dataesr/scientific-paragraphs-categorization huggingface dataset:
1 software (PerkinElmer) and transferred to the ImageJ image processing software. Then ROIs were automatically selected centering on the neuronal soma by the 'LC-Pro' plug-in in ImageJ by providing the ROI diameter (pixel) 30, frame rate (fps) 1. 6 and an intensity cutoff threshold with a p-value 0. 05. Finally, the raw intensity values of the calcium signal were extracted from selected ROIs. For the analysis, each ROIs data was transferred to 'Origin-Pro 9. 0'(OriginLab). Single neuronal dynamics were quantified via the calcium signal peak frequency and peak amplitude. The peaks were detected over time by 'positive maximum intensity peak finding method' using 'batch peak processing algorithm', with second derivatives of individual ROI's raw fluorescence intensities (r. f. u. ) as a change in fluorescence intensity from initial (dF)  [22].
We repeated the same analysis for each drugs treated group. For the generation of the correlation heat map among ROIs, the F/F0 values for a region of interest (ROI) were exported to a text file titled ROI Normalized in Image J. This text file was converted to a csv file and uploaded into Python for peak detection analysis. The code for this analysis is publicly available on GitHub [URL: gist. github. com/25086]. Briefly: First, data from all identified ROIs was normalized such that the minimum F/F0 value was equal to 1. Time series plots showing mean signal plus variability represented as 95% confidence interval were generated, along with heatmaps showing activity across every identified ROI, and a correlation matrix plotted as a heatmap representing correlation coefficients across all ROIs.
After 10mins of incubation with the dye at RT, live cell epifluorescence images were captured under 10X water objective with FITC (λEx=490nm; λEm=530nm) filter from all four groups of MMC, 1. Control; 2. VitC; 3. MMC; 4. MMC+VitC and represented here. The newly formed neurotransmitter vesicles at the cells synaptic terminals appeared as green-fluorescent puncta, as the lipophilic tail of the dye was embedded into the vesicle's membrane. (b) The mean raw fluorescence intensity (r. f. u. ) normalized by their respective background fluorescence from each group (3 fields of view/well; n=3 wells/group from 3 biological replicates) quantitated with by ImageJ analysis software and plotted as a bar graph. (c) The postsynaptic density of neural network was estimated by immunostaining method with Goat anti mouse PSD95 primary antibody (magenta) from all four groups.
PSD95, the post synaptic markers were co-stained with either Goat anti rabbit GFAP (green) or Goat anti rabbit MAP2 (yellow) to assess the presence of synaptic density with both iAstro and neurites respectively. Representative confocal images (10X water objective) from day 14 neuronal cultures from all four groups (6 wells/group) and measured mean fluorescent intensity of PSD95 (magenta) normalized by the background florescence from both sets of co-staining (3 field of views/well) was quantitated using ImageJ software and displayed as a bar plot (d). (e) and (f) show the bar plots for the percent area overlap of PSD95 over GFAP and MAP2, respectively. Scale bar: 50 µm; Error bar: standard error of mean; Statistical significance: Two-way ANOVA, *p<0. 05 **p<0. 01, ****p<0. 0001, n. s. =not significant.
Experimental site Di Prima et al. (  2021 ) evaluated the BEST-WR-2T model with measurements from the Berchidda site (40 • 48 ′ 57. 28 "N, 9 • 17 ′ 33. 09 "E), located in the Long-Term Observatory of Berchidda-Monti (NE Sardinia, Italy). For our study, we used the same dataset. The mean annual temperature at the Berchidda site is 14. 2 • C and the mean cumulative precipitation is 623 mm. The place is a Mediterranean wooded meadow with herbaceous species and oaks. The experimental site has two types of land cover: open spaces (grasslands) and tree canopies  (Seddaiu et al. , 2018). According to the USDA standards, the texture of the soil upper horizon ranged from sandy loam to loamy sand. More details about the soil properties can be found in Di  Prima et al. (2021 ). Di Prima et al. (2021)  performed water drop penetration time tests  (Wessel, 1988)  in the grasslands, and found that the soil surface was wettable in 47.
