dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion This study tested two different versions of the novel mesh-tomesh value comparison method, one manual and the other automated, for accuracy of pair-matching humeri. Both versions were assessed using sensitivity and specificity calculations. The most effective method was the manual MVC method, utilizing the LMI Technologies Flexscan3D software, which had a sensitivity of 100 % and a specificity of 100 %. The automated MVC method, utilizing the Viewbox software, had a sensitivity of 95 % and a specificity of 60 %. These values place the manual version of the MVC method among the most accurate methods available for pair-matching skeletal elements. There is further research to be done to improve both versions of the method including testing the effects of taphonomy, pathology, and fragmentation on the process, as well as expanding the method to other paired bones, reducing the overall cost, and fine-tuning the automated comparison algorithms.
Acknowledgments The authors would like to thank  Ángeles Martín Parrilla  and  Rosa Gurrea Barricarte  for allowing us access to the Ibizan collection, and the  Ibiza City Hall  for providing the facilities for the osteological study of the remains. We are extremely grateful to  Dr. Enrique Lopez  for facilitating the use of the CT scanner at the  Can Misses Hospital in Ibiza  and to  Dr. Neil Roberts  for scanning the Scottish material at the  Clinical Research and Imaging Centre (CRIC) ,  Queens Medical Research Institute (QMRI)  in Edinburgh. We would also like to thank  Julieta Gómez García-Donas  for her translation skills. Finally, we wish to thank all of the reviewers for their invaluable comments and corrections.
Conflict of interest Author DJH owns stock in dHAL Software, the company that markets Viewbox 4. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
S7  and text S1A, Supplementary Material online). The overall complexity and contradictory signals from the f3 tests and TreeMix could be explained by the putative complexity of the Siberian population history, which potentially includes recent shared ancestry, a multilayered history of admixture, and bottlenecks. These features of Siberian population dynamics violate the underlying assumptions of these methods, i. e. , instantaneous admixture at single time points and tree-like population history  (Patterson et al. 2012; Pickrell and Pritchard 2012). To test if isolation-by-distance alone can account for all of the observed genetic variation in Siberia, we applied the recently developed SpaceMix software  (Bradburd et al. 2016). This program uses a Bayesian framework to represent genetic relationships between populations as a "geogenetic" map, in which the distances are proportional to genetic differentiation between the sampled populations.
These geogenetic maps are expected to accurately capture the geographic distribution of samples under a model of pure isolation-by-distance; discrepancies between the geogenetic and geographic maps arise due to long-distance migration (resulting in larger than expected geogenetic distances) or admixture (resulting in lower than expected geogenetic distances). We applied SpaceMix to the Siberian data set and found that although the geogenetic map without admixture roughly captures the general geographical distribution of the populations (supple mentary fig. S8A  SpaceMix also identifies sources for the inferred admixture events; however, it allows for only a single admixture event per population  (Bradburd et al. 2016). As most Siberian populations show evidence of complex multilayered gene flow from distinct geographic locations (fig.
In the following section, we analyze signals of admixture and discuss the genetic prehistory of the populations of South Siberia, while the analyses pertaining to the other populations are described in supple mentary text S3 and figures S14-S17, Supplementary Material online. South Siberia (Altaians, Tuvans, and Buryats) The populations of southern Siberia are mainly pastoralists who speak Turkic (Altaians and Tuvans) or Mongolic languages (Buryats). Evidence for the use of domesticated animals (cattle, sheep, goats, and horses) in this region goes back to the Neolithic  (Laufer 1917; Mirov 1945; Vainshtein 1980; Clutton-Brock 1999; Clutton-Brock 2012). The results of the ADMIXTURE analysis for the populations of South Siberia are striking (fig. 3 ) and reveal the important role played by the Sayan and Altai Mountains in the human history of Siberia: All of the ancestry components found across Siberia are represented here (cf.
