dataesr/scientific-paragraphs-categorization huggingface dataset:
Raw accelerometer signal processing The pre-processing step aimed providing suitable datasets for the subsequent application of the classification algorithms. The pre-processing step was performed in Matlab R2018a. The total duration of observations led to 229 continuous signal sequences of 15-minutes. Due to a malfunction of 4 of the 30 sensors available, we collected only 199 continuous sequences. We first combined the 199 continuous sequences of raw accelerometer signal (thereafter referred to as observation sequences) with the corresponding field observations. It should be noted that the accelerometer signal and observations were time synchronized to ensure that direct observations were associated to the correct sequences of accelerometer signal. These 199 observation sequences were then split into segments (time-window) of the same duration (size of the time-window).
When more than one behaviour was associated to a time-window, this timewindow was removed from its sequence. In this way, the features extracted from the signal windows were representative of each behaviour specifically, which is the most suitable to train the models. Consequently, the 199 initial continuous sequences of observation were split into shorter continuous sequences. Furthermore, time-windows associated to the "Other" class were discarded from the dataset. Indeed, it combined several behaviours so it is not possible to extract representative features for this class in a supervised classification context. Therefore, we focused on the 6 main behaviours. As signal segmentation may drastically alter the results of the prediction  (Bersch et al. , 2014; Robert et al. , 2009; Smith et al.
The pitch and roll angle were calculated on a. static The sixty-seven calculated features are listed in Table  5. The formulas used to compute each feature are provided in the Appendix A. It should be mentioned that the features perfectly correlated were excluded. Thus, the RMS on a x , the mean and median on a y , the mean on a z , the RMS on a mag , the pitch and VeDBA were not considered in the rest of the study. The features were finally normalized according to the equation presented in the Appendix A. Therefore, the complete dataset consisted of 61 features associated to each 10 s-window.
Creation of two independent datasets As we are in a supervised classification context, the complete dataset was split into two independent datasets. Two-thirds of the 1794 observation sequences (Table  4 ) were randomly chosen to train the models and the remaining observation sequences were used to evaluate them. Random sampling of observations was stratified by behaviour in order to make sure that each behaviour was equally frequent in both the training and testing datasets. Therefore, the training dataset was composed of the 10 s-windows belonging to the selected two-thirds observation sequences. The testing dataset was composed of the remaining 10 s-windows.
The XGB algorithm was applied using the R package xgboost  (Chen et al. , 2018)  in R 3. 6. 1 software (R Core Team, 2019). • Support Vector Machine (SVM) SVM is a supervised binary classification method  (Burges, 1998). The basic idea of the multi-class SVM problem is to construct k classifiers, one for each class. The kth classifier constructs an optimal hyperplane between class n and the k -1 other classes. To construct the optimal separating hyperplane, a kernel function is used to transform the input data into a high-dimensional space. Hyperparameters to tune are:.
Thus, the final model converges to a strong classifier  (Subasi et al. , 2018). Hyperparameters to tune are: • max_depth: maximum depth of a tree. High values will tend to complicate the model with likely overfitting. • mfinal: number of iterations for which boosting is run or the number of trees to use. The Adaboost algorithm was applied using the R package adabag  (Alfaro et al. , 2013)  in R 3. 6. 1 software (R Core Team, 2019). • Random Forest (RF) Random Forest is a bagging algorithm where many decision trees are randomly built. A subset of variables and samples are randomly chosen to build each decision tree. Each node of the decision trees aims at segmenting the subset of samples using the randomly selected variables. The target is to find the segmentation leading to the purest classification, i.
e. leading to a maximum of instances of the same class in every node. For the classification tree, the Gini index is usually used to evaluate the decrease of impurity after each split. The final classification is carried out using every random decision trees  (Breiman, 2001). Hyperparameters to tune are: • ntree: number of iterations to carry out, i. e. number of decision trees to build. • mtry: number of variables to choose at each iteration to build the decision tree. The RandomForest algorithm was applied using the randomForest package  (Liaw and Wiener, 2002)  in R 3. 6. 1 software (R Core Team, 2019).
