A continuous wavelet transform and classification method for delirium motoric subtyping

The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a discrete accelerometer-based activity monitor. The continuous wavelet transform (CWT) with various mother wavelets were applied to accelerometry data from three randomly selected patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive, and mixed motor subtypes. A classification tree used the periods of overall movement as measured by the discrete accelerometer-based monitor as determining factors for which to classify these delirious patients. This data used to create the classification tree were based upon the minimum, maximum, standard deviation, and number of coefficient values, generated over a range of scales by the CWT. The classification tree was subsequently used to define the remaining motoric subtypes. The use of a classification system shows how delirium subtypes can be categorized in relation to overall motoric behavior. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behavior differ in electronically measured activity levels.

A method to weight three categories of adaptive thermal comfort

The adaptive thermal comfort theory considers people as active rather than passive recipients in response to ambient physical thermal stimuli, in contrast with conventional, heat-balance-based, thermal comfort theory. Occupants actively interact with the environments they occupy by means of utilizing adaptations in terms of physiological, behavioural and psychological dimensions to achieve ‘real world’ thermal comfort. This paper introduces a method of quantifying the physiological, behavioural and psychological portions of the adaptation process by using the analytic hierarchy process (AHP) based on the case studies conducted in the UK and China. Apart from three categories of adaptations which are viewed as criteria, six possible alternatives are considered: physiological indices/health status, the indoor environment, the outdoor environment, personal physical factors, environmental control and thermal expectation. With the AHP technique, all the above-mentioned criteria, factors and corresponding elements are arranged in a hierarchy tree and quantified by using a series of pair-wise judgements. A sensitivity analysis is carried out to improve the quality of these results. The proposed quantitative weighting method provides researchers with opportunities to better understand the adaptive mechanisms and reveal the significance of each category for the achievement of adaptive thermal comfort.

Effective LAI and CHP of a single tree from small-footprint full-waveform LiDAR

This letter has tested the canopy height profile (CHP) methodology as a way of effective leaf area index (LAIe) and vertical vegetation profile retrieval at a single-tree level. Waveform and discrete airborne LiDAR data from six swaths, as well as from the combined data of six swaths, were used to extract the LAIe of a single live Callitris glaucophylla tree. LAIe was extracted from raw waveform as an intermediate step in the CHP methodology, with two different vegetation-ground reflectance ratios. Discrete point LAIe estimates were derived from the gap probability using the following: 1) single ground returns and 2) all ground returns. LiDAR LAIe retrievals were subsequently compared to hemispherical photography estimates, yielding mean values within ±7% of the latter, depending on the method used. The CHP of a single dead Callitris glaucophylla tree, representing the distribution of vegetation material, was verified with a field profile manually reconstructed from convergent photographs taken with a fixed-focal-length camera. A binwise comparison of the two profiles showed very high correlation between the data reaching R2 of 0.86 for the CHP from combined swaths. Using a study-area-adjusted reflectance ratio improved the correlation between the profiles, but only marginally in comparison to using an arbitrary ratio of 0.5 for the laser wavelength of 1550 nm.

A NEW METHOD TO OBTAIN GOOD ANATOMICAL SLIDES OF HETEROGENEOUS PLANT PARTS

A new method is presented to prepare anatomical slides of plant materials including a combination of soft and hard tissues, such as stems with cambial variants, arboreal monocotyledons, and tree bark The method integrates previous techniques aimed at softening the samples and making them thereby more homogeneous, with the use of anti-tearing polystyrene foam solution In addition, we suggest two other alternatives to protect the sections from tearing adhesive tape and/or Mayer`s albumin adhesive, both combined with the polystyrene foam solution This solution is cheap and easy to make by dissolving any packaging polystyrene m butyl acetate It is applied before each section is cut on a sliding microtome and ensures that all the tissues in the section will hold together This novel microtechnical procedure will facilitate the study of heterogeneous plant portions, as shown in some illustrated examples

Relief influence on tree species richness in secondary forest fragments of Atlantic Forest, SE, Brazil

