dataesr/scientific-paragraphs-categorization huggingface dataset:
Automated Multi-Atlas Segmentation Methods Two automated multi-atlas segmentation methods were implemented, one using the maximum probability atlas, (MP method) and the other an ad-hoc maximum probability atlas created by propagation-fusion of the individual atlas dataset in the space of the MRI to be segmented (PF method). The MP method consisted in 1/computing the non-linear registration of an individual MRI on the MRI template; 2/ computing the inverse transformation field from template space to individual space; and 3/applying the inverse transformation to the maximum probability atlas and resampling it using the nearestneighbor (NN) interpolation method to preserve label values (Figure  2A ). The PF method consisted in propagating and fusing the multiatlas dataset directly in the native MRI space of the subject to be segmented (target), by 1/computing the non-linear registration of each individual MRI from the atlas dataset, onto the target MRI; 2/using the transformations to resample (NN) the label volumes of the atlas dataset in the target space, and 3/performing label fusion of the individual registered label volumes using maximum probability rules (Figure  2B ).
Evaluation The performance of the multi-atlas segmentation methods was assessed at two levels: morphometric and functional evaluation. Evaluation was performed on an independent set of coregistered MRI/PET data: i. e. , on the five additional rats of the Test group. Brain structures were manually delineated on the individual MRIs with respect to the protocol, this time by different investigators (C. B. , S. L. , E. L. ). All 27 structures were drawn. Accuracy was evaluated in terms of morphometric performance and by measuring functional PET data with automated segmentation. Morphometric evaluation. Three morphometric indices were used to evaluate the automated segmentation methods. To evaluate volumetric performance, relative volume difference (Eq. 1) and the magnitude (absolute value) of the relative volume.
Multi-atlas segmentation methods. MP method (A) using one maximum probability atlas previously created in a template space. PF method (B) using a multi-atlas dataset, and creating an ad-hoc maximum probability atlas in the target space. doi:10. 1371/journal. pone. 0109113. g002 difference (Eq. 2) were computed, quantifying bias in the structure volume extracted by manual versus automated delineation: Relative volume difference (%)Ñ automated {N manual N manual |100 ð1Þ Magnitude of the relative volume difference (%)D N automated {N manual D N manual |100 ð2Þ To evaluate overlap performance, the metric was the Dice similarity index (Eq. 3)  [21] , which quantifies overlap between manually and automatically delineated structures: Dice~2| N automated \ manual N automated zN manual ð3Þ Dice ranges from 0 (no overlap), to 1 (complete overlap).
For the MP and SA method, individual PET and MRI acquisitions were registered by computing the rigid spatial transformation of the static PET-FDG image to its MRI. This PET-to-individual-MRI registration was concatenated with the individual MRI-to-template-MRI non-linear transformation. The concatenated transformation was used to transform the single atlas (SA method) or the MP atlas (''MP with MRI'' method) in the PET space, where PET-FDG measures were extracted. Alternatively, direct registration between PET-FDG and MRI template  was computed. PET-FDG measures were extracted using the resliced MP atlas in the PET space (''MP without MRI''). For the PF method, propagation-fusion of the multi-atlas dataset was performed with the individual MRI as target, and the ad hoc maximum probability atlas was resliced in the PET space to extract regional PET-FDG measures.
Morphometric Evaluation of Multi-Atlas Segmentation Automated segmentation with a multi-atlas dataset did not induce significant bias in VOI measurement compared to manual delineation. The rat is an animal model widely used for the study of neurological diseases, including degenerative processes that may induce regional volumetric alterations. The present multi-atlas method is an accurate and reproducible means of studying large datasets, since it can be performed automatically in a reasonable computing time. Volumetric measurement was, however, assessed on healthy cases, and the inter-individual variability of the structural database was therefore limited to populations without lesions. Study of pathological cases that may be associated with greater morphological alteration might need more sophisticated non-linear registration methods  [22, 23]  and, further, methods using diffeomorphic registration and minimal deformation templates  [24].
