Elevated [CO2 ] effects on crops: Advances in understanding acclimation, nitrogen dynamics and interactions with drought and other organisms.

Future rapid increases in atmospheric CO2 concentration [CO2 ] are expected, with values likely to reach ~550 ppm by mid-century. This implies that every terrestrial plant will be exposed to nearly 40% more of one of the key resources determining plant growth. In this review we highlight selected areas of plant interactions with elevated [CO2 ] (e[CO2 ]), where recently published experiments challenge long-held, simplified views. Focusing on crops, especially in more extreme and variable growing conditions, we highlight uncertainties associated with four specific areas. (1) While it is long known that photosynthesis can acclimate to e[CO2 ], such acclimation is not consistently observed in field experiments. The influence of sink-source relations and nitrogen (N) limitation on acclimation is investigated and current knowledge about whether stomatal function or mesophyll conductance (gm ) acclimate independently is summarised. (2) We show how the response of N uptake to e[CO2 ] is highly variable, even for one cultivar grown within the same field site, and how decreases in N concentrations ([N]) are observed consistently. Potential mechanisms contributing to [N] decreases under e[CO2 ] are discussed and proposed solutions are addressed. (3) Based on recent results from crop field experiments in highly variable, non-irrigated, water-limited environments, we challenge the previous opinion that the relative CO2 effect is larger under drier environmental conditions. (4) Finally, we summarise how changes in growth and nutrient concentrations due to e[CO2 ] will influence relationships between crops and weeds, herbivores and pathogens in agricultural systems.

Photosynthesis, photochemistry and antioxidative defence in response to two drought severities and with re-watering in Allocasuarina luehmannii.

Gas exchange, chlorophyll fluorescence and water potentials, together with ascorbate and glutathione concentrations, were studied during moderate and severe drought stress and in response to re-watering in Allocasuarina luehmannii seedlings. Moderate drought stress (MS) decreased stomatal conductance (g(s)) and net CO(2) assimilation rates (A) to approximately 40% and approximately 60% of control values, respectively, and caused decreases in internal CO(2) concentration (C(i)) and maximum light use efficiency of light-acclimated photosystem II (PSII) centres (Fv'/Fm'). Severe drought stress (SS) decreased g(s) and A to approximately 5% and approximately 15% of the control values, respectively, and caused increases in C(i) and PSII excitation pressure (1 - qP), as well as decreases in water potentials, effective quantum yield of PSII (PhiPSII), maximum efficiency of PSII (Fv/Fm) and Fv'/Fm'. Ascorbate and glutathione concentrations remained unaffected by drought treatments, but ascorbate became more oxidised under severe stress. MS seedlings recovered within 1 day (C(i), Fv'/Fm') to 1 week (A, g(s)) of re-watering. In comparison, SS seedlings had longer-lasting after-stress effects, with recovery of many variables (g(s), water potentials, Fv/Fm, PhiPSII, Fv'/Fm') taking between 1 and 3 weeks from re-watering. We found no indication that interaction with antioxidants played a significant role in recovery. In conclusion, A. luehmannii seedlings appear to function normally under moderate drought, but do not seem to have particular metabolic tolerance mechanisms to endure severe drought, which may have implications for its persistence under climate change at the drier margins of its distribution.

Identification of transcription factor binding sites with variable-order Bayesian networks.

MOTIVATION: We propose a new class of variable-order Bayesian network (VOBN) models for the identification of transcription factor binding sites (TFBSs). The proposed models generalize the widely used position weight matrix (PWM) models, Markov models and Bayesian network models. In contrast to these models, where for each position a fixed subset of the remaining positions is used to model dependencies, in VOBN models, these subsets may vary based on the specific nucleotides observed, which are called the context. This flexibility turns out to be of advantage for the classification and analysis of TFBSs, as statistical dependencies between nucleotides in different TFBS positions (not necessarily adjacent) may be taken into account efficiently--in a position-specific and context-specific manner. RESULTS: We apply the VOBN model to a set of 238 experimentally verified sigma-70 binding sites in Escherichia coli. We find that the VOBN model can distinguish these 238 sites from a set of 472 intergenic 'non-promoter' sequences with a higher accuracy than fixed-order Markov models or Bayesian trees. We use a replicated stratified-holdout experiment having a fixed true-negative rate of 99.9%. We find that for a foreground inhomogeneous VOBN model of order 1 and a background homogeneous variable-order Markov (VOM) model of order 5, the obtained mean true-positive (TP) rate is 47.56%. In comparison, the best TP rate for the conventional models is 44.39%, obtained from a foreground PWM model and a background 2nd-order Markov model. As the standard deviation of the estimated TP rate is approximately 0.01%, this improvement is highly significant.

Estimation and filtering of potential protein-protein docking positions.

MOTIVATION: Software systems predicting automatically whether and how two proteins may interact are highly desirable, both for understanding biological processes and for the rational design of new proteins. As a part of a future complete solution to this problem, a bundle of programs is presented designed (i) to estimate initial docking positions for a given pair of docking candidates, (ii) to adjust them, and (iii) to filter them, thus preparing more detailed computations of free energies. RESULTS: The system is evaluated on a test set of 51 co-crystallized complexes aiming at redocking the subunits. It works completely automatically and the evaluation is performed using one single set of parameters for all complexes in the test set. The number of solutions is fixed to 50 positions with a median CPU time of 26 min. For 30 complexes, these contain a near-correct solution with root mean square deviation ( RMSD )

viwish: a visualization server for protein modelling and docking.

A visualization tool viwish for proteins based on the Tcl command language has been developed. The system is completely menu driven and can display arbitrary many proteins in arbitrary many windows. It isinstantly t o use, even for non computer experts and provides possibilities to modify menus, configurations, and windows. It may be used as a stand-alone molecular graphics package or as a graphics server for external programs. Communications with these client applications is established even across different machines (through the send command to Tk, an extension of Tcl). In addition, a wide rage of chemical data like molecular surfaces and 3D gridded samplings of chemical features can be displayed. Therefore the systmen is especially useful for the development of algorithms that need visual distributed freely, including the source code.

Protein docking combining symbolic descriptions of molecular surfaces and grid-based scoring functions.

With the growing number of known 3D protein structures, computing systems, that can predict where two protein molecules interact with each other is becoming of increasing interest. A system is presented, integrating preprocessing like the computation of molecular surfaces, segmentation, and searching for complementarity in the general framework of a pattern analyzing semantic network (ERNEST). The score of coarse symbolic computations is used by the problem independent control strategy of ERNEST to guide a more detailed analysis considering steric clash and judgements based on grid-based surface representations. Successful examples of the docking system are discussed that compare well with other approaches.

Determination of traces of rare earth elements in geological samples.

A procedure for the determination of traces of rare earth elements by inductively coupled plasma-optical emission spectrometry (ICP-OES) and X-ray fluorescence spectrometry (XRF) is presented. The samples are decomposed either by fusion with lithium tetraborate or by acid attack in a microwave oven. Separation of the rare earth elements from accompanying elements is achieved by anion exchange. For the determination by ICP-OES the samples are injected in hydrochloric acid solution, for the determination by XRF the rare earth element traces are co-precipitated with rhodizonate and tannin and measured as a thin film on a membrane filter. All preconcentration steps have been optimized and tested using radiotracers. Furthermore the rare earth element contents of some international standard reference rocks have been determined; the results are compared to the certified values and other values given in the literature. The procedure is applied to a series of petrographically identical metabasites with different degrees of metamorphism to show the mobility of those ions under metamorphic conditions.