Macrophage global metabolomics identifies cholestenone as host/pathogen cometabolite present in human Mycobacterium tuberculosis infection

Mycobacterium tuberculosis (M. tuberculosis) causes an enormous burden of disease worldwide. As a central aspect of its pathogenesis, M. tuberculosis grows in macrophages, and host and microbe influence each other\u27s metabolism. To define the metabolic impact of M. tuberculosis infection, we performed global metabolic profiling of M. tuberculosis-infected macrophages. M. tuberculosis induced metabolic hallmarks of inflammatory macrophages and a prominent signature of cholesterol metabolism. We found that infected macrophages accumulate cholestenone, a mycobacterial-derived, oxidized derivative of cholesterol. We demonstrated that the accumulation of cholestenone in infected macrophages depended on the M. tuberculosis enzyme 3β-hydroxysteroid dehydrogenase (3β-Hsd) and correlated with pathogen burden. Because cholestenone is not a substantial human metabolite, we hypothesized it might be diagnostic of M. tuberculosis infection in clinical samples. Indeed, in 2 geographically distinct cohorts, sputum cholestenone levels distinguished subjects with tuberculosis (TB) from TB-negative controls who presented with TB-like symptoms. We also found country-specific detection of cholestenone in plasma samples from M. tuberculosis-infected subjects. While cholestenone was previously thought to be an intermediate required for cholesterol degradation by M. tuberculosis, we found that M. tuberculosis can utilize cholesterol for growth without making cholestenone. Thus, the accumulation of cholestenone in clinical samples suggests it has an alternative role in pathogenesis and could be a clinically useful biomarker of TB infection

Spatial scale of correlated signals in 7T BOLD imaging

The spatial distribution of signals from magnetic resonance imaging (MRI) using measures of Blood Oxygen Level Dependent (BOLD) activations presents a fundamental limit on the ability of MRI to resolve the neural signals from the brain. Here we show that the multiple samples of low-level BOLD activity comprise a form of neural “imaging dust” with distinct spatial characteristics. We apply the distance-dependent measurement of variance to spatial maps of BOLD signals to deliver a new approach to estimating the empirical point-spread function (PSF) of MRI. We show that these new estimates are similar to earlier measures of the PSF of high field 7-T imaging, but deliver the advantage that they are specific to each individual tested in a single scanning session. We explore various potential applications of this approach

A digital twin model for enhancing performance measurement in assembly lines

Dynamic manufacturing processes are characterized by a lack of coordination, complexity and sheer volumes of data. Digital transformation technologies offer the manufacturers the capability to better monitor and control both assets and production. This provides also an ever-improving ability to investigate new products and production concepts in the virtual world while optimizing future production with IoT-captured data from different devices and shop floor machine centres. In this study, a digital twin is presented for an assembly line, where IoT-captured data is fed back into the digital twin enabling manufacturers to interface, analyse and measure the performance in real-time of a manufacturing process. The digital twin concept is then applied to an assembly production plan found in the automotive industry, where actual data is considered to analyse how the digital duplicate can be used to review activities and improve productivity within all production shifts