dataesr/scientific-paragraphs-categorization huggingface dataset:
We next discuss the technical limitations of the proposed model. First, node and edge labels are the only metadata included in our model. There is a lot of additional metadata in EHR systems (for example, lab data including values and units) that is of interest for analysis. Such data is represented as key/value pairs on nodes and edges in our graph model. Our generator is easily extended to node and edge attributes by coupling a multi-layer perceptron (MLP) to the model once the node type has been determined. Second, the present version of the model assumes that Encounter nodes are connected to only one type of Condition or Medication node. In practice, there can be more than one on the same node (if, for example, the patient is administered the same medication multiple times in the same encounter).
ACKNOWLEDGEMENTS M. V. is partially supported by the " Wallenberg AI, Autonomous Systems and Software Program " (WASP). M. L. is partially supported by  AIR Lund  ( Artificially Intelligent use of Registers at Lund University ) research environment and received funding from the  Swedish Research Council  (VR; grant No. 2019-00198 ). G. N. is supported by the  French National  research agency via the  AML-HELAS  ( ANR-19-CHIA-0020 ) project.
It is an over-set of JavaScript that adds additional features, such as static typing and object-oriented features, to enhance the development of large-scale JavaScript applications  [11] ,  Socket. io is a JavaScript library that allows developers to create real-time twoway web applications. It offers an efficient alternative to traditional HTTP connections, which only allow one-way communication between the client and the server  [12] ,  PeerJS is an open-source JavaScript library that facilitates peer-to-peer communication using the WebRTC (Web Real-Time Communication) protocol. It allows direct connections between web browsers without the need for an intermediate server. The main objective of PeerJS is to simplify the implementation of communications between users of a web application  [13] ,  [14] , 3.
To date, 2 retrospective studies have shown that statistical machine learning and deep learning can improve or outperform rule-based algorithms  [51, 52]. Further validation and impact studies are needed to improve the current dysfunctional EMD triage, and AI should be considered for enhancing the dispatch algorithms. Start-up companies are making proposals to help reduce response times and ensure data transmission from connected devices before or during calls. For example, the RapidSOS system is an emergency response data platform that securely links data from connected devices and sensors directly to first responders during emergencies. Another promising system provided by the Israeli start-up MDGo is the use of advanced AI technology to help dispatchers know if a car accident requires an ambulance.
ED Registration and Redirection Whether generated from a symptom checker with a self-triage step, from a call to an EMD center, or a connected device, all collected data concerning patients could benefit EDs. Linking emergency medical services to ED data allows a continuum of care assessment and improvement in patient outcomes  [53]. Concerns regarding interoperability, security, accurate patient match algorithms, and the reliability of wireless networks as potential barriers to adoption were identified in a review conducted by Martin et al  [54]. Several studies have demonstrated the feasibility of various statistical models for electronic health record (EHR) linking with EMD systems  [54]. For example, Redfield et al  [55]  used logistic regression to link Boston's EMD electronic patient care reports with their hospital EHR and achieved an unprecedented success rate of linkage without manual review (99.
The Massachusetts Institute of Technology clinical machine learning group, led by Gopinath et al  [46] , developed a tool called Medknowts that aims to autocomplete clinical terms in the EHR while note-taking. This tool was assessed in a real ED environment and showed a 67% reduction in the keystroke burden of clinical concepts  [46]. The model used is fully disclosed and is based on a shallow dual branch neural network for a minimal latency (time taken to process 1 unit of data) of approximately 0. 2 milliseconds. In addition, MedKnowts allows the retrieval and display of context-specific information from a patient's EHR while unifying the documentation and search process  [73]. However, the language aimed to be autocompleted with these systems is strictly medical and does not reflect the reality of clinical notes containing both nonmedical and medical concepts.
