dataesr/scientific-paragraphs-categorization huggingface dataset:
Lost to follow-up were not considered in these calculations. Sample size calculation results using the method proposed by Glick  [10]  are reported in Table  2. FEMCAT (Clinicaltrials. gov identifier: NCT01982006) is a multicenter, randomized, pragmatic clinical trial. Its aim is to compare the cost and effect of femtosecond laser-assisted cataract surgery to phacoemulsification cataract surgery. The primary economic endpoint is the cost per complication avoided at 3 months after surgery from the perspective of the French National Health Insurance. The resulting sample size calculated through our method is 1233 eyes per group (Table  3 ). a The monetary gain for an additional inclusion in each group (i. e. 2 participants in this example) is the difference between the EVPI n decrement and the costs induced by the inclusion and follow-up of two additional participants in the study (2257.
25 €/participant in this example) Fig. 1  Expected gain from including an additional participant in each group according to the sample size (n) (logarithmic scale) INTACT (Clinicaltrials. gov identifier: NCT02599389) is a multicenter triple-arm randomized clinical trial which primary objective is to estimate the incremental cost-effectiveness ratio of two innovative strategies for the treatment of femoropopliteal artery in-stent restenosis: drug-coated balloons (paclitaxel -antimitotic) used alone or in association with the Excimer Laser, both groups being compared to uncoated balloons. The primary endpoint is the cost per Quality adjusted life-years (Qaly) gained at 18 months from the perspective of the French Health System. The sample size calculation was based on the comparison of drug-coated balloons versus uncoated balloons, a comparison that would yield the lowest difference in effect.
The result of the sample size calculations using our method is 75 individuals per group (Table  3 ). OXYNAT (Clinicaltrials. gov identifier: NCT03078218) is a multicenter controlled before and after study where the cost-effectiveness of pulse oximetry (after), as compared to current medical examination (before), is estimated in the screening of critical congenital heart defects in asymptomatic newborns. The primary endpoint is the cost per complication avoided within a time horizon of 12 months of life from the perspective of the French Health System. The value of the ceiling cost-effectiveness ratio (λ) is 100,000 €/avoided complication because death accounts for 20% of complications of critical congenital heart defects. The resulting sample size calculated with our method is 12,951 newborns per group (Table  3 ).
Acknowledgements The authors would like to thank  Pr. Eric Ducasse ,  Dr. Cédric Schweitzer  and  Dr. Julie Thomas , respectively coordinating investigators of the INTACT study, the FEMCAT study and the OXYNAT study, for having kindly authorized the use of the their study as illustrations in this manuscript. The authors would also like to thank  Prs. Rodolphe Thiébaut , and  Jérôme Wittwer , and  Dr. Morgane Donadel  for their critical review of the manuscript.
This level, to introduce laws that describe the breakdown of composite materials, the Abaqus code offers the user the possibility of using pre-programmed constitutive models such as VUMAT (criterion of Hashin 3D)  (Tarfaoui et al. , 2008). In this section, the numerical investigation of damage in nozzle structures can be performed using finite element analysis implemented in commercial code Abaqus. It should be noted that the damage is examined by Hashin's criteria to evaluate the fiber and matrix damage initiation and propagation. The full model shows the turbine part and the impactor region used for simulation and presented in Fig. 2. The impactor has a hemispherical shape with 12 kg of mass and 3 m/s of velocity. Hydrodynamic loads are those loads that result from water flowing against and around our nozzle system.
Using Blade Element Momentum (BEM) theory to predict their hydrodynamic performances. The ABAQUS DLOAD user define function has been used to implement our interpolation procedure in order to use the hydrodynamic pressures computed with the panel method code as boundary conditions for the finite element analysis. The meshes of the nozzle for the calculation of the hydrodynamic performances and for the calculation of structure meet different criteria. For hydrodynamic calculation, the nozzle is represented by a surface mesh, composed of very elongated quadrangular meshes in the direction of the flow. This mesh is refined near the leading edge to correctly describe the flow around the nozzle. For the calculation of structure, the nozzle is represented by a mesh composed of shell or volume elements.
Intralaminar failure based continuum damage mechanics Among the fiber and matrix damage that can take place within a lamina. However, in many damage investigations, only the intralaminar damage mechanism is modeled and the interlaminar damage mechanism is ignored or vice-versa without adequate justification. In this purpose, a user material VUMAT subroutine was compiled in FORTRAN code and implemented by the finite element explicit Abaqus software to characterize the intralaminar damage. To simulate the evolution of the damage, modeling must take into account the various forms of damage occurring at the impact tests. It is not necessary that the numerical model take account all the physical phenomena observed if their presence does not affect in a relevant way the current turbine behavior.
Funding This work was supported by  EraNet Neuron  grant Deciphering hyperexcitable networks associated with neurodevelopmental lesions [DeCipher], # ANR-15-NEUR-0001-03 ,  FP7 the European Community 7th Framework programs (Development and Epilepsy-Strategies for Innovative Research  to improve diagnosis, prevention and treatment in children with difficult to treat Epilepsy [DESIRE], Health-F2-602531-2013, the  FRC-Rotary Club  "espoir en tête" 2009 and the  French National Agency for Research Young Researchers Programme  ( SILENCing excitability of epileptogenic networks in Early Epileptic Disorders [SILENCEED ],  ANR-16-CE17-0013  to JBM).
Security and privacy features in the TeSLA framework The security of the architectural components, as well as the intellectual property rights of TeSLA via software licenses, is ensured by using standard technologies like public key infrastructure (PKI) and X509 certificates. 12 The communication exchanges between all the components of the architecture use the Transport Layer Security (TLS) protocol. 13 Mutual authentication is enforced over the whole architecture, hence ensuring confidentiality and integrity of every data exchange. The underlying PKI infrastructure allows TLS deployment and management of certificates and authorities (cf. 14 and citations thereof, for further details about the TeSLA PKI infrastructure). Taking an e-assignment in this architecture first requires to log in on the VLE that contains the client-side plugin.
The learner can require the e-assignment using the learner tool available on the VLE as a third-party tool. The learner tool sends a request through the plugin to the TEP. The incoming request does not contain the name of the learner, but only the TeSLA ID, that the plugin requests from the TIP. Then, the TEP fetches the e-assignment in its database and sends it back to the VLE, where the learner will take the assignment while external tools and sample biometric data that will be regularly sent to instruments, for example, for anti-cheating analysis. The learner's identity verification performed in the TeSLA e-assessment system relies on specific data of the learners (such as the biometric samples), collected from their environment via some external tools embedded in the VLE. The communications between the institution and the TeSLA components rely on exchanges between the VLE and the TEP.
16 The matching between the learner's identity and the TeSLA ID is stored in the TIP database. The TIP database is placed at the university side and is not accessible from TeSLA. The TIP database shall be shared with all the VLEs. All the interactions between the university domain and the TeSLA domain will involve the plugin on the one hand, and the TEP on the other hand. This is sufficient to make sure that any request sent to the TEP through the plugin is first redirected to the TIP to retrieve the learner's TeSLA ID and use it in place of the learner's identity. An additional enhancement of the TeSLA architecture toward improved privacy features relies on the use of anonymous certification. As described in,  17, 18  anonymous certification allows to perform a privacy-friendly access control, in order to certify that users are allowed to access a resource because they own some attributes required by the verifier.
DEPLOYMENT USE CASE DURING THE PILOTS OF THE PROJECT As a EU-funded innovation action, the TeSLA project is a conducting large-scale pilots to evaluate the technological building blocks presented in the previous section. The evaluations are being performed taking into account quality assurance in education, privacy, and ethical issues, as well as educational and technological requirements throughout Europe. The pilots are used formatively to evaluate e-assessment scenarios among the institutional partners of the project. The pilots are conducted in order to asses constructed response tests, e-Portfolios, and peer review collaborative learning. 19 The response to an activity can be of various kinds (ie, the learner has to select, create, or perform the activity) but they all comprise technological actions such as text typing or code programming.
Pilots are being conducted by groups of 10 students working in parallel, which required about 80 Mbit/s. The deployment is performed over a series of virtual hosts running at the TeSLA server of TUS, using Docker Swarm. 20 The result of the infrastructure deployment is the initialization of all the TeSLA components discussed in Section 2, for example, TEP and TIP components, together with their databases. The instrument deployment presents the biometric and authorship instruments for learner authentication. Some other modules, such as the certification authority (CA) associated with the PKI of the TeSLA framework, are initialized as well (cf. Figure  3B ). The cloud structure of the deployment consists of the TEP, TIP, CA, and the biometric instruments. Both PKI and TeSLA ID deployments, based on,  [14] [15] [16]  allow protecting learners' identity.
Protection of the Docker containers and virtual Docker images follow well established recommendations. 21 igure  3D  shows the visualization of sample audit tool results. Data management is complemented with a series of remote database servers over on-demand cloud computing platforms for TeSLA at Amazon Web Services. Dedicated databases at TUS handle the anonymized learner data from all the assessment activities. The TeSLA plug-in has been tested over different operating systems including Windows-based operating systems (eg, Windows-7 and Windows-10, Professional, Home, Education editions) and GNU/Linux operating systems (eg, CentOS). In terms of web browsers, tests included browsers such as Firefox, Chrome, Internet Explorer, and Edge. Fault redundancy of the platform is ensured against physical (hardware, electrical supply failures, environmental factors) and virtual infrastructure (human and program errors) risks by: (1) support of alternative servers at another location; (2) server resources are enhanced with duplicate processor technologies, operating memory, and RAID massive for enhancing the disk capacity; (3) electric supply failure is mitigated with UPS devices of a market leader company; (4) external device backups of all virtual machines at periods of 72 hours in a month are maintained for eliminating human and program errors.
