dataesr/scientific-paragraphs-categorization huggingface dataset:
sph. umich. edu/abecasis/gas_power_calculator/), using summary statistics from Nalls et al. 2019, a disease prevalence of 0. 01, and a significance level of 0. 05 as input. We queried blood gene expression data included in the AMP PD version 2. 5 release to measure expression levels in PD cases and controls. We obtained TPM expression at baseline for samples that had case or control status and no PD mutations in whole-genome sequencing data, leaving a total of 1710 samples. Expression data for each gene was displayed in a violin plot and added to the expression section of the browser (Fig. 4b ). The literature section of the browser was updated to display a description, PubMed hit count, and word cloud plot for each gene within 1 MB of a PD risk variant. Our last addition to the browser was a display of user statistics.
We used the googleAnalyticsR package  34  to record and visualize the number of visits for the browser and each risk variant within a period specified by the user (Fig. 4b ). Project 8 (visualization of longitudinal UPDRS/HY scores). The Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn-Yahr (HY) stage are two of the most common measures of the severity of PD. We set out to develop a user-friendly and adaptable app to display a diverse set of visualizations of longitudinal UPDRS/HY scores, based on data from GP2, utilizing the Streamlit library from Python. During the Hackathon, we successfully integrated data from three cohorts: Parkinson's Progression Markers Initiative (PPMI), Parkinson's Disease Biomarkers Program (PDBP), and BioFIND  [35] [36] [37]. We were also able to produce four different visualizations (Fig.
As test data, we used a small subset of results from a study of amyloid-β levels in cerebrospinal fluid derived from healthy control individuals and individuals with PD from the PPMI dataset  35. Amyloid-β levels were measured at baseline and in follow-up visits; thus, results were available from both a cross-sectional and longitudinal GWAS. Using the R shiny  38  framework, we produced a skeleton framework for our web application, with several tabs, including (i) an "Upload data" tab where users could upload and query their data and (ii) a "Plots" tab where users could query the available test data and visualize it. In the "Plots" tab, we allowed users to query by a variant of interest, with the option to choose which biomarker(s) and cohort(s) they wished to visualize. The variant beta was visualized across cohorts and biomarkers using a forest plot, while the number of participants/observations was visualized using a bar plot (Fig.
ACKNOWLEDGEMENTS This project was supported by the  Global Parkinson's Genetics Program  ( GP2 ). GP2 is funded by the  Aligning Science Against Parkinson's (ASAP)  initiative and implemented by  The Michael J. Fox Foundation for Parkinson's Research  (https://gp2. org). For a complete list of GP2 members, see https://gp2. org. Data used in the preparation of this article were obtained from the  AMP PD Knowledge Platform. For up-to-date information on the study, see https://www. amp-pd. org. AMP PD-a public-private partnership-is managed by the  FNIH  and funded by  Celgene, GSK , the  Michael J. Fox Foundation for Parkinson's Research , the  National Institute of Neurological Disorders and Stroke ,  Pfizer ,  Sanofi , and  Verily. This research was supported in part by the  Intramural Research Program  of the  NIH, National Institute on Aging (NIA) ,  National Institutes of Health, Department of Health and Human Services ; project numbers  ZO1 AG000535  and  ZO1 AG000949 , as well as the  National Institute of Neurological Disorders and Stroke.
9 and ANNOVAR, as well as interaction tests with glm in R Both an internal and external dashboard can be used to investigate information about participating GP2 cohorts Locus zoom plots for conditional analyses, power calculations, violin plots for expression data, and user statistics were added to the Loci Browser Python Streamlit application for visualizing UPDRS and HY score progression Code for running an R shiny application that visualizes variant effects across cohorts Project title Goal Project 1: Post-GWAS analysis Develop a pipeline for some common post-GWAS follow-up analyses Project 2: Pathway and cell-Develop a pipeline for investigating type enrichment pipeline pathway and cell-type enrichment from GWAS summary statistics Project 3: Colocalization Develop a pipeline for colocalization pipeline analysis Project 4: Network generation Develop a pipeline that generates and and visualization pipeline visualizes gene regulatory networks Project 5: Variant interaction Develop a pipeline that investigates pipeline variant interaction Project 6: GP2 cohort tracker Develop a dashboard for tracking and visualization investigating the progress of cohort integration for GP2 Project 7: PD GWAS Loci Expand the functionality of the PD Browser expansions GWAS Loci Browser Project 8: Visualization of Visualize longitudinal clinical longitudinal UPDRS/HY scores measures of PD Project 9: Visualize Visualize longitudinal and cross- longitudinal and cross-sectional variant effects from GWAS sectional variant effects Topic GWAS-level and post- GWAS analyses Downstream analyses of genetic variation Data visualization Github: https://github.
