dataesr/scientific-paragraphs-categorization huggingface dataset:
2. The first activity (I) is to improve annotations of protein structures, in particular to bring 3rd party annotation resources into the PDBe-KB (knowledge-base) via a standard data exchange format (e. g. , json-based formats). These will include functional site annotations, predicted structures, and impact of disease-associated variants on structure. This is appealing to me, since my group generates these kinds of annotations, for example on antibodies, kinases, and protein-protein interactions. Access to these annotations via the PDB would greatly increase the impact of such resources and the depth of annotation of PDB structures. One example that is already in practice is the import of QSBIO and 3Dcomplex annotations for protein assemblies of X-ray structures in the PDB. Activity II is focused on integrating and benchmarking software for modeling proteins in 3D, including in particular modeling or annotating protein assemblies and conformational flexibility.
The inclusion of experimentally determined non-active compounds (for the construction of negative data sets) is an important suggestion in the paper. Another goal is to "open the world of structure-based modeling to the broader science community. " Worthy, but easier said than done, and it is not clear what this would look like exactly. Activity IV is focused on refinement of experimental structures, structural bioinformatics, structure prediction, and benchmarking studies of nucleic acid structures, which, as the authors point out, lag behind comparable efforts on proteins. As with the other Activities, the efforts will be on establishing benchmarks, collecting software tools, and enhancing interoperability. The authors explicitly propose bringing additional research groups outside of 3D-Bioinfo into Activity IV, presumably including those outside of Europe, which would greatly strengthen all of the activities of 3D-Bioinfo.
Specific comments: From the scientific point of view: Limits of structural data. Structural data is very important for many purposes, and the authors make this point clearly. However, it has also limits which should be spelled out clearly to avoid the impression of being a panacea. One limit is the availability of experimental data, with some molecules defying structural characterization. A more specific limit is also related to intrinsically disordered proteins (IDPs). This is a different (sub-)field which is not covered by 3D-Bioinfo and has spawned a separate ELIXIR user community with different priorities dictated by the lack of structural data.
From the ELIXIR point of view: Platforms. The description of interactions with ELIXIR platforms appears to denote a somewhat cursory knowledge of their activities. The interaction with OpenEBench is well described and convincing. The Data platform description is good for PBDe-KB but after this does not appear related to platform activities. The Compute platform description is also unclear. In all cases, the question is: how will 3D-Bioinfo make convincing use of platform services? Other user communities. A more thorough description of synergies with existing ELIXIR user communites would be welcome. This should also be separated from non-ELIXIR initiatives (e. g. Instruct, iNEXT, etc. ). Of note, the IDP user community is already established and not proposed. Likewise, "Synthetic Biology" should probably be Microbial Biotechnology.
Minor points: The manuscript is somewhat redundant and a bit lengthy at 16 pages without references. Streamlining it would improve focus and can contribute to mitigate some of the concerns above. The text refers extensively to "Europe" and "European" resources, ca. 60 times throughout. While this is a truism for ELIXIR, it sometimes feels just a bit too much emphasis in its present form. Reviewer Expertise: bioinformatics, computational biology, structural bioinformatics, intrinsically disordered proteins, databases, tools, infrastructure I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. "The first workshop of the Activity IV is going to take place in May 2020" => This should be updated "The above mentioned themes will not only form the initial focus for 3D-Bioinfo but the steering committee will actively monitor the emergence of new technologies and/or new research fields relevant for bioinformatics approaches, which then can be fostered further as new activities.
e. CASP27, CAMEO28 and CAPRI29,30 [ ) ] for assessment of the prediction " "Below we detail{,} the specific thematic aims of each Activity" "We will bring together the community experts to define data standards for different types of annotations [,] and integration of these annotations using a community-driven data exchange format will facilitate finding," "Extend the content of OpenEBench by adding {a} knowledge base integrating software tools for modeling the 3D struc-ture of proteins, protein complexes and assemblies. " "methods for generating[,?] scoring, and ranking models of protein complexes" "Benchmarking studies performed so far [-] including those carried out in the CSAR52 or D3R Grand Challenge53 (. ) -rely on datasets of limited size and diversity (…). " "Developing benchmarks dataset[s] / Developing [a] benchmarks dataset" "In order to foster new developments[,] focused courses/workshops will be organised on such topics" " exploit ELIXIR{s} platforms " Is the topic of the opinion article discussed accurately in the context of the current literature?.
