Do the A4c60 Fullerides Have a Broken-Symmetry Ground State?

Band theory predicts both K3C60 and K4C60 to be metals; various experimental probes show that while K3C60 is indeed metallic, K4C60 appears to be insulating. The standard view of this apparent failure of the single-particle picture is that electron correlation is predominant. We describe an alternative scenario, motivated on theoretical grounds, which invokes a spin- or charge-density-wave state to explain the observed insulating behavior.Comment: 9 pages + 3 figures, REVTeX 3.

Chloroplast membrane remodeling during freezing stress is accompanied by cytoplasmic acidification activating SENSITIVE TO FREEZING 2

Low temperature is a seasonal abiotic stress which restricts native plant ranges and crop distributions. Two types of low temperature stress can be distinguished: chilling and freezing. Much work has been done on the mechanisms by which chilling is sensed, but relatively little is known about how plants sense freezing. Recently, SENSITIVE TO FREEZING 2 (SFR2) was identified as a protein which responds in a non-transcriptional manner to freezing. Here, we investigate the cellular conditions which allow SFR2 activation. Using a combination of isolated organelle, whole tissue and whole plant assays, we provide evidence that SFR2 is activated by changes in cytosolic pH and Mg2+. Manipulation of pH and Mg2+ in cold acclimated plants is shown to cause changes similar to those of freezing. We conclude that pH and Mg2+ are perceived as intracellular cues as part of the sensing mechanism for freezing conditions. This evidence provides a specific molecular mechanism to combat freezing

Chloroplast membrane remodeling during freezing stress is accompanied by cytoplasmic acidification activating SENSITIVE TO FREEZING 2

Low temperature is a seasonal abiotic stress which restricts native plant ranges and crop distributions. Two types of low temperature stress can be distinguished: chilling and freezing. Much work has been done on the mechanisms by which chilling is sensed, but relatively little is known about how plants sense freezing. Recently, SENSITIVE TO FREEZING 2 (SFR2) was identified as a protein which responds in a non-transcriptional manner to freezing. Here, we investigate the cellular conditions which allow SFR2 activation. Using a combination of isolated organelle, whole tissue and whole plant assays, we provide evidence that SFR2 is activated by changes in cytosolic pH and Mg2+. Manipulation of pH and Mg2+ in cold acclimated plants is shown to cause changes similar to those of freezing. We conclude that pH and Mg2+ are perceived as intracellular cues as part of the sensing mechanism for freezing conditions. This evidence provides a specific molecular mechanism to combat freezing

Controlled biomineralization of magnetite (Fe₃O₄) by <i>Magnetospirillum gryphiswaldense</i>

Results from a study of the chemical composition and micro-structural characteristics of bacterial magnetosomes extracted from the magnetotactic bacterial strain Magnetospirillum gryphiswaldense are presented here. Using high-resolution transmission electron microscopy combined with selected-area electron diffraction and energy dispersive X-ray microanalysis, biogenic magnetite particles isolated from mature cultures were analysed for variations in crystallinity and particle size, as well as chain character and length. The analysed crystals showed a narrow size range (∼14-67 nm) with an average diameter of 46±6.8 nm, cuboctahedral morphologies and typical Gamma type crystal size distributions. The magnetite particles exhibited a high chemical purity (exclusively Fe3O4) and the majority fall within the single-magnetic-domain range

Detecting Fire and Grazing Patterns in Tallgrass Prairie Using Spectral Mixture Analysis

