cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11415051.v1
title: MOESM7 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 7: Table S7. Transcription factors (TFs) prediction for resistant hosts. Network analysis predicted potential TFs underlying the regulation of gene expression, based on the variation of TopDEGs. The output file was manually-curated, and only TFs found in cattle were classified according to their z-score. TopDEGs TFs were also indicated with their respective modulation and statistical significance. Network object name: network object name in MetaBase®; Actual: number of network objects in the activated dataset(s) which interact with the chosen object; n: number of network objects in the activated dataset(s); R: number of network objects in the complete database or background list which interact with the chosen object; N: total number of gene-based objects in the complete database or background list; Expected: mean value for hypergeometric distribution (n*R/N); Ratio: connectivity ratio (Actual/Expected); z-score: ((Actual-Expected)/sqrt(variance)); p-value: probability to have the given value of Actual or higher (or lower for negative z-score); Input IDs: original probe/gene IDs in the activated dataset(s); Signal: variation of gene expression in log2 FC; p-value2: statistical significance in the differential expression analysis. (XLS 52 kb)
DOI: 10.6084/m9.figshare.11415042
title: MOESM6 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 6: Table S6. Network prediction for TopDEGs from susceptible response to tick infestation. For each network, the key network objects and the GO process associated with them, followed by the number of total and seed nodes, and pathways are shown. The p-value, z-score, and g-score classified the networks. (XLS 40 kb)
DOI: 10.6084/m9.figshare.11415042.v1
title: MOESM6 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 6: Table S6. Network prediction for TopDEGs from susceptible response to tick infestation. For each network, the key network objects and the GO process associated with them, followed by the number of total and seed nodes, and pathways are shown. The p-value, z-score, and g-score classified the networks. (XLS 40 kb)
DOI: 10.6084/m9.figshare.11415036
title: MOESM5 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 5: Table S5 Network prediction for genes mined from the TagSNPs associated with resistance to ticks prospected in a genome-wide association study. For each network, the key network objects and the GO process associated with them, followed by the number of total and seed nodes, and pathways are shown. The p-value, z-score, and g-score classified the networks. (XLS 34 kb)
DOI: 10.6084/m9.figshare.11415033
title: MOESM4 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 4: Table S4. Network prediction for TopDEGs from resistant response to tick infestation. For each network, the key network objects and the GO process associated with them, followed by the number of total and seed nodes, and pathways are shown. The p-value, z-score, and g-score classified the networks. (XLS 42 kb)
DOI: 10.6084/m9.figshare.11415036.v1
title: MOESM5 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 5: Table S5 Network prediction for genes mined from the TagSNPs associated with resistance to ticks prospected in a genome-wide association study. For each network, the key network objects and the GO process associated with them, followed by the number of total and seed nodes, and pathways are shown. The p-value, z-score, and g-score classified the networks. (XLS 34 kb)
DOI: 10.6084/m9.figshare.11415030
title: MOESM3 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 3: Table S3. Enrichment analysis report of TopDEGs from intragroup comparisons. A) Enrichment by process networks of TopDEGs from resistant (Rpost vs. Rpre) and susceptible (Spost vs. Spre) hosts. Networks were classified according to the p-value, the false discovery rate (FDR), and the number of network objects found in the TopDEGs list (In Data). Total represents the number of total network objects in each network. Unique TopDEGs are shown in bold. B) Enrichment by pathway maps of TopDEGs from resistant (Rpost vs. Rpre) and susceptible (Spost vs. Spre) hosts. Maps were classified following the same scheme described for networks. (XLS 39 kb)
DOI: 10.6084/m9.figshare.11415033.v1
title: MOESM4 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 4: Table S4. Network prediction for TopDEGs from resistant response to tick infestation. For each network, the key network objects and the GO process associated with them, followed by the number of total and seed nodes, and pathways are shown. The p-value, z-score, and g-score classified the networks. (XLS 42 kb)
DOI: 10.6084/m9.figshare.11415030.v1
title: MOESM3 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 3: Table S3. Enrichment analysis report of TopDEGs from intragroup comparisons. A) Enrichment by process networks of TopDEGs from resistant (Rpost vs. Rpre) and susceptible (Spost vs. Spre) hosts. Networks were classified according to the p-value, the false discovery rate (FDR), and the number of network objects found in the TopDEGs list (In Data). Total represents the number of total network objects in each network. Unique TopDEGs are shown in bold. B) Enrichment by pathway maps of TopDEGs from resistant (Rpost vs. Rpre) and susceptible (Spost vs. Spre) hosts. Maps were classified following the same scheme described for networks. (XLS 39 kb)
DOI: 10.6084/m9.figshare.11415024
title: MOESM2 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 2: Table S2. Intragroup comparisons of differentially expressed genes in the skin of tick-resistant and -susceptible Braford cattle. DEGs are listed alphabetically with their Ensembl ID and description. The log2 FC and the FDR for each DEG are shown in the last two columns, respectively, with the results comparing Rpost vs. Rpre represented by white cells and Spost vs. Spre represented by gray cells. (XLS 866 kb)
DOI: 10.6084/m9.figshare.11415024.v1
title: MOESM2 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 2: Table S2. Intragroup comparisons of differentially expressed genes in the skin of tick-resistant and -susceptible Braford cattle. DEGs are listed alphabetically with their Ensembl ID and description. The log2 FC and the FDR for each DEG are shown in the last two columns, respectively, with the results comparing Rpost vs. Rpre represented by white cells and Spost vs. Spre represented by gray cells. (XLS 866 kb)
DOI: 10.6084/m9.figshare.11415018
title: MOESM1 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 1: Table S1. Sequencing throughput and mapping statistics for each resistance group. The average, minimum and maximum values of numbers (N) of reads passing filtering and aligned pairs, and the percentage (%) of overall read mapping, multiple alignments, and concordant pair alignment rate are shown. Values were compared inter- and intragroups. Different letters (a and b) represent statistical significance. (XLS 28 kb)
DOI: 10.6084/m9.figshare.11415018.v1
title: MOESM1 of Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

