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40 results for “plant growth regulation”

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zenodo40/100

Figure 1 in Laboratory evaluation of Beauveria bassiana, some plant oils and insect growth regulators against two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae)

Figure 1. Infection caused by Beauveria bassiana on Tetranychus urticae, (a, b) dead female mites due to mycosis, (c) dead mite in control.

opencc-by-4.0Jul 2017View details →

zenodo40/100

Fig. 1 in Effect of plant growth regulators on Blissus insularis (Hemiptera: Blissidae)

Fig. 1. Mean Blissus insularis densities (± SE) in St. Augustinegrass, Stenotaphrum secundatum, treated with mefluidide, trinexapac-ethyl, or untreated control. Means with the same letter within a sampling date did not differ statis- tically (ANOVA, P> 0.05); there were 5 replicates per treatment.

opencc-by-4.0Sep 2016View details →

zenodo36/100

Plant Growth Regulators in Barley in Northern Grains Region Australia

Project title: NGN Validating the use of plant growth regulators to manage excessive growth in barley in Northern Region warm growing environments (2022-24, AMP2205-004RTX).  Methodology: Seven barley PGR trials were run in 2022-23 at a range of locations in northern NSW. In 2022, three trials assessed PGRs Moddus Evo® (250 g/L Trinexapac-Ethyl) and ethephon (Promote® Plus 900, 900 g/L Ethephon) and a range of use patterns across 4 barley varieties. Following high levels of lodging in 2022, four trials in 2023 quantifed the impact of PGR treatments on different varieties, times of sowing and nitrogen levels.   Four varieties – Leabrook, Laperouse, Planet, Maximus CL – were selected to represent the range of lodging susceptibilities in commercial barley varieties. Moddus Evo® and ethephon use patterns were tested to allow for early and late control of barley biomass production as well as bounce back, where compensatory growth occurs under favourable conditions following a PGR application (Table 1). <a name="_Ref165987766"></a>Table 1. PGR use patterns tested on dryland barley in 2022 and 2023 <table> <tbody> <tr> <td>   </td> <td> Application 1 </td> <td> Application 2 </td> </tr> </tbody> <tbody> <tr> <td> PGR Treatment Name </td> <td> PGR </td> <td> Rate (ml) </td> <td> GS </td> <td> PGR </td> <td> Rate (ml) </td> <td> GS </td> </tr> <tr> <td> Moddus 31 </td> <td> Moddus Evo® </td> <td> 400 </td> <td> 31 </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td> Moddus 300 @ 31 </td> <td> Moddus Evo® </td> <td> 300 </td> <td> 31 </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td> Moddus 37 </td> <td> Moddus Evo® </td> <td> 400 </td> <td> 37 </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td> Moddus 31 + 37 </td> <td> Moddus Evo® </td> <td> 400 </td> <td> 31 </td> <td> Moddus Evo® </td> <td> 400 </td> <td> 37 </td> </tr> <tr> <td> Ethephon 41 </td> <td> Ethephon* </td> <td> 400 </td> <td> 41 </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td> Ethephon 45 </td> <td> Ethephon* </td> <td> 400 </td> <td> 45 </td> <td>   </td> <td>   </td> <td>   </td> </tr> </tbody> </table> GS=Growth Stage; 900 g/L Ethephon In 2022, the impact of PGR treatments (n=6) on four barley varieties were tested at three locations in northern NSW (Table 2 and Table 3). 1.    At Boomi and Gurley a variety (n=4) by PGR strategy (n=5) full factorial plot trial was established. Due to high rainfall and subsequent waterlogging the trial planted at Boomi failed to establish. 2.    At Tulloona a trial was established to replace the failed Boomi trial. PGR treatments (n=5) were applied over a commercial crop of Planet barley in a randomised complete block design. 3.    At Spring Ridge, a partial factorial experiment assessing varieties (n=4) was established with 6 PGR treatments. In 2023 trial sites were established at Boomi, Gurley, Pallamallawa and Breeza. 4. At Boomi full replicated randomised incomplete block design trials assessed the impact of time of sowing (n=2, early and mid time of sowing) and PGRs (n=6) for four barley cultivars. For the first time of sowing, PGRs (n=6) were applied to four varieties; Leabrook, Laperouse, Planet and Maximus CL, at the second time of sowing PGRs were only applied to lodging susceptible variety Leabrook. 5. At Gurley, the same trial was repeated as describe above for Boom 6. At Breeza, the four barley cultivars were tested with six PGR treatments with one time of sowing only. 7. At Pallamallawa, Leabrook was planted with four nitrogen rates (0, 75, 150 and 250 kg N/ha) applied as urea-N and six PGR treatments were applied. This trial failed due to residual herbicide damage and thus the results from this trial are not presented in this report. Consequently, yield responses to PGR treatment under a range of nitrogen levels was unable to be captured in this project. Seasonal conditions in 2022 and 2023 differed markedly for both in-crop temperatures and rainfall. In 2022, cooler than average spring maximum temperatures were experienced at Tulloona and Gurley while at Spring Ridge maximum spring temperatures were warmer than average. In contrast, in 2023 maximum winter and spring temperatures were 1.5⁰C higher than monthly averages between June and October (Appendix A). There were large differences in annual and growing season rainfall (May-October, GSR) between 2022 and 2023 (Table 4). In 2022, annual rainfall was 62-241mm above the long term average while in 2023 annual rainfall was 163-195mm below the long-term average. More importantly, were the differences in growing season rainfall. In 2022 growing season rainfall was 202-242mm above average (Decile 10) while in 2023 it was 136-175mm below average (Decile 1). However, high levels of soil water in 2023 compensated for the lack of in-crop rain. <a name="_Ref167779013"></a>Table 4. Plant available water and rainfall data for barley PGR trials in 2022 and 2023 <table> <tbody><tr> <td> Site </td> <td> Trial </td> <td> Planting PAW (mm) </td> <td> Annual rainfall (mm) </td> <td> Annual rainfall (mm) </td> <td> Growing season rainfall (mm)* </td> <td> Growing season rainfall (mm)* </td> </tr> </tbody><tbody> <tr> <td>   </td> <td>   </td> <td>   </td> <td> Year of trial </td> <td> Long Term Average </td> <td> Year of trial </td> <td> Long Term Average </td> </tr> <tr> <td> Tulloona </td> <td> PGR over commercial crop </td> <td> 165 </td> <td> 754 </td> <td> 548 </td> <td> 456 </td> <td> 214 </td> </tr> <tr> <td> Gurley </td> <td> PGR x Variety </td> <td> 165 </td> <td> 622 </td> <td> 560 </td> <td> 406 </td> <td> 204 </td> </tr> <tr> <td> Spring Ridge </td> <td> PGR x Variety </td> <td> 202 </td> <td> 870 </td> <td> 629 </td> <td> 465 </td> <td> 254 </td> </tr> <tr> <td> Boomi </td> <td> PGR x Variety x 2 Times of Sowing </td> <td> 201 </td> <td> 367 </td> <td> 543 </td> <td> 50 </td> <td> 201 </td> </tr> <tr> <td> Gurley </td> <td> PGR x Variety x 2 Times of Sowing </td> <td> 198 </td> <td> 365 </td> <td> 560 </td> <td> 68 </td> <td> 204 </td> </tr> <tr> <td> Breeza </td> <td> PGR x Variety </td> <td> 253 </td> <td> 484 </td> <td> 647 </td> <td> 122 </td> <td> 265 </td> </tr> <tr> <td> Pallamallawa# </td> <td> PGR x Nitrogen </td> <td> 171 </td> <td> 459 </td> <td> 641 </td> <td> 58 </td> <td> 233 </td> </tr> </tbody> </table> *Measured as rainfall from May to October, #Trial failed due to residual herbicide damage Plant height was measured during early grain fill in each replicate from ground level to the top of the spike excluding awns of the main tiller. Lodging was scored visually with a 0 for no lodging and 10 for 100% of the crop completely lodged and flat against the ground. In 2023 no lodging was evident at Boomi, Gurley or Pallamallawa thus scores were not recorded. Biomass cuts were taken at GS 30, GS 55 and GS 99 at 30mm above ground level with 1 x 0.5m quadrat.

