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RStudio r version 4 0 4
Positive correlations between 8 h TWA CO, Cu, and LDSA. A Correlation between 8 h TWA CO and LDSA, <t>R</t> 2 = 0.836, p
R Version 4 0 4, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "Exposure to lead-free frangible firing emissions containing copper and ultrafine particulates leads to increased oxidative stress in firing range instructors"

Article Title: Exposure to lead-free frangible firing emissions containing copper and ultrafine particulates leads to increased oxidative stress in firing range instructors

Journal: Particle and Fibre Toxicology

doi: 10.1186/s12989-022-00471-0

Positive correlations between 8 h TWA CO, Cu, and LDSA. A Correlation between 8 h TWA CO and LDSA, R 2 = 0.836, p
Figure Legend Snippet: Positive correlations between 8 h TWA CO, Cu, and LDSA. A Correlation between 8 h TWA CO and LDSA, R 2 = 0.836, p

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  • 90
    RStudio r version 4 0 4
    Positive correlations between 8 h TWA CO, Cu, and LDSA. A Correlation between 8 h TWA CO and LDSA, <t>R</t> 2 = 0.836, p
    R Version 4 0 4, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/r version 4 0 4/product/RStudio
    Average 90 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    r version 4 0 4 - by Bioz Stars, 2022-09
    90/100 stars
      Buy from Supplier

    93
    RStudio rstudio version 1 3 1093
    Pearson’s Correlation of Logarithmically-Transformed Prevalence Ratios of the Most Emergent SARS-CoV-2 Mutations of Concern and Interest Selected via the Algorithm This figure shows the graphical representation of the logarithmically-transformed prevalence data used to calculate the Pearson’s correlation of each of the twenty most emerged (of concern) and emergent (of interest) spike protein substitutions and deletions. The substitutions and deletions of concern here are in order of decreasing r value, and each has a unique alphabet identifier A) P681H, B) ΔV70, C) ΔH69, D) N501Y, E) S982A, F) D1118H, G) T716, H) A570D, I) ΔY144, and J) D614G. The algorithm uses the monthly prevalence data from these ten spike protein substitutions and deletions, and they are the most concerning of all spike changes. The substitutions and deletions of interest here are in order of decreasing r value and each unique substitution or deletion is denoted by a letter of the English alphabet, K) E484K, L) T478K, M) P26S, N) L452R, O) A701V, P) W152C, Q) T95I, R) H655Y, S) S13I, and T) D138Y. Graphs were generated using <t>RStudio</t> version 1.3.1093 (R version 4.0.3) and the ggplot2 package. Graphs were compiled and the final figure generated using Biorender.com.
    Rstudio Version 1 3 1093, supplied by RStudio, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    RStudio custom r script
    Sensitivity analysis reveals deviations of ratios of circRNA-located non-significant SNPs. To obtain a more distinct group delineation between significant and non-significant BMI-SNPs within the sensitivity analysis, the non-significant SNPs (P ≥ 5 × 10 −8 ; pink) were re-classified applying the additional P-value cut-offs of 5 × 10 −7 (green), 5 × 10 −6 (blue) and 5 × 10 −5 (orange). Non-significant SNPs with P-values ranging between these thresholds and the initial threshold of 5 × 10 −8 were removed from the respective group. The results were obtained by applying the <t>custom</t> <t>R</t> <t>script</t> to the newly defined dataset of non-significant SNPs and were compared to the initial group of significant SNPs (P
    Custom R Script, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Positive correlations between 8 h TWA CO, Cu, and LDSA. A Correlation between 8 h TWA CO and LDSA, R 2 = 0.836, p

    Journal: Particle and Fibre Toxicology

    Article Title: Exposure to lead-free frangible firing emissions containing copper and ultrafine particulates leads to increased oxidative stress in firing range instructors

    doi: 10.1186/s12989-022-00471-0

    Figure Lengend Snippet: Positive correlations between 8 h TWA CO, Cu, and LDSA. A Correlation between 8 h TWA CO and LDSA, R 2 = 0.836, p

    Article Snippet: These analyses were performed using R version 4.0.4 in RStudio and a p value < 0.05 was considered statistically significant.

