dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-cell segmentation is also very critical as it will constitute the very core of the future analysis of immune subpopulations and expression of biomarkers. Possible mistakes made at this step, e. g. the inability to separate immune cells in large infiltrates, would then result in misinterpretation of MANTIS-generated results. We tested different approaches to automatically segment healthy-looking and inflammatory skin samples, including random forest-based classifiers (e. g. , Ilastik machine learning). While such a method was suitable to segment healthy-looking images with a low concentration of immune cells, it failed to segment complex inflammatory lesions, where large and packed immune clusters were present (unpublished). We thus opted for a semi-supervised segmentation of single immune cells using the software Imaris, in which the segmentation of each inflammatory cluster was qualitycontrolled manually and in 3-D (Fig.
1F ). While this approach is probably more timeconsuming, we could ensure a precise 3-D segmentation and further extraction of an accurate single-cell database to be processed with MANTIS. A recent study has reported the use of an analysis pipeline, including a new segmentation strategy, adapted from the field of astronomy named "AstroPath"  47. Using this approach and only six biomarkers, they could identify important pathological features in biopsies from melanoma patients. These results, in line with our findings, highlight the importance of carefully selecting a list of biomarkers to be studied and of having the right analytic pipeline to draw reliable insights. Because immunologists are more commonly used to identify immune cell populations with manual gating of populations based on flow cytometry, we validated the quantitative performance of MANTIS by analyzing the extracted single-cell databases for the 15 patients analyzed with the conventional flow cytometry software, FlowJo.
We found that the MANTISbased analysis on 3-D images generated with two different panels, and just a minimal number of 10 antibodies per panel, were sufficient to distinguish 19 immune subsets and identify disease-specific trends in skin lesions. Because MANTIS attribution matrices can be quickly adjusted to any sets of markers, they could be compatible with single-cell databases generated using technology with high multiplexing capabilities such as CODEX  7, 8  , MIBI-TOF 9 , IMC 10 , MuSIC 11 , CyCIF  12  , Cell Dive 13 and others  14. We included in MANTIS the α-shape algorithm that enables us to define the precise shape of inflammatory immune clusters based on the principle of Delaunay triangulation  43. When applied to digital immune landscapes, the α-shape algorithm automatically identifies and quantifies dermal and epidermal inflammatory clusters (i.
Mosaic sequential images were acquired using the between-stack configuration in order to simultaneously collect individual 7/8 channels and tiles before merging them to obtain one single image. Use of the between-stack configuration and the modulation of the detectors' detection windows help to reduce the leaking of fluorophores. Finally, a digital zoom of 1. 9 was applied during the acquisition and a mosaic multicolor image was obtained and exported into a. lif format. Detection windows and microscope configuration used in our study are listed in Supplementary Table  2. Image deconvolution and correction of spectral spillover. 3-D mosaic images were then imported into Huygens SVI software, in order to correct the signal by applying deconvolution and crosstalk correction. Two deconvolution methods were used: the express deconvolution (theoretical and fast) or the deconvolution wizard (possibility to use experimental or theoretical parameters and to adjust the background value).
Automatic crosstalk correction estimation was obtained and the coefficients were slightly adjusted manually, if needed, for optimal spillover correction. Segmentation. 3-D mosaic images were imported into Imaris software to separate objects (cells) using a 3-D surface segmentation. Before creating the surface objects in Imaris, classical image processing was required. For instance, defining a threshold, adding a median filter, and/or normalizing the layers were sometimes applied in order to clean the background. Images were either cleaned using the CD45 surface objects or other channels by applying appropriate masks for each channel. Then, segmentation was applied on the CD45 channel surface. Statistics were exported into. csv format.
Segmentation troubleshooting. In some cases, the surface creation parameters were not efficient to automatically obtain good object creation, or the module was not sensitive enough to detect low intensity objects. In this case, the creation of small objects was done manually and the threshold selection was also reduced. If the detected object was below 1 µm, a manual object unification with surrounding objects of same intensity was performed. FlowJo analysis and gating strategies. Identification and density assessment of immune cell subsets were analyzed using classical histo-cytometry  32.
