dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). It did not provide improvement when applied to the data used in the current study (mean AUC value for leaveone-subject-out testing was 0. 59; in case of training and testing on the joint dataset of all participants AUC was equal to 0. 63), possibly due to higher variability of the MEG patterns or lower signal-to-noise ratio, but it is not unlikely that the classifier performance will improve with more participants involved. (4) Changing the preprocessing pipeline for better handling of the single-trial data. In the current study, MEG raw data were preprocessed using the MaxFilter software that applied the tSSS procedure. As it is applied blockwise, this procedure could likely introduce certain irregularities to the signal that do not affect the averaged signal (typically used in the studies of phase-locked MEG components) but may make more difficult classification of the singletrial data.
participants, respectively. Only in a few participants (4 in the case of LF-CNN and 3 in the case of VAR-CNN) was nonrandom performance not achieved in any test fold. Notably, all these participants had low number of available fixations (not higher than 100). Pearson correlation coefficient showed significant positive linear correlation between the naïve/ensemble testing AUC and the number of trials per class both in the case of LF-CNN (r = 0. 55/0. 64, p = 0. 0049/0. 0005) and in the case of VAR-CNN (r = 0. 57/0. 60, p = 0. 0030/0. 0014), i. e. , the more data were available for training, the better were the observed classification performance. For all participants strong overfitting of the CNNs were observed: mean group AUC values were about 0. 98 on the training subset. ROC AUC curves for training and testing procedures are represented on Supplementary Figure1in Supplementary Data Sheet 1 for datasets of three subjects containing more than 100 trials per class: the first one (subject 104), revealed below group classification performance; the second one (subject 114) with AUC values near group average; and the third one (subject 221) with above mean AUC values.
8 participants, respectively. Only in a few participants (4 in the case of LF-CNN and 3 in the case of VAR-CNN) was nonrandom performance not achieved in any test fold. Notably, all these participants had low number of available fixations (not higher than 100). Pearson correlation coefficient showed significant positive linear correlation between the naïve/ensemble testing AUC and the number of trials per class both in the case of LF-CNN (r = 0. 55/0. 64, p = 0. 0049/0. 0005) and in the case of VAR-CNN (r = 0. 57/0. 60, p = 0. 0030/0. 0014), i. e. , the more data were available for training, the better were the observed classification performance. For all participants strong overfitting of the CNNs were observed: mean group AUC values were about 0. 98 on the training subset. ROC AUC curves for training and testing procedures are represented on Supplementary Figure1in Supplementary Data Sheet 1 for datasets of three subjects containing more than 100 trials per class: the first one (subject 104), revealed below group classification performance; the second one (subject 114) with AUC values near group average; and the third one (subject 221) with above mean AUC values.
Analytical Methods The chemical composition of volcanic rocks was determined at SGS Laboratories in Canada by X-ray fluorescence spectrometry (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). The results are reported in Table  A1. The two types of felsic facies distinguished in the EAVC were investigated by the U-Pb zircon dating method: ISOD-11 (EAVC rhyolite) and ISOD-12 (EAVC pyroclastic breccia). Zircons were analyzed by high-precision isotope dilution thermal ionization mass spectrometry (ID-TIMS) at the Jack Satterly Geochronology Laboratory (Department of Geology, University of Toronto, Toronto, ON, Canada). U-Pb data for all samples are presented in Table  A2. In these, associated ages and errors were calculated using the IsoPlot/Ex (v. 3. 00) Excel Add-In  [44]. Sm and Nd concentrations and 143 Nd/ 144 Nd isotope ratios were measured by ID-TIMS at Université Blaise Pascal (Clermont-Ferrand, France), using the analytical methods reported in Pin et al.
Major Element Geochemistry Apart from chemical analyses specifically performed for this study, we also used two chemical analyses of fiamme-rich pyroclastic rocks available from studies published by the Regional Government of Andalucía (Spain)  [48]. Results are included in Table  A1. The list also includes the data we obtained for the basic rocks in the studied area. However, we have not displayed the data of basic rocks in the following figures for clarity, given that our work focused on the link between felsic rocks and VMS deposits. However, we did determine one exception with a diagram for rock classification based on immobile trace elements (see below), in order to show that in the studied area, as in most zones of the IPB, igneous rocks are bimodal in composition and mafic and felsic rocks are chemically unrelated  [29, 49].
