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393 results for “pesticides”
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
Software and suspect database for: "A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences"
<p>This upload contains the pesticide suspect list aggregated among the laboratories of work package 16 of the HBM4EU (https://www.hbm4eu.eu) project for a large-scale pesticide suspect screening and the resolving search templates for each pesticide. Additionally, we provide the used software version of MetAlign applied in this screening.</p>
Long-term trends in pesticide residues and physical chemical parameters of superficial water samples with accompanying macro-benthic invertebrate community surveys from the Palo Verde National Park, Costa Rica: 1993-1994; 2001; 2004-2005; 2009-2011
During the years 1993-1994, 2001, 2003-2005 and 2009-2011, the Central American Institute for Studies on Toxic Substances (IRET-UNA) executed independent research projects which quantified the presence of pesticide residues on superficial water samples from the Palo Verde National Park (PVNP) and surrounding areas. The PVNP (5460 sq km) is a RAMSAR wetland of international importance, which has been subjected to pesticide pressure from agricultural fields (mainly rice and sugarcane) since the 1960s and 1970s. In 1993, the PVNP wetlands were placed on the RAMSAR Montreux Record, indicating that it was considered an “impaired ecosystem” due to ecotoxicology concerns. Water is the key component of all issues regarding the biodiversity, management, restoration, and economic development of this region. Therefore, water quality is a critical component of many social ecological discussions and research efforts. This data package contains uniform pesticide, biological and water quality data from all PVNP wetland projects (1993- 2011) in order to present long-term trends in the environmental water quality and accompanying biological patterns for this conservation area. Study sites were spatially determined to compare clean upstream waters with a gradient of pesticide-affected waters. Superficial water samples were collected at various sites for chemical (pesticide) analysis and water quality parameters were recorded in situ for environmental monitoring. Corresponding biological sampling was completed to survey benthic macroinvertebrate communities and compare with local eco-toxicological profiles. This data package contains information from four separate projects.
S78 | SLUPESTTPS | Pesticides and TPs from SLU, Sweden
<p>This is the collection associated with list S78 SLUPestTPs Pesticides and TPs from SLU, Sweden on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Suspect list of pesticides and pesticide transformation products (TPs) from SLU, created based on Sweden’s national monitoring program and the pesticide properties database (PPDB) described in Frank Menger <em>et al </em>(2021) DOI: <a href="https://doi.org/10.1021/acs.est.1c00466">10.1021/acs.est.1c00466</a>.</p> <p>Updates: 27 Apr. 2021 - added new CIDs, replaced InChIKey file with *.txt version not *.inchikey. 10 May: added missing reference fields to transformations file. May 2023: updated 4 entries with new SMILES/CIDs according to feedback from <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/c3e0e4549bc00f67cc266d7dfbf064ea858b453e">PubChem</a>. April 2025: reverted 1 CID back to a <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem/-/commit/c42e0e1dadb1317f29a32652e62850341e6b2e59">new (old) preferred form</a> due to annotation disappearing. 2 Jun 2025: adjusted TFA synonym.</p>
S60 | SWISSPEST19 | Swiss Pesticides and Metabolites from Kiefer et al 2019
<p>This is the collection associated with list S60 SWISSPEST19 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Swiss pesticides (plant protection products) and metabolites from Kiefer et al 2019 (Eawag), Tables SI-B 1 and 2, DOI: <a href="https://doi.org/10.1016/j.watres.2019.114972">10.1016/j.watres.2019.114972</a></p> <p>Update 25 April 2020: fixed many naming issues in xlsx and csv file. No structural information changed. 25 Mar 2023: fixed date CAS. 25 May 2023: fixed non-live CIDs to <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/56abd8e2bacbbb6969c961101df792189d605bfd">live CIDs</a>. 6 Jul 2023: added <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/3ff26d704ad9ac831a5d276256c1c89020fb2e02">NOA 413161</a> structures and transformations file. 8 April 2025: fixed three CIDs that went non-live to match CIDs from NORMAN deposition. Mismatch of InChIKeys due to difference in treatment of the C=N-O groups between PubChem and Open Babel; files now match PubChem's output. 2 Jun 2025: updated TFA synonym. 31 Aug 2025: fixed triazole alanine structure for one entry (removing incorrect CID <span><a href="https://pubchem.ncbi.nlm.nih.gov/compound/139597001"><span>139597001</span></a></span>).</p>
Inter-Chemical Correlation results for the study: HHEARx2016-1449 (Environmental phenols and pesticide levels in relationship to autism)
Title: Environmental phenols and pesticide levels in relationship to autism <br>Species: Homo sapiens <br>Number of samples: 842 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=7 <br>
Dataset: Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment
<p>This dataset and these scripts supports the article 'Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment' as published in Cleaner Waste Systems. https://doi.org/10.1016/j.clwas.2024.100154</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we focused on exploring the potential of large-scale composting of rose waste in Kenyan rose cultivation. The objective of this study was to examine the potential of composting rose waste in this large-scale commercial setting with low operational costs, exploring its benefits and challenges.</p> <p>In piles of 4000 kg green waste the evolution of three mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the pesticide residue levels of mature rose waste were assessed. </p>
Exposure to sublethal concentrations of a pesticide or predator cues induces changes in brain architecture in larval amphibians, 2013.
