A molecular simulation analysis of producing monatomic carbon chains by stretching ultranarrow graphene nanoribbons

Atomistic simulations were utilized to develop fundamental insights regarding the elongation process starting from ultranarrow graphene nanoribbons (GNRs) and resulting in monatomic carbon chains (MACCs). There are three key findings. First, we demonstrate that complete, elongated, and stable MACCs with fracture strains exceeding 100% can be formed from both ultranarrow armchair and zigzag GNRs. Second, we demonstrate that the deformation processes leading to the MACCs have strong chirality dependence. Specifically, armchair GNRs first form DNA-like chains, then develop into monatomic chains by passing through an intermediate configuration in which monatomic chain sections are separated by two-atom attachments. In contrast, zigzag GNRs form rope-ladder-like chains through a process in which the carbon hexagons are first elongated into rectangles; these rectangles eventually coalesce into monatomic chains through a novel triangle-pentagon deformation structure under further tensile deformation. Finally, we show that the width of GNRs plays an important role in the formation of MACCs, and that the ultranarrow GNRs facilitate the formation of full MACCs. The present work should be of considerable interest due to the experimentally demonstrated feasibility of using narrow GNRs to fabricate novel nanoelectronic components based upon monatomic chains of carbon atoms.Comment: 11 pages, 6 figures, Nanotechnology accepted versio

Reconstruction and fast prediction of a 3D flow field based on a variational autoencoder

Reconstruction and fast prediction of flow fields are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN) and variational autoencoder (VAE) composite model is proposed for the reconstruction and prediction of 3D flowfields with high accuracy and efficiency. The VAE model is trained to extract features of the problem and to realize 3D physical field reconstruction. The ANN is employed to achieve the constructability of the extracted features. A dataset of steady temperature/velocity fields is acquired by computational fluid dynamics and heat transfer (CFD/HT) and fed to train the deep learning model. The proposed ANN-VAE model is experimentally proven to achieve promising field prediction accuracy with a significantly reduced computational cost. Compared to the CFD/HT method, the ANN-VAE method speeds up the physical field prediction by approximately 380,000 times, with mean accuracies of 97.3% for temperature field prediction and 97.9% for velocity field prediction, making it feasible for real-time physical field acquisition.Comment: 43 pages, 23 figure

Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.Comment: Accepted in the ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2023

Microstructure and texture analysis of δ-hydride precipitation in Zircaloy-4 materials by electron microscopy and neutron diffraction

This work presents a detailed microstructure and texture study of various hydrided Zircaloy-4 materials by neutron diffraction and microscopy. The results show that the precipitated δ-ZrH1.66 generally follows the δ (111) //α (0001) and δ[]//α[] orientation relationship with the α-Zr matrix. The δ-hydride displays a weak texture that is determined by the texture of the α-Zr matrix, and this dependence essentially originates from the observed orientation correlation between α-Zr and δ-hydride. Neutron diffraction line profile analysis and high-resolution transmission electron microscopy observations reveal a significant number of dislocations present in the δ-hydride, with an estimated average density one order of magnitude higher than that in the α-Zr matrix, which contributes to the accommodation of the substantial misfit strains associated with hydride precipitation in the α-Zr matrix. The present observations provide an insight into the behaviour of δ-hydride precipitation in zirconium alloys and may help with understanding the induced embrittling effect of hydrides.Fil: Wang, Zhiyang. University of Wollongong; Australia. Australian Nuclear Science and Technology Organisation; AustraliaFil: Garbe, Ulf. Australian Nuclear Science and Technology Organisation; AustraliaFil: Li, Huijun. University of Wollongong; AustraliaFil: Wang, Yanbo. University of Sydney; AustraliaFil: Studer, Andrew J.. Australian Nuclear Science and Technology Organisation; AustraliaFil: Sun, Guangai. Institute of Nuclear Physics and Chemistry, CAEP; ChinaFil: Harrison, Robert P.. Australian Nuclear Science and Technology Organisation, Institute of Materials Engineering; AustraliaFil: Liao, Xiaozhou. University of Sydney; AustraliaFil: Vicente Alvarez, Miguel Angel. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Santisteban, Javier Roberto. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kong, Charlie. University of New South Wales; Australi

Phenotypic Pattern-Based Assay for Dynamically Monitoring Host Cellular Responses to Salmonella Infections

The interaction between mammalian host cells and bacteria is a dynamic process, and the underlying pathologic mechanisms are poorly characterized. Limited information describing the host-bacterial interaction is based mainly on studies using label-based endpoint assays that detect changes in cell behavior at a given time point, yielding incomplete information. In this paper, a novel, label-free, real-time cell-detection system based on electronic impedance sensor technology was adapted to dynamically monitor the entire process of intestinal epithelial cells response to Salmonella infection. Changes in cell morphology and attachment were quantitatively and continuously recorded following infection. The resulting impedance-based time-dependent cell response profiles (TCRPs) were compared to standard assays and showed good correlation and sensitivity. Biochemical assays further suggested that TCRPs were correlated with cytoskeleton-associated morphological dynamics, which can be largely attenuated by inhibitions of actin and microtubule polymerization. Collectively, our data indicate that cell-electrode impedance measurements not only provide a novel, real-time, label-free method for investigating bacterial infection but also help advance our understanding of host responses in a more physiological and continuous manner that is beyond the scope of current endpoint assays

