Unraveling a Concerted Proton-Coupled Electron Transfer Pathway in Atomically Precise Gold Nanoclusters.

Metal nanoclusters (MNCs) represent a promising class of materials for catalytic carbon dioxide and proton reduction as well as dihydrogen oxidation. In such reactions, multiple proton-coupled electron transfer (PCET) processes are typically involved, and the current understanding of PCET mechanisms in MNCs has primarily focused on the sequential transfer mode. However, a concerted transfer pathway, i.e., concerted electron-proton transfer (CEPT), despite its potential for a higher catalytic rate and lower reaction barrier, still lacks comprehensive elucidation. Herein, we introduce an experimental paradigm to test the feasibility of the CEPT process in MNCs, by employing Au18(SR)14 (SR denotes thiolate ligand), Au22(SR)18, and Au25(SR)18- as model clusters. Detailed investigations indicate that the photoinduced PCET reactions in the designed system proceed via an CEPT pathway. Furthermore, the rate constants of gold nanoclusters (AuNCs) have been found to be correlated with both the size of the cluster and the flexibility of the Au-S framework. This newly identified PCET behavior in AuNCs is prominently different from that observed in semiconductor quantum dots and plasmonic metal nanoparticles. Our findings are of crucial importance for unveiling the catalytic mechanisms of quantum-confined metal nanomaterials and for the future rational design of more efficient catalysts.

Surface phase diagrams from nested sampling.

Studies in atomic-scale modeling of surface phase equilibria often focus on temperatures near zero Kelvin due to the challenges in calculating the free energy of surfaces at finite temperatures. The Bayesian-inference-based nested sampling (NS) algorithm allows for modeling phase equilibria at arbitrary temperatures by directly and efficiently calculating the partition function, whose relationship with free energy is well known. This work extends NS to calculate adsorbate phase diagrams, incorporating all relevant configurational contributions to the free energy. We apply NS to the adsorption of Lennard-Jones (LJ) gas particles on low-index and vicinal LJ solid surfaces and construct the canonical partition function from these recorded energies to calculate ensemble averages of thermodynamic properties, such as the constant-volume heat capacity and order parameters that characterize the structure of adsorbate phases. Key results include determining the nature of phase transitions of adsorbed LJ particles on flat and stepped LJ surfaces, which typically feature an enthalpy-driven condensation at higher temperatures and an entropy-driven reordering process at lower temperatures, and the effect of surface geometry on the presence of triple points in the phase diagrams. Overall, we demonstrate the ability and potential of NS for surface modeling.

Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades.

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.

PURPOSE: To develop an ultrafast and robust MR parameter mapping network using deep learning. THEORY AND METHODS: We design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan. Full data were acquired and retrospectively undersampled for training and testing using traditional and state-of-the-art techniques for comparison. Prospective data were also collected to evaluate the trained network. The performance of all methods is evaluated using the parameter qualification errors and other metrics in the segmented regions of interest. RESULTS: SuperMAP achieved accurate T1rho and T2 mapping with high acceleration factors (R = 24 and R = 32). It exploited both spatial and temporal information and yielded low error (normalized mean square error of 2.7% at R = 24 and 2.8% at R = 32) and high resemblance (structural similarity of 97% at R = 24 and 96% at R = 32) to the gold standard. The network trained with retrospectively undersampled data also works well for the prospective data (with a slightly lower acceleration factor). SuperMAP is also superior to conventional methods. CONCLUSION: Our results demonstrate the feasibility of generating superfast MR parameter maps through very few undersampled parameter-weighted images. SuperMAP can simultaneously generate T1rho and T2 relaxation maps in a short scan time.

Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.

PURPOSE: Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions. METHODS: A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects. RESULTS: The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar. CONCLUSIONS: The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large-scale patient studies.

Cryo-EM structure of influenza virus RNA polymerase complex at 4.3 Å resolution.