The system reached relatively stable exploitation status. It provided for monitoring and data gathering from enrollment and assessment activities from the pilots. TUS designed some test-bed scenarios suiting their existing distributed platforms for online distance education. To avoid collisions and problems with their existing learning environments, TUS implemented a parallel replica. The data from the different faculty servers were duplicated, and the test infrastructure was connected to TUS e-learning framework to test connectivity and student data transfers. This was achieved by a server Linux-based (KVM) virtualization. The results showed fault redundancy of educational process and compliance with the existing working infrastructure in TUS. Further formative evaluation results of the pilots at TUS are available online, at the website of TeSLA.
5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i. e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.
In this perspective, the collection of docking poses will be used to derive a consensus of predicted interface residues  15  both in single docking studies  [16] [17] [18] [19] [20]  , in which the docking poses result from the docking of two protein partners already known to interact, and in complete cross-docking (CC-D) studies  [21] [22] [23] [24]  , which involve performing docking calculations on all possible protein pairs within a given dataset. Several ! 4 benchmarking databases are available to evaluate protein-protein docking protocols, such as the Docking benchmark 5. 0  25  , DOCKGROUND  26  , or 2P2IDB  27. The Docking benchmark 5. 0  25  includes 230 protein-protein complexes and provides both bound and unbound structures for the majority of the protein partners. It is considered as the gold standard for protein-protein docking evaluation, notably thanks to the careful protein annotations (about the functional and docking difficulty categories).
One should note that our docking algorithm was evaluated in a previous study on the 168 proteins of the Docking benchmark 2. 0  28. DOCKGROUND  26  is a large protein-protein interactions database, with 396 co-crystallized protein-protein complexes and the corresponding X-ray unbound structures for both proteins. 2P2Idb  27  is a smaller database dedicated to orthosteric modulation of protein-protein interactions. Its specificity is to provide all interactions for which both the protein-protein and protein-inhibitor have been structurally resolved, resulting in a database of 31 protein-protein complexes, 619 protein-ligand complexes and 553 PPI inhibitors. Earlier studies  21, 23, 24  , using such benchmarking databases, showed that the cross-docking method could be used to accurately predict protein binding sites.
To our knowledge the impact on cross-docking binding site predictions of ! 5 the existence of multiple binding sites on the protein surface has never been systematically addressed. However, previous studies using a different approach based on sequence and structure analysis of a single protein, tackled the problem of predicting multiple binding sites for proteins  35, 36. In the cross-docking dataset of 358 proteins used in this work, even if the majority of proteins were associated with only one experimental partner, some multi-interface proteins presenting up to 5 experimental partners were also available. The first aim of our study was to evaluate both the binding site predictions obtained using cross-docking simulations with our dataset and the ability of the cross-docking method to detect multiple binding sites on protein surfaces.
Therefore, we compared the efficiency of two scoring schemes accounting for multiple binding sites. In a second step, we analyzed the cases where cross-docking calculations lead to the identification of alternate protein binding sites that were different from the reference experimental sites based on the interaction partners present in the protein dataset. For the large majority of these proteins, the predicted alternate binding sites were shown to correspond to interaction sites with other partners not included in the original cross-docking dataset. Finally, we tried to understand why some interfaces were better predicted than others using our CC-D results, and why, in some cases, protein-protein docking results could lead to the identification of binding sites for nucleic acids.
Protein dataset The HCMD2 dataset comprises 2246 non-redundant proteins. Among them around 400 are known to be involved in neuromuscular diseases (around 200 experimentally-determined structures and around 200 predicted structural models) and the 1800 remaining proteins, with a role in muscular dystrophy that is yet unknown, are human proteins, notably involved in the pathways monitoring essential cardiac or cerebral mechanisms. The HCMD dataset was constructed with the aim to study the potential interactions of the 400 proteins involved in neuromuscular diseases within the human body, to provide new insights on their molecular mechanism and to help biomedical researchers to develop therapies for neuromuscular diseases (for more information see Section 5 "Proteins list that will be analyzed on phase 2 of the HCMD2 project" in http://www.
ihes. fr/~carbone/HCMDproject. htm). Starting from the HCMD2 dataset, we extracted all the proteins for which a complex structure was available in the PDB, and where at least one experimental partner is also present in the dataset, i. e. proteins corresponding to different chains from the same Protein Data Bank (PDB)  37  structure. Any further reference to these proteins uses the PDB code of the experimental structure from which they were extracted, followed by their corresponding one letter chain denomination. For example, chain F of the 1LI1 PDB structure presented in the Results section will be denoted 1LI1_F. Within the 399 proteins originally extracted from the HCMD dataset, only 358 proteins, coming from 138 unique PDB structures, were used for this study after a quality control step, and constitute the SubHCMD dataset (listed in Table  S1 ).
