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Abstract This study aimed to examine the influence of COVID-specific stressors on cross-sectional and longitudinal bereavement outcomes. According to the Dual Process Model of grief these stress-inducing factors can relate to the loss (loss-oriented stressors) or to manage everyday life (restoration-oriented stressors) and require coping in the grief process. A total of 491 participants (94.1% female; 43.92 years on average; 44.4% loss of a parent) were included at the first measurement time point (T0), of whom 99 individuals also participated in a follow-up assessment 6 months later (T1). Participants frequently reported loss-oriented (on average 7.30 out of 21 queried) and restoration-oriented stressors (on average 6.99 out of 19 queried). Cross-sectionally, higher acute grief intensity was associated with a higher number of loss-oriented stressors, poorer mental well-being, and sociodemographic variables. This effect disappeared longitudinally, with only acute grief intensity and poorer mental well-being at T0 predicting higher prolonged grief at T1. Common resilience factors did not buffer the effects of the pandemic on grief. Loss-oriented stressors seem to be especially relevant for understanding grief and might be a mediator of higher long-term grief. The findings suggest that COVID-specific strains need to be specifically taken into account in the support of bereaved individuals.

Abstract Recent developments in deep learning have shown success in accurately predicting the location of biological markers in Optical Coherence Tomography (OCT) volumes of patients with Age-Related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). We propose a method that automatically locates biological markers to the Early Treatment Diabetic Retinopathy Study (ETDRS) rings, only requiring B-scan-level presence annotations. We trained a neural network using 22,723 OCT B-Scans of 460 eyes (433 patients) with AMD and DR, annotated with slice-level labels for Intraretinal Fluid (IRF) and Subretinal Fluid (SRF). The neural network outputs were mapped into the corresponding ETDRS rings. We incorporated the class annotations and domain knowledge into a loss function to constrain the output with biologically plausible solutions. The method was tested on a set of OCT volumes with 322 eyes (189 patients) with Diabetic Macular Edema, with slice-level SRF and IRF presence annotations for the ETDRS rings. Our method accurately predicted the presence of IRF and SRF in each ETDRS ring, outperforming previous baselines even in the most challenging scenarios. Our model was also successfully applied to en-face marker segmentation and showed consistency within C-scans, despite not incorporating volume information in the training process. We achieved a correlation coefficient of 0.946 for the prediction of the IRF area.

Abstract As the need for higher data rates for communication increases, the terahertz (THz) band has drawn considerable attention. This spectral region promises a much wider bandwidth and the transmission of large amounts of data at high speeds. However, there are still challenges that need to be addressed before the THz telecommunications technology hits the consumer market. One of the recurring concerns is that THz radiation is greatly absorbed by atmospheric water-vapor. Although many studies have presented the attenuation of THz signals under different atmospheric conditions, these results analyze specific temperature or humidity values, leaving the need for a more comprehensive analysis over a wider range of climate conditions. In this work, we present the first study of the attenuation of THz radiation over a broad range of temperatures and humidity values. It is worth noticing that all of our measurements have been undertaken at atmospheric pressure unlike many previous studies where the pressure was not kept constant for various temperatures. Furthermore, we extend our analysis beyond the impact of absolute humidity on the bit error rate in THz communications. We also discuss the refractivity of the atmosphere, examining its variations across different temperatures and humidity levels. THz propagation is studied using two different measurement systems, a long-path THz time-domain spectrometer as well as a quasi-optic setup with vector network analyze. We also compare the results with the ITU-R P.676-13 propagation model. We conclude that the attenuation at the absorption peaks increases linearly with water content and has no dependence on the temperature, while the refractive index, away from absorption lines, namely at 300 GHz shows a sub-linear increase with humidity.

Abstract Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.

Abstract A new integrated lens-antenna is designed and implemented for a nanocontact based terahertz (THz) photomixer. The new design replaces the standard conventional bulky silicon lens, which normally no THz photomixer can avoid. The Fresnel Zone Plate is used to design the new lens-antenna and is simulated by the MIT open-source tool called Meep. The final design showed, with only two simple fabrication technology processing steps (standard optical lithography) that the lens-antenna can be monolithically integrated with the THz nanophotomixer. With its compact design, the THz measurements showed a comparable behavior to the conventional bulky silicon lens, therefore it would be ready for photonic integrated circuits based THz systems.

