利用组织工程治疗骨缺损中的几点问题

骨缺损是临床上的常见病,也是骨科治疗的难题之一。近年来,骨组织工程发展迅速,利用骨组织工程治疗骨缺损成为研究的热点。组织工程应用的主要问题在于种子细胞的培养和细胞种植基质材料的优化。在此领域的发展过程中,渗透着丰富的哲学思想,理解和把握这些哲学思想对完善利用骨组织工程治疗骨缺损的工作将大有益处。     

A molar view of goal direction and habit

When we treat behavior within an evolutionary framework and as temporally extended flow, two fundamental questions arise: (a) What is an organism? and (b) What is behavior? An organism is a process that stays intact by constantly exchanging energy with the environment. It takes in resources and puts out waste. The behavior of an organism consists of those process parts of the organism process that make up the exchange. These activities serve the function of reproducing, which generally depends on surviving. Surviving and reproducing depend on responding to phylogenetically important events (PIEs). A PIE induces activities that enhance or mitigate the PIE. Organisms respond not only to a PIE but also to events that covary with the PIE. Both activities and environmental features may covary with a PIE. When either type of covariance is introduced to an organism, behavior adapts over time. The early stages of adaptation constitute what researchers call “goal direction,” and the later stages constitute what researchers call “habit.” Behavior and environment constitute a dynamic system, and manipulations of the covariances and environmental features of the system allow many experimental interventions. This molar approach allows experiments on goal direction and habit to be understood without appeal to everyday mentalistic terms.     

The effects of task-irrelevant threatening stimuli on orienting- and executive attentional processes under cognitive load

Human visual attention is biased to rapidly detect threats in the environment so that our nervous system can initiate quick reactions. The processes underlying threat detection (and how they operate under cognitive load), however, are still poorly understood. Thus, we sought to test the impact of task-irrelevant threatening stimuli on the salience network and executive control of attention during low and high cognitive load. Participants were exposed to neutral or threatening pictures (with moderate and high arousal levels) as task-irrelevant distractors in near (parafoveal) and far (peripheral) positions while searching for numbers in ascending order in a matrix array. We measured reaction times and recorded eye-movements. Our results showed that task-irrelevant distractors primarily influenced behavioural measures during high cognitive load. The distracting effect of threatening images with moderate arousal level slowed reaction times for finding the first number. However, this slowing was offset by high arousal threatening stimuli, leading to overall shorter search times. Eye-tracking measures showed that participants fixated threatening pictures more later and for shorter durations compared to neutral images. Together, our results indicate a complex relationship between threats and attention that results not in a unitary bias but in a sequence of effects that unfold over time.     

Analyzing individual differences in vocational, leisure, and family interests: A multitrait-multimethod approach

This study investigates the latent structure of individual differences in vocational, leisure, and family interests. The participants consisted of 302 French adults who rated their like or dislike for activities based on Holland’s RIASEC typology and presented in three life domains: work, family and leisure. The multitrait-multimethod model proposed by Eid [Eid, M. (2000). A multitrait-multimethod model with minimal assumptions. Psychometrika 65, 241-261.] was used with the vocational interests as standard comparison, and was applied separately on each of the 6 RIASEC types. Results indicated that interests strongly intercorrelated whatever their vocational vs. leisure or family contextualization. However, since these correlations were controlled for measurement error, the results also suggested that the effects of these contextualizations of the activities could not be ignored because they accounted for a non negligible part of the variance for several activities. Theoretical and practical implications are discussed and directions for future research are presented.     

Identifying Variables Responsible for Data not Missing at Random

When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology. A survey or questionnaire may contain many items; certain items may be responsible for NMAR values in other items. The paper develops statistical procedures to identify the responsible items. By comparing ML estimates (MLE), statistics are developed to test whether the MLEs are changed when excluding items. The items that cause a significant change of the MLEs are responsible for the NMAR mechanism. Normal distribution is used for obtaining the MLEs; a sandwich-type covariance matrix is used to account for distribution violations. The class of nonnormal distributions within which the procedure is valid is provided. Both saturated and structural models are considered. Effect sizes are also defined and studied. The results indicate that more missing data in a sample does not necessarily imply more significant test statistics due to smaller effect sizes. Knowing the true population means and covariances or the parameter values in structural equation models may not make things easier either. The research was supported by NSF grant DMS04-37167, the James McKeen Cattell Fund.     