description: Additional file 1: Table S1. Sequencing throughput and mapping statistics for each resistance group. The average, minimum and maximum values of numbers (N) of reads passing filtering and aligned pairs, and the percentage (%) of overall read mapping, multiple alignments, and concordant pair alignment rate are shown. Values were compared inter- and intragroups. Different letters (a and b) represent statistical significance. (XLS 28 kb)
DOI: 10.6084/m9.figshare.11415006
title: MOESM18 of Host plant adaptation in the polyphagous whitefly, Trialeurodes vaporariorum, is associated with transcriptional plasticity and altered sensitivity to insecticides

description: Additional file 18: Tables S16-S21 Genes identified as differentially expressed between lines of T. vaporariorum reared on different host plants when compared to a line reared on French bean. Each comparison is shown in a separate tab. Table S16 shows genes differentially expressed between the cucumber and bean-reared line. Table S17 shows genes differentially expressed between the pumpkin and bean-reared line. Table S18 shows genes differentially expressed between the tomato and bean-reared line. Table S19 shows genes differentially expressed between the tobacco and bean-reared line. Table S20 shows genes that are commonly differentially expressed in both nightshade (tobacco and tomato) reared comparisons. Table S21 shows genes that are commonly differentially expressed in both cucurbit (cucumber and pumpkin) reared comparisons.
DOI: 10.6084/m9.figshare.11415006.v1
title: MOESM18 of Host plant adaptation in the polyphagous whitefly, Trialeurodes vaporariorum, is associated with transcriptional plasticity and altered sensitivity to insecticides

description: Additional file 18: Tables S16-S21 Genes identified as differentially expressed between lines of T. vaporariorum reared on different host plants when compared to a line reared on French bean. Each comparison is shown in a separate tab. Table S16 shows genes differentially expressed between the cucumber and bean-reared line. Table S17 shows genes differentially expressed between the pumpkin and bean-reared line. Table S18 shows genes differentially expressed between the tomato and bean-reared line. Table S19 shows genes differentially expressed between the tobacco and bean-reared line. Table S20 shows genes that are commonly differentially expressed in both nightshade (tobacco and tomato) reared comparisons. Table S21 shows genes that are commonly differentially expressed in both cucurbit (cucumber and pumpkin) reared comparisons.
DOI: 10.6084/m9.figshare.11415000
title: MOESM2 of Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells

description: Additional file 2: Table S1. The microarray samples downloaded from NCBI GEO as the raw data to be used as the reference gene expression profiles, each being annotated with its source NCBI GEO sample accession numbers (GSM). Table S2. The reference gene expression signature (RefGES) matrix created in this study. Table S3. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 30% expression levels from breast tissues and 70% from immune cells. Table S4. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 50% expression levels from breast tissues and 50% from immune cells. Table S5. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 70% expression levels from breast tissues and 30% from immune cells. Table S6. The mapping of the cell types of NCBI GEO GSE65133 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S7. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 20 human PBMC samples of NCBI GEO GSE65133. Table S8. The mapping of the cell types of NCBI GEO GSE106898 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S9. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 12 human PBMC samples of NCBI GEO GSE106898. Table S10. The mapping of the cell types of NCBI GEO GSE107990 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S11. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 164 human PBMC samples of NCBI GEO GSE107990.
DOI: 10.6084/m9.figshare.11415000.v1
title: MOESM2 of Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells

description: Additional file 2: Table S1. The microarray samples downloaded from NCBI GEO as the raw data to be used as the reference gene expression profiles, each being annotated with its source NCBI GEO sample accession numbers (GSM). Table S2. The reference gene expression signature (RefGES) matrix created in this study. Table S3. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 30% expression levels from breast tissues and 70% from immune cells. Table S4. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 50% expression levels from breast tissues and 50% from immune cells. Table S5. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 70% expression levels from breast tissues and 30% from immune cells. Table S6. The mapping of the cell types of NCBI GEO GSE65133 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S7. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 20 human PBMC samples of NCBI GEO GSE65133. Table S8. The mapping of the cell types of NCBI GEO GSE106898 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S9. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 12 human PBMC samples of NCBI GEO GSE106898. Table S10. The mapping of the cell types of NCBI GEO GSE107990 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S11. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 164 human PBMC samples of NCBI GEO GSE107990.
DOI: 10.6084/m9.figshare.11414952
title: MOESM11 of Host plant adaptation in the polyphagous whitefly, Trialeurodes vaporariorum, is associated with transcriptional plasticity and altered sensitivity to insecticides

description: Additional file 11: Tables S9-S13. Identifier and sequences of curated T. vaporariorum genes belonging to detoxification enzyme families. Table S9 shows the P450 genes, Table S10 the carboxyl/cholinesterases, Table S11 the glutathione S-transferases, Table S12 the UDP- glucuronosyltransferases and Table S13 ATP-binding cassette transporters.
DOI: 10.6084/m9.figshare.11414952.v1
title: MOESM11 of Host plant adaptation in the polyphagous whitefly, Trialeurodes vaporariorum, is associated with transcriptional plasticity and altered sensitivity to insecticides

description: Additional file 11: Tables S9-S13. Identifier and sequences of curated T. vaporariorum genes belonging to detoxification enzyme families. Table S9 shows the P450 genes, Table S10 the carboxyl/cholinesterases, Table S11 the glutathione S-transferases, Table S12 the UDP- glucuronosyltransferases and Table S13 ATP-binding cassette transporters.
DOI: 10.6084/m9.figshare.11414823
title: MOESM1 of Effects of the Toll-like receptor 7 (TLR7) agonist, AZD8848, on allergen-induced responses in patients with mild asthma: a double-blind, randomised, parallel-group study

description: Additional file 1: Table S1. Treatment effect on sputum biomarkers at visit 12 which had to take place 48 to 72 h prior to Visit 13, which took place 1 week (±1 day) after the last dose. Figure S1. Consort flow diagram. Figure S2. Sputum cytokine levels measured pre- and post-allergen challenge before and after dosing with intranasal AZD8848 or placebo. (n = 12–19 for the various biomarker analyses). Figure S3. Individual AZD8848 concentration data
DOI: 10.6084/m9.figshare.11414823.v1
title: MOESM1 of Effects of the Toll-like receptor 7 (TLR7) agonist, AZD8848, on allergen-induced responses in patients with mild asthma: a double-blind, randomised, parallel-group study

description: Additional file 1: Table S1. Treatment effect on sputum biomarkers at visit 12 which had to take place 48 to 72 h prior to Visit 13, which took place 1 week (±1 day) after the last dose. Figure S1. Consort flow diagram. Figure S2. Sputum cytokine levels measured pre- and post-allergen challenge before and after dosing with intranasal AZD8848 or placebo. (n = 12–19 for the various biomarker analyses). Figure S3. Individual AZD8848 concentration data
DOI: 10.6084/m9.figshare.11414328
title: DATA Resultados y Anexos PAPER Q1.pdf

description: Supporting data for <b>Composting treatment of pelambre waste originating from tannery</b> p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Arial; color: #000000; -webkit-text-stroke: #000000} span.s1 {font-kerning: none}
DOI: 10.6084/m9.figshare.11414328.v1
title: DATA Resultados y Anexos PAPER Q1.pdf

description: Supporting data for <b>Composting treatment of pelambre waste originating from tannery</b> p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Arial; color: #000000; -webkit-text-stroke: #000000} span.s1 {font-kerning: none}
DOI: 10.6084/m9.figshare.11414040
title: Average codon usage pattern of Escherichia coli K-12

