Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation.

BACKGROUND: Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation. METHODS: Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2 +), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and then examined for over-represented functional signatures. RESULTS: We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n = 49), 0.05 Gray (n = 41), and 2 Gray (n = 38), as well as hyper-variable genes after any dose (n = 43). While after 2 Gray positive regulation of cell cycle was hypo-variable in N0, (regulation of) fibroblast proliferation was over-represented in hyper-variable genes of N1 and N2+. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose and were associated with the ERK1/ERK2 cascade. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data. CONCLUSION: N2+ showed high degrees of variability in pathways for the cell fate decision after genotoxic insults that may lead to the transfer and multiplication of DNA-damage via proliferation, where apoptosis and removal of the damaged genome would have been appropriate. Such a deficiency could potentially lead to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation, but following low-dose applications employed in diagnostics, as well.

[The situation of informal caregivers of working age in the COVID-19 pandemic-results of an online survey in Germany]. / Die Situation pflegender Angehöriger im erwerbsfähigen Alter in der COVID-19-Pandemie ­ Ergebnisse einer Onlinebefragung in Deutschland.

BACKGROUND: The consequences of the COVID-19 pandemic have posed major challenges to different groups. One of these are informal caregivers. This study investigates the changes the pandemic has caused for informal caregivers and the extent to which quality of life and burden of care have changed for specific subgroups. METHODS: Data for this cross-sectional study was gathered in the summer of 2020 in a convenient sample of informal caregivers (< 67 years of age, N = 1143). In addition to sociodemographic data, information on the care situation, compatibility of care and work, as well as stress and quality of life was collected in an online survey. The analysis of care situations and compatibility of care and work is done descriptively. Logistic regression models are used for a subgroup analysis of quality of life and care burden. RESULTS: The care situation has changed for 54.7% of participants and has become more time consuming. For 70.8% of respondents, the COVID-19 pandemic has made it even more difficult to balance care-giving and work. However, most respondents were satisfied with their employers' pandemic management (65.9%). A sharp decline in the quality of life and an increase in the burden of care for informal caregivers was ascertained. Both developments are stronger for young and female caregivers and for those caring for people with a greater need of support. DISCUSSION: The results indicate that living situations worsened for a substantial proportion of informal caregivers during the COVID-19 pandemic. Policymakers should recognize additional challenges that informal caregivers have faced since the outbreak of the COVID-19 pandemic and how they vary by subgroups. It is important to include home-based informal care as well as other care settings in future pandemic concepts.

Markers of cell death predict therapy response in patients with cirrhosis and hepatorenal syndrome.

BACKGROUND AND AIMS: Hepatorenal syndrome is a major complication in patients with cirrhosis and associated with high mortality. Predictive biomarkers for therapy response are largely missing. Cytokeratin18-based cell death markers are significantly elevated in patients with complications of chronic liver disease, but the role of these markers in patients with HRS treated with vasoconstrictors and albumin is unknown. METHODS: We prospectively analyzed a total of 138 patients with HRS, liver cirrhosis without HRS and acute kidney injury treated at the University Medical Center Mainz between April 2013 and July 2018. Serum levels of M30 and M65 were analyzed by ELISA and clinical data were collected. Predictive ability was assessed by Kaplan-Meier curves, logistic regression and c-statistic. Primary endpoint was response to therapy. RESULTS: M30 and M65 were significantly increased in patients with HRS compared to non-HRS controls (M30: p < 0.0001; M65: p < 0.0001). Both serum markers showed predictive ability for dialysis- and LTX-free survival but not overall survival. Logistic regression confirmed M30 and M65 as independent prognostic factors for response to therapy. A novel predictive score comprising bilirubin and M65 showed highest predictive ability to predict therapy response. CONCLUSIONS: Serum levels of M30 and M65 can robustly discriminate patients into responders and non-responders to terlipressin therapy with a good predictive ability for dialysis- and LTX-free survival in cirrhotic patients. Cell death parameters might possess clinical relevance in patients with liver cirrhosis and HRS.

Epidemiology of inherited epidermolysis bullosa in Germany.

