Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues depending on the developmental stage and affected organs. However, the effect of human developmental stages or mutations of different organs on the features of somatic mutations is still unclear. Here, we performed a systemic and comprehensive analysis of low-level somatic mutations using deep whole-exome sequencing (average read depth ~500×) of 498 multiple organ tissues with matched controls from 190 individuals. Our results showed that early clone-forming mutations shared between multiple organs were lower in number but showed higher allele frequencies than late clone-forming mutations  along with less nonsynonymous mutations and lower functional impacts. Additionally, early and late clone-forming mutations had unique mutational signatures that were distinct from mutations that originated from tumors. Compared with early clone-forming mutations that showed a clock-like signature across all organs or tissues studied, late clone-forming mutations showed organ, tissue, and cell-type specificity in the mutation counts, VAFs, and mutational signatures. In particular, analysis of brain somatic mutations showed a bimodal occurrence and temporal-lobe-specific signature. These findings provide new insights into the features of somatic mosaicism that are dependent on developmental stage and brain regions. Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues, and often show a degree of clonality depending on the time and origin of the mutation. Recent studies have identified the characteristics of postzygotic variants of somatic mutations at the single-cell or mono-clonal levels. However, the results may not be fully representative of the mutational processes involved. Here, we describe a comprehensive analysis of low-level somatic mutations identified after deep whole-exome sequencing in peripheral and brain tissues. We found that clone-forming mutations are uniquely defined by early and late-stage aspects in the mutational profiles. Thus, we identified reliable spatiotemporal characteristics of mosaic variants. Additionally, we found low-level clone-forming mosaic variants across multiple stages and tissues, and identified their intrinsic features. Somatic mutations persistently occur in normal cells during the entire human lifetime . AlthougTo address these issues, we performed a comprehensive analysis of low-level somatic mutations identified using deep whole-exome sequencing (WES) data (average read depth: ~500×) from 498 tissues of 190 individuals. The cohorts analyzed consisted of two or more organs, including the brain and matched-peripheral tissues, and covered a variety of neurological conditions. The pairwise analysis of age, brain locations, and diseases enabled multi-dimensional analysis and direct comparison with cancer mutations identified using the same analysis procedure.n = 301), blood (n = 100), liver (n = 60), heart (n = 13), and other peripheral tissues (n = 24). The 190 individuals from whom tissues were obtained included patients with ‘non-tumor’ neurological disorders (n = 133), brain tumors , and non-diseased controls (n = 38)  .We defined and used three different categories in the analysis: early-stage, late-stage, and tumor mutations . Early-sr = 0.84; P = 1.00×10−42) and between the WES and TASeq data  indicated the accuracy of the calls   . Howeverr, 0.36) .De novo signature extraction of the 1494 somatic SNVs  helped identify three novel signatures   identifiWe identified functional differences between the early and late-stage mutations. In particular, early-stage mutations showed a lower ratio of non-synonymous to synonymous substitutions (quantified as the dN/dS ratio by maximum-likelihood methods) (0.79) than did late-stage mutations (0.94), tumor mutations (0.94), and common germ-line coding variants  , indicatr = 0.45; p = 2.99×10−10; Next, we investigated the characteristics of late-stage mutations, with a particular focus on diversity among different organs and cell types. The numbers of mutations varied substantially by organ, with a smaller number in the brain  and a higher number in the blood  relative to other peripheral organs  . HoweverUnsupervised hierarchical clustering of the three signatures  in late-stage mutations showed that mutations in the brain primarily comprised signatures A (early-stage) and B2 (tumor), whereas blood mutations harbored signatures B1 (late-stage) and B2 (tumor) .+), oligogenic (Olig2+), and others (negative). TASeq of the separated cell populations showed that both early and late-stage mutations were present in multiple cell lineages, but a large asymmetry in mutation frequencies was present among cell types with late-stage mutations  contained early-stage mutations (5.47% VAFs) whereas the other (NLE-P-0225) contained five late-stage mutations (average: 8.00% VAF) , each ofutations .We further classified the late-stage brain mutations into temporal and non-temporal areas, and analyzed area-specific mutational signatures . As repoFurthermore, late-stage mutations in the blood and tumor mutations showed enrichment for T>C mutations on transcribed strands . TranscrBased on a large-scale deep whole exome sequencing data analysis using a total of 498 matched sample pairs from 190 individuals, we provided a detailed picture of low-level but clone-forming somatic mutations, the associated mutation counts, and characteristics that are distinguished in time and space. We found that early-stage mutations, which arise before gastrulation and are shared by multiple organs, are lower in number and have a reduced functional impact than late-stage mutations that are restricted within a single organ. Moreover, we showed that late-stage mutations are associated with human mutational processes in the late-embryonic and post-natal developmental stages, but vary by organ, tissue, and cell lineages. In particular, late-stage mutations in the brain showed a bimodal distribution for developmental stages and the asymmetry of mutational features across brain-cell types and regions.Using pairwise analysis of the brain and other matched organs, we identified mutational counts, variant fractions, and mutational processes with high confidence levels at each developmental or time stage. Our mutational numbers were roughly comparable to those from previous studies, which reported 0.53 and 3.15 shared and non-shared somatic mutations in the brain , respectively , 32. We In terms of mutagenic processes, we extracted three unique signatures that showed overall similarity but contained early stage, late stage, and tumor SNVs. This phenotype indicates that somatic mutations from different stages have different contexts. For mapping to aging-related COSMIC Mutational Signatures, the increased relative proportion of late-stage somatic mutations in SBS1 indicates the presence of active proliferation and clonal expansion during the late-embryonic and post-natal or aging periods , 34. Thede novo signatures of late-stage mutations showed that mutations in the brain had early-stage and tumor mutation characteristics. The former result reflects the characteristic of low stemness and little or slow progression of division/differentiation in brain cells. Further analysis of late-stage brain mutations in the temporal lobe regions revealed that these were more similar to the signature B2 (tumor signature). These results suggest that late-stage somatic mutations in the brain show a bimodal occurrence during the early embryonic and post-natal periods, with a pattern similar to that in tumor mutations. We hypothesized that tumor-like mutational signatures in the temporal lobe may originate via neurogenesis  that confers clonal proliferation properties, as reported previously . Lysis buffer (1 mL) was added to the homogenate and mixed by pipetting, after which the lysate was fixed in 1% paraformaldehyde at room temperature for 10 min. The fixed lysate was quenched with 0.125 M glycine at room temperature for 5 min. The homogenate was washed with suspension buffer (1 mM EDTA and 2% BSA), and filtered with a 40 μM cell strainer. The sample was incubated with anti-NeuN  and anti-Olig2  antibodies overnight at 4°C, followed by washing with suspension buffer and staining with the secondary antibody for 1 h at 4°C. After washing with suspension buffer, nuclei were passed through a 40 μM cell strainer and stained with 1 μg of 4′,6-diamidino-2-phenylindole. Nuclei used to isolate each cell type were analyzed and sorted using a MoFlo Astrios EQ cell sorter . Nuclei pellets were centrifuged for 10 min at 1500 ×S1 FigLate mutational analysis by mutagen in different organs  (A), (B) Age correlation with somatic mutation VAFs in late-stage variants and blood late-stage variants. (C) VAFs of late-stage somatic mutations in brains are classified by the diseases in individuals. (D) VAFs of late-stage somatic mutations in the heart, liver, and lipoid tissues. (E), (F) Number of mutations per individual and VAF distribution in individuals aged 50–75 years.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file. 1 Feb 2022Dear Dr Kim,Thank you very much for submitting your Research Article entitled 'Multi-organ analysis of low-level somatic mosaicism reveals stage- and tissue-specific mutational features in human development' to PLOS Genetics.The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review a much-revised version. We cannot, of course, promise publication at that time.In addition to the reviewers' concerns, I would request that in a revision you also carefully evaluate terminology that refers to mutations as pre- or post-gastrulation, since on my read it was not always clear that certain mutations were present in all three germ layers  such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at data availability policy requires that all numerical data underlying graphs or summary statistics are included with the submission, and you will need to provide this upon resubmission if not already present. In addition, we do not permit the inclusion of phrases such as "data not shown" or "unpublished results" in manuscripts. All points should be backed up by data provided with the submission.Please be aware that our Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool.  PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.While revising your submission, please upload your figure files to the Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, use the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.