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Background: Risk factors for dementia show inter-individually varying trajectories over the lifespan. However, risk factors have been mainly investigated with one time-point assessments. New research suggests that certain risk factor trajectories are associated with increased risk of adverse cognitive outcomes (Demnitz et al., 2020 [https://doi.org/10.1101/2020.05.20.20106963]; Singh-Manoux et al., 2018 [https://doi.org/10.1016/j.jalz.2017.06.2637]). However, it remains unclear how sequential and simultaneous changes of risk factors alter the individual risk for developing dementia. Testing the joint contribution of trajectories of depressive symptoms and self-perceived health on incidence of dementia, we hypothesized that consistently poor as well as deteriorating trajectories increase the risk for incident dementia, and explored possible interactions of the trajectories. Method: A total of 5,326 respondents to the SHARE survey, mean age 73.9 years, and 6 complete follow-ups spanning ~13 years, answered the EURO-D depression scale, self-perceived health (SPH) (t1-t5), and self-reported dementia diagnosis at last follow-up (t6). To investigate the predictive ability of distinct longitudinal trajectories, we applied unsupervised statistical learning methods (K-means cluster modelling). Clusters indicated distinct risk factor trajectories, which were used as exposures in stepwise logistic regressions to predict incident dementia, controlling for age, gender, education, and country. Result: Cluster analysis revealed five distinct trajectories each for SPH and EURO-D, with varying dementia incidence. In stepwise logistic regressions, respondents with trajectories “consistently poor health” and “consistently high depression” showed elevated risk of dementia (OR = 4.02 [1.39, 14.75] and OR = 2.26 [1.03, 4.95], respectively) compared to the combined baseline risk for “consistently low depression” and “consistently good health”. Interactions were not significant. However, respondents with the combination of “consistently high depression” and “consistently poor health” showed increased risk (N = 246; 6.1% dementia). Conclusion: Applying unsupervised machine learning is helpful to incorporate longitudinal information on depressive symptoms and self-perceived health and model these risk factors longitudinally to test their contribution to explain incidence of dementia. The predictive ability of the trajectories of depressive symptoms and self-perceived health for dementia indicates the potential for improving the identification of people at risk for developing dementia in late life by exploiting trajectory information readily accessible through regular medical check-ups in old age.

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic analyses of host- or environment-derived samples offer the prospect of resolving fundamental and realised niches in situ. In turn, this is considered a prerequisite for niche engineering in order to drive an individual population or a community towards a specific phenotype, e.g., improvement of a biotechnological process. Here, we sampled floating islets on the surface of an activated sludge tank in a time-series spanning 51 time-points over 14 months. Multi-omics datasets (metagenomics, metatranscriptomics, metaproteomics, and (meta-)metabolomics) were generated for all time-points. Leveraging nucleotide sequencing data, we analyzed the community structure and reconstructed genomes for specific populations of interest. Moreover, based on their metabolic potential, three major groups emerged, serving as proxies for their respective fundamental niches . Time-resolved linkage of the proteomic and transcriptomic data to the reconstructed genomes revealed a fine-grained picture of niche realization. In particular, environmental factors (temperature, metabolites, oxygen) were significantly associated with gene expression of individual populations. Furthermore, we subjected the community to controlled oxygen conditions (stable or dynamic) in a bioreactor experiment and measured the transcriptomic response. Our results suggest short-term adaptations of populations of interest with respect to lipid metabolism, among other pathways. In conclusion, our work demonstrates how longitudinal multi-omic datasets can be integrated in order to further our understanding of microbial niche ecology within a biotechnological process with potential applications beyond waste water treatment.

This research considers how best to cross the divides that exist between: (1) disparate practices between research fields (2) disparate interpretations of shared cultural heritage by the public and (3) disparate cultural heritage objects.

Despite the importance of Rayleigh-product multiple-input multiple-output channels and their experimental validations, there is no work investigating their performance in the presence of residual additive transceiver hardware impairments, which arise in practical scenarios. Hence, this paper focuses on the impact of these residual imperfections on the ergodic channel capacity for optimal receivers, and on the ergodic sum rates for linear minimum mean-squared-error (MMSE) receivers. Moreover, the low- A nd high-signal-to-noise ratio cornerstones are characterized for both types of receivers. Simple closed-form expressions are obtained that allow the extraction of interesting conclusions. For example, the minimum transmit energy per information bit for optimal and MMSE receivers is not subject to any additive impairments. In addition to the exact analysis, we also study the Rayleigh-product channels in the large system regime, and we elaborate on the behavior of the ergodic channel capacity with optimal receivers by varying the severity of the transceiver additive impairments. © 2017 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Peer reviewed

A powerful Monte Carlo variance reduction technique introduced in Cao and Zhang 2004 uses local derivatives to accelerate Monte Carlo estimation. This work aims to: develop a new derivative-driven estimator that works for SPDEs with uncertain data modelled as Gaussian random fields with Matérn covariance functions (infinite/high-dimensional problems) (Lindgren, Rue, and Lindström, 2011), use second-order derivative (Hessian) information for improved variance reduction over our approach in (Hauseux, Hale, and Bordas, 2017), demonstrate a software framework using FEniCS (Logg and Wells, 2010), dolfin-adjoint (Farrell et al., 2013) and PETSc (Balay et al., 2016) for automatic acceleration of MC estimation for a wide variety of PDEs on HPC architectures.