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In the present study, cryo‐electron tomography was used to investigate the localization of 2‐oxoacid dehydrogenase complexes (OADCs) in cardiac mitochondria and mitochondrial inner membrane samples. Two classes of ordered OADC inner cores with different symmetries were distinguished and their quaternary structures modeled. One class corresponds to pyruvate dehydrogenase complexes and the other to dehydrogenase complexes of α‐ketoglutarate and branched‐chain α‐ketoacids. OADCs were shown to be localized in close proximity to membrane‐embedded respirasomes, as observed both in densely packed lamellar cristae of cardiac mitochondria and in ruptured mitochondrial samples where the dense packing is absent. This suggests the specificity of the OADC–respirasome interaction, which allows localized NADH/NAD+ exchange between OADCs and complex I of the respiratory chain. The importance of this local coupling is based on OADCs being the link between respiration, glycolysis and amino acid metabolism. The coupling of these basic metabolic processes can vary in different tissues and conditions and may be involved in the development of various pathologies. The present study shows that this important and previously missing parameter of mitochondrial complex coupling can be successfully assessed using cryo‐electron tomography. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements. [ABSTRACT FROM AUTHOR]

Quantum confinement is known to affect a nanosized superconductor through quantum‐size variations of the electronic density of states. Here, it is demonstrate that there is another quantum‐confinement mechanism overlooked in previous studies. In particular, it is found that the electron–electron attraction can be enhanced due to quantum‐confinement modifications of electronic wave functions. The superconducting correlations are strengthened by such quantum mechanical effect, which creates a subtle interplay with surface–substrate phonon modifications. The combined effect depends on nanofilm thickness and can be controlled by nanoarchitechture. The calculations are in a reasonable agreement with experiments performed on high‐quality aluminum films. These findings shed light on the long‐standing problem of the size dependence of the critical temperature in low‐dimensional superconductors. Nanostructured superconductors attract significant interest because their properties may strongly vary with the system geometry and dimensions. This opens prospects of architecting superconducting nanodevices in order to tune their characteristics. Here the authors demonstrate that one of promising examples is high‐quality metallic nanofilms, where superconductivity is enhanced by quantum‐size effects and the enhancement is well controlled by the nanofilm thickness. [ABSTRACT FROM AUTHOR]