dataesr/scientific-paragraphs-categorization huggingface dataset:
Data acquisition. MEG data were recorded with a whole head 4D-neuroimaging system with 248 magnetometers. Participants were laying in horizontal position under the MEG dewar, facing a screen displaying a silent movie. MEG recordings were sampled at 678 Hz and bandpass filtered between 0. 3 and 500 Hz. Four head position coils (HPI) measured the head position of participants before each block. Prior to the session, 2 minutes of empty room recordings were acquired for the computation of the noise covariance matrix.
MRI-MEG co-registration and source reconstruction. The coregistration of MEG data with the individual's structural MRI was carried out by realigning the digitized fiducial points with MRI slices. Using MRILAB (Neuromag-Elekta LTD, Helsinki), fiducials were aligned manually on the MRI slice. Individual forward solutions for all source reconstructions located on the cortical sheet were next computed using a 3-layers boundary element model  98, 99  constrained by the individual aMRI. Cortical surfaces were extracted with FreeSurfer and decimated to about 10,240 sources per hemisphere with 4. 9 mm spacing. The forward solution, noise and source covariance matrices were used to calculate the depth-weighted (default parameter  = 0. 8) and noise-normalized dynamic statistical parametric mapping (dSPM)  100  inverse operator.
Acknowledgements YP is supported by the  Agence Nationale de la Recherche  and the  FWF  ( ANR-14-CE34-0011 ; project  RNALands ). SH is supported by the  German Federal Ministry of Education and Research  (BMBF support code 031A538B; de. NBI:  German Network for Bioinformatics Infrastructure ) and the  Future and Emerging Technologies programme (FET-Open  grant  323987 ; project RiboNets). We thank  Leonid Chindelevitch  for suggesting using the stable sets analogy to optimize our FPT algorithm, and  Arie Koster  for practical recommendations for computing tree decompositions.
A new module has been added in the workflow to locate phosphorylation (EPIYA-like) domains. S4TE 2. 0 consists of the S4TE 1. 4 tool and a web interface available to non-commercial users at http://sate. cirad. fr. The web interface is designed to make S4TE 2. 0 easy to use for biologists and more time efficient. Most of the genomes and plasmids available in the NCBI database of pathogenic bacteria that have a type IV secretion system have been loaded into the S4TE 2. 0 database so effectors can be predicted in only a few clicks. S4TE 2. 0 offers advanced users an expert mode (S4TE-EM) they can use to customize S4TE 2. 0 search parameters (e. g. exclude modules, modify module weightings). In this mode, S4TE 2. 0 can be used as 14 independent programs to search for particular features in a given bacterial genome (e.
Programming S4TE 2. 0 software consists in a graphical interface (website) to use the S4TE 1. 4 algorithm for genome analysis, Type IV effectors (T4Es) prediction and comparison of effectomes. S4TE 1. 4 is an update of S4TE 1. 0  [1]. It is written in Perl programming language and uses NCBI, Pfam, EMBOSS, BioPerl and MitoFates libraries and its own proper programs and database. It was developed to improve the prediction performances of S4TE 1. 0 and to provide new functionalities to search for new features, enable interactivity and comparative genomics. The 10 S4TE search modules in S4TE 1. 0 were kept in S4TE 1. 4. However, some modules have been modified (promoter motif search, homology, MLS, NLS, E-block and Pfam database) to improve their predictive power. A supplementary module (EPIYA search) has been added to the workflow.
Only alignments with a score above the chosen threshold are selected. The threshold that yielded the highest sensitivity and specificity for each motif in the corresponding bacterium was chosen (Fig  1). Homology. BLAST 2. 2 was used to compare proteins to search for homologies with known T4Es  [4]. The cut-off of the S4TE 1. 0 homology module was changed. S4TE 2. 0 compares the database containing all known T4Es with the query proteome and returns all orthologs with a cut-off of the expected value (E) <10-4. This E-value cut-off was selected to find accurate orthologs between phylogenetically distant bacterial species. Databases containing proven effectors have also been updated (S3 Dataset). Nuclear localization signals (NLS). NLS are protein sequences that target proteins in the nucleus of eukaryotic cells  [5].