The automated procedure proposed by  Concialdi et al. (2020)  to treat the transducer output was subsequently applied to determine the cumulative infiltration curves. This procedure uses an algorithm, implemented with the open-source software Scilab  (Campbell et al. , 2010) , that minimizes the effect of tension fluctuations caused by air bubble formation in the Mariotte system. The code can be downloaded from the website bestsoilhydro. net (https://bestsoilhydro. net/infiltrometer/), in the section entitled "automatic treatment of the raw data". α WR i s exp b s exp α WR S K s α WR S K s S K s (h -1 ) (mm h -1 ) (mm) (h -1 ) (mm h -0. 5 ) (mm h -1 ) (h -1 ) (mm h -0. 5 ) (mm h -1 ) (mm h -0. 5 ) (mm h -1 ).
Validation of the three-term formulation A specific program to process the cumulative infiltration curves was developed using Scilab  (Campbell et al. , 2010). The program first detects the steady state and measures the slope of the asymptote at long times, i exp +∞. We used the inverse function "lsrqsolve" implemented in Scilab  (Di Prima et al. , 2021)  to estimate S and α WR by fitting 1) the BEST-WR-2T model (Equation  7b ) to the experimental data corresponding to the transient state and 2) the BEST-WR-3T model (Equation  8b ) to the full data set. Note that in this approach we assumed that the BEST-WR-3T model was valid at for times. Finally, we calculated K s using Equation (  4 ) for both formulations. We compared the ability of the two models to estimate hydraulic properties from the synthetic infiltration curves by calculating the relative error, Er, for each estimated value of αWR , soil sorptivity ( ŜWR and Ŝ) and saturated soil hydraulic conductivity ( Ks,WR and Ks ) as compared to the corresponding reference value: Er(x) = x -x x ( 10 ) where x is the estimated value and x is the target, i.
The comparison of deviation times shows that the three-term formulation is valid (and overlapping with the QEI formulation) for much longer than the 2T approach (Fig. 2 ). Further, the BEST-WR-3T model only diverged from the QEI formulation for t greater than 4t s , whereas the BEST-WR-2T term diverged from the QEI formulation within the first hour for the sand and loamy sand soils. These results show that the three-term formulation was suitable for the complete dataset, whereas caution should be taken when using the two-term expansion. We note that, in contrast to the BEST-Slope and BEST-Intercept methods, BEST-WR-2T and BEST-WR-3T approaches do not have specific algorithms to ensure the time-validity of experimental data. Rather, in both approaches, all data points assigned to the transient state are used.
e. , increasing infiltration rates through time as water repellency diminishes). In such instances the BEST-WR-3T model provides lower Er FIT values than the BEST-WR-2T method. A second important point is that the BEST-WR-2T approach should only be used on transient-phase data, as the model is not appropriate for steadystate infiltration. Therefore, the correct detection of the transient versus steady-state regimes is necessary to delimit the data set to be used. If the complete dataset is used then the two-term model will deviate (Fig. 4 , red dotted lines). Thus, the three-term model has an undeniable advantage since it uses a model that is valid for much longer times.
At the same time, it avoids the need to delimit transient versus steady-state infiltration times, since the three-term expansion was determined to be valid for much longer times than the two-term version. This last point also implies that the BEST-WR-2T approach can suffer error when datasets that include steadystate condition are used to estimate S and K s. Since soil water repellency often dynamically changes during the course of an infiltration event, it is preferable to perform longer infiltration tests that can capture this range of behavior. We therefore recommend using the new BEST-WR-3T modelespecially for soils with strong to severe water repellencyto ensure proper estimation of soil hydraulic properties. ( β 2 -β + 1 ) ( i exp +∞ -AS 2 ) 2 S ∫ t 0 t 1 2 e -αWRt dt Using the integration by parts, the upper integral can be rewritten as follow:.
Acknowledgment This work was also supported through the  European Regional Development Fund (ERDF)  and the  Italian Ministry of Education, University and Research (MIUR)  through the  "Programma Operativo Nazionale (PON) Ricerca e Innovazione 2014-2020" (Linea 1 -Mobilità dei ricercatori ,  AIM1853149 , CUP:  J54I18000120001 ), and the  GAS-PAM  project " Gestione Agronomica Sostenibile dei Pascoli Arborati Mediterranei ", funded by  Regione Sardegna  ( L. 7/2007 ,  2019-21 ). The infiltration experiments at the Berchidda site were carried out by  Simone Di Prima ,  Filippo Giadrossich ,  Ludmila Ribeiro Roder  and  Sergio Campus.
