dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012)  was used to determine the optimal model of molecular evolution and gamma rate heterogeneity based on AIC. Both ML and BI were used to infer the phylogenomic relationships within the Fabids clade. ML analysis was performed using RAxML version 8. 2. 4  (Stamatakis, 2014). The GTR model of nucleotide substitution was selected for ML analysis, taking into account the gamma distribution of rate heterogeneity with five discrete categories (+G). To estimate the support of the ML topology, a bootstrap analysis was performed on 1,000 replicates. BI was run on MrBayes software, version 3. 1. 2 (Ronquist and Huelsenbeck, 2003)  with the GTR +G model. The MCMC algorithm ran for 5,000,000 generations, sampling one tree every 100 generations. The first 25% of trees were discarded as burn-in to estimate the values of posterior probabilities.
Thus, a 44-nucleotide sequence was obtained and added for closing the gap to produce the complete sequence of the circular molecule of 151,406 bp. The chloroplast genome sequence of P. edulis has been submitted to the NCBI GenBank and has received the accession number KX290855. The chloroplast genomes of Potentilla micrantha  (Ferrarini et al. , 2013)  and Aconitum barbatum var. puberulum  (Chen et al. , 2015)  were entirely sequenced using the PacBio RS II Platform, and both studies described the advantages of using this method. For instance,  Ferrarini et al. (2013)  highlight the generation of a single contig covering the entire cp genome of P. micrantha, whereas Illumina HiSeq2000 sequencing data have lower genome coverage (some regions have zero or very low coverage) and the resulting assembly consisted of seven contigs.
These sequences are informative, particularly at low taxonomic levels, due to their rapid evolution. In contrast, protein-coding genes evolve at a relatively slow rate  (Folk et al. , 2015). Regarding the Chrysobalanaceae, the placement of Licania heteromorpha near to the species of Hirtella with strong support (BS = 100%; PP = 1) makes this genus paraphyletic. Parinari campestris was the most basal species in this family and this position is consistent with the findings of  Bardon et al. (2013) , based on six chloroplast markers and one nuclear marker. Moreover, the placement of the eight Chrysobalanaceae species with strong support is consistent with the phylogenetic hypothesis proposed by  Malé et al. (2014)  analyzing the complete plastid DNAs of eight species of this tropical family. It is important to emphasize that our phylogenomic study is the first to take into account all the complete chloroplast genomes of the Malpighiales taxa available in databanks.
Malpighiales includes 40 families, which are poorly represented in sequence databanks. Consequently, a limited taxonomic sampling of the order was used in the analysis and this may have contributed to the high support of the nodes in the phylogenetic trees obtained. Therefore, it is not possible to reach any definitive conclusions concerning the monophyly or position of the families within the order without examining the chloroplast genome sequences of members of the other families. However, our study lends support not only to the utility of complete chloroplast genomes to infer phylogenetic relationships, but as well as the enormous utility of long sequence reads from PacBio sequencing and assembly for generating very highly accurate biological information. Subsequently, the complete nucleotide sequences of 43 chloroplast protein-coding genes of 42 species (including P.
edulis) that belong to the eight orders of the Fabids clade were compared to reconstruct the phylogenomic relationships based on the GTR +G model (Supplementary Table  S1 ), with V. vinifera (Vitales: Rosidae) as the outgroup. After alignment filtering and concatenating the nucleotide sequences into a single matrix, a total of 31,193 nucleotide positions were analyzed. Conservation analysis of positions was then performed and it is estimated that 61. 65% correspond to conserved sites and 38. 35% to variable sites. To summarize, these rates were estimated in Mega, a program that identifies a site as constant only if at least two sequences contain unambiguous nucleotides. In contrast, a variable site contains at least two types of nucleotide. The method of analysis (ML or BI) had no substantial effect on the resulting trees.