[LINK]We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Marshall S. Horwitz, M.D., Ph.D.Consulting Editor - PLoS GeneticsPLOS GeneticsGregory BarshEditor-in-ChiefPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors present an interesting study that leverages whole-exome sequencing (WES) data from multiple tissues from a large number of individuals (190) to gain insight into the nature of somatic mutations that occur in early and late embryonic development. The authors classify somatic mutations as early if they are shared by two different tissues and late if they are not. In addition, the authors also sequenced a subset of brain tumors for comparison. The main finding is that early mutations have higher allele frequency and lower functional impact than late mutations as revealed by multiple genetic metrics . In addition, novel mutational signatures and their contributions to different brain regions are described.While the approach is interesting and there is a high need in the field for this sort of comparative analysis, the article has some important caveats, described below.1. The study is far from being a systemic and comprehensive analysis of low-level somatic mutations, as claimed in the abstract. Only 3 tissue types  are analyzed in a large number (60+) of individuals. Authors claim multi-organ sequencing but for most individuals only two tissue types are analyzed: either brain and blood, or brain and liver. The authors should tone down the claim of multi-organ analysis since the study is essentially on brain tissue, just using different control tissues.2. The article is difficult to read because some key points are not clearly explained. For example, in the 19 individuals with brain tumors, the brain tissue analyzed was from tumor or from normal? That is unclear in figure 1 and it is not clearly explained in Material and Methods. The text only mentions: “If the source of the sample was related to a brain tumor, it was separately regarded as tumor mutations.” Please clarify the origin of samples in individuals with cancer and whether brain tumor tissue was used for the case-control comparisons explained in Fig. 1A. If it was used, this could be a source of confounding because in individuals with cancer, shared mutations tumor-blood could be due to ctDNA or cancer cells disseminated in the blood stream.3. The legend for Fig. 1A needs more explanation, especially regarding what samples are considered cases vs controls and the one-sample proportion test. What are the tests for organ specificity?4. When quantifying the number of early and late-stage somatic mutations per individual, how do the authors correct for the fact that different number of samples and different tissues were analyzed in different individuals? The article might benefit from removing the ‘other tissues’ and ‘heart’ subgroups since there are not many samples in those subgroups and will make the comparisons more similar across individuals. The different sample types used for different individuals is a concern.5. It is unclear how the mutational signature analysis might be relevant when the late-stage mutations come from a mixture of different tissues including brain, heart, liver, blood and others. Different tissues harbor different mutational tissues, please see PMID: 34433965 and 34433962. In addition, the number of early-stage mutations is small (94) for robust mutational signature analysis.6. The comparison of number and VAF of late-stage somatic mutations by tissue type should take into consideration the fact that the 3 groups of tissues are not age-matched. The is no data for older individuals for ‘other organs’.7. The brain tumors included in the study are glioblastoma and ganglioglioma. Glioblastoma is an adult tumor and ganglioglioma is a child tumor. Is it possible that the unique tumor mutational signature identified is the result of the mixture of mutations derived from these two very different brain tumors? If the ganglioglioma tumors were removed, would the mutations observed resemble to the landscape of mutations (driver genes) and mutational signatures reported for glioblastoma?8. Very low resolution of figures makes it impossible to read some of the content.Reviewer #2: Hyeonju Son and associates analyzed mosacism for SNVs and indels by means of deep whole-exome sequencing in 498 tissues from 190 individuals. Their findings were consistent with those of previous studies: somatic mutations with apparent early embryonic origin were fewer in number but had higher variant allele frequencies than variants that appear to have arisen later in life. Mutational signatures among early embryonic and later-arising variants were similar but differed somewhat.This research is well described, and the paper is generally well written, although there are occassional errors of English usage. There are a few issues that require attention:Line 233: "Early-stage mutations were not functionally tolerated than those of late-stage mutations." What does this mean? Are some words left out?Discussion: The authors should discuss the limitations of this study .Line 292: "Subjects with refractory epilepsy, including focal cortical dysplasia and non-lesional epilepsy, and who had undergone epilepsy surgery were enrolled through the Severance Children’s Hospital of Yonsei University College of Medicine." If these samples were obtained because of regional brain tissue excision for intractable epilepsy, could the mutations found have been responsible for the seizures? If so, might the frequency of variants and/or the VAF be biased to higher values in these samples? The same question could be raised in any other group of samples obtained by surgery to remove pathological tissue. This is an issue that the authors should discuss.Reviewer #3: Throughout its lifetime, human body accumulates mutations, which despite affecting multiple body parts rarely cause a pathology. To provide a better grasp of spatiotemporal characteristics of this genetic mosaicism, the Authors of the manuscript performed deep whole exome sequencing of human tissues comparing the pattern of clone-forming mutations in various organs. In its focus on differences between early (pre-gastrulation) and late (post-gastrulation) mutations, this well-designed study addresses an interesting, and largely unexplored problem of developmental shifts in somatic mutagenesis and makes up an important contribution to current studies about somatic mosaicism.The results include the finding that most somatic mutations are produced after gastrulation and that diversity of late-appearing mutations is higher than of the early ones. However, it is not clear if the observed numerical differences between early and late clone forming mutations reflect distinct mutation rates during these developmental stages. More details should be also provided to elucidate whether differences in mutational signatures across tissues stem from the action of distinct  mutagens in these sites. They Authors should refer to recent studies by Li et al., Nature 2021, 597 (7876): 398-403, who also considered the problem of genetic mosaicism and compared the mutational patter across tissues.I see the value of the present manuscript in its potential to contribute to ongoing studies of clonal evolution and suggest that the Authors elaborate on how their work contributes to recent studies on this subject :239-256; Swiatczak, Cell Mol Life Sci, 2021 78(21-22):6797-6806; Laconi et al. 2020, Br J Cancer 2020, 122(7):943-952). For example, the Authors hint that variant allele frequency (VAF) can be used as a measure of a degree of clonal expansion of mutated cells in various tissues but draw no conclusions pertaining to the question of somatic evolution based on the observed differences between VAF in the brain, blood and other tissues.All in all, the present manuscript provides an important contribution to cutting edge research on genetic mosaicism, which is getting a lot of attention now, but extending the present research to incorporate the above questions could help to shed light on the mechanisms underlying the observed mutational profiles.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.[LINK]Please let us know if you have any questions while making these revisions.Yours sincerely,Marshall S. Horwitz, M.D., Ph.D.Consulting Editor - PLoS GeneticsPLOS GeneticsGregory BarshEditor-in-ChiefPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have made a substantial effort to address my questions and concerns in the responses to reviewers and have added a very extensive paragraph about the limitations of the study in the Discussion section, which I appreciate. However, I believe the manuscript still lacks some of the necessary amendments.The first one relates to the problem of different ages for the different sample types, which is very obvious from Fig. 4C. The authors provided an analysis restricted to patients aged 50 to 75 in the responses to reviewers. I believe this data should be discussed in the text and included as a supplementary figure.The second issue relates to the selection of tissues for the study, which was done based on convenience. This leads to the “other organs” being a random mixture of tissues, which is still not well explained in the article, and to the brain tissue including a large proportion of samples from diseased tissue. The article would greatly benefit from more clarity on the types of samples included and from additional plots (which could be included as supplementary data) in which the authors further analyze the data to demonstrate that the choice of samples is not biasing the results. For example, in response to Reviewer #2, the authors indicate that the VAF of disease tissues is not different from the normal. All those comparisons should be clearly presented with dot plots in the article. Also, the authors have included this sentence in results: “VAF of late-stage mutations was inversely correlated with age, probably due to the accumulation of late-somatic mutations in blood with a low allelic fraction with aging”. This plot should be shown for all late-stage mutations and also for blood mutations only.Finally, the article still requires attention to the explanation of the figures and methods. What are the numbers in the bars of Fig. 1A? what do red and blue colors indicate? In Fig. 3B, how are the relative contributions calculated? In Methods, there are some sentences that are hard to understand. For example: “In the filtering process of paired-mode somatic calling using Mutect2, “normal artifact” is corresponding to it; variants are non-germline mutation, differ from reference sequence, and contained in matched pair samples. “ Overall, the article would benefit from careful reading to improve some language issues and to make some of the explanations in methods and results clearer.Reviewer #2: Hyeonju Son and associates have revised and substantially improved their analysis of somatic mosaicism using deep exome sequencing. They have adequately addressed all of my previous major concerns.There are still numerous errors of English grammar and word usage; this paper would benefit greatly from copy editing for English usage.Reviewer #3: The manuscript has been much improved in terms of clarity as the Authors have identified the origin of tumor samples and provided essential details about the mutation calling methods. They also brought the importance of their findings into light by relating their research to other somatic mutagenesis and clonal evolution studies.