dataesr/scientific-paragraphs-categorization huggingface dataset:
LabVIEW platform In order to develop algorithms for the AR. Drone by using the software LabVIEW, it is necessary to download and install the AR. Drone Toolkit LVH through the virtual instrument (VI) Package Manager, provided by National Instruments (Austin, Texas, USA). After installation, the AR. Drone Toolkit palette will be available in LabVIEW. In this palette, there are VIs that allow us to start and close the communication with the vehicle, called Open VI and Close VI, respectively. The Control Drone VI is used to control operations such as the takeoff and landing, hover mode, emergency landing, and movement commands. In order to obtain the navigation data of the AR. Drone, the Initialize NavData and Read NavData VIs are used. The provided information consists of the Euler angles and angular velocities, principally.
NET assembly. Once the above steps are performed, it is possible to use the functions included in the SDK, which allow to connect with and to request data from the VICON DataStream. Algorithm 2 summarizes the required steps to get the object's (in this case, a robot) position and orientation from the Vicon Motion Capture System by using the LabVIEW software. Configuration of multiple AR. Drones in LabVIEW. In order to design a LabVIEW program that allows us to manipulate multiple AR. Drones connected to a single personal computer (PC) at the same time, the PC and all the vehicles must be connected to the same router. This section explains how to develop the required procedure. On the one hand, users have to modify the network configuration for each drone. To configure the vehicle's network, it is necessary to Obtain the desired roll and pitch angles and the yaw and vertical speeds from the PPM decoder, from the GUI buttons or from the user control law VI 4: Send the control commands to the drone by using the Control drone VI 5: Read and display the navigation data with the Read NavData The previous configuration removes the AR.
0. 1 in order to command the Parrot AR. Drone versions 1. 0 and 2. 0. The node created from the ROS package is the ardrone_driver. This package uses dependencies such as roscpp, image_transport, sen-sor_msgs, camera_info_manager, and std_srvs (which are employed for programing on Cþþ language) to transport images in low-bandwidth compressed formats, to create messages for sensors, and to calibrate the cameras, respectively. Before running any package, it is necessary to execute the roscore command since it is a collection of nodes and programs necessaries in an ROS-based system. The package ardrone_autonomy consists of 4 input topics and 28 output topics. The input topics include the / ardrone/reset (emergency), /ardrone/land (landing), / ardrone/takeoff (takeoff), and /cmd_vel (roll, pitch, yaw, and thrust), while the output topics provide the navigation and camera data.
If the navigation data (telemetry) is required, it is necessary to create a node subscriber, which is the bridge between the information of the ardrone_autonomy and the user's application. However, a simple way to visualize the data is to read the topic in a terminal by using the command line rostopic list. The /ardrone/navdata topic displays the information of the battery level, the agent's position in x, y, and z axes, the angular position (roll, pitch, and yaw), the altitude, the atmospheric pressure, and other state variables, which, by setting the navdate_ demo parameter, can be configured to be transmitted at frequencies between 15 Hz and 200 Hz. Once all the parameters listed above have been configured, it is possible to develop a program under the ROS environment that allows us to control the Parrot AR.
Drone. This procedure provides a tool with the possibility of controlling the takeoff, landing, emergency, and the four control signals (roll, pitch, yaw, and thrust), with values from À1 to 1. In order to get the vehicle's x, y, and z positions, we use the motion capture system which provides the position and orientation of the vehicle through the communication protocol VRPN. This protocol is a set of classes within a library, and a set of servers interface between application programs and the set of physical devices use in a virtualreality system. In order to use this communication protocol, ROS employs the package called ros_vrpn_client which is a client for VRPN and publishes a transformation frame and TransformStamped of the tracked vehicle. In this sense, it is important to mention that this package was developed to work using the OptiTrack cameras system, but it could be modify to work using the Vicon cameras system.