Sex-and tissue-biased gene expression analysis We used the R package DESeq  (Anders and Huber 2010)  to normalize the libraries (upperquartile method with p = 0. 75) and calculate CPM. Only genes with CPM>1 in at least two libraries (out of 18) were considered as expressed and retained in the analyses, unless mentioned otherwise. To identify genes predominantly expressed in a specific tissue or a morph, we imposed that a minimal percentage (70%) of the reads mapping to a given gene was sequenced from that specific tissue/morph. Doing so avoided the reliance on p-values from differential expression analyses (which in turn depend on the absolute expression level of a gene). This 70% threshold allowed identifying genes showing considerable bias in expression toward a tissue/morph, and to retrieve a large number of genes for more powerful analyses.
have no financial conflict of interest with the content of this article. The raw data are publicly available on NCBI (projects PRJNA547535 and PRJNA748848. The bioinformatic and R scripts, as well as the R input files can be found on Zenodo: https://doi. org/10. 5281/zenodo. 6242803. We thank the editor Tanja Schwander, Ann Kathrin Huylmans and another anonymous referee who provided constructive comments to a previous draft of this manuscript.
RNA quality was checked on Bioanalyzer (Agilent) and quantified on Nanodrop (Thermo Scientific). The 18 RNA samples were subsequently sent to the GetPlage platform (Toulouse, France) for library preparation (TruSeq Stranded mRNA Library Preparation kit) and 150 bp RNA paired-end sequencing (Illumina HiSeq3000). After filtering for rRNA, reads from each library were mapped to the V2 assembly of the pea aphid genome (Acyr 2. 0, Genbank accession GCA_000142985. 2) using STAR version 2. 5. 2a (Dobin et al. 2013)  with default parameters, except: outFilterMultimapNmax = 5, outFilterMismatchNmax = 3, alignIntronMin = 10, alignIntronMax = 50000 and alignMatesGapMax = 50000. Then, we counted reads mapped on exons of each predicted gene (NCBI Annotation release ID: 102) using FeatureCounts version 1.
Acknowledgments This work was supported by a grant from the  University of Rennes 1 ("Défi émergent")  to JJ, as well as grants from the  French Research Agency (ANR)   SexAphid  ( ANR-09-GENM-017-001 ) and  Mecadapt  ( ANR-11-BSV7-005-01 ). The authors of this preprint declare that they.
The 2020 Edition of the Femto-UP School (the 6th occurrence) was proposed and organized by members of the Department of Ultrafast Optics and Nanophotonics of the Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS-DON). Initially planned to take place in Alsace (France) in the form of an on-site "École Thématique du CNRS" in November 2020, it had to be cancelled due to the Covid-19 pandemic. It was then organized in 2021, in a very different and unprecedented, online format, in the form of typically 2-hour-long video-conferences, two to four times a week, from March 8th to March 29th. In total, the online event iii included 20 h of Lectures and 6 h of Numerical Practical sessions, including a preliminary 2-hour-long Python "crash" tutorial organized one week before the School.
This Book The Chapters of this Book are independent, original Tutorials, written by the Lecturers of the School, closely following the corresponding Lectures delivered online during the School. The first part of the Book includes chapters 1-5 and focuses on general physical concepts and technical implementations of ultrafast laser pulses, with a historical testimony about the genesis of femtosecond laser pulses (chapter 1), followed by tutorials on generation (chapter 2), amplification (chapter 3), propagation (chapter 4), and characterization (chapter 5) of femtosecond laser pulses. Original, supplementary electronic materials, are likely becoming generic pedagogical material to illustrate femtosecond pulse propagation (Jupyter notebook, see chapter 4) or a generic technical tool to characterize femtosecond pulse durations (PyMoDAQ-femto module, see chapter 5).
Chapter 4 Basics of Femtosecond Pulse Manipulation: Simple Numerical Tools* Baptiste Fabre** and Yann Mairesse*** Many detailed courses already exist on the generation and characterization of femtosecond pulses  [1] [2] [3]. The goal of this chapter is not to provide an extensive review, but rather to give a few practical tools to develop an intuition on how simple effects can be at play in a femtosecond laboratory. This chapter is accompanied by a Jupyterlab Notebook containing the python code necessary to generate all presented examples. All "Let us practice" sections are detailed in the Notebook. To get Jupyterlab, the easiest way is to install Anaconda Individual Edition i , which is available for Windows, Linux and MacOs. We will start from the output of a femtosecond laser system. What happens to femtosecond pulses as they propagate along in the laboratory? They can travel through air, glass, birefringent media… They can be reflected on mirrors, focused by lenses… All optical elements placed on the path of a femtosecond pulse are susceptible to modify its temporal profile.
