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600 results for “Contamination”
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Ireland
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. </p> <p>The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. </p> <p>Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: </p> <ul> <li>Commission Regulation (EU) 625/2017, on the application of food and feed law</li> <li>Commission Delegated Regulation (EU) 2022/931</li> <li>Commission Implementing Regulation (EU) 2022/932</li> <li>Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006</li> </ul> <p>These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). </p> <p>More details are available in last year's finalised call for data ‘<span><a href="https://www.efsa.europa.eu/en/call/annual-call-continuous-collection-chemical-contaminants-occurrence-data-food-and-feed">Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA</a></span>’.</p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <p>OCC-CHEMMON2020 – Food Safety Authority of Ireland, Marine Institute, Department of Agriculture, Food and the Marine</p> <p>OCC-CHEMMON2021 – Food Safety Authority of Ireland</p> <p>OCC-CHEMMON2022 – Food Safety Authority of Ireland</p> <p>OCC-CHEMMON2023 – Food Safety Authority of Ireland; Department of Agriculture, Environment and Rural Affairs</p> <p>OCC-CHEMMON2024 – Food Safety Authority of Ireland; Department of Agriculture, Environment and Rural Affairs</p>
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Italy
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. </p> <p>The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. </p> <p>Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: </p> <ul> <li>Commission Regulation (EU) 625/2017, on the application of food and feed law</li> <li>Commission Delegated Regulation (EU) 2022/931</li> <li>Commission Implementing Regulation (EU) 2022/932</li> <li>Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006</li> </ul> <p>These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). </p> <p>More details are available in last year's finalised call for data ‘<span><a href="https://www.efsa.europa.eu/en/call/annual-call-continuous-collection-chemical-contaminants-occurrence-data-food-and-feed">Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA</a></span>’. </p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <p>OCC-CHEMMON2020 – Ministry of Health</p> <p>OCC-CHEMMON2021 – Ministry of Health</p> <p>OCC-CHEMMON2022 – Ministry of Health</p> <p>OCC-CHEMMON2023 – Ministry of Health</p> <p>OCC-CHEMMON2024 – Ministry of Health</p>
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Finland
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. </p> <p>The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. </p> <p>Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: </p> <ul> <li>Commission Regulation (EU) 625/2017, on the application of food and feed law</li> <li>Commission Delegated Regulation (EU) 2022/931</li> <li>Commission Implementing Regulation (EU) 2022/932</li> <li>Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006</li> </ul> <p>These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). </p> <p>More details are available in last year's finalised call for data ‘<span><a href="https://www.efsa.europa.eu/en/call/annual-call-continuous-collection-chemical-contaminants-occurrence-data-food-and-feed">Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA</a></span>’.</p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <p>OCC-CHEMMON2020 – Finnish Food Authority</p> <p>OCC-CHEMMON2021 – Finnish Food Authority</p> <p>OCC-CHEMMON2022 – Finnish Food Authority</p> <p>OCC-CHEMMON2023 – Finnish Food Authority; Finnish Customs</p> <p>OCC-CHEMMON2024 – Finnish Food Authority; Finnish Customs</p>
Results from national testing programs on the occurrence of chemical contaminants in food and feed - Bulgaria
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. </p> <p>The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. </p> <p>Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: </p> <ul> <li>Commission Regulation (EU) 625/2017, on the application of food and feed law</li> <li>Commission Delegated Regulation (EU) 2022/931</li> <li>Commission Implementing Regulation (EU) 2022/932</li> <li>Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006</li> </ul> <p>These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). </p> <p>More details are available in last year's finalised call for data ‘<span><a href="https://www.efsa.europa.eu/en/call/annual-call-continuous-collection-chemical-contaminants-occurrence-data-food-and-feed">Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA</a></span>’.</p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <p>OCC-CHEMMON2020 – Risk Assessment Center on Food Chain</p> <p>OCC-CHEMMON2021 – Risk Assessment Center on Food Chain</p> <p>OCC-CHEMMON2022 – Risk Assessment Center on Food Chain</p> <p>OCC-CHEMMON2023 – Risk Assessment Center on Food Chain</p> <p>OCC-CHEMMON2024 – Risk Assessment Center on Food Chain</p>
Maternal body condition affects the response of the gut microbiome to a widespread contaminant in larval spined toads
<p>Datasets (metadata and phyloseq object) </p> <p>Scripts used for the statistical analyses</p>
Fig 1 in Different responses of epigeic beetles to heavy metal contamination depending on functional traits at the family level
Fig 1. Diagram of non-metric multidimensional scaling of beetle assemblages classified to three groups of contamination (square- almost uncontaminated sites, circle- moderately contaminated sites, diamond- highly contaminated sites)
Fig 2 in Different responses of epigeic beetles to heavy metal contamination depending on functional traits at the family level
Fig 2. Mean total density ± SE of the most frequently occurring groups of beetles in three classes of contaminations along the season (circle- almost uncontaminated sites, square- moderately contaminated sites, triangle- highly contaminated sites).
Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation
<p>Inundation of coastal stormwater networks by tides is widespread due to sea-level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies are poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (1) documented temporal variation in concentrations of <em>Enterococcus spp</em>. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a two-month period and more intensively during two perigean spring tide periods, (2) measured ENT concentrations in roadway floodwaters during tidal floods, and (3) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving water body vary as a function of tidal stage and antecedent rainfall, but also site-specific characteristics of the stormwater network that drains to the waterbody. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceed thresholds for safe public use of recreational water. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR.</p>
Evaluation of point-of-use treatments and biochar to reduce 1,2,3-trichloropropane (TCP) contamination in drinking water
<p>From the 1940s to the 1980s, 1,2,3-trichloropropane (TCP) was widely present as an impurity in soil fumigants to eliminate plant parasitic nematodes. TCP also saw wide usage as a degreaser and solvent in industrial processes. In rural agricultural regions with a history of fumigant use, TCP is a common pollutant in groundwater. As a potent suspected carcinogen (California MCL 5 ng/L), TCP poses a risk to communities reliant on domestic wells. Lacking the populations needed for more centralized water treatment facilities, these communities often use point-of-use (POU) treatment technologies or buy bottled water. In this study, we tested commercially available water pitchers equipped with carbon filters for point-of-use TCP treatment efficacy. As a less costly carbon alternative, we also tested low-cost, locally sourced biochar made from almond shells. Biochar could serve as a sustainable alternative to the current coconut and coal-based carbon feedstocks. Pitcher point-of-use filters removed at or above 98% of TCP in tap water derived from untreated groundwater during their lifetime of use. In our batch studies, almond biochar did absorb TCP at a lower efficiency than commercially available granulated carbon. Ultimately, the study's findings could assist affected communities and households in identifying efficient and cost-effective treatment technologies at the domestic well and household levels.</p>
Chemical analysis dataset for contaminants of emerging concern and bioanalytical data for samples from a low mountain stream in Central Germany
<p>In 2022, river-water samples were collected at six sampling sites along the Holtemme River in Central Germany using large-volume solid phase extraction. The extracts were analysed by target chemical analysis for contaminants of emerging concern. In addition, the extracts were analysed in a bioanalytical test battery using effect-based tools. The battery included assays for cytotoxicity (neutral red retention assay), oxidative stress (Nrf2-CALUX®), endocrine disruption (ER-, AR-, anti-ER-, anti-AR-, GR- and PR-CALUX®) and the fish embryotoxicity test with zebrafish (<em>Danio rerio</em>). The data obtained are included in the .csv files in this repository.</p>
Simulation Files for Organic Contaminants and Atmospheric Nitrogen at the Graphene–Water Interface
<p>This data set provides files needed to run the simulations described in the manuscript entitled "Organic contaminants and atmospheric nitrogen at the graphene–water interface: A simulation study" using the molecular dynamics software NAMD and LAMMPS. The output of the simulations, as well as scripts used to analyze this output, are also included. The files are organized into directories corresponding to the figures of the main text and supplementary information. They include molecular model structure files (NAMD psf), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD or LAMMPS configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and some trajectories in dcd format (downsampled). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts. A modified LAMMPS C++ source file is also included.</p>
Dataset: A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern
<p>NORMAN Suspect List Exchange was used for the generation of the dataset. Datasets with clear label (LC or GC) were used. More specifically, we used S3 NORMANCT15, which contains a list of compounds that were detected in surface water from the Danube River in a pan-European collaborative trial employing both GC-HRMS and LC-HRMS. Moreover, the GC and LC target list were used by the following two institutes: National and Kapodistrian University of Athens (NKUA) and Helmholtz Centre for Environmental Research (UFZ). S21 UATHTARGETS is the LC target list of NKUA, S65 UATHTARGETSGC is the GC target list of NKUA and S53 UFZWANATARG contains the LC and GC target list of UFZ. Finally, two GC target lists (S51 WRIGCHRMS and S70 EISUSGCEIMS) were used. These lists contain GC substance lists and were provided by two Slovak institutes, the Water Research Institute (WRI) and Environmental Institute. The aforementioned compound lists were merged together to form a labelled dataset. The SMILES were used to calculate 1446 molecular descriptors. 1446 descriptors were produced by PaDEL-descriptor, logP was produced by JRgui and boiling point by USEPA ECOSAR.</p> <p>The dataset is used in the publication:</p> <p>"A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern" authored by</p> <p>Nikiforos Alygizakis, Vasileios Konstantakos, Grigoris Bouziotopoulos , Evangelos Kormentzas, Jaroslav Slobodnik and Nikolaos S. Thomaidis</p> <p>Github repository: https://github.com/nalygizakis/LCvsGC</p>
Analysis of pangolin metagenomic datasets reveals significant contamination, raising concerns for pangolin CoV host attribution
<p>Supplementary Figures, Information and Data to accompany:</p> <p>Analysis of pangolin metagenomic datasets reveals significant contamination, raising concerns for pangolin CoV host attribution<br> <em>Adrian Jones, Daoyu Zhang, Yuri Deigin and Steven C. Quay</em></p> <p>https://arxiv.org/abs/2108.08163</p> <p> </p> <p> </p>
River-groundwater Interaction and Recharge Effects on Microplastics Contamination of Groundwater in Confined Alluvial Aquifers: Original dataset
<p>The dataset includes all the microplastics analysed for the manuscript "River–Groundwater Interaction and Recharge Effects on Microplastics Contamination of Groundwater in Confined Alluvial Aquifers" by Edoardo Severini, Laura Ducci, Alessandra Sutti, Stuart Robottom, Sandro Sutti and Fulvio Celico (https://doi.org/10.3390/w14121913). The dataset "MP (ctrl and sample).txt" contains the raw data of environmental and ctrl samples. The values of the ctrl samples were used to analyse the statistical differences between ctrl and environmental samples and subsequently used to define the identification parameters (threshold) for the microplastics identification in surface water and groundwater, contained in the file "MP (after ctrl sample).txt".</p>
METHICILLIN-RESISTANT STAPHYLOCOCCUS AUREUS AND ITS DETERMINANTS OF RAW COW MILK CONTAMINATION IN SELECTED GAMO ZONE DISTRICT OF SOUTHERN ETHIOPIA
<p>We conducted this study, titled "Methicillin-Resistant Staphylococcus aurous and its Determinants of Raw Cow Milk Contamination in a Selected Gamo Zone District of Southern Ethiopia," in order to disseminate our findings to the scientific community. The research has its own strengths, such as the work we did on milk quality and potential milk borne pathogens, as well as the determinants of contamination at the selling point, which is only on fresh milk after it has been checked for freshness. As a result, the authors initiated this research based on practical challenges encountered while providing professional support to various producers in the milk selling point. Because of the irrational use of antibiotics among human and animal health in the study area, this research aimed to estimate the microbial load, prevalence of methicillin resistant <em>S. aurous</em> (MRSA), and determinants of raw cow milk contamination at selling points. This could be a source of drug resistance development in Ethiopia and in the study area in particular. Furthermore, due to resource constraints, the study focuses on bacteriological analysis, risk factor association, and drug sensitivity testing, which can serve as baseline data for future characterization and intervention. Hoping that we authors are interested to publish our research output in your journal with your eminent support for the article gets published.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.25% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.25% contaminant spike-in.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 1% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 1% contaminant spike-in.</p>
