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2,967 results for “secondary”
Figs. 6–7 in Complex of primary and secondary parasitoids (Hymenoptera: Encyrtidae and Signiphoridae) of Hypogeococcus spp. mealybugs (Hemiptera: Pseudococcidae) in the New World
Figs. 6–7. Leptomastidea debachi female (paratypes): 6, antenna; 7, fore wing.
Figs. 4–5 in Complex of primary and secondary parasitoids (Hymenoptera: Encyrtidae and Signiphoridae) of Hypogeococcus spp. mealybugs (Hemiptera: Pseudococcidae) in the New World
Figs. 4–5. Leptomastidea abnormis male (holotype of L. antillicola): 4, slide; 5, habitus.
Fig. 4 in The complete mitochondrial genome of Platygaster robiniae (Hymenoptera: Platygastridae): A novel tRNA secondary structure, gene rearrangements and phylogenetic implications
Fig. 4. (continued).
Fig. 3 in The complete mitochondrial genome of Platygaster robiniae (Hymenoptera: Platygastridae): A novel tRNA secondary structure, gene rearrangements and phylogenetic implications
Fig. 3. The secondary structure of 22 tRNA in Platygaster robiniae.
High-speed videos for the paper "The role of secondary recoil leaders in the formation of subsequent return strokes"
<p>This page contains high-speed video files as .cine files, that can be watched frame by frame to reproduce the analysis done in the paper titled "The role of secondary recoil leaders in the formation of subsequent return strokes", submitted for publication in Geophysical Research Letters.</p> <p><em>Instructions to watch the videos</em>: <strong>UP 44.cine </strong>and<strong> UP 154.cine:</strong></p> <p>Download and use the software Phantom Camera Control (PCC) available at:</p> <p><a href="https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware">https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware</a></p> <p>The Phantom Camera Control (PCC) software is compatible with Windows 7 Pro and Windows 8.1 and Windows 10, for both 32 and 64-bit operating systems.</p> <p>It is important to highlight that the time stamped on the video of the <strong>UP 44.cine</strong> is 97 ms delayed compared to the Earth Networks Total Lightning Network (ENTLN).</p>
Armitage et al., 2024. Multidecadal changes in coastal benthic species (primary and secondary data: SOTEAG)
<p>The primary data forms part of an ongoing monitoring programme by the Shetland Oil Terminal Environmental Advisory Group (SOTEAG). SOTEAG has undertaken biennial benthic surveys in Sullom Voe and the surrounding areas since 1974 to determine the status of infaunal communities and seafloor sediments. For more information on SOTEAG and the types of monitoring they practice please see: <a href="https://soteag.org.uk/">https://soteag.org.uk/</a>. A full description of sampling methods can be found here: https://soteag.org.uk/environmental-monitoring/monitoring-reports/</p> <p>These published data are a subset of the original SOTEAG data. They were used in a 32-year analysis of benthic infaunal invertebrates to investigate changes in species and trait composition, as well as detect long-term temperature-related shifts in benthic species and communities. The manuscript is entitled “Multidecadal changes in coastal benthic species composition and ecosystem functioning occur independently of temperature-driven community shifts” by Armitage et al., 2024. It should be noted that many of these data are a subset of the original and may have been transformed or standardised for statistical analyses. Below is a description of each .csv file, and whether the data are primary or secondary data, and how they are used in the analyses for the published paper. For details on how the data were reduced, please see the publication. In summary, a total of 15 stations were selected with a biennial year range of 1986 – 2018. The depth range covered 4 – 58m, where stations were classified into shallow, intermediate, and deep depth groups. </p> <p><strong>Env.csv</strong></p> <p><em>Primary data</em>: The environmental data that were collected during sampling by SOTEAG. The data are in long format with each column denoting: Station, Year, Water depth (m), Gravel %, Sand %, Mud %, Grain size, Water temperature (ᵒC), and water salinity. </p> <p><strong>Indices.csv</strong></p> <p><em>Secondary data</em>: These data are the various species and functional indices created from an analysis using species and trait data. The data are in long format and have row names consisting of the station and year, as well as column headings species richness (sp.rich), total species abundance (abundance) species diversity: Shannon (shannon), species diversity: Simpson (simpson), species evenness (evenness), functional richness (FRic), functional evenneness (FEve), functional diversity, also knowns as dispersion (FDis), functional redundancy (Red) species vulnerability (Vul), sampled station (Station), year of sampling (Year), classified depth group (Depth_group), and the decade (Decade). </p> <p><strong>Species.csv</strong></p> <p><em>Primary data</em>: This is the SOTEAG species abundance/community list that has been reduced (rare species were removed, a subset of stations were selected, and data were averaged and standardised across replicates). </p> <p><strong>Species_classification_all.csv </strong></p> <p><em>Secondary data</em>: This is extra supporting information for the species list highlighting the taxonomic ranking (coloumns) and species (rows). </p> <p><strong>STI_and_EcoFun_per_sp.csv</strong></p> <p><em>Secondary data</em>: This is data produced from a combination of species abundance and traits. The first two columns are row