scholarly journals Melon ethylene-mediated transcriptome and methylome dynamics provide insights to volatile production

2020 ◽  
Author(s):  
Ari Feder ◽  
Chen Jiao ◽  
Navot Galpaz ◽  
Julia Vrebalov ◽  
Yimin Xu ◽  
...  
Keyword(s):  
Transcriptional Repression ◽  
Large Scale ◽  
Cytosine Methylation ◽  
Acc Oxidase ◽  
Threonine Aldolase ◽  
Genome Methylation ◽  
Ester Biosynthesis ◽  
Small Set ◽  
Aldolase Activity ◽  
Mutant Complementation

AbstractDuring climacteric ripening large-scale transcriptional modifications are governed by ethylene. While ripening-related chromatin modifications are also known to occur, a direct connection between these factors has not been demonstrated. We characterized ethylene-mediated transcriptome modification, genome methylation dynamics, and their relation to organoleptic modifications during fruit ripening in the climacteric melon and an ethylene repressed line where the fruit-specific ACC oxidase 1 (ACO1) gene was targeted by antisense. The ACO1 antisense line exhibited mainly reduced transcriptional repression of ripening-related genes associated with DNA CHH hypomethylation at the onset of ripening. Additionally, transcription of a small set of ethylene-induced genes, including known ripening-associated genes, was inhibited by ACO1 repression and this inhibition was associated with CG hypermethylation. In the ACO1 antisense line, the accumulation of aromatic compounds, which are mainly derived from the catabolism of amino acids, is known to be inhibited. One of the ethylene-mediated transcriptionally up-regulated genes, CmTHA1, encoding a threonine aldolase, exhibited differential cytosine methylation. Threonine aldolase catalyzes the conversion of L-threonine/L-allo threonine to glycine and acetaldehyde and thus is likely involved in threonine-dependent ethyl ester biosynthesis. Yeast mutant complementation and incubation of melon discs with labeled threonine verified CmTHA1 threonine aldolase activity, revealing an additional ethylene-dependent amino acid catabolism branch involved in climacteric melon ripening.

scholarly journals Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

2016 ◽  
Author(s):  
Timothy N. Rubin ◽  
Oluwasanmi Koyejo ◽  
Krzysztof J. Gorgolewski ◽  
Michael N. Jones ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Brain Activity ◽  
Human Cognition ◽  
Brain Images ◽  
Whole Brain ◽  
Context Sensitive ◽  
Cognitive States ◽  
Small Set

AbstractA central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

scholarly journals Testing the Waters: Behavior across Participant Pools

2021 ◽  
Vol 111 (2) ◽  
pp. 687-719
Author(s):  
Erik Snowberg ◽  
Leeat Yariv
Keyword(s):  
External Validity ◽  
Large Scale ◽  
Student Population ◽  
University Student ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Lab Experiments ◽  
The Us ◽  
Small Set ◽  
Observer Effects

We leverage a large-scale incentivized survey eliciting behaviors from (almost) an entire undergraduate university student population, a representative sample of the US population, and Amazon Mechanical Turk (MTurk) to address concerns about the external validity of experiments with student participants. Behavior in the student population offers bounds on behaviors in other populations, and correlations between behaviors are similar across samples. Furthermore, non-student samples exhibit higher levels of noise. Adding historical lab participation data, we find a small set of attributes over which lab participants differ from non-lab participants. An additional set of lab experiments shows no evidence of observer effects. (JEL C83, D90, D91)

scholarly journals Temporal Relationship between Ester Biosynthesis and Ripening Events in Bananas

2002 ◽  
Vol 127 (6) ◽  
pp. 998-1005 ◽  
Author(s):  
Sastry Jayanty ◽  
Jun Song ◽  
Nicole M. Rubinstein ◽  
Andrés Chong ◽  
Randolph M. Beaudry
Keyword(s):  
Chlorophyll Fluorescence ◽  
Ethylene Production ◽  
Temporal Relationship ◽  
Photochemical Efficiency ◽  
Acc Oxidase ◽  
Ethylene Biosynthesis ◽  
Banana Fruit ◽  
Coa Transferase ◽  
Ester Biosynthesis ◽  
Natural Ester