Genotypes We genotyped 96 Siberian samples using Illumina 660W-Quad arrays. Quality filtering was performed as described previously  (Pugach et al. 2013 ). These data were merged with Illumina genotypes from three other studies  (Li et al. 2008; Rasmussen et al. 2010; Reich et al. 2012). After quality filtering and data integration, 542 individuals and 353,357 autosomal SNPs remained for analysis. Not all samples were used for all of the analyses.
Data Curation Quality filtering was performed as described previously  (Pugach et al. 2013). Twenty-five individuals were removed before analysis: Three due to low genotyping quality (one Nganasan, one Yamal Nenets, and one Taimyr Nenets), one because it was identified as a potential close relative to others (Yamal Nenets), while the others were removed because they The Complex Admixture History of Siberians. doi:10. 1093/molbev/msw055 MBE were determined via PCA to be outliers relative to their population (one Yamal Nenets, one Taimyr Nenets, one Yakut, four Nganasan, one Altaian, six Koryaks, one Selkup, one Japanese, five Chukchi). The Yakut, Nganasan, Chukchi, and Tundra Nenets populations from  Reich et al. (2012)  (supplementary table S1, (Supplementary Material online) were excluded from the IBD, regression, and dating analyses because no geographical coordinates were available for these samples, and because we had other samples from the same populations (supplemen tary table S1, Supplementary Material online) whose PCA and ADMIXTURE results overlapped with those of the  Reich et al.
Statistical Analyses Exploratory Analyses of Genetic Variation PCA. PCA was performed using the StepPCO software  (Pugach et al. 2011 ) on 353,357 markers for the entire data set and on 504,356 markers when analyzing a subset comprising only the Siberian populations. ADMIXTURE. We used the ADMIXTURE software  (Alexander et al. 2009)  to infer individual ancestry components and admixture proportions; the LD pruning for this analysis was done using the PLINK tool  (Purcell et al. 2007 ) with the following settings: -indep-pairwise 200 25 0. 4  (Rasmussen et al. 2011) , which reduced the data set to 148,620 markers. We ran ADMIXTURE for K ¼ 3 through K ¼ 10, and performed ten independent runs for each value of K. We confirmed consistency between runs and used the cross-validation procedure implemented in ADMIXTURE to find the best value of K (supplementary fig.
Methods of Demographic Inference For subsequent analyses, genetic distances between SNPs were interpolated using genetic coordinates and recombination rates estimated as part of the HapMap  (International HapMap Consortium et al. 2007 ) and the 1000 Genomes Projects (1000 Genomes Project Consortium 2010), and obtained using the SNAP tool  (Johnson et al. 2008). 3-Population Test and TreeMix. The f3 statistics were calculated with the threepop program implemented in TreeMix. Calculation of allele frequencies for the TreeMix analysis was performed using PLINK tool  (Purcell et al. 2007 ). The San samples were used as an outgroup, and we used the window size of 500 (-k option). IBD Segments. The data were phased using BEAGLE v3. 3. 2. IBD segments were inferred using the fastIBD method implemented in BEAGLE  (Browning SR and Browning BL 2007; Browning BL and Browning SR 2011).
To run PCA based on the results of the IBD calculation, we used the ade4 R package  (Dray and Dufour 2007) ; as a distance matrix we used the inverse of the pairwise matrix based on the number of shared IBD blocks (such that two populations sharing the smallest number of IBD blocks would be furthest apart from each other in terms of distance). Multiple Regression. Multiple regression on genetic and geographic distance matrices was performed and the statistical significance of the regression coefficients was determined using the ''ecodist'' R package  (Goslee and Urban 2007). For this, the inverse IBD-sharing matrix was used as a measure of genetic distance between populations. Geographic coordinates for the HGDP populations were taken from Ramachandran  Pugach et al. doi:10. 1093/molbev/msw055 et al.
