Although the relationship between dyslipidaemia (DL) and coronary artery disease (CAD) or between trace minerals intake and CAD is well known separately, the exact nature of this relationship remains unknown. We hypothesize that the relationship between trace mineral intake and CAD may differ depending on whether or not the individual has DL. The present study analysed the relationships among trace mineral intake, DL, and CAD in middle-aged and older adults living in Shika town, Ishikawa prefecture, Japan. This study included 895 residents following the exclusion of those with genetic risk carriers for familial hypercholesterolemia. Trace mineral intake was evaluated using the brief-type self-administered diet history questionnaire. Interactions were observed between DL and CAD with zinc (p = 0.004), copper (p = 0.010), and manganese intake (p < 0.001) in a two-way analysis of covariance adjusted for covariates such as sex, age, body mass index, and current smokers and drinkers. Multiple logistic regression analysis showed that zinc (odds ratio (OR): 0.752; 95% confidence interval (CI): 0.606, 0.934; p = 0.010), copper (OR: 0.175; 95% CI: 0.042, 0.726; p = 0.016), and manganese (OR: 0.494; 95% CI: 0.291, 0.839; p = 0.009) were significant independent variables for CAD in the dyslipidaemic group. The present results suggest that DL with a low trace mineral intake is associated with CAD. Further longitudinal studies are required to confirm this relationship.

Many components in diet have regulated oxidative stress, inflammatory reaction and even balance oestrogen levels. Because these factors are closely associated with depressive symptoms in postmenopausal women, it is considered that dietary factors are able to prevent and control depressive symptoms. On the other hand, a dietary pattern that considers the correlations and synergies between foods and nutrients is expected to have a greater impact on disease risk. The aim of the present study is to evaluate whether dietary patterns are associated with depressive symptoms in Chinese postmenopausal women. A cross-sectional study of 2051 postmenopausal women (mean age: 58·8 (sd 7·4) years) was conducted in Tianjin, China. Dietary consumption was assessed by a valid self-administered FFQ. Principal component analysis was used to derive three major dietary patterns: ‘healthy’, ‘sweets’ and ‘traditional Tianjin’ from eighty-eight food items. Depressive symptoms were assessed using the Zung Self-Rating Depression Scale, and cut-off point of 48 indicating serious depressive symptoms. The association between quartile of dietary patterns and depressive symptoms was assessed using multiple logistic regression analysis. The multivariable-adjusted OR of having depressive symptoms for increasing quartile of dietary patterns were as follows: healthy, 1·00, 0·79 (95 % CI 0·49, 1·28), 0·62 (95 % CI 0·37, 1·04) and 0·57 (95 % CI 0·33, 0·97); sweets, 1·00, 0·75 (95 % CI 0·42, 1·3), 1·08 (95 % CI 0·64, 1·81) and 1·66 (95 % CI 1·03, 2·71); and traditional Tianjin, 1·00, 1·02 (95 % CI 0·58, 1·79), 0·96 (95 % CI 0·54, 1·71) and 2·53 (95 % CI 1·58, 4·16), respectively. The present study demonstrated that a healthy dietary pattern was inversely associated with depressive symptoms. On the contrary, greater adherence to sweets and traditional Tianjin dietary patterns was associated with a higher prevalence of depressive symptoms.

Objective: To determine whether volumetric measures of the hippocampus, entorhinal cortex, and other cortical measures can differentiate between cognitively normal individuals and subjects with mild cognitive impairment (MCI). Method: Magnetic resonance imaging (MRI) data from 46 cognitively normal subjects and 50 subjects with MCI as part of the Boston University Alzheimer’s Disease Center research registry and the Alzheimer’s Disease Neuroimaging Initiative were used in this cross-sectional study. Cortical, subcortical, and hippocampal subfield volumes were generated from each subject’s MRI data using FreeSurfer v6.0. Nominal logistic regression models containing these variables were used to identify subjects as control or MCI. Results: A model containing regions of interest (superior temporal cortex, caudal anterior cingulate, pars opercularis, subiculum, precentral cortex, caudal middle frontal cortex, rostral middle frontal cortex, pars orbitalis, middle temporal cortex, insula, banks of the superior temporal sulcus, parasubiculum, paracentral lobule) fit the data best (R2 = .7310, whole model test chi-square = 97.16, p < .0001). Conclusions: MRI data correctly classified most subjects using measures of selected medial temporal lobe structures in combination with those from other cortical areas, yielding an overall classification accuracy of 93.75%. These findings support the notion that, while volumes of medial temporal lobe regions differ between cognitively normal and MCI subjects, differences that can be used to distinguish between these two populations are present elsewhere in the brain.

State-of-the-art item response theory (IRT) models use logistic functions exclusively as their item response functions (IRFs). Logistic functions meet the requirements that their range is the unit interval and that they are monotonically increasing, but they impose a parameter space whose dimensions can only be assigned a metaphorical interpretation in the context of testing. Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. This article illustrates how mechanical adoption of off-the-shelf logistic functions as IRFs for IRT models can result in off-the-shelf parameter estimates and fits to data. The results of a simulation study are presented, which show that logistic IRT models can fit a set of data generated by IRFs other than logistic functions just as well as they fit logistic data, even though the response processes and parameter spaces involved in each case are substantially different. An explanation of why logistic functions work as they do is offered, the theoretical and practical consequences of their behavior are discussed, and a testable alternative to logistic IRFs is commented upon.

We examined the sensitivity of the Rey Auditory Verbal Learning Test (AVLT), California Verbal Learning Test (CVLT), Boston Naming Test (BNT), and Multilingual Aphasia Examination Visual Naming subtest (MAE VN) to lateralized temporal lobe epilepsy (TLE) in patients who subsequently underwent anterior temporal lobectomy. For the AVLT (n = 189), left TLE patients performed more poorly than their right TLE counterparts [left TLE = 42.9 (10.6), right TLE = 47.7 (9.9); p < .002 (Cohen's d = .47)]. Although statistically significant, the CVLT group difference (n = 212) was of a smaller magnitude [left LTE = 40.7 (11.1), right TLE = 43.8 (9.9); (p < .03, Cohen's d = .29)] than the AVLT. Group differences were also present for both measures of confrontation naming ability [BNT: left LTE = 43.1 (8.9), right TLE = 48.1 (8.9); p < .001 (Cohen's d = .56); MAE VN: left TLE = 42.2, right TLE = 45.6, p = .02 (Cohen's d = .36)]. When these data were modeled in independent logistic regression analyses, the AVLT and BNT both significantly predicted side of seizure focus, although the positive likelihood ratios were modest. In the subset of 108 patients receiving both BNT and AVLT, the AVLT was the only significant predictor of seizure laterality, suggesting individual patient variability regarding whether naming or memory testing may be more sensitive to lateralized TLE. (JINS, 2008, 14, 394–400.)

Recursive estimation is considered for parameters of certain continuous stochastic models. Several optimality properties are shown to hold for the resulting recursive estimator, where a stochastic approximation viewpoint is taken when deriving statistical properties, like strong consistency and convergence in distribution. Applications are considered throughout, where for example explosion theory for diffusion processes is used as a modeling guide, in a particular application.