Statistical Immune cell populations were gated in FlowJo software as follows: B cells: CD45 + CD20 + NK cells: CD45 + CD20 -CD3 -CD57 + CD4 + T cells: CD45 + CD20 -CD3 + TCRγδ -CD4 + CD8 -CD57 low or high CD8 + T cells: CD45 + CD20 -CD3 + TCRγδ -CD4 -CD8 + CD57 low or high γδ T cells: CD45 + CD20 -CD3 + TCRγδ + dn T cells: CD45 + CD20 -CD3 + TCRγδ -CD4 -CD8 - dp T cells: CD45 + CD20 -CD3 + TCRγδ -CD4 + CD8 + Mast cells: CD45 + Tryptase + DC: CD45 + CD1c + CD207 -HLA-DR low or high LC: CD45 + CD1c -CD207 + HLA-DR low or high DC CD207 + : CD45 + CD1c + CD207 + HLA-DR low or high Neutrophils: CD45 + CD1c -CD207 -Tryptase -Siglec8 -MPO + Eosinophils: CD45 + CD1c -CD207 -MPO -Tryptase -Siglec8 + CD123 - Basophils: CD45 + CD1c -CD207 -MPO -Siglec8 + CD123 +.
). In order to standardize data processing, we scaled the reference tables and dynamically adapted, for each sample, the table values according to the MFI values. In practice, the value "1" in the binary table was replaced by the maximum MFI value acquired in the corresponding channel from the tested sample. Automatic cell type identification. To annotate the segmented objects, a correlation matrix between the MFI table and the adapted reference panel was generated by performing a pairwise Spearman's Rank Correlation using the R software (2021). Each object was then phenotypically assigned to the cell type having the highest correlation coefficient. Objects with multiple highest correlation coefficients were assigned as "Other" cell types. Accuracy validation. The accuracy of MANTIS automatic cell identification was verified by comparing quantification results to classical histo-cytometry  32.
Subsequently, the MFI corresponding to the first peak of the density curve is defined as the MFI value above which the cell is considered positive for the activation marker. Alpha (α)-shape calculation. α-shape was calculated using the alphashape Python package. Briefly, Delauney triangulation of a given set of points formed a bounding polygon that contains all the points of the set. The α parameter was defined by the value α, and a circle with 1/α radius was drawn in such a way that two points of the dataset are located on the boundaries of the circle and the circle is empty. For each empty circle found, the line between the two points formed a side of the bounding polygon, i. e. , the α shape. As α decreased, the alpha shape changed from a convex hull (e. g. , epidermis α shape, α = 0. 4) to a more tightly-fitting bounding box resulting in more refined alpha shapes (e.
g. , region of interest alpha shape [αROI], α = 0. 1). Cell to structure distance calculation and nearest neighbor search. x-y coordinates of epidermis α-shape contours were stored using the k-dimensional tree method, which allows data ranking and structuration. Briefly, data points were classified based on nodes and branches space-partitioning, allowing a fast nearest neighbor calculation. For a given point (cell) of the dataset, the nearest neighbor in the epidermis alpha shape was found and the distance defined by r was calculated using the scipy. spatial Python package  41. The distance of cells contained in the epidermis α shape was set to 0.
Data clustering and αROI analysis. Regions of interest (αROI, i. e. , inflammatory cell clusters) were identified using the α shape algorithm with a tuned α parameter (α = 0. 1), allowing correct detection of high cell density areas. αROI with less than 15 cells were removed from the analysis. For each selected αROI, specific characteristics were calculated and extracted, such as area, total number of cells, cell number, proportion by cell type and αROI center coordinates. Delauney triangulation of a given set of points formed a bounding polygon that contains all the points of the set. The alpha parameter was defined by the value α, and a circle with 1/α radius was drawn around each point of the dataset. The line between two circles meeting points formed a side of the bounding polygon, i. e.
