dataesr/scientific-paragraphs-categorization huggingface dataset:
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter (ECAL) and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. The online event selection is performed by a trigger system  [9] , which consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. In the simulation, pp collisions are generated using Pythia 6 and Pythia 8  [10]  with a specific LHCb configuration  [11]. Decays of hadronic particles are described by EvtGen  [12] , in which final-state radiation is generated using Photos  [13].
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  FOM  and  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FANO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF  (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
Methods: We used a large data set (n ¼ 965) of women with hormone receptor-positive and HER2-negative early breast cancer from the public data set of the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. We compared the overall performance of three validated web-based models: Adjuvant!, CancerMath. net and PREDICT, and we assessed concordance of these models in 10-year survival prediction. Results: Discrimination performances of the three calculators to predict 10-year survival were similar for the Adjuvant! Model, 0. 74 (95% CI 0. 71-0. 77) for the Cancermath. net model and 0. 72 (95% CI 0. 69-0. 75) for the PREDICT model). Calibration performances, assessed graphically, were satisfactory. Predictions were concordant and stable in the subgroup, with a predicted survival higher than 90% with a median score dispersion at 0.
In breast cancer management, the decision to give adjuvant treatments depends on several factors such as survival benefits and complication risk. Software tools have been developed to aid this decision-making process. They estimate the probability of cancerrelated mortality and relapse and calculate the percentage benefit of adjuvant treatments. The web-based software program Adjuvant online! (www. adjuvantonline. com) (Ravdin, 1996)  was developed from the American National Cancer Institute SEER registry (Surveillance, Epidemiology, End-Results data and estimates). The data represented B10% of all breast cancer patients in the United States aged between 36-59 years and treated between 1988 and 1992. It calculates the risk of relapse and 10-year cancer-related mortality with and without adjuvant therapy post cancer surgery.
It predicts the risk of death for the first 15 years after diagnosis and the impact of various adjuvant therapies. Survival rates are calculated using tumour size, lymph node status, ER/progesteron receptor/HER2 status, tumour grade and histological type. To date, one unit has published its validation of SNAP  (Michaelson et al, 2011). They found that SNAP provided accurate estimates of risk of death when compared against large patient data sets. A third web-based prediction tool, PREDICT, was described in 2010 (www. predict. nhs. uk), which estimates 5-and 10-year breast cancer survival and the benefits of hormonal therapy, chemotherapy and trastuzumab  (Wishart et al, 2010). The model was derived from the cancer registry information on 5694 breast cancer patients treated in East Anglia from 1999 to 2003 and used factors such as patient age, tumour size, tumour grade, number of positive nodes, ER status, Ki67 status and mode of detection.
An external data set of 5468 patients from the West Midlands Cancer Intelligence Unit was used for validation  (Wishart et al, 2010). A recent review on six prediction tools (including Adjuvant!, CancerMath. net and PREDICT) and 14 biomolecular features of breast cancer found that most models were inaccurate in particular patient subgroups  (Engelhardt et al, 2014). To date, there is limited data on the concordance of several prediction models. In addition, further validation of multiple models with several multinational large patient data sets is needed. The aim of this study was to validate and to determine the concordance of three web-based prediction tools Adjuvant!, PREDICT and CancerMath. net against a patient data set consisting of ER-positive and HER2-negative breast cancer.
PATIENTS AND METHODS Patients METABRIC data set. We retrieved 965 ER-positive and HER2negative cases from the METABRIC study (Molecular Taxonomy of Breast Cancer International Consortium)  (Curtis et al, 2012). This data set contains over 2000 clinically annotated primary fresh frozen breast cancer specimens and a subset of normals from tumour banks in the United States and Canada. Details about the METABRIC cohort have been published by  Curtis et al in 2012. This public data set accessible from the web (METABRIC Data for Use in Independent  Research-syn1688369, 2014). To automatically apply the models to the data set, we used the HTML code from the web pages of the calculators (Predict and Cancermath. net models) and we generated a partition tree for Adjuvant! (the information that the Adjuvant! algorithm uses to estimates the risk are binned into categories and the output is fixed in the number of possibilities).
