dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated datasets We generated realistic simulated datasets from the ensembles of normal and tumorous samples of the LUAD and LUSC TGCA studies. We first ranked all the gene expression values in normal samples and pooled them into consecutive packets. Each packet k contained 100 values of similar range {E(g k,1 ,S k,1 ), E(g k,2 ,S k,2 ),. , E(g k,100 ,S k,100 )} with E(g k,i ,S k,i ) the expression of gene g k,i in normal sample S k,i. Then for each group, we computed the ensemble of expression differences in normal samples defined as Δ n (k) = {E(g k,i ,S 0 )-E(g k,i ,S k,i ), 1�i�100 and 8 S 0 6 ¼S k, i }. Similarly, we defined the ensemble of expression differences between tumorous and normal samples as Δ c (k) = {E(g k,i ,T)-E(g k,i ,S k,i ), 1�i�100 and 8 tumor T}. From the 5% and 95% percentiles of Δ n (k), noted p 5 (k) and p 95 (k) respectively, we isolated the subset D 0 c ðkÞ of values in Δ c (k) that are smaller than p 5 (k) or greater than p 95 (k).
We assumed that D 0 c ðkÞ represents typical abnormal expression differences observed in cancer for the packet k and that the ratio r(k) between the number of elements in D 0 c ðkÞ and in Δ c (k) is representative of the probability for a gene in this group to be deregulated. Finally, to generate a simulated tumorous sample, we chose randomly one normal sample S. For each gene g, we determined the packet k containing E(g,S) and its expression was modified with a probability r(k) by adding a randomly-chosen element of D 0 c ðkÞ. In average 30% of the genes were up or down-regulated. Instead of r(k), we also used fixed proportions of deregulated genes from 0. 05 to 0. 9. We tested that the performance of PenDa on simulated datasets was not affected by the packet size (S11A Fig). The choice of the percentiles (5%, 95%) impacts on the ROC curves while PenDA still remains the best investigated methods in the low FPR range (S11B Fig).
Predictive power on simulated datasets To test the efficacy of PenDA or of other methods, we generated 10 simulated tumors (see above). For each dataset, in order to realize a fair comparison, we excluded the normal sample from which it was generated to the ensemble of normal samples used to define the reference properties of each method. For a given method and given parameters, true positive (TPR), false positive (FPR) and false discovery (FDR) rates were computed on these 10 simulations. ROC curves (TPR vs FPR) were obtained by varying one specific parameter for each method (threshold h for PenDA, percentile x for the percentile method, FDR level for Rankcomp and log2 fold change threshold for DESeq2). From each curve, we extracted the maximal informedness defined as the maximal value of the Youden's J statistics defined as the difference between TPR and FPR (TPR-FPR).
PenDA analysis of the lung cancer cohort from the TCGA The PenDA method was applied on preprocessed expression TCGA data (see Methods section: 'Data and preprocessing'). The PenDA vignette of the penda package version 1. 0 was executed on 18143 genes, using 98 control samples and 928 case samples. The data set was pretreated as following: 0 gene and 0 sample were removed during the NA values filtering step, and 1034 gene was removed for low because lowly expressed: under the threshold 'val_min' = 10 in at least 99% of cases. 98 controls were used to generate L and H lists using the following parameters: threshold LH = 0. 99 and s_max = 30. The penda method was then applied on 928 cases, with the following set of parameters: quantile = 0. 02, factor = 1. 2 and threshold = 0. 3.
