High-dose Vitamin C injection ameliorates against sepsis-induced myocardial injury by anti-apoptosis, anti-inflammatory and pro-autophagy through regulating MAPK, NF-κB and PI3K/AKT/mTOR signaling pathways in rats.

AIMS: This study aimed to evaluate the effects of VC on SIMI in rats. METHODS: In this study, the survival rate of high dose VC for SIMI was evaluated within 7 days. Rats were randomly assigned to three groups: Sham group, CLP group, and high dose VC (500 mg/kg i.v.) group. The animals in each group were treated with drugs for 1 day, 3 days or 5 days, respectively. Echocardiography, myocardial enzymes and HE were used to detect cardiac function. IL-1ß, IL-6, IL-10 and TNF-α) in serum were measured using ELISA kits. Western blot was used to detect proteins related to apoptosis, inflammation, autophagy, MAPK, NF-κB and PI3K/Akt/mTOR signaling pathways. RESULTS: High dose VC improved the survival rate of SIMI within 7 days. Echocardiography, HE staining and myocardial enzymes showed that high-dose VC relieved SIMI in rats in a time-dependent manner. And compared with CLP group, high-dose VC decreased the expressions of pro-apoptotic proteins, while increased the expression of anti-apoptotic protein. And compared with CLP group, high dose VC decreased phosphorylation levels of Erk1/2, P38, JNK, NF-κB and IKK α/ß in SIMI rats. High dose VC increased the expression of the protein Beclin-1 and LC3-II/LC3-I ratio, whereas decreased the expression of P62 in SIMI rats. Finally, high dose VC attenuated phosphorylation of PI3K, AKT and mTOR compared with the CLP group. SIGNIFICANCE: Our results showed that high dose VC has a good protective effect on SIMI after continuous treatment, which may be mediated by inhibiting apoptosis and inflammatory, and promoting autophagy through regulating MAPK, NF-κB and PI3K/AKT/mTOR pathway.

Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms.

BACKGROUND: Osteoarthritis and lipid metabolism are strongly associated, although the precise targets and regulatory mechanisms are unknown. METHODS: Osteoarthritis gene expression profiles were acquired from the GEO database, while lipid metabolism-related genes (LMRGs) were sourced from the MigSB database. An intersection was conducted between these datasets to extract gene expression for subsequent differential analysis. Following this, functional analyses were performed on the differentially expressed genes (DEGs). Subsequently, machine learning was applied to identify hub genes associated with lipid metabolism in osteoarthritis. Immune-infiltration analysis was performed using CIBERSORT, and external datasets were employed to validate the expression of these hub genes. RESULTS: Nine DEGs associated with lipid metabolism in osteoarthritis were identified. UGCG and ESYT1, which are hub genes involved in lipid metabolism in osteoarthritis, were identified through the utilization of three machine learning algorithms. Analysis of the validation dataset revealed downregulation of UGCG in the experimental group compared to the normal group and upregulation of ESYT1 in the experimental group compared to the normal group. CONCLUSIONS: UGCG and ESYT1 were considered as hub LMRGs in the development of osteoarthritis, which were regarded as candidate diagnostic markers. The effects are worth expected in the early diagnosis and treatment of osteoarthritis.

Alloying Driven Antiferromagnetic Skyrmions on NiPS3 Monolayer: A First-Principles Calculation.

Topological magnetic states are promising information carriers for ultrahigh-density and high-efficiency magnetic storage. Recent advances in two-dimensional (2D) magnets provide powerful platforms for stabilizing various nanometer-size topological spin textures within a wide range of magnetic field and temperature. However, non-centrosymmetric 2D magnets with broken inversion symmetry are scarce in nature, making direct observations of the chiral spin structure difficult, especially for antiferromagnetic (AFM) skyrmions. In this work, it is theoretically predicted that intrinsic AFM skyrmions can be easily triggered in XY-type honeycomb magnet NiPS3 monolayer by alloying of Cr atoms, due to the presence of a sizable Dzyaloshinskii-Moriya interaction. More interestingly, the diameter of the AFM skyrmions in Ni3/4Cr1/4PS3 decreases from 12 to 4.4 nm as the external magnetic field increases and the skyrmion phases remain stable up to an external magnetic field of 4 T. These results highlight an effective strategy to generate and modulate the topological spin texture in 2D magnets by alloying with magnetic element.

B63: The Most Stable Bilayer Structure with Dual Aromaticity.

