dataesr/scientific-paragraphs-categorization huggingface dataset:
05). In contrast, the level of HDL-C was significantly lower in plasma of the diabetic mice than in that of the control non-diabetic mice (*P < 0. 05). Supplementation of the diabetic mice with the ethanol-soluble derivative of propolis significantly restored the plasma The body weight and blood biochemical parameters were measured in the 3 groups of mice throughout the experiment period. The pooled data for 3 mice from each group are expressed as the mean value for each parameter ± SEM *P < 0. 05 for diabetic vs. control # P < 0. 05 for diabetic + propolis vs. diabetic + P < 0. 05 for diabetic + propolis versus control (ANOVA followed by Tukey's post-test) lipid profiles to nearly normal levels compared to diabetes induction alone ( # P < 0. 05).
Conclusions Our data suggest that treatment with propolis enhanced Band T-cell chemotaxis efficiency in diabetic mice. Taken together, our data suggest that propolis attenuates abnormal lipid profiles, oxidative stress, inflammation, and impaired lymphocyte proliferation and migration towards chemokines to maintain an efficient lymphocyte immune response. Therefore, the use of propolis is a potential strategy for the treatment of diabetic complications. (See figure on previous page. ) Fig. 4  Modulation of chemokine-mediated chemotaxis by B and T lymphocytes from propolis-treated diabetic mice. PBMCs were subjected to migration assays in response to CCL21 and CXCL12. a Representative dot plots showing the gating strategy to obtain viable lymphocytes based on forward and side scatter and to discriminate between the CD45R/B220 + cell population (B-lymphocytes) and the CD45R/B220 neg cell population (T-lymphocytes) using a PE-CD45R/B220 mAb and flow cytometric analysis.
The input cells and the migrated cells were stained with the PE-CD45R/ B220 mAb. The cells were then counted for 60 seconds via flow cytometry to calculate the percentage of cells that migrated nonspecifically (based on the number of cells that migrated to medium alone) or specifically (based on the number of cells that migrated to medium containing a chemokine). To calculate the percentage of specific migration induced by chemokines, the percentage of cells migrating to medium alone was subtracted from the percentage of cells migrating to the medium containing a chemokine. b & c The data from the different experiments (n = 10) are expressed as the mean percentage of chemokine-mediated specific migration of B and T lymphocytes ± SEM in control mice (open bars), diabetic mice (black bars) or propolis-treated diabetic mice (hatched bars).
In vitro data indicate that T1 values are accurately derived with the simulation approach and in vivo data suggest that myocardium T1 are 15% underestimated when processed with the standard 3-parameters fitting. At the cost of additional acquisitions, this method might be suitable in clinical research protocols for precise tissue characterization as it decorrelates T1 and T2 effects on parametric maps provided by MOLLI sequence and avoids inaccuracies when B1+ is not homogenous throughout the myocardium.
Thus, the T1 map estimation out of MOLLI data would be largely improved by taking into account T2, B1+ effects and inversion imperfection. In particular, estimation of parameters like extracellular volume fraction would greatly benefit from a more accurate quantification method. The aim of the present study was first to use Bloch equations simulation to precisely quantify the errors made when T1 maps are derived using standard post-processing of the MOLLI sequence. Then, we proposed a robust approach to derive more accurate T1 maps using MOLLI data, fast T2 and B1+ maps (compatible with CMR) and Bloch equations simulation. This novel approach was evaluated on phantoms and compared to the standard post-processing method on five healthy volunteers.
Simulation-based post-processing of MOLLI Bloch equations simulation of the MOLLI sequence was implemented using Matlab (The Mathworks, USA) as described in  [17]  and  [15]. Magnetization was described step by step using a general matrix representation: M kþ1 ¼AM k þB ð3Þ where M k is a 3D vector representing magnetization at the k th step of the sequence, A and B are respectively transformation matrix and vector that take into account RF pulses, free precession, longitudinal and transverse relaxation. The equilibrium magnetization is M 0 = [0 0 1] T. Due to magnetization decay during the application of the adiabatic inversion pulse, the inversion efficiency (η = cos(180-inversion pulse)) is commonly reduced  [18]. Using acquisition parameters typically used for MOLLI, the b-SSFP readout consists of 5 preparation pulses (α/6,-α/3, α/2, -2α/3, 5α/6) separated by TR, followed by a 69 ±α pulses train to read the k-space in a linear manner.
