dataesr/scientific-paragraphs-categorization huggingface dataset:
From these core area KGK-VI dispersed in most of the region by 4400 BC. This analysis also suggests that potentially two more clusters, seems to emerge, one in southsouthwestern Bulgaria and the other in southeastern Romania. Given the number of sites with radiocarbon dates, available for our study, we have not split the database into more clusters, as this might be the result of the current sample size and might have created artificial patterns in the SPD analysis and its model testing.
Several studies of Early and Middle Holocene societies in other European regions, have shown "booms" followed by important "busts" (drops) in the SPDs  (Shennan et al. 2013  Aubán  et al. 2016; Downey et al. 2016; Bevan et al. 2017; Roberts et al. 2018; Palmisano et al. 2019) , which are proposed to be related to long-term decline of Neolithic farming society. A similar pattern is shown in our study, when after a statistically significant "boom" and an equilibrium (of about 100 years-ca. 4450-4350 BC), a long period of higher than expected decline follows (about 350 years-ca. 4150-3800 BC), statistically significant against both exponential and logistic null models. However, we need a larger database to analyze SPDs for farming and climate and/or environmental co-variates, to help evaluate such potential explanations.
The current study brings some critical corrections related to the absolute chronology of the KGK-VI communities (humans, not pottery style!), especially regarding the end of this civilization, which cannot be placed around 4200 BC as recently postulated by various scholars, but much later. Why some communities in certain settlements are more resilient than others and the SEB collapse causes remain an open question, which we intend to answer in the future. Furthermore, the research approach outlined here, must involve in future studies on this topic the identification of potential biases, integration of additional dates, and the correlation with other proxies (e. g. , mortality juvenility index, data relative to food production, site and house counts, particular pottery style shift, innovations spreading, climate changes, and paleoenvironment alteration) or other constraints (e.
ACKNOWLEDGMENTS Many thanks to our colleagues  Radian Andreescu  (deceased),  Pavel Mirea ,  Valentin Parnic ,  Andrei Soficaru ,  Done Șerbănescu , and  Valentina Voinea  for providing samples for AMS radiocarbon dating from the archaeological sites where they excavated. Also, we would like to thank  Ovidiu Frujină  for his involvement in the sampling process and verifying the references. Special thanks go to  Oana Gâză ,  Maria Ilie ,  Doru Paceșilă ,  Gabriela Sava ,  Corina Simion , and  Iuliana Stanciu  ( IFIN-HH, Romania ) for their participation in sample preparation and analyzing of radiocarbon samples. We also want to thank two anonymous reviewers for their helpful comments and suggestions that improved the quality of the paper. This work was funded by  UEFISCDI Romania , through the research grant  351PED/2020  ( CALIB-RO ), project code:  PN-III-P2-2.
Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perf orm cr oss-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is pr o vided in our gr oup's GitHub page.
We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and o v arian cancer susceptibility genes. We ha ve sho wn that the GCPBayes pipeline could extract pleiotropic genes pre viousl y mentioned in the literature, while it also pr o vided new pleiotr opic genes and regions that are w orthwhile f or further investigation. We have also pr o vided some recommendations about parameter selection for decreasing computational time of GCP-Bayes on genome-scale data.
We de v eloped a Bayesian meta-analysis method (called GCP-Bayes) that can detect CP association both at the group le v el (gene or pathway le v el) and within groups (e. g. at the SNP le v el)  ( 10 ). Although there is an increment in publicly available GWAS summary statistics data, it is not trivial to use such data very easily while working with statistical packages for pleiotropy detection since various inputs including different file formats are needed for each package. Besides, some statistical methods are very time-consuming in the case of working with large GWAS da ta tha t included se v eral millions of variants. Ther efor e, it seems necessary to de v elop user-friendly platforms that could accelerate exploring pleiotropy analysis between pair of traits using GWAS summary statistics data.
Here, we have introduced a new pipeline designed to handle genome-le v el GWAS summary statistics data as inputs and would e v entually perform a feasible CP analysis by using the GCP-Bayes package. The pipeline checks the integrity of the summary sta tistics da ta, harmonizes the r efer ent alleles between the different datasets and provides the gene annotations for each variant. The pipeline also contains some functions and a Shiny application for visualization of results, which gi v e an ov erall ov ervie w to a user for further complementary analyses. To illustrate the application of the pipeline, we used it to identify CP associations at the gene le v el between breast cancer (BC) and ovarian cancer (OC) using GWAS summary statistics data from two large consortia: the Breast Cancer Association Consortium (BCAC)  ( 11 )  and the Ovarian Cancer Association Consortium (OCAC)  ( 12 ).