869 (95% confidence interval (CI), 0. 853-0. 884). With the present delineation protocol, the Dice index in the hippocampus was 0. 864 (CI, 0. 854-0. 874) with MP and 0. 868 (CI, 0. 848-0. 882) with PF. For comparison, results obtained with the single-atlas method were significantly poorer, with a mean Dice of 0. 833 (CI, 0. 817-0. 849; p,0. 001 compared to multi-atlas methods). We conclude that the present results are within the performance range of other multi-atlas methods, although rigorous comparison should be performed using a single database and single delineation protocol. Additionally, performance might be increased by more sophisticated fusion rules and atlas selection. The present study finally provided 12 atlases, since the initial multi-atlas dataset of 7 can be completed by the 5 of the Test group.
With a dataset of 12 atlases, we would be able to implement a multi-atlas method where decision fusion is not performed with the whole dataset, but by selecting a custom subset of atlases. It has been shown in human studies that selecting atlases with an image-based similarity criterion performs better than a random selection, or than using the whole dataset  [29]. These approaches would allow longitudinal study of an evolving brain lesion, or others pathophysiological brain processes in rodents. Table  3. Functional evaluation: regression against extraction with manual atlas. Segmentation for Functional Quantification PET regional measurements showed excellent agreement between the manual and automated multi-atlas delineation methods. It was firstly checked that activity extracted by individual manual delineation was completely equivalent in the individual PET, individual MRI and template spaces (r 2 <1, data not shown).
Measures were then extracted using multi-atlas automated delineation in the MRI space. Regression slopes for manually extracted data were close to 1; correlation coefficients were very good, with r 2 = 0. 996 (MP and PF), the regression slope was close to 1. It was concluded that, in spite of the imperfect overlap between manually and automatically delineated regions, the multiatlas methods were sufficiently precise to be able to extract PET measurements accurately. The tolerability of the slight mismatch of regions is due to the lower resolution of PET compared to MRI: the small segmentation error visible with the MRI resolution was negligible at the resolution of the PET acquisitions. Our data also shows that using a single individual to create an atlas is largely suboptimal in comparison to the use of a dataset of multiple atlases that are combined in a maximum probability atlas.
Conclusions A multi-atlas based method with non-linear transformation for automatically segmenting rat brain MRI and extracting PET activities was created and validated. Multi-atlas methods outperformed the single-atlas method for automated brain segmentation, and also performed comparably to manually defined regions for PET quantification. Additionally to rapid and reproducible VOI analysis, the definition of a template space will be helpful for intraand inter-individual voxel-based longitudinal multi-tracer analysis. The maximum probability atlas and the individual atlas dataset are available, with grant of license, from CERMEP, University of Lyon. Disclosure: All authors have read and agreed with the contents of the manuscript. The results of the study have not been published before and they are not under consideration to be published by another journal.
Study design and conduct This was a two-armed, parallel group, prospective, randomized, doubleblind, double-dummy, multicenter, Phase III trial (ClinicalTrials. gov: NCT01187953). De novo kidney transplant recipients were randomly assigned to study treatment in a 1:1 ratio, using a fixed-block randomization scheme via an interactive, automated system. Randomization was stratified by site and recipient race (black vs. nonblack). The randomization scheme was generated before the initiation of the study by an independent statistician/programmer who was not a member of the study team. The duration for the entire study was 24 months. However, the primary end point was efficacy and safety at 12 months. The 12-month data are currently available and reported here. Health Authority, Ethics Committee and Institutional Review Board approval was obtained at each participating center and informed consent was obtained from all patients.
Pancreatic adenocarcinoma (PDAC) is currently the fourth leading cause of cancer death with a median survival of 6 mo and a dismal 5-y survival rate of less than 5%, but could move up to second place as early as 2020 according to a new report from the Pancreatic Cancer Action Network. 1  Moreover, information from the Surveillance Epidemiology and End Results database indicates that the number of patients with PDAC in 2030 will represent more than a 2-fold increase over the current prevalence in the occidental world. 1  Based on these data, the number of deaths from PDAC will exceed those from breast and colorectal cancer, and will be surpassed only by the loss of life from lung cancer. PDAC progresses from precursor lesions called pancreatic intraepithelial neoplasias (PanINs). It is firmly established that oncogenic mutations in Kras are among the earliest stimuli for the formation of PanINs.