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , and click on the Fetch/Validate link next to the ORCID field.  This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.In the meantime, please log into Editorial Manager at If you have a press-related query, or would like to know about making your underlying data available , please see the end of this email. If your institution or institutions have a press office, please notify them about your upcoming article at this point, to enable them to help maximise its impact. Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,Marshall S. Horwitz, M.D., Ph.D.Consulting Editor - PLoS GeneticsPLOS GeneticsGregory BarshEditor-in-ChiefPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have made a substantial effort to address my concerns, including the addition of a supplementary figure that increases confidence on the results. I find a bit unusual, however, that panels C to F of Supplementary Figure 1 are not presented in Results, but in Discussion. That said, I believe that the extra figures have improved the manuscript. Clarity is still an issue and the manuscript would greatly benefit from language editing.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , one way to make that data available is to deposit it in the The following link will take you to the Dryad record for your article, so you won't have to re‐enter its bibliographic information, and can upload your files directly: http://datadryad.org/submit?journalID=pgenetics&manu=PGENETICS-D-21-01628R2http://www.datadryad.org/depositing. 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  13 Sep 2022PGENETICS-D-21-01628R2 Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development Dear Dr Kim, We are pleased to inform you that your manuscript entitled "Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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Over the last years it has been observed that the progress in data collection in life science has created increasing demand and opportunities for advanced bioinformatics. This includes data management as well as the individual data analysis and often covers the entire data life cycle. A variety of tools have been developed to store, share, or reuse the data produced in the different domains such as genotyping. Especially imputation, as a subfield of genotyping, requires good Research Data Management (RDM) strategies to enable use and re-use of genotypic data. To aim for sustainable software, it is necessary to develop tools and surrounding ecosystems, which are reusable and maintainable. Reusability in the context of streamlined tools can e.g. be achieved by standardizing the input and output of the different tools and adapting to open and broadly used file formats. By using such established file formats, the tools can also be connected with others, improving the overall interoperability of the software. Finally, it is important to build strong communities that maintain the tools by developing and contributing new features and maintenance updates. In this article, concepts for this will be presented for an imputation service. Data isThis article highlights current challenges and possible solution strategies in plant genotyping using the example of the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben. The transformation of genebanks into bio-digital resource centers  is a proThe advantages and effectiveness of this modular approach have been demonstrated by already finished and still ongoing research projects in the field of plant genetic resources. These include the creation of a homogeneous data space of plant genetic resources stored in genebanks and their web-based interactive exploration , the digEspecially genotyping and imputation as a subfield in particular, highly depends on the quality of the underlying RDM. So, based on the experience from these projects, an eye is cast on a current workflow for imputation of genotypic data. In particular, data for imputation is currently stored locally and processed sequentially on virtual machines. With a transition to a cloud-based, distributed solution, the efficiency as well as sustainability is expected to be increased significantly. In the following, the individual services involved are described, before explaining the current workflow in detail. Afterwards, suggestions are made to modernize the current imputation services as well as discussing the benefits and newly arisen challenges. This is enabled and accompanied by the modular approach and the specification of well-defined interfaces as well as the usage of standardized formats. Finally, it is shown how further synergies can be generated through integration into international as well as national programs such as the National Research Data Infrastructure (NFDI). In particular, the IPK is involved in the consortium for biodiversity research NFDI4Biodiversity, where one goal is to establish a cloud computing infrastructure with the Research Data Commons (RDC) to provide analysis services.2IPK’s RDM strategy aims to support a federated scenario of research groups under the umbrella of a central service portfolio to support all steps of the RDM data life cycle , 20, 21.the Research Data Management Organiser (RDMO)  as a quethe LIMS to support data collection in the labs, greenhouses and in the field,the data pre-processing according to common guidelines andthe provisioning of staging areas, a Galaxy instance to stage commonly used data analysis pipelines and a Slurm cluster for individual software pipelines,sharing through an ORACLE Application Express  for decldata sharing through data steward-based data publishing pipelines in central repositories, such as EMBL-ENA, EMBL-EVA or e!DAL-PGP , and lasthe collaboration and dissemination of international standards for data and metadata, such as MIAPPE , BioScheThe seven steps of the research data lifecycle  are fulfIn this section, the focus will be on the three steps Store, Share and (Re)Use of the data lifecycle. The selection refers to outcomes from various projects with IPK participation that are connected to the genotyping domain and are related to the use case of imputation. However, the scope could be extended further, but will be limited for the purposes of this article to the aspect of released or ongoing developments from IPK or community developed outcomes with IPK contribution.2.1As a generic data management backend, a stack of a highly available commercial relational database management system (RDBMS) and a file storage appliance, a hierarchical storage management system (HSM), is operated by core funded IT to host all IPK research data.The actual implementation of project specific data structures is in majority brokered by an institute-wide available laboratory information management system (LIMS) for structured data and an electronic laboratory book (ELN) for the documentation of experimental work . The comThe European Variation Archive (EVA) , which iA submission to EBI-EVA requires a valid Variant Call Format (VCF)  file thaThe recommended workflow for a FAIR compliant genotyping dataset submission: [if not yet available: (0) Submit reference assembly to INSDC public repository], (1) Registering sample metadata at EBI-BioSamples, (2) Submission of raw read files at EMBL-ENA by using previously registered Biosamples records, (3) Prepare, validate and submit VCF file; previous ENA run IDs obtained from step 2 should be linked to the EVA study; BioSample IDs from step 1 are re-used as genotype names.2.2One option to share data is by using files in established file formats. For genotypic diversity data, the Variant Call Format (VCF)  allows tIn addition to the above-mentioned sharing of data by using files, the same can also be fulfilled via domain-specific APIs. The Breeding API (BrAPI)  is a com2.3To analyze the diversity of various genotypes the method imputation is the attempt of completing sparse SNP data. The concept is based on the idea that unrelated samples in small regions are identical by descent (IBD). The differences between samples can be attributed to recombination of the originally underlying chromosomes. The goal of an imputation is now to identify those original haplotypes of the target haplotypes from a reference panel and to fill the gaps of the samples with the markers of the reference. In the following, reference markers are understood to be those markers that occur in the reference panel. Similarly, target markers are those from the target set and imputed markers are those that are not set in the target set and are taken from the reference panel . Target Imputation enables and enhances several variant data analysis methods. For example, it enables the prediction of missing data, which has a positive impact on data analysis of only variant data. However, imputation also strengthens the expressiveness of inter-domain analysis methods, such as Genome Wide Association Studies (GWAS). It has been shown that the use of imputed data gives a 10% better result than an GWAS analysis on the raw data . ImputatVarious algorithms are available to perform imputation. Most of them are based on Hidden Markov Models (HMM), but also on positional Burrows-Wheeler transformation or SNP-tagging approaches. HMM approaches have better accuracy, since the whole chromosome, as well as all mosaic configurations of the haplotype are considered. Among the most used methods are IMPUTE , FastPHATo visualize and analyze the genotypic diversity data before and after the imputation, an inhouse developed web tool called DivBrowse  is used.33.1The workflow for the imputation service, as depicted in 3.2Two different options are offered for the imputation service. On the one hand, it is possible to impute only on the target set, on the other hand via a reference panel. The reference panel are dense variant calls that are used to fill the gaps in the target set. Here it is important that there is an intersection between the markers in the reference panel and the targets. For the reference panel, a sequence of steps is provided as a script for a minimal quality control, which is based on steps suggested in . This enThe preferred process is the imputation on a reference panel. To ensure that the chromosome, position and