PenDA analysis of the lung cancer cohort from the Grenoble Hospital The PenDA method was applied on expression data (Affymetrix Human Genome U133 Plus 2. 0 Array) of the GSE30219 cohort. The PenDA vignette of the penda package version 1. 0 was executed on 19148 genes, using 14 control samples and 293 case samples. The data set was pretreated as following: 0 gene and 0 sample were removed during the NA values filtering step, and 0 gene was removed for low because lowly expressed: under the threshold 'val_min' = 0. 5 in at least 99% of cases. 14 controls were used to generate L and H lists using the following parameters: threshold LH = 0. 99 and s_max = 100. The penda method was then applied on 293 cases, with the following set of parameters: quantile = 0. 05, factor = 1. 05 and threshold = 0.
test q-value. Super-down-regulated genes were defined as follows: (i) P S T¼1 x D gT > medianð P S T¼1 x Tot T Þ, ii) counts > 10 in at least 80% of the control samples and iii) significant t. test q-value. Super-conservde genes were defined as follows: (i) P S T¼1 x Tot gT < medianð P S T¼1 x Tot T Þ, ii) counts > 10 in at least 80% of the control samples and iii) significant t. test q-value. c. PCA analysis (Fig  6 ) was performed using the function big_randomSVD of the R package bigstatr  [54]. SVM linear regression was performed on the 2 firsts Principle Component of PCA analysis, using the function svm of the R package "e1071", using the following arguments: kernel = linear, cost = 10 and scale = FALSE.
Use of Rankcomp The original Rankcomp and the RankcompV2 algorithms  [15, 16]  were tested using the Relative Expression Ordering Analysis (REOA) package downloaded from https://github. com/ pathint/reoa. We ran the program reoa on our simulated datasets using the options-s 1 -j 2 -a 2 to get individual predictions for both algorithms with default parameters. Results for different FDR levels were obtained using the -f option.
Use of DESeq2 The R-package of DESeq2  [7]  was imported from Bioconductor3. 7. To assess fold changes in expression from simulated datasets, we used DESeq2 default parameters. We performed 10 comparisons between individual simulated tumor sample and 97 independent TCGA control samples (we remove the control sample used for simulating the tumor sample from the reference dataset). As no replicate was available for tumor sample, DESeq2 allowed the variancemean dependence estimated from control samples to be used for case sample  [57]. The log2-foldChange estimation was used for sensitivity and specificity analysis. Performing DESeq2 with or without its internal normalization routine may impact the ROC analysis in particular if data are not standardized (S11C Fig). To assess fold changes in expression from TCGA datasets, we applied DESeq2 methods with default parameters, except for the significance cutoff which was set to 0.
the effect of the number of control samples in the reference dataset (Fig 2B) and of the number of investigated genes (Fig 2C) were analyzed by randomly choosing a set of control samples or a set of genes from the initial pools and by repeating these operations 10 times. TPR and FPR levels were computed on the ensemble of simulations and of random choices. In Fig 3B, the ROC curves were determined for a set of 10 control samples randomly picked from the original pool. In Fig 3C, effect of normalization was simulated by multiplying RNA-seq counts of control and tumorous samples by random factors uniformly drawn between 1 and 5: the same factor was applied for all the genes of a given sample.
Acknowledgments We thank the members of the DJ's and SK's groups, in particular  Ekaterina Flin , for inspiring discussions during regular joint group meetings. We are grateful to  Florian Prive ,  Michael Blum ,  Eric Fanchon  and members of the  BCM  team for algorithmic and methodological advices. We acknowledge computational resources from CIMENT infrastructure (supported by the  Rhone-Alpes region , Grant  CPER07 13 CIRA ). EB thanks  Centre de ressources (CRB) CHUGA Grenoble , French Ligue contre le cancer for transcriptomic platform and  Nicolas Lemaitre  for tumor data management.
The research leading to these results was supported by  ITMO Cancer (Plan Cancer   2014-2019 ,  Biologie des Systèmes   n˚BIO2015-08 ) [EB, SK, DJ] and  University Grenoble-Alpes  via the Grenoble Alpes Data Institute [MR] and the  SYMER program  [SK, DJ] (which are funded by the  French National Research Agency  under the " Investissements d'Avenir" program   ANR-15-IDEX-02 ). SK acknowledges additional funding from  Plan Cancer   ASC16079CSA ,  Pitcher ,  LIFE program of University Grenoble Alpes  ( ANR-15-IDEX-02 ),  Fondation   ARC "Canc'air"  ( RAC16042CLA ) and project  PGA1 RF20190208471. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Objectives Before evaluating the efficacy of the GEXP VR application on pain-related health outcomes, we needed to evaluate the basic parameters of the application's GEXP content and establish how potential users respond to the application. In this study, we aimed to measure the GEXP content (ie, How well did the module goals gradually challenge participants with fear-inducing movements) and feasibility (ie, Is it possible for participants with cLBP to use the app?). To measure feasibility, we specifically examined usability (ie, Can users accomplish module goals with effectiveness, efficiency, and satisfaction? [30] ), safety (ie, Can users complete the module tasks without experiencing harm?), and acceptance (ie, Do users find the potential treatment option fair, reasonable, and appropriate? [31] ).