Pipeline ov ervie w Ther e ar e four main sections available throughout the GCP-Bay es pipeline. A gener al ov ervie w of the pipeline is shown in Figure  1. As shown, three sections (Standardization, Annotation and Running GCPBayes) run for e v ery pair of traits, while there is also an optional section (Linkage Disequilibrium (LD) Clumping). Additionally, we provide an explanation of the analysis functions for input / output files through a visualization section. In Figure  1  , we provided a simplified version, while more details about each section, e v ery procedure, the scripts, and input and output files are provided in Supplementary File S1 and in our GitHub page for advanced users. Ther e ar e at least thr ee files needed for running the GCPBayes pipeline: GWAS summary statistics data for two traits and an annotation file that contains information about gene locations in human assembly.
We considered an annotation file from the GENCODE project ( 13 ) into the GCPBayes pipeline. Howe v er, it is possib le for a user to replace it with any other annotation file. In the Standardization section, GWAS summary statistics inputs are checked and harmonized. An important issue a user should consider before exploring pleiotropic effect on two traits is to make sure that the panel of SNPs is the same in both datasets, with valid alleles reported. Also, e v en it is not r equir ed, this is mor e convenient to analyze final results with similar effect alleles on the same strand in both datasets. If the alleles do not match in the two studies and switching alleles is needed, a user should notice to change the sign of beta values for the corresponding SNPs. Further information and details about running this step are provided in the GitHub page and Supplementary File S1 (Section 2.
We acknowledge that other methods exist and may offer distinct advanta ges, b ut the use of PLACO was justified in our study based on its specific featur es. Mor e information about how to pr epar e the annotation files for the SNPs with or without the LD clumping option is provided in the GitHub page and Supplementary File S1 (Sections 2. 2 and 2. 3). In the Running GCPBayes section, the pipeline reads the Rdata files created from the previous section and performs the GCPBayes method to find genes with potential pleiotropic effects on both traits (Supplementary File S1, Sections 2. 4. 2, 2. 4. 3, 2. 5. 2 and 2. 5. 3). We considered genes with θ > 0. 5 as potential pleiotropic genes, as explained in  ( 10 ) , where θ is defined as the probability of having group pleiotropy. So, θ > 0. 5 means that the probability to have a pleiotropic effect for the group is > 50%.
Also, HS could perform a mor e r elevant selection of signal at the SNP le v el as this method is designed for variable selection at both SNP and gene le v els. Howe v er, analysis with GCPBayes by consid-ering the HS function is more time-consuming than using DS  ( 10 ). Due to higher running time of the GCPBayes method while dealing with genes that include a large number of SNPs, the pipeline is also designed to divide genes into two groups according to whether their number of SNPs is lower or higher than a threshold that needs to be defined. It is then possible to restrict the GCPBayes analysis to genes with a number of SNPs lower than the gi v en threshold (Supplementary File S1, Sections 2. 4. 2 and 2. 5. 2). Genes with a number of SNPs higher than the gi v en threshold could also be processed through a separate script (Supplementary File S1, Sections 2.
4. 3 and 2. 5. 3) or a user could shrink the number of SNPs by considering the LD clumping step. More information is provided in the GitHub page and Supplementary File S1 (Section 2. 3). Ther e ar e two wa ys to launch the GCPBa yes pipeline: (i) by modifying directly the 'Definition section' that includes all file names and pa ths rela ted to input and output files, as well as threshold values (if needed) for each section, and by running the Bash or R script that will read all defined parameters and run e v ery step of the pipeline; or (ii) by using the Shiny app we de v eloped that will create the parameter file from the information the user provides (such as working directory path, GWAS column names, threshold values, etc. ) and that will run each step of the pipeline sequentially. An R script is also available (which could be run individually or through the Shiny app) to install r equir ed packages for the pipeline.