ID and species names. Columns 3-12 describe five ecosystem functions with the calculations/methodology provided in the publication. These data show the mean and scaled (0-1) scores for each function. The column headings equate to bioturbation (Bio), sediment stability (Sed), nutrient recycling (NutR), low trophic position (lowTP), high trophic position (highTP). The following columns X0 – X100 represent the quantiles of temperature affinities, with 50 being the 50% mark. The last column is the number of records (nrec) related to the thermal affinity search. </p> <p><strong>STImacroSoteag.csv </strong></p> <p><em>Secondary data</em>: Species thermal affinities (methods described in publication) with the first two columns as row ID and species names. The following columns X0 – X100 represent the quantiles of temperature affinities, with 50 being the 50% mark. The last column is the number of records (nrec) related to the thermal affinity search. </p> <p><strong>Traits.csv</strong></p> <p><em>Primary data</em>: Trait data where each species is listed as a row and traits as a coloumn heading. For trait column names please see publication. Note that the ranges of scores given to a species affinity to a trait is 0-3, which may be split depening on the affinity of the species (i.e., fuzzy coding). </p> <p><strong>Traits_EcoF.csv</strong></p> <p><em>Secondary data</em>: This is data produced from a combination of species abundance and traits. The first two columns are row ID and species names. Columns represent the trait used for the calculation of the Ecosystem Function (please see equations 2 – 6 in the publication). The column headings equate to bioturbation (Bio), sediment stability (Sed), nutrient recycling (NutR), low trophic position (lowTP), high trophic position (highTP), while the trait abbreviations can be found in Table 1 of the publication. The scores follow the fuzzy coding approach highlighted above (Traits.csv). </p> <p><strong>TrendsVsSTIHats.csv</strong></p> <p><em>Secondary data</em>: Results from species thermal index analysis and used to plot figure 4 of the main publication. Data is in long format. </p> <p><strong>tsSSTdata.csv</strong></p> <p><em>Primary data:</em> Sea surface temperatures used in the species distribution model to fit species thermal indices. See publication for details (section 2.3). </p> <p><strong>Vulnerability.csv </strong></p> <p><em>Secondary data:</em> A species index that was a result from the main species abundance/community data. Index values are given for each species (rows) by station/year (columns). Community means of the index can be found in “indices.csv”.</p>
Survey and secondary data on digitization
<p>Survey data: The data was gathered from October 15, 2023, to February 20, 2024 from 341 respondents through sharing of the questionnaire links on various social media platforms using Google Forms. <br>Data on "Voice and Accountability, Government Effectiveness, and Digital Payment"<br>Data on ITU database.<br>Data on DESI database</p>
Data for "Summertime Secondary Convection and Interaction with Sea Breeze Circulations"
<p>Datasets for manuscript entitled "Summertime Secondary Convection and Interaction with Sea Breeze Circulations"</p>
Genomic localization bias of secondary metabolite gene clusters and association with histone modifications in Aspergillus
<p>Table S4 (Distribution of Orthologous groups) associated with the publication 'Genomic localization bias of secondary metabolite gene clusters and association with histone modifications in Aspergillus' is deposited at Zenodo.</p>
Associated coordinate and mtz files for "Selective Oxidation of Secondary Coordination Sphere Tyrosine Residues in Engineered Cu Proteins"
<p>Coordinate and mtz files for the associated protein structures reported in "Selective Oxidation of Secondary Coordination Sphere Tyrosine Residues in Engineered Cu Proteins"</p> <p>9CST: Streptavidin-E101Q-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-Sav)</p> <p>9CSU: Streptavidin-E101Q-S112Y-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112Y-Sav)</p> <p>9CSV: Streptavidin-E101Q-S112Y-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor oxidized by hydrogen peroxide (Cu-2xm-S112Y-Sav + hydrogen peroxide)</p> <p>9CSW: Streptavidin-E101Q-S112A-K121Y bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112A-K121Y-Sav)</p> <p>9E6Z: Streptavidin-E101Q-S112F-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112F-Sav)</p>
Parental population range expansion before secondary contact promotes heterosis
<p>Population genomic analysis of hybrid zones is instrumental to our understanding of the evolution of reproductive isolation. Many temperate hybrid zones are formed by the secondary contact between two parental populations that have undergone post-glacial range expansion. Here we show that explicitly accounting for historical parental isolation followed by range expansion prior to secondary contact is fundamental for explaining genetic and fitness patterns in these hybrid zones. Specifically, ancestral population expansion can result in allele surfing where neutral or slightly deleterious mutations drift to high frequency at the expansion front. If these surfed deleterious alleles are recessive, they can contribute to substantial heterosis in hybrids produced at secondary contact, counteracting negative effects of Bateson-Dobzhansky-Muller incompatibilities (BDMIs) hence weakening reproductive isolation. When BDMIs are linked to such recessive deleterious alleles the fitness benefit of introgression at these loci can facilitate introgression at the BDMIs. The extent to which this occurs depends on the strength of selection against the linked deleterious alleles and the distribution of recombination across the chromosome. Finally, surfing of neutral loci can alter the expected pattern of population ancestry, thus accounting for historical population expansion is necessary to develop accurate null genomic models of secondary-contact hybrid zones.</p>
