cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4688775
title: Identification of beneficial and detrimental bacteria that impact sorghum responses to drought using multi-scale and multi-system microbiome comparisons

description: <strong>Background: </strong>Drought is a major abiotic stress that limits agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere and may lead to enrichment of specific groups of microbes, such as <em>Actinobacteria</em>. How these changes ultimately affect plant health is not well understood. In parallel, model systems have been used to tease apart the specific interactions between plants and single, or small groups of microbes. However, translating this work into crop species and achieving increased crop yields within noisy field settings remains a challenge. Thus, the next scientific leap forward in microbiome research must cross the great lab-to-field divide. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key beneficial and detrimental, root associated microbes that robustly affect drought stressed plant phenotypes. <strong>Results: </strong>Fifty-three bacterial strains, originally characterized for association with <em>Arabidopsis</em>, were applied to sorghum seeds and their effect on root growth was monitored for seven days. Two <em>Arthrobacter </em>strains, members of the <em>Actinobacteria </em>phylum, caused root growth inhibition (RGI) in <em>Arabidopsis</em> and sorghum. In the context of synthetic communities, strains of <em>Variovorax</em> were able to protect both <em>Arabidopsis </em>and sorghum from the RGI caused by <em>Arthrobacter</em>. As a transitional system, we tested the synthetic communities through a 24-day high-throughput sorghum phenotyping assay and found that during drought stress, plants colonized by <em>Arthrobacter</em> were significantly smaller and had reduced leaf water content as compared to control plants. However, plants colonized by both <em>Arthrobacter</em> and <em>Variovorax</em> performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across well-watered and drought conditions. Drought responsive microbes were identified, including an enrichment in <em>Actinobacteria</em>, consistent with previous findings. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a newly developed method and were then able to observe that the abundance of <em>Arthrobacter</em> strains negatively correlated with plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of high confidence bacterial taxa that positively associated with plant growth under drought stress. <strong>Conclusions: </strong>A three-tiered experimental system connected reductionist and ecological approaches and identified beneficial and deleterious bacterial strains for sorghum under drought stress.
DOI: 10.6084/m9.figshare.13466849
title: Additional file 1 of Insecticide resistance status of Aedes aegypti in Bangladesh

description: Additional file 1. All enzyme data.
DOI: 10.6084/m9.figshare.13466849.v1
title: Additional file 1 of Insecticide resistance status of Aedes aegypti in Bangladesh

description: Additional file 1. All enzyme data.
DOI: 10.6084/m9.figshare.13466851.v1
title: Additional file 2 of Insecticide resistance status of Aedes aegypti in Bangladesh

description: Additional file 2. All phenotype and genotype data.
DOI: 10.6084/m9.figshare.13525197
title: Additional file 1 of Subcutaneous ticks: a first report in a golden jackal, and their absence in non-canid carnivores

description: Additional file 1. Database with all examined carnivores.
DOI: 10.6084/m9.figshare.13525197.v1
title: Additional file 1 of Subcutaneous ticks: a first report in a golden jackal, and their absence in non-canid carnivores

description: Additional file 1. Database with all examined carnivores.
DOI: 10.6084/m9.figshare.13466851
title: Additional file 2 of Insecticide resistance status of Aedes aegypti in Bangladesh

description: Additional file 2. All phenotype and genotype data.
DOI: 10.6084/m9.figshare.16655466.v2
title: Additional file 1 of Criteria for assessing the quality of clinical practice guidelines in paediatrics and neonatology: a mixed-method study

description: Additional file 1. Hierarchical clustering.
DOI: 10.6084/m9.figshare.16655466
title: Additional file 1 of Criteria for assessing the quality of clinical practice guidelines in paediatrics and neonatology: a mixed-method study

description: Additional file 1. Hierarchical clustering.
DOI: 10.6084/m9.figshare.16655466.v1
title: Additional file 1 of Criteria for assessing the quality of clinical practice guidelines in paediatrics and neonatology: a mixed-method study

description: Additional file 1. Hierarchical clustering.
DOI: 10.6084/m9.figshare.16599575
title: Additional file 1 of Identifying FDA-approved drugs with multimodal properties against COVID-19 using a data-driven approach and a lung organoid model of SARS-CoV-2 entry

