  S3 ). The inverse relationship between the levels of expression deregulations of these pathways possibly suggests opposite roles in CNS disorders and Cancers.
Gene expression data Gene expression raw data (CEL files) were downloaded from NCBI GEO omnibus (GEO, http://www. ncbi. nlm. nih. gov/geo/), EBI ArrayExpress (AE, http://www. ebi. ac. uk/arrayexpress/) and Stanley Medical Research Institute, Online Genomics Database (SMRI, https://www. stanleygenomics. org) for Colorectal (CRC), Lung (LC) and Prostate (PC) Cancers, Alzheimer's disease (AD), Parkinson's disease (PD) and Schizophrenia (SZC), and for Asthma, HIV, Malaria, Dystrophy, Sarcoidosis (Text S1). For each disease, studies were filtered to select only the ones profiling at least 9 samples for disease and control cases, with Affymetrix arrays (GeneChip Human Genome U133 Plus 2. 0, GeneChip Human Genome U133A and GeneChip Human Genome U133A 2. 0 containing 23,945, 14,538 and 14,538 genes, respectively).
Microarray gene expression preprocessing and meta-analyses The collected microarray data from the different studies were normalized with frozen Robust Multiarray Analysis (fRMA)  [30]  from the R Affy package  [31]. Then, microarray meta-analyses were undertaken for each disease independently using the R MetaDE package  [32]. MetaDE implements meta-analysis methods for differential expression analysis, and we used the Fixed Effects Model (FEM)  [33]. This model assumes that the standardized effect sizes can be combined between the different studies, and that the variations in observed effects are only due to random error  [34] [35]. Similar results were obtained with the Random Effects Model (REM) approach that allows heterogeneity in the effect sizes between the different datasets (unpublished observations).
GSEA analyses For each CNS disorder and Cancer type independently, a gene set enrichment analysis was undertaken using GSEA  [36]  on the output of the meta-analyses, and focusing on KEGG  [24] , Biocarta (http://www. biocarta. com/) and Reactome  [26]  pathway databases. Significant pathways were selected as those with q-value (FDR),0. 05. Significant pathways in each disease were then compared to each others, and a network of pathways was built (Figure  2 , Figure  S2 ). For the KEGG pathways, further classification of the pathways in Metabolism, Genetic Information Processing, Cellular Processes, Environmental Processes and Organismal Processes, as provided by KEGG, was done (Figure  2 , Table  S2 ). Pathways corresponding to Human Diseases were discarded. Biocarta pathways (http://www.
The following two sheets list the DEGs that are deregulated in opposite direction in at least two CNS disorders and two Cancer types. Sheets number 5 and 6 show the DEGs deregulated in opposite directions in at least one CNS disorder and one Cancer type. The last four sheets enumerate the DEGs deregulated in opposite directions in at least two CNS disorders and one Cancer type and DEGs deregulated in opposite directions in at least one CNS disorder and two Cancer types. (XLS) Table  S3  Pathways significantly up-and down-regulated in each CNS disorder and Cancer type after Gene set enrichment analysis (GSEA, q-value,0. 05) considering KEGG, Reactome and Biocarta pathway databases. (XLS) Text S1 Workflow of the analysis pipeline and microarray expression datasets used in the meta-analyses. (PDF) Text S2 Dynamic report of the analysis pipeline.
Cross validation with 44 k Agilent microarray To analyze the gene expression changes triggered by over-expression of SRSF2 in H358 lung cancer cells, we performed an analysis using 44 k Agilent™ microarrays. These data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE50467. A lot of genes were differentially expressed between SRSF2-over-expressing H358 lung cancer cells and H358 control cells (1,709 deregulated probes; ≥ 2. 0 FC, P-value ≤ 0. 05 by t-test with FDR; Additional file 2: Table  S1 ), corresponding to 52% upand 48% down-regulations. Hence, in addition to its already reported role in the regulation of VEGFA splicing, over-expression of SRSF2 led to the regulation of transcript abundance of many additional genes, including genes present on the 15 k custom chip (Additional file 3: Table  S2 ), as demonstrated with the 44 k Agilent™ microarrays.