opencc-by-4.0Jun 2024View details →

dryad36/100

Data from: Enhanced phytoremediation of cadmium contaminated soil using chelating agents and plant growth regulators

Open the record for dataset details and reuse information.

publicSep 2024View details →

zenodo32/100

Stand age rather than soil moisture gradient dominantly regulates the compromise between plant growth and water use of Eucalyptus urophylla in hilly South China

These files contain the clinate data, the morphology characteristics, soil water content and tree normalized transpiration  collected in our studied site.

openother-openJul 2020View details →

zenodo32/100

Fig. 11 in Computational insight into the chemical space of plant growth regulators

Fig. 11. The results of testing the discovered hit-molecules (dots within the map) using the developed in silico model (background gradient corresponds to the distribution of Ref. PGRs). This case, the scale at the bottom indicates the number of hits in each node.

opennotspecifiedFeb 2016View details →

zenodo32/100

Fig. 10 in Computational insight into the chemical space of plant growth regulators

Fig. 10. The discrimination between two agro-categories using Ui: PGRs/pesticides (a), herbicides/PGRs (b).

opennotspecifiedFeb 2016View details →

zenodo32/100

Fig. 8 in Computational insight into the chemical space of plant growth regulators

Fig. 8. The distributions of PGRs (a), herbicides (b), fungicides (c), and insecticides (d). The scale at the bottom corresponds to the number of compounds; the axes indicate the node coordinates in the lattice; the contours are smoothed.

opennotspecifiedFeb 2016View details →

zenodo32/100

Fig. 9 in Computational insight into the chemical space of plant growth regulators

