Large-scale mutagenesis directed at specific chromosomes in wheat

Genome ◽  
2001 ◽  
Vol 44 (1) ◽  
pp. 45-49 ◽  
Author(s):  
Robert Koebner ◽  
James Hadfield
Keyword(s):  
Disease Resistance ◽  
Large Scale ◽  
Target Gene ◽  
Yellow Rust ◽  
Chemical Mutagenesis ◽  
Loss Of Function ◽  
Novel Approach ◽  
Large Numbers ◽  
Resistance Pathway ◽  
Order Of Magnitude

A novel approach has been developed to allow for the efficient selection of loss-of-function wheat mutants in the M1 generation, following either physical or chemical mutagenesis. This has generated an order of magnitude increase in the efficiency of identification of mutants, and also greatly increases the likelihood that selected individuals reflect mutation events at the target locus, rather than at genes acting elsewhere in the disease resistance pathway. The approach relies only on prior knowledge of the chromosomal location of the target gene, and uses the polyploidy of wheat to construct populations for mutagenesis in which large numbers of individuals are hemizygous for the target gene. The idea is illustrated with the mass identification of mutants at three independent genes for race-specific resistance to yellow rust, and one gene for resistance to powdery mildew.Key words: disease resistance mutant, hemizygotes, loss-of-function mutant.

scholarly journals Self-* properties through gossiping

2008 ◽  
Vol 366 (1881) ◽  
pp. 3747-3757 ◽  
Author(s):  
Ozalp Babaoglu ◽  
Márk Jelasity
Keyword(s):  
Cell Sorting ◽  
Mobile Agents ◽  
Large Scale ◽  
Overlay Networks ◽  
Biological Mechanism ◽  
Human Intervention ◽  
Differential Adhesion ◽  
Novel Approach ◽  
Large Numbers ◽  
Ubiquitous Systems

As computer systems have become more complex, numerous competing approaches have been proposed for these systems to self-configure, self-manage, self-repair, etc. such that human intervention in their operation can be minimized. In ubiquitous systems, this has always been a central issue as well. In this paper, we overview techniques to implement self-* properties in large-scale, decentralized networks through bio-inspired techniques in general, and gossip-based algorithms in particular. We believe that gossip-based algorithms could be an important inspiration for solving problems in ubiquitous computing as well. As an example, we outline a novel approach to arrange large numbers of mobile agents (e.g. vehicles, rescue teams carrying mobile devices) into different formations in a totally decentralized manner. The approach is inspired by the biological mechanism of cell sorting via differential adhesion, as well as by our earlier work in self-organizing peer-to-peer overlay networks.

scholarly journals Optimized strategy for in vivo Cas9-activation in Drosophila

2017 ◽  
Vol 114 (35) ◽  
pp. 9409-9414 ◽  
Author(s):  
Ben Ewen-Campen ◽  
Donghui Yang-Zhou ◽  
Vitória R. Fernandes ◽  
Delfina P. González ◽  
Lu-Ping Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Target Gene ◽  
In Vivo Studies ◽  
Transcriptional Activators ◽  
Loss Of Function ◽  
Guide Rnas ◽  
Genome Wide ◽  
A Genome ◽  
Endogenous Loci

While several large-scale resources are available for in vivo loss-of-function studies in Drosophila, an analogous resource for overexpressing genes from their endogenous loci does not exist. We describe a strategy for generating such a resource using Cas9 transcriptional activators (CRISPRa). First, we compare a panel of CRISPRa approaches and demonstrate that, for in vivo studies, dCas9-VPR is the most optimal activator. Next, we demonstrate that this approach is scalable and has a high success rate, as >75% of the lines tested activate their target gene. We show that CRISPRa leads to physiologically relevant levels of target gene expression capable of generating strong gain-of-function (GOF) phenotypes in multiple tissues and thus serves as a useful platform for genetic screening. Based on the success of this CRISRPa approach, we are generating a genome-wide collection of flies expressing single-guide RNAs (sgRNAs) for CRISPRa. We also present a collection of more than 30 Gal4 > UAS:dCas9-VPR lines to aid in using these sgRNA lines for GOF studies in vivo.

scholarly journals Experiences with Resource Provisioning for Scientific Workflows Using Corral

2010 ◽  
Vol 18 (2) ◽  
pp. 77-92 ◽  
Author(s):  
Gideon Juve ◽  
Ewa Deelman ◽  
Karan Vahi ◽  
Gaurang Mehta
Keyword(s):  
Large Scale ◽  
Genome Mapping ◽  
Resource Provisioning ◽  
Distributed Resources ◽  
Local Cluster ◽  
Loosely Coupled ◽  
Large Numbers ◽  
A Genome ◽  
Order Of Magnitude ◽  
Level Scheduling

The development of grid and workflow technologies has enabled complex, loosely coupled scientific applications to be executed on distributed resources. Many of these applications consist of large numbers of short-duration tasks whose runtimes are heavily influenced by delays in the execution environment. Such applications often perform poorly on the grid because of the large scheduling overheads commonly found in grids. In this paper we present a provisioning system based on multi-level scheduling that improves workflow runtime by reducing scheduling overheads. The system reserves resources for the exclusive use of the application, and gives applications control over scheduling policies. We describe our experiences with the system when running a suite of real workflow-based applications including in astronomy, earthquake science, and genomics. Provisioning resources with Corral ahead of the workflow execution, reduced the runtime of the astronomy application by up to 78% (45% on average) and of a genome mapping application by an order of magnitude when compared to traditional methods. We also show how provisioning can benefit applications both on a small local cluster as well as a large-scale campus resource.

Computer Analyses in Preventive Health Research

1967 ◽  
Vol 06 (01) ◽  
pp. 8-14 ◽  
Author(s):  
M. F. Collen
Keyword(s):  
Medical Research ◽  
Preventive Medicine ◽  
Health Research ◽  
Large Scale ◽  
Preventive Health ◽  
New Era ◽  
Large Numbers ◽  
Normal Test ◽  
Computer Analyses

The utilization of an automated multitest laboratory as a data acquisition center and of a computer for trie data processing and analysis permits large scale preventive medical research previously not feasible. Normal test values are easily generated for the particular population studied. Long-term epidemiological research on large numbers of persons becomes practical. It is our belief that the advent of automation and computers has introduced a new era of preventive medicine.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

Suppressors of a Cold-Sensitive Mutation in Yeast U4 RNA Define Five Domains in the Splicing Factor Prp8 That Influence Spliceosome Activation

Genetics ◽  
2000 ◽  
Vol 155 (4) ◽  
pp. 1667-1682 ◽  
Author(s):  
Andreas N Kuhn ◽  
David A Brow
Keyword(s):  
Large Scale ◽  
Splicing Factor ◽  
Cold Sensitivity ◽  
Splicing Factors ◽  
Loss Of Function ◽  
U1 Snrnp ◽  
Snrnp Protein ◽  
Cold Sensitive ◽  
Spliceosome Activation ◽  
U4 Rna

AbstractThe highly conserved splicing factor Prp8 has been implicated in multiple stages of the splicing reaction. However, assignment of a specific function to any part of the 280-kD U5 snRNP protein has been difficult, in part because Prp8 lacks recognizable functional or structural motifs. We have used a large-scale screen for Saccharomyces cerevisiae PRP8 alleles that suppress the cold sensitivity caused by U4-cs1, a mutant U4 RNA that blocks U4/U6 unwinding, to identify with high resolution five distinct regions of PRP8 involved in the control of spliceosome activation. Genetic interactions between two of these regions reveal a potential long-range intramolecular fold. Identification of a yeast two-hybrid interaction, together with previously reported results, implicates two other regions in direct and indirect contacts to the U1 snRNP. In contrast to the suppressor mutations in PRP8, loss-of-function mutations in the genes for two other splicing factors implicated in U4/U6 unwinding, Prp44 (Brr2/Rss1/Slt22/Snu246) and Prp24, show synthetic enhancement with U4-cs1. On the basis of these results we propose a model in which allosteric changes in Prp8 initiate spliceosome activation by (1) disrupting contacts between the U1 snRNP and the U4/U6-U5 tri-snRNP and (2) orchestrating the activities of Prp44 and Prp24.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

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