Analyzing and Dating Multiple Admixture Events To analyze signals of admixture in the Siberian populations involving more than two ancestral populations, we adopted the following workflow: The data were phased with the BEAGLE software  (Browning SR and Browning BL 2007) , then the PCAdmix tool was used to infer ancestry along individual chromosomes  (Brisbin et al. 2012). Unlike other existing methods for ancestry estimation, this algorithm allows for simultaneous inference of admixture from more than two sources of ancestry. In order to determine the sequence of admixture events we developed a new approach, the AHG (see below for details), based on the results of the ADMIXTURE analysis. Finally, we applied our previously published WT analysis  (Pugach et al. 2011) , adapted to include complex admixture scenarios, to the results obtained from PCAdmix and using the information on the sequence of events obtained from the AHG, to estimate the dates of the different admixture events (see supplementary text S1D, Supplementary Material online, for validation).
Because Greenland Inuit, whom we used as a proxy parental population for the admixture signal observed in Chukchi and Naukan Yupik, are themselves heavily admixed, we made an exception for this group and accepted up to 10% admixture in this parental population (as only three individuals met our 1% admixture criteria). Because WT analysis (see below) requires 2 n windows per chromosome  (Pugach et al. 2011) , we used custom scripts when running PCAdmix to ensure conformity to this requirement while maintaining relatively equal numbers of SNPs per window regardless of chromosome size. For this data set, this was achieved by taking 1024 windows across chromosomes 1-8 (n ¼ 10), 512 windows across chromosomes 9-18 and 20 (n ¼ 9), and 256 windows across chromosomes  19, 21, and 22 (n ¼ 8). AHG and WT Analysis.
Acknowledgments I. N. acknowledges financial support from the  Russian Foundation  for the Humanities ( 14-04-00346 ), V. O. acknowledges support within the  basic state program  of the  Russian Ministry of Science and Education , and B. P. is grateful to the  LABEX ASLAN  ( ANR-10-LABX-0081 ) of  Universitede Lyon  for its financial support within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR). This research was funded by the  Max Planck Society.
Acknowledgements We would like to thank  Peter Brunt  (from  Laser 2000 (UK) Ltd)  for his support with the multiple laser system (VersaLase) and the whole  Tomancak lab at the MBI-CBG in Dresden  and in particular  Christopher Schmied  for the valuable SPIM processing tips and help during our visits. We also want to thank  Anna Czarkwiani  for her input during writing the paper and  Paola Oliveri  for helping us setting up the microinjections at  UCL.
In 2013, DLR held a very similar SpaceBot competition which encouraged several robotic developments  (Kaupisch et al. , 2015). Heppner et al. (2015)  describe one of the participating systems, such as the six-legged walking robot LAURON V. LAURON is able to overcome challenging terrain, although its six legs limit the locomotion speed in comparison to wheeled robots. As with our system, the software architecture is based on the Robot Operating System [ROS  (Quigley et al. , 2009) ]. Sünderhauf et al. (2014)  developed a cooperative team of twowheeled robots, named Phobos and Deimos. The straightforward, rugged design with skid steering performed well, compared to more complicated locomotion approaches. We made the same observation in our participation at the SpaceBot Competition 2013 and opted to include wheels (opposed to a purely legged concept) in the Momaro robot.
The latter is partly ROS based but also has modules based on the Rock framework. Artemis demonstrated its navigation capabilities in the 2013 competition, but eventually its navigation planners became stuck in front of a trench, again highlighting the need to design systems with enough remote access, so that problems can be diagnosed and fixed remotely. A few articles on the SpaceBot Camp 2015 are already available. Kaupisch and Fleischmann (2015)  describe the event and report briefly on the performances of all teams. Wedler et al. (2015)  present the general design of their Lightweight Rover Unit (LRU), which competed in the SpaceBot Camp 2015, successfully solving all tasks except the optional soil sample task. The LRU is a four-wheeled rover with steerable wheels, similar to Momaro's drive.
Electronics Figure  3  gives an overview of the electrical components of Momaro. For onboard computation, an off-the-shelf mainboard with a fast CPU (Intel Core i7-4790K @4-4. 4 GHz) and We show USB 2. 0 data connections (red), LAN connections (dotted, blue), and the low-level servo bus system (dashed, green). 32 GB RAM is installed in the base. Communication with up to 1300 Mbit/s to the ground station is achieved through a NETGEAR Nighthawk AC1900 WiFi router. The hot-swappable six-cell 355 Wh LiPo battery yields around 1. 5-2 h run time. Momaro can also run from a power supply for more comfortable development. For more details on Momaro's hardware design, we refer to  Schwarz et al. (2016).
