dataesr/scientific-paragraphs-categorization huggingface dataset:
Results obtained for the seven methods are shown in Figure  3. The FCROS, FC, RP and TREAT methods had similar power of detection, with the TREAT and RP methods exhibited a slightly better performance for the case 3x3. The Student t-test and the WAD methods gave worse results than the other methods. In addition, we performed another run using the complete dataset (m 1 = 15, m 2 = 15). Table  1  shows the results obtained as well as the AUC (area under a ROC curve) values for the seven methods. The TREAT, FC and RP methods had the lowest error while the Ttest and WAD methods had the highest one. We recorded values for parameter δ for runs of the FCROS method. Results obtained are plotted in Additional file 1: Figure  S3. The values for δ are close to 1 if control and test samples are not randomized, see panel A of Additional file 1: Figure  S3.
Microarray data We used seven microarray datasets to evaluate the performance of the FCROS method. All data were generated with the Affymetrix technology. The first dataset ("Platinum Spike") is from  [32]  and consists of 18 spike-in samples (9 controls versus 9 tests). This dataset is available from the Gene Expression Omnibus website under the accession number GSE21344. The next six post-processed datasets are available from  [33]. For the second dataset, 58 diffuse large B-cell lymphoma (DLBCL) patients and 19 follicular lymphoma patients were used  [34]. The third dataset (Prostate) consists of 102 samples using 50 nontumor and 52 tumor prostate patients  [35]. The fourth dataset (Colon) consists of 22 control and 40 colon cancer samples  [36]. For the fith dataset (Leukemia), 47 acute lymphoblastic leukemia and 25 acute myeloblastic leukemia patients were used  [5].
The sixth dataset (Myeloma) was obtained using 36 patients without and 137 patients with bone lytic lesions  [37]. For the seventh dataset (ALL-1), 128 different individuals (95 B-cell leukemia and 33 T-cell leukemia) were used  [38]. (GSE21344) and used the RMA (robust multi-array average) method to obtain signals for probes  [39]. We downloaded the designated FC associated to probes from: www. biomedcentral. com/content/supplementary/ 1471-2105-11-285-s5. txt (accessed on 23 september 2013). Using this file, we retained 18952 probes, among which 1940 are known as DE. Each of these probes has an observed FC obtained using RMA normalized data and a designated FC read from the file we downloaded. Additional file 1: Figure  S4  shows the M-A plot  [31]  of the "Platinum Spike" dataset. We ran the seven methods and selected the top 1940 probes which were then crossed with the set of the designated DE probes.
"Platinum Spike" dataset We downloaded the Affymetrix CEL format files from the GEO website We further compared the performances of the 4 FCbased methods, Figure  4B. This comparison revealed that the WAD method outperformed the other methods, as it specifically detected 71 DE genes (Figure  4C ). However, it should be noted that the FC, FCROS and RP methods collectively identified 68 DE genes (mostly with low expression values, Figure  4D ) that were not detected by the WAD method. Results for six sets of microarray data We used the DLBCL, Prostate, Colon, Leukemia, Myeloma and ALL-1   datasets (see above) to compare results obtained with the FCROS method and with the other six methods. To select the list of the most DE genes for each method, we used the results of the RP method, for which a PFP value of zero was associated to some genes.
Except for the Myeloma dataset, all errors are under 10%. The error value for the RP method is the PFP. For the Prostate, the Myeloma and the ALL-1 datasets, we noted that the RP method detects some genes as down and up regulated at the same time. There are 19, 27 and 6 such genes for the Prostate, Myeloma and ALL-1 datasets, respectively. Most of these genes have a FC close to 1. The bad detection performance of the RP method for these datasets probably comes from the number of permutations (100) used. An increase in this number will lead to an increase of the runtime which is already long. Thus, the RP method is suitable for a small number of samples but is not advisable for larger numbers of samples. Table  4  shows the numbers of genes detected by all methods (common) and those uniquely detected by each method.
