dataesr/scientific-paragraphs-categorization huggingface dataset:
2019). Higher levels of biological organization and in particular the relationships between orthology and interaction networks have been another focus of the meeting discussions. The central premise is that orthologous proteins and genes which interact are likely a part of the same biological process. These interaction networks are often presented as graphs, whose nodes represent proteins and whose edges represent a functional association. Interaction networks encompass a broad variety of interaction types as well as methodologies to find those interactions  (Huang et al. 2018). Several network interaction databases were represented, with talks discussing more precisely the latest improvements in STRING  (Szklarczyk et al. 2019)  , FunCoup  (Ogris et al. 2018)  and KEGG  (Kanehisa et al. 2019 ).
These methods/databases focus on a variety of interaction types, including: protein-protein interactions, gene co-expression, protein-co-expression, genetic interaction profile similarities, shared transcription factor bindings, subcellular colocalization, domain interactions, cellular complex metabolic pathway, or signalling pathway co-membership, and shared genomic contexts such as operons or gene neighbourhoods. Additionally, there are several methodologies to find evidence of interactions. In general, experimental screens and computational inferences are used to populate the networks. These methodologies can range from direct measurements of physical protein-protein interactions, which are then stored in curated online databases, to more indirect methods such as phylogenetic profile similarity, text-mining of published works, or machine learning algorithms to predict protein-protein interactions.
STRING focuses on combining curated experimental databases, text-mining, and computational predictions  (Szklarczyk et al. 2019)  , whereas FunCoup uses only high-throughput experimental data  (Ogris et al. 2018). Orthology is a fundamental tool to relate the interaction information from one species to another. Based on the premise that the interaction among proteins is evolutionarily conserved, the term "interologs" refers to "orthologous pairs of interacting proteins in different organisms"  (Walhout et al. 2000; Yu et al. 2004 ). Simply put, if two proteins have been determined to interact in one species, one can infer that the two orthologs of those proteins in another species also interact. In FunCoup, functional associations from well-studied species are transferred to other organisms using orthologous relations from InParanoid  (Sonnhammer and Östlund 2015)  , while STRING uses hierarchical orthologous relations from EggNOG  (Huerta-Cepas et al.
2016). KEGG uses orthologous clusters, i. e. computationally generated quasi-cliques of bi-directional best hits, a subset of which are manually curated to form  KOs (22, 937 clusters). Roughly half of all 30 million proteins in KEGG have been assigned to KOs. From these orthology groups, KEGG pathway information from the curated molecular networks (KEGG pathway maps, BRITE hierarchies, and KEGG modules) can be assigned to orthologs belonging to the same KO group  (Kanehisa et al. 2019). Orthologs can also be used for not just relating the interaction network from one species to another, but also for comparing different gene interaction networks. For example, ManiNetCluster, a recent computational tool for comparing gene networks, can find functional links from multiple datasets  (Nguyen et al.
2016; Parks et al. 2017)  , raising new challenges as to how to map orthology relationships to this diversity. While single-copy gene trees revealed the overall contours of the tree of life (e. g. the analysis of 16sRNA,  Woese and Fox 1977 , or RpoB genes, Case et al. 2007  ), the phylogenomic approach involving concatenated sets of conserved (or highly conserved) orthologous proteins has aided in the acquisition of higher confidence species trees  (Segata et al. 2013; Asnicar et al. 2020)  , which in turn has major implications for taxonomy  (Parks et al. 2018). Despite this critical need of establishing high-quality orthology prediction to resolve novel clades, semi-automated genome annotation remains the norm for most sequencing projects. De facto , the posterior establishment of high quality gene models and high-quality reference proteomes is critical in the establishment of a reliable and stable orthology database, in particular when future functional annotations will propagate on the basis of these predictions.
g. number of introns, coding phases and lengths). This approach is implemented in SplicedFamAlign, a tool building splice-aware multiple sequence alignments  (Jammali et al. 2019 ). Improving quality with better exon alignments was similarly a focus in the last release of the OrthoMaM database  (Scornavacca et al. 2019). Its construction pipeline relies on the OMM_MACSE alignment pipeline  (Ranwez et al. 2018)  , which limits frameshifts and splicing errors. Another issue was raised by Yuichiro Hara, who identified "elusive" genes in amniotes, e. g. genes characterized by low phylogenetic conservation and lost in many taxa. He emphasized that making more complete reference proteomes is essential to distinguish genuine gene loss from information missing after an incomplete genome assembly or gene annotation  (Hara et al.
