dataesr/scientific-paragraphs-categorization huggingface dataset:
Import: A set of mechanisms ("uploaders") is provided in TVB to import data into the framework, including neuroimaging data generated independently by other applications. The following formats are supported: NIFTI-1 (volumetric time-series), GIFTI (surfaces) and CFF (connectome file). General compression formats, such as ZIP and BZIP2 are also supported for certain data import routines that expect a set of ASCII text files compressed in an archive. Hence the use of general compression formats means that preparing datasets for TVB is as simple as generating an archive with the correct ASCII files, in contrast to some of the other neuroscientific data formats found elsewhere. For instance, a Connectivity dataset (connectome) may be uploaded as a zip folder containing the following collection of files: (1) areas.
Database management system Internally, TVB framework uses a relational database (DB), for ordering and linking entities and as an indexing facility to quickly look up data. At install time, users can choose between SQLite (a file based database and one of the most used embedded DB systems) and PostgreSQL (a powerful, widely spread, open-source object-relational DB system which requires a separate installation by users) as the DB engine. In the database, only references to the entities are stored, with the actual operation results always being stored in files, due to size. A relational database was chosen as it provides speed when filtering entities and navigating entity relationship trees.
In the case of a FloatArray, the dtype attribute is fixed as being numpy. float64. More complex, higher-level, TVB-Datatypes are then built up with attributes that are themselves basic TVB-Datatypes. For example, TVB-Connectivity is datatype which includes multiple FloatArrays, as well as a number of other traited types, such as Integer and Boolean, in its definition. An example of a FloatArray being used to define an attribute of a Connectivity can be seen in Code 1. The high-level Datatypes currently defined in TVB are summarized in Table  2. An example indicating the usage and features of TVB-Datatypes is provided below. When a user uploads a connectivity dataset through the UI, an instance of a Connectivity datatype is generated. This Connectivity datatype is one of the required input arguments when creating an instance of SIMULATOR.
As a result of the execution of a simulation, other TVB-Datatypes are generated, for instance one or more TimeSeries datatypes. Specifically, if the simulation is run using the MEG and EEG recording modalities then TimeSeriesMEG, TimeSeriesEEG, which are subclasses of TimeSeries, are returned. and TimeSeries datatypes are accepted by a range of appropriate analysis and visualization methods. Further, TVB-Datatypes have attributes and metadata which remains accessible after exporting in TVB format. The metadata includes a technical description of the data (storage size for instance) as well as scientifically relevant properties and useful documentation to properly interpret the dataset. In the shell interface, the attributes of TVB-Datatype can be accessed by their key-names in the same way as Python dictionaries.
Noise Noise plays a crucial role for brain dynamics, and hence for brain function  (McIntosh et al. , 2010). The NOISE module consists of two base classes: RandomStream that wraps Numpy's RandomState class and Noise. The former provides the ability to create multiple random streams which can be independently seeded or set to an explicit initial state. The latter is the base class from which specific noises, such as white and colored  (Fox et al. , 1988) , are derived. In TVB's implementation Noise enters as an additional term within the stochastic integration schemes, and can be either an Additive or Multiplicative process  (Klöden and Platen, 1995). As well as providing a means to generate reproducible stochastic processes for the integration schemes, the related classes in NOISE are used to set the initial conditions of the system when no explicit initial conditions are specified.
Monitors The data from a simulation is processed and recorded while the simulation is running, that is, while the differential equations governing the system are being integrated. The base class for these processing and recording methods is the Monitor class in the MONITORS module. We consider two main types of online-processing: (1) raw or low-level; and (2) biophysical or high-level. The output of a Monitor is a 4-dimensional array (which can be wrapped in the corresponding TimeSeries datatype), i. e. , a 3D state vector as a function of time. For the first kind of Monitors these dimensions correspond to [time, state variables, space, modes] where "space" can be either brain regions or vertices of a cortical surface plus non-cortical brain regions. The number of state variables as well as the number of modes strictly depend on the Model.
