Quantifying quantum non-Markovianity based on quantum coherence via skew information

Based on the nonincreasing property of quantum coherence via skew information under incoherent completely positive and trace-preserving maps, we propose a non-Markovianity measure for open quantum processes. As applications, by applying the proposed measure to some typical noisy channels, we find that it is equivalent to the three previous measures of non-Markovianity for phase damping and amplitude damping channels, i.e., the measures based on the quantum trace distance, dynamical divisibility, and quantum mutual information. For the random unitary channel, it is equivalent to the non-Markovianity measure based on $l_1$ norm of coherence for a class of output states and it is incompletely equivalent to the measure based on dynamical divisibility. We also use the modified Tsallis relative $\alpha$ entropy of coherence to detect the non-Markovianity of dynamics of quantum open systems, the results show that the modified Tsallis relative $\alpha$ entropy of coherence are more comfortable than the original Tsallis relative $\alpha$ entropy of coherence for small $\alpha$.Comment: 13 pages, 5 figure

Contrastive Analysis of the Raman Spectra of Polychlorinated Benzene: Hexachlorobenzene and Benzene

Detection of persistent pollutants such as polychlorinated benzene in environment in trace amounts is challenging, but important. It is more difficult to distinguish homologues and isomers of organic pollutantd when present in trace amounts because of their similar physical and chemical properties. In this work we simulate the Raman spectra of hexachlorobenzene and benzene, and figure out the vibration mode of each main peak. The effect on the Raman spectrum of changing substituents from H to Cl is analyzed to reveal the relations between the Raman spectra of homologues and isomers of polychlorinated benzene, which should be helpful for distinguishing one kind of polychlorinated benzene from its homologues and isomers by surface enhanced Raman scattering

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

Background: Tumor microenvironment (TME) takes a non-negligible role in the progression and metastasis of bladder urothelial carcinoma (BLCA) and tumor development could be inhibited by macrophage M1 in TME. The role of macrophage M1-related genes in BLCA adjuvant therapy has not been studied well. Methods: CIBERSOR algorithm was applied for identification tumor-infiltrating immune cells (TICs) subtypes of subjects from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. We identified potential modules of M1 macrophages by weighted gene co-expression network analysis (WGCNA). Nomogram was determined by one-way Cox regression and lasso regression analysis for M1 macrophage genes. The data from GEO are taken to verify the models externally. Kaplan-Meier and receiver operating characteristic (ROC) curves validated prognostic value of M1 macrophage genes. Finally, we divided patients into the low-risk group (LRG) and the high-risk group (HRG) based on the median risk score (RS), and the predictive value of RS in patients with BLCA immunotherapy and chemotherapy was investigated. Bladder cancer (T24, 5637, and BIU-87) and bladder uroepithelial cell line (SV-HUC-1) were used for in vitro validation. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was employed to validate the associated genes mRNA level. Results: 111 macrophage M1-related genes were identified using WGCNA. RS model containing three prognostically significant M1 macrophage-associated genes (FBXO6, OAS1, and TMEM229B) was formed by multiple Cox analysis, and a polygenic risk model and a comprehensive prognostic line plot was developed. The calibration curve clarified RS was a good predictor of prognosis. Patients in the LRG were more suitable for programmed cell death protein 1 (PD1) and cytotoxic T lymphocyte associate protein-4 (CTLA4) combination immunotherapy. Finally, chemotherapeutic drug models showed patients in the LRG were more sensitive to gemcitabine and mitomycin. RT-qPCR result elucidated the upregulation of FBXO6, TMEM229B, and downregulation of OAS1 in BLCA cell lines. Conclusion: A predictive model based on M1 macrophage-related genes can help guide us in the treatment of BLCA

Gated recurrent unit neural network (GRU) based on quantile regression (QR) predicts reservoir parameters through well logging data

The prediction of reservoir parameters is the most important part of reservoir evaluation, and porosity is very important among many reservoir parameters. In order to accurately measure the porosity of the core, it is necessary to take cores for indoor experiments, which is tedious and difficult. To solve this problem, this paper introduces machine learning models to estimate porosity through logging parameters. In this paper, gated recurrent unit neural network based on quantile regression method is introduced to predict porosity. Porosity measurement is implemented by taking cores for indoor experiments. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity parameters measured in the laboratory are used as the output parameters. Experimental results show that the quantile regression method improves the accuracy of the gated recurrent unit neural network, and the RMSE (Root Mean Square Error) of the unoptimized GRU neural network is 0.1774, after optimization, the RMSE is 0.1061. By comparing with the most widely used BP neural network, the accuracy of the method proposed in this paper is much higher than that of BP neural network. This shows that the gated recurrent neural network method based on quantile regression is excellent in predicting reservoir parameters