Models fitting and prediction of behaviour time-windows The 4 Machine Learning models were fitted using the training data. For this purpose, several combinations of hyperparameters were tested from a generated grid of values specific to each algorithm (Section 2. 4. 1). For each combination of hyperparameters, the model was evaluated using a 10-fold cross validation procedure repeated three times. The accuracy and Cohen's Kappa associated with the prediction of each tested model were collected. This step was carried out with the R package caret  (Kuhn et al. , 2018)  in R 3. 6. 1 (R Core Team, 2019). For each algorithm, the model leading to the best performance, called the fitted model, was applied to predict a behaviour in each 10 s-window using the testing data. At the end of this supervised classification step, the sequences of predicted behaviours for every 10 s-window were obtained for the 4 Machine Learning algorithms.
The principle of the HMM-based Viterbi algorithm smoothing is illustrated in Fig. 3. All the matrices and vectors defining the HMM and mentioned in Fig. 3  are explained in the following sections. The HMM-model noted Λ was used to estimate the unknown states of the process noted S, i. e. the behaviours actually carried out by the cows, knowing the measurable observations noted V, i. e. the behaviours predicted by the Machine Learning algorithms  (Witten and Frank, 2011). The HMM Λ was also based on a transition matrix T providing the probability to move from a state, i. e. , a behaviour, to another at the next time-window. The transition matrix was obtained empirically, based on the data collected experimentally. The HMM was also defined by its emission matrix E, representing the probability that each observation was generated by each state.
Improved performance in behaviour prediction in comparison with similar studies Within the methodological framework proposed in the present study, the best prediction of dairy cow behaviours was obtained by combining (i) a preliminary study to find the most appropriate preprocessing of the raw accelerometer signal (window size of 10 s; overlap of 90%), (ii) a classification-prediction of the behaviours with the XGB algorithm, and (iii) a smoothing of the predictions using the HMM-based Viterbi algorithm. In the test dataset, the observed behaviours were predicted with an accuracy of 0. 98 and a Cohen's Kappa of 0. 96. The sensitivities and specificities were above 0. 90 for all the behaviours, except for the behaviour "Resting-standing" behaviour (sensitivity: 0. 82) and for the behaviour "Walking" (sensitivity: 0.
Reliable validation of the models In many similar studies, each labelled window is affected either to the training dataset or to the testing dataset, independently of the sequence of observations to which the window belongs to  (Lush et al. , 2018; Martiskainen et al. , 2009). Consequently, time-windows from the same cow at the same period of observation can be found in both training data and testing data. Due to the temporal structure of subsequent behaviours, the time-windows from the same sequence of observation are highly correlated. As explained by  Rahman et al. (2018) , the feature space distribution between the training data and the testing data is therefore very similar. The prediction is systematically better with this approach but this is not suitable to validate the algorithm. For instance,  Lush et al.
Field observation data As noted by other authors  (Rahman et al. , 2018) , the high betweencow variability in the recorded signal can decrease the performance of the prediction. For this reason, during data collection, particular attention was paid to cover as much variability as possible. Observations were carried out on 86 Holstein cows from 4 farms, of different parities and stages of lactation. The observations were also carried out in different paddocks (temporary versus permanent grasslands) and at different periods (summer of 2017 and spring and summer of 2018). In similar studies, the number of observed animals usually ranges between 5 and 40 animals from the same farm  (Alvarenga et al. , 2016; Lush et al. , 2018). To the authors' knowledge, the dataset collected is larger and covers a wider range of conditions than those usually met in literature.
Limitations of the studies and potential solutions 4. 4. 1. Overestimation of the model accuracy in an experimental context • Validation of the models with the same cows In this study, each algorithm was evaluated at the observation sequence level, as discussed in Section 4. 1. 3. Consequently, the signal windows from the same observation sequence were either in the training data or in the testing data. However, windows from the same cow can be found in both datasets as the cows were observed several times during the experiment. Considering the cow-level, i. e. with windows from the same cow either in the training data or in the testing data, is the most accurate way to assess the model performance. This approach is sometimes considered in similar studies  (Rahman et al. , 2018)  and leads to moderate performance, with an accuracy usually below 0.