Initializing a 12. Determine if the Integrator is deterministic or stochastic. If the latter, then configure the Noise and the integration method accordingly. 13. Set initial conditions. This is the state from which the simulation will begin. If none is provided, then random initial conditions are set based on the ranges of the model's state variables. Random initial conditions are fed to the initial history array providing the minimal state of the network with time-delays before t = 0. If initial conditions are user-defined but the length along the time dimension is shorter than the required horizon, then the history array will be padded using the same method of described for random initial conditions. 14. Configure the monitors for the simulation. Get variables of interest. As a working example, in Code 2, we show a code snippet which uses TVB's scripting interface and some of the classes and modules we have just described to generate one second of brain activity.
The for loop in the example code allows scripting users to receive time-series data as available and separately for each of the monitors processing simulated raw data. In this implementation, at each time step or certain number of steps, data can be directly stored to disk, reducing the memory footprint of the simulation. Such a feature is particularly useful when dealing with larger simulations. Likewise, data can be accessed while the simulation is still running, which proves to be advantageous for modeling paradigms where one of the output signals is fed back to the network model as stimulation for instance (see the paragraph about Dynamic modeling in section 3).
TESTING FOR SPEED In the context of full brain models there is no other platform against which we could compare the performance results for TVB and define a good ratio run-time/real-time. As a first approximation a simple network of 74 nodes, whose node dynamics were governed by the equations of the Generic2dOscillator model (see Code 3) was implemented in the Brian spiking neural network simulator. The integration step size was 0. 125 ms (dt = 2 -3 ms) and the simulation length was 2048 ms. This network was evaluated without time delays and using a random sparse connectivity matrix. Execution times were about 4. 5 s in Brian and 15 s in TVB. In contrast, when heterogeneous time delays were included, running times of the simulations implemented in Brian increased considerably (approximately 6.
They built parameter space maps using the Kuramoto synchronization index. Here, using TVB's scripting interface, we show it is easily possible to build a similar scheme and perform a parameter space exploration in the coupling strength (gcs) and transmission speed (s) space. The Connectivity upon which the large-scale network is built was the demonstration dataset. It is bi-hemispheric and consists of 74 nodes, i. e. , 37 regions per hemisphere. It includes all the cortical regions but without any sub-cortical structure such as the thalamic nuclei. Its weights are quantified by integer values in the range 0-3. The evolution of the local dynamics were represented by the model Generic2dOscillator, configured in such a way that a single isolated node exhibited 40 Hz oscillations (Figure  8 ).
We systematically stimulated the area corresponding to the primary visual cortex (V1) to demonstrate the functioning of TVB stimulation Patterns and observed how the activity elicited by a periodic rectangular pulse propagates to neighboring regions, especially V2. Benefiting from TVB's flexibility we show in Figure  11  that it is possible to systematically stimulate a specific brain region  Here brain regions are weakly coupled changing both the collective and local dynamics of the network. (B) Using TVB scientific library as a python module we can conveniently run thousands of simulations in parallel on a cluster. Note that TVB parallelizes different tasks e. g. , simulations and analyses, taking advantage of multi-core systems, however, it does not parallelize the processes themselves.
Simultaneous simulations allow for a systematic parameter space exploration to rapidly gain insights of the whole brain dynamics repertoire. In this plot, the magnitude and color scale correspond to one the variance computed over all the elements of the N-dimensional output array (Global Variance). Simulations were performed on a cluster based on the Python demo scripts available in the release packages. On of the major strengths of The Virtual Brain is that G-Users are enabled to launch parameter sweeps through the UI without the need to know how to submit parallel jobs (see Figure  10 ). (e. g. , V1) and to highlight the anatomical connection to its target region (e. g. , V2) by observing the arrival of the delayed activity; analyze the responses of the model; handle multi-modal simulated data; and extract metrics from computationally expensive algorithms to characterize both the "resting" and "evoked" states.
To allow a most optimal dissemination of knowledge in TVB we are currently developing a web-based educational platform that will allow training on the usage of TVB, as well as serve as a key reference. As simulations in TVB are built on the large-scale anatomical structure of the human brain, continued work to integrate new, reliable, sources of structural data is essential to the progress of the platform. An obvious future resource in this regard will be the newly developed database of the Human Connectome Project  (Essen and Ugurbil, 2012; Essen et al. , 2012).
2: Health states and clinical outcomes. Epidemiology Country-specific data are available on national statistics websites, which may be complemented by trade-and peer-reviewed literature searches. Health outcomes Given that the main objective of this analysis is to estimate the economic impact of a triptan Rx-to-OTC switch, and not the health benefit, the model did not include health outcomes. On the other hand, serious adverse events were considered, in particular, cardiovascular events that may occur when undiagnosed patients switch to OTC triptan. Only economic impact of serious adverse events was included in the model.
Input data Input data include epidemiological and resource utilization data. 1: Epidemiological Data. Populations This section presents the methodology used to estimate the numbers of patients of each sub-population. First we estimated the total country population aged over 18, and applied a country-specific migraine prevalence rate to calculate disease incidence. Then we used a diagnosis rate, to distinguish between the diagnosed and the undiagnosed patient populations. We created a treatment algorithm based on publically available prescription patterns as well as sales data. For diagnosed patients, we identified treated and untreated migraine patients. Subsequently, for the treated population, we estimated the population sizes depending on their treatment strategies (Rx, both Rx and OTC, or untreated).
Finally, by combining these estimates with sales data of triptan to be switched and other Rx triptans, we ended up with an approximate size of patients in Pop 1, Pop 2, Pop 3, and Pop 4. For the undiagnosed patients, we assumed that the percentage of treated patients (OTC, self-treated) corresponds to the percentage of those diagnosed and treated. This allowed an estimation of the size of Pop 5 and Pop 6. Switch rates Switch rates were mainly based on estimates from past examples of Rx-to-OTC switches in Europe. These were obtained from internal pharmaceutical sales data sources. In the base case scenario we assumed the peak uptake of OTC triptan to be switched would be 20% among both patients currently taking the Rx triptan and those on any other triptan, and 3% among patients either diagnosed with triptan but currently taking OTC drugs at the time of migraine attacks, or not using any pharmaceutical treatments.
Checks The first check we used to ensure the validity of our results was verifying that the sum of all bubble coefficients (2. 8) coincides numerically with the tree amplitude, as discussed in Section (2. 2). We ensured this for all distinct helicity configurations at 6, 7 and 8 points. As another strong and independent cross-check, we compared our results with numerical data kindly produced with the help of the powerful NGluon package  [58]  by one of its authors. To simulate the N = 1 chiral multiplet in the loop, we had to add separate contributions from the fermion and the scalar loop. Moreover, to remove the discrepancies due to different implementation of spinor-helicity formalism, we compared ratios of the master integral coefficients to the tree amplitude. In this way, we witnessed agreement for all types of coefficients up to machine precision of 13 digits for 8-point amplitudes and 12 digits for 17-point amplitudes.
Acknowledgments I would like to thank my supervisor  Ruth Britto  for her guidance and encouragement, as well as for useful comments on this manuscript. I would also like to express my gratitude to  Simon Badger  and  Edoardo Mirabella  who provided valuable numerical cross-checks of the presented analytic results. I am also very grateful to  Bo Feng ,  Gregory Korchemsky ,  David Kosower ,  Piotr Tourkine  and  Yang Zhang  for helpful discussions. I would like to extend my appreciation to the  Bethe Center for Theoretical Physics of the University of Bonn  for the hospitality at the initial stage of the project. This work was partially supported by the  Agence Nationale de la Recherche  under grant number  ANR-09-CEXC-009-01.