description: Additional file 1: Table S1. Genes differentially expressed in lung cancer cells after exposure to SARS-CoV-2. Source: Blanco-Melo et al. (2020). Table S2. Connectivity mapping results generated by querying the SARS-CoV-2 transcriptional signature into CMap. Table S3. Source of the drug structures utilized in the chemoinformatic analysis testing for interaction with SARS-CoV-2 enzymes.
DOI: 10.6084/m9.figshare.16599575.v1
title: Additional file 1 of Identifying FDA-approved drugs with multimodal properties against COVID-19 using a data-driven approach and a lung organoid model of SARS-CoV-2 entry

description: Additional file 1: Table S1. Genes differentially expressed in lung cancer cells after exposure to SARS-CoV-2. Source: Blanco-Melo et al. (2020). Table S2. Connectivity mapping results generated by querying the SARS-CoV-2 transcriptional signature into CMap. Table S3. Source of the drug structures utilized in the chemoinformatic analysis testing for interaction with SARS-CoV-2 enzymes.
DOI: 10.6084/m9.figshare.16735693
title: Additional file 2 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 2. Zip file containing all input mass spectral data for ALPINAC, for the training and validation sets. There is one text file per compound.
DOI: 10.6084/m9.figshare.16735693.v1
title: Additional file 2 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 2. Zip file containing all input mass spectral data for ALPINAC, for the training and validation sets. There is one text file per compound.
DOI: 10.6084/m9.figshare.16735696
title: Additional file 3 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 3. Zip file containing all output formulae produced by ALPINAC, for the training and validation sets, using the default settings. There is one text file per compound.
DOI: 10.6084/m9.figshare.16735696.v1
title: Additional file 3 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 3. Zip file containing all output formulae produced by ALPINAC, for the training and validation sets, using the default settings. There is one text file per compound.
DOI: 10.6084/m9.figshare.16735699
title: Additional file 4 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 4. Zip file containing all input mass spectral data, converted to .TRA files compatible with MOLGEN-MS.
DOI: 10.6084/m9.figshare.16735699.v1
title: Additional file 4 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 4. Zip file containing all input mass spectral data, converted to .TRA files compatible with MOLGEN-MS.
DOI: 10.6084/m9.figshare.16735702.v1
title: Additional file 5 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 5. ZIP file containing all input mass spectral data, converted to .jdx files compatible with the NIST EI library.
DOI: 10.6084/m9.figshare.16735702
title: Additional file 5 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 5. ZIP file containing all input mass spectral data, converted to .jdx files compatible with the NIST EI library.
DOI: 10.6084/m9.figshare.16735705
title: Additional file 6 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 6. Latex version of the supplementary material (Additional file 1), format .tex.
DOI: 10.6084/m9.figshare.16735705.v1
title: Additional file 6 of Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

description: Additional file 6. Latex version of the supplementary material (Additional file 1), format .tex.
DOI: 10.6084/m9.figshare.16735710
title: Additional file 3 of Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

description: Additional file 3. Table S1: Number of high-feedback topologies containing each node in the (a) T cell, (b) EMT, and (c) TRRUST2 strongly connected components, limiting cycles to 5 nodes and topologies to 10 nodes.
DOI: 10.6084/m9.figshare.16735710.v1
title: Additional file 3 of Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

description: Additional file 3. Table S1: Number of high-feedback topologies containing each node in the (a) T cell, (b) EMT, and (c) TRRUST2 strongly connected components, limiting cycles to 5 nodes and topologies to 10 nodes.
DOI: 10.6084/m9.figshare.16735719
title: Additional file 5 of Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

description: Additional file 5. Repository snapshot (ZIP): Archive of the HiLoop repository, including code and network structures.
DOI: 10.6084/m9.figshare.16735719.v1
title: Additional file 5 of Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

description: Additional file 5. Repository snapshot (ZIP): Archive of the HiLoop repository, including code and network structures.
DOI: 10.6084/m9.figshare.16735723
title: Additional file 2 of Exome sequencing implicates a novel heterozygous missense variant in DSTYK in autosomal dominant lower urinary tract dysfunction and mild hereditary spastic paraparesis

description: Additional file 2.
DOI: 10.6084/m9.figshare.16735723.v1
title: Additional file 2 of Exome sequencing implicates a novel heterozygous missense variant in DSTYK in autosomal dominant lower urinary tract dysfunction and mild hereditary spastic paraparesis

