dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, all generated beams can serve as input for building surrogate models using machine learning techniques to predict beam parameters. Section II starts with laser plasma injector parameters, introducing laser driver characteristics and plasma target configurations. Then the numerical setup for fast simulations recently allowed by the PIC code SMILEI  [15]  is presented and the massive random scan settings are introduced. Section III gives an overview of the generated dataset where correlations between plasma target input parameters and electron beam output parameters are highlighted. Several possible functions of merit to quickly grade and compare the generated beams are discussed. Finally, two different types of LPI configurations generating specific electron beams are extracted and further discussed.
A. Postprocessing A Python script based on HAPPI library [39] is used for postprocessing to extract the electron beam and laser parameters  [38]. For the injection, the inner shell electrons from N 5þ were tracked. The electron beam data are extracted at the last simulation time step (end of the plasma density out ramp) and a lower cutoff energy of 25 MeV is applied on the electron bunch energy distribution. Low charge beams below 0. 3 pC are not considered. Electrons originating from helium are not included in the resulting beam since they are very rarely trapped by self-injection (a 0 > 4) and do not contribute to the overall charge. All postprocessing scripts are available online  [38].
IV. CONCLUSION AND OPENING Starting from a robust plasma target design composed of two chambers, with dopant mitigated in the first part, the random scans dataset presented here allowed for the generation of a large number of electron beams parameters satisfying the initial filter "Q > 30 pC & E med > 150 MeV & δE mad < 5% & ϵ y < 2 μm" with origins from different input configurations (LPI working points). This was allowed by fast simulations with the SMILEI code combined with computing time allocated by GENCI at TGCC. Beams matching the filter corresponded to a laser focus in vacuum placed at the end of the accelerating chamber. Plasma self-focusing allowed for an earlier injection and longer accelerating distance (typically all along chamber 2). The divergence was outside of the scope but previous works proved the efficiency of plasma outramp to deal with this issue.
Physician Interviews Physicians who participated in the VED received an invitation via email from the implementation lead of the VED (JNH). Physicians who wanted to participate contacted the study coordinator who explained the study, provided a study information sheet, and obtained consent. All interviews were conducted by a qualitatively trained researcher (DS) and were recorded and transcribed verbatim. We collected descriptive information such as demographic details (ie, gender and age) and data on their ED experience (ie, number of shifts worked per month). Interviews explored overall provider experience, how the VED may have impacted access to care, continuity of care, and overall care, and the implementation of the VED.
Acknowledgments This initiative has been made possible because of funding from  Ontario Health and Sunnybrook Health Sciences Centre. The authors thank  Aikta Verma ,  Dan Cass ,  Andy Smith ,  Steffanye Michaelson , and  Antonia Alevantis  for their ongoing support of this work. JS is supported by a  Canadian Institute of Health Research Health System Impact postdoctoral  fellowship. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Generative artificial intelligence was not used in any portion of the paper.
Step 1: Statistical Representation Maps of Vessel Density and Direction To create a representation of the vessel structure at the population level, VBFL requires a large training set of healthy images where fovea position can be clearly identified. Here, we used the REFUGE data set  30  as the training set, referred to as D train. It contains 840 healthy eye fundus images with a manually annotated fovea location and optic disk position. As a preliminary step, we extracted a vessel information map v i for each single image u i. To do so, we used the recent retinal vessel segmentation network SA-UNet,  23  a convolutional neural network designed for biomedical image segmentation. We used the weights provided by the authors, pretrained on the DRIVE data set. 31 EFUGE and DRIVE images were acquired with different fundus cameras, all with a field of view of 45 degrees.
The REFUGE data set was collected with both a Zeiss Visucam 500 (resolution 2124 × 2056 pixels) and a Canon CR-2 (resolution 1634 × 1634 pixels). DRIVE images were taken with a Canon CR5 NM (resolution 768 × 584 pixels). To follow SA-UNet requirements, all images were resized to 592 × 592 pixels before they were fed to the network. This method outputs a binary map v(x, y) indicating the presence or not of a vessel at each pixel (x, y) of the fundus image, so that v (x, y) = 1 if (x, y) belongs to a vessel, and v (x, y) = 0 otherwise. Aggregating these individual vessel maps v i , we estimated two distribution representations: a vessel density map and a vessel direction map. The vessel density map ( V ) aims at giving, for each position (x, y), the likelihood to have a vessel passing through.