These VR activities included goals such as fighting monsters, crouching under branches, saving animals that challenged participants to reach, bend to get under obstacles, and walk quickly. We then incorporated the VR activities into six 3-min modules that encouraged participants to complete progressively more difficult combinations of the movement tasks (Table  1 ). We created the VR application, named Lucid, specifically for this study through a Small Business Innovation Research grant partnership with From the Future, LLC. We used Unity to develop the VR application, which ran on a Windows 10-based personal computer. In the HMD, participants navigated a walking path and utilized a VR sword and shield (Figure  3 ). Participants walked to translate along the path and moved the HTC-Vive controllers to wield the sword and shield.
Primary Measures of GEXP Content Validity (Hypothesis 1) Our primary GEXP content validity outcome was the avoidance rank, expected pain, and expected concern participants assigned to each Lucid module at baseline. Developed in the same style as the PHODA, we developed a card for each module that showed an avatar performing the movement required in the module. We laid the 6 cards randomly in front of each participant. Participants organized the cards in a row from the activities they would be least to most likely to avoid. We discreetly recorded the card's position as the ranked avoidance (1 for least likely to avoid to 6 for most likely to avoid), which we used as a measure of how participants perceived the difficulty of the modules in relation to the other modules. Then, to encourage participants to more critically evaluate the tasks in each module, participants reported their expected pain if they were to perform the activity (0 for no pain to 100 for worst possible pain) and their expected concern and worry for harming their back (0 for not at all concerned to 10 for extremely concerned) for each card.
Data Analysis This study aimed to (1) determine the GEXP content validity of the VR application and (2) determine the feasibility of individuals with cLBP performing integrated physical activities. We descriptively summarized demographic data. To examine the GEXP content validity of the graded modules, we used Friedman tests and Dunn post hoc pairwise comparisons with Bonferroni corrections to analyze ordinal data across sessions (avoidance rank and RPE). For continuous data (expected pain and expected concern), we performed a repeated measures analysis of variance (ANOVA) across the VR sessions and performed post hoc pairwise comparisons with Bonferroni corrections. We reported the means and SDs of how participants rated each session's difficulty compared with the previous sessions. To evaluate the feasibility of the VR application, we calculated the means and SDs of the TEI-sf, SUS, and postsession questionnaire.
We performed paired t tests to compare participant pain and pain-related fear from baseline to follow-up. We also used paired t tests to examine differences in VAS pain from before to after each module. We performed Shapiro-Wilk normality tests to confirm the normality of the distribution for dependent variables. For all analyses, we collapsed the VR modules into 3 groups (session 1, session 2, and session 3), based on the 3 VR sessions. We set the alpha level of significance to. 05 (two tailed) for all statistical tests. We checked the data for underlying assumptions, and data were described and analyzed using IBM SPSS 25 (IBM Corp).
Principal Findings In this study, we aimed to (1) determine the GEXP content validity of the VR application and (2) determine the feasibility of individuals with cLBP performing integrated physical activities. Participant responses supported that the VR modules provided progressive exposure to fearfully perceived tasks by assigning greater avoidance to the modules designed to elicit greater fearful appraisals and present greater challenges. In concurrence with the FAM, participants assigned the highest expected pain and concern for harming their back to the modules that they ranked with higher avoidance. For feasibility, the TEI and SUS scores indicated that participants with high fear and cLBP found the VR application an acceptable approach to treat cLBP and usable as a system. Additionally, the VR application was safe, as participants successfully completed the GEXP VR protocol without negative effects on pain or pain-related fear.