Monopartite NLS consist of [KR]-[KR]-[KR-][KR]- [KR], X-K-[KR]-[KRP]-[KR]-X, X-R-K- [KRP]-[KR]-X, X-R-K-X-[KR]-[KRP], X-K-[KR]-[KR]-X-[KRP], X-R-K-[KR]-X-[KRP], X-K-[KR]-X-[KR]-X-X, X-R-K-X-[KR]-X-X, X-K-[KR]-[KR]-X-X-X and X-R-K-[KR]-X-X-X motifs. Bipartite NLS were also searched with S4TE 1. 0 motif (K-[KR]-X(6,20)-[KR]-[KR]-X-[KR]). The new module was tested with a dataset of 32 NLS and 32 no-NLS containing proteins (S1 Dataset). The module selected 24 true positives (TP) and only three false positives (FP). This represents a sensitivity (Se) of 75% and a specificity (Sp) of 91%. Mitochondrial localization signals (MLS). MLS are signal sequences located in the Nterminus of proteins that are targeted to mitochondria. This sequence is cleaved after translocation of the protein inside the mitochondria  [5, 7].
To predict MLS in S4TE 2. 0, we used the MitoFates tool  [8]. MitoFates predicts mitochondrial presequences, a cleavable localization signal located in the N-terminal, and its cleaved position. E-block. The E-block domain consists of a glutamate sequence rich in C-terminal 30 amino acids and is associated with T4Es translocation in L. pneumophila. Huang et al. showed that an E-block motif is also important for the translocation of T4SS substrates  [9]. In S4TE 2. 0, the E-block module was modified according to Lifshitz et al. [10]. The E-block was searched in a window of 22 amino acids between position -4 C-terminal and -26 C-terminal. The motif that is searched for is a motif of 10 amino acids containing three or more glutamate (E) residues. The module was tested on 98 E-block and 98 no-E-block containing proteins (S2 Dataset).
This module selected 60 TP and only 6 FP (Sensitivity of 61%, Specificity of 94%). Pfam database. The local Pfam database has been updated to find more eukaryotic domains of known effectors of Legionella pneumophila  [10]. Eukaryotic domains were extracted from the whole Pfam database and added to the S4TE 2. 0 workflow. All eukaryotic domains used for this search are listed in S4 Dataset. EPIYA search. EPIYA search is a new module implemented in S4TE 2. 0. Bacterial EPIYA effectors are delivered into host cells by T4SS, where they undergo tyrosine phosphorylation at the EPIYA motif and thereby manipulate host signalling by tight interaction with SH2 domain-containing proteins  [11]. In H. pylori, EPIYA has been shown to contribute to the secretion of a CagA effector  [12]. Moreover the functional versatility of EPIYA motif highlights the importance of this emerging family of bacterial effectors  [11].
Result Validation S4TE 2. 0 is a software with 14 independent modules. We tested all the modules independently. The 14 modules were weighted to make S4TE 2. 0 efficient. The weighting of each module was calculated according to its performance in finding effectors in L. pneumophila Philadelphia I which has been shown to have the most extensive repertoire of T4Es ever identified, with 286 confirmed effectors  [10]. Each module has its own weighting in S4TE 2. 0 searches. The weightings were calculated for each module based on their Positive Predictive Value (PPV [PPV = TP/(TP+FP)]) for L. pneumophila (Table  1 ). The S4TE 2. 0 prediction threshold was then defined to enable the best prediction by disregarding homology with known effectors. The threshold was chosen by examining the Sensitivity (Se), Specificity (Sp), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (Acc) for thresholds ranging from 40 to 120 on the test dataset (Fig  2 ).
Availability and future directions Command-line interface. S4TE 2. 0 is a web interface but the S4TE 1. 4 package is freely available to non-commercial users at http://sate. cirad. fr/S4TE-Doc. php. All programming was done using Perl 5. 18 and BioPerl 1. 6. 1. The software runs on Linux platforms (Ubuntu 14. 04 and Mac OS X). All required packages and the installation process are described in the user guide included in the package. The user guide also details S4TE options for running S4TE. By default, the command line to launch S4TE is. /S4TE. pl -f "Genbank_file" in the S4TE folder (cd way_to_S4TE/S4TE/). Some options are available for the user to launch S4TE: -c, suppression of a module in the pipeline; -w, modification of the weight of each module in the pipeline; -t, imposition of a threshold for effector selection.
