dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, it is multithreaded, allowing an efficient use of modern CPUs. Finally, Geant4 is open source and developed by a large international collaboration. Medical physicists began using Geant4 around the year 2000. A search on the PubMed citation database [16] finds almost 1000 medical physics publications related to Geant4. The first of them dates back to 2002 with a clearly increasing tendency, as can be seen in Fig. 2. Geant4 simulates radiation transport and interaction with matter and has the capability to import the Computed Tomography (CT) scan of a patient in DICOM format  [17] , allowing the simulation of the interaction of the beam with a specific patient. Moreover, many programs specifically tailored to medical simulations have been developed using Geant4 as the physics engine, and -therefore-they use its nuclear reaction models.
A Geant4 package dedicated to the simulation of early biological damage induced by ionising radiation at the DNA scale also exists: Geant4-DNA  [18]. Despite the high impact of low energy nuclear reaction models and the wide usage of Geant4, recent literature has shown the limitations of the models implemented in Geant4 in reproducing the measured secondary yields of ion interactions below 100 MeV/u, in terms of production rates, angular and energy distributions. For example: • Braunn et al. [19] have shown discrepancies up to one order of magnitude in 12 C fragmentation at 95 MeV/u on thick Polymethyl methacrylate (PMMA) target; • De Napoli et al. [20] highlighted the limitations on the angular distribution of the secondaries emitted in the interaction of a 62 MeV/u 12 C beam with a thin carbon target.
Possible analysis of MIDAS data A planned future study aims to investigate whether these results are also valid for MIDAS particles. The aspect ratios of dust particles collected by MIDAS should be calculated and their distribution reviewed. It will be of great interest if the distribution falls in different groups, and if they match those found in the simulation and with COSIMA particles. As MIDAS particles are one order of magnitude smaller than those of COSIMA, this will allow us to understand how the initial structures of dust particles of comet 67P might look and if they remain similar over the 1 µm to 100 µm size range.
In both brains, these cells were sparse and scattered in a relatively even fashion across the entire CA1 region. We examined the molecular content of CA1 ebGABAs to infer the putative cell types comprising this GABAergic population. Staining for single neurochemical markers, we found that many ebGABAs expressed SOM (49 ± 16%, mean ± standard deviation) and, in a progressively lower extent, PV (29 ± 7%), NPY (24 ± 11%) and M2R (22 ± 12%, Figure  2b  and Supplementary Figure  3a-b ). These data are in line with previously published results on the whole hippocampus  10. Using an antibody that allows discrimination between weak and strong levels of nNOS expression, we found that a small but consistent proportion of ebGABAs (8 ± 4%) expressed strong nNOS levels, a marker of long-range projection cells  17  (Figure  2c  and Supplementary Figure  3c ).
When we compared the maximum amplitude and paired pulse ratio for CA3, EC and VMT inputs, we observed similar values in the two cell populations (Supplementary Figure  6f-g ), indicating that an early birth date biases GABA cells' input connectivity but not synaptic strength or release probability. Given the sparse connectivity and the fact that EPSCs evoked in ebGABAs by stimulation of the EC were small (maximum amplitude 19 ± 33 pA, mean ± standard deviation), these data suggest that extra-hippocampal afferents play a small role in the recruitment of ebGABAs. Furthermore, we examined spontaneous EPSCs (sEPSCs), many of which are likely to be action potential-dependent, and thereby arise from intra-hippocampal connections in coronal slices. We confirmed that sEPSCs were mediated by AMPA/KA receptors because they were completely blocked by NBQX (10-20 µM, 4/4 cells).
Analysis of ex vivo calcium imaging data during development We used custom designed MATLAB software allowing automatic identification of loaded cells, measurement of the average fluorescence transients from each cell as a function of time and detection of onsets and offsets of calcium signals  15. Network synchronizations (GDPs) were detected as synchronous onsets peaks including more neurons than expected by chance, as previously described  15  , and their time stamp denoted by. The 'Inter GDP intervals' (IGI) was defined as the interval between two consecutive GDPs. To establish whether the stimulation of a single neuron was able to influence the frequency of GDPs occurrence, we employed a previously published analysis  15. We first calculated the average IGI in the three epochs: pre-stimulus (control), during the stimulation period and poststimulus.
