dataesr/scientific-paragraphs-categorization huggingface dataset:
Converting pixel data to NIfTI image DICOM volume loading and conversion to NIfTI format is performed using the pydicom and nibabel python packages  [18, 19]. These packages were integrated in a python class handling multi-dimensional volumes from lists of DICOM files enabling the building of pixel data arrays and managing data orientation to improve NIfTI encoding. Limitations remain for oblique orientations, frequently encountered in large animal models, special care should be provided when handling NIfTI conversions and manual corrections might be necessary. Therefore, the orientation of the pixel data was manually validated using the ITK-snap software  [20]. The provided data were acquired on a clinical PET-MRI system without stereotaxic frame, therefore a unique subject position was established close to a patient position.
The oblique encoding of MRI sequences led to potential errors in conversions. The closest orientation was Right-Superior-Anterior (RSA) for the majority of subjects. Other subjects were oriented oblique closest to Right-Anterior-Inferior (RAI). In order to provide uniform data, the identified RAI subject's data were manually oriented to RSA. Additionally, diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) require bval and bvec files to qualify the sequences. These files are obtained with the dicom2nifti python package  [21].
Generating the metadata file To collect the necessary metadata for each acquisition sequence, compensate missing tags and integrate subject-specific radioactivity parameters, three configuration files are necessary. Sequence overview: The sequence overview configuration file provides a list of sequences to integrate in the BIDS formatting. It provides details on tags necessary for each sequence and replacement values if the tag is missing in the DICOM metadata. In this configuration file, the naming format for the sequence in the BIDS database is also defined along with instructions on where to find and store the corresponding data. Figure  2  provides a description of each column required in the sequence overview configuration file along with examples. Each tag is attributed a value depending on the sequence-specific requirements, the tags availability and format in the DICOM metadata and the modality: -0-Tag not included in the json metadata file of the sequence.
Data quality assessment To provide an indication of the quality of the database formatted to BIDS standards, we analyzed the distribution of three relevant metrics: signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for MRI acquisitions, and median intensity for PET and MRI acquisitions. The metrics were measured on voxels with tissue signal (volume of interest, VOI) following Otsu thresholding on the volume with highest intensity  [23]. Voxels below the given threshold were considered background for noise measurement. The metrics were calculated using the following formulas: SN R = µ V OI σ noise (1) CN R = max V OI -min V OI σ noise (2) M edianIntensity = median V OI (3) Categorical violin plots were plotted for each metric. These plots show the distribution of the metric values across subjects.
Strip-plots were superimposed to the violin plots to provide additional information on time-distributions. In these plots, each category corresponds to an MRI sequence or PET tracer, and the points represent the metric values for each acquisition within that sequence. For dynamic MRI sequences and PET acquisitions, we averaged the metrics across time points or frames and plotted the resulting values. For multi echo sequences, we averaged the median of intensity across echo times and plotted the resulting values. In terms of noise evaluation, only the first echo was plotted as signal is maximal at this echo time. A sample of the data was manually inspected to ensure that the results were accurate and to identify any issues that might have been missed. Based on these evaluations, we were able to provide an indication of the overall quality of the database for potential future users of the dataset, including the detection of potential outliers and the assessment of the homogeneity and consistency of the data.
Results After converting the data to BIDS standards, the sequences' availability was assessed using an automated counter. The data availability is provided with Figure  5. This analysis highlighted the presence of a test-retest for two subjects in the dataset and a few missing acquisitions. The lack of data is explained by issues with radio-synthesis, contrasting agent or movement during acquisition for PET, contrast imaging and MRI sequences respectively. This figure also indicates the study year for each subject, ranging from 2016 to 2019. This parameter is relevant to explain variability in data quality due to software updates on the PET-MRI system. As previously described, the quality of the acquisitions in the shared BIDS database is expressed with SNR, CNR and median of intensity. The quality assessment highlighted the consistency of the acquisition quality (averaged and over time) as displayed in Figure  6.
Discussion In line with human neuroimaging advances, the NHP scientists community is evolving toward more reproducibility and transparency in biomedical sciences, notably through the means of data sharing. To the best of our knowledge, this NHP imaging database is the first open-source collection including simultaneous dynamic PET-acquisition and MRI sequences. We provide a wide range of NHP MRI acquisitions for download, including structural, diffusion and perfusion imaging. BIDS standards were originally designed to guide best practices for storage and sharing of fMRI data sets  [1]. PET imaging modalities came afterwards as extensions to the original BIDS specifications  [12]. In this context, Drs Gitte Knudsen and Robert Innis initiated a collaborative working program to address specific PET imaging challenges through the definition of standards for organizing and sharing  [11].
We followed the current recommendations regarding the description of data acquisition and reconstruction methods as well as the molecular description of the radio-tracers. The purpose of the present brain NHP database is to provide the molecular imaging community a full data set with detailed quality description. We included quality control measures of PET images using the median of intensity metric. Although the methods we propose here is derived from MRI applications, we believe that they can be used to quantify the variability of the different PET data acquisition. This variability is due to the injected radioactivity, subject's weight and physiological constants' variations. While corrections are applied to normalize weight and dose variations using standardized uptake value (SUV) quantification, variability of physiological constants can be compensated using reference region ratios (SUVr).
We provided uncorrected data enabling future users to apply their own normalization and modeling methods. We are aware of the quality limitations of the MRI data in comparison to the standards in NHP imaging  [24]. These drawbacks are largely due to the specifics of our research protocol dedicated to translational stroke research. Therefore, baseline acquisition (before stroke induction) were acquired in the same conditions as further occlusion-reperfusion acquisitions  [14, 25]  which precludes the use of a stereotaxic frame for a repeatable animal position in the scanner. Moreover, due to the specificity of our model, acquisitions had to be shortened to fit experimental conditions in stroke phases. While we observed variability in our data quality which might alter the automation of pipelines; the diversity of our database can represent a source of interest for a wide range of applications from noise reduction to anatomical studies.
When formatting a retrospective and/or pre-clinical database to BIDS standards, we frequently encounter missing data and missing DICOM tags. These inconsistencies are difficult to identify and compensate. Therefore, we found a need for a self-designed tool to format data following BIDS guidelines. Additionally, this tool enables the selection of specific setting/experimental phase to include in the converted database. We found this functionality could encourage scientists to share parts of their data while holding the remaining settings until results are published. We also support a variety of raw data folder organization as we know it varies between structures and institutes. A common issue faced in the development of data formatting and sharing is the time and resources these initiatives require.
This highlights the need for data scientists in research teams dedicated to these tasks. We are hoping that the provided tool will facilitate such initiatives that are urgently needed to address societal challenges raised by the use of NHP in biomedical research. To conclude, we generated an original and diverse NHP hybrid PET/MR database available for the community through PRIME-DE platform and hope that the present note, describing the quality of the published data and metadata will encourage the neuroimaging community to use it.
Acknowledgments This work was supported by  ANR CYCLOPS  and  CMRO2  ( ANR-15-CE17-0020  and  ANR-21-CE17-0028 ), the  RHU MARVELOUS  ( ANR-16-RHUS-0009 ) of Lyon University, under the  Investissements d'Avenir program  of the  French National Research Agency (ANR). The Ph. D. salary of  LC (Cifre, OLEA Medical ) is co-funded by the  French Ministry of Higher Education and Research (ANRT).
The approach suggests that hydrate accumulation grows at a rate of 1% of the pore volume over a period of 10e5 years, as represented by a phase graph and specific fluid velocity. In 1999 and 2002 , Tinivella (1999)  utilized Domenico's approach to quantify the saturations of hydrate and hydrate-related free gas in pores. Her model was applied to the acoustic impedances of in-situ sediments of Bottom Simulating Reflector (BSR). The study used data sets of travel time inversion (tomography) and sonic logs to infer the amounts of hydrate and free gas in the sediments by fitting the theoretical and empirical velocity curves. Clarke et al. (1999)  proposed a mathematical model that utilizes surface energy, mean pore radius, and wetting angle parameters to estimate the conditions required for hydrate growth in porous media.
Methods Seismic Data and Reinterpretation The reinterpreted two seismic profiles in the present study had acquired during two surveys, carried out in the 1989/90 and 1996/97 Austral summers  (Tinivella et al. , 1998; Tinivella and Accaino, 2000)  onboard the R/V OGS Explora, in the frame of a project supported by the Italian National Antarctic Program (PNRA). These data had processed and interpreted by  Tinivella et al. (2009)  and  Loreto et al. (2011). The authors had produced Pre-stack depth migrated (PreSDM) sections by using Kirchhoff algorithm under the open source Seismic Unix software and home codes created by  Tinivella et al. (2009;  see also  Vargas-Cordero et al. , 2010)  and they had obtained 2D velocity fields by using the output of the pre-stack depth migration iteratively. Loreto et al.
(2011)  estimated total volume of gas hydrate in a range of 16 × 109-20 × 109 m3 and total volume of free gas that ranges from 1. 68 to 2. 8 × 1012 m3 by seismic velocity, using the modified Biot-Geerstma-Smit theory  (Tinivella, 1999 and 2002). As already mentioned, gas hydrates have been recognized as BSR in different seismic profiles. Here, we considered two seismic lines called 209 and 213 characterised by lateral velocity variations (Figs. 3  and 4 ). While BSR is continuously traced in Profile 213 (oriented WSW-ENE) by mimicking the seafloor morphology (Fig. 3 ), it is locally marked in the middle of Profile 209 (oriented SE-NW; Fig. 4 ). In this study, the two seismic profiles were displayed and analyzed by using the commercial software Echos AspenTech. The PreSDM sections were reinterpreted to depths of about 4.
The backbone of this study was ANSYS FLUENT, a state-of-the-art, finite volume-based Computational Fluid Dynamics (CFD) software, which was employed to provide timedependent numerical solutions  (ANSYS, 2018). The Navier-Stokes equations are solved numerically through the use of CFD software, as demonstrated by previous research such as  Combarnous and Bories (1975) ,  Patankar (1980) , and  Holmes and Connell (1989). The density of the fluid is considered a function of temperature based on the Boussinesq approximation  (Fontaine and Wilcock, 2007; Düşünür-Doğan and Üner, 2019; Loreto et al. , 2019, Üner and Düşünür-Doğan, 2021). The model geometry was constructed using ANSYS Design Modeler, incorporating the fault geometry and sediment thickness derived from depth-migrated seismic sections (213, 209)  (Loreto et al.
Acknowledgements This study was supported by  Memorandum of Understanding between Istanbul Technical University (ITU)  and  Department of Geophysical Engineering and Istituto Nazionale di Oceanografia e Geofisica Sperimentale (OGS)  for the years 2018-2023. The geophysical data were acquired in 1989-2004 in the frame of the project " Gas Hydrates: impact on climate environmental of the sub-Antarctic regions (BSR) " supported by the  Italian National Antarctic Program (PNRA). We also gratefully acknowledge the  AspenTech  Software Grant for  Istanbul Technical University  which enabled seismic data analysis with their software package Echos™. Bathymetry maps were created under ArcGIS software, freely available. Manuscript is dedicated to the memory of Serkan Üner.
Data are presented as mean values +/-SEM. Source data are provided as Source data file. lipid concentrations. Then, we introduced fluorescently labeled recombinant viral Gag proteins in the bulk and, after 20 min of incubation, we performed spot-variation FCS experiments and analyzed the diffusion laws as in  43  to monitor the variation of HIV-1 Gag monomers membrane diffusion coefficients and their membrane partitioning in the different actin meshes (Fig. 5E -G, Supplementary Fig. 9A ) To avoid any self-assembly of Gag molecules on the surface of the SLB, that would have immediately modified the membrane diffusion coefficient (D bound ) as well as the estimated partition coefficient (Kp = membrane Bound/ bulk Free Gag), we have tuned Gag concentrations to be sufficiently low (10 nM)  13, 43.
Exact p-values are given in graphs. Data are presented as mean values ± SEM. Source data are provided as Source data files. Whereas others observed by cryo-EM the presence of branched actin and actin filaments, or sometimes its absence, underneath HIV-1 buds  15, 50  , reinforcing possible functional roles of F-actin in particle assembly. Other studies showed a role for actin regulating cofactors, such as filamin A, required for HIV-1 release  51  or that the Rac1dependent IRSp53/Wave2/Arp2/3 signaling pathway was required for HIV-1 particle production  16  in which the membrane curvature factor IRSp53 fosters HIV-1 Gag assembly at the host cell membrane  16, 27. Several recent studies rely on the use of drugs perturbating F-actin polymerization  19, 52, 53. Rac1 or CDC42 are both regulators of F-actin and IRSp53, at the crossed road of lamellipodia or filopodia generation  54, 55  which itself depends upon Arp2/3 regulation.
