dataesr/scientific-paragraphs-categorization huggingface dataset:
CLUSTATIS provides several indices and tools to select the appropriate number of clusters and to assess the relevance of the obtained solution, the homogeneity of each cluster, the centrality of each dataset within its cluster, the similarity among the clusters, etc. We have discussed the application of CLUSTATIS to projective mapping/Napping and free sorting data. Obviously, the range of application of this strategy of clustering is very wide and is getting wider as time goes by since the collection of multiblock data is becoming more and more frequent in several domains of applications such as metabolomics, genomics, etc. From a technical point of view, CLUSTATIS offers two distinct strategies of clustering (i. e. , hierarchical and partitioning algorithms) and it is advocated using both of them since they complement each other.
The hierarchical cluster analysis helps the practitioner to choose the appropriate number of clusters and provides an initial solution to the partitioning algorithm. Notwithstanding, each of these two strategies of clustering can stand by itself. We have seen in the first case study that the solution provided by the hierarchical clustering was but very marginally improved since only one subject changed membership. In the second case study, the partitioning algorithm could not further improve the solution obtained by means of the hierarchical algorithm. One should not jump to the conclusion that the partitioning algorithm is redundant. Indeed, there may be situations where it can significantly improve the solution from the hierarchical strategy of clustering. Moreover, we should bear in mind that the hierarchical clustering is time consuming particularly if the number of datasets is very large.
In such situations, the partitioning algorithm can be used by itself by considering several initial solutions and, eventually, choosing the best solution that corresponds to the smallest criterion D. The difficulty with this strategy of analysis is the choice of the appropriate number of clusters, which is a tricky problem. CLUSTATIS was designed to be an extension of the CLV approach (Vigneau and  Qannari, 2003; Vigneau, Chen and Qannari, 2015). As stated above, this latter approach is concerned with the cluster analysis of variables and have enjoyed some popularity among practitioners in various domains of analysis. As a matter of fact, this method of analysis has several interesting options that can be adapted to the CLUSTATIS approach. For instance, we may be interested in clustering several consumers' datasets (e.
g. , liking scores for several criteria) taking account of an external dataset (e. g. , sensory data). We may also be interested in clustering a collection of datasets (e. g. , from a sorting or Napping tasks) while setting aside outlier panellists  (Vigneau, Qannari, Navez, Cottet, 2016). This will result in a more stable and better interpretable clustering of the datasets. Investigations regarding these extensions are currently underway and the findings will be reported elsewhere. For the illustration of CLUSTATIS, we have used case studies which involve a relatively small number of subjects. The efficiency of the clustering approach would be better demonstrated with case studies involving more subjects. Furthermore, besides the homogeneity indices, more validation tools can be used to better assess the relevance of the results  (Vigneau et al, 2016; Brock, Pihur, Datta, Datta, 2008).
Clinical Outcomes Clinical data extracted from interrogation carried out during initial care was collected on an electronic Case Report Form (CRF). This CRF included age, gender, medical and surgical history, otolaryngological comorbidities, onset of general symptoms and main symptoms presented during the first two days of the disease, among the following ones: cough, fever, myalgia, headaches, diarrhea, rhinorrhea. We also looked in the patients' medical records for the main reason for hospitalization and possible resuscitation transfer. Lastly, the phone interview entailed a search for qualitative or quantitative olfactory and/or gustatory disorders, not related to nasal obstruction or rhinorrhea.
Statistical Methods Excel V R Software was used to perform the statistical analyses. Data were collected in Excel V R tables, including statistical descriptions according to the following terms: For quantitative variables: median, minimal values, maximal values; for qualitative variables: absolute values and percentages (missing data were excluded from denominator). The average equality hypothesis was tested for quantitative variables. The cohort homogeneity hypothesis was checked for qualitative variables. Concerning quantitative variables, after variance equality and normal distribution of the values were checked, we used the Student t-Test. In case of variance inequality or non-Gaussian distribution of the values, a nonparametric Mann-Whitney/Wilcoxon test was performed. For the analyses of qualitative variables, a Chi-square Test was performed, or a Fisher Test in case of small-size samples.
