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Single-atom catalysts are becoming increasingly significant to numerous energy conversion reactions. However, their rational design and construction remain quite challenging due to the poorly understood structure-function relationship. Here we demonstrate the dynamic behavior of CuN2C2 site during operando oxygen reduction reaction, revealing a substrate-strain tuned geometry distortion of active sites and its correlation with the activity. Our best CuN2C2 site, on carbon nanotube with 8 nm diameter, delivers a sixfold activity promotion relative to graphene. Density functional theory and X-ray absorption spectroscopy reveal that reasonable substrate strain allows the optimized distortion, where Cu bonds strongly with the oxygen species while maintaining intimate coordination with C/N atoms. The optimized distortion facilitates the electron transfer from Cu to the adsorbed O, greatly boosting the oxygen reduction activity. This work uncovers the structure-function relationship of single-atom catalysts in terms of carbon substrate, and provides guidance to their future design and activity promotion.Here, we show that metal oxide surfaces catalyze the formation of intermediate defluorinated tetrafluoroethylene (TFE) radicals, resulting in enhanced binding on the corresponding metal oxide surfaces. We attribute the preferential adsorption and radical formation of TFE on Cr2O3(0001) relative to TiO2(110) to the low oxygen coordination of Cr surface atoms. This hints at a possible dependence of the TFE binding strength to the surface stoichiometry of metal-oxide surfaces.We demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input data. An example of naturally occurring correlated auxiliary noise is the noise due to decoherence. Our results could, therefore, also be of interest, for example, for machine-learned quantum error correction.The concept of depth induces an ordering from centre outwards in multivariate data. Most depth definitions are unfeasible for dimensions larger than three or four, but the Modified Band Depth (MBD) is a notable exception that has proven to be a valuable tool in the analysis of high-dimensional gene expression data. This depth definition relates the centrality of each individual to its (partial) inclusion in all possible bands formed by elements of the data set. selleck chemicals llc We assess (dis)similarity between pairs of observations by accounting for such bands and constructing binary matrices associated to each pair. From these, contingency tables are calculated and used to derive standard similarity indices. Our approach is computationally efficient and can be applied to bands formed by any number of observations from the data set. We have evaluated the performance of several band-based similarity indices with respect to that of other classical distances in standard classification and clustering tasks in a variety of simulated and real data sets. However, the use of the method is not restricted to these, the extension to other similarity coefficients being straightforward. Our experiments show the benefits of our technique, with some of the selected indices outperforming, among others, the Euclidean distance.The fans' importance in sports is acknowledged by the term 'the 12th man', a figurative extra player for the home team. Sport teams are indeed more successful when they play in front of their fans than when they play away. The supposed mechanism behind this phenomenon, termed Home Advantage (HA), is that fans' support spurs home players to better performance and biases referees, which in turn determines the outcome. The inference about the importance of fans' support is, however, indirect as there is normally a 12th man of this kind, even if it is an opponent's. The current pandemic, which forced sporting activities to take place behind closed doors, provides the necessary control condition. Here we employ a novel conceptual HA model on a sample of over 4000 soccer matches from 12 European leagues, some played in front of spectators and some in empty stadia, to demonstrate that fans are indeed responsible for the HA. However, the absence of fans reduces the HA by a third, as the home team's performance suffers and the officials' bias disappears. The current pandemic reveals that the figurative 12th man is no mere fan hyperbole, but is in fact the most important player in the home team.The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.Vast efforts have been devoted to the development of antifungal drugs targeting the cell wall, but the supramolecular architecture of this carbohydrate-rich composite remains insufficiently understood. Here we compare the cell wall structure of a fungal pathogen Aspergillus fumigatus and four mutants depleted of major structural polysaccharides. High-resolution solid-state NMR spectroscopy of intact cells reveals a rigid core formed by chitin, β-1,3-glucan, and α-1,3-glucan, with galactosaminogalactan and galactomannan present