dataesr/scientific-paragraphs-categorization huggingface dataset:
Last but not least, we emphasize that owing to its particular ascertainment design  (Bos et al. , 2018)  the EMIF-AD MBD dataset does not (attempt to) constitute a representative sample from the "general population. " Accordingly, the results presented here cannot be generalized to the general population. We note that the same is true for many GWAS in this and other fields, which typically use clinic-based ascertainment which is not representative of the population as a whole.
Summary of PGS results significant after multiple testing correction. Threshold" refers to P-value cut-off used for PGS construction in prior GWAS summary statistics and "Number of SNPs" refers to the LD-pruned SNPs passing this threshold that are included in PGS calculations. "R 2 " denotes the phenotypic variance explained by the SNPs of the prior GWAS in the EMIF-AD MBD dataset. A full listing of results from these PRS analyses can be found in Supplementary Material. MMSE, Mini Mental State Examination.
ABOARD. ABOARD also received funding from  Edwin Bouw Fonds and Gieskes-Strijbisfonds. RD was supported by the  NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London , United Kingdom; Health Data Research UK, which was funded by the  UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England) ,  Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government ),  Public Health Agency (Northern Ireland) ,  British Heart Foundation  and  Wellcome Trust ; the  BigData@Heart Consortium , funded by the  Innovative Medicines Initiative-2 Joint Undertaking  under grant agreement No.
ACKNOWLEDGMENTS We acknowledge the assistance of  Ellen De Roeck ,  Naomi De Roeck , and  Hanne Struyfs  (UAntwerp) with data collection. We thank  Tanja Wesse  and  Sanaz Sedghpour Sabet  at the  Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany  for technical assistance with the GSA genotyping. We thank  Fabian Kilpert  for his assistance with the QC and genotype imputations and  Marcel Schilling  for his assistance on visualizing the results. The LIGA team acknowledges computational support from the  OMICS compute cluster at the University of Lübeck.
FUNDING The present study was conducted as part of the EMIF-AD  MBD  project, which has received support from the  Innovative Medicines Initiative Joint Undertaking  under EMIF grant agreement No. 115372 , the resources of which were composed of financial contribution from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. Parts of this study were made possible through support from the  German Research Foundation  ( DFG grant   FOR2488 :  Main support by subproject "INF-GDAC "  BE2287/7-1  to LB) and the  Cure Alzheimer's Fund  (to LB). RV acknowledges support by the  Stichting Alzheimer Onderzoek  (# 13007 , # 11020 , # 2017-032 ) and the  Flemish Government  ( VIND IWT 135043 ). KB was supported by the  Swedish Research Council  (# 2017-00915 ), the  Alzheimer Drug Discovery Foundation (ADDF) ,  United States  (# RDAPB-201809-2016615 ), the  Swedish Alzheimer Foundation  (# AF-742881 ),  Hjärnfonden, Sweden  (# FO2017-0243 ), the  Swedish state  under the agreement between the  Swedish Government and the County Councils , the  ALF -agreement (# ALFGBG-715986 ),  European Union   Joint Program for Neurodegenerative Disorders  ( JPND2019-466-236 ), and the Alzheimer's  Association 2021 Zenith Award  ( ZEN-21-848495 ).
HZ was a  Wallenberg Scholar  supported by grants from the  Swedish Research Council  (# 2018-02532 ), the  European Research Council  (# 681712 ), and  Swedish State Support for Clinical Research  (# ALFGBG-720931 ). SV received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under ROADMAP grant agreement No. 116020  and from ZonMw during the conduct of this study. Research at VIB-UAntwerp was in part supported by the  University of Antwerp Research Fund  and  SAO-FRA 2018 0016. The Lausanne study was funded by a grant from the  Swiss National Research Foundation  ( SNF 320030_141179 ) to JP. Research of CT was supported by the  European Commission  [ Marie Curie International Training Network , grant agreement No  860197  (MIRIADE), and JPND],  Health Holland , the  Dutch Research Council (ZonMW) ,  Alzheimer Drug Discovery Foundation , the  Selfridges Group Foundation ,  Alzheimer Netherlands , Alzheimer Association.
DATA AVAILABILITY STATEMENT GWAS summary statistics for the top (P-value < 1E-05) results are listed in the Supplementary Tables. Full GWAS summary statistics are available from the authors upon request. Clinical data and genome-wide genotyping data are stored on an online data platform using the "tranSMART" data warehouse framework. Access to the genome-wide genotyping data can be requested from the corresponding author of this study who will forward each request to the EMIF-AD data access team.
Dispersion curve of the odd mode Dispersion curve was simulated by means of the eigenmode solver of Ansys HFSS software. Fig. 1  shows the unit cell of the meandered TL with the geometric parameters and the simulation conditions used to calculate the dispersion curve. As illustrated in Fig. 1 (a) and according to the magnetic field which is mainly oriented in the z-direction, a periodic condition (master-slave) was put in the y-z plane, and a Perfect Electric Conductor (PEC) and Perfect Magnetic Conductor (PMC) was used in the x-y and y-z plane, respectively. The dispersion curve plotted in Fig. 2  is attributed to an odd mode as shown by the magnetic field mapped in the inset of Fig. 2. This propagation mode corresponds to a symmetric magnetic field around the two meandered strips produced when the current in each meandered strip circulates in the same direction.
Acknowledgments 6 E3S Web of Conferences 351, 01090 (2022) https://doi. org/10. 1051/e3sconf/202235101090ICIES'22 This work was funded by the  ANR  and  DGA (Agence Nationale de la Recherche  and  Direction Générale de l'Armement)  through the project named  CONTEXT ,  ANR-17-CE24-0013. The authors would like to thank the characterization platform CHOP at IEMN and  François Dassonville  at  ENSAIT.
It also cast doubt on the reliability of the system and on a potential participation of the EMSC in the so-called plot. Originally, the LastQuake information system used to collect felt reports for all the crowdsourced detections that it recorded but published these testimonies only when the seismic activity had actually been confirmed by seismic data from partner institutes. In the case of Mayotte swarm, not enough sensors were there to seismically confirm the information of the system. Despite the testimonies collected, the earthquakes of the Mayotte swarm were not being displayed -a very frustrating user experience! As a matter of fact, earthquake eyewitnesses were able to report their experience for the first 15 min from the shaking and associate it to the crowdsourced detection displayed on the LastQuake app.
The Las Palmas seismic swarm On 19 September 2021, the Cumbia Vieja volcano started erupting on La Palma Island. The eruption garnered seismic activity in the form of a swarm, particularly active in October 2021. An interactive map on the EMSC website unintentionally became "evidence" for false information and even conspiracy theories. Indeed, the local seismicity map, when zoomed, displayed a grid shape (Figure  1 ). Theories would then explain that these earthquakes were artificial, linked to military activities of the United States of America (including the HAARP system ) or heralded a giant tsunami (Figure  2 ). In reality, this "grid" of earthquake locations was an artifact, due to the fact that the EMSC rounds longitude and latitude coordinates to two decimal points, resulting in a less-granular, less-defined dataset.
Earthquake predictors The first type of earthquake predictions occurs regularly and are often produced by seismology enthusiasts or selfproclaimed scientists who often use seismic data produced by the EMSC or other well-known seismic centers to predict earthquakes. Some of them use EMSC's notoriety and audience to give visibility to their predictions by mentioning the EMSC account. For example, a person publishing content on Twitter regularly uses EMSC and USGS data to make videos in which he makes and comments on earthquake predictions. He now has a large community on Twitch (50 K followers) and YouTube (530 K followers). This type of prediction occurs throughout the earthquake cycle since these "experts" constantly monitor the seismic situation on a local or global scale. The number of views generated by these contents suggests that it could be a source of income for their authors  (Mathew, 2022).
Informational improvements The EMSC has become aware of the importance of explaining to the public how the LastQuake information system works in order to limit false information. We created a short explanatory video of the system without including any text, so that it is understandable and accessible to as many people as possible. The video is permanently pinned to the EMSC's Twitter account @LastQuake and it serves as an educational presentation. In parallel we created a repository of answers for our Frequently Asked Questions. The questions are organized around 6 mains categories: (1) about the Site (2) about LastQuake, (3) about earthquakes, (4) I felt an earthquake, (5) data and confidentiality, and (6) citizen seismology. The answers to these questions meet users' expectations in a precise, sourced, and comprehensive way.
