dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiple B = 0 image noise variance Estimator (MUBE) If multiple non-DWI (i. e. , b = 0 s/mm 2 , termed b0 henceforth) images are available in the dataset we can use image differences to robustly estimate the noise pattern. Landman et al. [40]  presented a method for local noise variance estimation using the differences of two non diffusion weighted images that robustly estimated the noise pattern assuming that Rician noise can be well approximated by a Gaussian distribution if the local SNR is high enough (SNR. 5). We propose a similar approach, albeit without assuming Gaussian distributed noise by using the analytical correction scheme proposed by Koay and Basser  [38]. Given a set of n b0 images (n. = 2), we first compute global PCA decomposition of image series (i. e. we perform a PCA decomposition of a matrix formed by as many columns as image voxels and as many rows as images, see Fig.
Simulated dataset A numerical phantom consisting of a set of diffusion-weighted images was generated using the Numerical Fiber Generator (NFG) software package  [41]. The simulated images were generated with a diffusion-weighted response function based on the diffusion tensor model with a fractional anisotropy of FA = 0. 8, apparent diffusion coefficient ADC = 0. 9610 -3 mm 2 /s, 7 b = 0 s/mm 2 images and 60 DWIs along 60 uniformly distributed diffusiongradient directions (b = 3000 s/mm 2 ), with volume field of view dimensions of 10061006100 voxels, with a voxel size of 26262 mm 3.
Although we have no news about the use of this method for DWI denoising we decided to include this method in the comparison since this method is able to deal with spatially varying noise fields typically present on parallel acquired DWI. This method was also applied to each DWI gradient image separately. All three methods were run with their default parameters. In the two NLM variants, we used our own implementation of the method (Matlab mex file) while in the second case we used the command JointLMMSE utility integrated in the Slicer 3. 0 software  [45]. The default routines available in ''dtifit'' (part of the FSL  [46]  software distribution, FMRIB, Oxford) were used to process the data. In particular, the tensor model was fitted using least squares  [47]. The tensor was diagonalized and quantitative measures extracted (i.
e. , FA and ADC). Probabilistic tractography was performed using bedpostx (also part of FSL), which uses Markov Chain Monte Carlo sampling to build distributions of diffusion Figure  9. Tractography of the genu of the corpus callosum (first row), the posterior limb of the internal capsule (second row) and right crus of fornix (third row), overlaid on the FA map derived from the LPCA-denoised data. Isosurfaces represent those voxels that are intersected by at least 100 tracts in the case of the corpus callosum and internal capsule, and by at least 7 in the case of the fornix. Notice the limited propagation of all the tracts derived from the original, low SNR, data sets (first column), with the crus of the fornix barely visible (arrowhead). The corpus callosum was extended by all algorithms, but reached even farther in the LPCA-denoised data set (curved arrow).
The crus of the fornix is extended and its width increased by all denoising methods, but JLMMSE and ANLM-denoised data sets produced spurious tracks (double arrows), which were not produced in the LPCA and NLM-denoised data sets. doi:10. 1371/journal. pone. 0073021. g009 parameters, including that of the principal diffusion orientation, which is then used to repeatedly seed tracks that sample from such a distribution to follow particular trajectories in a stochastic fashion. 5000 tracks were initiated per seed voxel, and all defaults defined in the bedpostx and probtrackx programs were used.
Simulated data experiment The simulated phantom data was corrupted with different levels of Rician noise ranged from 1% to 9% of the maximum non-DWI signal amplitude (both stationary and spatially varying noise patterns were applied). To measure the different filter performances the root-mean-square-error (RMSE) measure was used. For the spatially stationary noise case all JLMMSE, NLM and our proposed method used the noise estimation provided by MUBE method while ANLM internally estimated the amount of noise. For the spatially varying noise case, both NLM and JLMMSE methods assume constant noise across the volume and therefore they were run using the noise level present at the background which is typically used to estimate noise level on MRI. Similarly to the stationary noise case ANLM method internally estimated the local noise level and finally our proposed method used the noise estimation provided by MUBE method.
In-vivo real data experiment The in-vivo real dataset was filtered with the four methods compared and the resulting images were analyzed. Noise was estimated using the proposed SIBE method since only one b0 image was available for this dataset. As expected, the noise field estimated by SIBE showed a spatially varying noise pattern due to the use of a parallel imaging SENSE sequence (see Fig. 7 ). ANLM, NLM and JLMMSE methods were run with their default parameters. In NLM method the noise standard deviation parameter was estimated from the image background. ANLM and JLMMSE methods internally estimated the noise level. As can be noticed in fig. 7 , the proposed LPCA method produced a clear and contrasted image. The NLM method showed some residual noise due to the noise level underestimation at image central areas.
