dataesr/scientific-paragraphs-categorization huggingface dataset:
The bridge resistance R b was adjusted to obtain an initial voltage of 2. 5 V. The experimental set-up is shown in Figure  5. Sensors 2017, 17, 1337 9 of 14 The electrical response of the sensor to strain was recorded over time by a data acquisition system with a built-in voltage source from Keithley. The internal power supply provided a voltage set at 5 V and allowed for measurements with high accuracy. This apparatus was connected to a computer to record, plot, and process the sensor electrical voltage during the tensile elongation of the substrate. A simple divider bridge was implanted between the Keithley and the MTS 2/M bench, which recorded the strain applied to the sensor over its deformation. The bridge resistance Rb was adjusted to obtain an initial voltage of 2. 5 V. The experimental set-up is shown in Figure  5.
The sensor electrical resistance was obtained using the simple divider bridge, as defined in Equation (3). A test procedure similar to that described in Section 2. 3. 2 was used for recording the mechanical behavior of the sensors. The sensor was caught in the jaws. The conductive track was positioned parallel to the direction of the extension, and at the center of the tested sample. During the tests, given the disposition of the connecting wires on the conductive track, the current flowed in the same direction as the sensor extension. Thus, the longitudinal piezoresistive effect was measured. Six sensors systems were prepared as described in the methods. The recorded signal from the sensor was plotted over the strain applied, the obtained elongation and the time spent. Data were expressed and a normalized relative resistance was defined by (Equation (  4 )) to characterize the electrical response of the sensors independently from the resistance variations observed from an individual sensor to another.
Six sensors systems were prepared as described in the methods. The recorded signal from the sensor was plotted over the strain applied, the obtained elongation and the time spent. Data were expressed and a normalized relative resistance was defined by (Equation (  4 )) to characterize the electrical response of the sensors independently from the resistance variations observed from an individual sensor to another. R n = (R -R 0 )/R 0 (4) where R (Ω) is the electrical resistance of the sensor during the measurements; and R 0 (Ω) its initial value (without extension). The results were obtained by plotting the variation of the normalized relative resistance R n versus the sensor relative elongation ε r. The slope of the obtained curve was the gauge factor K, as defined in Equation (  5 ) as follows: K = R n /ε r.
Acknowledgments: The  National Center for Space Studies (CNES) of Toulouse  is acknowledged for the financial support of this research project. We are also grateful to the  Zodiac Marine Corporation (Toulouse)  for performing electromechanical tests. We also would like to thank  Hanifa Skhara  and  Aurélie Blanchon-Martin  ( Bluestar Silicone ) for providing the aqueous silicone.
The most of the earlier studies on the topic were dedicated essentially to measure the amplitude of deflected beam  [12] [13] [14]  and to evaluate its impact on the analysis. Recent works propose some experimental equipment and methods to minimize the effect of the gas interaction electron beam and to study the dielectric samples  [15]. The interaction was restricted to the primary electron beam with atoms/molecules of gas. Consequently, the interaction volume due to the presence of the skirt, which is the key parameter for spatial resolution, is not considered. Recent studies  [16]  have compared the skirt radius with X-ray emission volume radius and the generation volume radius of the backscattered electrons in the material calculated from the Monte Carlo computation (CASINO V2. 42 software) in high vacuum mode.
The maximum PA intensity of ICG in liposomes was observed after laser irradiation at 806 nm and shifted upon dilution to 800 nm. The PA signal displayed in red in Figure  3B  was overlaid with the US signal from the tubes (grey). PA average intensity (IPA) was calculated in the selected regions of interest (ROIs) and then plotted against ICG amount in pmol. The data show that liposomes were still detectable at a concentration of 2 pmol. The concentration dependence of the PA signal was logarithmic, expressed by the equation y=0. 2872ln(x) +0. 3643, where y represents the PA signal intensity and x represents the quantity of ICG liposomes in pmol (Figure  S1 ). To determine the maximum depth of signal penetration, 2 nmol of liposomes in 50 µL were placed at different depths in a chicken muscle phantom.