Heat generated by nanoparticles The GATE code simulates the absorption of optical photons by the nanoparticle-infused tissue and the diffusion of the deposited photon energy (or heat) in biological tissues. The code provides a voxelized energy map (photon deposited energy in eV). The deposited energy is then converted (in an automated post-processing step) into heat following the work from Qin and Bischof  [30]. In their review, they focused on the thermophysical and biological responses of media heated by laser activated gold nanoparticles. With a concentration N (number of NPs/m 3 ) of nanoparticles, the heat generation  [30]  is given by: Q heat = N × Q NP = N ×C abs × I (2) where Q NP is the heat generated by a single nanoparticle, C abs is the nanoparticle absorption cross-section area and I is the local laser irradiance (in W•m -2 ) within the nanoparticle-infused tissue.
Hybrid Monte-Carlo and analytical simulation: a dynamic process During NIR light illumination of a biological tissue, the thermal heat produced by the optical photons deposited energy does not accumulate locally in the tissue; it diffuses in biological tissues. This dynamic effect has been taken into account in the GATE code following the procedure described in Fig. 1. The biological tissue is illuminated for a duration of n seconds. The simulation  of the optical photons absorption by the tissue produces an absorption map in which optical photons deposited energy is accumulated and does not take into account the fact that the heat diffuses during light illumination. This accumulated absorption map is sampled (discretized) into p time frame images by setting the simulation parameter setNumberOfTimeFrames to p.
Validation of the heat diffusion in GATE versus an analytical simulation code In this section, the heat diffusion implemented in GATE is compared to an analytical code that solves the diffusion equation via the finite difference method where continuous derivatives are replaced by their finite difference approximations. This method is applied on the following general convection-diffusion heat transfer equation  [34] : ∂ T ∂t = k ρc ∇ 2 T -u∇T + Q (11) where u is the blood velocity (in m•s -1 ) considered as uniform. Q represents the thermal capacity of external sources. The Pennes model from Eq. (  5 ) can be derived from Eq. (  11 ) in which the second term, describing the heat transfer in blood vessels, is modeled to be proportional to the difference between the arterial temperature and the temperature at a given location  [35].
After setting the simulation parameter setDiffusionTime to 100 s and using a thermal diffusivity of 1 mm 2 •s -1 , the new energy distribution within the phantom is derived from the initial conditions. Taking into account the symmetry of the problem, the solution to the heat diffusion equation via finite difference method was implemented in MATLAB allowing for diffusion only along one dimension (x axis) for simplicity. This numerical method approximates the temperature at given grid points with a spacing of ∆x = 2. 1 mm and the time evolution is computed with a time step of ∆t = 0. 1 s. 1000 time steps were computed to follow the diffusion of heat up to 100 s post illumination start. The blood velocity u was set to 31. 75 cm•s -1  [36]. Boundary conditions were set to the values obtained following the GATE simulation (i.
Validation of heat diffusion with experimental data Figure  4  shows the benchmark used to validate GATE against experimental data. It consisted of a parallelepipedic phantom of 4×4×1 cm 3 made of a common thermoplastic polymer, the acrylonitrile butadiene styrene (ABS), whose density is 1. 05 g•cm -3. Illumination was achieved using a 633 nm laser coupled to a 0. 96 mm diameter fiber and a 0. 48 numerical aperture (NA). The maximal half-angle (θ ) of the light cone that can exit the fiber is given by: NA = n × sin(θ ), ( 13 ) where n is the ABS refraction index (i. e. 1. 4  [38] ). It was inserted 1 mm under the surface of the phantom, which pointed towards a Gobi 640 low noise thermal camera (Xenics, Louvain, Belgium) recording 3 images per second. This camera, which has a resolution of 0.
1 • C, provides an accurate measurement of temperature at the surface of the phantom (within 100 µm). The total power at the fiber exit was measured to be 150 mW. The experimental acquisition started with images of the phantom recorded for 10 seconds, followed by 30 seconds laser illumination. When the laser was switched off, images were recorded for 1 more minute. The phantom initial temperature, measured from the data using a 2D region of interest (ROI) of 0. 5×0. 5 mm 2 positioned at the center of the phantom surface, was 26. 9 • C. The phantom thermal diffusivity (K 1 ), extracted from the data using Eq. (  12 ), was 0. 32 mm 2 •s -1. The phantom absorption coefficient was also extracted from the data using the thermal image recorded before the laser was switched on (image 1) and the thermal image recorded one second later (image 2).