/doi. org/10. 12688/f1000research. 20559. 1 22 Apr 2020, (ELIXIR):278 Latest published: 9 https://doi. org/10. 12688/f1000research. 20559. List of abbreviations 3D-Bioinfo: name of the ELIXIR Community of structural bioinformatics BioExcel: Center of excellence for biomolecular research Biomedinfra: authenticaton and authorisation infrastructure (ELIXIR AAI) of ELIXIR Finland CAMEO: Continuous Automated Model Evaluation CAPRI: community-wide experiment on the comparative evaluation of protein-protein docking for structure prediction CASP: critical Assessment of protein structure prediction ChEMBL: a manually curated database of bioactive molecules with drug-like properties COOT: crystallographic object-oriented toolkit, a graphics for refinement of experimental biomolecular structures COST: (European) cooperation in science and technology EMDB: Electron microscopy data bank ELIXIR: intergovernmental organisation that brings together life science resources from across Europe EM, cryo-EM: electron microscopy, cryo-electron microscopy EOSC-Hub: European Open Science Cloud EU-OPENSCREEN: integrates high-capacity screening platforms throughout Europe FAIR: data which meet principles of findability, accessibility, interoperability, and reusability FARFAR: fragment assembly of RNA with full-atom refinement FARNA,: fragment assembly of RNA Instruct, Instruct-ERIC: pan-European research infrastructure in structural biology MD: molecular dynamics microED: electron micro-crystallography MMB: MacroMolecularBuilder.
A program suite for macromolecular modelling MX macromolecular X-ray crystallography NMR: nuclear magnetic resonance, a spectroscopic method OpenEBench: infra-structure designed to establish a continuous automated benchmarking system for bioinformatics PDB: Protein data bank PDBe-KB: Protein data bank in Europe -knowledge base PDB-Redo: procedure to optimise crystallographic structure models Phenix: software suite for the automated determination of molecular structures PHYRE: automatic fold recognition server for predicting the structure and/or function of the protein sequence Proteopedia: wiki and 3D encyclopedia of proteins and other biomolecules Pubchem: database of chemical molecules and their activities Refmac: program for refinement of experimental structures of biomolecules RNA Puzzles: collective experiment for blind RNA structure prediction SAXS: small angle X-ray scattering SBDD: structure-based drug design Swiss-model: structural bioinformatics web-server dedicated to homology modelling of 3D protein structures TeSS portal: ELIXIR's training portal UniProt: Universal protein esource, a comprehensive resource for protein sequence and annotation data Web-Beagle: web server for the alignment of RNA secondary structures.
the topic of the opinion article discussed accurately in the context of the current literature? Yes Are all factual statements correct and adequately supported by citations? Yes Are arguments sufficiently supported by evidence from the published literature? Yes Are the conclusions drawn balanced and justified on the basis of the presented arguments? Yes No competing interests were disclosed. Competing Interests: Reviewer Expertise: computational structural biology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. 09 June 2020 Reviewer Report https://doi. org/10. 5256/f1000research. 22602. r63141 © 2020 Dunbrack R. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited.
22 May 2020 Reviewer Report https://doi. org/10. 5256/f1000research. 22602. r62621 © 2020 Tosatto S. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited. Silvio Tosatto Department of Biomedical Sciences, BioComputing Laboratory, Padova, I-35121, Italy The manuscript by Orengo et al. describes the proposed activities of the ELIXIR 3D-Bioinfo user community. This is a useful and necessary contribution to describe the scope and plans of a new ELIXIR user community. The introduction provides a recapitulation of the uses and relevance of structural bioinformatics in different contexts. As the authors note, this is a mature field where several key data resources and initiatives alredy exist.
1 v1 1 /doi. org/10. 12688/f1000research. 20559. 1 22 Apr 2020, (ELIXIR):278 Latest published: 9 https://doi. org/10. 12688/f1000research. 20559. List of abbreviations 3D-Bioinfo: name of the ELIXIR Community of structural bioinformatics BioExcel: Center of excellence for biomolecular research Biomedinfra: authenticaton and authorisation infrastructure (ELIXIR AAI) of ELIXIR Finland CAMEO: Continuous Automated Model Evaluation CAPRI: community-wide experiment on the comparative evaluation of protein-protein docking for structure prediction CASP: critical Assessment of protein structure prediction ChEMBL: a manually curated database of bioactive molecules with drug-like properties COOT: crystallographic object-oriented toolkit, a graphics for refinement of experimental biomolecular structures COST: (European) cooperation in science and technology EMDB: Electron microscopy data bank ELIXIR: intergovernmental organisation that brings together life science resources from across Europe EM, cryo-EM: electron microscopy, cryo-electron microscopy EOSC-Hub: European Open Science Cloud EU-OPENSCREEN: integrates high-capacity screening platforms throughout Europe FAIR: data which meet principles of findability, accessibility, interoperability, and reusability FARFAR: fragment assembly of RNA with full-atom refinement FARNA,: fragment assembly of RNA Instruct, Instruct-ERIC: pan-European research infrastructure in structural biology MD: molecular dynamics microED: electron micro-crystallography MMB: MacroMolecularBuilder.
