dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, according to requirement 5 it will be able to add these capabilities to its Ambient set, and to execute either the program that has been selected by the user through the optional -c parameter, or start a shell. Figures  24  and 25  in the Appendix provide detailed flow charts for the implementation of sr and sr aux respectively. The sr process checks whether the role exists and whether the administrator has authorised the user or its group to assign the requested role with the requested program. If authorized, then sr authenticates the users by asking them to provide their password. When the authentication is successful, sr will prepare the program or shell to run with the requested capabilities. According to Requirement 2, the sr process cannot add the list of capabilities requested by the user to its own Permitted set and cannot add them to its Ambient set either (because they are not present in its Permitted set).
Thus, we also have to add the cap setpcap capability to the Permitted set of the sr file during installation. Now the user's requested capabilities can be added to the Permitted set of the sr aux file and to the Inheritable set of the sr process. According to Requirement 1, when sr makes an exec call to sr aux the capabilities of the file's Permitted set will be copied into the Permitted set of the resultant process and the capabilities of the Inheritable set of the sr process will be copied into the Inheritable set of the resultant process of sr aux. According to Requirement 5, the resultant process of sr aux can dynamically add the user's list of requested capabilities into its own Ambient set. Finally, the resultant sr aux process makes an exec call to the program requested by the user (or the shell).
The kernel does not tell the calling process that cap setuid is needed. This issue represents a major obstacle to the adoption of Linux capabilities, because administrators and users do not have commands that can tell them what kind of capabilities the used programs are asking for. We have built the capable command to address this issue. There are two functions that are called by Linux syscalls in order to check whether a process has the required capability or not (Figure  19 ). The first function is called capable() and the second function is called ns capable(). The difference between them is that ns capable() has the ability to check the capability in a namespace. However, both of them are implemented by a function called cap capable() in the capability LSM. As Listing 10 shows, cap capable() checks whether the calling process has the required capability in its Effective set.
If not, it returns the "EPERM" error code without indicating the required capability to the calling process. One way to resolve this issue would be to modify the cap capable() source code. However, discovering the impact of this modification is a very difficult task. Consequently, we preferred to opt for another solution, specifically, using one of the Linux tracing technologies such as Kprobe  [14] , tracepoints  [15]  or Ftrace  [16]  with eBPF  [13]. The basic idea of all the tracing technologies is to insert trace points in specific locations of the kernel. When the processor executes one of these points, a trace event is generated. This event contains the execution context information that is stored in the registers of the processor. We used Kprobe  [14]  to get the context execution whenever cap capable() is executed by the kernel.
Conclusions and Future Works Our module RootAsRole provides new functionality to the Linux community by providing them with a module that allows Linux privileges to be given to users through the assignment of roles. Our module also allows Linux administrators to constrain the use of these privileges to certain programs (e. g. , the Apache service or tcpdump). In addition, RootAsRole allows administrators to assign roles to sets of users through the group concept. Our sr command is more secure than sudo/su commands because it comes only with two capabilities which are cap setpcap and cap setfcap, while sudo/su commands need the full list of Linux administrative privileges. However we need to conduct a user study to analyze the usability issues of our module. We need to test the usability regarding the configuration of RootAsRole and the invocation of roles by users.
Statistical Evaluation Descriptive statistic was performed and data were analyzed by Shapiro-Wilk-test for conformance with a normal distribution. Observation time was defined as the time from the submission of muscle and nerve biopsies to the last contact with the cat owner. Recovery time vs. age was studied using Kruskal-Wallistest because age did not follow a normal distribution. Recovery time vs. all variables and groups inside of those variables between each other were studied via Fisher tests. Disease onset, age, clinical and electrodiagnostic parameters were analyzed for any association with outcome (recovered/not-recovered) via logistic regression. P < 0. 05 was considered significant. Furthermore, P-values were adjusted for multiple comparisons by the Holm method. All analyses were done by the R Statistical Software (version 4.