90 when a single sensor has been used. This method led also to a decrease of performance in our study, with an accuracy of 0. 85 and a Cohen's Kappa of 0. 76 using the XGB algorithm (data not shown). This decrease in performance is mainly due to the differences in the physical movement between dairy cows, leading to different motion patterns from a cow to another  (Rahman et al. , 2018). A way to improve the genericity of the algorithm may consist in considering a higher number of cows from various farms. Adding other sensors in the same electronic box fixed on the neck-collar could be also a way to better predict posture behaviours and then improve the genericity of the model. • Removal of the remaining behaviours from the dataset As explained in Section 2. 1, behaviours other than the 6 under investigation, i.
e. the "Other" class, were removed from the dataset. However, dairy cows can express these behaviours in real field situations. In these cases, the model will fail in the prediction because it has not been trained to predict these specific behaviours. Consequently, the prediction accuracy in real field situations will certainly be lower than in our development context. As the "Other" class is a combination of several behaviours, it is not possible to extract representative features for this class. Consequently, training a Machine Learning algorithm to predict the "Other" class does not seem appropriate. A solution would be to collect observations about each specific additional behaviour and then train the model to predict each event, as proposed by  Lush et al. (2018)  for the urination event.
Conclusion This study aimed to develop a methodological framework to predict feeding behaviour and posture of dairy cows at pasture from accelerometer data. For this purpose, a suitable pre-processing of the raw accelerometer signal was applied (10 s time-window, 90% overlap) and several methods of Machine Learning were compared (eXtreme Gradient Boosting, Random Forest, Support Vector Machine and Adaboost). The subsequent application of a Viterbi algorithm was used to account for the temporal structure within behaviour sequences. The best prediction capacity was obtained using the XGB algorithm followed by the Viterbi smoothing, leading to an accuracy of 0. 98 and a Cohen's Kappa of 0. 96 on a test dataset. These prediction results were higher than those obtained in the similar studies. Our methodology therefore has great potential for the development of decision-support tools in grazing systems, both for assisting pasture management and for monitoring animal health and welfare.
e. , the most probable hidden states given a sequence of observations, is obtained with the Viterbi algorithm. where p o is the relative observed agreement among raters and p e is the hypothetical probability of chance agreement. The index i is the considered behaviour; TP i (True positives) is the number of instances where behaviour i was observed and correctly predicted; FN i (False negatives) is the number of instances where behaviour i was observed but another behaviour was predicted, FP i (False positives) is the number of instances where behaviour i was predicted but another behaviour was observed; TN i (True negatives) is the number of instances where behaviour i was not observed and not predicted. = = P q s ( ) i i 1 ∀ i, Appendix E. Confusion matrices obtained with every model using the test dataset before the HMM-based Viterbi smoothing.
Confusion matrices obtained with every model using the test dataset after the HMM-based Viterbi smoothing ADA Prediction Grazing Resting -Lying Ruminating -Lying Resting -Standing Ruminating -Standing Walking Reference Grazing 16,508 0 0 0 0 131 Resting -Lying 0 2501 99 446 36 3 Ruminating -Lying 0 48 5458 31 87 18 Resting -Standing 0 429 21 901 64 1 Ruminating -Standing 2 0 148 18 1948 2 Walking 8 0 13 0 4 546 RF Prediction Grazing Resting -Lying Ruminating -Lying Resting -Standing Ruminating -Standing Walking Reference Grazing 16,514 0 0 0 0 131 Resting -Lying 0 2844 33 359 3 1 Ruminating -Lying 0 11 5589 51 35 11 Resting -Standing 0 123 20 981 66 1 Ruminating -Standing 0 0 94 5 2035 0 Walking 4 0 3 0 0 557 Appendix F. XGB Prediction Grazing Resting -Lying Ruminating -Lying Resting -Standing Ruminating -Standing Walking Reference Grazing 16,514 0 0 0 0 103 Resting -Lying 0 2840 5 237 0 0 Ruminating -Lying 0 16 5666 62 5 11 Resting -Standing 0 122 25 1088 74 1 Ruminating -Standing 2 0 43 9 2060 0 Walking 2 0 0 0 0 586 SVM Prediction Grazing Resting -Lying Ruminating -Lying Resting -Standing Ruminating -Standing Walking Reference Grazing 16,480 0 10 0 10 154 Resting -Lying 24 2768 59 204 9 19 Ruminating -Lying 0 32 5573 36 53 15 Resting -Standing 0 178 22 1149 61 3 Ruminating -Standing 1 0 75 7 2001 9 Walking 13 0 0 0 5 501 ADA Prediction Grazing Resting -Lying Ruminating -Lying Resting -Standing