Statistical Analysis Sample size calculation was based on a median deteriorationfree survival of 1 month versus 2 months, with a hazard ratio (HR) of 0. 50, a bilateral type I error of. 0166 (three targeted dimensions), and a statistical power of 80%. This corresponded to a total enrolment of 96 patients followed and 89 events to be observed, that is, patients who had a significant deterioration in HRQoL or died, whichever occurred first. Considering a 10% rate of switching between treatment arms as a result of the use of Zelen's randomization, a total of 106 patients with available data were required. The intent to treat (ITT) population was defined as all randomized patients score available, regardless of whether eligibility criteria were met and regardless of treatment received. The primary endpoint was analyzed in a modified intention-to-treat (mITT) population, that is, considering all ITT patients with at least a baseline HRQoL score available.
A post hoc analysis for the primary endpoint was conducted for the first 6 months of follow-up only in order to reduce the bias caused by long survivors. Deterioration-free survival was estimated using the Kaplan-Meier method. Adverse events were analyzed in the as treated population and were considered for patients receiving at least 1 day of treatment in the PN arm and only patients without artificial nutrition in the OF arm. All analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC). All analyses were two sided, and the statistical significance value was fixed at. 0166 to take account of the number of comparisons performed.
DISCUSSION In this study, PN failed to improve QoL for patients with cancer-related cachexia as well as survival. Moreover, PN caused more serious adverse events. This is the first study to assess PN for patients with advanced cancer with estimated life expectancy under 1 year, so comparison with data in the literature is difficult. In the only previous study (in Sweden), 339 patients were randomized if they had cancer-related cachexia and functional gastrointestinal tract to receive nutritional support (including possibility of home PN) or not  [34]. Unfortunately, HRQoL was not assessed, and the median overall survival did not differ in the ITT analysis. The authors mentioned the complexity of their current model, as additional interventions (i. e. , cyclooxygenase inhibitors and erythropoietin) were offered to the best possible extent for patients in both the study and control arms.
Current analytical methods reflect a heterogeneous mix of chemical entities, with no information on any subspecies of fibre or any information on the structural characteristics of the fibre present. One example of how dietary fibre is measured is by using the AOAC enzyme-gravimetric method, which is intended to simulate the physiological conditions of digestion, and measures all the components of fibre, as currently defined by CODEX Alimentarius. This kind of analysis is limited when being used to interpret mechanistic data on the functional properties of cell walls, individual cell wall polysaccharides and storage oligosaccharides. More informative methods, notably dissolution kinetics, molecular weight of individual polysaccharides, and cell wall porosity are urgently required for characterising dietary fibre in nutritional and epidemiological studies, if food sources of dietary fibre for health are to be optimised.
A. C. : is in the advisory board for BD (Beckton Dikinson), Eli Lilly, Mundipharma; gave lectures sponsored by Astra Zeneca, Berlin Chemie, Boehringer Ingelheim, Eli Lilly, Mundipharma, Novo Nordisk and Roche Diagnostics; and received research grants from Mitsubishi. P. R. E. : some of his almond studies were funded by the Almond Board of California. M. -A. H. : Director of East Anglia Food. J. C. H. : his Clinical Nutrition Research Centre conducts food and nutrition research for several companies including Beneo, Roquette, Tate and Lyle, Wilmar and Nestle. D. J. A. J. : has received research grants from Saskatchewan & Alberta Pulse Growers Associations, the Agricultural Bioproducts Innovation Program through the Pulse Research Network, the Advanced Foods and Material Network, Loblaw Companies Ltd.
Acoustic Stimuli All sounds were generated using MATLAB software (Mathworks, United States) at a sampling frequency of 48 kHz. We created -Consonant and dissonant chords by summing a reference note (C7, 2093 Hz) and its harmonics, all with equal amplitude, to another note, see Figure  1B. Chords were presented in sequences of 300 ms of sound separated by 100 ms silence. -Amplitude Modulated (AM) sounds: broadband noise or a harmonic complex tone including 4 kHz and its harmonics, all with equal amplitude, were amplitudemodulated (100% of modulation depth) at rates of 2, 5 (see Figure  1Cii ),  15, 25, 35, 70, and 140  Hz. Sounds were presented at 77 (±2) dB SPL using a dome tweeter (22TAF/G, Seas prestige). Roughness of sounds was estimated using  Vassilakis' estimation method (2001)  implemented in the free Matlab MIR toolbox.
