dataesr/scientific-paragraphs-categorization huggingface dataset:
In silico chromosome painting is a technique by which contributions of distinct genetic groups are represented along chromosomes of hybrid individuals. This type of analysis is used to study the mechanisms by which these individuals were formed. Such techniques are well adapted to identify genetic groups contributing to these individuals as well as hybridization events. It can also be used to follow chromosomal recombinations that occurred naturally or were generated by selective breeding. Here, we present GeMo, a novel interactive web-based and user-oriented interface to visualize in a linear-based fashion results of in silico chromosome painting. To facilitate data input generation, a script to execute analytical commands is provided and an interactive data curation mode is supported to ensure consistency of the automated procedure.
A few online applications were developed but are specific to some organisms like DNAPainter for Humans (https://dnapainter. com) or favor circular-based visualization rather than linear  (19). Until now, there were no web-based interfaces handling datasets in an interactive and linear fashion, and none addressing the challenges of polyploidy, which is often encountered in plants. Here we present GeMo, a web-based tool for visualization, curation and data sharing of genome ancestry mosaics. It comes with a script and step-by-step instructions, using two local ancestry inference-based methods, TraceAncestor  (20)  and VCFHunter  (21) , which assign alleles to genetic groups and use the allelic ratio observed in individuals to assign an origin to genomic blocks. GeMo can guide users through in silico chromosome painting starting with a Variant Call Format (VCF) file and acts as an online repository for genomics ancestry mosaics.
Tool implementation and usage GeMo is a lightweight web-based interface implemented in JavaScript. The display of the interactive graph is managed with the JavaScript D3JS library (https://d3js. org) and chromosome visual rendering builds on the Ideogram library (https://eweitz. github. io/ideogram). The web page design uses the Bootstrap library (https://getbootstrap. com), which ensures compatibility with modern web browsers. On the back-end, a NodeJS server (https://nodejs. org/en/) ensures data transfer and analysis.
Visualization of preloaded ancestry mosaics/datasets GeMo contains ancestry mosaics of preloaded individuals resulting from published studies conducted on subsets of plant genotypes  (13, (20) (21) (22). When selecting a preloaded individual (Figure  1A ), a form is automatically filled with the genomic coordinates of the genomic blocks and the visualization parameters (e. g. colors, reference sequence coordinates, ploidy). GeMo displays the ancestry mosaic drawing for each chromosome (Figure  1B ). The chromosomal segments are colored according to their corresponding genetic group. An interactive legend labels each genetic group with a corresponding color (Figure  1B ). After modifying the color of a group directly in the legend (free-mode and colorblind mode available), the visualization of the chromosomes is automatically updated.
Input user datasets Users have the possibility to enter their own files using the menu panel (Figure  1A ). GeMo requires at least two types of datasets to generate the ideogram visualization: (i) a tabulated file for the reference chromosome metadata (names and respective sizes) and (ii) a tabulated file for drawing the genome ancestry mosaic of analyzed individuals. GeMo comes with a list of defined plant chromosomes, but users can enter their own chromosomes as well. The position of the centromeres can be optionally indicated in this file. The file containing genome ancestry mosaics can either be encoded as block position (genomic segment along a chromosome with defined start and stop positions) or normalized curve (observed allelic ratio for each gene pool along the chromosomes). Once data is provided, the karyotype diagram is generated dynamically.
As a complement, and to avoid extracting genomic information already available in a JBrowse instance, GeMo allows the user to extract the genomic block information as JBrowse tracks (Figure  1C ). To support data import in GeMo, step-by-step tutorials with two possible programs using VCFHunter and TraceAncestor are provided for users starting with a VCF file as well as a single script to facilitate execution (https://gemo. readthedocs. io/en/latest/QuickStart. html). The approach is compatible with genome-wide genotyping methods (e. g. GBS, RADseq, DaRTseq), transcriptomics (i. e. RNAseq) or wholegenome resequencing data, including both individuals to investigate and ancestral genetic groups. The complete data processing of these typically takes less than 1 h per individual. The tutorials describe the creation of either block-positionbased mosaics or curve-based mosaics for interactive display, the latter enabling manual curation of the ancestry mosaics via the GeMo Interface.