The emergence of a bilayer B48 cluster, which has been both theoretically predicted and experimentally observed, as well as the recent experimental synthesis of bilayer borophene sheets on Ag and Cu surfaces, has generated tremendous curiosity in the bilayer structures of boron clusters. However, the connection between bilayer boron cluster and bilayer borophene remains unknown. By combining a genetic algorithm and density functional theory calculations, a global search for the low-energy structures of the B63 cluster was conducted, revealing that the Cs bilayer structure with three interlayer B-B bonds is the most stable bilayer structure. This structure was further examined in terms of its structural stability, chemical bonding, and aromaticity. Interestingly, the interlayer bonds induce strong electronegativity and robust aromaticity. Furthermore, the dual aromaticity stems from diatropic currents originating from virtual translational transitions for both σ and π electrons. This unprecedent bilayer boron cluster is anticipated to enrich the concept of dual aromaticity and serve as a potential precursor for bilayer borophene.

Tin-oxo nanoclusters for extreme ultraviolet photoresists: Effects of ligands, counterions, and doping.

The extreme ultraviolet (EUV) nanolithography technology is the keystone for developing the next-generation chips. As conventional chemically amplified resists are approaching the resolution limit, metal-containing photoresists, especially tin-oxo clusters, seize the opportunity to embrace this challenge owing to their small sizes, precise atomic structures, and strong EUV absorption. However, atomistic insights into the mechanism for regulating their photolithographic behavior are lacking. Herein, we systematically explored the effects of ligands, counterions, and endohedral doping on the photophysical properties of tin-oxo cage clusters by first-principles calculations combined with molecular dynamics simulations. Photoresists assembled by allyl-protected clusters with small-size OH- or Cl- counterions have a high absorption coefficient at the EUV wavelength of 13.5 nm and a low energy cost for ligand detachment and superior stability to ensure high sensitivity and strong etch resistance, respectively. The photoresist performance can further be improved by endohedral doping of the metal-oxo nanocage with Ag+ and Cd2+ ions, which exhibit superatomic characteristics and are likely to be synthesized in laboratory. These theoretical results provide useful guidance for modification of metal-oxo clusters for high-resolution EUV photolithography.

Efficient Full-Frequency GW Calculations Using a Lanczos Method.

The GW approximation is widely used for reliable and accurate modeling of single-particle excitations. It also serves as a starting point for many theoretical methods, such as its use in the Bethe-Salpeter equation (BSE) and dynamical mean-field theory. However, full-frequency GW calculations for large systems with hundreds of atoms remain computationally challenging, even after years of efforts to reduce the prefactor and improve scaling. We propose a method that reformulates the correlation part of the GW self-energy as a resolvent of a Hermitian matrix, which can be efficiently and accurately computed using the standard Lanczos method. This method enables full-frequency GW calculations of material systems with a few hundred atoms on a single computing workstation. We further demonstrate the efficiency of the method by calculating the defect-state energies of silicon quantum dots with diameters up to 4 nm and nearly 2,000 silicon atoms using only 20 computational nodes.

Optical Activity and Excitonic Characteristics of Chiral CdSe Quantum Dots.

Introduction of chirality to colloidal semiconductor quantum dots (QDs) triggers a chiroptical effect. However, there remains a knowledge gap in the mechanism of chirality transfer and amplification from molecules to QDs. By time-dependent density functional theory calculations combined with a correlated electron-hole picture, we explored the chiroptical activity of CdSe QDs decorated with different chiral monocarboxylic acids from an excitonic perspective. Our calculations showed strong circular dichroism (CD) signals in the visible region for the chiral CdSe QDs. The excitonic states with large CD originate from QDs, while the chiral molecules break the orthogonality between electric and magnetic transition dipoles, which synergistically facilitates the prominent dissymmetric effect. The considered monocarboxylic acid chiral molecules all favor the bidentate adsorption configuration of the carboxyl group on the CdSe surface, endowing an identical CD signature but distinct excitonic characteristics. These findings are crucial for the regulation of chirality and excitons in semiconductor QDs to develop excitonic devices.

On the nature of Con±/0 clusters reacting with water and oxygen.

