A dumbbell probe-mediated rolling circle amplification strategy for highly sensitive microRNA detection

We herein report the design of a dumbbell-shaped DNA probe that integrates target-binding, amplification and signaling within one multifunctional design. The dumbbell probe can initiate rolling circle amplification (D-RCA) in the presence of specific microRNA (miRNA) targets. This D-RCA-based miRNA strategy allows quantification of miRNA with very low quantity of RNA samples. The femtomolar sensitivity of D-RCA compares favorably with other existing technologies. More significantly, the dynamic range of D-RCA is extremely large, covering eight orders of magnitude. We also demonstrate miRNA quantification with this highly sensitive and inexpensive D-RCA strategy in clinical samples

Research on Speech Emotion Recognition Based on the Fractional Fourier Transform

Speech emotion recognition is an important part of human–computer interaction, and the use of computers to analyze emotions and extract speech emotion features that can achieve high recognition rates is an important step. We applied the Fractional Fourier Transform (FrFT), and then constructed it to extract MFCC and combined it with a deep learning method for speech emotion recognition. Since the performance of FrFT depends on the transform order p, we utilized an ambiguity function to determine the optimal order for each frame of speech. The MFCC was extracted under the optimal order of FrFT for each frame of speech. Finally, combining the deep learning network LSTM for speech emotion recognition. Our experiment was conducted on the RAVDESS, and detailed confusion matrices and accuracy were given for analysis. The MFCC extracted using FrFT was shown to have better performance than ordinal FT, and the proposed model achieved a weighting accuracy of 79.86%

BroadBand-Adaptive VMD with Flattest Response

A mixed signal with several unknown modes is common in the industry and is hard to decompose. Variational Mode Decomposition (VMD) was proposed to decompose a signal into several amplitude-modulated modes in 2014, which overcame the limitations of Empirical Mode Decomposition (EMD), such as sensitivity to noise and sampling. We propose an improved VMD, which is simplified as iVMD. In the new algorithm, we further study and improve the mathematical model of VMD to adapt to the decomposition of the broad-band modes. In the new model, the ideal flattest response is applied, which is derived from the mathematical integral form and obtained from different-order derivatives of the improved modes’ definitions. The harmonics can be treated via synthesis in our new model. The iVMD algorithm can decompose the complex harmonic signal and the broad-band modes. The new model is optimized with the alternate direction method of multipliers, and the modes with adaptive broad-band and their respective center frequencies can be decomposed. the experimental results show that iVMD is an effective algorithm based on the artificial and real data collected in our experiments

Research on Speech Emotion Recognition Based on AA-CBGRU Network

Speech emotion recognition is an emerging research field in the 21st century, which is of great significance to human–computer interaction. In order to enable various smart devices to better recognize and understand the emotions contained in human speech, in view of the problems of gradient disappearance and poor learning ability of the time series information in the current speech emotion classification model, an AA-CBGRU network model is proposed for speech emotion recognition. The model first extracts the spectrogram and its first and second order derivative features of the speech signal, then extracts the spatial features of the inputs through the convolutional neural network with residual blocks, then uses the BGRU network with an attention layer to mine deep time series information, and finally uses the full connection layer to achieve the final emotion recognition. The experimental results on the IEMOCAP sentiment corpus show that the model in this paper improves both the weighted accuracy (WA) and the unweighted accuracy (UA)

Convolutional Network Research for Defect Identification of Productor Appearance Surface

The accurate and rapid identification of surface defects is an important element of product appearance quality evaluation, and the application of deep learning for surface defect recognition is an ongoing hot topic. In this paper, a lightweight KD-EG-RepVGG network based on structural reparameterization is designed for the identification of surface defects on strip steel as an example. In order to improve the stability and accuracy in the recognition of strip steel surface defects, an efficient attention network was introduced into the network, and then a Gaussian error linear activation function was applied in order to prevent the neurons from being set to zero during neural network training, leaving neuron parameters without being updated. Finally, knowledge distillation is used to transfer the knowledge of the RepVGG-A0 network to give the lightweight model better accuracy and generalization capability. The outcomes of the experiments indicate that the model has a computational and parametric volume of 22.3 M and 0.14 M, respectively, in the inference phase, a defect recognition accuracy of 99.44% on the test set, and a single image detection speed of 2.4 ms, making it more suitable for deployment in real engineering environments

Research on Speech Emotion Recognition Based on AA-CBGRU Network

Speech emotion recognition is an emerging research field in the 21st century, which is of great significance to human–computer interaction. In order to enable various smart devices to better recognize and understand the emotions contained in human speech, in view of the problems of gradient disappearance and poor learning ability of the time series information in the current speech emotion classification model, an AA-CBGRU network model is proposed for speech emotion recognition. The model first extracts the spectrogram and its first and second order derivative features of the speech signal, then extracts the spatial features of the inputs through the convolutional neural network with residual blocks, then uses the BGRU network with an attention layer to mine deep time series information, and finally uses the full connection layer to achieve the final emotion recognition. The experimental results on the IEMOCAP sentiment corpus show that the model in this paper improves both the weighted accuracy (WA) and the unweighted accuracy (UA)

Flood Influence Characteristics of Rail Transit Engineering of Tunnel, Viaduct, and Roadbed through Urban Flood Detention Areas

Many subways, light rails, and trains travel through urban flood retention regions via tunnels, viaducts, and roadbeds; however, less is known about the flood influence laws of rail transportation by the crossing ways. Rail transit projects were chosen as research objects for the ordinary subway, light rail, and railway passing through urban flood detention areas in Wuhan, and the flood influence characteristics were systematically compared for the three crossing ways. The study revealed that crossing ways primarily affected the flood storage volume occupied per unit length of lines and that the flood influence of rail projects on flood detention areas was proportionate to the flood storage volume occupied per unit length of lines. Specifically, the flood storage volume occupied per unit length of tunnels was about 1/8.9 that of viaducts and 1/19.7 that of roadbeds. Moreover, the tunnel way had the least influence on the main aspects, such as flood control, floods on engineering, and engineering-related aspects; the roadbed-based way had the largest; and the viaduct way was in the middle. These findings may provide technical support for the decision-making, engineering planning, construction, and management of rail transit and other projects in urban flood detention areas

Pharmacogenetic study of drug-metabolising enzyme polymorphisms on the risk of anti-tuberculosis drug-induced liver injury: a meta-analysis.

Three first-line antituberculosis drugs, isoniazid, rifampicin and pyrazinamide, may induce liver injury, especially isoniazid. This antituberculosis drug-induced liver injury (ATLI) ranges from a mild to severe form, and the associated mortality cases are not rare. In the past decade, many investigations have focused the association between drug-metabolising enzyme (DME) gene polymorphisms and risk for ATLI; however, these studies have yielded contradictory results.PubMed, EMBASE, ISI web of science and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. A meta-analysis was performed to examine the association between polymorphisms from 4 DME genes (NAT2, CYP2E1, GSTM1 and GSTT1) and susceptibility to ATLI. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Heterogeneity among articles and their publication bias were also tested.38 studies involving 2,225 patients and 4,906 controls were included. Overall, significantly increased ATLI risk was associated with slow NAT2 genotype and GSTM1 null genotype when all studies were pooled into the meta-analysis. Significantly increased risk was also found for CYP2E1*1A in East Asians when stratified by ethnicity. However, no significant results were observed for GSTT1.Our results demonstrated that slow NAT2 genotype, CYP2E1*1A and GSTM1 null have a modest effect on genetic susceptibility to ATLI