reference as well as allele nucleotides are identical, an additional program is provided. If there is no reference panel available the Beagle software can be run without one, imputing the missing data from the other samples of the target set.3.3The described workflow was tested on a dataset to obtain a runtime comparable to the proposed improvements. For this test, a dataset consisting of 8070 wheat samples with a total of over 1.6 M unphased variants was used. The data set is submitted to the EMBL-EVA repository with the project identifier PRJEB52759. The imputation was performed on this data on a virtual machine in the de.NBI Cloud  that has3.4Following imputation, the resulting VCF can be sent back to the local machine, so that it is available for further processing. Possible uses have already been mentioned in the previous section and, as far as they exceed the domain of genotypic data, will not be discussed further. A direct use case solely on the imputed data would be to load the resulting VCF file into a DivBrowse instance for visualization and analysis.4To enable the best user experience for a cloud based imputation service, we implemented a handy data import and optimised the performance using a distributed, cloud based execution pipeline. This improved on one hand, the user-friendliness, on the other hand, the runtimes of the individual imputations. The following sections outline additional possible future steps to be taken into those directions.4.1A potential hurdle in usability is that the imputation service runs on a virtual machine that is controlled via the command line. The user then executes a number of scripts in a predefined order with individualized parameters, thereby semi-automatically managing the jobs. This whole process can be simplified by providing a web interface. Such an interface should allow the user to (1) submit the target data, (2) change the reference, (3) start and manage jobs, (4) monitor currently running jobs, and (5) request the imputed data. This spares the user the need to use the command line and familiarize themselves with the parameters of the scripts. In addition, this automation of job submission and management would minimize the risk of user-caused errors in the workflow.Furthermore, the service can be connected to API-based data sources, which allow the data set to be pre-filtered by requesting only a small slice rather than the entire data set. By making several requests, either a merged VCF file can be generated, or the individual regions can be stored as independent VCF files. This would depend on the further processing. With DivBrowse, a BrAPI endpoint is offered for genotypic data, as described before. In order to be able to use this source, a consumer  is devel4.2Bottleneck for the imputation on a single node is the construction of the HMM for the regions on the dataset. By running the imputation as described in the previous section, those regions get imputed sequentially without any parallelization due to hardware limitations. However, by simply extending the environment to a multi-node setup and running independent imputation jobs for the different chromosomes in parallel, the run-time will decrease significantly. Furthermore, it is possible to run independent jobs in parallel for genomic windows on the chromosomes based on the identical by descent (IBD) segments that can be identified beforehand, decreasing the run-time even more. In DivImpute, the parallelization by chromosome-based job batches is already available, the imputation on genomic windows is yet to be implemented.To reduce the work the user has to put in to start the imputation, one can decide to use workflow languages like Common Workflow Language (CWL)  or nextfWith SparkBeagle  there ex4.3To test the procedure, the imputation of the test data set from the previous section was repeated. This time, however, the environment was extended by 2 additional virtual machines. Firstly, a head node was created, which is smaller in specification and is only used to access the computational nodes and manage the jobs. This node has 4 VCPUs, 4 GB RAM and 20 GB disk as well as 2 TB additional volume storage. This head node has access to 2 computational nodes. Both of these nodes are identical and have the same specifications as before in the single node test run.In order to start the imputation, the data set was first divided according to chromosomes in the head node and then distributed batch wise to the computational nodes. The batches are based on the three sub genomes of wheat, so that the A, B and D genomes were imputed separately. The A and B genomes were imputed in parallel, and the D genome as soon as one node finished imputing either the A or B genome. The A genome was imputed in almost 128 h and the B genome in almost 182 h. When the imputation of the A genome was finished, the D genome jobs were started, taking roughly 69 h. The significant differences in imputation time for the three sub genomes can easily be explained by taking a look at the number of markers for the different chromosomes. Comparing the number of markers for chromosome 7 of all sub genomes reveals that with 58k markers the D genome has almost half the number of markers of A (ca. 101k) and B (ca. 110k) genomes cf. . The tes4.4In summary, some advantages and disadvantages can be derived for the described concept. In addition to the improvements in usability, the acceleration or the reduction of the runtime of the imputation is a main advantage. Moreover, this is expected to happen without a loss of accuracy of the results. In addition, the introduction of modern infrastructure solutions such as Spark and Kubernetes allow jobs to be automated and the workload to be distributed among the computational nodes. This can reduce the runtime even further. By running ready-to-use solutions such as SparkBeagle, the management of merging the individual partial results, as well as cleaning up the executor is implemented in a fault tolerant way. In general fault tolerance is ensured by the usage of for example Kubernetes, as pods will be rescheduled, when a node is lost. In the current solution a node fault will result in the processes to be lost and need to be started again manually.However, by using ready-to-use solutions, one is less flexible and limited in the decisions and customization of the system. SparkBeagle requires Hadoop as well as Hadoop Distributed File System. Therefore, one would need to deploy as well as maintain those additional systems. Besides technical requirements, such solutions also limit the usage of the imputation software, as SparkBeagle seems, based on the documentation, to not support imputation solely based on the target set, but only based on a reference panel. Therefore, reducing the flexibility of the service. Furthermore, it seems the active development of SparkBeagle has been stopped, showing – with stopped maintenance – another disadvantage of ready-to-use solutions. Apart from using ready-to-use solutions, the introduction of Kubernetes and Spark require one to coordinate the complex interplay of the additional components, maintain their functionality as well as introduce security policies on those systems. One way of dealing with this, is to rely on specialized services providing such environments in a platform-as-a-service (PAAS) style, which allow for an easier and faster deployment of such an imputation service.5In this article, a use case for the analysis of genotyping data in the field of crop plant research has been described. Here, the transfer of analyses pipelines from local, institutional IT systems to external distributed infrastructures has been presented. As an exemplary application, the research field of genotyping with the subfield of imputation was considered. For an research institute like the IPK Gatersleben, which holds over 150,000 different samples of cultivated plants in its genebank, their development and activation is an important task and challenge in order to further develop the IPK Genebank in particular as well as the whole IPK in general into a bio-digital resource center. Currently, these genebank samples are systematically genotyped in many projects including following publication of the results in scientific articles and the provision of the data sets regarding the FAIR criteria in international repositories, in information systems hosted at IPK and accessible via the web as well as comprehensive data publications. Further analysis and integration of this published information with datasets from interesting users in the scientific community is currently one of the greatest challenges. Thus, the easy reuse of the research data is currently in the focus.Hence, the availability of research data that meets the FAIR criteria is thus increasingly fulfilled. This is achieved by consistently applying metadata standards for description, by offering the data in standard formats, and by providing access via standardized APIs. But how can reuse of the research data be further advanced? The current task is to build up infrastructures, offer them sustainably and operate them permanently. A single research institute cannot fulfill this task alone, because the necessary resources are not available locally or cannot be acquired.In the area of life sciences, a decentralized, federated infrastructure was established with the mentioned de.NBI network, which is currently being sustained. One component of de.NBI is the de.NBI cloud, which allows the scientific community to perform its analyses in data centers distributed all over Germany. Thereby, the offered compute resources can be used with publicly available as well as own data. The de.NBI cloud was used to compute the imputation described in the use case in this present article.It was recognized that the development to build sustainable infrastructures needs to go further. In Germany, for example, the National Research Data Infrastructure (NFDI) has been under establishment since 2020. Here, nearly all scientific domains far beyond the life sciences are working together to offer sustainable solutions so that research data can be published in accordance with the FAIR criteria, remain accessible and also be reused. Through this collaboration across all scientific disciplines, it is expected to exploit appropriate synergies in building and operating the infrastructures to achieve sustainability more effectively. This concept is called the Research Data Commons (RDC).The IPK Gatersleben is contributing actively to the NFDI in the consortia NFDI4Biodiversity and FAIRagro and is working together with NFDI collaborators to deploy and sustain the imputation use case described in this article into the NFDI RDC infrastructure as one part of Plant Data Commons.


