Personalized Lens Correction Improves Quantitative Fundus Autofluorescence Analysis.

Purpose: Quantitative fundus autofluorescence (QAF) currently deploys an age-based score to correct for lens opacification. However, in elderly people, lens opacification varies strongly between individuals of similar age, and innate lens autofluorescence is not included in the current correction formula. Our goal was to develop and compare an individualized formula. Methods: One hundred thirty participants were examined cross-sectionally, and a subset of 30 participants received additional multimodal imaging 2-week post-cataract-surgery. Imaging included the Scheimpflug principle, anterior chamber optical coherence tomography (AC-OCT), lens quantitative autofluorescence (LQAF), and retinal QAF imaging. Among the subset, least absolute shrinkage and selection operator regression and backward selection was implemented to determine which lens score best predicts the QAF value after lens extraction. Subsequently, a spline mixed model was applied to the whole cohort to quantify the influence of LQAF and Scheimpflug on QAF. Results: Age and LQAF measurements were found to be the most relevant variables, whereas AC-OCT measurements and Scheimpflug were eliminated by backward selection. Both an increase in Scheimpflug and LQAF values were associated with a decrease in QAF. The prediction error of the spline model (mean absolute error [MAE] ± standard deviation) of 32.2 ± 23.4 (QAF a.u.) was markedly lower compared to the current age-based formula MAE of 96.1 ± 93.5. Both smooth terms, LQAF (P < 0.01) and Scheimpflug (P < 0.001), were significant for the spline mixed model. Conclusions: LQAF imaging proved to be the most predictive for the impact of the natural lens on QAF imaging. The application of lens scores in the clinic could improve the accuracy of QAF imaging interpretation and might allow including aged patients in future QAF studies.

TGF-ß specifies TFH versus TH17 cell fates in murine CD4+ T cells through c-Maf.

T follicular helper (TFH) cells are essential for effective antibody responses, but deciphering the intrinsic wiring of mouse TFH cells has long been hampered by the lack of a reliable protocol for their generation in vitro. We report that transforming growth factor-ß (TGF-ß) induces robust expression of TFH hallmark molecules CXCR5 and Bcl6 in activated mouse CD4+ T cells in vitro. TGF-ß-induced mouse CXCR5+ TFH cells are phenotypically, transcriptionally, and functionally similar to in vivo-generated TFH cells and provide critical help to B cells. The study further reveals that TGF-ß-induced CXCR5 expression is independent of Bcl6 but requires the transcription factor c-Maf. Classical TGF-ß-containing T helper 17 (TH17)-inducing conditions also yield separate CXCR5+ and IL-17A-producing cells, highlighting shared and distinct cell fate trajectories of TFH and TH17 cells. We demonstrate that excess IL-2 in high-density T cell cultures interferes with the TGF-ß-induced TFH cell program, that TFH and TH17 cells share a common developmental stage, and that c-Maf acts as a switch factor for TFH versus TH17 cell fates in TGF-ß-rich environments in vitro and in vivo.

Processivity and specificity of histone acetylation by the male-specific lethal complex.

Acetylation of lysine 16 of histone H4 (H4K16ac) stands out among the histone modifications, because it decompacts the chromatin fiber. The metazoan acetyltransferase MOF (KAT8) regulates transcription through H4K16 acetylation. Antibody-based studies had yielded inconclusive results about the selectivity of MOF to acetylate the H4 N-terminus. We used targeted mass spectrometry to examine the activity of MOF in the male-specific lethal core (4-MSL) complex on nucleosome array substrates. This complex is part of the Dosage Compensation Complex (DCC) that activates X-chromosomal genes in male Drosophila. During short reaction times, MOF acetylated H4K16 efficiently and with excellent selectivity. Upon longer incubation, the enzyme progressively acetylated lysines 12, 8 and 5, leading to a mixture of oligo-acetylated H4. Mathematical modeling suggests that MOF recognizes and acetylates H4K16 with high selectivity, but remains substrate-bound and continues to acetylate more N-terminal H4 lysines in a processive manner. The 4-MSL complex lacks non-coding roX RNA, a critical component of the DCC. Remarkably, addition of RNA to the reaction non-specifically suppressed H4 oligo-acetylation in favor of specific H4K16 acetylation. Because RNA destabilizes the MSL-nucleosome interaction in vitro we speculate that RNA accelerates enzyme-substrate turn-over in vivo, thus limiting the processivity of MOF, thereby increasing specific H4K16 acetylation.

A wall-time minimizing parallelization strategy for approximate Bayesian computation.

Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often necessary. However, the existing parallelization strategies leave computing resources unused at times and thus do not optimally leverage them yet. We present look-ahead scheduling, a wall-time minimizing parallelization strategy for ABC Sequential Monte Carlo algorithms, which avoids idle times of computing units by preemptive sampling of subsequent generations. This allows to utilize all available resources. The strategy can be integrated with e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Our key contribution is the theoretical assessment of the strategy of preemptive sampling and the proof of unbiasedness. Complementary, we provide an implementation and evaluate the strategy on different problems and numbers of parallel cores, showing speed-ups of typically 10-20% and up to 50% compared to the best established approach, with some variability. Thus, the proposed strategy allows to improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.

Spectral Analysis of Human Retinal Pigment Epithelium Cells in Healthy and AMD Eyes.

Purpose: Retinal pigment epithelium (RPE) cells show strong autofluorescence (AF). Here, we characterize the AF spectra of individual RPE cells in healthy eyes and those affected by age-related macular degeneration (AMD) and investigate associations between AF spectral response and the number of intracellular AF granules per cell. Methods: RPE-Bruch's membrane flatmounts of 22 human donor eyes, including seven AMD-affected eyes (early AMD, three; geographic atrophy, one; neovascular, three) and 15 unaffected macula (<51 years, eight; >80 years, seven), were imaged at the fovea, perifovea, and near-periphery using confocal AF microscopy (excitation 488 nm), and emission spectra were recorded (500-710 nm). RPE cells were manually segmented with computer assistance and stratified by disease status, and emission spectra were analyzed using cubic spline transforms. Intracellular granules were manually counted and classified. Linear mixed models were used to investigate associations between spectra and the number of intracellular granules. Results: Spectra of 5549 RPE cells were recorded. The spectra of RPE cells in healthy eyes showed similar emission curves that peaked at 580 nm for fovea and perifovea and at 575 and 580 nm for near-periphery. RPE spectral curves in AMD eyes differed significantly, being blue shifted by 10 nm toward shorter wavelengths. No significant association coefficients were found between wavelengths and granule counts. Conclusions: This large series of RPE cell emission spectra at precisely predefined retinal locations showed a hypsochromic spectral shift in AMD. Combining different microscopy techniques, our work has identified cellular RPE spectral AF and subcellular granule properties that will inform future in vivo investigations using single-cell imaging.

Modeling early treatment response in AML from cell-free tumor DNA.

Monitoring disease response after intensive chemotherapy for acute myeloid leukemia (AML) currently requires invasive bone marrow biopsies, imposing a significant burden on patients. In contrast, cell-free tumor DNA (ctDNA) in peripheral blood, carrying tumor-specific mutations, offers a less-invasive assessment of residual disease. However, the relationship between ctDNA levels and bone marrow blast kinetics remains unclear. We explored this in 10 AML patients with NPM1 and IDH2 mutations undergoing initial chemotherapy. Comparison of mathematical mixed-effect models showed that (1) inclusion of blast cell death in the bone marrow, (2) transition of ctDNA to peripheral blood, and (3) ctDNA decay in peripheral blood describes kinetics of blast cells and ctDNA best. The fitted model allows prediction of residual bone marrow blast content from ctDNA, and its scaling factor, representing clonal heterogeneity, correlates with relapse risk. Our study provides precise insights into blast and ctDNA kinetics, offering novel avenues for AML disease monitoring.

FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes.

MOTIVATION: Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. RESULTS: Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AVAILABILITY AND IMPLEMENTATION: FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.

pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.

SUMMARY: Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. AVAILABILITY AND IMPLEMENTATION: pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto).

Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.

Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter and prediction uncertainties. Yet, generating representative samples from the posterior distribution is often computationally challenging. Here, we present an approach that lowers the computational complexity of sample generation for dynamical models with scaling, offset, and noise parameters. The proposed method is based on the marginalization of the posterior distribution. We provide analytical results for a broad class of problems with conjugate priors and show that the method is suitable for a large number of applications. Subsequently, we demonstrate the benefit of the approach for applications from the field of systems biology. We report an improvement up to 50 times in the effective sample size per unit of time. As the scheme is broadly applicable, it will facilitate Bayesian inference in different research fields.

Retest variability and patient reliability indices of quantitative fundus autofluorescence in age-related macular degeneration: a MACUSTAR study report.