The SAM and TREAT methods have the smallest number of genes not detected by any other method. We examined genes detected only by the Ttest, WAD or FC methods. The Ttest method detected some genes with a fold change close to 1. The WAD method detected some highly expressed genes with a small FC but missed other genes with a high FC but low expression values. The FC method detected some genes for which only one sample has a large impact on the average values of all samples. Interestingly, for some datasets (e. g. Myeloma, Prostate, DLBCL), the FCROS method detected a relatively high number of probes not detected by other methods. The proportion of such probes varied highly between datasets, and was as high as 75% of the number of commonly detected probes (Myeloma dataset). This observation suggests that the FCROS method can significantly enrich the number of candidate DE genes.
We conducted a close inspection of the Colon and the Prostate datasets and used the interactive Venn Diagram plotter software  [40]  to search for specific and common DE genes detected by the FCROS, RP, SAM and TREAT methods. As expected, the FCROS method shared more genes with the RP method than with the SAM or TREAT methods. Figure  5A  shows that 58 genes were detected by these four methods for the Colon dataset. The FCROS method also shared 16 genes with the SAM and the TREAT methods, which were not detected by the RP method. In contrast, no gene was detected jointly by the RP, SAM or TREAT methods which was not detected by the FCROS method. For the Prostate dataset (Figure  5B ), 519 genes were detected by all four methods. Many genes were detected only by the FCROS (105) or shared by the FCROS, SAM and TREAT methods (161), and not detected by the RP method.
Again, only few genes  (12)  were detected by the RP and the SAM or TREAT methods that were not detected by FCROS. We further used the Prostate dataset to compare the 4 FC based methods (Figure  5C ). We considered that genes that were detected by 2 or more methods are good candidates for true DE genes. There were 1095 such genes, of which 56% were commonly identified by all methods. No method clearly outperformed the other, as each of them failed to detect ≈ 10% of genes that were detected by at least 2 of the other methods. Surpringly, the simple FC method detected the fewest number of genes that were not detected by other methods.
Effect of sample number A literature survey performed in  [41]  shows that many biological microarray studies use  very small number of replicates (e. g. 3 to 5). To evaluate the consequence of such choices on the detection power, we used the Colon dataset, and conducted analyses with varying numbers of control and test samples selected from the original dataset. In a first analysis and for a given (m 1 , m 2 ) pair, we proceeded as follows: a) random selection of m 1 control and m 2 test samples from their true sample groups, b) run the seven methods to obtain results for each, c) select the 100 top DE probes and assign 1 to them and 0 to all other probes. These three steps were repeated 100 times and the total occurences of 1 for each probe was calculated as its score. To set a threshold for the score, we performed a second analysis where control and test samples were chosen without regard for the biological sample groups to which they belong.
Analysis of reproducibility We further used the Colon dataset to assess the reproducibility of DE genes identification in a complex noisy dataset. We conducted 100 runs, in which we used half of the dataset, i. e. 11 control and  20 test samples that were randomly selected. The top 100 genes identified as DE in each run were assigned a score of 1 while all other genes were assigned a score of 0. The overall score for each gene was calculated as the sum of its scores. We evaluated the reproducibility of each method by counting the number of genes with perfect (100) or good (≥ 90) global scores (Table  5 ). As expected, the FC based methods were better than the t-test based methods in reproducibly identifying DE genes. Among these methods, FCROS and WAD were more reproducible than FC and RP.
Conclusion We have described here a new FC-based method and shown that it is powerful to detect DE genes in noisy datasets. Importantly, the FCROS method assigns a statistic to DE genes, which can be used as a selection criterion. FCROS appears to be more specific and much faster than RP, and as sensitive and reproducible as WAD. The FCROS method has two possible applications, when used in combination with other methods: 1) identification of a core set of high confidence DE genes detected by all methods, 2) identification of additional potentially DE genes not detected by other methods. This last possibility may be especially relevant when studying samples with a high degree of intrinsic biological variability (like tumor samples). Our results indeed show that the FCROS method can detect many DE genes in tumor datasets, which escape identification with other methods (Figure  5 ).