Acknowledgments This work was supported by grants from  La Ligue Contre le Cancer ,  INCa ,  Canceropole PACA ,  DGOS (labelisation SIRIC ) and INSERM to JLI;  National Institutes of Health  (grant  DK52913 ), the  Mayo Clinic Center for Cell Signaling in Gastroenterology  ( P30DK084567 ), and the  Mayo Foundation  to RU; and by the  Fraternal Order of Eagles Cancer Award  to GL.
Flow cytometry Flow cytometry was performed with the FACScanto flow cytometer (BD, Franklin Lakes, NJ), DIVA Flow Cytometry acquisition software, and Kaluza Flow Cytometry analysis software (Beckman Coulter, Miami, FL). Blood was drawn and directly stained with antibodies by incubating 50 mL of blood with 1 mg/mL purified anti-CD16/32 (2. 4G2, BD Biosciences) for 10 min at 4uC to block Fc-mediated binding and for an additional 20 min with the appropriate dilution of specific antibodies. The panel of antibodies used was: anti-CD11b (clone M1/70), anti-Ly6C (clone AL-21), and anti-Ly6G (clone 1A8), NK1. 1 (clone PK136) (all Becton Dickinson, San Jose, CA). After incubation, cell suspensions were washed once in PBS 0. 5% BSA EDTA 2 mM and were directly analyzed by flow cytometry. Absolute cell numbers were calculated by adding to each vial a fixed number (10,000) of nonfluorescent 10-mm polybead carboxylate microspheres (Polysciences, Niles, IL) according to the formula: number of cells = (number of acquired cells610,000)/(number of acquired beads).
This work was supported by grants from  Inserm ,  UPMC emergence , European Grant ENDOSTEM (FP7 # 241440 ),  ANR Emergence  '' I-CKAIRE''  ( ANR-12-EMMA-0050 ). KK is supported by an  NHMRC career development fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
e. from groups 2a and 2b). We then sequenced exons 2 and 3 in the remaining 280 participants. All genetic variations were compared with the 1000 Genomes (http://browser. 1000genomes. org/Homo_sapiens) and Exome Variant Server (EVS) (http://evs. gs. washington. edu/ EVS/) databases. Genomic DNA was extracted from blood samples using the Wizard® GenomicDNA Purification kit (Promega, Southampton, UK).
DNA sequencing DNA was sequenced using 1 μl Big Dye Terminator on an Eppendorf thermocycler with the following program: hot start: 96°C for 5 min; 25 cycles (denaturation at 96°C for 30 s, hybridization at 50°C for 15 s, elongation at 60°C for 4 min); final elongation: 60°C, 10 min. After purification on a Sephadex G50 plate Millipore column, raw sequences were analyzed and interpreted with Seqscape® software (Applied Biosystems, Life Biotechnologies, NY, USA).
Statistical analysis Intergroup comparisons of polymorphism and haplotype frequencies were performed with Fisher's exact test and  Results (Table  3 ) First, all four RHOXF2 exons were sequenced in 47 patients with oligospermia or non-obstructive azoospermia. The sequences corresponded to RHOXF2. In view of the high degree of sequence homology (>99. 9%), a pseudobiallelic nomenclature was adopted for the SNPs' genotype. Five previously described heterozygous SNPs  [14]  were identified: 3 on exon 2, one on exon 3 and one on exon 4. These SNPs were also present in the 1000 Genomes and EVS databases. A previously described frameshift variant in exon 2 (a guanine insertion leading to a premature stop codon (TGA) of the cDNA (c. 381dupG). p. (L128Afs*34)) was also identified. Given that we did not find any new SNPs in exons 1 and 4, we decided to only study exons 2 and 3 in the remaining 280 patients.
