dataesr/scientific-paragraphs-categorization huggingface dataset:
In line with our previous dataset, a significant increase in RSV-neutralizing and PLA titers was observed in mice boosted epicutaneously with V-306 compared to mice that received an excipient only patch (Fig. 3D  and E ). PLA titers obtained in mice boosted epicutaneously were significantly lower than those obtained from mice boosted subcutaneously with V-306 (Fig. 3D ). However, RSV-neutralizing titers were similar (Fig. 3E ). As expected, and in agreement with the low F-specific antibody titers measured by ELISA, FI-RSV and RSV infection did not induce any RSV-neutralizing and PLA antibodies. To evaluate the capacity of epicutaneous boosting to provide protection against RSV infection, vaccinated mice were challenged intranasally with 10 6 pfu of RSV/A2 on day 42, 21 days after the booster immunization.
Methods This study was a retrospective and observational single centre study. We contacted all the patients who underwent SLF between January 1, 2008 and December 31, 2018 at Foch hospital (Suresnes, France). If the patient agreed to enter the study, an information letter was sent. Medical and surgical history, initial purpose of consultation, urinary and anorectal symptoms, physical examination using the POP-Q system  [2]  to assess prolapse stage, perioperative data, hospitalisation data, postoperative data and recurrence or the need for secondary urinary incontinence surgery were collected retrospectively from medical files. To assess medium and long-term anatomical results patients underwent a clinical examination using POP-Q prolapse stage and measuring the different POP-Q points and a detailed interview with quality-oflife questionnaires between January and April 2019.
Chi2 and Fisher tests were used for categorical variables and T student or Mann-Whitney tests were used for average comparisons. SAS ® software (V. 9. 4) was used for the analysis. Statistical tests were 2-sided and significance was evaluated at an α level of 0. 05. Our trial was approved by the French Authority, the Advisory Committee on Information Processing in Healthcare Research (ID-CRB: 2018-218 C25). This protocol obtained the agreement of the National Commission for Data Protection and Liberties (CNIL-France) by respecting the reference methodology.
Phylogenetic Analysis DNA sequences obtained from Sanger sequencing were assembled and the consensus sequence edited using VectorNTI11. 5 (Invitrogen). The identities levels were obtained by aligning the sequences using the 'align' function of VectorNTI and the identity matrix was constructed using the RStudio software. For the phylogenetic analyses, the sequences were aligned with the ClustalW function of MEGA7 or MEGA10  [14, 15]. Maximumlikelihood (ML) analyses were performed using a transition-to-transversion ratio of 2. 0 with empirical base frequencies and the HKY85 substitution model implemented in the.
Acknowledgments: We acknowledge the sequencing facilities at the  ANSES Ploufragan-Plouzané-Niort laboratory  and the ' Plate-forme de microbiologie mutalisée  ( P2M )' at the  Institut Pasteur (Paris)  for technical assistance with NGS sequencing. We thank  Stéphanie Bougeard  for her invaluable help in preparing Figure 3. Peter Walker  and  Gael Kurath  are acknowledged for their invaluable help in proposing a new classification of the perhabdoviruses. Yannick Ledoré  is acknowledged for providing healthy fish as controls. Carolyn Engel-Gautier  is acknowledged for correcting the English of the manuscript.
Patients and diagnosis The data of all patients treated in 4 reference centers between January, 1st 2002 and December, 31th 2017 were analyzed retrospectively. Inclusion criteria were unilateral HNCUP, histologically proven SCC with no mucosal tumour detectable on panendoscopy and 18FDG PET-CT and patients eligible for surgical treatment. Exclusion criteria were distant metastases at time of diagnosis, prior history of skin or HN SCC or cervical radiotherapy. The panendoscopy evaluating the upper aerodigestive tract was combined with rigid oesophagoscopy under general anesthesia with biopsy of any suspicious mucosal sites macroscopically visualized or guided by PET-CT. No patient underwent prophylactic tonsillectomy. P16 status was assessed on formalin-fixed, paraffin-embedded sections from neck dissection tissues by immunohistochemistry.