Presentation of Useful Functions Throughout the paper we will introduce some functions and show the output of code which is available in the Supplementary Material. All coding relies on Python 3. 8, but should work with all Python 3. X versions. Let us first import the classical libraries numpy and matplotlib. pyplot. In general, python libraries, and in particular numpy, should be imported using an alias (for example np in the case of numpy), but in order to lighten the writing and make it easier for beginners, we will omit this alias. This means that for instance to use the exponential function, we will type exp() instead of np. exp(). We can do this here because we do not import many libraries. This could be a problem if one function was defined in two different libraries. from numpy import * from matplotlib.
Influence of the Spectral Phase In this part, we use the basic tools presented above to investigate the influence of various spectral phases on the temporal profile of a femtosecond pulse. The initial pulse is defined as being a Gaussian spectrum centered at 800 nm, with a 40 nm FWHM bandwidth and a flat spectral phase presented in figure 4. 6 (see the corresponding code in the Jupyter Notebook). Ultrafast Lasers Technologies and Applications The pulse is centered around x 0 ¼ 2:356 Â 10 15 rad/s. Its temporal profile is Gaussian, with a 23. 4 fs duration (FWHM). The temporal phase is flat, once the linear component associated with the non-zero central frequency is removed. This pulse is the shortest pulse that can be achieved with the given spectrum. It is said to be Fourier-limited. In the following, we establish the influence of the spectral phase on the temporal profile, by adding polynomial phases of increasing order.
Introduction Measuring short laser pulses is a mandatory and difficult task when dealing with experimental ultrashort measurements. To overcome the non-existence of fast enough measuring devices at the picosecond or femtosecond scale, many techniques have been invented. Most of them rely on a non-linear interaction to gain information on the spectro-temporal features of the laser pulse, and yield the complete phase and *Supplementary electronic material available at: https://github. com/CEMES-CNRS/pymodaq_ femto. **sebastien. weber@cemes. fr ***romain. geneaux@cea. fr amplitude profile of the measured pulse. This spectro-temporal information sought is an experimental equivalent to Gabor or Wiegner functions presented in the previous chapter. So far, each invented technique has required one specific algorithm, experimental layout, and acquisition program to retrieve pulse characteristics.
The recently developed COPRA approach 1 solves the first of these issues: it provides one algorithm for all methods after noticing that most of these algorithms share a common mathematical formulation. Indeed, all methods do measure the light spectrum after a non-linear process, as a function of a parameter (delay, dis-persion…). COPRA formulates these methods together as "Parameterized Nonlinear Process Spectra" (PNPS). The COPRA article  1  gives a very detailed analysis of the reconstruction mathematical problem where both concepts and comparisons between existing algorithms are presented. With PyMoDAQ-Femto, we take a step further and incorporate this algorithm (available in the Python for Pulse Retrieval PyPRet 2 package published under MIT license) within the PyMoDAQ 3 framework, in an attempt to provide an open-source and widely applicable application for pulse retrieval and simulation purposes.
Simulation and Retrieval of Synthetic Laser Pulses PyMoDAQ-Femto has two main python modules: the Simulator and the Retriever. Both will be illustrated here using two typical shaped femtosecond pulses. The first one has second-order and third-order spectral phase due, for instance, to dispersion during propagation in air and materials. The second one presents a Gaussian temporal phase due to self-phase modulation typically used for production of ultrashort pulses (\10 fs).
Other Shaping At the moment, only Taylor spectral phase and Gaussian temporal phase can be simulated. Sequence of pulses with identical shape can also be produced. If the need arises, please contact the authors to add some other shaping types or dive into python and propose some modification of the source code on GitHub  12  ! Using the Simulator module it is therefore easy to explore various pulse shaping and experimentally achievable  13  time-frequency representations in order to be familiar with what to expect in an experiment. The chosen non-linearity has an impact on the clarity of the raw trace, but has no real impact on the retrieval details of the tested pulse (except the direction of time if using SHG-FROG, which can be of importance in some circumstances). 116  Ultrafast Lasers Technologies and Applications.
Interfacing a Dispersion Scan Measurement in PyMoDAQ The d-scan module must work with two hardware elements: a motorized translation for the moving glass wedge (in our case from Smaract), and a spectrometer covering the spectrum of interest (StellarNet Inc). The interfacing and data acquisition are performed with PyMoDAQ, as shown in figure  5. 8: we use one adequate PyMoDAQ plugin to communicate with each instrument. In our case, both plugins had already been written  19  and were freely available in the PyMoDAQ plugin database  20. The plugins are wrappers of manufacturer functions, which are typically given as dll/py/pyd/… files upon purchase. The plugin translates all hardware functionalities onto a set of mandatory functions common to all PyMoDAQ instruments, so that their operation is transparent regardless of the actual hardware.
Here, the wedge linear translation is cast as a DAQ_Move object, and the spectrometer as a DAQ_Viewer generating 1D data. Once the plugins are installed, all PyMoDAQ functionalities can be used directly. When the plugins already exist (which is increasingly the case as more users do contribute to PyMoDAQ), the scanning program is thus constructed in a few FIG. 5. 8 -PyMoDAQ Workflow to construct an interface and perform a dispersion scan. The hardware is connected using PyMoDAQ plugins and combined in a dashboard. Then, the scan module performs the measurement and stores the results and metadata as a HDF5 file. For a demonstration of PyMoDAQ features, see the videos at https://youtu. be/ZdYpQIZHMCY or https://youtu. be/PWuZggs_HwM. 120  Ultrafast Lasers Technologies and Applications minutes and later loaded in less than a minute.
As illustrated in figure  5. 8, the DAQ_Move and 1D DAQ_Viewer are combined into a dashboard, providing an interface to optimize the acquisition settings. Then, the scan module is used to scan the wedge insertion while measuring the SHG spectrum. Figure  5. 8 shows a screenshot of the live display of a d-scan measurement; this particular trace is not exceptionally nice (it is tilted and a lot of rebounds are present), directly indicating poor post-compression conditions. At the end, all data and metadata are stored into an HDF5 file, which is an open source, portable and self-describing file format well suited to storing heterogeneous and potentially large datasets.
Retrieving the Temporal Profile of Few-Cycle Pulses Using PyMoDAQ-Femto From this point, the PyMoDAQ-Femto retriever module can be directly loaded from the scan program, and will automatically load the last measured trace. Otherwise, it can also be launched separately and load any previously measured experimental file, in a manner similar to that of synthetic traces (see section 5. 2. 3). The module requires two inputs: a measured PNPS trace, and the measurement of the fundamental spectrum of the characterized pulses. This spectrum can serve as a starting point for the algorithm, as a comparison to gauge the quality of the retrieval, or even be used to constrain the solution to have the same spectrum. In practice, its measurement can be done sequentially (by moving the spectrometer or by switching the beam path arrangement), or in parallel using two spectrometers.
Pre-Processing Upon loading the trace and fundamental spectrum, much information is extracted from the data: the central wavelengths, the spectral widths, as well as the size of each axis. An option allows to rescale either axis to standard units in case the instruments used different ones. The next important step is the pre-processing of the PNPS trace. Because of the variety of measurement types that can be retrieved in PyMoDAQ-Femto, as well as the various kinds of hardwares that can be used, it seemed unwise to try to automatize this process in a way that would work for all users. Instead, our philosophy has been to provide as much freedom to the users as possible for all parameters. This is perhaps off-putting at first, especially compared to more specialized and integrated commercial softwares, but the interface could easily be simplified by each user to fit their specific needs, after finding the optimal parameters.
Retrieval The pre-processing step converts the measured data onto instances of pypret classes: a MeshData object (the trace), a Pulse object (the fundamental spectrum), and a PNPS object. Again, we purposely chose to give the user as many options for the retrieval as possible: Algorithm type: The pypret module implements several reconstruction algorithms (COPRA, PCGPA. ), which allows to compare their efficiency in various situations. Femtosecond Pulse Shaping and Characterization 123 However, we have only used and tested the COPRA algorithm, which readily works on all PNPS methods. In the future, other algorithms could be tested and debugged, and the interface could be modified to display only algorithms compatible with the chosen PNPS. Verbose information: Displays the error between retrieved and measured traces at each iteration, together with the spectral and temporal intensity of the current solution.