dataesr/scientific-paragraphs-categorization huggingface dataset:
According to  [34] , a negative correlation between the car speed and THW can exist. In  [35] , it was shown that the speed and THW follow different distribution patterns under different traffic density levels. e THW can illustrate better the driver's characteristics in all conditions of car-following behavior than the TTC, which is only important for the braking behavior. In this work, the classification of different driver profiles begins with the observations from the NGSIM data. Figure  3  shows the distribution of each car driver's maximum THW, mean THW, minimum THW, and standard deviation of THW, for the 1993 vehicle trajectories of the first 15 min NGSIM 101 dataset. Figure  3  shows evidence of the heterogeneity of human driving profiles as the mean THW ranges from near 0s to 5s and the minimum value of THW ranges from near 0s to more than 3s.
In addition, the parameter δ (exponential parameter of speed) is fixed at δ � 4 as proposed in  [43]. In our approach, the identification of drivers with extreme profiles is performed by calibrating the parameters of the IDM model, using a genetic algorithm referring to the work in  [30]. e used GA method is the default model in Matlab 2020b  [46] , and for the algorithm description, it is presented in the book  [47]. In general, the mean square error (MSE) is widely used for calibration of car-following models. Here, another error metric is used as in  [30] , root mean square percentage errors (RMSPE). RMSPE(θ) � ���������������������� � 1 P 􏽘 P i�0 x simul i (θ) -x data i (θ) 􏼐 􏼑 2 x data i (θ) 2 􏽶 􏽴 , ( 5 ) where x data i (respectively, x simul i ) denotes the car trajectory (positions in time) from data (respectively, from simulation) and where θ represents all the parameters of the considered model.
e car longitudinal position is chosen as the target variable in the objective function. e IDM model calculates car acceleration, as described in  (6) , with the driver's reaction time (n * Δt). e reaction time is a delay of decision that driver takes for updating the acceleration with current situation with the leading vehicle. Car's speed and position can be calculated as follows in  (7)  and  (8) , based on the Euler method. a simul (t + nΔt) � IDM(θ; variables(t)), (6) v simul (t + Δt) � v simul (t) + a simul (t) * Δt, (7) x simul (t + Δt) � x simul (t) + v simul (t) * Δt. (8) In addition, for drivers with normal profile, we simulate them using an average profile. eir calibration of IDM parameters is extracted directly from the dataset by their physical meanings and combined with the default values in the SUMO simulator.
Specific Driver Profiles in the NGSIM Dataset In a traffic dataset with a large number of trajectories, it is always possible to distinguish good drivers from risky ones. Following the definition of the two extreme driver profiles of Section 2. 2, we consider the following four driver profile groups in the NGSIM dataset: group 1: the 2. 5% most aggressive drivers, group 2: the second 2. 5% most aggressive drivers (drivers ranked between 2. 5% and 5% of mean THWs), group 3: the 2. 5% most inattentive drivers, and group 4: the second 2. 5% most inattentive drivers (drivers ranked between 2. 5% and 5% in terms of inattentiveness). Mathematically, the four groups are defined as follows. Let us denote the set I agg (t), the of drivers i whose THW(i) is less than t: I agg (t) ≔ i, THW(i) < t 􏽮 􏽯.
(9) Now, we denote by N the total number of drivers of the considered dataset and by N agg (t) the cardinal (number of elements) of I agg (t): N agg (t) ≔ Card I E (t) 􏼁. (10) Finally, we denote by p agg (t) the proportion of the number of drivers in I agg (t) with respect to the total number of drivers (N): p agg (t) ≔ N agg (t) N. ( 11 ) Let us now define t * 1 and t * 2 as follows: t * 1 � p -1 agg (0. 025) ≔ max t, p agg (t) ≤ 0. 025 􏽮 􏽯, t * 2 � p -1 agg (0. 05) ≔ max t, p agg (t) ≤ 0. 05 􏽮 􏽯. ( 12 ) Groups 1 and 2 are then defined as follows: Group 1 � I agg t * 1 􏼁 � I agg p -1 agg (0. 025) 􏼐 􏼑, Group 2 � I agg t * 2 􏼁\I agg t * 1 􏼁 � I agg p -1 agg (0. 05) 􏼐 􏼑\I agg p -1 agg (0. 025) 􏼐 􏼑. (13) e groups 3 and 4 are defined similarly, that is, I inatt (t) ≔ i, min THW(i) > t { }, N inatt (t) ≔ Card I inatt (t) 􏼁, p inatt (t) ≔ N inatt (t) N , t * 3 � p -1 inatt (0.