Transparency, Accountability, and Liability In the interest of patient safety and trust, a certain amount of transparency must be ensured. Transparency reflects the extent to which information about an AI system or application is available to individuals. Its scope ranges from design decisions to training data, the structure of the model, its intended use case, and how and when deployment or end-user decisions were made and by whom. Transparency and participation can be increased by the use of open-source software for the underlying design of an AI technology or by making the source code of the software publicly available (eg, Babylon Health). However, there may be some legitime issues related to intellectual property protection  [131]. The use of AI technologies in health care requires the assignment of responsibility within complex systems in which responsibility is distributed among different actors.
Explainability and Interpretability Explainability refers to a representation of the mechanisms underlying the operation of an algorithm or model, whereas interpretability refers to the meaning of an AI system's output. Laws and regulations such as the European General Data Protection Regulation (GDPR) state that automated (or guided) decision-making should come along with the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject (Article 13{2}). When considering the possible application of emotion detection in voice during emergency calls to detect urgent conditions, the transparency and explainability of an AI solution is challenging. In emergency situations, the time requirements and explanation details collide. Thus, information regarding the outputs of an AI application should be meaningful and straightforward.
A virus particle, known also as a virion, consists of a nucleic acid (DNA or RNA) that is surrounded with a protective coat referred to as a capsid. The first structural studies of such viral particles were possibly conducted by Bernal and Fankuchen in 1938, using crystallographic data to show that the Tomato Bushy Stunt Virus (TBSV) crystallizes in a body-centered cell structure  [25]. Their analyses were based on powder photographs instead of single crystals. It was only in the 1970s that crystallographic experimental techniques and associated computing methods enabled atomic-level resolution of whole viral particles. One of the first of such structures was of TBSV at 5. 5 Å resolution  [26]. This structure was deposited in the then-recently assembled Protein Data Bank (PDB)  [27, 28]. Since then, the number of virion structures in the PDB as well as the number of proteins expressed by viral genomes has increased drastically (see for example  [29]  for a review of icosahedral virus structures in the PDB  [30] ).
To help virologists with exploring those structures, Reddy and colleagues developed a virus particle explorer, VIPER  [31] [32] [33] [34] [35]. There are five main geometries for virions: helical (such as tobacco mosaic virus and inovirus), icosahedral (see  [30]  for a full review), prolate (often found in bacteriophage), enveloped (in which the capsid is enveloped by a modified form of a cell membrane, found in influenza viruses, HIV, and coronaviruses, for example), and complex (i. e. , different from the other types). Similarly, the proteins of the envelope of those viruses adopt many different folds  [36] [37] [38]. The PDB and VIPERDB  [34] , the database associated with VIPER, have proven to be essential resources for understanding virions and viral protein structures. The experimental resolutions of such structures remain, however, a significantly time-consuming and expensive task.
e. , membrane) protein. The high resolution structure of several WNV viruses are available in the Protein Data Bank. Note that most structural information available only includes the outer shell, as it is difficult to observe the lipid bilayer membrane and the nucleocapsid, although recently, a cryo-EM model of the capsid of immature Zika virus was described  [109]. Here, we focus on the structure of the Kunjin virus, a subtype of WNV endemic to Oceania. The all-atom, high resolution structure (3. 1 Å) derived from Cryo Electron Microscopy (cryo-EM) of the outer shell of the virus is available in the PDB with the identifier 7KVA  [110]. A cartoon representation of this structure is given in Figure  2A. Each asymmetrical unit contains 3 E proteins and 3 M proteins, with a total of 1728 residues (3 × 501 E protein residues and 3 × 75 M protein residues) that include 12,835 atoms.
Full Viral Envelope We analyze the geometry of the WNV-K virus outer shell from the PDB file 7KVA using our program UnionBall  [103]. It takes a total of 10 s on an AMD Threadripper multicore CPUs running at 2. 2 GHz, with 32 cores (64 threads) to fully characterize its outer shell, i. e. , to compute the Delaunay triangulation, extract the dual complex, and find the pockets for the union U W NV of 770,100 balls that represents this outer shell. Note that the Delaunay computation is the dominant part of the overall computing cost, taking 5. 3 s. As computing the Delaunay is mostly sequential, the whole calculation does not benefit from the multiple cores. Each ball in U W NV is assigned a radius set to the vdW radius of the corresponding atom, to which we add 3 Å (approximately twice the radius of a water molecule).