Although we intentionally used a GEXP mechanism in the Lucid VR application to decrease the physical limitations caused by activity avoidance, other factors may have helped participants complete the modules. Distraction from pain is a well-studied mechanism commonly used in acute pain  [20, 55]. We added components in each module to increase movement exposure, such as adding coins to collect or animals to rescue. These novel components may have helped hold the attention of participants and maintained distraction. Many participants reported that they were less aware of their pain and that the modules distracted them from their pain, and this likely contributed to their expressed acceptance of the VR application. Therefore, although VR GEXP may be a targeted way to progressively challenge participants with cLBP, distraction may be an important component to include in future VR applications.
Limitations As our study design was to establish the content validity and feasibility of the GEXP app, all participants received the same activities in the same order. The lack of personalization could have limited the GEXP experience for some of our participants as we only provided exposure to these predefined movement combinations. We do not believe this limitation had a significant effect on our study outcomes as participants generally ranked the activities by avoidance in the expected order. In addition, although we used RPE to measure perceived effort, we did not ask participants to specifically rate the difficulty of each module given their back pain. Participants may have assigned greater effort to the higher intensity modules for reasons other than back-related challenges. Participants who walked faster may have had higher exposure as they were able to progress further along the trail in 3 min.
Future Directions The next step in this line of research is to explore the efficacy of the VR application on pain-related health outcomes. For this, we would need a larger sample size and a longer study duration. Although this study focused on GEXP content and feasibility, we would also need to explore how the VR exposure training translates to real-world activities by measuring changes in avoidance, disability, and physical function outside of the VR setting. Additionally, future iterations could improve the GEXP experience by allowing the participants to rank a greater number of activities and then experience modules tailored to how they rank their avoidance of each task. This would improve the personalization of the VR application and could allow participants to experience a tailored program more relevant to their daily living.
Conclusions We have established that the VR modules provided progressive challenges and were feasible for individuals with cLBP and high pain-related fear. The locomotion-enabled VR modules allowed users to freely walk and complete challenging physical activities in a motivating environment that participants thought was acceptable, usable, and safe. Expectation ratings, RPE, and module difficulty responses support that the sessions and comprising modules provided a progressive challenge, in line with GEXP protocols. Despite presenting activities likely for individuals with high fear to avoid, the graded VR walking challenges did not increase pain or fear of movement. The VR modules provided exposure to physical activity challenges that integrate reaching, walking, crouching, and carrying weights while also providing a safe bout of exercise and an enjoyable gaming experience.
Acknowledgments This research was supported by the  National Institutes of Health/National Institute of Child Health and Human Development of the National Institutes of Health  under award number # 1R43HD093522-01  and through a grant from the  University of Alabama at Birmingham Health Services Foundation General Endowment Fund  (PIs:  Carmen Capo-Lugo [PT, PhD ] and DB [PT, PhD]). The authors would like to thank  Jennifer Uzochukwu  and  Caroline Springer  for their assistance with data collection and data entry.
There are clearly two practical challenges to conducting such collaborations, namely the "establishment of the data sharing agrizones" and "procedures for integrating data" from different MS testing schemes, without imposing a strict uniformity of testing method. F I G U R E 3 Feeding value of grass and legume species/cultivars from the French NL since 2010 (www. herbe -book. org). Spring yield is all cuts before July 10 in years 2 and 3 from 5 to 8 locations. Feed unit for milk is calculated from the weighed ADF content in the first three cuts year 1. One feed unit refers to 1 kg of barley as 1,700 kJ/kg DM of net energy [Colour figure can be viewed at wileyonlinelibrary. com].
| EU-VCU stakeholder commitment A successful implementation of an EU-VCU scheme will require all stakeholders to be convinced of the benefits. The process will initially require coordination, probably at the EU level, to implement the key steps which are as follows: • MS agree the cross-border agrizones for a species and the division of tasks for assessing "standard" and "additional proxy" characters, ideally in a workload neutral arrangement. • MS retain their current protocol for existing "standard" characters within an agrizone. • Test centres comply with agreed quality assurance standards and best practices, including management of a shared database with each MS having "real-time" full access. • MS accept to test each other's candidates in their agrizone sites on a full cost recovery basis.
