dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In the present study we showed that supervised multivariate techniques, such as machine learning ones, may successfully address different multiclass analysis problems in metabolomics. We employed modern machine learning methods to analyse diverse aspects of two different multiclass problems. First, we classified 14 raspberry cultivars on the basis of their GC-MS profiles and PTR-MS fingerprints. Good results were achieved with both techniques, with slightly lower classification errors for the PTR-MS dataset, probably because of the larger number of analyzed samples. In fact, PTR-MS is a high-throughput technique that allows the reduction of measurement time by about 100 times compared to standard GC-MS analysis. Groups of cultivars with similar volatile emission were consistently identified using confusion matrices.
These similarities were related to genetic affinities, varieties sharing common parents are generally grouped together. Among the classification methods considered, Random Forest showed the best classification performance, in particular for PTR-MS data, but the other methods also showed to be effective in both cases. Feature selection by RF-RFE allowed the identification of the peaks/compounds that are more relevant to these classification problems and suggested that VOCs of very diverse compound classes are needed for the full discrimination of the considered raspberry cultivars. The same analysis procedure was employed to grade the raspberry cultivars on levels of gray mold susceptibility. Models based on the GC-MS dataset and on the PTR-MS one displayed similar grading errors but marked differences in the confusion matrices.
an insight in the classification performance provided by RF on the GC-MS dataset. The corresponding average classification error is 0. 298, meaning that on average about 70% of the samples are assigned to the correct class by the model. The generally good prediction performance on the analyzed cultivars entangles a significant and robust difference in their VOC profiles. A close look at Table 2 suggests the existence of groups of cultivars which are generally confused. This is particularly true in the case of Polka VV3-536 and Polka VV5-657. In fact, these two classes correspond to the same cultivar (Polka), confirmed by genetic analysis (data not shown) and have been considered separately because at the beginning of the harvesting campaign their identity was doubtful. Thus, it does not A C C E P T E D M A N U S C R I P T.
classes, where the two Polka cultivars are grouped together. The results improve in all cases, showing that these two mixed and isolated classes correspond in fact to a single class. With 13 classes the best prediction errors are reduced to 0. 191 for GC-MS and to 0. 156 for PTR-MS. 3. 2 Feature selectionIn this section we address the problem of highlighting the most relevant variables (GC-MS compounds or PTR-MS peaks) for separating the 14 raspberry classes. As we explained in Section 2, feature selection based on RF-RFE proceeds as follows. As a first step we assess the behaviour of the mean discrimination error of RF as a function of the number of variables used in the model. In order to clarify the procedure, in the case of the GC-MS dataset the results are showed in Figure1. A trade-off between model simplicity and discrimination error is represented by 20 variables in this case, from the original total of 45.
Statistical Analysis Data are reported in mean ± standard deviation (SD) apart from EDSS which is reported in median and percentiles (25. and 75. ). To compare means t-test was used for comparisons between control and patient groups; to compare means or medians within the group of controls or patients one sided ANOVA or Kruskal-Wallis procedure was used. For correlation analysis Spearman's rho (r) was used. For all analysis Matlab (Release R2011a, The MathWorks, Inc. , Natick, MA, USA) was used. A p-value of <0. 05 was considered significant.
DISCUSSION The width of the third ventricle has now been assessed by several groups. The mean widths vary between 3. 6 ±1. 8 mm [19] and 6. 2 ±1. 9 mm  [10]  with several studies in between Age in years; MS (multiple sclerosis) duration in years; 3v width, width of 3. ventricle in mm; 3v ↑, number of patient with third ventricular enlargement as indicated by being above the upper limit of the 95% confidence interval of the control population. All values in mean ± SD apart from EDSS which is presented in median and percentiles (25. and 75. ). n. s. , not significant. (3. 9 ± 2. 5 mm  [13] , 4. 5 ± 1 mm (Berg et al  [11] , derived from figure  1 ). The values of Seidel et al  [10]  were published in 1995, hence the data were collected one or two years earlier which could implicate that the then older technologic equipment might be one reason for their results.
