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189 results for “Vineyards”
Data from: Native grass ground covers provide multiple ecosystem services in Californian vineyards
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Data from: Landscape diversity and crop vigor influence biological control of the western grape leafhopper (E. elegantula Osborn) in vineyards
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Data from: Effect of tillage intensity on the inter-row plant community and microlepidoptera predation rates in arid vineyards in Argentina
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Data from: Fungal adaptation to contemporary fungicide applications: the case of Botrytis cinerea populations from Champagne vineyards (France)
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Data from: Genetic diversity and structure of Lolium perenne ssp. multiflorum in California vineyards and orchards indicates potential for spread of herbicide resistance via gene flow
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Data from: Pest consumption in a vineyard system by the lesser horseshoe bat (Rhinolophus hipposideros)
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Data from: Deployment of organic farming at a landscape scale maintains low pest infestation and high crop productivity in vineyards
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Data from: A unique ecological niche fosters hybridization of oak-tree and vineyard isolates of Saccharomyces cerevisiae.
Differential adaptation to distinct niches can restrict gene flow and promote population differentiation within a species. However, in some cases the distinction between niches can collapse, forming a hybrid niche with features of both environments. We previously reported that distinctions between vineyards and oak soil present an ecological barrier that restricts gene flow between lineages of Saccharomyces cerevisiae. Vineyard isolates are tolerant to stresses associated with grapes while North American oak strains are particularly tolerant to freeze-thaw cycles. Here, we report the isolation of Saccharomyces cerevisiae strains from Wisconsin cherry trees, which display features common to vineyards (e.g. high sugar concentrations) and frequent freeze-thaw cycles. Genome sequencing revealed that the isolated strains are highly heterozygous and represent recent hybrids of the oak x vineyard lineages. We found that the hybrid strains are phenotypically similar to vineyard strains for some traits, but are more similar to oak strains for other traits. The cherry strains were exceptionally good at growing in cherry juice, raising the possibility that they have adapted to this niche. We performed transcriptome profiling in cherry, oak, and vineyard strains and show that the cherry-tree hybrids display vineyard-like or oak-like expression, depending on the gene sets, and in some cases the expression patterns linked back to shared stress tolerances. Allele-specific expression in these natural hybrids suggested concerted cis-regulatory evolution at sets of functionally regulated genes. Our results raise the possibility that hybridization of the two lineages provides a genetic solution to the thriving in this unique niche.
BSC Post-processed Sub-seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the subeasonal climate prediction system NCEP-CFSv2.</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term, while a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: National Centers for Environmental Prediction (NCEP) CFSv2, post-processed by BSC (create a lagged ensemble, downscaling and calibration).</p> </li> <li> <p>Issue frequency: Weekly (Initialization every Thursday, post-processed prediction every Friday).</p> </li> <li> <p>Lead times: weeks 1 to 4 (e.g. For a forecast issued on Friday 4th November, forecasts will be weekly averages starting the following Monday-Thursday and the 4 following weeks (e.g. week 1 will be 8th-15th November). The initialization date is indicated in the name of each file (e.g. 20211104). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, and incoming solar radiation.</p> </li> <li> <p>Ensemble size: 48 members</p> </li> <li> <p>Postprocessing: Create a lagged ensemble of 48 ensemble members, downscaling from the original (1°x 1°) resolution to 0.1°x 0.1° for the three domains and weekly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ‘douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p>The specific latitude and longitude indices to extract the predictions corresponding to each vitiGEOSS site are indicated in Table 2.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20211104.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (Always a Thursday).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20211104.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (Always a Thursday).</p> <p>It contains the 48 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania_percentiles_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33rd percentile is the boundary between below-normal and normal, and the 66th percentile is the boundary between the normal and above-normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania_skill_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the week of the year for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization and lead time (valid week).</p>
Smart Sprayer Working in Laboratory and Vineyard - Videos
<p>Smart Sprayer Working in Laboratory and Vineyard. Videos of the performed tests.</p>
Vineyard Dataset
<p>Vineyard Dataset for training the DIscriminator and the FC Layer</p>
Supporting data and code for: Investigation on the sensitivity of Plasmopara viticola to amisulbrom and ametoctradin in French vineyards using bioassays and molecular tools
<p>This is a small modification of the first release of the final data and code for the article entitled "Investigation on the sensitivity of Plasmopara viticola to amisulbrom and ametoctradin in French vineyards using bioassays and molecular tools" accepted for publication in Pest Management Science.</p>
Fig. 1 in Myrmecofauna (Hymenoptera: Formicidae) present in vineyards infested with Eurhizococcus brasiliensis (Hemiptera: Margarodidae) in southern Brazil
Fig. 1. Relative frequencies of occurrence of the main ant species in vineyards where Eurhizococcus brasiliensis occurred in Rio Grande do Sul.