In studies of rare diseases, the number of patient samples can be very low while the number of control samples from healthy people is high. The results from Figures  3  and 7  show that the FCROS method performs well in such Colon dataset: scores obtained using the seven methods. situations. The FCROS method has also other advantages. (1) It does not require prefiltering to improve the statistic associated with each gene. In contrast, prefiltering is important for other methods, as it decreases the computational load and the FDR. where k 1 and k 2 are the numbers of pairwise comparisons from the first and the second batch, respectively. We provide an R package which is deposed on the Comprehensible R Archive Network (CRAN) server for download, see http://www. r-project. org. The function fcros2() allows to deal with datasets from two batches.
We use standardized ranks, i. e. each component in r i is divided by n. Hence, the mean and standard deviation in step 3 of the algorihm above should be divided by n. In the FCROS algorithm, necessary parameters are computed from the dataset except the trimmed mean percentage parameter noted trim. Theorem 1 gives theoretical values for many parameters, more precisely δ = b-a n-1 , R = b+a 2n and σ 2 R = ( 1 12 -1 12n 2 )δ 2. For the ideal situation (a = δ = 1, b = n) theoretical mean and variance are 1. Let us examine the role of parameters k, δ and trim.
06 0. 58% Leukemia FCROS 0. 028, 0. 9717 493 4. 7 5. 63% (n=7129) FC 1. 942 494 0. 12 na (m 1 =47,m 2 =25) WAD 1. 1668 494 0. 17 na RP 0 494 768. 65 0% Ttest 0. 00052 494 1. 43 0. 75% SAM 0. 00051 494 28. 71 0. 74% TREAT 0. 00153 494 0. 13 2. 2% Myeloma FCROS 0. 01296, 0. 987 450 34. 77 2. 59% (n=12625) FC 1. 5189 452 0. 4 na (m 1 =36,m 2 =137) WAD 1. 212 449 0. 49 na RP 0 451 4721. 73 0% Ttest 0. 0051 449 2. 9 14. 34% SAM 0. 0055 450 94. 84 15. 43% TREAT 0. 0192 451 0. 54 53. 75% ALL-1 FCROS 0. 0355, 0. 9644 1187 22. 72 7. 11% (n=12625) FC 1. 4176 1187 0. 5 na (m 1 =95,m 2 =33) WAD 1. 1165 1185 0. 64 na RP 0 1188 3108. 43 0% Ttest 0. 000057 1186 2. 66 0. 06% SAM 0. 000145 1187 69. 48 0. 15% TREAT 0. 00117 1184 0. 26 1. 25% Comparative results for six microarray datasets (na = not available).
Furthermore, complementary expressions were included to facilitate the distinction between emotional and nonemotional expressions. This is particularly important for children and adolescents with ASC without functional language, to whom a verbal explanation of this distinction is often inefficient. In this way, a face without emotional expression corresponded to a neutral/non-emotional facial expression and a 'funny face' reflected an intentional inappropriate facial expression. Each expression was associated to one facial expression and three gestures. Each facial expression was different from another by mouth, eyes and eyebrows shape, opening or tilt. Only one emotional valence was presented for each emotion (see Figure  1a ). JeStiMulE is a virtual reality game with a multi-sensory environment.
Visual, tactile, and auditory stimulations are provided to facilitate game immersion. In JeStiMulE, each expression was associated to a visual non-verbal code (see Figure  1b ), a corresponding verbal written code (which comprises emotional words and idiomatic expressions), and a tactile pattern (see further details below). Visual non-verbal codes corresponded to colors and a symbol. Each basic emotion was associated to a specific color from Plutchick's emotional wheel (happiness = yellow, anger = red, disgust = purple, fear = green, sadness = light blue, surprise = dark blue)  [55]  (see Figure  1b ). Pain was associated with black. Neutral was associated with the color white and 'funny face' was associated with a trash can. This last choice was made to introduce the notion of inappropriate facial expressions, in particular to participants without functional language.
Each tactile pattern could be considered as a word in an emotional language corresponding to an icon in visual communication  [58, 59]. Tactile patterns reinforce emotional meaning. The patterns were developed in a number of iterative user tests. The tactile stimulations were produced by eight actuators distributed over the gamepad body (see Figure  1c ). The actuators position was defined by measuring the contact zones between the hand and the gamepad. Auditory stimulations were repetitive musical sequences, emotional onomatopoeias and environmental noises presented along and according to the social scenes. JeStiMulE was designed based on specific user requirements. In order to offer adapted response options to all participants, in JeStiMulE each expression was presented not only with its corresponding non-verbal code but also with two corresponding verbal written codes (emotional words and idiomatic expressions).