It is to note that all the proteins are in the bound form. The SubHCMD dataset protein sizes range ! 7 from 21 to 789 residues, with a median value around 150 residues per protein. In the quality control step, alpha-carbon only structures, proteins with no interaction with other monomers of the same PDB present in the HCMD dataset and proteins with missing docking results were excluded. The large majority of proteins was associated with only one experimental partner but for some proteins over 5 experimental partners were available in the dataset (see Table  S1  and Figure  S1 ). Among the 358 proteins of the SubHCMD dataset, 16 proteins are antibody monomers, 6 are antigen monomers, 82 are enzyme monomers, 8 are inhibitor monomers and 246 are classified as « other ».
Systematic docking simulations Our systematic rigid body docking algorithm MAXDo (Molecular Association via Cross Docking) was derived from the ATTRACT protocol  38  and uses a multiple energy minimization scheme. For each pair of proteins within the SubHCMD dataset, the first molecule (called the receptor) is fixed in space, while the second (termed the ligand) is used as a probe and placed at multiple positions on the surface of the receptor. The initial distance of the probe from the receptor is chosen so that no pair of probe-receptor pseudoatoms comes closer than 6 Å. Starting probe positions are randomly created around the receptor surface with a density of one position per 70 Å 2. The same protocol is then repeated for the ligand protein. For each pair of receptor/ligand starting positions, different starting orientations were generated by applying 5 rotations of the gamma Euler angle defined with the axis connecting the centers of mass of the 2 proteins (Figure  S2a ).
Computational implementation Each energy minimization for a starting configuration of a pair of proteins typically takes 5 s on a single 2 GHz processor. Between 313 and 2552230 minimizations are needed to probe all possible interaction conformations (with a mean value of 61564 minimizations). Therefore, a CC-D search on the SubHCMD dataset, involving namely = 64261 receptor/ligand pairs, would require several thousand years of computation on a single processor. However, since each minimization is independent of the others, this problem belongs to the « embarrassingly parallel » category and is well adapted to multiprocessor machines, and particularly to gridcomputing systems. In the present case, our calculations have been carried out using the World Community Grid (WCG, www. worldcommunitygrid.
org) during the second phase of the Help Cure Muscular Dystrophy (HCMD2, https://www. worldcommunitygrid. org/research/ hcmd2/overview. do?language=en_US) project. It took approximately three years to perform CC-D calculations on the complete HCMD dataset of 2246 proteins. More technical details regarding the execution of the program on WCG can be found in Ref. 45.
For each protein pair in the benchmark, between 1 and 10134 docking poses were kept using the 2. 7 kcal. mol -1 energy criterion, with an average of 73 docking poses (Figure  S3a ), and a median value of 26 docking pose kept. These low statistics on each individual protein pair are compensated by the fact that every protein was docked with 358 different partners. Eventually, for each protein in the dataset, between 1293 and 241367 docking poses were used to calculate the residues PIP values, with an average of 25968 docking poses and a median value of 17136 docking poses (Figure  S3b ).
Individual ROC curves and individual AUC values can be derived from the study of a single protein, with the TP, TN, FP and FN values being calculated ! 13 considering only the surface residues of the query protein. For example, an individual ROC curve can be plotted for the 1LI1_F protein, the sensitivity and 1-specifity values being computed on the 177 surface residues of the 1LI1_F protein and the corresponding individual AUC value is equal to 0. 360 (BI score without using alternate interfaces). In addition, a general ROC curve and a general AUC value can be computed for the complete SubHCMD dataset, with TP, TN, FP and FN values being processed using all the 50718 surface residues from all proteins in the SubHCMD dataset.
Specific case of protein with multiple experimental partners In a simple case of binding site prediction evaluation, the studied protein presents only one experimental partner, and thus one reference interaction site. The evaluation of the ability of the method to detect this interface then comes down to comparing the predicted interface to the reference experimental one. However, the dataset used in this study includes protein presenting more than one experimental partner and thus more than one interaction site. To evaluate our binding sites predictions while accounting for multiple experimental binding sites, we compared the efficiency of 2 scoring schemes. The first score, called Global Interface (GI) score, was obtained by comparing the predicted interface with one single global reference experimental interface generated by concatenating all the available experimental interfaces (Figure  S4a ).