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13,113 results for “Resistivity”
Figure 3 Seasonal abundance ofEotetranychus carpiniobserved during 2010 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 3 Seasonal abundance ofEotetranychus carpiniobserved during 2010 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 6 Seasonal abundance ofKampimodromus aberransobserved during 2009 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 6 Seasonal abundance ofKampimodromus aberransobserved during 2009 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Figure 1 Seasonal abundance ofPanonychus ulmiobserved during 2009 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 1 Seasonal abundance ofPanonychus ulmiobserved during 2009 (months are indicated in x-axis) on different treatments in vineyards
Figure 5 Seasonal abundance ofAmblyseius andersoniobserved during 2010 in Biological control of spider mites in North-Italian vineyards using pesticide resistant predatory mites
Figure 5 Seasonal abundance ofAmblyseius andersoniobserved during 2010 (months are indicated in x-axis) on different treatments in vineyards of Farm A.
Resistivity Tomography Imaging of the Substratum of the Bedestan Monumental Complex at Nicosia, Cyprus
<p>M. Cossolino et al., "Resistivity Tomography Imaging of the Substratum of the Bedestan Monumental Complex at Nicosia, Cyprus." Substratum at 1.25 metres.</p>
QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species Brassica oleracea var. villosa [Main code]
<p>This is the main code supplement for my computational analysis for the manuscript: "QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species <em>Brassica oleracea </em>var<em>. villosa".</em> The main code is availabe in separate html-files. DOI will be added if available.</p>
Arcing Fault Electrical Signatures Data Base - Sinusoidal power supply (230 V - 400 Hz) - Resistive loads - part 1
<p>The dataset contains series arc faults voltage and current signatures in a AC low power network.</p> <p>Sinusoidal power supply (230 V – 400Hz, 600Hz and 800Hz) - Resistive Loads</p> <p>The data provided can be used for the development of methods for the detection of arcing faults.</p> <p>The data files are current and voltage signatures experimentally measured.</p> <p>Two technique are used to produce an arcing fault : Open contact electrodes and Carbonized path wires</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p>
Resistivity Saturation in Kondo Insulators
<p> </p> <p>Data and codes associated with <br> <strong> Resistivity saturation in Kondo insulators</strong><br> M. Pickem, E. Maggio, J. M. Tomczak <br> <a href="https://doi.org/10.1038/s42005-021-00723-z">Communications Physics 4, 226 (2021)</a><br> (preprint arXiv:2008.05846)</p> <p>When using the provided data, please cite the Zenodo DOI 10.5281/zenodo.4355597 as well as the article.</p> <p>Available are</p> <ul> <li>density functional theory + dynamical mean-field theory (DFT+DMFT) data for Ce<sub>3</sub>Bi<sub>4</sub>Pt<sub>3</sub>.<br> Data is generated using <ul> <li>WIEN2k 18.2 (http://susi.theochem.tuwien.ac.at/)</li> <li>DFT + embedded DMFT Functional (http://hauleweb.rutgers.edu/tutorials/)</li> </ul> </li> <li>electrical conductivity data. <br> Data is obtained using <ul> <li>LinReTraCe: the Linear Response Transport Centre.<br> This code will be released at http://github.com/linretrace/linretrace in 2021.</li> </ul> </li> <li>python tool for analysis of experimental measurements using the new formalism.<br> The python tool is available and described at https://github.com/linretrace/quantum_conductivity_fit</li> </ul> <p> </p>
Resistance tests to anthracnose in common bean
<p>This video shows the steps in resistance tests in controlled conditions to anthracnose (caused by <em>Colletotrichum</em> <em>lindemuthianum</em> (Sacc. & Magnus) Briosi & Cavara,,<em><strong>)</strong></em> in common bean.</p> <p>A dissemination task developed by the Plant Genetic Group (SERIDA) for the BRESOV project.</p> <p>This work is part of the BRESOV project funded by the EU (Grant agreement ID: 774244).</p> <p><strong>Also available in the link</strong>: https://www.youtube.com/watch?v=dLPAq9_7M60</p>
Pooled DNA sequencing to identify SNPs associated with a major QTL for bacterial wilt resistance in Italian ryegrass (Lolium multiflorum Lam.)