This phase also included a user-test to ensure that participants were able to use the gamepad to move the avatar around the platform. 2) JeStiMulE learning phase (see Figure  3 ): this phase included three levels with gradually increasing complexity. On level 1, emotions were displayed on faces and associated with a visual non-verbal code and a specific tactile stimulation. On level 2, emotions were displayed on faces combined with gestures and associated with a visual non-verbal code and a specific tactile stimulation. On level 3, emotions were displayed on faces combined with gestures and associated with a visual non-verbal code, a verbal written (emotional words and idiomatic expressions) code and a specific tactile stimulation. Participants were first trained to recognize different emotional faces and gestures in static avatars and then in animated avatars.
3). Note that in this last case, the content of conversation was not perceptible. For this reason, the participant had to use his/her attention skills to detail and base his/her judgments on non-verbal cues only. In order to promote the transfer of skills from one context to another (based on analogical reasoning), several variations were introduced from one module to the other within the same scenarios. Furthermore, the fact that  modules followed a probabilistic progression encouraged participants to deduce rules, based on implicit learning, from one scene to the other. Thus, JeStiMulE is based on ASC-specific skills and offers a model of logical processing of social information in order to compensate their difficulties with intuitive social processing.
Adaptability JeStiMulE design paid special attention to the sensory and learning profile of ASC in order to allow a heterogeneous (age, IQ, reading, and so on) group of individuals with ASC to play. It included the following adaptations: (1a) a possibility to choose the intensity of tactile stimulations (strong, medium, weak and inactive) in accordance with each participant's sensory profile; (1b) a learning phase based on a learning-by-association procedure (visual non-verbal code-expression during the learning phase); (1c) a possibility to choose a user-adapted response option (color, emotional words or idiomatic expressions) in accordance with each participant's age, level of semantic-syntactic language acquisition or reading abilities; (1d) games adapted to the cognitive style of the individuals with ASC: matching, attention to detail, implicit learning, categorization, analogical reasoning.
Thirty-nine real-life character faces (six trials for each basic emotion + three for pain a , see Figure  7c ) taken from the Radbout Faces Database  [62]. Forty-two real-life character gestures (six trials for each emotion) presented in a full-body display where faces were masked by a grey circle to make participants focus on the gestures (see Figure  7d ). Twenty-eight real-life character social scenes (four trials for each emotion; see Figure  7e ). Photographs were selected by two independent experts. The characters' faces were masked in order to make the participants focus on contextual cues to recognize the emotion expressed in the given social situation  [10]. In order to verify these tasks reliability, a preliminary data collection was conducted on typically developing individuals (n = 17).
Mean results are presented in Additional file 3. At the beginning of each trial, the stimuli were displayed in the left side of the screen and the response options were displayed in the right side. The response options corresponded to seven rectangles of different colors according to the different expression-color associations included in JeStiMulE. In addition, the word corresponding to the expression was displayed inside each rectangle and every expression was read aloud. Participants were asked to determine the expression displayed by choosing and clicking (mouse click) on the rectangle corresponding to it. Participants were tested individually in a quiet room with pauses, if necessary. They were seated in front of a computer screen. The stimuli were presented in E-Prime 2 (Psychology Software Tools, Inc.
Procedure The study procedure is illustrated in Figure  8. After inclusion, all participants were tested on emotion recognition (pre-JeStiMulE testing, two weeks before training). Following this, they were presented with JeStiMulE, starting with a calibration phase, followed by a learning and a training phase. All sessions were conducted with a caregiver and took place in the same room. Each training session lasted one hour maximum. Participants played twice a week until they completed JeStiMulE. They had four weeks (corresponding to eight sessions) maximum to achieve this goal. The number of sessions thus varied from one participant to the other, according to their performance. Each game level could only be performed once. In order to verify JeStiMulE's usability (adaptability, effectiveness and efficiency), game data were collected for each participant.
Effectiveness (Table  3 ) -After the learning phase, 91% of the participants were able to make JeStiMulE visual non-verbal code-expression faces associations and 79% of them were able to make JeStiMulE visual non-verbal code-expression gestures associations. After the training phase, 91% of the participants completed JeStiMulE module 1, 85% completed module 1 and 2, and 73% completed all modules, independently of their response modality. Efficiency (Table  4 ) -These data are based only on the performance of participants who completed the two key stages of JeStiMulE (which are visual non-verbal codeexpression associations and recognition of emotions displayed in social scenes). These participants (73%) played, on average, 6 sessions (range 3 to 8 sessions) of 49 minutes (range 30 to 60 minutes) each.
As every participant was tested before and after training, he/she would serve as his/her own control. This experimental decision helped us evaluate the effectiveness of the training on each individual. In this study, emotion recognition skills were assessed before and after fourweek JeStiMulE training. A significant improvement was found in most of the tasks despite the heterogeneity of the group of participants. To date, most of the studies conducted in this field have focused on homogeneous groups with ASC. Very often, these studies focused on individuals with HFA including AS. There are few studies that include individuals with LFA. For example, Hopkins et al. [38]  assessed the efficacy of 'FaceSay' , a computer-based social skills training program for children with LFA and HFA. Their results suggested that providing children with ASC with a controlled, structured, and interactive environment with avatar assistants can help them to improve their social skills.
JeStiMulE was developed specifically to provide a rulebased computer game relying on 'autistic intelligence' to develop other, more social, skills. The underlying working hypothesis was thus that rule-based learning could be a relevant pathway to reach gradually more complex, social learning in ASC. The idea that rule-based environments could compensate for difficulties in the domain of emotion recognition in ASC  [39]  is not novel. When designing JeStiMulE, we assumed that individuals with ASC could use their logical skills to learn emotions and could evolve in a systemizing environment. JeStiMulE uses a virtual environment to simulate different social situations, offering thus more ecological learning opportunities than those of devices using static stimuli  [26]. Participants included in this study only played once each game level and none of them repeated a successfully completed level or module.
In line with current advances (ASC-Inclusion project  [66] ), future versions of JeStiMulE are expected to extend to other relevant skills such as imitation and emotion expression (JEMImE  [67] ). Preliminary findings of our own group have shown that logical processing such as involved in JeStiMulE can also be used to develop academic skills, such word decoding, in children with ASC and without functional language (SEMA-TIC; manuscript in preparation).
Acknowledgements We are grateful to all children, adolescents and their families for their participation in this study. We also thank our partners ( Idées3 com, HLP Technologies, Autism Resources Center Nice, Day-care Units  for children and adolescents with autism (' La Caravelle', 'Les Coteaux d'Azur' and 'Les Noisetiers')  for their participation in this study. Written informed consent was obtained from all participants and their families. The consent form is held by the corresponding author and is available for review by the Editor-in-Chief. We also acknowledge the following associations for their support: Monaco Against Autism (MONAA),  ABA Apprendre Autrement, Collectif Handicap 06. We acknowledge the support provided by  CoBteK (Cognitive Behaviour Technology). The study promoter was the  Nice University Hospital (CHUN).
Rivaroxaban Rivaroxaban was the first oral, direct Factor Xa inhibitor to receive approval from regulatory authorities for stroke prevention in patients with AF, based mainly on the results of the Phase III ROCKET AF trial (Rivaroxaban Once-Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation; NCT00403767). 7 In addition, rivaroxaban has received approval in many countries, including the US  8  and the countries of the European Union,  9  and for use in several other different thromboembolic conditions. 9 ] [12]  The drug undergoes renal clearance of active compound in plasma of approximately 33%. 11 Pharmacokinetic analyses from two Phase II evaluations of rivaroxaban for treatment of deep-vein thrombosis and prevention of recurrent venous thromboembolism in patients with deep-vein thrombosis  13, 14  were adapted to provide estimates of rivaroxaban exposure in simulated patients with AF;  15  these analyses indicated that 20 mg once daily would be an appropriate dosage for stroke prevention in AF.