(Relief influence on tree species richness in secondary forest fragments of Atlantic Forest, SE, Brazil). The aim of this work was to explore the relationship between tree species richness and morphological characteristics of relief at the Ibiuna Plateau (SE Brazil). We sampled 61 plots of 0.30 ha, systematically established in 20 fragments of secondary forest (2-274 ha) and in three areas within a continuous secondary forest site, Morro Grande Reserve (9,400 ha). At each plot, 100 trees with diameter at breast height > 5 cm were sampled by the point centered quarter method, and total richness and richness per dispersal and succession class were obtained. The relief was characterized by the mean and variance of slope, elevation, aspect and slope location. There was no significant relationship between relief heterogeneity and tree species richness. Relief parameters generally did not affect tree richness, but elevation was particularly important especially in the continuous forest. Despite the limited range of altitudinal variation (150 m), species richness increases with elevation. The highest areas were also those with the largest forest cover and the lowest disturbance degree, which should contribute to the greater richness of those sites. Our results suggest an indirect influence of relief, due to the fact that deforestation is less intense in higher regions, rather than a direct influence of abiotic factors related to the altitudinal gradient.

Fast heuristics for the Steiner tree problem with revenues, budget and hop constraints

This article describes and compares three heuristics for a variant of the Steiner tree problem with revenues, which includes budget and hop constraints. First, a greedy method which obtains good approximations in short computational times is proposed. This initial solution is then improved by means of a destroy-and-repair method or a tabu search algorithm. Computational results compare the three methods in terms of accuracy and speed. (C) 2007 Elsevier B.V. All rights reserved.

Crystal structure and statistical coupling analysis of highly glycosylated peroxidase from royal palm tree (Roystonea regia)

Royal palm tree peroxidase (RPTP) is a very stable enzyme in regards to acidity, temperature, H(2)O(2), and organic solvents. Thus, RPTP is a promising candidate for developing H(2)O(2)-sensitive biosensors for diverse applications in industry and analytical chemistry. RPTP belongs to the family of class III secretory plant peroxidases, which include horseradish peroxidase isozyme C, soybean and peanut peroxidases. Here we report the X-ray structure of native RPTP isolated from royal palm tree (Roystonea regia) refined to a resolution of 1.85 angstrom. RPTP has the same overall folding pattern of the plant peroxidase superfamily, and it contains one heme group and two calcium-binding sites in similar locations. The three-dimensional structure of RPTP was solved for a hydroperoxide complex state, and it revealed a bound 2-(N-morpholino) ethanesulfonic acid molecule (MES) positioned at a putative substrate-binding secondary site. Nine N-glycosylation sites are clearly defined in the RPTP electron-density maps, revealing for the first time conformations of the glycan chains of this highly glycosylated enzyme. Furthermore, statistical coupling analysis (SCA) of the plant peroxidase superfamily was performed. This sequence-based method identified a set of evolutionarily conserved sites that mapped to regions surrounding the heme prosthetic group. The SCA matrix also predicted a set of energetically coupled residues that are involved in the maintenance of the structural folding of plant peroxidases. The combination of crystallographic data and SCA analysis provides information about the key structural elements that could contribute to explaining the unique stability of RPTP. (C) 2009 Elsevier Inc. All rights reserved.

Optimization of a method for determination of phenolic acids in exotic fruits by capillary electrophoresis

In this work, the separation of nine phenolic acids (benzoic, caffeic, chlorogenic, p-coumaric, ferulic, gallic, protocatechuic, syringic, and vanillic acid) was approached by a 32 factorial design in electrolytes consisting of sodium tetraborate buffer(STB) in the concentration range of 10-50 mmol L(-1) and methanol in the volume percentage of 5-20%. Derringer`s desirability functions combined globally were tested as response functions. An optimal electrolyte composed by 50 mmol L(-1) tetraborate buffer at pH 9.2, and 7.5% (v/v) methanol allowed baseline resolution of all phenolic acids under investigation in less than 15 min. In order to promote sample clean up, to preconcentrate the phenolic fraction and to release esterified phenolic acids from the fruit matrix, elaborate liquid-liquid extraction procedures followed by alkaline hydrolysis were performed. The proposed methodology was fully validated (linearity from 10.0 to 100 mu g mL(-1), R(2) > 0.999: LOD and LOQ from 1.32 to 3.80 mu g mL(-1) and from 4.01 to 11.5 mu g mL(-1), respectively; intra-day precision better than 2.8% CV for migration time and 5.4% CV for peak area; inter-day precision better than 4.8% CV for migration time and 4.8-11% CV for peak area: recoveries from 81% to 115%) and applied successfully to the evaluation of phenolic contents of abiu-roxo (Chrysophyllum caimito), wild mulberry growing in Brazil (Morus nigra L.) and tree tomato (Cyphomandra betacea). Values in the range of 1.50-47.3 mu g g(-1) were found, with smaller amounts occurring as free phenolic acids. (C) 2009 Elsevier B.V. All rights reserved.

