Disorder-Induced Critical Phenomena in Hysteresis: Numerical Scaling in Three and Higher Dimensions

We present numerical simulations of avalanches and critical phenomena associated with hysteresis loops, modeled using the zero-temperature random-field Ising model. We study the transition between smooth hysteresis loops and loops with a sharp jump in the magnetization, as the disorder in our model is decreased. In a large region near the critical point, we find scaling and critical phenomena, which are well described by the results of an epsilon expansion about six dimensions. We present the results of simulations in 3, 4, and 5 dimensions, with systems with up to a billion spins (1000^3).Comment: Condensed and updated version of cond-mat/9609072,``Disorder-Induced Critical Phenomena in Hysteresis: A Numerical Scaling Analysis'

Hysteresis, Avalanches, and Disorder Induced Critical Scaling: A Renormalization Group Approach

We study the zero temperature random field Ising model as a model for noise and avalanches in hysteretic systems. Tuning the amount of disorder in the system, we find an ordinary critical point with avalanches on all length scales. Using a mapping to the pure Ising model, we Borel sum the $6-\epsilon$ expansion to $O(\epsilon^5)$ for the correlation length exponent. We sketch a new method for directly calculating avalanche exponents, which we perform to $O(\epsilon)$. Numerical exponents in 3, 4, and 5 dimensions are in good agreement with the analytical predictions.Comment: 134 pages in REVTEX, plus 21 figures. The first two figures can be obtained from the references quoted in their respective figure captions, the remaining 19 figures are supplied separately in uuencoded forma

Identification of genes differentially expressed in a resistant reaction to Mycosphaerella pinodes in pea using microarray technology

<p>Abstract</p> <p>Background</p> <p>Ascochyta blight, caused by <it>Mycosphaerella pinodes </it>is one of the most important pea pathogens. However, little is known about the genes and mechanisms of resistance acting against <it>M. pinodes </it>in pea. Resistance identified so far to this pathogen is incomplete, polygenic and scarce in pea, being most common in <it>Pisum </it>relatives. The identification of the genes underlying resistance would increase our knowledge about <it>M. pinodes-</it>pea interaction and would facilitate the introgression of resistance into pea varieties. In the present study differentially expressed genes in the resistant <it>P. sativum </it>ssp. <it>syriacum </it>accession P665 comparing to the susceptible pea cv. Messire after inoculation with <it>M. pinodes </it>have been identified using a <it>M. truncatula </it>microarray.</p> <p>Results</p> <p>Of the 16,470 sequences analysed, 346 were differentially regulated. Differentially regulated genes belonged to almost all functional categories and included genes involved in defense such as genes involved in cell wall reinforcement, phenylpropanoid and phytoalexins metabolism, pathogenesis- related (PR) proteins and detoxification processes. Genes associated with jasmonic acid (JA) and ethylene signal transduction pathways were induced suggesting that the response to <it>M. pinodes </it>in pea is regulated via JA and ET pathways. Expression levels of ten differentially regulated genes were validated in inoculated and control plants using qRT-PCR showing that the P665 accession shows constitutively an increased expression of the defense related genes as peroxidases, disease resistance response protein 39 (DRR230-b), glutathione S-transferase (GST) and 6a-hydroxymaackiain methyltransferase.</p> <p>Conclusions</p> <p>Through this study a global view of genes expressed during resistance to <it>M. pinodes </it>has been obtained, giving relevant information about the mechanisms and pathways conferring resistance to this important disease. In addition, the <it>M. truncatula </it>microarray represents an efficient tool to identify candidate genes controlling resistance to <it>M. pinodes </it>in pea.</p

Single-feature polymorphism discovery by computing probe affinity shape powers

<p>Abstract</p> <p>Background</p> <p>Single-feature polymorphism (SFP) discovery is a rapid and cost-effective approach to identify DNA polymorphisms. However, high false positive rates and/or low sensitivity are prevalent in previously described SFP detection methods. This work presents a new computing method for SFP discovery.</p> <p>Results</p> <p>The probe affinity differences and affinity shape powers formed by the neighboring probes in each probe set were computed into SFP weight scores. This method was validated by known sequence information and was comprehensively compared with previously-reported methods using the same datasets. A web application using this algorithm has been implemented for SFP detection. Using this method, we identified 364 SFPs in a barley near-isogenic line pair carrying either the wild type or the mutant <it>uniculm2 </it>(<it>cul2</it>) allele. Most of the SFP polymorphisms were identified on chromosome 6H in the vicinity of the <it>Cul2 </it>locus.</p> <p>Conclusion</p> <p>This SFP discovery method exhibits better performance in specificity and sensitivity over previously-reported methods. It can be used for other organisms for which GeneChip technology is available. The web-based tool will facilitate SFP discovery. The 364 SFPs discovered in a barley near-isogenic line pair provide a set of genetic markers for fine mapping and future map-based cloning of the <it>Cul2 </it>locus.</p

Transcriptome Analysis of H2O2-Treated Wheat Seedlings Reveals a H2O2-Responsive Fatty Acid Desaturase Gene Participating in Powdery Mildew Resistance

Hydrogen peroxide (H2O2) plays important roles in plant biotic and abiotic stress responses. However, the effect of H2O2 stress on the bread wheat transcriptome is still lacking. To investigate the cellular and metabolic responses triggered by H2O2, we performed an mRNA tag analysis of wheat seedlings under 10 mM H2O2 treatment for 6 hour in one powdery mildew (PM) resistant (PmA) and two susceptible (Cha and Han) lines. In total, 6,156, 6,875 and 3,276 transcripts were found to be differentially expressed in PmA, Han and Cha respectively. Among them, 260 genes exhibited consistent expression patterns in all three wheat lines and may represent a subset of basal H2O2 responsive genes that were associated with cell defense, signal transduction, photosynthesis, carbohydrate metabolism, lipid metabolism, redox homeostasis, and transport. Among genes specific to PmA, ‘transport’ activity was significantly enriched in Gene Ontology analysis. MapMan classification showed that, while both up- and down- regulations were observed for auxin, abscisic acid, and brassinolides signaling genes, the jasmonic acid and ethylene signaling pathway genes were all up-regulated, suggesting H2O2-enhanced JA/Et functions in PmA. To further study whether any of these genes were involved in wheat PM response, 19 H2O2-responsive putative defense related genes were assayed in wheat seedlings infected with Blumeria graminis f. sp. tritici (Bgt). Eight of these genes were found to be co-regulated by H2O2 and Bgt, among which a fatty acid desaturase gene TaFAD was then confirmed by virus induced gene silencing (VIGS) to be required for the PM resistance. Together, our data presents the first global picture of the wheat transcriptome under H2O2 stress and uncovers potential links between H2O2 and Bgt responses, hence providing important candidate genes for the PM resistance in wheat

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages