dataesr/scientific-paragraphs-categorization huggingface dataset:
For a regression, the mean of the predicted values by each tree is the final result. In this study we exploit Random Forests as a regression tool to "predict" the mass and the velocity of the rockfalls from the features of the seismic signal generated by each impact at the ground. The methodology consisted in: 1) defining relevant and we consider each impact seismic signal as a sample in the dataset. As for the A 0 and E S computation, we considered only the impact for which the attenuation regression model yields a determination coefficient above 0. 6. The maximum amplitude at the source A 0 and the seismic energy E S are not included in the features used. By analysing the machine learning model produced we can determine which features of the seismic signals carry the important information that the model is using to successfully predict the value of the mass and the velocity of the block at each impact.
This might suggest that the feature of the seismic signals recorded at the closer stations are more important for the determination of the velocity, as the high frequency are the first attenuated by the propagation of the seismic waves. The detail of the process and understanding why it only affects the velocity prediction are however difficult to unravel from our dataset and should be more thoroughly investigated, through laboratory experiments for example. This observation is not inconsistent with the Hertz theory. Regarding the frequency content, according to the feature importance the full spectrum (FFT) of the whole signal carries less information than the spectrograms and the filtered waveforms. This is unexpected as according to the Hertz theory the full spectrum of the signal (maximum amplitude, width, corner frequency) should all be highly dependent on the mass and the velocity of the impactor.
This removes the need to make assumptions which are necessary in the classical approaches used until now but which are carrying strong uncertainties, such as the velocity of the seismic waves, the density of the soil, the anelastic attenuation factor and the attenuation model used. The machine learning approach also removes the need to know the exact localisation of the impacts and to correct for site effects. Those are major advantages for an operational implementation of such methods for rockfall risks assessment and mitigation. An implementation in any context will only require to perform several, well-monitored, controlled launches of rockfalls to produce a dataset to train the machine learning model, which will then be able to predict the mass and the velocity of future rockfalls. Another strength of the Random Forest approach is its ability to perform well even with few events used to train the algorithm.
signals. An example of the seismic signals recorded at one station and of the selected impact seismic signals is presented in Figure 2. This resulted in a dataset of 384 seismic signals of impacts. 5 https://doi. org/10. 5194/egusphere-2022-522 Preprint. Discussion started: 12 July 2022 c Author(s) 2022. CC BY 4. 0 License.
seismological features to characterize the data; 2) defining a subset of the dataset to train the Random Forests model; 3) training the model and 4) testing the model on a subset of the dataset (the test set) not selected for the training. To assess the robustness and estimate uncertainties associated, steps 2 to 4 are repeated hundreds of times, by increasing, from 10 to 100, the number of events in the training set. When selecting seismic signals features we must find those that might carry the most relevant information on the source properties. We choose 57 features proposed by Provost et al. (2017)  andHibert et al. (2017c)  and given in Appendix A. Those features are used for many applications of the Random Forests as an automated seismic source classifier. They can be 7 https://doi. org/10.
5194/egusphere-2022-522 Preprint. Discussion started: 12 July 2022 c Author(s) 2022. CC BY 4. 0 License. categorized into three families: 1) waveform features (temporal); 2) spectral (frequency) features and 3) pseudo-spectrogram (evolution of the frequency content with time) features. When analysing a dataset from multiple stations, it might be complicated to merge the information carried by all signals in the same set of features. Thus, the strategy chosen consisted in computing the feature on each station for a given impact and taking the mean of a given feature value on all stations. We also computed the standard deviation of the values of each feature over all the stations and included those standard deviations in our array of features. Hence we have a set of 114 features for each impact (57 mean values and 57 standard deviation values).
Acknowledgements. This work was carried with the support of the  French National Research Agency (ANR)  through the projects  HY-DROSLIDE "Hydrogeophysical Monitoring of Clayey Landslides ", the  Open Partial Agreement "Major Hazards" of Council of Europe  through the project " Development of Cost-effective Ground-based and Remote Monitoring Systems for Detecting Landslide Initiation ", the  Research Council of Norway through its Centres of Excellence funding scheme , Project No. 262644. and the  Observatoire Multi-disciplinaire des Instabilités de Versant (OMIV)  (RESIF/OMIV, 2015). The authors thank  H. Collomb  ( ONF-RTM / Alpes-de-Haute-Provence ) for facilitating the access to the  Riou-Bourdoux  experimental site and  Pierre Bottelin  for insightful comments on the manuscript. 14 https://doi.