com/ipdgc/GP2pathway-enrichment-pipeline Zenodo: https://doi. org/10. 5281/ zenodo. 6477914 Github: https://github. com/ipdgc/ Colocalization-Pipeline Zenodo: https://doi. org/10. Github: https://github. com/ipdgc/GP2network-generation Zenodo: https://doi. org/10. Github: https://github. com/ipdgc/GP2-Variant-Interaction-Pipeline Zenodo: https://doi. org/10. 5281/ zenodo. 6477931 Github: https://github. com/ipdgc/GP2visualize-longitudinal-variant-effects Zenodo: https://doi. org/10. 5281/ zenodo. 6477935 Access to the code or applications developed during the hackathon is included in the "Links to Code or Applications" column.
Physically realistic models of stellar spectra are needed in a variety of astronomical studies, from the analysis of fundamental stellar parameters, to studies of exoplanets and stellar populations in galaxies. Here we present a new version of the widely used radiative transfer code Turbospectrum, which we update so that it is able to perform spectrum synthesis for lines of multiple chemical elements in non-local thermodynamic equilibrium (NLTE). We use the code in the analysis of metallicites and abundances of the Gaia FGK benchmark stars, using 1D MARCS atmospheric models and the averages of 3D radiation-hydrodynamics simulations of stellar surface convection. We show that the new more physically realistic models offer a better description of the observed data, and we make the program and the associated microphysics data publicly available, including grids of NLTE departure coefficients for H, O, Na, Mg, Si, Ca, Ti, Mn, Fe, Co, Ni, Sr, and Ba.
g. ,  Nordlund et al. 2009; Freytag et al. 2012). Convective energy transport is typically modeled using the mixing-length theory  (Böhm-Vitense 1958) , and turbulence is represented using the "macroturbulence" and "microturbulence" parameters. The classical 1D HE models have the advantage of including a very detailed account of opacities, although the opacities are still computed in LTE. The main goal of this paper is to introduce an open-source NLTE version of the widely used Turbospectrum synthesis code (TS,  Alvarez & Plez 1998; Plez 2012)   1. Our main motivation is to provide a public software that allows the user to generate realistic wide-band synthetic stellar spectra of FGKM-type stars, relying on state-of-the-art NLTE model atoms for multiple chemical elements simultaneously and at low computational cost, comparable to standard 1D LTE codes such as MOOG A&A 669, A43 (2023)  (Sneden 1973)  or Synthe  (Kurucz 1970).
Turbospectrum Turbospectrum  (Alvarez & Plez 1998; Plez 2012 ) is an LTE radiative transfer code based on the same methods and input physics as the MARCS model atmosphere code. It uses the Feautrier scheme  (Feautrier 1964; Nordlund 1984)  to solve the radiative transfer equation including scattering, and works in plane-parallel and spherically symmetric geometry for flux and intensities at various angles. Rayleigh and electron scattering in the continuum is fully taken into account, although we note that this effect is mostly relevant in the atmospheres of extremely metal-poor red giants and it influences only the spectra in the near-UV  (Gustafsson et al. 1975; Cayrel et al. 2004; Sobeck et al. 2011). TS uses exactly the same EoS as MARCS, with up to four ions of all 92 natural elements, and hundreds of molecules and radicals.
Line list Concerning atomic and molecular data, we make use of the Gaia-ESO line list  (Heiter et al. 2021) , which covers the wavelength ranges from 475 to 685 nm and from 850 to 895 nm. The atomic part of the line list is mainly based on experimental data supplemented by the most reliable theoretical data (see  Heiter et al. 2021  for a detailed description of the data sources). For the wavelength ranges not covered by the Gaia-ESO line list, we use data from the VALD database 7   (Piskunov et al. 1995; Ryabchikova et al. 2015). It is important to note that the line list was not calibrated on any observational data, which is sometimes done in the literature (e. g. ,  Smith et al. 2021) , and therefore it is not tied to any particular type of star or stellar model, which makes it universally applicable to any object in the entire parameter space of cool stars.