Each S4TE module creates a. txt file in the folder way_to_S4TE/S4TE/Jobs/job<Name _of_genome_folder><year><month><day><hour><min> All the results are compiled in the CompilationFile. txt and Results. txt in the same folder. Web interface. The S4TE 2. 0 website is powered from scratch on the 'CIRAD web server'. All the features of the web site were tested on common web browsers. S4TE 2. 0 found T4Es in large genome database (S5 Dataset) available to all users. A user account is available and necessary to keep your jobs for up to three months, to import your own genome in a S4TE 2. 0 temporary database and to ask to add a new proved effector in the database. The addition of an effector to the database must be accompanied by a reference (scientific article) and will be checked manually before the effector is added to the database.
Those who subscribed to the newsletter will be notified by email about the addition of new effectors to the database and the effector will be visible in the S4TE 2. 0-tab strip. This free account allows users to search for proteins in the S4TE 2. 0 database using the name, the locus tag or NCBI number of a protein in the search bar. The account also allows the user to subscribe to the S4TE newsletter that summarizes any changes made to the software and provide updates on the latest research on Type IV Effectors. S4TE 2. 0 is a simple and user-friendly tool. S4TE 2. 0 is a web-based user-friendly tool that gets results in only a few clicks. The user can locate a chromosome in more than 340 bacterial genomes and plasmids available in the database and the results can be viewed by clicking on run S4TE 2.
0 (S5 Dataset). If the desired genome is not available in the database, the user can import it with a GenBank file (. gbk). S4TE 2. 0 will import the file to a temporary database for three months. All S4TE tools (S4TE-EM and S4TE-CG) can then be used on the uploaded genome. The S4TE 2. 0 web page allows users to read some of the news published in the newsletter. Five news items are visible on the S4TE2. 0 web page, but all the news can be found by clicking on the bottom right link. Fig  4  presents some results obtained with S4TE 2. 0. All the proteins in the selected genome are represented on the S4TE 2. 0 web results page. A score was calculated for each protein based on the weighting of each module. Proteins were ranked according to the same score. All proteins whose scores are above the threshold are considered as belonging to the S4TE 2.
Conclusions This paper presents updated S4TE software. The computational tool is designed to predict the presence of T4SS effector proteins in bacteria. The identification of T4Es and some characteristics are improved in this update. Compared with a machine learning approach, using S4TE 2. 0 to predict T4Es in Legionella and Coxiella species  [10, 13, 14]  improved sensitivity (98% for S4TE 2. 0 and 89% for Wang et al. ) and equivalent specificity (97% for Wang et al. and 93% for S4TE 2. 0). S4TE 2. 0 is easy to use: only an internet connection and a few clicks are needed to search for T4Es in more than 340 bacterial genomes and plasmids. The results are displayed instantaneously for easy reading. An automated pipeline is also provided to analyze and visualize effector distribution in the genome according to G+C content and local gene density.
S4TE 2. 0 results are linked to bioinformatics databases like NCBI and Pfam. The S4TE 2. 0 database is designed to evolve and will be updated by adding new proven effectors and new bacterial genomes. S4TE 2. 0 not only predicts the T4Es but also their subcellular localization (NLS, MLS, prenylation) and the function of these proteins (Coiled coils, EPIYA, Euk-like, etc. ). All these features make S4TE 2. 0 a powerful software for studies of T4Es. S4TE 2. 0 also offers an expert mode, which allows users to make manual adjustments to the weight of the modules. Each module that searches for a feature or a characteristic can be used independently. Thus, S4TE-EM can be viewed and used as 14 independent programs. This could facilitate the annotation of new genomes by looking for specific features such as NLS, prenylation domains, etc.
S4TE-EM will find all proteins with a NLS in the genome, meaning users can use S4TE-EM as a new genome analysis tool. S4TES4TE-CG is a new tool designed to compare different effectomes predicted by S4TE 2. 0. Users can choose up to four effectomes in S4TE 2. 0 databases or upload a genome present in the temporary database. S4TE-CG displays results in a Venn diagram and in an interactive table. Users can easily find different subsets of information in the appropriate table by referring to the different colors in the Venn diagram (Fig3). Information about each effector can easily be found by clicking on the name of the effector in the table. Or users can simply copy and paste the table in a. csv file.