A greater accumulation of mutations on the   molecular surface and the loops of the protein ensures the preservation of structurally and functionally important core residues. Clearly, a large number of residues across the entire vertebrate NGF lineage appear to be highly conserved (Figure  S5 ). Moreover, it has been demonstrated on numerous occasions that snake venom NGFs exhibit biological activities similar to their mammalian counterparts  [44, 74]. This would seemingly free the duplicate elapid NGF gene to participate exclusively in prey envenoming. In contrast to the Elapidae NGF, only one copy was found in the venom glands of the Anguimorpha lizard Abronia graminea, which was identical to the one recovered from genomic sequencing for use in taxonomy studies  [27]. Moreover, despite extensive BLAST searches using sequence templates from a wide array of mammalian and reptilian NGFs against the genomes of Anolis carolinensis (Iguania) and Python molurus (Henophidia), we were only able to retrieve a single copy of the NGF gene.
Phylogenetic Analyses Phylogenetic analyses were performed to allow the reconstruction of the molecular evolutionary history of vertebrate NGF, BDNF and NT-3 genes. Nucleotide sequences were downloaded from the National Center for Biotechnology Information database (NCBI: http://www. ncbi. nlm. nih. gov/) and a dataset of 1183 sequences (NGF: 308; BDNF: 434; NT-3: 441) was compiled. Accession numbers of all sequences analysed in this study are available in Tables S5. 1-S5. 3. Unpublished sequences are available in the File S1. Resultant sequence sets were aligned using MUSCLE  [87]. The phylogenetic relationships were determined using Bayesian and maximum-likelihood approaches. MrBayes version 3. 2  [88]  was used for Bayesian inference. Tree searches were run using four Markov chains for a minimum of 10 million generations, sampling every 100th tree.
Test for Recombination To overcome the effects of recombination on the phylogenetic and evolutionary interpretations  [90] , we employed Single Breakpoint algorithm implemented in the HyPhy package and assessed recombination on all the toxin forms examined in this study  [91, 92]. When potential breakpoints were detected using the small sample Akaike information Criterion (AICc), the sequences were compartmentalized before conducting selection analyses, so as to allow recombining units to have distinct phylogeny.
SLAC, FEL, REL and FUBAR  [99, 100]  implemented in HyPhy  [101]  were employed to provide additional support to the aforementioned analyses and to detect sites evolving under the influence of positive and negative selection. MEME  [102]  was also used to detect episodic diversifying selection. Further support for the results of the nucleotide-level selection analyses was obtained and the radicalness of mutations were assessed using a complementary protein-level approach implemented in TreeSAAP  [103]. Direct comparison of ω values computed from different datasets can be misleading, as they can have different proportion of sites under selection. Hence, we assessed the selection pressures shaping the evolution of NGF along various toxicoferan reptilian lineages by employing clade model analyses implemented in codeml and simultaneously estimated ω values  [104].
KS was funded by PhD grant ( SFRH/BD/61959/2009 ) from F. C. T ( Fundação para a Ciência e a Tecnologia ). BGF was funded by an  Australian Research Council   Future Fellowship  and by the  University of Queensland. TNWJ was funded by an  Australian Postgraduate Award. NRC was funded by fellowship award  ( NE/J018678/1 ) from the  Natural Environment Research Council, UK. EABU acknowledges funding from the  University of Queensland  ( International Postgraduate Research Scholarship, UQ Centennial Scholarship , and  UQ Advantage Top-Up Scholarship)  and the  Norwegian State Education Loans Fund. NV was funded by the  'Service de Systématique Moléculaire ' of the  Muséum National d'Histoire Naturelle [UMS 2700 (OMSI) ]. GFK was funded by  Australian Research Council Discovery  Grant  DP130103813.
WAHREN routes the data of one given station not only to its nearest neighbors but also to its second-nearest neighbors, providing the redundancy required to bypass a station gone "dark". More information about the implementation of the WAHREN structure at the RDA site can be found in  [6, 7]. During August 2011, a series of integration tests of the RDA SD communication system was performed. The goal of this exercise was to test and debug the entire SD communication chain from a LS controller, through the Central Data Acquisition System (CDAS). As illustrated in Figure  4 , the path comprised seven interacting processes running on five computing platforms, including: -A LS controller to generate simulated second-level triggers and routine monitoring data, and to forward that data over a wired CAN Bus connection to the Local Radio (LR), -A WAHREN Baseband Board (BB) serving as a LR for a typical tank, -A second BB serving as a Concentrator Radio (CR) for the sector CS, -A Single Board Computer (SBC) interface between the CR and the CS, -A Computer running the CS, Postmaster (PM), and CDAS software.
