Objective To find a useful index for real-time detecting of speech endpoint and improving the performance of speech processing under low SNR by analyzing fluctuation complexity of speech signals. Method The influence of state space partition method, window size and partition numbers on detecting performance was analyzed. The comparison experiments of speech signals corresponding to different SNR and noise type was designed using the measure of complexity behaviors based on the information gain.Result It was found that fluctuation complexity was more effective in detecting low-SNR speech than spectral entropy. Conclusion Fluctuation complexity is a valid feature to make speech/non-speech decision for the low SNR cases. The presented method can achieve robust performance and has a good real-time behavior.

Objective To investigate the potential relationship between the paroxysmal Atrial Fibrillation(PAF) and serum uric acid level.Methods Consecutive patients with (patient group,n =65) and without(control group,n =41) PAF,who were hospitalized in the Second Hospital of Tianjin Medical University from September 2011 to June 2012,were included in this study.We excluded subjects with congestive heart failure,acute coronary syndrome,congenital heart disease,valvular heart disease,cardiomyopathy,thyroid dysfunction and acute infection or inflammatory conditions.Baseline clinical data,complications and laboratory examination results were collected.Left atrium diameter (LAD),left ventricular end-diastolic dimension (LVEDD) and left ventricular ejection fraction (LVEF) were determined by echocardiography.Univariate and logistic regression was conducted to detect risk factors for PAF.Results Serum uric acid level were significantly increased in patients with PAF compared with controls ((360.2 ± 103.9) μmol/L vs (296.0 ±68.1) μmol/L,P =0.001).Multivariate logistic regression analysis showed that higher level of serum uric acid (OR:1.007,95％ CI:1.000-1.015) and LAD (OR:1.142,95％ CI:1.031-1.265) were independent risk factors for the occurrence of PAF.Conclusion High serum uric acid level is an independent risk factor for the development of PAF.Future larger studies should further evaluate this potential association as well as the underlying mechanisms.

Automatic speaker gender identification based on voice feature is an important task in voice processing and analysis fields. In this paper non-linear parameters such as fractal dimension are applied to be one part of feature space for improving the ability of describing speaker gender feature through conventional linear parameters method. Pitch is picked using lifting scheme, and audio fractal dimension is extracted. Then based on Takens theory, the time delay method is used to reconstruct the phase space of fractal dimension sequence. And fractal dimension complexity is obtained by calculating Approximate Entropy. Three dimension feature vectors, including the pitch, the fractal dimension and the fractal dimension complexity, are applied to speaker gender identification. Experiment results show that through adding non-linear parameters, compared with the linear parameter using one dimension only such as pitch, the proposed method is more accurate and robust, and thus provides a new way for speaker gender identification.
Algorithms ; Artificial Intelligence ; Biometry ; methods ; Humans ; Nonlinear Dynamics ; Pattern Recognition, Automated ; methods ; Pitch Discrimination ; Sex Characteristics ; Signal Processing, Computer-Assisted ; Speech ; Speech Acoustics ; Voice

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.
Artificial Intelligence ; Humans ; Pattern Recognition, Automated ; methods ; Signal Processing, Computer-Assisted ; Speech ; Speech Production Measurement ; methods ; Speech Recognition Software

In order to more accurately and effectively understand the intermuscular coupling of different temporal and spatial levels from the perspective of complex networks, a new multi-scale intermuscular coupling network analysis method was proposed in this paper. The multivariate variational modal decomposition (MVMD) and Copula mutual information (Copula MI) were combined to construct an intermuscular coupling network model based on MVMD-Copula MI, and the characteristics of intermuscular coupling of multiple muscles of upper limbs in different time-frequency scales during reaching exercise in healthy subjects were analyzed by using the network parameters such as node strength and clustering coefficient. The experimental results showed that there are obvious differences in the characteristics of intermuscular coupling in the six time-frequency scales. Specifically, the triceps brachii (TB) had relatively high coupling strength with the middle deltoid (MD) and posterior deltoid (PD), and the intermuscular function was closely connected. However, the biceps brachii (BB) was independent of other muscles. The intermuscular coupling network had scale differences. MVMD-Copula MI can quantitatively describe the relationship of multi-scale intermuscular coupling strength, which has good application prospects.
Arm ; Electromyography ; Exercise ; Humans ; Muscle, Skeletal ; Upper Extremity

Emerging and re-emerging RNA viruses occasionally cause epidemics and pandemics worldwide, such as the on-going outbreak of the novel coronavirus SARS-CoV-2. Herein, we identified two potent inhibitors of human DHODH, S312 and S416, with favorable drug-likeness and pharmacokinetic profiles, which all showed broad-spectrum antiviral effects against various RNA viruses, including influenza A virus, Zika virus, Ebola virus, and particularly against SARS-CoV-2. Notably, S416 is reported to be the most potent inhibitor so far with an EC of 17 nmol/L and an SI value of 10,505.88 in infected cells. Our results are the first to validate that DHODH is an attractive host target through high antiviral efficacy in vivo and low virus replication in DHODH knock-out cells. This work demonstrates that both S312/S416 and old drugs (Leflunomide/Teriflunomide) with dual actions of antiviral and immuno-regulation may have clinical potentials to cure SARS-CoV-2 or other RNA viruses circulating worldwide, no matter such viruses are mutated or not.
Animals ; Antiviral Agents ; pharmacology ; therapeutic use ; Betacoronavirus ; drug effects ; physiology ; Binding Sites ; drug effects ; Cell Line ; Coronavirus Infections ; drug therapy ; virology ; Crotonates ; pharmacology ; Cytokine Release Syndrome ; drug therapy ; Drug Evaluation, Preclinical ; Gene Knockout Techniques ; Humans ; Influenza A virus ; drug effects ; Leflunomide ; pharmacology ; Mice ; Mice, Inbred BALB C ; Orthomyxoviridae Infections ; drug therapy ; Oseltamivir ; therapeutic use ; Oxidoreductases ; antagonists & inhibitors ; metabolism ; Pandemics ; Pneumonia, Viral ; drug therapy ; virology ; Protein Binding ; drug effects ; Pyrimidines ; biosynthesis ; RNA Viruses ; drug effects ; physiology ; Structure-Activity Relationship ; Toluidines ; pharmacology ; Ubiquinone ; metabolism ; Virus Replication ; drug effects