Figure 3 from: Sharma L, Oliveira I, Torres L, Marques G (2018) Entomopathogenic fungi in Portuguese vineyards soils: suggesting a 'Galleria-Tenebrio-bait method' as bait-insects Galleria and Tenebrio significantly underestimate the respective recoveries of Metarhizium (robertsii) and Beauveria (bassiana). MycoKeys 38: 1-23. https://doi.org/10.3897/mycokeys.38.26970
Figure 3 Principal component analysis (PCA) and hierarchical clustering of the observations based on the fungal isolations. aPC1 vs. PC2. bPC1 vs. PC3. cPC2 vs. PC3. d PCA 3D plot e Hierarchical clustering dendrogram to access the ecological proximities of obtained fungi based on their respective isolation profiles. Software R 4.3.2 was used to obtain the PCA plots and the hierarchical clustering. There was no fungal isolation from hedgerows from the farm Granja when bait-insect T.molitor was used and hence, it could not be included in any of the analysis which relies on proportions, i.e. PCA plots, hierarchical clustering. To reduce any bias, the authors also discarded the soil samples (N=1) which yielded the fungal isolations, when G.mellonella was used, from the hedgerows of the farm Granja. The blue balls represent relatively more frequent EPF, i.e. Beauveriabassiana, Beauveriapseudobassiana, Clonostachysroseaf.rosea, Cordycepscicadae, Purpureocilliumlilacinum and Metarhiziumrobertsii. The red balls represent other fungi such as Cordyceps sp., Lecanicilliumaphanocladii, Lecanicilliumdimorphum, Metarhiziumguizhouense and Purpureocilliumlavendulum. Hierarchical clustering based dendrogram classified isolated EPF into two clusters, i.e. rarely occurring EPF (cluster 1) and relatively more frequent EPF (cluster 2). Abbreviations used are: Beauveriabassiana (B.b), Beauveriapseudobassiana (B.p), Cordycepscicadae (C.c), Cordyceps sp. (C.sp), Lecanicilliumaphanocladii (L.a), Lecanicilliumdimorphum (L.d), Metarhiziumguizhouense (M.g), Purpureocilliumlavendulum (P.la), Purpureocilliumlilacinum (P.l), Clonostachysroseaf.rosea (C.rr) and Metarhiziumrobertsii (M.r).
Figure 2 from: Sharma L, Oliveira I, Torres L, Marques G (2018) Entomopathogenic fungi in Portuguese vineyards soils: suggesting a 'Galleria-Tenebrio-bait method' as bait-insects Galleria and Tenebrio significantly underestimate the respective recoveries of Metarhizium (robertsii) and Beauveria (bassiana). MycoKeys 38: 1-23. https://doi.org/10.3897/mycokeys.38.26970
Figure 2 Effect of insect baiting and habitat-type on the isolation of the entomopathogenic fungi. a Occurrence (% of soil samples ± SE) of entomopathogenic fungi when different bait-insects were incorporated b Occurrence (% of soil samples ± SE) of entomopathogenic fungi when soils were collected from different habitat-types. Bars with asterisk (*) show significant isolations, i.e. (P<0.05).
