dataesr/scientific-paragraphs-categorization huggingface dataset:
More in detail, in  Baizid et al (2014)  two NSB approaches for obstacle avoidance of multi-UAVMs systems involved in transportation tasks are proposed. In  Antonelli et al (2014a,b) , a Control software Architecture for cooperative multi-UAVM (CA-VIS) is developed. The use of multi-priority control, both at kinematic or dynamic level, is not new. A good state of the art may be found, e. g. , in Chiaverini et al  (2008). By limiting our attention to floating-base manipulation, it is worth mentioning the underwater case study, where re-cently some experimental results have been obtained in a grasping operation  (Simetti et al, 2013)  with a system characterized by 13 degrees of freedom taking into account several prioritized tasks run by means of proper activating functions. In a sense, the humanoid case is also a floating-base manipulation task with a limited mobility of the torso when the robot is not walking.
Also, the standard estimation algorithms for the aerial robotic platform navigation are implemented at this level. The level uses a modelbased development framework based on Matlab/ Simulink and implemented over the autopilot, using the real-time operating system QNX. -Functional level: this level includes the integration of the perception and cooperation algorithms that will run on-board of the aerial robot. A Linux processing unit with ROS  (Quigley et al, 2009)  is used as the framework for the integration of the different functionalities. -Multi-vehicle level: this level includes the integration of the software modules that require the information from multiple vehicles. The standard DDS middleware is used to interconnect the different software modules that will be integrated at this level.
With regards to the control level, the autopilot in use allows full control of all the hardware and software in order to integrate the robotic arm and the control algorithms developed in this paper. To test the control algorithms, the ARCAS project is using a Model-Based Design (MBD) methodology  (Santamaria et al, 2012) , based on Simulink c , relying on code generation tools. This methodology allows not only to easily integrate the code generated from Simulink into the target autopilot, but also, to access to all the internal variables and change parameters while the platform is flying. Also, the same software (Simulink) can be used in the design and simulation phase, as well as in software-in-the-loop testing and in real flying experiments. For costly computing code, such as image processing or coordinated control, the system is equipped with an i7 Asctec Mastermind on board that runs ROS.
This This is a post-peer-review, pre-copyedit version of an article published in AUTONOMOUS ROBOTS. The final authenticated version is available online at: https://doi. org/10. 1007/s10514-016-9590-0 unit implements what it has been called the functional level (Fig. 9 ). This functional level has a well-defined API with the control level, formed by two software modules: the UAV Abstraction layer and the Robotic Arm specific layer. These two layers allowed to develop a simulation system based on Gazebo simulator with exactly the same interfaces what the aerial robot actually has. Finally, the experiments have been performed in the CATEC indoor testbed (Fig. 10 ) that has a useful volume, i. e. , the area in which the UAVM is allowed to move, of 15 × 15 × 5 meters, where this entire software infrastructure is deployed.
Acknowledgements This research has been supported by the  European Commission 's  7th Framework Program  under grant agreement No. 287617  ( IP  project  ARCAS -Aerial Robotics Cooperative Assembly system ) and grant agreement No. 608849  ( IP  project  EUROC -European Robotics Challenges ), and by the  European Commission 's  Horizon 2020 Program  under grant agreement No. 644271  ( IP  project  AE-ROARMS -AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance ).
D. Mathematical ranking In addition to the empirical ranking by the review panel, we also performed a mathematical ranking based on plan indices to explore potential automated plan quality comparison. We used the secondary plan review software ARTIVIEW (AQUILAB, Loos les Lille, France) to calculate all plan indices based on the original plans. On the basis of the planning objectives and the review panel ranking, the following indices were analyzed in three categories: A) spinal cord V 14Gy and V 18Gy and PTV coverage at 21 Gy (prescription dose), B) conformity index (ratio between the total volume receiving 21 Gy and the PTV), total monitor units, and the estimated treatment time (includes patient setup, robot motion, and beam-on time), and C) PTV D min , spinal cord D max , kidney D max , 5 mm skin D max , and volume10 cm V 10Gy (the volume receiving 10 Gy in the 10 cm volume surrounding the PTV).
g. , case 10). Due to the sequential steps implementation of SMOO in MultiPlan (version 4. 5), the optimizer could be stuck on a local optimum after a few steps. (11) The use of short and simple optimization scripts  (23)  and/or manual adjustment of the constraints combined with iterative optimization with different scripts can help to explore the various trade-offs of the planning constraints and goals in order to create a well-balanced treatment plan for a specific case. 3. Regardless of the distance to the target area, critical structures should in general be dosimetrically optimized according to the as-low-as-reasonably-achievable (ALARA) principle (i. e. , the minimization of critical structure maximum doses is advised) if this is not detrimental to the plan quality (e. g. , case 10 where the maximum kidney dose was limited without impairing the spine dose or target coverage).
