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457 results for “growth regulation”
Raw data: Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium
<p>What regulates organ size and shape remains one fundamental mystery of modern biology. Research in this area has primarily focused on deciphering the regulation in time and space of growth and cell division, while the contribution of cell death has been overall neglected. This includes studies of the <i>Drosophila</i> wing, one of the best characterised systems for the study of growth and patterning, undergoing massive growth during larval stage and important morphogenetic remodeling during pupal stage. So far, it has been assumed that cell death was relatively neglectable in this tissue both during larval and pupal stage and as a result the pattern of growth was usually attributed to the distribution of cell division. Here, using systematic mapping and registration combined with quantitative assessment of clone size and disappearance as well as live imaging, we outline a persistent pattern of cell death and clone elimination emerging in the larval wing disc and persisting during pupal wing morphogenesis. Local variation of cell death is associated with local variation of clone size, pointing to an impact of cell death on local growth which is not fully compensated by proliferation. Using morphometric analyses of adult wing shape and genetic perturbations, we provide evidence that patterned death affects locally and globally adult wing shape and size. This study describes a roadmap for precise assessment of the contribution of cell death to tissue shape, and outlines an important instructive role of cell death in modulating quantitatively local growth and morphogenesis of a fast-growing tissue.</p><p>This repository contains the raw data (local projection images, ROI and quantificatiosn) of the Current Biology article "<strong>Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium".</strong></p>
Fig. 3 in Asymbiotic germination, multiplication and development of Alatiglossum fuscopetalum (Orchidaceae) as affected by culture medium, sucrose and growth regulators
Fig. 3. Growth index of Alatiglossum fuscopetalum on Murashige and Skoog (MS and ½MS), Knudson (KN), and Vacin and Went (VW) media, 90 days after the onset of seed germination. Histobars with the same letters are not significantly different according to the Tukey's test at the 5% probability level.
Fig. 2 in Asymbiotic germination, multiplication and development of Alatiglossum fuscopetalum (Orchidaceae) as affected by culture medium, sucrose and growth regulators
Fig. 2. Germination percentages of Alatiglossum fuscopetalum seeds on Murashige and Skoog (MS and ½MS), Knudson (KN), and Vacin and Went (VW) media. Histobars with the same letters are not significantly different according to the Tukey's test at the 5% probability level.
Figs. 1 A-D in Asymbiotic germination, multiplication and development of Alatiglossum fuscopetalum (Orchidaceae) as affected by culture medium, sucrose and growth regulators
Figs. 1 A-D. Protocorm developmental stages of Alatiglossum fuscopetalum from seed germination in vitro. A. Stage 1 swollen green embryos (protocorm phase); B. Stage 2 protocorm bearing one leaf; C. Stage 3 protocorm bearing two leaves; D. Stage 4 protocorm with leaves and one root (seedling stage). Bars = 1 mm.
Data from: The roles of growth regulation and appendage patterning genes in the morphogenesis of treehopper pronota
<p>Treehoppers of the insect family Membracidae have evolved enlarged and elaborate pronotal structures, which is hypothesized to involve co-opted expression of genes that are shared with the wings. Here, we investigate the similarity between the pronotum and wings in relation to growth. We show that the ontogenetic allometry of the pronotum is similar to that of wings in Membracidae, but not the outgroup. Using transcriptomics, we find genes related to protein synthesis and translation are mutually upregulated. These genes are implicated in the eIF2, eIF4/p70S6K, and mTOR pathways, and have known roles in regulating cell growth and proliferation. We show that species-specific differential growth patterning of the pronotum begins as early as the third instar. This finding suggests that co-option of appendage patterning genes must occur before the metamorphic molt. We propose that a network related to growth and size determination is the more likely mechanism shared with wings. However, regulators upstream of the shared genes in the pronotum and wings need to be elucidated to substantiate whether co-option has occurred. Finally, we<span> believe it will be helpful to distinguish the mechanisms leading to pronotal size from those regulating pronotal shape as we make sense of this spectacular evolutionary innovation. </span></p>
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.
