dataesr/scientific-paragraphs-categorization huggingface dataset:
This latter observation in mice is consistent with those of studies reporting a higher expression of ACACA alternative transcripts derived from the bidirectional promoter shared with the divergently oriented TADA2A gene in mouse brains  [75, 78]. While ACACA is a key rate-limiting enzyme involved in FA biosynthesis and plays a pivotal role in cellular energy homeostasis, the biological link between the three other genes and energy regulation is not clear. C19H17orf78 is the ortholog of human C17ORF78 and mouse Gm11437. It was referenced in the Ensembl database V76 (August 2014) and is still referenced in the current NCBI gene database but is absent from Ensembl V87 (Galgal 5. 0). The role of this gene is still unknown. DUSP14 belongs to the dualspecificity phosphatase family that includes critical regulators for many biological processes such as T-cell development, immune regulation and tumorigenesis  [79, 80].
Acknowledgements The authors thank the staff of the  INRA experimental poultry unit  ( UE1295 PEAT, Nouzilly, France ) for producing and rearing animals and the technicians of the research units for measurements on birds and sample collection. The authors also thank  F. Martins  ( GeT GenoToul platform, Toulouse, France ) for his technical contributions on Fluidigm data production. English was improved by  Nature Research Editing Service from Springler Nature  (http://authorservices. springernature. com/language-editing/).
In clear contrast, the morphology of inhibitory interneurons is shaped radially, yielding sinks and sources that cancel each other, contributing (in principle) little to EEG and MEG signals  (Murakami and Okada, 2006). In sum, the alpha waves recorded with EEG and MEG are mainly driven by spatio-temporally coordinated post-synaptic currents generated by pyramidal cells. MEG and EEG pharmacological trials involve higher complexity during data acquisition and analysis than standard experiments. In this section, I will highlight two of these challenges and I will suggest possible ways to approach them. For an excellent methodological review about pharmaco-MEG, the reader is referred to  Muthukumaraswamy (2014).
A more advanced procedure to monitor and correct head movements was provided by  Stolk et al. (2013). They created a real-time head-localizer tool, which is a part of the FieldTrip Matlab toolbox  (Oostenveld et al. , 2011). For example, in the CTF275 MEG system, head coil positions can be placed at anatomical landmarks (nasion, left and right ear canals) to record head movements. At the beginning of an MEG experiment, head position can be tracked and stored. In case of excessive head movement (i. e. , > 5 mm), the experimenter can stop the task and instruct the participant to correct his/her head position to recover the original coordinates. Very often, pharmaco-MEG studies employ cross-over designs: the same participant receives placebo and treatment/s in different sessions separated by days.
ACKNOWLEDGMENTS I would like to thank to Frontiers in Computational Neuroscience Editors-in-Chief Profs. Misha Tsodyks ,  Si Wu , and  Idan Segev  for their kind invitation to participate in this 10th anniversary Research Topic. I am thankful to  Prof. Wolfgang Klimesch  and  Dra. Stephanie R. Jones  for their insightful discussions regarding the relationship between GABA and alpha. I am indebted to  Dr. Tushar Chauhan  for his helpful comments and proofreading.
In order to ease access to logical modelling by biologists, this protocol proposes a stepwise introduction to the framework, relying on its implementation into the software GINsim (release 3. 0). The following section introduces the biological system used as an illustration. Next, in Section 3, we proceed with the stepwise construction and analysis of a logical model. The article then ends with some conclusions and prospects. damage. An external stress induces a damage to the DNA, which promotes Mdm2 degradation. The level of p53 can then increase and activate DNA repair mechanisms. In parallel, p53 inhibits Mdm2 translocation from the cytoplasm to the nucleus through an indirect inactivation of AKT. However, in the nucleus, high level of p53 activates Mdm2 transcription, while Mdm2 induces the degradation of p53, thereby forming a negative feedback circuit.
