Many works have focused on speech emotion recognition algorithms. However, most rely on the proper selection of speech acoustic features. In this paper, we propose a novel emotion recognition algorithm that does not rely on any speech acoustic features and combines speaker gender information. We aim to benefit from the rich information from speech raw data, without any artificial intervention. In general, speech emotion recognition systems require manual selection of appropriate traditional acoustic features as classifier input for emotion recognition. Utilizing deep learning algorithms, and the network automatically select important information from raw speech signal for the classification layer to accomplish emotion recognition. It can prevent the omission of emotion information that cannot be direct mathematically modeled as a speech acoustic characteristic. We also add speaker gender information to the proposed algorithm to further improve recognition accuracy. The proposed algorithm combines a Residual Convolutional Neural Network (R-CNN) and a gender information block. The raw speech data is sent to these two blocks simultaneously. The R-CNN network obtains the necessary emotional information from the speech data and classifies the emotional category. The proposed algorithm is evaluated on three public databases with different language systems. Experimental results show that the proposed algorithm has 5.6%, 7.3%, and 1.5%, respectively accuracy improvements in Mandarin, English, and German compared with existing highest-accuracy algorithms. In order to verify the generalization of the proposed algorithm, we use FAU and eNTERFACE databases, in these two independent databases, the proposed algorithm can also achieve 85.8% and 71.1% accuracy, respectively.
The present study attempted to elucidate whether intravesical instillation of platelet-rich plasma (PRP) could decrease bladder inflammation and ameliorate bladder hyperactivity in ketamine ulcerative cystitis (KIC) rat model. Female Sprague Dawley (S-D) rats were randomly divided into control group, ketamine-treated group, ketamine with PRP treated group, and ketamine with platelet-poor plasma (PPP) treated group. Cystometry and micturition frequency/volume studies were performed to investigate bladder function. The morphological change of bladder was investigated by Mason’s trichrome staining. Western blotting analysis were carried out to examine the protein expressions of inflammation, urothelial differentiation, proliferation, urothelial barrier function, angiogenesis and neurogenesis related proteins. The results revealed that treatment with ketamine significantly deteriorated bladder capacity, decreased voiding function and enhanced bladder overactivity. These pathological damage and interstitial fibrosis may via NF-κB/COX-2 signaling pathways and muscarinic receptor overexpression. PRP treatment decreased inflammatory fibrotic biosynthesis, attenuated oxidative stress, promoted urothelial cell regeneration, and enhanced angiogenesis and neurogenesis, thereafter recovered bladder dysfunction and ameliorate the bladder hyperactivity in KIC rat model. These findings suggested that the PRP therapy may offer new treatment options for those clinical KIC patients.
Postmenopausal women who have ovary hormone deficiency (OHD) may experience urological dysfunctions, such as overactive bladder (OAB) symptoms. This study used a female Sprague Dawley rat model that underwent bilateral ovariectomy (OVX) to simulate post-menopause in humans. The rats were treated with platelet-rich plasma (PRP) or platelet-poor plasma (PPP) after 12 months of OVX to investigate the therapeutic effects of PRP on OHD-induced OAB. The OVX-treated rats exhibited a decrease in the expression of urothelial barrier-associated proteins, altered hyaluronic acid (hyaluronan; HA) production, and exacerbated bladder pathological damage and interstitial fibrosis through NFƘB/COX-2 signaling pathways, which may contribute to OAB. In contrast, PRP instillation for four weeks regulated the inflammatory fibrotic biosynthesis, promoted cell proliferation and matrix synthesis of stroma, enhanced mucosal regeneration, and improved urothelial mucosa to alleviate OHD-induced bladder hyperactivity. PRP could release growth factors to promote angiogenic potential for bladder repair through laminin/integrin-α6 and VEGF/VEGF receptor signaling pathways in the pathogenesis of OHD-induced OAB. Furthermore, PRP enhanced the expression of HA receptors and hyaluronan synthases (HAS) enzymes, reduced hyaluronidases (HYALs), modulated the fibroblast-myofibroblast transition, and increased angiogenesis and matrix synthesis via the PI3K/AKT/m-TOR pathway, resulting in bladder remodeling and regeneration.
Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.
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