Trends and hotspots in non-motor symptoms of Parkinson’s disease: a 10-year bibliometric analysis

Non-motor symptoms are prevalent among individuals with Parkinson’s disease (PD) and seriously affect patient quality of life, even more so than motor symptoms. In the past decade, an increasing number of studies have investigated non-motor symptoms in PD. The present study aimed to comprehensively analyze the global literature, trends, and hotspots of research investigating non-motor symptoms in PD through bibliometric methods. Studies addressing non-motor symptoms in the Web of Science Core Collection (WoSCC), published between January 2013 and December 2022, were retrieved. Bibliometric methods, including the R package “Bibliometrix,” VOS viewer, and CiteSpace software, were used to investigate and visualize parameters, including yearly publications, country/region, institution, and authors, to collate and quantify information. Analysis of keywords and co-cited references explored trends and hotspots. There was a significant increase in the number of publications addressing the non-motor symptoms of PD, with a total of 3,521 articles retrieved. The United States was ranked first in terms of publications (n = 763) and citations (n = 11,269), maintaining its leadership position among all countries. King’s College London (United Kingdom) was the most active institution among all publications (n = 133) and K Ray Chaudhuri was the author with the most publications (n = 131). Parkinsonism & Related Disorders published the most articles, while Movement Disorders was the most cited journal. Reference explosions have shown that early diagnosis, biomarkers, novel magnetic resonance imaging techniques, and deep brain stimulation have become research “hotspots” in recent years. Keyword clustering revealed that alpha-synuclein is the largest cluster for PD. The keyword heatmap revealed that non-motor symptoms appeared most frequently (n = 1,104), followed by quality of life (n = 502), dementia (n = 403), and depression (n = 397). Results of the present study provide an objective, comprehensive, and systematic analysis of these publications, and identifies trends and “hot” developments in this field of research. This work will inform investigators worldwide to help them conduct further research and develop new therapies

6G Network Operation Support System

6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional perception, global coverage, green and low-carbon, native network security, etc. 6G will realize the transition from serving people and people-things communication to supporting the efficient connection of intelligent agents, and comprehensively leading the digital, intelligent and green transformation of the economy and the society. As the core support system for mobile communication network, 6G OSS needs to achieve high-level network automation, intelligence and digital twinning capabilities to achieve end-to-end autonomous network operation and maintenance, support the operation of typical 6G business scenarios and play a greater social responsibility in the fields of environment, society, and governance (ESG).This paper provides a detailed introduction to the overall vision, potential key technologies, and functional architecture of 6G OSS . It also presents an evolutionary roadmap and technological prospects for the OSS from 5G to 6G.Comment: 103 pages, 20 figures, 52 references (chinese version

Association between serum potassium and Parkinson’s disease in the US (NHANES 2005–2020)

BackgroundEvaluating the correlation between serum potassium and Parkinson’s disease (PD) in US adults.MethodsA cross-sectional study was conducted on 20,495 adults aged 40 years or older using NHANES data from 2005 to 2020. The study utilized one-way logistic regression and multifactorial logistic regression to examine the correlation between serum potassium levels and PD. Additionally, a smoothed curve fitting approach was employed to assess the concentration-response relationship between serum potassium and PD. Stratified analyses were carried out to investigate potential interactions between serum potassium levels and PD with variables such as age, sex, race, marital status, education, BMI, smoking and medical conditions like coronary, stroke, diabetes, hypertension, and hypercholesterolemia.ResultsIn this study, a total of 20,495 participants, comprising 403 PD and 20,092 non-PD individuals, were included. After adjusted for covariates, multivariable logistic regression revealed that high serum potassium level was an independent risk factor for PD (OR:1.86, 95% CI:1.45 ~ 2.39, p < 0.01).The linear association between serum potassium and PD was described using fitted smoothing curves. Age, sex, race, education, marital, BMI, coronary, stroke, diabetes, hypertension and hypercholesterolemia were not significantly correlated with this positive connection, according to subgroup analysis and interaction testing (P for interaction >0.05).ConclusionSerum potassium levels are elevated in patients with Parkinson's disease compared to non-PD patients. Additional prospective studies are required to explore the significance of serum potassium levels in individuals with Parkinson's disease

Activating mutations of STAT5B and STAT3 in lymphomas derived from gamma delta-T or NK cells

Peer reviewe

The Pathogenesis of Cytomegalovirus and Other Viruses Associated with Hearing Loss: Recent Updates

Virus infection is one of the most common etiologies of hearing loss. Hearing loss associated with viral infection can be unilateral or bilateral, mild or severe, sudden or progressive, and permanent or recoverable. Many viruses cause hearing loss in adults and children; however, the pathogenesis of hearing loss caused by viral infection is not fully understood. This review describes cytomegalovirus, the most common virus causing hearing loss, and other reported hearing loss-related viruses. We hope to provide a detailed description of pathogenic characteristics and research progress on pathology, hearing phenotypes, possible associated mechanisms, treatment, and prevention measures. This review aims to provide diagnostic and treatment assistance to clinical workers