To change the motion capture system platform, it is necessary to arrange the position of the quaternions since the Vicon cameras system gives a different arrangement than the OptiTrack cameras system (see Table  3 ). Once this procedure has been done, it is necessary to create a new package to control the AR. Drone using the ardrone_autonomy and ros_vrpn_client packages and implementing the different algorithms (Algorithms 3 and 4). These packages read the information provided in a Translation. Z ¼ pos. z() Rotation. x ¼ q. rot. x() Rotation. x ¼ q. rot. x() Rotation. y ¼ q. rot. y() Rotation. y ¼ q. rot. z() Rotation. z ¼ q. rot. z() Rotation. z ¼ -q. rot. y() Rotation. w ¼ q. rot. w() Rotation. w ¼ q. rot. w() file. msg created for the ros_vrpn_client package, which contains the position of each vehicle.
In order to change the default configuration network of the AR. Drone, the TelNet protocol is required, which enables to connect remotely to the embedded operating system of the drone (based on Linux version 2. 6. 32), as it was described in the previous section. In this sense, the vehicles must be connected to a router in order to control multiple drones with a single PC. Once the vehicles are connected to the router, we need to change the SDK 2. 0. 1, which is located inside the package of ardro-ne_autonomy. This procedure is needed due to an issue that does not allow the connection of two or more AR. Drones using the same package. To solve this problem, we need to access the file called vp_com_socket. c, which is located in the path of ARDroneLib/VP_SDK/VP_Com, and to replace the line of code 90, which is: res ¼ VP\_COM\_ERROR; with this code res ¼ VP\_COM\_OK; and then, to compile the package again.
Configuration of multiple Bebop Drones. To employ multiple Bebop Drones (1. 0 or 2. 0), it is necessary to perform the following steps: For each one of the Bebop platforms to use, a Bebop Autonomy ROS package must be installed, changing only the installation directory. All the instructions to install and compile the Bebop Autonomy ROS package are found in the web page http://bebop-auton omy. readthedocs. io/en/latest/index. html For each one of the Bebop platforms, change the communication port in the Bebop ROS package. In order to do this, change the file ARDISCOVERY_-DEVICES_wifi. c located in =bebop=devel=src= libARDiscovery=sources=wifi. The line of code to change is the following: #define BEBOP_DEVICE_TO CONTROLLER_ PORT 43210 Next, it is necessary to rebuild the package. Change the network configuration, converting Bebop to client instead of access point.
Then, integration of sensors, actuators, and filters is straightforward, and each of them can be linked to a different module automatically. The framework also manages the ground station, and then, the programmer does not have to worry about how to exchange information between UAV and ground station. The framework sends data to the ground station (for plotting), and each item's configuration (sensor, filter, and other modules) can be modified from the ground. Moreover, a simulation environment allows to analyze previously the performance of the scripts executed onboard the real UAV platforms, but in a ground station computer. The aim is to test the programs of all UAVs in a single computer, in order to verify that there are no bugs in the code and that everything is working as expected. Therefore, the simulation environment avoids unnecessary crashes on real flights and saves time on the developments.
Conclusion In this article, the development of an experimental test bed for applications involving heterogeneous unmanned vehicle systems was presented. The test bed consists of four different variations of a laboratory setup, which considers the use of aerial vehicles (Parrot AR. Drone and Bebop Drones) as well as ground vehicles (Rovio). The laboratory platforms enable the end user to choose between using a graphical programing software (LabVIEW), a Linux-based operating system (ROS), or to replace the original software of the AR. Drone for implementing novel control algorithms in multiple vehicles. Diverse algorithms were described for configuring both the vehicles as well as the motion capture systems (VICON or OptiTrack), which allowed a simple setup procedure to communicate with the employed unmanned vehicles.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially supported by the  Mexican Program PRODEP UPPACH-004  Grant, by the  Project Red Tematica de Sistemas Autonomos y Ciber-Fı ´sicos , and by the  Mexican National Council for Science and Technology  Grant  263777. This work has been partially sponsored by the  French government research program Investissements d'avenir through the Robotex Equipment of Excellence  ( ANR-10-EQPX-44 ). This work was carried out in the framework of the Labex MS2 T, which was funded by the  French Government , through the program Investments for the future managed by the  National Agency for Research (Reference   ANR-11-IDEX-0004-02 ).