Spot variation fluorescence correlation spectroscopy (svFCS) svFCS Experiments were performed on a Zeiss LSM780 confocal microscope (Zeiss, Iena, Germany) using an immersive 40X water objective equipped with a variable pupil coverage system to obtain different excitation laser waists. Argon 488-nm laser line was used for excitation of Rhodamine or Gag/Phalloidin Alexa Fluor 488, and HeNe 633-nm laser line for Atto647N-PI(4,5)P 2. Acquisition was controlled by the Zeiss Zen software. The laser waists for the different pupil coverage values were calibrated with the diffusion time of rhodamine (for 488 nm excitation) or Cy5 (for 633 nm excitation) in solution (D = 360 µm². s -1 at 20 °C for rhodamine and 320 µm². s -1 at 20 °C for Cy5 (based on Picoquant application note 69 and references therein)).
For each waist, at least 50 measurements of 10 seconds are made. Autocorrelation functions are analyzed using PyCorrFit 1. 1. 7 software  70  to extract the average decorrelation half time (τ 1/2 ) for each probed waist. The svFCS diffusion laws were then established by plotting τ 1/2 as a function of the square of the probed waists (w²). These diffusion laws were then fitted either a linear model τ 1/ 2 = w²/4D + t 0 in the case of lipid membrane diffusion (Atto647N PI(4,5) P 2 in Jurkat T-cells and SLBs), or using equation 6 and 7 in ref. 43 in the case of the membrane partitioning HIV-1 Gag protein dynamics measurements to determine its membrane bound diffusion coefficient (D bound ) and the partition coefficient (Kp) in the different SLBs, using a MATLAB function and scripts previously described in ref.
The pellet was resuspended with TNE buffer 1X overnight at 4 °C. Viral release was estimated by performing anti-CAp24 immunoblot on cell extract as well as purified virions. Full (uncropped/ unprocessed scans) western blots with molecular weight (MW) markers are provided in the "Western blots" Source Data file. Quantification of Gag pr55 and p24, as well as GAPDH are done using Fiji software. The percentage of viral release was calculated based on the following formula: % of viral release = Gag released/(Gag released + Gag intracellular normalized to GAPDH) *100 % of Gag maturation is = Capsid/ (Gag + Capsid)*100 released in virion. Cell viability is measured by CellTiter 96 AQ (Promega ref G3581) kit when in vitro infection is designed in 96 well plates, or by trypan blue in the case of 6 well plate culture.
F-actin quantification is performed using Fiji software after z-projection and F-actin intensity per cell using manual countering for each cell. Sample preparation for TIRF and STORM microscopy 48 hours post infection, Jurkat T cell were washed one time with warm PBS and seeded on poly-l-lysine (Sigma) coated 25 mm round #1. 5 coverslips (VWR) in microscopy phenol-red free medium L15 supplemented with 20 mM Hepes for 30 minutes at 37 °C. Cells were then fixed using 4% PFA + 4% sucrose in PBS for 15 min at room temperature and quenched after in 50 mM NH 4 Cl for 5 min. Samples were then washed in PBS. For STORM imaging, an immunofluorescence protocol was applied: Samples were incubated with Triton 0. 2% in PBS for 5 minutes at room temperature, and washed then blocked for 15 min at room temperature using 2% BSA in PBS.
TIRF and STORM Imaging TIRF and Single-molecule localization microscopy was performed on a Nikon inverted microscope with an oil immersion objective 100×. STORM imaging was performed with 120 mW of 561 nm laser and 200 mW of 641 nm laser. Illumination was performed in TIRF-mode. For STORM aquisitions, 30,000 frames were acquired for each cell with 20 ms exposure time with 561 nm laser and 50,000 frames with 30 ms exposure time with 641 nm laser (adapted from  27  ). Tetraspeck 100 nm beads (Life Technologies) were used as fiducial markers to correct for drift and chromatic aberration. TIRF and STORM analysis for virus assembly density quantification TIRF acquisitions were segmented using Ilastik software 1. 4. 0. Intensity-based threshold was applied to identify clusters size and intensity.