Results and discussion The unit of analysis of this study is the structural elements of a building. Initially, the authors used a non-parametric test to evaluate if there are statistically significant differences between the types of structural elements (question B1) regarding the independent and dependent variables asked in the questionnaires. The null hypothesis is that there is no difference between the groups of structural elements. The purpose of this test is to make sure that combining the responses for all the components for further analysis will not affect the overall reliability of the dataset. Using SPSS version 25, the Kruskal-Wallis H test is performed at the 5% significance level to determine if the type of the element (question B1) affects the scores provided for the factors affecting the reusability of the structural components  [30].
As presented in Appendix B, the Kruskal-Wallis H test results indicate that none of the p-values of the technical factors is less than 0. 05 and that there is not enough evidence to reject the null hypothesis. Therefore, it can be concluded that the entire dataset can be used for further analysis regardless of the type of structural element the respondents considered to complete the survey. Based on the reliability analysis illustrated in Section 2 and the result of the Kruskal-Wallis H test (the above discussion), the type of the element (B1), the amount of load supported by the component (B10), and its life expectancy (B11) are excluded from the list of features used for developing the predictive models.
Oversampling After converting the multi-scale responses to binary ones, it was observed that there was a considerable imbalance in the classification with 34% (non-reusable) and 66% (reusable) responses. While the questionnaire was distributed to a wide range of professionals in the building sector, it was observed that the respondents with successful reuse experience were more responsive. It can be argued that the initial data collection could be continued to have more balanced responses; however, due to the time constraints, as well as the limited number of experts in this field, this option was not practical. Nevertheless, even if the data collection continues, since the respondents are free to choose any structural component with any level of reusability to complete the questionnaire, it is impossible to guess the outcome of the new survey, which might end up with a similar imbalanced dataset.
In this study, the oversampling technique developed by  [33]  is employed to pre-process the datasets and minimise the class imbalance impact. This technique is identified as Synthetic Minority Over sampling Technique (SMOTE) by the developers. Unlike other oversampling techniques that rely on replacement in data space  [34] , the SMOTE technique creates synthetic examples of the minority class in feature space using the K-Nearest Neighbours (KNN) algorithm (with the default value for k=5)  [33]. In this study, following the approach adopted by  [35] [36] [37] [38] , the SMOTE was performed on the imbalanced dataset discussed above, which resulted in a new dataset with 192 observations. This new dataset, which contains the collected data using the online survey, is used to develop the predictive models in this study.
Feature selection Feature selection is a vital stage in supervised machine learning  [40]. It includes selecting a subset of features (independent variables) in a dataset for efficient and optimum analysis of the problem in hand  [40, 41]. In supervised machine learning, there is always a chance that some variables are irrelevant to the response or redundant. In such cases, their presence negatively affects the performance of a predictive model. Proper feature selection results in the development of predictive models that perform optimally on both seen and unseen data. Therefore, feature selection focuses on identifying relevant features and discards irrelevant or redundant independent variables  [42]. In this study, feature selection is performed using recursive feature elimination (RFE) methods.
Based on the above discussion, it seems reasonable to choose interpretable methods such as linear discriminant analysis to develop the model  [49]. Nevertheless, interpretable models are not always accurate and might have a high bias in their predictions  [46]. It is because these models are mostly less flexible, and some of them consider a functional form for the relationship between the predictors and the response (parametric models)  [46]. On the other hand, there are very flexible models such as the support vector machine (SVM) or KNN classifier (mostly nonparametric methods) that produce models with very accurate predictions on the training dataset  [46, [50] [51] [52]. However, this flexibility comes at the cost of losing interpretability, high variance, and sometimes overfitting, which results in inaccurate predictions on unseen data  [46].
Therefore, in selecting the proper method for developing a predictive model, this trade-off between bias and variance should be considered  [51, 53, 54]. Besides, the limited number of observations in the dataset and unawareness of the nature of the relationship between the predictors (independent variables) and the response brought new dimensions to the challenge of selecting a proper machine learning method. Therefore, it was decided to study a wide range of machine learning methods to develop an optimum predictive ΡΩ͆͊Λ΄ ΐΆ͊ ̮̻ΩϬ͊ ̼͆͊ΉμΉΩ Ήμ Ή ΛΉ͊ ϭΉφΆ φΆ͊ ΆΩ ͔θ͊͊-Λϡ̼Ά͞ φΆ͊Ωθ͊Ρμ μϡͼͼ͊μφ͊͆ ̻ϳ  [55]. These models are listed in Table  4. Details of these models could be found in Appendix D and  [51, 53, 56]. It should be noted that, as described in Appendix D and mentioned in the footnote of Table  4 , K-Nearest Neighbours (KNN), Random Forests (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) use hyperparameters, which require to be set before training\predicting the reusability of the structural elements using these methods.
