dataesr/scientific-paragraphs-categorization huggingface dataset:
Most additive parts with curved or sloping surfaces have a characteristic step-wise surface roughness because models are sliced to produce layer shapes which are extruded in a single direction. Post-processing operations, such as grinding process, solvent immersion, EDM (Electrical Discharge Machining) subtraction, improve the surface accuracy but, only when the parts are slightly larger than the required shape. It is not guaranteed in the normal chain because missing volume creates problems. Finally, the data format used for AM processes is a tessellated model which means increasing the size of the data, if accuracy needs to be high. The first serious examination of the needs of manufacturing was performed by Renan Bonnard, then at the Ecole Centrale de Nantes  [1]. His work led to the development of the STEP-NC standard for additive processes which defines a consistent format for additive processes as part of a manufacturing chain.
Medical products, aircraft, racing cars and dental work are recent applications of additive manufacturing, but STEP-NC does not cover all AM specialised operations and features  [10]   [11]. Various application industries, as well as typical manufacturing industry, utilise AM, as mentioned by Lee  [12]. Advanced production technology, such as 4D printing , has also been launched  [13]. This paper proposes a novel data representation based on STEP-NC and a new geometric procedure for producing slice geometry. In section 2 the basic concept and existing model for this paper are introduced. Section 3 describes new features and representation of AM features based on the STEP-NC representation. Slicing operations and a way to ensure that volume is added for post-processing by machine tools follows in section 4.
Multi-material Additive manufacturing Even though many existing data formats have been developed, there is still a gap to satisfy the requirements of improving geometric processing. Software systems for additive process require high slice density, multi material data format, and fine slicing operations in order to produce accurate shape. 1) In this section, data formats which are used are investigated from STL  [2]  to STEP-NC  [14]  and AMF  [5] , recently established as an international standard. Then, 2) existing data representation formats for additive manufacturing which are specifically for multi-material processes are discussed. Some representations and strategies for multi-material structures, a key function for slicing whole shapes into fractional pieces, are introduced from aspects of improving the accuracy of the final output shape.
The format also allows post-processing, if required by the manufacturer, to either STL or AMF with parameters determined from user requirements. Also Bonnard et al. [26]  described that the two formats STL and AMF are enablers for rapid evolution but not suitable for delicate manufacturing. The STEP-NC format is also more appropriate if manufacture switched between machines with different characteristics. Since STEP-NC records what is commonly termed a micro process-plan it is also suitable for archiving where the process parameters are recorded explicitly. Zhao et al. [27]  explained by using STEP-NC formats advantage, richness of information, are also more proper format for maching simulation. For further considerations, see Um et al. [8]  which compares existing data representations with the STEP-NC data representation in an additive manufacturing process for re-manufacturing.
Data representation "How should the target objects be designed" is the question to legacy CAM software for additive processes. Using STEP-NC is a key way for deriving exact slices to fit the final shape the designer wants. ISO 14649-17 is the standard of STEP-NC for additive processes and is integrated with the other ISO 14649 parts for hybrid process plans. The intention of this section is twofold, 1) Firstly, STEP-NC data format is introduced with the comparison to voxelisation method. 2) Secondly, gradient boundary representation generated by various source types is introduced to describe multiple material or multiple colour object. Finally, we discuss how to apply both features to the build-up phase.
STEP-NC standards Multiple materials structure and complex structures inside the product are also applications of additive manufacturing. The ways in which these shapes are expressed is broadly used in tessellation or in the Voxelisation method. Voxelisation methods generally make the data size larger depending on the shape complexity, as with point clouds. It also has the disadvantage of not having the high resolution of the original features once converted. BRep is a way of expressing complex shape accurately with smaller capacity compared with voxels or triangles, as well as direct inputs of the squash operation which can produce accurate and high-quality results. In the case of STEP-NC, additive features present some ways of expressing BRep based representation. Figure  1  shows the data structure for STEP-NC additive manufacturing.
Case studies This section investigates the differences of representation methods in the design and manufacturing phases using STEP-NC additive manufacturing features. This section illustrates the differences and advantages of the proposed method which is defined in ISO 14649-17 [? ] among STEP-NC standardisation methods. The method presented in this paper is applied to multi-material shapes and squash slices to increase the accuracy in the geometry definition process. In particular, this section shows data loss and changes in accuracy through manufacturing phases from computer-aided design tools and process planning tools, like CAM systems, in order to show the verification and comparison. In the design phase, the accuracy and data size of additive shapes are analysed and, in particular, representation methods for controlling distributions to multi-colour are compared.
Recent innovations in genetics and imaging are providing the means to reconstruct cell lineages, either by tracking cell divisions using live microscopy, or by deducing the history of cells using molecular recorders. A cell lineage on its own, however, is simply a description of cell divisions as branching events. A major goal of current research is to integrate this description of cell relationships with information about the spatial distribution and identities of the cells those divisions produce. Visualizing, interpreting and exploring these complex data in an intuitive manner requires the development of new tools. Here we present CeLaVi, a web-based visualization tool that allows users to navigate and interact with a representation of cell lineages, whilst simultaneously visualizing the spatial distribution, identities and properties of cells.
g. (4) (5) (6) (7) (8) ). The visualization and integration of these complex data re- quires new specialised tools. Here we present CeLaVi (Cell Lineage Visualization tool), a web based visualization tool for exploring a cell lineage while viewing a 3D representation of the same cells, which can be annotated in any way required. CeLaVi is an open-source web tool that requires no installation of software. Example data sets, and text and video tutorials are provided (http://www. celavi. pro/tutorial. html). The simple requirements for using CeLaVi, namely a personal computer with a modern browser, makes it easy to use by cell lineage researchers, but also opens the possibility of exploring the data emerging from the new cell lineaging technologies to the wider developmental biology community, and to the general public.
Input/output To visualise a cell lineage tree and to relate it to a 3D representation of the positions of each cell, the user must provide two files: one containing the lineage tree and one containing the 3D coordinates of the same cells (with identical labels). The lineage tree file describes the topology of the tree either in Newick or Json format. When uploading a file (Figure  1A ), CeLaVi automatically identifies the format of the lineage tree file and displays the tree in the Lineage tree viewer (Figure  1E ). To ensure the format of the lineage tree is correct, Json and Newick files are parsed with the Javascript standard method JSON. parse() or with the newick-tools software (available at https://github. com/ xflouris/newick-tools), respectively. Branch lengths can be recorded in the input file, either as 'absolute' branch lengths (distances of each node from the root) or as 'relative' blanch lengths (distances between nodes of the tree) and the user must identify the appropriate mode.
To visualise the same cells in the 3D viewer, the user needs to upload a file of the 3D coordinates of cells (Figure  1B ) in comma-separated-value (csv) format. The first row of the file is a header specifying the identities of each column ('cell', 'X', 'Y' and 'Z') and each subsequent row giving the name/ID and 3D coordinates (X, Y, Z) of each cell. The cells are represented in an interactive visualization (Figure  1F ) in the 3D viewer. The image of the lineage tree or the 3D cells, as currently displayed in the visualization areas, can be exported as an image file (in SVG or PNG format) at any time. For MaMuT  (10)  users, it is possible to convert a Ma-MuT single XML output file into CeLaVi lineage and 3D coordinates input files (in json and csv formats respectively) using the python script 'mamut to celavi' by Ko Sugawara, available at https://github.
Visualizing cell annotations and gene expression Two additional files can be loaded to provide information associated with the cells in the two viewer windows. Cell type information (or indeed any other cell annotation) can be uploaded as a csv file using the 'Cell annotations file' input box (Figure  1C ). The information from this file is displayed as an interactive table with the annotations as coloured rows (Figure  1G ). For example, selecting the 'muscle' annotation in the Ciona dataset, labels the muscle cells in both the 3D viewer and the lineage viewer. Gene expression data (Figure  1D ), can be uploaded as a. csv file using the 'Gene expression file' input box. To see the relative expression levels of a gene of interest, the user types the gene name in the search box (which has an autofill function) and clicks 'submit'.
Example datasets We include four example datasets that demonstrate CeLaVi's features (see Table  2 ). Caenorhabditis elegans L1 larva. The embryonic cell lineage of the nematode Caenorhabditis elegans was first described by Sulston and collaborators in 1983  (2). The data used for the cell lineage and the cell types were extracted from a json file made available by Nikhil Bhatla at wormweb. org/celllineage. The complete cell lineage was trimmed back to the L1 larval stage. The 3D coordinates come from the study of Long and collaborators  (11) , where they mapped the positions of 357 cells (out of the 558 cells) of the L1 larval stage, using confocal image stacks from 15 individual worms. Ciona intestinalis gastrula. The cell lineage of the ascidian Ciona intestinalis was described by Conklin in 1905 (  12 ).
The cell lineage example/test file (in newick format) was created using Conklin's lineage and nomenclature, from the zygote to the 110-cell stage. The 3D coordinates of the cells in the Ciona gastrula were obtained from a reconstructed 3D embryo model (Mid 112-cell stage Amira 1. obj file; (13)), available in the Aniseed database  (14). The 3D embryo model contains information in cell shapes. We used the cell centroid to represent each cell as a single point in 3D space. The gene expression data were obtained from the single-cell sequencing dataset published by Levine and collaborators  (15). We used the raw expression data matrix (expression matrix C110. 1. tsv), containing the expression levels of 15 307 genes in 1731 cells, together with the associated metadata file (C110. 1. clusters. upload.
rename. 1. txt) that contains the cell type identity of each cell at the early gastrula stage. We used the R software version 3. 6. 3 (https://www. R-project. org/) with the Seurat package version 3. 1. 4 (16)  to identify the 500 genes with the most variable expression between cells and to obtain their average expression values for each cell. The gene names correspond to human gene IDs, derived from the Ciona intestinalis genome assembly KH2012 with NCBI Gene Model to Best Blast Hit mapping, available in the Aniseed database. Only the gene ID with the highest Blast e-value is shown. Parhyale hawaiensis limb. The cell lineage and 3D coordinates of the limb of the crustacean Parhyale hawaiensis were obtained by Pavlopoulos and collaborators  (10). Transgenic fluorescently-labeled embryos were imaged with multi-view light-sheet microscopy at high spatiotemporal resolution over several days of embryogenesis.
The cell lineage was reconstructed with the aid of the MaMuT software. In silico organoid. The 'organoid' dataset comes from a simulation of morphogenesis using the ya||a software  (17). The organoid is the product of a simulation of branching morphogenesis with epithelium and mesenchyme (only the epithelial cells are recorded in this example). The simulation was kindly provided by Miquel Marín-Riera, coauthor of the ya||a software and is based on the example 'branching. cu' available in https://github. com/germannp/yalla.
Comparison with other software CeLaVi is, to our knowledge, the first interactive web-based visualization of cell lineages. It was inspired by web based visualizations of phylogenetic trees, such as phylo. io and Evolview3  (18, 19). The most similar existing implementation of some of these features is found in Morphonet  (20)  which is a webserver software for visualizing morphological data. Morphonet shares some commonalities with CeLaVi but also has some important differences we discuss below. The main feature of Morphonet is the visualization of complex 3D morphological information with the possibility of including multi-time point segmentation data. The morphological units to be visualized (as mesh objects) can be organs, tissues or cells. Morphonet offers the possibility of integrating additional information like gene expression and cell lineage, but cell lineage viewing is not its main focus, and the interactive manipulation of the cell lineage data as well as its integration with the spatial information is limited.
Limitations and perspectives All computations in CeLaVi are performed on the client side. This makes CeLaVi scalable and responsive as the data visualized in CeLaVi are not sent to or stored in the webserver. We believe this is an advantage as it can alleviate concerns that researchers might have about uploading unpublished data to servers. The main disadvantage of client-side computing is that there is a maximum amount of data that can be handled without experiencing delays that depends on the client computer. We have noted that CeLaVi becomes less responsive when >10 000 cells are loaded. We provide some suggestions to improve the responsiveness of CeLaVi when dealing with these very large datasets in the tutorial. A second limitation of CeLaVi is that the 3D viewer shows the spatial distribution of the cells at the final time point only.
ACKNOWLEDGEMENTS We thank  Anastasios Pavlopoulos  for kindly providing lineage data and cell coordinates for the Parhyale limb dataset,  Chen Cao  and  Michael Levine  for kindly providing the scR-NAseq data for the Ciona gastrula,  Miquel Marín-Riera  for kindly providing the organoid simulation, and  Ko Sugawara  for producing the script to input MaMuT data. We thank  Tomáš Flouri  for fruitful early discussions on the implementation of CeLaVi and general guidance and  Laura Piovani  for helping designing the CeLaVi logo. We also want to thank the members of the  Telford, Oliveri and Averof labs for testing CeLaVi  and providing useful feedback.
Genome instability has been recognized as a key driver for microbial and cancer adaptation and thus plays a central role in many diseases. Genome instability encompasses different types of genomic alterations, yet most available genome analysis software are limited to just one type of mutation. To overcome this limitation and better understand the role of genetic changes in enhancing pathogenicity we established GIP, a novel, powerful bioinformatic pipeline for comparative genome analysis. Here, we show its application to whole genome sequencing datasets of Leishmania, Plasmodium, Candida and cancer. Applying GIP on available data sets validated our pipeline and demonstrated the power of our tool to drive biological discovery. Applied to Plasmodium vivax genomes, our pipeline uncovered the convergent amplification of erythrocyte binding proteins and identified a nullisomic strain.
As such, genome instability often determines disease outcome (  [19] [20] [21] [22]  and is a key driver of phenotypic and genetic variability of microbes and other pathogenetic systems that rely on genome instability for adaptation, such as cancer. In this context, several consortia-based projects have been established with the goal to produce WGS for the study of different biological systems  (5) , and a number of publicly available databases have been compiled or updated  (23). Parallel to data availability, many bioinformatics tools have been developed to perform specific genome analysis tasks  (24, 25). For instance, tools such as Freebayes  (26) , CN-Vnator  (27)  and DELLY  (28)  have been respectively used for the detection or characterization of DNA single nucleotide variants (SNVs), copy number variations (CNVs), 2 Nucleic Acids  Research, 2021  and structural variations (SVs), but their scope is limited to the analysis of one genomic feature at the time.
A number of integrative WGS pipelines and workflows have been established combining the execution of multiple bioinformatics algorithms serving different analysis steps  (29). Even though continuous progress has been made, there is no standardized or unified approach for genomic investigation. For the development of improved WGS data analysis pipelines, several important requirements need be considered, including portability, reproducibility, scalability and compatibility with high-performance computing (HPC) clusters and remote cloud computing. Here we introduce a novel genome instability pipeline (GIP) that fulfills all these requirements. GIP facilitates the genome-wide detection, quantification, comparison and visualization of chromosome aneuploidies, gene CNVs, SNVs and SVs. GIP is implemented in Nextflow  (30) , a workflow language that allows to execute GIP seamlessly in local workstation, on an HPC or remotely in the cloud.
All required environment and software dependencies of GIP are fulfilled and provided with a Singularity container, thus making GIP reproducible, easy-toinstall and easy-to-use. GIP allows the use of giptools, a tool-suit of R-based modules for genome data exploration, enabling the comparison of sample sub-sets. GIP and giptools generate a summary report with publication-quality figures and spreadsheet tables. GIP and giptools constitute a single framework for WGS analysis suitable both for large scale batch analysis of individual genomes and comparison of samples from different experimental conditions or origins. Lastly, a key strength of GIP and giptools is the general applicability to different eukaryotic species. We already successfully applied GIP on the analysis of Leishmania genomes  (16, 31, 32).