Bulk cobalt does not react with water at room temperature, but cobalt nanometals could yield corrosion at ambient conditions. Insights into the cobalt cluster reactions with water and oxygen enable us to better understand the interface reactivity of such nanometals. Here we report a comprehensive study on the gas-phase reactions of Con±/0 clusters with water and oxygen. All these Con±/0 clusters were found to react with oxygen, but only anionic cobalt clusters give rise to water dissociation whereas the cationic and neutral ones are limited to water adsorption. We elucidate the influences of charge states, bonding modes and dehydrogenation mechanism of water on typical cobalt clusters. It is unveiled that the additional electron of anionic Con- clusters is not beneficial to H2O adsorption, but allows for thermodynamics- and kinetics-favourable H atom transfer and dehydrogenation reactions. Apart from the charge effect, size effect and spin effect play a subtle role in the reaction process. The synergy of multiple metal sites in Con- clusters reduces the energy barrier of the rate-limiting step enabling hydrogen release. This finding of water dissociation on cobalt clusters put forward new connotations on the activity series of metals, providing new insights into the corrosion mechanism of cobalt nanometals.

Two-dimensional fullerene-based monolayer materials assembled by C80 and Sc3N@C80.

Construction of two-dimensional (2D) materials using fullerenes as building blocks has attracted particular attention, primarily due to their ability to integrate desired functionalities into devices. However, realization of stable 2D phases of polymerized fullerenes remains a big challenge. Here, we propose two stable 2D monolayer phases with covalently bridged C80 cages, namely α-C80-2D and ß-C80-2D, which are semiconductors with strong absorption in the long wave range and appreciable carrier mobility, respectively. The high stability originates from the bond energy released by the [2+2] cycloaddition polymerization of C80 is greater than the deformation energy of a cage. Starting from α-C80-2D, endohedral incorporation of the Sc3N molecule into each C80 cage leads to 2D semiconductors of α-Sc3N@C80-2D and α'-Sc3N@C80-2D, which possess exceptional stability and diverse physical properties, including unique electronic band structures, strong optical absorption in the visible (VIS) to near-infrared (NIR) regime, and anisotropic optical characteristics. Remarkably, a temperature-induced order-disorder transition in the α-Sc3N@C80-2D phase has been observed at elevated temperatures above 600 K. These findings expand the family of 2D carbon materials and provide useful clue for the potential applications of fullerene-assembled monolayer networks.

Effect of fission defects on tensile strength of U3Si2 Σ5(210) grain boundary from first-principles calculations.

U3Si2 is regarded as a promising accident tolerant fuel (ATF) to replace the commercial fuel UO2; however, grain boundary (GB) embrittlement of U3Si2 caused by irradiation-induced defect segregation remains to be clarified. In this work, the U3Si2 Σ5(210) symmetrically tilted GB is taken as a representative to elucidate the individual effect of xenon (Xe) and vacancy on the tensile strength and failure of GBs using first-principles calculations. Compared with the predicted segregation energies of defects at the most energetically favourable positions of GBs, Si vacancy (VSi) has a much stronger preference to segregate to GBs than that of Xe substitution on the Si sublattice (XeSi). Moreover, the strengthening/embrittlement potency of GBs with single vacancy/Xe is evaluated using the first-principles-based uniaxial tensile test. Although both VSi and XeSi yield a weakening effect on the strength of the U3Si2 Σ5(210) GB, such defective GBs exhibit significantly stronger interface strengths compared to the corresponding defects segregated to the UO2 Σ3(111) GB. The underlying mechanism of strength change of U3Si2 GBs is discussed in terms of charge analysis. Our results can provide a fundamental understanding of the mechanical behavior of irradiated GBs from an atomic perspective.

Assisted Self-Assembly of Nanoporous Ices via Carbon Nanomaterial Templates.

Self-assembly is a widely used synthetic method in nanoscience to assemble well-organized structures. Self-assembly processes usually occur in a water solvent environment. However, the self-assembly of water molecules is rarely studied. Herein, we show a strategy to fabricate porous ice via carbon nanomaterial-assisted self-assembly. Diverse frameworks of nanoporous ice are formed by using orthorhombic and tetragonal arrays of carbon nanotubes or carbon-atom chains as templates. In contrast to many bulk ices discovered in nature, nanoporous ices are shown to be stable only under negative pressure. Hence, nanoporous ices cannot be produced through the direct nucleation of water at negative pressure. The template-assisted self-assembly method is shown to be the most effective method to fabricate nanoporous ice in quantity. Several key factors for the self-assembly of nanoporous ices are identified, including proper gap spacings in the carbon nanomaterial template and suitable interactions between water and the carbon nanomaterials.

Cyclical palmitoylation regulates TLR9 signalling and systemic autoimmunity in mice.