Data from: Live-cell analysis of IMPDH protein levels during yeast colony growth provides insights into the regulation of GTP synthesis
<p>Here we present real-time, live-cell analysis of accumulation of the Imd2 isoform of IMPDH in <em>Saccharomyces cervisiae</em> yeast cells forming a monolayer colony in a microfluidic device over a 50-hour time course. We observe two distinct phases of increased Imd2 accumulation: a guanine-insensitive phase early in outgrowth and a guanine-sensitive phase later, when cells become crowded. We show that the IMPDH inhibitor mycophenolic acid enhances both phases of increase. Deletion of a transcription attenuator upstream of the mRNA start site that decreases Imd2 mRNA synthesis in the presence of high GTP increases the baseline level of Imd2 protein ten-fold and abolishes guanine-sensitive but not guanine-insensitive induction. Our results suggest that at least two mechanisms of yeast Imd2 regulation exist, the known GTP-dependent attenuation of RNA polymerase II elongation and a GTP concentration-independent pathway that may be controlled by cell growth state.</p>
Figure 1 in Growth regulators and their reflection on different hop genotypes cultivated under in vitro conditions
Figure 1. Explants of a hop genotype, grown in different culture media (yellow, blue and pink). Each medium provided different development behavior for the number of nodal segments. The larger the number of nodal segments, the more new plants will be obtained.
Figure 2 in Growth regulators and their reflection on different hop genotypes cultivated under in vitro conditions
Figure 2. Estimates of the direct and indirect effects of the variables root length (RL), shoot height (SH) and number of shoots (NS) on the variable number of nodal segments (NNS) (main variable).
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.
Hepatic transcriptomic analysis reveals differential regulation of metabolic and immune pathways in three strains of chickens with distinct growth rate exposed to mixed parasites infections
<p><span>This dataset was generated from the study investigating hepatic gene expression in three strains of chickens: Ross-308 (R), Lohmann Brown Plus (LB), and Lohmann Dual (LD), 2 weeks after either an experimental infection (n = 18) with both <em>A. galli</em> and <em>H. gallinarum or kept as uninfected control (n = 12)</em>. </span></p>
Figure 2 in Response of Little Fire Ant (Hymenoptera: Formicidae) Colonies to Insect Growth Regulators and Hydramethylnon
Figure 2. Number* of sexual brood and abnormal alates produced within Wasmannia auropunctata colonies after exposure to baits containing IGRs.
Figure 1 in Response of Little Fire Ant (Hymenoptera: Formicidae) Colonies to Insect Growth Regulators and Hydramethylnon
Figure 1. Mean worker mortality* (%) in Wasmannia auropunctata colonies after IGR-bait exposure over time.
Data from: Live-cell analysis of IMPDH protein levels during yeast colony growth provides insights into the regulation of GTP synthesis
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Data from: The roles of growth regulation and appendage patterning genes in the morphogenesis of treehopper pronota
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Data from: HIF-signaling in the prothoracic gland regulates growth and development in hypoxia but not normoxia in Drosophila
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Data from: Detrital nutrient content and leaf species differentially affect growth and nutritional regulation of detritivores
Resource nutrient content and identity are common bottom-up controls on organismal growth and nutritional regulation. One framework to study these factors, ecological stoichiometry theory, predicts that elevated resource nitrogen (N) and phosphorus (P) contents enhance organism growth by alleviating constraints on N and P acquisition. However, the regulatory mechanisms underlying this response – including whether responses depend on resource identity – remain poorly understood. In this study, we tested roles of detrital N and P contents and identity (leaf species) in constraining growth of aquatic invertebrate detritivores. We synthesized results from seven detritivore species fed wide nutrient gradients of oak and maple detritus in the laboratory. Across detritivore taxa, we used a meta-analytic approach quantifying effects of detrital leaf species and N and P contents on growth, consumption, and N- and P-specific assimilation and growth efficiencies. Detritivore growth rates increased on higher-N and P detritus and on oak compared to maple detritus. Notably, the mechanisms of improved growth differed between the responses to detrital nutrients versus leaf species, with the former driven by greater consumption rates despite lower assimilation efficiencies on higher-nutrient detritus, and the latter driven by improved N and P assimilation and N growth efficiencies on oak detritus. These findings suggest animal nutrient acquisition changes flexibly in response to resource changes, altering the fate of detrital N and P throughout regulation. We affirm resource identity and nutrients as important bottom-up controls, but suggest these factors act through separate pathways to affect organism growth and thereby change detrital ecosystems under anthropogenic forest compositional change and nutrient enrichment.