This simulation was used to emphasize the deviation between true T1 and T1 values estimated by the standard post-processing approach as a function of T1, T2, nominal flip angle (α), and inversion efficacy η. Four different datasets were simulated using actual timings of the MOLLI sequence, and varying either T1, T2, α or η (Table  1 ). For each set of parameters, the 11 MOLLI points were reordered as a function of TI and the 3-parameter model corresponding to Eq 1 was adjusted to the recovery curve. T1 was then derived from Eq 2 and compared with the true T1. A novel post-processing procedure was then proposed to derive a more accurate T1 map from experimental MOLLI data. First, inversion efficiency was evaluated using Bloch simulation of the hyperbolic secant pulse as well as B0 and B1+ maps.
Monte Carlo simulation A Monte Carlo simulation was implemented to quantify the influence of T2 and B1+ measurements on T1 quantification. A large set of data (10 5 ) was generated using the Bloch equations simulation and standard in vivo parameters: nominal flip angle α nom = 24°, T2 nom = 42 ms, 400 < T1 < 2400 ms and a simulated 60 b. p. m. heart rate. For each dataset, the 11 points of the MOLLI sequence were generated, including a noise corresponding to the experimental SNR. Datasets were then processed either by the standard or the novel post-processing approach using modified T2 and flip angle values (T2 mod and α mod ). These modified values were generated from nominal values and mean standard deviations of T2 and B1+ mapping sequences (std T2 = 3ms and std α = 2. 5°respectively) observed in vivo through the septum: T2 mod ¼ T2 nom þ randnðstd T2 Þ ð 5Þ a mod ¼ a nom þ randnðstd a Þ ð 6Þ where randn(std T2 ) and randn(std α ) are random values picked up in normal distribution of standard deviations std T2 and std α respectively.
Simulations The robustness of the standard post-processing approach of the MOLLI sequence was evaluated by simulations, varying either intrinsic tissue parameters (T1/T2) or sequence parameters (α/η). Different datasets were simulated, and the T1 obtained with the 3-parameters fit was compared to the input T1. As depicted by  Fig 2,  the error made on T1 measurement was quite sensitive to the different parameters. Although the method seemed suitable to measure short T1 values, with high T2, using low nominal flip angle and with good inversion efficiency, discrepancies might become higher than 15% for large-T1 measurement, with high nominal flip angle and poor inversion efficacy (η < 0. 9). As expected, the standard method underestimated longitudinal relaxation times while the novel approach remained consistent for the entire exploration windows (Fig  3B ).
). The accuracy of the measurement of T2 and excitation flip angle α will strongly determine the sensitivity of the post-processing method based on Bloch equations simulation. In particular, it was shown that standard deviations of T1 values are significantly higher using the simulation method compared to the standard 3-parameter fitting approach. Results given by the latter will mainly depend on the signal-to-noise ratio (SNR) of the 11 data points of the MOLLI sequence, whereas the simulation approach sensitivity will be driven by SNR as well as B1 + and T2 confidence intervals. Monte Carlo simulations were performed using confidence intervals for α and T2 given by standard deviations observed in vivo for both parameters. They showed that coefficient of variation are significantly increased when MOLLI data are processed by the simulation approach.
Various methods were recently presented for cardiac T1 mapping. A shortened version of MOLLI with conditional curve fitting (ShMOLLI) was proposed as a means of shortening the breath-hold  [32]. ShMOLLI sequence has demonstrated more insensibility to heart rate variability but at the cost of a loss of precision due to a lower number of fitted data points. Furthermore, as MOLLI sequence, ShMOLLI is also biased by the same confounders (T2 decay during b-SSFP train, imperfect inversion) to estimate long T1 values. With minor modifications, the proposed method can be applied to post-process ShMOLLI data, and may be adaptable to any version of MOLLI-derived sequence. For example, in the present study, we demonstrated that MOLLI could be acquired without recovery period before the second and third inversions without corrupting reconstructed T1 maps.