RESULTS In the Standardization steps (Figure  1  ), we removed all SNPs with missing effect or incomplete allele information. For the BCAC, a total of 9 149 691 SNPs remained, while the final OCAC GWAS summary sta tistics da ta included 8 929 032 SNPs. The OCAC GWAS data were also recoded in order to keep the same r efer ent and effect alleles as in the BCAC data. For details about cleaning and reformatting steps, see Supplementary File S1 (Section 2. 1). By keeping shared entries for BCAC and OCAC GWAS summary sta tistics da ta, 7 079 969 SNPs wer e consider ed for further analyses. We first compared the GWAS summary statistics data from the two datasets to find out common significant loci. As shown in Supplementary Figure  S1 , there were three loci (5p15. 33, 10p12. 31 and 15q26. 1) that were significant in both BC and OC data.
DISCUSSION We have provided a pipeline for our recently developed method GCPBayes  ( 10 )  that explores pleiotropy at the gene le v el. The pipeline starts with standardization of GWAS summary sta tistics da ta f or two traits and perf orms an annota tion step, crea tes inputs f or the GCPBa yes package and then provides results for CP association at the gene le v el using GCPBayes. We have also developed a visualization tool using Shiny application that loads the GCPBayes output data and creates various plots automatically. To illustrate applicability of the pipeline, we run it on GWAS data for BC and OC. A total of 151 genes from 79 loci were detected to be associated with BC and OC (Supplementary Table  S2 ). We could retrie v e three genes ( TERT , RCCD1 and B AB AM1 ) that have been reported to be associated with both BC and OC in previous fine-mapping studies at 5p15.
33, 15q26. 1 and 19p13 loci  ( 19 , 21 , 22 ). We also highlighted the 8q24. 21 locus that was reported to be associated with multiple cancers  ( 20 ). Fehringer et al. ( 25 )  have previously explored pleiotropy across multiple cancers (lung, ovarian, breast, prostate, colorectal) using the method ASSET that conducts a fixed effect meta-analysis by examining the association between each SNP and multiple subsets of cancer. This study reported 130 SNPs from 21 loci to be pleiotropic between BC and OC (Supplementary Table  S4 ). Our study could replicate se v en loci (3p24. 1, 5p15. 33, 5q11. 2, 9p21. 3, 10q26. 13, 19p13. 11 and 20q11. 22)  reported in their study (Supplementary Table  S4 ). In  ( 23 ) , an SNP-le v el cross-cancer genome-wide association meta-analysis focusing on breast, ovarian and prostate cancers was conducted using GWAS statistics data from the BCA C and OCA C based on a lower number of individuals than the datasets we used.
DA T A A V AILABILITY All scripts, Bash files and detailed information about how to run on any dataset (also how to run a Bash file on BCA C and OCA C GWAS summary sta tistics da ta), and a step-by-step tutorial for running the GCPBayes pipeline on BCAC and OCAC GWAS summary sta tistics da ta are available on our group's GitHub page ( https://github. com/ CESP-ExpHer/GCPBayes-Pipeline , permanent doi: https: //doi. org/10. 5281/zenodo. 8042138 ). Input GWAS summary sta tistics da ta are accessible through the w e b page of the.
ACKNOWLEDGEMENTS The authors acknowledge the  BCAC  and  OCAC , international multidisciplinary consortiums, for providing GWAS summary sta tistics da ta. The breast cancer GWAS for the BCAC was supported by  Cancer Research UK  ( PPRPGM-Nov20 \ 100002 ,  C1287 / A10118 ,  C1287 / A16563 ,  C1287 / A10710 ,  C12292 / A20861 ,  C12292 / A11174 ,  C1281 / A12014 ,  C5047 / A8384 ,  C5047 / A15007 ,  C5047 / A10692 ,  C8197 / A16565 ) and the  Gray Foundation , the  National Institutes of Health  ( CA128978 ,  X01HG007492 -- the  DRIVE  consortium), the  PERSPECTIVE  project supported by the  Government of Canada  through  Genome Canada  and the  Canadian Institutes of Health Research  (grant  GPH-129344 ) and the  Minist ère de l' Économie, Science et Innovation du Qu ébec  through  Genome Qu ébec  and the  PSRSIIRI-701  grant, the  Quebec Breast Cancer Foundation , the European Community's  Se v enth Frame wor k Programme  under grant agreement no.
• Users can forward model the effect that diverse thermal histories and mechanical mixing processes characteristic of silicic magma bodies have on zircon age distributions as measured by high-precision, chemical abrasion thermal ionization mass spectrometry (CA-ID-TIMS) U-Pb geochronology. • Zircon CA-ID-TIMS datasets from silicic magmatic systems can be easily compared with model output to gain semi-quantitative information on thermo-mechanical history of the system of interest. • We demonstrated  (Tavazzani et al. , in press ) that distribution of high-precision zircon ages in crystallized remnants of shallow ( ∼ 250 MPa), silicic magma reservoirs can discriminate between systems that experienced catastrophic, caldera-forming eruptions and systems that underwent monotonic cooling histories.