Replication and transcription of influenza virus genome mainly depend on its RNA-dependent RNA polymerase (RdRP), composed of the PA, PB1, and PB2 subunits. Although extensively studied, the underlying mechanism of the RdRP complex is still unclear. Here we report the biochemical characterization of influenza RdRP subcomplex comprising PA, PB1, and N terminus of PB2, which exist as dimer in solution and can assemble into a tetramer state, regulated by vRNA promoter. Using single-particle cryo-electron microscopy, we have reconstructed the RdRP tetramer complex at 4.3 Å, highlighting the assembly and interfaces between monomers within the tetrameric structure. The individual RdRP subcomplex contains all the characterized motifs and appears as a cage-like structure. High-throughput mutagenesis profiling revealed that residues involved in the oligomer state formation are critical for viral life cycle. Our results lay a solid base for understanding the mechanism of replication of influenza and other negative-stranded RNA viruses.

A Fast Na-Ion Conduction Polymer Electrolyte via Triangular Synergy Strategy for Quasi-Solid-State Batteries.

Polymer electrolytes provide a visible pathway for the construction of high-safety quasi-solid-state batteries due to their high interface compatibility and processability. Nevertheless, sluggish ion transfer at room temperature seriously limits their applications. Herein, a triangular synergy strategy is proposed to accelerate Na-ion conduction via the cooperation of polymer-salt, ionic liquid, and electron-rich additive. Especially, PVDF-HFP and NaTFSI salt acted as the framework to stably accommodate all the ingredients. An ionic liquid (Emim+ -FSI- ) softened the polymer chains through a weakening molecule force and offered additional liquid pathways for ion transport. Physicochemical characterizations and theoretical calculations demonstrated that electron-rich Nerolin with π-cation interaction facilitated the dissociation of NaTFSI and effectively restrained the competitive migration of large cations from EmimFSI, thus lowering the energy barrier for ion transport. The strategy resulted in a thin F-rich interphase dominated by NaTFSI salt's decomposition, enabling rapid Na+ transmission across the interface. These combined effects resulted in a polymer electrolyte with high ionic conductivity (1.37×10-3  S cm-1 ) and tNa+ (0.79) at 25 °C. The assembled cells delivered reliable rate capability and stability (200 cycles, 99.2 %, 0.5 C) with a good safety performance.

Photoinduced Rotation of Colloidal Semiconductor Nanocrystals in an Electric Field.

We demonstrate that solution-phase semiconductor nanocrystals (NCs) undergo photoinduced rotation in an external electric field. Present measurements backed by theoretical calculations show that the rotation of colloidal NCs is driven by the excited-state dipole moment, which is counterbalanced by the solvent viscosity drag. Corresponding angular velocities range from 0.5°/ns for cubic CsPbBr3 NCs to 3°/ns for nanoparticles with a large photoinduced charge separation (CdSe/CdS core-shell and dot-in-a-rod NCs). Because of photoinduced rotation, solution-phase semiconductor NCs exhibited an order-of-magnitude increase in the spectral changes caused by the quantum confined Stark effect (QCSE), compared to solid NC assemblies. The enhanced QCSE of colloidal NCs reflected their global alignment in solution, which could be retained in a solid environment by slow crystallization. Overall, we expect that the demonstrated phenomenon of the colloidal nanocrystal rotation in an electric field will open up new avenues for developing electro-optical and voltage-sensitive applications.

Tuning the Dimensionality of Excitons in Colloidal Quantum Dot Molecules.

Electrically coupled quantum dots (QDs) can support unique optoelectronic properties arising from the superposition of single-particle excited states. Experimental methods for integrating colloidal QDs within the same nano-object, however, have remained elusive to the rational design. Here, we demonstrate a chemical strategy that allows for the assembling of colloidal QDs into coupled composites, where proximal interactions give rise to unique optoelectronic behavior. The assembly method employing "adhesive" surfactants was used to fabricate both homogeneous (e.g., CdS-CdS, PbS-PbS, CdSe-CdSe) and heterogeneous (e.g., PbS-CdS, CdS-CdSe) nanoparticle assemblies, exhibiting quasi-one-dimensional exciton fine structure. In addition, tunable mixing of single-particle exciton states was achieved for dimer-like assemblies of CdSe/CdS core-shell nanocrystals. The nanoparticle assembly mechanism was explained within the viscoelastic interaction theory adapted for molten-surface colloids. We expect that the present work will provide the synthetic and theoretical foundation needed for building assemblies of many inorganic nanocrystals.

