cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7057916.v1
title: Supporting data for "a draft genome assembly of the sea slug Elysia chlorotica"

description: Elysia chlorotica, a sacoglossan sea slug found off the East Coast of the United States, is well-known for its ability to sequester chloroplasts from its algal prey and survive by photosynthesis for up to 12 months in the absence of food supply. Here we present a draft genome assembly of E. chlorotica that was generated using a hybrid assembly strategy with Illumina short reads and PacBio long reads. The genome assembly comprised 9,989 scaffolds, with a total length of 557 Mb and a scaffold N50 of 442 kb. BUSCO assessment indicated that 93.3 % of the expected metazoan genes were completely present in the genome assembly. Annotation of the E. chlorotica genome identified 176 Mb (32.6 %) of repetitive sequences and a total of 24,980 protein-coding genes. We anticipate that the annotated draft genome assembly of the E. chlorotica sea slug will promote the investigation of sacoglossan genetics, evolution, and particularly, the genetic signatures accounting for the long-term functioning of algal chloroplasts in an animal.<br><b>Genome assembly and annotation files provided in this dataset:</b>1. <i>Elysia_chlorotica.fa.gz</i>: genome assembly of Elysia chlorotica in fasta format.2. <i>Elysia_chlorotica.gene.gff.gz</i>: protein-coding gene annotation in GFF3 format.3. <i>Elysia_chlorotica.gene.cds.gz</i>: coding sequences of the protein-coding genes in fasta format.4. <i>Elysia_chlorotica.gene.pep.gz</i>: peptide sequences of the protein-coding genes in fasta format.<br>5. <i>Elysia_chlorotica.ProteinMask.gff.gz</i>: homology-based repetitive elements identified by searching against TE protein database with RepeatProteinMask in GFF3 format.<br>6. <i>Elysia_chlorotica.RepeatMasker.gff.gz</i>: homology-based repetitive elements identified by searching against Repbase with RepeatMasker in GFF3 format.<br>7. <i>Elysia_chlorotica.RepeatModeler.gff.gz</i>: denovo-based repetitive elements identified by RepeatModeler followed by RepeatMasker in GFF3 format.<br>8. <i>Elysia_chlorotica.TRF.gff.gz</i>: tandem repeats identified by Tandem Repeats Finder in GFF3 format.<br>
DOI: 10.6084/m9.figshare.7057871
title: Supplementary Material for: Switching from Epoetin Alfa (Epogen®) to Epoetin Alfa-Epbx (Retacrit<sup>TM</sup>) Using a Specified Dosing Algorithm: A Randomized, Non-Inferiority Study in Adults on Hemodialysis

description: <b><i>Background:</i></b> For patients with anemia undergoing hemodialysis, erythropoiesis-stimulating agents (ESAs) are typically dosed via precise algorithms. Using one such algorithm, we assessed the maintenance of hemoglobin levels in patients switched from epoetin alfa reference product (Epogen®) to epoetin alfa-epbx (Retacrit<sup>TM</sup>; a biosimilar to US-licensed Epogen®/Procrit®). <b><i>Methods:</i></b> This randomized, open-label, non-inferiority study was conducted at Fresenius Medical Care North America (FMCNA) hemodialysis centers. Patients with anemia and chronic kidney disease undergoing maintenance hemodialysis and receiving routine intravenous (IV) Epogen® were randomized 1: 1 to switch to IV Retacrit<sup>TM</sup> or continue standard-of-care (Epogen®) for 24 weeks, using analogous versions of the FMCNA ESA-dosing algorithm. The primary endpoint was the proportion of time patients’ hemoglobin was 9–11 g/dL during weeks 17–24. <b><i>Results:</i></b> Of 432 randomized patients, 418 received treatment (Retacrit<sup>TM</sup>, <i>n</i> = 212; standard-of-care, <i>n</i> = 206) and comprised the full analysis set. A similar proportion of patients discontinued from each arm. The proportion of time patients’ hemoglobin was within the target range was 61.9% (95% CI 57.5–66.2) in the Retacrit<sup>TM</sup> arm and 63.3% (95% CI 58.7–67.7) in the standard-of-care arm. The difference in proportions between treatment arms was –1.4% (95% CI –7.6 to 4.9), and the lower bound of the confidence interval was within the pre-specified non-inferiority margin of –12.5%. There was no statistically significant difference between arms in the mean change from baseline in the weekly mean ESA dose during weeks 17–24, and no clinically relevant differences in safety outcomes. <b><i>Conclusions:</i></b> Switching to Retacrit<sup>TM</sup> was non-inferior to continuing ­Epogen® in maintaining hemoglobin levels in patients receiving hemodialysis, when both ESAs were dosed using a specified algorithm (ClinicalTrials.gov, NCT02504294).
DOI: 10.6084/m9.figshare.7057880
title: Supplementary Material for: Prognostic Significance of Asymptomatic Brain Natriuretic Peptide Elevation at Nephrology Referral in Patients with Chronic Kidney Disease