This discrepancy between police reports and those of other rescue and treatment services occurs especially often in low-and middle-income countries  [14]. While solutions involving capture-recapture methodologies  [15, 16]  can be applied to the police and hospital databases to estimate the number of deaths and injuries, even those have significant limitations in certain countries such as Burkina Faso. For example, neither the National Police nor the Yalgado Ouédraogo Hospital have computerized databases on road traffic crashes and injuries. As such, there is a need in Burkina Faso, and in other LICs, to develop tools for reporting road crashes and traumatic injuries so that evidence can be produced to better understand this phenomenon that has become a major problem. The use of mobile technologies and social media has already been shown to be effective in public health surveillance  [17] , although this was in middle-income countries, such as South Africa.
A back-end application was developed to handle the GPS devices' SMSs and link them with the Ushahidi mapping system. This was activated by a police officer at the crash scene. Data were sent in real-time to the platform, which analyzed the SMS content and extracted the relevant information (geographic coordinates) to integrate it into the Ushahidi interface within the map. The capability to detect and remove duplicate reports of the same crash or vulnerable road user (pedestrian or cyclist) collision was also added. The platform was Internet-accessible (http://traumatismes. africasys. com/) for consultation and data exportation (Figure  1 ). To complete the information on the event, data regarding sex, age, type(s) of vehicle(s), injuries, and deaths among the populations involved were collected on paper from the reports prepared by the officers.
These data were entered daily into a database. The core systems ran on a Linux server powered by Ubuntu 12. 04 LTS with 4 Gb of RAM, 2 CPU and 20 Gb of disk space. This main server ran all the applications (Ushahidi, database system, SMS management system, incidents filtering/duplication removal, web interface, etc. ). The platform's data (files and databases) were synchronized automatically at 2:00 a. m. daily on another backup server (which had the same hardware specifications as the master server). With this architecture, it was possible to restore backups in case of corruption and also switch to the backup server in case of outage to minimize platform downtime. Twenty-one National Police officers were trained in the use and maintenance of the geotracer during a one-day training session.
Other equipment-related problems appeared near the end of the six-month period. Several geotracers malfunctioned, mainly due to lack of protection from the heat of the vehicles in which they were installed. Table  1  presents the total cost of the surveillance system over three months, without counting the research activities. Both the methodology and the implementation of this simple surveillance system were nevertheless satisfactory and the results over a period of three months during which the process functioned perfectly illustrated the system's knowledge-producing capacity. The SMS-based system for disseminating crash data validated the proposed process, because it could be replaced by a simple telephone equipped with geolocalization. In total, 873 crash scenes were recorded over 3 months.
The system was accessible on the Internet (Figure  2 ) for open consultation of the map of crash sites, with restricted access to data on precise locations and time-related information. With the map interface it is possible to zoom in and observe the geographic distribution of crashes in a district, and even, for example, on a specific street (Figure  3 ). The interface also makes it possible to monitor crashes over a defined period to assess fluctuations and carry out temporal interpretations of the distribution of crashes (Figure  4 ). All data acquired and integrated into the database could be exported, with no particular conversion problems, from the platform into statistical or spatial analysis software. Descriptive statistical analyses revealed that crashes occurred more frequently in the evenings and during rush hours, as well as on weekends and Mondays.
Acknowledgments The authors thank the  National Police of Ouagadougou. This research project is part of the 'Community research studies and interventions for health equity in Burkina Faso'. We thank the  Canadian Institutes of Health Research (CIHR) , who funded the program [Grant no. ROH-115213 ]. VR holds a CIHR-funded Research Chair in Applied Public Health [Grant no. CPP-137901 ].
Acknowledgements. The authors are grateful to the  Portuguese Environment Agency (APA)  for providing the bathymetry data for the coast of the  Costa da Caparica. The first author was financed by the  Brazilian National Council for Scientific and Technological Development (CNPq)  under the  Ciências Sem Fronteiras programme  (research grant no. 237448/2012-2 ). MARETEC acknowledges the ERDF funds of the  Competitiveness Factors Operational Programme (COMPETE) , and national funds from the  Foundation for Science and Technology (FCT)  (project  UID/EEA/50009/2013 ). Edited by:  John M. Huthnance Reviewed  by:  André B. Fortunato  and one anonymous referee.
