Parkinson's disease detection and classification using EEG based on deep CNN-LSTM model.

The progressive loss of motor function in the brain is a hallmark of Parkinson's disease (PD). Electroencephalogram (EEG) signals are commonly used for early diagnosis since they are associated with a brain disorder. This work aims to find a better way to represent electroencephalography (EEG) signals and enhance the classification accuracy of individuals with Parkinson's disease using EEG signals. In this paper, we present two hybrid deep neural networks (DNN) that combine convolutional neural networks with long short-term memory to diagnose Parkinson's disease using EEG signals, that is, through the establishment of parallel and series combined models. The deep CNN network is utilized to acquire the structural features of ECG signals and extract meaningful information from them, after which the signals are sent via a long short-term memory network to extract the features' context dependency. The proposed architecture was able to achieve 97.6% specificity, 97.1% sensitivity, and 98.6% accuracy for a parallel model and 99.1% specificity, 98.5% sensitivity, and 99.7% accuracy for a series model, both in 3-class classification (PD patients with medication, PD patients without medication and healthy).

Evolving protein-protein interaction networks: A model based on duplication and mutation at different rates.

We present a model describing the evolution of protein-protein interaction networks. The model is based on gene duplication and mutation. Considering rates of gene duplication and mutation, the average node degree and cluster coefficient are calculated for different parameters. The predicted degree distribution and cluster coefficient are in good agreement with the observed statistical properties obtained from the analysis of the yeast proteome.

Quantitative proteomic analysis for radiation-induced cell cycle suspension in 92-1 melanoma cell line.

Melanoma is a malignant tumor with high invasive and metastatic properties. Though radiation is the major therapy for melanoma, its radio-resistance has been shown to severely influence the clinical outcome. So it is imperative to enhance the sensitivity of uveal melanoma cells to radiotherapy. Previously, we found that the cell cycle of 92-1 uveal melanoma cells was suspended and remained unchanged for up to 5 days after exposure to 10 Gy of X-rays, which might be relevant to the high radio-sensitivity of 92-1 cells. To further investigate the cell cycle suspension-associated proteins, we employed two analyses with stable isotope labeling with amino acids in cell culture technology and two-dimensional liquid chromatography tandem mass spectrometry. Cells were incubated for 15 h or 48 h after irradiation with 10 Gy of X-rays. We identified a total of 737 proteins at 15 h (Group A) and 530 proteins at 48 h post-irradiation (Group B). The gene ontology biological pathway was used to obtain a systems level view of proteome changes in 92-1cells under cell cycle suspension. We further selected the significantly changed proteins for investigation of their potential contribution to cell cycle suspension, growth arrest and cell senescence. These proteins are involved in the cell cycle, stress response, glycolysis and the tricarboxylic acid cycle, etc. Our study expected to reveal potential marker proteins associated with cell suspension induced by irradiation, which might contribute to understanding the mechanism beyond the cell cycle suspension.

[Characteristics of gas release during combustion of main arbor and shrub species in Xiaoxing'an Mountain].

The amounts of CO2, CO, CxHy, SO2 and NO released from the combustion of 10 arbor and 9 shrub species in Liangshui Forest Farm of Xiaoxing'an Mountain were determined, with the release amounts and emission factors of these gases calculated. The results showed that the released CO2, the total amount of carbon-containing gases, and the mean value of the total amount of the 5 gases during combustion were larger for arbors than for shrubs, being 1277.04 and 1149.06 mg x g(-1), 1476.27 and 1147.18 mg x g(-1), and 1486.21 and 1459.67 mg x g(-1), respectively, while an opposite pattern of the mean release of CO, NO and SO2 was observed, being 231.58 and 282.93 mg x g(-1), 3.61 and 5.03 mg x g(-1), and 6.32 and 7.46 mg x g(-1) for the arbor and shrub species, respectively. The emission factors of CO2 and CO were also larger for arbors than for shrubs, being 2.8853 and 2.7718, and 0.4558 and 0.2425, respectively.

THE DETECTION OF FOOT-AND-MOUTH DISEASE VIRUS IN ANIMAL TISSUE BY RT-PCR TECHNIQUE / 微生物学通报

A set of primers amplified the VP1 gene of foot-and-mouth disease vims (FMDV) was designed and synthesized. A reverse transcription-polymerase chain reaction (RT-PCR) technique detected the RNA of FMDV was established after selecting the best purification method, reagents and reaction conditions. Samples of fresh milk, lymph node, spinal cord, vesicular skin, milk powder, cotton swab, mouse and meat in daughter-house were detected by RT-PCR, positive rates were41.4% (24/58), 13.33% (2/15), 20% (1/5), 100% (1/1), 100% (1/1), 37.5% (12/32), 100% (2/2) and 10% - 70%, respectively. However, positive rate of cockroach detected by RT-PCR was 0. The results showed that the established FMDV RT-PCR technique provided a more sensitive, specific and reliable method for diagnosis and epizootic study of the foot-and-mouth disease.