GINsim The GINsim software supports the definition, the simulation and the analysis of regulatory graphs, based on the (multilevel) logical formalism. GINsim is freely available for academic users from the GINsim website (http://ginsim. org), along with documentation and a model repository. For this tutorial, we use the recently released GINsim-3. 0, which is available for all platforms with version 8 of the Java Virtual Machine. To get started with GINsim, download the corresponding Java ARchive (JAR file), with dependencies included, from the download section of GINsim website (http://ginsim. org/downloads). On your computer, double-click on the file icon to start the application or launch it with the command: java -jar GINsim-version. jar in a terminal. Detailed instructions, troubleshooting and further options are available on the website.
3 Figure  2 : GINsim main window displaying the nodes of the p53-Mdm2 logical regulatory graph. The upper part of the window displays five scrolling menus. These menus provide access to classical file management options, as well as exports into various formats. The central area displays the regulatory graph (here the nodes of the p53-Mdm2 model), while the lower panel contains two tabs: the Modelling Attributes tab (selected here) and the Style tab, corresponding to the selected node, here p53. The graphical appearance of the nodes have been modified using the Style tab. The Edit button on the top is selected and emphasised with a green contour, enabling the edition of the attributes of the selected node, including its id and name, its maximal level (Max, here set to 2), and also the insertion of annotations in the form of free text (bottom right panel) or of links to relevant database entries (bottom middle panel).
Taking advantage of the multiple export formats supported by GINsim, it is also possible to use complementary tools, including stochastic simulations software (e. g. MaBoSS, see Stoll et al. [42] ), model checking tools (e. g. NuSMV, see Abou-Jaoudé et al. [1, 3] , Traynard et al. [48] , or yet various graph visualisation and analysis packages (see Note 5 for a list of export options). As mentioned in the introduction, various logical models for different cellular processes have been proposed during the last decades, many of them available in the repository included along with GINsim on the dedicated website (http://ginsim. org). The interested reader can thus download the model of his choice and play with it, reproduce some of the results reported in the corresponding publication, or modify and extend it according to his own research aims.
Naldi et al. [33]  presents a method to determine the functionality context of a circuit in terms of constraints on the levels of its external regulator. A circuit functionality context can be interpreted as the part of the state space where the circuit is functional, i. e. , generates the expected dynamical property  [13]. 5. GINsim allows the user to export logical regulatory graphs as well as state transition graphs towards various formats, facilitating the use of other software: • SBML-qual, the qualitative extension of the popular model exchange format  [9]. • MaBoSS, a C++ software for simulating continuous/discrete time Markov processes, applied on a Boolean networks (https://maboss. curie. fr/). • BoolSim (http://www. vital-it. ch/software/genYsis/). • GNA, a software for the piecewise linear modelling of regulatory networks (http://ibis.
inrialpes. fr/article122. html). • NuSMV, a symbolic model-checking tool (http://nusmv. fbk. eu/). • Integrated Net Analyzer (INA) supporting the analysis of Place/Transition Nets (Petri Nets) and Coloured Petri nets (http://www2. informatik. hu-berlin. de/ ~starke/ina. html). • Snoopy, a tool to design and animate hierarchical graphs, among others Petri nets (http:// www-dssz. informatik. tu-cottbus. de/DSSZ/Software/Snoopy). • Graphviz, an open source graph visualization software offering main graph layout programs (http://www. graphviz. org/). • Cytoscape, a popular open source software platform for visualizing molecular interaction networks (http://www. cytoscape. org/). • Scalable Vector Graphics (SVG) format, an XML standard for describing two-dimensional graphics (http://www. w3. org/Graphics/SVG/).
Acknowledgments The authors acknowledge numerous constructive comments from GINsim users over the years, in particular insightful feedback from  Laurence Calzone ,  Samuel Collombet ,  Karla Corral ,  Adrien Fauré ,  Swann Floch  lay,  Asmund Floback ,  Anna Niarakis ,  Elisabeth Remy ,  Otoniel Rodriguez , and  Gautier Stoll.
Funding CC and PTM acknowledge support from the  Fundação para a Ciência e a Tecnologia , through grants  PTDC/BEX-BCB/0772/2014  and  PTDC/EEI-CTP/2914/2014. DT acknowledges support from the  French Plan Cancer  ( 2014-2017 ), in the context of the projects CoMET and SYSTAIM, as well as from the  French Agence Nationale pour la Recherche , in the context of the project  SCAPIN  [ ANR-15-CE15-0006-01 ].