To assess the models' concordance, we first compared the three predicted survival values using paired t-test analysis, ANOVA measure and correlation coefficient. We then calculated the maximal dispersion corresponding to the difference between the highest and the lowest prediction for the same patient. We also calculated differences between the mean of the three predictions and each predictor. Finally, we defined three risk groups: low-, intermediate-and high-risk groups. These groups were constituted using the average risk predicted by the three models, then cut into tercile. The R 2. 15. 2 software (R Development Core Team (2012), http://cran. r-project. org/) was used for data analysis, using the Hmisc, rms, and pROC libraries.
Fig. 1  Effects of celastrol on number of living cells and miR-223 in two cell lines. a Celastrol's effects on viable cell numbers. MCF-7 or PC3 cells were cultured with different doses of celastrol (with solvent DMSO as control) for 24 h. Then the number of viable cells was quantified by flow cytometry with one-platform protocol, as detailed in Methods. b Inducing miR-223 with celastrol. After incubation with celastrol at the indicated doses for the indicated time, the cells' total RNA was extracted and reverse transcription was performed. Then miR-223 levels were determined by quantitative PCR with specific primers. Cel represents celastrol. The tests were repeated three times. Data is presented as mean ± SD; statistics were determined between the celastrol-treated group and DMSO, * indicates P < 0.
2b ). The observed decrease in viable cells from up-regulating miR-223 (Fig. 2b ) is also explainable through NF-κB dominance: this manipulation inhibits NF-κB while activating mTOR and HSF-1 and elevating HSP70 (Fig. 5b ). (adjusted density of X in test sample)/(adjusted density of X in control sample), with the relative expression of X protein in the control set as 1. The mean of relative expressions of all specific proteins in different treatments is displayed by heat map, which was created by ggplot2 package of R language (https://www. r-project. org/). The experiments were repeated three times Interestingly and strangely, the above data showed that miR-223 down-regulation could activate NF-κB in cells not treated with celastrol, however, when celastrol was used, cells with knocked-down miR-223 showed lower NF-κB and mTOR activity than cells with mock transfection.
Based on the following facts, we thought that celastrol-induced miR-223 might be related to NF-κB inhibition and rely on transient mTOR activation. One: NF-κB inhibition and mTOR activation (both observable at 5 min after celastrol treatment, though mTOR was inhibited by 1 h) preceded miR-223 elevation (at 6 h). Two: like celastrol, NF-κB inhibitor PDTC could induce miR-223. This effect was accompanied by mTOR activation. Three: mTOR inhibitor could reduce celastrol-or NF-κB inhibitor-induced miR-223 elevation. Kumar et al. reported that in combination with Notch, NF-κB activation, rather than inhibition, elevated miR-223  [22]. This difference in results might be cell-type specific. To support this notion, our bioinformatics work with ChIPBase software  [39]  identified multiple NF-κB binding sites up-and down-stream of the miR-223 loci in the human X chromosome.
Acknowledgements This work is supported by the following grants:  Shanghai Gongli Hospital Youth Project  (No. 2013GLQN05  for LC and No. 2012GLQN09  for XZ);  Shanghai Pudong Youth Talent Project in Medicine  (No. PWRq2013-11  for FC);  Shanghai Yang Fan  project (No. 15YF1410800  for FC); the  Shanghai Pudong District Science and Technology Innovation Project  (No. PKJ2013-Y03  for YW);  Shanghai Gongli Hospital Youth Project  (No. 2014GLQN16  for YS); the  International Science & Technology Cooperation Project of China  (Grant  2011DFB30010  for GU);  National Natural Science Foundation of China  (No. 81102349  for  BP  and No. 81400793  for YW);  Shanghai Excellent Academic Leader  in Medicine (No. XBR2011054  for DZ) and  Shanghai Traditional Chinese Medicine Content Construction Innovation Project  ( ZY3-CCCX-3-7001  for DZ).