Toll-like receptor 9 (TLR9) recognizes self-DNA and plays intricate roles in systemic lupus erythematosus (SLE). However, the molecular mechanism regulating the endosomal TLR9 response is incompletely understood. Here, we report that palmitoyl-protein thioesterase 1 (PPT1) regulates systemic autoimmunity by removing S-palmitoylation from TLR9 in lysosomes. PPT1 promotes the secretion of IFNα by plasmacytoid dendritic cells (pDCs) and TNF by macrophages. Genetic deficiency in or chemical inhibition of PPT1 reduces anti-nuclear antibody levels and attenuates nephritis in B6.Sle1yaa mice. In healthy volunteers and patients with SLE, the PPT1 inhibitor, HDSF, reduces IFNα production ex vivo. Mechanistically, biochemical and mass spectrometry analyses demonstrated that TLR9 is S-palmitoylated at C258 and C265. Moreover, the protein acyltransferase, DHHC3, palmitoylates TLR9 in the Golgi, and regulates TLR9 trafficking to endosomes. Subsequent depalmitoylation by PPT1 facilitates the release of TLR9 from UNC93B1. Our results reveal a posttranslational modification cycle that controls TLR9 response and autoimmunity.

Rational Design of Full-Color Fluorescent C3N Quantum Dots.

Carbon-based quantum dots (QDs) exhibit unique photoluminescence due to size-dependent quantum confinement, giving rise to fascinating full-color emission properties. Accurate emission calculations using time-dependent density functional theory are a time-costing and expensive process. Herein, we employed an artificial neural network (ANN) combined with statistical learning to establish the relationship between geometrical/electronic structures of ground states and emission wavelength for C3N QDs. The emission energy of these QDs can be doubly modulated by size and edge effects, which are governed by the number of C4N2 rings and the CH group, respectively. Moreover, these two structural characteristics also determine the phonon vibration mode of C3N QDs to harmonize the emission intensity and lifetime of hot electrons in the electron-hole recombination process, as indicated by nonadiabatic molecular dynamics simulation. These computational results provide a general approach to atomically precise design the full-color fluorescent carbon-based QDs with targeted functions and high performance.

Room-Temperature Ferromagnetism in Epitaxial Bilayer FeSb/SrTiO3(001) Terminated with a Kagome Lattice.

Two-dimensional (2D) magnets exhibit unique physical properties for potential applications in spintronics. To date, most 2D ferromagnets are obtained by mechanical exfoliation of bulk materials with van der Waals interlayer interactions, and the synthesis of single- or few-layer 2D ferromagnets with strong interlayer coupling remains experimentally challenging. Here, we report the epitaxial growth of 2D non-van der Waals ferromagnetic bilayer FeSb on SrTiO3(001) substrates stabilized by strong coupling to the substrate, which exhibits in-plane magnetic anisotropy and a Curie temperature above 390 K. In situ low-temperature scanning tunneling microscopy/spectroscopy and density-functional theory calculations further reveal that an Fe Kagome layer terminates the bilayer FeSb. Our results open a new avenue for further exploring emergent quantum phenomena from the interplay of ferromagnetism and topology for application in spintronics.

Predicting Binding Energies and Electronic Properties of Boron Nitride Fullerenes Using a Graph Convolutional Network.

As isoelectronic counterparts of carbon fullerenes, medium-sized boron nitride clusters also prefer cage structures composed of even-sized polygons. As the cluster size increases, the number of cage isomers grows rapidly, and determining the ground state structure requires a tremendous amount of DFT calculations. Herein, we develop a graph convolutional network (GCN) that can describe the energy of a (BN)n cage by its topology connection. We define a vertex feature vector on a dual polyhedron by the permutation of the neighbor vertices' degree and aggregate the information on vertices by two graph convolutional layers to learn the local feature of the dual polyhedron. The GCN is trained on (BN)28 and subsequently tested on (BN)23 and (BN)24 data sets, which satisfactorily reproduce the order of isomer energies from DFT calculations. We further employ the trained GCN to predict the ground state structures within the size range of n = 25-32, which agree well with DFT results. Using the same GCN framework, we also successfully trained the highest-occupied or lowest-unoccupied orbital energies of (BN)28 isomers. The present graph convolutional network establishes a direct mapping between the topological connection and the energetic or electronic properties of a cage-like cluster or molecule.

Self-healing FBG sensor network fault-detection based on a multi-class SVM algorithm.

We propose a three-layer ring architecture with enhanced reconfigurable capabilities for fiber Bragg grating (FBG) sensor networks. The proposed network is capable of self-healing when three fiber links fail. In addition to self-healing, soft faults in the FBG sensors can be detected using a multi-classification support vector machine (multi-class SVM) algorithm. The detection accuracy reached 99%. Additionally, we used an artificial neural network (ANN) reliability estimation model to estimate the reliability of the FBG self-healing network. The results show that the ANN reliability analysis model can accurately estimate the reliability of the architecture at a reasonable cost.

Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic.

BACKGROUND: Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cases in China before and during the COVID-19 pandemic. METHODS: A generalized regression model was used to investigate the association of time trends between imported malaria cases from 45 African countries and P. falciparum cases in 31 provinces of China from 2012 to 2018 before the COVID-19 pandemic and during the COVID-19 pandemic from October 2020 to May 2021. Based on the analysis, we proposed a statistical and deep learning hybrid approach to model the resurgence of malaria in China using monthly data of P. falciparum from 2004 to 2016. This study builds a hybrid model known as the ARIMA-GRU approach for modeling the P. falciparum cases in all provinces of China and the number of malaria deaths in China before and during the COVID-19 pandemic. RESULTS: The analysis showed an emerging link between the rise of imported malaria cases from Africa and P. falciparum cases in many provinces of China. Many imported malaria cases from Africa were P. falciparum cases. The proposed deep learning model achieved a high prediction accuracy score on the testing dataset of 96%. CONCLUSION: The study provided an analysis of the reduction of P. falciparum cases and deaths caused by imported P. falciparum cases during the COVID-19 pandemic due to the control measures regarding the limitation of international travel in China. The Chinese government has to prepare the imported malaria control measures after the normalization of international travel, to prevent the resurgence of malaria disease in China.

High-performance gas sensor with symmetry-protected quasi-bound states in the continuum.

A high-performance optical sensor with a vertical cavity structure comprising high-contrast gratings (HCGs) and a distributed Bragg reflector was designed. The structure has two peaks with different mechanisms, among which the first peak is formed by breaking the symmetry of the structure and coupling between the incident wave and the symmetric protection mode. The joint action of the HCG resonance and Fabry-Perot resonance formed a second peak. Moreover, changing the structural parameters, such as the grating width, period, and cavity length, can tune the spectral reflection dips. The sensitivity of the designed structure was as high as 674 nm/RIU, and the corresponding figure of merit was approximately 34741. The presented gas sensor provides a method for applying a vertical cavity structure to the sensing domain.

Core-Packing-Related Vibrational Properties of Thiol-Protected Gold Nanoclusters and Their Excited-State Behavior.

Thiolate-protected gold nanoclusters, with unique nuclearity- and structure-dependent properties, have been extensively used in energy conversion and catalysis; however, the mystery between kernel structures and properties remains to be revealed. Here, the influence of core packing on the electronic structure, vibrational properties, and excited-state dynamics of four gold nanoclusters with various kernel structures is explored using density functional theory combined with time-domain nonadiabatic molecular dynamics simulations. We elucidate the correlation between the geometrical structure and excited-state dynamics of gold nanoclusters. The distinct carrier lifetimes of the four nanoclusters are attributed to various electron-phonon couplings arising from the different vibrational properties caused by core packing. We have identified specific phonon modes that participate in the electron-hole recombination dynamics, which are related to the gold core of nanoclusters. This study paints a physical picture from the geometric configuration, electronic structure, vibrational properties, and carrier lifetime of these nanoclusters, thereby facilitating their potential application in optoelectronic materials.

Electric Field-Controlled Magneto-Optical Kerr Effect in A-Type Antiferromagnetic Fe2CX2 (X = F, Cl) and Its Janus Monolayer.

The magneto-optical Kerr effect (MOKE) is a powerful probe of magnetism and has recently gained new attention in antiferromagnetic (AFM) materials. Through extensive first-principles calculations and group theory analysis, we have identified Fe2CX2 (X = F, Cl) and Janus Fe2CFCl monolayers as ideal A-type collinear AFM materials with high magnetic anisotropy and Néel temperatures. By applying a vertical external electrical field (Ef) of 0.2 V/Å, the MOKE is activated for Fe2CF2 and Fe2CCl2 monolayers without changing their magnetic ground state, and the maximum Kerr rotation angles are 0.13 and 0.08°, respectively. Due to the out-of-plane spontaneous polarization, the intrinsic and nonvolatile MOKE is found in the Janus Fe2CFCl monolayer and the maximal Kerr rotation angle without external electronic field is 0.25°. Moreover, the intrinsic built-in electronic field also gives origin to more robust A-type AFM ordering and reversible Kerr angle against external Ef. Our study suggests that Ef is an effective tool for controlling MOKE in two-dimensional (2D) AFM materials. This research opens the possibility of related studies and applications in AFM spintronics.