This study aimed to determine the retest variability of quantitative fundus autofluorescence (QAF) in patients with and without age-related macular degeneration (AMD) and evaluate the predictive value of patient reliability indices on retest reliability. A total of 132 eyes from 68 patients were examined, including healthy individuals and those with various stages of AMD. Duplicate QAF imaging was conducted at baseline and 2 weeks later across six study sites. Intraclass correlation (ICC) analysis was used to evaluate the consistency of imaging, and mean opinion scores (MOS) of image quality were generated by two researchers. The contribution of MOS and other factors to retest variation was assessed using mixed-effect linear models. Additionally, a Random Forest Regressor was trained to evaluate the extent to which manual image grading of image quality could be replaced by automated assessment (inferred MOS). The results showed that ICC values were high for all QAF images, with slightly lower values in AMD-affected eyes. The average inter-day ICC was found to be 0.77 for QAF segments within the QAF8 ring and 0.74 for peripheral segments. Image quality was predicted with a mean absolute error of 0.27 on a 5-point scale, and of all evaluated reliability indices, MOS/inferred MOS proved most important. The findings suggest that QAF allows for reliable testing of autofluorescence levels at the posterior pole in patients with AMD in a multicenter, multioperator setting. Patient reliability indices could serve as eligibility criteria for clinical trials, helping identify patients with adequate retest reliability.

Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort.

Background: Lack of specific definitions of clinical characteristics, disease severity, and risk and preventive factors of post-COVID-19 syndrome (PCS) severely impacts research and discovery of new preventive and therapeutics drugs. Methods: This prospective multicenter cohort study was conducted from February 2020 to June 2022 in 5 countries, enrolling SARS-CoV-2 out- and in-patients followed at 3-, 6-, and 12-month from diagnosis, with assessment of clinical and biochemical features, antibody (Ab) response, Variant of Concern (VoC), and physical and mental quality of life (QoL). Outcome of interest was identification of risk and protective factors of PCS by clinical phenotype, setting, severity of disease, treatment, and vaccination status. We used SF-36 questionnaire to assess evolution in QoL index during follow-up and unsupervised machine learning algorithms (principal component analysis, PCA) to explore symptom clusters. Severity of PCS was defined by clinical phenotype and QoL. We also used generalized linear models to analyse the impact of PCS on QoL and associated risk and preventive factors. CT registration number: NCT05097677. Findings: Among 1796 patients enrolled, 1030 (57%) suffered from at least one symptom at 12-month. PCA identified 4 clinical phenotypes: chronic fatigue-like syndrome (CFs: fatigue, headache and memory loss, 757 patients, 42%), respiratory syndrome (REs: cough and dyspnoea, 502, 23%); chronic pain syndrome (CPs: arthralgia and myalgia, 399, 22%); and neurosensorial syndrome (NSs: alteration in taste and smell, 197, 11%). Determinants of clinical phenotypes were different (all comparisons p < 0.05): being female increased risk of CPs, NSs, and CFs; chronic pulmonary diseases of REs; neurological symptoms at SARS-CoV-2 diagnosis of REs, NSs, and CFs; oxygen therapy of CFs and REs; and gastrointestinal symptoms at SARS-CoV-2 diagnosis of CFs. Early treatment of SARS-CoV-2 infection with monoclonal Ab (all clinical phenotypes), corticosteroids therapy for mild/severe cases (NSs), and SARS-CoV-2 vaccination (CPs) were less likely to be associated to PCS (all comparisons p < 0.05). Highest reduction in QoL was detected in REs and CPs (43.57 and 43.86 vs 57.32 in PCS-negative controls, p < 0.001). Female sex (p < 0.001), gastrointestinal symptoms (p = 0.034) and renal complications (p = 0.002) during the acute infection were likely to increase risk of severe PCS (QoL <50). Vaccination and early treatment with monoclonal Ab reduced the risk of severe PCS (p = 0.01 and p = 0.03, respectively). Interpretation: Our study provides new evidence suggesting that PCS can be classified by clinical phenotypes with different impact on QoL, underlying possible different pathogenic mechanisms. We identified factors associated to each clinical phenotype and to severe PCS. These results might help in designing pathogenesis studies and in selecting high-risk patients for inclusion in therapeutic and management clinical trials. Funding: The study received funding from the Horizon 2020 ORCHESTRA project, grant 101016167; from the Netherlands Organisation for Health Research and Development (ZonMw), grant 10430012010023; from Inserm, REACTing (REsearch & ACtion emergING infectious diseases) consortium and the French Ministry of Health, grant PHRC 20-0424.

How European Research Projects Can Support Vaccination Strategies: The Case of the ORCHESTRA Project for SARS-CoV-2.