Nd Isotope Geochemistry Although only a limited Nd isotope dataset was available, these data indicated that the felsic rocks in the area under study were generated from an evolved, crustal source (-4. 7 < εNd < -4. 5). In addition, a comparison between chemical and isotopic results in the EAVC zone and those obtained in other areas of the Spanish VSC showed that these values also occurred in other areas in the IPB such as the Paymogo Volcanic Sedimentary Alignment (PVSA), where felsic rocks, generated from evolved crustal segments, are related to VMS deposits [14]. This comparison, however, is not strict because, in this latter area, the felsic volcanic rocks showed a wider range of Nd values, comprised between -5. 3 and -4. 1 for the highly evolved dacites and rhyolites, and between -3. 2 and -2.
DenSeeMammo (DSM) has been validated for density assessment and shows a higher degree of agreement than that reported in studies on other vendors' automated volumetric assessment tools  20. However, the software has not yet been validated for prediction of breast cancer risk and for assessing the risk of masking (i. e. , the risk of having interval or subsequent round cancer instead of screendetected cancer). The primary aim of this study was to evaluate a recently developed automated system for breast density assessment (DenSeeMammo) and its ability to stratify breast cancer risk, by comparing discriminative accuracy for BC risk prediction to a non-inferiority threshold defined on the basis of visual assessment by radiologists. The secondary aim was to estimate how the fully automated BD assessment is able to measure the masking effect.
Methods Study population. This study represents a pooled analysis of images collected within the RETomo trial  21  , Florence study  17  and PROCAS case-control study  18  , with a total of 14,267 women 48-55 years old, of whom 322 developed breast cancer. A summary of studies included is provided in Supplementary Table  1  while the flowchart of study participants is presented in Fig. 1. Florence study. A cohort study including 15,952 women who had their first screening digital mammography aged 49-54 years old during the period 2006-2013. Women were routinely reinvited after 2 years, and the study follow-up lasted 2. 5 years. Only 5359 2D full-field digital mammography images were available for breast density analysis since some images were performed with mammography equipment (GE 2000D) which is not supported by DenSeeMammo software.
Acquisition of images. Cranio-caudal (CC) and mediolateral oblique (MLO) pairs (left and right breast) of 2D full-field digital mammography (FFDM) of all women recruited were obtained from Picture Archiving and Communication Systems (PACS) and processed by DSM. Ten women were excluded as it was not possible to obtain suitable images. Images were from different vendors and systems (GE Essential and Siemens Mammomat Inspiration). Detailed description of image acquisition is provided in Supplementary Table  S1. Breast density assessment. DenSeeMammo 1. 2 (Predilife, Villejuif, France)-is a Food and Drug Administration (FDA) approved, fully automated software for assessing breast density providing a BI-RADS category density grade. DenSeeMammo handles processed 'for-presentation' images extracted from DICOM files as input (CC + MLO, or CC or MLO).
It provides results on a per patient basis, using the maximum density category of the two breasts. The method is based on a comparison with databases containing images previously scored by the Mammography Quality Standards Act (MQSA) radiologists using BI-RADS 5th Edition  4. With DenSeeMammo, all assessments are based on BI-RADS 5th Edition which takes into account percent density and density distribution in the breast to reflect risk of masking  4. DenSeeMammo received 510(k) FDA clearance in 2017 and in 2018 for automatic breast density evaluation on GE and Hologic equipment. Endpoints. Cancers included in the analysis were: (1) prevalent cancers, i. e. , cancers detected at inclusion to the study, (2) incident cancers i. e. , cancer not detected at the study inclusion mammogram but occurred at a subsequent screening round including (3) interval cancers i.