7] [8]  PpL enables pure and homogeneous antibody fragments to be obtained from very heterogeneous samples, even when these antibody molecules are present in limited (trace) amounts. In addition, PpL-affinity purification prevents the use of potentially immunogenic tags. PpL is now produced in recombinant form and marketed as a high affinity matrix with improved flow properties for purification, manufacturing and even industrial applications. In addition PpL conjugates have been engineered in order to facilitate detection of antibody CONTACT Nicolas Aubrey aubrey@univ-tours. fr fragments, either free or in complex with their antigen, allowing detection and titration in a wide range of assays. 0] [11]  To elucidate PpLbinding, interaction studies between Fab 2A2 (Homo sapiens IGKV1-39 Ã 01 or VkI subtype) and PpL domain C Ã (PDB code 1HEZ) have been performed.
The new open-source quadrotor platform called X4-MaG presented here was developed for academic and research applications. X4-MaG is a small, low-cost open quadrotor of only 307-grammes which offers two levels of controllers providing a manual mode and an automatic mode thanks to powerful Linux-based controller embedded onboard. The experiments presented here show the reliability of the open hardware and software embedded onboard the quadrotor. To estimate the robot s attitude, a quaternion-based complementary filter requiring very few computational resources was developed and implemented on an 8-bit Arduino board. It was also established that the stabilization feedback system based on quaternions tracks the attitude setpoints with precision up to twice greater than a classical cascaded PI controller.
The controllers and estimators were designed in the Matlab/Simulink environment and directly implemented onboard the tiny Linux-based autopilot board using a custom made toolbox (RT-MaG toolbox). The autopilot was tested in the brand-new Marseilles Flying Arena with various 3-D flight trajectories and found to be highly accurate with errors of only 0. 7cm in hover and less than 3. 2cm at 1. 2m. s -. The X4-MaG quadrotor was able to reach speeds greater than 2m. s -1 and reject attitude disturbances of 20' within 0. 8s.
For example, the Openpilot and MultiWii projects were launched by RC hobbyists (  [10]  and  [11] ). Members of the DIYdrone community have also developed the well-known Arduino-based autopilot Arducopter. As a summary, many low cost quadrotors have been described in the literature (e. g. ,  [12] ,  [8] ) or are available on the market. However, none of them are both open-source and directly programmable with Matlab-Simulink tools. In addition, few reports are available so far in the literature on the reference tracking performances and the repeatability of the results obtained with these various open-source quadrotors. This paper presents a new open-source and a new open-hardware quadrotor platform called X4-MaG, developed jointly by the ISM laboratory (Marseille, France) and the Gipsa-Lab (Grenoble, France), in which a particular care have been taken to assess and quantify how accurately the present small and low cost quadrotor operates in a flying arena.
From the hardware point of view, the X4-MaG quadrotor is mainly composed of low cost commercial hobbyist components. From the software point of view, the Linux-based Computer-On-Module is fully supported by our new open-source Matlab/Simulink toolbox RT-MaG (see  [13] ), which makes the prototyping and implementation of new algorithms very fast, reliable, versatile and easy. In addition, the present X4-MaG quadrotor operates using a fully quaternion-based open-source autopilot making the implementation more efficient. The attitude estimation was performed by a complementary filter inspired by  [14]  and  [3] , which was implemented in the present work in an optimized version using quaternions. A new version of the geometric feedback controller presented in  [15]  was used and enhanced by an integral action and was fully expressed with quaternions.
THE LINUX-BASED X4-MAG QUADROTOR Matlab/Simulink was often used in ground station because it allows users to test and design new control algorithms very easily. [16]  used Matlab/Simulink, a Vicon motion capture system and a custom-made RF module for steering a quadrotor vehicle along a trajectory. But the control algorithms are not executed onboard. Programming the X4-MaG quadrotor via Matlab/Simulink was achieved by means of our new open-source toolbox (  [13] ) which enable to run Simulink model in real-time on Linux based Computer-On-Module (more information are given in section 2. 3. At the hardware level, the quadrotor X4-MaG is composed entirely of low cost off-the-shelf components. Its light weight (307g) and its small frame made of a printed circuit board (PCB) make it highly resistant and robust to crashes.