description: Awareness of different codon usage pattern in different microorganisms led to the concept of codon optimization in genetic engineering studies. Briefly, codon optimization seeks to adjust the codon usage profile of a heterologous gene to match that of the host organism to help ensure higher expression levels. In cases where codon optimization was not done, codons in the gene that are less frequently used in the host species might not be efficiently translated, and this would result in poorer translation level. But, at the fundamental level, codon usage pattern of an organism holds greater implications than its role in informing codon optimization for heterologous gene expression. Specifically, understanding the codon usage pattern of a species could guide thinking on the evolutionary forces experienced by the species. This work sought to determine the codon usage pattern of the model prokaryotic organism, <i>Escherichia coli </i>K-12 through analysing codon usage for all genes at the global level using an in-house MATLAB software. Results reveal that codon AGA and AGG are less frequently used in <i>E. coli </i>K-12. Preference for codons for each amino acid was determined, and are as follows: phenylalanine (TTT), leucine (CTG), isoleucine (ATT), valine (GTG), serine (TCG), proline (CCA and CCG), threonine (ACC), alanine (GCG), tyrosine (TAT), histidine (CAT), glutamine (CAG), asparagine (AAC), lysine (AAA), aspartate (GAT), glutamate (GAA), cysteine (TGC), arginine (CGC), serine (AGC), and glycine (GGC). Overall, the data reveals that codon usage is fairly well distributed between alternative codons for each amino acid except for leucine, and lysine. But, this does not necessarily imply that <i>E. coli </i>K-12 has a deficit in availability of amino-acyl tRNA for less often used codons. This work should find utility in informing heterologous gene expression in <i>E. coli </i>K-12, as well as more fundamental studies seeking to understand why <i>E. coli </i>K-12 chose such a codon usage pattern. <br><br>
DOI: 10.6084/m9.figshare.11414028
title: CardenasEtAl_GRL2020_SupplementaryData.xlsx

description: <b>Supporting Information for</b> Submarine groundwater and vent discharge in a volcanic area associated with coastal acidification M. Bayani Cardenas<sup>1</sup>*, Raymond S. Rodolfo<sup>2,3</sup>, Mark R. Lapus<sup>2</sup>, Hillel B. Cabria<sup>2</sup>, Jose Fullon<sup>4</sup>, Gordos Gojunco<sup>5</sup>, Daniel O. Breecker<sup>1</sup>, Danica M. Cantarero<sup>6</sup>, Jaivime Evaristo<sup>7</sup>, Fernando P. Siringan<sup>6</sup>, and Tongwei Zhang<sup>8</sup> <sup>1</sup> Dept. of Geological Sciences, The University of Texas at Austin, Austin, Texas USA<br> <sup>2</sup> Agricultural Sustainability Initiatives for Nature, Inc., Quezon City, PHILIPPINES <sup>3</sup> Dept. of Environmental Science, Ateneo de Manila University, Quezon City, PHILIPPINES <br> <sup>4</sup> Planet Dive Resort, Mabini, Batangas, PHILIPPINES<br> <sup>5</sup> Scuba Academy Manila, Makati, PHILIPPINES<br> <sup>6</sup> Marine Science Inst., University of the Philippines-Diliman, Quezon City, PHILIPPINES <sup>7</sup> Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, THE NETHERLANDS<br> <sup>8</sup> Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, USA *<sup> </sup>corresponding author: cardenas@jsg.utexas.edu<b></b><br><b>Supplementary Table 1</b>. This table shows the chemistry of water samples collected throughout the site. The samples correspond to those presented in Figure 3. Locations where the samples were collected are shown in Figure S2 or described in the text. See Excel spreadsheet. <b>Supplementary Table 2</b>. This table shows the results of radon-222 measurements along the coast of Calumpan Peninsula done at three different times. The measurements were done using RAD7 instruments connected to the RAD-AQUA gas equilibration chamber. The data covers the results shown in Figures 1B and 1C. See Excel spreadsheet. <b>Supplementary Table 3</b>. This table shows the results of surface seawater pCO2 measurements. The measurements were done with an infrared gas analyzer (Pro-Oceanus Pro-CV). The data in the table corresponds to what is presented in Figure 1E. See Excel spreadsheet. <b>Supplementary Table 4</b>. This table shows the results of in-situ bottom seawater pCO2 measurements. The measurements were done with an infrared gas analyzer (Pro-Oceanus Pro-CV). The results are broken into segments and the arclength along each segment is also calculated. This data covers those plotted in Figures 1F and 2H. See Excel spreadsheet.