Features of leaf form emerge soon after primordia initiation To understand how the three dimensional form of the leaf arises from cellular growth patterns, we repeatedly imaged living wild-type (WT) and jaw-D mutant first leaves from 3 days after sowing (DAS) to 8 or 9 DAS (n = 3 leaves for each genotype; Figure  1A ). Cell boundaries were marked with a fluorescent plasma membrane marker. To increase the surface of the leaf that could be imaged, the cotyledons were dissected off. We used confocal microscopy to image nearly the entire abaxial (bottom) epidermis of the leaf at cellular resolution so that we could make cellular and organ scale measurements. Then we used MorphoGraphX 2. 0 image analysis software to represent the confocal stacks as 2. 5D curved mesh surfaces  (Strauss et al. , 2022).
Cell segmentations were manually corrected immediately after segmentation or through the process of manual cell lineage tracing and cell junction correction using the check correspondence process. Cell and growth parameters were measured and heat maps generated using a python script run through MorphoGraphX 2. 0. All >77,000 cells in the final analysis were selected based on the presence of lineage tracking ensuring that only cells that were manually curated were included in the final dataset. All segmented meshes and measures are included in Supplemental Data.
Data analysis All data processing, analysis and plotting was performed in  RStudio (2020; 2021). Sigmoidal models were fit using  (Ritz et al. , 2015). CVs were compared using  (Ben Marwick and Kalimuthu Krishnamoorthy, 2019). Scripts used to process the data and create each figure can be found at https://github. com/kateharline/jawd-paper. We have provided the segmented meshes, measures of cellular growth, divisions and characteristics to enable further exploration into the variety of cell lineages we captured in this data set in Supplemental Data.
Funding This work was funded by an  NSF   Graduate Research Fellowship (NSF Graduate Research Fellowship  ( DGE-1650441 ) to K. H. ,  NSF   MCB-2203275 ; The  Schwartz Research Fund Award and National Institute Of General Medical Sciences  of the  National Institutes of Health  under Award Number  R01GM134037  to A. H. K. R. ;  Agence Nationale de la Recherche (FR)  Grant#  ANR-21-CE30-0039-01  to A. B. ;  Institute Strategic Program Grant  from the  BBSRC  to the  John Innes Centre  ( BB/P013511/1 ) to R. S. S. and B. L. ;  Deutsche Forschungsgemeinschaft  ( FOR2581 ) to S. S. and G. M. ;  NSF  Grant  ECCS-1711548  to A. S. ;  Investissements d'Avenir du LabEx PALM  ( ANR-10-LABX-0039-PALM ) to A. F. and paired PhD programme of the  Faculty of Science, Stockholm University  [ SU FV-1. 2. 1-0124-17 ) to C-B.
The unprecedented growth of publicly available SARS-CoV-2 genome sequence data has increased the demand for effective and accessible SARS-CoV-2 data analysis and visualization tools. The majority of the currently available tools either require computational expertise to deploy them or limit user input to preselected subsets of SARS-CoV-2 genomes. To address these limitations, we developed ViralVar, a publicly available, point-and-click webtool that gives users the freedom to investigate and visualize user-selected subsets of SARS-CoV-2 genomes obtained from the GISAID public database. ViralVar has two primary features that enable: (1) the visualization of the spatiotemporal dynamics of SARS-CoV-2 lineages and (2) a structural/functional analysis of genomic mutations. As proof-of-principle, ViralVar was used to explore the evolution of the SARS-CoV-2 pandemic in the USA in pediatric, adult, and elderly populations (n > 1.
Especially within the Spike open reading frame, a greater proportion of missense compared to synonymous mutations is indicative of strong positive selection for Spike proteins with altered structure and function  [16, 17]. Continued SARS-CoV-2 genomic surveillance is essential to identify new emergent variants with novel phenotypic properties that may alter best practices in public health and clinical care. Viruses 2022, 14, 2714 2 of  18  The remarkable global scientific response to the COVID-19 pandemic has led to the generation of vast amounts of publicly available SARS-CoV-2 whole-genome sequence data. Worldwide, most genome sequences are deposited in the GISAID public database (gisaid. org) [18] , and more than 13 million viral sequences from around the world have been deposited as of 12 September 2022.
