Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.

Polyunsaturated fatty acids (PUFAs) have been widely applied in the food and medical industry. In this study, malonyl-CoA: ACP transacylase (MAT) was overexpressed through homologous recombination to improve PUFA production in Schizochytrium. The results showed that the lipid and PUFA concentration were increased by 10.1 and 24.5% with MAT overexpression, respectively. Metabolomics analysis revealed that the intracellular tricarboxylic acid cycle was weakened and glucose absorption was accelerated in the engineered strain. In the mevalonate pathway, intracellular carotene content was decreased, and the carbon flux was then redirected toward PUFA synthesis. Furthermore, a glucose fed-batch fermentation was finally performed with the engineered Schizochytrium. The total lipid yield was further increased to 110.5 g/L, 39.6% higher than the wild strain. Docosahexaenoic acid and eicosapentaenoic acid yield were enhanced to 47.39 g/L and 1.65 g/L with an increase of 81.5 and 172.5%, respectively. This study provided an effective metabolic engineering strategy for industrial PUFA production.

A model of the Sb 2 Se 3 solar cell with a hole transport layer (HTL) has been investigated by solar cell capacitance simulator (SCAPS). The influence of different HTLs on device performance has been firstly analyzed, and CuO has been found to be the best HTL. Then, Sb 2 Se 3 thickness, CuO thickness, the doping concentration of CuO, the hole mobility of CuO, the defect density of Sb 2 Se 3 layer, the defect density at the CdS/Sb 2 Se 3 interface, and the work function of metal electrode on device performance have been systematically studied. The optimum thicknesses of Sb 2 Se 3 and CuO are 300 nm and 20 nm, respectively. To achieve ideal performance, the doping concentration of CuO should be more than 10 19 cm −3 , and its hole mobility should be over 1 cm 2 V −1 s −1 . The defect densities in the Sb 2 Se 3 layer and at the CdS/ Sb 2 Se 3 interface play a critical role on device performance, both of which should be as low as 10 13 cm −3 and 10 14 cm −2 , respectively. In addition, the work function of the metal electrode should be more than 4.8 eV to avoid formation of Schottky junction at the metal electrode interface. After optimization, a best efficiency of 23.18% can be achieved. Our simulation results provide valuable information to further improve the efficiency of Sb 2 Se 3 solar cells in practice.

DNA fingerprinting was used to examine genetic variation in populations of Puccinia striiformis Westend. f.sp. tritici, an obligate fungus that causes wheat stripe rust, using as a probe a moderately repetitive DNA sequence PSR331 that shows species specificity in the genome of this pathogen. One hundred and sixty isolates sampled from six provinces throughout China were examined for genetic variation over 26 putative genetic loci defined by PSR331 and the restriction enzyme BglII. Because of the dikaryotic nature of this fungus, DNA fingerprints can not differentiate heterozygotes from homozygotes. We refer to the PSR DNA fingerprints as phenotypes rather than genotypes. Phenotypic diversity analysis revealed a high level of genetic variation. A total of 97 phenotypes was detected among 160 isolates. Phenotypic diversity varied among regions, ranging from 0.3742 in Shaanxi to 0.9380 in Gansu, as calculated with the normalized Shannon's index. Genetic subdivision analysis revealed a low level of genetic differentiation (GST = 0.0084) among regions (Gansu, Henan, Shaanxi, Sichuan, and Yunnan provinces) as well as within regions (Gansu and Sichuan provinces). This, together with the detection of the same phenotypes among regions, provided the molecular evidence for gene flow in P. striiformis f.sp. tritici. The results support conclusions from virulence surveys that Tianshui of southern Gansu is probably the most important "hotspot" area with respect to the potential to generate and maintain virulence variation. DNA polymorphism analysis also detected potential hotspot areas in addition to southern Gansu. This may result in more difficulties in management of genetic variation and thus the potential virulence variation in P. striiformis f.sp. tritici as well as providing opportunities for searching disease resistance factors.Key words: genetic diversity, Puccinia striiformis, DNA fingerprinting, virulence variation.

No abstract

Copy number variation (CNV) is a kind of chromosomal structural reorganization that has been detected, in this decade, mainly by high-throughput biological technology. Researchers have found that CNVs are ubiquitous in many species and accumulating evidence indicates that CNVs are closely related with complex diseases. The investigation of chromosomal structural alterations has begun to reveal some important clues to the pathologic causes of diseases and to the disease process. However, many of the published studies have focused on a single disease and, so far, the experimental results have not been systematically collected or organized. Manual text mining from 6301 published papers was used to build the Copy Number Variation in Disease database (CNVD). CNVD contains CNV information for 792 diseases in 22 species from diverse types of experiments, thus, ensuring high confidence and comprehensive representation of the relationship between the CNVs and the diseases. In addition, multiple query modes and visualized results are provided in the CNVD database. With its user-friendly interface and the integrated CNV information for different diseases, CNVD will offer a truly comprehensive platform for disease research based on chromosomal structural variations. The CNVD interface is accessible at

No abstract