In vivo evaluation The accumulation of control and targeted ICG-labeled liposomes was evaluated in nude female mice bearing epidermoid carcinoma (A431) xenografts. The animals were divided into two groups with n=5 in each and received single intravenous injection of either control or targeted formulation (100 µL of liposome solution with the total amount of 6 nmol of ICG per mouse). All tumors were imaged with the PA Vevo ® LAZR system (FUJIFILM VisualSonics, Amsterdam, the Netherlands) at 800 nm before injection and 15 minutes and 4 hours after injection. The tumors were scanned in three dimension (3D) with the help of an automated motor system. For each time point, the tumors were reconstructed in 3D (Figure  S2 ), and the relative increase in the mean PA signal per tumor volume (related to the value before injection) was calculated (photoacoustic signal [PS]%) using the Vevo ® LAB software (Figure  4 ).
4  For this purpose, the PA data were registered at various wavelengths -700, 750, 800, 850, and 900 nm -in two-dimensional (2D) tumor slices. Then, the acquired data were automatically unmixed using the Vevo LAB software considering the PA spectrum of ICG-labeled liposomes (Figure  3 ) and the blood components (existing as a preset in the software). Figure  5A  demonstrates an example of an unmixed 2D tumor slice 4 hours after the injection of control and targeted liposomes. Similar to the non-unmixed PA image (Figure  4A ), the ICG signal was non-homogeneously distributed within the tumor and localized next to tumor blood vessels and surrounding areas. The calculated PA intensities (IPA) from tumor ROIs in unmixed images (Figure  S3 ) after the injection of either targeted or control liposomes are presented in Figure  5B.
Experimental section Reagents and solvents were purchased from Sigma Aldrich (Taufkirchen, Germany), Glen Research (Sterling, VA, USA), or Alfa Aesar (Thermo Fisher Scientific, Waltham, MA, USA) and were used without further purification. Phospholipids were purchased from Bachem AG (Bubendorf, Switzerland) or Avanti Polar Lipids (Alabaster, AL, USA). Fluorescent dyes were purchased from Sigma Aldrich (Germany). Cell culture media and additives were from Gibco ® (Thermo Fisher Scientific, Waltham, MA, USA). For the mass spectrometry analysis, an Ultraflex III MALDI-TOF-TOF instrument (Bruker Daltonics GmbH, Bremen, Germany) equipped with a Smartbeam 2 laser with a repetition rate up to 200 Hz (500 laser shots) was used. The obtained mass spectrum was analyzed with Flex-analysis version 4. 0 software (Bruker Daltonics GmbH).
The HPLC separation and analysis were performed with Waters ® Alliance 2695 (Waters ® Corporation, Milford, MA, USA) apparatus equipped with the ultraviolet (UV) detector Waters ® 486 (Waters Corporation) and analyzed with Millennium 32 software (Waters Corporation). The reverse phase analytical C8 column (Macherey-Nagel, GmbH & Co, KG, Hoerdt, France) was used for all separations.
To separate the liposomes from the free dye, they were purified by sizeexclusion chromatography with Sephadex G75 resin (Sigma Aldrich). The liposome solutions were stored at 4°C. The size, distribution, and zeta potential measurements of control and targeted liposomes were performed in triplicate by dynamic light scattering (DLS) using Zetasizer Nano ZS (Malvern Instruments, Malvern, UK) at 25°C. Calculations of size distribution based on light scattering intensity were performed by the Zetasizer Nano software from the correlation functions using the general purpose algorithm (Malvern Instruments). The liposomal stock dispersions were diluted by a factor of 15 with 10 mM HEPES buffer before measurements performed by DLS. The size distribution data are presented in Figure  S6. The liposome fluorescence intensity and absorbance were measured with Tecan Infinite 200 Pro (Tecan Trading AG, Männedorf, Switzerland) and analyzed with Tecan i-control, v.
