dataesr/scientific-paragraphs-categorization huggingface dataset:
The peak database was tested in GSEA software with pre-ranked option to determine whether our a priori defined peak sets are significantly enriched in the CM + group. Peak sets are generated separately for each organ by pvclust and assembled within a peak set database. We retained clusters having at least 95% of confidence. In the presence of missing data, peak values are imputed using the multiple imputation process (see Material and Methods). Peaks were then ranked according to the pooled t-statistic value comparing uninfected samples to CM + samples. GSEA reports (Fig 5A) identified a peak set that was significantly enriched in the CM + group in the spleen (NES = 2. 1744, q-value = 0), in the blood (NES = 2. 3300, q-value = 0) and in the brain (NES = 2. 2873, q-value = 0). This peak set contains peaks from TRBV1, TRBV3, TRBV4, TRBV5, TRBV12-2, TRBV13-2, TRBV19, TRBV26 and TRBV29 (S2 Fig) suggesting a rather restricted pathogenic T cell response.
In order to assess the predictive power of this signature, we applied the process described in the methodology section separately for spleen and blood. For validation, we used previous data published in  [22]  as a test set to predict ("AC set"). As a training set, we used our current data from spleen and blood ("EMF set"). Due to a high number of missing data on the "EMF set", we applied the multiple imputation process for peak data. Random forest models were trained on 20 CTR and 23 CM + in spleen and on 20 CTR and 21 CM + in blood and tested on 6 CTR and 9 CM + in spleen and on 7 CTR and 13 CM + in blood. Accuracy was assessed on train and test sets for each step. Random forest models showed very good accuracy > 90% of good prediction (Table  2 ). Altogether, the identified peak set allows to discriminate CM + from CTR mice regardless of the organs, as validated on two independent datasets.
Acknowledgments This work has been funded by  Assistance Publique-Hôpitaux de Paris, Université Pierre and Marie Curie (Paris VI) ,  LabEx Transimmunom  ( ANR-11-IDEX-0004-02 ),  LabEx Parafrap  ( ANR-11-LABX-0024 ) and  LIA CNRS DBT SIGID ("Systems Immunology and genetics of.
This work has been funded by  Assistance Publique-Hôpitaux de Paris, Université Pierre and Marie Curie (Paris VI) ,  LabEx Transimmunom  ( ANR-11-IDEX-0004-02 ),  LabEx Parafrap  ( ANR-11-LABX-0024 ) and  LIA CNRS DBT SIGID ("Systems Immunology and genetics of infectious diseases "). EMF & HPP have been supported by a doctoral fellowship from  Ministère de la Recherche et de la Technologie , as well as by  Fondation de France and Institut Pasteur for EMF.
75 m-spaced rows). (iii) Real-time tree canopy detection systems. (6) Graphical user interfaces for monitoring autonomous outdoor vehicles. (7) Safety systems for humans and animals near the vehicles and the robots themselves (obstacle detection in the robot's path). Although the main aim of the RHEA project was to check fleets of robots and all software applications were developed to manage a large number of robots, the limited budget and limited duration of the project forced the consortium to build only 3 UGV and 2 UAV. Thus, in this article, the real RHEA robots will be just referred to as a multi-robot system.
Study site The RHEA entire system was tested in maize (Zea mays L. ), wheat (Triticum aestivum L. ) and olive trees (Olea europaea L. ), and the experiments were performed in two fields located in Arganda del Rey, Madrid, Spain, (40818 0 50. 241 00 , -3829 0 4. 653 00 for wheat and 40818 0 57. 924 00 , -3829 0 3. 7134 00 for maize and olive trees). These fields are inside the facilities owned by CSIC (RHEA project co-ordinator). Experimental data were recorded with elemental sensors (ultrasonic sensors, encoders, etc. ) and sophisticated devices (GNSS, laser), measuring mainly positions (m) and volumes (l). The recorded data were analyzed based on average values.
System architecture To accomplish these specific objectives, the RHEA multi-robot system was broken down into seven main systems organized into two parts: stationary equipment and movable equipment (Fig. 2 ). The stationary equipment contains the systems and devices allocated to fixed positions during the mission, close to the working field. All of these elements (antennas, ethernet switches, routers and receivers) are physically installed in the base station (BS), which consists of a cabin that provides housing for the equipment and shelter for the operator; the BS is the operator's work station. The BS is supplied with AC voltage and equipped with a computer to which the stationary systems are connected and the relevant Fig. 2  RHEA system architecture Precision Agric software applications are run.
The BS computer allows the operator to interact with the pertinent systems and modules through the Graphical User Interface (GUI), to define missions and control the multi-robot system through the Mission Manager. This software module computes and supervises the missions, controlling the main components of the movable parts, i. e. , the UGVs, UAVs (through the UAV High-Level Controller) and related equipment, whenever needed. Figure  3  illustrates the BS system architecture. The mobile robots (unmanned vehicles) are the elements of the movable part responsible for providing motion over the entire field for sensing crops and acting on them. Thus, the unmanned vehicles carry the Perception System through the fields. This system is composed of the Aerial Remote Perception System shipped on the UAVs and the Ground Perception System on board the UGVs.
This system is also distributed and provides the BS and the vehicles with the base antenna and the vehicle antennas, respectively. RHEA is a complex system that requires intensive use to make it economically feasible. This efficiency can be accomplished using the contractor approach, i. e. , a specialized company provides the requested services to individual farmers in different fields. Both stationary and movable equipment are moved from field to field using tracks or autonomously if the local legislation allows it. The primary goal of the RHEA project was to design, develop and integrate all of these systems with their relevant modules, as detailed in the following sections.
This controller was also modified to allow activation of the system that provides the agricultural tool with power [power take-off (PTO)] and selection of the gears by CAN messages. The High-Level Decision Making System is responsible for co-ordinating all the systems on board the UGV and making decisions on future actions. The hardware architecture for the HLDMS was designed, implemented and assessed relying on a compactRIO 9082 computer (National Instruments Corporation, Austin, Texas, USA) and the software structure consisted of a number of modules and functions, organized as illustrated in Fig. 5. This structure defines the communication (in terms of commands, responses and related parameters) between the HLDMS and every other sub-system connected to it. In particular, the communication between the HLDMS and the Mission Manager, the Weed Detection System, the UGV Controller and the User Portable Device was designed to allow the operator to supervise and control the multi-robot system.
Managing the multi-robot system The Mission Manager is a software application developed for handling the multi-robot system. This application runs on the BS computer and is responsible for (a) generating the trajectories and actions for both aerial and ground vehicles, (b) supervising the trajectory and action executions and co-ordinating behaviors among vehicles when a conflicting situation or system malfunction is detected, (c) obtaining data from the remote perception system, and (d) interacting with the operator through the GUI (see  Figs. 2, 3). Two types of missions can be defined: (a) inspection missions carried out by the aerial vehicles and (b) treatment missions performed by the ground vehicles. At the outset of the process, the operator provides information related to the type of mission and the field specifications (crop type, field dimensions, geographical position, crop headings, etc.
). This type of information allows the Mission Manager to make decisions on the number of UAVs for inspecting the mission field, to select the number of UGVs needed to accomplish the task and to provide an action plan for each vehicle (Mission Planner) (Fig. 6 ). During the inspection mission, the Mission Manager uses the Remote Perception System to obtain information to compute a weed distribution map, which is essential for generating the treatment mission plan in narrow-row crops. The Mission Manager also supervises the mission, making real-time decisions when unexpected events occur (Mission Supervisor) using the status information provided by the aerial and ground vehicles (mainly position, orientation, states of different modules, errors, etc. ) during the execution of their missions.
4 s, i. e. , all required data duly processed and ordered with correct synchronization between them. Experimentally, the final average time used by the perception system was lower than 0. 35 s. Weed detection relies on the spatial identification of crop rows. Thus, determination of crop-row positions with respect to the UGV becomes a crucial task for weed patch detection and UGV guiding. Crop-row detection In the last two decades, several strategies have been proposed for crop row detection based on (a) exploration of horizontal strips, (b) Hough transformation, (c) vanishing point-base, (d) stereo-based approach, (e) blob analysis, (f) accumulation of green plant, (g) frequency analysis and (h) linear regression. These methods are described in  Guerrero et al. (2013). In this specific case, the crop row positions were determined with respect to the UGV for UGV guiding and weed detection purposes.
Communication system The RHEA Network system was realized by using a Linux-based wireless router providing IEEE 802. 11b/g, IEEE 802. 11a, IEEE 802. 14. 5 ZigBee PRO and ITU GPRS interfaces. The high-level communication system architecture was based on the multi-technology management approach, where different communication paradigms (publish/subscribe and request/reply) and services (best effort and enhanced reliability) were designed to utilize multiple interfaces. An interface based on TCP/UDP messages, implemented as a Network API, was used by all communication components. Accordingly, an IP addressing scheme for the overall system was defined, unambiguously addressing every component located on board a UGV or on the BS. To assure traffic prioritization and quality of service, the IEEE 802.
Graphical user interface The graphical user interface (GUI) is a software application running in the BS computer (Fig. 3 ), where the model of the real system is represented in 3D and the user can interact in different ways to obtain information regarding what is currently occurring with the real system or to send commands to the system. The system displays the status of the mission, the position of the vehicles in the field, the sensor measurements, the camera images and other information received from the Mission Supervisor, and it can control the UGVs remotely as well as manage the mission execution. Moreover, the same program allows the operator to execute a real mission in the field or simply run the planned mission in simulation to evaluate its efficiency and test emergency maneuvers.