BACKGROUND: Epidermolysis bullosa (EB) is a rare genetic disorder manifesting with skin and mucosal membrane blistering in different degrees of severity. OBJECTIVE: Epidemiological data from different countries have been published, but none are available from Germany. METHODS: In this population-based cross-sectional study, people living with EB in Germany were identified using the following sources: academic hospitals, diagnostic laboratories and patient organization. RESULTS: Our study indicates an overall EB incidence of 45 per million live births in Germany. With 14.23 per million live births for junctional EB, the incidence is higher than in other countries, possibly reflecting the availability of early molecular genetic diagnostics in severely affected neonates. Dystrophic EB was assessed at 15.58 cases per million live births. The relatively low incidence found for EB simplex, 14.93 per million live births, could be explained by late or missed diagnosis, but also by 33% of cases remaining not otherwise specified. Using log-linear models, we estimated a prevalence of 54 per million for all EB types, 2.44 for junctional EB, 12.16 for dystrophic EB and 28.44 per million for EB simplex. These figures are comparable to previously reported data from other countries. CONCLUSIONS: Altogether, there are at least 2000 patients with EB in the German population. These results should support national policies and pharmaceutical companies in decision-making, allow more precise planning of drug development and clinical trials, and aid patient advocacy groups in their effort to improve quality of life of people with this orphan disease.

A Multilevel Model of Older Adults' Appropriation of ICT and Acquisition of Digital Literacy.

Digital literacy refers to a set of competencies related to the skilled use of computers and information technology. Low digital skills can be a barrier for older adults' full participation in a digital society, and COVID-19 has increased this risk of social exclusion. Older adults' digital inclusion is a complex process that consists of the interplay of structural and individual factors. The ACCESS project unwrapped the complexity of the process and developed an innovative, multilevel model that illustrates how societal, institutional, material and pedagogical aspects shape adults' appropriation of digital literacy. A holistic model describes factors contributing to older adults' digital literacy, acknowledging sociocultural contexts, environments, learning settings and instruction practices for learning digital literacy. Instead of seeing older adults' reasons for learning digital skills purely as individual choice, this model recognizes the interpersonal, institutional and societal aspects that implicitly or explicitly influence older adults' acquisition of digital literacy. The results offer a tool for stakeholders, the research community, companies, designers and other relevant stakeholders to consider digital skills and the given support. It demands diverse communication between different stakeholders about the things that should be discussed when organizing digital support in digitalized societies.

Radiation-response in primary fibroblasts of long-term survivors of childhood cancer with and without second primary neoplasms: the KiKme study.