IDM Model Calibration for Extreme Driver Profiles As mentioned above, the calibration is considered as the optimization problem with the aim at minimizing the error between the simulated car trajectories by the IDM model and the real car trajectories from the dataset. We chose here to apply a genetic algorithm (the default algorithm in Matlab  [48] ) and to attempt to find the global optimum parameters of selected drivers. Moreover, we repeated the genetic algorithm 10 times for every driver trajectory, in order to find a global optimum solution for the parameters of the calibration. In the application of the genetic algorithm, upper and lower bounds need to be set for all parameters in the IDM model. For the reaction time and desired THW, the bounds for these two parameters need to be set differently for the aggressive and inattentive driver profiles because the parameters' real distribution for these two profiles are different(Figure  4 ).
From the calibration results, given in Tables 2-5, we notice that group 1 driver profiles have shorter reaction times and desired headway times compared to group 2 driver profiles. On the other hand, group 3 driver profiles have longer reaction times and desired headway times than group 4 driver profiles. Moreover, the desired THW (parameter T of formula (  4 )) of attentive drivers is much larger than the one of aggressive drivers. is result of calibration of IDM parameters for aggressive drivers and inattentive drives is consistent with the findings from the dataset(Figure  4 ).
Numerical Simulation Experiments Setup Before generating the collisions in traffic, we need to ensure that the NGSIM traffic can be simulated realistically. e reproduction of the NGSIM traffic data by numerical simulation consists on the creation of the road section, the selection of the microscopic traffic model (IDM car-following model plus the default lane changing model in SUMO), and the creation of vehicles. e road network of NGSIM is drawn manually using NETEDIT  [49]  provided by the SUMO simulator, which is presented as a road of 640 meters with 5 straight lanes. e creation of each vehicle is provided by its longitudinal origin position, longitudinal destination position, entering lane, and entering time (in seconds). is necessary information is extracted from the car trajectory data.
As what we supposed in Section 3, each extreme driver profiles have 2. 5% in the initial traffic simulation. In Figure  5 , we give car mean speed from the NGSIM real data and from numerical simulation in SUMO. is figure shows that the traffic simulation represents well the state of traffic (car speed) over time. is first simulation with initial driver profiles distribution in the traffic show that we can simulate the real NGSIM traffic in SUMO. Under this condition, we can begin the experiments for generating the collisions. In addition, during the simulation, SUMO can detect physical collisions (front and back bumper meet or overlap)  [36]. We can get the collision counts at the end of each simulation. Four experiments of car collision simulation are proposed for different combinations of the two selected groups of drivers.
Each experiment is carried out using different combinations of one group of aggressive drivers and one group of inattentive drivers. Driver profiles used in each experiment are given in Table  6 , and the different groups of driver profiles are described in Section 3. For generating the car collisions, we assume here that the car collisions can be generated in each simulation by increasing the number of aggressive and/or inattentive drivers in the traffic. e increasing of the percentages of extreme driver profiles is done artificially and randomly by replacing the normal drivers by the chosen extreme driver profiles. In each experiment, we reset numbers of simulations by using different rates of drivers associated to each extreme driver profile (2. 5% in the origin dataset and then 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%, respectively, for aggressive drivers and inattentive drivers in each simulation).
The Results In this section, we give the results of the numerical simulations. In Subsection 6. 1, we provide the number of car collisions generated by numerical simulation, based on the first 15 minutes data, and, respectively, for the defined four experiments. In Subsection 6. 2, we focus on the analysis of the car collisions obtained in the simulations of experiment 1 (E1). In addition, we show that the number of simulated car collisions can be written as a function of the rates of aggressive and inattentive driver profiles in traffic. In Section 7, we use another 15-minute time period data for the validation of our approach.