HER1/EGFR protein expression analysis The 15 k custom microarray predicted multiple exon skipping in the 3′ region of HER1/EGFR in SRSF2-overexpressing H358 lung cancer cells, which was confirmed by quantitative RT-PCR. These observations led us to test whether these splicing events would have an impact on the amount of the HER1/EGFR protein. Western blotting analysis was performed using various anti-EGFR antibodies directed against the N-terminal (31G7) or the C-terminal (D38B1) portion of the protein, as well as against the phosphorylated active form of EGFR (P-HER1/ EGFR-Tyr1068). The results demonstrated that SRSF2 overexpression in H358 cells led to a decrease in EGFR protein amount, as detected using all antibodies (Figure  2 ). These data suggested that SRSF2-regulated EGFR pre-mRNA splicing strongly affects EGFR protein expression.
Microarray hybridization Analyses of the H358/Tet-On/SRSF2 mRNA content were performed on both the 15 k custom microarray and the 44 k Whole Human Genome microarray (Agilent™) that contains roughly 41,000 probes, providing full coverage of human transcripts. Double-stranded cDNA was synthesized from 500 ng of total RNA using the Quick Amp Labeling kit, One-color, as instructed by the manufacturer (Agilent™). Labeling with cyanine3-CTP, fragmentation of cRNA, hybridization and washing were performed according to the manufacturer's instructions. The microarrays were scanned and the data were extracted with the Agilent™ Feature Extraction Software.
Gene expression analysis The bioinformatics analysis of the 15 k custom microarray data and the comparison of 15 k chip results with 44 k commercial chip results were performed by GenoSplice technology™. Concerning the 15 k custom microarray data analysis, data were normalized using median normalization based on Agilent™ control genes. Gene expression level was assessed using constitutive probes only (i. e. , probes targeting regions that are not known to be alternative regions). For each gene of interest, all possible splicing patterns were defined and analyzed. All types of alternative events can be analyzed: alternative first exons, alternative terminal exons, cassette exons, mutually exclusive exons, alternative 5′ donor splice sites, alternative 3′ acceptor splice sites, and intron retentions.
Analyses were performed using unpaired Student's t-test on the splicing-index as previously described  [46, 47]. Results were considered statistically significant for unadjusted P-values ≤ 0. 05 and fold-changes ≥ 1. 5. After bioinformatics analysis of microarray data, a manual inspection using the GenoSplice EASANA™ interface was conducted to select highconfidence events. An alternative 44 k bioinformatics analysis was carried out. Raw gene expression data were imported into the GeneSpring GX 11. 0. 2 software program (Agilent™). Genes with missing values in more than 25% of the samples were excluded from the analysis. A 2-fold cut-off difference was applied to select the up-and down-regulated genes (P-value ≤ 0. 05 by ttest with Benjamini-Hochberg false discovery rate).
Real-time polymerase chain reaction analysis Regulation events detected in the 15 k custom and 44 k commercial microarrays were analyzed by quantitative RT-PCR using RNA isolated from cell preparations separate from those originally used for microarray hybridization. Reverse transcription was performed as instructed by the manufacturer (Applied Biosystems), as described previously, and quantitative RT-PCR was conducted using the SYBR GREEN PCR Master Mix (Applied Biosystems), according to the manufacturer's instructions, with an ABI 7300 real-time PCR system (Applied Biosystems). All determinations were performed in duplicate, normalized against beta-2-microglobulin or GAPDH as internal control genes. These reference transcripts were found to be stable when surveyed in several cell culture systems (data not shown).
Acknowledgements We thank  Dr. Pascal Loyer  for his gift of the E1A plasmid minigene, and  Dr. Sandrine Jacolot  for her help with analysis of the microarray data. MP was recipient of a fellowship from the  Région Bretagne (ARED). This work was supported by the  INSERM , the  Université de Bretagne Occidentale , the  Ligue Contre le Cancer (Comité du Finistère) , and the  BioIntelligence program.