For analyzing limb movements during optogenetic activations, animals were placed in a custom transparent open-field (40 3 40 cm) and filmed from below at 100 images/sec (Jai GO-2400-USB). For optogenetic activations, open-field recording sessions lasted for a maximum of 30 min per animal. For silencing and ablation experiments, several 3-min videos were recorded per animal. For analyzing snout motion, animals were head-fixed for a maximum of 10 min, and filmed from above at 400 images/sec (Jai GO-2400-USB). Images were streamed to disk on a computer using 2 nd Look (IO Industries). Timings of photo-activations were recorded using the TTL output of the laser connected to a National Instruments acquisition card (USB-6211) and the LabScribe NI software (iWorxs). Both recordings were synchronized using hardware trigger.
Custom scripts were written in MATLAB (Mathworks) and used for computation. The orientation of the animal is defined as the angle a between the vector from T to the median point of E L and E R (E M ) and the origin axis x ! (Figure  S2B ). TE M ! kTE M ! k = X 1 Y 1 X 1 Y 1 = cosðaÞ ÀsinðaÞ sinðaÞ cosðaÞ Ã 1 0 amod360 = signðY 1 Þ Ã cos À1 ðX 1 Þ The head rotation is defined by the angle b between the vector from T to E M and the vector from E L to E R. A bias of 90 is introduced to set the zero when the 2 vectors are orthogonal, resulting in a positive angle when the head turns to the left and a negative angle when the head turns to the right (Figure  S2C ). E L E R ! kE L E R ! k = X 0 Y 0 X 1 Y 1 = cosðbÞ ÀsinðbÞ sinðbÞ cosðbÞ Ã X 0 Y 0 bmod360 = sign Y 1 À ðX 0 X 1 + Y 0 Y 1 ÞY 0 X 0 Ã cos À1 ðX 0 X 1 + Y 0 Y 1 Þ À 90 The barycenter (G) of the 4 POIs was computed to generate the locomotor trajectory of each mouse and its position derived to obtained the instantaneous speed (s) of the animal across the recording (Figure  S2D ).
Calculations of limb velocity To produce color-plots histograms, we attempted to label the 4 paws using DeepLabCut. However, this led to significant number of errors and inversions between paws. All errors were then manually corrected on 9 videos from 3 mice. Points were then filtered (maximum speed and group cutoff followed by linear interpolation) and the instantaneous speed of each limb speed was then computed, and a color matrix produced using MATLAB.
ACKNOWLEDGMENTS This work was funded by an  Agence Nationale pour la Recherche  grant ( ANR-17-CE16-0027 ) to J. B. and by  NeuroPSI ,  CNRS , and  Universit e Paris-Saclay. G. U. holds a  Universit e Paris-Saclay doctoral fellowship. C. H. holds doctoral fellowships from  R egion Ile-de-France  and  Fondation pour la Recherche M edicale. We thank  S everine Diem  for assistance with genotyping,  Tihana Jovanic  for critical reading of the manuscript, and  Beatrice Cerocchi  for the mouse drawings in the graphical abstract. Other schematics are from Scidraw. io.
It is hoped that these studies will allow older adults to receive preventive treatment at the correct institutions and by the appropriate professionals as early as possible, which will prevent loss of autonomy. Altogether, ILM is conceived as an emerging property of databases ("digital twins") and not the reverse. Furthermore, ILM should facilitate a coordinated set of actions by the caregivers, which is a complex challenge in itself. This approach should be gradually extended to all ages, because frailty has no age, as is testified by overwork, burnout, and post-traumatic syndrome.
On the one hand, human groups that are supposedly reasonably homogeneous (for example, stroke patients and airplane pilots, who have been investigated by the current authors) turned out to be extremely polymorphic, with large interindividual variability. On the other hand, the behavior of these people at an individual level tends to be surprisingly stable over time. Several studies undertaken to explore the feasibility of building and mining databases that have been used to implement the ILM of normal and diseased humans will be briefly summarized below. 6] [37] [38] [39] [40] [41]  These studies have confirmed two well-known hypotheses and have opened up a series of questions: • Detecting pre-frailty and frailty at an early stage, in clinics, companies, sports stadiums, and war zones, and so forth, should indeed be based on ILM and personalized treatment.
In short, "tailor-made" solutions are created for each person. • The construction and mining of large databases that bring together behavioral, biological, psychological, economical, and sociological variables collected during ILM will be the key to "maintenance" of an individual as a biological and cognitive machine. These databases will allow for the detection of pre-frailty, and hence allow the prevention of frailty to be maximized. • The construction and mining of these databases for human behavior will provide important political and economic leverage. Hence, the following questions are already arising: Who should control this process: the public sector, the private sector, or both? How can data confidentiality be controlled? What ethical rules should be enforced? How might we tackle the risks surrounding the right of access to personal raw data?.
| Objectives Maintaining robustness of HMCs required three objectives to be fulfilled. The first objective was to quantify their behavior by making measurements in real time and at the correct sampling frequencies. Most importantly, the measurements were noninvasive, or at least minimally invasive. That is, the individuals being monitored were able to continue their in-the-field activities. This was basically an engineering problem and in some cases it was successfully solved (see below). The second objective was to enrich these behavioral databases with a variety of data. This included the evolution of several biological variables during the observation period. 6] [47] [48]  Other variables of interest include psychological (responses to questionnaires), neurocognitive (batteries of tests), and sociological variables; and the genotype.
