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233 results for “Impact Structure”
Fig. 2 in Changes in the structure of fish assemblages in streams along an undisturbed-impacted gradient, upper Paraná River basin, Central Brazil
Fig. 2. Fish assemblage rarefaction plot by stream stretches. P1 = Macaúba; P2 = Carapina; P3 = Barreiro; P4 = Cana Brava; NP1 = Palmito; NP2 = Onça; NP3 = Unnamed Stream 3; NP4= Bandeira; NP5 = Pedreira; NP6 = Unnamed Stream 2.
Fig. 1 in Changes in the structure of fish assemblages in streams along an undisturbed-impacted gradient, upper Paraná River basin, Central Brazil
Fig. 1. Location of the streams sampled in the João Leite River basin, Goiás, Central Brazil. The broken line represents the limits of the basin. Clear grey area = Altamiro de Moura Pacheco State Park; Goiânia and Anápolis are the main cities in the basin. P1 = Macaúba; P2 = Carapina; P3 = Barreiro; P4 = Cana Brava; NP1 = Palmito; NP2 = Onça; NP3 = Unnamed Stream 3; NP4 = Bandeira; NP5 = Pedreira; NP6 = Unnamed Stream 2.
Fig. 3 in Changes in the structure of fish assemblages in streams along an undisturbed-impacted gradient, upper Paraná River basin, Central Brazil
Fig. 3. Fish assemblage dendogram resulting from the Morisita-Horns analysis. Roman numerals indicate the groups and the small box the scale of variance. P1 = Macaúba; P2 = Carapina; P3 = Barreiro; P4 = Cana Brava; NP1 = Palmito; NP2 = Onça; NP3 = Unnamed Stream 3; NP4 = Bandeira; NP5 = Pedreira; NP6 = Unnamed Stream 2.
Fig. 4 in Changes in the structure of fish assemblages in streams along an undisturbed-impacted gradient, upper Paraná River basin, Central Brazil
Fig. 4. ABC curves per year of fish assemblages from stream stretches located in preserved areas. P1 = Macaúba; P2 = Carapina; P3 = Barreiro; P4 = Cana Brava. W = statistical value for each stream.
Fig. 6 in Changes in the structure of fish assemblages in streams along an undisturbed-impacted gradient, upper Paraná River basin, Central Brazil
Fig. 6. ABC curves per year of fish assemblages from stream stretches located in impacted areas. NP4 = Bandeira; NP5 = Pedreira; NP6 = Unnamed Stream 2. W = statistical value for each stream.
Data for: Heavy metal pollution impacts soil bacterial community structure and antimicrobial resistance at the Birmingham 35th Avenue Superfund Site
<p>The data in this archive are the results of a study on the impact of heavy metals (HMs) on the soil microbiota of an urban Superfund site in Alabama. HMs are known to modify bacterial communities both in the laboratory and in situ. Consequently, soils in HM-contaminated sites such as the U.S. Environmental Protection Agency (EPA) Superfund sites are predicted to have altered ecosystem functioning, with potential ramifications for the health of organisms, including humans, that live nearby. Further, several studies have shown that heavy metal-resistant (HMR) bacteria often also display antimicrobial resistance (AMR), and therefore HM-contaminated soils could potentially act as reservoirs that could disseminate AMR genes into human-associated pathogenic bacteria. To explore this possibility, topsoil samples were collected from six public locations in the zip code 35207 (the home of the North Birmingham 35th Avenue Superfund Site) and in six public areas in the neighboring zip code, 35214. 35027 soils had significantly elevated levels of the HMs As, Mn, Pb, and Zn, and sequencing of the V4 region of the bacterial 16S rRNA gene revealed that elevated HM concentrations correlated with reduced microbial diversity and altered community structure. While there was no difference between zip codes in the proportion of total culturable HMR bacteria, bacterial isolates with HMR almost always also exhibited AMR. Metagenomes inferred using PICRUSt2 also predicted significantly higher mean relative frequencies in 35207 for several AMR genes related to both specific and broad-spectrum AMR phenotypes. Together, these results support the hypothesis that chronic HM pollution alters the soil bacterial community structure in ecologically meaningful ways and may also select for bacteria with increased potential to contribute to AMR in human disease.</p>
Data for: Age structure eliminates the impact of coinfection on epidemic dynamics in a freshwater zooplankton system
<p>Parasites often coinfect host populations, and, by interacting within hosts, might change the trajectory of multi-parasite epidemics. However, host-parasite interactions often change with host age, raising the possibility that within-host interactions between parasites might also change, influencing the spread of disease. We measured how heterospecific parasites interacted within zooplankton hosts and how host age changed these interactions. We then parameterized an epidemiological model to explore how age-effects altered the impact of coinfection on epidemic dynamics. In our model, we found that in populations where epidemiologically relevant parameters did not change with age, the presence of a second parasite altered epidemic dynamics. In contrast, when parameters varied with host age (based on our empirical measures), there was no longer a difference in epidemic dynamics between singly and coinfected populations, indicating that variable age structure within a population eliminates the impact of coinfection on epidemic dynamics. Moreover, infection prevalence of both parasites was lower in populations where epidemiologically relevant parameters changed with age. Given that host-population age structure changes over time and space, these results indicate that age-effects are important for understanding epidemiological processes in coinfected systems and that studies focused on a single age group could yield inaccurate insights.</p>
Fig. 4A-D in The impact of urban warfare on the structure of ant assemblages on trees (Hymenoptera: Formicidae)
Fig. 4A-D – Multivariate linear regression (1 independent, n dependent) for different parameters: A – between degree of damage and number of ants; B – between number of ants and tree diameter; C – between dendrobiont (nesting in trees) ants and degree of damage; D – between herpetobiont (nesting in soil) ants and degree of damage.