In the selection of the participants, as there are now more than 300 installed CyberKnife units worldwide, we tried to find a balanced mix between very experienced (> 10 yrs) and rather new users (< 1 yr) to the CyberKnife. However, the experience of the user did not reflect in the resulting plan quality -for example, the planner of case 6 (2nd in ranking) did not have access to MultiPlan version 4. 5 before this study. From the results of our study, it also became clear that treatment plans generated with simple optimization scripts (i. e. , case 6 or case 10) may yield better results than those with complex scripts and multiple tuning structures. Since a very simple treatment planning technique yielded good results, our planning guidelines may also be valid for simple targets and other indications, requiring further validation.
Practical guidelines for robotic radiosurgery treatment planning can be found in Appendix A. A simple approach to CyberKnife treatment planning could be to start with known critical structure limitations as hard constraints,  (21)  maximize the target coverage in the first step and minimize the dose to the tuning shell structures for dose conformity (e. g. , planning for case 5 and 6). This simple approach can already lead to high-quality treatment plans. (23) Furthermore, the resulting shell doses could be used as constraints in a second optimization script in order to minimize the dose to the critical structures (e. g. , planning for case 2). It may be noted that some specific optimization steps (e. g. , minimum volume dose optimization) could potentially yield inferior results compared to others especially with nearby critical structures (i.
e. , planning for cases 3, 4, and 8 with the highest spinal cord doses). To still archive acceptable results while using the minimum volume dose constraint, the goal relaxation value needs to be large enough to warrant the competition between the different objectives in those cases. Another approach to CyberKnife treatment planning could be to manually derive the tuning shell and critical structure doses by subsequently reducing the maximum and volume constraints until the target receives ≤ 95% coverage (e. g. , planning of case 10). This iterative optimization technique is the basis of the sequential multiobjective optimization. (11) Yet, since the implementation of SMOO in MultiPlan (since version 3. 0), plan optimization scripts are used more commonly. Regardless of the type of optimization, skin entry doses and hot spots outside the target should be controlled as to avoid severe skin or organ reactions.
F. First optimization The first optimization should be as simple as possible (e. g. , 2-3 steps, low resolution) and can be used to determine the maximum shell doses as they are dependent on collimator selection, MU limitations, and target shape and generally not known a priori. Furthermore, the first optimization may be used to determine a good collimator selection and should be quick to reduce the overall planning time. Therefore, the number of constraint points should be limited to lower than 10,000 for the PTV and lower than 5,000 for OAR or shell structures. The optimization grid resolution and number of constraint points may then be increased in the subsequent optimizations to generate the final plan. A simple script to explore basic plan qualities is presented in the following steps: Step 1: Optimize PTV Coverage (OCO at Rx + X Gy with Y Gy relaxation).
G. Second optimization For many targets without any close critical structures (e. g. , peripheral brain or lung tumors), the first optimization script alone (e. g. , with higher resolution) may be used to create highquality treatment plans. (23) If there are close critical structures, either a manual adjustment of maximum or pseudo-volume-constraints or a second optimization script can be used to optimize OAR doses, which requires the setting of the maximum dose constraints for the shell structures determined in the first optimization. A simple script for OAR optimization is presented in the following steps: Step 1: Optimize PTV Coverage (OCO at Rx + X Gy with Y Gy relaxation) -see First Optimization above. Step 2: Optimize OAR Mean Dose (OME at H Gy with Z Gy relaxation). Using a specific optimization dose target for OME may likely result in a lower volume which this OAR receives at the given dose target compared to optimizing the overall OAR mean dose (OME at 0 Gy).
Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on crossvalidation benchmarks using 'gold standard' gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation.
There exist other methods that do not rely on seed genes, and are therefore more suited for diseases of unknown etiology. These methods rely on disease relevant keywords, or on complementary disease specific experimental data sets. For instance, the Génie web server ranks genes using a textmining approach that is fed with user-selected keywords and can be complemented with orthology information  (7). Alternatively, MetaRanker integrates multiple disease specific data sets, including genetic association data, copy number variation data and differential expression profiles  (8). In this article, we introduce a much-improved version of Endeavour, our gene prioritization method, which is available for six species--now also including zebrafish. Endeavour currently integrates 75 data sources that represent different and complementary views about the genes, which is more than twice as many as our previous version  (9).