Pathological Fundus Images Eighty-nine fundus images from eyes diagnosed with maculopathy and a manually annotated fovea position were used. These annotated images were part of the training data set released during the ADAM challenge, which goal was to evaluate automated algorithms for the detection of AMD in retinal fundus images. 22 These 89 images included all forms and stages of AMD, showing representative retinal lesions, such as drusen's, exudates, hemorrhages, and/or scars. They were acquired using a Zeiss Visucam 500 fundus camera (resolution 2124 × 2056 pixels). Images were manually annotated by seven independent ophthal-mologists who located the fovea position, as well as visible lesions. For each image, fovea's final coordinates were obtained by averaging the seven annotations and were quality-checked by a senior specialist.
Statistical Analysis Statistical analyses were carried out using R software  33  with the following additional R packages: tidyr, dplyr, stringr, nlme, emmeans, and ggplot2. In validation 1, we analyzed the performance of VBFL on healthy images by comparing it to the performance of the NAM method for each of the 25 crossedconditions (5 head orientations * 5 fixation positions). We fitted a liner mixed-effect (LME) model  34  with prediction error as a dependent variable. The following variables: "method," "head orientation," "fixation location," and "axial length of the eye" were set as fixed effects and "participants" was modeled as the random effect. Prediction error was transformed in natural logarithm (ln) units and eye axial length was centered around its mean. In validation 2, we inspected the performance of VBFL on healthy images with simulated lesions of varying sizes and shapes by comparing it across all 18 conditions presented in Figure  4  (3 shapes * 6 sizes).
In order to validate our approach, we applied it on three different fundus image datasets: nonpathological images from healthy eyes (validation 1), nonpathological images with a simulated central lesion (validation 2), and pathological images with maculopathy (validation 3). For nonpathological images, we found that the VBFL method gives accurate results, with prediction error smaller than 1 degree of visual angle. Hence, our method appears to be slightly more precise than the commonly used method that relies on NAMs. Furthermore, in case of atypical data acquisition conditions, such as eccentric fixation or head tilt, VBFL predictions remain accurate, whereas the NAM method may provide off predictions, by up to 7 degrees. In this sense, the proposed method may be considered as an improvement over the literature standard.
However, it remains to be determined how frequent those extreme data acquisition conditions may be encountered in a clinical environment. In our sample of nonpathological eyes, we included three eyes with high myopia, for which axial length was greater than 26 mm. 38 In these eyes, higher axial length can cause a distorted retinal shape, resulting in an altered geometry of the apparent vessels. 39 Therefore, we expected our method to yield greater fovea localization error in those eyes with high axial length. However, we did not find a significant effect of eye axial length on the VBFL prediction error. Because our sample size of high myopic eyes was quite small, it remains to be tested in a greater sample of eyes present-ing high myopia, whether the VBFL method can still be considered reliable on extreme values of axial length.
For instance, smartphone-based fundus imaging, which couples a smartphone with a retinal camera, now allows for mobile and inexpensive fundus examination. 41, 42 Despite the poorer image quality they convey, it remains useful, especially in low-resource settings. It remains to be tested whether VBFL can accurately estimate the position of the fovea under such quality images with very low resolution. The pathological images we used in validation 3 were made available specifically for the ADAM challenge, whose aim was to make artificial intelligence (AI) research teams compete during the International Symposium on Biomedical Imaging. The challenge objective was to evaluate and compare automated algorithms for the detection of AMD on a common dataset of retinal fundus images. 32 Results were published recently  22  and show that all methods proposed for fovea localization were based on machine learning (segmentation and/or regression) relying on neural networks.
However, comparing their results to ours is quite difficult for several reasons. First of all, their estimation was based on both normal and pathological images. Therefore, the reported value of prediction error average (in pixels only) may appear better than ours given that the majority of their images were nonpathological (311 normal against 89 pathological images). Furthermore, because they reported one single average value of prediction error for the whole dataset, it is impossible to compare our results on a case-by-case basis. Because we already stated that VBFL is not robust with absent optic disk or dark images, it would have been useful to compare our results only for cases where VBFL can be reliable to assess its efficiency. However, in Ref. 22, it is reported that participating teams can get better fovea localization results when the macular region has higher contrast and cleaner texture, regardless of AMD or non-AMD images, which is in line with our own conclusion.