Validation of the heat diffusion in GATE versus an other analytical simulation code This validation concerns the heat diffusion process only and was performed by comparing two analytical simulation codes which solve the heat diffusion equation respectively via Fourier transformations and convolution theorem (GATE implementation) and via finite difference method (MATLAB implementation). Figure  6  shows the radiant energy (in eV) in the voxels located in the middle of the XY plane of the phantom from Fig. 2  for both simulation codes. There is a good agreement between both implementations. The relative error between both simulations ranges from -1. 31 to 4. 0%. This validates our approach of using the Fourier transformations and convolution theorem to solve the heat diffusion equation analytically.
Discussion Hyperthermal therapy has been developed as a new module of the GATE open-source simulation platform. This new hybrid Monte-Carlo and analytical simulation module allows for a wide range of applications going from localized hyperthermia (i. e. skin hyperthermia or direct heating with a fiber coupled laser) to more complex nano-mediated near-infrared hyperthermal therapy. The Monte-Carlo simulation models the absorption and scattering of optical photons by the medium of interest (i. e. biological tissue). The analytical simulation models the diffusion of heat in the biological tissue by solving the Pennes bioheat equation. The solution to this equation, obtained via Fourier transformations and convolution theorem, was found to be equivalent to the convolution of the initial conditions with a Gaussian function whose standard deviation is related to the tissue thermal diffusivity.
e. skin (connective tissue), the relative change in heat is 13. 5% (-10. 5%). When local hyperthermia is used for cancer near or on the skin, the region of interest can be associated to the true skin thermal diffusivity. In nanoparticle-mediated hyperthermia therapy, the treatment heats a larger part of the body such as an entire organ and the illumination light is delivered directly at the tumor site using an interstitial laser. In this application, it is reasonnable to use a mean value of the thermal diffusivity for a region of interest composed of the tumor and surrounding tissues. Localised hyperthermal therapy has been first demonstrated in a phantom made of acrylonitrile butadiene styrene (ABS). As shown in Fig. 9 , the GATE simulation code was able to reproduce the relative temperature changes as a function of the acquisition time.
They reported that the average temperature increase of the nanoshell-treated tumors was 37. 4 ± 6. 6 • C on near-infrared exposure of 4-6 minutes, which leads to an accuracy of 17. 6% in the value of the temperature rise. They also explained that observed variances in temperature change could be due to different nanoshell distributions within the tumor, the source-to-skin distance of the laser fiber as well as the placement of the MR plane for observation. Despite several sources of experimental systematic errors, the GATE code reproduces the experimental data published by Hirsch et al. [7]  reasonably well. The experimental data are within the blood perfusion rate bands representing the range of simulated temperature rise (see Fig. 10 ).
Preprocessing We preprocessed the corpus using cTAKES  (Savova et al. , 2010) , an open-source natural language processing system for the extraction of information from electronic health records. We extracted sentence and token boundaries, as well as token types and semantic types of the entities that have a span overlap with a least one gold standard EVENT entity of the THYME corpus. Semantic types are a set of subject categories (organized as a tree) that are used to categorize concepts in the UMLS ® (Unified Medical Language System) Metathesaurus. There are currently 135 types  (Bodenreider, 2004). This information was added to the set of Gold Standard attributes available for EVENT entities in the corpus. An overview of the attributes available for each token is presented in Table  2.
Experiments and Discussion We experimented with three configurations. In the first one, we used only word embeddings and character embeddings. In the second one, we added the feature embeddings related to the Gold Standard (GS) attributes. Finally, in a third experiment, we added the feature embeddings related to cTAKES. For each experiment, we report precision (P), recall (R) and F1-measure (F1) computed with the official evaluation script 2  provided during the Clinical TempEval challenges. Results of the experiments are presented in Table  4 : Experimentation results. We report precision (P), recall (R) and F1-measure (F1) for each configuration of our model, for the best system of the Clinical TempEval 2016 challenge  (Lee et al. , 2016)  and for the best result obtained so far on the corpus  (Lin et al.