In addition, we have validated Endeavour using a time-stamped benchmark in which predictions are made and only benchmarked later on when enough new data have accumulated (similar to the CAFA strategy  (10) ). Other researchers have used Endeavour to decipher the mechanisms underlying several human diseases. For instance, Yu et al. used Endeavour to prioritize candidate genes extracted from a whole-exome sequencing of familial cases of congenital diaphragmatic hernia  (11). After validation, GATA4 (ranked 3rd) was associated with congenital diaphragmatic hernia. Zielinski et al. studied hemifacial microsomia and identified a 1. 3 Mb duplication on chromosome 14q22 by analyzing five affected individuals  (12). Their final result suggests a role for OTX2 (ranked 1st) in human craniofacial development.
g. Gene Ontology) only the features overrepresented within the set of seed genes are kept in the submodel. In the second step, the candidate genes are scored using the model built in the first step. More precisely, for a given data source, the scores are computed using the associated sub-model and represent how well the candidate genes fit this sub-model. For instance, for annotation-based data sources, the score of a candidate gene is the Fisher's omnibus combination of the P-values of its annotations that are present in the sub-model (i. e. annotations not present in the sub-model are ignored). At the end of this step, each data source is associated with a ranking of the candidate genes, with the most promising candidate genes on top. In the third step, these rankings, which correspond to prioritizations made using different data sources, are then integrated using order statistics to obtain a single global ranking (13).
This method allows candidate genes with little data (e. g. poorly annotated genes) to be fairly compared with candidate genes that have a lot more data. The algorithm then outputs a ranked list of candidate genes, with P-values that represent the significance of this ranking. In addition, the prioritization results are now displayed graphically using a parallel coordinate representation so that users can easily check which data sources contributed the most to the global ranking. Endeavour integrates 75 data sources for six species that can be classified into broad categories that describe what we know about genes. They are briefly described below, and a detailed list of all available databases is available in Supplementary Material 1. The 'Gene and protein function' category includes resources such as Gene Ontology  (14)  and InterPro  (15) , which are usually organized as annotations between ontologies (that describe function in a broad sense) and biological entities (i.
e. genes or gene products). In addition, the category 'Biomolecular pathways' includes pathway databases such as Reactome  (16) , which are complementary to the purely ontological annotations described above. Several resources that describe gene or protein interaction networks, such as BioGrid  (17)  and IntAct  (18) , are also integrated and classified into the 'Interaction networks' category. Chemical data sets are also integrated in Endeavour within the 'Chemical information' category. These contain annotations between gene or gene products and other entities such as drugs. One example is the Drug-Bank database  (19). The 'Phenotypic information' category gathers all resources that collect associations between genes or gene products and phenotypes or diseases, possibly using model organism data.
Examples are the OMIM database for human Mendelian disorders  (20)  and the Rat Disease Ontology from the Rat Genome Database  (21). Expression data are split into two categories depending on whether complete expression profiles are available (category 'Expression profiles'), such as large expression data sets, or whether the data have already been summarized into annotations, such as within PaGenBase (  22 ) (category 'Expression ontologies'). The last category 'Sequence-based features' contains all resources that are based on gene, transcript or protein sequences. This includes protein sequence similarities computed using BLAST or predicted miRNA regulation using transcript sequences  (23, 24). The web server is based on a three-tier architecture, developed using Microsoft. Net technology and Microsoft SQL database.
The core Endeavour library is written in Perl and the data are stored in a MySQL database. We have introduced a parallel coordinate representation to visualize our multidimensional numerical results. This gives the users the opportunity to easily recognize patterns in the data and identify possible correlation among the different sources. Uploaded data and prioritization results are kept private and not viewable by other users, and are in any case deleted after 30 days. The web site is free and open to all and there is no login requirement. Two examples derived from the literature are available for users who simply want to try out Endeavour. In addition, a manual describes how to run a simple prioritization and a help page contains hints on how to solve the more frequent issues.
EVALUATION RESULTS For the first validation, we have performed a leave-oneout cross-validation on 'gold standard' gene sets. These sets contain genes already associated with phenotypes or biological processes and have been extracted from reference databases, such as OMIM and Gene Ontology. These sets are mostly derived from studies on Mendelian disorders that represent the majority of our current knowledge. However, we have also included a benchmark data set extracted from the Genetic Association Database that focuses on complex disorders, since these are the focus of most of the studies nowadays. Briefly, for each iteration of the crossvalidation, the prioritization is done by training Endeavour with all genes from the 'gold standard' set except one (hereafter called the left-out gene), and the aim is to rank this leftout gene together with 99 randomly selected genes.