Qualitative variables were compared using χ 2 tests. We additionally calculated effect sizes (Cohen's d) and its 95% confidence interval based on marginal means and standard error output from the general linear model (after accounting for the appropriate covariates) for the main contrasts between SA and both control groups. A binary logistic regression model was used for sensitivity and specificity analyses. When applying, the alpha level was set at 0. 05 unless a Bonferroni correction was necessary. The threshold for ROI analyses was set at a very conservative Bonferroni-corrected Po 0. 002 (Po0. 05 divided by four ROIs, two sides and four different measures). Statistical analyses were carried out with SPSS 20 (SPSS, Chicago, IL, USA).
40; P = 0. 001) and right VLPFC thickness (r = -0. 26, P = 0. 03). No measure was correlated with Suicide Intent Scale. In patients, HDRS score was correlated with all measures except left VLPFC area and right VLPFC thickness (r between -0. There was no significant assocation between the Iowa Gambling Task total score and any measure, but data were only available in the small Sample 1. Potential for clinical application. We examined the sensitivity and specificity of the neuroimaging measures in correctly classifying individuals with histories of suicide attempt among the 231 participants. As expected, a history of mood disorder had a 100% sensitivity (as all SAs suffered from mood disorder in our study) but a lower specificity (71%). Adding left VLPFC area or volume into the model improved specificity in identifying attempters in a significant but limited manner, reaching 74.
DISCUSSION This study examined structural alterations associated with the vulnerability to suicidal behavior in mood disorders using two complementary analyses in 231 subjects including 67 SAs. It represents the largest neuroimaging study of suicidal behavior in mood disorders to date and was specifically designed to investigate the neural basis of suicidal behavior. After covarying for sample, intracranial volume, gender, age, education, bipolar disorder and medication intake, ROI analyses showed significant group differences in left VLPFC volume measured by Freesurfer, the only measure that discriminated SA from both control groups in our study. Additional measures in VLPFC, OFC and DPFC, although not surviving a very conservative multiple comparison correction, were also different between SAs and HCs, with moderate effect sizes (Cohen's d up to 0.
This should notably be tested with the imaging markers revealed here. Our study presents several limitations. First, pooled data analysis adds heterogeneity when not designed a priori as a multicenter study, due to different acquisition parameters and scanners, which contributes to increased risk of type II errors  47  but not type I error and does not undermine highlighted findings. Second, we included moderately to severely ill and often medicated patients to be more representative of the general clinical population. This may have added heterogeneity although several clinical factors (including bipolar disorder and medication) were controlled for in analyses. Finally, determination of ROIs largely depends on their definition and the atlases, and these only partly overlap for SBM and VBM.
ACKNOWLEDGMENTS Preliminary data and partial data were previously presented at the 2012 and 2013 Organization for  Human Brain Mapping Conferences , and 2013  International Academy of Suicide Research Conferences. Study 1 was supported by the  American Foundation for Suicide Prevention  (to FJ),  Fondation pour la Recherche Médicale  ( Action Dynamique en Psychiatrie  to FJ) and the  Royal Society and University of London Central Research Fund  (to NL). Study 2 was funded by a  Projet Hospitalier de Recherche Clinique  (to FJ and PC). EO received a grant from  Académie Nationale de Médecine. Study 3 was funded by the  Institut Servier  (to FJ), who also funded FC's M. c. YD currently holds a  Fond de Recherche du Québec-Santé (FRQS)  PhD Grant # 24117. FJ currently holds a 'FRQS chercheur-boursier clinicien' salary award.
Furthermore, the dosimetric accuracies were found to be similar. A major strength of this study is that the improved model relies on far fewer free parameters than previous works. The inclusion of measured data from a second passively-scattered proton therapy beamline is another strength of this study. Specifically, the analytical model was configured for use at the lower energy (75 MeV) and compared against experimental data to validate its utility to predict stray radiation from an ocular beamline. One limitation of this study is that we only benchmarked the model with measured data at a single proton beam energy for the ocular beamline. Additionally, the measured data was taken in-air and not in a water phantom. These limitations are minor because we demonstrated good agreement for the model compared with Monte Carlo simulated / values in a water phantom and at many different energies for the general purpose beamline.