FUNDING BG was supported by a  Junior Grant  from the  French National Research Agency  ( ANR-15-CE37-0007-01 ). OP was supported by the  Fondation pour la Recherche Medicale  ( FDM201806005994 ). NM was supported by the grants from the  ANR  as part of the  second Investissements d'Avenir program LIGHT4DEAF  ( ANR-15-RHUS-0001 ) and  LabEx LIFESENSES  ( ANR-10-LABX-65 ), LHW-376  Stiftung , and by the  Fondation pour l'Audition.
8… kcal/day to breast milk and 208. 3… kcal/day to infant formula for 18-month-olds. We used data on kcal/L for breast milk (3,103. 7 kJ/L for 2-6-montholds, 3,683. 2 kJ/L for 12-39-month-olds) during prolonged lactation from Mandel et al. [59]  with these energy intake figures to estimate breast milk intakes of 0. 579… L/day for 7-month-olds and Page 11 0. 2303… L/day for 18-month-olds. For formula-fed infants, we used average energy content reported on the labels for the products we tested in our market basket survey (669. 0… kcal/L for 7-month-olds, 621. 6… kcal/L for 18-month-olds) together with the energy estimates due to formula, yielding estimated formula intakes of 0. 671… L/day for 7-month-olds and 0. 3351… L/day for 18-month-olds  [42].
Acknowledgments This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This study received institutional support from  Better Life Laboratories, Inc. ,  Norwich University , and the  Centre d'Etudes Nucléaires de Bordeaux Gradignan (CENBG). We thank  Deborah Ahlers  and  Tammy Hunt  from  Norwich University , and  Florelle Domart  and  Francesco Porcaro  from  CENBG  for their valuable assistance with this project.
A flow diagram of the screening, recruitment and enrollment procedures, and participation through the phases of the study is provided in Fig. 1. At the time of the medical screening, body weight and height were measured and BMI (kg/m 2 ) was computed. The Three Factor Eating Questionnaire  (Stunkard & Messick, 1985)  was administered in order to identify and exclude candidates with excessive Dietary Restraint (cutoff restraint score = 8). Participants were then asked to record their food intake over three consecutive days (2 week day and one week-end day) using standardized food diaries. Mean daily energy intake was computed from the diaries (Nutri-7® software, CIQUAL Food  Composition Table, 2017). All participants signed an informed consent form and were informed of their right to withdraw from the study at any time.
The protocol was approved by the Institutional review Board (CPP Nord-Ouest II, ref  2018 -5, February 21, 2018)) , registered and authorized by the French health authorities (ANSM, January 22, 2018, N°ID-RCB: 2017-A03167-46). The study was registered in the Protocol Results Registration System (PRS) at ClinicalTrial. gov (Identifier: NCT03526120). The first participant was enrolled on February 27, 2018 and the experimental procedure ended on July 28, 2018.
The following meals were ingested in free-living conditions. A registered dietician instructed the participants about weighing all the food they consumed at dinner on day 1 and on the next day (using a portable household scale lent by the research center) and reporting the quantity in a food diary (leftovers deducted). Total energy, macronutrient (total lipids, protein and carbohydrates; mono-and disaccharides; saturated, monounsaturated and polyunsaturated fats) and micronutrients (thiamine, pyridoxine, riboflavin, potassium, zinc, magnesium, copper, manganese) intakes were computed. Energy and nutrient intakes computations were based either on the data provided on food labels or from the validated Nutri-7® software  (Fantino et al. , 2018; Fantino & Gourmet, 2008; Ghisolfi, Fantin, Turck, de Courcy, & Vidailhet, 2013)  based on the CIQUAL French Food Composition Table  (Tables Ciqual-Anses, 2017).
