Crop Response to Lime in the Western United States

The western states include a complex combination of geological, topographical, climatic, and vegetative features. The region extends through 1900 km of latitude and contains three major north-south, storm-directing mountain systems. Most of the region is arid (annual precipitation < 50 em); however, sizeable agriculturally important areas west of the Cascade and Sierra Nevada mountains, and at higher elevations along all the major mountain systems, receive substantial annual precipitation (120-200 em). Mean annual temperatures vary from 24°C in the desert areas near our border with Mexico to 3°C in the mountain valleys of the Northwest. This wide range of climates superimposed over a variety of parent materials and landforms has favored the development of many widely differing soils. To this variety of soils and climates, farmers have introduced agricultural systems that include a large number of economically important crops grown under management systems that vary from minimal to the most intensive. As a result, acid soils and soil acidification, although not always extensive, are important in every area of the region

Stochastic Dynamics of Lexicon Learning in an Uncertain and Nonuniform World

We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning - whereby information from multiple episodes is combined to eliminate incorrect meanings - can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally-efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially-efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.Comment: 7 pages, 3 figures. Version 2 contains additional discussion and will appear in Phys. Rev. Let

Reprogramming of hepatic fat accumulation and 'browning' of adipose tissue by the short-chain fatty acid acetate

Background/Objectives: Short-chain fatty acids, produced by microbiome fermentation of carbohydrates, have been linked to a reduction in appetite, body weight and adiposity. However, determining the contribution of central and peripheral mechanisms to these effects has not been possible. Subjects/Methods:C57BL/6 mice fed with either normal or high-fat diet were treated with nanoparticle-delivered acetate, and the effects on metabolism were investigated. Results:In the liver, acetate decreased lipid accumulation and improved hepatic function, as well as increasing mitochondrial efficiency. In white adipose tissue, it inhibited lipolysis and induced 'browning', increasing thermogenic capacity that led to a reduction in body adiposity. Conclusions:This study provides novel insights into the peripheral mechanism of action of acetate, independent of central action, including ‘browning’ and enhancement of hepatic mitochondrial function

Acetate supplementation modulates brain histone acetylation and decreases interleukin-1β expression in a rat model of neuroinflammation

<p>Abstract</p> <p>Background</p> <p>Long-term acetate supplementation reduces neuroglial activation and cholinergic cell loss in a rat model of lipopolysaccharide-induced neuroinflammation. Additionally, a single dose of glyceryl triacetate, used to induce acetate supplementation, increases histone H3 and H4 acetylation and inhibits histone deacetylase activity and histone deacetylase-2 expression in normal rat brain. Here, we propose that the therapeutic effect of acetate in reducing neuroglial activation is due to a reversal of lipopolysaccharide-induced changes in histone acetylation and pro-inflammatory cytokine expression.</p> <p>Methods</p> <p>In this study, we examined the effect of a 28-day-dosing regimen of glyceryl triacetate, to induce acetate supplementation, on brain histone acetylation and interleukin-1β expression in a rat model of lipopolysaccharide-induced neuroinflammation. The effect was analyzed using Western blot analysis, quantitative real-time polymerase chain reaction and enzymic histone deacetylase and histone acetyltransferase assays. Statistical analysis was performed using one-way analysis of variance, parametric or nonparametric when appropriate, followed by Tukey's or Dunn's post-hoc test, respectively.</p> <p>Results</p> <p>We found that long-term acetate supplementation increased the proportion of brain histone H3 acetylated at lysine 9 (H3K9), histone H4 acetylated at lysine 8 and histone H4 acetylated at lysine 16. However, unlike a single dose of glyceryl triacetate, long-term treatment increased histone acetyltransferase activity and had no effect on histone deacetylase activity, with variable effects on brain histone deacetylase class I and II expression. In agreement with this hypothesis, neuroinflammation reduced the proportion of brain H3K9 acetylation by 50%, which was effectively reversed with acetate supplementation. Further, in rats subjected to lipopolysaccharide-induced neuroinflammation, the pro-inflammatory cytokine interleukin-1β protein and mRNA levels were increased by 1.3- and 10-fold, respectively, and acetate supplementation reduced this expression to control levels.</p> <p>Conclusion</p> <p>Based on these results, we conclude that dietary acetate supplementation attenuates neuroglial activation by effectively reducing pro-inflammatory cytokine expression by a mechanism that may involve a distinct site-specific pattern of histone acetylation and histone deacetylase expression in the brain.</p

Temporal Controls of the Asymmetric Cell Division Cycle in Caulobacter crescentus

The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine (e.g., Rhizobium, Brucella)

Identification and characterization of a direct activator of a gene transfer agent

Gene transfer agents (GTAs) are thought to be ancient bacteriophages that have been co-opted into serving their host and can now transfer any gene between bacteria. Production of GTAs is controlled by several global regulators through unclear mechanisms. In Rhodobacter capsulatus, gene rcc01865 encodes a putative regulatory protein that is essential for GTA production. Here, I show that rcc01865 (hereafter gafA) encodes a transcriptional regulator that binds to the GTA promoter to initiate production of structural and DNA packaging components. Expression of gafA is in turn controlled by the pleiotropic regulator protein CtrA and the quorum-sensing regulator GtaR. GafA and CtrA work together to promote GTA maturation and eventual release through cell lysis. Identification of GafA as a direct GTA regulator allows the first integrated regulatory model to be proposed and paves the way for discovery of GTAs in other species that possess gafA homologues

Hierarchy of Scales in Language Dynamics

Methods and insights from statistical physics are finding an increasing variety of applications where one seeks to understand the emergent properties of a complex interacting system. One such area concerns the dynamics of language at a variety of levels of description, from the behaviour of individual agents learning simple artificial languages from each other, up to changes in the structure of languages shared by large groups of speakers over historical timescales. In this Colloquium, we survey a hierarchy of scales at which language and linguistic behaviour can be described, along with the main progress in understanding that has been made at each of them − much of which has come from the statistical physics community. We argue that future developments may arise by linking the different levels of the hierarchy together in a more coherent fashion, in particular where this allows more effective use of rich empirical data sets