    Techniques:

    Pearson’s Correlation of Logarithmically-Transformed Prevalence Ratios of the Most Emergent SARS-CoV-2 Mutations of Concern and Interest Selected via the Algorithm This figure shows the graphical representation of the logarithmically-transformed prevalence data used to calculate the Pearson’s correlation of each of the twenty most emerged (of concern) and emergent (of interest) spike protein substitutions and deletions. The substitutions and deletions of concern here are in order of decreasing r value, and each has a unique alphabet identifier A) P681H, B) ΔV70, C) ΔH69, D) N501Y, E) S982A, F) D1118H, G) T716, H) A570D, I) ΔY144, and J) D614G. The algorithm uses the monthly prevalence data from these ten spike protein substitutions and deletions, and they are the most concerning of all spike changes. The substitutions and deletions of interest here are in order of decreasing r value and each unique substitution or deletion is denoted by a letter of the English alphabet, K) E484K, L) T478K, M) P26S, N) L452R, O) A701V, P) W152C, Q) T95I, R) H655Y, S) S13I, and T) D138Y. Graphs were generated using RStudio version 1.3.1093 (R version 4.0.3) and the ggplot2 package. Graphs were compiled and the final figure generated using Biorender.com.

    Journal: bioRxiv

    Article Title: Algorithm for the Quantitation of Variants of Concern for Rationally Designed Vaccines Based on the Isolation of SARS-CoV-2 Hawaiʻi Lineage B.1.243

    doi: 10.1101/2021.08.18.455536

    Figure Lengend Snippet: Pearson’s Correlation of Logarithmically-Transformed Prevalence Ratios of the Most Emergent SARS-CoV-2 Mutations of Concern and Interest Selected via the Algorithm This figure shows the graphical representation of the logarithmically-transformed prevalence data used to calculate the Pearson’s correlation of each of the twenty most emerged (of concern) and emergent (of interest) spike protein substitutions and deletions. The substitutions and deletions of concern here are in order of decreasing r value, and each has a unique alphabet identifier A) P681H, B) ΔV70, C) ΔH69, D) N501Y, E) S982A, F) D1118H, G) T716, H) A570D, I) ΔY144, and J) D614G. The algorithm uses the monthly prevalence data from these ten spike protein substitutions and deletions, and they are the most concerning of all spike changes. The substitutions and deletions of interest here are in order of decreasing r value and each unique substitution or deletion is denoted by a letter of the English alphabet, K) E484K, L) T478K, M) P26S, N) L452R, O) A701V, P) W152C, Q) T95I, R) H655Y, S) S13I, and T) D138Y. Graphs were generated using RStudio version 1.3.1093 (R version 4.0.3) and the ggplot2 package. Graphs were compiled and the final figure generated using Biorender.com.

    Article Snippet: Pearson’s was calculated using RStudio version 1.3.1093 (R version 4.0.3) and plotted with the ggplot2 package.

    Techniques: Transformation Assay, Generated

    Pearson’s Correlation on Logarithmically-Transformed Prevalence Ratios of the Remaining SARS-CoV-2 Variant Amino Acid Substitutions and Deletions Not Currently Selected via the Algorithm This figure shows the graphical representation of the SARS-CoV-2 spike protein amino acid substitutions and deletions not currently concerning due to low previous month prevalence, low r value, or insignificant P value. Though not yet of concern as of April ‘21, those substitution and deletions represented by high r values should be cause for close monitoring. Each graph denoted by an alphabetical character or characters represents a unique amino acid substitution or deletion in the spike protein of SARS-CoV-2 (A) A845S, B) A67V, C) P681R, D) Q677H, E) K417N, F) ΔL242, G) Q52R, H) T20N, I) T1027I, J) ΔA243, K) R190S, L) ΔL244, M) K417T, N) D80A, O) E154K, P) L18F, Q) E484Q, R) Q1071H, S) F888L, T) ΔY145, U) M1229I, V) V1176F, W) R246I, X) G142D, Y) D950N, Z) Y453F, and AA) I692V). Graphs were generated using RStudio version 1.3.1093 (R version 4.0.3) and the ggplot2 package. Graphs were compiled and the final figure generated using Biorender.com.