Abstract In this paper we analyse the performance of machine learning methods in predicting patient information such as age or sex solely from retinal imaging modalities in a heterogeneous clinical population. Our dataset consists of N = 135,667 fundus images and N = 85,536 volumetric OCT scans. Deep learning models were trained to predict the patient’s age and sex from fundus images, OCT cross sections and OCT volumes. For sex prediction, a ROC AUC of 0.80 was achieved for fundus images, 0.84 for OCT cross sections and 0.90 for OCT volumes. Age prediction mean absolute errors of 6.328 years for fundus, 5.625 years for OCT cross sections and 4.541 for OCT volumes were observed. We assess the performance of OCT scans containing different biomarkers and note a peak performance of AUC = 0.88 for OCT cross sections and 0.95 for volumes when there is no pathology on scans. Performance drops in case of drusen, fibrovascular pigment epitheliuum detachment and geographic atrophy present. We conclude that deep learning based methods are capable of classifying the patient’s sex and age from color fundus photography and OCT for a broad spectrum of patients irrespective of underlying disease or image quality. Non-random sex prediction using fundus images seems only possible if the eye fovea and optic disc are visible.

Abstract Magnetic resonance imaging (MRI) of the brain combined with voxel-based morphometry (VBM) revealed changes in gray matter volume (GMV) in various disorders. However, the cellular basis of GMV changes has remained largely unclear. We correlated changes in GMV with cellular metrics by imaging mice with MRI and two-photon in vivo microscopy at three time points within 12 weeks, taking advantage of age-dependent changes in brain structure. Imaging fluorescent cell nuclei allowed inferences on (i) physical tissue volume as determined from reference spaces outlined by nuclei, (ii) cell density, (iii) the extent of cell clustering, and (iv) the volume of cell nuclei. Our data indicate that physical tissue volume alterations only account for 13.0% of the variance in GMV change. However, when including comprehensive measurements of nucleus volume and cell density, 35.6% of the GMV variance could be explained, highlighting the influence of distinct cellular mechanisms on VBM results.

Abstract Various types of DNA viruses are known to elicit the formation of a large nuclear viral replication compartment and marginalization of the cell chromatin. We used three-dimensional soft x-ray tomography, confocal and electron microscopy, combined with numerical modelling of capsid diffusion to analyse the molecular organization of chromatin in herpes simplex virus 1 infection and its effect on the transport of progeny viral capsids to the nuclear envelope. Our data showed that the formation of the viral replication compartment at late infection resulted in the enrichment of heterochromatin in the nuclear periphery accompanied by the compaction of chromatin. Random walk modelling of herpes simplex virus 1–sized particles in a three-dimensional soft x-ray tomography reconstruction of an infected cell nucleus demonstrated that the peripheral, compacted chromatin restricts viral capsid diffusion, but due to interchromatin channels capsids are able to reach the nuclear envelope, the site of their nuclear egress.

Abstract Correlative light and electron microscopy (CLEM) is a powerful approach to investigate the molecular ultrastructure of labeled cell compartments. However, quantitative CLEM studies are rare, mainly due to small sample sizes and the sensitivity of fluorescent proteins to strong fixatives and contrasting reagents for EM. Here, we show that fusion of a self-labeling protein to insulin allows for the quantification of age-distinct insulin granule pools in pancreatic beta cells by a combination of super resolution and transmission electron microscopy on Tokuyasu cryosections. In contrast to fluorescent proteins like GFP organic dyes covalently bound to self-labeling proteins retain their fluorescence also in epoxy resin following high pressure freezing and freeze substitution, or remarkably even after strong chemical fixation. This enables for the assessment of age-defined granule morphology and degradation. Finally, we demonstrate that this CLEM protocol is highly versatile, being suitable for single and dual fluorescent labeling and detection of different proteins with optimal ultrastructure preservation and contrast.