In this study, we intend only to differentiate the positions of the constituent elements of the solutions and their intrinsic stiffness so as to adapt them as well as possible to the different degrees of disc degeneration. As a result, a simplified dimensioning tool is needed in this upstream solution search phase  [9]. Different models can then be selected. In an earlier study, we used a model consisting of rigid bodies subjected to forces in mechanical equilibrium  [10]. The main advantage of this model is that it is possible to assess the solution behavior with a very short calculation time (resolution of an analytical model); however, the equation system is associated with a single specific architecture of the solution. In order to put in place a tool that can be more generally applied, we develop a FE model consisting of beam elements and springs, using Matlab.
Strategies for promoting neural regeneration are hindered by the difficulty of manipulating desired neural fates in the brain without complex genetic methods. The subventricular zone (SVZ) is the largest germinal zone of the forebrain and is responsible for the lifelong generation of interneuron subtypes and oligodendrocytes. Here, we have performed a bioinformatics analysis of the transcriptome of dorsal and lateral SVZ in early postnatal mice, including neural stem cells (NSCs) and their immediate progenies, which generate distinct neural lineages. We identified multiple signaling pathways that trigger distinct downstream transcriptional networks to regulate the diversity of neural cells originating from the SVZ. Next, we used a novel in silico genomic analysis, searchable platform-independent expression database/connectivity map (SPIED/CMAP), to generate a catalogue of small molecules that can be used to manipulate SVZ microdomain-specific lineages.
Introduction Controlling the fate of neural stem cells (NSCs) is a key therapeutic strategy in neuroregenerative medicine. The most promising and direct approach would be to use small molecules to promote the generation of a particular neural lineage, without the need to introduce complex genetic methods. A novel strategy consists of identifying drug-like compounds with the ability to induce transcriptional changes that are similar to those observed within neurogenic niches and are associated with acquisition of a specific cell fate  [1, 2]. Such a strategy is facilitated by the accumulation of publicly available datasets that allows the systematic comparison and identification of similarities between transcriptional signatures of biological and drug-induced samples  [3] , a principle that lies behind the connectivity map (CMAP) project  [4].
Identification of divergent signaling pathways in SVZ microdomains The SVZ contains NSCs and their progeny, the transient amplifying progenitors (TAPs), which generate both NPs and OPs. The SVZ can be subdivided into discrete spatial microdomains (or niches) from which distinct neural lineages originate. While subtypes of GABAergic interneurons originate from all SVZ regions, the dorsal SVZ (dSVZ) additionally gives rise to glutamatergic NPs and is the primary source of forebrain OPs (reviewed in  [6, 7, 13] ). To identify the molecular hallmarks that determine cell fate within these microdomains, we previously generated whole transcriptome datasets of NSCs, TAPs, and their respective SVZ niches at postnatal day (P)4, P8, and P11  [1] , which correspond to the postnatal period of greatest germinal activity and lineage diversity  [6, 14].
Here, we interrogated these datasets to identify signaling and metabolic processes that are unique to NSCs, TAPs, and their respective SVZ niches. Transcripts enriched in dorsal versus lateral datasets were compared using GeneGO Metacore for Process Networks, and the function of individual genes were classified using http://www. genecards. org. The top ten Metacore categories in each microdomain were ranked (Fig 1A  and 1B ), and only two categories overlapped, namely "Chemotaxis" and "Notch signaling," stressing the importance of these pathways within the neurogenic niche as well as highlighting the existence of discrete signaling processes that are specific to the dorsal and lateral SVZ microdomains (Fig 1A  and 1B ). Among enriched transcripts generic to the SVZ niche, those coding for secreted signaling factors (grouped here for simplicity as "morphogens") were prominent in both SVZ microdomains (Fig 1C ); many of these were enriched in NSCs/TAPs  (Fig 1D).