2019). For instance, PANTHER infers horizontal transfer events  (Mi et al. 2016)  , and corresponding xenolog pairs, among a set of 142 fully sequenced genomes. Notably, HGTree is the first database dedicated to the detection of horizontal transfer and allows comparison of gene sets to species trees for thousands of bacterial and archaeal genomes  (Jeong et al. 2016). More recently, a formal definition of xenolog classes has been proposed by  Darby et al. , 2017  and implemented in the reconciliation tool NOTUNG  (Stolzer et al. 2012). Primary xenolog, sibling donor xenolog, sibling recipient xenolog and outgroup xenolog are the four proposed classes that reflect the events associated with the divergence of a xenologous gene pair and help to grasp the relative timing of the transfer and speciation events  (Darby et al.
2013  ). In addition, some viral families show specific evolutionary mechanisms such as reassortments (e. g. genome segments shuffling during coinfection), breaking the classical assumption of descendancy. At the same time, the viral pan genome is often considered as the largest genetic reservoir on the planet. When a new virus of previously unknown lineage is discovered, most genes encoded in its genome have no homologs in extant databases. Likewise for metaviromic analysis, metagenomic analysis targeting subcellular fraction often results in 60-90 percentage of reads with unknown origin; significantly higher proportion of those compared to the cellular fraction. Such reads or assembled contigs cannot be properly assigned to its biological origin -whether its host was archaeal , bacterial or eukaryal, and even often not sure whether it was cellular or viral origin -and often such reads are omitted from further analysis  (Roux et al.
2012; Yoshida et al. 2013). These genes of unknown, but putatively of viral origin, are often referred to as the "biological dark matter". Compared to cellular organisms, the tremendous challenge of exploring the viral biosphere clearly shows that building virus orthology models is in its infancy and calls for specific research. To our knowledge, the first resource dedicated to virus orthology was the phage orthologous groups (POGs), a database of bacteriophages orthologs built from simple 3-way reciprocal blast matches  (Kristensen et al. 2013). More recently, the Prokaryotic Virus Orthologous Groups (pVOGs) used a similar approach but enlarged the inference to nearly 3000 bacterial or archaeal hosts  (Grazziotin et al. 2017 ) and came with tools of functional annotations and phylogenetic profiling.
Three generalist orthology databases (EggNOG, OrthoDB, and PhylomeDB) also offer viral orthologs but, similarly to POGs and pVOGs, their inference pipelines were initially developed for eukaryotic and prokaryotic genomes. It appears that the Vipr database (a general resource for virus genomics,  Pickett et al. 2012 ) , is the first resource using an orthology inference protocol developed specifically for viruses. A Domain-Architecture Aware Inference of Orthologs (DAIO), is used to classify viral proteins into "Strict Ortholog Groups" (SOGs), e. g. groups where orthology relationships are confirmed via phylogenetic inference at low taxonomic levels and where domain architecture is conserved  (Zmasek et al. 2019). This approach helped for the dissemination of functional annotation and naming conventions throughout the numerous viral families present in Vipr  (Zmasek et al.
2019). A more recent resource is VOGDB (http://vogdb. org), a database proposing V irus O rtholog G roups (VOGs) inferred from phage and non-phage virus genomes. VOGDB pipeline uses a graph-based approach complemented by filters designed specifically for virus genomes structures  (Kiening et al. 2019 ). Notably, it includes steps of polyprotein re-annotation and post-clustering refinements based on HHalign-KBest  (Yu et al. 2015)  , a hidden Markov model alignment method computing sub-optimal alignments by using structural models, specifically designed for cases of low sequence identity (<35%). The database also provides tools developed for metagenomic applications such as identification of virus-specific markers or identification of orthologous groups which encode essential genes for viral lineages.
fDOG (github. com/BIONF/fDOG), a software package facilitating a targeted ortholog search for individual proteins across large taxon collections in linear time. fDOG is a profile-based ortholog search algorithm (Ebersberger et al. 2009) with the option to compile the training data for pHMM generation iteratively on the fly. fDOG is integrated with an automatic scoring of the pair-wise domain architecture similarities between the seed protein and its orthologs. He demonstrated how orthology-based Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab098/6209019 by guest on 05 May 2021.