Given that models adjusted for age and study centre yielded similar results as crude models, the results of the crude model are presented. As a single measure of accuracy, i. e. , ability of the automated system to discriminate between cases and controls, area under the receiver operating characteristic curve (ROC)-AUC with 95% CI, according to binomial exact distribution, was computed based on the four BI-RADS categories. In order to establish a clinically and statistically acceptable threshold for accuracy of the breast density software system, the non-inferiority margin for the AUC was defined as the observed value for the routine evaluation made by radiologists in the Florence study. Since our outcome was a single ROC curve and the direct comparison with radiologist visual assessment was not possible, a non-inferiority margin was calculated based on the modification of confidence interval method suggested by Ahn et al.
22. In the Florence study, consisting of 15,952 mammograms classified by radiologists' visual assessment in the four BI-RADS categories, the AUC for predicting incident cases was 0. 579 (Supplementary Fig. S1 ). The non-inferiority threshold was set at 10% reduction in area exceeding 0. 5, if the lower bound of the 95% CI of AUC value for DSM was higher than 0. 571. Statistical analysis was performed using Stata version 10. 0 (Stata Corporation, College Station, TX, USA). Power of the study. For the power calculation we used AUC of VolparaDensity's automated system (version 3. 1, Matakina Technology, Ely-Cambridgeshire, UK) from the Florence study as an expected value of software AUC. VolparaDensity is a commercial volumetric breast density method that operates on 'for processing' mammograms  23.
Expected value of the software AUC in the Florence study was 0. 637 (re-analysis of data from Florence study)  17. With an expected sample size of about 300 cancers and an underlying cohort of about 25,000 women aged 48-55, applying the same BI-RADS specific risk of cancer observed in the Florence study  17  , the power to exclude a non-inferiority threshold of 0. 571 was expected to be over 99%.
Discussion In the present study we have evaluated the ability of a recently developed automated system for breast density assessment (DenSeeMammo) to predict risk of breast cancer in the pooled analysis of three European studies. Positive association of automatically assessed breast density and risk of incident, interval (masking effect) and prevalent breast cancers and all cancers was observed in the pooled data, and no important heterogeneity was observed among the studies. Accuracy of DSM in breast cancer risk prediction was acceptable in our study with an AUC (0. 589), not inferior to, and actually slightly higher than, the radiologist's visual assessment observed in Florence  17. It was also similar to those reported previously and estimated by using Volpara software  14, 16, 19. Although these studies found positive associations of both automatically and visually assessed BD with breast cancer risk, their automated systems were inferior to BI-RADS visual assessment in the discrimination of cancer risk.
Astley et al. also showed that the average of two radiologists estimates of breast density recorded on Visual Analogue Scales (VAS) was a stronger predictor for breast cancer, compared to four different automated systems  18. In our study overall DSM accuracy outperformed the single radiologists' visual breast density assessment, suggesting that DSM or other software with similar performance could be used in screening, thereby overcoming some of the organizational barriers that impede the radiologist's visual assessment without the risk of decreasing accuracy. This study did not aim to provide direct comparison with other automated software and Volpara was used as the only automated software available for the power calculation. Although a recent study demonstrated that DSM has a comparable or even better degree of agreement with expert radiologists' visual assessment than Volpara 24 , Total 13,691 98 35 Table  3.
Cancers OR (95% CI) Cancers OR (95% CI) A 1063 ( the most studied and one of the best performing quantitative volumetric assessment method  14, 18  , other studies performed on the same set of mammographic images with direct comparison with other automated systems are necessary to answer whether DSM is a valid alternative to other automated systems. As expected, excess risk was higher for incident cancer (OR 2. 6) than for overall cancers (OR 1. 8) in our study. Data for incident cancers were less consistent between studies ranging from an OR of 2. 8 in the PROCAS study  18  and a HR of 8. 3 in the study by Wanders et al. 25. Considering only incident cases overestimates the true prediction performance of the software; of note is that both studies are case-control studies and for the latter, density was evaluated at the time of cancer detection, augmenting additionally the ability of the software to predict breast cancer.