FISH analysis did not reveal 13q14 nor 17p deletions. The search for translocation t(4;14) was inconclusive. The patient was enrolled in the clinical trial IFM 2007-02 (NCT00910897). A 4-cycle treatment associating Bortezomib and Dexamethasone was initiated followed by high dose therapy with Melphalan 200 mg/m 2 followed by autologous stem cell transplantation in February 2009. The patient achieved a very good partial response (VGPR) with a serum M-protein undetectable on IFE but monoclonal lambda FLCs were still detectable on urinary IFE. During this  period, serum lambda FLC and IgD were within the normal range (Table  1  and Figure  1 ). The patient relapsed in December 2010 as he presented with a skull vault tumor. IgD was quantified at 204 mg/L and FLCs were measured at 779 mg/L. Patient started on radiotherapy plus high dose Dexamethasone.
Materials and methods The ESTEEM 2 study, conducted at 40 sites in Austria, Canada, Denmark, France, Germany, Italy, Spain, Switzerland and the U. S. A. (NCT01232283), enrolled the first patient in November 2010. The last patient completed the first 52 weeks of the study in December 2012. All patients provided written informed consent. The protocol and consent were approved by the institutional review boards/ethics committees at all investigational sites. The study was conducted in accordance with the principles of good clinical practice and the Declaration of Helsinki.
Statistical analysis Efficacy assessments were conducted for the modified intention-to-treat (mITT) population or the full analysis set (FAS), which included all patients who were randomized as specified in the protocol; patients randomized in error and not dispensed study medication were excluded from the mITT population. The safety population comprised all randomized patients who received at least one dose of study drug. Approximately 405 patients were planned to be randomized into the study. This proposed sample size was to provide a sufficient database for safety evaluations. This sample size provided > 90% power to detect a 20% difference between apremilast and placebo in PASI 75 response. impute missing efficacy measurements. Multiple sensitivity analyses (including nonresponder imputation) were conducted.
Acknowledgments The authors would like to thank  Irina Khanskaya ,  John Marcsisin ,  Claire Barcellona  (clinical),  Zuoshun Zhang ,  Rongdean Chen  (statistics),  Monica Bilbault ,  Dale McElveen  (clinical operations),  Marlene Kachnowski  (data management),  Ann Marie Tomasetti ,  Trisha Zhang  (programming) and  Joana Goncalves  (medical affairs) of  Celgene Corporation  for their contributions to the study and/or the manuscript. The authors received editorial support in the preparation of the manuscript from  Vrinda Mahajan, PharmD , and  Kristin E. Larsen, PhD , of  Peloton Advantage, LLC , funded by  Celgene Corporation.
jouy. inra. fr/mima2). All biofilms were scanned at 400 Hz, with a 40× 0. 8 N. A. (Leica HCX Apo) water immersion objective lens, and a 488 nm argon laser set at 25% maximum intensity. Syto9 fluorescence was visualized by recording fluorescence in the 500-600 nm range. Three stacks of horizontal plane images (512×512 pixels) with a z-step of 1 μm were acquired for each biofilm, from different areas of the well. Three independent experiments were performed for each strain, and the same image acquisition steps were repeated on the same wells after a series of three rinses with water. Three-dimensional projections of biofilm structures were reconstructed with the Easy 3D function of IMARIS 7. 0 software (Bitplane, Switzerland). The quantitative structural parameters of the biostructures, such as biofilm biovolume, substrate coverage and mean thickness, were calculated with PHLIP, a freely available Matlab-based image analysis toolbox (htpp:// sourceforge.
thermophilus strains by PCR amplification, from the genomic DNA of the strains tested, with the EG771/EG772, EG758/ EG759 and EG762/EG763 primers, respectively (S1 Table ). The genomic region lying between metK (STH8232_1369) and murA1 (STH8232_1353) was amplified by PCR from the genomic DNA of S. thermophilus strains with the MURA1 and METK primers. The sequences of the STH8232_0714 alleles from S. thermophilus strains JIM10001, JIM10010, JIM10031, JIM10116 and CNRZ1575 has been deposited in the GenBank database under accession numbers KF717044, KF717043, KF717042, KF717041, and KF717045, respectively.