Acknowledgments We thank the  National Institute for Basic Biology , the  Japanese Society for Bioinformatics  and the  Daiko Foundation  for supporting the 67th  NIBB  Conference / The 6th Quest for Orthologs Meeting. We wish to acknowledge the following support for attending the meetings and/or writing this manuscript:  EMBO Young Investigator meeting grant  (to C. D. ). Service and  Infrastructure grant  from the  Swiss Institute of Bioinformatics, Swiss National Science Foundation  [183723] (to C. D. ) ;  IdEX Unistra in the framework of the Investments for the future program  of the  French government  (to O. L. and Y N. ). NSF  (Award No. 1724300 ),  KAKENHI  (Grant No. JP18H01325 ) (to S. E. M. ). Support by the research funding program  Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz (LOEWE) of the State of Hessen, Research Center for Translational Biodiversity Genomics (TBG)  (to I.
As such, the commercial interfaces available for use with ROS often rely on the user writing custom programs for each new application. A first contribution of this paper is the evaluation of the open-source control interface based on standard Motoman driver package  [27]  created with the cooperation of Yaskawa Motoman, in order to check its effectiveness for teleoperation tasks. It should be noted that Yaskawa robots are used in different fields such as surgery  [28] , pick and place  [29] , welding  [30] , etc. Recently, NASA selected its Motoman SIA50 robots for teleoperation demonstration  [31]. It successfully confirmed the ability to robotically transfer oxidizer to a satellite valve in flight-like conditions. The standard Motoman driver package uses ROS-Industrial basic nodes and interfaces them with Yaskawa Motoman robot controllers.
The research work in  [32]  proposesda setup of the Motoman SDA_10 dual-arm robot with ROS-Industrial for industrial applications. Real-time response of robotic system and its communication latency analysis leads to better controlling, as confirmed for ROS-controlled KUKA robots in  [33]. This analysis highlights the required extra developments to be carried out. The resulting performance degradation sources are identified: A second contribution of this paper deals with the proposition of an improved version of Motoman ROS-based control suitable for teleoperation tasks. The modifications of the standard Motoman ROS package is proposed to compensate the depicted delays to deal with teleoperation. It manifests in the addition of a new velocity control mode into the Motoman ROS driver. The experimental evaluation of the improved ROS-based control, applied on a 6 Degrees-of-Freedom (DoFs) Yaskawa GP8 robot, teleoperated by a 3DConnexion Space-Mouse Pro device, confirms an improvement on the robot teleoperation performance, in terms of latency and trajectory tracking, which can achieve 43% with respect to the standard control.
• The "Joystick node" allows reading the peripheral components as well as the buttons state. This node corresponds to the "joystick_drivers" metapackage of ROS, which is used for interfacing common joysticks and human input devices with ROS. It provides "joy", "Spacenav_node", "ps3joy" and "wiimote" packages, which are compatible with generic Linux joystick, 3Dconnexion SpaceNavigator 6-DOF joystick, Playstation 3 SIXAXIS or DUAL SHOCK 3 joystick and Nintendo Wiimote, respectively. The piloting mode is supposed to be a mixture between the articular and the Cartesian modes, so that both modes are enabled simultaneously. This mixture is defined by the joystick instruction vector c joy ∈ R m. m is the degree of freedom of the joystick. c joy = [c joy a ; c joy c ] T , where c joy a ∈ R k is the articular displacement instruction, c joy c ∈ R j is the Cartesian displacement instruction and j + k = m.
• The "Motoman ROS node" is the interface between simulation and reality. It is responsible for communicating motion tasks q goal to the robot and retrieving information on its current positions q ∈ R n. This feedback is then transmitted, as q m ∈ R n , to the Gazebo node to perform the servo-control. The ROS and ROS-Industrial Motoman community provides a set of nodes and mechanisms to facilitate the communication with the robot, namely the ROS Motoman driver  [27]. The Motoman driver controller interface was created with the cooperation of Yaskawa Motoman, to provide a more high-performance interface for controlling Motoman robots. The software works on all FS100, DX100, DX200, YRC1000 and YRC1000micro robot controllers. For each communication between the different nodes, we consider that there is an information transmission delay.