For this purpose, we use the database proposed in  [31]. Images were acquired by the single-band near-infrared sensor from the JAI AD080 GE camera, coupled with a linear polarizer from Thorlabs (LPNIRE100-B). A precision motorized rotation stages (Agilis™ Piezo Motor Driven Rotation Stages) allowed to take the four images at four orientation angles ([I 0 , I 45 , I 90 , I 135 ] T ). A registration procedure aligned the images  [32]  pixel-to-pixel. The images were also calibrated with respect to the spatial deviation of the illuminant and the non-linearities. There are ten multispectral images, each one being provided with four different polarization angles k ∈ {0, 45, 90, 135}. Scenes imply different objects with materials like fabrics, plastics, food, color checkers, glass, and metal. Conditions of acquisition are constant for all scenes, i.
9504 on average over all channels and polarization angles), which validates the use of the spatial correlation assumption for both SFA and PFA demosaicking. According to Table  1a ,d, the spatial correlation has the same behavior for the four polarization angles. It also highlights that the channel C 4 has low spatial correlation. We believe that it is due to the database acquisition setup, which uses the dual-RGB method leading to unbalanced channel sensitivities. In this configuration, the spectral sensitivity function associated with the channel C 4 is lower than other channels over the spectrum. Thus, all channels don't share the same noise level, and poor information recovery (especially for C 4 ) could lead to low correlation values. Regarding the spectral inter-channel correlation, the usual behavior is that close spectral channels in term of wavelength band are more correlated than distant ones, and channels in the visible are weakly correlated with the near-infrared channel  [11].
CuBic (CB) and Cubic-SPline (CBSP) An article was published by Gao et al. [37]  to compare bilinear, weighted bilinear, cubic, and cubic-spline. In our work, the cubic and bicubic interpolation methods have been implemented using built-in functions from Matlab software (The MathWorks, Inc. , Natick, MA, USA). Cubic interpolation uses the third order polynomial fitting to interpolate an area delimited by four corners, and uses three directional derivatives (horizontally, vertically and diagonally) as input. The cubic-spline method is a sequence of an horizontal interpolation and a vertical interpolation. Polynomial fitting (third order) is also used to reconstruct missing values from adjacent pixels, but with the additional constraint that the first and second derivative at the interpolation points are continuous.
Experimental Setup PFA image simulation is employed to assess the interpolation strategies. As for the correlation study in Section 2, the polarimetric images from the database of Lapray et al. [31]  was used as references. All methods of Table  3  were either re-coded (R), adapted to PFA (A), or provided by authors in Matlab/ImageJ language software (P). They are further integrated into the framework presented in Figure  4  in order to assess and compare the performances of demosaicking. Stokes descriptors are then computed for both reference and estimated images, according to Equations (  1 )-(3). To avoid precision errors during image conversions, all considered images and processing are using 32-bit float data representation. We consider the Peak Signal-to-Noise Ratio (PSNR) as quality metric.
Results and Discussion Table  4  displays the PSNR values provided by the demosaicking methods on average over the ten database scenes. Results show that among bilinear filters, B 3 provides the best results for I 0 , I 45 , I 90 , I 135 , S 0 , S 1 , and DOLP, while B 4 slightly exceeds it for S2 and AOLP. Among PFA-oriented methods, CB and CBSP generally provide the best results. Our proposition to adapt RI and ARI CFA demosaicking methods to the PFA case (with S 0 as guide) provides better results than classical PFA-oriented methods. We remark that RI and ARI are very close together in the PSNR results. RI also provides the best results among all tested methods regarding S 2 parameter and DOLP descriptor. For PFA methods, it is important to note that the output interpolated pixel is shifted by half pixel when using bilinear kernels B 1 , B 4 , and B 5 , compared to other bilinear kernels B 2 , B 3.