The students' and the graduates' characteristics are described. General data regarding duration of studies for the II department are studied. For the purpose of this study, data were collected by the secretariat office of the department in April of 2010. In total, data were collected for 2675 students who have been registered in the department from 1999 to 2010. Moreover, with the use of a questionnaire addressed to active students of the department, their evaluation for courses, professors and students is studied. The goal of this study is to study how many of the registered students graduate, how long is the duration of studies, how many overcome the upper limit that the state has set, how many and why are deleted and whether the above are effected by factors such as the semester of each student is at, sex, origin and the way of each student has registered to the department.
Data analysis of the Industrial Information Department For the purposes of this study, data were collected by the secretariat office of the department in April 2010. In total, data were collected for the 2675 registered' students from 1999 to 2010. For each student sex, origin, registration year, way of insertion, semester of insertion (registrations used to took place in winter and spring semester as well) have been recorded. Moreover, for each student has been recorded if he/she is deleted and why, if he/she is active of in suspension and if he/she has graduated. For those who have graduated, the year of graduation, the duration of studies and the bachelor degree have been recorded. In Table  1  the frequency and the percentages of all students from 1999 to 2009 are presented. The biggest percent of 34% of the students are active, from which 64% are male and 36% are female.
Empirical research With the use of a questionnaire addressed to active students of the department, their evaluation for courses, professors and students is studied. It was distributed in October of 2010 and the number of students that have responded is 106. The statistical analysis was realized with the statistical package SPSS 17 (Statistical Package for the Social Sciences). From the descriptive statistic analysis, male are 66% of the sample and female are 34%. The biggest mass of the sample origin from west and central Macedonia 35. 8%. Almost 60% origin form near-by areas. Regarding students' secondary education, the majority of them come from Lyceum at 80%. From the possible education directions in secondary school, 52. 8% chose technological, 18. 9% informatics and 16% theoretical. Finally, 42.
The quartz shows mostly intra-or transgranular, curved cracks, whereas in micas and feldspars rather straight cracks often follow the mineral cleavages and are interconnected into intergranular cracks. Microstructural analysis of the three sections was performed to determine the sample mineral composition, grain size and grain aspect ratio. A total of 779 grains was analysed using ESRI ArcGIS software for manual tracing of digital photographs and MATLAB TM PolyLX toolbox  (Lexa et al. 2005)  for geometric evaluation of the traced objects. The results of our thin section analysis are summarized in Table  1. Mercury porosimetry yielded a sample density of 2. 626 g cm -3 and a total porosity of 1. 4 per cent, taking into account pores with access diameter between 0. 3 and 5 nm. TC was measured by optical scanning method  (Popov et al.
Tracing of grain boundaries and microcracks We used microphotographs of three mutually perpendicular, epoxysaturated thin sections to manually trace grain boundaries, intergranular cracks and intragranular cracks in quartz, feldspars and in micas using ESRI ArcMap 9. 3 GIS software. Grain boundaries were traced on microphotographs obtained under cross-polarized light, whereas microphotographs obtained under UV-excited fluorescence light were used for tracing of the cracks. The sample material was epoxy-saturated prior to preparation of the thin-section and so any cracks due to the preparation do not appear in the UV image and are consequently not accounted for during the tracing. The traced networks were processed in MATLAB TM FracnetLX toolbox  (Lexa 2010) , in order to visualize the orientation distribution functions on rose diagrams and to calculate the bulk anisotropy of preferred orientation [SURFOR 'A(α)' function after  Panozzo (1984) ] for each of the traced features.
Ultrasonic P-wave velocity data We plotted the results of our V P measurements on the lower hemisphere of equal area projection using grey scale look up for each of the plot in order to accentuate the anisotropy evolution throughout the experiment. For easier visual comprehension the plot data are contoured by isolines. In total, we constructed three types of stereo plots that we use for interpretation: (1) V P plots for each of the 13 confinement levels (seven during pressurization, six during depressurization). These plots show the V P range and directional dependence of V P with confining pressure (depth). (2) V P ( p) plots showing changes of V P pattern between successive levels of confining pressure, for both pressurization and depressurization. Changes in V P are correlated to the closing or opening of microporosity as a function of confining pressure (depth).