In fact, this method does not take into account the spatially varying noise. The JLMMSE method showed some artifacts at frontal areas and a slightly change of contrast probably due to an inaccurate Rician noise bias correction. ANLM perform very well but seemed to over smooth some image details. Diffusion measures were also performed to evaluate the performance of the four methods compared (fig. 8 ). It can be noted that LPCA and ANLM methods provided the greatest improvements, particularly evident in the FA and ADC maps. The deep brain structures in the FA map of the LPCA-denoised data still show slight noise, albeit considerably less than the original data. All denoising methods improved the diffusion measurements with the exception of JLMMSE (this could be caused by an inaccurate noise estimation since this method was not designed to deal with spatially varying noise patterns).
It is also worth to note that the uncertainty on the estimation of the principal diffusivity was drastically reduced by all denoising methods. The greatest reduction was obtained using the LPCA method. Finally, tractography experiments were performed to evaluate the effect of the denoising algorithms on the directional information of water diffusion. Tractography of the genu of the corpus callosum, the posterior limb of the internal capsule and right crus of fornix were obtained. Tracts for the corpus callosum and internal capsule result from seeding a single voxel, whereas the fornix was seeded in 40 voxels at the level of the body of the fornix, with an AND operator at the level of the hippocampus. The same tracking criteria were used for all data sets. Figure  9  shows tractography results of the different denoised data using the compared methods.
As can observed LPCA produced the most coherent results of all compared methods. JLMMSE and ANLM methods produced spurious tracks not present on LPCA and NLM-denoised data. Regarding the processing times of the compared methods, the fastest one was the JLMMSE with only 40 seconds, the second was the proposed LPCA method with around 3 minutes and finally NLM and ANLM methods took over 8 and 15 minutes correspondently. All experiments were run on an i7 intel machine with 16 GB RAM using Matlab 2011a and Windows 7.
Discussion In this paper, a new method for DWI denoising is proposed and evaluated using both synthetic and real clinical data. In addition, a comparison with related state-of-the-art methods is provided. Despite its simplicity, we demonstrated improvements on DWI denoising and diffusion parameter estimation using our LPCA filter compared to previously proposed methods. Our proposed filter removes noise in multi-directional DWI data by using an overcomplete local PCA-based decomposition. Such noise reduction capabilities can be understood due to high level of local profile redundancy which allows representing diffusion profiles using only a small number of components thus enabling to remove non signal related components (i. e. noise) efficiently. In addition, we have shown that our proposed filter not only reduces the noise present in the images, but also the bias induced by the Rician nature of the noise, thus producing diffusion parameters that better reflect the characteristics of the tissue, rather than noise-biased measurements.
This has been clearly shown with the ADC and FA results where the lower parameter estimation error was coherent for all the noise levels tested. Moreover, tractography results were improved using our denoised method, considerably reducing the uncertainty of fiber directions and increasing the probability of connections between voxels within a given tract. Therefore, tracts can be reconstructed more reliably and thus the analyses of the tissue microstructure by means of tensor-derived metrics would be more sensitive. The quantitative diffusion maps derived from the simulated data sets showed that the bias introduced by Rician noise in the estimation of quantitative diffusion parameters are minimized when denoising of the DWI is performed, with a clear advantage of the LPCA method compared to JLMME and NLM based algorithms.
This was also true in the case of inhomogeneous noise, a scenario ever more commonly encountered, as parallel imaging methods are becoming commonplace. Moreover, the directional information of the diffusion data is not affected by our method, showing negligible deviation from the true principal diffusivity compared to the noise-free phantom and clearly outperforming both the noisy phantom as well as the other denoising algorithms tested. For clinical dataset, this translated into better depictions of connectivity with a higher degree of confidence. Our current method can be easily applied to any mutidirectional DWI data set, independently of the diffusion analysis performed (e. g. , DTI, HARDI, q-ball, etc. ), potentially improving any quantitative measure derived from them. This has immediate benefits for the analysis of brain connectivity, as well as the study of tissue microstructure in the healthy and diseased brain.
