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INTRODUCTION: Increasing evidence suggests that metabolic impairments contribute to early Alzheimer's disease (AD) mechanisms and subsequent dementia. Signals in metabolic pathways conserved across species can facilitate translation. METHODS: We investigated differences in serum and brain metabolites between the early‐onset 5XFAD and late‐onset LOAD1 (APOE4.Trem2*R47H) mouse models of AD to C57BL/6J controls at 6 months of age. RESULTS: We identified sex differences for several classes of metabolites, such as glycerophospholipids, sphingolipids, and amino acids. Metabolic signatures were notably different between brain and serum in both mouse models. The 5XFAD mice exhibited stronger differences in brain metabolites, whereas LOAD1 mice showed more pronounced differences in serum. DISCUSSION: Several of our findings were consistent with results in humans, showing glycerophospholipids reduction in serum of apolipoprotein E (apoE) ε4 carriers and replicating the serum metabolic imprint of the APOE ε4 genotype. Our work thus represents a significant step toward translating metabolic dysregulation from model organisms to human AD. Highlights: This was a metabolomic assessment of two mouse models relevant to Alzheimer's disease.Mouse models exhibit broad sex‐specific metabolic differences, similar to human study cohorts.The early‐onset 5XFAD mouse model primarily alters brain metabolites while the late‐onset LOAD1 model primarily changes serum metabolites.Apolipoprotein E (apoE) ε4 mice recapitulate glycerophospolipid signatures of human APOE ε4 carriers in both brain and serum. [ABSTRACT FROM AUTHOR]

Biological organisms achieve robust high-level behaviours by combining and coordinating stochastic low-level components.sup.1-3. By contrast, most current robotic systems comprise either monolithic mechanisms.sup.4,5 or modular units with coordinated motions.sup.6,7. Such robots require explicit control of individual components to perform specific functions, and the failure of one component typically renders the entire robot inoperable. Here we demonstrate a robotic system whose overall behaviour can be successfully controlled by exploiting statistical mechanics phenomena. We achieve this by incorporating many loosely coupled 'particles', which are incapable of independent locomotion and do not possess individual identity or addressable position. In the proposed system, each particle is permitted to perform only uniform volumetric oscillations that are phase-modulated by a global signal. Despite the stochastic motion of the robot and lack of direct control of its individual components, we demonstrate physical robots composed of up to two dozen particles and simulated robots with up to 100,000 particles capable of robust locomotion, object transport and phototaxis (movement towards a light stimulus). Locomotion is maintained even when 20 per cent of the particles malfunction. These findings indicate that stochastic systems may offer an alternative approach to more complex and exacting robots via large-scale robust amorphous robotic systems that exhibit deterministic behaviour. A stochastic robotic system shows deterministic behaviour--such as locomotion, object transport and phototaxis--from the collective motion of many loosely coupled disk-shaped 'particles' that perform only volumetric oscillations., Author(s): Shuguang Li [sup.1] [sup.2] , Richa Batra [sup.2] , David Brown [sup.3] , Hyun-Dong Chang [sup.3] , Nikhil Ranganathan [sup.3] , Chuck Hoberman [sup.4] [sup.5] , Daniela Rus [sup.1] [...]

Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.

Introduction: Alzheimer's disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD‐associated metabolic changes in the brain has been missing. Methods: We metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late‐life cognitive performance and AD neuropathology measures. Results: We observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD‐relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation, and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than amyloid beta pathology. Discussion: This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow‐up studies. [ABSTRACT FROM AUTHOR]

This article presents maplet , an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results and visualizations. maplet' s key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques. Availability and implementation The maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet [ABSTRACT FROM AUTHOR]