When evaluating risk of overall cancers, i. e. , considering also prevalent cases, our results (OR 1. 8) are in agreement with results of studies utilising other automatically assessed breast density software. In particular, prediction of cancer was very similar to the estimates of Puliti et al. who reported 2. 0 times higher risk of invasive BC in women within the highest breast density category compared to those in the three lower categories  17  , as estimated by VolparaDensity software in the entire Florence cohort. It must be noted that only a small part of available cancers and mammograms were included in both of the density evaluation studies, because only one third of the Florence cohort used for validating Volpara software was also suitable for processing by DSM. With the limitation of the difference in age of the participants, screening frequency and breast density distribution, other studies reported similar results for the association between fully automatically assessed BD and risk of cancer, with OR ranging from 1.
Image acquisition and processing The 10 × 10 cm area of the five granitoids was imaged using a digital field camera (Nikon D5300 DSLR) and sunshine/natural lighting. A reference color chart was shot preliminary to correct RAW images for white balance, color accuracy, and tonal response under given lighting conditions. Each RAW image was then cropped, converted, and exported as a JPEG file (see Fig. 1a ). These images were then imported into the Fiji-ImageJ app  (Schindelin et al. , 2012) , scaled, and enhanced (for contrast and exposure). Images were then segmented for each phase (minerals or groundmass) distinguished on the macroscopic sample, using the Weka trainable machine-learning (ML) algorithm in Fiji-ImageJ  (Arganda-Carreras et al. , 2017; see Fig. 1b  for the TARN sample). Phases proportion was then computed and exported using ImageJ Measurement Tool and grain size distribution using SPO software  (Launeau et al.
Certified concentrations for major and trace elements can be found on the GeoReM database and GGR Review  (Jochum et al. , 2005 (Jochum et al. , , 2014)). Each pressed pellet was analyzed for 75 s, as recommended in  de Winter et al. (2017) , with the same source and sensor conditions mentioned above. The raw spectra (PDZ filetype) of the first dataset were converted to CSV filetype using S1PXRF software (www. bruker. com). X-ray spectra were then imported into the CloudCal app. For each element, a region of interest (ROI) where to find the ED peak of interest was preliminary adjusted. A calibration curve was then generated for each chemical element using non-linear regression and the Lucas-Tooth and Price equation regression models (see procedure details in  Sapkota et al. , 2020a ,b). The quality of the calibration process and regression line equations are summarized in appendix A2 along with interference corrections, amount of omitted SRMs (being outliers in the calibration curves) and Compton normalization range used for each element peak energy.
Most chemical elements showed promising  calibration curves except for V, Cr and As; hence, the latter was removed from the calibration method. A subset of twelve additional SRMs' press-pellets (except for BHVO) of igneous rocks was then analyzed as known-unknowns (BIR, DNC-1, GA, GSN, BHVO-1, JA-2, JB-3, JR-1, JR-2, MRG-1, NIM-N, SY-2; see the GeoReM database for certified compositions) to validate our calibration method (Fig. 2  a-r). Reference composition and quantified results are compared and show a good correlation (close to 1:1 line in Fig. 2 ) for most chemical elements. We used the determination coefficient (R 2 ), the slope value, and standard errors to characterize the quality of each element's regression line (see appendix A3 for a summary). Three chemical elements (Mg, Al and Zr) show > 10% of std error with low R 2 values (<86%); five other elements (Si, Mn, Ga, Y, Nb) are characterized by 5 and 10% of std error with moderate R 2 values (ranging between 92 and 98%) and the nine others (K, Ca, Ti, Fe, Cu, Zn, Rb, Sr, Ba) with <5% of std error with highest R 2 (>99.
7 ). The spreading of averaged values is strongly minimized from 5 × 5 for the coarser-grained granitoids (TARN and PLOU) and 6 × 6 for Fig. 7. Results of the bootstrapped Magnetic Susceptibility analysis applied to in house granitoid samples (in a similar manner as for pXRF analyses; see data table in appendix A7). Boxes extend from the lower to upper MS quartile values of each sampling size and the red line is for the median value. Black whiskers show the range of the dataset except for identified outliers in whitish symbols. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article. ) medium to fine-grained granitoids (GAL, RGR and QUE; see Fig. 7 ). Interestingly, the magnetic susceptibility values and the spread of their averaged value are less affected by the rocks' mean grain size than for PXRF analysis.