A theoretical investigation of 38-atom CuPd clusters: the effect of potential parameterisation on structure and segregation.

Understanding the structure of bimetallic clusters is increasingly important due to their emerging practical applications. Herein we investigate the structure of 38-atom CuPd clusters using a genetic algorithm with cluster energies described by the semi-empirical Gupta potential. Selected clusters are then refined with density functional theory. Three different parameterisations of the Gupta potential are used and their performance assessed to understand what features of bulk and surfaces are necessary to capture for accurate description of small clusters. Three general regions of motif stability exist; for the Pd majority clusters (Pd38 to Cu4Pd34) the truncated octahedron is most stable, while for clusters of intermediate compositions (Cu5Pd33 to Cu25Pd13) a "pancake" icosahedron is most stable, and for the Cu majority clusters (Cu26Pd12 to Cu38) again the truncated octahedron is most stable. CuPd clusters tend to segregate to a Cu-core, Pd-shell structure if possible, and at higher Cu compositions, the Pd segregates to the faces of the cluster. Using multiple parameterisations of the Gupta potential ensures the full variety of possible structures is found, and improves the search for the most stable CuPd clusters.

Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks.

PURPOSE: Fully automatic tissue segmentation is an essential step to translate quantitative MRI techniques to clinical setting. The goal of this study was to develop a novel approach based on the generative adversarial networks for fully automatic segmentation of knee cartilage and meniscus. THEORY AND METHODS: Defining proper loss function for semantic segmentation to enforce the learning of multiscale spatial constraints in an end-to-end training process is an open problem. In this work, we have used the conditional generative adversarial networks to improve segmentation performance of convolutional neural network, such as UNet alone by overcoming the problems caused by pixel-wise mapping based objective functions, and to capture cartilage features during the training of the network. Furthermore, the Dice coefficient and cross entropy losses were incorporated to the loss functions to improve the model performance. The model was trained and tested on 176, 3D DESS (double-echo steady-state) knee images from the Osteoarthritis Initiative data set. RESULTS: The proposed model provided excellent segmentation performance for cartilages with Dice coefficients ranging from 0.84 in patellar cartilage to 0.91 in lateral tibial cartilage, with an average Dice coefficient of 0.88. For meniscus segmentation, the model achieves 0.89 Dice coefficient for lateral meniscus and 0.87 Dice coefficient for medial meniscus. The results are superior to previously published automatic cartilage and meniscus segmentation methods based on deep learning models such as convolutional neural network. CONCLUSION: The proposed UNet-conditional generative adversarial networks based model demonstrated a fully automated segmentation method with high accuracy for knee cartilage and meniscus.

Improved walker population control for full configuration interaction quantum Monte Carlo.

Full configuration interaction quantum Monte Carlo (FCIQMC) is a stochastic approach for finding the ground state of a quantum many-body Hamiltonian. It is based on the dynamical evolution of a walker population in Hilbert space, which samples the ground state configuration vector over many iterations. Here, we present a modification of the original protocol for walker population control of Booth et al. [J. Chem. Phys. 131, 054106 (2009)] in order to achieve equilibration at a pre-defined average walker number and to avoid walker number overshoots. The dynamics of the walker population is described by a noisy damped harmonic oscillator and controlled by two parameters responsible for damping and forcing, respectively, for which reasonable values are suggested. We further introduce a population growth witness that can be used to detect annihilation plateaus related to overcoming the FCIQMC sign problem. Features of the new population control procedure such as precise walker number control and fast equilibration are demonstrated. The standard error of the shift estimator for the ground state energy as well as the population control bias is found to be unaffected by the population control procedure or its parameters. The improved control of the walker number, and thereby memory consumption, is a desirable feature required for automating FCIQMC calculations and requires minimal modifications to the existing code.

Delayed Photoluminescence in Metal-Conjugated Fluorophores.