-CG comparative genomics tool to compare effectomes. S4TE-EM will find all proteins with a NLS in the genome, meaning users can use S4TE-EM as a new genome analysis tool. S4TES4TE-CG is a new tool designed to compare different effectomes predicted by S4TE 2. 0. Users can choose up to four effectomes in S4TE 2. 0 databases or upload a genome present in the temporary database. S4TE-CG displays results in a Venn diagram and in an interactive table. Users can easily find different subsets of information in the appropriate table by referring to the different colors in the Venn diagram (Fig3). Information about each effector can easily be found by clicking on the name of the effector in the table. Or users can simply copy and paste the table in a. csv file.
It also reduces the active sensor area to avoid the synchrotron radiation fan, making the BPM less sensitive. This could be an issue for accurate measurement of the pilot bunch, as this requires an order of magnitude improvement over what is currently achieved in the LHC. An absolute alignment accuracy with respect to the quadrupoles of better than 200 µm is needed in the arcs, with tighter tolerances in the insertion regions. The system must be capable of providing measurements of the closed orbit with submicron resolution and a fill-to-fill reproducibility of better than 20 µm. The data will be provided in real-time to allow orbit and radial position feedback. Discussions are ongoing about the tolerances required to ensure alignment of the beam orbit with the synchrotron radiation slits in the beamscreen and the impact that this has on the BPM alignment.
The system will also provide turn-by-turn data for injection oscillations, optics measurements and post mortem analysis. A few special BPMs will provide bunchby-bunch and turn-by-turn, sub-micron resolution data for special purposes such as instability observations or interlock purposes. A modern, state-of-the-art BPM acquisition system should be able to meet all of these requirements. The sensor and front-end electronics, comprising analogue signal shaping and digitisation circuitry, will be located in the tunnel. A radiation-hard, bidirectional, fibre-optic link  [232]  connects each station to the processing electronics located out of the radiation environment, on the surface. The challenge will be to design a cost-effective solution, where the front-end electronics can function in a tunnel radiation environment with a high energy hadron fluence 500 times higher than in the LHC and a total dose a 200 times higher.
Once the scenario is validated by the office of the President of the Republic, which is where the regional and national stakeholders come together, the preparation of the legally required process for the public to participate in the project ("débat public")  [430]  can begin. The goal of this process is to develop an acceptance and appreciation of the project amongst the population, which forms the basis for a governmental clearance for the project owner to continue with the preparation. A commission defines which topics are relevant for the population and where there can be reasonable involvement in this process. The formal procedure lasts six months, but it requires about two years of preparation and is concluded with a one-year phase for its implementation. After this internal preparatory phase, the project owner in close cooperation with the public debate commission, prepares information material (documents, databases, Web contents, booklets, videos, travelling information stands, town meetings), organises the nation-wide process, employs consultants and companies experienced with this process, sets up the required technical infrastructure and finally, defines a schedule that will allow all relevant public representatives to be involved.
Milestones and deliverables The milestones and deliverables are presented in Table  12. 10. origins, impacts and mitigation measures. It merely serves to give a glimpse of the comprehensive risk management process, which has been set up and which is currently ongoing in the Future Circular Collider study. A risk assessment database is maintained by the study group in order to establish an early warning and mitigation process that is expected to grow into a comprehensive project risk management scheme during the project preparatory phase. Invest in R&D to develop magnet designs that are easier to manufacture, in particular aiming at streamlined production, testing and installation. Optimise the internal system interfaces with respect to number and simplicity as well as the external system interfaces, including test points for total quality management.
Decades of research in cancer genomics have allowed to identify several hundreds of such cancer genes. Regularly updated databases such as the Cancer Gene Census (CGC)  [11] , provide catalogues of genes likely to be causally implicated in cancer, with various levels of experimental validations. Many cancer genes have been identified recently by systematic analysis of somatic mutations in cancer genomes, as provided by large-scale collaborative efforts to sequence tumors such as The Cancer Genome Atlas (TCGA)  [12]  or the International Cancer Genome Consortium (ICGC)  [13]. Indeed, cancer genes tend to be more mutated than noncancer genes, providing a simple guiding principle to identify them. In particular, the COS-MIC database  [14]  is the world's largest and most comprehensive resource of somatic mutations in coding regions.