Neuroimaging Magnetic resonance imaging (MRI) is acquired to assess anatomical and functional (fMRI activation maps) brain changes due to intervention and surgery. For instance, the development of novel activation sites distant from the surgical target may indicate that NICP was effective in promoting a reduction of functional relevance for targeted areas, in favor of a more distributed network. Therefore, all patients undergo three identical MRI sessions: (1) before NICP, (2) after NICP but before surgery, and (3) after surgery. Each session consists of MRI data acquisition using a 3 Tesla Siemens PRISMA scanner and a 32-channel head coil. The protocol includes accelerated multiband sequences adapted from the Human Connectome Project and provided by the Then, task fMRI is acquired during three language and three motor paradigms, which adhere to the following procedures: -Word generation task: Block paradigm consisting of five cycles.
Neurophysiological assessment By performing neuronavigated TMS mapping, we compare the anatomical distribution of active targets pre-vs. post-intervention. The same targets and intensity of stimulation defined at baseline are used at the end of the intervention, to allow comparisons. A figure of 8 coil (MagPro Cool B65-AP-RO Coil) connected to a transcranial magnetic stimulator (MagVenture MagPro x100) is driven by a robotic arm (Axilum Robotics TMS-Cobot) controlled manually or through a dedicated neuronavigation software (Brainsight TMS neuronavigation). We apply biphasic current with an anterior-posterior followed by a posterior-anterior current direction in the brain; the TMS coil is held tangential to the scalp, the handle pointing backward with a 45-degree deviation from the sagittal plane.
For curvilinear 3D brain reconstruction (MNI coordinates), participant's structural and functional MRI data are uploaded. Grids of targets (inter-target distance: 10 mm) are defined based on anatomical landmarks for the subsequent motor/language mapping protocol and to determine the hotspot for neuromodulation. Grids are placed in correspondence with the primary sensorimotor and premotor areas (motor mapping) and over the pars triangularis and the gyrus supramarginalis (language mapping). The procedure for motor and language mapping (at baseline and at the end of the prehabilitation program) is as follows: Set up. The patient is seated on an electromechanics treatment chair to ensure comfort and stability during the assessment. An optic tracker (Polaris Vicra) detects trackers for 3D localization of the Cobot, the patient (a Brainsight Adhesive Subject Tracker is attached to the patient's forehead), and the pointer (Brainsight P-970, for the registration of anatomical landmarks).
After skin preparation (alcohol swab), self-adhesive electrodes for EMG recording are attached bilaterally to the olecranon (ground), the muscle belly of the first dorsal interosseus (FDI, negative electrode), and the muscle tendon of the FDI (positive electrode). A pillow is placed underneath the forearm of the side being assessed, to ensure that muscles are completely at rest during the protocol. PowerLab 8/35 and Quad Bio Amp (ADInstruments, data acquisition hardware devices) register the EMG response and send data to the dedicated software (ADInstruments, LabChart, data analysis software). Every time a TMS pulse is delivered, a trigger signal is sent automatically from the MagPro x100 to the PowerLab through a D-type 26-BNC interface cable, to initiate the recording of the EMG response.
For language mapping, a screen for image presentation is placed in front of the patient. One computer runs Brainsight, while another computer runs LabChart and a dedicated MATLAB script for language mapping. Mapping protocol. The whole protocol is performed for both the affected and unaffected sides. For motor mapping, the entire session is recorded in LabChart. A preliminary search of the hotspot is performed around the hand knob of the precentral gyrus, starting from an intensity of 35-40% and rising progressively up the intensity until stable MEPs (here defined as signals with peakto-peak amplitude larger than 500 µV) are produced. Then, a formal hotspot search is performed, by applying five stimuli to each selected target; the target showing the largest average MEPs is considered the hotspot to determine the RMT, defined as the lowest intensity capable of eliciting three MEPs (signals with peak-to-peak amplitude larger than 50 µV) out of six consecutive stimuli (5-to 10-s interval).
Dissemination A summarized version of the study protocol has been published online with ClinicalTrials. gov Identifier NCT05844605 (more information of the SPIRIT WHO trial registration data set are reported in Table  1 ). Study data are collected and managed using REDCap electronic data capture tools hosted at Guttmann Institute  (76, 77). To ensure confidentiality, each study participant is identified with an alphanumerical code. Data from neurophysiology and neuroimaging are anonymized and stored on online cloud platforms. Accessibility to data files is granted by the principal investigator only to researchers involved in data management. The hard copy of informed consent forms and other data collected on paper is stored in a locked closet at the Guttmann Institute, accessible only by the principal investigator.
Anonymized data supporting study findings are available from the corresponding author, upon reasonable request. Alternatively, an online data repository named "Joan Ribas Araquistain Dataset on Brain Tumor Prehabilitation" is created and made accessible upon reasonable request to accredited clinicians, researchers, and institutions in the field of neuro-oncology. Furthermore, we sought to establish collaboration agreements with international oncology databases, such as the Georgetown Database of Cancer (G-DOC), REMBRANDT (Repository of Molecular Brain Neoplasia Data), and the Cancer Imaging Archive. The results of the study will be submitted to a peer-reviewed journal and presented at scientific congresses.
Methods We implemented all the simulations in The Virtual Brain (TVB; https://www. thevirtualbrain. org) software, a framework for the simulation of dynamics in largescale brain networks, constrained by an imposed structural connectome, with customizable choice of local regional dynamics and possibility to apply arbitrary spatiotemporal patterns of stimulation  (87). TVB software and thus required a specific adaptation of the source code. Such excitatory-toinhibitory coupling, besides its realism, contributes to make the collective dynamics of the model richer and more complex. In reference to well-documented ratios of excitatory to inhibitory populations in the brain, the input to the inhibitory population is scaled to be five times larger than Gee  (97). All connections were instantaneous (no delay was used) and all simulations deterministic (no noise).
Acknowledgements We thank  Giovanni Rabuffo , and  Jan Fousek  for the technical support in creating the model within the TVB software. Funding: Funded by  Agence National de la Recherche (France ; Grant " ERMUNDY ",  ANR-18-CE37-0014-02 ) and the  Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high-performance computing resources. We perform identical stimulation experiments in the five most typical windows of each FC state and in five subsequent oscillatory cycles in each typical window equalling 25 stimulation experiments for each parameter combination of stimulated phase, FC state, and region. First, we calculate the phase difference between stimulated and unstimulated time series for 50000 timesteps post stimulation and wrapped the difference in a range between -0.
The Grand Radio Array for Neutrino Detection (GRAND) is an envisioned observatory that aims to detect the radio emission from air showers induced by ultra-high-energy cosmic particles; in particular, by neutrinos. Because these are rare, GRAND requires a large detection area, necessitating the use of inexpensive radio-detection units that must trigger autonomously. Such a trigger must achieve a high rejection efficiency of the dominant transient radio background, while keeping a high detection efficiency for air shower radio pulses. Fortunately, air shower simulations and field data suggest that air shower radio pulses exhibit characteristic features whose exploitation would lead to a powerful background rejection. We present the results of a machine learning signal classification method that has been tested on simulations and data recorded by a GRAND prototype in the Gansu province of China.
In the present work, we focus on the First Level Trigger (FLT), i. e. the treatment developed within NuTrig to identify radio pulses associated with Extensive Air Showers (EAS) at the level of each antenna alone. In section 2, we introduce the principle of an advanced FLT algorithm. In section 3, we present the data that are used to train and evaluate a neural network-based FLT. We then detail the neural network itself in section 4, the training process in section 5, and the results in section 6.
This precise knowledge of both (EAS) signal and background can surely be used to build an advanced FLT. In NuTrig, we are focusing on a neural network-based FLT. This FLT is motivated by a first successful study based on the TREND experimental data  [8]. Hundreds of EAS candidates were identified within the TREND experimental data through an offline analytical algorithm, with a ∼80% confidence level  [9]. The time traces associated with these EAS candidates, and the same amount of random TREND background traces (that were rejected by the same identification algorithm), were used to train a neural network. When applied to a billion of recorded TREND traces, the neural network achieved a background rejection efficiency of 82% at the antenna level, and of 98% at the array level (where space-time coincidences between antenna-level triggers were requested), while 90% of the EAS candidates were successfully identified.