Nuclear magnetic resonance characterization of metabolite disorder in orange trees caused by citrus sudden death disease

Citrus sudden death (CSD) is a new disease of sweet orange and mandarin trees grafted on Rangpur lime and Citrus volkameriana rootstocks. It was first seen in Brazil in 1999, and has since been detected in more than four million trees. The CSD causal agent is unknown and the current hypothesis involves a virus similar to Citrus tristeza virus or a new virus named Citrus sudden death-associated virus. CSD symptoms include generalized foliar discoloration, defoliation and root death, and, in most cases, it can cause tree death. One of the unique characteristics of CSD disease is the presence of a yellow stain in the rootstock bark near the bud union. This region also undergoes profound anatomical changes. In this study, we analyse the metabolic disorder caused by CSD in the bark of sweet orange grafted on Rangpur lime by nuclear magnetic resonance (NMR) spectroscopy and imaging. The imaging results show the presence of a large amount of non-functional phloem in the rootstock bark of affected plants. The spectroscopic analysis shows a high content of triacylglyceride and sucrose, which may be related to phloem blockage close to the bud union. We also propose that, without knowing the causal CSD agent, the determination of oil content in rootstock bark by low-resolution NMR can be used as a complementary method for CSD diagnosis, screening about 300 samples per hour.

Ensembling MML causal discovery

This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with any single learner in terms of learning accuracy and correctness; (2) Among all the ensemble causal induction algorithms examined, the weighted voting without seeding algorithm outperforms all the rest; (3) It seems that the ensembled CI algorithms could alleviate the local minimum problem. The only drawback of this method is that the time complexity is increased by δ times, where δ is the ensemble size.

A grid-based index method for time warping distance

Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree structures are quite weak, resulting in inefficient search. In this paper, we propose a dimensionality reduction method motivated by observations about the inherent character of each time series. A very compact index file is constructed. By scanning the index file, we can get a very small candidate set, so that the number of page access is dramatically reduced. We demonstrate the effectiveness of our approach on real and synthetic datasets.

DDR: an index method for large time-series datasets

The tree index structure is a traditional method for searching similar data in large datasets. It is based on the presupposition that most sub-trees are pruned in the searching process. As a result, the number of page accesses is reduced. However, time-series datasets generally have a very high dimensionality. Because of the so-called dimensionality curse, the pruning effectiveness is reduced in high dimensionality. Consequently, the tree index structure is not a suitable method for time-series datasets. In this paper, we propose a two-phase (filtering and refinement) method for searching time-series datasets. In the filtering step, a quantizing time-series is used to construct a compact file which is scanned for filtering out irrelevant. A small set of candidates is translated to the second step for refinement. In this step, we introduce an effective index compression method named grid-based datawise dimensionality reduction (DRR) which attempts to preserve the characteristics of the time-series. An experimental comparison with existing techniques demonstrates the utility of our approach.

A new indexing method for high dimensional dataset

Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.

An empirical study of encoding schemes and search strategies in discovering causal networks

Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithm proposed by Wallace et al. [10] has demonstrated its ability in discovering Linear Causal Models from data. To explore the ways to improve efficiency, this research examines three different encoding schemes and four searching strategies. The experimental results reveal that (1) specifying parents encoding method is the best among three encoding methods we examined; (2) In the discovery of linear causal models, local Hill climbing works very well compared to other more sophisticated methods, like Markov Chain Monte Carto (MCMC), Genetic Algorithm (GA) and Parallel MCMC searching.

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.