The core of the GUI consists of the commercial package Webots 7 robot simulator (developed by project partner Cyberbotics, Lausanne, Switzerland) that computes dynamic simulations using a realistic physics engine and represents the robots and relevant equipment in a 3D space. Another independent software module was developed to provide a single interface for the different partners' modules (for example, the UAV Mission Planner and the UGV Mission Planner). This additional program constituted the entry point for the human operators and allowed them to set the parameters and manage the 3D environment to simulate missions or supervise them. Some models of the real operational setup were developed to be able to accurately reproduce the setup in the Webots simulation. Figure  13  illustrates what the GUI looks like.
7 ) around the parcel and manually geo-referenced using a RTK GPS receiver (0. 01 m accuracy). Pictures of the crop were obtained and stored in the camera memory cards during the flight. The complete set of pictures was then uploaded at the BS computer after the landing of the UAVs. Image mosaicking and weed mapping The memory card was inserted in the BS computer and the data (geo-referenced pictures) obtained, then aerial image mosaicking of the field was performed by using tailor-made software as described above (Fig. 7 ). Figure  8a  presents a picture of the demo field in standard colors, and Fig. 8b  illustrates the image in its false color representation including the NIR channel. These pictures were the basic inputs to identify and locate weed patches by using a computer application implemented through the commercial software packages described above.
One more step forward would be the integration of critical modules in the compactRIO FPGA (Field Programmable Gate Array), improving the speed of some processes and allowing the vision software to be extended. The GUI has been assessed as customized modular and extensible software able to compute and execute missions on specific agricultural fields and environments, but its application to other types of scenarios and surroundings will require the generalization and extension of rules for automatic mission computation and representations. Moreover, as with other large software applications, intensive tests have to be conducted to improve the GUI's ease of use, ergonomics and reliability. Regarding the communication system, implemented communication features contributed to a successful proof of concept of the overall communication in real scenarios.
Nevertheless, safety for humans, animals and the vehicle itself is an important issue for these types of vehicles, and new standards are being defined by different working groups. For example, the group ISO/IEC TC 23/WG 15 (ISO 2016) is working on the new standard ISO 18947-Safety of Highly Automated Agricultural Machines. UAVs and UGVs require a significant amount of energy to perform their tasks. Battery innovations will increase the mission time for UAVs, and the improvement of fuel cells will allow, in the near future, configuration of a complete ecological, efficient ground vehicle for carrying heavy equipment. Regarding the Mission Manager, the supervision modules (distributed and multi-level) greatly facilitate supervision in other applications by replacing, adjusting or adding only the individual pieces of code related to the specific treatment of new alarms.
Furthermore, many of the alarms defined and the code developed for monitoring them are basic in the supervision of autonomous robots regardless of the type of application. Thus, the software developed is largely reusable. However, the proposed planning strategies for the mission planners, based on Simulated Annealing, NSGA II or Genetic Algorithms, are applicable to other more general optimization problems, such as the Vehicle Routing Problem. In addition, the Planner can be used in other agricultural activities that require planning, e. g. , crop distribution prior to sowing. In fact, the developed planner can work stand-alone, i. e. , disconnected from the rest of the modules of the Base Station or the GUI. The Mission Manager could be improved by including mechanisms to define and use different policies for alarm management, even allowing the changing of management strategy during the mission execution if needed.
Acknowledgments The research leading to these results received funding from the  European Union 's  Seventh Framework Programme  [FP7/2007-2013] under Grant Agreement n8 245986. Support for publishing this article has been provided by  CSIC. The authors appreciate the contributions of  Airrobot Gmbh & Co Kg, Bluebotics SA  and the CM S. r. l members of the RHEA consortium not participating in this article. 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.
F. W. Howell et al. : Arctic sea ice in the PlioMIP ensemble that there was year-round coverage of sea ice at this location, whilst there are indications from ostracode assemblages and ice-rafted debris sediments as far north as Meighen Island (approx. 80 • N) that Pliocene Arctic sea ice was seasonal  (Cronin et al. , 1993; Moran et al. , 2006; Polyak et al. , 2010). The prospect of the Arctic becoming ice-free in summer in the future increases the importance of the investigation of past climates which may have had seasonal Arctic sea ice. Whilst many studies have focused on the simulation of Arctic sea ice for present and future climate by a variety of modelling groups (e. g. Arzel et al. , 2006; Parkinson et al. , 2006; Stroeve et al. , 2007 Stroeve et al. , , 2012 Stroeve et al. , , 2014;; Johnson et al.