The E Protein-M Protein Complex The E protein monomer of flaviviridae viruses comprises three domains, I, II, III, and a transmembrane segment, itself composed of a stem and a transmembrane element, TM. Domain I serves as a scaffold to the global organization of the E protein structure, domain II includes the dimerization interface and two glycosylation sites, and the peptide of fusion with the cellular membrane, while domain III is compact and immunoglobulinlike  [112, 113]. E proteins form dimers on the outer shell of mature flaviviridae viruses. The protein M is located underneath the E protein. We repeated the analysis described above on a single copy of the E protein-M protein complex from two viruses, the WNV Kunjin viruses already analyzed above (PDB code 7KVA), and of the chimeric Binjari virus-Dengue virus type 2 (bDENV2).
Application of UnionBall to Modeling: A Toy Problem on the Capsid Protein of Flaviviruses As mentioned above, detecting and measuring internal cavities of biomolecules is often performed as a first step for drug design. Here, we demonstrate how UnionBall can be used to perform such as task on a protein structure available in the PDB dataset and how this information can be used for a protein whose structure is not yet known. The genome of a flavivirus encodes for a large polyprotein. This protein is cleaved by host and viral proteases to generate three structural proteins, the C or core protein, the prM or membrane protein, and the E or envelope protein, as well as several nonstructural (NS) proteins. The geometries of the E protein and prM protein were described above. Here, we are concerned with the C protein that is used by the virus to form a spherical or isometric nucleocapsid or core to encapsulate its genome.
A structure exits for such a tetramer, under the PDB code 1SFK  [114] ; however, it is missing a large portion of the N-terminal region of two of its monomers, a region that is unfortunately part of the interface between two dimers where the inhibitor binds. To circumvent this problem, we used ColabFold, the open interface to AlphaFold2  [119] , to generate five models of the full structure of a C protein dimer. We compared all five models to the conformation of the incomplete C protein dimer in 1SFK: all those models show remarkable resemblance to the experimental structure, with RMS deviations in the range 0. 7 Å to 0. 9 Å over 717 atoms (RMS calculations were performed using the "align" function of Pymol  [111] ). In Figure  5A , the superposition of model 3 with the experimental structure 1SFK is shown.
Examples We have implemented the algorithm described above in the program NormalModes  [141, 153]. We used it to analyze the dynamics of the outer shell of WNV-K, whose geometry is studied above. We use the Go potential to represent the energy of the molecular system. As the Go potential is computed from the Cα atoms, we isolated those from the PDB file 7KVA. We excluded the M protein and only considered the E protein. The Go potential was parametrized as follows: K r = 100 , K θ = 20 , K φ1 = , K φ3 = 0. 5 , with = 0. 36. In addition, the nonbonded term V nb is computed over all edges of the Delaunay complex associated with the Cα atoms of the outer shell. We refer to the union of those edges as the Elastic Network (EN) of the virus outer shell. We considered the E protein in four different environments: isolated, MONO, corresponding to chain A in the asymmetric unit of 7KVA, as a dimer, DIMER, corresponding to chain A in the asymmetric unit of 7KVA, within a raft, RAFT, and within the whole outer shell structure, FULL.
Computing Time for NormalModes The procedure described above and implemented in the program NormalModes was developed to enable the analysis of the dynamics of large molecular systems, including viruses on standard desktop computers. To ascertain that this is indeed the case for NormalModes, we measured the computing required to up to 2000 normal modes for the whole outer shell of WNV-K. As described above, this outer shell is large, including more than 90,000 residues. Using the Go potential, this means that we need to perform a normal mode calculation over 90,000 atoms. In theory, the corresponding Hessian for the Go potential is a matrix of size 270,000 × 270,000, whose storage would require more than 580 GB of memory, a number that is not compatible with a desktop computer. We have shown, however, that there are ways to circumvent this issue.