SOFTWARE ARCHITECTURE Both the Momaro robot and the scenarios we are interested will require highly sophisticated software. To retain modularity and maintainability and encourage code re-use, we built our software on top of the popular ROS [Robot Operating System  (Quigley et al. , 2009) ] middleware. ROS provides isolation of software components into separate nodes (processes) and inter-and intraprocess communication via a publisher/subscriber scheme. ROS has seen widespread adoption in the robotics community and has a large collection of freely available open-source packages. To support the multitude of robots and applications in our group, 2  we have a set of common modules, implemented as Git repositories. These modules (blue and green in Figure  4 ) are used across projects as needed.
Communication Architecture Our communication architecture is shown in Figure  10. The DLRprovided network emulator is the central element limiting all communication between robot and operator crew. To be able to exploit the different link characteristics, we place an additional field computer between the network emulator and the robot. Thus, it is connected to the network emulator via a reliable Ethernet connection and communicates directly with the robot over WiFi. As the WiFi link is unreliable, but has low latency, while the network emulator link is reliable, but has high latency, this places the field computer in an ideal position to exploit both link characteristics. As the network emulator allows communication only through a single port per direction, we use the Linux tun interface to create a network tunnel over two ports.
Communication Software Module Since our participation in the DLR SpaceBot Cup 2013  (Stückler et al. , 2015) , our group develops a robust software module (nimbro_network) for communication between multiple ROS masters over unreliable and high-latency networks. We used it with very good results in the DLR SpaceBot Cup 2013 and in the DARPA Robotics Challenge  (Schwarz et al. , 2016). Since the DRC, the module is now freely available 7 under BSD-3 license. In contrast to custom-engineered network stacks for a particular purpose, it allows the generic transport of ROS topics and services. The module is ideally suited for situations where the connection drops and recovers unexpectedly, since it avoids any configuration/discovery handshake. Several specific transports and compression methods exist, such as a ROS log transport, tf snapshotting, or H264 video stream compression.
Remote Introspection and Debugging To be able to react to software problems or mechanical failures, operators first need to be aware of the problem. Our system addresses this concern by • providing direct access to the remote ROS log, • showing the state of all ROS processes, and • transmitting and displaying 3D visualization data from the autonomous behaviors. Once aware of the problem, the operators can interact with the system through ROS service calls over our nimbro_network solution, parameter changes, or ROS node restarts through rosmon. In extreme cases, it is even possible to push small Git code patches over the network and trigger re-compilation on the robot. If everything else fails, the operators can access a remote command shell on the robot using the mosh shell  (Winstein and Balakrishnan, 2012) , which is specifically optimized for highlatency, low-bandwidth situations.
Full System Performance at DLR SpaceBot Camp 2015 After a restart caused by a failed actuator (described below), Momaro solved all tasks of the SpaceBot Camp with supervised autonomy. Our team was the only one to demonstrate all tasks including the optional soil sample extraction. Figure  14  gives an overview of the sequence of performed tasks. A video of our performance can be found online. 9 While overall the mission was  successful, we experienced a number of problems which will be discussed in detail. In our run, Momaro failed to take the soil sample in the first attempt. During the vigorous scooping motion, the scoop turned inside the hand (cf. Figures  2  and 13 ). We found the problem to be a malfunctioning finger actuator in the hand holding the scoop. Since we were confident that Momaro would be able to solve all tasks even in the remaining 50:20 min, we restarted the whole run after performing a software reset on the affected finger and letting it cool down.
For instance, adaptive roll stabilization could advance Momaro's locomotion capabilities. • Actuator Monitoring. Our system provides extensive diagnostic actuator feedback such as temperature and current consumption. Still, this was not enough to prevent the failure of the finger actuator during our run. Actuator monitoring and damage prevention should have a high priority during development. • Software Design: Autonomy Follows Teleoperation. Our unique history of competing previously in the DARPA Robotics Challenge, a competition heavily focused on intuitive teleoperation, set us apart from other teams. In particular, resulting from the DRC competition, we had extensive intuitive teleoperation abilities before starting work on the higher autonomy required by the SpaceBot Camp. We suspect that most other teams followed the opposite approach, augmenting the autonomy later on with teleoperation facilities, which can be difficult if the system was not designed for teleoperation from the start.