Methods and design The Trivalve trial is a single-center, prospective, randomized trial. It will evaluate the short-term (six month) hemodynamic performance of three pericardial bioprostheses: the Trifecta valve (St. Jude Medical, Minneapolis, MN, USA), the Mitroflow valve (Sorin Group, Saluggia, Italy), and the Carpentier-Edwards Magna Ease valve (Edwards Lifesciences, Irvine, CA, USA), Figure  1. ClinicalTrials. gov Identifier: NCT01522352.
Patient's enrollment and randomization All patients scheduled for surgical aortic valve replacement by bioprosthesis will be screened according to inclusion and exclusion criteria (Table  2 ). Patients who have given their signed informed consent to participate in this clinical trial will undergo, preoperatively, a CT-scan and a transthoracic echocardiogram to measure the aortic annulus. Included patients will be randomly allocated to receive one of the three bioprostheses, in a 1:1:1 ratio. When a patient is considered eligible and informed consent has been obtained, randomization will be performed automatically (using STATA software (StataCorp, College Station, TX, USA) before surgery by an independent biostatistician. No stratification will be done. The selected bioprosthesis will be implanted.
The study was coordinated from Southampton and collaborators met every 6 months at EORTC Quality of Life Study Group meetings. Questionnaires and data collection. All patients completed the EORTC QLQ-C30 (version 3. 0) and QLQ-ELD15 at baseline. A 'not applicable' option was added at the request of the UK ethics committee to the three items of the QLQ-ELD15 which mentioned family (questions 35-37). In 29% of baseline questionnaires, the 'not applicable' option was omitted in error. A subset of patients, who were predicted to be clinically stable, completed the questionnaires again 1 week later (test-retest analysis) and a different subset, predicted to have a different clinical status, completed the questionnaires again 3 months after baseline (response to change analysis, RCA). EORTC translation guidelines  (Koller et al, 2007)  were used to produce questionnaires in all the relevant languages.
The QLQ-ELD15 contains 15 items in five scales: mobility (Q31-34), family support (Q35-36), worries about the future (Q37-41), maintaining autonomy and purpose (Q42-Q43), and burden of illness (Q44-45)  (Johnson et al, 2010). All responses were converted to a score of between 0 and 100 using a linear transformation following EORTC guidelines  (Fayers et al, 2001). High scores indicate poor mobility, good family support, much worry about the future, good maintenance of autonomy and purpose, and high burden of illness. At baseline, participants completed a debriefing questionnaire that recorded time for completion, whether any help was needed and whether any of the items were upsetting, confusing or difficult to answer. Additional comments were invited. Sociodemographic and clinical data were recorded at each completion of the questionnaires, along with the Charlson Comorbidity Index  (Charlson et al, 1987) , Eastern Cooperative Oncology Group (ECOG) Common Toxicity Criteria and Performance Status  (Oken et al, 1982) , G-8 Geriatric Screening tool  (Bellera et al, 2012)  and Instrumental Activities of Daily Living (IADL)  (Lawton and Brody, 1969).
Statistical analysis. Standard psychometric analyses were employed to evaluate the QLQ-ELD15. All analyses were performed using Stata/IC version 12 statistical software (Stata Corporation, College Station, TX, USA). Scaling. The construct validity of the QLQ-ELD15, that is whether the individual items composing the questionnaire could be aggregated into the five hypothesised scales described above, was examined using multi-trait scaling. Construct validity comprises convergent validity and discriminant validity. Convergent validity is demonstrated when an item correlates highly with its own hypothesised scale, defined as a correlation of X0. 40 (corrected for overlap)  (Fayers and Machin, 2007). Discriminant validity is demonstrated when an item does not correlate highly with the scales it is not part of.
