An Augmented Sample Selection Framework for Prediction of Anticancer Peptides.

Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction.

Strong Generalized Speech Emotion Recognition Based on Effective Data Augmentation.

The absence of labeled samples limits the development of speech emotion recognition (SER). Data augmentation is an effective way to address sample sparsity. However, there is a lack of research on data augmentation algorithms in the field of SER.

Multi-Stream Convolution-Recurrent Neural Networks Based on Attention Mechanism Fusion for Speech Emotion Recognition.

The quality of feature extraction plays a significant role in the performance of speech emotion recognition. In order to extract discriminative, affect-salient features from speech signals and then improve the performance of speech emotion recognition, in this paper, a multi-stream convolution-recurrent neural network based on attention mechanism (MSCRNN-A) is proposed. Firstly, a multi-stream sub-branches full convolution network (MSFCN) based on AlexNet is presented to limit the loss of emotional information.

Spatiotemporal characterization of microdamage accumulation in rat ulnae in response to uniaxial compressive fatigue loading.

Repetitive fatigue loading can induce microdamage accumulation in bone matrix, which results in impaired mechanical properties and increased fracture susceptibility. However, the spatial distribution and time-variant process of microdamage accumulation in fatigue-loaded skeleton, especially for linear microcracks which are known to initiate bone remodeling, remain not fully understood. In this study, the time-varying process of the morphology and distribution of microcracks in rat ulnae subjected to uniaxial compressive fatigue loading was investigated.

Pulsed electromagnetic fields (PEMF) attenuate changes in vertebral bone mass, architecture and strength in ovariectomized mice.

Pulsed electromagnetic fields (PEMF) has been investigated as a noninvasive alternative method to prevent bone loss for postmenopausal osteoporosis (OP), and the bone tissue involved in these studies are usually long bones such as femur and tibia in OP patients or rat models. However, few studies have investigated the effects of PEMF on the vertebral bone in mice with OP. This study aimed to investigate whether PEMF preserve lumbar vertebral bone mass, microarchitecture and strength in ovariectomized (OVX) mouse model of OP and its associated mechanisms.

Effects of four kinds of electromagnetic fields (EMF) with different frequency spectrum bands on ovariectomized osteoporosis in mice.

Electromagnetic fields (EMF) was considered as a non-invasive modality for treatment of osteoporosis while the effects were diverse with EMF parameters in time domain. In present study, we extended analysis of EMF characteristics from time domain to frequency domain, aiming to investigate effects of four kinds of EMF (LP (1-100 Hz), BP (100-3,000 Hz), HP (3,000-50,000 Hz) and AP (1-50,000 Hz)) on ovariectomized (OVX) osteoporosis (OP) in mice. Forty-eight 3-month-old female BALB/c mice were equally assigned to Sham, OVX, OVX + LP, OVX + BP, OVX + HP and OVX + AP groups (n = 8).