Naturally occurring environmental factors shape developmental trajectories to produce variable phenotypes. Such developmental phenotypic plasticity can have important effects on fitness, and has been demonstrated for numerous behavioral and morphological traits. However, surprisingly few studies have examined developmental plasticity of the nervous system in response to naturally occurring environmental variation, despite accumulating evidence for neuroplasticity in a variety of organisms. Here, we asked whether the brain is developmentally plastic by exposing larval amphibians to natural and anthropogenic factors. Leopard frog tadpoles were exposed to predator cues, reduced food availability, or sublethal concentrations of the pesticide chlorpyrifos in semi-natural enclosures. Mass, growth, survival, activity, larval period, external morphology, brain mass, and brain morphology were measured in tadpoles and after metamorphosis. Tadpoles in the experimental treatments had lower masses than controls, although developmental rates and survival were similar. Tadpoles exposed to predator cues or a high dose of chlorpyrifos had altered body shapes compared to controls. In addition, brains from tadpoles exposed to predator cues or a low dose of chlorpyrifos were narrower and shorter in several dimensions compared to control tadpoles and tadpoles with low food availability. Interestingly, the changes in brain morphology present at the tadpole stage did not persist in the metamorphs. Our results show that brain morphology is a developmentally plastic trait that is responsive to ecologically relevant natural and anthropogenic factors. Whether these effects on brain morphology are linked to performance or fitness is unknown.
Exposure to pesticides data for residents and bystanders, and for environmental risk assessment
<p>In 2014, EFSA has commissioned a study to review and evaluate all published data related to the exposure to pesticides for residents and bystanders and for environmental risk assessment. The aim was to conduct a literature review and to produce a database containing all published data (predominately peer-reviewed publications supplemented by grey-literature) for the last 25-years, which will support the non-dietary exposure assessment to pesticides for bystanders and residents, as well as daily air concentration (vapours and aerosols) of pesticides, drift values from spray, seed and granular applications, and dislodgeable foliar residues.</p> <p>The data has been collated via a systematic and extensive literature review defined and managed according to a pre-defined 'review protocol'. The data was also exported in a format that meets the requirements of the EFSA Data Collection Framework (DCF).</p> <p>Based on quality and relevance criteria, articles and related studies have been selected. For dislodgeable foliar residues the assessment includes 27 articles (containing 49 discrete studies); for air concentrations, 26 articles (containing 84 discrete studies); for resident and bystander exposure, 5 articles (containing 8 discrete studies); and for drift values 55 articles (containing 275 discrete studies). </p> <p>For dislodgeable foliar residues the data retained covered 17 crops (including grass, glasshouse crops, lucerne, and citrus) and 29 pesticides; for air concentrations the data retained covered 21 crops (including fruit, glasshouse crops, ornamentals, grass, vegetables and cereals) and 39 pesticides. For drift values, the data covers a range of crops and landscapes from cereals, grass and turf, orchards, vineyards and regenerated forestry. The vast majority of the data retrieved applies to field studies for liquid spray drift, measured either as ground deposits or collected at various heights and were conducted using fluorescent tracers rather than pesticides. No data was found for microbials (biopesticides). For resident and bystander exposure, many articles were rejected due to the applied inclusion/exclusion criteria.</p>
Montenegro results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. </p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2022 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2021 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2020 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2019 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2018 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2017 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.