Ruminating -Standing Walking Reference Grazing 16,510 0 0 0 0 127 Resting -Lying 0 2487 68 406 20 2 Ruminating -Lying 0 39 5521 24 69 22 XGB Prediction Resting -Standing Ruminating -Standing Grazing 6 2 Resting -Lying 452 0 Ruminating -Lying 16 122 Resting -Standing 942 24 Ruminating -Standing 79 1971 1 Walking 0 Reference Grazing Walking 16,513 0 0 0 0 12 0 0 0 0 118 549 Resting -Lying 0 2857 10 269 4 1 Ruminating -Lying 0 25 5663 64 12 11 RF Resting -Standing Ruminating -Standing Prediction 0 3 Grazing 96 0 Resting -Lying 20 46 Ruminating -Lying 1057 6 Resting -Standing 71 2052 Ruminating -Standing 0 0 Walking Reference Walking Grazing 2 16,514 0 0 0 0 0 0 0 0 571 128 Resting -Lying 0 2820 23 295 0 0 Ruminating -Lying 0 9 5604 46 29 11 SVM Prediction Resting -Standing Ruminating -Standing Grazing 0 0 Resting -Lying 149 0 Ruminating -Lying 25 84 Resting -Standing 1048 7 Ruminating -Standing 64 2046 2 Walking 0 Reference Grazing Walking 16,479 4 0 0 11 3 0 0 10 0 163 560 Resting -Lying 26 2751 68 271 12 20 Ruminating -Lying 0 39 5549 38 69 13 Resting -Standing 0 188 23 1080 60 4 Ruminating -Standing 1 0 86 7 1983 9 Walking 12 0 2 0 5 492.
For Nocardia genus, HTS has been used to study phylogeny among species and to explore the genetic diversity inside phylogroups of the same species. 37, 38 ince the advent of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) in clinical microbiology laboratories, MS identification of Nocardia at the species level has been met with several difficulties. The main limitation is the large number of Nocardia species (more than 100 so far), only a limited number of which were included in initial databases. 39 1] [42]  A preliminary step of protein extraction (with glass beads and ethanol, followed by formic acid) and repeated spotting of different extracts is frequently required to increase the likelihood of identification. 43, 44 For other clinically frequent species, such as N.
Follow-Up and Prognosis No data have been published to describe how quickly a patient with nocardiosis should improve after initiating antibiotic treatment; the following proposals are therefore based on expert opinion. Apyrexia is usually achieved after a few days of antibiotic treatment and skin lesions improve within weeks. Follow-up imaging focusing on the body site that was initially infected is mandatory to study the patient's response to treatment. During follow-up, clinical or radiological worsening should encourage consideration of several possibilities. First, treatment failure may be caused by antibiotic under-dosing, poor adherence to treatment, interaction with other treatments, or poor distribution of antibiotics to infected sites. Therapeutic drug monitoring can be performed to rule out these hypotheses.
Statistical analyses Statistical analyses were performed with R software (The R Core Team, 2019, 3. 4. 4 version). As variables were all transformed into qualitative variables, a multiple correspondence analysis was conducted followed by hierarchical classification with the FactoMineR package  (Lê et al. , 2008). To establish whether FEC was significantly associated with the farming system, a linear regression was performed. We built a full model regressing FEC (n = 46) on farming system (two classes), the number of treatments (three classes), the proportion of time each horse spent on mixed grasslands, grassland stocking rate and time since the end of the ERP. A variable selection procedure was implemented with the stepAIC() function of the MASS package that retained the farming system, the time since the end of ERP and stocking rate.
Acknowledgements This research was supported by grants from  IFCE  and  INRA-Phase Division. Additional funding was obtained through the  PSDR4  project new-DEAL co-funded by  INRA, Irstea  and the  Auvergne-Rhône-Alpes region. We are grateful to the farmers who agreed to answer our questions and let us collect dung samples in their fields. We also thank  Margot Mouilleau , Dilution of strongyle load in mixed horse farms.
CLARITY study group Session: P-71. Treatment of Antimicrobial Resistant Infections Background. Advances in the survival of patients with invasive aspergillosis (IA) are jeopardized by the emergence of azole resistance in Aspergillus fumigatus, which has been associated with high probability of azole treatment failure. The clinical implications of azole-resistant IA compared to azole-susceptible IA remain unclear. Thus, we seek to describe the epidemiology and to determine the efficacy of antifungal therapy in patients with documented azole-resistant IA compared to azole-susceptible IA in patients with hematological malignancy. Methods. For proven and probable IA (EORTC/MSG 2019) caused by A. fumigatus in patients with hematological malignancies retrospective data were documented, comprising demographics, diagnosis, treatment, response, and outcome.