Acknowledgments This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (Grant agreement no. 771829 ). We thank the  ERC  for the financial support of the PhD thesis of LG. The  CNRS  and  University Paris-Saclay  are also acknowledged for financial support. The authors thank  Stéphanie Denis  ( IGPS, Univ Paris-Saclay ) and  Valérie Nicolas  ( UMS IPSIT, Univ Paris-Saclay ) for technical assistance in cell culture and confocal microscopy, respectively. Dr Julie Mougin  ( IGPS, Univ Paris-Saclay ),  Claire Boulogne  and  Cynthia Gillet  ( I2BC, Gif-sur-Yvette, France ) are acknowledged for technical assistance in TEM imaging. Dr Amaury Bossion  and  Dr Chen Zhu  are acknowledged for fruitful discussions and advice.
Acknowledgements The present work has benefited from the core facilities of Imagerie-Gif, (http://www. i2bc. parissaclay. fr), member of IBiSA (http://www. ibisa. net), supported by  "France-BioImaging " ( ANR-10-INBS-04-01 ), and the  Labex "Saclay Plant Science " ( ANR-11-IDEX-0003-02 ).
The accuracy of real-time identification of motor neuron discharge activity was determined from the rate of agreement calculated between the motor neuron spike trains identified in real-time and those identified offline after manual editing. Of note, we did not assess the capabilities of the software with simulated signals because the approach we used for identifying motor neurons has previously been validated for offline analysis  (18, 20). Data, codes, and a user manual are available at https://github. com/simonavrillon/I-Spin.
Our software uses a fast independent component analysis (fasICA) to optimize a set of separation vectors (i. e. , filters) for each motor unit. To this end, each separation vector is iteratively optimized to maximize the sparseness of the source. At the end of this step, the source is refined, and a K-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). Increasing the number and spatial distribution of recording electrodes increases the likelihood that each motor unit will have a unique motor unit action potential profile (shape), i. e. , a temporal and spatial profile that differs from all the other active motor unit within the recorded volume (10, 25). The uniqueness of motor unit action potential profiles is necessary for the blind source separation to accurately estimate the motor unit discharge times.
Overview of the software The software was coded as a MATLAB app (version 2021a, The MathWorks, Inc, USA). It allows researchers to record and process signals from one to four grids of surface electrodes or intramuscular arrays with up to 64 channels. Of note, the current version has been developed to interface with only one commercialised multichannel EMG system (EMG-Quattrocento, 400 channel EMG amplifier; OT Bioelettronica, Italy). However, the blind source separation algorithm in the code can be used with all systems that record EMG signals with grids of surface electrodes or intramuscular arrays of electrodes. As the accuracy of the algorithm relies on the consistency of motor unit filters, it is recommended to record these EMG signals during stationary conditions -e. g. , isometric contractions -to limit changes in muscle geometry or position/orientation of the active muscle fibres relative to the electrodes  (26, 27).
Electromyographic Recordings The accuracy and boundary capabilities of the online EMG decomposition algorithm was tested on a series of experimental data collected with either a high-density grid or an intramuscular array of electrodes from five different muscles. Surface EMG signals were recorded from the gastrocnemius medialis and lateralis (GM and GL; triceps surae protocol) or the vastus medialis and lateralis (VM and VL; quadriceps protocol) with two-dimensional adhesive grids of 64 electrodes [13 × 5 electrodes with one electrode absent on a corner, gold-coated, interelectrode distance: 8 mm; (GR08MM1305, OT Bioelettronica, Italy)]. Surface EMG signals were recorded from the tibialis anterior with a two-dimensional adhesive grid of 64 electrodes [13 × 5 electrodes with one electrode absent on a corner, gold-coated, interelectrode distance: 4 mm; (GR04MM1305, OT Bioelettronica, Italy)].