At the software level, the robot is equipped with two different controllers which trigger a rescue mode in case of failure of the main controller. In addition, the high level controller (the main controller) is based on the Gumstix Overo, a powerful low consumption Computer-On-Module providing the robot with huge computational resources at no cost to the payload and the endurance of the quadrotor. Since the Gumstix COM is fully supported by our new opensource Matlab/Simulink toolbox RT-MaG (see the website  [13] ), the robot is directly piloted via Matlab/Simulink. Readers are invited to consult the RT-MaG website (see  [13] ), which gives all the information needed to assemble and program the X4-MaG quadrotor. In this work, the quadrotor performances were evaluated using the well known Vicon s motion capture system as a localisation system (more information are available in  [17] ).
The hardware As shown in figure  1 , the robot is equipped with the 20-cm span Nanoframe (Flyduino  [18] ), four "5030" propellers, four 3800KV motors, four 6A ESCs (Electronic Speed Controllers for the motors) and a 2200mAh lithium-polymer battery. The maximum flight time is about 10 to 12 minutes in normal use, the total span (including the propellers) is 30cm and the weight is about 307g including the battery pack. The X4-MAG is based on the NanoQuad frame (Flyduino), which is a small (20cm), light (60g) and genuinely crash-proof platform. This platform, which can be purchased for less than 250€, is piloted manually via a radio transmitter. It was decided to add the Gumstix Overo as a high level controller, which is a powerful Computer-On-Module (COM) providing additional computational resources.
Embedded sensors and computational resources The robot is equipped with a low-level autopilot based on a microcontroller with its 6-axis IMU (NanoWii) and a high-level autopilot based on a powerful Linux-based Computer-On-Module (Gumstix COM). The built-in NanoWii autopilot enables the robot to be piloted manually and takes over from the Gumstix if failure of the latter occurs. This architecture enabled to safely test and validate all the new algorithms while limiting the risk of crashing in the case of failure. The Gumstix COM can be programmed directly via Simulink, as it is fully supported by the RT-MaG toolbox developed at our laboratory. This considerably reduces the time elapsing before it is possible to run flight tests when developing new algorithms. The X4-MaG quadrotor is equipped with three different electronic boards: • The NanoWii  [18]  stabilizes the platform in the manual mode and sends sensors' values to the high-level controller in the automatic mode.
A Matlab/Simulink capable quadrotor The X4-MaG quadrotor is equipped with a Gumstix Overo processor board featuring a Linux operating system patched to ensure real-time capabilities. The RT-MaG toolbox generates reliable real-time applications corresponding to a Simulink model, can run control loop at frequencies up to 1kHz and gives access to the various I/Os classically used in robotic application (UDP, SPI, I2C, RS232, PWM, GPIO and ADC). It also provides efficient debugging modes and feedback information about the realtime performances, giving users several possible metrics for optimizing their applications. This toolbox also makes it possible to perform real-time monitoring via a ground station and tune the parameters of the algorithms in real time. More complete descriptions about the RT-MaG toolbox as the sources can be found on the RT-MaG website: http://www.
gipsa-lab. fr/projet/RT-MaG/ (  [13] ). As a consequence, the X4-MaG quadrotor can be fully programmed via Matlab/Simulink, and all the control algorithms can be tested in simulation, in processor-in-the-loop mode and implemented on the final hardware using the same environment. This makes the development of new control strategies drastically faster. Figure  4  gives a classic scheme for the communications between the embedded quadrotor autopilot and the ground station. The autopilot consists in a Simulink model executed in real time on a Computer-On-Module (COM). The real-time host application consists in another Simulink model executed in real time on a host computer (or a ground station). On one hand, the host computer sends high level setpoints and parameters to the embedded application.