Comparison with NLTE calculations from MULTI We tested our method for calculating NLTE line profiles using TS by generating NLTE model spectra using the MULTI code, and by comparing the equivalent widths (EWs) of various Ca lines. MULTI is the code that we use to calculate the NLTE statistical equilibrium and NLTE grids of departures, and so it provides the most self-consistent anchor point for our TS results. The same four sets of atmospheric parameters as in Fig. 2  were taken from grid points of the MARCS model atmosphere grid and chosen as test cases to represent giant and dwarf stars at solar and low metallicity. As mentioned previously, the giant stars were given an effective temperature of 4000 K and a surface gravity of log g = 2. 0, and the dwarfs were given an effective temperature of 6000 K and a surface gravity of log g = 4.
5. Our low-metallicity model used an [Fe/H] of -2. 0 dex. The results of this test for each of the model spectra are shown in Table  2. Direct comparisons of NLTE EWs are not useful, because small discrepancies may arise due to other differences between the two codes in quantities such as EoS, background opacities, or radiative transfer solvers and angle quadrature. We therefore calculated with each code the ratio of NLTE to LTE EWs for a given set of Ca lines. Ca was chosen as the element, for which the NLTE abundance corrections show a large dynamic range, as it is positive for some lines and negative for the others. The average percent difference of the ratio of EWs (EW NLTE /EW LTE ) computed with MULTI and TS is given in the final column of the table. All lines show a difference below 0.
However, there does appear to be a trend in the difference between NLTE and LTE abundance with the excitation potential for ⟨3D⟩ models. As seen in Fig. 6 , lines with higher excitation potential tend to have a lower ∆(NLTE -LTE). This is consistent with the results by  Bergemann et al. (2012b)  and  Amarsi et al. (2016) , who found the same trends in their analyses of Fe lines in FGK-type stars. Figure  7  shows the differences between 1D NLTE and ⟨3D⟩ NLTE abundances and the 1D LTE results. We note that the available ⟨3D⟩ model atmosphere grid is limited in terms of the 12 https://github. com/JGerbs13/TSFitPy A43, page 8 of 14 Notes. ( a) Van der Waals broadening parameter from  Barklem et al. (2000). ( b) References for g f -values: (1) average of  Bard et al. (1991)  and O'Brian et al.
Discussion and conclusions We have presented a new version of Turbospectrum capable of handling computations of synthetic spectra using NLTE physics. This new capability, plus the existing capability for Turbospectrum to handle ⟨3D⟩ model atmospheres, means that Turbospectrum can now be used to compute model spectra in 1D LTE, 1D NLTE, ⟨3D⟩ LTE, and ⟨3D⟩ NLTE. We compared the calculation of EWs of Ca line profiles in NLTE using Turbospectrum to a similar calculation using MULTI (a code well established in the literature for its ability to handle NLTE calculations), and found that the EWs using NLTE profiles agreed within 1% for all test cases. This result indicates that the computations by Turbospectrum using NLTE physics are correct, or at least, match well with those from a previous verified source in the literature that has been used to successfully model various observed stellar spectral features in NLTE.
As part of this example, we provide a code that involves a Python wrapper that can be used to automate generating spectra with the Fortran based Turbospectrum code, and automates the fitting procedure of spectral features. Our code also includes a script that cross matches information in an LTE Turbospectrum line list with a model atom to create an NLTE formatted Turbospectrum line list. Our abundance values from the example fits agree well with literature values, and show some of the 1D NLTE and ⟨3D⟩ NLTE effects on abundances determined through spectral fitting as a function of metallicity.
Acknowledgements. E. M. and M. B. acknowledge support by the  Collaborative Research centre SFB 881  (projects  A5 ,  A10 ),  Heidelberg University, of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ). B. P. is supported in part by the  Centre National d'Etudes Spatiales (CNES)  in the frame of a  PLATO grant. This  project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research  and  A43 , page 13 of 14 A&A 669, A43 ( 2023) innovation programme  (Grant agreement No. 949173 ). T. O. and U. H. acknowledge support from the  Swedish National Space Agency (SNSA/Rymdstyrelsen ). This work benefitted from support by the  National Science Foundation  under Grant No. OISE-1927130  (IReNA).
