dataesr/scientific-paragraphs-categorization huggingface dataset:
Impact of corrupted GD on individual FA maps Hypointense slices indicating corruption in single volumes of an individual DTI data set (Figure  2 , upper panel) were identified by reduced Q-values (Q < 0. 8) within the respective volume (Figure  2 , lower panel). In more than 50% of the HD patients' DTI data, a constellation could be found where GD elimination led to an adjustment of ROI-based mean FA values. Figure  3  illustrates an example for a ROI analysis (HD subject 589). Here, visits 1 and 3 had no GD  eliminations while nine corrupted GD were found in visit 2. A ROI was placed in the position where the difference matrix of visit 2 (with vs. without GD elimination) showed its local maximum in FA differences. Mean FA values in the ROI were compared to mean FA values of visits 1 and 3 at the identical ROI positions.
Impact of corrupted GD on cross-sectional comparison of FA maps In the next step, the impact of QC-based GD elimination on the cross-sectional comparisons between FA maps from HD participants against controls was investigated: results clusters, i. e. patterns of significant FA reductions and increases, from crosssectional differences between HD subjects and controls were compared with or without QC-based GD elimination for the three visits. The application of the QC algorithm resulted in specific QC characteristics for each DTI data set with identified volumes to be eliminated for FA calculation. The statistics for the number of eliminated volumes for all DTI data sets are summarized in Figure  4  as GD elimination statistics. The numbers of GDs excluded during the QC process for each participant are displayed separately for each visit.
Effect of corruptions in a DTI data set Movement associated image corruptions are a matter of concern in neuroimaging research. These artefacts may reduce the potential of DTI as a biomarker in HD. Algorithms are therefore needed that compensate for corrupted volumes in a DTI scan. In this study, we used an algorithm for automatic and objective detection of volumes that contain corrupted slices. Identified volumes with corrupted GD were then excluded from further calculations of DTI metrics. The question to what extent GD could be removed from a DTI data set without relevant changes to FA maps had previously been addressed  [6]. The present study is a continuation of QC-based studies of our group as well as of other groups  [2, 4, 5, 7]. It could be demonstrated in the present study that corrupted volumes in DTI acquisitions have impact on the results of crosssectional group comparisons of FA maps.
The effects of corrupted volumes on cross-sectional result patterns were two-fold: first, corrupted volumes influenced FA values alone, and secondly, as a consequence, they could have impact on registration of the subject's FA image to the study-specific group template (which is based on study specific FA templates calculated from single subject FA maps). This means that the MNI normalization process of all DTI data sets was also influenced by differences in the FA maps of individual participants. In this study, no entire scans had to be eliminated although the first QC showed some scans with a greater number of corrupted GD. For these cases, a Q threshold reduction could detect the most corrupted volumes and the number of remaining GD was considered still sufficient for accurate tensor detection.
Conclusion In summary, QC of DTI data clearly impacted upon the results pattern of FA reductions and increases at the group level when comparing HD subjects and controls. We could show that some small clusters were observed in cross-sectional results without QC-based GD elimination, suggesting that corrupted GDs may increase the risk of type I errors and should consequently be eliminated during a QC process. Furthermore, QC-based GD elimination was associated with emergence of otherwise undetected clusters of group differences indicating that QC-based GD elimination may also protect against type II errors at the level of group comparisons.
Figure1Analysis schemes for cross-sectional comparison. (A) Schematic example for an iterative template-specific MNI-normalization: after a 1 st normalization step based on landmarks, first templates T 1 ((b = 0) template and FA-template) were obtained by arithmetic averaging of DTI-data I 0. Analyses were performed with or without quality control (QC) and subsequent gradient direction elimination. Subsequently, in an iterative procedure, normalized DTI-data I 1 were obtained by non-linear normalization to the previously defined templates (T 1 ). From these newly normalized DTI-data I 1 , new templates (T 2 ) were derived which again could be used for normalization. This iterative process is stopped when a predefined coincidence (measure by correlation) between DTI-data and templates was reached.
