dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, some categorical behavioural variables used in this study may be suboptimal. Cigarette and alcohol consumption categories were based on arbitrary cut-offs, and the education phenotype was dominated by the category capturing university education. Low levels of trait morphometricity may reflect these caveats and may require reevaluation in other samples. It is unknown whether our results are directly translatable to analyses using different software and pipelines. For reproducibility, we make all our analysis code available online (https://annafurtjes. github. io/Comparing_atlases/). Study results would likely differ across FreeSurfer versions  (Gronenschild et al. , 2012; we used v6. 0. 0) , and between processing software, especially compared with other approaches than surface-based analyses (e.
Statement of transparency for secondary analyses AEF is the lead analysist on this project and has been granted access to the UKB data through UKB application 40933, of which JHC is the principal investigator. AEF previously worked with pre-processed Desikan-Killiany IDPs through another UKB application, but has not seen or worked with the bulk MRI data used in this project, or any of the behavioural traits. She therefore is naı ¨ve to any potential associations between variables considered in this study. The UKB application used here is unrelated to the one used in Couvy-Duchesne et al. (  2020 ). An application to download the required data was submitted to UKB on the 28th of March 2021. It has been approved on the 11th of May, neuroimaging data download occurred successively between November 2021 and January 2022 (our pipeline downloaded individual-by-individual, processed, and deleted data to keep cluster storage on King's College London's high performance computer Rosalind at a minimum).
Acknowledgements We thank the three anonymous reviewers of this Registered Report for their valuable input that has significantly expanded and improved the content presented in this project. AEF is funded by the  Social, Genetic and Developmental Psychiatry Centre, King's College, London , and the  National Institute of Health  grant  R01AG054628. SJR is funded by the  Jacobs Foundation. JHC is funded by a  UK Research & Innovation (UKRI)   Innovation Fellowship  ( MR/R024790/1 ;  MR/R024790/2 ). BCD is directly supported by a  CJ Martin Fellowship  funded by the  NHMRC  ( App1161356 ) and the  Inria , as well as indirectly by the  NHMRC grant  ( App1161356 ), the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche - 10-IA Institut Hospitalo-Universitaire-6 ) and reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ), the  European Union   H2020 program  (project  EuroPOND , grant number  666992 , the joint  NSF/NIH/ANR program  " Collaborative Research in Computational Neuroscience"  (project  HIPLAY7 , grant number  ANR-16-NEUC-0001-01 ), the  ICM Big Brain Theory Program  (project  DYNAMO , project  PredictICD ), and the  Abeona Foundation  (project  Brain@Scale ).
Purpose: To validate MRI Analyzer Quality Control (MA-QC), a free and open-source online software designed to facilitate MR data acquisition quality control and PI-QUAL score calculation. Material and methods: MA-QC is a web-based software, designed for analysing DICOM data related to MR acquisition parameters. The software allows automatic extraction of 18 technical criteria, and manual input of 12 visual criteria, to calculate the PI-QUAL score. We collected 100 prostate MRI datasets from four MR device manufacturers to test data compatibility, automatic sequence recognition, and robustness of technical criteria extraction from DICOM data. The main issue was to determine the spatial resolution in the phase and frequency directions, due to variable encoding of the DICOM datasets. Results: Acquisition data could be extracted from all sample examinations (100%), with a median analysis speed of 15.
While the technical requirements of PI-QUAL are accessible in the DICOM data, gathering these technical parameters can be complex and time-consuming for the average radiologist. To address this issue, our team developed a free online software called "MRI Acquisition Quality Control" (MA-QC) that automatically extracts technical parameters from DICOM images metadata and allows interactive PI-QUAL score calculation based on this data. The aim of this study is to describe the design of the MA-QC software, and to validate it on a large set of DICOM prostate examinations from multiple vendors.
Software design The MRI Acquisition Quality Control (MA-QC) software can be accessed from any internet browser via the following link: https://www. pcih. fr/mr. This free-to-use academic tool was developed without thirdparty funding from April 2022 to March 2023 and is free of advertising. The software was designed as a web application for improved accessibility without requiring local software installation, VPN connection, or additional web-browser plug-ins. The software was developed using PHP (v. 5. 6. 40) and Javascript, and open-source third-party libraries (dicom-parser v. 1. 8. 12-2022-02-07 and cornerstone-core-2. 6. 1-2021-11-19)  for DICOM tags extraction  [24]. It is released as open-source under the GNU GPL v3 license. The end-user interface was developed in HTML5, using the dataTransfer technology for DICOM files drag-anddrop.
MR data analysis When a set of files (multiple files or folders) is dragged and dropped directly from the computer's desktop, the software automatically starts their analysis. No data is transferred to the server, and all processing is performed on the user's local machine using a Javascript script. The first step of the process involves filtering of the files suitable for analysis, which excludes non-DICOM files like thumbnails and metadata, as well as DICOMDIR files, non-MR files like CT and US, and enhanced MR objects incompatible with the dicom-parser library. Next, the DICOM data from the selected files is parsed and 46 DICOM tags are read, using vendor-specific algorithms (Table  1 ). Relevant MR information may be stored in different tags, depending on the manufacturer's DICOM conformance statements document.
e TR/TE, sequence name, series description…) or vendor-specific rules. For each series a thumbnail is extracted from the pixel data. After processing, the software displays a summary of the examination on the web page (total number of images, total examination time, device manufacturer, model, field strength, and software version) and an ordered list of the series grouped by type, with a thumbnail and all relevant technical parameters. The processing time and progress are constantly available for the end-user to let him know that the software is running. See Fig. 1  for a screenshot of the application after processing a MR dataset.
Validation data collection To ensure that the software and PI-QUAL calculator are validated on a wide range of MR devices, we reached out to local representatives of major MR manufacturers (GE Healthcare, Canon Medical Systems, Siemens Healthineers, and Philips Healthcare). We requested prostate MR examinations installed by default in new facilities, eventually optimized by the MRI application engineers. We received 67 DICOM examinations directly from three manufacturers, whereas one of them provided a list of centres equipped with their machines for which we collected 10 anonymised examinations. We also collected 23 other examinations from older or previously unavailable devices from our centres or personal contacts. A total of 100 anonymized multiparametric prostate MR examinations were obtained and stored into a single hard drive without renaming, modifying, or sorting the files.
We also did not remove any "non DICOM" files to test the software's ability to process datasets under real-life conditions. Since our study's objective was to technically validate the tool, we only assessed if the software could read all DICOM images, extract relevant series data correctly (e. g. , instance matrix sizes, DWI b values), determine sequence types correctly (e. g. , T2, DWI, DCE), calculate technical criteria correctly and display them in the interface, and calculate the PI-QUAL score. We assumed that all visual criteria were valid for all examinations and reported only if the PI-QUAL was correctly reported in the interface. In case of technical failures during data extraction or analysis, our developer identified and analyzed the corrupted datasets.
Results A total of 100 multiparametric MRI examinations of the prostate were tested, originating from four different manufacturers: 45 from Philips Medical Systems, 15 from Canon Medical Systems, 19 from General Electric Healthcare, and 21 from Siemens Healthineers. The datasets were acquired in 2022, but the devices had a median official release date of 2018 (minimum 2003; maximum 2022). Table  2  provides detailed information on the main characteristics of the devices used to produce these examinations, including the number of examinations per device, mean processing time, and mean image count per examination.
Browser compatibility This web software is compatible with all modern HTML5 browsers. We have successfully tested it on Microsoft Windows 10 using Google Chrome v106 and Microsoft Edge v112, as well as on Apple OSX Monterrey v12. 6 with Safari 16. 3, Google Chrome v112, Microsoft Edge v105, and Firefox v112. The software is also compatible with all mobile devices, although file input can theoretically only be performed from a desktop computer. Study and device information displayed at the top of the page, allowing quick identification of the dataset and its origin. The blue number corresponds to the number of images processed in memory, and is updated in real time, as images are analysed. The estimated duration of the examination is calculated, based on series acquisition times and durations.
Processing time The mean processing time for a prostate MR examination was 96 s , with a median speed of 15. 2 ± 4. 4 files per second (Table  2 ). Since processing is performed by a Javascript script on files stored locally, processing time was not affected by the Internet connection speed but instead by the local machine's performance (e. g. , processor speed, other computations in the background). The main factors affecting processing time were therefore the number of files per examination (which ranged from 158 to 4177) and the performance of the computers used for testing.
DICOM parsing The software was able to load DICOM datasets for all examinations, except one that had to be re-exported from our Picture Archiving and Communication System (PACS), as it was encoded in enhanced MR objects. We adapted the source code to exclude this type of DICOM file encoding. Study information (study description, protocol name, body part, and study date) and Device information (manufacturer, model, software version, and magnetic field strength) were extracted from standard DICOM tags without error in all cases. The DICOM parser never crashed or generated memory leaks. It identified numerous non-DICOM files (such as DICOMDIR, XML or JPEG files) stored with the exported Fig. 2. Screenshot of the "Perform quality test" tab of the MA-QC web application after a prostate MR examination was loaded in memory.
Image geometry Although mandatory standard DICOM tags "Pixel spacing", "Slice thickness" and "Space between slices" were highly reliable, we encountered some difficulties to calculate image resolution in phase and frequency directions in a few cases: For instance, we found discrepancies between the DICOM tag "Phase encoding direction", and the encoding of DICOM tag "Matrix" in two different examinations from two different devices of the same manufacturer, but using the same software version. We created an exception in the source code. In another case, we noticed that one manufacturer did not use the common DICOM tag "Field of view". In two examinations, we observed that recent motion reduction sequences such as BLADE, PROPELLER, MULTIVAN, or JET, which sample k-space in a rotating fashion with radially directed bands, acquire multiple slices during one "blade" and did not provide a traditional matrix.
DCE data recognition Out of 100 examinations, DCE data extraction failed in 10 cases (10%) due to data corruption or incompleteness: anonymized dates and times affected 5 (5%) examinations, while 1 (1%) had only one dynamic phase, and 4 (4%) were biparametric. Noticeably, these errors were not due to the software itself, but to the datasets. DICOM datasets for the remaining 91 examinations had inconsistent availability of parameters such as "Temporal position identifier," "Temporal resolution," and "Total acquisition duration," which varied depending on the manufacturer or device model. Additionally, the creation time of images could be encoded differently across tags such as "Image Creation Time," "Image time," or "Acquisition time. " To overcome these issues, the DICOM parser applied manufacturer-based exceptions.
DICOM parsing and series identification The software demonstrated the capability to extract information from DICOM datasets across all MR models and constructors. The extraction process was fast, with an average of 96 s per examination and 15. 2 files per second, making it a suitable and efficient tool for everyday use. The DICOM data extraction was facilitated by a robust open-source library  [24]  that is constantly being improved. The only limitation of this library is its inability to read multi-frame or enhanced DICOM objects, which we addressed by converting one of the examinations before analysis. We encountered issues with the encoding of certain parameters in the DICOM files. For example, the phase encoding direction, which was important to calculate the separate spatial resolutions in the phase and frequency directions, was missing in some cases or encoded in a different DICOM tag in others.