description: Additional file 2.
DOI: 10.6084/m9.figshare.16735729
title: Additional file 3 of Exome sequencing implicates a novel heterozygous missense variant in DSTYK in autosomal dominant lower urinary tract dysfunction and mild hereditary spastic paraparesis

description: Additional file 3.
DOI: 10.6084/m9.figshare.16735735
title: Additional file 4 of Exome sequencing implicates a novel heterozygous missense variant in DSTYK in autosomal dominant lower urinary tract dysfunction and mild hereditary spastic paraparesis

description: Additional file 4.
DOI: 10.6084/m9.figshare.16735735.v1
title: Additional file 4 of Exome sequencing implicates a novel heterozygous missense variant in DSTYK in autosomal dominant lower urinary tract dysfunction and mild hereditary spastic paraparesis

description: Additional file 4.
DOI: 10.6084/m9.figshare.16735729.v1
title: Additional file 3 of Exome sequencing implicates a novel heterozygous missense variant in DSTYK in autosomal dominant lower urinary tract dysfunction and mild hereditary spastic paraparesis

description: Additional file 3.
DOI: 10.6084/m9.figshare.13535669
title: Additional file 2 of Validation of loop-mediated isothermal amplification for the detection of Loa loa infection in Chrysops spp in experimental and natural field conditions

description: Additional file 2: Table S2. The Different parasite stages found in the dissected Chrysops flies.
DOI: 10.6084/m9.figshare.13535669.v1
title: Additional file 2 of Validation of loop-mediated isothermal amplification for the detection of Loa loa infection in Chrysops spp in experimental and natural field conditions

description: Additional file 2: Table S2. The Different parasite stages found in the dissected Chrysops flies.
DOI: 10.6084/m9.figshare.13364426.v1
title: Additional file 11 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 11: Table S5: Summary of host blood-meal composition. Table shows, the number of mosquitoes carrying mammalian DNA, the percent match of sequence to the NCBI database, and average count of reads per mammal.
DOI: 10.6084/m9.figshare.13364426
title: Additional file 11 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 11: Table S5: Summary of host blood-meal composition. Table shows, the number of mosquitoes carrying mammalian DNA, the percent match of sequence to the NCBI database, and average count of reads per mammal.
DOI: 10.6084/m9.figshare.13364429.v1
title: Additional file 12 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 12: Table S6: Eukaryotic parasite and virus identification. Table shows the primer name, sample name, collection site, mosquito species, and taxonomic information per sequence identified. Table also gives, for each sequence, the frequency per sample (count), percentage match to NCBI database (% Identity), length of sequence (in bp), and length of the match (in bp) to NCBI sequence
DOI: 10.6084/m9.figshare.13364432.v1
title: Additional file 13 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 13: Figure S7: PCoA plot showing the dissimilarity between the microbial composition of individual mosquitoes based on Bray-Curtis dissimilarity metric for An. gambiae mosquitoes only, from sites in Guinea (A) and all Anopheles mosquitoes identified in Boffa, Guinea (B). Each dot represents the bacterial composition of a single mosquito. The numbers in brackets near the axes indicate the proportion of the variance explained by the PC 1 and 2.
DOI: 10.6084/m9.figshare.13364432
title: Additional file 13 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 13: Figure S7: PCoA plot showing the dissimilarity between the microbial composition of individual mosquitoes based on Bray-Curtis dissimilarity metric for An. gambiae mosquitoes only, from sites in Guinea (A) and all Anopheles mosquitoes identified in Boffa, Guinea (B). Each dot represents the bacterial composition of a single mosquito. The numbers in brackets near the axes indicate the proportion of the variance explained by the PC 1 and 2.
DOI: 10.6084/m9.figshare.13364429
title: Additional file 12 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 12: Table S6: Eukaryotic parasite and virus identification. Table shows the primer name, sample name, collection site, mosquito species, and taxonomic information per sequence identified. Table also gives, for each sequence, the frequency per sample (count), percentage match to NCBI database (% Identity), length of sequence (in bp), and length of the match (in bp) to NCBI sequence
DOI: 10.6084/m9.figshare.13364441.v1
title: Additional file 3 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 3: Figure S2: Distribution of reads counts for the mammalia_16S primer across mosquito samples and negative control samples.
DOI: 10.6084/m9.figshare.13364441
title: Additional file 3 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 3: Figure S2: Distribution of reads counts for the mammalia_16S primer across mosquito samples and negative control samples.
DOI: 10.6084/m9.figshare.13364456
title: Additional file 8 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 8: Table S3: Pairwise comparison of cox1 and S200X.6 primers for resolution of mosquito species. Table shows, for each pairwise-comparison between two Anopheles species, the mean nucleotide difference and range for cox1 and S200X.6 loci
DOI: 10.6084/m9.figshare.13364456.v1
title: Additional file 8 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 8: Table S3: Pairwise comparison of cox1 and S200X.6 primers for resolution of mosquito species. Table shows, for each pairwise-comparison between two Anopheles species, the mean nucleotide difference and range for cox1 and S200X.6 loci
DOI: 10.6084/m9.figshare.13364459
title: Additional file 9 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 9: Figure S6: Distribution of L1014F mutation (kdr-w) in mosquitoes grouped by Anopheles species. Numbers above each bar represent the total number of mosquitoes that kdr-w genotype is identified, per site. RR Homozygous resistant, SS homozygous sensitive, R/S heterozygous.
DOI: 10.6084/m9.figshare.13364459.v1
title: Additional file 9 of Relative contributions of various endogenous and exogenous factors to the mosquito microbiota

description: Additional file 9: Figure S6: Distribution of L1014F mutation (kdr-w) in mosquitoes grouped by Anopheles species. Numbers above each bar represent the total number of mosquitoes that kdr-w genotype is identified, per site. RR Homozygous resistant, SS homozygous sensitive, R/S heterozygous.
DOI: 10.6084/m9.figshare.13534886
title: Additional file 10 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 10: Table S8. Differential regulation statistics for transcripts of dipteran immune pathways of interest (Toll, Imd, DUOX and JAK-STAT) across samples. ns Not significantly differentially regulated.
DOI: 10.6084/m9.figshare.13534886.v1
title: Additional file 10 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 10: Table S8. Differential regulation statistics for transcripts of dipteran immune pathways of interest (Toll, Imd, DUOX and JAK-STAT) across samples. ns Not significantly differentially regulated.
DOI: 10.6084/m9.figshare.13534898
title: Additional file 3 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 3: Table S1. Read mapping summaries. This table shows the read mapping information for each sample; for example, number of reads, percentage of read mapped, among others.
DOI: 10.6084/m9.figshare.13534898.v1
title: Additional file 3 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 3: Table S1. Read mapping summaries. This table shows the read mapping information for each sample; for example, number of reads, percentage of read mapped, among others.
DOI: 10.6084/m9.figshare.13534901
title: Additional file 4 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 4: Table S2. Transcripts associated with trypanosomatid presence in the blood meal. This table shows the fold-changes and differential regulation statistics (including P values) for transcripts whose abundance differed between trypanosomatid fed flies and blood-fed control flies.
DOI: 10.6084/m9.figshare.13534901.v1
title: Additional file 4 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 4: Table S2. Transcripts associated with trypanosomatid presence in the blood meal. This table shows the fold-changes and differential regulation statistics (including P values) for transcripts whose abundance differed between trypanosomatid fed flies and blood-fed control flies.
DOI: 10.6084/m9.figshare.13534904.v1
title: Additional file 5 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 5: Table S3. Transcripts associated with specific trypanosomatids in the blood meal. This table shows the fold changes and differential regulation statistics (including P values) for transcripts whose abundance differed between trypanosomatid infections.
DOI: 10.6084/m9.figshare.13534907
title: Additional file 6 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 6: Table S4. Transcripts significantly differentially regulated between 1 day and 4 days post blood meal (blood-only) in P. papatasi.
DOI: 10.6084/m9.figshare.13534904
title: Additional file 5 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 5: Table S3. Transcripts associated with specific trypanosomatids in the blood meal. This table shows the fold changes and differential regulation statistics (including P values) for transcripts whose abundance differed between trypanosomatid infections.
DOI: 10.6084/m9.figshare.13534907.v1
title: Additional file 6 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 6: Table S4. Transcripts significantly differentially regulated between 1 day and 4 days post blood meal (blood-only) in P. papatasi.
DOI: 10.6084/m9.figshare.13534910
title: Additional file 7 of The Phlebotomus papatasi systemic transcriptional response to trypanosomatid-contaminated blood does not differ from the non-infected blood meal

description: Additional file 7: Table S5. Transcripts significantly differentially regulated between 4 day and 9 days post blood meal (blood-only) in P. papatasi.