Figure 1 from: Sharma L, Oliveira I, Torres L, Marques G (2018) Entomopathogenic fungi in Portuguese vineyards soils: suggesting a 'Galleria-Tenebrio-bait method' as bait-insects Galleria and Tenebrio significantly underestimate the respective recoveries of Metarhizium (robertsii) and Beauveria (bassiana). MycoKeys 38: 1-23. https://doi.org/10.3897/mycokeys.38.26970
Figure 1 Geographic coordinates and altitudes of the farms and details of the soil sampling strategy adopted. a Details of the six farms of the Douro Wine Region, Portugal, which were considered in this study b Details of the soil sampling strategy from vineyards and adjacent hedgerows.
Supplementary material 1 from: Sharma L, Oliveira I, Torres L, Marques G (2018) Entomopathogenic fungi in Portuguese vineyards soils: suggesting a 'Galleria-Tenebrio-bait method' as bait-insects Galleria and Tenebrio significantly underestimate the respective recoveries of Metarhizium (robertsii) and Beauveria (bassiana). MycoKeys 38: 1-23. https://doi.org/10.3897/mycokeys.38.26970
Supplementary tables :
Figure 4 from: Bouaziz-Yahiatene H, Inäbnit T, Medjdoub-Bensaad F, Colomba MS, Sparacio I, Gregorini A, Liberto F, Neubert E (2019) Revisited – the species of Tweeting vineyard snails, genus Cantareus Risso, 1826 (Stylommatophora, Helicidae, Helicinae, Otalini). ZooKeys 876: 1-26. https://doi.org/10.3897/zookeys.876.36472
Figure 4 Cantareus apertus. Italy, Calabria, Amantea, Marincola, NMBE 560941 A shell in frontal and B lateral views C genital anatomy, situs D, E details showing the male genital tract and the atrium. Photographs E. Neubert, shell × 1.5.
Figure 1 from: Bouaziz-Yahiatene H, Inäbnit T, Medjdoub-Bensaad F, Colomba MS, Sparacio I, Gregorini A, Liberto F, Neubert E (2019) Revisited – the species of Tweeting vineyard snails, genus Cantareus Risso, 1826 (Stylommatophora, Helicidae, Helicinae, Otalini). ZooKeys 876: 1-26. https://doi.org/10.3897/zookeys.876.36472
Figure 1 Combined RaxML and Bayesian tree of Cantareus, Cornu, and Erctella, using COI, 16S, H3, 28S, and ITS2.
Figures 5-9 from: Bouaziz-Yahiatene H, Inäbnit T, Medjdoub-Bensaad F, Colomba MS, Sparacio I, Gregorini A, Liberto F, Neubert E (2019) Revisited – the species of Tweeting vineyard snails, genus Cantareus Risso, 1826 (Stylommatophora, Helicidae, Helicinae, Otalini). ZooKeys 876: 1-26. https://doi.org/10.3897/zookeys.876.36472
Figures 5-9 Cantareus subapertus (Ancey, 1893). 5–7 syntypes Helix subapertaNHMW, Djurdjura, Kabylie ex Ancey 5NHMW 7861, D = 23.44 mm 6NHMW 7862, D = 23.51 mm 7NHMW 7863, D = 29.44 mm 8Helix mazzulopsis lectotype ANSP 63133, Jurjura Mts. Shell in frontal (A) lateral (B) and dorsal (C) view (D, E) labels 9 "Helix aspersa", original specimen of Iconographie (2) 3, pl. 69, fig. 359. Shell in frontal (A) lateral (B) ventral (C) and apical (D) views. Photographers 5–7 H. Wood, NHMW; photograph 8 E. Wildner, ANSP; photograph 9 E. Bochud, NMBE; all shells × 1.5.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.