Numerical simulations of radon exhalation from surrounding rocks of single and double underground roadways.

The radon exhalation rate of surrounding rocks in underground roadways is an important parameter in determining radon exhalation capacity and ventilation flowrate for radon removal. By approximating the roadways as thick-walled, porous cylinders, this study investigates radon exhalation from their surrounding rocks via simulations using computational fluid dynamics (CFD). Radon exhalation rates of single and double underground roadways were computed and analysed under different pressure differences, radon diffusion coefficients, permeabilities of rocks, single roadway locations and additional parallel roadway orientation. The radon regulating zone was presented and the effect of pressure difference on it was analysed. By fitting the data from simulation results, an estimation model was obtained for the radon exhalation rate of a single roadway. For two adjacent parallel roadways with a distance greater than or equal to 50m, the model is also suitable for estimating the radon exhalation rate when the rock permeability is less than 1 × 10-14 m2 and the ratio of permeability to diffusion coefficient is less than 5 × 10-9.

Optimization of the operating parameters for radon reduction on brattice induced cavern ventilation using CFD.

Cavern is a place where workers often work in underground spaces, where radon is constantly released from surrounding rock surfaces. It is of great significance to develop effective ventilation to reduce radon in underground space for safe production and occupational health. For the purpose of controlling the radon concentration level in the cavern, the influence of upstream and downstream brattice length, upstream and downstream brattice to wall width on the volume average radon concentration and the plane average radon concentration at the height of the human respiratory zone (Z = 1.6 m) in the cavern, was studied by using the CFD (Computational fluid dynamics) method, and the operating parameters of ventilation induced by the brattice are optimized. The results show that the radon concentration in the cavern can be significantly reduced by using the brattice induced ventilation compared with no ventilation auxiliary facilities. This study provides a reference for local radon-reducing ventilation design of underground cavern.

Partial Oxidation of Methane to Methanol on the M-O-Ag/Graphene (M = Ag, Cu) Composite Catalyst: A DFT Study.

Partial oxidation of methane (CH4) to methanol (CH3OH) remains a great challenge in the field of catalysis due to its low selectivity and productivity. Herein, Ag-O-Ag/graphene and Cu-O-Ag/graphene composite catalysts are proposed to oxidize methane (CH4) to methanol (CH3OH) by using the first-principles calculations. It is shown that reactive oxygen species (µ-O) on both catalysts can activate the C-H bond of CH4, and in addition to CH4 activation, the catalytic activity follows the order of Ag-O-Ag/graphene (singlet) > Ag-O-Ag/graphene (triplet) ≈ Cu-O-Ag/graphene (triplet) > Cu-O-Ag/graphene (singlet). For CH3OH* formation, the catalytic activity follows the order of Cu-O-Ag/graphene (triplet) > Ag-O-Ag/graphene (triplet) > Ag-O-Ag/graphene (singlet) > Cu-O-Ag/graphene (singlet). It can be inferred that the introduction of Cu not only reduces the use of noble metal Ag but also exhibits a catalytic effect comparable to that of the Ag-O-Ag/graphene catalyst. Our findings will provide a new avenue for understanding and designing highly effective catalysts for the direct conversion of CH4 to CH3OH.

Pb2+ removal based on the confinement effect in polygonal carbon nanotubes: a molecular dynamics simulation.

Heavy metal Pb2+ pollutants have become an important environmental problem, which threatens public health and ecosystems worldwide. In this study, to explore the effective treatment of trace Pb2+ pollution in water, molecular dynamics simulation combined with DFT calculations was used to study the transportation behavior of Pb2+ using polygonal carbon nanotubes (PCNT: P = 4, 5, 6, 8)/graphene composites (PCNTs/G). It is shown that due to the confinement effect of PCNTs, both H2O and H3O+ can form a hydrogen-bonding network and transport them in the form of proton exchange through the PCNT channels. The trajectory shows that with the help of a hydrogen-bonding network, the probability of Pb2+ passing through the 8N channel is enhanced. Then, upon the fluorine modification of PCNTs, mutual effects of both the hydrogen-bonding network and electrophilic attraction make Pb2+ get through the channel of 8F. It is indicated that with respect to 4CNT/G, 5CNT/G, and 6CNT/G, 8CNT/G is not accurate for Pb2+ interception at the outlets. In addition, the RDF, and HOMO-LUMO orbitals indicate that the affinity from the hydrogen-bonding network and PCNT walls both play important roles in particle transportation. This work can not only provide a basic understanding of Pb2+ transportation in PCNTs from the perspective of diffusion but also be helpful to guide the strategy on how to deal with Pb2+ pollution in waters.