Acknowledgments The authors would like to thank  Julien Kaplan ,  Mate ó Borlat  and  Nicolas Lanoux  for designing the user interface of the VBFL method. Supported by  ANR  (grant  ANR-20-CE19-0018 ); PACA Region Council; and  Monticelli-Paradis Ophthalmology Center (Marseille -France ) -Cofunding of Microperimeter with PACA Region in 2019. Disclosure:  A. Calabrèse , None;  V. Fournet , None;  S. Dours , None;  F. Matonti , None;  E. Castet , None;  P. Kornprobst , None.
Cell line and reagents Cells were cultured in DMEM (Sigma-Aldrich, St. Louis, MO, USA) supplemented with 10% fetal bovine serum (Sigma-Aldrich), 1% glutamax (Biowest, Nuaillé, France), and 1% penicillin/streptomycin (Sigma-Aldrich), except ChaGo-K-1 and DU145 cells that were cultured in RPMI (Sigma-Aldrich). All cells were grown in humidified air at 37 °C and 5% CO 2 atmosphere. Palbociclib was purchased from Sigma-Aldrich. Experiments were performed in mycoplasma-free cells. A549 (RRID:CVCL_0023), ChaGo-K-1 (RRID: CVCL_1121), and HT-29 (RRID:CVCL_0320) cells were purchased from the European Collection of Authenticated Cell Cultures (ECACC). MCF7 (RRID: CVCL_0031) cells were purchased from Eucellbank (Barcelona, Spain). BT-474 (RRID:CVCL_0179) cells were a kind gift from R. Wright. DU145 (RRID: CVCL_010) cells a kind gift from M.
Olivan (Vall d'Hebron Institut de Recerca, Spain). MDA-MB-231 (RRID:CVCL_0062) and LoVo (RRID:CVCL_0399) cells were a kind gift from Mezquita (Universitat de Barcelona, Spain). HCA7 (RRID:CVCL_0289) cells were a kind gift from R. Mangues (Hospital Sant Pau, Barcelona). NCI-H1437 (RRID:CVCL_1472) cells were a kind gift from M. Esteller (IDIBELL, Barcelona). All cell lines have been authenticated in the past 3 years by Short Tandem Repeat (STR) analysis. Epstein-Barr virus (EBV)-immortalized lymphocytes were a kind gift from M. Sánchez (Universitat Internacional de Catalunya, Barcelona, Spain) and were grown in RPMI supplemented with 15% fetal bovine serum, 1% glutamax, and 1% penicillin/streptomycin.
Structural analysis A search of the Protein Data Bank  [16]  was carried out using the 'Molecular Function'-go term 'cyclin binding' (GO ID: 30332). A set of 577 models containing cyclin structures was retrieved that hold 10 unique structures of Human CDKs bound to cyclin partners (PDB codes: 1F5Q, 1W98, 2F2C, 2JGZ, 2W96, 3G33, 3MI9, 3QHW, 4EOJ). For each complex, we defined interface residues as those having ≥ 20% of unbound Solvent Accessible Surface Area hidden upon complexation. Common interface positions present in all CDK/cyclin complexes were defined from a multiple sequence alignment carried out with the MAFFT program  [17]. Interchain contact maps were calculated with the shadow map algorithm  [18]  to define the set of common CDK/cyclin interactions as all the interchain contacts found in all 10 CDK/cyclin complexes.
The comparative modeling for CCNI was constructed using the automated protocol of the I-TASSER software  [19]. The sequence of the Uniprot entry of CCNI protein (ID: Q14094) was used as an input with default options. The best five predictions were energy minimized searching the local conformational space using the relax protocol of ROSETTA3 software  [20]. A thousand minimization trajectories were produced from each I-TASSER prediction, making 5000 conformations. Only the top I-Tasser model generated significantly low energy structures and the lowest energy one was selected as the final comparative model of CCNI. The CCNI-CDK6 complex structures were generated using protein-protein docking calculations. The comparative model of CCNI was docked against the crystal structure of CDK6 kinase (PDB code: 2F2C) using RosettaDock local optimization protocol  [21].