The temporal relationship between changes in ethylene production, respiration, skin color, chlorophyll fluorescence, volatile ester biosynthesis, and expression of ACC oxidase (ACO) and alcohol acyl-CoA transferase (AAT) in ripening banana (Musa L. spp., AAA group, Cavendish subgroup. `Valery') fruit was investigated at 22 °C. Ethylene production rose to a peak a few hours after the onset of its logarithmic phase; the peak in production coincided with maximal ACO expression. The respiratory rise began as ethylene production increased, reaching its maximum ≈30 to 40 hours after ethylene production had peaked. Green skin coloration and photochemical efficiency, as measured by chlorophyll fluorescence, declined simultaneously after the peak in ethylene biosynthesis. Natural ester biosynthesis began 40 to 50 hours after the peak in ethylene biosynthesis, reaching maximal levels 3 to 4 days later. While AAT expression was detected throughout, the maximum level of expression was detected at the onset of natural ester biosynthesis. The synthesis of unsaturated esters began 100 hours after the peak in ethylene and increased with time, suggesting the lipoxygenase pathway be a source of ester substrates late in ripening. Incorporation of exogenously supplied ester precursors (1-butanol, butyric acid, and 3-methyl-1-butanol) in the vapor phase into esters was maturity-dependent. The pattern of induced esters and expression data for AAT suggested that banana fruit have the capacity to synthesize esters over 100 hours before the onset of natural ester biosynthesis. We hypothesize the primary limiting factor in ester biosynthesis before natural production is precursor availability, but, as ester biosynthesis is engaged, the activity of alcohol acyl-CoA transferase the enzyme responsible for ester biosynthesis, exerts a major influence.

Muhkam Algorithmic Models of Real World Processes for Intelligent Technologies

2013 ◽  
Vol 1 (2) ◽  
pp. 56-82 ◽  
Author(s):  
Tom Adi ◽  
O.K. Ewell ◽  
Tim Vogel ◽  
Kim Payton ◽  
Jeannine L. Hippchen
Keyword(s):  
Real World ◽  
Large Scale ◽  
Adaptive Method ◽  
Emotional Learning ◽  
Sound Symbolism ◽  
Small Set ◽  
Software Implementations

This paper significantly revises and expands a chapter in a handbook of research on synthetic emotions published in 2009. The authors extend the scope beyond emotions to all real world processes. The authors use a new adaptive method to create, improve, and correct algorithmic models (models in the form of algorithms) of emotional, learning, communication, memory, perception, biological, physiological, social, legal, and spiritual processes. The models are constructed from the sound symbolism that is indicated by the usage of the Arabic names of these processes in so-called muhkam text passages. These muhkam models have been validated by successful large-scale software implementations and by clinical emotions research. Naturally, models in the form of algorithms are easy to implement in intelligent technologies. These models also lend themselves to integration and interoperability because they share a small set of seven general concepts and their symmetrical combinations.

scholarly journals Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data

2020 ◽  
Vol 34 (04) ◽  
pp. 6853-6860
Author(s):  
Xuchao Zhang ◽  
Xian Wu ◽  
Fanglan Chen ◽  
Liang Zhao ◽  
Chang-Tien Lu
Keyword(s):  
Real World ◽  
Large Scale ◽  
Learning Algorithm ◽  
Noisy Data ◽  
Training Set ◽  
Robust Learning ◽  
Robust Model ◽  
Small Set ◽  
Real World Datasets ◽  
Theoretical Analyses

The success of training accurate models strongly depends on the availability of a sufficient collection of precisely labeled data. However, real-world datasets contain erroneously labeled data samples that substantially hinder the performance of machine learning models. Meanwhile, well-labeled data is usually expensive to obtain and only a limited amount is available for training. In this paper, we consider the problem of training a robust model by using large-scale noisy data in conjunction with a small set of clean data. To leverage the information contained via the clean labels, we propose a novel self-paced robust learning algorithm (SPRL) that trains the model in a process from more reliable (clean) data instances to less reliable (noisy) ones under the supervision of well-labeled data. The self-paced learning process hedges the risk of selecting corrupted data into the training set. Moreover, theoretical analyses on the convergence of the proposed algorithm are provided under mild assumptions. Extensive experiments on synthetic and real-world datasets demonstrate that our proposed approach can achieve a considerable improvement in effectiveness and robustness to existing methods.