Also, data can be extracted from GPS, presence detectors, pressure sensors, smart water, gas and electricity meters, and so forth. This is an ongoing study. The third objective underway is to merge these composite databases for the benefit of HMCs. In particular, a complex problem was to determine intra-and interindividual standards for statistical bases in order to understand the fine lines between physiological and pathological states, and reversible and non-reversible states. For instance, it was necessary to be able to predict the risk of falls for persons that had never fallen before. In brief, maintaining robustness requires multidisciplinary teams working together from the outset.
Once the data were recorded, reliable and useful information must have been provided to the persons and those surrounding them (doctors, coaches, social workers, senior staff, etc) almost instantaneously. This was essential if the data collection was to be experienced as a fruitful exchange. Being monitored should not be cumbersome for the subjects due to the installation of IFL on a long-term basis. Until recently, this seemed utopian, but it has become progressively easier to implement IFL with the development of pervasive (or even ubiquitous) computing. In short, the sensors, data collection from the sensors, pre-processing of the recorded information, and transmission to databases can now be integrated into utensils, furniture, doctor's consultation room instruments, athlete's training room tools, and so forth.
| Smart Check Smart Check aims to automatically measure the five criteria of  Fried 7, 8  to assess the degree of robustness of a person in consultation and in the field. It is a portable device that includes a tablet, various questionnaires, five inertial motion units (IMUs), a force platform, a grip pressure sensor, a reference database, and dedicated algorithms hosted on a Cloud server. Smart Check allows the user to obtain a weight measurement, multiple features concerning static equilibrium with eyes closed and eyes open, locomotion, U-turn, head movement, grip force, the sense of fatigue, and an approximate measurement of cognitive function using questionnaires. On paper, the concept was simple. Considerable progress in sensor technology, the IOT, and the construction and processing of databases allows ILM of the robustness to be performed for each individual from age 60 years onward.
| CON CLUS ION These projects focus on collecting quantitative sensorimotor, cognitive, and psychological data to assess frailty. Later genetic and biological data for community-dwelling seniors or HMCs will be collected. It is progressively being learned how to monitor individuals at home and in outpatient clinics, intermediate housing, retirement homes, hospitals, and so forth. To implement ILM, databases have been built and mined in order to quantify normal and pathological human behavior. With appropriate algorithms, faint physiological signals can begin to detect transitions from robustness to pre-frailty states and from pre-frailty to frailty states. It is hoped that these studies will allow older adults to promptly receive preventive treatment at the right institution and from the correct professional in order to prevent loss of autonomy.
Altogether, ILM is conceived in the same way as shown in the emerging study of database properties (ie, to be a digital twin). Furthermore, ILM should facilitate coordinated action of the caregivers (ie, analogous to predictive maintenance), which is a complex challenge in itself. This approach, which has been initiated among communitydwelling seniors, should be gradually extended to all ages, because frailty has no age in this complex society, as is testified by overwork, burnout, and post-traumatic syndrome. Building and mining appropriate databases for ILM requires multidisciplinary teams, which brings together several types of health professionals, neurophysiologists, engineers, and mathematicians, and results in the development of a preventive health policy. Also, these projects call for further development of noninvasive sensors to more accurately capture physiological data.
We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous Segment-and-Decode-based system and reduced the error rate by 20%-40% relative for most languages. Further, we report new state-ofthe-art results on IAM-OnDB for both the open and closed dataset setting. The system combines methods from sequence recognition with a new input encoding using Bézier curves. This leads to up to 10x faster recognition times compared to our previous system. Through a series of experiments we determine the optimal configuration of our models and report the results of our setup on a number of additional public datasets.
Introduction In this paper we discuss online handwriting recognition: Given a user input in the form of an ink, i. e. a list of touch or pen strokes, output the textual interpretation of this input. A stroke is a sequence of points (x, y, t) with position (x, y) and timestamp t. Figure  1  illustrates example inputs to our online handwriting recognition system in different languages and scripts. The left column shows examples in English with different writing styles, with different types of content, and that may be written on one or multiple lines. The center column shows examples from five different alphabetic languages similar in structure to English: German, Russian, Vietnamese, Greek, and Georgian. The right column shows scripts that are significantly different from English: Chinese has a much larger set of more complex characters, and users often overlap characters with one another.