We expect this left-out gene to rank toward the top since it comes from the 'gold standard' set. All data sources except the ones that contain explicitly the association we want to predict are used in the process (i. e. OMIM is not used for any phenotype-based 'gold standard'). In the end, an Area Under the ROC curve (AUC) is computed for each 'gold standard' set, and AUC values are averaged over the different sets to derive a global estimate. These are then compared to control AUC values obtained with randomly built gene sets. A summary of the results is presented in Table  1. The full results, including ROC curves, are available in Supplementary Materials 2 and 3. We observe that performance varies from 88. 34% (for human phenotypes) to 94. 93% (for worm gene function), which indicates that Endeavour methodology is performing as expected.
In addition, our benchmark reveals that the performance on complex disorders (GAD, 88. 96%) is similar to the performance on pure Mendelian disorders For each benchmark (row), the columns contain the number of gene sets, the number of genes, the AUC and the control AUC respectively. (OMIM, 93. 41%). Based on the GAD benchmark, we also observe that the average running time for the prioritization of 100 human genes is 311 s (using 34 data sources). However, cross-validation is often not the best method to estimate actual performance (because leakage of information across multiple sources can lead to overly optimistic results), and time-stamped strategies are often preferable  (25). We have therefore defined a time-stamped benchmark that resembles the CAFA strategy  (10). The principle is that predictions have been made in January 2013 and only benchmarked in December 2015 to avoid data contamination (i.
e. data used for both training and testing, even if indirectly). Our benchmark relies on annotations between human genes and phenotypic terms from the Human Phenotype Ontology (HPO). More precisely, Endeavour was first used to make predictions for a wide range of HPO terms, using the whole genome and only data from January 2013 or before. We have then collected from HPO the 3854 phenotypegene associations that have been discovered between January 2013 (build 44) and December 2015 (build 102), and used these associations to benchmark the Endeavour predictions from the first step. The AUC over the 3854 novel associations is 81. 51%, which indicates that Endeavour is able to efficiently prioritize disease candidate genes in a realistic setup (details are in Supplementary Material 4). However, this AUC value is lower than the estimate obtained by the cross-validation, therefore confirming the suspicion that cross-validation on a 'gold standard' most likely provides an overestimation of the actual performance.
Introduction The constantly increasing number of macromolecular crystal structures deposited in the Protein Data Bank (PDB;  Berman et al. , 2007) , the increasing number of protein folds that these contain and the advent of more sensitive methods  (McCoy et al. , 2007; Read, 2001; DiMaio et al. , 2011)  has meant that molecular replacement (MR) is the overwhelming choice for structure solution in macromolecular crystallography (MX). However, the solution of the crystal structures of biological macromolecules of unknown fold and of large protein-protein or protein-nucleic acid complexes still often requires experimental phase determination. Most experiments for de novo structure determination routinely exploit anomalous scattering via the multiwavelength anomalous dispersion (MAD;  Smith, 1991; Hendrickson, 1991)  or single-wavelength anomalous dispersion (SAD;  Rice et al.
The MXCuBE2 concept User control of experiments on ID23-1, ID29 and ID30B is, as for all of the ESRF facilities for MX, via the MXCuBE2 (http://www. esrf. eu/mxcube2 and https://github. com/mxcube) graphical user interface (GUI), the full functionality of which will be presented elsewhere  (Oscarsson et al. , in preparation). Briefly, MXCuBE2, the successor to MXCuBE  (Gabadinho et al. , 2010) , has been designed to simplify the planning and execution of MX experiments via a single, integrated and intuitive interface, which facilitates the carrying out of experiments with elaborate data-collection strategies by minimizing error-prone iterative manual intervention. Two main aspects of MXCuBE2 are relevant for experiments exploiting anomalous scattering in de novo crystal structure determination: firstly, MXCuBE2 relies on the concept of 'saved positions' for data collection from any given sample (Fig.
Good data-collection practice as implemented in MXCuBE2 4. 1. Calculation of data-collection strategies Calculation of a suitable data-collection strategy is among the 'good practices' handed down by crystallographers  (Dauter, 1999; Flot et al. , 2006). Typically, the goal is to determine the minimal angular range and a convenient oscillation range (slicing) and to ensure that complete data with the desired multiplicity are collected. To ensure the calculation of optimal data-collection strategies at ESRF MX beamlines, the EDNA pipeline  (Incardona et al. , 2009)  is used via the MXCuBE2 'Characterization' tab. The successor to DNA  (Leslie et al. , 2002) , EDNA has been developed to take advantage of the evolution of software such as LABELIT  (Sauter et al. , 2004) , BEST  (Bourenkov & Popov, 2010)  and RADDOSE  (Paithankar & Garman, 2010)  to produce data collection strategies which take into account global radiation damage.