• Some common control cultivars with putative contrasting adaptation patterns are sown at all test sites in an agrizone to integrate results from different MS test systems into the agrizone database. • MS retain own listing authority, applying their own decision standards and indices. • EU adopt CC listings that denote the EU agrizone(s) for which each cultivar is adapted. If two MS protocols differ so widely for a species that using linkage controls alone to combine data would not be effective, then some harmonization of methodology might be required of the test sites sharing data within an agrizone. However, a single unified protocol is still not an essential requirement and variations in MS protocols can be accommodated. Among the various stakeholders, EU policymakers should readily favour an EU-VCU, as its more pan-EU functionality is a better fit with the single market policy.
schemes. By sharing common control cultivars, it is possible to transform these data and incorporate the results from each MS site into a shared agrizone database. As already states, MS would apply their own pass/fail standards and policy priorities for their NL. So, where an agrizone was a minor region in a MS, EU-VCU could provide the data needed for major species level decisions, so standardizing NL pass/fail stringency across the EU. Retaining MS automony is vital as the decision makers in some MS are government officials (e. g. Ireland, Germany-Federal and.
This value also depends on weights fixed during the training phase which itself relies on a dataset given by the trainer. The common question Q1: How well is the model performing ? is usually answered with various evaluation metrics, but since the model inner workings are dependent on the architecture, the training dataset and the learning process at once, it is difficult to understand and explain those performances, and neural networks are then of-ten considered as "black boxes". For this reason, it is difficult to improve or debug models and answer the following questions without proceeding to expensive tries, testing different network architectures and/or parameters in order to verify various hypotheses: Q2: Should we add or remove layers in the model? Q3: Should we change the number of filters of that convolutional layer? Q4: Is our training dataset efficient enough to train our model? Recently, various works leverage information visualization in order to progress into the field of explainable artificial intelligence.
, 2016; Olah et al. , 2018). Other strategies exist, such as model comparison  (Zeng et al. , 2017; Zhang et al. , 2018)  which consists in comparing models to detect learning differences. Several tools emphasize on different data analysis and visualization techniques at once. ActiVis  (Kahng et al. , 2017)  choses to focus on multiple analysis methods for fully connected layers while others tempt to collect as much information as possible on the full network and complete dataset  (Liu et al. , 2018) ; some focus on the manner data is being progressively classified using data flow visualizations and techniques previously mentioned. Such systems can be seen in CNNVis  (Liu et al. , 2016) , Summit  (Hohman et al. , 2020) , and some experiments on Distill  (Olah et al. , 2018) , which are focused on image classification, and Neural Network Playground  (Smilkov et al.
, 2016) , which proposes an interactive neural network building tool mixing network representation and forward data projection. Those visualization systems give hints on active parts of the model and justify its decision. The method presented in this paper belongs to this category. By combining previously cited analysis methods and presenting their results into a data flow visualization. However, instead of clustering data based on neuron behavior as seen in CNNVis (using mean neuron activation values) or Summit (using top k active neurons), this method clusters data based on the simi-larities of activation maps using an existing analysis method. Displayed flows on the visualization tool can be considered as data being similarly processed, showing the network's progressive dataset classification.
Data analysis and clustering Algorithm 1 depicts the procedure which is expensive to compute. As mentioned earlier by addressing R1 and R2, it should be processed using distributed computing on a computer cluster. As it can be seen, the procedure is performing two iterations: the first one over the evaluation dataset to collect and perform analysis over model computations, the other on each layer to prepare those results into specific encodings. They can be efficiently performed across computer clusters, since neither iterations is depending on their previous state. getActivationMaps (R1): The first step is to collect the data transformations of each model's layers for the current input data. This collection may be done by various ways, but it is necessary to keep a record of which activation map belongs to which layer.