Validation of the Approach on the Second 15-Minute Time Period of Data In this section, the same approach (Figure  1 ) for car collision generation is carried out based on the second 15-min time period of the NGSIM 101 dataset with the purpose of validation of the approach, where 1495 vehicles are registered in this period on the 5 normal traffic lanes. e thresholds on THW (mean THW) and min THW for each driver profile are given in Table  11. e simulation of traffic in SUMO for this period is shown in Figure  10. e simulated resulting car collisions, for the 4 experiments (E1-E4), are given in Tables  12 13 14 15. e result using the second 15-minute period data is close to the result using the first 15-minute period. e thresholds obtained from the four experiments are similar. e simulated car collisions using the second 15-minute data show the same trend as the first 15-minute data.
Car collision severity is much lighter between two aggressive drivers who have an average of relative speed at 15. 7 km/h. SCANeR studio  [52, 53]  is a driving simulation software, which also includes Bullet physics engine (a free and open-  source software of simulation of collision detection, soft, and rigid body dynamics) and provides the opportunity to simulate vehicle car collisions with a vehicle physical model. For every simulated car collision in SUMO, the involved two vehicles' trajectory can be registered. en, the car collision scene can be replayed in SCANeR Studio. us, the result of a car collision simulated in SUMO can be resimulated in SCANeR Studio (Figure  14 ). In Figure  15 , the collision force for certain replayed car collisions is shown. Each point corresponds to one resimulated car collision by SCANeR.
Conclusion and Scope for Further Work In this work, the main contributions consist of the following: extracting three proposed driver profiles based on real driving datasets, replicating these profiles in a simulated environment, and establishing a relationship between car collision occurrences and these different driver profiles by varying their percentages in the whole traffic via virtual simulation. Based on the NGSIM 101 dataset, two specific driver profiles related to car collisions on road networks and a normal driver profile have been classified, defined as (i) aggressive drivers who keep short time headways with cars ahead of them, (ii) inattentive drivers with long reaction times, and (iii) normal drivers with intermediate values of reaction time and time headway. ese three driver profiles have been simulated by using the intelligent driver model (IDM) car-following model, with an extension including driver reaction time.
As a result, rather than only being able to distinguish patients with specific tumor types from the healthy ones, the identified miRNA biomarker should suffice for such an objective. Furthermore, biomarkers that can be used across species should provide us with extensive information about their universal roles in the oncogenesis of each specific tumor type. Sequencing technology has been widely used in canine and human exosome-derived miRNA profiling  [4, 10, 12]. Despite the limited evidence from the direct comparison study of canine and human tumor exosome-derived miRNA profiles, their available datasets in the database allowed for meta-analysis among them. It should also be noted that most intraspecies exosome-derived miRNA studies in tumors interpret statistical changes in target miRNA expression levels.
Exosome-derived miRNA data The sequence read archive database was used to retrieve small RNA sequencing data derived from exosomes in the culture medium of normal mammary epithelium, mammary tumor cell lines, and various lymphoid tumor cell lines from both humans and dogs which were retrieved from the SRA database (https:// www. ncbi. nlm. nih. gov/sra)  [4, 6, 11, 17, 18]. All samples of canine and human lymphoid tumor cell lines were B-cell origin. Except for canine mammary tumor cell lines, which were generated from biopsies obtained from canine mammary tumor cases, most cell lines, including human mammary tumor cell lines, were commercially available (Table  -1).
Data pre-processing All exosome-derived RNA sequencing datasets (Table-1) were assessed for quality, aligned with their corresponding genome assembly (GRCh38 for humans and CanFam3. 1 for dogs), and counted for each annotated miRNA ortholog (CanFam3. 1 Ensembl gene annotation) using a method similar that described previously  [19]. For consistency, human gene symbols were used to refer to all orthologs in this study. In brief, the genome assembly and annotated nucleotide sequences of both humans and dogs were obtained from the Ensembl database (https://asia. ensembl. org/info/data/ftp/index. html). The "Flexbar 3. 0" software archived the adapter trimming and quality trimming processes  [20, 21]. The sequences chosen for genome alignment were 18-30 nucleotides long, with Phred scores ≥30 for at least 50% of the bases.
The selected sequences were aligned and counted using the "STAR" aligner  [22] , and the suitable genome assembly -CanFam3. 1 and GRCh38 for canine and human sequences, respectively. With batch correction by the Combat-Seq method, all sequences aligned to annotated miRNA orthologs would be included in the pooled human-dog miRNA library  [23]. Finally, the pooled library was normalized using the Transcripts Per Million metric followed by log2 transformation.
Candidate exosome-derived miRNA selection The principle presented in our previous studies and was used to select candidate miRNA orthologs for classifying lymphoid and mammary tumor cell lines  [24, 25]. The miRNA orthologs were differentially expressed among lymphoid tumor cell lines, mammary tumor cell lines, and normal epithelium cell cultures, which were identified using F-test results (acquired from 1000 learning datasets generated through 3-fold cross-validation) and the "CMA" package  [26]. Differentially expressed miRNA orthologs with an importance value ≥10 would be further validated using the Rank Product (RP) analysis (p≤0. 01) and the "RankProd" package  [27]. Because this study aimed to identify the exosome-derived miRNA candidates with high potential to classify different tumor cell lines, only miRNA orthologs with high-ranking expression orders from RP analysis (RP score ≤0) would be considered due to their significant differences in expression levels.
In similar situations, the generalization of acquired knowledge for clinical practice must be considered. Since the purpose of this study was only to introduce the concept of universal exosome-derived miRNA biomarkers using cell culture sequencing data, further clinical application of the knowledge required further validation of both markers and models with proper miRNA sequencing data such as those obtained from tumor patients' peripheral exosomes. Exosome-derived miRNA orthologs were abundantly expressed in lymphoid and mammary tumor cell lines from humans and dogs (Figure  -1 ). Furthermore, several of them replicated those previously reported in other studies  [4, 10, 11, 18] , thereby  validating the pooled cross-species datasets library. It should be noted that this study will only look at candidate miRNAs that have a significant impact on tumor classification model.
The Greengenes v. 13_5 97% database (available from http://greengenes. secondgenome. com (accessed on 18 January 2017)) was used for classification of 16S datasets, while the UNITE v. 7. 2 (2017-06-28) 97% with singletons database (available from https://unite. ut. ee/repository. php (accessed on 23 August 2017)) was used for classification of the ITS datasets. The resulting OTU tables were filtered to remove all unknown reads. Reads classified as "chloroplast" at the class level in the 16S data analysis were omitted, since they represent plant/algae sequences and not bacteria.
8 g/L, manganese sulfate (MERK) 0. 16 g/L, ferrous chloride (Panreac) 0. 04 g/L, sodium chloride (Sigma) 0. 04 g/L, agar (ROKO) 15 g/L) and (iii) Malt Extract Agar (MEA: malt extract powder (CultiMed) 19 g/L, agar 15 g/L)). DNA was isolated using the Wizard™ Genomic DNA Purification Kits (Promega, Stockholm, Sweden) following the supplier instructions. PCR amplification and subsequent sequencing of 1000-1500 and 400-600 nucleotides were carried out for the 16S rRNA and the ITS regions for bacteria and fungi, respectively (PCR primers: Table  2 ). Consensus sequences of each strain were obtained by using the Seqman tool (DNASTAR Lasergene software package), and identification was done through the GenBank and EMBL databases by BLAST (https://blast. ncbi. nlm. nih. gov (accessed on 11 July 2018);  [40] ).
Different measures to can be used to describe the microbial diversity. The alpha and beta diversities were calculated from the OTU classification tables using the Estimate Alpha and Beta Diversities workflow in CLC Genomic Workbench (Qiagen). In the alpha diversity analyses, maximum depths of 10,000 and 5000 were used for rarefaction of the bacteria and fungi, respectively, and the number of points was set to 20. In both cases, data reported were for the maximum levels.
Acknowledgments: Special thanks to (i)  ESTELLA  project (" DESign of bio-based Thermoset polymer with rEcycLing capabiLity by dynAmic bonds for bio-composite manufacturing ") (Project no. :  101058371 ) funded by the  European Union  through the  Horizon Europe Framework Programme (HORIZON)  (call:  HORIZON-CL4-2021-RESILIENCE-01-11 ); and ( ii) Syntheroids project (Synthetic biology  for industrial production of steroids; ERA CoBioTech 1st call) through the APCIN call of the  Spanish Ministry of Science, Innovation and Universities  (Project  ID :  PCI2018-093066 ). The authors wish to thank the invaluable help and selfless support of  Villavieja's (El Bierzo; León, Spain)  inhabitants. Besides, the authors also wish to thank the scientific and administrative staff of INBIOTEC, SINTEF, and  University of León.