Genetic disruption of synthesis pathways of Arabidopsis secondary metabolites dramatically affects root-associated nematode populations directly and via modulation of microbial communities
<p>Dataset of nematode, fungal and bacterial sequence reads of Arabidopsis roots. Dataset of fungal and bacterial sequence reads of Arabidopsis microbial suspension. DNA concentration of Arabidopsis root microbial suspension. Meloidogyne incognita J2 invasion into tomato roots. qPCR dataset of Meloidogyne hapla infection pressure into Arabidopsis roots. </p>
Afrotropical secondary forests exhibit fast diversity and functional recovery, but slow compositional and carbon recovery after shifting cultivation
<p>Question: Human disturbance is increasingly affecting forest dynamics across the tropics. Forests can recover via natural secondary succession to pre-disturbance levels of biodiversity, species composition, and ecosystem carbon stocks. Central Africa will be subject to increasingly high shifting cultivation pressure in the next decades, but succession trajectories of these ecosystem properties are still poorly known for the Congo basin. We addressed two questions: (1) how does taxonomic and functional composition and diversity shift? (2) How fast do aboveground carbon stocks recover during secondary succession in tropical forests?</p> <p>Location: Central Congo basin</p> <p><span>Methods: We conducted an inventory of trees (DBH ≥10 cm diameter), measured species traits and soil texture and carbon content in 18 plots, located along six secondary succession stages (i.e. from agricultural to old growth forest sites). We measured tree diameter, height for 20% of trees distributed across diameter classes, wood traits from all species, and leaf traits from species that contributed to 85% of the plot basal area.</span></p> <p>Results: We showed that secondary forests recover relatively fast in terms of tree species diversity, alpha functional diversity, and fine root carbon, with near-old-growth forest values after six decades past disturbance, while floristic composition exhibited slower recovery. Secondary forests only partially shifted from acquisitive to a conservative life-history, with shifts in leaf traits being largely decoupled from wood traits. Only 43% of above-ground carbon recovered after 60 years of forest regrowth, potentially through a slow recovery of the large-sized tree stems that dominate carbon stocks of old-growth forests.</p> <p>Conclusions: Our findings underline the capacity of Afrotropical forests to recover species and alpha functional diversity after clear-cutting through shifting cultivation. Simultaneously, old-growth forests harbors a particular floristic community and store a large quantity of carbon with much longer recovery trajectories, stressing the need for conservation of these forests in the Congo Basin.</p>
Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study
<p>Simple Summary: Pain is one of the most common and debilitating symptoms in cancer patients. A clinical peculiarity of cancer pain is the breakthrough cancer pain (BTcP), which is defined as a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The NP-BTcP occurs in the absence of any specific activity. In this paper, we addressed the topic through a mathematical approach to provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management. Abstract: Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting—Multicentric Survey (IOPS-MS). A correlation analysis was performed to characterize the NP-BTcP profile about its intensity, number of episodes per day, and type. The multiple correspondence analysis (MCA) determined the identification of four groups (phenotypes). A univariate analysis was performed to assess differences between the four phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (phenotype 1) to the worst (phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the phenotype 1 (37.3%)’ vs. the onset > 10 min in phenotype 4 (25.8%) (p < 0.001). Phenotype 1 was characterized by the gastrointestinal type of cancer (26.4%) with respect to phenotype 4, where the most frequent cancer affected the lung (28.8%) (p < 0.001). Phenotype 4 was mainly managed with rapid-onset opioids, while in phenotype 1, many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p = 0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. Although requiring validation, this strategy can provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management.</p> <p> </p>
Primary Versus Secondary Gravity Wave Responses at F-region Heights Generated by a Convective Source
<p>Simulation outputs for the paper "Primary Versus Secondary Gravity Wave Responses at F-region Heights Generated by a Convective Source" by Heale et al.</p> <p>The .mat file consist of 3D (t,x,z) arrays of the temperature perturbation (K) for the full amplitude (T_fullamp), 1/4 amplitude (T_quarteramp), and 1/100th amplitude (T_hundrethamp) simulations. The time resolution is 60 seconds and the spatial resolution is 1km. The .mat file also included an x array and height array (km)</p>
Is there hybridisation between diploid and tetraploid Euphrasia in a secondary contact zone?