A strap electrode dampened with water was placed around the ankle (ground electrode) for each data collection. The EMG signals were recorded in monopolar mode and digitized together with the torque signal at a sampling rate of 2048 Hz for the grids of surface electrodes and 10240 Hz for the intramuscular array of electrodes (EMG-Quattrocento, 400 channel EMG amplifier; OT Bioelettronica, Italy).
Experimental procedure Eight physically active male volunteers (mean ± standard deviation; age: 28 ± 5 yr, height: 181 ± 5 cm, body mass: 77 ± 18 kg) participated in the triceps surae protocol, and eight physically active male volunteers (age: 31 ± 6 yr, height; 181 ± 5 cm; body mass: 78 ± 16 kg) participated in the quadriceps protocol; with data recorded using a pair of surface grid electrode. Two individuals participated in both the triceps surae and quadriceps protocols. Five physically active male volunteers (age: 26 ± 4 yr; height: 174 ± 7 cm; body mass: 66 ± 15 kg) participated in the tibialis anterior protocol, with data recorded using a surface grid electrode; and one physically active volunteer (age: 30 yr, height: 176 cm, body mass: 70 kg) participated in the tibialis anterior protocol with an intramuscular array of electrodes.
Real-time identification of motor neuron activities EMG signals were transmitted by packages of 256 data points for the surface grid recordings (125 ms with a sampling frequency of 2048 Hz) or 1280 data points for the intramuscular array recordings (125 ms with a sampling frequency of 10240 Hz). The electrode mask determined from the baseline contractions was applied to remove the channels with noise or artifacts and the data was extended using the same extension factor as for the baseline contraction (see the section on EMG decomposition above). The matrix of separation vectors (motor unit filters) identified during the baseline contraction was applied over the extended EMG signal. Local peaks were identified for each motor unit using the MATLAB function "islocalmax" with a minimal separation of 25 ms between peaks to limit the number of false positives (Figure  2B ).
03 for GM (Figure  3 ). The above results highlight that the motor units kept for the online EMG exhibit a SIL value well above 0. 8. For the TA experiments, we chose to set a threshold at 0. 9 for the minimal SIL for the offline decomposition to significantly decrease the burden of visual checking and manual editing. On average, we identified 15 ± 4 motor units with a SIL value of 0. 94 ± 0. 01 during the baseline contraction performed with grids of surface electrodes placed over the TA muscle. We identified 10 motor units with an average SIL value of 0. 95 ± 0. 02 during the baseline contraction performed with the intramuscular array of electrodes inserted into the TA (Figure  3A ). Only one motor unit was removed from each dataset after manual editing. It is noteworthy that manual editing did not change the SIL value for any of the motor unit, either recorded with the grid of surface electrodes or the intramuscular array (Figure  3B ).
Discussion We have introduced an open-source software based on blind source separation for decoding the activity of alpha motor neurons in real-time. We first presented the full framework, from the initialisation of the real time decoding using an offline decomposition of EMG signals with fastICA, to the real-time feedback. We demonstrated that: i) the computational time to decompose the EMG signals in real-time is low, i. e. , 0. 37 ms per motor unit when highdensity EMG electrodes are used, ii) the highest accuracy of the real time decoding is observed with intramuscular array comparing to grids of surface electrodes, iii) the highest accuracy of the real time decoding is observed for the tibialis anterior relatively to the other muscles, iv) the smoothed motor unit discharge rates displayed to the participant is accurately estimated from real time EMG decomposition, with a RMSE value lower than 2 pulses per second.
Reviewer #2 (Public Review): Rossato et al present I-spin live, a software package to perform real-time blind-source separation-based sorting of motor unit activity. The core contribution of this manuscript is the development and validation of a software package to perform motor unit sorting, apply the resulting motor unit filters in real-time during muscle contractions, and provide realtime visual feedback of the motor unit activity. I have a few concerns with the work as presented: -I found it challenging to specifically understand the technical contributions of this manuscript. The authors do not appear to be claiming anything novel algorithmically (with respect to spike sorting) or methodologically (with respect to manual editing of spikes before the use of the algorithms in real-time).