Rhyolite-MELTS zircon crystallization distribution Model zircon crystallization distributions are calculated from rhyolite-MELTS  [ 25 ]  major-element equilibrium crystallization (EQ), fractional crystallization (FC) and recharge fractional crystallization (RFC) simulations (run on the software Magma Chamber Simulator, https://mcs. geol. ucsb. edu ;  [ 26 , 27 ] ) using the saturation model of Boehnke et al. [ 28 ]. A rhyolitic composition (from  [ 29 ] , given in Table  1  ; Fig. 1  ) is used as starting melt composition at a pressure of 250 MPa. This pressure corresponds to the optimal depth range for magma chamber nucleation and growth as suggested by physical models  [ 30 ]  and a compilation of natural datasets  [ 31 ]. Initial magmatic water content is set to 2. 5 wt% H 2 O and ƒO 2 to the QFM (quartz-fayalite-magnetite) buffer.
The random selection algorithm undergoes 10 3 iterations for each simulation. 𝑛 = 𝐹 -1 𝑝 𝑍𝑟 ( 𝑋 ) where 𝑋 = 𝐹 𝑝 𝑍𝑟 ( 𝑛 ) (9) The n variables randomly selected from a non-uniform zircon crystallization probability density function 𝑓 𝑝 𝑍𝑟 ( 𝑛 ) correspond to specific simulation steps at which zircons are observed ( Fig. 2  ). This set of equations is implemented using the MATLAB R ○ function randsrc(). The number of randomly selected n variables (N Zr ) can be defined by the user ( Figs. 2 , 3  ). For each simulation, as to ensure robustness of the random selection process, the random selection algorithm undergoes 10 3 iterations.
Visualization of model output -zircon crystallization age density functions (natural and synthetic) Zircon age distributions are expressed as relative zircon crystallization density functions f xtal (t r ) (after  [2]  ), where t r is relative time, scaled from zircon saturation ( t sat ) to full plutonic solidification ( t sol ) or eruptive truncation ( t er ): 𝑡 𝑟 = ( 𝑡 -𝑡 𝑠𝑜𝑙 ) Δ𝑡 where Δ𝑡 = 𝑡 𝑠𝑎𝑡 -𝑡 𝑠𝑜𝑙 (10a) 𝑡 𝑟 = ( 𝑡 -𝑡 𝑒𝑟 ) Δ𝑡 where Δ𝑡 = 𝑡 𝑠𝑎𝑡 -𝑡 𝑒𝑟 (10b) While in natural samples t sat and t sol or t er correspond to absolute radiometric dates, in the case of model zircon distributions t sat corresponds to the first simulation step in which [ Zr ] melt calc > [ Zr ] melt sat and t sol or t er correspond to the simulation step at which the simulation is interrupted. The form of relative crystallization distributions f xtal (t r ) estimated from natural or synthetic data are then visualized through a truncated kernel density estimate of the scaled crystallization times 𝑡 𝑟 𝑜𝑏𝑠 where: 𝑡 𝑟 𝑜𝑏𝑠 = 𝑡 𝑜𝑏𝑠 -min ( 𝑡 𝑜𝑏𝑠 ) max ( 𝑡 𝑜𝑏𝑠 ) -min ( 𝑡 𝑜𝑏𝑠 ) (11) To produce the kernel density estimate of f xtal (t r ) from 𝑡 𝑟 𝑜𝑏𝑠 we use the MATLAB R ○ function ksdensity() with a Gaussian kernel and bandwidth determined by Gaussian approximation.
The resulting kernel density estimate is truncated at t r = -0. 05 for natural volcanic samples and model f xtal (t r ) produced by simulations interrupted at 750°C (i. e. modeling pre-eruptive zircon crystallization). Kernel density estimates are then scaled between x = 1, which denotes the initiation of zircon crystallization at t sat and x = 0 the termination of crystallization at t sol or t er ( Figs. 2 , 3  ). This methodology (first introduced by  [2]  ) allows a reliable estimate of f xtal (t r ) , which can reproduce broad fluctuations in the zircon relative crystallization rate, while maintaining the abrupt cutoff in the crystallization distribution that should be caused by an eruption. The kernel density estimates of natural, CA-ID-TIMS datasets represent averaged zircon crystallization distributions of all the samples collected from a single magmatic system.