<p>We used pooled DNA sequencing to characterize a major QTL for bacterial wilt resistance of Italian ryegrass and to develop inexpensive sequence-based markers to efficiently target resistance alleles for marker-assisted recurrent selection. From the mapping population segregating for the QTL, DNA of 44 of the most resistant and 44 of the most susceptible F<sub>1</sub> individuals were pooled and sequenced using the Illumina HiSeq2000 platform. Allele frequencies of 18 x 10<sup>6</sup> single nucleotide polymorphisms (SNP) were determined in the resistant and susceptible pool. A total of 271 SNPs on 140 scaffold sequences of the reference parental genome showed significantly different allele frequencies in both pools. We converted 44 selected SNPs to KASP markers, genetically mapped these proximal to the major QTL and thus validated their association with bacterial wilt resistance.</p>
Insecticide resistance triggers a reduction of virulence to host-plant defenses in the brown planthopper
<p>This dataset contains R scripts to analyze the virulence of resistance rice cultivars and to draw figures. Data contains the LD<sub>50</sub> values of imidacloprid, virulence test and figure data. This study was supported by grants-in-aid from Japan's National Agriculture and Food Research Organization (NARO) project 315 and the NARO Innovation Project 2017 for the NARO.</p>
dataset for bioRxiv preprint titled 'Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease'
<p>This data supports the Figures in the preprint titled</p> <p><strong>Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease</strong></p> <p><strong>working abstract</strong></p> <p>The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS). During the progression of the disease, some individual patients acquire -drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the ‘flap region’ of the HIV-1 protease, which is a critical dynamic region involved in non-specific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and polypeptide binding of the main protease. We use a multi-agent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs (SIV and FIV). We also investigate changes in dynamics due to common drug-resistant mutations in many patients. We find that a key functional site in the flap region, a solvent-exposed isoleucine (ILE50) and surrounding sites that control flap dynamics is often targeted by drug-resistance mutations, likely leading to malfunctional molecular dynamics affecting the overall flexibility of the flap region. We conclude that better long term patient outcomes may be achieved by designing drugs that target protease regions which are less dependent upon single sites with large functional binding effects.</p>
Magnetotelluric data from Santos basin (SE Brazil) and inversion resistivity models exploring basin wedge and deep crustal structure beneath.
<p><strong>Magnetotelluric data</strong></p> <p>Processed data from 90 magnetotelluric broadband stations acquired in are available in Electrical Data Interchange (EDI) and ModEM format.</p> <p>The MMT data were recorded in 2007 by WesternGeco Electromagnetics as part of the National Observatory Rio de Janeiro project funded by Petrobras. The campaign comprised a total of 92 sites from shallow water (about 50 m depth) to deep water (about 1600 m depth). The stations are placed along three NW-SE parallel profiles in the northwest part of Santos basin. The central profile is approximately 160 km long and consists of 56 stations, while the west profile and east profile extend about 55 km each and contain 18 and 16 stations, respectively.</p> <p> </p> <p><strong>Models</strong></p> <p>Inversion models and predicted data are present for two different starting resistivity model testes 10 and 1 Ohm.m. The inversion models were estimated using ModEM - modular system for inversion of electromagnetic geophysical data.</p>
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
<p>Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at <a href="http://bench.cs.vt.edu/deeparg">http://bench.cs.vt.edu/deeparg</a>.</p>
Figure 2 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 2. (A) Silicon leaf, total protein and phenol contents, (B) Activity of POD, CAT, and PPO of S. melongena- treated plants. Means followed by the same letter are not significantly different using Tukey's HSD Test at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3= OSAB 4 mL L−1, T4= Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Figure 1 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 1. Mean number ± SE of the different stages of T. urticae on S. melongena leaves 10, 30 and 50 days after spraying (DAS). Means followed by the same letter are not significantly different using Tukey's HSD at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3 = OSAB 4 mL L−1, T4 = Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Data and Code for: Resistance is futile: Weaker selection for resistance by abundant parasites increases prevalence and depresses host density
<p>We model host evolution of costly resistance to infection and its dependence on environmental factors, such as nutrients. We find that higher nutrients can increase infection prevalence AND select for lower resistance. In turn, the model predicts that lower resistance drives infection prevalence even higher while depressing host density. The attached code performs the model analysis, produces the published figures, and conducts statistical analysis on the data (described below). We conducted a mesocosm experiment with mixtures of zooplankton host (<em>Daphnia dentifera</em>) genotypes, algal resources (<em>Ankistrodesmus falcatus</em>), and fungal parasites (<em>Metschnikowia bicuspidata</em>). Mesocosm populations were supplied with low or high nutrients (5 or 50 ug/L phosphorus and 100 or 1000 ug/L nitrogen). We measured densities of hosts along with age class (juvenile or adult), sex, infections status, and egg number and chlorophyll densities; these data are a subset of data published previously Walsman et al. <em>Functional Ecology </em>(<a href="https://doi.org/10.1111/1365-2435.14030">https://doi.org/10.1111/1365-2435.14030</a>; data at <a href="https://doi.org/10.5061/dryad.mw6m905zg">https://doi.org/10.5061/dryad.mw6m905zg</a>). For the first time, we also report genotype frequencies for the mixed genotype treatments. Importantly, we found that high nutrients increased infection prevalence as well as selecting for the host genotype less resistant to infection; the resulting host evolution increased infection prevalence further and depressed host density. These data and code may be reused with citation of the corresponding publication ("'Resistance is futile': Weaker selection for resistance by abundant parasites increases prevalence and depresses host density" in <em>The American Naturalist</em>).</p>