This massive and ongoing SARS-CoV-2 sequencing effort has provided a unique opportunity to study the virus's evolution in exquisite detail. However, at the same time, the volume and diversity of available sequences exacerbates the complexity of the data analysis and calls for effective tools to allow researchers with little or no computational expertise to perform detailed analyses of relevant genomic data. In part to address this problem, several web-based tools have been developed to facilitate the study of SARS-CoV-2 spatiotemporal dynamics, mutational frequency, and/or three-dimensional (3D) protein structures  [19] [20] [21] [22]. Though useful for gaining broad insights, these applications are often limited to the analysis of predetermined datasets with minimal user control, such as COVIDCG  [23] , outbreak info  [24] , covariants  [25] , 2019nCoVR  [26] , CoV-GLUE  [27] , and COG-UK  [28].
However, even tools that allow the processing of user-defined data often accept a limited number of sequencing data such as covdb (limit = 100)  [29] , coronApp  [30]  (limit ~100 MB or ~3500), and VirusViz (limit = 50)  [31]. In addition to the lack of options for large-scale data analysis, these tools have limited analytical features for the multilevel analysis and visualization of SARS-CoV-2 lineages and their mutations (e. g. , spatiotemporal visualization of lineages, linear or 3D visualizations of mutations in the context of proteins and genomes). Other tools and databases have been developed to study SARS-CoV-2 protein structures. One of these applications is SARS-CoV-2 3D  [32]  which provides tools for 3D structure predictions and energy calculations to evaluate targets and design new potential therapeutics.
CoV3D  [33]  is a repository for 3D protein structures of SARS-CoV-2 and host antibodies. Neither tool provides information on mutational changes in the context of the 3D structures. Other webservers such as the GISAID  [18] , covariants  [25] , and COG-UK  [28]  provide limited 3D structural visualizations for only fixed sets of mutations (mostly cladedefining) and only for the Spike protein. To the best of our knowledge, there are currently two webservers that enable the visualization of mutations in the context of 3D protein structures for all SARS-CoV-2 proteins: Coronavirus3D  [19]  and COVID-3D  [21]. However, both servers have a fixed list of SARS-CoV-2 genomes/mutations in their databases and lack the option to visualize mutations based on user-provided genomes. Although COVID-3D  [21]  provides an option for the user to input variants, this is limited to only one protein at a time and thus requires multiple file uploads to visualize genomic mutations in the context of more than one protein.
General Software Workflow The ViralVar webtool is implemented in the R programming language using Shiny, an open-source R package for developing interactive web applications. Shiny implements layout features available in Bootstrap, an HTML 4. 01/shiny-css 1. 7. 1/shiny-javascript 1. 7. 1 framework. To add more advanced content to ViralVar, the user interface was customized with HTML and Shiny's HTML tag attributes, as well as custom cascading style sheets (CSS) and other R packages listed in the context of the relevant sections below. Briefly, SARS-CoV-2 genomic data retrieved from the GISAID  [18]  were used as input for Viral-Var. The webapp is divided into two modules (Figure  1 ). In the first module, "Lineage Dynamics", data are processed to depict the spatiotemporal dynamics of SARS-CoV-2 lineages and clades in the form of stacked bars, area plots, and pie charts.
General Software Workflow The ViralVar webtool is implemented in the R programming language using Shiny, an open-source R package for developing interactive web applications. Shiny implements layout features available in Bootstrap, an HTML 4. 01/shiny-css 1. 7. 1/shiny-javascript 1. 7. 1 framework. To add more advanced content to ViralVar, the user interface was customized with HTML and Shiny's HTML tag attributes, as well as custom cascading style sheets (CSS) and other R packages listed in the context of the relevant sections below. Briefly, SARS-CoV-2 genomic data retrieved from the GISAID  [18]  were used as input for Vi-ralVar. The webapp is divided into two modules (Figure  1 ). In the first module, "Lineage Dynamics", data are processed to depict the spatiotemporal dynamics of SARS-CoV-2 lineages and clades in the form of stacked bars, area plots, and pie charts.
