The molecular and electronic structure of the four-iron cluster of the ferredoxin (Fd) from the hyperthermophilic archaeon, Pyrococcus furiosus, Pf (which has only three Cys in the cluster binding consensus sequence), has been investigated by 1H NMR in order to determine the identity of the noncysteinyl cluster ligand in each of the four redox states [Gorst, C. M., Zhou, Z. H., Ma, K., Teng, Q., Howard, J. B., Adams, M. W., & La Mar, G. N. (1995) Biochemistry 34, 8788-8795], and to characterize the electron spin ground state for the reduced cluster which at 10 K exhibits an unusual predominant S = 3/2 ground state [Conover, R. C., Kowal, A. T., Fu, W., Park, J. -B., Aono, S., Adams, M. W. W., & Johnson, M. K. (1990) J. Biol. Chem. 265, 8533-8541]. It is demonstrated that a combination of 1D and 2D NMR tailored to relaxed resonances allows the location of four hyperfine shifted and paramagnetically relaxed spin systems which dictates that all four cluster ligands are amino acid side chains, rather than a solvent water/hydroxide at the unique non-Cys ligation site. Three of the ligands could be sequence-specifically assigned to the three Cys residues (positions 11, 17, and 56) in the consensus sequence for cluster binding, hence identifying the fourth ligand as Asp 14. It is concluded that the identification of Asp ligation to a 4Fe cluster is readily achieved in the reduced, but not in the oxidized cluster of Fd. Analysis of the relaxation properties and pattern of the hyperfine shifts in Pf Fd reveals very strong similarities to other Fds with S = 1/2 ground states, leading to the conclusion that the S = 3/2 ground state is not detected in solution at ambient temperatures, and this in independent of the redox state of the two remaining Cys residues in the protein (positions 21 and 48). However, the electron self-exchange rate for 4Fe Pf Fd is significantly slower than for other 4Fe Fd with complete Cys ligation. Changes in the pattern of hyperfine shifts between oxidized and reduced clusters for the four ligands in Pf Fd reveal that the most significant variation occurs for the Asp 14 orientation, suggesting that the altered Asp orientation may "gate" the electron transfer.
Single-molecule conductance of a B−N substituted phenanthrene derivative and its isoelectronic CC counterpart was investigated by the scanning tunneling microscopy break junction (STM-BJ) technique. The incorporation of the B−N motif results in a better single-molecule conductivity than the CC analogue. Furthermore, the Lewis acid−base reaction between F − and the B atom of the B−N motif leads to a decrease of the conductance of the BN derivative, which can be understood due to the shifting of the energy positions of the LUMO, as revealed by quantum transport calculations, even though the HOMO−LUMO gap decreases in the B−F Lewis acid−base. These findings provide insights for modulating electron transport properties by isoelectronic structure design. The B−N isoelectronic substituted structure could be a feasible way to design single-molecule devices such as switches and chemical sensors.
Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with stateof-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin-10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin-100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin-100 is much lower than the accuracy of skin-10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01% for Skin-10 and 53.54% for Skin-100. We also present an object detection based approach by introducing bounding boxes into the Skin-10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes.
In order to design molecular electronic devices with high performance and stability, it is crucial to understand their structure-to-property relationships. Single-molecule break junction measurements yield a large number of conductancedistance traces, which are inherently highly stochastic. Here we propose a weakly supervised deep learning algorithm to classify and segment these conductance traces, a method that is mainly based on transfer learning with the pretrain-finetune technique. By exploiting the powerful feature extraction capabilities of the pretrained VGG-16 network, our convolutional neural network model not only achieves high accuracy in the classification of the conductance traces, but also segments precisely the conductance plateau from an entire trace with very few manually labeled traces. Thus, we can produce a more reliable estimation of the junction conductance and quantify the junction stability. These findings show that our model has achieved a better accuracy-to-manpower efficiency balance, opening up the possibility of using weakly supervised deep learning approaches in the studies of single-molecule junctions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.