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600 results for “Contamination”
Figure 1 in Formicidae fauna in pig carcasses contaminated by insecticide: implications for forensic entomology
Figure 1 Median (square), quartile range (box) and total range (vertical line) of the species richness in each stage of decomposition in non-contaminated carcasses (A) and in contaminated carcasses (B). Different lowercase letters (a, b, c) indicate significant differences between stages in each treatment (p <0.05). The stages are 1= Fresh; 2= bloated; 3= deterioration; 4= post-deterioration; 5= skeletonization.
Figure 2 in Formicidae fauna in pig carcasses contaminated by insecticide: implications for forensic entomology
Figure 2 Detrended Correspondence Analysis (DCA) using species occurrence to assess the change in the composition of ant species that occurs in both types of carcasses. The numbers correspond to the species appear in Table 1. On the right are those that occur more effectively in noncontaminated carcasses, and on the left are those that occurred in contaminated carcasses.
Figure 3 in Formicidae fauna in pig carcasses contaminated by insecticide: implications for forensic entomology
Figure 3 Nonmetric multidimensional scaling analysis (nMDS), using species occurrence to assess the change in the composition of ant species that act on different types of carcasses, along the different stages of decomposition. The numbers correspond to the species appear in Table 1. On the right are those that occur in contaminated carcasses, and on the left are those that occurred more effectively in non-contaminated carcasses. Noncont. = Non-contaminated and Cont. = Contaminated, I= Fresh; II= bloated; III= deterioration; IV= post-deterioration; V= skeletonization.
Remondis Contamination Dataset (RCD)
<p>The Remondis Contamination Dataset (RCD) is a novel and challenging collection of images designed to facilitate the development and evaluation of computer vision models for the detection of plastic-bag contamination in waste collection scenarios. This dataset was meticulously curated from historical records of Remondis, a waste management company, and open source online resources which provides real-world visuals captured by cameras installed on waste collection trucks. The images cover various scenarios including waste bins, truck hoppers, different camera zooms, angles, and lighting conditions, accurately reflecting the complexities of waste collection environments.</p> <p><strong>Dataset Details:</strong></p> <ul> <li><strong>Format:</strong> JPEG images, RGB color scheme</li> <li><strong>Dimensions:</strong> 640 x 480 pixels</li> <li><strong>Resolution:</strong> 72 pixels-per-inch</li> <li><strong>Annotations:</strong> Bounding box annotations for plastic-bag candidates</li> <li><strong>Annotation Format:</strong> KITTI</li> <li><strong>Contaminant Types:</strong> Plastic bags including coles bags, woolie bags, color bags, white bags, black bags, and packaging material</li> <li><strong>Total Images:</strong> 1125 samples (968 for training, 157 for validation)</li> <li><strong>Total Bounding Box Annotations:</strong> 1851 (1588 for training, 263 for validation)</li> </ul> <p><strong>Key Challenges:</strong> The RCD dataset poses unique challenges due to visual similarities between plastic bags and non-contaminants, such as white paper or dark portions in the image. The diverse range of plastic bag types, along with the intricacies of waste collection environments, make the task of plastic-bag contamination detection particularly demanding.</p> <p><strong>Applications:</strong> The RCD dataset serves as a benchmark for practical waste segregation tasks, including the detection of various waste contaminants, characterization of waste contents, and counting occurrences of specific waste types. The dataset is a valuable resource for researchers, practitioners, and enthusiasts working on computer vision applications in waste management and environmental monitoring.</p> <p><strong>Citation:</strong> If you use the RCD dataset in your research, please consider citing it using the following reference:</p> <p>U. Iqbal, J. Barthelemy, P. Perez, and T. Davies, “Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis,” <em>Sensors</em>, vol. 22, no. 20, p. 7821, Oct. 2022, doi: 10.3390/s22207821.</p>
Data from: Maternal deposition of hormones and contaminants shape the gonadal transcriptome in American alligators
Open the record for dataset details and reuse information.
Evaluation of point-of-use treatments and biochar to reduce 1,2,3-trichloropropane (TCP) contamination in drinking water
Open the record for dataset details and reuse information.
Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation
Open the record for dataset details and reuse information.
IscoKin database of rate constants for reaction of organic contaminants with the major oxidants relevant to In Situ Chemical Oxidation
<p>Database of second-order rate constants for aqueous phase reaction of organic contaminants with the major oxidants involved in groundwater remediation by In Situ Chemical Oxidation (ISCO)—including permanganate, hydroxyl radical (from activated peroxide), and sulfate radical (from activated persulfate)—plus other comparable oxidants of environmental interest (carbonate radical, chlorine dioxide, and ozone).</p> <p>The data were compiled between 2003 and 2005 by Rachel Waldemer, Jaimie Powell, and other students under the direction of Professor Paul G. Tratnyek at the Oregon Graduate Institute in Beaverton, Oregon, USA. In 2006, a script written by Kaylie Langley was used to make the data available online via an interface that allowed searching by chemical name, CAS-RN, etc. The result was named the “IscoKin” database and was available at http://cgr.ebs.ogi.edu/iscokin/search.php until the server was retired in 2019.</p> <p>In place of IscoKin, we have made all of the data available as a spreadsheet in .xlsx format. The file includes two sheets, the first contains one “recommended” rate constant for each combination of organic compound and oxidant (i.e., one row per compound). The second sheet contains all of the individual rate constants that were compiled (i.e., one row per experimental result).</p> <p>The "best" value is most often an average of the individual values listed in the "All_values_with_references" sheet; but occasionally this value represents a single value, usually the "best" value given in Buxton et al. (1988) “Critical review of rate constants for reactions of hydrated electrons. hydrogen atoms and hydroxyl radicals (•OH/•O-) in aqueous solution” J. Phys. Chem. Ref. Data. 17, 513-886.</p> <p>While the database was checked thoroughly, it still is unlikely to be completely accurate or complete. For critical applications, we recommend tracking down the primary sources (listed in the spreadsheet) and use them for data, conditions, and other caveats. Obviously, we do not accept any responsibility for what anyone does with information obtained from this document.</p> <p>The work was funded by the Strategic Environmental Research and Development Program (SERDP), under grant number ER-1289, titled “Improved Understanding of In Situ Chemical Oxidation”. More details on this work can be found in Part 1 of the final project report, which is located at https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Persistent-Contamination/ER-1289/.</p>
Data from: Intraspecific DNA contamination distorts subtle population structure in a marine fish: decontamination of herring samples before restriction-site associated (RAD) sequencing and its effects on population genetic statistics
Wild specimens are often collected in challenging field conditions, where samples may be contaminated with the DNA of conspecific individuals. This contamination can result in false genotype calls, which are difficult to detect, but may also cause inaccurate estimates of heterozygosity, allele frequencies, and genetic differentiation. Marine broadcast spawners are especially problematic, because population genetic differentiation is low and samples are often collected in bulk and sometimes from active spawning aggregations. Here, we used contaminated and clean Pacific herring (Clupea pallasi) samples to test (i) the efficacy of bleach decontamination, (ii) the effect of decontamination on RAD genotypes, and (iii) the consequences of contaminated samples on population genetic analyses. We collected fin tissue samples from actively spawning (and thus contaminated) wild herring and non-spawning (uncontaminated) herring. Samples were soaked for 10 minutes in bleach or left untreated, and extracted DNA was used to prepare DNA libraries using a restriction-site associated DNA (RAD) approach. Our results demonstrate that intraspecific DNA contamination affects patterns of individual and population variability, causes an excess of heterozygotes, and biases estimates of population structure. Bleach decontamination was effective at removing intraspecific DNA contamination and compatible with RAD sequencing, producing high-quality sequences, reproducible genotypes, and low levels of missing data. Although sperm contamination may be specific to broadcast spawners, intraspecific contamination of samples may be common and difficult to detect from high-throughput sequencing data, and can impact downstream analyses.
Microplastic contamination in Corpus Christi Bay blue crabs, Callinectes sapidus
<p>Microplastic pollution has been observed in marine environments around the world and has the potential to negatively impact marine organisms when ingested. One organism of particular concern is the blue crab, <i>Callinectes sapidus</i>, whose feeding strategies increase their likelihood of exposure to plastic pollution. To assess microplastic contamination in blue crabs, 39 individuals were collected from Corpus Christi Bay, TX and processed using a hydrogen-peroxide based tissue destruction method followed by confirmation using micro-attenuated total reflectance Fourier transform infrared spectroscopy (µ-FTIR). From the 39 blue crabs sampled, 28 fully synthetic fragments and fibers and 24 semi-synthetic fibers were found within their stomachs. After correcting for possible contamination, 36% of collected blue crabs were estimated to contain 20 synthetic fragments and fibers and 14 semi-synthetic fibers (0.87 items crab<sup>-1</sup>). This study shows that blue crabs ingest microplastic items in Corpus Christi, TX and populations in other locations likely face similar risks.</p>
Single-cell ATAC-seq control of cross-contaminations (experiment 2)
<p>On the Fluidigm C1 platform for single-cell analysis, the cells are captured in 96 chambers arranged serially, and then washed before further processing. Thus, debris present from the loading medium or released by captured cells upstream are a possible source of contamination. We generated a control datasets using the single-cell ATAC-seq protocol available from Fluidigm's ScriptHub. We cultivated human Hep G2 and mouse Hepa 1-6 (both are liver cancer cell lines), stained them with green and red calceins (respectively), and loaded them at equal concentration in a Fluidigm medium flow cell (old design), before running the C1 single-cell ATAC-seq program. To evaluate damage and carry-over of debris from FACS-sorting, two IFCs were run in two C1 machines in parallel. In the first (flowcell ID 1772-123-155), the cells not washed and in the second, they were washed (ID 1772-123-158).</p> <p>The data deposited here is a sequencing run (Illumina MiSeq) of these ATAC-seq libraries. The metadata indicating the contents of each well is being uploaded separately and this record will be updated once the DOIs are available.</p>
Single-cell ATAC-seq control of cross-contaminations (experiment 1)
<p>On the Fluidigm C1 platform for single-cell analysis, the cells are captured in 96 chambers arranged serially, and then washed before further processing. Thus, debris present from the loading medium or released by captured cells upstream are a possible source of contamination. We generated a control datasets using the single-cell ATAC-seq protocol available from Fluidigm's ScriptHub. We cultivated human Hep G2 and mouse Hepa 1-6 (both are liver cancer cell lines), stained them with green and red calceins (respectively), and loaded them at equal concentration in a Fluidigm medium flow cell (old design), before running the C1 single-cell ATAC-seq program.</p> <p>The data deposited here is a sequencing run (Illumina MiSeq) of these ATAC-seq libraries. The metadata indicating the contents of each well is being uploaded separately and this record will be updated once the DOIs are available.</p>
Dianthus sylvestris subsp. sylvestris as a promising candidate for phytostabilization of copper-contaminated post-mining sites in Alpine ecosystems
<p>The lack of remediation of inactive mine sites is a serious global concern, as they pose risks to the environment, human health, and safety. The potential of <em>Dianthus sylvestris</em> subsp. <em>sylvestris</em> to remediate post-mining sites contaminated with copper (Cu) at high altitudes, which is a challenging task for most management strategies, was explored in this study. More than 1300 mg Cu kg<sup>-1</sup> in shoots were found in plants collected at the Monte Avanza legacy mine site (Alps). However, it is unclear whether this is due to hyperaccumulation or foliar contamination. To address this gap, field samples were washed with two different protocols, and a controlled Cu-tolerance test was conducted. While very high Cu concentrations, exceeding the Cu hyperaccumulation threshold of 300 mg kg<sup>-1</sup>, were found in samples washed with water, results for the plants cleaned with a more rigorous approach with EDTA suggested Cu exclusion strategy. Under controlled conditions, the plant showed Cu hypertolerance but did not hyperaccumulate Cu. Thus <em>D. sylvestris</em> presents a Cu exclusion strategy rather than hyperaccumulation potential, making it a suitable candidate for Cu phytostabilization at high-altitude legacy mine sites. The study emphasizes the need for experiments under controlled conditions when assessing the phytoremediation potential.</p>
Long-term changes in pituitary gene expression following developmental exposure to environmental contaminants (BPS, BDE-47, or TBBPA) in male mice.