To resolve this issue, we manually analysed the DICOM files in some cases, used the DICOM conformance statements of the manufacturer to locate the correct DICOM tag or deduced the encoding direction from the "Acquisition matrix" DICOM tag in cases where it was missing. We also observed that some recent motion reduction sequences do not provide a traditional matrix. We adapted the code to detect those sequences and calculate the matrix in a different way. In some cases, we could not detect the b-values of the trace DWI sequences, which are necessary for PI-QUAL. Although this information is supposedly encoded in the standard "Diffusion b-value" DICOM tag, we identified some calculated b-2000 series that did not contain this information and only encoded it in a highly unreliable way: within the series description label (e.
g. , "DWI b2000 axial"). Another difficulty was to determine the exact acquisition time of each phase of a DCE series: we had to extract multiples DICOM tags ("Content Time", "Series Time", "Instance Creation Time", "Trigger Time"), and calculate which of them varied within the images of the series. Depending on the manufacturer, and even the software version, this information could be encoded differently. Added to that, DCE series can be encoded into one or multiple series, and the DICOM tag "Temporal position identifier" was not constantly available. To address this, we used our "series fingerprint" technique combined with the analysis of all timestamps to identify the correct DCE sequence, the different acquisition phases, their count and interval, which are essential for calculating the PI-QUAL score.
PI-QUAL calculation PI-QUAL score is a combination of both technical and visual criteria. MA-QC offers a rapid and automated way to assess the technical criteria that radiologists may struggle to accurately and easily assess, given the variability of their encoding in DICOM datasets and the lack of dedicated tools. Although these criteria could be evaluated only once when the acquisition protocol is defined by default for a device, they are usually adjusted by the radiographers to accommodate patient characteristics, making it difficult for radiologists to check them for every patient/examination, due to time and resource constraints. Inter-reader agreement is strong among highly experienced radiologists, but moderate among those with variable experience  [26] [27] [28] , highlighting the value of training and hardware-based scoring assistance  [29].
Emerging technologies, such as artificial intelligence software and easily accessible tools, may aid in the assessment of visual criteria, allowing for objective and rapid evaluation, leading to a PI-QUAL score that approaches the highest degree of objectivity  [25, 30, 31]. In the MA-QC software, the final PI-QUAL score is calculated using the description of Giganti et al  [21]. The criteria used for qualifying sequences as "optimal", "acceptable" or "unacceptable", which are not described in the original publication of PI-QUAL  [20]  are the same as those used in the literature for a similar software  [25] , and have been discussed with the authors of the original publication. In this study, we did not report the number of technical or visual PI-QUAL criteria that the sample examinations fulfilled, because it was not the objective, and because most parameters are not linked with the device model itself, but with the protocol and the radiographer's preferences at time of acquisition (acquisition matrix, slice thickness, total DCE acquisition duration…).
Potential developments and limitations MA-QC is a tool for quick and reliable analysis of the technical parameters of prostate MRI examinations. This software allows users to save and load protocols to and from a database, allowing for a quick retrieval of the dataset of a prostate MR protocol, provided it is constant over a period of time, to prevent repeating the analysis of the exact same dataset. But, the real benefit of the database is the ability to analyse multiple protocols from a specific MR model from other sites around the world and potentially improve the local protocol. For example, if the user's T2-w sequence only validates 4 out of 5 technical criteria, it is possible to search for a protocol in the database that fulfils all technical criteria and modify and improve the local protocol by reviewing series acquisition parameters in the "Current dataset" tab.
This feature potentially helps improve the overall PI-QUAL score in a facility. In future developments, we plan to automatically check for similar and better-ranking datasets during the PI-QUAL quality test and inform the end-user if some improvements are technically possible. We believe this software is as useful individually as collectively, helping to bring about a global reflection on our practices. To our knowledge, only one software including PI-QUAL assessment has been published  [25]. Amongst other differences, MA-QC is free, requires no installation, and was tested on a larger set of examinations. This software has some limitations. Firstly, as described above, we experienced some technical failures with enhanced DICOM objects, with some recent TSE-T2 sequences, and with a few DCE or DWI sequences.
These errors can be easily corrected when detected and are not major obstacles to using the software. Secondly, it requires the export of DICOM datasets to a file system (Windows or OSX), which can be timeconsuming. An integration of MA-QC with PACS systems and the addition of a such "MR Acquisition Quality Control" or "PI-QUAL" quality control within the PACS systems could bring a real benefit to the process of optimising the quality of prostate MR examinations.
Acknowledgements We would like to acknowledge. • The local MRI specialists from the listed vendors :  Ms. DUFOUR CLAUDE Isabelle  ( General Electric );  Mr. ROGER Maxime  ( Siemens Healthineers );  Mr. GRIGNION Lois  ( Canon Medical systems );  Mr. ROBERT Benjamin  ( Philips Medical systems ). • Colleagues that helped us by providing examinations from their centres :  Dr BOUDA Damien ,  Dr LOOCK Mélanie ,  Dr GAILLANDRE Loïc ,  Dr BORDE Paul ,  Dr GAILLARD Vianney.
Quantification of circulating levels of pro-inflammatory cytokines Serum levels of cytokines were quantified with the fully automated immunoassay platform, Ella (ProteinSimple/Bio-Techne, CA, USA) with the Simple Plex Cytokine Storm Panel (Ref. # ST01A-PS-003229). The lower limits of quantification were provided by the supplier as a threshold for normal baselines, e. g. , 3 pg/ml for IL-6. Serum samples were run in triplicate and results in pg/mL were calculated by the instrument software (Simple Plex Explorer, ProteinSimple/Bio-Techne, CA, USA).
The denatured protein, blocking reagent, washing buffer, antibodies and chemiluminescent substrate were dispended into the microplate. Samples were loaded in duplicate into individual capillaries on a 25capillary cartridge (12-230 kDa separation matrix). Protein separation and immunodetection were automatically performed on individual capillaries using default settings. The area under the curve (AUC) for the electropherograms peak corresponding to the immunodetection of each protein at the expected size, was measured using Compass Software. The threshold for positivity was equal to 1. 2 × 107 total under the curve [AUC] values encompassing all SARS-CoV-2-related antigens, as determined by QC validation of the Kit.
Statistical analyses A detailed description of the statistical tests used is provided in the Supplementary Information. All statistical analyses were performed using SPSS Statistics (version 25. 0, IBM, Armonk, NY, USA) and Prism (version 8. 0; GraphPad Software, La Jolla, California), with statistical significance set at p < 0. 05. Of note, when some sera could not be tested because of quantity limitation or technical issues for test interpretation with requested QC criteria, the numbers analyzed in the successive analyses may differ from one to two PSD or CG.
ACKNOWLEDGEMENTS All the authors would like to acknowledge funding by agence nationale de la recherche  I-GIVE ANR-13-SAMA-0004-01 ),  Network of European funding for Neuroscience (ERA-NET NEURON)  Grant ( ANR-18-0008-01 ). We also thank  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Fondation FondaMental  and the  LVMH group  for their financial support.
After these optimisations, bilobalide and ginkgolide contents in buds and leaves of G. biloba are quantified. In the second part of the paper, three aglycones found in the G. biloba leaves (quercetin, kaempferol and isorhamnetin) are quantified after acidic hydrolysis of the extracts. Finally, we use Principal Component Analysis (PCA) to identify groups of similar individuals in the dataset.
Equipments For the analysis of flavonoids, the equipment used was a reversed-phase HPLC Thermo Separation Product system (San Jose, CA, USA) fitted with an Interchim (Montluçon, France) Nucleosil C18 5 µm (250 mm × 4. 6 mm I. D. ) column. A UV/visible light detector (TSP, UV 2000) operating at 370 nm was also used. The HPLC software and data acquisition system we utilized was AZUR 5. 0 (DATALYS, 38400 St Martin D'heres, France). For terpene trilactone analysis and quantification, the equipment used was an LC system that consisted of a U3000-Dionex equipped with a 1 µL loop injector. Separations were carried out using an Acclaim Pepmap C18 1 mm I. D. column (150 mm × 3 µm × 100 µm) (Thermo Fischer, Illkirch, France). All mass spectra were acquired with Electrospray Ionization-High Resolution Mass Spectrometry (ESI-HRMS) using a micrOTOF Q TM (Bruker Daltonics, Bruker, Bremen, Germany) apparatus, equipped with an electrospray source in the negative mode at 10 eV.