Introduction: The Influence of Field Layers as Units of Analysis For archaeology, time is key: reliably documenting diachronic patterns of cultural and environmental change is amongst the most intensely studied and debated issues, especially in prehistory. While absolute dates occasionally provide highly resolved datasets, archaeologists frequently address chronological changes by using arbitrary "slices" of time. More often than not, attempts to trace shifts in material culture, subsistence strategies, or other aspects of human behaviour are based on comparisons of material assigned to 'distinct' stratigraphic layers defined during fieldwork. As such, time is partitioned into discrete spatial units, namely layers, levels, assemblages, spits, phases, or periods. These analytical units used to perceive and organise time have a dramatic impact on archaeological inference; they define its resolution and constrain the chronological grain, and they influence the way cultural assemblages or techno-complexes are defined, interpreted, and compared (e.
Laplace and Méroc's (1954)  early call for a systematic, objective, spatial exploration of stratigraphies remained, for decades, technically impractical for large datasets. It is only much later thanks to the availability of computers and electronic devices during the 1970s, 1980s, and 1990s that the approach they advocated realistically and progressively became a reality. Since the 1950s, recording the spatial provenience of artefacts beyond simple layer attributions gradually became widespread in Palaeolithic archaeology. Dibble (1987)  and McPherron  (Dibble & McPherron, 1988)  pioneered the introduction of the electronic theodolite with a distance metre (a total station) during excavations, facilitating the 3D piece-plotting of all artefacts above a chosen size. In the late 1990s and early 2000s, computer power increasingly available to researchers allowed multiple statistical and spatial analyses thanks to new tools for exploring spatial data (e.
However, spatial analyses are seldom used to explore data beyond the diachronic units provided by field layers. To better understand this practice in the context of the present study, we reviewed > 1400 journal articles, published in 7 journals (Bulletin de la Société Préhistorique Française, Journal of Archaeological Science: Reports, Journal of Paleolithic Archaeology, PALEO, PaleoAnthropology, PLoS ONE, Proceedings of the National Academy of Sciences) in the last decade (2012 to 2022-these limits were chosen arbitrarily to sample the most recent research). Of these papers, 192 present diachronic site-based datasets concerning Palaeolithic or Mesolithic material culture or faunal assemblages, in the form of artefact counts by layer (Fig. 1 ). While less than a third use spatial data, only 13% (n = 25) analyse the vertical distribution of the attributes studied (e.
Even if this is the case, the absence of PES analyses in publications still poses serious questions concerning the reproducibility of the stratigraphic analysis of archaeological sequences. In some respects, the spatial analysis of piece-plotted artefacts can constitute a more objective view of a site, allowing for the redefinition of units based on explicit, clearly stated criteria that can be statistically investigated. Moreover, these types of analyses move us away from a reliance on layers defined during fieldwork following difficult-to-test criteria. In a scientific community that increasingly relies on open-data, the publication of XYZ coordinates underlying PES constitute important datasets for future studies, allowing reproducibility as well as the archiving of archaeological heritage data.
While the interpretation of vertical spatial data and the definition of post-excavation assemblages remains somewhat subjective, the field data itself is significantly more objective. With this being the case, whenever possible, making freely available field data and publishing the process behind such PES studies are vital first steps towards better reproducibility (e. g. , databases made available on the http:// www. oldst oneage. com website; supplementary data in  Gravina et al. , 2018 and Discamps et al. , 2019). As excavation is an irreversible process, field data, notably spatial information (piece-plotted data and the location/depth of spits), allow analysts to reconstruct sites and build as many PES as necessary, using techniques and methods that improve over time. Pushing forward PES requires the development of tools for facilitating the spatial exploration of archaeological data in three dimensions as well as more advanced statistical treatment of data.
For the former, several software or R packages specifically designed for the three-dimensional exploration of intra-site spatial data exist, such as the NewPlot program  (Dibble & McPherron, 1988) , scripts adapted for the DataDesk software (Lacrampe-Cuyaubère, 1997), Python scripts for ArcGIS  (Roy Sunyer, 2015) , and Shiny R applications designed for the Poeymaü  (Plutniak, 2021b)  and Saint-Césaire  (Couillet et al. , 2022)  sequences. The development of new user-friendly tools for people unfamiliar with GIS would be useful to the more widespread application of PES studies. As a starting point, a quick and simple tutorial for basic PES analysis in QGIS is provided in the Supplementary Information (SI #3). Open-source R Shiny applications dedicated to the intra-site spatial analysis of piece-plotted archaeological remains have also recently been published by some of us: the SEAHORS application  (Royer et al.
, 2023) , the archeoViz application/R package  (Plutniak, 2023) , and its related web portal (https:// analy tics. huma-num. fr/ arche oviz/ home). For the latter issue of statistical developments, specifically, adapted statistical packages would go some way in ensuring a more robust definition of assemblages through spatial analysis, including its automatization. The TSAR (topological study of archaeological refitting) approach  (Plutniak, 2021c) , implemented through the archeofrag R package  (Plutniak, 2022) , constitutes an important step in this regard, as it allows for measurements of cohesion and admixture of spatial units based on the structural properties of groups of refits. Additionally, a R script adapting Ripley's K functions to PES studies is concurrently made available at https:// github.
Acknowledgements We are grateful to all the participants of the research seminar "(Re-)définir des ensembles archéo-stratigraphiques en Préhistoire après la fouille: quels enjeux, quels outils et de la nécessité des approches spatiales et taphonomiques" (held at the  Université Toulouse Jean Jaurès  on the 14th of April 2022) and notably to  S. Costamagno ,  A. Couillet ,  S. Ducasse ,  L. Feyfant ,  D. Giusti ,  X. Muth ,  J. -M. Pétillon ,  C. Renard , and  D. Todisco  for constructive discussions concerning many of the concepts and methods underlying post-excavation stratigraphies. Comments on an earlier draft by  J. -Ph. Faivre , and those of the three anonymous reviewers, helped to improve the manuscript.
Statistical analysis The statistical analysis was performed by software IBM SPSS Statistics v. 26. 0 and Microsoft Excel 2019. We performed the calculation of mean values, standard deviation, and confidence interval with α = 0. 05, and paired T-Student test was performed for dependent pools. A comparison of dynamic indicators of motor abilities was carried out with the baseline level. The Pearson correlation analysis was done for the age and motor abilities of the subjects. A comparison of quantitative MRI data was carried out among the results before and after training for each of the stages.
Bioinformatical analysis Verticillium dahliae PLL sequences were retrieved using available genome database (ftp. broadinstitute. org/). SignalP-5. 0 Server (http://www. cbs. dtu. dk/services/SignalP/) was used for identifying putative signal peptide and putative glycosylation sites were predicted using NetOGlyc 4. 0 Server (http://www. cbs. dtu. dk/services/NetOGlyc/ ). Sequences were aligned and phylogenetic analysis was carried out using MEGA multiple sequence alignment program (https://www. megasoftware. net/). Homology models used for molecular replacement were created using I-TASSER structure prediction software (https://zhanglab. ccmb. med. umich. edu/I-TASSER/) and UCSF Chimera (http://www. cgl. ucsf. edu/chimera/) was used for creation of graphics.
Cloning, heterologous expression and purification of VdPelB V. dahliae PelB gene (VdPelB, UNIPROT: G2X3Y1, GenBank: EGY23280. 1, VDAG_04718) consists of 1 single exon of 1002 bp length. The coding sequence, minus the nucleotides encoding signal peptide, was PCR-amplified using cDNA and gene-specific primers while VdPelB mutants were generated by PCR mutagenesis, a method for generating site-directed mutagenesis, using specific primers carrying mutations (Table  S1 ). Cloning of the PCR-amplified sequences in pPICZαB, sequencing and expression in P. pastoris heterologous expression system as well as purification steps were done as previously described in Safran et al. [37].
Crystallization of VdPelB Pectate lyase VdPelB was concentrated at 10 mg. mL -1 in Tris-HCl pH 7. 5 buffer. Crystallization conditions were screened using the sitting-drop vapor-diffusion method. Pectate lyase VdPelB (100nL) was mixed with an equal volume of precipitant (1:1) using Mosquito robot (STP Labtech). The crystals that resulted in best diffraction data were obtained with 0. 1 M MIB (Malonic acid, Imidazole, Boric acid system) at pH 8. 0, with 25 % PEG 1500 as the precipitant (condition B5 from the PACT premier kit, Molecular Dimensions, Sheffield, UK) after 1 month. Optimization was realized using the hanging drop vapor-diffusion method forming the drop by mixing 1 μL of precipitant solution with 1 μL of the enzyme. The crystals (120 μm × 30 μm) were cryo-protected by increasing PEG 1500 concentration to 35 %, before mounting them in a loop and flash-cooling them in liquid nitrogen.