Since the mid-1990s, oncologists and geriatricians have worked to integrate CGA approaches into oncologic practice. The International Society of Geriatric Oncology created a taskforce to determine the best CGA format for use in oncology. 8 ndependent of these recommendations, the feasibility and effectiveness of the CGA in managing older cancer patients and the evidence of its usefulness in everyday oncology practice deserve consideration. ] [11]  The objectives of this review were to depict CGA components in everyday oncology practice and to assess the usefulness of the CGA in assessing health problems, guiding decisions about cancer treatments, predicting outcomes, and developing a coordinated program of tailored geriatric interventions. We also reviewed the available data on the benefits of specific CGA-based interventions.
Study eligibility criteria We used four eligibility criteria to select studies for our review: a focus on older patients (65 years or older) with solid cancer (excluding hematologic malignancies) who were seen in oncology or surgery or geriatric-oncology clinics (as outpatients or inpatients); prospective data collection and observational or interventional design; a sample size of at least 100 patients; and assessment of at least five CGA domains (from nutrition, cognition, mood, functional status, mobility and falls, polypharmacy, comorbidities, and social environment). ] [11]  For assessment of the ability of the CGA to detect previously unrecognized health problems, the studies had to contain information on the frequencies of CGA domain alterations or on the data needed to compute these frequencies.
55 In a recent randomized trial in older patients undergoing elective surgery for solid cancer, an individualized geriatric intervention plan based on patient-related risk factors for delirium failed to decrease the occurrence of postoperative delirium, other complications, or death. 56 We urgently need randomized controlled trials of patient outcomes after CGA-based geriatric interventions. The available data suggest that these trials will demonstrate significant improvements, thus helping to convince health authorities that geriatric oncology teams must receive strong support. Seven such trials are ongoing and are registered on clinicaltrials. gov (Table  3 ).
Acknowledgments This paper is published on behalf of the  ELCAPA Study Group , which comprises three oncologists ( S Culine ,  Ch Tournigand ,  B Rousseau ), one radiotherapist ( J-L Lagrange ), five geriatricians ( P Caillet ,  M Laurent ,  E Liuu ,  E Paillaud ,  H Vincent ), three epidemiologists ( F Canouï-Poitrine ,  S Bastuji-Garin ,  E Audureau ), one pharmacist ( M Carvahlo-Verlinde ), one biostatistician (A Le Thuaut), one clinical research medical doctor (N Reinald), and one clinical research assistant (A Radenne). We thank  A Wolfe  for editing the manuscript.
A research grant was provided by  Serdia Pharmaceuticals (India) Pvt. Ltd. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. the authors received honoraria, research grants, or both, from  Servier or Serdia Pharmaceuticals (India) Pvt. Ltd. This study was funded by  Serdia Pharmaceuticals (India) Pvt. Ltd. Co-author Paramesh Shamanna  is employed by  Bangalore Diabetes Centre, Medisys Clinisearch India.
Fabric measurements The NEEM fabric data were measured in the field, at the NEEM camp, during field seasons 2009 to 2011, from 33 m to 2461 m depth at a 10 m resolution. First, vertical thin ice core sections were produced directly from the extracted cores, except for the brittle ice zone that was drilled in 2009 but studied at the NEEM site in 2010. The brittle ice zone, extending from about 600 m to 1300 m depth, corresponds to ice highly affected structurally by the pressure from air bubbles that have not yet turned into clathrates  (Lipenkov, 2000; Kipfstuhl et al. , 2001). A 1-year period is considered necessary to relax these constrains enough for ice to be processed. The raw fabric data were obtained using two automatic ice texture analyzers (AITA)  (Russell-Head and Wilson, 2001)  of similar versions.
Conclusions This paper presents the overall features of the fabric data measured in the field, every 10 m along the NEEM ice core, Greenland. The main observations are the following: i. Similar to what was observed along other deep ice cores, the initial fabric, measured from 33 m depth downward, is not isotropic and may therefore be associated with fabric evolution during firnification. ii. Down to about 1500 m depth, the fabric evolves toward a single maximum, with a slight girdle tendency. This is coherent with the position along a ridge, characterized by a vertical compression and a slight asymmetry in the horizontal extension. iii. A sharp fabric strengthening is measured at the Holocene-Wisconsin transition, with a maximum reached at about 1800 m depth, and maintained down to 2300 m depth.