For PXRF analyses, the same protocol optimized in the lab was applied for each quarrying site or monument investigated. For each sampling site, a 4 × 4 cm grid was drawn in which sixteen regularly spaced PXRF analyses were performed. Each analysis was then processed through our in-house calibration using the CloudCal app. Each set of sixteen chemical concentrations data was then averaged to provide a representative value for the whole rock (error bars in Fig. 11  are 2 σ applied on the entire dataset). Two sites were analyzed in Cavallo and San Bainzu quarries and three sites in Lavezzu to check for good reproducibility of the method and its potential application as a high spatial resolution tracer. At first sight, chemical data of Cavallo and San Bainzu quarry sites are well clustered, while those from the Lavezzu quarry show larger variability between sampling areas (Fig.
g. ,  Kido et al. , 2006; Forster et al. , 2011; Hahn et al. , 2020) , their combined effects with coarse-grained materials remain poorly documented. These developments should be conducted on different types and models of PXRF instruments to provide comparable results between different studies. All these aspects are also of great importance for the PXRF embedded in future rover missions  (Young et al. , 2016)  and the analysis of coarse-grained rocks during future planetary surface exploration. Future directions also concern the testing, the development of calibration and optimization of other non-invasive, handheld instruments, including portable Raman spectrometer, recent Gamma-ray spectrometer, and portable Laser Induced Breakdown Spectroscopy (pLIBS). For the geoarchaeological community, the explosion and diversification of various spectroscopic data require the implementation of an integrated, collaborative, and open-access database to characterize archeological materials.
Acknowledgments The first author (A. T) was an  FRS-FNRS post-doctoral research fellow  for the  PROBARC  project (Grant CR n • 1. B. 414. 20 F) during the conception and writing of this manuscript. NM and AT were supported by the  F. R. S. -FNRS  research credit CDR/OL J. 0150. 20. AC. Da Silva thanks Liege university  for the support " Equipement 2018"  for its support on the project « XRF Portable -geochemistry as a climatic proxy in paleo-records. »P. Kaskes thanks  Research Foundation Flanders (FWO)  for the awarded Ph. D. Fellowship (projects  11E6619N ,  11E6621N ). Ph. Claeys thanks the  Research Foundation Flanders (Hercules)  for purchasing the PXRF. This research is authorized and subsidized by the  Regional Service of Archaeology (SRA)  directed by  Laurent Sévègnes of the Regional Direction of Cultural Affairs of Corsica (DRAC) , directed by  Franck Leandri  within the annual archeological program.
Similarly, it should be noted that the smart contracts have been implemented with Hyperledger Fabric and Ethereum. Therefore, the differences that can be observed in Section 5. 2 are only due to the performance of these two blockchain implementations. To compare the performance of these blockchain-based solutions, we also used a Python implementation ("HappyEmu", 2018) of a locally deployed centralized ACE-OAuth 2 authorization server as a basis for comparison. The defined smart contracts and the scripts used in the implemented environment, for the evaluation of the results are available online  [43].
Online, open access databases for biological knowledge serve as central repositories for research communities to store, find and analyze integrated, multi-disciplinary datasets. With increasing volumes, complexity and the need to integrate genomic, transcriptomic, metabolomic, proteomic, phenomic and environmental data, community databases face tremendous challenges in ongoing maintenance, expansion and upgrades. A common infrastructure framework using community standards shared by many databases can reduce development burden, provide interoperability, ensure use of common standards and support long-term sustainability. Tripal is a mature, open source platform built to meet this need. With ongoing improvement since its first release in 2009, Tripal provides full functionality for searching, browsing, loading and curating numerous types of data and is a primary technology powering at least 31 publicly available databases spanning plants, animals and human data, primarily storing genomics, genetics and breeding data.