For the 4°C uptake studies, the cells were preincubated in the refrigerator for 30 minutes. After incubation with liposomes, the cells were washed three times in 1× PBS to remove free liposomes and fixed with 4% paraformaldehyde. The cell nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI) following the manufacturer's protocol (Life Technologies). The cellular uptake was monitored by fluorescence microscopy with an Axio Imager M2 microscope and a high-resolution AxioCam MRm Rev. 3 camera (Zeiss Group, Oberkochen, Germany) under 20× or 40× magnifications. Various channels were imaged -DAPI (ex. 365 nm, em. 455 nm) to visualize nuclei and DsRed (ex. 554 nm, em. 605 nm) to observe liposome uptake trough RhB fluorescence. For each individual well, more than ten images were collected, and the fluorescence intensity was then quantified with the ImageJ software (US National Institutes of Health, Bethesda, MD, USA).
The addition of reactions allows the ECS be modeled as an active medium glial buffering of K +. Ischemia impedes ATP production which results in a failure of the Na + /K + -ATPase pump and a rise in extracellular K +. Once extracellular K + exceeds a threshold it will cause neurons to depolarize, further increasing extracellular K +. NEURON's reaction-diffusion module NRxD  [2]  provides a platform where detailed neurons models can be embedded in a macroscopic model of tissue. This is demonstrated with a multiscale biophysical model of ischemic stroke where the rapid intracellular changes are coupled with the slower diffusive signaling. A neuron's electrical activity is governed not just by presynaptic activity, but also by its internal state. This state is a function of history including prior synaptic input (e.
g. cytosolic calcium concentration, protein expression in SCN neurons), cellular health, and routine biological processes. The NEURON simulator  [1] , like much of computational neuroscience, has traditionally focused on electrophysiology. NEURON has included NRxD to give standardized support for reaction-diffusion (i. e. intracellular) modeling for the past 5 years  [2] , facilitating studies into the role of electrical-chemical interactions. The original reactiondiffusion support was written in vectorized Python, which offered limited performance, but ongoing improvements have now significantly reduced run-times, making larger-scale studies more practical. New accelerated reaction-diffusion methods are being developed as part of a separate NEURON module, crxd. This new module will ultimately be a fully compatible replacement for the existing NRxD module (rxd).
The objective of the present study is to assess the impact of these changes on parameter estimates and stability. We therefore compared performance between an early version of DCM (v6303) and a newer version of DCM (v6801) in combination with the parametric empirical Bayesian (PEB) framework  [2]. Both were compared regarding (1) ability to explain observed cross spectral densities (CSD), (2) estimated network structure, and (3) stability of parameter estimates. An extensive single-subject longitudinal dataset, including 101 resting state fMRI sessions, was analyzed (myconnectome. org/wp) [3]. Eight resting state sessions were chosen for our analyses: occipital and lateral visual, auditory, somatomotor, left and right frontoparietal, default mode, and executive control network. Results showed that the newer spDCM-PEB combination explained the data (i.
In this study, we compared global biophysical model parameters between brain tumor patients and healthy controls. To this end, we used The Virtual Brain (TVB;  [1] ), a neuroinformatics platform that utilizes empirical structural connectivity data to create dynamic models of an individual's brain. Ten glioma patients (WHO grade II and III, mean age 41. 1yo, 4 females; 5 from open access dataset  [2] ), 13 meningioma patients (mean age 60. 23y, 11 females), three pseudo-meningioma patients (subtentorial brain tumors, mean age 58yo, 2 females) and 11 healthy partners (mean age 58. 6y, 4 females) were included in this study. From all participants, diffusion MRI, resting-state fMRI and T1-weighted MRI data were acquired. Data were preprocessed and converted to a subjectspecific structural and functional connectivity matrix using a modified version of the TVB preprocessing pipeline  [3].