Enzyme Fractionation by Means of Large- Scale Continuous- Flow Electrophoresis

1966 ◽  
Vol 12 (8) ◽  
pp. 497-504 ◽  
Author(s):  
Seymour Winsten ◽  
Jeanne Jackson ◽  
Paula Wolf
Keyword(s):  
Lactic Acid ◽  
Enzyme Activities ◽  
Continuous Flow ◽  
Large Scale ◽  
Leucine Aminopeptidase ◽  
Ph Effect ◽  
Good Recovery ◽  
Acid Dehydrogenase ◽  
Aldolase Activity ◽  
Single Serum Sample

Abstract Large- scale, microglass bead, continuous- flow electrophoresis was used to separateand collect protein fractions from a single serum sample. These fractions were assayed for a multiplicity of enzyme activities. Relatively good recovery was obtained with lactic acid dehydrogenase, leucine aminopeptidase, and phosphohexoseisomerase. Artifactually high recoveries were observed with aldotase and isocitric acid dehydrogenase. The increase observed in aldolase activity may be due to a pH effect.

Measurement of l-Threonine Aldolase Activity in Rat Liver

1979 ◽  
Vol 7 (6) ◽  
pp. 1274-1276 ◽  
Author(s):  
MICHAEL I. BIRD ◽  
PETER B. NUNN
Keyword(s):  
Rat Liver ◽  
Threonine Aldolase ◽  
Aldolase Activity

Pivotality and Turnout: Evidence from a Field Experiment in the Aftermath of a Tied Election

2014 ◽  
Vol 2 (2) ◽  
pp. 309-319 ◽  
Author(s):  
Ryan D. Enos ◽  
Anthony Fowler
Keyword(s):  
Field Experiment ◽  
Large Scale ◽  
Electoral Competition ◽  
Political Process ◽  
The Political ◽  
Scale Field ◽  
Large Scale Field ◽  
Political Office ◽  
Small Set ◽  
Rare Opportunity

Many citizens abstain from the political process, and the reasons for this abstention are of great interest and importance. Most scholars and pundits assume that greater electoral competition and the increased chance of pivotality will motivate citizens to participate. We test this hypothesis through a large-scale field experiment that exploits the rare opportunity of a tied election for major political office. Informing citizens that an upcoming election will be close has little mobilizing effect. Any effect that we do detect is concentrated among a small set of frequent voters. The evidence suggests that increased pivotality is not a solution to low turnout and the predominant models of turnout focusing on pivotality are of little practical use.

scholarly journals Epigenetic Gene Regulation in the Bacterial World

2006 ◽  
Vol 70 (3) ◽  
pp. 830-856 ◽  
Author(s):  
Josep Casadesús ◽  
David Low
Keyword(s):  
Dna Methylation ◽  
Dna Replication ◽  
Transcriptional Activation ◽  
Transcriptional Repression ◽  
Cytosine Methylation ◽  
Regulation Of Gene Expression ◽  
Phase Variation ◽  
E Coli ◽  
Adenine Methylation ◽  
Methylation Patterns

SUMMARY Like many eukaryotes, bacteria make widespread use of postreplicative DNA methylation for the epigenetic control of DNA-protein interactions. Unlike eukaryotes, however, bacteria use DNA adenine methylation (rather than DNA cytosine methylation) as an epigenetic signal. DNA adenine methylation plays roles in the virulence of diverse pathogens of humans and livestock animals, including pathogenic Escherichia coli, Salmonella, Vibrio, Yersinia, Haemophilus, and Brucella. In Alphaproteobacteria, methylation of adenine at GANTC sites by the CcrM methylase regulates the cell cycle and couples gene transcription to DNA replication. In Gammaproteobacteria, adenine methylation at GATC sites by the Dam methylase provides signals for DNA replication, chromosome segregation, mismatch repair, packaging of bacteriophage genomes, transposase activity, and regulation of gene expression. Transcriptional repression by Dam methylation appears to be more common than transcriptional activation. Certain promoters are active only during the hemimethylation interval that follows DNA replication; repression is restored when the newly synthesized DNA strand is methylated. In the E. coli genome, however, methylation of specific GATC sites can be blocked by cognate DNA binding proteins. Blockage of GATC methylation beyond cell division permits transmission of DNA methylation patterns to daughter cells and can give rise to distinct epigenetic states, each propagated by a positive feedback loop. Switching between alternative DNA methylation patterns can split clonal bacterial populations into epigenetic lineages in a manner reminiscent of eukaryotic cell differentiation. Inheritance of self-propagating DNA methylation patterns governs phase variation in the E. coli pap operon, the agn43 gene, and other loci encoding virulence-related cell surface functions.

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