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393 results for “pesticides”
Fig. 4 in The threat of pesticide and disease co-exposure to managed and wild bee larvae
Fig. 4. Brood pathogens studies per bee genera found from Web of Science searches using search terms "brood disease", "brood pathogen", "brood virus", "larvae disease", "larvae pathogen" and "larvae virus" across bee genera. Red squares indicate that a pathogen on the x-axis has been found to infect at least one species in the bee genus corresponding to its position in the phylogeny shown on the y-axis; yellow squares indicate that the pathogen on the x-axis has been found in individuals from at least one species in the genus on the y-axis but no symptoms were reported in the studies; dark grey squares indicate that the pathogen on the x-axis has been tested for in at least one species in the genus corresponding on the yaxis, however has not been found; white squares indicate that the searches found no studies where any bee species of the genus on the y-axis were tested for in the corresponding pathogens on the x-axis. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in The threat of pesticide and disease co-exposure to managed and wild bee larvae
Fig. 2. Proportion of results per search term across bee genera on the Web of Science search engine (n = the total number of studies corresponding to each search term). Search terms related to brood disease (A) and pesticide exposure (B) are both compared to species diversity at the genus level. Genera with less than 5 studies in any brood disease search term (A) and less than 10 studies across any pesticide exposure search term (B) have been classified as 'Understudied' (brown) and grouped. Genera with no studies related to any brood disease search term (A) and no studies related to any pesticide exposure search term (B) have been classified as 'Unexplored' (grey) and grouped. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in The threat of pesticide and disease co-exposure to managed and wild bee larvae
Fig. 1. Flowers contaminated with brood pathogens and pesticides can lead to simultaneous exposure to pesticides and brood pathogens from flowers in adult foraging bees (A). This leads to brood being co-exposed to the stressors via food provisioning (B). Pesticides may increase larval mortality from brood infections directly through compromised immunocompetence (C), and/or indirectly through manipulating microbial communities and compromising food provisions from adult bees (D) (Icons8, 2022).
Pesticide application data and agronomic practices accross Europe and Argentina
<p>A farm survey in 135 farms accross 10 european countries and Argentina was conducted in 2021. Data collected and reported in this database include</p> <p>(i) General information such as the type of farming practice, size of farm and of fields, types of crops and livestock, as well as working capacity (employees).</p> <p>(ii) For a selected one or two fields, a detailed description of the agronomic activities and yield parameters was compiled, which included crop and variety and e.g. position in crop rotation, as well as all crop-specific activities such as tillage, fertilisation, irrigation, pruning, and sowing. </p> <p>(iii) Activities related to crop protection were requested in detail, including but not limited to the use of PPP formulations and related tools and techniques, and the strategy and motivation of the farm manager for PPP applications. The assessment of the crop protection strategy on the respective fields included variety, dates of PPP applications, product tank mixtures, type of products, product brand name, product distributor, target organisms, mode of action, area treated, quantity of product applied, total volume applied (total tank volume: product and water volume), formulation, active substances and concentrations, technique of application, speed of application, type of nozzles, and costs of products applied.</p> <p>(iv) Soil fertility management assessment included pre-crop, dates of application, types of product and nutrient specifications (NPK, micronutrients) as well as quantities applied. The information on agronomic practices was compiled in a ‘log book’, which listed all agronomic management practices on the respective fields during the whole season from planting/sowing to harvest as well as the underlying assumptions and motivations.</p> <p>(vi) Finally, econometric data included costs of products and yield</p> <p>Raw data was thoroughly screened for completeness, plausibility and data integrity, and revised/supplemented where necessary. Missing/unknown data was tagged and complemented where possible with proxies from e.g. product technical leaflets to allow for subsequent analysis. The data set was further complemented with related publicly available data, such as phenological codes (BSV, 2021.; Schweizerische Eidgenossenschaft, 2021), standard codes for crops and pests (EPPO secretariat, 2022.), mode of action of active substances and resistance risks (FRAC Committee, 2022.; HRAC Committee, 2022.; IRAC Committee, 2022.; Sparks & Nauen, 2015), and application recommendations of PPPs and fertilizers (collection of regional crop-specific or manufacturer-specific technical leaflets). If expenditures for PPPs were not available, costs for PPPs were assessed based on publicly available catalogues issued by manufacturers or regional PPP distributors. Data was also amended by hazard statements of active substances (Burtscher-Schaden et al.,)</p>