Statistical analysis The primary objective was the demonstration that the daily consumption of the pistachio snack for 12 weeks would not induce weight changes significantly different from changes observed in the controls. To this aim, a non-inferiority statistical test was conducted comparing body weight before and after the intervention in both groups. The sample size was computed according to Julious  (Julious, 2004 ) using NQuery 7. 0 software, considering a 10% non-inferiority margin, i. e. the maximum value over which two BW means can be considered not different according to the considered margin, with a 2-sided 5% type 1 error and a statistical power of 80%. The non-inferiority margin was determined according to the method prescribed by the Food and Drug Administration  (FDA, 2016)  and European Medicines Agency  (EMA, 2005).
The assumption of a 30% difference was based to be clinically relevant in iron-overloaded population. Statistical analysis was performed using SAS® version 9. 4 software (Cary, NC, USA); hypothesis testing was two-sided with significance interpreted as p < 0. 05. The AUC, Cmax (maximum observed value of serum iron concentration) and Clast (last observed value of serum iron concentration) were calculated using Phoenix 64® Winnolin V7 (Certara Princeton NJ, USA). The AUCs were calculated using the linear trapezoidal rule. For continuous parameters, mean ± SD or median and interquartile range were calculated, according to the statistical distribution. The Shapiro-Wilk test was used to investigate the assumption of normality for continuous data. The primary endpoint was compared between the groups using analysis of variance for correlated data including sequence, visit, treatment and time fixed effects in addition to the patient effect (as a random effect).
When appropriate, the pathology was added to the model as a fixed effect. Type III sum of squares and least squares (LS) means were used. We used the Bonferroni correction for multiple comparisons. The normality of residuals was studied using the Shapiro-Wilk test. Owing to negative AUC values no logarithmic transformation was proposed. First, analysis of the values observed at T0 was carried out (without correction for missing values) to evaluate within-and between-subject variability; following this, V0 data (under fasting conditions) were analyzed to estimate the difference between the pathologies in the fasting state (basal values), followed by the evaluation of the meal and treatment. To identify the effect of procyanidin supplementation, we used a two-step correction of the mean iron concentrations.
French regulations, ethical approval was obtained from the "Comité de Protection des Personnes Sud-Méditerranée I" on 20 February 2018 (reference 18-10). The study was conducted in accordance with the Declaration of Helsinki and the Good Clinical Practice recommendations. Written informed consent was obtained from each participant. The study was registered on clinicaltrials. gov (NCT 03453918) and on the European Clinical Trials Database (EudraCT 2017-A01955-48). Patients were screened for participation during their normal medical follow-up in the Internal Medicine Department of Clermont-Ferrand University Hospital, France. The sequence of administration was randomized through a computer generator using block randomization with stratification by disease. Patients, experimenters, care-providers and statisticians were blinded to the sequence of administration.
Study design This preregistered,  15  cross-sectional online study was approved by the Office of Research Protections of The Pennsylvania State University (STUDY00014904); it is in accordance with the revised Declaration of Helsinki, and compliant with privacy laws in the U. S. A. and European Union. Data reported here were collected between April 7 and July 3, 2020 from the Global Consortium for Chemosensory Research (GCCR) core questionnaire (Appendix 1 and https://gcchemosensr. org),  8  an online crowdsourced survey deployed in 32 languages that used binary response and categorical questions (e. g. Appendix 1, Questions 6,9) and visual analog scales (e. g. , Appendix 1, Question 13) to measure self-reported chemosensory ability and other symptoms in adults with current or recent respiratory illness.
Participants (272) A convenience sample of 52,334 volunteers accessed the GCCR questionnaire; 25,620 met e criteria (≥19 years old, respiratory illness or suspicion thereof within the past two weeks). After applyi registered exclusion criteria, 15,747 participants were included in reported analyses (Figure  1 ). Based on the self-reported outcome of a COVID-19 lab test, participants were labeled as eithe (positive result) or C19-(negative result). The specific collider bias characterizing this sample (high fra C19+ participants and high prevalence of chemosensory disorders in both groups) underestimates the p correlation between smell loss and COVID-19 (Figure  S1 ). Thus, it represents a conservative scenario the hypothesis that smell loss reliably predicts COVID-19 status. We benchmarked the GCCR datase representative samples collected with the Imperial College London YouGov Covid 19 Behaviour T (henceforth, YouGov; countries shared across datasets: Brazil, Canada, Denmark, Finland, France, Ge Italy, Mexico, Netherlands, Norway, Spain, Sweden, UK, USA; YouGov: N=8,674, GCCR: N=3,96 publicly available at https://github.