In synthetic datasets, kernel density   2  ), and for (b) a recharge fractional crystallization simulation (ID: 14-3d from Table  2  ). (c), (d) Box plots of temperature values from the same simulations. In panel (e) and (f), the randomly sampled zircon crystallization distributions are displayed as kernel density estimates of the scaled simulation steps (n * ). Synthetic zircon age and temperature distributions are scaled from initiation (x = 1) to termination (x = 0) of zircon crystallization. Box plots show median, interquartile ranges (IR) and extreme values for bins corresponding to 1/10 of the total zircon crystallization history. estimates are obtained for both zircon age distributions from individual stochastic outputs and zircon age distributions averaged over 10 3 stochastic outputs.
Uncertainty parametrization in the generation of CA-ID-TIMS synthetic zircon populations To parameterize variation of U-Pb zircon age uncertainties with absolute age of zircon crystallization  [ 38 , 39 ]  and thus simulating an actual CA-ID-TIMS output, we: (1) Compiled ∼1700 single-crystal, Th-corrected CA-ID-TIMS zircon 206 Pb/ 238 U dates from 1 to 1000 Ma published between 2011-2021 (Supplementary Table  2 ; see reference list under Fig. 5  ) together with their reported analytical uncertainties in Ma (2 𝜎). (2) Applied a linear regression (least square, LSQ) to the compiled dataset in the interval 1 to 1000 Ma to obtain an empirical relation between 206 Pb/ 238 U age and analytical 2 𝜎 uncertainty. As analytical uncertainties in U-Pb dates are symmetric, we only plotted one data point per analysis.
Given the spread of the published uncertainties within individual time bins (up to one order of magnitude), the goodness of fit is lacking (r 2 = 0. 40). Fitting the dataset with higher order polynomial functions does not seem to improve the regression procedure ( Fig. 5 a ). A least square regression of this large dataset provides a first order indication of the evolution of typical analytical uncertainties in CA-ID-TIMS 206 Pb/ 238 U dates with zircon age. [ 21, 22, 37,. Each line represents a different polynomial fit to the dataset, and points on a line corresponding to interpolated 2 𝜎 uncertainties for three magmatic systems of increasing crystallization ages ( ∼ 7 Ma, Capanne pluton,  [ 37 ] ; ∼ 95 Ma, Mt. Stuart pluton, Matzel et al. [ 66 ] ; ∼285 Ma, Laghi granites,  [ 41 ] ) (b), (c), (d) Visualization of the output of recharge fractional crystallization simulations (simulation ID: 14-3d from Table  2  ) with added analytical uncertainty corresponding to the three age brackets at 7, 95 and 285 Ma (thin grey lines) compared to an average of 10 2 iterations for the same crystallization scenario without added uncertainty (bold red line).
(3) To add the effect of analytical uncertainty corresponding to zircons crystallized at a specific time, the model calculates the median age of the input dataset and uses that value to calculate the characteristic uncertainty from the linear regression ( Fig. 5 a ). A random value picked from the Gaussian uncertainty distribution is then added to each randomly selected zircon date from the cumulative distribution function 𝐹 𝑝 𝑍𝑟 ( 𝑛 ). The increasing absolute uncertainty with increasing zircon crystallization age (here only 206 Pb/ 238 U ages are discussed) is reflected in the scatter of resampled populations. In simulations of zircon crystallization in younger systems ( < 10 Ma) the effect of accounting for analytical uncertainty during probability distribution resampling is nearly insignificant ( Fig.
The zircon populations that can be created through such mixing will have age distributions that cannot be reproduced with a purely thermodynamic model. Here the stochastic nature of the mechanical mixing process is simulated by juxtaposing and randomly sampling a number of zircon populations that have a parametrized temporal distribution, with an additional MATLAB R ○ routine [Resampling_for_mixing. m] that allows the simulation of complex mixing of zircon populations: (1) The model is based on zircon growth curves for a number of temporal intervals (n = 7) that correspond to CA-ID-TIMS dated samples of the Bergell pluton ( Fig. 6  a, b;  [ 68 ] ). The temporal distribution of samples in this intrusion represents a benchmark example of incrementally added magma batches, and nearly every sample shows a zircon age distribution that is expected for monotonic cooling of a magma batch  [ 14 ].