FAM171B as a Novel Biomarker Mediates Tissue Immune Microenvironment in Pulmonary Arterial Hypertension.

The purpose of this study was to uncover potential diagnostic indicators of pulmonary arterial hypertension (PAH), evaluate the function of immune cells in the pathogenesis of the disease, and find innovative treatment targets and medicines with the potential to enhance prognosis. Gene Expression Omnibus was utilized to acquire the PAH datasets. We recognized differentially expressed genes (DEGs) and investigated their functions utilizing R software. Weighted gene coexpression network analysis, least absolute shrinkage and selection operators, and support vector machines were used to identify biomarkers. The extent of immune cell infiltration in the normal and PAH tissues was determined using CIBERSORT. Additionally, the association between diagnostic markers and immune cells was analyzed. In this study, 258DEGs were used to analyze the disease ontology. Most DEGs were linked with atherosclerosis, arteriosclerotic cardiovascular disease, and lung disease, including obstructive lung disease. Gene set enrichment analysis revealed that compared to normal samples, results from PAH patients were mostly associated with ECM-receptor interaction, arrhythmogenic right ventricular cardiomyopathy, the Wnt signaling pathway, and focal adhesion. FAM171B was identified as a biomarker for PAH (area under the curve = 0.873). The mechanism underlying PAH may be mediated by nave CD4 T cells, resting memory CD4 T cells, resting NK cells, monocytes, activated dendritic cells, resting mast cells, and neutrophils, according to an investigation of immune cell infiltration. FAM171B expression was also associated with resting mast cells, monocytes, and CD8 T cells. The results suggest that PAH may be closely related to FAM171B with high diagnostic performance and associated with immune cell infiltration, suggesting that FAM171B may promote the progression of PAH by stimulating immune infiltration and immune response. This study provides valuable insights into the pathogenesis and treatment of PAH.

Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation.

Hemiplegia is a common motor dysfunction caused by a stroke. However, the dynamic network mechanism of brain processing information in post-stroke hemiplegic patients has not been revealed when performing motor imagery (MI) tasks. We acquire electroencephalography (EEG) data from healthy subjects and post-stroke hemiplegic patients and use the Fugl-Meyer assessment (FMA) to assess the degree of motor function damage in stroke patients. Time-varying MI networks are constructed using the adaptive directed transfer function (ADTF) method to explore the dynamic network mechanism of MI in post-stroke hemiplegic patients. Finally, correlation analysis has been conducted to study potential relationships between global efficiency and FMA scores. The performance of our proposed method has shown that the brain network pattern of stroke patients does not significantly change from laterality to bilateral symmetry when performing MI recognition. The main change is that the contralateral motor areas of the brain damage and the effective connection between the frontal lobe and the non-motor areas are enhanced, to compensate for motor dysfunction in stroke patients. We also find that there is a correlation between FMA scores and global efficiency. These findings help us better understand the dynamic brain network of patients with post-stroke when processing MI information. The network properties may provide a reliable biomarker for the objective evaluation of the functional rehabilitation diagnosis of stroke patients.

Commissuroplasty as a Main Operative Technique in Rheumatic Mitral Valve Repair: Surgical Experiences and Mid-Term Results.

AIM: We aimed to evaluate the therapeutic effectiveness of commissuroplasty in mitral valve repair for rheumatic mitral valve disease. For this purpose, we summarise our experience with this technique and analyse the mid-term postoperative outcomes. METHOD: We retrospectively evaluated the records of patients with rheumatic valve disease who underwent mitral valve repair between January 2011 and January 2018 at our centre. Detailed follow-up data were collected. A Kaplan-Meier survival curve for survival free from reoperation and valve failure was constructed. Multivariate Cox regression analyses were performed to identify predictors of relevant end points (death, reoperation, and valve failure). RESULTS: A total of 362 patients underwent rheumatic mitral valve repair during the study period. Mitral valve stenosis was the primary pathological feature. Almost all surgeries were accomplished via commissuroplasty. The mean duration of follow-up was 25.57 ± 19.91 months. Twenty-two (22) endpoint events were noted during follow-up. The 2- and 7-year rates of survival free from reoperation and valve failure were 93.9%±1.4% and 91.5%±2.0%, respectively. Multivariate Cox regression analysis revealed that left atrial anteroposterior diameter >60 mm (hazard ratio, 5.2; p < 0.001) was an independent predictor of all endpoints. CONCLUSIONS: Most Chinese patients with rheumatic valve disease were treated effectively via commissuroplasty combined with other surgical procedures, and the mid-term postoperative outcomes were satisfactory.