Statistical analyses Statistical analyses were performed in Python 3. 7. 6 using the pandas,  17  scikit-learn,  18  and statsmodels  19  packages. The data and annotated code is included as supplemental material and will be publicly available on GitHub (http://github. com/GCCR/GCCR002) upon publication. Missing values for covariates used in prediction models of COVID-19 status and smell recovery were imputed as follows: binary features = 0. 5, numeric features = median, categorical variables = "Missing". Prediction targets themselves were never imputed. Responses incompatible with model generalization (e. g. , open ended questions) were excluded. A one-hot encoding was applied to all categorical variables to produce binary indicators of category membership. L1regularized logistic regression (penalty α=1) consistently produced sparse models with comparable crossvalidation accuracy and were therefore the prediction test of choice.
Model quality was measured using receiver operating characteristic (ROC) area under the curve (AUC). Cross-validation was performed in 100 random splits of 80% training set and 20% test set, and ROC curves are concatenated over each test set. ROC curves are computed on predicted probabilities from each model, circumventing the high-cardinality bias of AUC. For single feature models, AUC is independent of most modeling details, including all rank-invariant decisions. To correctly compute p-values for model coefficients, the normalized data were standardized (mean 0, variance 1) and then coefficients back-transformed to normalized form after fitting.
This collider bias indicates that the positive correlation between smell loss and C19+ is underestimated in the present sample. Indeed, a direct comparison of the binary (y/n) smell loss questions in the two empirical samples yields an C19 odds ratio of 5. 96 in the YouGov sample (Table  S1 ) but only 4. 89 for GCCR. Therefore, our analyses represent a conservative scenario for the prediction of C19+ and C19-based on chemosensory alterations. Column 1 includes the list of countries available in the YouGov database. Column 2 indicates the number of participants over the history of their survey up to July 3, 2020. Minimum N=1000 per time point. Column 3 reports the percentage of participants who reported C19+. Column 4 reports the percentage of participants who reported C19-. Columns 5-7 report the odds ratios using Smell Loss for either C19+ vs.
Sample description Based on responses to question 7 of the GCCR survey ("Have you been diagnosed with COVID-19?", Appendix 1), participants can be split into six groups (see Figure  1 ). Participants who responded with Option 2 ("Yes -diagnosed with viral swab") or 3 ("Yes -diagnosed with another lab test") were classified as C19+; participants who responded with option 5 ("No -I had a negative test, but I have symptoms") were classified as symptomatic C19-; participants who responded with option 4 ("No -I was not diagnosed, but I have symptoms") were classified as C19 Unknown; participants who responded with option 6 ("No -I do not have any symptoms"), with option 7 ("Don't know"), or with option 8 ("Other") were classified as undefinable and excluded from the final analyses. To replicate our previous findings,  8  we first compared individuals newly included in the GCCR dataset (responses from 14 May to 2 July, 2020, replication sample in Figure  1 ) with COVID-19 who were lab tested and those who were diagnosed by a clinician based on the self-reported quantitative changes in smell, taste, chemesthesis, and nasal obstruction (Figure  S3 ).
Acknowledgements The authors wish to thank all study participants, patients, and patient advocates that have contributed to this project, including members of the  AbScent Facebook group. The authors wish to thank  Micaela Hayes, MD  for her input on the clinical relevance of this project,  Shannon Alshouse  and  Olivia Christman  for their help in implementing the survey,  Sara Lipson  for her support, and the international online survey research firm YouGov for providing data gathered with the  Imperial College London YouGov Covid 19 Behaviour Tracker.