Conventional detection methodology is limiting our ability to understand the roles and functions of fine roots

We lack a thorough conceptual and functional understanding of fine roots. Studies that have focused on estimating the quantity of fine roots provide evidence that they dominate overall plant root length. We need a standard procedure to quantify root length/biomass that takes proper account of fine roots. Here we investigated the extent to which root length/biomass may be underestimated using conventional methodology, and examined the technical reasons that could explain such underestimation. Our discussion is based on original X-ray-based measurements and on a literature review spanning more than six decades. We present evidence that root-length recovery depends strongly on the observation scale/spatial resolution at which measurements are carried out; and that observation scales/resolutions adequate for fine root detection have an adverse impact on the processing times required to obtain precise estimates. We conclude that fine roots are the major component of root systems of most (if not all) annual and perennial plants. Hence plant root systems could be much longer, and probably include more biomass, than is widely accepted.

A multiresolution terrain model for efficient visualization query processing

Multiresolution Triangular Mesh (MTM) models are widely used to improve the performance of large terrain visualization by replacing the original model with a simplified one. MTM models, which consist of both original and simplified data, are commonly stored in spatial database systems due to their size. The relatively slow access speed of disks makes data retrieval the bottleneck of such terrain visualization systems. Existing spatial access methods proposed to address this problem rely on main-memory MTM models, which leads to significant overhead during query processing. In this paper, we approach the problem from a new perspective and propose a novel MTM called direct mesh that is designed specifically for secondary storage. It supports available indexing methods natively and requires no modification to MTM structure. Experiment results, which are based on two real-world data sets, show an average performance improvement of 5-10 times over the existing methods.

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

Longitudinal associations between children's phonological processing abilities, ADHD-like behaviour ratings and early reading develpment

The current study examined the contribution of phonological processing abilities and ADHD-like behaviours to first-grade word reading ability. 136 children were tested at the beginning and end of first grade. At both times, teachers rated children on hyperactive, inattentive, and oppositional behaviour. Children were given tests of letter knowledge at T1 and tests of word reading, phonological sensitivity, phonological memory, rapid automatised naming, and vocabulary at T1 and T2. Regression analyses revealed that, of the behavioural measures, inattention made the strongest contribution to T2 reading, even after controlling for the effects of T1 reading, hyperactivity, and oppositional behaviour. Hyperactivity did not explain variance in T2 reading once the effect of inattention was controlled. Inattention predicted 4.7% independent variance in T2 word reading ability, even after the effects of T1 reading, vocabulary, and phonological processing were controlled. Although phonological processing predicted 9.3% independent variance in T2 word reading, even after the effects of reading, vocabulary, and inattention were controlled, the effects of phonological processing may have been partly mediated by inattention. This research indicates that inattention contributes to the prediction of early reading development in unselected populations, and that this influence is independent of other key cognitive predictors of reading ability.