description: <b><i>Background:</i></b> It is unclear whether asymptomatic elevation of brain natriuretic peptide (BNP) is associated with cardiovascular events (CVEs) or heart failure (HF) in predialysis chronic kidney disease (CKD) patients. <b><i>Methods:</i></b> We measured BNP in 482 asymptomatic predialysis patients with CKD stages 2–5 at nephrology referral between August 2004 and October 2010, and followed them prospectively to investigate the prognostic significance of BNP using Cox models and receiver operating characteristic (ROC) analyses. The primary composite end point was the time to death or the first nonfatal CVEs. Secondary end points included CVEs including sudden death, HF and all-cause death. <b><i>Results:</i></b> The median age was 67 years (male, 67.4%; diabetic nephropathy, 33.4%), and estimated glomerular filtration rate was 20.1 mL/min/1.73 m<sup>2</sup>. The primary end point occurred in 92 patients. CVEs including sudden death, HF and all-cause death occurred in 66, 35, and 54 patients, respectively during a median follow-up period of 37.7 months. Multivariate analyses showed that BNP level was significantly associated with the primary end point (hazard ratio [HR] 1.241; 95% CI 1.020–1.511; <i>p</i> = 0.031), CVEs (HR 1.337; 95% CI 1.067–1.675; <i>p</i> = 0.012) and HF (HR 1.489; 95% CI 1.059–2.091; <i>p</i> = 0.022), but not associated with all-cause death (HR 1.081; 95% CI 0.829–1.410; <i>p</i> = 0.565). The ROC curves showed that the optimal predictive BNP levels for the primary end point, CVEs and HF were 92.5, 127.0, and 274.6 (pg/mL) respectively. <b><i>Conclusion:</i></b> Asymptomatic elevation of BNP is strongly predictive for CVEs and HF, which might help to integrate cardio-renal risk stratification in predialysis CKD patients.
DOI: 10.6084/m9.figshare.7057862
title: Supplementary Material for: A Review of Diagnostic Impact of Amyloid Positron Emission Tomography Imaging in Clinical Practice

description: <b><i>Background:</i></b> Molecular imaging of brain amyloid for the diagnosis of Alzheimer’s disease (AD) using positron emission tomography (PET) has been approved for use in clinical practice by the Food and Drug Administration (FDA) since 2012. However, the clinical utility and diagnostic impact of amyloid PET imaging remain controversial. We conducted a review of the recent studies investigating clinical utility of amyloid PET imaging with focus on changes in diagnosis, diagnostic confidence, and patient management. <b><i>Summary:</i></b> A total of 16 studies were included in the final analysis. Overall rate of changes in diagnosis after amyloid PET ranged from 9 to 68% (pooled estimate of 31%, 95% CI 23–39%). All studies reported overall increase in diagnostic confidence or diagnostic certainty after amyloid PET. Changes in patient management ranged from 37 to 87%; the most common type of change in management reported was either the initiation or discontinuation of planned AD medications. <b><i>Key Messages:</i></b> Amyloid PET imaging led to moderate to significant changes in diagnosis, diagnostic confidence, and subsequent patient management. It may be most useful in patients with high level of diagnostic uncertainty even after the completing the standard workup.
DOI: 10.6084/m9.figshare.7057871.v1
title: Supplementary Material for: Switching from Epoetin Alfa (Epogen®) to Epoetin Alfa-Epbx (Retacrit<sup>TM</sup>) Using a Specified Dosing Algorithm: A Randomized, Non-Inferiority Study in Adults on Hemodialysis

