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1AbstractCortical microinfarcts are caused by blood flow disturbances and are linked to pathologies like cerebral amyloid angiopathy and dementia. Despite their relevance for disease progression, microinfarcts often remain undetected and the smallest scale of disturbance has not yet been identified. We employ blood flow simulations in realistic microvascular networks from the mouse cortex to quantify the impact of single capillary occlusions. We reveal that the severity of a microstroke is strongly affected by the local vascular topology and the baseline flow rate in the occluded capillary. The largest changes in flow rate are observed in capillaries with two in- and two outflows. Interestingly, this specific topological configuration only occurs with a frequency of 8%, while the majority of capillaries are likely designed to efficiently supply oxygen and nutrients. Taken together, microstrokes likely induce a cascade of local disturbances in the surrounding tissue, which might accumulate and impair energy supply locally.

When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma.

The high levels of serine (S) and threonine (T) residues within the Presenilin 1 (PS1) N-terminus and in the large hydrophilic loop region suggest that the enzymatic function of PS1/γ-secretase can be modulated by its ‘phosphorylated’ and ‘dephosphorylated’ states. However, the functional outcome of PS1 phosphorylation and its significance for Alzheimer’s disease (AD) pathogenesis is poorly understood. Here, comprehensive analysis using FRET-based imaging reveals that activity-driven and Protein Kinase A-mediated PS1 phosphorylation at three domains (domain 1: T74, domain 2: S310 and S313, domain 3: S365, S366, and S367), with S367 being critical, is responsible for the PS1 pathogenic ‘closed’ conformation, and resulting increase in the Aβ42/40 ratio. Moreover, we have established novel imaging assays for monitoring PS1 conformation in vivo, and report that PS1 phosphorylation induces the pathogenic conformational shift in the living mouse brain. These phosphorylation sites represent potential new targets for AD treatment.

Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data. We predicted 10 phenotypes of AAM at age 22 using brain MRI features at ages 14, 19, and 22. Binge drinking was found to be the most predictable phenotype. The most informative brain features were located in the ventricular CSF, and in white matter tracts of the corpus callosum, internal capsule, and brain stem. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. We also experimented with four different ML models and several confound control techniques. Support Vector Machine (SVM) with rbf kernel and Gradient Boosting consistently performed better than the linear models, linear SVM and Logistic Regression. Our study also demonstrates how the choice of the predicted phenotype, ML model, and confound correction technique are all crucial decisions in an explorative ML study analyzing psychiatric disorders with small effect sizes such as AAM.

AbstractYellow Fever (YF) is an arbovirus capable of causing haemorrhagic fever which is endemic in tropical regions of Africa and South America. In recent years, it has resurged – leading to large outbreaks and expanding its endemic zone, the causes of which are unknown. In Africa, the disease is currently considered endemic in 34 countries where it is estimated to cause 78,000 deaths a year. As the mosquito vectors of YF sensitive to environmental conditions, climate change may have substantial effects on the transmission of YF. Here we present the first analysis of the potential impact of climate change on YF transmission and disease burden. We extend an existing model of YF transmission in Africa to account for rainfall and a temperature suitability index. From this, we project transmission intensity across the African endemic region in the context of four climate change scenarios (representative concentration pathways (RCPs) 2.6, 4.5, 6.0 and 8.5). We use these transmission projections to assess the change from current to future disease burden in 2050 and 2070 for each emission scenario. We find that disease burden changes heterogeneously with temperature and rainfall across the region. In RCP 2.6, we find a 93.0% [95% CI 92.7, 93.2%] chance that deaths will increase in 2050. We find that the annual expected number of deaths may increase by between 10.8% [95% CrI -2.4, 37.9%] for RCP 2.6 and 24.9% [95% CrI -2.2, 88.3%] for RCP 8.5 in 2050, with the most notable changes occurring in East and Central Africa. Changes in temperature and rainfall will affect the transmission dynamics of YF. Such a change in epidemiology will complicate future control efforts. As such, we may need to consider the effect of changing climactic variables on future intervention strategies.

Quantifying the relative impact of environmental conditions and host community structure on disease is one of the greatest challenges of the 21st century, as both climate and biodiversity are changing at unprecedented rates. Both increasing temperature and shifting host communities toward more fast-paced life-history strategies are predicted to increase disease, yet their independent and interactive effects on disease in natural communities remain unknown. Here, we address this challenge by surveying foliar disease symptoms in 220, 0.5 m-diameter herbaceous plant communities along a 1100-m elevational gradient. We find that increasing temperature associated with lower elevation can increase disease by (1) relaxing constraints on parasite growth and reproduction, (2) determining which host species are present in a given location, and (3) strengthening the positive effect of host community pace-of-life on disease. These results provide the first field evidence, under natural conditions, that environmental gradients can alter how host community structure affects disease.