Acknowledgments The authors would like to thank members of the earthquake misinformation working group, especially,  Dr. Irina Dallo ,  Dr. Michele Marti ,  Dr. Sara McBride ,  Dr. Femke Mulder ,  Dr. Sarah Dryhurst ,  Giulia Luoni ,  Dr. Julia Becker , and  Max Schneider. Authors would also like to thank LastQuake users and Twitter followers for their help in better understanding earthquake misinformation and testing solutions. Finally, authors would like to thank  Dr. Robert Steed  for his proof-reading of the paper.
wiley. com/doi/10. 1002/dep2. 212 by Ifremer -Centre Atlantique, Wiley Online Library on [20/03/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License both 10 and 30 kV. Log-ratios of two elements measured by XRF core scanning can be interpreted as the relative concentrations of two elements and minimises the effects of down-core changes in sample geometry and physical properties  (Weltje & Tjallingii, 2008). Each measurement was performed over an area of 1. 2 × 1 cm using 30 s integration time. The core was sub-sampled at irregular intervals based on the XRF data obtaining an average sample spacing of about 5 cm. The samples were sieved using mesh sizes of 63 and 150 μm.
All 61 grain-size classes identified by the Sedigraph measurements, belonging to the silt fraction, have been used in an endmember modelling, an analysis performing a deconvolution on subpopulations of the grain-size spectrum  (Weltje, 1997). To obtain the endmembers, the 'Emmageo v0. 9. 7' scripts of  Dietze and Dietze (2019)  have been used in R (version 4. 2. 1). The interval between 520 and 580 cm core depth was further analysed for bulk grain-size distribution using a Malvern Mastersizer 3000 at the Department of Geology (Ghent University). Bulk grain size was measured at a resolution of 1 cm from samples ranging from 3 to 6 mg. Samples were boiled in a hydrogen peroxide solution to remove traces of organic matter, and to assist in dispersion of the particles. Boiling was continued until the reaction could no longer be visually detected, usually about 15 minutes.
One millilitre of 0. 2% sodium hexametaphosphate solution was added to each sample prior to measurement to limit the formation of particle aggregates  (McCave & Hall, 2006). Samples were introduced into the Malvern using a Hydro MV module with a stirrer speed of 2,500 rpm. A continuous ultrasonic bath was set at 2% to avoid breaking the foraminiferal material. Each sample was run three times, each time using a 12 s integration interval, with the results averaged. In order to verify the co-variance of several sets of proxies, correlation coefficients have been calculated using the statistical program 'SPSS Statistics 27' and its 'dimension reduction' option. These coefficients are mentioned in the discussion. F I G U R E 2 Overview of the condensed section (dashed black lines) within core MD08-3227.
Upper panel: core picture between 500 and 600 cm core depth modified in terms of saturation, contrast and temperature to optimise the condensed section visualisation. The sedimentation rate (Figure  3 ) and error on the age model are superimposed. Lower panel: gridded bulk grain-size data (Malvern Mastersizer; grid display) along with the XRF log(Fe/Ca) ratio (white dots). No grain-size data were obtained between 526 and 527 cm. Grain-size axis is logarithmic, the boundary between sand and silt is indicated. The upper X-axis indicates the age, while the lower one displays core depth.
| Power spectrum In order to detect statistically relevant temporal variations within the dataset, a time series analysis has been performed  (Alonso et al. , 2021; Hoogakker et al. , 2011; Pardo-Iguzquiza & Rodríguez-Tovar, 2011). A multivariate Lomb-Scargle periodogram  (Scargle, 1982)  has been applied on the MD08-3227 core data. The Lomb-Scargle periodogram  (Lomb, 1976; Scargle, 1982)  is a well-known algorithm for detecting and characterising periodicity in unevenly sampled time-series  (VanderPlas, 2018). An important factor in the Lomb-Scargle periodogram is the amount of data; more data will yield statistically more relevant periodograms, which will be reflected in the statistical confidence level  (VanderPlas, 2018). The statistical confidence levels are inversely related to the probability of a 'false alarm' of the spectral peaks.
Spectral peaks with confidence levels below 95% are not considered. The periodograms (Figure  4 ) for planktonic δ 18 O values (indicator of palaeoclimate cycles) and sortable silt (indicator for bottom current cycles) have been constructed using Matlab-scripts. Both proxies contain insufficient data points (101 for sortable silt and 56 for planktonic δ 18 O values) in the post-condensed section period to calculate meaningful periodograms for this interval (their peaks do not surpass the 50% statistical confidence level). Therefore, only the pre-condensed section periodograms (MIS3) are considered. In these, peaks not followed by minima below 90% confidence level are  considered as the same peak and the highest peak of the group is considered the dominant cycle. Such groups of peaks are connected by a solid black line in Figure  4.