<p><strong>Experimental Design</strong></p><p>In this study we evaluate the long-term gene expression changes in pituitary in male mice exposed developmentally to one of three known endocrine disrupting chemicals: bisphenol-S (BPS), 2,2',4,4'-tetrabromodiphenyl ether (BDE-47), and 3,3',5,5'-tetrabromobisphenol A (TBBPA). Male mice were exposed to chemical treatment through their mothers' during pregnancy (umbilical blood flow) and nursing, from pregnancy day 8 through weaning at postnatal day 21 (PND21). Each chemical exposure was calculated to equal 0.2mg/kg bw/day. The details of exposure protocol are described elsewhere (Kim<i> et al</i>, 2015). The male pups were allowed to grow untreated until adulthood at PND140. At PND140, male mice were euthanized, then their pituitaries removed, snap frozen in liquid nitrogen, and then stored at -80°C. </p><p>The data-files described below represent major steps of our analysis:</p><p><strong>1. FASTQ files for mouse pituitary RNA-seq data.</strong></p><p>Male mouse pituitary RNA was isolated using a Trizol protocol, checked for purity and concentration and then processed for mRNA sequencing using the Illumina TruSeq kit and protocol (TruSeq Stranded mRNA LP, Cat # 20020594 and TruSeq RNA Sg Idx SetB, Cat # 20020493, Illumina, San Diego, CA) following the manufacturer's recommended procedures. High throughput sequencing was conducted using the NextSeq500 sequencing system. cDNA libraries were single-end sequenced in 76 cycles using a NSQ 500/550 Hi Output KT v2.5 (Cat #20024906 Illumina, San-Diego, CA) in one multiplex run (N=3/exposure group). Read filtering, trimming, and de-multiplexing were performed using the BaseSpace cloud service by Illumina (<a href="https://basespace.illumina.com/home/index">https://basespace.illumina.com/home/index</a>, RRID:SCR_011881). Processed reads were mapped to the mouse reference genome (MM10) using the RNA-Seq Alignment v. 1.1.1. software with Bowtie 2. Each FASTQ file is a compressed file representing data from one sequencing flow cell lane for each sample (4 files per sample). Sample identifiers are coded for treatment: X = Vehicle control, R = TBBPA, C = BDE-47, E = BPS. </p><p><strong>2. Differential expression data.</strong></p><p>Aligned reads were used to assemble transcripts and analyze differential expression using Cufflinks Assembly & DE v. 2.1.0. package. Reads aligned to known annotated regions for both control and exposed groups were used to calculate log2 FPKM ratios. Differentially expressed genes were identified as genes altered with false discovery rate significance ≤ 0.05 (FDR, q ≤ 0.05). Data on all exposure groups are shown in different sheets of the same file - Differential_expression.xlsx.</p><p><strong>3. Enrichment of biological categories associated with DEGs induced by chemical exposures. </strong></p><p>All differentially expressed genes were uploaded to Metascape for the analysis of enriched biological categories using default settings. Results of Metascape analysis are shown in two MS Excel files per exposure group, one showing negatively enriched categories and one positively enriched categories. The title of each file consists of three parts connected via underscore sign: the name of the chemical, the direction of enrichment, and the name of analysis - metascape (e.g., BDE-47_negative_metascape.xlsx).</p><p><strong>4. Pathway analysis for chemical exposures.</strong></p><p>All differentially expressed genes were uploaded to Ingenuity Pathway Analysis and enriched canonical pathways were identified using default settings . Altered molecular or disease pathways, their p-values, and associated differentially expressed genes are provided for each exposure. Data for all exposure groups are shown in different sheets of the same file - IPA_pathway_analysis.xlsx.</p><p><strong>References:</strong></p><p>Kim B, Colon E, Chawla S, Vandenberg LN, Suvorov A. Endocrine disruptors alter social behaviors and indirectly influence social hierarchies via changes in body weight. Environ Health. 2015 Aug 5;14:64. doi: 10.1186/s12940-015-0051-6. PMID: 26242739; PMCID: PMC4524022.</p>
Supplementary digital data for "Widespread contamination of soils and vegetation with Current Use Pesticide residues along altitudinal gradients in a European Alpine valley"
<p>Supplementary digital data for the publication:</p> <p>Widespread contamination of soils and vegetation with Current Use Pesticide residues along altitudinal gradients in a European Alpine valley</p> <p>Data that was no publicly available but is used for Figure 1 - 3 in the manuscript is provided in this repository in addition to the raw data describing the occurrence in plant and soil matrices throughout the Vinschgau valley along the altitudinal transects.</p> <p> </p>
Supplementary materials of the article "First report of plastic biomedia contamination in Brazilian beaches – evidence from the Paraná coast"
<p>Supplementary materials of the article "First report of plastic biomedia contamination in Brazilian beaches – evidence from the Paraná coast", published in Ocean and Coastal Research.</p>
Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Integrating GIS Tools and Probability-Risk Matrix – Case Study: Guarda Region, Portugal
<p>In these files we can find the final risk map of heavy metal contamination for the guarding area in Portugal obtained according to the methodology explained in the paper "Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Instegrating GIS Tools and Probability-Risk Matrix - Case Study: Guarda Region (Portugal)</p> <p>Final Risk Equal.tiff: GeoTiff with a pixel size of 30m. EPSG:3763 - ETRS89 / Portugal TM06</p> <p>Also attached is the symbolisation for the image in .qml (Quantum GIS Layer Style File) format.</p> <p>A file called RISK RECLASS is also available, where you can find the risk classification maps for each of the studied factors: </p> <ul> <li>Proximity to roads</li> <li>Proximity to industrial areas</li> <li>Ph</li> <li>Soil organic content</li> <li>Slope</li> <li>Soil texture</li> <li>Mining extraction areas </li> <li>Drainage</li> </ul> <p>finally a DATABASE file where the data of the 360 points for the calculation of the risk maps can be found. </p>
Human health impact based on adult European consumers' dietary exposure to chemical contaminants and consumption of unprocessed red meat, processed meat, and legumes
<p>This dataset was generated within the PRISMA project (Marie Skłodowska-Curie Grant Agreement No. 101110615) for assessing the health impact for adult consumers from 25 European countries due to dietary exposure to chemical contaminants and consumption of food based on different dietary patterns.</p><p>The risk coefficients used to generate data are presented in Table S1.</p><p>File S2 presents the health impact based on the chronic dietary exposure to chemical contaminants for each country.</p><p>File S3 presents figures regarding the health impact for each country for adult consumers due to dietary exposure to chemical contaminants and consumption of food based on different dietary patterns.</p><p>File S4 presents the health impact based on the consumption of unprocessed red meat, processed meat, and legumes for each country.</p><p> </p>
Supplementary material for "Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers"
<p><span>Dataset presented and discussed in the manuscript of the research article “</span><span>Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers</span><span><span>” by Zocher et al. The manuscript will be submitted to <em>Environmental Pollution</em> and was prepared by the following authors: </span></span></p> <p> </p> <p><span><span>Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></p> <p><span> </span></p> <p><span><span>(1) School of Science, Constructor University, Bremen, Germany</span></span></p> <p><span><span>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</span></span></p> <p><span><span>(3) </span></span><span><span>Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</span></span></p> <p><span><span>(4) SINTEF Ocean, Trondheim, Norway</span></span></p> <p> </p> <p><span>This work was conducted within the ELEMENTARY project, and we appreciate funding from the Norwegian Research Council (grant No. 301236).</span></p>
Are we contaminating our own laboratory experiments on microfibers?
<p>Data set of the publication 'Are we contaminating our own laboratory experiments on microfibers?'.</p> <p>Raw data and statistical analysis data of the study.</p>
ScienceDex guides
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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.