Cell cycle analysis by flow cytometry Cells were plated in 6-multi well plates and transfected as described above. After 72 h, cells were trypsinized and centrifuged at 200 g for 4 min: the culture medium was discarded and the pellet was fixed overnight at -20 °C with a solution of cold 70% ethanol. Cells were then centrifuged at 200 g for 4 min and ethanol was discarded. The pellet was resuspended in a solution of PBS containing RNAse (20 μgÁmL -1 ) and propidium iodide (50 μgÁmL -1 ) and incubated for 45 min in the dark, at 37 °C. The propidium iodide fluorescence was measured on a FACScan flow cytometer (BD FACSCalibur™). The data were gated to exclude cell debris and aggregates and analyzed using the FLOWING Software 2. 5. 1 (Turku Centre for Biotechnology, Turku, Finland). The results are expressed as % of total cells.
Likewise, a discernible effect on S807/ 811 pRb phosphorylation in either A549 or MDA-MB-231 cells was only observed after silencing both CCNI and CCND1 (Fig. 4D ), supporting the idea of some additivity in the regulation of pRb phosphorylation. Furthermore, we extracted data from the Depmap portal (depmap. org/, DepMap 20Q2 Public)  [25] , which contains gene expression profiles of 1304 cell lines of different histological types, and investigated which genes are coexpressed with endogenous CCNI. First, we generated a list of genes ordered by how closely their expression correlated with that of CCNI across all the cell lines. The first 1000 genes were selected for a GSEA using the GSEA portal and the hallmark gene set collection of the Molecular Signatures Database (MSigDB)  [26]. The top hit is a list of 34 genes regulated by E2F transcription factors (Pvalue = 10 -18 , FDR = 10 -17 ) (Fig.
4E ), supporting a model where the CCNI-CDK6 complex phosphorylates pRb to unleash E2F-dependent transcription. To confirm that the expression correlations obtained from the DepMap portal are a consequence of CCNI upregulation, we randomly selected eight genes from the list of 34 genes regulated by E2F transcription factors, all of them considered cell cycle genes according to the Target Gene Regulation Database  [40] , and analyzed their expression by RT-qPCR in MDA-MB-231 cells overexpressing CCNI (Fig. 4F ); we have also included in this analysis 15 other genes that are wellestablished E2F targets (Fig. S13 ). We found that CCNI upregulation led to significantly higher expression of several E2F target genes (Fig. 4F ; Fig. S13 ). To investigate whether the results obtained in cell lines can be extrapolated to human cancer, we exploited the RNA-seq database from The Cancer Genome Atlas (TCGA).
Acknowledgements This work is part of the projects  I+D+I PGC2018-096597-B-I00  and  PID2021-127302NB-I00  (JC), and  BIO2017-83650-P  (JV-F) financed by  MCIN/AEI/ 10. 13039/501100011033/FEDER  'Una manera de hacer Europa'. We would like to thank  Marta Pérez  and  Andrea Mourin  for their technical support. We also thank  Javier Jiménez  and  Jane Endicott  for their valuable scientific discussion. Graphical abstract made in BioRender. com.
Introduction: Early after the start of the COVID-19 pandemic, a major drop in the number of influenza B/Yamagata detections was observed globally. Given the potential public health implications, particularly with regards to influenza vaccination, we conducted a systematic review of influenza B/Yamagata virus circulation data from multiple complementary sources of information. Methods: We searched articles published until 20 th March 2023 in PubMed and EMBASE; examined epidemiological and virological influenza data for 2020-2023 contained in the publicly available WHO-FluNet and GISAID (Global Initiative on Sharing All Influenza Data) global databases, or collected by the multi-national Global Influenza Hospital Surveillance Network (GIHSN) study; and looked for influenza data in the webpages of respiratory viruses surveillance systems from countries worldwide.
This updated review builds upon prior assessments while aiming to have a higher degree of comprehensiveness than any previously published. To achieve this, our search has been extended from global databases for influenza virological surveillance (e. g. WHO-FluNet and GISAID)  [Paget 2022 ], to include another surveillance database (GISHN), biomedical literature databases (PubMed and EMBASE), as well as the webpages of national surveillance systems around the world.