Additionally, peaks which appear less than four times in the time frame of the dataset (6. 5 kyr for sortable silt and 11. 5 kyr for planktonic δ 18 O values) are considered statistically insufficient. This threshold can be calculated by dividing the time frame by four (the minimum number of required occurrences) and is indicated by a vertical dashed line in Figure  4. T A B L E 1 AMS 14 C ages and tie-points of core MD08-3227 (calibration method) described in detail in  Waelbroeck et al. (2019).
| Power spectrum The sortable silt record suggests one cycle meeting the 95% significance level of 1. 47 kyr, while planktonic δ 18 O values contain three cycles surpassing the 95% confidence level: 9. 62, 5. 04 and 3. 20 kyr. The first two cycles do not meet the threshold of at least four possible cycles within the timeframe of the dataset, yielding only a 3. 20 kyr cycle for planktonic δ 18 O values (Figure  4 ).
(2010). However, they do not exclude the possible (additional) influence of bottom currents. Comparing the sedimentation rates of cores MD08-3227 and GeoB9064  (Wienberg et al. , 2010) , situated only 25 km north of MD08-3227, shows that rates up to ca 25 cm/ kyr are reported for GeoB9064, while rates exceeding 50 cm/kyr are regularly observed for core MD08-3227, with peaks even exceeding 100 cm/kyr. If the entire region is affected solely by changes in aeolian dust input, equal sedimentation rates would be expected for sites so close to each other. Given the much higher rates for core MD08-3227, the influence of bottom currents is likely, which can affect the sortable silt variations of core MD08-3227 as well. Finding out which factor (aeolian input or bottom currents) is the most important at each stage is difficult, although the dataset does give some indications.
ACKNOWLEDGEMENTS This study was carried out within the framework of a  Ghent University BOF  'Starting Grant'. It was additionally part of the  FWO  project  Contourite-3D  (n°  1524713N ). The authors wish to acknowledge the captains and crews of the campaigns on board of the R/V Marion Dufresne cruise MD169-MICROSYSTEMS (founded by  European Science Foundation ,  EuroDIVERSITY  within the framework of project  EC FP6 IP HERMES ) and of the R/V Belgica cruise 2013/16-COMIC. Shiptime on board R/V Belgica was provided by  BELSPO  and  RBINS-OD Nature. Stable isotopes analyses were performed on Panoply Platerform Mass spectrometer at LSCE. A special thanks to  Gulay Isguder  who helped us in the foraminifera determination and to  Alice Matossian  and  Lotte Verweirder  for providing additional bulk grain sizes on the condensed section.
22°, 24. 42°, 32. 85°, and 50. 12° respectively in square root scale (Figure  S3 , Supporting Information). Additionally, according to simulations, based on the powder diffraction database,  [22]  the (001), (002), and (003) reflections should have almost the same intensities, while our data show the intensity of the (001) reflection to be much higher than those of the other two. From our result and based on the depths of Cu K α X-ray probing for Pb and I matrices for the (006) peak, at θ value ≈25. 06 o , it can Adv. Funct. Mater. 2022, 32, 2204283 be assumed that the thickness of the ordered monohydrate layer is ≈2 µm. However, because in-diffusion from a surface yields a gradient of decreasing concentration of the diffusing species (H 2 O, in our case) with depth, it is likely that disordered clusters of MAPbI 3.
Acknowledgements NPJ thanks  Dr. Shay Tirosh  for technical support and guidance. N. P. J. and D. C. thank  Reut Cohen ,  Michal Weitman  for earlier work on this problem at  Bar Ilan University , and  Drs. Irit Goldian  and  Sidney Cohen  ( Weizmann Institute of Science ) for the AFM measurements. The authors thank the reviewers for their constructive comments. NPJ acknowledges funding from the  European Union 's  Horizon 2020 MSCA Innovative Training Network MAESTRO  under grant agreement no. 764787. Additional support was obtained from the  Nancy and Stephen Grand Technion Energy Program (GTEP). G. S. G. acknowledges the support of the  Arturo Gruenebaum Chair in Materials Engineering. At Bar-Ilan University  work was supported by the  Israel Ministry of Energy  as part of the  Solar ERAnet PerDry consortium  and at the  Weizmann Institute of Science  by the  Minerva Centre for Self-Repairing Systems for Energy & Sustainability , and the  CNRS-Weizmann program.