Data curation of ancestry mosaic predictions In silico chromosome painting resulting from an automated bioinformatic procedure may not be exact, requiring expert review and adjustment. GeMo proposes a curation mode to edit thresholds for single nucleotide polymorphism (SNP) ratios that are by default defined based on expected ploidy (0. 5 for heterozygous diploids, 0. 33/0. 66 for heterozygous triploids, etc. ). Indeed, such ratios are expected in the ideal case, but some variations relative to the expected value can exist, either associated with the sequencing and genotyping techniques, errors in the attribution of ancestral alleles or sampling of the individuals used as representatives of a genetic group. For example, if a genetic group is represented by only a few members representing only a small portion of its diversity, identified alleles will under-predict the contribution of this genetic group in individuals descending from it.
In this context, the threshold may need to be adapted. As illustrated in Figure  2 , the curation mode can be used to significantly improve automated results. When normalized curve datasets are available (from preloaded datasets or loaded by users online), GeMo displays interactive graphs that enable the curation of predicted  Comparison of genome ancestry mosaic results before (A) and after (B) curation in GeMo. In A, the genome ancestry mosaic is not well resolved (dark gray for undefined origin) with default parameters (0. 5 for diploid). Once the curve-based mode is activated, an interactive graph facilitates threshold adjustment. In B, the genome ancestry mosaic is better resolved after reducing expected thresholds for the two curves with the main ratios (as indicated in circles). Downloaded from https://academic.
oup. com/database/article/doi/10. 1093/database/baac057/6645005 by Universiteé Fédérale Toulouse Midi-Pyrénées -SICD user on 20 July 2022 genome ancestry mosaics. These graphs represent the proportion of haplotypes of each genetic group by chromosomes resulting from a normalization of the number of reads supporting each origin on a given window. This allows for visualizing the contribution of each genetic group along the chromosomes and checking the threshold that led to the colored painting of blocks (Figure  2A ). With GeMo, users can change the threshold on the graph to recalculate the ancestry mosaic block contribution. Once a threshold is changed, the karyotype diagram is dynamically updated, which facilitates threshold adjustment (Figure  2B ).
In silico chromosome painting visualization can help the user inspect chromosome structures in progenies to select the most desirable individual. Thus, GeMo has the potential to support the visualization of crosses in breeding programs working with molecular approaches  (23). For functional genomics, drawing genome ancestry mosaics in which genomic segments are co-localized with a QTL (Figure  3C ) may reveal traces of adaptative introgressions to be further investigated  (5). The recurrent occurrence of a genomic block in natural hybrids, for instance, or co-localization with a given QTL may indicate positive selection. For transcriptomics studies, it provides a way to overlay the chromosomes with genes that display differential expression in a specific haplotype  (24). The software interoperability with genome browsers allows to zoom into specific genes in a genomic block or to export the genome ancestry mosaics as tracks into a JBrowse instance.
Acknowledgements This work was supported by the  CIRAD-UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (http://www. southgreen. fr/). We thank colleagues for their careful testing of the software and feedback, in particular  Alberto Cenci (Bioversity), Valentin Guignon (Bioversity)  and  Eloi Durant  (IRD) as well as  Manuel Ruiz  and  Ang é lique Dhont (CIRAD)  for their support in the project. We also thank  Alexandr Zelenin  for committing useful features of the ideogram. jslibrary;  Rachel Chase  for text editing of the manuscript and the anonymous reviewer for the constructive and comprehensive feedback to improve the manuscript.
As such, when comparing to timings given in  [DF20]  where they used a 128-core server, one should remember that we could improve our timing further by conducting the search in parallel, allocating 8 threads for each instance of Gurobi, which would be very close to dividing our solving times by n if using 8n threads in the cases considered. Our code is available at https://github. com/FastMILPDivisionProperty/FastMILPDivision.