Fiji (ImageJ v1. 53t) software was then used to identify particles size and intensity. SMLM acquisitions were analyzed using the ThunderSTORM plugin in Fiji (ImageJ v1. 53t). Post-processing imaging was applied to first eliminate the background noise with a threshold within 50 nm radius. Next, duplicate localization were removed and repeating molecules within 20 nm were merge together. Drift was corrected by the drift correction module using fiducial markers applied on TetraSpeck beads. STORM localizations, found after post processing in ThunderSTORM reconstruction, were used to quantify viral clusters size using DBSCAN  71. Intensity based threshold was applied after on each ROI to quantify the number of particles per surface using Fiji software (ImageJ v1. 53t). TIRF F-actin and Gag cluster analysis TIRF acquisitions were analyzed using Fiji (ImageJ v1.
Acknowledgements The following reagent was obtained through the  NIH   HIV Reagent Program ,  Division of AIDS, NIAID , NIH:  Human Immunodeficiency Virus Type 1  ( HIV-1 )  NL4-3   Gag-iGFP ΔEnv Non-Infectious Molecular Clone ,  ARP-12455 , contributed by  Dr. Benjamin Chen. We thank  Christophe Leterrier  ( Marseille, France ) for the first introduction to F-actin imaging by STORM many years ago and  H. Berry  ( Lyon, France ) for the Matlab code of FCS diffusion law analysis. RD is a recipient of a SIDACTION PhD fellowship. We thank  CEMIPAI facility  for STORM/TIRF microscopy in BSL3, and  MRI facility  for FCS and confocal microscopies. The study was supported by  CNRS ,  SIDACTION  # LS200599-DM , ANRS # 248756-DM  ( French Agency for AIDS, Hepatitis Research)  granted to DM. CF and DM are members of the  CNRS Imabio consortium.
Data availability All relevant data supporting the key findings of this study are available within the article and in Supplementary Information files. Source data are provided as a Source Data file in a ZIP folder containing the data of each graph and in Supplementary information for the uncropped Western blots. Due to size constrains, relevant raw data of FCS and single molecule localization microscopy (STORM) are provided and available at the following address https://doi. org/10. 5281/zenodo. 8366334, the remaining raw data are available upon reasonable request. Source data are provided with this paper.
Image Processing, Co-Registration, and Statistical Analysis We have performed three types of data analysis: 1) Z-score analysis: The nine cerebral images of rats without lesion were realigned and underwent an elastic registration allowing the creation of a template (FSL software). Then, a descriptive voxel-wise analysis with SPM software was carried out in order to determine a Z-score map between injured and controls rats. Z-score = [(individual value)-(control mean)]/(control SD). With MRICron software, Z-score map between each injured rat and control rats template obtained 24 and 72 h after injury were co-registered with elastic transformations to an average rat brain atlas (Rat W. Schiffer,  [40] ). Left and right cortex were then delineated on each Z-score map. Anatomical regions of interest were classified as follows: QA_left_cortex, no_NaCl_right_cortex and NaCl_right_cortex.
Then, the percentage rate of voxels with a Z-score value exceeding the threshold value of 3 was determine in each anatomical region in order to highlight differences of [ 18 F]FNM uptake with QA or NaCl stereotaxic injection. 2) SUVR of each brain area after brain segmentation: With PMOD Software (v3. 905), the rat W. Schiffer brain atlas was applied on injured and control rats (Fig. 1 ) in order to delineate volumes of interest (VOI) corresponding to brain areas. The standard uptake values (SUV) of each VOI were computed. In order to standardize data, a ratio of brain areas to that in the whole cerebellum was calculated (SUVR). The homogeneity of cerebellar SUVs between groups was tested using one-way ANOVA. The difference in [ 18 F]FNM uptake in control and injured rats 24 or 72 h after injury in each cortical area was then interrogated with an unpaired one-tailed Student t-test applied on the mean SUVR between each group.
Acknowledgements We thank the  CREFRE-Oncopole Experimental Zootechny and Non-Invasive Exploration Teams-US006/CREFRE Inserm/UPS/ ENVT Anexplo Platform  for animal imaging. We thank  IMAVITA  for providing animal tissue for in vitro experimentations. We thank the fluorination platform of  Toulouse University Hospital  for providing [ 18 F]FNM. This study was funded by the  European Regional Development Fund  (Grant No. 16015075/MP0014063 ) and  Occitania Region (Mutualized Platform  Grant No. 16015090 )  PIR2  and  Labex IRON  ( 11-LABX-0018 ).