There are very few details of manual editing shown in this manuscript (I believe the authors reference the Hug et al. 2021 paper for these details), and little discussion of the core challenges and variability of that process, even though it seems to be a critical step in the proposed workflow. So this is very hard to evaluate and would be challenging for readers to replicate. -I found the User Guide in the Github package to be easy to follow. Importantly, it seems heavily tied to the specific hardware (Quattrocento). I understand it may be difficult to make the full software package work with different hardware, but it seems important to at least make an offline analysis of recorded data possible for this package to be useful more broadly. -While this may be a powerful platform, it is also very possible that without more details and careful guidance for users on potential pitfalls, many non-experts in sorting could use this as a platform for somewhat sloppy science.
-The authors mention that data is included with the Github software package. I could not find any included data, or instructions on how to run the software offline on example data. -Given the centrality of the real-time visual feedback to their system, the authors should show some examples of the actual display etc. so readers can understand what the system in action actually looks like (I believe there is no presentation of the actual system in the manuscript, just in the User Guide). Similarly, it would be helpful to have a schematic figure outlining the full workflow that a user goes through when using this system. -The authors note all data was collected with male subjects because more motor units can be decomposed from male subjects relative to females. But what is the long-term outlook for the field if studies avoid female subjects because their motor units may be harder to decompose? This should at least be discussed -it is an important challenge for the field to solve, and it is unacceptable if new methods just avoid this problem and are only tested on male subjects.
It seems that the objective of the algorithm is the speed of analysis, not the number of units, which makes the elimination of female participants not justified. 7. Human curation is often used in spike sorting, but the description of criteria used in this step or how the human curation choices are documented is missing. 8. The authors might try to add text to be more circumspect about the contributions of this method. I would recommend emphasizing the conceptual advances over the specifics of the performance of the algorithm since processor speed and implementation of the ideas in a faster environment (Matlab can be slow) will change those outcomes in a trivial way. Yet, much of the results section is very focused on these metrics. Minor Ln 115, "inversing" is not a word. "inverse" is not a verb Ln 186, typo, bioadhesive MVC should be defined on first use.
Characterization techniques Inductively coupled plasma optical emission spectroscopy (ICP-OES) was carried out on an Optima 7000 DV spectrometer (Perkin Elmer), after a microwave-assisted acidic dissolution in aqua regia at 185 • C under autogenic pressure. Independently on the preparation method, real Fe and Ni weight contents were measured at 5. 0% ± 0. 2%. ". X-ray diffraction (XRD) measurements were collected using a PANalyticalX'Pert Pro MPD diffractometer. The X-ray source was a copper long fine focus X-ray diffraction tube operating at 40 kV and mA. Data were collected in the 5-90 • 2θ range with 0. 0167 • step. Crystalline phases were identified by references to the ICDD PDF-2 (version 2004) database. Surface area and porosimetry measurements were carried out on ASAP2020 Micromeritics using N 2 as adsorbant at -196 • C, with a prior outgassing under vacuum at 200 • C for 3 h in order to desorb the impurities or moisture.
Acknowledgements This research was performed thanks to the contest  « Programme d'Investissements d'Avenir » of the French Government  under the supervision of the  French National   Radioactive Waste Management Agency  (ANDRA), and thanks to the  MIGNON  project, financially supported by the  French National Research Agency (ANR) , under grant agreement No. ANR-22-CE42-0012. The authors also wish to warmly thank the PhD students and colleagues involved on this topic:  Flavien Barcella ,  Laurent Colin ,  Luis Iglesias-Hernandez ,  Thierry Leïchlé ,  Maria Dolores Manrique-Juarez ,  Fabrice Mathieu ,  Liviu Nicu ,  Priyadarshini Shanmugam.