The project will also develop protocols, standards, and mechanisms that allow the community to add data, analysis tools, and model content to KnowledgeSpace. The initial development of KnowledgeSpace has been driven and supported by the International Neuroinformatics Coordinating Facility (INCF; incf. org), the Neuroscience Information Framework (NIF; neuinfo. org) and the Blue Brain Project (BBP; bluebrain. epfl. ch). The KnowledgeSpace also represents an important component of the Neuroinformatics Platform being deployed in the Human Brain Project web portal. KnowledgeSpace is currently transitioning to a shared governance model, with a Governing Board composed of members of the neuroscience community who are currently funded to generate or share data and/or code as part of a lab, project or organization, and who will rotate off the board when their project ends.
The results have been obtained by using the Brain Simulation Platform (BSP) of the Human Brain Project and two open-source packages, the Electrophys Feature Extraction Library (eFEL, https://github. com/BlueBrain/ eFEL) and the Blue Brain Python Optimization Library (BluePyOpt)  [1]. They have been integrated into the BSP in an intuitive graphical user interface guiding the user through all steps, from selecting experimental data to constrain the model, to run the optimization generating a model template and, finally, to explore the model with in silico experiments. Electrophysiological features were extracted from somatic traces obtained from intracellular paired recordings performed using sharp electrodes on CA1 principal cells and interneurons with classical accommodating (cAC), bursting accommodating (bAC) and classical non-accommodating (cNAC) firing patterns.
The results obtained in this study, by combining experimental datasets with our neural network computational model, shows that while the synaptic contribution is mainly involved in shaping the network burst, the key player in the generation of network bursts could be found in the local properties of the neuronal network. Specifically, with functional connectivity analysis, we found and detected, both in simulation and in experiments, a few and specific 'hot spots' of the networks that matched with the ignition sites of the propagations. In particular, in the model, the neurons of to the hot spots were much more responsive than any other region to mild stimulations delivered to these regions. Although the connectivity was truly uniform by design we found that the 'hot spots' were characterized by local graph properties (i.
Examples include Matlab GPU extensions  [1] , Ten-sorFlow GPU support  [2] , Theano GPU extensions  [3]  and so on. Here we present the first public release of Brian2GeNN  [4] , a software package that connects the popular Brian 2 simulator  [5]  to the GPU enhanced neuronal networks (GeNN) framework  [6]  to provide effortless GPU support for computational Neuroscience investigations to Brian 2 users. Brian2GeNN was first announced at CNS*2014 and has undergone a long phase of maturation and development until its first public release this year. It is a Python based package that allows users to deploy their Brian 2 models to a device named "genn", using the simple command "set_ device('genn')". This triggers the use of Brian2GeNN, which generates code that can be executed on GPUs using GeNN.
Combining the parallel TetOpSplit molecular solver developed by OIST and EPFL's parallel EField solver based upon the PETSc library, our new release addresses the limitations of above test cases, and allows full scale parallel simulation of the complete Purkinje cell calcium burst model. It also contains new changes that are essential to parallel STEPS modelling and simulation pipeline, such as the improved python binding using Cython technology. In this poster, we will use this model as an example to showcase the general procedure of converting a serial STEPS simulation to its parallel counterpart using these new changes. We will also analyze the performance and scalability of our integrated solution, and discuss the direction of future STEPS development. The cortex of the cerebellum is one of the most well-characterized regions of the brain, comprising three distinct layers whose connectivity is well understood.
They are, in general, massively parallel multicore co-processors and have become a cornerstone of modern high performance computing  [4]. The hardware architecture of these two accelerator families differ significantly and thus require different software approaches. While both are programmable via the common programming interface OpenCL, important features such as unified memory or remote direct memory access (RDMA) are often only supported in the native hardware architecture specific programming frameworks  [5, 6]. These not only need to be integrated into an overall parallel software system performing a coherent spatial simulation but also need to scale well over several accelerators and compute nodes. Previous research has shown that we can exploit the computational power of accelerators to improve spatially homogenous stochastic simulations by two orders of magnitude while avoiding the limitation imposed to the size of the reaction system to be simulated by the small fast memory space  [7].