Bayesian journey-to-crime modelling of juvenile and adult offenders by gender in Manchester

This study tested the Bayesian journey-to-crime (JTC) model by gender and age for serial offenders from Manchester, England. The data were 4056 crimes committed by 171 serial offenders between 2003 and 2006. The data were subdivided by gender and age group to examine whether accuracy and precision varied by these subgroups. In general, the centre of minimum distance was found to be the most accurate measure, but the Bayesian risk and product measures were found to be the most precise measurements. The traditional ‘distance decay’ type of JTC function did not produce estimates that are as accurate nor as precise as the Bayesian approach. Tests were conducted on whether specific gender and age group JTC functions and origin–destination matrices improved predictability. With the exception of juvenile male offenders, the general functions were more accurate and precise. In terms of building an accurate and precise geographical profiling methodology, the results point to the need to include information on the likely predisposition of neighbourhoods to produce offenders as well as information on the crime opportunities available to offenders. Copyright © 2009 John Wiley & Sons, Ltd.     

An alternative two stage least squares (2SLS) estimator for latent variable equations

The Maximum-likelihood estimator dominates the estimation of general structural equation models. Noniterative, equation-by-equation estimators for factor analysis have received some attention, but little has been done on such estimators for latent variable equations. I propose an alternative 2SLS estimator of the parameters in LISREL type models and contrast it with the existing ones. The new 2SLS estimator allows observed and latent variables to originate from nonnormal distributions, is consistent, has a known asymptotic covariance matrix, and is estimable with standard statistical software. Diagnostics for evaluating instrumental variables are described. An empirical example illustrates the estimator. I gratefully acknowledge support for this research from the Sociology Program of the National Science Foundation (SES-9121564) and the Center for Advanced Study in the Behavioral Sciences, Stanford, California. This paper was presented at the Interdisciplinary Consortium for Statistical Applications at Indiana University at Bloomington (March 2, 1994) and at the RMD Conference on Causal Modeling at Purdue University, West Lafayette, Indiana (March 3-5, 1994).     

A note on distributional properties of the Jöreskog-Sörbom fit indices

In introducing the LISREL model for systems of linear structural equations, Jöreskog and Sörbom proposed two goodness-of-fit indices, GFI and AGFI. Their asymptotic distributions and some statistical properties are discussed.The authors would like to acknowledge the helpful comments and suggestions from the Associate Editor and two anonymous reviewers.     

Effect analysis and causation in linear structural equation models

This paper considers total and direct effects in linear structural equation models. Adopting a causal perspective that is implicit in much of the literature on the subject, the paper concludes that in many instances the effects do not admit the interpretations imparted in the literature. Drawing a distinction between concomitants and factors, the paper concludes that a concomitant has neither total nor direct effects on other variables. When a variable is a factor and one or more intervening variables are concomitants, the notion of a direct effect is not causally meaningful. Even when the notion of a direct effect is meaningful, the usual estimate of this quantity may be inappropriate. The total effect is usually interpreted as an equilibrium multiplier. In the case where there are simultaneity relations among the dependent variables in tghe model, the results in the literature for the total effects of dependent variables on other dependent variables are not equilibrium multipliers, and thus, the usual interpretation is incorrect. To remedy some of these deficiencies, a new effect, the total effect of a factorX on an outcomeY, holding a set of variablesF constant, is defined. When defined, the total and direct effects are a special case of this new effect, and the total effect of a dependent variable on a dependent variable is an equilibrium multiplier.For helpful comments, I am grateful to G. Arminger, K. Bollen, W. Faris, R. m. Hauser, T. Petersen, three anonymous Psychometrikas reviewers, and the Editor. For computational assistance, I am grateful to B. D. Kim.