The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

What is the best size descriptor to use for pharmacokinetic studies in the obese?

The prevalence of obesity in the western world is dramatically rising, with many of these individuals requiring therapeutic intervention for a variety of disease states. Despite the growing prevalence of obesity there is a paucity of information describing how doses should be adjusted, or indeed whether they need to be adjusted, in the clinical setting. This review is aimed at identifying which descriptors of body size provide the most information about the relationship between dose and concentration in the obese. The size descriptors, weight, lean body weight, ideal body weight, body surface area, body mass index, fat-free mass, percent ideal body weight, adjusted body weight and predicted normal body weight were considered as potential size descriptors. We conducted an extensive review of the literature to identify studies that have assessed the quantitative relationship between the parameters clearance (CL) and volume of distribution (V) and these descriptors of body size. Surprisingly few studies have addressed the relationship between obesity and CL or V in a quantitative manner. Despite the lack of studies there were consistent findings: (i) most studies found total body weight to be the best descriptor of V. A further analysis of the studies that have addressed V found that total body weight or another descriptor that incorporated fat mass was the preferred descriptor for drugs that have high lipophilicity; (ii) in contrast, CL was best described by lean body mass and no apparent relationship between lipophilicity or clearance mechanism and preference for body size descriptor was found. In conclusion, no single descriptor described the influence of body size on both CL and V equally well. For drugs that are dosed chronically, and therefore CL is of primary concern, dosing for obese patients should not be based on their total weight. If a weight-based dose individualization is required then we would suggest that chronic drug dosing in the obese subject should be based on lean body weight, at least until a more robust size descriptor becomes available.

A standard weight descriptor for dose adjustment in the obese patient

Objective: To develop a standard weight descriptor that can be used for estimation of patient size for obese patients. Patients and methods: Data were available from 3849 patients: 2839 from oncology patients (index data set) and 1010 from general medical patients (validation data set). The patients had a wide range of age (16-100 years), weight (25-165kg) and body mass index (BMI) [12-52 kg/m(2)] in both data sets. From the normal-weight patients in the oncology data set, an equation for male and female patients was developed to predict their normal weight as the sum of the lean body mass and normal fat body mass. The equations were evaluated by predicting the weight of patients in the general medical data set who had a normal BMI (30 kg/m(2)).

Influence of particle morphology on nanostructural feature and conducting property in Sm-doped CeO2 sintered body

Doped ceria (CeO2,) compounds are fluorite type oxides, which show oxide ionic conductivity higher than yttria stabilized zirconia (YSZ), in oxidizing atmospheres. As a consequence of this, considerable interest has been shown in application of these materials for 'low (500-650 degreesC)' or 'intermediate (650-800 degreesC)' temperature operation, solid oxide fuel cells (SOFCs). In this study, the authors prepared two kinds of nanosize Sm-doped CeO2 particles with different morphologies: one type was round and the other was elongated. Processing these powders with different morphology produced dense materials with very different ionic conducting properties and different nanoscale microstructures. Since both particles are very fine and well dispersed, sintered bodies with high density (relative density >95% of theoretical) could be prepared using both types of powder particles. The electrical conductivity of sintered bodies prepared from these powders with different starting morphologies was very different. Materials prepared from particles having a round shape were much higher than those produced using powders with an elongated morphology. Measured activation energies of the corresponding sintered samples showed a similar trend; round particles (60 kJ/mol), elongated particles (74 kJ/mol). While X-ray diffraction (XRD) profiles of these sintered materials were identical, diffuse scatter was observed in the back.-round of selected area electron diffraction pattern recorded from both sintered bodies. This indicated an underlying structure that appeared to have been influenced by the processing technology. Detailed observation using high-resolution transmission electron microscopy (HR-TEM) revealed that the size of microdomain with ordering of cations in the sintered body made from round shape particles was much smaller than that of the sintered body made from elongated particles. Accordingly, it is concluded that the morphology of doped CeO2 powders strongly influenced the microdomain size and electrolytic properties in the doped CeO2 sintered body. (C) 2004 Elsevier B.V. All rights reserved.

Widespread sensory hypersensitivity is a feature of chronic whiplash-associated disorder but not chronic idiopathic neck pain

Objectives: To investigate sensory changes present in patients with chronic whiplash-associated disorders and chronic idiopathic neck pain using a variety of quantitative sensory tests to better understand the pain processing mechanisms underlying persistent symptoms. Methods: A case control study was used with 29 subjects with chronic whiplash-associated disorders, 20 subjects with chronic idiopathic neck pain, and 20 pain-free volunteers. Pressure pain thresholds were measured over the articular pillars of C2-C3, C5-C6, the median, radial, and ulnar nerve trunks in the arm and over a remote site, the muscle belly of tibialis anterior. Heat pain thresholds, cold pain thresholds, and von Frey hair sensibility were measured over the cervical spine, tibialis anterior, and deltoid insertion. Anxiety was measured with the Short-Form of the Spielberger State Anxiety Inventory. Results: Pressure pain thresholds were decreased over cervical spine sites in both subject groups when compared with controls (P < 0.05). In the chronic whiplash-associated disorders group, pressure pain thresholds were also decreased over the tibialis anterior, median, and radial nerve trunks (P < 0.001). Heat pain thresholds were decreased and cold pain thresholds increased at all sites (P < 0.03). No differences in heat pain thresholds or cold pain thresholds were evident in the idiopathic neck pain group at any site compared with the control group (P > 0.27). No abnormalities in von Frey hair sensibility were evident in either neck pain group (P > 0.28). Discussion: Both chronic whiplash-associated disorders and idiopathic neck pain groups were characterized by mechanical hyperalgesia over the cervical spine. Whiplash subjects showed additional widespread hypersensitivity to mechanical pressure and thermal stimuli, which was independent of state anxiety and may represent changes in central pain processing mechanisms. This may have implications for future treatment approaches.

Normalized Gaussian Networks with Mixed Feature Data

With mixed feature data, problems are induced in modeling the gating network of normalized Gaussian (NG) networks as the assumption of multivariate Gaussian becomes invalid. In this paper, we propose an independence model to handle mixed feature data within the framework of NG networks. The method is illustrated using a real example of breast cancer data.