The gene duplication in primates was probably mediated by non-allelic recombination via flanking endogenous retroviral sequences  [15]. Furthermore, an analysis of eight non-synonymous variant sites in humans has suggested that the gene selection process is on-going. Furthermore, it was recently reported that RHOXF2B is selectively expressed in male germ cells  [16]. These various datasets support the hypothesis whereby RHOXF2 variants may be present in patients with impaired spermatogenesis. Our two-step strategy (based on the preliminary identification of new variants in a small group of patients with impaired spermatogenesis and then extension of the analysis to control patients) enabled us to identify (i) new synonymous and missense variants and (ii) two frame-shift variants (one of which was associated with a premature stop codon).
In the mouse, low Rhox5 expression was shown to have a harmful effect on spermatogenesis  [13]. Furthermore, we did not study exon 4, which carries a polymorphism that is defined as harmful by the PANTHER classification system (http://www. pantherdb. org). We identified two rare duplications in our patients with impaired spermatogenesis. The first duplication is a guanine insertion that shifts the reading frame, induces a premature stop codon and thus produces a truncated protein. This duplication was found in two patients with spermatogenesis failure and none of the control patients. The EVS database lists this duplication for only one individual, the fertility of which is not stated. Given that Rhox gene mutation alter testicular function in rodents  [27] , this variant may indeed be pathogenic.
Background: Previous studies have shown that survey methodology can greatly influence prevalence estimates for alcohol and illicit drug use. The aim of this article is to assess the effect of data collection modes on alcohol misuse and drug use reports by comparing national estimates from computer-assisted telephone interviews (CATI) and audio-computer-assisted self interviews (A-CASI). Methods: Design: Two national representative surveys conducted in 2005 in France by CATI (n = 24,674) and A-CASI (n = 8,111). Participants: French-speaking individuals aged [18-64] years old. Measurements: Alcohol misuse according to the CAGE test, cannabis use (lifetime, last year, 10+ in last month) and experimentation with cocaine, LSD, heroin, amphetamines, ecstasy, were measured with the same questions and wordings in the two surveys.
Introduction More and more general population surveys using representative samples are attempting to assess adult drug use in European countries. In France, various surveys have been conducted since the 1990s. The National Health Barometer (HB) is the primary source of information about prevalence, correlates, and trends in substance use and misuse in France  [1]. This survey collects data every five years from a nationally representative sample on patterns and correlates of licit and illicit drug use and related problems, with a focus on cannabis use. It is a telephoneadministered interview survey. The last but one iteration of this cross-sectional survey was conducted in 2005. A few months later, the Life Events and Health Survey (LEHS) was conducted, a face-toface interview survey mainly dealing with violence and including an audio computer-assisted self-administered section (the A-CASI) to assess licit and illicit drug use  [2].
Data collection Both surveys used a household-based two-stage sampling procedure (household and individual selected using the next birthday method) and the final sample was large: 9,953 individuals aged  (among which 8,111 aged ) were interviewed by A-CASI and 30,514 aged  (among which 24,674 aged ) were interviewed by CATI. A letter was sent prior to the survey to improve response rates. Given the questionnaire was long (45 minutes on average for the CATI and 1 hour for the A-CASI) and sometimes sensitive, the interviewers had received specific training. The illicit substance use part of the questionnaires was restricted to respondents aged. The Health Barometer (HB) was collected by a private firm on behalf of the National Institute for Prevention and Health Education (Inpes), a public establishment created in 2002 whose mission is to implement prevention and health education policies.
The HB is a repeated cross-sectional telephone survey (CATI) representative of the non-institutionalized, civilian population of France. Data were collected between November 2004 and February 2005. To ensure representativeness, a subsample of 3. 842 individuals with only a cell phone were added to the 26. 672 individuals possessing a land-line in their homes. For this cell phone sample, the letter was not sent before the call but its sending was proposed by the interviewer. The participation rate was 65%. The data were weighted according to the number of telephone lines and the number of individuals in the household to compensate for the greater probability of being selected in households with several telephone lines and the lesser probability in households made up of many individuals. Data were then adjusted to match the French population structure in terms of gender, age group, region, town size and education, as derived from the 2005 French census, by using a calibration procedure issued from a general regression (GREG) carried out with R software.