This work has been supported by the  Spanish  grant  TIN2011-26727  from  Ministerio de Ciencia e Innovacion. This work has been also partially supported by the  French grant  '' HR-DTI ''  ANR-10-LABX-57  funded by the TRAIL from the  French Agence Nationale de la Recherche  within the context of the Investments for the  Future program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Fibrin polymerization (37 °C, 7% CO 2 , 2 h) led to a rectangular fibrin-cell-gel formation around the tips of the silicone posts. The racks were then transferred to fresh 24-well plates containing culture medium (DMEM (Biochrom F0415), 10% horse serum (Gibco 26050), 2% chick embryo extract, 1% Penicillin/ Streptomycin (Gibco 15140), insulin (10 μg/ml, Sigma I9278) and aprotinin (33 μg/ml, Sigma A1153)) and kept in cell culture (21% oxygen) for up to 4 weeks. On day 5 cytosine β-D-arabinofuranoside (25 μg/ml, Sigma C1768) was added to culture medium for 48 h. Spontaneous contractility of EHTs was recorded over time by a video camera and automatically evaluated by a customized CTMV software (Pforzheim, Germany) as recently published  [18]. Contractility parameters analyzed were frequency, force, contraction and relaxation times (T1, T2 determined at 10% of peak maximum, respectively).
To analyze contractility under constant beating rate and perfusion a novel microscope-based set-up was constructed as described in detail in the Supplemental Material. EHTs were positioned in 24-well glass bottom plates (IWAKI 090615) in a cellculture-like incubation chamber, electrically paced (6 Hz; 7 V; 5 ms duration, S88X Stimulator, Grass, Astro-Med, Inc. ) and continuously perfused (37 °C, 95% O 2 , 5% CO 2 ) with Tyrode's solution (in mM: NaCl 120, KCl 5. 4, MgCl 2 1. 0, CaCl 2 0. 2-1. 8, NaH 2 PO 4 0. 4, NaHCO 3 22. 6, glucose 5. 0, Na 2 EDTA 0. 05, ascorbic acid 0. 3; 6 ml/min). Contractile parameters were evaluated by averaging 50-100 peaks using a customized software (CTMV).
Statistical Analysis. Values obtained for , ,  :  ratio in IgG4-RD patients were compared with values from healthy controls. Values from patients with active disease (serum sample collected at diagnosis or relapse) were compared with values obtained in few patients with inactive disease. Comparison of distributions was performed by using the Mann-Whitney test. Differences were considered significant when  < 0. 05. Correlation was evaluated using Spearman's test. Graphic representation and statistical analysis were obtained using GraphPad Prism software (San Diego, CA, USA).
0 = α∈I 1 Λ 4 Γ α = H 3 (Γ; C 1 )    β 3 Z = Λ 4 Γ = H 3 (Γ; C 0 0 )    H 2 (Γ; C 2 ) = H 0 (Ω)    0 = α∈I 1 Λ 3 Γ α = H 2 (Γ; C 1 )    β 2 Z 4 = Λ 3 Γ = H 2 (Γ; C 0 0 )    H 1 (Γ; C 2 ) = H 1 (Ω)    Z L 1 = α∈I 1 Λ 2 Γ α = H 1 (Γ; C 1 )    β 1 Z ( 2ν 2 ) = Λ 2 Γ = H 1 (Γ; C 0 0 )    H 0 (Γ; C 2 ) = H 2 (Ω)    0 → α∈I 1 Λ 1 Γ α → H 0 (Γ; C 1 ) → α∈I 1 ker ǫ α → 0    β ′ 0    β 0    β ′′ 0 0 → Λ 1 Γ → H 0 (Γ; C 0 0 ) → ker ǫ → 0    0 5. 3. Example computations. All examples discussed below have to some extent been calculated by computer. For this purpose, we have used the computer algebra system GAP  [26] , the GAP package Cryst  [16, 17] , as well as further software written in the GAP language. It should be emphasized that these computations are not numerical, but use integers and rationals of unlimited size or precision.
Neglecting the possibility of programming errors, they must be regarded as exact. One piece of information that needs to be computed is the set of all intersections of singular affine subspaces, along with their incidence relations. This is done with code based on the Wyckoff position routines from the Cryst package. The set of singular affine subspaces is invariant under the action of a space group. Cryst contains routines to compute intersections of such affine subspaces and provides an action of space group elements on affine subspaces, which allows to compute space group orbits. These routines, or variants thereof, are used to determine the space group orbits of representatives of the singular affine subspaces, and to decompose them into translation orbits. The intersections of the affine subspaces from two translation orbits is the union of finitely many translation orbits of other affine subspaces.