01 mBar overnight. The stabilization occurred by annealing the sponges in ethanol vapors, followed by freeze-drying again. The porosity, thickness, and pore size of the sponges (n = 3) were evaluated by microcomputed tomography (microCT) using a Bruker SkyScan 1272 (Kontich, Belgium). Scans were acquired at a voltage of 50 kV and a current of 200 μA, with a rotation step of 0. 3 °, pixel size of 5 μm, and no filter. Reconstruction of the obtained tomograms was carried out using NRecon software (Bruker) and 3D rendered images of the samples were generated through original volumetric reconstructed images by CTVox (Bruker). The quantification of structure properties was evaluated using a cylindric volume of interest (VOI) of 30 mm 3 centered in the middle of the samples. Before the analysis, datasets were binarized using a global threshold of 70-255 and a 3D despeckle process was applied to reduce image noise.
Boyden chamber assay U87MG-CXCR4 + cells were starved in serum-free DMEM (SFM) for 24 h. Then cells were collected with Acutase solution, washed with PBS and resuspended in SFM. 5 × 10 4 cells were deposited on top of 8-μm pore PET inserts (Corning 353,097) in 100 μL of SFM. Then, 650 μL of SF DMEM containing 0, 40 or 120 ng/mL SDF-1 α was deposited in the bottom well. After 18-h incubation cells were fixed with 4 % PFA for 15 min at room temperature (RT) and stained with 0. 1 % crystal violet solution for 30 min. After washing, images were obtained in a VHX-70 0 0 microscope (Keyence, France), and cells were counted with the QuPath software.
The constructs were left in culture for 6 days, fixed with 4 % PFA, permeabilized, and stained with DAPI before incubation in 0. 3 % Soudan Black solubilized in 70 % ethanol to reduce the autofluorescence of sponges. Confocal images were obtained from the top view and the cross-section of sliced constructs. The on-top cell area and the cross-section invaded area were analyzed with the QuPath software.
Acknowledgements For critical platform assistance in developing this work, authors would like to thank  Romain Mallet ,  Florence Manero  and  Rodolphe Perrot  from  SCIAM  ( Common Service for Imaging and Microscopy Analysis, SFR ICAT 4208, Université d'Angers, France ),  Florence Franconi  and  Samuel Bonnet  from  PRISM  ( Imagerie et spectroscopie multi- modales,  SFR ICAT 4208, Université d'Angers, France ),  Catherine Guillet ,  Jérôme Cayon  and  Lydie Bonneau  from  PACeM  ( Plateforme d'Analyses Cellules et Moléculaire, SFR ICAT 4208, Université d'Angers, France ), and  Aurélia Rolland  from  IMAC (Imagerie Cellulaire, INRAE, Angers, France ). The authors are also grateful to  Luis Diaz-Gomez  from  USC (Universidade de Santiago de Compostela, Spain ) for help with microCT analysis andLaurence Sindji and  Anne Clavreul  from  CRCI2NA , team 5 GLIAD (Design and application of innovative local treatments in glioblastoma, Angers, France) for their useful advices.
"Silk-fibroin interventional nano-trap for the treatment of glioblastoma") [grant ANR-19-ENM3-0 0 03-01]. It was also supported by the Instituto de Salud Carlos III ( ISCIII ) [ AC19/0 0 067 ] Cofinanciado FEDER, Spain, by the Italian Ministry of Health-Ricerca Corrente Annual Program 2023, by the Institut National de la Santé et de la Recherche Médicale (INSERM) and by the University of Angers (Angers, France). The work was in addition related to: (i) to the French ANR under the frame of the LabEx IRON (Innovative Radiopharmaceuticals in Oncology and Neurology) as part of the French government Investissements d'Avenir program (ANR-11-LABX-0018 to E. G.
a b s t r a c t The land cover data presented here is a reconstruction of the past landscape (1993) at Very High Resolution (VHR) for the city of Poitiers, France. This reconstruction is based on multiple sources of images and data. We combined the strengths of both mono-temporal and multi-temporal classifications. Orthophotos were created at a spatial resolution of 0. 5 m using aerial raw images from the French National Geographic Institute (IGN), taken during two aerial missions in July and August 1993. These orthophotos were merged at a spatial resolution of 5 m to conduct a first object-based classification using Landsat-5 TM images. The goal was to identify croplands, grasslands, coniferous and deciduous forests, urban areas, water bodies, and shadows. This learning-based classification employed a dataset consisting of 1371 polygons and demonstrated strong classification performances, achieving an overall accuracy of 86.