Finally, we included articles focusing on influenza in which the collection of respiratory specimens for influenza testing extended from at least February 28 th , 2020, and in which the virus lineage was determined in all or a subset of the detected influenza B cases. We excluded articles in which: (a) the data collection period was entirely prior to 2020 or was not specified; (b) no respiratory specimens tested positive for influenza; (c) all influenza detections were due to type A viruses, or the virus type was not determined; (d) none of influenza B detections was tested to determine the virus lineage; or (e) there were no original data (e. g. commentaries, editorials, or letters without data). Articles that were entirely based on data from the WHO-FluNet, the Global Initiative on Sharing All Influenza Data (GISAID), or the Global Influenza Hospital Surveillance Network (GISHN) study were not considered, since an extensive search of these databases was also conducted as part of the present study (see below).
We applied no restrictions in terms of study design: therefore, studies as diverse as surveillance reports, hospital-based clinical studies, vaccine-effectiveness studies, and phylogenetic analysis of influenza isolates (e. g. to determine their genetic and antigenic characteristics) were all considered as potentially eligible for inclusion as long as they matched the criteria listed above. The data extracted from each eligible study included: study country, start and end of data collection, number of respiratory specimens that were collected and tested for influenza, number of those testing positive for influenza and specifically for type B influenza, and, among the type B influenza, the number of those that were characterized and the number that had a B/Yamagata lineage.
Influenza B/Yamagata viruses reported in the WHO-FluNet, GIHSN, and GISAID The FluNet (https://www. who. int/tools/flunet) is a web-based database that is coordinated by the WHO and is freely available to researchers. The FluNet database contains information on the weekly number of laboratory-confirmed influenza detections that are uploaded from WHO regional databases or directly entered by national influenza centres (NICs) and other influenza reference laboratories from 194 countries and territories (in August 2023) participating in the GISRS (Global Influenza Surveillance and Response System) initiative. Information on the influenza virus type is available for the vast majority of reported cases as well as the influenza A virus subtype, while the proportion of influenza B cases for which the lineage is reported varies considerably across countries and years  [Zanobini 2022 ].
Influenza data from week 1 of 2020 onwards was downloaded from the FluNet on August 28 th , 2023. The total number of influenza B/Yamagata detections and the number per 100,000 tested specimens, total influenza cases, and influenza B cases (overall and the subset of those that were characterized) were calculated for each of the following five periods: 2020 weeks 1-17 (January-April), 2020 weeks 18-53 (May-December), 2021, 2022, and 2023 weeks 1-33. Moreover, we collected the names of the countries in which influenza B/Yamagata detections occurred in each of these five periods. The GIHSN (https://gihsn. org/) initiative was launched in 2012 through a partnership between industry, public health institutions, and sentinel hospitals in several countries around the world. The overarching objective of GIHSN is to collect data on the epidemiology of severe influenza, defined as a laboratory-confirmed influenza infection resulting in hospitalization, and provide annual estimates of the effectiveness of the influenza vaccine in preventing influenzaassociated hospitalization.
The GIHSN database was queried for severe influenza B/Yamagata cases reported from week 1 of 2020 to week 20 of 2023, with 21 countries contributing data on influenza in at least one season during this period (Brazil, Canada, China, France, India, Ivory Coast, Kenya, Lebanon, Mexico, Nepal, Pakistan, Peru, Romania, Russia, Senegal, Serbia, South Africa, Spain, Turkey, Ukraine, and the USA). GISAID (Global Initiative on Sharing All Influenza Data; https://gisaid. org/) is a publicly accessible repository established in 2008 to share viral genomic data for influenza (later expanded to include SARS-CoV-2 and other pathogens). The GISAID database includes genetic sequences for all influenza viruses as well as related epidemiological data, and access is provided free-of-charge to users around the world in order to stimulate research and gain understanding on how influenza viruses evolve over time and spread globally.
As GISAID includes influenza virus sequences from other sequence databases, such as GenBank, these other databases were not queried separately. The GISAID database was queried on 9 September 2023 for all B/Yamagata and B/unknown-lineage labelled sequences with a collection date from March 1 st , 2020 onwards. Sequences were analysed using GISAID and GenBank blast and phylogenetic analysis using MEGA version 7. 0. 21 taking into account sequences of all available segments, and Nextclade version 2. 14. 1 (https://clades. nextstrain. org/) for hemagglutinin segment assessment.