Exact Modelization : Convex Hull The first technique used to modelize the propagation through an S-box is using the Convex Hull of S, the smallest convex set containing all points of S. It can be represented by its H-representation, a set of linear inequalities L such that the only feasible solutions of L are exactly the points of S. This H-representation is typically computed using the SageMath software  [The20] , which in backend is using the Parma Polyhedra Library  [BHZ08]. Such approach was first proposed in the context of differential cryptanalysis [SHW + 14], and later used to actually search for division trails  [XZBL16]. Note that the number of inequalities in the H-representation can be rather large. But hopefully, since in our case all coefficients u i and v i belong to {0, 1}, one can significantly reduce the size of L while still maintaining its correctness [SHW + 14] (i.
Lossy Modelization : Piecewise Linear Constraints Piecewise linear functions are functions defined such that there exists a set of intervals where the function restricted to each interval is an affine function. Such a function is defined by a list of points defining the start and end of each segment. We give an example of such a function in dimension 2 in Figure  1 , where the list of points is L = [(0, 0), (1, 1), (2, 1), (3, 2), (4, 4)]. These functions can be modelized in MILP so that a pair of variables is constrained to take the values of a piecewise linear function. A nice thing about the Gurobi solver is that it has a direct way to add such a constraints (called PWL constraints) without having to generate the corresponding set of linear inequalities (e. g. using addGenConstrPWL in the Python interface).
We will use such a constraint to get a lossy modelization of S-boxes as follows. Given the list of all valid propagations u S → v, we first compute w i = min wt(u)=i (wt(v) s. t. u S → v), 0 ≤ i ≤ n that is, w i is the minimum weight of any v such that u S → v where the hamming weight of u is i. Our goal is to add a constraint so that if wt(u) = i, then wt(v) ≥ w i. While this does not look very restrictive, it was enough to prove some results (see Section 5). Actually, it is very close to asking Gurobi to first search for word-based division property distinguishers before searching for bit-based division property. We can easily do so by using the following constraints s 1 = n i=1 u i s 2 ≤ n i=1 v i addGenConstrPWL(s 1 , s 2 , [0, 1,. , n], [w 0 , w 1 ,. , w n ]) u i , v i binary variables , 1 ≤ i ≤ n 0 ≤ s 1 ≤ n 0 ≤ s 2 ≤ n where addGenConstrPWL(x, y, T x , T y ) defines a PWL constraints where the piecewise linear function f (x) = y is defined by the pairs (T x [i], T y [i]).
** The representative study on landscape perception was done by  Kienast et al. , 2013 [30]. The noise calculation is based on the Swiss GIS noise database sonBASE. Empirical evidence obtained by Kopf et al. (2017)  [28]  shows that only 6% of Vorarlberg's area is undeveloped, with negligible infrastructure rates ranging from 0-5%. Landscape "chambers" are delimited with less than 20% infrastructural development. These "white zones" account for 19% of the territory. More than three-quarters of those are at high altitudes (the mean elevation of white zones is 1942 m above sea level), 28% are used for Alpine meadows, and over 50% are barren Alpine land. The potential for valuable landscapes worthy of protection is especially high in white zones: 50% of white zones are officially designated as valuable biotopes (without protection status).
Data and Methodology The approach of the GIS analysis is based on the selection of geodata representing building development, soil sealing, and technical infrastructures. The most precise and up-to-date geodata sets for these criteria are usually only available from national or regional authorities. However, these national or regional data often differ in terms of their structure and the definition of infrastructure categories, and not all of them are publicly available for research purposes. To cover all states and regions of the Alps equally, geodata available across Europe are used for the analyses presented, as shown in Table  2. The main data sources are provided by the European Copernicus Land Monitoring Service (LMS): the comparatively coarse-resolution Corine Land Cover 2018  [42]  and the high-resolution Impervious Built-up 2018  [43]  dataset.
However, many linear infrastructure elements (such as roads, cable cars, or power lines) can only be integrated with the freely available data of the OpenStreetMap community. No fundamental guarantee can be given that these OpenStreetMap data are always accurate, comprehensive, and up to date. However, several studies  [44] [45] [46]  and our pilot samples in selected alpine regions (comparisons of OpenStreetMap and official geodata for selected infrastructures) suggest that they are suitable for analyses on an alpine-wide scale. Accuracy analysis of used geodata (with specialized remote sensing methods) would go beyond the scope of this article. Suggested reading for evaluation of OpenStreetMap and Copernicus HRL datasets includes, e. g. ,  [47, 48]. In the methodology of this paper (aggregated buffers resulting from different infrastructure input datasets), potential accuracy deficits of single datasets are compensated by the mix of several datasets (e.