, 2010). These boundary conditions are based on inferences from the SST reconstruction, and evidence from diatoms and sedimentological data  (Dowsett et al. , 2010). In both Experiments 1 and 2, atmospheric CO 2 is 405 ppm, and a modern orbital configuration is used. In Table  1 , details of the eight models which ran PlioMIP Experiment 2 simulations are summarised. With the exception of GISS-E2-R, each model was also used for Experiment 1 simulations. Four of the models (CCSM4, GISS-E2-R, HadCM3, and IPSLCM5A) are also represented in the CMIP5 ensemble, the results for which are contrasted with the PlioMIP results. Higher-resolution versions of MIROC4m and NorESM-L, and an updated version of MRI-CGCM also ran CMIP5 simulations. For COSMOS, results from the model MPI-ESM-LR, which has a higher resolution and an updated version of the ECHAM model in COSMOS, are shown.
Comparison to CMIP5 simulations Before examining the simulations of Arctic sea ice for the mid-Pliocene, the simulations of pre-industrial sea ice cover by individual models are assessed. A comparison with observed sea ice characteristics is a suitable methodology. Ideally, we would have compared the output of the pre-industrial simulations to observations of sea ice from the same time period. However, the most spatially and temporally comprehensive observations of sea ice originate from satellites. Respective data sets date back only as far as 1979, which is more than 100 years after the time period that the pre-industrial simulations represent. Whilst there are observations of sea ice characteristics available dating back to the early 20th century that could have been used for the comparison, most, particularly the earliest, are ship-based observations of ice margins.
Acknowledgements. F. W. Howell  acknowledges  NERC  for the provision of a doctoral training grant. A. M. Haywood acknowledges that the research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 278636. B. Otto-Bliesner  and  N. Rosenbloom  recognise that NCAR is sponsored by the  US National Science Foundation  (NSF) and computing resources were provided by the  Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory (CISL) , sponsored by the  NSF  and other agencies. C. Stepanek  acknowledges financial support from the  Helmholtz Graduate School  for Polar and  Marine Research  and from the  Helmholtz Climate Initiative REKLIM.
Background: Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of bio-samples. The joint analysis of the data matrices associated with the different data types of a MMDS should provide a more focused view of the biology underlying complex diseases such as cancer that would not be apparent from the analysis of a single data type alone. As multi-modal data rapidly accumulate in research laboratories and public databases such as The Cancer Genome Atlas (TCGA), the translation of such data into clinically actionable knowledge has been slowed by the lack of computational tools capable of analyzing MMDSs. Here, we describe the Joint Analysis of Many Matrices by ITeration (JAMMIT) algorithm that jointly analyzes the data matrices of a MMDS using sparse matrix approximations of rank-1.
In what follows, we describe in greater detail a workflow for the joint analysis of multiple data types based on the JAMMIT algorithm. A section on methods provides technical detail on the algorithm and the computational tools used to evaluate the statistical significance, biological coherence, and clinical relevance of JAMMIT-derived signatures. We then present and discuss results of: 1) a study that compared JAMMIT detection performance against that of other joint analysis algorithms on simulated data; ii) a JAMMIT analysis of global mRNA, microRNA and DNA methylation data for ovarian cancer down-loaded from TCGA; and iii) a JAMMIT analysis of whole-genome mRNA data for liver cancer supervised by quantitative features derived from PET imaging data. A discussion and conclusions are presented in a final section.
nih. gov/). This data download resulted in three high-dimensional data matrices of dimensions 16020 × 291 (mRNA), 799 × 291 (micro-RNA) and 15418 × 291 (DNA methylation), each of which were log-transformed, quantile-normalized, centered, and scaled by their respective Frobenius norms prior to formation of an ovarian MMDS denoted by D OVCA. Clinical meta-data for each patient were also downloaded from TCGA and aligned with the columns of the super-matrix of D OVCA. These data included censored survival time, age, stage, and treatment information. Subsequent to formation of D OVCA , additional wholegenome mRNA data for tumors obtained from 99 patients with Stage 3 disease were downloaded from TCGA along with associated clinical metadata. These data were organized to form a mRNA data matrix that was used to assess the robustness of any associations with overall survival with mRNA expression patterns found in the discovery data set represented by D OVCA.
JAMMIT analysis of transcriptomic and PET imaging data for liver cancer Twenty patients referred for surgical resection of liver tumors were prospectively recruited to participate in an institutional review-board approved clinical research study with written informed consent. Prior to surgery, these patients underwent liver imaging with a Philips Gemini TF-64 PET/CT scanner (Philips Healthcare, Andover, Massachusetts) using 18F-fluorocholine under an investigational new drug protocol. In a previous singleinstitution clinical trial, 18F-fluorocholine, a tracer of choline phospholipid synthesis, affords PET/CT with relatively high diagnostic sensitivity for HCCs  [32, 33]. Presently, less is known regarding the diagnostic utility of 18F-fluorocholine for ICCs and other subtypes of liver cancer.