Acknowledgements. This work is a contribution to the NEEM ice core project that is directed and organized by the  Centre of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST) ,  France (IPEV, CNRS/INSU, CEA and ANR), Germany (AWI), Iceland (RannIs), Japan (NIPR) ,  South Korea (KOPRI) , the  Netherlands (NWO/ ALW), Sweden (VR) ,  Switzerland (SNF) , the  United Kingdom (NERC)  and the  USA (US NSF, Office of Polar Programs)  and the  EU Seventh Framework programmes Past4Future and WaterundertheIce. IW  acknowledges financial support by the  Helmholtz Association  ( VH-NG-802 ). MM and FGC are part of  Labex OSUG@2020  ( ANR10 LABX56 ).
Microarray processing and data analysis A microarray analysis was performed, in a mono color experimental design, to compare gene expression profiles of MFG at 4 time points of infection (0, 12 h, 18 h and 24 h) on the 5 goats. RNA samples were labelled using T7 RNA polymerase, which simultaneously amplifies target material and incorporates cyanine 3-labelled CTP (Agilent Technologies) according to the manufacturer's instructions. Briefly, total RNA (120 ng) was first reverse transcribed and then converted to labeled cRNA with Cy3 dyes. Before hybridization, quantity and quality of purified cRNA was assessed with the Agilent 2100 Bioanalyzer. Since goat microarrays were not available, we used the Sheep Gene Expression Microarray, 8 × 15 K (Agilent Technologies) developed for a closely related species (Ovis aries), to profile gene expression.
Hybridization was performed according to the manufacturer's instructions. Briefly, each microarray was hybridized with fluorescent labelled (Cy3) cRNA samples. Three hundred ng of each labelled cRNA were fragmented for 30 min at 60 °C in 25X fragmentation buffer. Then, fragmented and labelled cRNA samples were diluted in 2X GE x Hybridization Buffer Hi-RPM and hybridized onto the 15 K ovine microarray for 17 h at 60 °C in a rotating hybridization oven. After hybridization, microarrays were washed twice with washing buffer 1 at room temperature for 1 min and then with washing buffer 2 at 37 °C for 1 min. Microarrays were scanned on an Agilent scanner G2565BA using the extended dynamic range scan mode. Data were processed (including Lowess normalisation) and extracted with Feature Extraction software version 10.
5 (Agilent, Technologies). Statistical analysis of microarray data was carried out using the commercially available software package GeneSpring® GX v10. 0. 2 software (Agilent Technologies), using the latest gene annotations available on a web-accessible resource established by SIGENAE  [22]. Information about this experiment has been deposited in NCBI Gene Expression Omnibus (GEO) with accession number (GSE39315). All data are "Minimum Information About a Microarray Experiment" (MIAME) compliant. First, we applied a quantile normalization on data, which assumes that the distribution of gene abundance is the same for all samples, and spots that did not meet minimum signal intensity were removed. The resulting signal information was analyzed using repeated measures ANOVA with a Benjamini-Hochberg correction for multiple comparisons.
05. The output of this analysis was additionally filtered by fold expression, generating lists of differentially expressed genes with at least 1. 5-fold change. This final set of genes was then referenced to biological functions with Ingenuity Pathways Analysis software v6. 0 (IPA, Ingenuity Systems, Redwood City, CA, USA). The contribution of genes to the identified function was explained with an associated p-value, calculated with a right-tailed Fisher's exact test. To identify temporal relationships among this subset of genes, a gene regulatory network was reconstructed from the expression data using an approach specifically developed for replicated longitudinal data, called Empirical Bayes Dynamic Bayesian Networks (EBDBN), implemented in the ebdbNet package in R  [23]. Briefly, the EBDBN method makes use of an iterative empirical Bayes estimation procedure for a linear Gaussian statespace model.