Two of them, STH8232_1361 and STH8232_0714, were each inactivated by several independent transposition events, i. e. the transposon was found at different positions within these genes in different insertion mutants (14 mutants for STH8232_1361, 3 mutants for STH8232_0714), which had lost their biofilm formation capability. This observation constitutes a strong indication for their potentially critical role in biofilm formation and these genes were therefore selected for further study. STH8232_1361 and STH8232_0714 encode a transmembrane protein from the polysaccharide transporter (PST) family (TCDB database) and a cell surface-exposed protein with a MucBP domain (MUCin-Binding-Protein, Entry PF06458), respectively. STH8232_0171 was also selected for further study because this gene is present in only 3 of 18 currently sequenced S.
thermophilus strains and the one available for testing, JIM8232, formed biofilms. It encodes a predicted cytoplasmic protein (Psort prediction) with unknown function and containing a NACHT domain (nucleoside-triphosphatase domain, Entry PF05729). The remaining five genes were not studied further because they were targeted only once by the transposon and they are present in most sequenced S. thermophilus strains, including those unable to form biofilms (S2 Table ). For independent confirmation of the involvement of STH8232_1361, STH8232_0714 and STH8232_0171 in JIM8232 biofilm formation, and to exclude the possibility of secondary mutations, we inactivated these genes in the wild-type strain and evaluated the biofilm-forming ability of the corresponding knockout mutants (Fig  4 ). The biofilm-forming capability of each of the three mutants was similar to that of the corresponding ISS1 transposon mutants and was significantly lower (approximately 70%; P < 0.
STH8232_0714, which is flanked by a promoter and terminator sequence, is predicted to encode a cell surface protein containing a MucBP domain and belonging to a group of adhesins facilitating the attachment of bacteria to host cells. MucBP-containing proteins have been found in more than 315 bacterial species (Pfam analysis with PF06458 as the input, http:// pfam. xfam. org), mostly from the Streptococcaceae (211 species) and Lactobacillaceae (86 species), many found in association with animal mucosa. The putative protein encoded by STH8232_0714 thus has several features of proteins involved in adhesion to surfaces. This gene is highly polymorphic and is subject to gene loss in S. thermophilus.
Acknowledgments We thank  Sophie Chat  for TEM observations and  Julien Deschamps  for MATH experiments at the  MIMA2 microscopy platform (INRA)  (http://www6. jouy. inra. fr/mima2). We thank  Nicolas Lapaque  and  Alexandra Gruss  for critical reading of the manuscript, and  Bob Petersen  and  Alex Edelman  and Associates (http://www. alexedelman. com)for improving English usage in this paper.
The bacterial DNA restriction patterns were analyzed using the Bionumerics software 7. 5 (Applied Maths, Kortrijk, Belgium). The similarity of PFGE profiles was compared and a dendrogram was created using the band-based Dice unweighted-pair group method, using average linkages (UPGMA), based on 1% position tolerance and 0% position optimization. Branch quality was evaluated using Cophenetic correlation. PFGE patterns were considered clonally related when they had a similarity coefficient higher than 80%.
Plates were dried at 37 • C for 15 min and biofilms were stained with crystal violet (0. 1% wt/vol) for 2 min. After removal of crystal violet solution, the biofilms were washed three times with PBS and dried at 37 • C for 15 min. The biofilm stain was dissolved with 150 μl of 80% (vol/vol) ethanol and 20% (vol/vol) acetone and biofilms were quantified by measuring the absorbance at 590 nm (OD 590 ) with a microplate reader (Powerwave; BioTek Instruments, Winooski, VT, USA). The results for the static biofilms formed at 18 and 30 • C were compared using two-way analysis of variance (ANOVA) followed by a Bonferroni post hoc comparison using GraphPad Prism, version 4. 02 (GraphPad Software, San Diego, CA, USA). Strains were divided into three groups based on the OD 590 of bacterial biofilm: strong (A 590 > 0.
Environmental Data and Statistical Analysis Rainfall data (2-days cumulative rainfall before sampling date) were provided by the meteorological stations from Meteo France at Pleurtuit (site 1) and at Coutances (sites 2 and 3). The water temperature was measured manually at each sampling. The data on temperature and precipitation were categorized into three groups whose boundaries were defined so that they are likely to categorize the data significantly for the studied sites and they allow to have in each category a number of sample consistent with a reliable statistical analysis. Comparisons of STEC and EPEC prevalence between the type of samples, the site, the season, the temperature and the precipitation were analyzed by the chi-square test. A p-value of <0. 05 was considered statistically significant.