Fig. 9. The distributions of the descriptor values within the map for HBA (a) and RBN (b) demonstrate that separation could be achieved with a holistic approach. The scale at the bottom shows the number of the compounds; the axes indicate the node coordinates in the lattice; the contours are smoothed.

opennotspecifiedFeb 2016View details →

zenodo32/100

Fig. 4 in Computational insight into the chemical space of plant growth regulators

Fig. 4. The diversity in heterocycles for the reference compounds: PGRs (a), the whole reference set (b), herbicides (c).

opennotspecifiedFeb 2016View details →

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Fig. 3 in Computational insight into the chemical space of plant growth regulators

Fig. 3. The representative examples of compounds from the reference dataset (the corresponding patent application numbers are provided).

opennotspecifiedFeb 2016View details →

zenodo32/100

Fig. 6 in Computational insight into the chemical space of plant growth regulators

Fig. 6. The Sammon map showing the distribution of the compounds within the related chemical space; outliers indicated by the ellipse are spread beyond the scope of the major set population. Axis per se do not make sense as they simply organize outputted 2D-lattice.

opennotspecifiedFeb 2016View details →

zenodo32/100

Fig. 7 in Computational insight into the chemical space of plant growth regulators

Fig. 7. The Kohonen map constructed for the whole reference dataset. The scale at the bottom identifies the number of compounds located in a node; the axes indicate the coordinates of neurons within the lattice; the contours are smoothed.

opennotspecifiedFeb 2016View details →

dryad28/100

Data from: Constitutive and herbivore-induced plant defenses regulate herbivore population growth

1. Induced plant defenses regulated by the phytohormones jasmonic acid and salicylic acid are predicted to influence herbivore population dynamics, in part because they can operate in a density-dependent manner. While there is ample evidence that induced plant responses affect individual performance and growth of herbivores, whether they scale-up to regulate herbivore population dynamics is still unclear. 2. We evaluated the consequences of variation in plant defenses and herbivore density on herbivore development, reproduction and density-dependent population growth. We investigated potential mechanisms affecting the strength of herbivore density-dependent processes by manipulating jasmonate expression, quantifying plant defensive traits (phytohormones jasmonic acid and salicylic acid and serine proteinase inhibitors) and adding aphids (Macrosiphum euphorbiae) at different densities to plants to simulate different initial population density and herbivore load. We manipulated jasmonate defenses by using genetically modified lines of tomato plants (Solanum lycopersicum) with elevated or suppressed jasmonate-dependent defenses. Jasmonate-insensitive plants cannot induce the jasmonic acid pathway, while jasmonate-overexpressing plants constitutively express jasmonate-dependent defenses. 3. We found that jasmonate defenses provided resistance against aphids and influenced density-dependent processes. Jasmonate-overexpressing plants reduced aphid reproduction, prolonged developmental time, dampened aphid populations across all aphid densities, and caused density-independent aphid population growth. Aphid feeding on jasmonate-overexpressing plants did not activate the salicylic acid pathway, thus on this plant line jasmonate defenses affected aphid responses. In contrast, jasmonate-insensitive plants increased aphid reproduction, shortened the developmental time, reduced population growth only at high initial densities, and promoted strong negative density-dependent population growth. Aphid feeding on jasmonate-insensitive plants did not induce jasmonate-dependent defenses, but induced the salicylic acid pathway in a density-dependent manner, which resulted in negative density-dependent aphid population growth. 4. Aphid feeding on jasmonate-insensitive and overexpressing plant differentially activated the salicylate pathway, revealing a negative crosstalk between the defensive phytohormones jasmonic acid and salicylic acid. By muting or enhancing jasmonate-mediated responses and quantifying salicylic acid phytohormone induction, we demonstrated that plant defenses are a key factor driving not only the performance, but also the density dependence processes of herbivore populations.

opencc-zeroDec 2018View details →

zenodo28/100

Fig. 1 in Computational insight into the chemical space of plant growth regulators

Fig. 1. Representative examples of phytohormones.

opennotspecifiedFeb 2016View details →

zenodo28/100

Fig. 5 in Computational insight into the chemical space of plant growth regulators

Fig. 5. The representative examples of the reference scaffolds.

opennotspecifiedFeb 2016View details →

zenodo28/100

Fig. 2 in Computational insight into the chemical space of plant growth regulators

Fig. 2. The mechanisms of action for herbicides (examples).

opennotspecifiedFeb 2016View details →

dryad28/100

Data from: Constitutive and herbivore-induced plant defenses regulate herbivore population growth

Open the record for dataset details and reuse information.

publicApr 2019View details →

geo24/100

The AP2/ERF Transcription Factor TINY Modulates Brassinosteroid-Regulated Plant Growth and Drought Response in Arabidopsis

GEO Series GSE128946. Arabidopsis thaliana. 9 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2019View details →

geo24/100

Genome-wide binding of Arabidopsis growth regulating factors uncovers their broad regulatory functions in plant growth, development and stress response [RNA-Seq]

GEO Series GSE128170. Arabidopsis thaliana. 12 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenFeb 2020View details →

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allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

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neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record