Primer design and real time quantitative PCR RNA isolated from goat MFG and microdissected MEC were used to determine the expression of selected genes of interest by real time quantitative PCR (qPCR) as described by Bevilacqua et al. [24] , using the SYBR green PCR master mix and an ABI Prism 7900 Sequence Detection System (Applied Biosystems, Life Technologies). The reference sequence for primers was taken from a bovine database. Expressed sequence tags (EST) of caprine sequences found by the BLASTn search and reference were aligned to create one consensus. To eliminate the risk of genomic DNA amplification we systematically chose primers that hybridize on exon-exon junctions. Only junctions with great homology between sequences were chosen for primer design. Then primers were designed using Primer Express Software version 2.
Statistical analysis Reliability of reference genes (RPS24 and PPIA) was evaluated with the GeNorm Visual Basic application for Microsoft Excel as described by Vandesompele et al. [27]. Statistical analyses were performed using the R software, with the Rcmdr package. The non-parametric Mann-Whitney U-test was used to analyse the differences in relative expression for each gene of interest between MG and MFG, non infected versus infected, at 12 h, 18 h and 24 hpi but also between infected MG and microdissected MEC, at 30 hpi.
Microarray results showed that the most highly expressed genes, at the 4 time points of infection (including 24 hpi), were the signature of lactating MEC gene expression. Indeed, genes coding for milk proteins (LALBA, CSNs and BLG) and ribosomal proteins involved in protein synthesis, RPL and RPS, gave strong signals. Likewise, amongst these highly expressed genes, we also found genes involved in synthesis and secretion of \fatty acids and triglycerides (FASN, XDH, ADRP, SCD and DGAT1), as well as the gene encoding UDPgalactosyltransferase (B4GALT1) which participates in glycoprotein post-translational modification and lactose synthesis. Furthermore, microarray data are consistent with the results of qPCR analyses performed to assess the level of contamination of MFG by immune cells. Indeed, specific macrophage and neutrophil gene markers present on the sheep microarray indicate an absence of immune cells up to 18 hpi whereas the contamination by neutrophils and macrophages began to be noticeable at 24 hpi.
Gene network analysis of differentially-expressed gene lists Systemic identification and grouping of differentiallyexpressed genes into biological networks was performed using the software packages Ingenuity Pathway Analysis. Analysis of gene interactions and pathways was performed using the differentially-expressed gene sets (0-12 h and 12-18 h) after staphylococcal stimulation, independently. We observed that top networks obtained at 12 hpi deal with cellular movement with 12 molecules on 14 molecules annotated taking part in this network. At 18 hpi, the most relevant biological networks were related to inflammatory response and infectious disease with 27 molecules on 32 molecules annotated (Figure  5 ). Moreover, for each comparison, inflammatory response and cell to cell signalling and interaction were among the top biological functions that came up with highly significant p-values (1.
Acknowledgements This work has received a financial support from " La Région Centre" (programme Capricel , coordinated by  Rachel Rupp ,  SAGA ,  INRA Toulouse ) and from the  EADGENE  ( EU  Contract No. FOOD-CT-2004-506416 ). We gratefully acknowledge  Jérome Lecardonnel  ( Centre de Ressources Biologiques GADIE, INRA, Jouy-en-Josas, France ) for technical skillful and help in performing microarray experiments. We warmly thank  Leonardo Bianchi  for invaluable help in collecting samples. We would also thank  Sophie Dauchy  and  Caroline Morgenthaler  for helpful assistance in performing cryosection and  LCM. Pauline Brenaut  was supported by a PhD fellowship from the  ABIES graduate school, AgroParisTech, Paris.