Evaluation of Standard Motoman ROS-Based Control for Teleoperation Tasks In this section, the open-source control interface based on standard Motoman driver package  [27] , created with the cooperation of Yaskawa Motoman, is experimentally analyzed in order to check its effectiveness for teleoperation tasks of Yaskawa manipulators. Thanks to ROS, it is possible to tag precisely the times when data is sent and received on all nodes. Response time and tracking delay generated by the standard Motoman ROS-based control are investigated. The evaluation of standard Motoman ROS-based control for teleoperation tasks is performed with an Intel Core i7-6700HQ processor computer. The motion task is done using a 3DConnexion Space-Mouse Pro device, which requires the implementation of "Spacenav_node" from "joystick_drivers" metapackage of ROS.
Performance degradation of teleoperated robot is supposed to be related to software development. Figure  3  presents the Motoman ROS node architecture provided by Yaskawa Motoman documentation  [35]. This node is based on the principle of two-point trajectories. Each trajectory is discretized by the Motoman ROS node and then sent to the robot controller. The MotoRos code then takes care of applying this interpolation between the different trajectory waypoints. The diagrams of Figures  4  and 5  from MotoRos documentation  [35] , describe the Motoman ROS node overall behavior. The ROS node is designed to send trajectories to the robot. Each path is made up of a waypoint sets, which are then packaged into a message sent to MotoROS. The robot is then responsible for making a position control to follow this trajectory.
Conclusions This paper proposed an improved ROS-based control for Motoman Yaskawa robots. A first contribution of this paper deals with the analysis of the opensource control interface based on standard Motoman driver package  [27] , in order to check its usefulness for teleoperation tasks. The effectiveness of this method for robotic teleoperation is discussed in detail. Experimentation depicts non-continuous resulting motion of the robot and non-acceptable time delays for teleoperation. Therefore, a second contribution of this paper deals with the improvement of the ROS-based standard control architecture. Adding a new velocity control mode in addition to the conventional position control one leads to the improvement of the real-time response of the robot, whose motion becomes continuous and smooth.
Acute myeloid leukemias (AMLs) are hematologic malignancies with varied molecular and immunophenotypic profiles, making them difficult to diagnose and classify. Highdimensional analysis algorithms might increase the utility of multicolor flow cytometry for AML diagnosis and follow-up. The objective of the present study was to assess whether a Compass database-guided analysis can be used to achieve rapid and accurate diagnoses. We conducted this study to determine whether this method could be employed to pilote the genetic and molecular tests and to objectively identify differentfrom-normal (DfN) patterns to improve measurable residual disease follow-up in AML. Three Compass databases were built using Infinicyt 2. 0 software, including normal myeloid-committed hematopoietic precursors (n = 20) and AML blasts harboring the most frequent recurrent genetic abnormalities (n = 50).
The diagnostic accuracy of the Compass database-guided analysis was evaluated in a prospective validation study (125 suspected AML patients). This method excluded AML associated with the following genetic abnormalities: t(8;21), t(15;17), inv(16), and KMT2A translocation, with 92% sensitivity [95% confidence interval (CI): 78. 6%-98. 3%] and a 98. 5% negative predictive value (95% CI: 90. 6%-99. 8%). Our data showed that the Compass database-guided analysis could identify phenotypic differences between AML groups, representing a useful tool for the identification of DfN patterns.
One such tool is Infinicyt Compass, which was developed by the EuroFlow ™ (EF) Consortium, and allows for the recognition of complex immunophenotypic patterns through multivariate analyses of flow cytometric data. The present study aimed to assess whether the Compass database analysis could be used to guide the genetic and molecular testing of AML to achieve a rapid and accurate diagnosis and to perform DfN analyses. The results of this study showed that the comparison of new cases against reference databases composed of well-classified AML cases represents a user-friendly method that can facilitate the orientation of genetic and molecular biology testing to achieve a rapid, accurate diagnosis. In addition, the Compass database-guided analysis of MFC data can be used as a nonsubjective method for DfN evaluation.