Association between functional polymorphisms in the promoter of the miR-143/145 cluster and risk of conotruncal heart defects.

Aim: Conotruncal heart defects (CTDs) are the most common form of congenital heart disease. We investigated the association of these two single-nucleotide polymorphisms (SNPs) in the promoter of miR-143/145 (rs353292 and rs4705343) with the susceptibility to CTDs in a Chinese population. Materials & methods: Two SNPs in the promoter of miR-143/145 (rs353292 and rs4705343) have been examined by PCR-RFLP methodology for 259 CTDs patients and 303 control subjects. Results: An association between SNP rs4705343 of miR-143/145 and CTDs has been confirmed in the Chinese Han population. Conclusion: Our results indicated that SNP rs4705343 in miR-143/145 is a potential genetic marker for CTDs in the Chinese Han population.

4'-C-Trifluoromethyl modified oligodeoxynucleotides: synthesis, biochemical studies, and cellular uptake properties.

Herein, we report the synthesis of 4'-C-trifluoromethyl (4'-CF3) thymidine (T4'-CF3) and its incorporation into oligodeoxynucleotides (ODNs) through solid-supported DNA synthesis. The 4'-CF3 modification leads to a marginal effect on the deoxyribose conformation and a local helical structure perturbation for ODN/RNA duplexes. This type of modification slightly decreases the thermal stability of ODN/RNA duplexes (-1 °C/modification) and leads to improved nuclease resistance. Like the well-known phosphorothioate (PS) modification, heavy 4'-CF3 modifications enable direct cellular uptake of the modified ODNs without any delivery reagents. This work highlights that 4'-CF3 modified ODNs are promising candidates for antisense-based therapeutics, which will, in turn, inspire us to develop more potent modifications for antisense ODNs and siRNAs.

Noncooperative Game Strategy in Cyber-Financial Systems With Wiener and Poisson Random Fluctuations: LMIs-Constrained MOEA Approach.

The financial market is a nonlinear stochastic system with continuous Wiener and discontinuous Poisson random fluctuations. Most managers or investors hope their investment policies to be with the not only high profit but also low risk. Managers and investors involved pursue their own interests which are partly conflicting with others. Stochastic game theory has been widely applied to multiperson noncooperative decision making problem of financial market. However, for the nonlinear stochastic financial system with random fluctuations, it still lacks an analytical or computational scheme to effectively solve the complex noncooperative game strategy design problem. In this paper, the stochastic multiperson noncooperative game strategy in cyber-financial systems is transformed to a multituple Hamilton-Jacobi-Isacc inequalities (HJIIs)-constrained multiobjective optimization problem (MOP). This HJIIs-constrained MOP solution is also found to be the Nash equilibrium solution of multiperson noncooperative game strategy in nonlinear stochastic financial systems. In order to simplify design procedure by the global linearization theory, a set of local linear systems are interpolated to approximate the nonlinear stochastic financial system so that the m-tuple HJIIs-constrained MOP for noncooperative game strategy of cyber-financial system could be converted to a linear matrix inequalities (LMIs)-constrained MOP. Finally, an LMIs-constrained multiobjective evolution algorithm is explored for effectively solving the multiperson noncooperative game strategy in cyber-financial systems. Two design examples are also given for the illustration of the design procedure and the performance validation of the proposed stochastic noncooperative investment strategy in the nonlinear stochastic financial systems.

Finite-time stability and stabilization for stochastic markov jump systems with mode-dependent time delays.

This paper is concerned with the problems of finite-time stability and stabilization for stochastic Markov systems with mode-dependent time-delays. In order to reduce conservatism, a mode-dependent approach is utilized. Based on the derived stability conditions, state-feedback controller and observer-based controller are designed, respectively. A new N-mode algorithm is given to obtain the maximum value of time-delay. Finally, an example is used to show the merit of the proposed results.

Genetic variance of transforming growth factor ß2 gene in conotruncal heart defects.

OBJECTIVE: The aim of this study was to evaluate the association between two haplotype-tag single nucleotide polymorphisms (SNPs) (rs6658835 and rs10495098) of TGF-ß2 and conotruncal heart defects (CTDs). METHODS: Two polymorphisms of TGF-ß2 gene were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) from 259 CTDs patients and 310 control subjects. RESULTS: The association between SNP rs6658835 in TGF-ß2 and CTDs has been found. The frequency of G allele in CTDs patients was significantly higher than that in control subjects (52.7% versus 40.3%, p < 0.001, OR =1.649). CONCLUSION: TGF-ß2 gene polymorphisms may serve as a novel genetic marker for the risk of CTDs.

The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification.

