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13,113 results for “Resistivity”
Supporting data and code for: Myzus persicae resistance to neonicotinoids - unravelling the contribution of different mechanisms to phenotype
<p>This is the first release of the final data and code for the article accepted for publication in <em>Pest Management Science</em> journal. It contains the necessary scripts to produce most of the analyses and figures of the manuscript. All the necessary data can be found in the 'data' folder.</p>
MS-UMG: MALDI-TOF Mass Spectra and Resistance Information on Antimicrobials from University Medical Center Göttingen
<p>During routine diagnostic procedures, we aggregated MALDI-TOF MS data of organisms isolated from clinical specimens from the University Medical Center Göttingen (UMG) in 2020 / 2021. We integrated these with corresponding antimicrobial susceptibility profiles. This amounted to 26,961 mass spectra and 26,961 corresponding metadata entries for the year 2020, and 50,381 mass spectra and 50,381 corresponding metadata entries for 2021, respectively. The dataset reflects 348 different species of bacterial and fungal organisms and 72 different antimicrobial susceptibility testing (AST) results.</p> <p> </p> <p>Please cite: </p> <div> <div>Effect of Data Heterogeneity in Clinical MALDI-TOF Mass Spectra Profiles on Direct Antimicrobial Resistance Prediction through Machine Learning</div> </div> <div><span><span><span>Youngjun</span> <span>Park</span></span>, <span><span>Michael</span> <span>Weig</span></span>, <span><span>Christine</span> <span>Noll</span></span>, <span><span>Oliver</span> <span>Bader</span></span>, <span><span>Anne-Christin</span> <span>Hauschild</span></span></span></div> <div><span>bioRxiv </span><span>2024.10.18.617592; </span><span><span>doi:</span> https://doi.org/10.1101/2024.10.18.617592</span></div>
MULTIPLIERS_WP3/4/5_Science learning project on Anti-microbial resistance_Public data_UCY_20241018_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Anti-microbial resistance</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with teachers and students (</span><span>Pseudo-/Anonymised)</span></li> <li>Summary<span> of transcripts from focus group discussions with students (</span><span>Pseudo-/Anonymised)</span></li> </ul>
Linked collectors and determiners for: V. N. Karazin Kharkiv National University herbarium, Department of Mycology and Plant Resistance.
Natural history specimen data linked to collectors and determiners held within, "V. N. Karazin Kharkiv National University herbarium, Department of Mycology and Plant Resistance". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f0ecd5eb-bc62-40fe-ab53-294cbb34a290">https://bionomia.net/dataset/f0ecd5eb-bc62-40fe-ab53-294cbb34a290</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f0ecd5eb-bc62-40fe-ab53-294cbb34a290">https://gbif.org/dataset/f0ecd5eb-bc62-40fe-ab53-294cbb34a290</a>. Formatted as a Frictionless Data package.
Metallomics in childhood obesity, insulin resistance, and related susceptibility factors
<p>Metallomics data from plasma and red blood cell (RBC) samples collected from a population-based cohort of children with obesity and healthy controls</p>
Metabolomics in childhood obesity, insulin resistance, and related susceptibility factors
<p>Metabolomics data from plasma and red blood cell (RBC) samples collected from a population-based cohort of children with obesity and healthy controls</p>
Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'
<p>GENERAL INFORMATION</p> <p>1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?'</p> <p>2. Author Information<br> A. Investigator Contact Information<br> Name: Philip G. Madgwick<br> Institution: Syngenta <br> Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK<br> Email: philip.madgwick@syngenta.com</p> <p> B. Investigator Contact Information<br> Name: Ricardo Kanitz<br> Institution: Syngenta <br> Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland<br> Email: ricardo.kanitz@syngenta.com</p> <p><br> 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01 </p> <p>4. Geographic location of data collection: UK </p> <p>5. Information about funding sources that supported the collection of the data: </p> <p>This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC).</p> <p><br> SHARING/ACCESS INFORMATION</p> <p>1. Licenses/restrictions placed on the data: NA</p> <p>2. Links to publications that cite or use the data: [UPDATE]</p> <p>3. Links to other publicly accessible locations of the data: NA</p> <p>4. Links/relationships to ancillary data sets: NA</p> <p>5. Was data derived from another source? No</p> <p>6. Recommended citation for this dataset: [UPDATE]</p> <p><br> DATA & FILE OVERVIEW</p> <p>1. File List: <br> PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION)<br> Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance </p> <p>2. Relationship between files, if important: </p> <p>Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'.</p> <p>3. Additional related data collected that was not included in the current data package: NA</p> <p>4. Are there multiple versions of the dataset? No</p> <p><br> METHODOLOGICAL INFORMATION</p> <p>1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>4. Standards and calibration information, if appropriate: NA</p> <p>5. Environmental/experimental conditions: NA</p> <p>6. Describe any quality-assurance procedures performed on the data: NA</p> <p>7. People involved with sample collection, processing, analysis and/or submission: NA </p> <p><br> DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv</p> <p>1. Number of variables: </p> <p>17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript): <br> Population Size = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9<br> Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1<br> Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1<br> Female Exposure = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1<br> Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1<br> Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 1 = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1<br> Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1<br> Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1<br> Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1<br> Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 2 = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1<br> Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1<br> Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1<br> Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA </p> <p>4. Missing data codes: NA</p> <p>5. Specialized formats or other abbreviations used: NA</p> <p><br> DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv) </p> <p>1. Number of variables: </p> <p>10 variables with column names that have the following meanings:<br> A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency <br> A_f_250 = the frequency of resistance allele A at the 250th generation <br> A_f_bar = the mean frequency of resistance allele A over the first 250 generations <br> B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency <br> B_f_250 = the frequency of resistance allele B at the 250th generation <br> B_f_bar = the mean frequency of resistance allele B over the first 250 generations <br> nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation<br> nf_250 = the female population size at the 250th generation<br> nf_bar = the mean female population size over the first 250 generations <br> nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA</p> <p>4. Missing data codes: all missing data is recorded as 0 </p> <p>5. Specialized formats or other abbreviations used: NA</p>
Knockdown resistance (kdr) genotypes and collection information for Aedes aegytpi from Iquitos, Peru (2000 - 2017)
<p>This study describes the evolution of <i>knockdown resistance (kdr)</i> haplotypes in <i>Aedes aegypti</i> in response to pyrethroid insecticide use over the course of 18 years in Iquitos, Peru. Based on the duration and intensiveness<span> of sampling (~10,000 samples), this is the most thorough study of kdr population genetics in <i>Ae. aegypti</i> to date within a city.</span> We provide evidence for the direct connection between programmatic citywide pyrethroid spraying and the increase in frequency of specific <i>kdr</i> haplotypes by identifying two evolutionary events in the population. The relatively high selection coefficients, even under infrequent insecticide pressure, emphasize how quickly <i>Ae. aegypti </i>populations can evolve. In our examination of the literature on mosquitoes and other insect pests, we could find no cases where a pest evolved so quickly to so few exposures to low or non-residual insecticide applications. <span>The observed rapid increase in frequency of resistance alleles might have been aided by the incomplete dominance of resistance-conferring alleles over corresponding susceptibility alleles.</span> In addition to dramatic temporal shifts, spatial suppression experiments reveal that genetic heterogeneity existed not only at the citywide scale, but also on a very fine scale within the city.</p>
I alternate therefore I generalize: how the intrinsic resistance risk of fungicides counterbalances their durability
<p>The evolution of resistance to pesticides is a major burden in agriculture. Resistance management involves maximizing selection pressure heterogeneity, particularly by combining active ingredients with different modes of action. We tested the hypothesis that alternation may delay the build-up of resistance not only by spreading selection pressure over longer periods, but also by decreasing the rate of evolution of resistance to alternated fungicides, by applying an experimental evolution approach to the economically important crop pathogen <i>Zymoseptoria tritici. </i>Our results show that alternation is either neutral or slows the evolution of resistance, relative to continuous fungicide use, but results in higher levels of generalism in evolved lines. We demonstrate that the relative risk of resistance intrinsic to fungicide alternation probably underlies a trade-off between the number of fungicides and the frequency of alternation. This trade-off is also dynamic over the course of resistance evolution. These findings open up new possibilities for tailoring resistance management effectively while optimizing interplay between alternation components.</p>
Structure-Dependent Influence of Moisture on Resistive Switching Behavior of ZnO Thin Films - Dataset
<p>This is the dataset of "Structure-Dependent Influence of Moisture on Resistive Switching Behavior of ZnO Thin Films"</p>
Valproic Acid Synergizes With Cisplatin and Cetuximab in vitro and in vivo in Head and Neck Cancer by Targeting the Mechanisms of Resistance - Unpublished data
<p>Antitumor effects of valproic acid (VPA) in combination with Cisplatin/Cetuximab doublet in head and neck squamous cell carcinoma (HNSCC) models. We reported unpublished data of the effects of this combination on cell cycle and 3D cell cultures</p>
Novel Genomic Regions linked to Ascochyta blight Resistance in two differentially resistant cultivars of chickpea
<p><em>Ascochyta</em> blight (AB) caused by the fungal pathogen <em>Ascochyta rabiei</em> is a devastating foliar disease of chickpea (<em>Cicer arietinum</em> L.). Genotyping-by-sequencing (GBS) has been used in the current study for the identification of AB associated quantitative trait loci (QTLs) and their gene(s). We evaluated genotyping-by-sequencing (GBS)-based approach for mapping QTLs associated with AB resistance in chickpea using two recombinant inbred lines populations (AB<sub>3279</sub> and AB<sub>482</sub>) derived from two crosses ILC 1929 X ILC 3279 and ILC 1929 X ILC 482, under six different environments. In total, twenty-one different genomic regions were identified on linkage groups CalG02 and CalG04 pertaining to AB resistance in both populations AB<sub>3279</sub> and AB<sub>482</sub>. Four genomic regions were detected on CalG02 in the population AB<sub>3279</sub> and nine major genomic regions were associated with AB resistance on CaLG04, five out of them were common to both resistant parents ‘ILC3279’ and ‘ILC482’, and eight minor genomic regions with two out of them common between both populations. These regions contain 1,118 SNPs significantly associated (p ≤ 0.001) with AB resistance. Gene ontology (GO) assigned these QTLs to 319 genes, many of which were associated with stress and disease resistance, with most important genes belonging to resistance gene families including Leucine-Rich Repeat (LRR), and transcription factors families. Our results may refer to the flowering-associated gene GIGANTEA as a possible key factor in AB resistance in chickpea. The results have narrowed the AB resistance associated regions on the chickpea physical map and the associated markers will help in breeding programs for chickpea improvement.</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).
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Allen Brain Atlas
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