Data Input The GISAID is one of the largest global resources for sharing SARS-CoV-2 genome sequences and associated clinical and demographic information  [18]. GISAID data are accessible to users through free registration via the GISAID website (https://gisaid. org/) accessed on 31 May 2022. The database provides genome consensus sequences, reference-based multiple sequence genome alignments, and lists of mutations for each genome with the associated lineage or clade designations in tabular format. Data to be downloaded from the database can be readily filtered to focus on dates of collection, specific geographical regions, or selected lineages or clades. ViralVar accepts input data from the GISAID in a tabular format that includes three sets of information for each genome: (1) PANGO lineage (users can opt to manually add Nextclade designations), (2) sample collection date, and (3) a list of protein mutations (denoted as "AA Substitutions" in the GISAID data; required for "Mutational Analysis").
Detailed guidance on retrieving GISAID data in the proper format for input into ViralVar is provided on the "Home" tab of ViralVar. A limited set of 3892 SARS-CoV-2 sequences collected through the Northwestern Medicine Healthcare (NMH) system in Chicago, IL, between February 2020 and May 2022, are included in the ViralVar webtool for example purposes and can be viewed by checking the "Visualize Example Data" checkbox in each module. The GISAID IDs are provided in Table  S1.
This method does not consider virus counts in its calculation (i. e. , the number of uploaded genomes with a particular mutation) such that each mutation event is counted only once. This avoids potential biases in reporting mutational frequency due to unequal amplification or sequencing across the genome as well as bias sampling  [16]. In the "Protein Distribution" tab, the frequencies of genomes (virus counts) with mutations at specific protein residues are visualized using the R package ggplot2  [34]  and plotly R package (interactive visualization). Separate plots can be generated for all SARS-CoV-2 proteins, both structural and nonstructural. Protein domain boundaries are indicated as described in the literature  [16, 17]. The IEDB server (Bepipred Linear Epitope Prediction 2. 0 at http://www.
iedb. org/) [36]  (accessed on 31 October 2021) was used to predict B-cell epitopes, which are indicated above the protein schematic. In the "3D Protein Structure" tab, the R library package r3dmol is used to visualize mutations in the context of 3D protein structures. The 3D coordinates were obtained from the Protein Data Bank (PDB) with PDB accession numbers provided for each structure  [37]. For proteins with no available 3D structure, models, as predicted by AlphaFold, were used when available  [38]. Alternatively, the positions of transmembrane helices for proteins with no available 3D structures were identified using the TMHMM 2. 0 algorithm  [39]. Lists of the top mutations along with their frequencies for each protein can be downloaded in the form of tab delimited tables. The 3D protein illustrations can be downloaded as portable network graphics (PNG) files.
Each of the above tabs includes a date slider to allow users to restrict data to a specific date range and a "Select VOC/VOI" option to limit output to a specified VOC or VOI. The above mutational analysis tabs are further complemented by two tabs for statistical analysis and k-means clustering. In the "Statistical Analysis" tab, ViralVar utilizes the binomial test to identify individual proteins within the uploaded dataset that have significantly different mutation frequencies. The method has been previously applied to identify significantly under-and over-mutated SARS-CoV-2 proteins  [16, 17]. Briefly, the arguments for the binomial test are the observed number of distinct protein mutations in a certain protein (the "number of successes"), the total number of distinct protein mutations in all SARS-CoV-2 proteins (the "number of trials"), and the length of a given protein divided by the length of all SARS-CoV-2 proteins (the "expected probability of success").
13) P(MP, MT) = 325 66 0. 13 66 (1 -(0. 13) 325-66 = 0. 00046 Based on the null hypothesis, we expect only 42 mutations in Spike, given that 66 out of the total 325 mutations identified in SARS-CoV-2 proteins are located in Spike, the length of the Spike protein is of 1273 amino acids, and the entire SARS-CoV-2 proteome is 9930 long. However, the binomial test p-value (0. 00046) suggests rejection of the null hypothesis and indicates a significantly higher number of mutations in the Spike proteins compared to the background (entire proteome). ViralVar conducts the above calculation for user input data; therefore, MP, MT, and P(p) will be different for each input dataset. An option to exclude clade signature mutations is provided to avoid bias in the binomial test across highly divergent clades.