in the mobile phase. Gene deletion reshuffles the composition and spatial organization of polysaccharides, with significant changes in their dynamics and water accessibility. The distribution of α-1,3-glucan in chemically isolated and dynamically distinct domains supports its functional diversity. Identification of valines in the alkali-insoluble carbohydrate core suggests a putative function in stabilizing macromolecular complexes. We propose a revised model of cell wall architecture which will improve our understanding of the structural response of fungal pathogens to stresses.The phase diagrams of LaMnO3 perovskites have been intensely studied due to the colossal magnetoresistance (CMR) exhibited by compositions around the [Formula see text] doping level. However, phase segregation between ferromagnetic (FM) metallic and antiferromagnetic (AFM) insulating states, which itself is believed to be responsible for the colossal change in resistance under applied magnetic field, has prevented an atomistic-level understanding of the orbital ordered (OO) state at this doping level. Here, through the detailed crystallographic analysis of the phase diagram of a prototype system (AMn[Formula see text]Mn[Formula see text]O12), we show that the superposition of two distinct lattice modes gives rise to a striping of OO Jahn-Teller active Mn3+ and charge disordered (CD) Mn3.5+ layers in a 13 ratio. This superposition only gives a cancellation of the Jahn-Teller-like displacements at the critical doping level. This striping of CD Mn3.5+ with Mn3+ provides a natural mechanism though which long range OO can melt, giving way to a conducting state.The practical application of room-temperature Na-S batteries is hindered by the low sulfur utilization, inadequate rate capability and poor cycling performance. To circumvent these issues, here, we propose an electrocatalyst composite material comprising of N-doped nanocarbon and Fe3N. The multilayered porous network of the carbon accommodates large amounts of sulfur, decreases the detrimental effect of volume expansion, and stabilizes the electrodes structure during cycling. Experimental and theoretical results testify the Fe3N affinity to sodium polysulfides via Na-N and Fe-S bonds, leading to strong adsorption and fast dissociation of sodium polysulfides. With a sulfur content of 85 wt.%, the positive electrode tested at room-temperature in non-aqueous Na metal coin cell configuration delivers a reversible capacity of about 1165 mA h g-1 at 167.5 mA g-1, satisfactory rate capability and stable capacity of about 696 mA h g-1 for 2800 cycles at 8375 mA g-1.Previous neuroimaging studies in rodents investigated effects of the controlled cortical impact (CCI) model of traumatic brain injury (TBI) within one-month post-TBI. This study extends this temporal window to monitor the structural-functional alterations from two hours to six months post-injury. Thirty-seven male Sprague-Dawley rats were randomly assigned to TBI and sham groups, which were scanned at two hours, 1, 3, 7, 14, 30, 60 days, and six months following CCI or sham surgery. Structural MRI, diffusion tensor imaging, and resting-state functional magnetic resonance imaging were acquired to assess the dynamic structural, microstructural, and functional connectivity alterations post-TBI. There was a progressive increase in lesion size associated with brain volume loss post-TBI. Furthermore, we observed reduced fractional anisotropy within 24 h and persisted to six months post-TBI, associated with acutely reduced axial diffusivity, and chronic increases in radial diffusivity post-TBI. Moreover, a time-dependent pattern of altered functional connectivity evolved over the six months' follow-up post-TBI. This study extends the current understanding of the CCI model by confirming the long-term persistence of the altered microstructure and functional connectivity, which may hold a strong translational potential for understanding the long-term sequelae of TBI in humans.A novel non-linear beamforming method, namely, filtered delay optimally-weighted multiply and sum (F-DowMAS) beamforming is reported for conventional focused beamforming (CFB) technique. The performance of F-DowMAS was compared against delay and sum (DAS), filtered delay multiply and sum (F-DMAS), filtered delay weight multiply and sum (F-DwMAS) and filter delay Euclidian weighted multiply and sum (F-DewMAS) methods. Notably, in the proposed method the optimal adaptive weights are computed for each imaging point to compensate for the effects due to spatial variations in beam pattern in CFB technique. F-DowMAS, F-DMAS, and DAS were compared in terms of the resulting image quality metrics, Lateral resolution (LR), axial resolution (AR), contrast ratio (CR) and contrast-to-noise ratio (CNR), estimated from experiments on a commercially available tissue-mimicking phantom. The results demonstrate that F-DowMAS improved the AR by 57.04% and 46.95%, LR by 58.21% and 53.40%, CR by 67.35% and 39.25%, and CNR by 44.04% and 30.