description: <b><i>Background:</i></b> For patients with anemia undergoing hemodialysis, erythropoiesis-stimulating agents (ESAs) are typically dosed via precise algorithms. Using one such algorithm, we assessed the maintenance of hemoglobin levels in patients switched from epoetin alfa reference product (Epogen®) to epoetin alfa-epbx (Retacrit<sup>TM</sup>; a biosimilar to US-licensed Epogen®/Procrit®). <b><i>Methods:</i></b> This randomized, open-label, non-inferiority study was conducted at Fresenius Medical Care North America (FMCNA) hemodialysis centers. Patients with anemia and chronic kidney disease undergoing maintenance hemodialysis and receiving routine intravenous (IV) Epogen® were randomized 1: 1 to switch to IV Retacrit<sup>TM</sup> or continue standard-of-care (Epogen®) for 24 weeks, using analogous versions of the FMCNA ESA-dosing algorithm. The primary endpoint was the proportion of time patients’ hemoglobin was 9–11 g/dL during weeks 17–24. <b><i>Results:</i></b> Of 432 randomized patients, 418 received treatment (Retacrit<sup>TM</sup>, <i>n</i> = 212; standard-of-care, <i>n</i> = 206) and comprised the full analysis set. A similar proportion of patients discontinued from each arm. The proportion of time patients’ hemoglobin was within the target range was 61.9% (95% CI 57.5–66.2) in the Retacrit<sup>TM</sup> arm and 63.3% (95% CI 58.7–67.7) in the standard-of-care arm. The difference in proportions between treatment arms was –1.4% (95% CI –7.6 to 4.9), and the lower bound of the confidence interval was within the pre-specified non-inferiority margin of –12.5%. There was no statistically significant difference between arms in the mean change from baseline in the weekly mean ESA dose during weeks 17–24, and no clinically relevant differences in safety outcomes. <b><i>Conclusions:</i></b> Switching to Retacrit<sup>TM</sup> was non-inferior to continuing ­Epogen® in maintaining hemoglobin levels in patients receiving hemodialysis, when both ESAs were dosed using a specified algorithm (ClinicalTrials.gov, NCT02504294).
DOI: 10.6084/m9.figshare.7057862.v1
title: Supplementary Material for: A Review of Diagnostic Impact of Amyloid Positron Emission Tomography Imaging in Clinical Practice

description: <b><i>Background:</i></b> Molecular imaging of brain amyloid for the diagnosis of Alzheimer’s disease (AD) using positron emission tomography (PET) has been approved for use in clinical practice by the Food and Drug Administration (FDA) since 2012. However, the clinical utility and diagnostic impact of amyloid PET imaging remain controversial. We conducted a review of the recent studies investigating clinical utility of amyloid PET imaging with focus on changes in diagnosis, diagnostic confidence, and patient management. <b><i>Summary:</i></b> A total of 16 studies were included in the final analysis. Overall rate of changes in diagnosis after amyloid PET ranged from 9 to 68% (pooled estimate of 31%, 95% CI 23–39%). All studies reported overall increase in diagnostic confidence or diagnostic certainty after amyloid PET. Changes in patient management ranged from 37 to 87%; the most common type of change in management reported was either the initiation or discontinuation of planned AD medications. <b><i>Key Messages:</i></b> Amyloid PET imaging led to moderate to significant changes in diagnosis, diagnostic confidence, and subsequent patient management. It may be most useful in patients with high level of diagnostic uncertainty even after the completing the standard workup.
DOI: 10.6084/m9.figshare.7057097
title: Additional file 7: of Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

description: Table in Excel format containing the information about target mRNAs, CAI and tAI values of mRNA interacting regions using the data from Melamed et al. (2016) [21]. (XLSX 538 kb)
DOI: 10.6084/m9.figshare.7057097.v1
title: Additional file 7: of Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

description: Table in Excel format containing the information about target mRNAs, CAI and tAI values of mRNA interacting regions using the data from Melamed et al. (2016) [21]. (XLSX 538 kb)
DOI: 10.6084/m9.figshare.7057085
title: Additional file 5: of Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

description: Table in Excel format showing the number of interactions for each sRNA zones and for each site of interaction in target mRNAs. (XLSX 45 kb)
DOI: 10.6084/m9.figshare.7057085.v1
title: Additional file 5: of Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

description: Table in Excel format showing the number of interactions for each sRNA zones and for each site of interaction in target mRNAs. (XLSX 45 kb)
DOI: 10.6084/m9.figshare.7057079
title: Additional file 2: of Delftia sp. LCW, a strain isolated from a constructed wetland shows novel properties for dimethylphenol isomers degradation

description: Table S2. Proteome raw data for DMP treatments (XLSX 22980 kb)
DOI: 10.6084/m9.figshare.7057079.v1
title: Additional file 2: of Delftia sp. LCW, a strain isolated from a constructed wetland shows novel properties for dimethylphenol isomers degradation

description: Table S2. Proteome raw data for DMP treatments (XLSX 22980 kb)
DOI: 10.6084/m9.figshare.7057058
title: Additional file 2: of Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