Δt Where Δt is the average IGI interval in the control condition, and <    > = i × Δ is the expected occurrence of the i-th GDP, according to the control condition. The phase of each real GDP was set to zero at the peak of synchronous activation and increased linearly reaching 2π at the peak of the next GDP. By subtracting the phase of real GDPs from the expected GDP, we could instantaneously measure the effect of phasic stimulation. The cell was included in the dataset only if the difference between expected and observed cycles did not exceed ± 2 cycles during resting conditions. To assess whether single cell stimulation was able to lock GDP occurrence, we isolated the trials following each stimulation (trial length was either 2. 5 s or 5 s). Next, we calculated an average histogram of single cell calcium onsets including all trials.
For evoked PSC experiments, 60% compensation was applied to the Rs. Only recordings with Rs <30 MΩ were included in the dataset. The cell was discarded if Rs changed by more than 20% throughout the protocol. For electrically-evoked and optically-evoked PSCs, PSC onset delays were consistent with monosynaptic responses (3. 3 ± 1. 6 ms, mean ± standard deviation).
Analysis of ex vivo patch clamp recordings in adulthood Analysis of intrinsic membrane properties was performed using custom-made MATLAB scripts. The resting membrane potential was estimated by averaging a 60 s current-clamp trace recorded at 0 pA holding current. The input resistance was calculated from the slope of steady-state voltage responses to a series of subthreshold current injections lasting 500 ms (from -50 pA to last sweep with a subthreshold response, 5 or 10 pA step size). The membrane time constant (τ) was estimated from a bi-exponential fit of the voltage response to a -30 pA hyperpolarizing pulse. The membrane capacitance was calculated as the ratio between membrane τ and input resistance. The first spike in response to a juxta-threshold positive current injection was used to determine: the threshold potential (the first point >0.
The maximum firing rate was defined as the maximum value reached in a f/I curve with positive current injections from +20 pA to +720 pA (step size: 50 pA). Analysis of spontaneous postsynaptic currents (sPSCs) was performed with MiniAnalysis (Synaptosoft). PSC parameters were calculated from two minutes recording time. The decay time (10-90%) was calculated from a mono-exponential fit of mean sPSCs. Analysis of evoked postsynaptic current data was performed with Clampfit 10 (Molecular Devices) and MATLAB custom-made scripts. Since membrane and synaptic parameters may be affected by age, for all adult datasets we tested that the distributions of the ages of the mice used did not differ statistically between ctrlGABA and ebGABA. Additionally, for optogenetics experiments we verified that the time of opsin expression did not differ between the groups.
Imaging was performed with a single beam multiphoton laser scanning system coupled to a microscope (TriM Scope II, Lavision Biotech). The Ti: sapphire excitation laser (Chameleon Ultra II, Coherent) was operated at 1040 nm for jRGECO1a excitation and 920 nm for GFP excitation. Fluorescence emission was acquired using a 16x objective (Nikon, NA 0. 8) and split in two detectors (GaSP PMT, H7422-40, Hamamatsu) with bandpass filters of 510/42 nm for GFP and 580/624 nm for GECO. Scanner and PMT were controlled by a commercial software (Imspector, Lavision Biotech). To optimize the signal-to-noise ratio of fluorescence variation, we used a dwell time exposition of 1. 85 µs and a spatial resolution of 2 µm/pixel that allowed us to acquire at 9. 85 Hz at a field of view of 400 × 400 µm. Locomotion and imaging triggers were synchronously acquired and digitized using a 1440A Digidata (Axon instrument, 2 kHz sampling) and the Axoscope 10 software (Molecular Devices).
Analysis of in vivo calcium imaging data in adulthood In vivo calcium movies were pre-processed using the CaImAn toolbox for MATLAB  44. First, movies were motion-corrected using a rigid registration method  45. Then, contours and calcium transients were detected using a constrained non-negative matrix factorization framework allowing denoising and demixing of fluorescence signals  46. To ensure correct segmentation of somatic calcium activity, the automatic detection was manually refined by adding and removing ROIs using the correlation image based on neighboring pixels. Fluorescence signals were then deconvolved after detrending and extraction of the 20% F/F. An additional manual refinement was carried out with visual inspection of each ROI in the correlation image and the corresponding trace.
