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1,514 results for “Widespread”
Targeting DNA topoisomerases or checkpoint kinases forces a shift in global protein homeostasis, triggering widespread protein aggregation
GEO Series GSE173940. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
Data from: Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection
Identifying local adaptation is crucial in conservation biology in order to define ecotypes and establish management guidelines. Local adaptation is often inferred from the detection of loci showing a high differentiation between populations, the so-called FST outliers. Methods of detection of loci under selection are reputed to be robust in most spatial population models. However, using simulations we showed that FST outlier tests provided a high rate of false positives (up to 60%) in fractal environments such as river networks. Surprisingly, the number of sampled demes was correlated with parameters of population genetic structure, such as the variance of FSTs, and hence strongly influenced the rate of outliers. This unappreciated property of river networks therefore needs to be accounted for in genetic studies on adaptation and conservation of river organisms.
Widespread increasing vegetation sensitivity to soil moisture
<p>This repository contains the datasets used in the research article "Widespread increasing vegetation sensitivity to soil moisture".</p> <p>The repository contains the following folders: 1) demo_data.zip - contains all the raw datasets used for training the random forests and inferring sensitivity analysis using Shapley Additive Explanations method. 2) fig_data.zip - contains all the processed data and additional datasets for creating main figures in the article.</p> <p>Formats: All scripts are in the programming language Python. The datasets are in the NumPy array format.</p> <p>The codes related to the research article are available in the following repository: https://doi.org/10.5281/zenodo.6554966</p>
Figure 2 from: Soares IM, Azevedo-Santos VM, Benine RC (2017) Redescription of Moenkhausia megalops (Eigenmann, 1907), a widespread tetra from the Amazon basin (Characiformes, Characidae). Zoosystematics and Evolution 93(2): 255-264. https://doi.org/10.3897/zse.93.10837
Figure 2 - Moenkhausia megalops, INPA 48895, 41.9 mm SL, Brazil, Amazonas State, rio Japurá basin.
Figure 4 from: Maddison DR, Anderson R (2016) Hidden species within the genus Ocys Stephens: the widespread species O. harpaloides (Audinet-Serville) and O. tachysoides (Antoine) (Coleoptera, Carabidae, Bembidiini). Deutsche Entomologische Zeitschrift 63(2): 287-301. https://doi.org/10.3897/dez.63.10748
Figure 4 - Diagram of gonocoxite of a female Ocys showing measurements taken.
Phylogeny and disease links of a widespread and ancient gut phage lineage
<p>Viruses are a core component of the human microbiome, impacting health and disease through interactions with gut bacteria<sup>1</sup> and the immune system<sup>2</sup>. Most viruses in the human microbiome are bacteriophages, which exclusively infect bacteria. Individual gut bacteriophages can affect bacterial bile acid deconjugation<sup>3</sup>, and can alter their infection strategy based on dietary content<sup>4</sup>. Up to recently, most studies of the gut virome have focused on low taxonomic scales (e.g., viral operational taxonomic units), hampering population-level analyses. We previously identified the expansive and widespread Candidatus <em>Heliusviridae</em> bacteriophage family in a cohort with inhabitants of Amsterdam, the Netherlands. Here, we study their biodiversity and evolution in a wide variety of human populations. With a detailed phylogeny based on sequences from six viral genome databases, we now propose the Candidatus order <em>Heliusvirales</em> to accommodate these viruses. We identify <em>Ca.</em> <em>Heliusvirales</em> viruses in 80% of 5,441 individuals across 39 studies, and also in thirteen out of sixteen analyzed metagenomes from ancient humans that lived in Europe and North America between 1,000 and 5,000 years ago. We show that a large <em>Ca.</em> <em>Heliusvirales</em> lineage has diversified starting with human dispersion across the globe some 50,000 years ago. Ancient peoples and modern hunter-gatherers further have distinct <em>Ca.</em> <em>Heliusvirales</em> populations that are characterized by lower richness than modern urbanized people. Within urbanized people, those suffering from type 1 and type 2 diabetes, as well as inflammatory bowel disease, have higher <em>Ca.</em> <em>Heliusvirales </em>richness than healthy controls. We thus conclude that these ancient core members of the human gut virome have thrived with increasingly westernized lifestyles of the human population.</p>
Description of three widespread new Peronospora species parasitising Caryophyllales
<p><span>The phylogenetic relationships based on a concatenated alignment </span><span>from </span><span>two nuclear loci (</span><span>ITS, <em>HSP90</em>) and four mitochondrial loci (<em>COX</em>2, cox2-1 spacer, <em>COX</em>1, <em>NAD1</em>, and <em>RPS10</em>)</span><span> revealed no supported conflicting topologies with the trees based on individual genes. The concatenated alignment</span><span> </span><span>contained</span><span> </span><span>a total of 86 specimens</span><span>, each with all loci amplified and included, resulting in an overall alignment length of 4378 sites.</span></p>
Figures 28-29 from: van Nieukerken EJ, Gilrein DO, Eiseman CS (2018) Stigmella multispicata Rociene. & Stonis, an Asian leafminer on Siberian elm, now widespread in eastern North America (Lepidoptera, Nepticulidae). ZooKeys 784: 95-125. https://doi.org/10.3897/zookeys.784.27296
Figures 28-29 Stigmellamultispicata, distribution.