description: Table in Excel format containing the information about sRNA, target mRNA, sRNA subzone, site of sRNA-mRNA interaction, CAI values, tAI values, and Î G of sRNA-mRNA interaction using the data from BRSD. (XLSX 111 kb)
DOI: 10.6084/m9.figshare.7057058.v1
title: Additional file 2: of Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

description: Table in Excel format containing the information about sRNA, target mRNA, sRNA subzone, site of sRNA-mRNA interaction, CAI values, tAI values, and Î G of sRNA-mRNA interaction using the data from BRSD. (XLSX 111 kb)
DOI: 10.6084/m9.figshare.7057004
title: Additional file 2: of Bio-hybrid inorganic microparticles derived from CO2 for highly efficient and selective removal of antibiotics

description: Table S1. Physical properties of mag-SiCC. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.7057004.v1
title: Additional file 2: of Bio-hybrid inorganic microparticles derived from CO2 for highly efficient and selective removal of antibiotics

description: Table S1. Physical properties of mag-SiCC. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.7056998
title: Additional file 3: of Reliability of a participant-friendly fecal collection method for microbiome analyses: a step towards large sample size investigation

description: Table S1. Read counts information per samples collected and stored by three different collection/storage methods (Fresh, O24h, O7d) and extracted using two different procedures (PF, QIA). Table S2. Comparison of the read counts per samples between three different collection/storage methods (Fresh, O24h, O7d) and two different DNA extraction procedures (PF, QIA). Paired sample t-test showed no statistical differences between any of the groups in terms of read count. (XLSX 15Â kb)
DOI: 10.6084/m9.figshare.7056998.v1
title: Additional file 3: of Reliability of a participant-friendly fecal collection method for microbiome analyses: a step towards large sample size investigation

description: Table S1. Read counts information per samples collected and stored by three different collection/storage methods (Fresh, O24h, O7d) and extracted using two different procedures (PF, QIA). Table S2. Comparison of the read counts per samples between three different collection/storage methods (Fresh, O24h, O7d) and two different DNA extraction procedures (PF, QIA). Paired sample t-test showed no statistical differences between any of the groups in terms of read count. (XLSX 15Â kb)
DOI: 10.6084/m9.figshare.7056953
title: Additional file 1: of Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle

description: Table S1. The full list of the significant gene from the gene-based analysis. Table S2. The full list of gene significant both in gene-based analysis and differential expression. (XLSX 72 kb)
DOI: 10.6084/m9.figshare.7056953.v1
title: Additional file 1: of Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle

description: Table S1. The full list of the significant gene from the gene-based analysis. Table S2. The full list of gene significant both in gene-based analysis and differential expression. (XLSX 72 kb)
DOI: 10.6084/m9.figshare.7056929
title: Additional file 7: of Human Alzheimer’s disease gene expression signatures and immune profile in APP mouse models: a discrete transcriptomic view of Aβ plaque pathology

description: Table S4. Laser captured plaque transcriptomic signature. RNAseq data of laser-capture microscopy of TgCRND8 plaques versus non-plaque tissue as outlined in schematic in Fig. 2. Data shows statistical analysis of TgCRND8 plaques versus non-plaque tissue at 6 months of age (p-value) and log10 analysis of the ratio of TgCRND8 plaques versus non-plaque tissue. Differentially expressed genes were identified by Pearson correlation or T-test using Matlab R2010b (Mathworks). A p-value cutoff of &lt; 0.001 was used to identify differentially expressed genes. The FDR corresponding to this p-value is given in each of the comparisons to convey relative signature confidence. Set annotation analysis was performed by comparing input sets to GeneGo ( www.genego.com ), Ingenuity ( www.ingenuity.com ) and KEGG ( www.genome.jp/kegg/ ) pathway sets. Bonferroni corrected hypergeometric p-values (expectation (e)-values) of less than 0.1 were considered significant overlap between sets. (XLS 1410 kb)
DOI: 10.6084/m9.figshare.7056929.v1
title: Additional file 7: of Human Alzheimer’s disease gene expression signatures and immune profile in APP mouse models: a discrete transcriptomic view of Aβ plaque pathology