LUCS (Light-Up Cell System) is a new live cell test that allows assessment of a cell's homeostasis and its alteration by a toxic agent. To evaluate the effectiveness of LUCS as an alternative test method for acute oral toxicity, we compared EC 50 s determined in HepG2 cells treated with 53 chemicals selected from the ACuteTox EU database with corresponding human blood LC 50 s derived from human acute poisoning cases. Linear regression analysis showed that LUCS results predict human data to 69 %. Rodent oral LD 50 s and LUCS EC 50 s were then correlated to human LC 50 s using shared data sets. Linear regression analyses comparing LUCS and animal data clearly showed that LUCS always predicts human toxicity better than animal data do. These successful prediction values prompted us to simplify the LUCS test, adapting it to regulatory and high throughput applications, resulting in a new protocol with consistent dose-response profiles and EC 50 s.
Here, we present a new cell-based approach called LUCS and report that regression analysis of human acute lethal blood concentrations with in vitro LUCS data resulted in a better correlation than with standard in vivo rodent oral lethal doses. In other words, LUCS data predict human toxicity better than animal data do. Aware of limitations in LUCS procedure, we also present an optimization of the protocol allowing the LUCS test to be easily implemented on any commercially available fluorescence reader. This study demonstrates both suitability and benefit of the LUCS method test as a dedicated test of acute oral toxicity for both regulatory purposes and industrial high throughput applications.
Selection of chemicals 53 chemicals were selected from the ACuteTox database (97 reference chemicals, www. acutetox. eu) on the criterium of commercial availability. The compounds described in Table  1. S. (Supplementary data) included pharmaceutical drugs (30 substances), industrial chemicals (12 substances), and biocides (11 substances). The 53 compounds have been assigned to their different toxicity categories according to the EU Classification, Labelling and Packaging (CLP) system. This set of compounds comprised only 1 substance in category 1 (LD 50 < 5 mg/kg b. w. ), 7 substances in category 2 (5 < LD 50 < 50 mg/kg b. w. ), 12 substances in category 3 (50 < LD 50 < 300 mg/kg b. w. ), 20 substances in category 4 (300 < LD 50 < 2000 mg/kg b. w. ) and 11 substances in the non classified (NC) (LD 50 > 2000 mg/kg b.
Rodent LD 50 , human LC 50 and LUCS EC 50 data collection The human LC 50 s (the 50 % Lethal Concentrations) were taken from a study by Sjöström et al. [40]  who calculated them from sub-lethal and lethal blood concentrations derived from nearly 2800 human poisoning cases and compiled within the ACuteTox project (www. acutetox. eu). The LUCS EC 50 data were collected from a previous study on human hepatic cells (HepG2)  [41]. Mean in vivo oral rodent LD 50 s were extracted from two EURL ECVAM's published studies: 1) Hoffmann et al. [26] , a statistical review of compiled rat and/or mouse data, providing means of LD 50 values (estimated from 10 and 20 individual values for most compounds) and where the principal sources of the in vivo animal data from oral acute toxicity studies were the internet databases Che-mIDplus (http://chem.
sis. nlm. nih. gov/chemidplus) and HSDB Hazardous Substances Data Bank (http://toxnet. nlm. nih. gov/newtoxnet/ hsdb. htm), and 2) Kinsner-Ovaskainen et al. [42] , providing rat LD 50 data only. Finally, the LD 50 values available for the 53 compounds selected in our study were presented as three datasets, named "Mean of rat oral LD  50   1 ).
EC 50 and coefficient of determination (R 2 ) evaluations For dose-response experiments, F post /F pre ratios obtained for each dose were fitted using GraphPad Prism software with a mathematical non linear regression model: Y = Bottom + (Top-Bottom)/(1 + 10( (LogEC 50 -X)*HillSlope)), where X is the measured value and HillSlope the slope of the straight line defining the tangent at the inflection point. 50 % efficacy concentration (EC 50 ) and R 2 values were calculated from the regression model.