BACKGROUND: The etiology and most risk factors for a sporadic first primary neoplasm in childhood or subsequent second primary neoplasms are still unknown. One established causal factor for therapy-associated second primary neoplasms is the exposure to ionizing radiation during radiation therapy as a mainstay of cancer treatment. Second primary neoplasms occur in 8% of all cancer survivors within 30 years after the first diagnosis in Germany, but the underlying factors for intrinsic susceptibilities have not yet been clarified. Thus, the purpose of this nested case-control study was the investigation and comparison of gene expression and affected pathways in primary fibroblasts of childhood cancer survivors with a first primary neoplasm only or with at least one subsequent second primary neoplasm, and controls without neoplasms after exposure to a low and a high dose of ionizing radiation. METHODS: Primary fibroblasts were obtained from skin biopsies from 52 adult donors with a first primary neoplasm in childhood (N1), 52 with at least one additional primary neoplasm (N2+), as well as 52 without cancer (N0) from the KiKme study. Cultured fibroblasts were exposed to a high [2 Gray (Gy)] and a low dose (0.05 Gy) of X-rays. Messenger ribonucleic acid was extracted 4 h after exposure and Illumina-sequenced. Differentially expressed genes (DEGs) were computed using limma for R, selected at a false discovery rate level of 0.05, and further analyzed for pathway enrichment (right-tailed Fisher's Exact Test) and (in-) activation (z ≥|2|) using Ingenuity Pathway Analysis. RESULTS: After 0.05 Gy, least DEGs were found in N0 (n = 236), compared to N1 (n = 653) and N2+ (n = 694). The top DEGs with regard to the adjusted p-value were upregulated in fibroblasts across all donor groups (SESN1, MDM2, CDKN1A, TIGAR, BTG2, BLOC1S2, PPM1D, PHLDB3, FBXO22, AEN, TRIAP1, and POLH). Here, we observed activation of p53 Signaling in N0 and to a lesser extent in N1, but not in N2+. Only in N0, DNA (excision-) repair (involved genes: CDKN1A, PPM1D, and DDB2) was predicted to be a downstream function, while molecular networks in N2+ were associated with cancer, as well as injury and abnormalities (among others, downregulation of MSH6, CCNE2, and CHUK). After 2 Gy, the number of DEGs was similar in fibroblasts of all donor groups and genes with the highest absolute log2 fold-change were upregulated throughout (CDKN1A, TIGAR, HSPA4L, MDM2, BLOC1SD2, PPM1D, SESN1, BTG2, FBXO22, PCNA, and TRIAP1). Here, the p53 Signaling-Pathway was activated in fibroblasts of all donor groups. The Mitotic Roles of Polo Like Kinase-Pathway was inactivated in N1 and N2+. Molecular Mechanisms of Cancer were affected in fibroblasts of all donor groups. P53 was predicted to be an upstream regulator in fibroblasts of all donor groups and E2F1 in N1 and N2+. Results of the downstream analysis were senescence in N0 and N2+, transformation of cells in N0, and no significant effects in N1. Seven genes were differentially expressed in reaction to 2 Gy dependent on the donor group (LINC00601, COBLL1, SESN2, BIN3, TNFRSF10A, EEF1AKNMT, and BTG2). CONCLUSION: Our results show dose-dependent differences in the radiation response between N1/N2+ and N0. While mechanisms against genotoxic stress were activated to the same extent after a high dose in all groups, the radiation response was impaired after a low dose in N1/N2+, suggesting an increased risk for adverse effects including carcinogenesis, particularly in N2+.

T-cell exhaustion and residency dynamics inform clinical outcomes in hepatocellular carcinoma.

BACKGROUND & AIMS: Despite recent translation of immunotherapies into clinical practice, the immunobiology of hepatocellular carcinoma (HCC), in particular the role and clinical relevance of exhausted and liver-resident T cells remain unclear. We therefore dissected the landscape of exhausted and resident T cell responses in the peripheral blood and tumor microenvironment of patients with HCC. METHODS: Lymphocytes were isolated from the blood, tumor and tumor-surrounding liver tissue of patients with HCC (n = 40, n = 10 treated with anti-PD-1 therapy). Phenotype, function and response to anti-PD-1 were analyzed by mass and flow cytometry ex vivo and in vitro, tissue residence was further assessed by immunohistochemistry and imaging mass cytometry. Gene signatures were analyzed in silico. RESULTS: We identified significant enrichment of heterogeneous populations of exhausted CD8+ T cells (TEX) in the tumor microenvironment. Strong enrichment of severely exhausted CD8 T cells expressing multiple immune checkpoints in addition to PD-1 was linked to poor progression-free and overall survival. In contrast, PD-1 was also expressed on a subset of more functional and metabolically active CD103+ tissue-resident memory T cells (TRM) that expressed few additional immune checkpoints and were associated with better survival. TEX enrichment was independent of BCLC stage, alpha-fetoprotein levels or age as a variable for progression-free survival in our cohort. These findings were in line with in silico gene signature analysis of HCC tumor transcriptomes from The Cancer Genome Atlas. A higher baseline TRM/TEX ratio was associated with disease control in anti-PD-1-treated patients. CONCLUSION: Our data provide information on the role of peripheral and intratumoral TEX-TRM dynamics in determining outcomes in patients with HCC. The dynamics between exhausted and liver-resident T cells have implications for immune-based diagnostics, rational patient selection and monitoring during HCC immunotherapies. LAY SUMMARY: The role of the immune response in hepatocellular carcinoma (HCC) remains unclear. T cells can mediate protection against tumor cells but are frequently dysfunctional and exhausted in cancer. We found that patients with a predominance of exhausted CD8+ T cells (TEX) had poor survival compared to patients with a predominance of tissue-resident memory T cells (TRM). This correlated with the molecular profile, metabolic and functional status of these cell populations. The enrichment of TEX was independently associated with prognosis in addition to disease stage, age and tumor markers. A high TRM proportion was also associated with better outcomes following checkpoint therapy. Thus, these T-cell populations are novel biomarkers with relevance in HCC.

Interpretable generative deep learning: an illustration with single cell gene expression data.

Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered by various approaches, such as variational auto-encoders, are useful to get a better understanding of the relations between observed gene expressions and experimental factors or phenotypes. Furthermore, by providing a generative model for the latent and observed variables, deep generative models can generate synthetic observations, which allow us to assess the uncertainty in the learned representations. While deep generative models are useful to learn the structure of high-dimensional omics data by efficiently capturing non-linear dependencies between genes, they are sometimes difficult to interpret due to their neural network building blocks. More precisely, to understand the relationship between learned latent variables and observed variables, e.g., gene transcript abundances and external phenotypes, is difficult. Therefore, we also illustrate current approaches that allow us to infer the relationship between learned latent variables and observed variables as well as external phenotypes. Thereby, we render deep learning approaches more interpretable. In an application with single-cell gene expression data, we demonstrate the utility of the discussed methods.

Stratified neural networks in a time-to-event setting.

Deep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their non-stratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.

Identification of Genetic Predispositions Related to Ionizing Radiation in Primary Human Skin Fibroblasts From Survivors of Childhood and Second Primary Cancer as Well as Cancer-Free Controls: Protocol for the Nested Case-Control Study KiKme.

BACKGROUND: Therapy for a first primary neoplasm (FPN) in childhood with high doses of ionizing radiation is an established risk factor for second primary neoplasms (SPN). An association between exposure to low doses and childhood cancer is also suggested; however, results are inconsistent. As only subgroups of children with FPNs develop SPNs, an interaction between radiation, genetic, and other risk factors is presumed to influence cancer development. OBJECTIVE: Therefore, the population-based, nested case-control study KiKme aims to identify differences in genetic predisposition and radiation response between childhood cancer survivors with and without SPNs as well as cancer-free controls. METHODS: We conducted a population-based, nested case-control study KiKme. Besides questionnaire information, skin biopsies and saliva samples are available. By measuring individual reactions to different exposures to radiation (eg, 0.05 and 2 Gray) in normal somatic cells of the same person, our design enables us to create several exposure scenarios for the same person simultaneously and measure several different molecular markers (eg, DNA, messenger RNA, long noncoding RNA, copy number variation). RESULTS: Since 2013, 101 of 247 invited SPN patients, 340 of 1729 invited FPN patients, and 150 of 246 invited cancer-free controls were recruited and matched by age and sex. Childhood cancer patients were additionally matched by tumor morphology, year of diagnosis, and age at diagnosis. Participants reported on lifestyle, socioeconomical, and anthropometric factors, as well as on medical radiation history, health, and family history of diseases (n=556). Primary human fibroblasts from skin biopsies of the participants were cultivated (n=499) and cryopreserved (n=3886). DNA was extracted from fibroblasts (n=488) and saliva (n=510). CONCLUSIONS: This molecular-epidemiological study is the first to combine observational epidemiological research with standardized experimental components in primary human skin fibroblasts to identify genetic predispositions related to ionizing radiation in childhood and SPNs. In the future, fibroblasts of the participants will be used for standardized irradiation experiments, which will inform analysis of the case-control study and vice versa. Differences between participants will be identified using several molecular markers. With its innovative combination of experimental and observational components, this new study will provide valuable data to forward research on radiation-related risk factors in childhood cancer and SPNs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32395.

Attitudes towards working in retirement: a latent class analysis of older workers' motives.

One of the fastest growing labour market groups is working pensioners, meaning those who work past the statutory retirement age whilst receiving a pension. Previous research has investigated the motives of this group and found very heterogeneous reasons for employment in retirement. However, little is known about the expectations and preferred work arrangements of older workers regarding a potential post-retirement employment. Using data from the German survey transitions and old age potential, we explore older workers' motives, preferences and expectations towards working in retirement. Results show that about half of the respondents plan to work in addition to receiving a pension; however, the share is higher amongst men and those with higher levels of education. The motives for staying in post-retirement employment vary as well: using latent class analysis, we find four distinct patterns of motives that can be classified as (1) financially-driven, (2) status-driven, (3) contact and fun-driven, as well as (4) generativity-driven, underlining the complexity of retirement decisions. Furthermore, preferences regarding arrangements when combining work and retirement are very heterogeneous. Whilst highly educated men want to work as self-employed, women and those with lower qualifications want to stay in their old jobs. Only small differences were found regarding preferred hours (about 17) and days per week (2.24). In summary, the results show that the rapidly growing group of working pensioners and their preferences should be seen as characterised by differences by those responsible for creating these post-retirement employment opportunities.

Classification of psychiatric symptoms using deep interaction networks: the CASPIAN-IV study.

Identifying the possible factors of psychiatric symptoms among children can reduce the risk of adverse psychosocial outcomes in adulthood. We designed a classification tool to examine the association between modifiable risk factors and psychiatric symptoms, defined based on the Persian version of the WHO-GSHS questionnaire in a developing country. Ten thousand three hundred fifty students, aged 6-18 years from all Iran provinces, participated in this study. We used feature discretization and encoding, stability selection, and regularized group method of data handling (GMDH) to classify the a priori specific factors (e.g., demographic, sleeping-time, life satisfaction, and birth-weight) to psychiatric symptoms. Self-rated health was the most critical feature. The selected modifiable factors were eating breakfast, screentime, salty snack for depression symptom, physical activity, salty snack for worriedness symptom, (abdominal) obesity, sweetened beverage, and sleep-hour for mild-to-moderate emotional symptoms. The area under the ROC curve of the GMDH was 0.75 (CI 95% 0.73-0.76) for the analyzed psychiatric symptoms using threefold cross-validation. It significantly outperformed the state-of-the-art (adjusted p < 0.05; McNemar's test). In this study, the association of psychiatric risk factors and the importance of modifiable nutrition and lifestyle factors were emphasized. However, as a cross-sectional study, no causality can be inferred.

Planned Retirement Timing in Europe: Are Europeans Adapting to the Policy of Extending Working Lives.

As populations are ageing concerns regarding the sustainability of European welfare states have come to the forefront. In reaction, policy makers have implemented measurements aimed at the prolongation of working lives. This study investigates weather older workers have adapted their planned retirement age, as a result of this new policy credo. Based on data from Survey of Health, Ageing and Retirement in Europe (SHARE) the analysis shows an increase of the planned retirement age (1.36 years) across all ten European countries investigated, albeit with country-specific variations. Variations on the individual level can be detected in regard to gender, education and self-reported health status.

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the [Formula: see text] variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10[Formula: see text] Genomics and Smart-seq2 technologies, we could show that for 10[Formula: see text] datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

Deep generative models in DataSHIELD.

BACKGROUND: The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. METHODS: The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package "BoltzmannMachines" from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. RESULTS: We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size. CONCLUSIONS: Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

Synthetic observations from deep generative models and binary omics data with limited sample size.

Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g. as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios (ORs) is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 300 variables, with a tendency of over-estimating ORs when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of ORs. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful.

Comparison of time and dose dependent gene expression and affected pathways in primary human fibroblasts after exposure to ionizing radiation.