Scaling between cell cycle duration and wing growth is regulated by Fat-Dachsous signaling in Drosophila
<p>The atypical cadherins Fat and Dachsous (Ds) signal through the Hippo pathway to regulate growth of numerous organs, including the <em>Drosophila</em> wing. Here, we find that Ds-Fat signaling tunes a unique feature of cell proliferation found to control the rate of wing growth. The duration of the cell cycle increases in direct proportion to the size of the wing, leading to linear rather than exponential growth. Ds-Fat signaling enhances the rate at which the cell cycle lengthens with wing size, thus diminishing the linear rate of wing growth. We show that this results in a complex but stereotyped relative scaling of wing growth with body growth in <em>Drosophila</em>. Finally, we examine the dynamics of Fat and Ds protein distribution in the wing, observing graded distributions that change during growth. However, the significance of these dynamics is unclear since perturbations in expression have negligible impact on wing growth.</p>
Plant Growth Regulators in Barley in Northern Grains Region Australia
<p><strong>Project title: </strong>NGN Validating the use of plant growth regulators to manage excessive growth in barley in Northern Region warm growing environments (2022-24, AMP2205-004RTX). </p> <p><strong>Methodology: </strong>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<sup>®</sup> (250 g/L Trinexapac-Ethyl) and ethephon (Promote<sup>®</sup> 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. </p> <p>Four varieties – Leabrook, Laperouse, Planet, Maximus CL – were selected to represent the range of lodging susceptibilities in commercial barley varieties. Moddus Evo<sup>®</sup> 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).</p> <p><a name="_Ref165987766"></a>Table 1. PGR use patterns tested on dryland barley in 2022 and 2023</p> <table> <tbody> <tr> <td> <p><strong> </strong></p> </td> <td> <p><strong>Application 1</strong></p> </td> <td> <p><strong>Application 2</strong></p> </td> </tr> </tbody> <tbody> <tr> <td> <p><strong>PGR Treatment Name</strong></p> </td> <td> <p><strong>PGR</strong></p> </td> <td> <p><strong>Rate (ml)</strong></p> </td> <td> <p><strong>GS</strong></p> </td> <td> <p><strong>PGR</strong></p> </td> <td> <p><strong>Rate (ml)</strong></p> </td> <td> <p><strong>GS</strong></p> </td> </tr> <tr> <td> <p>Moddus 31</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>31</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Moddus 300 @ 31</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>300</p> </td> <td> <p>31</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Moddus 37</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>37</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Moddus 31 + 37</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>31</p> </td> <td> <p>Moddus Evo<sup>®</sup></p> </td> <td> <p>400</p> </td> <td> <p>37</p> </td> </tr> <tr> <td> <p>Ethephon 41</p> </td> <td> <p>Ethephon*</p> </td> <td> <p>400</p> </td> <td> <p>41</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Ethephon 45</p> </td> <td> <p>Ethephon*</p> </td> <td> <p>400</p> </td> <td> <p>45</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>GS=Growth Stage; 900 g/L Ethephon</p> <p>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).</p> <p>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.</p> <p>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.</p> <p>3. At Spring Ridge, a partial factorial experiment assessing varieties (n=4) was established with 6 PGR treatments.</p> <p>In 2023 trial sites were established at Boomi, Gurley, Pallamallawa and Breeza.</p> <p><span><span>4. </span></span>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.</p> <p>5. At Gurley, the same trial was repeated as describe above for Boom</p> <p>6. At Breeza, the four barley cultivars were tested with six PGR treatments with one time of sowing only.</p> <p>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.</p> <p>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 1.5<span>⁰</span>C higher than monthly averages between June and October (Appendix A).</p> <p>There were large differences in annual and growing season rainfall (May-October, GSR) between 2022 and 2023 (Table <span>4</span><span></span>). 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.