Staphylococcus aureus (S. aureus) is a worldwide leading versatile pathogen that causes a wide range of serious infections. The emergence of antimicrobial resistance against S. aureus resulted in an urgent need to develop new antimicrobials in the new era. The methicillin-resistant S. aureus (MRSA) prevalence in hospital and community settings necessitates the discovery of novel anti-pathogenic agents. Staphyloxanthin (STX) is a key virulence factor for the survival of MRSA against host innate immunity. The current work aimed to demonstrate the anti-virulence properties of meloxicam (MXM) as compared to diclofenac (DC), which was previously reported to mitigate the virulence of multidrug-resistant Staphylococcus aureus and test their activities in STX production. A total of 80 S. aureus clinical isolates were included, wherein a qualitative and quantitative assessment of STX inhibition by diclofenac and meloxicam was performed. The quantitative gene expression of STX biosynthetic genes  and hla (coded for α-hemolysin) as a virulence gene with and without DC and MXM was conducted, followed by molecular docking analysis for further confirmation. DC and MXM potently inhibited the synthesis of STX at 47 and 59 µg/mL to reach 79.3–98% and 80.6–96.7% inhibition, respectively. Treated cells also revealed a significant downregulation of virulence genes responsible for STX synthesis, such as crtM, crtN and global transcriptional regulator sigB along with the hla gene. Furthermore, computational studies unveiled strong interactions between the CrtM binding site and DC/MXM. In conclusion, this study highlights the potential role and repurposing of DC and MXM as adjuvants to conventional antimicrobials and as an anti-virulent to combat MRSA infections. Staphylococcus aureus (S. aureus) is one of the most predominant Gram-positive bacteria responsible for a wide range of bacterial infections, including bacteremia, pneumonia, osteomyelitis, skin infections, and heart valve infections  × 100 [The STX extraction and quantification was performed according to Al-kazaz et al. . The mosm] × 100 .v/v [The change in proteolytic activity of MRSA isolates was evaluated using skimmed milk agar. The skimmed milk agar was prepared by adding fresh skimmed milk to sterile molten nutrient agar  to a final concentration of 5% v/v . Overnigv/v . This asThe nuclease activity alterations were detected using DNase agar  according to Lagacé-Wiens et al. with some modifications. Overnight cultures of treated (with sub-MICs of the tested drugs) and untreated MRSA isolates in TSB were centrifuged at 6000 rpm for 10 min, then the supernatants  were injected into cups made in DNase agar plates. The DNase agar plates were then incubated for 24 h at 37 °C . The clehttp://insilico.ehu.es/mini_tools/discriminatory_power/) (accessed on 10 December 2022). The similarity index (Jaccard/Tanimoto Coefficient and number of intersecting elements) between all samples was calculated using an online tool  (accessed on 10 December 2022) [Genotyping of the MRSA isolates was performed by Enterobacterial Repetitive Intergenic Consensus Polymerase Chain Reaction (ERIC-PCR) using a pair of published primers (F: 5′-ATG TAA GCT CCT GGG GAT TCA C-3′) (R: 5′-AAG TAA GTG ACT GGG GTG AGC-3′) . The PCRer 2022) .mecA gene using a pair of primers (F: 5′-GTA GAA ATG ACT GAA CGT CCG ATA-3′) (R: 5′-CCA ATT CCA CAT TGT TTC GGT CTA A-3′) [The genotypic identification of the MRSA isolates was conducted by conventional PCR for detection of TA A-3′) . The PCRTA A-3′) .crtM and crtN, and those involved in virulence, such as hla  and sigB . Total RNA was extracted from DC/MXM treated and untreated isolates after 24 h of incubation by the Trizol method. Conversion into cDNA and qPCR analysis was performed in a 1-step reaction using GoTaq® 1-Step RT-qPCR System A6020  as per the manufacturer’s protocol. The qPCR data were analyzed using QuantStudio 5 . Expression of the selected genes was quantified by 2(–ΔΔCt) method after the normalization of cycle threshold (Ct) values of all tested genes to that of the housekeeping gene 16srRNA [The qPCR analysis was performed to assess the effect of DC/MXM treatment on selected genes encoding for STX biosyntheis in MRSA, such as  16srRNA . The priMolecular docking was performed to assess the binding energy and interactions of DC and MXM with CrtM of MRSA. The 3D crystal structure of CrtM (ID: 2ZCQ) was downloaded from the Protein Data Bank. The 3D structures of the target compounds used in the study were sketched using the BIOVIA Discovery Studio v 4.5 program . The docking step was performed using the cDOCKER protocol, in which the scoring system was based on the cDOCKER energy. The 3D and 2D structures were visualized through BIOVIA Discovery studio visualizer 2016 v16.1.0.15350 [p ≤ 0.05. Statistical analysis was performed with IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY, USA: IBM Corp.Numerical data were explored for normality by checking the distribution of data and by using tests of normality (Kolmogorov–Smirnov and Shapiro–Wilk tests). All the experiments were carried out in triplicates. Data were presented as median, range, mean and standard deviation (SD) values. The repeated measures ANOVA test and Mann–Whitney U test were implemented. The significance level was set at sigB and other virulence genes such as the hla gene. Genotypic analysis was in accordance with phenotypic analysis, with no significant difference between the anti-virulence potential of both drugs. Thus, the present study proposes the repurposing of DC/MXM as adjuvants to conventional antimicrobial treatments against serious MRSA infections. The current study unveiled the anti-virulence potential of DC/MXM treatment against MRSA. Both drugs revealed significant STX inhibition that was further confirmed by molecular docking analysis, emphasizing the strong interaction between the CrtM binding site and DC/MXM. Additionally, real-time PCR analysis highlighted the efficiency of the proposed treatment in downregulating the expression of genes responsible for STX synthesis, along with the transcriptional regulator gene


The development of Fricke gel (FG) dosimeters based on poly (PVA) as the gelling agent and glutaraldehyde (GTA) as the cross-linker has enabled significant improvements in the dose response and the stability over time of spatial radiation dose distributions. However, a standard procedure for preparing FG in terms of reagent concentrations is still missing in the literature. This study aims to investigate, by means of spectrophotometric analyses, how the sensitivity to the radiation dose and the range of linearity of the dose–response curve of PVA-GTA-FG dosimeters loaded with xylenol orange sodium salt (XO) are influenced by ferrous ammonium sulphate (FAS) and XO concentrations. Moreover, the effect of different concentrations of such compounds on self-oxidation phenomena in the dosimeters was evaluated. PVA-GTA-FG dosimeters were prepared using XO concentrations in the range 0.04–0.80 mM and FAS in the range 0.05–5.00 mM. The optical absorbance properties and the dose response of FG were investigated in the interval 0.0–42.0 Gy. The results demonstrate that the amount of FAS and XO determines both the sensitivity to the absorbed dose and the interval of linearity of the dose–response curve. The study suggests that the best performances of FG dosimeters for spectrophotometric analyses can be obtained using 1.00–0.40 mM and 0.200–0.166 mM concentrations of FAS and XO, respectively. The final concentration of radiation-induced Fe3+ ions is proportional to the energy deposited by ionizing radiation in the dosimeter, i.e., the absorbed dose. Three-dimensional (3D) spatial information on the absorbed dose is obtainable within the gel volume, and it can be captured and retrieved by a suitable readout technique ..3+)-(XO)3+ and XO concentrations [3+ ions or XO favors the formation of the (Fe3+)2-(XO) complex or the (Fe3+)-(XO)2 complex, respectively. Upon Fe3+ binding, the yellow-orange color of FG dosimeters loaded with XO changes to violet, allowing us to point out the formation of the complex in the visible range. In fact, the (Fe3+)2-(XO) and (Fe3+)-(XO) complexes present an absorption band in the range of approximatively 500–620 nm, while the (Fe3+)-(XO)2 complex absorbs light at a shorter wavelength in the spectral region overlapping the tail of the main absorption peak of the free XO at 430 nm [The probability of each complex’s formation depends on the Fetrations . For exatrations ,62 that t 430 nm .2+ in the dosimeters.Examples of ΔOA spectra of XO-PVA-GTA-FG dosimeters prepared with an XO concentration of 0.200 mM and two different FAS concentrations , independently of the employed gelling matrix see .3+ ions produced by self-oxidation phenomena and the formation of (Fe3+)2-(XO) and (Fe3+)-(XO) complexes. Indeed, such complexes are characterized by a main OA peak at 585 nm [3+-(XO)2 complexes are expected to be the major species. Such complexes absorb light at a shorter wavelength [An absorption band centered at approximately 585 nm can be also observed in the OA spectra of the dosimeters prepared with very low XO concentrations . This peak can be explained by the presence of Fet 585 nm . For higvelength , i.e., iExamples of ΔOA spectra of XO-PVA-GTA-FG dosimeters prepared with an FAS concentration of 0.40 mM and different XO concentrations and irradiated at various doses are shown in 3+)-(XO)2 is predominant, presenting an absorption peak under 500 nm. When decreasing the concentration, the main complex becomes the 1:1 (Fe3+)-(XO) complex with a peak at about 585 nm in an acidic medium. However, the complex (Fe3+)2-(XO), prevailing when the iron concentration is higher than the XO concentration, also shows an absorption peak at the same wavelength [Only the spectral region of interest for dosimetric purposes  was considered. It is worth noting that the boundary of the absorption region strictly depends on the XO concentration. In fact, the shape of the ΔOA spectra of the dosimeters prepared with the highest XO concentration of 0.800 mM a was difvelength ,63. Thus3+)-(XO)2 complexes make a greater contribution than the other ones due to the effect of the availability of XO molecules that can be bounded with radiation-induced