    Journal: bioRxiv

    Article Title: Algorithm for the Quantitation of Variants of Concern for Rationally Designed Vaccines Based on the Isolation of SARS-CoV-2 Hawaiʻi Lineage B.1.243

    doi: 10.1101/2021.08.18.455536

    Figure Lengend Snippet: Pearson’s Correlation on Logarithmically-Transformed Prevalence Ratios of the Remaining SARS-CoV-2 Variant Amino Acid Substitutions and Deletions Not Currently Selected via the Algorithm This figure shows the graphical representation of the SARS-CoV-2 spike protein amino acid substitutions and deletions not currently concerning due to low previous month prevalence, low r value, or insignificant P value. Though not yet of concern as of April ‘21, those substitution and deletions represented by high r values should be cause for close monitoring. Each graph denoted by an alphabetical character or characters represents a unique amino acid substitution or deletion in the spike protein of SARS-CoV-2 (A) A845S, B) A67V, C) P681R, D) Q677H, E) K417N, F) ΔL242, G) Q52R, H) T20N, I) T1027I, J) ΔA243, K) R190S, L) ΔL244, M) K417T, N) D80A, O) E154K, P) L18F, Q) E484Q, R) Q1071H, S) F888L, T) ΔY145, U) M1229I, V) V1176F, W) R246I, X) G142D, Y) D950N, Z) Y453F, and AA) I692V). Graphs were generated using RStudio version 1.3.1093 (R version 4.0.3) and the ggplot2 package. Graphs were compiled and the final figure generated using Biorender.com.

    Article Snippet: Pearson’s was calculated using RStudio version 1.3.1093 (R version 4.0.3) and plotted with the ggplot2 package.

    Techniques: Transformation Assay, Variant Assay, Generated

    Post-Quarantine Days to VOC Arrival vs. Quarantine Days to VOC Arrival in Hawai’i and Utah as of May, 20, 2021 This figure shows the days-to-arrival of variants of concern (VOC) compared to VOC categorized as ‘quarantine’ and ‘post-quarantine.’ Depicted as a box-and-whisker/violin plot displaying median (line), mean (black circle), interquartile range, minimum, and maximum. Quarantine VOC are those VOC that emerged worldwide during the 43-state collective quarantine in the United States (2020-03-11 to 2020-06-16). Post-quarantine VOC are those VOC that emerged worldwide following the collective quarantine in the United States (2020-06-16 to 2021-05-20). The comparison was done using an independent t-test. A) Partitioning days to VOC arrival in Hawai’i into quarantine (M = 320 days, SD = 45) and post-quarantine (M = 132 days, SD = 21) time periods demonstrates that quarantine delayed the arrival of VOC to Hawai’i (t(45) = 17.38, p = 1.38e-21). B) For Utah, a state that did not quarantine during the 43-state collective quarantine, a difference is also demonstrated between the two time periods (quarantine M = 285 days, SD = 71; post-quarantine M = 116 days, SD = 40) regarding time to VOC arrival (t(40) = 9.37, p = 1.2e-11). C) Comparing Hawai’i and Utah by the quarantine time period demonstrates that Hawai’i (M = 320 days, SD = 45), a quarantine state, saw a greater delay in days to VOC arrival compared to Utah (M = 285 days, SD = 71), a non-quarantine state (t(47) = 2.11, p = 0.04). D) Comparing Hawai’i and Utah by the post-quarantine time period demonstrates no difference between Hawai’i (M = 132 days, SD = 40) and Utah (M = 116 days, SD = 40) (t(38) = 1.59, p = 0.12) in days to VOC arrival. Created with RStudio version 1.3.1093 (R version 4.0.3) using ggplot2 and ggstatsplot packages. The final figure was created with BioRender.com .

