Direct evidence showing the effect of root surface iron plaque on arsenite and arsenate uptake into rice (Oryza sativa) roots

The present study aimed to investigate the effects of root surface iron plaque on the uptake kinetics of arsenite and arsenate by excised roots of rice (Oryza sativa) seedlings. The results demonstrated that the presence of iron plaque enhanced arsenite and decreased arsenate uptake. Arsenite and arsenate uptake kinetics were adequately fitted by the Michaelis-Menten function in the absence of plaque, but produced poor fits to this function in the presence of plaque. Phosphate in the uptake solution did not have a significant effect on arsenite uptake irrespective of the presence of iron plaque; however phosphate had a significant effect on arsenate uptake. Without iron plaque, phosphate inhibited arsenate uptake. The presence of iron plaque diminished the effect of phosphate on arsenate uptake, possibly through a combined effect of arsenate desorption from iron plaque.

NO3- transport across the plasma membrane of Arabidopsis thaliana root hairs:Kinetic control by pH and membrane voltage

High-affinity nitrate transport was examined in intact root hair cells of Arabidopsis thaliana using electrophysiological recordings to characterise the response of the plasma membrane to NO₃^-challenge and to quantify transport activity. The NO₃^--associated membrane current was determined using a three-electrode voltage clamp to bring membrane voltage under experimental control and to compensate for current dissipation along the longitudinal cell axis. Nitrate transport was evident in the roots of seedlings grown in the absence of a nitrogen source, but only 4-6 days postgermination. In 6-day-old seedlings, additions of 5-100 μm NO₃^-to the bathing medium resulted in membrane depolarizations of 8-43 mV, and membrane voltage (V_m) recovered on washing NO₃^-from the bath. Voltage clamp measurements carried out immediately before and following NO₃^-additions showed that the NO₃^--evoked depolarizations were the consequence of an inward-directed current that appeared across the entire range of accessible voltages (-300 to +50 mV). Both membrane depolarizations and NO₃^--evoked currents recorded at the free-running voltage displayed quasi-Michaelian kinetics, with apparent values for K_m of 23 ± 6 and 44 ± 11 μm, respectively and, for the current, a maximum of 5.1 ± 0.9 μA cm^-2. The NO₃^-current showed a pronounced voltage sensitivity within the normal physiological range between -250 and -100 mV, as could be demonstrated under voltage clamp, and increasing the bathing pH from 6.1 to 7.4-8.0 reduced the current and the associated membrane depolarizations 3- to 8-fold. Analyses showed a well-defined interaction between the kinetic variables of membrane voltage, pH_o and [NO₃^-]_o. At a constant pH_o of 6.1, depolarization from -250 to -150 mV resulted in an approximate 3-fold reduction in the maximum current but a 10% rise in the apparent affinity for NO₃^-. By contrast, the same depolarization effected an approximate 20% fall in the K_m for transport as a function in [H⁺]_o. These, and additional characteristics of the transport current implicate a carrier cycle in which NO₃^-binding is kinetically isolated from the rate-limiting step of membrane charge transit, and they indicate a charge-coupling stoichiometry of 2(H⁺) per NO₃^-anion transported across the membrane. The results concur with previous studies showing a high-affinity NO₃^-transport system in Arabidopsis that is inducible following a period of nitrogen-limiting growth, but they underline the importance of voltage as a kinetic factor controlling NO₃^-transport at the plant plasma membrane. © 1995 Springer-Verlag New York Inc.

High performance VLSI architecture for division and square root

A novel high performance bit parallel architecture to perform square root and division is proposed. Relevant VLSI design issues have been addressed. By employing redundant arithmetic and a semisystolic schedule, the throughput has been made independent of the size of the array.

A VLSI architecture for multiplication, division and square root

A high-performance VLSI architecture to perform combined multiply-accumulate, divide, and square root operations is proposed. The circuit is highly regular, requires only minimal control, and can be reconfigured for every cycle. The execution time for each operation is the same. The combination of redundancy and pipelining results in a throughput independent of the wordsize of the array. With current CMOS technology, throughput rates in excess of 80 million operations per second are achievable.

VLSI module for high-performance multiply, square root and divide

A high-performance VLSI architecture to perform multiply-accumulate, division and square root operations is proposed. The circuit is highly regular, requires only minimal control and can be pipelined right down to the bit level. The system can also be reconfigured on every cycle to perform any one of these operations. The gate count per row has been estimated at (27n+70) gate equivalents where n is the divisor wordlength. The throughput rate, which equals the clock speed, is the same for each operation and is independent of the wordlength. This is achieved through the combination of pipelining and redundant arithmetic. With a 1.0 µm CMOS technology and extensive pipelining, throughput rates in excess of 70 million operations per second are expected.

Fast VLSI algorithms for division and square root

Real time digital signal processing demands high performance implementations of division and square root. This can only be achieved by the design of fast and efficient arithmetic algorithms which address practical VLSI architectural design issues. In this paper, new algorithms for division and square root are described. The new schemes are based on pre-scaling the operands and modifying the classical SRT method such that the result digits and the remainders are computed concurrently and the computations in adjacent rows are overlapped. Consequently, their performance exceeds that of the SRT methods. The hardware cost for higher radices is considerably more than that of the SRT methods but for many applications, this is not prohibitive. A system of equations is presented which enables both an analysis of the method for any radix and the parameters of implementations to be easily determined. This is illustrated for the case of radix 2 and radix 4. In addition, a highly regular array architecture combining the division and square root method is described. © 1994 Kluwer Academic Publishers.