Within the EU-Hydro dataset, Copernicus LMS offers a drainage model (also called Drainage Network), derived from EU-DEM (digital elevation model), with catchments and drainage lines and nodes  [32]. From this dataset, the basic units of "basins" are incorporated, which feature a mean size of 22. 5 km 2 within the Alpine Convention perimeter. Before processing the basins, lakes were extracted, using the "inland water" layer as well from the EU-hydro river network. After calculating the level of infrastructure development for every natural landscape unit, statistical summaries are created for different political (Alpine Convention perimeter and EUSALP macro-region) and administrative territories (countries), the results of which are presented in Section 3. It is important to mention that when overlaying the perimeters, the natural landscape units are not cut off at the respective administrative boundary (only for cartographic purposes).
The data and classification of highly protected (National Park: core zone, nature reserve, nature/regional park from Italy) and other protected areas are provided by ALPARC from its database. Concrete data on spatial planning designations could not be used in the alpine-wide analysis, as there exist no transnational databases. However, similar planning data was used for the specific example of the cross-border area between the German federal state of Bavaria and the Austrian federal state of Salzburg (cf. Figure  6 ). The geodata is either freely available from state authorities in the respective region or obtained upon request.
Spatially, however, it is noticeable that Switzerland and France have a high degree of protected area coverage. This is mainly because of weaker types of protected areas, such as, e. g. , large-scale nature parks in the Southwest of the French Alps. The observation of a missing overlay does not necessarily mean that these areas are completely unprotected. It is rather difficult to establish an alpine-wide database on other open space protection instruments. Here, spatial planning can play a coordinating role in bridging bottlenecks between protected areas and safeguarding important open space corridors. Therefore, an example of cross-border analysis of spatial planning (also concerning nature conservation and water protection designations) between Germany and Austria is presented in Section 4.
The use of OpenStreetMap data for key components of the GIS analysis (infrastructure layers such as roads, railways, cable cars, and power lines) has provided a solid basis for analyzing the status quo but is unsuitable for continuous monitoring. This is because it cannot be determined whether land use changes result from real changes or only from an update/improvement of the data from the OpenStreetMap community. In contrast, the continuously updated data of the Copernicus Land Monitoring Service is of great value. The Corine Land Cover data  [42]  offer the longest time series but only a low spatial resolution with a minimum mapping unit of 25 ha. In particular, the high-resolution layers, especially the Impervious Built-up Layer  [43] , which has been available since 2018, are promising for continuous transnational monitoring but also feature some methodological weaknesses  [47].
The method carried out is only an initial step in the analysis of open spaces. To generate more relevant databases for spatial planning decisions, analyses of this kind must, in the future, include more qualitative aspects of open spaces such as ecosystem services (e. g. , specific soil functions or habitat functions). The long-term evolution and prospects of open spaces should be supported by the monitoring of commonly defined key indicators, which are derived from the binding obligations of the Alpine Convention Protocols. Based on common quantitative and qualitative criteria and a harmonized international data basis, a practical definition should be agreed on among alpine countries to support transnational coordination of spatial planning. Furthermore, a standardized permanent Alps-wide monitoring system might be implemented to observe and identify the development of open spaces and their modification by quantitative and qualitative impacts  [50].
Data Availability Statement: Publicly available datasets from Copernicus Land Monitoring Service and OpenStreetMap were analyzed and processed in this study. The resulting geodata for the central result (level of infrastructure development) can be found here (ArcGIS Feature Server): https://services5. arcgis. com/qjjtr5pMpt3PDiiR/arcgis/rest/services/Job_et_al__2022__Level_of_infrastructure_development/ FeatureServer (accessed on 10 September 2022).