e. , the main postnatal forebrain source of OLs) with publicly available transcriptional datasets of oligodendroglial lineage cells  [25]. The results are presented in S3 Table and summarized in Table  1C. Most small molecules were related to "Gene regulation" and"Signaling" (Fig  2D ). Many from the latter category, including two of the top ten ranked pro-oligodendrogenesis drugs LY-294002 and sirolimus, are inhibitors of PI3K/Akt/mTor signaling (Table  1C ), which acts downstream of several ligands enriched in the dSVZ (Fig  1E ). Notably, many of the small molecules associated with oligodendrogenesis were within "Epigenetic" and "Cell cycle" categories, which barely featured in the other SPIED analyses (Fig 2B  -2E ). The highest ranking amongst these were trichostatin-A and vorinostat (Table  1C ), which are potent inhibitors of histone deacetylases (HDACs) with broad epigenetic activities, consistent with evidence that HDACs repression is required during NSC differentiation into OPs  [26, 27].
Bioinformatics Whole genome transcriptome datasets of the isolated SVZ microdomains and region-specific NSCs and TAPs are described in detail in a recent study that described transcriptional regulators acting in SVZ regionalization  [1]. Briefly, the rostral periventricular regions of postnatal mice of different ages (P4, P8, and P11) of the Ascl1-EGFP Bac transgenic reporter mouse line were carefully microdissected under a fluorescent binocular microscope in RNAse-free and sterile conditions. Isolated SVZ microdomains were derived from brain coordinates of +1 and 0 relative to Bregma. The Hes5-EGFP reporter mouse line was used in combination with Prominin-1 immunodetection to isolate NSCs from microdissected dorsal and lateral microdomains by fluorescence-activated cell sorting. Similarly, the Ascl1-EGFP Bac transgenic reporter mouse line was used to isolate the 25% brightest cells, i.
e. , corresponding to TAPs, from either microdomain. Half a litter of animals were used to pool for each replicate throughout. In the present study, these datasets (recently made publicly available from NCBI Gene Expression Omnibus [http://www. ncbi. nlm. nih. gov/geo] GEO Series accession number GSE60905) were analyzed using previously applied bioinformatics methods, with only minor modifications  [1]. In brief, data were cured (background subtraction, normalization, and summarization) using robust multi-chip analysis (RMA) using the Partek Genomic Suite software package version 6. 6 using stringent false discovery rate (FDR) with p-values where necessary in the analysis. All data were normalized collectively with datasets from previous studies of isolated NSC, NPs, and glia (i. e. , GSE60905, GSE9566, GSE18765) for optimal parameters.
Partek was used to assemble affymetrix data and generate hierarchical clustering and gene lists. GO sets were generated using the latest MGI mouse GO datasets via the Broad Institute (http://www. broadinstitute. org/gsea/index. jsp). The numbers of probes that were differentially expressed across the ten samples analyzed (dNSCs, dTAPs, lNSCs, lTAPs, P4 dSVZ, P4 lateral SVZ, P8 dSVZ, P8 lateral SVZ, P11 dSVZ, and P11 lateral SVZ) represented a total of ~37K probe sets within the 10% FDR range. Genego Metacore (https://portal. genego. com/) and GSEA (http:// www. broadinstitute. org/gsea/msigdb/index. jsp) were used to filter and select for probes associated as secreted morphogens (tropic factors, growth factors, extracellular signaling molecules, mitogens, and secreted inhibitors of signaling pathways).
The numbers of morphogen from this filtered list that were significantly altered amounted to 530 probes, representing approximately 330 individual genes. Identification of spatially enriched signaling ligands, regardless of sample type (Fig 1A  and 1B ), was done by comparing all dorsal versus all lateral samples. This gene list was uploaded onto Genego Metacore and Process Network option selected using the default parameters. Determination of the spatial expression profiles of secreted signaling factors in SVZ microdomains (Fig 1C  -1H ) was performed by comparing datasets using appropriate fold changes and FDR cut-offs (Partek, 1. 65-fold change and FDR <5%). For all analyses, raw expression values are provided and Heatmaps are presented in the manuscript.