ferric ions. Consequently, in the samples with the highest XO concentration the absorption band related to (Fe3+)2-(XO) and (Fe3+)-(XO) that peaked at 585 nm appeared to only be a shoulder of the main absorption band that peaked at a lower wavelength and was related to the (Fe3+)-(XO)2 complexes [Actually, the shape of the ΔOA spectra of omplexes .A similar shape was observed for the ΔOA spectra of the samples prepared with a XO concentration of 0.400 mM, but only for doses ≤14 Gy b.Actually, the relative ratio between the concentration of xylenol orange and the concentration of ferric ions in complexes with different stoichiometric ratios (and consequently their absorption bands) depends on the dose, i.e., on the concentration of ferric ions produced in the dosimeters after exposure to ionizing radiation ,59.The samples with XO concentrations of 0.200 mM and 0.166 mM c,d were The observed saturation effects of the response of these dosimeters were attributable to the low concentration of XO molecules that can be bounded with the radiation-induced ferric ions.A thorough analysis of the dose–response curve of the XO-PVA-GTA-FG dosimeters prepared with an FAS concentration of 0.40 mM and different XO concentrations is shown in For the samples with an XO concentration ranging from 0.080 mM to 0.800 mM, straight lines were fitted to the experimental data. The results of the fit parameters, together with details of the dose interval considered for the fitting procedure, are given in The slope values of In addition to the use of the cumulative ΔOA, dose–response curves similar to those of A straight line was fitted to each dose–response curve and the sensitivity to the radiation dose  for each sample at each individual wavelength was accordingly obtained.The complete results of the wavelength-dependence of the sensitivity to the radiation dose for XO-PVA-GTA-FG dosimeters prepared with different XO concentrations are shown in The results of the analysis of the dose–response curves of XO-PVA-GTA-FG dosimeters prepared with different FAS and XO concentrations indicate that the use of an FAS concentration in the interval 0.40–0.60 mM, coupled with the use of an XO concentration in the interval 0.133–0.200 mM, guaranteed satisfactory dosimetric properties of the samples both in terms of sensitivity and linearity .The dose–response curves of these samples are plotted in i and that obtained at the time t0.Besides the optimization of FAS and XO concentrations to guarantee an adequate level of sensitivity and a wide range of linearity, the effects of such compounds on the self-oxidation features of XO-PVA-GTA-FG dosimeters were investigated. The results suggest that the self-oxidation rate did not significantly depend on the XO concentration when an FAS concentration of 0.40 mM was used b. SimilaA systematic study on the effects of variation in ferrous ammonium sulfate (FAS) and xylenol orange (XO) concentrations on the dosimetric properties of Fricke gel dosimeters prepared with poly (PVA) cross-linked by glutaraldehyde (GTA) was carried out. The investigated properties concerned the dose–response curves , the self-oxidation rate, and the level of self-oxidation.From the outcomes achieved in this study, some conclusions can be drawn about the behavior of the tested XO-PVA-GTA-FG dosimeters. Firstly, increasing the FAS concentration does not significantly increase the absorbed dose–optical response range, nor does it increase the optical sensitivity. However, a more pronounced level of self-oxidation was noticed; thus, an increase in the FAS concentration tends to decrease the temporal stability. On the other hand, lower FAS concentrations reduce the dosimeter’s response range. However, there was no evidence of variations for the optical sensitivity. Furthermore, it was found that the XO concentration is the main factor responsible for the limited absorbed dose response.Starting from these considerations, the experimental data were in line with the literature data on traditional and natural gel matrices. In particular, 1.00–0.40 mM and 0.200–0.166 mM are the optimal intervals of FAS and XO concentrations, respectively, to be used in the preparation of dosimeters in order to maximize their performances in the case of spectrophotometric analyses.The results obtained in this paper allow us to begin a new investigation on the possibility of improving the dosimetric stability of the FG by adding alternative chelating agents and/or antioxidants, such as sulfosalicylic acid (SSA), methylthymool blue sodium salt (MTB), ethylenediaminetetraacetic acid (EDTA), and dimethylsulfoxide (DMSO).


In December 2019, coronavirus disease 2019 spread worldwide, causing acute respiratory distress syndrome. Coronavirus disease 2019 presents from an asymptomatic infection to severe disease causing multiorgan failure. Neurological manifestations were observed in some patients, including intracerebral hemorrhage. Bilateral basal ganglia hemorrhage is rare due to trauma.Our patient was a 14-year-old Iranian boy with multiple trauma and loss of consciousness who tested positive for coronavirus disease 2019. The brain computed tomography scan reported bilateral basal ganglia hemorrhage. Bilateral ground glass opacity was reported through a chest computed tomography scan.In this study, we reported a 14-year-old boy referred to the emergency room due to multiple trauma. Through the medical interventions, bilateral basal ganglia hemorrhage was discovered incidentally. Coronavirus disease 2019 was detected in this patient on the basis of findings in chest computed tomography scan and positive real reverse transcription polymerase chain reaction test. Several clinical reports and series exploring the relationship between coronavirus disease 2019 and ischemic strokes have been published. Coronavirus disease 2019, like other acute respiratory syndromes, can invade the central nervous system through hematogenous and neuronal dissemination or it can be an immune response to the cytokine storm. In conclusion, it is vital to know the pathophysiology of the neurological manifestations of coronavirus disease 2019 and prevent the mild neurological manifestations leading to severe conditions. Severe acute respiratory syndrome–coronavirus-2 (SARS–CoV-2) caused coronavirus disease 2019 (COVID-19) in Wuhan, China in December 2019. COVID-19 is a contagious respiratory disease spreading worldwide . Intracr2 saturation was 93%, temperature was 38.5 °C, and the other vital signs were stable. In the ER, his Glasgow Coma Scale (GCS) score was 3/15 (E1V1M1). No free fluid in the abdomen and pelvic cavity in focused assessment with sonography for trauma was reported. A right bundle branch block was observed in his electrocardiogram (ECG). Echocardiography was performed and reported as normal. He was then referred to the neurosurgery ward and was hospitalized for almost 40 days.The case is a 14-year-old Iranian boy with no past medical history presenting with multiple trauma from a motorcycle accident who was transferred to the Poursina hospital emergency room (ER) in Rasht on 9 June 2020. In addition, he had an extensive laceration on his right knee and a star-shaped laceration on his parietal region. OHe had no signs and symptoms related to COVID-19. However, during hospitalization, spiral high-resolution computed tomography (HRCT) scan of his lung without contrast revealed patchy ground-glass opacity in both lungs (mostly in the right lung) .In this study, we reported a 14-year-old boy referred to the ER due to multiple trauma. He had no history of coagulopathy or anticoagulant consumption. In this case, trauma precipitated parietal contusion. Through medical interventions, bilateral basal ganglia hemorrhage was discovered incidentally.Several clinical reports and series exploring the relationship between COVID-19 and ischemic strokes have been published .Neurologic symptoms in COVID-19 can be divided into several categories, ranging from nonspecific symptoms such as a headache to severe forms such as cerebrovascular disease .Basal ganglia hemorrhage due to traumatic events is rare .Systemic and metabolic disease, neurodegenerative disease, and vascular disease can involve basal ganglia and thalamus , 9.COVID-19, like other acute respiratory syndromes, can invade the central nervous system (CNS) through hematogenous and neuronal dissemination, or it can be an immune response to the cytokine storm , 11.There is not enough data about the underlying mechanisms of neurological disorders caused by COVID-19. A total of 2% of patients have neurological symptoms. One of the ways in which COVID-19 can invade the neurological system is through the mucous layer of the nose that contains the olfactory receptor cells. Olfactory nerves might deliver viruses and infections to the brain. On the other hand, COVID-19 causes respiratory infection and the level of oxygen in blood may decrease. Brain function can be impaired through hypoxemia Fig.  12].Fig.Fig12].FSome other pathophysiology of ischemic stroke in COVID-19 are hypoperfusion of the brain, hypertension leading to posterior reversible encephalopathy syndrome, and septic embolization in patients with bacterial superinfection Fig.  13]..13].ACE2 serves important roles in various parts of the body, such as the lung, kidney, brain, liver, and endothelial cells. COVID-19 decreases the expression of ACE2. Due to reduced ACE2 levels, the renin–angiotensin–aldosterone system (RAAS) will deteriorate and cause multiorgan damage. Diminished ACE2 can cause intracerebral hemorrhage through several mechanisms. For instance, increasing the blood pressure, endothelial dysfunction, damage to the brain vessels, and finally depression of angiotensin-(1-7) and Mas receptor [Ang (1-7)/MasR] signaling, which has a neuroprotective, antifibrotic, and vasodilatory effect , 15.Immune response to the cytokine storm and chemokines can disrupt the blood–brain barrier and neuroinflammation. The breakdown of the blood–brain barrier and inflammation both have a main role in ischemic stroke , 17.Intracranial hemorrhage was documented in some cases as a complication of COVID-19. Some risk factors are arterial hypertension and anticoagulation therapy (pretreatment of unrelated disease to COVID-19 thromboprophylaxis) .Hyperinflammatory state may be the main cause of complication in patients with diabetes mellitus (DM) as well as COVID-19. A case series was reported in October 2020 that included three patients with COVID-19 and ICH. All three patients had risk factors for ICH such as hypertension and DM. DM and ICH are associated through some mechanisms. High blood glucose can cause endothelial dysfunction. Dysfunction of the brain’s small vessel endothelial can lead to ICH .There is a hypothesis that the state of hyperinflammation in diabetic patients is related to higher mortality in these patients . High C-We reported a 14-year-old boy with bilateral basal ganglia hemorrhage and documented COVID-19. Our patient had no special past medical history of hypertension, DM, and other diseases. There is no evidence that he consumed anticoagulant agents before. There is some research that ICH is propounded as a complication of COVID-19. Basal ganglia hemorrhage related to trauma is rare .The underlying pathophysiology for COVID-19 related ICH can involve hyperinflammatory state due to increased cytokine production. Although a few case series and case reports were published on some patients COVID-19 as well as ICH, more investigation and research regarding the association between COVID-19 and ICH are needed.It is essential to discover the pathophysiology of neurological manifestations of COVID-19. Neurological findings of COVID-19 range from mild conditions such as headache to severe conditions such as stroke. Therefore, acknowledgement as to the causes and prevention of the situations leading to critical events should be considered in treatments of COVID-19.


The idea of EAE  is put forward on the basis of the mature development of ecological aesthetics and AE theory. Starting with EAE, establishing people's aesthetic attitude and improving people's spiritual realm will help to reverse people's hostile attitude towards nature and rebuild the harmonious relationship between man and nature. This paper studies the application optimization of AE (aesthetic education) resources in universities based on ML (machine learning) from the perspective of resource development. The recommendation algorithm based on ML is the main idea of the classification of AE resources, and the classification model of AE resources is constructed. Through deep learning, we can learn the effective features of items from the content data in advance and then transform the learned features into CF (Collaborative Filtering) target learning task. Optimize the voting mechanism of the algorithm, and compare the RF_VM (random forest with optimized voting mechanism) algorithm with the traditional RF (random forest) algorithm. Experiments show that the algorithm proposed in this paper can effectively classify texts and has high feasibility. The core of basic education reform is curriculum reform, and AE is an aspect that cannot be ignored in curriculum reform. The development and utilization of AE curriculum resources is an important part of AE. The essence of EAE  is AE with vivid ecological beauty, which exerts a subtle influence on college students, which is unmatched by other ecological education and AE forms . It shouThe smooth implementation of education requires a well-designed school curriculum. Process is a means and tool for achieving educational and teaching objectives, as well as an important link in determining educational quality . AE  –17 is thStein et al. conducted a study to eliminate heterogeneous influence by removing sensitive attributes from the decision-making process and adding fairness constraints to avoid heterogeneous processing. Observe that the standard fairness constraint is nonconvex; then use covariance to turn the nonconvex problem into a convex problem. Finally, measure the output results and sensitive attributes of parameters to investigate the sensitive attributes of multiple classification and analysis. Some scholars looked at the fairness metrics of a variety of algorithms to see how they correlated with different definitions and discovered that they were very closely related. The transformed data retains most of the characteristic signals of insensitive attributes, intersection-sensitive attributes are proposed, and the effects of two sensitive attributes do not overlap . Nguyen Ecological problems are closely related to people, and ecological imbalance is the deviation of people's ideas and consciousness. To solve ecological problems, we must first solve the problem of people's ideology, which is actually a problem of people's education. AE aims to establish people's aesthetic attitude and create aesthetic living conditions, as an education to improve people's living standards. Starting with AE, it is of great significance to establish people's aesthetic attitude for solving ecological problems and improving people's living conditions, and EAE is still a new field of AE in China.EAE's mission is to educate and infect people through art and various forms of EAE so that they can achieve true enlightenment, good education, and emotional edification in a pleasant spirit and gradually develop people's ecological aesthetic sensibility and consciousness. Realize the survival of ecology and aesthetics and perform a one-of-a-kind and irreplaceable role. On the one hand, we should focus on infecting and educating people about the aesthetic method of perceptual freedom so that they can consciously educate beauty or “give freedom with freedom.” On the other hand, understanding the natural laws of the ecosystem should be a priority in the implementation of EAE so that people can correctly apply the natural laws and realize the harmonious unity between man and nature. EAE imbues educated people with a sense of beauty and encourages them to pursue and yearn for ecological beauty. Colorful ecological landscapes, endless back and forth, and endless phenomena make people feel a new rhythm of life, ideal living conditions, and a stirring sense of life, which makes them indulge and miss. Ecological beauty education will have a positive impact if we can make conscious use of these resources and educate people about ecological beauty.The difference of EAE resources in schools is caused by the imbalance of social economy, the difference of management system and supply mode, and the information asymmetry of social demand for talents. The difference of school EAE resources constitutes the difference of school EAE process and effect. The diversity and complexity of school EAE resources determine the instability of education resources, among them are human factors, material factors, policy orientation, development and changes of social and economic conditions, and other factors. Its mobility is mainly manifested in the flow of teachers' resources and students' resources and funds.The aesthetic course of university is mainly realized through aesthetic activities, so the appreciation and creation of various kinds of beauty is an important part of the course content. If you want to appreciate various forms of beauty, you must know and understand the basic knowledge of various forms of beauty. Through the study of various meanings, features, forms, elements, and other knowledge of beauty, we can accumulate certain theoretical knowledge and become the basis of aesthetic practice. The forms of beauty can be divided into natural beauty, social beauty, and artistic beauty, and the aesthetic practice part of the application of university AE resources is also selected from these four parts. It can be concluded that the basic framework of university AE resource application is shown in The frame diagram only shows what the AE courses in universities should have, which can be emphasized or supplemented in the implementation of specific courses. For example, in the part of aesthetic practice, you can appreciate and create natural beauty, artistic beauty, and social beauty at the same time, or you can choose one kind of beauty as the main part of the course and another form. Even in the part of basic theory, theoretical study can be appropriately increased or decreased according to class time, students' aesthetic quality, classroom content arrangement, and so on.Social ecological beauty is the beauty reflected in the social ecosystem, which widely exists in all fields of people's production and life, is reflected in the harmonious and orderly social life, and embodies people's ecological aesthetic ideal. Therefore, we can cultivate people's sensibility and ecological aesthetic consciousness through various social and ecological aesthetic forms. In the implementation of EAE in schools, the development of material resources such as educational environment resources should reflect the spirit of the times, and the architectural style should fully reflect the characteristics of vitality and simplicity. At the same time, we should also pay attention to the nationalized and localized material resources, which contain our precious culture and fine traditions, and embody the harmonious unity between man and nature, tradition and modernity, and ideas and modernity.The world, including humans, is an interconnected life ecosystem that appears as an organic whole. The concept of ecological science highlights the inseparable relationship between man and nature, breaking the existing concept of separation and opposition between man and nature. Everything in this place is connected. Life and life, life and environment, life and environment, and life and environment, communicate and coordinate with one another, cooperate with one another, displaying a beautiful form. The use of a recommendation system  is one oThe HHR_L model is proposed to solve the data sparseness, cold start, and scalability problems in recommendation system. HHR_L constructs a recommendation algorithm based on hash, which enables the recommendation system to handle large-scale data recommendation, improves the recommendation efficiency, and provides an effective method for scalable recommendation. The model framework as an example of HHR_L is shown in k suggestions are given by Hamming distance sorting.At first, we initialize the hidden layer feature representation of the project by DAE (Denoising Autoencoder); then we fine-tune the DAE network by combining the paired sorting objective function