Assemblies of metal nanostructures and fluorescent molecules represent a promising platform for the development of biosensing and near-field imaging applications. Typically, the interaction of molecular fluorophores with surface plasmons in metals results in either quenching or enhancement of the dye excitation energy. Here, we demonstrate that fluorescent molecules can also engage in a reversible energy transfer (ET) with proximal metal surfaces, during which quenching of the dye emission via the energy transfer to localized surface plasmons can trigger delayed ET from metal back to the fluorescent molecule. The resulting two-step process leads to the sustained delayed photoluminescence (PL) in metal-conjugated fluorophores, as was demonstrated here through the observation of increased PL lifetime in assemblies of Au nanoparticles and organic dyes (Alexa 488, Cy3.5, and Cy5). The observed enhancement of the PL lifetime in metal-conjugated fluorophores was corroborated by theoretical calculations based on the reverse ET model, suggesting that these processes could be ubiquitous in many other dye-metal assemblies.

Low rank approximation methods for MR fingerprinting with large scale dictionaries.

PURPOSE: This work proposes new low rank approximation approaches with significant memory savings for large scale MR fingerprinting (MRF) problems. THEORY AND METHODS: We introduce a compressed MRF with randomized singular value decomposition method to significantly reduce the memory requirement for calculating a low rank approximation of large sized MRF dictionaries. We further relax this requirement by exploiting the structures of MRF dictionaries in the randomized singular value decomposition space and fitting them to low-degree polynomials to generate high resolution MRF parameter maps. In vivo 1.5T and 3T brain scan data are used to validate the approaches. RESULTS: T1 , T2 , and off-resonance maps are in good agreement with that of the standard MRF approach. Moreover, the memory savings is up to 1000 times for the MRF-fast imaging with steady-state precession sequence and more than 15 times for the MRF-balanced, steady-state free precession sequence. CONCLUSION: The proposed compressed MRF with randomized singular value decomposition and dictionary fitting methods are memory efficient low rank approximation methods, which can benefit the usage of MRF in clinical settings. They also have great potentials in large scale MRF problems, such as problems considering multi-component MRF parameters or high resolution in the parameter space. Magn Reson Med 79:2392-2400, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A Simple Method of Predicting Spin State in Solution.

A simple method, using density functional theory (DFT), of predicting spin-state in advance of synthesis is reported. Specifically, an excellent correlation is observed between the switching temperatures (T1/2) in CDCl3 solution of five spin-crossover (SCO)-active [FeII(Lazine)2(NCBH3)2] complexes and the DFT-calculated (and observed) 15N NMR chemical shift (δNA) of the five different azine-substituted 1,2,4-triazole ligands employed, Lazine = 4-(4-methylphenyl)-3-phenyl-5-(azine)-1,2,4-triazole, where azine = pyridine, pyridazine, 4-pyrimidine, pyrazine, and 2-pyrimidine. To test the generality of this finding, DFT was also employed to readily predict the δNA values for a family of 16 literature ligands, known as bppX,Y [X,Y-substituted 2,6-(pyrazol-1-yl)pyridines], which have produced 16 SCO-active [FeII(bppX,Y)2](Z)2 complexes (Z = BF4 or in one case PF6) in (CD3)2CO solution: again an excellent correlation was found between the computed δNA and the observed T1/2. These correlations represent a key advance in the field, as they allow a simple DFT calculation on a modified ligand to be used to reliably predict, before synthesis of the ligand or complex, the T1/2 that would result from that modification. Achieving such easily predictable tuning of T1/2, and hence of spin-state, is a significant step forward in the field of SCO and also has big implications in many other fields in which spin-state is key, including catalysis, metallo-enzyme modeling studies, and host-guest chemistry.

One-Dimensional Carrier Confinement in "Giant" CdS/CdSe Excitonic Nanoshells.