Participants Two groups of adult participants, either normal weight or overweight/obese, were studied in free-living conditions. During the preliminary visit, they were given a resting electrocardiogram validated by a cardiologist (ECG cardimax FX-7202, Fukuda Denshi). They were weighed, and their height, neck, waist and hip circumferences were measured. Moreover, their diastolic and systolic arterial pressure and resting heart rate were measured using a Spengler manual tensiometer. The participants were told to maintain their current habits for the waking period of one day, i. e. , free and spontaneous activities. Each participant was instructed to wear an Android smartphone (Samsung Galaxy xCover or LG Nexus4 or Nexus 5 or the participant's smartphone) in the left pants pocket. The eMouveRecherche application collects the smartphone accelerometry data at 6 Hz.
The raw accelerometry data were then sent via Internet to the ActivCollector Web platform (https:// activcollector. clermont. inra. fr) where they were immediately compiled and analyzed according to the algorithms designed in  [15, 16]. The algorithms were validated under controlled and free-living conditions against reference methods (Armband and Fitmate Pro). The mean gaps in absolute value between the total energy expenditure (TEE) estimated by the algorithms and the reference methods in free-living conditions were approximately 5. 7% and 8. 5%, respectively, in normal weight and overweight/obese participants. The treatments results, i. e. , time spent in each activity category expressed in minutes or in percentage, and energy expenditure during the waking period were available on the Web platform.
The protocol was approved by the French Committee for the Protection of Human Subjects (Sud-Est VI). It was registered under the references 2013-A00188-37 and 2013-A01140-45 in the ANSM system, and under the references NCT01995162 and NCT02348554 in Clinical Trials. The participants signed an informed consent form before beginning the clinical trial. Data collection took place in 2013 and 2014. The data analyses were performed in 2015. The participants' characteristics are presented in Table  1  and correspond to a population-based approach dedicated to men and women within an extended age range and with a normal BMI in the first group. Being overweight or obese, the participants of the second group were older and had a higher BMI than those of the first group. The mean recording time for the normal weight and overweight/obese participants was 756 min ± 154 min and 805 ± 120 min, respectively.
Statistical analyses A two-way analysis of variance was carried out to determine the effect of weight status, sex and their interaction on time spent in each activity category expressed in minutes or in percentage of the waking period. When time spent in each activity category between weight status or sex differed significantly, the means were compared using a Newman & Keuls test. Statistical significance was set at p<0. 05. The linear relationships between variables were studied by calculating the correlation coefficients for the whole sample and by gender. All statistical analyses were performed using SAS 9. 4 software.
Very few of the studies used objective research devices such as ActiGraph to assess light-, moderate-and vigorous-intensity activities in large populations, probably because of the high cost of research devices and/or the need for a laboratory environment. Like the smartphone, ActiGraph uses accelerometry data to assess time spent in different activity categories. [20]  used ActiGraph to study physical activity and sedentary behaviors of European participants and found that obese people had a significantly higher odds ratio of sitting more than 10 hours per day than normal weight people. However, regardless of their BMI (< 25, 25 -30, > 30 kg. m -2 ), the participants spent about 60-62% of their time in sedentary behavior, 34-35% in light activity, 3-4% in moderate-intensity activities and 0.
This article addresses the development and implementation of a test bed for applications of heterogeneous unmanned vehicle systems. The test bed consists of unmanned aerial vehicles (Parrot AR. Drones versions 1 or 2, Parrot SA, Paris, France, and Bebop Drones 1. 0 and 2. 0, Parrot SA, Paris, France), ground vehicles (WowWee Rovio, WowWee Group Limited, Hong Kong, China), and the motion capture systems VICON and OptiTrack. Such test bed allows the user to choose between two different options of development environments, to perform aerial and ground vehicles applications. On the one hand, it is possible to select an environment based on the VICON system and LabVIEW (National Instruments) or robotics operating system platforms, which make use the Parrot AR. Drone software development kit or the Bebop_autonomy Driver to communicate with the unmanned vehicles.