The Huanan Market Origin of SARS-CoV-2 is unlikely: The ancestral lineage containing specimen appears to have arisen from laboratory contamination
<p>• There is universal agreement that the lineage B/L is not the ancestral SARS-CoV-2; lineage A/S is the most ancestral lineage<br> • Until the Gao paper, no lineage A/S virus was identified at the Huanan Market, making the market an unlikely origin for the pandemic<br> • The Gao paper found one specimen, A20, with both lineage A/S and B/L<br> • The lineage B/L reads in A20 and the specimen Ct matched the expected findings<br> • The lineage A/S reads were anomalously high compared to the Ct and meet the definition of a statistical outlier<br> • The SARS-CoV-2 reads from the A20 sample also had two SNVs not seen in GISAID sequences until at least 60-90 days after the specimen was collected from the market<br> • This analysis supports a finding that the ancestral lineage A/S virus sequences in sample A20 were not present on the glove on January 1, 2020 when the sample was collected but instead arose by inadvertent laboratory contamination later, probably during metagenomic sequencing</p> <p>The absence of an unimpeachable ancestral lineage specimen at the Hunan Market makes it unlikely the market was the origin of the pandemic.</p>
Iron Nitride Nanoparticles for Rapid Dechlorination of Mixed Chlorinated Ethene Contamination: DFT Calculations
<p>This dataset contains input and output files of density functional theory calculations on which the computational part of the peer-reviewed article "Iron nitride nanoparticles for rapid dechlorination of mixed chlorinated ethene contamination" by M. Brumovský et al., doi 10.1016/j.jhazmat.2022.129988, is based. Please cite this article when using the dataset.</p> <p>The dataset includes:<br> * Structures and energies of adsorbed cis-DCE and PCE molecules on the Fe(110), Fe4N(001), and FeS(001) surfaces in the folder "Adsorption_calculations", including structures and energies of adsorbed TCE on the FeS(001) surface<br> * cis-DCE and PCE homolytic bond dissociation energies calculated using VASP and TURBOMOLE in the folder "BDE_calculations"<br> * Relaxed structures of dechlorinated intermediates on the Fe(110), Fe4N(001), and FeS(001) surfaces in the folder "Intermediates"<br> * Transition state calculations of chloroethene dechlorination reactions, including frequency calculations of transitions states, in the folder "NEB_calculations"<br> * Energies of reactants, products, and transitions states calculated with the inclusion of the solvent effect in the folder "VASPsol_calculations"</p> <p>The final geometries and energies calculated using VASP are reported in the CONTCAR and OUTCAR files, respectively. Note that the POTCAR files are not allowed to be made publicly accessible. However, their description is given in the OUTCAR files. Please consult VASP/TURBOMOLE manual for more information regarding input and output files.</p> <p>The fully relaxed unit cells of Fe, Fe4N, and FeS, as well as adsorption calculations of TCE, and transition state calculations of TCE chemisorption and its first dechlorination step on the Fe4N(001) surface are accessible in a previously published dataset, doi 10.5281/zenodo.6338412.</p> <p>Funding: This work was supported by the Austrian Science Fund (FWF) project M 2892-N. The Vienna Scientific Cluster (Project No. 70544) is gratefully acknowledged for providing computational resources.</p> <p>Terms of use: These data are provided "as is", without any warranty. The data are provided under the Creative Commons Attribution 4.0 International license.</p>
Fig. 1. A in Agricultural Trichothecene Mycotoxin Contamination Affects The Life-History And Reduced Glutathione Content Of Folsomia Candida Willem (Collembola)
Fig. 1. A relative growth of Folsomia candida on the relation of the contaminated food; significant difference in mean B. Total reproduction of Folsomia candida in the contaminated
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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