The emerging generation of quantum dot optoelectronic devices offers an appealing prospect of a size-tunable band gap. The confinement-enabled control over electronic properties, however, requires nanoparticles to be sufficiently small, which leads to a large area of interparticle boundaries in a film. Such interfaces lead to a high density of surface traps which ultimately increase the electrical resistance of a solid. To address this issue, we have developed an inverse energy-gradient core/shell architecture supporting the quantum confinement in nanoparticles larger than the exciton Bohr radius. The assembly of such nanostructures exhibits a relatively low surface-to-volume ratio, which was manifested in this work through the enhanced conductance of solution-processed films. The reported core/shell geometry was realized by growing a narrow gap semiconductor layer (CdSe) on the surface of a wide-gap core material (CdS) promoting the localization of excitons in the shell domain, as was confirmed by ultrafast transient absorption and emission lifetime measurements. The band gap emission of fabricated nanoshells, ranging from 15 to 30 nm in diameter, has revealed a characteristic size-dependent behavior tunable via the shell thickness with associated quantum yields in the 4.4-16.0% range.

pH-dependent conformational changes of a Thogoto virus matrix protein reveal mechanisms of viral assembly and uncoating.

Orthomyxoviruses are a family of ssRNA virus, including influenza virus, infectious salmon anaemia virus and Thogoto virus. The matrix proteins of orthomyxoviruses play crucial roles in some essential processes of the viral life cycle. However, the mechanisms of the matrix proteins involved in these processes remain incompletely understood. Currently, only the structure and function of the matrix protein from influenza virus have been studied. Here, we present the crystal structures of the N-terminal domain of matrix protein from Thogoto virus at pH 7.0 and 4.5. By analysing the structures, we identified the conformational changes of monomers and dimers in different pH conditions, mainly caused by two flexible loops, L3 and L5. These structural deviations would reflect the basis of viral capsid assembly or disassembly.

Primary Sjogren syndrome - A bibliometric analysis via CiteSpace.

This study employs CiteSpace software to analyze the research status, hotspots, and trends of primary Sjogren syndrome (pSS). Relevant publications from 1999 to 2023 were searched in the Web of Science Core Collection (WoSCC) set, followed by generating a network map using CiteSpace software to identify top authors, institutions, countries, keywords, journals, references, and research trends. A total of 3564 valid articles were included in this study. The People Republic of China had the highest number of articles (n = 524), while the University of Bergen emerged as the institution with the highest publication count (n = 94). Mariette X was identified as the author with the most publications (n = 67), whereas Vitali C received recognition as the most cited author (n = 1706). Annals of Rheumatic Diseases stood out as the journal with the highest citation count (n = 2530). Notably, an article published in the Annals of Rheumatic Diseases in 2017 garnered significant attention by being cited a remarkable 304 times. The bibliometric analysis reveals that key areas of research in pSS encompass investigating pathogenesis; advancing and applying targeted biological agents; and establishing treatment and diagnostic standards.

"All-in-one" self-healing and injectable cationic guar gum hydrogel dressing functionalized by bioactive complexes composed of natural small molecules.

Reducing the risk of wound infection is an urgent issue health priority. Antibacterial polysaccharide-based hydrogels have attracted great attention for infectious wounds, attributed to their safe antimicrobial performance and natural non-toxicity and biodegradability advantages. In this study, the "all-in-one" self-adaptive and injectable cationic guar gum (CG)-based polysaccharide hydrogels (FA-TOB/CG) loaded with bioactive complexes were developed for infectious wound healing. The constructed antioxidant and antibacterial ferulic acid (FA)-tobramycin (TOB) bioactive complexes (FA-TOB) were used as the cross-linking agent and introduced into the CG matrix to construct the FA-TOB/CG hydrogel with a three-dimensional porous structure. The sterilization rates of FA-TOB/CG hydrogel against S. aureus and E. coli reached 98 % and 80 % respectively. In addition, the FA-TOB/CG also exhibits enhanced antioxidant performances (DPPH: > 40 %; ABTS: > 90 %; ·OH: > 50 %). More importantly, FA-TOB/CG hydrogel also showed the ability to sustain the release of FA and TOB. These superiorities of the FA-TOB/CG hydrogel enabled it to provide a moist wound environment and promote wound healing by eliminating bacteria, modulating the local inflammatory response, and accelerating collagen deposition and vascular regeneration. Thus, this study may enlarge a new sight for developing multifunctional dressings by incorporating bioactive complexes into polysaccharide hydrogels for infected wounds.