STEPS implements a spatial version of Gillespie's stochastic simulation algorithm computing reaction-diffusion systems on a mesh of tetrahedral sub-volumes  [1, 8]. Operator splitting techniques allow to separate the reaction of molecules within a sub-volume from the diffusion of molecules between them. We develop a layered hybrid software architecture using classic central processing units as well as multiple accelerators, integrated into STEPS. Multiple sub-volumes are assigned to an accelerator. To accommodate the different hardware characteristics, NVidia GPUs are applied within a sub-volume and the Intel Xeon Phi at the level of the operator splittings. Furthermore, due to differences in the performance characteristics of the accelerators the use of load balancing at the tetrahedral mesh level will be important.
Our architecture will be a plug-in solution to STEPS not requiring any changes to the interfaces towards the user or other software systems of STEPS itself. The relationship between the structure, or topology, of a neural network and its dynamics remains largely unexplored. This relationship may be particularly significant for the place cell network in region CA3 of the hippocampus. Place cells are believed to encode position by firing when the animal is in a specific spatial location  [1]. Multiple "charts" mapping place cells to locations for several different environments may be stored simultaneously in the network  [2]. Given hippocampal neurogenesis and synaptic plasticity, the place cell network should be robust to small perturbations in its topology: it shouldn't "forget" charts if the pattern of synaptic connections changes slightly.
Given that Granger causality (GC) and DCM are two main causality methods and have distinct but complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity  [4] [5] , therefore it's important to use a same dataset to compare two models. We used the dataset of college students previously reported in our study  [2]. All fMRI data were collected using a 3-Telsa Siemens Skyra scanner and processed using the Data Processing Assistant for Resting-State fMRI, which is based on SPM and Resting-State fMRI Data Analysis Toolkit  [2] [3]. For fMRI analyses, we conducted the standard procedures included slice timing, motion correction, regression of WM/CSF signals and spatial normalization  [3]. A standard GC analysis was also applied to test the causality among key regions involved in smoking  [5] [6].
bsSoTS is based on integrate-and-fire neurons that are tighter connected when they encode a specific characteristic of an item presented in one position on the Visual Field and loosely connected when they present the same characteristics but items presented in different positions on the visual field. Moreover, the model incorporates a number of synaptic currents and processes that allowed us to successfully simulate the Visual Search experiment  [4, 5]. In research, cultural membership is usually investigated between collectivists (Asian cultures) and individualists' groups (Western Europeans cultures)  [1, 2]. The experiments that bsSoTS simulated so far are based on individualists' groups  [4, 5]. To simulate therefore the difference in behavior between collectivists and individualists, we need to simulate the difference observed in collectivists cultures.
To do that we tested the coupling between the neurons that encode a specific item presented in one position on the Visual Field as a saliency parameter. The same parameter was used in preliminary modelling work in our lab  [3]. The results showed that the saliency parameter successfully simulates the behavioral results. Additionally, further behavioral work is proposed by investigating the relationship between the different saliency levels and the observed effect. The University of Sheffield, Sheffield, S1 3JD, UK; 3  NeuroNLP, a key application on the Fruit Fly Brain Observatory  [1]  platform (FFBO, http://fruitflybrain. org), provides a modern web-based portal for navigating fruit fly brain circuit data. Increases in the availability and scale of fly connectome data demand new, scalable and accessible methods to facilitate investigation into the functions of the complex circuits being uncovered.
Combining data from multiple sources into a single database, with a common data model, Neu-roNLP facilitates access to data from various sources simultaneously. It is built on top of the NeuroArch database  [2]  which codifies fly connectome data from both the FlyCircuit database  [3]  and the Janelia Fly Medulla data  [4]. The former hosts meso-scale connectome data on the whole-brain level and the latter contains detailed, micro-scale synaptic information about the Medulla neuropil. NeuroNLP allows users to probe biological circuits in the NeuroArch database with plain English queries, such as "show glutamatergic local neurons in the left antennal lobe" and "show neurons with dendrites in the left mushroom and axons in the fan-shaped body", replacing the cumbersome menus prevalent in today's neurobiological databases.