based on CF, and finally get the hash codes and items of users, and the top-The basic idea of hashing algorithm is to compress input data of arbitrary length and map it to data output of fixed length. A key skill is dimension reduction. For test samples, if the information from adjacent samples and related markers is known, the maximum posterior criterion is used to predict a group of markers in the test samples.ith mark of sample x isThen, through the maximum posterior probability criterion, it can be shown that the yx(i) represents the posterior probability of the label corresponding to sample x. Because the posterior probability is difficult to be directly calculated, it can be converted into prior probability and conditional probability according to Bayesian theorem. Therefore, if the label of the sample is unknown, the labeled sample can be expressed asP(Hib) represents the prior probability of Hib, P(x|Hib) represents the conditional probability that the tag belongs to sample x when Hib is known, and P(Hib) and P(x|Hib) can be obtained from a given data set.Among them, D > 0, the hash function Rk⟶Z satisfies the formulaα ∈ Rk represents a K-dimensional feature vector that obeys N standard normal distribution, β ∈ R represents a feature vector randomly selected from the range of , and D represents the width of the bucket.And change the distribution of hashed points on the machine by selecting a specific hash function. First, given the parameter k groups s={s1, ⋯, sk}. For example, the ith group will have a cluster center point μi. We distinguish data points based on the median points of two neighboring groups, and the median plane between them is hyperplane. The definition of the vertical plane is as follows:From the above steps, we can get the quantization result from B, D, Y, Θ, when the intermediate variable X is updated, the items unrelated to X in formula equation . In particular, the update rules of Ub, Vb is a stack of left singular vector and right singular vector of matrix Vb, which can be determined by A collection of natural languages is referred to as a document set. To classify and organize the document set, all that is required is to extract elements from the document set that can express the content of each document. Because “word” is a fully expressible semantic object in Chinese documents, it is frequently chosen as the metadata of text features. The metadata of text features must first be extracted before text mining can begin. Currently, all types of text representation methods suffer from the same issue. There are many dimensions that can be used to represent a document, and many documents that are not useful for text clustering. As a result, text features must be extracted twice. After the text feature vectors have been dimension reduced, they can be clustered, and the clustering results can be analyzed in light of the current situation.M isP is the adjacent co-occurrence probability of X, Y, and X, Y represent two Chinese characters. P(X), P(Y) are the probability of X, Y appearing separately in the text. This can indicate the tightness between Chinese characters. When the value of M is greater than a certain threshold, it can be considered that X, Y can form a word.“Thesaurus,” or machine dictionary, has as many entries as possible. Word segmentation based on Thesaurus is the most commonly used, the simplest operation method and the most effective method. Match the item to be processed with Thesaurus, and if the match is successful, display the item string as a word. If they appear more at the same time, they are most likely to form a word. Therefore, the confidence of two adjacent words can usually be obtained by calculating their co-occurrence frequency. The mutual information CI  is a simple and effective dimension reduction method. For supervised learning, CI constructs CI subspaces, takes the cyclic vector composed of these CI subspaces as the base vector, and projects the text cyclic vector onto this subspace.ith classification is Centeri, its calculation method is as follows:Ni is the number of texts in category Ci and Docij represents the j prototype vector in category Ci.Classified prototype vector: assuming that the prototype vector of the PCA  is a widely used method of pattern recognition and signal processing, which is especially effective for data compression and feature extraction. In this paper, we apply PCA method to text classification, hoping to obtain low-dimensional representation of text vectors.M categories in the training set sample, the following is how to calculate the covariance matrix of each internal document category separately:Assuming that there are Xi is the covariance matrix of the internal document classification in the ith classification, x is the text vector in the ith classification, and μi represents the prototype vector in the ith classification.Among them, M-dimensional space to D-dimensional space. This is because the sum of error squares of the data obtained by the algorithm can always be kept to a minimum. At the same time, the mutual information between the original vector x and the mapping vector y is the largest.The essence of PCA method is that you can always get the best linear mapping from When dealing with multiclassifier combinations in classification problems, the voting mechanism is frequently used in machine learning. In the above rules, the relative majority voting rule, which selects the class with the highest output among all machine classification algorithms, is a commonly used voting rule. The RF (random forest) algorithm must aggregate the classification results of each DT in the forest and vote on classification categories when determining the classification results. The DT (decision tree) model's similarity can be calculated from two perspectives: DT semantics and DT structure. The concrete implementation idea for improving the voting mechanism is to combine the DT classification effect with the weighted voting method of class probability to improve the RF voting mechanism. The accuracy of data classification outside the bag reflects the classification effect of DT. The DT classification effect improves as classification accuracy improves.X and the number of correctly classified samples is Xcorrect, then the accuracy rate of DT's out-of-bag data is the ratio of the two. The accuracy of DT data outside the bag is used to weight DT, and the weight of DT in RF algorithm isBefore making the classification decision, it is necessary to calculate the accuracy rate of each DT's out-of-bag data. If the total number of samples is Pi, the probability that the sample belongs to class y is Pi(y), the weight of DT is wi, and the number of DTs in RF is k. The weighted value z(y) of the sample belonging to class y is obtained by the formulaRemember that the probability that DT outputs that the sample belongs to each class is The probability that the sample belongs to each class is calculated by the above method, and the class with the largest weighted value is selected as the classification result.The RF_VM (Random Forest Classification Using Optimized Voting Mechanism) model can be divided into three modules: learning module, weight calculation module, and decision module. The algorithm flow is shown in First, we implement a training module, which is almost the same as the traditional RF algorithm. Next, the weight calculation module calculates the weight of each DT according to formula . FinallyThe computer used in the experiment is running Microsoft Windows 10. In this article, we choose Python as the development language of our experiment. Try PyCharm software to make Python language development more efficient.As an excellent Python integrated development software, PyCharm can provide intelligent Python support through intelligent code completion, code inspection, and navigation. PyCharm supports project and file management and various scientific packages, such as Anaconda, matplotlib, and NumPy. In order to verify the effectiveness of the proposed algorithm in cold start and sparse scenarios, we choose to experiment on two public datasets (Amazon dataset and Yelp 9th round dataset), and the sparsity is as high as 99.9%. For content data, first, delete punctuation marks and numbers. And words with stop words and length less than 2 usually have no clear meaning, so the remaining words are processed by English word segmentation.In this paper, Accuracy and MRR  are used to evaluate the recommendation performance of HHR_L in cold start environment. The experimental results are shown in It can be seen that the recommendation accuracy of HHR_L is higher than that of comparison method. This is because  and 20]20] are hIn addition, although the recommendation accuracy of HHR_L increases steadily with the increase of data density, the performance of  is unstaK-Nearest Neighbors) algorithm is used for classification, where K=10 is shown in Keep more commonly used words in text documents to improve classification accuracy. CI_PCA and DF (Document frequency) are compared in dimension reduction effect, so KNN  for the superparameters of the algorithms, which also proved the importance of value optimization.EAE is extremely important for environmental protection and improving human living conditions. The EAE educational method should focus on aesthetics, psychological discipline, and infection, and its practical implementation should be integrated with all aspects of school operations. Teachers' human resources are the main body, curriculum resources are the foundation, and material resources are the carrier in the development of school EAE resources, in order to create a humanized environment, build an information platform, and carry out various activities. When designing an ML algorithm, try to avoid data sparsity and a cold start. Data scarcity is not a big deal because of the curriculum limitations, so we pay attention to the new curriculum. By projecting the high-dimensional prototype text onto a low-dimensional subspace and then reducing the dimension according to the classification covariance matrix, the CI PCA algorithm significantly reduces the classification cost. When the proposed RF algorithm is combined with the optimized voting mechanism, the algorithm's classification ability can be greatly improved.