    Journal: Research Square

    Article Title: Genomic Analysis of SARS-CoV-2 Variants of Concern Circulating in Hawai’i to Facilitate Public-Health Policies

    doi: 10.21203/rs.3.rs-378702/v3

    Figure Lengend Snippet: Post-Quarantine Days to VOC Arrival vs. Quarantine Days to VOC Arrival in Hawai’i and Utah as of May, 20, 2021 This figure shows the days-to-arrival of variants of concern (VOC) compared to VOC categorized as ‘quarantine’ and ‘post-quarantine.’ Depicted as a box-and-whisker/violin plot displaying median (line), mean (black circle), interquartile range, minimum, and maximum. Quarantine VOC are those VOC that emerged worldwide during the 43-state collective quarantine in the United States (2020-03-11 to 2020-06-16). Post-quarantine VOC are those VOC that emerged worldwide following the collective quarantine in the United States (2020-06-16 to 2021-05-20). The comparison was done using an independent t-test. A) Partitioning days to VOC arrival in Hawai’i into quarantine (M = 320 days, SD = 45) and post-quarantine (M = 132 days, SD = 21) time periods demonstrates that quarantine delayed the arrival of VOC to Hawai’i (t(45) = 17.38, p = 1.38e-21). B) For Utah, a state that did not quarantine during the 43-state collective quarantine, a difference is also demonstrated between the two time periods (quarantine M = 285 days, SD = 71; post-quarantine M = 116 days, SD = 40) regarding time to VOC arrival (t(40) = 9.37, p = 1.2e-11). C) Comparing Hawai’i and Utah by the quarantine time period demonstrates that Hawai’i (M = 320 days, SD = 45), a quarantine state, saw a greater delay in days to VOC arrival compared to Utah (M = 285 days, SD = 71), a non-quarantine state (t(47) = 2.11, p = 0.04). D) Comparing Hawai’i and Utah by the post-quarantine time period demonstrates no difference between Hawai’i (M = 132 days, SD = 40) and Utah (M = 116 days, SD = 40) (t(38) = 1.59, p = 0.12) in days to VOC arrival. Created with RStudio version 1.3.1093 (R version 4.0.3) using ggplot2 and ggstatsplot packages. The final figure was created with BioRender.com .

    Article Snippet: We then compared the quarantine group to the post-quarantine group by days-to-arrival using an independent t-test in RStudio version 1.3.1093 (R version 4.0.3) and plotted with ggplot2 and ggstatsplot packages.

    Techniques: Whisker Assay

    Sensitivity analysis reveals deviations of ratios of circRNA-located non-significant SNPs. To obtain a more distinct group delineation between significant and non-significant BMI-SNPs within the sensitivity analysis, the non-significant SNPs (P ≥ 5 × 10 −8 ; pink) were re-classified applying the additional P-value cut-offs of 5 × 10 −7 (green), 5 × 10 −6 (blue) and 5 × 10 −5 (orange). Non-significant SNPs with P-values ranging between these thresholds and the initial threshold of 5 × 10 −8 were removed from the respective group. The results were obtained by applying the custom R script to the newly defined dataset of non-significant SNPs and were compared to the initial group of significant SNPs (P

    Journal: Scientific Reports

    Article Title: Evidence for correlations between BMI-associated SNPs and circRNAs

    doi: 10.1038/s41598-022-16495-7

    Figure Lengend Snippet: Sensitivity analysis reveals deviations of ratios of circRNA-located non-significant SNPs. To obtain a more distinct group delineation between significant and non-significant BMI-SNPs within the sensitivity analysis, the non-significant SNPs (P ≥ 5 × 10 −8 ; pink) were re-classified applying the additional P-value cut-offs of 5 × 10 −7 (green), 5 × 10 −6 (blue) and 5 × 10 −5 (orange). Non-significant SNPs with P-values ranging between these thresholds and the initial threshold of 5 × 10 −8 were removed from the respective group. The results were obtained by applying the custom R script to the newly defined dataset of non-significant SNPs and were compared to the initial group of significant SNPs (P

    Article Snippet: A custom R script (R version: 4.0.5; RStudio version 1.4.1106) was applied to match the genomic coordinates of the circRNAs with those of the GWAS SNPs.