CONCLUSION In this article, we presented the mobile manipulation robot Momaro and its ground station. We provided details on the software and hardware architecture of the integrated robot system and motivate design choices. The feasibility, flexibility, usefulness, and robustness of our design were evaluated with great success at the DLR SpaceBot Camp 2015. Novelties include an autonomous hybrid mobile base combining wheeled locomotion with active stabilization in combination with fully autonomous object perception and manipulation in rough terrain. For situational awareness, Momaro is equipped with a multitude of sensors such as a continuously rotating 3D laser scanner, IMU, RGB-D camera, and a total of seven color cameras. Although our system was build with comprehensive autonomy in mind, all aspects from direct control to mission specification can be teleoperated through intuitive operator interfaces.
Autoantibodies refer to antibodies that target selfantigens, which can play pivotal roles in maintaining homeostasis, distinguishing normal from tumor tissue and trigger autoimmune diseases. In the last three decades, tremendous efforts have been devoted to elucidate the generation, evolution and functions of autoantibodies, as well as their target autoantigens. However, reports of these countless previously identified autoantigens are randomly dispersed in the literature. Here, we constructed an AA-gAtlas database 1. 0 using text-mining and manual curation. We extracted 45 830 autoantigen-related abstracts and 94 313 sentences from PubMed using the keywords of either 'autoantigen' or 'autoantibody' or their lexical variants, which were further refined to 25 520 abstracts, 43 253 sentences and 3984 candidates by our bio-entity recognizer based on the Protein Ontology.
Finally, we identified 1126 genes as human autoantigens and 1071 related human diseases, with which we constructed a human autoantigen database (AAgAtlas database 1. 0). The database provides a user-friendly interface to conveniently browse, retrieve and download human autoantigens as well as their associated diseases. The database is freely accessible at http://biokb. ncpsb. org/aagatlas/. We believe this database will be a valuable resource to track and understand human autoantigens as well as to investigate their functions in basic and translational research.
Using bead-based arrays, Ayoglu et al. profiled the sera from multiple sclerosis patients with 11 520 proteins and identified Anoctamin 2 as an autoimmune target in multiple sclerosis  (14). In addition, some tumor AAgs might be ideal targets for targeted immunotherapy such as p53, HER2 and NY-ESO-1  (15, 16). For example, HER2 is highly expressed on the surface of some cancer cells, and several tar- geted therapies directly against HER2 have been employed to treat breast and stomach cancers, i. e. trastuzumab (Her-ceptin®)  (17). However, the information of previously identified AAgs is dispersed in thousands of published papers, and the systematic investigation of human AAgs is hampered due to unavailability of a comprehensive human AAg collection. To address this need, Wang et al. constructed a Human Potential Tumor Associated Antigen (HPtaa) database that collected 3518 potential tumor antigens identified by in silico computing (http://www.
hptaa. org) based on tumorspecific expression, coding capacity, chromosomal location, subcellular location and the knowledge of gene function  (18). Almeida et al. constructed a cancer testis antigen database, CTdatabase (http://www. cta. lncc. br), which contains 276 genes that were compiled manually from the literature and computational prediction  (19). However, these two databases only cover a small fraction of the published AAgs and their inclusion of predicted but not documented AAgs can be confusing (Figure  2B ). We have built an AAgAtlas database using text-mining and manual curation with the aim to provide a comprehensive AAg repository that supports basic and translational studies. We extracted 45 830 AAg-related abstracts and 94 313 sentences from PubMed by the keywords of either 'autoantigen' or 'autoantibody' or their lexical variants, which were further refined to 25 520 abstracts, 43 253 sentences and 3984 candidates by bio-entity recognizer based on the Protein Ontology  (20, 21).