ORCHESTRA ("Connecting European Cohorts to Increase Common and Effective Response To SARS-CoV-2 Pandemic") is an EU-funded project which aims to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. Here, we describe the early results of this project, focusing on the strengths of multiple, international, historical and prospective cohort studies and highlighting those results which are of potential relevance for vaccination strategies, such as the necessity of a vaccine booster dose after a primary vaccination course in hematologic cancer patients and in solid organ transplant recipients to elicit a higher antibody titer, and the protective effect of vaccination on severe COVID-19 clinical manifestation and on the emergence of post-COVID-19 conditions. Valuable data regarding epidemiological variations, risk factors of SARS-CoV-2 infection and its sequelae, and vaccination efficacy in different subpopulations can support further defining public health vaccination policies.

Accessibility of covariance information creates vulnerability in Federated Learning frameworks.

MOTIVATION: Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side. RESULTS: We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks. AVAILABILITY AND IMPLEMENTATION: The code examples are provided at GitHub (https://github.com/manuhuth/Data-Leakage-From-Covariances.git). The CNSIM1 dataset, which we used in the manuscript, is available within the DSData R package (https://github.com/datashield/DSData/tree/main/data).

Cox proportional hazards deep neural network identifies peripheral blood complete remission to be at least equivalent to morphologic complete remission in predicting outcomes of patients treated with azacitidine-A prospective cohort study by the AGMT.

The current gold standard of response assessment in patients with myelodysplastic syndromes (MDS), chronic myelomonocytic leukemia (CMML), and acute myeloid leukemia (AML) is morphologic complete remission (CR) and CR with incomplete count recovery (CRi), both of which require an invasive BM evaluation. Outside of clinical trials, BM evaluations are only performed in ~50% of patients during follow-up, pinpointing a clinical need for response endpoints that do not necessitate BM assessments. We define and validate a new response type termed "peripheral blood complete remission" (PB-CR) that can be determined from the differential blood count and clinical parameters without necessitating a BM assessment. We compared the predictive value of PB-CR with morphologic CR/CRi in 1441 non-selected, consecutive patients diagnosed with MDS (n = 522; 36.2%), CMML (n = 132; 9.2%), or AML (n = 787; 54.6%), included within the Austrian Myeloid Registry (aMYELOIDr; NCT04438889). Time-to-event analyses were adjusted for 17 covariates remaining in the final Cox proportional hazards (CPH) model. DeepSurv, a CPH neural network model, and permutation-based feature importance were used to validate results. 1441 patients were included. Adjusted median overall survival for patients achieving PB-CR was 22.8 months (95%CI 18.9-26.2) versus 10.4 months (95%CI 9.7-11.2) for those who did not; HR = 0.366 (95%CI 0.303-0.441; p < .0001). Among patients achieving CR, those additionally achieving PB-CR had a median adjusted OS of 32.6 months (95%CI 26.2-49.2) versus 21.7 months (95%CI 16.9-27.7; HR = 0.400 [95%CI 0.190-0.844; p = .0161]) for those who did not. Our deep neural network analysis-based findings from a large, prospective cohort study indicate that BM evaluations solely for the purpose of identifying CR/CRi can be omitted.

Using machine learning to predict antibody response to SARS-CoV-2 vaccination in solid organ transplant recipients: the multicentre ORCHESTRA cohort.

OBJECTIVES: The study aim was to assess predictors of negative antibody response (AbR) in solid organ transplant (SOT) recipients after the first booster of SARS-CoV-2 vaccination. METHODS: Solid organ transplant recipients receiving SARS-CoV-2 vaccination were prospectively enrolled (March 2021-January 2022) at six hospitals in Italy and Spain. AbR was assessed at first dose (t0), second dose (t1), 3 ± 1 month (t2), and 1 month after third dose (t3). Negative AbR at t3 was defined as an anti-receptor binding domain titre <45 BAU/mL. Machine learning models were developed to predict the individual risk of negative (vs. positive) AbR using age, type of transplant, time between transplant and vaccination, immunosuppressive drugs, type of vaccine, and graft function as covariates, subsequently assessed using a validation cohort. RESULTS: Overall, 1615 SOT recipients (1072 [66.3%] males; mean age±standard deviation [SD], 57.85 ± 13.77) were enrolled, and 1211 received three vaccination doses. Negative AbR rate decreased from 93.66% (886/946) to 21.90% (202/923) from t0 to t3. Univariate analysis showed that older patients (mean age, 60.21 ± 11.51 vs. 58.11 ± 13.08), anti-metabolites (57.9% vs. 35.1%), steroids (52.9% vs. 38.5%), recent transplantation (<3 years) (17.8% vs. 2.3%), and kidney, heart, or lung compared with liver transplantation (25%, 31.8%, 30.4% vs. 5.5%) had a higher likelihood of negative AbR. Machine learning (ML) algorithms showing best prediction performance were logistic regression (precision-recall curve-PRAUC mean 0.37 [95%CI 0.36-0.39]) and k-Nearest Neighbours (PRAUC 0.36 [0.35-0.37]). DISCUSSION: Almost a quarter of SOT recipients showed negative AbR after first booster dosage. Unfortunately, clinical information cannot efficiently predict negative AbR even with ML algorithms.

Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation.

Calibrating model parameters on heterogeneous data can be challenging and inefficient. This holds especially for likelihood-free methods such as approximate Bayesian computation (ABC), which rely on the comparison of relevant features in simulated and observed data and are popular for otherwise intractable problems. To address this problem, methods have been developed to scale-normalize data, and to derive informative low-dimensional summary statistics using inverse regression models of parameters on data. However, while approaches only correcting for scale can be inefficient on partly uninformative data, the use of summary statistics can lead to information loss and relies on the accuracy of employed methods. In this work, we first show that the combination of adaptive scale normalization with regression-based summary statistics is advantageous on heterogeneous parameter scales. Second, we present an approach employing regression models not to transform data, but to inform sensitivity weights quantifying data informativeness. Third, we discuss problems for regression models under non-identifiability, and present a solution using target augmentation. We demonstrate improved accuracy and efficiency of the presented approach on various problems, in particular robustness and wide applicability of the sensitivity weights. Our findings demonstrate the potential of the adaptive approach. The developed algorithms have been made available in the open-source Python toolbox pyABC.

Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease.

Systemic inflammation is established as part of late-stage severe lung disease, but molecular, functional, and phenotypic changes in peripheral immune cells in early disease stages remain ill defined. Chronic obstructive pulmonary disease (COPD) is a major respiratory disease characterized by small-airway inflammation, emphysema, and severe breathing difficulties. Using single-cell analyses we demonstrate that blood neutrophils are already increased in early-stage COPD, and changes in molecular and functional neutrophil states correlate with lung function decline. Assessing neutrophils and their bone marrow precursors in a murine cigarette smoke exposure model identified similar molecular changes in blood neutrophils and precursor populations that also occur in the blood and lung. Our study shows that systemic molecular alterations in neutrophils and their precursors are part of early-stage COPD, a finding to be further explored for potential therapeutic targets and biomarkers for early diagnosis and patient stratification.

Nucleocapsid-specific T cell responses associate with control of SARS-CoV-2 in the upper airways before seroconversion.

Despite intensive research since the emergence of SARS-CoV-2, it has remained unclear precisely which components of the early immune response protect against the development of severe COVID-19. Here, we perform a comprehensive immunogenetic and virologic analysis of nasopharyngeal and peripheral blood samples obtained during the acute phase of infection with SARS-CoV-2. We find that soluble and transcriptional markers of systemic inflammation peak during the first week after symptom onset and correlate directly with upper airways viral loads (UA-VLs), whereas the contemporaneous frequencies of circulating viral nucleocapsid (NC)-specific CD4+ and CD8+ T cells correlate inversely with various inflammatory markers and UA-VLs. In addition, we show that high frequencies of activated CD4+ and CD8+ T cells are present in acutely infected nasopharyngeal tissue, many of which express genes encoding various effector molecules, such as cytotoxic proteins and IFN-γ. The presence of IFNG mRNA-expressing CD4+ and CD8+ T cells in the infected epithelium is further linked with common patterns of gene expression among virus-susceptible target cells and better local control of SARS-CoV-2. Collectively, these results identify an immune correlate of protection against SARS-CoV-2, which could inform the development of more effective vaccines to combat the acute and chronic illnesses attributable to COVID-19.

Validation of genetic variants from NGS data using deep convolutional neural networks.

Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessary step in state-of-the-art processing pipelines. This limits reproducibility and introduces a bottleneck with respect to scalability. We demonstrate that the validation of genetic variants can be improved using a machine learning approach resting on a Convolutional Neural Network, trained using existing human annotation. In contrast to existing approaches, we introduce a way in which contextual data from sequencing tracks can be included into the automated assessment. A rigorous evaluation shows that the resulting model is robust and performs on par with trained researchers following published standard operating procedure.

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach. The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.