New algorithms and VLSI architectures for SRT division and square root

In real time digital signal processing, high performance modules for division and square root are essential if many powerful algorithms are to be implemented. In this paper, a new radix 2 algorithms for SRT division and square root are developed. For these new schemes, the result digits and the residuals are computed concurrently and the computations in adjacent rows are overlapped. Consequently, their performance should exceed that of the radix 2 SRT methods. VLSI array architectures to implement the new division and square root schemes are also presented.

Landfill cap models under simulated climate change precipitation::Impacts of cracks and root growth

Desiccation crack formation is a key process that needs to be understood in assessment of landfill cap performance under anticipated future climate change scenarios. The objectives of this study were to examine: (a) desiccation cracks and impacts that roots may have on their formation and resealing, and (b) their impacts on hydraulic conductivity under anticipated climate change precipitation scenarios. Visual observations, image analysis of thin sections and hydraulic conductivity tests were carried out on cores collected from two large-scale laboratory trial landfill cap models (∼80 × 80 × 90 cm) during a year of four simulated seasonal precipitation events. Extensive root growth in the topsoil increased percolation of water into the subsurface, and after droughts, roots grew deep into low-permeability layers through major cracks which impeded their resealing. At the end of 1 year, larger cracks had lost resealing ability and one single, large, vertical crack made the climate change precipitation model cap inefficient. Even though the normal precipitation model had developed desiccation cracks, its integrity was preserved better than the climate change precipitation model.

Localized Flux Maxima of Arsenic, Lead, and Iron around Root Apices in Flooded Lowland Rice

In wetland-adapted plants, such as rice, it is typically root apexes, sites of rapid entry for water/nutrients, where radial oxygen losses (ROLs) are highest. Nutrient/toxic metal uptake therefore largely occurs through oxidized zones and pH microgradients. However, the processes controlling the acquisition of trace elements in rice have been difficult to explore experimentally because of a lack of techniques for simultaneously measuring labile trace elements and O2/pH. Here, we use new diffusive gradients in thin films (DGT)/planar optode sandwich sensors deployed in situ on rice roots to demonstrate a new geochemical niche of greatly enhanced As, Pb, and Fe(II) mobilization into solution immediately adjacent to the root tips characterized by O2 enrichment and low pH. Fe(II) mobilization was congruent to that of the peripheral edge of the aerobic root zone, demonstrating that the Fe(II) mobilization maximum only developed in a narrow O2 range as the oxidation front penetrates the reducing soil. The Fe flux to the DGT resin at the root apexes was 3-fold higher than the anaerobic bulk soil and 27 times greater than the aerobic rooting zone. These results provide new evidence for the importance of coupled diffusion and oxidation of Fe in modulating trace metal solubilization, dispersion, and plant uptake.

Synergy Between an Improved Covariate Unit Root Test and Cross-sectionally Dependent Panel Data Unit Root Tests, with two Applications

This paper proposes the use of an improved covariate unit root test which exploits the cross-sectional dependence information when the panel data null hypothesis of a unit root is rejected. More explicitly, to increase the power of the test, we suggest the utilization of more than one covariate and offer several ways to select the ‘best’ covariates from the set of potential covariates represented by the individuals in the panel. Employing our methods, we investigate the Prebish-Singer hypothesis for nine commodity prices. Our results show that this hypothesis holds for all but the price of petroleum.

Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland

Summary

1.While plant–fungal interactions are important determinants of plant community assembly and ecosystem functioning, the processes underlying fungal community composition are poorly understood.
2.Here, we studied for the first time the root-associated eumycotan communities in a set of co-occurring plant species of varying relatedness in a species-rich, semi-arid grassland in Germany. The study system provides an opportunity to evaluate the importance of host plants and gradients in soil type and landscape structure as drivers of fungal community structure on a relevant spatial scale. We used 454 pyrosequencing of the fungal internal transcribed spacer region to analyse root-associated eumycotan communities of 25 species within the Asteraceae, which were sampled at different locations within a soil type gradient. We partitioned the variance accounted for by three predictors (host plant phylogeny, spatial distribution and soil type) to quantify their relative roles in determining fungal community composition and used null model analyses to determine whether community composition was influenced by biotic interactions among the fungi.
3.We found a high fungal diversity (156 816 sequences clustered in 1100 operational taxonomic units (OTUs)). Most OTUs belonged to the phylum Ascomycota (35.8%); the most abundant phylotype best-matched Phialophora mustea. Basidiomycota were represented by 18.3%, with Sebacina as most abundant genus. The three predictors explained 30% of variation in the community structure of root-associated fungi, with host plant phylogeny being the most important variance component. Null model analysis suggested that many fungal taxa co-occurred less often than expected by chance, which demonstrates spatial segregation and indicates that negative interactions may prevail in the assembly of fungal communities.
4.Synthesis. The results show that the phylogenetic relationship of host plants is the most important predictor of root-associated fungal community assembly, indicating that fungal colonization of host plants might be facilitated by certain plant traits that may be shared among closely related plant species.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.