Unstable or noisy traces were removed because this led to spurious spike inference. A Markov Chain Monte Carlo approach  47  initialized by the fast OOPSI algorithm  48  was used to model rise and decay time constants using a second order autoregressive process, which allowed spike inference from the fluorescence traces. A final visual inspection of overlapping calcium traces and spike raster plots was performed to ensure that the spike times reflected the dynamics of the fluorescence traces. Temporal alignment of the treadmill movement signal and spike raster as well as subsequent analyses were performed using custom-made MATLAB scripts. Locomotion epochs were defined as time periods with deflections in the photo-sensors signal reading the treadmill movement. Rest epochs were defined as periods > 200 ms without treadmill movement.
Statistical analyses Pairwise comparisons between distributions were performed using the Mann-Whitney U test. Comparisons between multiple groups were performed using the Friedman test with post hoc Dunn's correction. Two-way ANOVAs were performed with Bonferroni post hoc correction. Data are expressed as either means ± standard deviations or medians and interquartile ranges (IQR). Statistical tests were performed with Graphpad Prism 8 (GraphPad Software, Inc. ).
Patch clamp recordings were performed from visually identified neurons (GFP+ cells for ebGABAs, random putative GABAergic cells for ctrlGABAs). EbGABAs and ctrlGABAs were sampled from roughly equal numbers from CA1 layers to avoid biases towards specific cell types or inputs. We ensured that no pyramidal cell was included in the ctrlGABA dataset: first, we avoided pyramidal-shaped neurons in the strata pyramidale and oriens; second, we verified in current clamp experiments that all targeted cells (21/21) displayed stereotypical GABA cell firing; third, we verified histologically a subset of ctrlGABAs that were filled with neurobiotin (n = 28, see below for details), and all of them exhibited GABA cell anatomical features. An optoLED system (Cairn Research) consisting of two LEDs was used to visualize fluorescence signals and stimulate Chronos-expressing afferents in CA1.
Acknowledgements We thank  Loris Cagnacci ,  Catherine Pauchet-Lopez ,  François Michel  ( InMAGIC imaging facility ) and  INMED 's animal facility technicians for excellent technical support. We are grateful to  Valerie Crépel  and  Alfonso Represa  for sharing equipment. We thank  Vitor Lopes-Dos Santos  for sharing the assembly detection code and  Yannick Bollmann  and  Julien Denis  for analytical help. We also thank all Cossart lab members for helpful feedback and discussions.
Funding This work was supported by the  European Research Council  under the European Union's  FP7 and Horizon 2020 research and innovation program  (grant no. 646925 ). M. B. was supported by grants from the  Fyssen Foundation  and the  Fondation pour la Recherche Medicale  (grant no. SPF20170938593 ), and by a  Marie Skłodowska-Curie individual fellowship  (grant no. 794861 -IF-2017 ). D. A. was supported by an  A*MIDEX  grant (grant no. ANR-11-IDEX-0001-02 ) and by the  I-Site Paris Seine Excellence Initiative  (grant no. ANR-16-IDEX-0008 ). T. Tr. was supported by The  French National Research Agency  (grant no. ANR-14-CE13-0016 ).
Characterization 2. 2. 1 Pair Distribution Function analysis XRD data for real space analysis were collected at the ID15A beamline (ESRF, Grenoble, France), selecting an energy of E = 65. 35 keV (λ = 0. 18971 Å), of the incoming beam. Using a Pilatus CdTe detector, a 2D detector system and placing the samples to a sample-to-detector distance of 12. 05 cm, a maximum momentum transfer Q = 4π sin θ/λ of Qmax ~ 35 Å -1 was reached. Power samples of C4F, C2F, CF and graphite were loaded into 1. 5 mm diameter kapton tubes. 64 images of 10 s each were collected (for a total data collection time of about 10 minutes). Data on empty tube was also collected to obtain sufficient statistics for background subtraction. Sample spinning was used to increase powder averaging. Data collected on a CeO2 powder sample were used for geometrical calibration (7 images of 10 s).