Flux Data We updated our POC and mineral export database derived from 234 Th measurements published in Le Moigne et al. [2012]  with data from six additional cruises (JC068, D357, D361, D369, JR271, and JR274). This extended data set includes observations from the Arctic ocean, the high-latitude North Atlantic (HLNA), the equatorial Atlantic, and the SO (Figure  S1  in the supporting information). Locations, sampling dates, and references are given in Table  S1  (supporting information). We used the 234 Th "small-volume" technique with inductively coupled plasma-mass spectrometry measurement of 234 Th extraction efficiency  [Pike et al. , 2005]. Vertical profiles of 234 Th activity were integrated to 100 m and converted to estimates of downward 234 Th flux using a one-dimensional steady state model  [Buesseler et al.
, 2002; Honda and Watanabe, 2010; Le Moigne et al. , 2012]. We then used the geographically weighted regression (GWR) technique. The application of GWR to ocean particle flux data was carefully described and tested by  Wilson et al. [2012]. We followed a similar approach to estimate geographically weighted carrying coefficients for each of the ballast components and the fraction of POC not associated with any mineral. In essence, the GWR analysis calculates "local carrying coefficients" at each sampling location for the ballast minerals and the residual, nonassociated POC  [Wilson et al. , 2012]. We used the GWR 4. 0 software  [Nakaya et al. , 2009]  (freely available at http://www. st-andrews. ac. uk/ geoinformatics/gwr/gwr-software/). We used an adaptive Gaussian Kernel, which groups the data by the nearest neighbor approach.
Acknowledgments We would like to thank the captains and crews of the R. R. S Discovery,  R. R. S James Cook , and  R. R. S. James Clark Ross. We also gratefully thank  Andy Milton ,  Mark Stinchcombe ,  Adrian Martin ,  Gary Watmough ,  Elena Ceballos-Romero ,  Mathew Cooper ,  Jeffrey Benson , and the National Marine Facilities technicians. We would like to give thanks to anonymous reviewers for their comments. This work was supported by the  UK's Ocean acidification (NERC ,  NE/H017097/1 ),  NERC  ( NE/G015732/1 );  UK GEOTRACES (NE/H006095/1 ) and the  EU CalMarO  ( 215157 ) programs. Data are held at the  British Oceanographic Data Centre  (http://www. bodc. ac. uk/). Peter Strutton  thanks two anonymous reviewers for their assistance in evaluating this paper.
The Compact Muon Solenoid (CMS) detector has excellent capabilities to perform b-jet identification (b tagging) measurements as demonstrated in Ref. [32]. Measurements of the b-jet cross section  [33]  and b-jet angular correlations  [34]  have been performed in pp collisions at 7 TeV. This Letter presents the first measurements of b-jet production in heavy-ion collisions using a dataset corresponding to an integrated luminosity of 150 µb -1 of PbPb collisions at √ s NN = 2. 76 TeV delivered by the LHC in 2011. The comparison measurements are performed with a dataset consisting of pp data recorded in 2013 and corresponding to an integrated luminosity of 5. 3 pb -1 at √ s = 2. 76 TeV. The central feature of the CMS apparatus is a superconducting solenoid providing a magnetic field of 3. 8 T.
The pp luminosity has an uncertainty of 3. 6%, while the uncertainty in T AA varies from about 4% for centrality 0-10% to 15% for 50-100%  [16]. Figure  3  shows the centrality-integrated b-jet nuclear modification factor (R AA ), which is the ratio of the T AA -normalized PbPb yield and the measured pp cross section in Fig. 2 , as a function of p T. The jet and b-tagging systematic uncertainties in R AA are obtained by varying the pp and PbPb data simultaneously. This results in partial cancellation, giving a systematic uncertainty of 16-21%, which is dominated by the b-tagging uncertainty. A significant suppression of the yield with respect to the pp expectation is observed in b jets, which is indicative of the parton energy loss in the hot medium. No strong trend is observed as a function of p T , although the data hint a modest rise at higher p T.
Simulation has been widely adopted for the evaluation of novel protocols or other designs for networked low-power embedded systems, especially for wireless sensor networks (WSNs). A large number of simulation tools have been developed for WSNs in the past few years. However, different tools may emphasize on different features. For example, general network simulators mainly focus on the high-level performance evaluation with certain assumptions, while some SystemC-based simulators have been developed recently to realize the hardware and software co-design of sensor node at the system level. Therefore, it is necessary to study the different modeling methodologies and to distinguish the various features of the existing WSN simulators. In this paper, we propose a taxonomy that categorizes the existing simulation tools into four groups, i.