Facile Synthesis of SiO2@C Nanoparticles Anchored on MWNT as High-Performance Anode Materials for Li-ion Batteries

Carbon-coated silica nanoparticles anchored on multi-walled carbon nanotubes (SiO2@C/MWNT composite) were synthesized via a simple and facile sol-gel method followed by heat treatment. Scanning and transmission electron microscopy (SEM and TEM) studies confirmed densely anchoring the carbon-coated SiO2 nanoparticles onto a flexible MWNT conductive network, which facilitated fast electron and lithium-ion transport and improved structural stability of the composite. As prepared, ternary composite anode showed superior cyclability and rate capability compared to a carbon-coated silica counterpart without MWNT (SiO2@C). The SiO2@C/MWNT composite exhibited a high reversible discharge capacity of 744 mAh g−1 at the second discharge cycle conducted at a current density of 100 mA g−1 as well as an excellent rate capability, delivering a capacity of 475 mAh g−1 even at 1000 mA g−1. This enhanced electrochemical performance of SiO2@C/MWNT ternary composite anode was associated with its unique core-shell and networking structure and a strong mutual synergistic effect among the individual components

The Regulation of Surface-Enhanced Raman Scattering Sensitivity of Silver Nanorods by Silicon Sections

Vertically aligned silver nanorods were good substrates for surface-enhanced Raman scattering. The surface-enhanced Raman scattering sensitivity of nanorods can be regulated through the method that the silver nanorod is divided into four uniform silver sections using five uniform silicon sections. And the length of silicon sections is the key factor in regulating the surface-enhanced Raman scattering sensitivity. In the regulation, the best surface-enhanced Raman scattering performance is about 4 times as large as the worst performance. The study provides an effective way to regulate the surface-enhanced Raman scattering sensitivity of silver nanorods and its possible explanation about mechanism

The Special Neuraminidase Stalk-Motif Responsible for Increased Virulence and Pathogenesis of H5N1 Influenza A Virus

The variation of highly pathogenic avian influenza H5N1 virus results in gradually increased virulence in poultry, and human cases continue to accumulate. The neuraminidase (NA) stalk region of influenza virus varies considerably and may associate with its virulence. The NA stalk region of all N1 subtype influenza A viruses can be divided into six different stalk-motifs, H5N1/2004-like (NA-wt), WSN-like, H5N1/97-like, PR/8-like, H7N1/99-like and H5N1/96-like. The NA-wt is a special NA stalk-motif which was first observed in H5N1 influenza virus in 2000, with a 20-amino acid deletion in the 49th to 68th positions of the stalk region. Here we show that there is a gradual increase of the special NA stalk-motif in H5N1 isolates from 2000 to 2007, and notably, the special stalk-motif is observed in all 173 H5N1 human isolates from 2004 to 2007. The recombinant H5N1 virus with the special stalk-motif possesses the highest virulence and pathogenicity in chicken and mice, while the recombinant viruses with the other stalk-motifs display attenuated phenotype. This indicates that the special stalk-motif has contributed to the high virulence and pathogenicity of H5N1 isolates since 2000. The gradually increasing emergence of the special NA stalk-motif in H5N1 isolates, especially in human isolates, deserves attention by all

Exploration of comorbidity mechanisms and potential therapeutic targets of rheumatoid arthritis and pigmented villonodular synovitis using machine learning and bioinformatics analysis

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease. Pigmented villonodular synovitis (PVNS) is a tenosynovial giant cell tumor that can involve joints. The mechanisms of co-morbidity between the two diseases have not been thoroughly explored. Therefore, this study focused on investigating the functions, immunological differences, and potential therapeutic targets of common genes between RA and PVNS.Methods: Through the dataset GSE3698 obtained from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) were screened by R software, and weighted gene coexpression network analysis (WGCNA) was performed to discover the modules most relevant to the clinical features. The common genes between the two diseases were identified. The molecular functions and biological processes of the common genes were analyzed. The protein-protein interaction (PPI) network was constructed using the STRING database, and the results were visualized in Cytoscape software. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression and random forest (RF) were utilized to identify hub genes and predict the diagnostic efficiency of hub genes as well as the correlation between immune infiltrating cells.Results: We obtained a total of 107 DEGs, a module (containing 250 genes) with the highest correlation with clinical characteristics, and 36 common genes after taking the intersection. Moreover, using two machine learning algorithms, we identified three hub genes (PLIN, PPAP2A, and TYROBP) between RA and PVNS and demonstrated good diagnostic performance using ROC curve and nomogram plots. Single sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the biological functions in which three genes were mostly engaged. Finally, three hub genes showed a substantial association with 28 immune infiltrating cells.Conclusion: PLIN, PPAP2A, and TYROBP may influence RA and PVNS by modulating immunity and contribute to the diagnosis and therapy of the two diseases