ACKNOWLEDGEMENTS This study was funded by the  European Organisation for Research and Treatment of Cancer Quality of Life  (EORTC QOL) Group who approved the manuscript before submission. We would like to thank the reviewers from the  EORTC QOL group ,  Mogens Grønvold ,  Galina Velikova ,  Susanne Singer ,  Bernhard Holzner ,  Eva Greimel ,  Jaap Reijneveld  and  Andrew Bottomley. The following all contributed to participant recruitment, for which we are grateful:  Dr Valerie Quipourt ,  Dr Genevie  `ve Imbert,  Dr Bernhard Holzner ,  Dr Annemie Courtens ,  Dr Jessie Wu ,  Ms Anne Jacob ,  Ms Janet Prouse ,  Mrs Christine Brannan  and  Mrs Melanie Winterbotham. We acknowledge all the centres, investigators and patients who took time to participate in the study. This work is published under the standard license to publish agreement.
Data collection Patient data were recorded retrospectively from 12 months before dialysis then prospectively for 12 months after the beginning of dialysis. The data were collected from either electronic or written medical records. All the data from medical visits and biological measurements, including at dialysis start, were collected over the predialysis year. Over the first dialysis year, all events occurring within and between dialysis sessions were recorded. Estimated GFR (eGFR) was calculated by using simplified MDRD equation  [20]. Measurement of BP was made in sitting position at rest during physician visit.
Statistical analysis Characteristics of our study samples were compared to national data by one-sample Student t-tests (for means) or one-sample z-tests (for proportions). Variables were presented as mean, standard deviation for quantitative variables and as percentage for qualitative variables. Survival was estimated by the product-limit procedure of Kaplan Meier, and survival curves were compared with the log rank test. Biological or clinical parameters were compared using conditional logistic regression  [21]. The calculation of the samples size, with α=0. 05 and 1-β=0. 8 were based on the following assumptions: risk of one year mortality or cardiovascular events of 20% with the follow up in the network allowing to decrease this risk to 5%. We found that 38 patients had to be included in the network group and 114 in the control group.
Assuming a proportion of patient lost of follow up of almost 5%, we included 40 patients in the network group and 120 in the control group. The GFR annual slope was computed with all values available between 12 months before dialysis and dialysis starting date. This slope was compared between the network group and the control group with a mixed model accounting for matching  [22, 23]. Using regression equations, we defined the theoretical value for entering dialysis. Only parameters with less than 15% of missing data were considered. Statistical analysis was performed using SAS 9. 2 software (SAS Institute Inc. , North Carolina, USA) and the threshold of 0. 05 was considered as statistically significant.
Patient characteristics As expected from the matching, the patients from the two groups had similar age, gender and prevalence of diabetes (Table  1 ). The distribution of primary renal disease was similar except for the percentage of patients with hereditary nephropathies that was significantly higher in the network group. In order to verify the representativeness of the two groups of patients, we compared major characteristics of study patients to those of incident dialysis patients registered during the same period in the French national database of ESRD patients, the REIN (Renal Epidemiology Information Network) Registry. In the whole study group, there were no differences for age and the proportion of women (respectively 65. 6±14. 7 vs 65. 3±15. 6 and 35% vs 38%) but there were less diabetic patients (22.
The authors showed that a distributed network could improve the progression of CKD in patients with stage 3-5 CKD comparatively to usual care. However, in this study, renal follow up was only performed by the GP, without visits with the nephrologist who gave the treatment adaptations remotely  [11]. This is quite different from our approach because the absence of direct contact between patients and nephrologist may affect the clinical evolution of the patients. This study describes for the first time in France the effects of coordinated renal care provided by a dedicated network. Although accurate epidemiological data on the treatment of stages 4-5 CKD are lacking, data from the REIN registry show a trend towards a stabilization of the incidence rate of ESRD over the five last years. However, the prevalence of CVD remains high in incident patients, contributing to their early mortality: French data network REIN showed that the risk of death is highest in the first year after the start of dialysis treatment (17% for the whole population), especially in older patients  [3].
Acknowledgments We thank study investigators:  E Decullier  (pôle  IMER ,  Hospices Civils de Lyon ),  P Chauveau  ( AURAD Aquitaine ),  C Michel  ( AURA Paris ),  F Combarnous  ( Clinique du Tonkin, Villeurbanne ),  H My  ( Hospital St Joseph, Lyon ),  C Chazot  ( NephroCare Tassin-Charcot, Ste Foy les Lyon ),  C Jacquelinet  and  M Lasalle  ( REIN Registry, St Denis ). We also thank  M Bailly ,  K Maurizi  and  M Mercier  for their help in the course of this study. This study was supported by a research grant from  Roche  laboratory.