Fig. 2A-F in The impact of urban warfare on the structure of ant assemblages on trees (Hymenoptera: Formicidae)
Fig. 2A-F – Degrees of damaged trees. A – undamaged trees, B – 1st degree of damage, C – 2nd degree, D – 3rd degree; E – 4th degree; F – 5th degree. Black arrows indicate superficial damage to the tree bark, red arrows indicate deep damage to conductive tissues, yellow arrows - destruction of the upper part of the tree trunk, blue arrows - irreparable damage to the tree (destruction of the trunk).
Fig. 1A-B in The impact of urban warfare on the structure of ant assemblages on trees (Hymenoptera: Formicidae)
Fig. 1A-B – Investigated locations in the Kyiv region: A – Bucha; B – Irpin. Areas of cities affected by military operations are highlighted in red. Data by UN Satellite Center.
Data for: Age structure eliminates the impact of coinfection on epidemic dynamics in a freshwater zooplankton system
Open the record for dataset details and reuse information.
Data for: Heavy metal pollution impacts soil bacterial community structure and antimicrobial resistance at the Birmingham 35th Avenue Superfund Site
Open the record for dataset details and reuse information.
Impact of intercept trap type on plume structure: a potential mechanism for differential performance of intercept trap designs for Monochamus species
<p>Studies have demonstrated that semiochemical-baited intercept traps differ in their performance for sampling insects, but we have an incomplete understanding of how and why intercept trap design effects vary among insects. This can significantly delay both the development of new and optimization of existing survey and detection tools. The development of a mechanistic understanding of why trap performance varies within and among species would mitigate this delay. The primary objective of this study was to develop methods to characterize and compare the odor plumes associated with intercept traps that differ in their performance for forest Coleoptera. We released CO<sub>2</sub> and measured fluctuations of this tracer gas from 175-point locations arranged in a 2-by-3-by-2-m grid cuboid downwind of a standard multiple-funnel, a modified multiple-funnel, a panel, a canopy malaise trap, and a blank control (i.e., no trap) in a greenhouse. Significant differences in trapping efficacy between these different trap designs were observed for <i>Monochamus scutellatus</i> (Say) and <i>Monochamus notatus</i> (Drury) in a field trial. Significant differences were also observed in how CO<sub>2</sub> accumulated in time at different positions downwind among these different trap designs. Turbulent dispersion is the dominant force structuring odor plumes and creates intermittency in the odor plume that is important for sustained upwind flight in insects. Methodological and instrumental limitations resulted in the inability to determine instantaneous plume structures and vortex shedding frequencies for different intercept trap designs. Although we observed differences in the odor plumes emanating downwind of the different intercept trap designs, we were unable to reconcile these differences with capture rates of the different trap designs for <i>M. scutellatus</i> and <i>M. notatus</i>.</p>
Of Mojave milkweed and mirrors: The population genomic structure of a species impacted by solar energy development
<p>A rapid renewable energy transition has facilitated the development of large, ground‐mounted solar energy facilities worldwide. Deserts, and other sensitive aridland ecosystems, are the second most common land‐cover type for solar energy development globally. Thus, it is necessary to understand existing diversity within environmentally sensitive desert plant populations to understand spatiotemporal effects of solar energy siting and design. Overall, few population genomic studies of desert plants exist, and much of their biology is unknown. To help fill this knowledge gap, we sampled Mojave milkweed (<em>Asclepias</em> <em>nyctaginifolia</em>) in and around the Ivanpah Solar Electric Generating Station (ISEGS) in the Mojave Desert of California to understand the species' population structure, standing genetic variation, and how that intersects with solar development. We performed Restriction‐site Associated Sequencing (RADseq) and discovered 9942 single nucleotide polymorphisms (SNPs). Using these data, we found clear population structure over small spatial scales, suggesting each site sampled comprised a genetically distinct population of Mojave milkweed. While mowing, in lieu of blading, the vegetation across the solar energy facility's footprint prevented the immediate loss of the ISEGS Mojave milkweed population, we show that the effects of land‐cover change, especially those impacting desert washes, may impact long‐term genetic diversity and persistence. Potential implications of this include a risk of overall loss of genetic diversity, or even hastened extirpation. These findings highlight the need to consider the genetic diversity of impacted species when predicting the impact and necessary conservation measures of large‐scale land‐cover changes on species with small population sizes.</p>