This paper presents an effective mutual information-based feature selection approach for EMG-based motion classification task. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the successive and non-overlapped sub-bands. The energy characteristic of each sub-band is adopted to construct the initial full feature set. For reducing the computation complexity, mutual information (MI) theory is utilized to get the reduction feature set without compromising classification accuracy. Compared with the extensively used feature reduction methods such as principal component analysis (PCA), sequential forward selection (SFS) and backward elimination (BE) etc., the comparison experiments demonstrate its superiority in terms of time-consuming and classification accuracy. The proposed strategy of feature extraction and reduction is a kind of filter-based algorithms which is independent of the classifier design. Considering the classification performance will vary with the different classifiers, we make the comparison between the fuzzy least squares support vector machines (LS-SVMs) and the conventional widely used neural network classifier. In the further study, our experiments prove that the combination of MI-based feature selection and SVM techniques outperforms other commonly used combination, for example, the PCA and NN. The experiment results show that the diverse motions can be identified with high accuracy by the combination of MI-based feature selection and SVM techniques. Compared with the combination of PCA-based feature selection and the classical Neural Network classifier, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and MI in EMG motion classification.

Joint application of rough set-based feature reduction and Fuzzy LS-SVM classifier in motion classification.

This paper presents an effective classification scheme consisting of the rough set theory (RST)-based feature selection and the fuzzy least squares support vector machine (LS-SVM) classifier for the surface electromyographic (sEMG)-based motion classification. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the non-overlapped sub-bands and the energy characteristic of each sub-band is adopted to form the original feature set. In order to reduce the computation complexity, the RST is utilized to get the reduction feature set without compromising classification accuracy. In the feature reduction phase, cluster separation index (CSI) is introduced to evaluate the performance of the proposed algorithm. In the sequel, the Fuzzy LS-SVM is constructed for the multi-class classification task. The RST-based feature selection is independent of the classifier design. Consequently the classification performance will vary with different classifiers. We make the comparison between the proposed classification scheme and the commonly used classification scheme, such as the combination of the principal component analysis (PCA)-based feature selection and the neural network (NN) classifier. The results of comparative experiments show that the diverse motions can be identified with high accuracy by the proposed scheme. Compared with other feature extraction and selection algorithms and classifiers, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and RST in EMG motion classification.

Classification of surface EMG signals using harmonic wavelet packet transform.

In this paper, an efficient method based on the discrete harmonic wavelet packet transform (DHWPT) is presented to classify surface electromyographic (SEMG) signals. After the relative energy of SEMG signals in each frequency band had been extracted by the DHWPT, a genetic algorithm was utilized to select appropriate features in order to reduce the feature dimensionality. Then, the selected features were used as the input vectors to a neural network classifier to discriminate four types of prosthesis movements. Compared with other classification methods, the proposed method provided high classification accuracy in experimental research. In addition, this method could also save a lot of computational time because the DHWPT has a fast algorithm based on the fast Fourier transform for numerical implementation.

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition.

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short perioda9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.

Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials.

We have studied methods for noise reduction of myoelectric signals and for extraction of motor unit action potentials from these signals. Effective MUAP peak detection is the first important step in EMG decomposition. We first combined independent component analysis and wavelet filtering to remove power line interference, and then applied a wavelet filtering method and threshold estimation calculated using wavelet transform to suppress background noise and Gaussian white noise. The technique was applied to single-channel, short-period real myoelectric signals from normal subjects and to artificially generated EMG recordings. In contrast to existing methods based on amplitude single-threshold filtering of the original myoelectric signal or a conventional digitally filtered signal, our technique is fast and robust. Moreover, the proposed algorithm is substantially automatic. The performance has been evaluated with a set of synthetic and experimentally recorded myoelectric signals. The basic tool for testing was power spectrum density (PSD) estimation by the Welch method, which allowed us to analyze the PSD of nonstationary signals.

[Classification of surface EMG signal based on wavelet transform with nonlinear scale].

Surface EMG (sEMG) signal is a complex nonlinear, non-stationary signal. In this paper, wavelet transform with nonlinear scale (NWT) is introduced. Due to the gradual shortening of its time-resolution, NWT is good at extracting the precise time-frequency information from sEMG signal. First, every sEMG signal (30 sets are for forearm supination and 30 sets are for forearm pronation) is transformed into intensity distribution (time-frequency distribution) by NWT. And then the feature vector is determined from the characteristic roots which are obtained from the intensity distribution by principle component analysis. At last, the two patterns of sEMG signals are identified by BP neural network. The results show that the accurate classification rate is higher gained by NWT than by two conventional time-frequency distributions. At the same time, the calculating complexity of neural network is decreased greatly.