• The X4-MaG quadrotor equipped with a Gumstix COM and programmed via the RT-MaG toolbox. The robot receives its 3-D position and the position setpoints from the ground station, and then computes its trajectory autonomously. The communications and the programs running on the ground station and the robot are both managed via Matlab/Simulink. During the experiments, the ground station monitors (in the Simulink environment) all the robot's data via a WIFI connection and sends the position setpoints and the Vicon measurements to the robot at a frequency of 100Hz. The Simulink model of the ground station runs in real time thanks to the Quanser's software QUARC® (  [21] ). The robot runs the Simulink model described by the figure  6  in real time via the RT-MaG toolbox  [13]. All the computations are performed onboard by the Gumstix, i.
B. 2 Procedure and conditions A given object was placed at various points in the flying arena for 30 seconds, and the data obtained with the Vicon system during this time were recorded. The structure holding the camera was motorized and could be moved up and down to adapt the "working volume" of the arena as required. All the results presented here were obtained with the maximum working volume (6m×6m×8m). The mean position measured, the standard deviation of the measurements, and the maximum deviation of each position in the arena were then calculated. The object used in these tests was the Wand which is also a calibration tool provided by Vicon Motion Systems Ltd and endowed with IR reflectors. This object is shown in the figure  12    By extension, the overall standard deviation, denoted E(σ), can be defined as the mean standard deviation of all the positions.
B. 4 Results The above procedure was applied to 36 different positions (9 positions at 4 different heights, covering a large part of the whole volume of the flying arena). The data obtained at each position were recorded for 30 seconds at a frequency of 500Hz. The arena was equipped with 17 T-40s cameras arranged as shown in the figure  13. Before the experiment started, the calibration was repeated until an "image error" of less than 0. 35 pixels was reached with each camera (in the Tracker 2. 0 program), i. e. , until the position of a marker's centroid could be measured with a precision greater than 0. 35 pixels in the case of each camera (see figure  13   Figure  13 : Screenshot of the Tracker software just before the experiment. The precision was by the Tracker in terms of the "image error", which is the estimated camera's positioning error in pixels.
Linux patched with PREEMPT-RT, is able to run Simulink models thanks to the RT-MaG toolbox. With its breakout board (Pinto-TH), it features 4 PWM outputs (and 2 are already translated to 5. 0V by the RCB), 1 SPI bus (with 2 Chip Select), 2 serial ports, 1 WIFI interface (54 MB/s), 8 GPIO and 6 10-bit ADC. The Overo AirSTORM can be easily programmed via the RT-MaG toolbox, which makes it easy to test and validate various control strategies. In addition, the board provides a wireless link (WIFI) making it really easy to monitor data and tune parameters in real time via a ground station. The various links between the hardware components of X4-MaG are shown in figure 2. The Gumstix and the NanoWii communicate with each other via an RS232 link at 115200 Baud. The Gumstix sends the four motor input reference signals to the Nanowii, which sends the IMU's data back 92 X4-MaG: A Low-Cost Open-Source Micro-Quadrotor and Its Linux-Based Controller International Journal of Micro Air Vehicles.
ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their fruitful comments,  M. Boyron  and  J. Diperi  for their great work on the quadrotor,  F. Expert  for his help with the experiments, and  F. Colonnier ,  J. Dumon  and  J. Minet  for their fruitful discussions. This work was supported by  CNRS ,  Aix-Marseille University , the  Provence-Alpes-Côte d'Azur region  and the  French National Research Agency (ANR)  with the EVA, IRIS projects (EVA and  IRIS  project under ANR grant numbers  ANR608-CORD-007-04  and  ANR-12-INSE-0009 , respectively) and the  Equipex/Robotex project. Augustin Manecy ,  Nicolas Marchand ,  Franck Ruffier  and  Stéphane Viollet 107 Volume  7 • Number 2 • 2015 Table 4: The minimum standard deviation observed in the arena was found to be about 0. 0025mm, which was probably greatly underestimated; this value cannot be valid because it is well below the accuracy ("trueness") of the Vicon system.