The results of some of these tools are available at the Driver DB database  [25]. Some methods integrate information on mutation frequency and functional impact of mutations, or other types of data such as genome position, copy number variations (CNVs) or gene expression. The underlying idea is that combining data should improve the prediction performance over tools that use a single type of information. For example, TUSON  [26]  or DOTS-Finder  [27]  combine mutation frequencies and functional impact of mutations to identify OGs and TSGs. Also in this category, the 20/20+ method  [28]  encodes genes with features based on their frequency and mutation types, in addition to other biological information such as gene expression level in difference cancer cell lines  [29]  or replication time.
Cross-validation performance for pan-cancer driver gene prediction We first study the pan-cancer regime, where cancer is considered as a single disease, and where we search for driver genes involved in at least one type of cancer. Several computational methods have been proposed to solve this problem in the past, and we compare LOTUS with five well-known state-of-the-art methods  [28] : MutSigCV  [21] , which is a frequency-based method, TUSON  [26] , 20/20+  [28] , which combines frequency and functional information, MUFFINN  [32]  and DiffMut  [35] , which takes the mutation patterns of genes into account. As explained in the Materials and methods section, to perform a fair comparison between different methods we use different training databases for LOTUS adapted to each competing methods.
This reflects the fundamental difference between TSG mutations and OG mutations: the first lead to loss-of-function and can pile up, while the second are gain-of-function mutations and have a much more subtle nature. In addition, gain-of-function can also be due to overexpression of the OG, which can arise from other mechanisms than gene mutation. One way to improve the OG prediction performance may be to include descriptors better suited to them, such as copy number. Moreover, as mutations affecting OGs are not all likely to provide them with new functionalities, many mutations on OGs present in the database and used here might not bear information on OGs. Therefore, relevant information on OGs is scarce, which makes OG prediction more difficult. In addition, the data themselves might also contribute to difference in performance between TSG and OG prediction.
These results show that, both for OG and TSG, using both mutation and PPI information dramatically improves the prediction performance over using only one of them. This underlines the fact that mutation and PPI bear complementary information that are both useful for the prediction tasks. The performances obtained with only PPI information are similar for OG and TSG, which seems to indicate that this information contributes similarly to both prediction tasks. On the contrary, the performances obtained using only mutation information are much better for TSG than for OG. This is consistent with the above comment that mutation information is more abundant in the database and more relevant in nature for TSG than for OG. It is also consistent with the fact that using K mutation alone outperforms using K PPI alone for TSGs, while the opposite is observed for OGs.
For that purpose, we binned the genes in 20 groups of roughly equal sizes, by decreasing degree in the network, and randomly shuffled the genes within each group. After this random shuffling, each gene has approximately the same degree as in the initial PPI network, but not the same neighborhood. We repeated the random shuffling 100 times and computed the performance of LOTUS with the corresponding PPI kernels. We observed that the performance was worse with the randomized PPI network than with the original PPI network in 95% of the cases, confirming that LOTUS with the PPI kernel uses more than the mere degree information to predict cancer genes. Furthermore, we examined the first predictions (excluding already known driver genes) of LOTUS with the 20/20 datasets, when both the mutation and the PPI kernels are used.
Performance on CGCv86 prediction in the pan-cancer regime We now evaluate the generalization properties of the different methods on new unseen data as external test set. This could mitigate the potential bias in the evaluation of the performance of TUSON, DiffMut and 20/20+ based on cross-validation experiments, as in the previous paragraph. For that purpose, we evaluate the performance of the different methods when predicting supposedly "difficult" new cancer genes (an independent test set), which have only been added recently in CGCv86. We train on the one hand LOTUS, MUFFINN and TUSON with the TUSON mutation database and driver gene train sets, and on the other hand LOTUS, DiffMut and 20/20+ with the 20/20 mutation database and driver gene train sets. Then, we make predictions on the remaining genes in COSMIC, and count how many driver genes in CGCv86 appear among the 20, 50 and 100 first predictions.