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13,113 results for “Resistivity”
Figure 1 in Herbicide-resistance management: a common pool resource problem?
Figure 1. Diagram of pesticide resistance as common property resource based on Miranowski and Carlson (1986). In this conceptualization, the common property resource is pest susceptibility, which is composed of a stock variable and a flow variable. Pest resistance is initially a renewable resource but becomes depleted over time through repeated use of chemicals. Thus, the actions of certain individuals may deplete the resource stock for others.
Figure 2 in Herbicide-resistance management: a common pool resource problem?
Figure 2. This diagram conceptualizes herbicide resistance as a common pool resource problem. Importantly, two conjoined common pool resources—herbicides and the weed gene pool—make up this resource system. Following common pool resource theory, this diagram illustrates the interconnectedness of four stock variables: (1) supply of a herbicide; (2) supply of a weed gene pool susceptible to a herbicide; (3) supply of a weed gene pool resistant to a herbicide; and (4) supply of herbicide efficacy on a weed gene pool. We have also diagramed corresponding flow variables or resource units (RU). In a generalized way, the use of a herbicide application (F1) influences the weed gene pool. However, the weed gene pool (S2 and S3) also acts independently of herbicide use and is influenced by both biological dynamics and social dynamics. Importantly, dynamics involving the weed gene pool are complex and include spatial and temporal variability in both the plant population and weed seedbank. The characteristics of the weed gene pool (S2 and S3) then affect the efficacy of the herbicide (S4) and whether its effectiveness is renewable or whether it becomes a finite stock resource. The quality of the herbicide (S4) may ultimately affect the supply of the herbicide (S1), if declining efficacy takes away from the herbicide's economic and chemical utility. In particular, the quality of these two common pool resources and not simply the quantity makes it a very complex resource arrangement. Factors adding complexity include that the weed gene pool is simultaneously both a pest and a resource. Furthermore, when the weed gene pool is characterized as a resource (its susceptibility to herbicides), the quality of this resource depends primarily upon provisioning practices of the common pool resource that keep the quality intact.In other words, following resource practices that do not allow internal or external resistance into the gene pool is key to maintaining its quality. The lack of quality from underprovisioning may result in a finite stock supply of the resource (i.e., weed gene pool susceptible to herbicides).Overappropriation (i.e., quantity or overharvesting of the resource) is a concern,in that it can be connected to poor provisioning practices.Aside from using a resource unit of herbicide in an application, the resource user does not directly appropriate or harvest from the system. This schematic only covers a generalized scenario, and more finescale analysis is needed to tease apart the complex relationships existing among herbicides and the weed gene pool.
Figure 5 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 5. Weighted co-expression network analysis results. (A) Gene expression dendrogram for module assignment where a total of 33 modules were identified. (B) Traitmodule correlation plot with values outside parentheses representing Pearson correlation and values inside parentheses representing the significance correlation P-values. Correlation values range from −1 to 1, with red values indicating a positive association and blue values indicating a negative association with dicamba resistance. ME refers to modules followed by their color code.
Figure 1 in Identification of candidate genes involved with dicamba resistance in waterhemp (Amoronthus tuberculotus) via transcriptomics analyses
Figure 1. Plant selection and phenotype classification for RNA-seq. Photos show the differences in phenotypes of some of the individuals selected for sequencing: (A) resistant plants and (B) sensitive plants. Selection was done based on visual damage estimation, biomass, and plant area measured via image analysis (Bobadilla et al. 2022). Photos were taken 14 d after treatment with dicamba at 560 g ai ha−1. The graph shows the relationship between biomass and plant area across resistant and sensitive individuals.
Figure 7 in Interference and management of herbicide-resistant crop volunteers
Figure 7. Individual rows of weedy rice accessions or cultivated rice cultivars 8 d following a post-flood application of benzobicyclon at 371 g ai ha−1. Healthy rows are cultivated rice or resistant weedy rice accessions, whereas chlorotic rows are benzobicyclon-sensitive weedy rice accessions.
Figure 9 in Interference and management of herbicide-resistant crop volunteers
Figure 9. Field-scale evaluation of imidazolinone-resistant (ClearfieldṜ) wheat compared with non–herbicide resistant wheat (including volunteers the following year) in Saskatchewan, Canada, in the early 2000s (adapted from Beckie et al. 2011).
Figure 4 in Interference and management of herbicide-resistant crop volunteers
Figure 4. Symptoms of (A) glufosinate on glyphosate-resistant volunteer corn in glufosinate-resistant soybean and (B) sethoxydim on glyphosate/glufosinate-resistant volunteer corn in dicamba/glyphosate-resistant soybean.
Figure 2 in Interference and management of herbicide-resistant crop volunteers
Figure 2. Soybean after corn is a typical rotation in the midwestern United States. If not controlled, volunteer corn is a problem weed in soybean fields.
Figure 1 in Interference and management of herbicide-resistant crop volunteers
Figure 1. Glyphosate- and glufosinate-resistant canola volunteers in adjacent fields in Saskatchewan, Canada, due to bidirectional pollen-mediated gene flow the previous year.
Figure 3 in Interference and management of herbicide-resistant crop volunteers
Figure 3. Volunteer corn in a cornfield in Nebraska. Highly productive soils and easy access to irrigation have encouraged growers to adopt a corn-on-corn cropping system in south-central Nebraska that results in corn volunteers.
Figure 2 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 2. Agarose gel (1,8%) showing polymerase chain reaction products (89 bp) of the chloroplastic acetyl-coenzyme A carboxylase gene carboxyl-transferase domain targeted by HRMA primers RottF and RottR.
Figure 5 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 5. High-resolution melting analysis (HRMA) for detection of the Trp-2027-Cys mutation in Rottboellia cochinchinensis carboxyl-transferase domain of the acetylcoenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. (B) Normalized plot and (C) difference plot using susceptible (wild type) as the reference genotype.
Figure 4 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 4. High-resolution melting analysis (HRMA) for detection of mutation Trp-2027-Cys in Rottboellia cochinchinensis carboxyl-transferase domain of the acetyl-coenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. Three genotypes are included: wild type (homozygous TGG, susceptible), mutant homozygous (TGC, resistant), and artificial mutant heterozygous (TGG/TGC, possibly resistant). (A) Representative profiles of the melting curves (derivative melt curves), (B) normalized plot, and (C) difference plot using susceptible (wild type) as the reference genotype.
Fig. 1 in Detection of Escherichia fergusonii - an emerging pathogen harbouring drug resistant genes from seafood samples of Tamil Nadu, India
Fig. 1 — Gene specific PCR amplification of Escherichia fergusonii (lane 1 – 100 bp DNA ladder, lane 2 – positive control (clinical E. fergusonii), lane 3 – negative control, lane 4 – E011, lane 5 – E060)
Assessing Hydrodynamic resistance in Microfluidics: A Case Study - datasets
<p><strong><span>Abstract: </span></strong><span>Hydrodynamic resistance is a critical parameter in microfluidics, affecting device functionality and performance.</span><span> However, quantifying hydrodynamic resistance in microfluidics is a challenge due to many influencing factors and the difficulties associated with the precise measurements of low flow rates (< 10 </span><span><span>m</span></span><span>L/min) and pressure drops (< 5 kPa). This article presents a simple experimental test method for assessing hydrodynamic resistance, correlating with theoretical and numerical calculations. The results demonstrate good agreement between benchtop and theoretical data, suggesting a potential standardized method for assessing hydrodynamic resistance in microfluidic devices.</span></p> <p> </p> <p><span>In the files attached: Dataset</span></p> <p> </p> <p><strong><span>Funding:</span></strong><span> This project (20NMR02 MFMET) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. V.S. would like to acknowledge the FCT, I.P., for funding of the Research Unit INESC MN (UID/05367/2020) through pluriannual BASE and PROGRAMATICO and project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy – i4HB</span></p>
Figure 6 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 6: Field trial R. reniformis eggs per gram of root collected from four root systems. LS means for PHY 332 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2021. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0441. Nematicide treatments included no-nematicide control, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), C-LV (0.28 +1.24 L/ha), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha).
Figure 1 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 1: M. incognita PHY 340 W3FE susceptible variety on the left and the resistant PHY 360 W3FE on the right at 106 DAP.
Figure 5 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 5: Field trial R. reniformis eggs per gram of root collected from four root systems LS means for PHY 332 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2020. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0178. Nematicide treatments included no-nematicide control, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), C-LV (0.28 +1.24 L/ha), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha).
Figure 2 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 2: Field trial M. incognita eggs per gram of root collected from four root systems LS means for PHY 360 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2020. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0368. Nematicide treatments included no-nematicide control, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), C-LV (0.28 +1.24 L/ha), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha).
Figure 3 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 3: Field trial M. incognita eggs per gram of root collected from four root systems LS means for PHY 360 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2021. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0272. Nematicide treatments included no-nematicide control, Reklemel (0.21 L/ha) + Vydate C-LV (0.88 L/ha) Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha, Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.21 L/ ha) + Vydate C-LV (0.88 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.50 L/ha).
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