Background was systematically subtracted on the raw images before azimuthal integration (using in-house software). In this work, the PDF is described with the G(r) formalism  [21] , which indicates the probability of finding a pair of atoms separated by a distance r. G(r) is experimentally determined via sine Fourier transform of the total scattering function F(Q): where Q is the magnitude of the scattering vector, Q = 4π sin θ/λ, and r is the interatomic distance. S(Q) is the experimental coherent X-ray scattering intensity, after correcting the raw data for sample self-absorption, multiple and Compton scattering. Data integration was performed using pyFAI  [22]  and extraction of the G(r) functions was performed using PDFGetX3 software  [23] , employing data for the Fourier Transformation to a Qmax = 35 Å -1.
Neutron powder diffraction (NPD) data  [24]  for real space analysis were collected at the D4C instrument (ILL, Grenoble, France)  [25]  at an incident neutron wavelength λ = 0. 4964(5) Ǻ, covering a Q-range between 0. 35 and 23. 5 Ǻ -1. Powder samples were placed inside a sealed 7 mm diameter Vanadium cylindrical cell and measured under ambient temperature and vacuum conditions. Vanadium rod, empty cans and boron powder patterns were also measured in order to properly subtract the background and normalize the data. Backgrounds were measured periodically during the experiment in order to assess their stability. PDFs from NPD data were obtained using the standard Placzek correction and Fourier transform, implemented into a software procedure developed at the D4C instrument  [26]. In particular, an appropriated background, taking into account a linear combination of the diffraction data for the empty bell jar and empty can and Placzek corrections, was subtracted from the raw data.
X-ray Raman scattering All XRS data were collected using the large solid angle spectrometer of the beamline ID20 at the ESRF (Grenoble, France) dedicated to XRS studies  [19, 20]. The pink beam from four U26 undulators was monochromatized by using a nitrogen cooled Si(111) monochromator and a Si (311) Channel Cut post-monochromator and then focused to a spot size of approximately 10 × 20 μm 2 (V × H) at the sample position with a mirror system in Kirkpatrick-Baez geometry. Measurements were performed by scanning the incident energy Ei so that the energy loss [Ef -Ei] matches the core-electron excitation of interest, in this case C and F K-edge. XRS data were collected in the vertical scattering plane using 36 spherically bent Si(660) analyzer crystals at the final energy Ef of 9. 8 KeV and using 3 pixelated Maxipix detectors  [28].
The overall energy resolution was 0. 7 eV and the mean momentum transfer was 3. 2 ± 0. 3 Å -1. All the samples were handled in air, ground into fine powder and pressed in an ad-hoc grooved flat Aluminium sample holder. The sample holder was placed on the goniometer stage at an incident beam angle of 10 degrees. All measurements were collected at room temperature and checked for consistency. The inelastic data were integrated over appropriated Region Of Interest (ROIs), averaged over the 36 crystals and treated with the XRS tools program package  [29]. A background accounting for the valence Compton profile was subtracted following the procedure described in  [29]. Finally normalization over the area across the edge was applied. XRS spectra are presented in energy loss scale.
Computational details All the calculations were performed within the ab-initio framework of the density functional theory using the pseudopotential and plane waves method, as implemented in the VASP code  [30, 31]. The parameters of the calculation are similar to those previously used in Ref  [13, 14] , with the van der Waals corrections to the total energy taken into account at the DFT level using the so-called Grimme's D2 method  [32]. The two 2s and two 2p outermost valence electrons of C and the two 2s and five 2p outermost valence electrons of F were dealt with explicitly in the calculations, while the rest of the electrons were considered frozen at the core. A projector augmented wave scheme was used  [33, 34]  with a kinetic-energy cutoff in the plane waves expansions of 600 eV whereas for the integration over the Brillouin zone 15×15×5 Monkhorst-Pack grids were used.
All these calculations correspond to hydrostatic pressures spanning the region of interest and zero temperature, with the small effect of the zero point energy neglected. The analysis of the electron localization function (ELF) was performed once electronic self-consistency was achieved and has provided very useful information on the bonding features for the studied materials. ELF was calculated from the valence electronic density obtained within the pseudopotential scheme implemented in VASP and the results were visualized using the VESTA 3. 4. 3 software and Jmol Visualization tool  [35, 36]. The net atomic charges (NAC) were also calculated by means of the DDEC6 code  [37, 38]  (the frozen core electrons were included in the charge analysis). We have also carried out calculations of the core level binding energies for the C-1s and F-1s core state, including final-state core hole effects (core-level relaxation energy), which involve the recalculation of the Kohn-Sham eigenvalues for the core states for the chosen atom using a half core-hole  [39].