To visually compare the results provided by demosaicking methods on S 0 , AOLP, and DOLP descriptors, we select a zoomed area from the "macbeth_enhancement" scene of the database. Among demosaicking methods, we show the results of the most intuitive method (bilinear interpolation using B 3 filter), and the pseudo panchromatic image difference (PPID) that globally provides the best PSNR results. Figure  7  shows that there is no significant difference regarding the S 0 parameter, except that the two highlight dots are more apparent in PPID demosaicked image. Computing AOLP and DOLP parameter from a bilinearly interpolated image generates many artifacts that are fairly reduced using PPID demosaicking method. Generally speaking, we found that demosaicking that are dedicated to PFA don't necessary give better PSNR result.
Thus, it was not obvious that considering color and spectral demosaicking techniques applied to PFA arrangement could be beneficial. The results highlights that this can benefit the pre-processing of PFA. However, we can express some reservations about the results obtained. First, we limited our study on a relatively small database. Other polarization database in the literature  [59]  furbish only the Stokes parameters and polarization descriptors, but no fully-defined reference image I = {I k } k∈{0,45,90,135} are available. Natural scene samples could also be beneficial for a complementary algorithm classification. Secondly, the database used in this work was made with the same experimental conditions, i. e. , constant angle/plan of incidence and a unique non-polarized illuminant. We think that supplementary tests of the best identified algorithms in an extended database containing a better statistical distribution of data could be valuable.
Since these properties are data-dependent, we have chosen to not use them in our study, despite that they are used in few SFA demosaicking methods. We remarked that some algorithms need more computation time than others, without necessary giving better results. No computational complexity consideration has been reported in this work. We think that there is a lake of information about these aspects in the original articles. Moreover, Matlab or ImageJ can not provide a consistent evaluation of the complexity of the selected algorithms, e. g. , for their potential ability to be parallelized in a hardware acceleration for real-time computing. (g-i) The pseudo-panchromatic image difference (PPID) interpolation  [11].
Conclusions By considering the inter-channel correlation, CFA and SFA schemes aim to improve the spatial reconstruction of channels from the information of other channels. Experiments on the only available polarization image database have shown that such methods provides better results in term of PSNR than PFA-oriented methods. More particularly, we proposed to adapt two CFA demosaicking algorithms based on residual interpolation to the PFA case, and showed that they provide better results than classical PFA-oriented methods. Moreover, the SFA PPID method provides the overall best results, and largely reduces visual artifacts in the reconstructed polarization descriptors in comparison with bilinear method. Correlation study has shown that the spectral band considered in the acquisition of polarization channels has no influence on the correlation between polarization channels.
The correlation results from this study could be an input and provide assumptions for the design of new demosaicking algorithms applied on cameras that mix spectral and polarization filters. All algorithms were tested on a small database of images. As future work, we hope that an extensive database of polarization and spectral images will be available soon in the research community. Thus, further evaluations on more various materials and image statistics would validate more deeply our conclusions. Furthermore, we believe that a real-time pipelined implementation of the PPID method using GPU or FPGA needs to be investigated, that would be a valuable tool for machine vision applications.
Real signal A pre surgical intracerebral EEG signal from a pharmaco resistant subject with a symptomatic focal cortical dysplasia in the right occipito-temporal junction was investigated. The implantation technique was Stereotaxic EEG (SEEG), consisting in depth electrodes. The acquisition and preprocessing phases were applied in the Clinical Neurophysiology Department of 'La Timone hospital' in Marseille as in  [13]  studied and validated by an expert neurologist. This particular IEEG recording was chosen because it presented clear pre-ictal patterns with regular spiking and visible epileptic oscillations as validated by the expert. Accurate position of depth electrodes was defined upon a non-invasive phase that allows defining a putative epileptogenic zone (ZE). The IEEG data was recorded on a Deltamed System, sampled at 256 Hz, with anti-aliasing low-pass analog filter set to 100 Hz.
Methods: SWT as a case study The stationary wavelet transform SWT is a filtering technique that relies on time and scale signals representation. The stationary property ensures time invariance: a translated version of a signal X leads to a translation of its transformation. This is thus an effective tool for spontaneous signals representation, separation, detection and filtering processes  [18]. In fact, The redundancy of SWT leads to translationinvariance, which is an interesting property as spontaneous oscillations are not always aligned to the same 'tiles' of the time-frequency plane. This, it facilitates salient signal features identification, as in continuous wavelet transform, as oscillations are more easily separated from the high frequency part of the transient spikes. Interestingly, and contrary to the continuous wavelet transform, the SWT is a reversible technique, as the discrete wavelet transform DWT, which allow to reconstruct the inverse function ISWT (from selected coefficient to a filtered dataset).
More precisely, non contaminated pre-ictal gamma oscillations spatio temporal representation were carried out as in  [17]. A thresholding step based on a rectangular mask with a time spread equal to gamma oscillatory length (200 ms for 45 Hz, 180 for 55 Hz and 150 ms for 85 Hz) and 3 to 2 scales of width (3 scales for 45 Hz and 2 scales for the rest) was used as in  [13]. The last step of separation is the reconstruction of thresholded coefficients (masked approximation and detail coefficients) using the inverse function ISWT on matlab. The SWT performance for separating transient and oscillatory activities was studied in previous work  [13]. For simulated data the SWT goodness of fit (match between separated recovered activities and simulated ones) was shown to be above 80%, and it depends of the frequency range of oscillations (in our case the separation is above 90%: gamma rhythms), and signal to noise ratio.
Chosen platform The hardware/software codesign approach of our application was applied on Xilinx Virtex-5ML505 (an Embedded Development Kit EDK software and FPGA XC5VLX50T-1FFG1136). The Xilinx Virtex-5ML505 features a large numbers of Input/Output peripherals and an important memory storage that allows a high level of smart development of connected, and differentiated systems.
Demonstration For simulated data the SWT is iterated 3*200 ¼ 600 times and for real signal 96*4 ¼ 384 (number of channel by realizations/events) times. Our algorithm could be explained through two rings: a data vector, and a correlation process, proceeding in a parallel way. A data vector contains a window of 5000 samples: 20 s, for both simulated and real IEEG signal with an amplitude range of [-  20 30]  mv, which lead to 50 values stored in a multi IO memory  [28]. Our original signal was convolved with two filters 4 k and J k , followed by a thresholding step using a rectangular shape transfer function (see Eq. (  3 )). The final results were added and stored in a memory bloc using MATLAB Simulink with Xilinx system generator library for VHDL files generation and required netlist. thre d ¼ hC d ; recti; thre a ¼ hC ad ; recti ð3Þ With thre d and thre a are the SWT details and approximate coefficients in our rectangular mask adopted for oscillatory activities.
A stimulation test in Xilinx ISE was run;  about 400 times per tics comparing with SWT software, and 22 tics of time gain was obtained in comparison to a solution without accelerator. Fig. 9  illustrates the integration of pre-ictal gamma oscillations spatio temporal mapping. Our adopted platform is composed of three blocks, each one involving three sets of convolution (Morlet transform, smoothing and normalization). Each block Table  1. Time consumption of integrating SWT.
SWT Integration Time per Tics Software 420 No Accelerator 45 2 Accelerators 23 Fig. 9. Integration of spatio temporal mapping: a data vector, a correlation block with three convolution sets. The resulting data will be incorporated into a comparator to display the highest energy in space and time (in order to detect the seizure build up in real time). is composed of addition and multiplication operators, linked to an internal memory unit to store the results (Mw for wavelet transform, Ms for smoothing and Mn for normalization). The input J, H, sin and Z denote the used transfer functions. After compiling, a comparator was used to detect the sensors' maximum energy. Final output result would be stored in an external memory unit then mapped on a screen. Embedding spatio temporal mapping was profiled using the same architecture for SWT integration: a dynamic partial reconfiguration on Xilinx platform, composed of a data vector bloc to access the input signal (original signal/pure gamma) and a correlation block with three multiplication/addition rings, 3 memory units and an external memory bloc to store the highest energetic component of the involved sensors during seizure build-up.