For instance, a common feature of SARS-CoV-2 genomes with the N501Y spike mutation (e. g. , Alpha, Beta and Gamma strains) was enhanced infectivity and transmissibility over the previous variants  [14]. The clustering of genomes based on pairwise distance-based methods is computationally intensive and might take days to run depending on the computational resources. The runtime for the first step of these approaches (the calculation of distance matrices for all pairs of genomes) increases exponentially with the increase in the number of genomes (Figure  S3 ). In contrast, k-means clustering of SARS-CoV-2 genomes has been proposed in the recent literature as a rapid method to investigate emerging variants and tackle the computational challenges in large-data analysis  [40, 41]. Due to its simplicity and being computationally inexpensive, the k-means clustering of genomes, based on mutations in specific proteins, can be quickly and repeatedly run on large-scale genomic datasets (such as ~11.
1 M SARS-CoV-2 genomes). ViralVar uses k-means to group genomes-based on protein mutations. To avoid the effects of spurious mutations (e. g. , due to sequencing or assembly errors), the clustering of the genomes is calculated only from protein mutations with a default minimum mutation frequency (MMF) of >0. 005, although this cutoff is user-adjustable. To determine the optimal number of clusters, ViralVar repeats k-means clustering for numbers of clusters (determined based on the number of variables in the input file) and calculates the average silhouette width (ASW) index using the R package NbClust  [42]. In the calculation of the ASW, ViralVar uses unique genomes (duplicated genomes with identical mutational patterns are removed) to make calculations less computationally expensive. However, the final clustering is applied to all of the genomes in the input data to produce counts of the genomes in each cluster.
Applying ViralVar to Assess Dynamics of SARS-CoV-2 Evolution The following was retrieved from the GISAID for the collection dates between 1 January 2020 and 15 May 2022 (downloaded 31 May 2022): a total of 1,739,797 SARS-CoV-2 high-quality complete genome assemblies (the GISAID criteria, including N content <5%) from the USA for which patient age, collection date (day/month/year), and geographic location available. To study the dynamics of the SARS-CoV-2 evolution using ViralVar, sequenced samples were classified into three populations by age: children (0-18 years), adults (18-64 years), and elderly (65+ years) (Table  1 ). The list of the GISAID identifiers that compose each group is provided in Table  S1. Sequence data for each age group were uploaded separately and analyzed using ViralVar.
Spatiotemporal Dynamics of SARS-CoV-2 VOCs in the USA The United States has experienced one of the world's highest COVID-19 burdens during the pandemic, with a total of 86. 4 M confirmed cases and 1. 01 M deaths as of 31 May 2022. Whereas some reports detailing the evolution of the COVID-19 pandemic in select cities and states are available  [43, 44] , there are few comprehensive reports at a national level. To demonstrate the capabilities of ViralVar, we downloaded all high-quality whole genome sequence data available in the GISAID on specimens collected in the USA between 1 January 2020 and 15 May 2022 (n = 1,739,797 SARS-CoV-2 sequences total). These data were sorted by age (children, adults, elderly) and uploaded into the ViralVar webtool for analysis. The temporal dynamics of the VOCs across age groups in the USA were visualized using the "Area Chart" tab in the "Lineage Dynamics" module of ViralVar (Figure  2A ).
Mutational Analysis of Alpha Variant Sublineages in the USA We subset the USA data explained earlier (n = 1,739,797 SARS-CoV-2 sequences) to only include genomes assigned to the Alpha lineage (n = 140,100). Additionally, genomes assigned to the Alpha lineage collected from the entire world (n = 906,114 excluding the USA) were retrieved from the GISAID as of 31 May 2022. Using the ViralVar "Mutational Analysis" module, the mutation profile for the Alpha VOC in the USA was compared to specimens from other countries. All ages were grouped together for this analysis due to the relatively small sample size of the under 18 and over 65 populations compared to adults in this dataset. Using the "Protein Distribution" tab, we visualized the mutational frequency in the Alpha VOC sequences at sites across Spike and NSP12 in both the USA and in the rest of the world (Figure  4 ).