<p>There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.</p>
Intraspecific variation in the sensitivity of bees to pesticides: a comparative analysis in Bombus terrestris and Osmia bicornis
<p>These files describe the archived CSV files associated with the publication "Intra-specific variation in sensitivity of Bombus terrestris and Osmia bicornis to three pesticides"</p> <p>By Alberto Linguadoca, Margret Jürison, Sara Hellström, Edward A. Straw1, Peter Šima, Reet Karise, Cecilia Costa, Giorgia Serra, Roberto Colombo, Robert J. Paxton, Marika Mänd, Mark J. F. Brown<br> </p>
Raw (main) dataset for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Raw dataset for farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
HydroGeoSphere Model Input Files and Results for Validation of Pesticide Leaching to Groundwater for Nine EU FOCUS Scenarios
<p>This dataset includes HydroGeoSphere (HGS) model (Aquanty, 2024) input and output files for nine Forum for the Co-ordination of Pesticide Models and their Use (FOCUS) scenarios (EC, 2014) for simulation of leaching of four test contaminants to groundwater. It is recommended that users are familiar with HGS software in order to best make use of the available files. Scenarios are included in separate subfolders named using the first four letters of the FOCUS scenario location name, e.g. folder "chat" contains the model run for the "Chateaudun" scenario. It is recommended that users familiarize themselves with the (EC, 2014) groundwater scenarios. HGS model inputs are specified in the *.grok ASCII text file for each scenario in each subfolder. Soil material properties and evapotranspiration properties are included in HGS input files in ASCII text format in the "material_properties" subfolder. Solute application timing for each scenario are include in the "solute_app" subfolder. And climate times series inputs are included in the "weather" subfolder.</p>
S69 | LUXPEST | Pesticide Screening List for Luxembourg
<p>This is the collection associated with list S69 LUXPEST Pesticide Screening List for Luxembourg on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A pesticide screening list for Luxembourg, compiled from multiple sources by Jessy Krier, uni.lu. Dataset DOI: 10.5281/zenodo.3862688.</p> <p>NOTE: the presence of pesticides on this list means that they are potentially relevant for Luxembourg and surrounding regions, but does not imply that they have been detected in Luxembourg. This list has been compiled to enable the efficient screening of data using the background knowledge contained within this list.</p> <p>The sources used were:</p> <p>Classification links & Authorization in Luxembourg</p> <p>https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/public/?event=activesubstance.selection&language=EN</p> <p>https://sitem.herts.ac.uk/aeru/bpdb/search.htm</p> <p>https://sitem.herts.ac.uk/aeru/ppdb/en/search.htm<br> <br> Origin:</p> <p>ASTA: https://saturn.etat.lu/tapes/tapes_de_lst_pdt.jsp?sel=_</p> <p>SWISSPEST16: https://comptox.epa.gov/dashboard/chemical_lists/swisspest</p> <p>Structure mapping: CompTox Batch Search with CID mapping via PubChem; InChIs were generated from SMILES by OpenBabel. MS_READY SMILES were used for mass spectral screening; parent forms were used for structural information and to map classification content.</p> <p>Update 29/7/2021: TP permission information updated.<br> </p>
Raw data files associated with the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees"
<p>These are the raw data CSV files associated with the results described in the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees".</p> <p>By Sara Hellström, Verena Strobl, Lars Straub, Wilhelm H. A. Osterman, Robert J. Paxton, Julia Osterman</p>
Pesticide residue monitoring in South Tyrol (Italy)
<ul> <li>Pesticide residue monitoring programm in South Tyrol (Province Bolzano, Italy)</li> <li>The programm is organized and conducted by the Department of Public Health, Servizio Igiene e Sanità<br> Pubblica—Azienda Sanitaria dell’Alto Adige, Bolzano, Italy</li> <li>Further despription and data available at: <ul> <li>https://www.sabes.it/gesundheitsvorsorge/umweltmedizin.asp</li> <li>https://umwelt.provinz.bz.it/pestizide-suedtirol.asp</li> </ul> </li> <li>Residue concentrations of grass samples per sampling site and sampling date can be found in the file "Pesticide_monitoring_SouthTyrol_2018_2021.csv "</li> <li>Available period: 2018 to 2021</li> <li>All sampling sites were public sites</li> <li>Names of active agents are in Italian</li> <li>Residue concentartions are given in "mg kg<sup>-1</sup>"</li> <li>"n.d." means "not detected"</li> <li>Lower limit of quantification (LOQ) was by default 0.01 mg kg<sup>-1</sup></li> <li>GPS coordinates of the sampling sites are can be found in thefile "Coordinates_sampling_sites.csv"</li> </ul>
Effects of life stage on the sensitivity of Folsomia candida to four pesticides
<p>This submission provides R-code and data files for our peer-reviewed work.</p><p>The R-notebook "Analysis_likelihood_ratio_test" contains the code used to estimate the parameters of concentration-response curves (EC10, EC50, LC10, LC50, and slopes) and perform likelihood ratio tests to compare curves from different tested life stages.</p><p>The R-notebook "Figures_Concentration_response_curves" showcases the code used to generate the figures presented in the manuscript.</p><p>The dataset files are provided in CSV format with Comma Separated Values:</p><ul><li>Cyproconazole_FolsomiaCandida_10days_20days_RawData_New.csv</li><li>Imidacloprid_FolsomiaCandida_10days_20days_RawData_New.csv</li><li>Teflubenzuron_FolsomiaCandida_10days_20days_RawData_New.csv</li><li>Thiacloprid_FolsomiaCandida_10days_20days_RawData_New.csv</li></ul><p>The submission includes the following:</p><ul><li>R files: R notebooks described above.</li><li>CSV files: Count data of springtail juveniles and adults.</li></ul><p> </p><p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 859891.</p><p>This publication reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains.</p>