Seismic Reflection Data from the Kentland Impact Structure, Indiana from Robitaille MSc (2024)
<p>This repository contains the correlated and stacked shot gathers and the final unmigrated and migrated files (all in SGY format) collected near the Kentland Crater Impact Structure. These data are associated with the MSc thesis of Brian Robitaille at Purdue University (2024). See citation below.</p>
Metabarcoding of canopy arthropods reveals negative impacts of forestry insecticides on community structure across multiple taxa
<p>1. Insecticides used to combat outbreaks of forest defoliators can adversely affect non-target arthropods. Forest use insecticides typically suppress Lepidoptera larvae which are the keystone of the canopy community of deciduous oak forests. The abrupt removal of this dominant component of the food web could have far-reaching implications for forest ecosystems, yet it is rarely investigated in practice owing to several methodological shortcomings. The taxonomic impediment and the biased nature of arthropod sampling techniques particularly impede the assessment of insecticide impacts on diverse communities.</p> <p>2. To tackle this issue, we propose an experimental approach combining pyrethrum knockdown sampling and species determination via DNA metabarcoding, using community subsampling to derive estimates of species abundances. We applied this protocol to investigate the short-term effects of the insecticides diflubenzuron (DFB) or <i>Bacillus thuringiensis</i> var. <i>kurstaki</i> (BTK) on canopy-dwelling arthropod communities in German oak woodlands.</p> <p>3. Our approach allowed us to include most of the detected diversity and integrate species abundances in our analyses. By classifying arthropod species into assemblages based on their expected sensitivity rather than coarse taxonomic groupings, we could unveil substantial effects of DFB across multiple taxa five weeks after application.</p> <p>4. Although strong effects on single species appear related to direct toxicity, substantial impacts of DFB on parasitoids and xylophagous beetles suggest that anti-defoliator treatments can have previously unsuspected indirect effects on some components of forest arthropod communities. The impacts of BTK on community structure were consistent with but much weaker than that of DFB.</p> <p>5. <i>Synthesis and applications</i>. Comparing diversity patterns in the arthropod communities of sprayed and unsprayed oak canopies, our results show that selective insecticides can alter species diversity in presumably non-sensitive taxa. Even though the ecological significance of these impacts has yet to be assessed in an operational setting, their existence calls for increased regulatory scrutiny on indirect effects. As community approaches become more attainable with the rapid development of DNA metabarcoding, we suggest the inclusion of community level endpoints as regulatory requirements for the approval of forest use insecticides.</p>
Datasets to quantify the impact of lianas on 3D tree structure and biomass
<p>This dataset consists of terrestrial laser scanning (TLS) point clouds and their corresponding quantitative structure models (QSMs) of 182 trees. There are two point clouds for each tree, one containing only the wood points and the other containing the leaf points of the tree. The QSMs of the trees are in .mat format and the detailed readME file to read the QSMs can be found here: https://github.com/InverseTampere/TreeQSM.</p> <p> </p>
The Impact of Structural Modification on Electrochromic and Electroluminescent Properties of D-A-D Benzothiadiazole Derivatives with a Fluorene Linker and (Bi)Thiophene Units
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The Impact of Hurricane Harvey on Pavement Structures in the South East Texas and South West Louisiana