</p> <p><a name="_Ref167779013"></a>Table <span><span>4</span></span>. Plant available water and rainfall data for barley PGR trials in 2022 and 2023</p> <table> <tbody><tr> <td> <p><strong><span>Site</span></strong></p> </td> <td> <p><strong><span>Trial</span></strong></p> </td> <td> <p><strong><span>Planting PAW (mm)</span></strong></p> </td> <td> <p><strong><span>Annual rainfall (mm) </span></strong></p> </td> <td> <p><strong><span>Annual rainfall (mm)</span></strong></p> </td> <td> <p><strong><span>Growing season rainfall (mm)*</span></strong></p> </td> <td> <p><strong><span>Growing season rainfall (mm)*</span></strong></p> </td> </tr> </tbody><tbody> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>Year of trial</span></strong></p> </td> <td> <p><strong><span>Long Term Average</span></strong></p> </td> <td> <p><strong><span>Year of trial</span></strong></p> </td> <td> <p><strong><span>Long Term Average</span></strong></p> </td> </tr> <tr> <td> <p><span>Tulloona</span></p> </td> <td> <p><span>PGR over </span><span>commercial crop</span></p> </td> <td> <p><span>165</span></p> </td> <td> <p><span>754</span></p> </td> <td> <p><span>548</span></p> </td> <td> <p><span>456</span></p> </td> <td> <p><span>214</span></p> </td> </tr> <tr> <td> <p><span>Gurley</span></p> </td> <td> <p><span>PGR x Variety</span></p> </td> <td> <p><span>165</span></p> </td> <td> <p><span>622</span></p> </td> <td> <p><span>560</span></p> </td> <td> <p><span>406</span></p> </td> <td> <p><span>204</span></p> </td> </tr> <tr> <td> <p><span>Spring Ridge</span></p> </td> <td> <p><span>PGR x Variety</span></p> </td> <td> <p><span>202</span></p> </td> <td> <p><span>870</span></p> </td> <td> <p><span>629</span></p> </td> <td> <p><span>465</span></p> </td> <td> <p><span>254</span></p> </td> </tr> <tr> <td> <p><span>Boomi</span></p> </td> <td> <p><span>PGR x Variety </span><span>x 2 Times of Sowing</span></p> </td> <td> <p><span>201</span></p> </td> <td> <p><span>367</span></p> </td> <td> <p><span>543</span></p> </td> <td> <p><span>50</span></p> </td> <td> <p><span>201</span></p> </td> </tr> <tr> <td> <p><span>Gurley</span></p> </td> <td> <p><span>PGR x Variety </span><span>x 2 Times of Sowing</span></p> </td> <td> <p><span>198</span></p> </td> <td> <p><span>365</span></p> </td> <td> <p><span>560</span></p> </td> <td> <p><span>68</span></p> </td> <td> <p><span>204</span></p> </td> </tr> <tr> <td> <p><span>Breeza</span></p> </td> <td> <p><span>PGR x Variety</span></p> </td> <td> <p><span>253</span></p> </td> <td> <p><span>484</span></p> </td> <td> <p><span>647</span></p> </td> <td> <p><span>122</span></p> </td> <td> <p><span>265</span></p> </td> </tr> <tr> <td> <p><span>Pallamallawa#</span></p> </td> <td> <p><span>PGR x Nitrogen</span></p> </td> <td> <p><span>171</span></p> </td> <td> <p><span>459</span></p> </td> <td> <p><span>641</span></p> </td> <td> <p><span>58</span></p> </td> <td> <p><span>233</span></p> </td> </tr> </tbody> </table> <p><span>*Measured as rainfall from May to October, #Trial failed due to residual herbicide damage </span></p> <p>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. <span> </span></p>
Crown morphology in Norway spruce (Picea abies [Karst.] L.) as adaptation to mountainous environments is associated with single nucleotide polymorphisms (SNPs) in genes regulating seasonal growth rhythm
Trees growing at high altitude or latitude have to be adapted, amongst others, to the lower temperatures, a shorter vegetation period, heavier snow load and frost desiccation. Association between molecular genetic markers and climatic variables may provide evidence for the genetic control of climatic adaptation. With increasing genomic resources, several genes with importance to climatic adaptation are identified over a wide range of tree species. Commonly, circadian clock genes are linked to the adaptation to lower temperatures and especially to a shortened vegetation period, as they are regulating metabolic and phenological processes in the day-night shift and seasonal change. Potentially adaptive "candidate" genes associated with latitudinal and elevational gradients were identified in several Picea spp. Before molecular markers became available to study climatic adaptation, phenotypic traits measured in natural populations and/or common garden studies were used to search for their association with climate variables. In Norway spruce, the crown architecture is the most noticeable trait associated with altitude and the related environment. The mountainous narrow-crowned morphotype is characterised by superior resistance to snow breakage in regions with heavy snow fall. In total, the crown shape was assessed in 765 individual trees from mountainous regions in the Thuringian Forest, the Ore Mountains (Saxony) and Harz Mountains (Lower-Saxony/Saxony-Anhalt), and they were genotyped at 44 single nucleotide polymorphisms (SNPs) in 24 adaptive trait related candidate genes. Six SNPs in three genes, APETALA 2-like 3 (AP2L3), GIGANTEA (GI), and mitochondrial transcription termination factor (mTERF) were associated with variation in crown shape. GI has previously been identified in angiosperms and gymnosperms to be associated with temperature and growth cessation. Our results showed that crown morphology in Norway spruce is associated with genetic markers which are putatively involved in the complex process of genetic adaptation to climatic conditions at high altitudes.
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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)
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.
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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.
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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.