On the other hand, it is possible to employ a platform that uses the OptiTrack system and that allows users to develop their own applications, replacing AR. Drone's original firmware with original code. We have developed four experimental setups to illustrate the use of the Parrot software development kit, the Bebop Driver (AutonomyLab, Simon Fraser University, British Columbia, Canada), and the original firmware replacement for performing a strategy that involves both ground and aerial vehicle tracking. Finally, in order to illustrate the effectiveness of the developed test bed for the implementation of advanced controllers, we present experimental results of the implementation of three consensus algorithms: static, adaptive, and neural network, in order to accomplish that a team of multiagents systems move together to track a target.
Diverse communication and control interfaces have been developed for quad rotorcrafts in order to collaborate and to execute complex missions. In the studies of Cavett et al. 1  and Visser et al. ,  2  the authors developed the communication and control interfaces for the AR. Drone. Gururaj et al. 3  controlled the AR. Drone position and velocity through a Visual Cþþ interface whose communication is implemented using the AT commands and the UDP protocol. A control interface was developed for a quad rotorcraft with fire detection capabilities by using Visual C and OpenGL in the study of Hernandez et al. 4  In this sense, a ground station for a quad rotorcraft, based on Java and Visual Cþþ software, was developed by Yang et al. 5  and Xiao-yan et al. 6  Mellado et al. ,  7  Zhekui et al. ,  8  and Garcia Carrillo et al.
Drone and the Bebop Drone that allows users to choose between four different development environments for testing control algorithms involving more than one vehicle, as well as vehicles of different nature (heterogeneous systems). The four development environments differ each other either in the employed vehicles or in the software used for controlling them. The employed software includes LabVIEW (a graphical programing software) and robotics operating system (ROS, Open Source Robotics Foundation). Moreover, the test bed allows users to choose different ways of controlling vehicles based on the replacement of the original firmware which reads the raw data from sensors and controls directly the motors, or more intuitively, by using the available Parrot software development kits (SDKs) or drivers, which have been modified for accepting commands to communicate and control multiples drones.
Figures  1  and 2  sketch the developed test beds for applications of vehicle tracking and multiagent application, respectively. The rest of the article is organized as follows: ''Test bed description'' section presents the description of the main elements of the test bed. ''Developed platforms'' section describes the developed platforms by using the LabVIEW software, the ROS, and the method which does not use the Parrot SDK but uses a custom program. ''MASs application'' section presents the control law used to verify the effectiveness of the proposed test bed. Then, ''Experimental results'' section shows the numerical and experimental results obtained by using the test bed in a controlled laboratory environment. Finally, ''Conclusion'' section provides conclusions of this research work.
Unmanned Aircraft Vehicle (UAV) description The UAVs employed for the implementation of algorithms in the proposed test bed were the Parrot AR. Drone version 2, which is a quad rotorcraft controlled through a Wi-Fi connection. The technical characteristics of this vehicle, such as the processor type and sensors, can be found in the studies of Bristeau et al. 21 and Montufar et al. 22  We have selected the AR. Drone to develop the proposed platform since it is a lowcost platform, very resistant to damages from crashes. In order to use the AR. Drone for custom applications, two approaches are available: (i) an SDK and (ii) the original program of the AR. Drone replacement. The SDK is presented in the next subsection, as it is common to both LabVIEW (see ''LabVIEW platform'' section) and ROS (see ''ROS platform'' section) methods.
Parrot AR. Drone SDK The AR. Drone creates its own Wi-Fi network, and, using the user datagram protocol (UDP), it receives command signals generated by a device connected to the network. Likewise, by means of the UDP protocol, the AR. Drone shares its navigation information (telemetry). The information exchange is carried through three ports: 5554, 5555, and 5556, which are used for (i) navigation data reception, (ii) video package reception, and (iii) command control transmission and parameters configuration, respectively. The SDK provided by Parrot is a set of libraries that allow us to communicate with the AR. Drone by an external device in order to get the navigation data and video package allowing the host computer to configure and to control the AR. Drone. The core of the SDK is the attention commands (AT), which are composed by the string ''AT*'' plus the command name followed by the equal sign, and finally by a sequential number and an optional arguments list.