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Functional data are data that can be represented by suitable functions, such as curves (potentially multi-dimensional) or surfaces. This paper gives an introduction to some basic but important techniques for the analysis of such data, and we apply the techniques to two datasets from biomedicine. One dataset is about white matter structures in the brain in multiple sclerosis patients; the other dataset is about three-dimensional vascular geometries collected for the study of cerebral aneurysms. The techniques described are smoothing, alignment, principal component analysis, and regression., (Copyright © 2013 John Wiley & Sons, Ltd.)

Background: Depression and anxiety are prevalent mental disorders among the working population with potentially high personal and financial cost. The overall aim of this study was to test the applicability of an outreach collaborative model for early identification and treatment of clinical and sub-clinical mental disorders among Danish employees. This applicability was examined by I) investigating the fractions of identified and treated clinical and subclinical cases, II) describing the distribution and characteristics of cases identified and III) investigating the effect of allocated treatment. Methods: A longitudinal study design with four assessments (T0-T3) over 16 months was applied. Self-reporting questionnaires probing for psychopathology were distributed to all employees in six consecutively enrolled companies at the four time points. Employees meeting the screening criteria at T1 were assessed diagnostically. Subjects diagnosed with a clinical mental disorder were allocated to outpatient psychiatric treatment, and subjects with subclinical conditions were allocated to preventive cognitive behavioural therapy. Follow-up was conducted 6 and 12 months after initiation of treatment. We used chi-squared test and F-test to compare the different groups on baseline characteristics and mixed effects linear regression to analyse the treatment effects. Results: Forty (6.8%) of the 586 responders at T1 were diagnosed with a clinical mental disorder and referred to outpatient psychiatric treatment. Thirty-three (5.6%) were affected by a subclinical condition and referred to preventive treatment. Nearly two-thirds (63%) of the employees diagnosed with a clinical condition had never received treatment before. Symptom severity decreased significantly for both treated groups until follow-up. When compared to a composed control group, subclinical cases displayed a more rapid initial significant symptomatic decrease on the global symptom scale (coefs = − 0.914, 95% CI [− 1.754, − 0,075]) and anxiety sub-scale (coefs = − 1.043, 95% CI [− 2.021, − 0.066]). This did not apply to the clinical cases as no significant difference in change were identified. Conclusions: The outreach collaborative model demonstrated an applicability to identify both clinical and subclinical cases, among these a high number of employees with an unmet need for treatment. We found evidence of a positive initial effect on symptomatology from the allocated preventive treatment among the subclinical cases, but not for clinical cases. Trial registration: Retrospectively registered at December 18, 2018 at clinicaltrials.gov, identifier: NCT03786328. [ABSTRACT FROM AUTHOR]

The article discusses a study on the efficacy of an intervention programme for improving physical health in long-term-treated psychiatric patients. Topics discussed include the focus of the intervention described as active awareness on the individual patient and the staff members and indication of the possibility of stopping further increase in waist circumference (WC) and stabilizing the level of fasting glucose resulting in the prevention or delay of cardiovascular disease (CVD) and diabetes.

Synergism and antagonism are often defined in relation to the model of Concentration Addition (CA). Hence, it is vital for the conclusion of mixture toxicity studies to be able to test whether an observed deviation from CA reflects a true deviation or whether it is simply due to random variation. In this paper we consider a non-linear regression model for the classical ray designs for binary mixture experiments. The model combines dose–response curves for each mixture in the experiment with an isobole model, describing possible deviations from CA. The method allows us to test whether the chosen isobole model is reasonable for the data and to test the hypothesis of CA. Furthermore, it provides us with a measure of the degree of synergism/antagonism. The method is flexible since both the dose–response relationships and the isobole model can be chosen arbitrarily. We demonstrate the use of the method on datasets where combinations of pesticides are tested on a floating plant, Lemna minor, and an algae, Pseudokirchneriella subcapitata. Furthermore, we conduct a simulation study in order to explore the power with which a specific deviation from CA can be distinguished in different test-systems. [ABSTRACT FROM AUTHOR]