A battery of in silico models application for pesticides exerting reproductive health effects: assessment of performance and prioritization of mechanistic studies
<p>Dataset of Table 1-7</p> <p>Data of Table 1, “Pesticides and their classification”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab1.PNG). Corresponding raw data is regarding classification in the hazard class reproductive toxicity available on line. All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK__Tab1_PPP_27_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 2, “PDB structures of nuclear receptors used in VTL and ED” </p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab2 15 meta data files as pdf-format with information sources of PDB structures used in employed in silico models (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M15.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab2_27_2_M.txt) in txt format.</p> <p> </p> <p>Data of Table 3, “Results of in vivo studies (Shepelska et al., 2021; Shepelskaya and Kolyanchuk, 2021; Shepelskaya and Kolianchuk, 2018)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Table3.PNG). Three meta data file as pdf-format with data of in vivo studies (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M3.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab3_27_3_M.txt) in txt format.</p> <p> </p> <p>Data of Table 4, “Results of in silico modelling of pesticides interaction with nuclear receptors”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab4.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf). All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab4_24_1-2_M.txt) in txt format.</p> <p> </p> <p>Data of Tabe 5, “Combination of in silico results with in vitro results by considering as positive result only where both in silico models predict a hit (Combined 1)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab5.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf).</p> <p>Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab5_24_25_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 6, “Combination of in silico results with in vitro results by considering as a positive any in silico hit independently of the employed model (Combined 2)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab6.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf).</p> <p>Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab6_24_25_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 7, “Metrics of performance of in silico models separately and combined.”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab7.PNG). Corresponding raw data with calculation of relevant performance metrics provided as one file in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1.csv). One meta data file as pdf-format with detailed description of the method used for calculation (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1_M1.pdf).</p> <p>All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab7_26_1_M.txt) in txt format.</p>
Sublethal effects of pesticides on predator-prey interactions in amphibians, 2008.
Increasing evidence suggests that contaminants in the environment can have important consequences on organismal interactions. While we have a good understanding of the lethal effects of contaminants on organisms, we have a weak understanding of how contaminants can affect organisms by altering the interactions that they have with other species in the community. Using tadpoles of two anuran species (Bullfrogs, Lithobates [Rana] catesbeianus; Green Frogs, L. clamitans), we investigated the effects of low nominal concentrations (1 and 10 ppb) of two pesticides (malathion and endosulfan) on tadpole activity and survival when exposed to four predator treatments (no predators; water bugs, Belostoma flumineum; newts, Notophthalmus viridescens; and dragonfly larvae, Anax junius). In both anuran species, adding predators reduced tadpole activity and survival, with increasing rates of mortality occurring with water bugs, newts, and dragonflies, respectively. Additionally, the highest concentration of endosulfan caused tadpole mortality after 48 hrs. Most significant, tadpole species also experienced interactive effects of predators and pesticides on survival after 48 hrs. In Bullfrog treatments, all predators reduced the amount of tadpole mortality when exposed to endosulfan. In Green Frogs, additive negative effects occurred, except that newts increased the tadpole mortality when exposed to endosulfan. Our findings illustrate that pesticide effects on predator–prey interactions are often complex and have the potential to alter aquatic community composition.
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Allen Brain Atlas
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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.
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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
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