<p>• Premise of the study: Hybridisation between species with contrasting ploidy is usually considered rare in nature due to strong ploidy related postzygotic reproductive isolating barriers. However, genomic sequencing has revealed previously overlooked examples of natural cross-ploidy hybridisation, suggesting this phenomenon may be more common than once thought. Here, we investigate potential cross-ploidy hybridisation in British eyebrights (Euphrasia, Orobanchaceae), a group where thirteen putative cross-ploidy hybrid combinations have been reported based on morphology. • Methods: We analysed a contact zone between diploid E. rostkoviana and tetraploid E. arctica in Wales. We sequenced part of the internal transcribed spacer of nuclear ribosomal DNA (ITS1) and used Genotyping by Sequencing (GBS) to look for evidence of cross-ploidy hybridisation and introgression. • Key results: All variant sites in the ITS1 region were fixed between diploids and tetraploids, indicating a strong barrier to hybridisation. Clustering analyses of 356 SNPs generated using GBS clearly separated samples by ploidy and revealed strong genetic structure (FST = 0.44). However, the FST distribution across all SNPs was bimodal, indicating potential differential selection on loci between diploids and tetraploids. Demographic inference with dadI suggested potential gene flow – with this limited to around one or fewer migrants per generation. • Conclusions: Our results suggest recent cross-ploidy hybridisation is rare or absent in a site of secondary contact in Euphrasia. While a strong ploidy barrier prevents hybridisation over ecological time-scales, such hybrids may form in stable populations over evolutionary time-scales and may allow for cross-ploidy introgression to take place.</p>
Data for "Resource use divergence facilitates the evolution of secondary syntopy in a continental radiation of songbirds (Meliphagoidea): insights from unbiased co-occurrence analyses"
<p>These two files contain data needed to replicate analyses in the associated article.</p> <p>AUTHOR of data files: Vladimir Remes<br> CONTACT: vlad.remes/at/gmail,com<br> AUTHORS of the article: V. Remes, L. Harmackova<br> DATE CREATED: 8 November 2022<br> ARTICLE: published in Ecography</p> <p> </p> <p>The file named "data_syntopy.xlsx" has two sheets:<br> "data": contains the data<br> "legend": contains explanations of data columns</p> <p>The file was saved in MS Excel for Mac 16.65.</p> <p><br> The file named "tree_Mel.tre" is the phylogenetic tree in the parenthetic format used for the analyses. It has been pruned from the tree published by Marki et al. (2017) Mol Phyl Evol 107, 516–529. It was saved using the write.nexus function from the "ape" package for R software.</p>
Dataset from "Electrifying secondary settlers to enhance nitrogen and pathogens removal"
<p>The dataset contains the raw data of the figures and tables reported in the open access publication “Botti A., Pous N., Cheng H., Colprìm J., Zanaroli G., Puig, S. (2023). Electrifying secondary settlers to enhance nitrogen and pathogens removals. Chemical Engineering Journal 451, 138949”</p>
Landscape structure, predictability of forest regeneration trajectories, and recovery rate on secondary forests
<p>Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in non- linear ways. This dataset is the synthesis outcome of 22 independent databases from studies of woody plant species recovery as part of the research project entitled "Impacts of landscape structure on secondary tropical forest regeneration". This work aimed to understand the effect of landscape-level disturbance on forest regeneration, specifically through the predictability of trajectories and the recovery rate of these forests.</p> <p>Using a multiscale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density, and basal area are less predictable in landscapes (4-km radius) with intermediate (40-60%) forest cover than in landscapes with high (>60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation in key ecological factors for forest recovery (e.g. seed dispersal, seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only the species richness is positively related to forest cover in relatively small (1-km radius) landscapes. These findings highlight the importance of using a spatially-explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1-4 km radius. </p>
Mass spectrometry-based aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol
<p>Dataset of Thoma et al. published at Atmospheric Measurements and Techniques:</p> <p>Mass spectrometry-based aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol</p>
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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.