<p>Corresponding data set for Tran-SET Project No. 18PUTA02. Abstract of the final report is stated below for reference:</p> <p>"This study developed a methodology to estimate the damage caused by flooding, such that caused by Hurricane Harvey, on a road or street network. The flooded street or pavement sections are identified using GIS flood maps with street GIS maps used for pavement management systems (PMS) by cities or state authorities. Then the damage caused by flooding directly through the increase moisture in foundation layers or indirectly due to the increase heavy traffic during the relief effort is estimated. An example Excel macro was created to illustrate the estimation process. The methodology estimates the increase in rehabilitation costs since the flooding imposes that many rehabilitation works must be done earlier than anticipated before the flooding. The methodology also estimated the increase in fuel consumption caused by the increased in pavement roughness if the rehabilitation works are done when anticipated before the flooding. The methodology and the Excel macro can also be used to identify the pavement structures with better resilience to the flooding by grouping sections based on the flooding duration (no flooding, single and multiple day flooding) and on design features such as pavement type, functional class, age or time from the most recent resurfacing or reconstruction, subgrade soil type, traffic volume, layer thickness."</p>
Impact of water models on structure and dynamics of enzyme tunnels
<ul> <li>1-initial_topologies_coordinates.tar.gz <ul> <li>primary input coordinates and parameter-topology files of all initial systems (LinBwt, LinB32, and Linb86 variants of haloalkane dehalogenase) in OPC and TIP3P water models</li> <li>prepared with the tleap module of AMBER18 package</li> <li>parm7 and crd formatted</li> </ul> </li> <li>2-cap_domain_gate_distances.tar.gz <ul> <li>datasets with minimum distance calculation between Asp146 and Leu176</li> <li>calculated by CPPTRAJ module of AMBER 18 for each performed simulation</li> <li>plain text formatted</li> </ul> </li> <li>3-protein_trajectories-linbwt.tar.gz <ul> <li>three replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of LinBwt in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-closed.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-closed.tar.gz <ul> <li> two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>4-basic_analyses.tar.gz <ul> <li>datasets on RMSF, RMSD, RoG, and RDF from the CPPTRAJ module of AMBER 18</li> <li>for selected replicas 3x LinBwt, 2x LinB32-closed, 2x LinB32-open, 2x LinB86-closed and 2x LinB86-open</li> <li>plain text and PDB formatted</li> </ul> </li> <li>5-caver_analyses.tar.gz <ul> <li>results of tunnel analyses for two replicas of open & closed state each for LinB32 & LinB86 in OPC and TIP3P, and three replicas of LinBWT in OPC and TIP3P</li> <li>generated by CAVER 3.0 using "Divide-and-conquer approach" (MethodsX, 10, 2023, 101968)</li> <li>comprising csv and pdb formatted: tunnel_profiles.csv and bottlenecks.csv, stripped_system.10001.pdb, v_origins.pdb</li> <li>For this and following analyses, the names of the trajectories were modified as follows: <ul> <li>linbwt_opc1_2 = md1_opc_linbwt; linbwt_opc2_2 = md2_opc_linbwt; linbwt_opc3_2 = md3_opc_linbwt;</li> <li>linbwt_tip3p1_2 = md1_tip3p_linbwt; linbwt_tip3p2_2 = md2_tip3p_linbwt; linbwt_tip3p3_2 = md3_tip3p_linbwt;</li> <li>linb32-closed_opc1_2 = md1_closed_opc_linb32; linb32-closed_opc2_2 = md2_closed_opc_linb32;</li> <li>linb32-open_opc1_2 = md1_open_opc_linb32; linb32-open_opc2_2 = md2_open_opc_linb32;</li> <li>linb32-closed_tip3p1_2 = md1_closed_tip3p_linb32; linb32-closed_tip3p2_2 = md2_closed_tip3p_linb32;</li> <li>linb32-open_tip3p1_2 = md1_open_tip3p_linb32; linb32-open_tip3p2_2 = md2_open_tip3p_linb32;</li> <li>linb86-closed_opc1_2 = md1_closed_opc_linb86; linb86-closed_opc2_2 = md2_closed_opc_linb86;</li> <li>linb86-open_opc1_2 = md1_open_opc_linb86; linb86-open_opc2_2 = md2_open_opc_linb86;</li> <li>linb86-closed_tip3p1_2 = md1_closed_tip3p_linb86; linb86-closed_tip3p2_2 = md2_closed_tip3p_linb86;</li> <li>linb86-open_tip3p1_2 = md1_open_tip3p_linb86; linb86-open_tip3p2_2 = md2_open_tip3p_linb86.</li> </ul> </li> </ul> </li> <li>6-transport_tools_analyses.tar.gz <ul> <li>results of comparative analyses for all simulations generated in 5-caver_analyses.tar.gz</li> <li>generated by TransportTools 0.9.3</li> <li>comprising csv, pdb, plain text and py formatted: configuration file (config_TT.ini), tunnel_profiles (data folder) for all filtered tunnels and bottlenecks (data folder) for all filtered tunnels, statistics (statistics folder) and visualization (visualization folder)<br> </li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
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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.