PDF analysis A direct analysis of the Real Space data is carried out for the three C4F, C2F and C1F samples together with pristine powder graphite, for both X-ray and neutron diffraction data. The results are listed in Table  1. In Fig. 1  X-ray data in both reciprocal (left panel) and direct space (right panel) are plotted while the reference simulated models for the considered structures are sketched in Fig. 2  and described in Ref  [13, 14]. Neutron data are reported in the SI file. XRD data in reciprocal space clearly evidence the presence of the (002) pure graphite reflection at Q ∼1. 9 Å -1 in all the samples, broader in C4F while sharp and well-defined in C2F and CF samples. X-ray data in direct space show one single oscillation in the low r region of the G(r) of C4F, with the first shell distances centred at 1.
This contrast is related to the number of free available electron states in the conduction band and gets modulated by the spatial orientation of the C-F bonds. Data of C4F do not correlate well with NEXAFS spectra of ionic C6F  [46] , even though a precise comparison cannot be done as the samples were synthetized differently and XRS provides a powder averaged signal with high sensitivity to the core. For both the edges, the agreement with previous soft X-ray NEXAFS characterization of the same CxF (x=4,2,1) compounds  [12]  is very good: the global spectral shape, energy position and relative intensity of the peaks in the two datasets show an excellent match. However it should be noticed that, for all the stoichiometries and in particular at high F content, the fine structure of the XRS spectra is less clear and defined than the NEXAFS data.
Acknowledgments We thank the all the staff of the ID20 beamline of the  ESRF  for help and for support during the experiments. We acknowledge  ESRF  and  ILL  for beamtime allocation. We are grateful to to  ESRF  for funding,  F. Ghelfi  for participating to the XRS experiment and  S. Huotari  for providing the diamond powder sample. S. R. acknowledges the support from  MINECO  Project No. MAT2016-75586-C4-3-P. This work was supported by the  LABEX iMUST  ( ANR-10-LABX-0064 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
Motivation: Expanding research highlights the importance of guanine quadruplex structures. Therefore, easy-accessible tools for quadruplex analyses in DNA and RNA molecules are important for the scientific community. Results: We developed a web version of the G4Hunter application. This new web-based server is a platform-independent and user-friendly application for quadruplex analyses. It allows retrieval of gene/nucleotide sequence entries from NCBI databases and provides complete characterization of localization and quadruplex propensity of quadruplex-forming sequences. The G4Hunter web application includes an interactive graphical data representation with many useful options including visualization, sorting, data storage and export.
, 2011)  and replication origin definition  (Comoglio et al. , 2015; Valton et al. , 2014). G4 sequences can be predicted in silico and several tools have been developed  (Garant et al. , 2018; Hon et al. , 2017; Huppert and Balasubramanian, 2005; Kikin et al. , 2006; Wong et al. , 2010). A newly developed and validated algorithm, G4Hunter, overcomes several limitations of previous algorithms  (Bedrat et al. , 2016). G4Hunter is a powerful and widely used tool for G4 prediction which takes into account G-richness and G-skewness of a DNA or RNA sequence and provides a quadruplex propensity score. However, three major limitations of the G4Hunter have become evident. (i) The current implementation requires advanced computational expertise and software packages including Python v. 2. 7 and libraries NumPy, Matplotlib and Biopython; (ii) The software runs in the command line only; (iii) results are exported in two files in text format without any additional tools for direct postprocessing or visualization.
Workflow and implementation The original G4Hunter requires the user to install Python and Python's libraries Biopython, Matplotlib and NumPy, runs only in console and stores results in a text file. The basic input parameters and result of our new application are the same; the user specifies input sequence(s), window size and G4Hunter score threshold, and outputs the sequences and scores identified. Compared to the original Python program, our web-based application has a user-friendly interface and rich graphical presentation of results. It also shows a heatmap of quadruplex forming sequences and provides statistical information. Another advantage is the ability to analyze multiple sequences at once. Results can also be exported as a text file for further processing. The G4Hunter web application runs on a central web server and clients/visitors can execute analyses using server resources, sequences and results are stored in a relational database.