New indicators of ecological resilience and invasion resistance to support prioritization and management in the sagebrush biome, United States
<p>Ecosystem transformations to altered or novel ecological states are accelerating across the globe. Indicators of ecological resilience to disturbance and resistance to invasion can aid in assessing risks and prioritizing areas for conservation and restoration. The sagebrush biome encompasses parts of 11 western states and is experiencing rapid transformations due to human population growth, invasive species, altered disturbance regimes, and climate change. We built on prior use of static soil moisture and temperature regimes to develop new, ecologically relevant and climate-responsive indicators of both resilience and resistance. Our new indicators were based on climate and soil water availability variables derived from process-based ecohydrological models that allow predictions of future conditions. We asked: (1) Which variables best indicate resilience and resistance? (2) What are the relationships among the indicator variables and resilience and resistance categories? (3) How do patterns of resilience and resistance vary across the area? We assembled a large database (n = 24,045) of vegetation sample plots from regional monitoring programs and derived multiple climate and soil water availability variables for each plot from ecohydrological simulations. We used USDA Natural Resources Conservation Service National Soils Survey Information, Ecological Site Descriptions, and expert knowledge to develop and assign ecological types and resilience and resistance categories to each plot. We used random forest models to derive a set of 19 climate and water availability variables that best predicted resilience and resistance categories. Our models had relatively high multiclass accuracy (80% for resilience; 75% for resistance). Top indicator variables for both resilience and resistance included mean temperature, coldest month temperature, climatic water deficit, and summer and driest month precipitation. Variable relationships and patterns differed among ecoregions but reflected environmental gradients; low resilience and resistance were indicated by warm and dry conditions with high climatic water deficits, and moderately high to high resilience and resistance were characterized by cooler and moister conditions with low climatic water deficits. The new, ecologically-relevant indicators provide information on the vulnerability of resources and likely success of management actions and can be used to develop new approaches and tools for prioritizing areas for conservation and restoration actions.</p>
Diet can alter the cost of resistance to a natural parasite in Caenorhabditis elegans
<p>Resistance to parasites confers a fitness advantage, yet hosts show substantial variation in resistance in natural populations. Evolutionary theory indicates that resistant and susceptible genotypes can coexist if resistance is costly, but there is mixed evidence that resistant individuals have lower fitness in the absence of parasites. One explanation for this discrepancy is that the cost of resistance varies with environmental context. We tested this hypothesis using Caenorhabditis elegans and its natural microsporidian parasite, Nematocida ironsii. We used multiple metrics to compare the fitness of two near-isogenic host genotypes differing at regions associated with resistance to N. ironsii. To quantify the effect of the environment on the cost associated with these known resistance regions, we measured fitness on three microbial diets. We found that the cost of resistance varied with both diet and the measure of fitness. We detected no cost to resistance, irrespective of diet, when fitness was measured as fecundity. However, we detected a cost when fitness was measured in terms of population growth, and the magnitude of this cost varied with diet. These results provide a proof-of-concept that, by mediating the cost of resistance, environmental context may govern the rate and nature of resistance evolution in heterogeneous environments.</p>
Data from: Diversification of the ruminant skull along an evolutionary line of least resistance
<p>Clarifying how microevolutionary processes scale to macroevolutionary patterns is a fundamental goal in evolutionary biology, but these analyses, requiring comparative datasets of population-level variation, are limited. By analyzing a previously published dataset of 2859 ruminant crania, we find that variation within and between ruminant species is biased by a highly conserved mammalian-wide allometric pattern, CREA (CR-aniofacial E-volutionary A-llometry), where larger species have proportionally longer faces. Species with higher morphological integration and species more biased towards CREA have diverged farther from their ancestors, and Ruminantia as a clade diversified farther than expected in the direction of CREA. Our analyses indicate that CREA acts as an evolutionary 'line of least resistance' and facilitates morphological diversification due to its alignment with the browser-grazer continuum. Taken together, our results demonstrate that constraints at the population-level can produce highly directional patterns of phenotypic evolution at the macroevolutionary scale. Further research is needed to explore how CREA has been exploited in other mammalian clades.</p>
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