The structural model was refined using BUSTER/TNT  (27). The Protein Data Bank accession code is 4AUP; see Table  1  for refinement statistics. Phospholipase and Protease Activity Assays-PLA 2 activity was measured by quantifying free linoleic acid released from 2-linoleoyl-1-palmitoyl-sn-glycero-3-phosphocholine (Sigma-Aldrich) using a colorimetric assay (NEFA-HR(2), Wako). Reaction mixtures contained, in a final volume of 40 l, 20 mM HEPES (pH 8. 0), 150 mM NaCl, 30 mM CaCl 2 , and 5 mM phospholipid substrate dissolved in 2% (v/v) Triton X-100, plus 1 or 0. 025 M enzyme for the 22-and 14. 5-kDa forms of TbSP1, respectively. After a 10-min incubation at 37 °C, 7-l aliquots of each reaction mixture were treated with NEFA-HR(2) according to the manufacturer's instructions, followed by determination of absorbance at 546 nm and quantification of the released fatty acids using oleic acid as an external standard.
Autoproteolytic activity was routinely assayed by incubating recombinant TbSP1, TbSP1-thioredoxin (Trx) fusion proteins, or the natural protein purified from T. borchii mycelia (see below) at a concentration of 20 M for various lengths of time (from a few min up to several h, depending on the source of the protein and on reaction conditions) in 20 mM Tris-HCl at 37 °C. Proteolytically inactive pro-TbSP1 derived from inclusion bodies was used as substrate (30 M) for some experiments, as specified under "Results. " Digestion products were quantified by SDS-PAGE, followed by data analysis with the Multi-Analyst/PC software (Bio-Rad) and by MALDI-TOF MS. The latter was performed by mixing 5 l of each sample with an equal volume of 1% trifluoroacetic acid (TFA), followed by salt removal on a ZipTip C18 pipette tip (Millipore) prewashed with acetonitrile and 0.
1% TFA, followed by an additional wash with 10 l of 0. 1% TFA. Protein was eluted with 2 l of the matrix solution containing sinapinic acid (10 mg/ml) dissolved in a 55% acetonitrile/water mixture plus 0. 1% TFA. MS data were analyzed with Masslynx TM (version 4. 1). Eluted proteins were spotted on a target plate, dried under a flow of nitrogen, and analyzed by a MALDI mass spectrometer (Waters-Micromass) operated in linear positive mode with the following parameters: 15,000 V source voltage, 1,550 V pulse voltage, 1,800 V MCP detector. For temperature dependence and heat stability experiments, TbSP1 (BL21-Ori), dissolved in 20 mM Tris-HCl (pH 8. 0) at a 20 or 0. 05 M concentration, in the case of autoproteolysis (15 h) or phospholipase (10 min) assays, respectively, was incubated at increasing temperatures (from 25 to 100 °C) in a gradient thermal cycler (Mastercycler Eppendorf).
Unless otherwise indicated, incubation mixtures contained the 2-linoleoyl-1-palmitoyl-sn-glycero-3-phosphocholine (PC) substrate (5 mM final concentration). After heating at 100 °C (20 min), the protein was incubated for 20 min at 4 °C prior to functional assays. Self-digestion of pro-TbSP1 and linoleic acid release were monitored and quantified by SDS-PAGE and the NEFA-HR(2) colorimetric assay, respectively, as described above. Unless otherwise indicated, phospholipase and protease activity values are the mean Ϯ S. D. ) of at least three independent experiments. Self-cleavage reaction (apparent rate constants) and calcium dependence (apparent Ca 2ϩ dissociation constants) data were analyzed with the SigmaPlot software package (SPSS Inc. , Chicago, IL). Purification and Immunoblot Analysis of TbSP1 Extracted from T.
C, map of the cleavage sites identified by MALDI-TOF analysis of the digestion products generated upon incubation of TbSP1 from the BL21-Ori (empty arrowheads) or the BL21-CP (filled arrowheads) strain; upward pointing gray arrowheads indicate the N-terminal amino acid residues revealed by x-ray analysis of TbSP1 crystals. D, recombinant 22-kDa TbSP1, prepurified by metal affinity chromatography, was run on a Superdex 75 5/150 GL column at 4 °C (solid line), and the autoproteolytic activity of individual fractions was assayed by SDS-PAGE analysis as in A (dots); the data shown are from one of three replicates that produced nearly identical results. The elution times and molecular weights of protein standards run on the same column are reported above the chromatogram. E, same as D with the mature (14.
Experimentally determined (TbSP1, p15, and S. violaceoruber sPLA 2 s) and predicted (TmelPLA 2 , sPlaA, and sPlaB) secretion signal peptides are shown as light gray bars. GenBank TM accession numbers are as follows: AAF80454 (TbSP1), CAZ81513 (TmelPLA 2 ), BAD01581 (sPlaA), BAD01582 (sPlaB), BAB70714 (p15), and AAQ55264 (S. violaceoruber sPLA 2 ).