BACKGROUND: Exposure to ionizing radiation induces complex stress responses in cells, which can lead to adverse health effects such as cancer. Although a variety of studies investigated gene expression and affected pathways in human fibroblasts after exposure to ionizing radiation, the understanding of underlying mechanisms and biological effects is still incomplete due to different experimental settings and small sample sizes. Therefore, this study aims to identify the time point with the highest number of differentially expressed genes and corresponding pathways in primary human fibroblasts after irradiation at two preselected time points. METHODS: Fibroblasts from skin biopsies of 15 cell donors were exposed to a high (2Gy) and a low (0.05Gy) dose of X-rays. RNA was extracted and sequenced 2 h and 4 h after exposure. Differentially expressed genes with an adjusted p-value < 0.05 were flagged and used for pathway analyses including prediction of upstream and downstream effects. Principal component analyses were used to examine the effect of two different sequencing runs on quality metrics and variation in expression and alignment and for explorative analysis of the radiation dose and time point of analysis. RESULTS: More genes were differentially expressed 4 h after exposure to low and high doses of radiation than after 2 h. In experiments with high dose irradiation and RNA sequencing after 4 h, inactivation of the FAT10 cancer signaling pathway and activation of gluconeogenesis I, glycolysis I, and prostanoid biosynthesis was observed taking p-value (< 0.05) and (in) activating z-score (≥2.00 or ≤ - 2.00) into account. Two hours after high dose irradiation, inactivation of small cell lung cancer signaling was observed. For low dose irradiation experiments, we did not detect any significant (p < 0.05 and z-score ≥ 2.00 or ≤ - 2.00) activated or inactivated pathways for both time points. CONCLUSIONS: Compared to 2 h after irradiation, a higher number of differentially expressed genes were found 4 h after exposure to low and high dose ionizing radiation. Differences in gene expression were related to signal transduction pathways of the DNA damage response after 2 h and to metabolic pathways, that might implicate cellular senescence, after 4 h. The time point 4 h will be used to conduct further irradiation experiments in a larger sample.

Exploring generative deep learning for omics data using log-linear models.

MOTIVATION: Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for uncovering structure by generating synthetic samples. However, exploration and visualization is not as straightforward as with image applications. RESULTS: We demonstrate how log-linear models, fitted to the generated, synthetic data can be used to extract patterns from omics data, learned by deep generative techniques. Specifically, interactions between latent representations learned by the approaches and generated synthetic data are used to determine sets of joint patterns. Distances of patterns with respect to the distribution of latent representations are then visualized in low-dimensional coordinate systems, e.g. for monitoring training progress. This is illustrated with simulated data and subsequently with cortical single-cell gene expression data. Using different kinds of deep generative techniques, specifically variational autoencoders and deep Boltzmann machines, the proposed approach highlights how the techniques uncover underlying structure. It facilitates the real-world use of such generative deep learning techniques to gain biological insights from omics data. AVAILABILITY AND IMPLEMENTATION: The code for the approach as well as an accompanying Jupyter notebook, which illustrates the application of our approach, is available via the GitHub repository: https://github.com/ssehztirom/Exploring-generative-deep-learning-for-omics-data-by-using-log-linear-models. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Use of Internet-Based Health and Care Services by Elderly People in Europe and the Importance of the Country Context: Multilevel Study.

BACKGROUND: Digital health care is becoming increasingly important, but it has the risk of further increasing the digital divide, as not all individuals have the opportunity, skills, and knowledge to fully benefit from potential advantages. In particular, elderly people have less experience with the internet, and hence, they are in danger of being excluded. Knowledge on the influences of the adoption of internet-based health and care services by elderly people will help to develop and promote strategies for decreasing the digital divide. OBJECTIVE: This study examined if and how elderly people are using digital services to access health and social care. Moreover, it examined what personal characteristics are associated with using these services and if there are country differences. METHODS: Data for this study were obtained from the Special Eurobarometer 460 (SB 460), which collected data on Europeans' handling of and attitudes toward digital technologies, robots, and artificial intelligence, including data on the use of internet-based health and social care services, among 27,901 EU citizens aged 15 years or older. Multilevel logistic regression models were adopted to analyze the association of using the internet for health and social care services with several individual and country-level variables. RESULTS: At the individual level, young age, high education, high social class, and living in an urban area were positively associated with a high probability of using internet-based health and social services. At the country level, the proportion of elderly people who participated in any training activity within the last month was positively associated with the proportion of elderly people using these services. CONCLUSIONS: The probability of using internet-based health and social services and their accompanying advantages strongly depend on the socioeconomic background. Training and educational programs might be helpful to mitigate these differences.