Model selection The accuracy and interpretability of any machine learning model play an important role in choosing the best predictive model for the study at hand  [46]. In this study, a k-fold Cross-Validation (kfCV) method with k=10 is employed to assess the performance of the developed models. In the kfCV method, the original dataset is randomly divided into k folds (k groups of observations) with approximately equal size  [46]. Then, the first fold is used as the testing set, and the k-1 remaining folds are used to train a predictive model. Next, the performance of the fit is determined using the held-out set. The process repeats k times with all folds, and each time a different group of observations is considered as the validation set. Simultaneously, the performance results are recorded for all k folds, and eventually, the performance of the predictive model is determined using the mean performances of the k folds.
According to James et al. [46] , while k can take any number less than n (n is the number of observations in a dataset), values of k equal to 5 or 10 have empirically shown resistance against high bias or variance. The choice of k=10 in this study enables a higher number of training observations at each fold, which improves the performance of the classifiers  [46]. In a binary classification problem such as the ones of interest in this study, where the methods classify the test observations to one of the two classes as reusable (1) or non-reusable (0), the outcomes (predictions) fall under one of the following categories. To evaluate whether the selected classifier correctly predicts and classifies the reusable and non-reusable items into correct classes, the true negative (TN) and true positive (TP) criteria, as represented in the confusion matrix (Table  3 ), were used.
The confusion matrix provides additional information about the rates of the predicted responses that were misclassified, which is a reusable item is classified as non-reusable (false negative or FN) or vice-versa (false positive or FP)  [46]. It should be noted that the rows and columns of Table  3  represent the actual and predicted values of the responses, respectively. Based on Table  3 , there are two types of misclassification. The first one, which is called Type I error, is when a non-reusable item is by mistake classified as reusable. Eq. 1 represents the Type-I error rate, which is the number of non-reusable items misclassified as reusable by a classifier divided by the total number of non-reusable components in the test dataset.
𝐹𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑒𝑟𝑟𝑜𝑟 𝑟𝑎𝑡𝑒 = 𝑇𝑃 + 𝐹𝑁 Model classification error rates or Type I and Type II errors are significant indicators of the performance of a predictive model. According to James et al. [46] , low error rates on a given dataset guarantees the safe use of a particular supervised learning model. While both error rates should be minimum, Type I error has a pronounced impact on the success of a project with recovered building structural elements. Type I error happens when a predictive model (Table  4 ) classifies a non-reusable component as reusable. This mistake causes several logistic, financial, and technical costs by providing a false indication about the reusability of an element, which could risk the entire project. However, the consequences of a Type II error are manageable. While reuse aims to improve the circularity of materials in the building sector, a Type II error only troubles the design team to focus on other available recovered structural components.
𝑂𝑣𝑒𝑟𝑎𝑙𝑙 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁 + 𝑇𝑃 The third metric used to compare the performance of the developed models is the area under the receiver operating characteristics (ROC) curve (also known as the AUC). The ROC curve is used to observe the performance of a classifier with different threshold values and to decide which threshold value works the best for a classifier (Figure  2 ). The Y-axis of this graph shows the sensitivity or true positive rate (the number of correctly classified reusable items by a model divided by the total number of reusable components in the test dataset), and the X-axis shows the false positive or Type-I error rate. Then, for different threshold values, these two metrics are calculated, and a graph is drawn by connecting the identified points on the X-Y plane  [46].
The ROC curve in Figure  2  shows that the selected threshold value of 0. 5 works fine because of the low falsepositive error rate. Moreover, this figure shows that the AUC is almost equal to 1, which is desirable. The summary of the Type-I error rate, overall accuracy (or predictive accuracy), and the area under the ROC curve (AUC) used to compare the performance of different models is provided in Table  4. 4  Non-parametric 0. 07 0. 93 0. 98 Hyperparameters (calculated using 70% of the dataset that was selected randomly): 1 k = 6 2 ntree = 500, mtry = 5, nodesize = 1 3 Size = 9, Decay = 0. 09 4 Cost = 1. 601470833, Sigma = 0. 047078172 In this study, following  [57] , a maximum threshold of 10% is considered acceptable for the Type-I error rate. Moreover, the minimum threshold values of 85% and 90% are considered acceptable for the models' overall accuracy and AUC, respectively.
The DSA method randomly selects several samples from the dataset, alters the values of an independent variable for all data points and records the responses while not changing other features. This process is performed for all reusability factors in the dataset. The sensitivity responses identified using the DSA method can be used to determine the feature importance using a sensitivity measure. This research uses the Average Absolute Deviation (AAD) from the Median as the sensitivity measure, as advised by  [50]. According to Cortez and Embrechts  [50] , 𝐿 ∑ 𝑗=1 |𝑦 ̂𝑎𝑗 -𝑦 ̃𝑎| Eq. 4  =  where L =7 (seven intervals between one to five),  ̂ is the sensitivity response for    ∈ {1, 1. 67, 2. 33, 3, 3. 67, 4. 33, 5} (jth level of input   :  ∈ {1, … , } for p features), and  ̃ is the median of the responses.
The higher the value of the AAD for an independent variable, the more important is the feature. This measure is then used to develop the relative importance of the input variables. It is noteworthy that following Cortez and Embrechts  [50] , this research uses the complete dataset to perform the SA. Figure  3  shows the results of the feature importance for the RF model. In this figure, the x-axis shows the relative importance of the variables, and the y-axis shows the features. Based on Figure  3 , only some of the variables are relevant, and others have negligible importance. In this study, features with relative importance greater than 2% are considered for developing the rules in Section 3. 2. 2, and the remaining are ignored. It results in a total number of fourteen independent variables including, B3, B5, B7, B8, C6, C12, C15, C16, C20, C25, C27, C28, D23, and D24.
It means that checking should start with rule number one, and if its conditions are not satisfied, the next rule should be checked. This sequential process continues until a rule͞μ ̼Ω͆ΉφΉΩμ are satisfied. At this point, checking stops and the rule number and prediction result should be recorded against the observation. The second column shows the length of a condition, which is the count of variable-value pairs in a rule  [59]. For example, rule number 7 has three circumstances to be satisfied; hence, the length of its condition is equal to 3. The third column is the frequency of a rule, which is defined as the proportion of the observations in the training dataset that satisfy the rule condition(s). For instance, the total number of observations in the training set is equal to 134, out of which twenty-one fall under the first rule.
Therefore, the frequency of the first rule becomes 0. 157 (the sum of frequency values is equal to one). The fourth column is the error rate of each set of conditions, which is equal to the number of misclassifications made by a rule divided by the number of observations satisfying the rule condition(s) in the training dataset. According to Column five of Table  5  shows the conditions of the rules. And the last column contains the predicted responses by the rules, which are equal to zero (0) for non-reusable elements and to one (1) for reusable components. As an example, rule number one states that if C12 (damage caused by living organisms), C20 (lack of earlier certificates), and D23 (the process of matching the design of the new building with the strength of the recovered element) are less than or equal to 3, then the component is not reusable.
While the above set of rules provides an easy-to-understand and implement collections of conditions, it is essential to make sure that the resulting predictions on the unseen data satisfy the minimum requirements set in Section 3. 1. 4. Therefore, the authors used the remaining 30% of the dataset (unseen observations by the learner (classifier) in Table  5 ) to evaluate the performance of the learner presented in Table  5. For this purpose, we followed the rules sequentially (from 1 to 15), identified the applicable set of conditions to each observation, and recorded the resulting prediction for each element. Next, we compared the prediction results with the correct responses and recorded the errors to evaluate the performance of the learner. As a result, the classifier misclassified two (2) non-reusable elements as reusable (Type-I errors) and eight (8) reusable components as non-reusable (Type-II errors), resulting in the Type-I error rate equal to 6.