-A fully functional GPS receiver to synchronize timing between the LR and CR. For this indoor testing, no radio frequency (RF) daughter cards were needed. Rather, the wireless link between the LR and CR was emulated by a ribbon cable carrying the analog signals that would normally run between each baseband board and its RF daughter card. During testing, end-to-end communication was successfully established. The system was cold-started from a completely shut-down condition. First, the CR established two-way communication with the CS computer via the SBC. Then, the LR established communication with the CS via the CR and SBC, and was integrated into the running system. Finally, the LS began generating simulated second level trigger messages, which were successfully relayed to the CDAS software.
In addition, error-injection code was written that exercised some of the fault-detection and response mechanisms in the CS computer. Specifically, the CR was programmed to corrupt a specified fraction of second level trigger messages en route to the CS. Whenever an incorrect second level trigger message was received by the CS, it issued a Negative Acknowledgement (NACK) message which was properly relayed to, and received by the LR. The last elements of the communication electronics, the RF boards, are currently being tested and will likely go in production over the summer 2012.
ΛCDM Simulation One of the goals of this study is a comparison of the observed evolution in the COSMOS LSS with current theoretical models. For this, we make use of mock simulation catalogs generated for an area and volume equivalent to the COSMOS survey. The mock catalogs are based on ΛCDM simulations which start at z = 127 evolved down to z = 0  (Wang et al. 2008 ). For comparison with the COSMOS data we make use of their WMAP3YC simulation, which adopts cosmological parameters derived from a combination of third-year WMAP data on large scales, and Cosmic Background Imager and extended Very Small Array data on small scales  (Spergel et al. 2007)  (with Ω M = 0. 226 and Ω Λ = 0. 774). Their mass and force resolution are the same as used in the Millennium Simulation  (Springel et al. 2005) , while the volume is smaller by a factor of 64.
The galaxy formation model of De Lucia & Blaizot (2007) was adopted to calculate the galaxy properties. This model has been able to reproduce many aspects of local galaxy populations (e. g. Croton et al. 2006; De Lucia & Blaizot 2007)  and high redshift galaxy properties  (Kitzbichler & White 2007; Guo & White 2009). For WMAP3, two sets of parameters are found to reproduce the local observational data (see  Wang et al. 2008; De Lucia & Blaizot 2007; Croton et al. 2006; Springel et al. 2005). The simulations track halo dark matter masses, star formation rates (SFRs) and stellar masses. This simulation was extremely valuable for evaluating the effectiveness of our techniques for identifying LSS in the presence of redshift errors similar to those of the COSMOS photoz, and for analysis of the scaling between the derived 2-d surface densities of galaxies and the 3-d volume density of galaxies, for the range of LSS expected to be present at high redshift.
We thank the referee for helpful suggestions which have very much improved this work and we thank  Zara Scoville  for proofreading of the manuscript. Support for this work was provided by  NASA  through Contract Number  12712786  issued by JPL. Additional information on the COSMOS survey is available from the main COSMOS web site at <http://www. astro. caltech. edu/cosmos>. It is a pleasure to acknowledge the excellent services provided by the  NASA IPAC/IRSA  staff in providing online archive and server capabilities for the COSMOS datasets. The environmental densities and mpeg versions with all 127 redshift slices of the maps shown in Fig. 6-8 will be available in FITS format from the  NASA IPAC/IRSA  archive at < http://irsa. ipac. caltech. edu/data/COSMOS/>. KS acknowledges support from the  National Radio Astronomy Observatory  which is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc.
GQ acknowledges support from the  National basic research program of China (program 973  under grant No. 2009CB24901 ), the  Young Researcher Grant  of  National Astronomical Observatories, CAS , the  NSFC grants program  (No. 11143005 ) and the  Partner Group program  of the  Max Planck Society. Facilities: HST (ACS), Subaru (SCAM), Spitzer (IRAC).