Precise descriptions of sampling and other survey procedures are available elsewhere  [1]. The final sample used here comprised 24. 674 persons aged 18-64. The Life events and health survey (LEHS) was collected by National Institute of Statistics and Economic Studies (INSEE) interviewers for the Research, Studies, Evaluation and Statistics Directorate (DREES) of the French Ministry of Health and Sport. It was a face-to-face survey mainly exploring health and difficult life events. This cross-sectional survey is representative of the non-institutionalised, civilian population of France, aged 18-75, based on the most recent national census. Data were collected from September 2005 to November 2005. The participation rate was 72%. The most sensitive questions (sexual behaviour, alcohol and drug use) were asked via A-CASI.
The respondents' privacy was ensured as the interviewer, who was alone with the respondent, left the room for the A-CASI part of the questionnaire. Out of 9,953 people interviewed face-to-face, 9,538 (95. 8%) agreed to complete the interview with the A-CASI. Data were also adjusted to the French population structure as obtained from the ''Continuous employment survey'' carried out in 2005, both for the whole sample and for respondents who answered the A-CASI questionnaire. Precise descriptions of sampling and other survey procedures are available elsewhere  [2]. Both HB and LEHS surveys rely on complex sampling: two stages of simple random sampling (SRS) for HB (phone number and then individual), stratified and three stage sampling for LEHS (stratification according to the size and socio-economic status of the area of residence, urban area for the first stage, household for the second stage then individual for the third stage).
Both surveys aimed to interview the same population, but with different sampling strategies and each individual could be interviewed for both surveys, although this may be very rare (less then 0. 001%). The two datasets were pooled together in order to estimate adjusted differences of A-CASI (LEHS) vs CATI (HB) in multivariate logistic regressions. The sampling design (stratification and clustering in LEHS) and the sampling weights were taken into account in the analyses, by considering HB as a separate stratum containing as many clusters as individuals.
Analyses Bivariate analyses with qualitative variables were performed using design-based Pearson chi-squared tests, for the whole sample and by gender. Logistic regressions controlled for age, gender, marital and employment status, and educational level and were used to compute adjusted odds ratios (aOR) by data collection mode for a large set of substance use indicators. Analyses were first performed on the whole sample (18-64 years old), and then stratified by gender and age group: 18-29 and 30-44 years old, using SAS V9. 3. 2 and Stata V10. 1. This strategy makes it possible, first, to take into account the strong age-and gender-related variations in psychoactive substance use  [49]  and second, to avoid for possible interactions between these variables and the data collection mode. Data from respondents aged 45-64 were not analysed because of very low illicit drug use prevalences.
Interpretations of the findings Apart from data collection mode, which is considered in the literature as a major influence on the way respondents answer sensitive questions  [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] 15] , several methodological differences could influence prevalence estimates for substance use. Nevertheless, the sampling procedures and the sampling weights were taken into account in the analyses, by considering HB as a separate stratum containing as many clusters as individuals. Some of the more salient differences between the Health Barometer and the LEHS include privacy considerations (presence of the interviewer or of other people) and response rate. On one hand, the LEHS was performed face-to-face by interviewers from the French National Institute for Statistics and Economic Studies (INSEE), using computerized self-administration methods (A-CASI) to collect drug-related data.
This effect is particularly strong for specific populations such as professional sportsmen  [56]  and which is mainly a ''January effect'' in most European countries and the Northern Hemisphere  [57, 58]. But this factor probably plays a minor role because the HB fieldwork was interrupted from 20 th December 2004 to 10 th January 2005 in order to minimize both overindulgence during the holiday season and New Year's resolutions toward temperance. Moreover, except for cannabis regular use, which is calculated on the last 30 days, the indicators relied on large period of time (lifetime or last 12 months, including all seasons for all the respondents). Such indicators are much less impacted by seasonal effects than daily or recent use of alcohol or drugs. The databases used in the two surveys (persons living in households in LEHS, members of households equipped with a landline or mobile telephone in HB) could also explain part of the differences in prevalences.