Study design and data collection We designed a multicentre study on the individual and organisational predictors of stress and fatigue among HCWs at the Hospitals of Paris, the STRIPPS (Stress au Travail et Risque Infectieux chez les Patients et le Personnel Soignant) study. 22 The study was conducted between February 2018 and July 2019 and data were collected on midwives, registered and auxiliary nurses. An auxiliary nurse, also known as a healthcare assistant, works closely with healthcare professionals (nurses and physicians,). He/she performs duties such as washing and dressing patients, or making beds, in healthcare facilities. HCWs were recruited from four voluntary French University general care hospitals. Eight wards per participating hospital were drawn at random among wards employing at least 30 HCWs.
Data were collected longitudinally every 4 months during 1 year by two different interviewers for all included participants as follows: t0, corresponding to the first collection during the HCWs inclusion visit; t1, t2, t3, corresponding to follow-up visits at 4 months, 8 months and 12 months. For the first data collection (t0), dates and times of visits by an interviewer were drawn randomly for each participating ward. For later data collections, individual appointments were made with each included HCWs. Data were collected through questionnaire-based interviews at both levels (ward and individual levels).
Individual-level variables To collect individual-level variables, HCWs of included wards were interviewed individually at 4 times of visits (t0, t1, t2 and t3). The individual questionnaire collected demographic and occupational characteristics, including age, sex, professional status, contractual situation, experience in the hospital and daily working hours. Individual data related to work organisation, including schedule, nightshift, extra hours, mealtimes and rest periods, were also collected. Social support from colleagues and from supervisor was also assessed using the social support dimension of the Job Content Questionnaire (JCQ), which was developed and validated by Karasek et al. 23  Social support dimension includes eight 4-points Likert scale items (from 1 to 4), with a resulting score ranging from 8 to 32 (from very low to very high social support).
Missing data imputation For missing data, multiple imputations were performed on validated questionnaire items only (JCQ, PSS-10, Pichot and ERI questionnaires). Data were imputed using multiple imputation, using the R mice package. 32 The mice package allows performing imputation of continuous and categorical variables in a context of multilevel and longitudinal data. For all questionnaire items included in the imputation model, missing data were assumed to be missing at random.
CD44 levels influence the progression of intestinal bowel disease in humans By investigating publicly available gene expression data (GEO database, GSE137344  [29] ), we assessed the expression levels of CD44 in Crohn's disease (CD) patients. Low CD44 expression (below the median) correlated significantly with a shorter time of relapse-free survival whereas high CD44 expression correlated with sustained remission in this patient cohort (Fig. 5d ). Since CD is associated with a compromised epithelial barrier function, these data support the role of CD44 in sustaining the mucosal integrity in a condition susceptible to chronic inflammation. Coherently, the study of RNAseq data from 10 CD patients (GEO database, GSE171244  [30] ) shows a positive correlation between the expression levels of CD44 and Wnt target genes, PCs, and stem cell markers (Fig.
METHODS A resource table can be found in the Supplementary Material (Supplementary Table  S1 ). (ATCC Cat# CRL-588, RRID: CVCL_1490) were obtained from ATCC (Wesel, Germany) and maintained in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% FBS, 1 mM sodium pyruvate, and 1% PenStrep at 37 °C. All experiments were performed using mycoplasmafree cells.
The staining against β-catenin (1:500; BD Biosciences Cat# 610154, RRID: AB_397555), was performed using the EnVision+ System-HRP (Dako) protocol. Prior to the addition of the primary antibody, the slices were incubated for 10 min with a power block solution, blocking the peroxidases. After washing with PBS-T, the primary antibody (β-catenin 1:500) was added to the slides and incubated overnight at 4 °C. Slices were washed to remove the primary antibody and then incubated with Rabbit EnVision+System-HRP (Dako) or Mouse EnVision+System-HRP (Dako) for 30 min. Sections were rinsed in PBS-T before applying DAB. Slices were washed and counterstained with hematoxylin, rehydrated, and mounted as described above. Immunohistochemistry staining against β-catenin of Swiss-roll sections of the colon of two Cd44 +/+ and three Cd44 Δie mice after tamoxifen induction were and the images were analyzed using the Aperio Imagescope -Pathology Slide Viewing Software (Leica Biosystems).