An analysis of the effects of aeroelasticity on static longitudinal stability and control of a swept-back-wing airplane

A theoretical analysis of the effects of aeroelasticity on the stick-fixed static longitudinal stability and elevator angle required for balance of an airplane is presented together with calculated effects for a swept-wing bomber of relatively high flexibility. Although large changes in stability due to certain parameters are indicated for the example airplane, the over-all stability change after considering all parameters was quite small, compared to the individual effects, due to the counterbalancing of wing and tail contributions. The effect of flexibility on longitudinal control for the example airplane was found to be of little real importance

An analysis of the effects of aeroelasticity on static longitudinal stability and control of a swept-wing airplane

A theoretical analysis has been made of the effects of aeroelasticity on the static longitudinal stability and elevator angle required for balance of an airplane. The analysis is based on the familiar stability equation expressing the contribution of wing and tail to longitudinal stability. Effects of wing, tail, and fuselage flexibility are considered. Calculated effects are shown for a swept-wing bomber of relatively high flexibility

Coordinated analysis of age, sex, and education effects on change in MMSE scores

Objectives. We describe and compare the expected performance trajectories of older adults on the Mini-Mental Status Examination (MMSE) across six independent studies from four countries in the context of a collaborative network of longitudinal studies of aging. A coordinated analysis approach is used to compare patterns of change conditional on sample composition differences related to age, sex, and education. Such coordination accelerates evaluation of particular hypotheses. In particular, we focus on the effect of educational attainment on cognitive decline.Method. Regular and Tobit mixed models were fit to MMSE scores from each study separately. The effects of age, sex, and education were examined based on more than one centering point.Results. Findings were relatively consistent across studies. On average, MMSE scores were lower for older individuals and declined over time. Education predicted MMSE score, but, with two exceptions, was not associated with decline in MMSE over time.Conclusion. A straightforward association between educational attainment and rate of cognitive decline was not supported. Thoughtful consideration is needed when synthesizing evidence across studies, as methodologies adopted and sample characteristics, such as educational attainment, invariably differ. © 2012 The Author

Founder effects facilitate the use of a genotyping-based approach to molecular diagnosis in Swedish patients with familial hypercholesterolaemia

Aim To investigate whether genotyping could be used as a cost-effective screening step, preceding next-generation sequencing (NGS), in molecular diagnosis of familial hypercholesterolaemia (FH) in Swedish patients. Methods and results Three hundred patients of Swedish origin with clinical suspicion of heterozygous FH were analysed using a specific array genotyping panel embedding 112 FH-causing mutations in the LDLR, APOB and PCSK9 genes. The mutations had been selected from previous reports on FH patients in Scandinavia and Finland. Mutation-negative cases were further analysed by NGS. In 181 patients with probable or definite FH using the Dutch lipid clinics network (DLCN) criteria (score >= 6), a causative mutation was identified in 116 (64%). Of these, 94 (81%) were detected by genotyping. Ten mutations accounted for more than 50% of the positive cases, with APOB c.10580G>A being the most common. Mutations in LDLR predominated, with (c.2311+1_2312-1)(2514)del (FH Helsinki) and c.259T>G having the highest frequency. Two novel LDLR mutations were identified. In patients with DLCN score A was higher than previously reported in Sweden. The lack of demonstrable mutations in the LDLR, APOB and PCSK9 genes in similar to 1/3 of patients with probable FH strongly suggests that additional genetic mechanisms are to be found in phenotypic FH.Peer reviewe

Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings