Grief and loss in old age: Exploration of the association between grief and depression.

BACKGROUND: The proportion of older adults is increasing due to demographic changes. Depression belongs to the most common mental disorders in late life. The loss of an emotionally significant person is a risk factor for the development of depression. The aim of this study is to analyze the association between depression and grief burden resulting from loss. Based on prior evidence, we examined loneliness as a possible mediator and social support as possible moderator of this association. METHODS: The cross-sectional analyses are based on a sample (N = 863) of study participants aged 75+ (M = 81.4 years, SD = 4.4, 62.2% female) with loss experience deriving from the multicenter prospective German cohort study AgeMooDe. Regression analyses (moderated mediation) were performed. RESULTS: With increasing age (ß = 0.10, p = .005) and grief burden (ß = 0.33, p <. 001) depression severity increased. There was an indirect mediating effect of loneliness on the correlation of grief burden and depression (b = 0.04, CI [0.03, 0.05]), but no moderating effect of social support on the correlation of grief burden and loneliness. People living alone had a significantly higher risk of depression, increased loneliness and lack of social support. LIMITATIONS: Assessments were based on self-reporting and recorded dimensionally. The cross-sectional design limits conclusions about directions and causality of associations. Sampling bias cannot be completely excluded. CONCLUSION: The study provides empirical evidence and a better understanding of the association between grief and depression among the very old and the mediating role of loneliness.

Reconstruction of highly porous structures from FIB-SEM using a deep neural network trained on synthetic images.

Combining scanning electron microscopy with serial slicing by a focused ion beam yields spatial image data of materials structures at the nanometer scale. However, the depth of field of the scanning electron microscopic images causes unwanted effects when highly porous structures are imaged. Proper spatial reconstruction of such porous structures from the stack of microscopic images is a tough and in general yet unsolved segmentation problem. Recently, machine learning methods have proven to yield solutions to a variety of image segmentation problems. However, their use is hindered by the need of large amounts of annotated data in the training phase. Here, we therefore replace annotated real image data by simulated image stacks of synthetic structures - realizations of stochastic germ-grain models and random packings. This strategy yields the annotations for free, but shifts the effort to choosing appropriate stochastic geometry models and generating sufficiently realistic scanning electron microscopic images.

Three-dimensional image analytical detection of intussusceptive pillars in murine lung.

A variety of diseases can lead to loss of lung tissue. Currently, this can be treated only symptomatically. In mice, a complete compensatory lung growth within 21 days after resection of the left lung can be observed. Understanding and transferring this concept of compensatory lung growth to humans would greatly improve therapeutic options. Lung growth is always accompanied by a process called angiogenesis forming new capillary blood vessels from preexisting ones. Among the processes during lung growth, the formation of transluminal tissue pillars within the capillary vessels (intussusceptive pillars) is observed. Therefore, pillars can be understood as an indicator for active angiogenesis and microvascular remodelling. Thus, their detection is very valuable when aiming at characterization of compensatory lung growth. In a vascular corrosion cast, these pillars appear as small holes that pierce the vessels. So far, pillars were detected visually only based on 2D images. Our approach relies on high-resolution synchrotron microcomputed tomographic images. With a voxel size of 370 nm we exploit the spatial information provided by this imaging technique and present the first algorithm to semiautomatically detect intussusceptive pillars. An at least semiautomatic detection is essential in lung research, as manual pillar detection is not feasible due to the complexity and size of the 3D structure. Using our algorithm, several thousands of pillars can be detected and subsequently analysed, e.g. regarding their spatial arrangement, size and shape with an acceptable amount of human interaction. In this paper, we apply our novel pillar detection algorithm to compute pillar densities of different specimens. These are prepared such that they show different growing states. Comparing the corresponding pillar densities allows to investigate lung growth over time.

Quantitative comparison of segmentation algorithms for FIB-SEM images of porous media.

Focused ion beam tomography has proven to be capable of imaging porous structures on a nano-scale. However, due to shine-through artefacts, common segmentation algorithms often lead to severe dislocation of individual structures in z-direction. Recently, a number of approaches have been developed, which take into account the specific nature of focused ion beam-scanning electron microscope images for porous media. In the present study, we analyse three of these approaches by comparing their performance based on simulated focused ion beam-scanning electron microscope images. Performance is measured by determining the amount of misclassified voxels as well as the fidelity of structural characteristics. Based on this analysis we conclude that each algorithm has certain strengths and weaknesses and we determine the scenarios for which each approach might be the best choice.

Morphological segmentation of FIB-SEM data of highly porous media.

Nanoporous materials play an important role in modern batteries as well as fuel cells. The materials microstructure needs to be analyzed as it determines the electrochemical properties. However, the microstructure is too fine to be resolved by microcomputed tomography. The method of choice to analyze the microstructure is focused ion beam nanotomography (FIB-SEM). However, the reconstruction of the porous 3D microstructure from FIB-SEM image data in general has been an unsolved problem so far. In this paper, we present a new method using morphological operations. First, features are extracted from the data. Subsequently, these features are combined to an initial segmentation, that is then refined by a constrained watershed transformation. We evaluate our method with synthetic data, generated by a simulation of the FIB-SEM imaging process. We compare the ground truth in the simulated data to the segmentation result. The new method is found to produce a much smaller error than existing techniques.

Quantitative micro-CT.

In this paper, the field of quantitative microcomputed tomography arising from the combination of microcomputed tomography and quantitative 3D image analysis, is summarized with focus on materials science applications. Opportunities and limitations as well as typical application scenarios are discussed. Selected examples provide an insight into commonly used as well as recent methods from mathematical morphology and stochastic geometry.

Analysis of spatial cross-correlations in multi-constituent volume data.

We investigate spatial cross-correlations between two constituents, both belonging to the same microstructure. These investigations are based on two approaches: one via the measurement of the cross-correlation function and the other uses the spatial distances between the constituents. The cross-correlation function can be measured using the fast Fourier transform, whereas the distances are determined via the Euclidean distance transform. The characteristics are derived from volume images obtained by synchrotron microtomography. As an example we consider pore formation in metallic foams, knowledge of which is important to control the foam production process. For this example, we discuss the spatial cross-correlation between the pore space and the blowing agent particles in detail.

Modelling a ceramic foam using locally adaptable morphology.

In this paper, a ceramic open foam is modelled using a two-step procedure. First, a random Laguerre tessellation is fitted to the edge system of the polyurethane foam forming the core of the ceramic foam. In a second phase, a model of the ceramic foam is obtained using dilations with balls of locally varying size. The model fitting is based on geometric characteristics of both polyurethane and ceramic foam estimated from reconstructed tomographic images of these structures.