Taxometrics in Psychopathology Research: An Introduction to Some of the Procedures and Related Methodological Issues.

This article introduces a special section on the use of taxometrics to examine the categorical versus the dimensional structure of various forms of psychopathology. Paving the way into the special section, this introduction briefly describes 3 taxometric methods--mean above minus below a sliding cut (MAMBAC), maximum covariation (MAXCOV), and maximum eigenvalue (MAXEIG)--and discusses possible threats to statistical conclusion validity that often emerge when such techniques are applied in psychopathology research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Heterogeneity in Youth Symptom Trajectories Following Psychiatric Crisis: Predictors and Placement Outcome.

The authors examined heterogeneity in symptom trajectories among youths following psychiatric crises as well as the psychosocial correlates and placement outcomes associated with identified trajectories. Using semiparametric mixture modeling with 156 youths approved for psychiatric hospitalization, the authors identified 5 trajectories based on symptoms over the 16 months following crisis: high improved, high unimproved, borderline improved, borderline unimproved, and subclinical. Membership in unimproved symptom groups was associated with less suicidality, younger age, more youth hopelessness, and more caregiver empowerment. Improved symptom group membership predicted long-term decreases in days in out-of-home placements. More important, and in contrast with general impressions from the existing literature, findings suggest that a substantive proportion of youths with serious emotional disturbance sustain high levels of symptomatology following intensive mental health services. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work

The effects of parameter uncertainty on optimal policy have been a matter of interest for academics, and even for some policymakers, for a long time. Two lines of literature have developed analytical results on this matter. The first line uses static models and the second dynamic models. In this dynamic line most of the results are confined to models with a single state and a single control variable. In this paper we want to encourage the analysis of more general dynamic cases. To do so, the results in the dynamic line are extended from one-state and one-control finite horizon models to models with a pair of control variables. We then discuss some of the hurdles which must be surmounted for the results to be made more general and suggests some lines for further research. JEL classification: C61; E61     

Steady-state response of the parametrically excited axially moving string constituted by the Boltzmann superposition principle

Summary.  The steady-state transverse vibration of a parametrically excited axially moving string with geometric nonlinearity is investigated in this paper. The Boltzmann superposition principle is employed to characterize the material property of the string. The method of multiple scales is applied directly to the governing equation, which is a nonlinear partial-differential-integral equation. The solvability condition of eliminating the secular terms is established. Closed form solutions for the amplitude and the existence conditions of nontrivial steady-state response of the summation resonance are obtained. Some numerical examples showing effects of the viscoelastic parameter, the amplitude of excitation, the frequency of excitation, and the transport speed are presented. Received February 12, 2002; revised October 25, 2002 Published online: May 8, 2003 The research was supported by the National Natural Science Foundation of China (Project No. 10172056).     

Finite element analysis of electromagnetic scattering from a cavity

A finite element method (FEM) is implemented to compute the radar cross section of a two-dimensional (2D) cavity embedded in an infinite ground plane. The method is based on the variational formulation which uses the Fourier transform to couple the fields outside the cavity and those inside the cavity; hence, the scattering problem can be reduced to a bounded domain. The convergence of the discrete finite element problem is analyzed. Numerical results are presented and compared with those obtained by the standard finite element-Green function method and by the 2D integral equation method.     

Sputtering mechanisms near the threshold energy

Threshold energies for sputtering cannot be calculated directly but have to be evaluated from the energy dependence of the sputtering yields. This paper investigates trajectories of projectile and recoils near the threshold energy for sputtering, where the collision cascade becomes increasingly simple. Statistics of the different collision events show which processes dominate the sputtering close to the threshold energy for selfbombardment of different light and heavy targets. The differential cross-sections for scattering and recoil production explain qualitatively the probability for the various processes.     

Implicit Motor Learning in Parkinson's Disease.

Objective: This study examined whether disruption of performance is moderated in Parkinson's disease (PD) patients who acquire their motor behaviors in an implicit manner. Method: Twenty-seven patients with PD learned a hammering task in errorless (implicit) or errorful (explicit) conditions and were tested for robustness of motor performance under a secondary task load, which required them to continuously count backward as they performed the hammering task. Results: Patients in the errorless (implicit) motor learning condition exhibited robustness to secondary task loading, whereas patients in the errorful (explicit) motor learning condition did not. Conclusions: Implicit motor learning techniques should be considered by PD rehabilitation specialists in cases in which existing disruption to movements is exacerbated by conscious control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mining constrained gradients in large databases

Many data analysis tasks can be viewed as search or mining in a multidimensional space (MDS). In such MDSs, dimensions capture potentially important factors for given applications, and cells represent combinations of values for the factors. To systematically analyze data in MDS, an interesting notion, called "cubegrade" was recently introduced by Imielinski et al. [2002], which focuses on the notable changes in measures in MDS by comparing a cell (which we refer to as probe cell) with its gradient cells, namely, its ancestors, descendants, and siblings. We call such queries gradient analysis queries (GQs). Since an MDS can contain billions of cells, it is important to answer GQs efficiently. We focus on developing efficient methods for mining GQs constrained by certain (weakly) antimonotone constraints. Instead of conducting an independent gradient-cell search once per probe cell, which is inefficient due to much repeated work, we propose an efficient algorithm, LiveSet-Driven. This algorithm finds all good gradient-probe cell pairs in one search pass. It utilizes measure-value analysis and dimension-match analysis in a set-oriented manner, to achieve bidirectional pruning between the sets of hopeful probe cells and of hopeful gradient cells. Moreover, it adopts a hypertree structure and an H-cubing method to compress data and to maximize sharing of computation. Our performance study shows that this algorithm is efficient and scalable. In addition to data cubes, we extend our study to another important scenario: mining constrained gradients in transactional databases where each item is associated with some measures such as price. Such transactional databases can be viewed as sparse MDSs where items represent dimensions, although they have significantly different characteristics than data cubes. We outline efficient mining methods for this problem.