Datasets The neural network datasets rely on the experimental data of GP13, a GRAND prototype composed of 13 butterfly antennas, deployed in a remote area of the Gobi desert, in the Gansu province of China  [7]. During a running phase of 11 days in May 2023, 3-arms-traces of 2048 ns were recorded every 10 seconds. The present study uses as an input a realistic reproduction of real run conditions. Prior to a neural network treatment, a simple yet efficient pre-trigger algorithm is applied : only traces with at least 2 points over 3 within a time window of 25 ns are kept for further analysis. This treatment reject (useless) signals without detectable transients, and allows to restrict the finite neural network computation time for those of interest for the search of EAS. The following subsections describe the process to produce transients datasets, for a 'background' and a EAS (further referred as 'air shower') origin.
Background datasets Traces are randomly picked within 669 748 daytime GP13 experimental traces. They are selected if they pass the aforementioned windowed double 3 pre-trigger, for at least 1 of the 3 channels, and if the maximum does not stand at the edges of the trace (see section 3. 2. 3). 10 000 'background' 3D-traces, such as as the one on the left side of figure  1 , are devoted to training, and another dataset of 10 000 3D-traces is dedicated to the test phase.
Air shower electric fields In order to cover the full possible range of signal shapes, signal frequency spectra, and simulated signal over experimental noise ratio, that we would like to be able to trigger on, the air shower signal datasets must be generated from a various range of simulated air showers. We therefore use a simulation set containing air-shower-induced electric fields computed with ZHaireS  [11] , for an array of 277 antennas. The set covers the full azimuth range, zenith angles are between 30 • and 87 • , and core positions are within the array. The air showers come from protons and iron nuclei, of energies up to 4 × 10 18 eV. We only selected air showers above 10 18 eV, in order to obtain a significant portion of high signals.
PoS(ICRC2023)224 GRAND First Level Trigger Sandra Le Coz randomly shifted. The shift must belong to the [0,1848] ns range of the 2048 ns trace, not to cut part of the signal, which length is typically below 200 ns. To avoid a consequent bias, the 'background' and 'air shower' datasets have to satisfy a requirement on the position of the trace maximum. It must be at least 200 ns after the beginning of the trace, and at most 200 ns before its end. To be included in the 'air shower' datasets, the maximum of the air shower signal has to be at least 3 times the standard deviation of the experimental trace in which it is inserted, for at least one channel. We further refer to this as the 'SNR' requirement (Signal to Noise Ratio). After the superimposition, the trace has to pass the windowed double 3 pre-trigger, as it is requested for the background datasets.
Results In this section, we evaluate the performances of the trained neural network on the test-dedicated sets. For air showers, data overlap between training and test sets has no chance to occur. For background, the overlap is estimated to be 2 instances or less, with a probability of 99%. Results  The accuracy is the proportion of orange dots that are below 0. 5 for background, and above 0. 5 for air showers. Considering this decision threshold of 0. 5, the plain red lines show a background rejection rate between 97% and 99%, that does not significantly depend on the standard deviation related maximum. Regarding the air shower signals, one can note that, despite generally being outshined by transients in their background insertion trace, the ability to trigger on SNR between 3 and 4 has reached 66%.
Acknowledgment This work is supported by the  'Emergences' and 'Initiative des 2 Infinis' programs of Sorbonne Université , and by the  Programme National des Hautes Energies of CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. It is a part of the  NuTRIG  project, supported by the  ANR  ( ANR-21-CE31-0025 ) and the  DFG (Projektnummer 490843803 ). Simulations were performed using the computing resources at the  CCIN2P3 Computing Centre (Lyon/Villeurbanne -France ), a partnership between  CNRS/IN2P3  and  CEA/DSM/Irfu.