Construction of the Databases The Compass databases are composed of fcs-exported data files featuring characteristics of leukemic blasts from well-classified AML cases, according to WHO recommendations (1), including t(8;21) AML (n = 8), t(15;17) AML (n = 19), inv(16)/t(16;16) AML (n = 12), and AML with MLL gene translocations (MLL-r AML, n = 11); all samples were diagnosed by the Hematology Laboratory from the University Hospital of Saint-Etienne between 2013 and 2019. Normal myeloid hematopoietic precursor cells (my-HPCs) committed toward a neutrophil lineage (CD45 +low/int CD117 + CD34 +/-HLA-DR +int CD13 + CD14 -IREM2 -CD33 + CD36 -), a monocyte lineage (CD45 +low/int CD117 + CD34 +/-HLA-DR +hi CD64 + CD14 -CD300e[IREM-2] -CD13 + CD33 + ), and an erythroid lineage (CD45 -/+low CD117 + CD34 +/-HLA-DR +low/int CD36 + CD105 + CD33 -CD35 +low ) were also included (Figure  2 ).
The immunophenotypic characterization of my-HPCs using the EF AML/MDS panel was performed in the present study using normal BM samples obtained from healthy individuals with normal blood counts (eleven healthy BM donors (HDs) and nine patients undergoing sternotomy for cardiac surgery (CS)) from the University Hospitals of Saint-Etienne and Clermont-Ferrand, France. The strategy used for the selection of normal my-HPCs was based on recently published data  (5, 6). Pregating for intact singlets, followed by the discrimination of normal my-HPCs, was performed using the Infinicyt 2. 0 software based primarily on the backbone markers CD117, HLA-DR, and CD45, in addition to several lineage-specific markers, such as CD13 for neutrophilcommitted HPCs; CD64 and CD33 for monocyte-committed HPCs; and CD105 and CD36 for erythroid-committed HPCs (Figure  2 ).
Several exclusion gates were used to avoid the inclusion of undesirable events that may fall into the CD45 +low CD117 + HLA-DR + blast gate, such as CD11b + hypogranular neutrophils and basophils (tube 1), CD14 +low granulocytes and CD14 + IREM-2 + monocytes (tube 2), and CD10 + hematogones (tube 1) (Figure  2 ). Three databases corresponding to the first three tubes of the EF AML/MDS panel which are dedicated to the immunophenotypic analysis of the principals myeloid lineages were built from merged fcs files containing AML blasts from the four AML groups and the normal my-HPCs: tube 1 of the EF AML/ MDS panel (neutrophil lineage), tube 2 of the EF AML/MDS panel (monocytic lineage), and tube 3 of the EF AML/MDS panel (erythroid cell lineage). The fcs files used to build the databases are available at FlowRepository (ID: FR-FCM-Z3JL).
Database-Guided Analysis of New AML Cases at Initial Diagnosis The BM samples used in this prospective study were obtained between January 2019 and June 2021 from 125 consecutive patients who were hospitalized with suspected AML at the Institut de Canceŕologie Lucien Neuwirth, Saint-Priest-en-Jarez, the Estaing University Hospital of Clermont-Ferrand, France, and the Oncological Institute Prof. Dr. Ion Chiricuța, Cluj-Napoca, Romania. Written informed consent was obtained from each patient and healthy donor (HD), as approved by the institutional procedures of the independent ethics committee and the Comitéde Protection des Personnes -Ile de France (NCT03233074/17. 07. 2017). All participants' characteristics are summarized in Table  1  and detailed in Table  S1. The blast events from each individual AML case were compared against each well-classified AML group and with the normal my-HPC populations using balanced APS plots.
The similarity between blast events from the new case and any defined populations was scored based on the position of the median from the new case relative to the 1 and 2 standard deviation (SD) curves for the defined AML groups or normal my-HPC groups included in the database. A total of 101 cases were correctly classified, whereas 24 cases were incorrectly assigned to the wrong AML group or to the other group. The incorrectly assigned t(15;17) AML case was an NPM1 + AML with an acute promyelocytic leukemia (APL)-like phenotype; the three false-positive t(8;21) AML cases were RUNX1-mutant AML cases, and for the false-positive MLL-r AML case, the array-comparative genomic hybridization (CGH) analysis identified a PICALM/ MLLT10 (CALM-AF10) fusion gene. (C) Phase 3: Evaluation of the Compass database-guided DfN analysis.