Global grasslands are typically under management practices (such as fire and grazing) that alter nutrient cycling, ecosystem composition, and distribution of organic matter from the unmanaged condition. We evaluated landscape-level response to fire and grazing treatments in the Konza Tallgrass Prairie Research Natural Area, Kansas, using spectral mixture analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired 31 August 1990. Spectral mixture analysis derives the fractional abundances of spectrally unique components in the landscape. The reflectance spectra of these components are called endmembers. Endmember fractions values were compared against ground values of live biomass, current standing dead biomass, and litter for 12 watersheds. Analysis of variance (ANOVA) was performed on 37 watersheds with known burning and grazing histories for each of the remote sensing variables. Seven endmembers were selected from the AVIRIS data using a manual endmember selection method: nonphotosynthetic vegetation (NPV), soil, rock, shade, and three green vegetation endmembers (GV1, GV2, and GV3). Each vegetation endmember correlated differently to biomass measurements and revealed unique relationships to management treatments. From regressions, ANOVAs, and image analysis, these three endmembers were inferred to represent canopy vertical structure or leaf area index (LAI), greenness, and fractional cover of grass, respectively. There was a stronger relationship between the sum of GV1 and GV3 fractions and live grass biomass values than there was with the (unsummed) individual fractions. In an ANOVA, the sum separated both burn and grazing treatments as well as the treatment interaction. The NPV fraction was strongly correlated with ground measurements of litter and standing dead biomass, and significantly separated burn treatments. The soil fraction differentiated grazing treatments, and analysis of the soil fraction image revealed a spatial coherence of grazing patterns along drainages. Similar analyses were perfomed on the Normalized Difference Vegetation Index (NDVI), a commonly used two-band index computed from red and near-infrared reflectance. NDVI, shown in previous studies to estimate the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR), was a poor indicator of canopy biomass, but it successfully separated fire treatments. Broad-scale assessment of the state and structure of managed grassland systems requires the identification of several indicator variables. Spectral mixture analysis, unlike NDVI, not only separated treatments but also allowed for the identification of five remotely sensible factors affected by the management treatments, namely, vertical structure, percentage cover or patchiness, greenness, and distribution of soil and litter

Higher biomass accumulation by increasing phosphoribosylpyrophosphate synthetase activity in Arabidopsis thaliana and Nicotiana tabacum

Plants are able to produce all the organic compounds required for development and growth. As developmental processes and metabolic pathways use a common resource pool, the tight regulation of the distribution of metabolites between growth, production of defence compounds and storage products can be assumed. A transgenic approach was used to investigate the importance of supplying the key intermediate phosphoribosylpyrophosphate (PRPP) for plant growth and biomass accumulation in the model plant Arabidopsis thaliana and in Nicotiana tabacum. For this purpose, the Ashbya gossypii genes coding for either PRPP synthetase (PRS) or a mutated variant of the same gene were over-expressed under the control of a constitutive promoter. It was shown that increased PRS activity in A. thaliana or N. tabacum leads to a substantial increase in biomass accumulation under different standardized growth conditions. Growth enhancement was accompanied by significant changes in the amount of sugars and other metabolites. This study provides evidence that the supply of PRPP co-limits growth rates, and has obvious implications for biotechnological strategies aiming to increase plant biomass as an alternative renewable energy source

Deep learning as optimal control problems

We briefly review recent work where deep learning neural networks have been interpreted as discretisations of an optimal control problem subject to an ordinary differential equation constraint. We report here new preliminary experiments with implicit symplectic Runge-Kutta methods. In this paper, we discuss ongoing and future research in this area

A comparison of spectral mixture analysis an NDVI for ascertaining ecological variables

In this study, we compare the performance of spectral mixture analysis to the Normalized Difference Vegetation Index (NDVI) in detecting change in a grassland across topographically-induced nutrient gradients and different management schemes. The Konza Prairie Research Natural Area, Kansas, is a relatively homogeneous tallgrass prairie in which change in vegetation productivity occurs with respect to topographic positions in each watershed. The area is the site of long-term studies of the influence of fire and grazing on tallgrass production and was the site of the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) from 1987 to 1989. Vegetation indices such as NDVI are commonly used with imagery collected in few (less than 10) spectral bands. However, the use of only two bands (e.g. NDVI) does not adequately account for the complex of signals making up most surface reflectance. Influences from background spectral variation and spatial heterogeneity may confound the direct relationship with biological or biophysical variables. High dimensional multispectral data allows for the application position of techniques such as derivative analysis and spectral curve fitting, thereby increasing the probability of successfully modeling the reflectance from mixed surfaces. The higher number of bands permits unmixing of a greater number of surface components, separating the vegetation signal for further analyses relevant to biological variables