System Setup The coordinates of the atoms of the solute as well as their partial charges are read from a single file with the PQR format. PQR files can be readily generated using the service PDB2PQR  [194]. For all examples described below, we used the AMBER parameter dataset to assign charges. The PQR file is then modified to add hydrophobic "charges" to selected atoms. All atoms identified as aliphatic carbon, extended carbon, with 1, 2, or 3 hydrogens, ring carbons, and sulfur atom with one hydrogen were assigned a nonzero hydrophobic charge of +1. The HDPBL system of equations include 12 parameters: the numbers of vertices in the mesh in each dimension, the lattice size a, the temperature T, the concentrations of water c w , ions, c s , and hydrophobic probes, c h , the valence z of the anions and cations from the salt, the strength of the water dipole, p 0 , and the parameters of the Yukawa potential, l Y (= βw Y ) and l h (= 1/κ Y ).
0 Å resolution, in the presence of sinefungin (SIN), a hydrophobic inhibitor (PDB code 3LKZ  [197] ). We analyzed the geometry and energetics of WNV Mtase using UnionBall and AquaVit, respectively. The PDB structure 3LKZ corresponds to this protein bound to a hydrophobic ligand, SIN. All ligands, ions, and crystallographic water molecules were removed prior to running UnionBall and AquaVit. UnionBall only includes one parameter, the radius of the water probe. We set it to 3. 0 Å, to detect deep pockets within the protein. As explained above, AquaVit involves 12 parameters. Those were set as described. In Figure  11 , we show the resulting pockets detected by UnionBall and hydrophobic particle occupancy map detected by AquaVit, superimposed on the PDB structure of WNV Mtase, with and without the ligand.
Conclusions Viruses are pathogens that raise serious threats for human health: the recent SARS-CoV-2 pandemic can unfortunately attest to this statement. Fortunately, our knowledge of those pathogens is improving. In particular, we have more and more structural information on virions (the complete, infective form of a virus that includes its genomic material and surrounding envelope) and on their gene products. This information is available in the PDB database (see, for example, Ref. [30]  for a review on icosahedral virus structures in the PDB). It is important to adapt the tools and software platforms to enable the analyses of such structural information on very large macromolecular assemblies. In this paper, we have reviewed some of those methods. We focused on understanding the geometry of virions and viral structural proteins, on their dynamics, and their energetics, with the ambition that this understanding can help design antiviral agents  [199].
We have discussed those methods in light of the specificities of those structures, mainly that they are very large. We focused three of our own methods that can analyze the geometry, dynamics, and energetics of such structures within computing times that are compatible with the use of regular desktop computers. We provided (brief) explanations of the theories behind them and described the corresponding software platforms, i. e. , UnionBall, NormalModes, and AquaVit. Those programs are available in open source format at the address https: //www. cs. ucdavis. edu/~koehl/Projects/. We showed examples of their applications on the outer shell and some structural proteins of the West Nile Virus (WNV). The main assumption behind all the analyses described in this review is that structural information is available on the virus that is the target for drug development.
Indeed, experimental structural information on virions is becoming widely available, as mentioned above. However, it remains that such information may sometimes be still insufficient. The recent successes of AlphaFold  [39]  and its successor AlphaFold2  [40] , two softwares that are designed to predict the structure of a protein from its sequence only, raises hope that experimental structure determination will not be a bottleneck from antiviral drug developments. The successes of using AlphaFold2 for drug discovery, however, are currently limited [44], but provide hope for better computational drug design  [45]. In this review, we treated geometry, dynamics, and energetics independently: this is clearly a limitation that needs to be addressed. For example, AquaVit, our program for analyzing the environment of a protein is fast, and as such, compares favorably with the ligand mapping in molecular dynamics simulations that have been designed for detecting and characterizing binding sites in proteins.
Figure5. Modeling the interaction of the inhibitor ST148 [118]  with a tetramer of C protein from WNV. (A) Superposition of the experimental structure of a C protein from WNV (PDB code 1SFK, [114] ), in magenta, with the third model generated by AlphaFold2 using ColabFold [119] , in wheat color. The third model is the closest to the experimental structure, with an RMS of 0. 71 Å. (B) Superposition of the AlphaFold2 tetramer model 3 of the C protein from WNV (see text for detail), in wheat, with the experimental structure of the equivalent tetramer of DENV-2 [116], in magenta. The overall RMS is 1. 2 Å. (C) The largest pocket (red) superimposed onto model 3 for the WNV tetramer of C protein. The putative position of the inhibitor ST148 is shown using a ball model in cyan. (D) The two largest pockets (red) superimposed onto model 4 for the WNV tetramer of C protein.
Motivation: In population genetics, the detection of genomic regions under positive selection is essential to understand the genetic basis of locally adaptive trait variation. We propose a principled approach to detect those regions that combines a robust moment based F ST estimator with a segmentation algorithm. Results: Our approach allows for pairwise comparisons of populations and does not require any prior knowledge about the size of the regions to be detected. The procedure runs within seconds even for large genome datasets with millions of SNPs, and provides a complete landscape of the F ST distribution over the chromosome. The procedure comes with a grounded estimator of the baseline F ST level, allowing the detection of regions exhibiting high departures from this reference value.
WLS criterion optimization For a given D the number of possible segmentations in D is large: n-1 D-1 but several efficient dynamic programming algorithms already exist to solve the optimization problem efficiently  (Rigaill, 2015; Maidstone et al. , 2017; Haynes et al. , 2017; Runge et al. , 2020). We first adapted the pDPA algorithm of  Rigaill (2015)  to take into 4/15 account the weights. As this adaptation was rather straightforward we do not describe it here, but the code is available on the fpopw package on the CRAN. The pDPA algorithm recovers C D 1:n for all D in between 1 and a user defined D max on average in O(D max n log(n)) time. Its space complexity is in O(D max n) which is problematic as we want to consider rather large D max (i. e. 10 2 -10 3 ) for n typically of order 10 5 -10 6.
Model selection To select the number of segments we use the penalized LS criterion proposed by  Lebarbier (2005) , that has been shown to have good performances in a recent simulation benchmark  (Fearnhead and Rigaill, 2020). The penalty of  Lebarbier (2005)  has the following form pen(D) = constant * (2 log(n/D) + 5D), where the constant is related to the variance of the signal and needs to be calibrated from the data. For this calibration we followed the strategy proposed by  Lebarbier (2005)  and implemented in the segmentor3isback R package  (Cleynen et al. , 2014). Additionally, we also implemented the penalty proposed by  Yao (1988)  which is linear in the number of changes: pen(D) = 2D log(n)σ 2. As the variance σ 2 is unknown it must be estimated, using e. g. the estimator proposed in  Hall et al.
In some applications populations may be gathered into "super-" or "meta-" populations, i. e. subsets of populations defined from prior knowledge. For instance the 1000 Genomes dataset  (Consortium et al. , 2010 ) collects information about 26 human populations that have been divided into 5 super-populations (African,Admixed American, East Asian, European and South Asian). One may then be interested in comparing two subsets of populations rather than analyzing the whole set. In such cases the previous procedure can be adapted as follows: rather than considering all pairwise segmentations one can focus on pairwise segmentations contrasting a population from subset 1 with a population from subset 2, then apply the rest of the procedure as previously described. This application case is illustrated in the Results section.
Sheep dataset The Sheep dataset includes 507 individuals from 23 domestic sheep populations raised in France, genotyped at 527,823 biallelic markers spread over the 26 autosomal chromosomes. The data are described in  Rochus et al. (2018) , where a selection sweep detection was performed using the FLK  (Bonhomme et al. , 2010)  and hapFLK  (Fariello et al. , 2013)  methods. The different aspects of the methodology presented in section 2 are illustrated through 3 different applications: a simple pairwise comparison between 2 populations, a contrast between two sets of populations, and the global analysis of the full dataset.