9%, and the overall accuracy equal to 85. 3%. Therefore, this learner satisfies the minimum performance requirements defined in Section 3. 1. 4. Moreover, the learner in Table  5  is transparent, easy-to-understand, and can be easily implemented in practice. It should be noted that the Type-II error rate is the number of the reusable items misclassified as non-reusable by a model divided by the total number of reusable components in the test dataset. According to Table  5 , C25 is not available in any of the rules. Hence, a practitioner may not need to collect data on this variable. Appendix C summarises the survey that the practitioners need to perform before being able to use the learner in Table  5. In Appendix C, the variable codes (Code) are kept equal to the original survey (Appendix A) to maintain uniformity.
𝑝𝑟(𝑿 = 𝒙|𝑟𝑒𝑢𝑠𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝑘)𝑝𝑟(𝑟𝑒𝑢𝑠𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝑘) Eq. (D. 5)  pr( = | = ) = () In Eq. (D. 5), (| = ) is known as the density function of  for a structural element that belongs to class , ( = ) is the prior probability which is the probability that a given observation belongs to class , and () is the overall probability of  in the dataset  [46]. In Eq. (D. 5), the prior probability is simply the result of the number of elements in each of the training classes divided by the total number of components in the training dataset  [46]. The conditional probability in Eq. (D. 5) can be re-written as follows  [46] : 𝑓 𝑘 (𝒙)𝜋 𝑘 Eq. (D. 6) 𝑝 𝑘 (𝑿) = 𝑘 ∑ 𝑠=1 𝜋 𝑠 𝑓 𝑠 (𝒙) In Eq. (D. 6),   () is the posterior probability that an observation is reusable or not, given the values of its features  [46]. Therefore, the LDA classifier needs to estimate the value of   () (the density function) and   (the prior probability) and plug them into Eq.
(D. 6) to evaluate the posterior probability  [46, 53]. The LDA method assumes a one-dimensional normal distribution for each independent variable in Eq. (D. 6) (a multivariate Gaussian distribution) and equal variance for the class responses  [46]. The density function in Eq. (D. 6) can be then converted to the following (for further details, refer to  [46, 53] ): 1 Eq. (D. 7) 𝛿 𝑘 (𝒙) = 𝒙 𝑇 ∑ -1 𝜇 𝑘 -𝜇 𝑘 𝑇 ∑ -1 𝜇 𝑘 + 𝑙𝑜𝑔 𝜋 𝑘 2 The above is known as the discriminant function  [46, 53]. The LDA method estimates ∑ (the covariance matrix that is common to reusable and non-reusable components), and   (mean vector of the features in each class) to evaluate   () in the training dataset  [46, 53]. The Bayes classifier then classifies a new observation as reusable or non-reusable for which the value of the corresponding   () is higher  [46, 53] ΄ ΐΆ͊ ϭΩθ͆ ΆΛΉ̮͊θ͞ Ή φΆΉμ Ρ͊φhod stems from the fact that the discriminant function is a linear function of   [46, 53].
D. 4 Quadratic Discriminant Analysis (QDA) Quadratic discriminant analysis (QDA) is a similar approach to the LDA with the exception that, in the QDA method, each class has its covariance matrix  [46, 53]. Moreover, in QDA, the discriminant function is a quadratic function of predictors   [46, 53]. The QDA method is more flexible and can handle the possible non-linear relationship between the features and the response in each dataset  [46, 53]. For further details, please refer to  [46, 53].
At each stage, the recursive binary splitting method selects an independent variable   with a cut-point value of  ( is any value belong to   ) and splits the feature space of an existing node into the new terminal nodes { |   < } and { |   ≥ } with the highest possible purity in response classification  [46]. It is noteworthy that the split happens on the training observations available in a region and not the entire training dataset. The DT method uses the Gini index or the entropy impurity function measures to assess the purity of the splits at each stage  [46, 53]. After each split, if the resulting purity of the new leaves is not satisfactory, the splitting continues to decrease the impurity of the new terminal nodes  [46]. This process continues until no further improvement is possible, resulting in a deep tree  [46].