Annexes to the EFSA external scientific report "Proposed prospective scenarios for cumulative risk assessment of pesticide residues"
<p>In the context of prospective cumulative risk assessment of pesticides, different options and scenarios for a tiered approach were investigated by means of 15 case studies for the cumulative assessment group associated with an effect on the motor division of the nervous system (CAG-NAM) and 15 case studies for the cumulative assessment group associated with an effect on hypothyroidism (CAG-TCF) (doi:10.2903/sp.efsa.2021.EN-6811). The results of the prospective exposure calculations are reported in the following annexes:</p> <p><strong>Annex A</strong>: Acute exposure calculations - CAG-NAM</p> <p><strong>Annex B</strong>: Chronic exposure calculations - CAG-TCF</p> <p><strong>Annex C</strong>: Results supporting the discussion on prospective acute scenarios - CAG-NAM</p> <p><strong>Annex D</strong>: Results supporting the discussion on prospective chronic scenarios - CAG-TCF</p> <p>The case studies reported above also include fictitious data, which were included for assessing the relevance of the various parameters in these calculations. The results of these case studies do not represent real estimates of exposure or risk, nor do they represent the formal outcome of a risk assessment.</p>
Figure 2 in Application of demographic analysis for assessing effects of pesticides on the predatory mite, Phytoseiulus persimilis (Acari: Phytoseiidae)
Figure 2. Age specific survival rate (lx), fecundity (mx), maternity (lxmx) and age-stage specific fecundity (fxj) of offspring from Phytoseiulus persimilis females treated with LC25 of three pesticides compared with untreated females.
Figure 1 in Application of demographic analysis for assessing effects of pesticides on the predatory mite, Phytoseiulus persimilis (Acari: Phytoseiidae)
Figure 1. Age-stage specific survival rate (sxj) of offspring from Phytoseiulus persimilis females treated with LC25 of three pesticides compared with untreated females.
Epigenome-wide DNA Methylation and Pesticide Use in the Agricultural Lung Health Study
<p>An epigenome-wide association study of blood DNA methylation and pesticide use was conducted in adults in the Agricultural Lung Health Study. Sixteen specific pesticides were analyzed: dicamba, picloram, mesotrione, acetochlor, metolachlor, glyphosate, 2,4-Dichlorophenoxyacetic acid (2,4-D), atrazine, malathion, aldrin, chlordane, DDT, dieldrin, heptachlor, lindane, and toxaphene. 162 differentially methylated CpGs across 9 specific pesticides (acetochlor, atrazine, dicamba, glyphosate, malathion, metolachlor, mesotrione, picloram, and heptachlor.</p>
Figure 12 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 12 Seasonal abundance of predatory mites observed during 2010 (months are indicated in x-axis) on different treatments in Farm B.
Figure 11 Seasonal abundance ofEotetranychus. carpiniobserved during 2010 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 11 Seasonal abundance ofEotetranychus. carpiniobserved during 2010 (months are indicated in x-axis) on different treatments in Farm B.
Figure 7 Seasonal abundance ofKampimodromus aberransobserved during 2010 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 7 Seasonal abundance ofKampimodromus aberransobserved during 2010 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 2 Seasonal abundance ofEotetranychus carpiniobserved during 2009 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 2 Seasonal abundance ofEotetranychus carpiniobserved during 2009 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 8 Seasonal abundance ofTyphlodromus pyriobserved during 2009 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 8 Seasonal abundance ofTyphlodromus pyriobserved during 2009 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 9 Seasonal abundance ofTyphlodromus pyriobserved during 2010 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 9 Seasonal abundance ofTyphlodromus pyriobserved during 2010 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 4 Seasonal abundance ofAmblyseius andersoniobserved during 2009 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 4 Seasonal abundance ofAmblyseius andersoniobserved during 2009 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 10 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 10 Canopy's feature parameters observed in different vineyards of Farm A. Different letters indicate significant differences at Tukey test (α = 0.05).
Figure 3 Seasonal abundance ofEotetranychus carpiniobserved during 2010 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 3 Seasonal abundance ofEotetranychus carpiniobserved during 2010 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 6 Seasonal abundance ofKampimodromus aberransobserved during 2009 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 6 Seasonal abundance ofKampimodromus aberransobserved during 2009 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 1 Seasonal abundance ofPanonychus ulmiobserved during 2009 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 1 Seasonal abundance ofPanonychus ulmiobserved during 2009 (months are indicated in x-axis) on different treatments in vineyards
Figure 5 Seasonal abundance ofAmblyseius andersoniobserved during 2010 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 5 Seasonal abundance ofAmblyseius andersoniobserved during 2010 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
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.