Immunohistochemical Analysis The TMAs sections underwent immunohistochemical staining against CDX2, FRMD6, HTR2B, and ZEB1 for the CMS classification. This IHC-based screening panel was used as a surrogate for gene expression profiling  [37]. The antibodies used were anti-FRMD6 (Clone HPA001297; 1:50; Sigma), anti-HTR2B (Clone HPA012867; 1:50; Sigma), anti-ZEB1 (Clone HPA027524; 1:50; Sigma), using Roche's BenchMark Ventana automatic immunostainer. The anti-CDX2 (Clone PA0535; RTU; Novocastra), anti-cytokeratin (Clone PA0909; RTU; Leica), and the MMR proteins MLH1 (Clone PA0610; RTU; Biocare), MSH2 (Clone FE-11; 1:100; Calbiochem), MSH6 (Clone PM265AA; RTU; Biocare) and PMS2 (Clone PM344AA; RTU; Biocare), were used to determine dMMR status using Leica Biosystems' Bond automatic immunostainer, and the BRAF V600E mutation was determined through anti-BRAF (Clone VE1, 1:1, Roche) by IHC.
Finally, to determine PD-L1 expression, TMAs underwent staining using the antibody anti-PD-L1 (SP142; RTU; Roche) following the specifically approved protocol. Staining was performed in each section, after antigenic recovery and endogen peroxidase blockage, by sequentially incubating the specific primary and secondary antibodies, and revealed with the Optiview Universal DAB Detection Kit using an automatic BenchMark XT (VEN-TANA/Roche). Each stained TMA array was then scanned and digitalized using the VENTANA iScan HT Slide scanner. Images were processed using the integrated Virtuoso image and workflow management software (VENTANA/Roche).
Statistical Analysis The primary endpoint OS was defined as time from surgery to death due to any cause and disease-free survival (DFS) was defined as time from surgery to relapse or death due to any cause. A predetermined subgroup analysis of OS and DFS for the expression of PD-L1 in the CMS2/CMS3 population was performed. The statistical analysis was performed by using the SPSS 24. 0 software (IBM, New York, USA). Associations between variables among groups were determined using the ttest, Mann-Whitney U test or ANOVA for quantitative variables and using Fisher's exact Cancers 2021,  13, 1943.
Statistical Analysis The primary endpoint OS was defined as time from surgery to death due to any cause and disease-free survival (DFS) was defined as time from surgery to relapse or death due to any cause. A predetermined subgroup analysis of OS and DFS for the expression of PD-L1 in the CMS2/CMS3 population was performed. The statistical analysis was performed by using the SPSS 24. 0 software (IBM, New York, USA). Associations between variables among groups were determined using the t-test, Mann-Whitney U test or ANOVA for quantitative variables and using Fisher's exact test and the χ 2 test for categorical ones. Univariate and multivariate Cox proportional hazard regression models were used for OS and DFS analysis. The multivariate model was adjusted for the factors that proved significant with survival in the univariate analysis, which included baseline clinical variables such as age and sex.
Patient Cohort The patient cohort consists of 68 patients with metastatic colorectal cancer, receiving metastasectomy with curative intent at the Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University, Munich, Germany. A liver metastasis (LM, n = 53) or a lung metastasis (LuM, n = 15) was analyzed from each patient. Doublecoded tissues and the corresponding data used in this study were provided by the Biobank of the Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany. This Biobank operates under the administration of the Human Tissue and Cell Research (HTCR) Foundation. The framework of HTCR Foundation, which includes obtaining written informed consent from all donors, has been approved by the ethics commission of the Faculty of Medicine at the LMU (approval number 025-12) as well as the Bavarian State Medical Association (approval number 11142) in Cancers 2022,  14, 1939  Germany.
Statistical Analysis All statistical analyses were performed with IBM SPSS v. 23. Mean biomarker expression between liver and lung metastases was compared using the Mann-Whitney U-test. The prognostic impact of single and dual biomarker expression was evaluated using Kaplan-Meier analysis (log rank test, 'pairwise over strata') and multivariate Cox regression analysis (biomarker expression used as 'categorical covariate', 'First' as reference category). OS was defined as the time from metastasectomy until the last follow-up or death of the patient. PFS was defined as the time from metastasectomy until the next progression. A p-value of ≤0. 05 was considered as significant.