Acknowledgments-We thank Francis Martin and his group ("Ecogenomics of Interactions" Laboratory UMR "Interactions Arbres/Micro-Organismes" INRA-Nancy, France) for sharing T. melanosporum gene expression data and Arturo Roberto Viscomi (Department of Biochemistry and Molecular Biology, University of Parma) for technical support. The contributions of undergraduate research students (Marianna Blengino, Monica Campanini, Giuliana Cirillo, Mariangela Coletta, Cristina Copelli,  Marta Fragnito ,  Elisabetta Lombardi , and  Marilena Margiotta ) to different phases of this work are also gratefully acknowledged.
* This work was supported by grants from the  Italian Ministry of Education and Research (MIUR-PRIN)  (to G. L. R. and S. O. ) and by a grant from the  Fondazione Cariparma  (to S. O. ). □ S This article contains supplemental Table 1 and Figs. S1-S4. The atomic coordinates and structure factors (code  4AUP ) have been deposited in the Protein Data Bank (http://wwpdb. org/).
Production of recombinant HMGB1 (rHMGB1) The full length version of human HMGB1 (clone image 5268698, accession number BC066889) was modified by directed mutagenesis to remove the endogenous NdeI restriction site using 59-AGGCAAAATGTCATCTTATGCATTTTTTGTG-39 and 59-CACAAAAAATGCATAAGATGACATTTTGCCT-39. It was then amplified by PCR using primers 59-AACCATGGG-CAAAGGAGATCCTAA-39 and 59-TTTGAATTCTTGCTGCCGCGCGGCACCAGTTCAT-CATCATCATCTTCTT-39 and subsequently cloned in pET28 vector using NcoI and EcoRI sites. This lead to a sequence encoding HMGB1 -thrombin site -C-terminal his-tag. After transformation of BL21-codon plus E. Coli bacteria (Agilent), culture and induction were done as described  [23]. Cells were harvested at 3,000 g for 15 min at 4uC. All subsequent steps were done at 4uC. The pellet was resuspended in 10 mL of 25 mmol.
ELISA quantification and Dot-Blot analysis Secretion of cytokines (IL-6, TNF-a) was measured by ELISA kit (eBiosciences, Austria) according to manufacturer's instructions. Of note, no exogenous HMGB1, LPS and IL-6 were detected in commercial cell culture media or complete media by Dot-blot, LAL-assay and ELISA, respectively (data not shown). Secretion of HMGB1 was measured by ELISA kit from IBL international according to manufacturer's instructions (IBL, Germany). 100 mL of SW872 cell culture supernatant were spotted on nitrocellulose membrane (Hybon-C extra, GE Healthcare, France) with a Dot-blot apparatus (Biorad). Membranes were processed as for the Western-blot technique.
Cells were incubated in the following primary antibody, anti-HMGB1 (Clone 2F6, Sigma) 1:200 dilution (2. 5 mg. mL 21 ) in PBS-BSA 1% (w/v) and incubated with the secondary antibody Alexa Fluor 594conjugated anti-mouse IgG (1:1000 dilution). Nuclei were stained with DAPI (Sigma, D9542) at a final concentration of 0. 1 ng. mL 21 ). Coverslips were mounted in Vectashield (Vector Labs, Clinisciences, France), and fluorescence was observed using a Nikon Eclipse E2000-U microscope (Nikon, Tokyo, Japan). Images were obtained using the Nikon Digital sight PS-U1 camera system and the imaging software NIS-Element AR (Nikon, Tokyo, Japan).
HMGB1 localization in the human preadipocyte cell line SW872 We analyzed the basal expression of HMGB1 in the human preadipocyte SW872 cells by RT-PCR. The transcript of HMGB1 was detected in SW872 cells in basal culture conditions (Figure  1A ). We next investigated the cellular localization of HMGB1 in SW872 cells by immunofluorescence. We found that 100% of SW872 cells expressed HMGB1 and with an immunostaining mainly localized to the cytoplasm (Figure  1B ). To confirm that SW872 cells predominantly expressed cytoplasmic HMGB1, we performed Western blot of cellular fractions of cytoplasm (Cy) and nucleus (Nu). The proteins b-actin and lamin B were used as internal controls to assess the enrichment for cytoplasmic and nuclear proteins, respectively. High levels of cytoplasmic and low amount of nuclear HMGB1 were detected in SW872 cells confirming the immunofluorescence data (Figure  1C ).