TGIRT-seq of Inflammatory Breast Cancer Tumor and Blood Samples Reveals Widespread Enhanced Transcription Impacting RNA Splicing and Intronic RNAs in Plasma
<p>Supplemental file containing results of mapping statistics, DESeq2 analysis, peak calling analysis, and IDR analysis.</p>
Data from: Veronica officinalis product authentication using DNA metabarcoding and HPLC-MS reveals widespread adulteration with Veronica chamaedrys
Studying herbal products derived from local and traditional knowledge and their value chains is one of the main challenges in ethnopharmacology. The majority of these products have a long history of use, but non-harmonized trade and differences in regulatory policies between countries impact their value chains and lead to concerns over product efficacy, safety and quality. Veronica officinalis L. (common speedwell), a member of Plantaginaceae family, has a long history of use in European traditional medicine, mainly in central eastern Europe and the Balkans. However, no specified control tests are available either to establish the quality of derived herbal products or for the discrimination of its most common substitute, V. chamaedrys L. (germander speedwell). In this study, we use DNA metabarcoding and high performance liquid chromatography coupled with mass spectrometry (HPLC-MS) to authenticate sixteen V. officinalis herbal products and compare the potential of the two approaches to detect substitution, adulteration and the use of unreported constituents. HPLC-MS showed high resolution in detecting phytochemical target compounds, but did not enable detection of specific plant species in the products. DNA metabarcoding detected V. officinalis in only 15% of the products, whereas it detected V. chamaedrys in 62% of the products. The results confirm that DNA metabarcoding can be used to test for the presence of Veronica species, and detect substitution and/or admixture of other Veronica species, as well as simultaneously detect all other species present. Our results confirm that none of the herbal products contained exactly the species listed on the label, and all included substitutes, contaminants or fillers. This study highlights the need for authentication of raw herbals along the value chain of these products. An integrative methodology can assess both the quality of herbal products in terms of target compound concentrations and species composition, as well as admixture and substitution with other chemical compounds and plants.
Figure 67 from: Bouzan RS, Iniesta LFM, Pena-Barbosa JPP, Brescovit AD (2021) Six new species of the widespread Brazilian millipede genus Eucampesmella (Polydesmida: Chelodesmidae). Zoologia 38: 1-22. https://doi.org/10.3897/zoologia.38.e66300
Figure 67 Distribution map of Eucampesmella species.
Figure 66 from: Bouzan RS, Iniesta LFM, Pena-Barbosa JPP, Brescovit AD (2021) Six new species of the widespread Brazilian millipede genus Eucampesmella (Polydesmida: Chelodesmidae). Zoologia 38: 1-22. https://doi.org/10.3897/zoologia.38.e66300
Figure 66 Distribution map of Eucampesmella species.
Stop codon recoding is widespread in diverse phage lineages and has the potential to regulate translation of late stage and lytic genes
<p>Genomes and supplementary materials for Borges et al. 2021 (<strong>Stop codon recoding is widespread in diverse phage lineages and has the potential to regulate translation of late stage and lytic genes) </strong></p>
Analysis and data for "No evidence of widespread algal bloom intensification in hundreds of lakes"
<p>Analysis code (written for R) and data used for Wilkinson et al. "No evidence of widespread algal bloom intensification in hundreds of lakes" in Frontiers in Ecology and the Environment</p>
Figure 3 from: Edgecombe GD, Vahtera V, Giribet G, Kaunisto P (2015) Species limits and phylogeography of Newportia (Scolopendromorpha) and implications for widespread morphospecies. In: Tuf IH, Tajovský K (Eds) Proceedings of the 16th International Congress of Myriapodology, Olomouc, Czech Republic. ZooKeys 510: 65-77. https://doi.org/10.3897/zookeys.510.8573
Figure 3 - Maximum likelihood tree (lnL = -14054.372302). Abbreviations for countries as in Fig. 2.
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
Sleep Disturbances and Chronic Widespread Pain
ClinicalTrials.gov study NCT03908905. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Local and Widespread Hypoalgesic Effects of Neurodynamic Mobilization in Healthy Controls
ClinicalTrials.gov study NCT03674489. IPD Sharing: NO. Countries: 1. Publications: 0.
Chronic Widespread Pain After Rapid Weight Loss in Non-Hispanic Black and Hispanic/Latino/a/x Adults
ClinicalTrials.gov study NCT06795386. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.