    Techniques:

    Deviations of genome-wide significant BMI-SNPs located on circRNAs between females and males. To explore whether the enrichment of significant BMI-associated SNPs deviates between females and males, we extracted data of a BMI-GWAS 37 analysing both sexes separately. We applied the custom R script to the significant (grey; total of 27,653 SNPs in females; total of 22,833 SNPs in males) and non-significant (black; total of 27,352,598 SNPs in females; total of 27,357,420 SNPs in males) SNP data concomitantly with the circRNA data extracted from circBase separately for both sexes. Subsequently, we compared the number of circRNA-located significant SNPs for females against the quantity of SNPs encompassed in circRNA loci for males (Chi-square test). The statistical results are stated in Supplementary Table S9 . ****P

    Journal: Scientific Reports

    Article Title: Evidence for correlations between BMI-associated SNPs and circRNAs

    doi: 10.1038/s41598-022-16495-7

    Figure Lengend Snippet: Deviations of genome-wide significant BMI-SNPs located on circRNAs between females and males. To explore whether the enrichment of significant BMI-associated SNPs deviates between females and males, we extracted data of a BMI-GWAS 37 analysing both sexes separately. We applied the custom R script to the significant (grey; total of 27,653 SNPs in females; total of 22,833 SNPs in males) and non-significant (black; total of 27,352,598 SNPs in females; total of 27,357,420 SNPs in males) SNP data concomitantly with the circRNA data extracted from circBase separately for both sexes. Subsequently, we compared the number of circRNA-located significant SNPs for females against the quantity of SNPs encompassed in circRNA loci for males (Chi-square test). The statistical results are stated in Supplementary Table S9 . ****P

    Article Snippet: A custom R script (R version: 4.0.5; RStudio version 1.4.1106) was applied to match the genomic coordinates of the circRNAs with those of the GWAS SNPs.

    Techniques: Genome Wide, GWAS

    Genome-wide significant SNPs for BMI are enriched in genomic loci of circRNAs. SNPs were classified as significant and non-significant based on the genome-wide P-value threshold of 5 × 10 −8 . Correspondingly, SNPs with a lower P-value than this threshold were considered significant (grey; total of 40,835 SNPs), while SNPs with P-values exceeding this cut-off were defined as non-significant (black; total of 2,283,734 SNPs). Genomic positions of these SNPs were checked if they matched the genomic coordinates of circRNAs extracted from the databases circAtlas v2.0 (GRCh38), circBase, CIRCpediaV2 and circVAR using a custom R script. The numbers shown in the parentheses indicate the number of circRNAs included in the respective dataset (see also Supplementary Table S1 ). The results of the statistical tests can be found in Supplementary Table S5 . ****P

    Journal: Scientific Reports

    Article Title: Evidence for correlations between BMI-associated SNPs and circRNAs

    doi: 10.1038/s41598-022-16495-7

    Figure Lengend Snippet: Genome-wide significant SNPs for BMI are enriched in genomic loci of circRNAs. SNPs were classified as significant and non-significant based on the genome-wide P-value threshold of 5 × 10 −8 . Correspondingly, SNPs with a lower P-value than this threshold were considered significant (grey; total of 40,835 SNPs), while SNPs with P-values exceeding this cut-off were defined as non-significant (black; total of 2,283,734 SNPs). Genomic positions of these SNPs were checked if they matched the genomic coordinates of circRNAs extracted from the databases circAtlas v2.0 (GRCh38), circBase, CIRCpediaV2 and circVAR using a custom R script. The numbers shown in the parentheses indicate the number of circRNAs included in the respective dataset (see also Supplementary Table S1 ). The results of the statistical tests can be found in Supplementary Table S5 . ****P

    Article Snippet: A custom R script (R version: 4.0.5; RStudio version 1.4.1106) was applied to match the genomic coordinates of the circRNAs with those of the GWAS SNPs.

    Techniques: Genome Wide