An SOC Benchmark for Smart Process Control The simulation benchmark shown in Figure  3  was built in Matlab/Simulink to evaluate FOSOC controller performance. The simulation benchmark is composed of (1) the GCNM controller and (2) the FOPDT system  (12)  with the discrete FOPI controller implemented using Stateflow. The FOPDT system is normalized and evaluated in this benchmark, following Remark 11. 1. 3 presented in Appendix A  [38]. The FOSOC is tested under three conditions of the FOPDT system: lag dominated (L = 0. 1), balanced time (L = 1), and delay dominated (L = 10). A closed-loop stable FOPI controller is designed as the initial condition for each case using the FMIGO method  [33]. In addition, the FOSOC benchmark uses as performance indices the GCNM convergence time, the closed-loop overshoot and settling time, the root mean square value (RMS) of the control action, the integral square error (ISE), the integral absolute error (IAE), and the root mean square error (RMSE) during the benchmark execution time given by  (13).
The code for the benchmark can be downloaded from https://github. com/tartanus/FOSOCBenchmark, accessed on 10 July 2022. In addition, there is a 300s period reference square signal r. The benchmark parameters are shown in Table  1. Notice that the maximum and minimum constraint values of K p , K i , and λ change from each test to ensure the stability of the closed-loop system. For all the benchmark tests, the cost function weights are The time response of the FOSOC controller for the FOPDT system with L = [0. 1, 1, 10] s are shown in Figures  4 5 6. As can be observed, the FOSOC controller satisfies the overshoot and settling time specifications with a convergence time closer to 2000 s after performing some probabilistic restarts, with an improved performance compared with the initial condition IC given by the FOPI parameters obtained using the FMIGO  [39]  tuning method, shown in the last columns of Table  1  for each value of L.
Conclusions and Future Work This paper presented a FOSOC framework to improve a closed-loop system performance. A benchmark was built in Matlab/Simulink to evaluate the FOSOC controller performance using a FOPI controller under three different tests. In addition, a more optimal self optimizing control (SOC) employing fractional-order Gaussian noise (fGn) with long range dependence (LRD) to improve the optimization convergence time of the GCNM (globalized constrained Nelder-Mead) algorithm was presented. The obtained results show that the FOSOC controller enhances the system closed-loop performance and ensures stability and robustness for different dead times. Additionally, the accelerated SOC using fractional order stochasticity was evaluated using fractional Gaussian noise with different LRD given by the Hurst exponent.
, 2014; Lazaridis et al. , 2014; Olalde et al. , 2014 Olalde et al. , , 2015;; Skoglund et al. , 2014; Jones et al. , 2015 Jones et al. , , 2017;; Broushaki et al. , 2016; Hofmanová et al. , 2016; Kılınç et al. , 2016; González-Fortes et al. , 2017; Sikora et al. , 2017; Günther et al. , 2018; Marchi et al. , 2022) , Table  S1 ), including four from the Danube Gorges (  (Marchi et al. , 2022) , Table  1 ), and 20 modern samples from Africa and West Eurasia retrieved from the SGDP database (  (Mallick et al. , 2016) , Table  S1 ). To ensure comparability and increase the sensitivity of population genomic analyses, we focused on 54 whole genomes and 54 5Mb neutralomes (with seven overlaps, Table  1 , S1) that passed rigorous quality assessments mainly evaluating the reproducibility of genetic diversity estimates in face of data bootstrapping (see Methods, Figure  S1-S2 ).
Interactions between foragers and farmers in the Danube Gorges While the results shown above attest to a certain degree of cultural syncretism during the Transformation Period, this does not seem to apply to all sites in the Danube Gorges. All Danube Gorges individuals for which we estimate at least some Neo Aegean-like ancestry were buried at Lepenski Vir. A single additional individual with some degree of Neo Aegean-like ancestry was previously reported from Padina (PADN_4;  (Mathieson et al. , 2018) ), the only other Danube Gorges site at which trapezoidal houses were found. In contrast, at the nearby site of Vlasac, the genome-wide ancestry of all seven individuals analyzed, as well as mtDNA haplogroups identified for additional 9 individuals, are all consistent with Meso European-like ancestry (Supplementary Data Table  1 ), in line with previous reports for Vlasac and Hajducka Vodenica  (Mathieson et al.