This was assessed for all three conditions (serum/LIF, 2i, and 2i/LIF). When single HV low (HV À ) or HV high (HV + ) cells were cultured in 2i or 2i/LIF, they regenerated heterogeneous cultures, although a proportion of cells maintained a bias toward regenerating mixed populations that contained a higher fraction of the seeding cell type (Figure  2B ). We asked whether HV + and HV À cells derived from serum/LIF or 2i/LIF culture conditions represented equivalent cell types by carrying out RNA sequencing (RNA-seq) genome-wide expression analysis on each population (Gene Expression Omnibus accession number GSE45182). Hierarchical clustering of gene expression based on an alternative pairwise comparison of the different populations (Figure  2C ) suggested that HV + and HV À states were more distinct in 2i/LIF than in serum/LIF (>2-fold change, false discovery rate [FDR] 0.
Extraembryonic endoderm and trophoblast genes were both upregulated upon the addition of LIF, while neuroectoderm and mesoderm markers were reduced (Figure  S6G ). Moreover, genes upregulated by LIF in 2i demonstrated the strongest correlation with placental gene expression (Genomics Institute of the Novartis Research Foundation [GNF] Gene Expression Database; Figure  5E ). In 2i, LIF increased the proportion of proliferating HV + cells while decreasing the proportion of HV À proliferating cells  (Figures 5F and 5G) , suggesting that LIF promoted the expansion of the HV + totipotent population.
, 1992; Takahashi et al. , 2008). In embryoid body differentiation, LIF selectively blocks primitive ectoderm differentiation while permitting PE differentiation  (Shen and Leder, 1992). Additionally, the downstream effector (E) Statistical significance of correlation of gene expression differences between ESCs cultured in the presence and absence of LIF and tissue-specific gene expression in the GNF Ver. 3 database. Correlation was estimated from data on 5,085 genes, which represent the overlap between 10,000 most informative genes in our RNA-seq data and 10,000 most informative genes in the GNF database. Organs/tissues sorted by decreasing correlation. The model relates serum/LIF and 2i ESC cultures to embryonic development. 2i/LIF cultures contain totipotent single cells that are comparable to early preimplantation stages when embryonic cells are still totipotent.
All qRT-PCR transcript levels in this figure were normalized to TBP and are shown as mean ± SD. (E) Statistical significance of correlation of gene expression differences between ESCs cultured in the presence and absence of LIF and tissue-specific gene expression in the GNF Ver. 3 database. Correlation was estimated from data on 5,085 genes, which represent the overlap between 10,000 most informative genes in our RNA-seq data and 10,000 most informative genes in the GNF database. Organs/tissues sorted by decreasing correlation. (F) Confocal optical section of HV cells immunostained for the proliferation marker KI67. White arrowheads indicate KI67-negative cells. (G) Quantification of KI67 immunostaining in HV + and HV À ESCs. The absolute number of proliferating cells (expressing KI67, red bar) and quiescent cells (not expressing KI67, black bar) were counted.
ACKNOWLEDGMENTS We thank  Lynsey Robertson ,  Sally Inverarity ,  Javier Martin Gonzalez ,  Kasper Bonderup ,  Ron Wilkie ,  Simon Monard ,  Gelo de la Cruz , and  Valerie Wilson  for technical assistance;  Austin Smith ,  Heiko Lickert , and  Ian Chambers  for reagents; and  Sophie Astrof ,  Sally Lowell , and the entire  Brickman lab  for critical discussion of this manuscript. This work was supported by grants from the  Medical Research Council (MRC)  ( G0701428 ) and the  Novo Nordisk Foundation  (to J. M. B. ), the  Intramural Research Program of the National Institutes of Health , and the  National Institute on Aging  ( Z01AG AG000656  and  Z01AG000662  to M. S. H. K. ). S. M. M. is supported by an  MRC  studentship, and J. M. B. is an MRC senior nonclinical fellow. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.