Genome sequencing and genome assembly data WGS reads were downloaded from the Sequence Read Archive (SRA)  (33) , the European Nucleotide Archive (ENA)  (34)  repositories and the Encyclopedia of DNA Elements (ENCODE) dashboard  (35)  (Supplementary Table  S1 ). For L. infantum the GCA 900500625 genome reference and gene annotations available from the ENSEMBL protists server (release-48) were used  (36). For C. albicans the assembly 21 of the SC5314 strain genome reference and gene annotations available from the Candida Genome Database (CGD) were used  (37). For P. vivax the P01 reference genome and gene annotations available from Plas-moDB (release-50) were used  (38). For the cancer cell lines, the human genome GRCh38 primary assembly and gene annotations available from ENSEMBL (release-102) were used  (36).
GIP and giptools All results presented in this study were generated using GIP and giptools version 1. 0. 9. GIP code is maintained and freely distributed at the github page: https://github. com/ giovannibussotti/GIP. Figure  1  and Supplementary Figure  S1  provide a schematic representation of the GIP workflow. The giptools container is accessible from the Singularity cloud at https://cloud. sylabs. io/library/giovannibussotti/ default/giptools. The GIP configuration files (Supplementary Data 1) and the giptools command options used to generate all results (Supplementary Data 2) are provided. The full documentation of GIP and giptools including a description of all options is available from https://gip. readthedocs. io/en/latest/.
Read alignment The repetitive elements of reference genomes were softmasked by GIP using Red  (39). WGS reads were mapped by GIP using BWA-mem (version 0. 7. 17)  (40, 41)  run with option -M to label shorter split hits as secondary. Then the alignment files were sorted, indexed and reformatted by GIP using Samtools (version 1. 8)  (42). Finally, read duplicates were removed by GIP using Picard MarkDuplicates (http://broadinstitute. github. io/picard) (version 2. 18. 9) with the option 'VALIDA-TION STRINGENCY = LENIENT'. In the four considered datasets, WGS reads were aligned against full assemblies, including unsorted contigs if present. However just the canonical assembled chromosomes were considered for all downstream analyses ('chrs' option, Supplementary Data 1). A minimum read alignment MAPQ score was adopted to select genes for cluster analysis, and to call for SNVs and SVs ('MAPQ' option, Supplementary Data 1).
GIP enabled the CNV analysis of genes sharing high sequence identity by clustering the nucleotide sequences of the genes with low mean MAPQ score into groups with cd-hit-est (version 4. 8. 1) (43)  with options '-s 0. 9 -c 0. 9 -r 0 -d 0 -g 1'. Then for each gene cluster GIP computed the mean gene coverage normalized by median chromosome coverage (Figure  2E , Supplementary Figure  S3B ). The predictions of CNV regions returned by GIP for the P. vivax dataset were compared to the ones available from previously published work  (44). For the analysis we utilized the 178 available samples not affected by national export restrictions. The sample list is available from https://www. malariagen. net/sites/default/ files/PvGV May2016 sample data 2. xlsx. To enable the comparison, GIP was re-executed considering the same genome refence (Pvax Sal1) and CNV length cutoff (>3 kb) used in the published work.
4'). Further, GIP discarded SNVs with sequencing coverage above or below 4 median absolute deviations (MADs) from the median chromosome coverage ('-MADrange'). SnpEff (version 4. 3t) (49) was used to predict the impact of SNVs on coding sequence. The predicted effects that GIP considered synonymous mutations are: 'synonymous variant', 'stop retained variant' and 'start retained'. The predicted effects that GIP considered non-synonymous mutations are: 'missense variant', 'start lost', 'stop gained', 'stop lost' and 'coding sequence variant'. The phylogenetic tree was computed by the giptools module 'phylogeny' using IQtree2 (version 2. 1. 2) (50,51) with options '-seqtype DNA -alrt 1000 -B 1000'. The Venn-diagram comparison considered the strains QS0044 C, QS0001 C, QS0037 C, QS0016 C and SGH 358 that were sampled from different locations in Ethiopia, respectively Habala, Badowacho, Arbaminch, Hawassa and Jimma.