Cronbach's α coefficient was 0. 84 for emotionality, 0. 76 for expectancy, 0. 66 for compulsivity, and 0. 63 for purposefulness. 38 Items are rated on a 1 to 7 scale (strongly disagree to strongly agree). To reduce acquiescence, we reverse keyed four items. During data analysis, we inverted raw scores on the reverse keyed items. We calculated scores for tobacco craving by summing item scores for each dimension. For FTCQ-12 we analysed the sum of all dimension scores (range 12-84) and for the Minnesota nicotine withdrawal scale the sum of all items (range 0-32). Self reported compliance was based on how many patches (15 mg or 10 mg patch) participants reported using since the last visit and how many patches (15 mg or 10 mg) participants brought to their current visit. We subtracted the number of patches participants self reported using since the last visit from that of the prescription/delivery at the previous visit and expressed this as the percentage of the prescribed dose.
Data analysis We analysed data on an intention to treat basis. The intention to treat population was defined as all pregnant smokers having received a randomisation number and a prescription. We considered those participants who missed a visit as smokers. Smoking (lapse or relapse) was not a reason for discontinuation. We contacted those women who missed a visit by telephone; missed visits were not a criterion for discontinuation. The women were strongly encouraged to stay in the trial up to delivery. We defined the end of follow-up as end of pregnancy: delivery or other (for example, miscarriage). We hypothesised that even a short period of smoking abstinence could lead to improved birth weight. Therefore we made every effort to track down all the newborn babies of the randomised women. Data were analysed blinded to treatment.
Complete abstinence rates were compared using Fisher's exact test. We used the log rank test to compare the time to lapse or relapse. The exact date of the relapse after quit date was not collected because of the uncertainty of recall and the lack of biochemical measure (carbon monoxide level in expired air); the date of lapse or relapse was defined as the date of the visit at which the non-abstinence was first ascertained. Using a mixed effect logistic model for longitudinal data we compared the point prevalence abstinence rate. To take into account the clustering of outcomes (twin pregnancies), we compared birth outcome measures using a linear mixed model for quantitative variables and a generalised linear model with a logit link function for qualitative variables. To test whether changes over time of blood pressure, weight, craving tobacco, withdrawal symptoms, number of cigarettes smoked, carbon monoxide level in expired air, and saliva cotinine concentration were different for the two treatment groups, we used a linear mixed model with time, group, and their interaction as fixed effects.
51  established a model of MLST for S. suis using seven different house-keeping genes (cpn60, dpr, recA, aroA, thrA, gki and mutS). Since being established, this MLST model has been used by multiple laboratories throughout the world to determine the STs of S. suis strains isolated from pig and human cases of infection. When used alongside serotyping, MLST allows gathering further information about the genetic diversity of the S. suis strains within the different serotypes. The popularity of this typing method for S. suis continues to increase due to the ease in carrying out the technique (PCR and sequencing) and due to the fact that the data are transferrable and comparable from one laboratory to another. Thanks to the S. suis MLST Database, the global distribution of the different S. suis STs can be shared and compared.
METHODOLOGY USED IN THE PRESENT REVIEW In the present review, literature searches were completed using the following databases: Medline (PubMed; http://www. ncbi. nlm. nih. gov/pubmed/), Web of Science (http://thomsonreuters. com/web-ofscience-core-collection/) and Science Direct (http://www. sciencedirect. com/). Relevant articles in English were used, while references in other languages were considered when available. MLST results were obtained from the S. suis MLST Database (http://ssuis. mlst. net/). Regarding the distribution of serotypes, the results reported for clinical cases in pigs correspond only to those where serotyping was carried out using antisera and not PCR, while for human cases, reports where serotyping was performed by either antisera or PCR (for serotypes 2 and 14) are both reviewed.
This consideration is primarily based on the fact that serotype 1 and serotype 1/2 isolates have not yet been recovered (or confirmed) from human cases of infection. It is important to note that when the correlation between STs and serotypes are reported in this review, it is impossible to confirm the methodology used for the serotyping for many cases. 51 We decided to take the data into consideration, especially for those included in the S. suis MLST Database, since the number of cases used for this review would otherwise have been highly limited. For the distribution of STs, the ST complexes represent hyperinvasive lineages to which two or more STs belong as a result of genetic proximity and are based on the MLST phylogeny diagram presented by Lachance et al. 54  STs considered to be unrelated to another ST according to this phylogeny diagram are identified as being 'unrelated' to any.