description: Table S4. Laser captured plaque transcriptomic signature. RNAseq data of laser-capture microscopy of TgCRND8 plaques versus non-plaque tissue as outlined in schematic in Fig. 2. Data shows statistical analysis of TgCRND8 plaques versus non-plaque tissue at 6 months of age (p-value) and log10 analysis of the ratio of TgCRND8 plaques versus non-plaque tissue. Differentially expressed genes were identified by Pearson correlation or T-test using Matlab R2010b (Mathworks). A p-value cutoff of &lt; 0.001 was used to identify differentially expressed genes. The FDR corresponding to this p-value is given in each of the comparisons to convey relative signature confidence. Set annotation analysis was performed by comparing input sets to GeneGo ( www.genego.com ), Ingenuity ( www.ingenuity.com ) and KEGG ( www.genome.jp/kegg/ ) pathway sets. Bonferroni corrected hypergeometric p-values (expectation (e)-values) of less than 0.1 were considered significant overlap between sets. (XLS 1410 kb)
DOI: 10.6084/m9.figshare.7056914
title: MOESM1 of The effects of land cover change on carbon stock dynamics in a dry Afromontane forest in northern Ethiopia

description: Additional file 1. Sheet 1. Aboveground vegetation properties and soil organic carbon data. Sheet 2. Total carbon data for all land cover types. Sheet 3. Vegetation parameters data for individual species.
DOI: 10.6084/m9.figshare.7056914.v1
title: MOESM1 of The effects of land cover change on carbon stock dynamics in a dry Afromontane forest in northern Ethiopia

description: Additional file 1. Sheet 1. Aboveground vegetation properties and soil organic carbon data. Sheet 2. Total carbon data for all land cover types. Sheet 3. Vegetation parameters data for individual species.
DOI: 10.6084/m9.figshare.7056911
title: Additional file 6: of Human Alzheimer’s disease gene expression signatures and immune profile in APP mouse models: a discrete transcriptomic view of Aβ plaque pathology

description: Table S3. Human AD translational transcriptomic profiling. Comparative gene network analysis was carried out from the mouse models used (tgCRND8 and Tg2576 models) in our study with that of: 1) Human aging (up-regulated) 2) Human aging (down-regulated) 3) Human inflammation signature 3) ITIM/ITAM-domain associated network signature 3) Mouse microglial gene signature (Barres et al., 2013) and 4) Mouse disease-associated microglia or DAM gene signature (Keren-Sheul et al., 2017). Differentially expressed genes were identified by Pearson correlation or T-test using Matlab R2010b (Mathworks). A. p-value cutoff of &lt; 0.001 was used to identify differentially expressed genes. The FDR corresponding to this p-value is given in each of the comparisons to convey relative signature confidence. Set annotation analysis was performed by comparing input sets to GeneGo ( www.genego.com ), Ingenuity ( www.ingenuity.com ) and KEGG ( www.genome.jp/kegg/ ) pathway sets. Bonferroni corrected hypergeometric p-values (expectation (e)-values) of less than 0.1 were considered significant overlap between sets. (XLSX 26 kb)
DOI: 10.6084/m9.figshare.7056911.v1
title: Additional file 6: of Human Alzheimer’s disease gene expression signatures and immune profile in APP mouse models: a discrete transcriptomic view of Aβ plaque pathology

description: Table S3. Human AD translational transcriptomic profiling. Comparative gene network analysis was carried out from the mouse models used (tgCRND8 and Tg2576 models) in our study with that of: 1) Human aging (up-regulated) 2) Human aging (down-regulated) 3) Human inflammation signature 3) ITIM/ITAM-domain associated network signature 3) Mouse microglial gene signature (Barres et al., 2013) and 4) Mouse disease-associated microglia or DAM gene signature (Keren-Sheul et al., 2017). Differentially expressed genes were identified by Pearson correlation or T-test using Matlab R2010b (Mathworks). A. p-value cutoff of &lt; 0.001 was used to identify differentially expressed genes. The FDR corresponding to this p-value is given in each of the comparisons to convey relative signature confidence. Set annotation analysis was performed by comparing input sets to GeneGo ( www.genego.com ), Ingenuity ( www.ingenuity.com ) and KEGG ( www.genome.jp/kegg/ ) pathway sets. Bonferroni corrected hypergeometric p-values (expectation (e)-values) of less than 0.1 were considered significant overlap between sets. (XLSX 26 kb)
DOI: 10.5281/zenodo.3997180
title: Tempus results

description: This document shows the results after executing four test cases using Tempus: Ecological Island, Terminal Self-Service, DENMEvaP-BruteForce, and DENMEvaP-SnifferCongestion.
DOI: 10.5281/zenodo.3997179
title: Tempus results

description: This document shows the results after executing four test cases using Tempus: Ecological Island, Terminal Self-Service, DENMEvaP-BruteForce, and DENMEvaP-SnifferCongestion.