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Wastewater-based epidemiology (WBE) has demonstrated its importance to support SARS-CoV-2 epidemiology complementing individual testing strategies. Due to their immune-evasive potential and the resulting significance for public health, close monitoring of SARS-CoV-2 variants of concern (VoC) is required to evaluate the regulation of early local countermeasures. In this study, we demonstrate a rapid workflow for wastewater-based early detection and monitoring of the newly emerging SARS-CoV-2 VoCs Omicron in the end of 2021 at the municipal wastewater treatment plant (WWTP) Emschermuendung (KLEM) in the Federal State of North-Rhine-Westphalia (NRW, Germany).
Initially, available primers detecting Omicron-related mutations were rapidly validated in a central laboratory. Subsequently, RT-qPCR analysis of purified SARS-CoV-2 RNA was performed in a decentral PCR laboratory in close proximity to KLEM. This decentralized approach enabled the early detection of K417N present in Omicron in samples collected on 8th December 2021 and the detection of further mutations (N501Y, Δ69/70) in subsequent biweekly sampling campaigns. The presence of Omicron in wastewater was confirmed by next generation sequencing (NGS) in a central laboratory with samples obtained on 14th December 2021. Moreover, the relative increase of the mutant fraction of Omicron was quantitatively monitored over time by dPCR in a central PCR laboratory starting on 12th December 2021 confirming Omicron as the dominant variant by the end of 2021.
In conclusions, WBE plays a crucial role in surveillance of SARS-CoV-2 variants and is suitable as an early warning system to identify variant emergence. In particular, the successive workflow using RT-qPCR, RT-dPCR and NGS demonstrates the strength of WBE as a versatile tool to monitor variant spreading.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Wastewater-based SARS-CoV-2 epidemiology (WBE) has been established as an important tool to support individual testing strategies. The Omicron sub-variants BA.4/BA.5 have spread globally, displacing the preceding variants. Due to the severe transmissibility and immune escape potential of BA.4/BA.5, early monitoring was required to assess and implement countermeasures in time. In this study, we monitored the prevalence of SARS-CoV-2 BA.4/BA.5 at six municipal wastewater treatment plants (WWTPs) in the Federal State of North Rhine-Westphalia (NRW, Germany) in May and June 2022. Initially, L452R-specific primers/probes originally designed for SARS-CoV-2 Delta detection were validated using inactivated authentic viruses and evaluated for their suitability for detecting BA.4/BA.5. Subsequently, the assay was used for RT-qPCR analysis of RNA purified from wastewater obtained twice a week at six WWTPs. The occurrence of L452R carrying RNA was detected in early May 2022, and the presence of BA.4/BA.5 was confirmed by variant-specific single nucleotide polymorphism PCR (SNP-PCR) targeting E484A/F486V and NGS sequencing. Finally, the mutant fractions were quantitatively monitored by digital PCR, confirming BA.4/BA.5 as the majority variant by 5 June 2022. In conclusion, the successive workflow using RT-qPCR, variant-specific SNP-PCR, and RT-dPCR demonstrates the strength of WBE as a versatile tool to rapidly monitor variants spreading independently of individual test capacities.