Identification of metabolically quiescent Leishmania mexicana parasites in peripheral and cured dermal granulomas using stable isotope tracing imaging mass spectrometry

Leishmania are sandfly-transmitted protists that induce granulomatous lesions in their mammalian host. Although infected host cells in these tissues can exist in different activation states, the extent to which intracellular parasites stages also exist in different growth or physiological states remains poorly defined. Here, we have mapped the spatial distribution of metabolically quiescent and active subpopulations of Leishmania mexicana in dermal granulomas in susceptible BALB/c mice, using in vivo heavy water labeling and ultra high-resolution imaging mass spectrometry. Quantitation of the rate of turnover of parasite and host-specific lipids at high spatial resolution, suggested that the granuloma core comprised mixed populations of metabolically active and quiescent parasites. Unexpectedly, a significant population of metabolically quiescent parasites was also identified in the surrounding collagen-rich, dermal mesothelium. Mesothelium-like tissues harboring quiescent parasites progressively replaced macrophage-rich granuloma tissues following treatment with the first-line drug, miltefosine. In contrast to the granulomatous tissue, neither the mesothelium nor newly deposited tissue sequestered miltefosine. These studies suggest that the presence of quiescent parasites in acute granulomatous tissues, together with the lack of miltefosine accumulation in cured lesion tissue, may contribute to drug failure and nonsterile cure. IMPORTANCE Many microbial pathogens switch between different growth and physiological states in vivo in order to adapt to local nutrient levels and host microbicidal responses. Heterogeneity in microbial growth and metabolism may also contribute to nongenetic mechanisms of drug resistance and drug failure. In this study, we have developed a new approach for measuring spatial heterogeneity in microbial metabolism in vivo using a combination of heavy water (2H2O) labeling and imaging mass spectrometry. Using this approach, we show that lesions contain a patchwork of metabolically distinct parasite populations, while the underlying dermal tissues contain a large population of metabolically quiescent parasites. Quiescent parasites also dominate drug-depleted tissues in healed animals, providing an explanation for failure of some first line drugs to completely eradicate parasites. This approach is broadly applicable to study the metabolic and growth dynamics in other host-pathogen interactions

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures