Transcriptome and metabolomics analysis of adaptive mechanism of Chinese mitten crab (Eriocheir sinensis) to aflatoxin B1.

Aflatoxin B1 (AFB1), with the strong toxicity and carcinogenicity, has been reported to great toxicity to the liver and other organs of animals. It cause huge economic losses to breeding industry, including the aquaculture industry. Chinese mitten crabs (Eriocheir sinensis), as one of important species of freshwater aquaculture in China, are deeply disturbed by it. However, the molecular and metabolic mechanisms of hepatopancreas and ovary in crabs underlying coping ability are still unclear. Hence, we conducted targeted injection experiment with or without AFB1, and comprehensively analyzed transcriptome and metabolomics of hepatopancreas and ovary. As a result, 210 and 250 DEGs were identified in the L-C vs. L-30 m and L-C vs. L-60 m comparison, among which 14 common DEGs were related to six major functional categories, including antibacterial and detoxification, ATP energy reaction, redox reaction, nerve reaction, liver injury repair and immune reaction. A total of 228 and 401 DAMs in the ML-C vs. ML-30 m and ML-C vs. ML-60 m comparison both enriched 12 pathways, with clear functions of cutin, suberine and wax biosynthesis, tyrosine metabolism, purine metabolism, nucleotide metabolism, glycine, serine and threonine metabolism, ABC transporters and tryptophan metabolism. Integrated analysis of metabolomics and transcriptome in hepatopancreas discovered three Co-enriched pathways, including steroid biosynthesis, glycine, serine and threonine metabolism, and sphingolipid metabolism. In summary, the expression levels and functions of related genes and metabolites reveal the regulatory mechanism of Chinese mitten crab (Eriocheir sinensis) adaptability to the Aflatoxin B1, and the findings contribute to a new perspective for understanding Aflatoxin B1 and provide some ideas for dealing with it.

Histopathological image recognition of breast cancer based on three-channel reconstructed color slice feature fusion.

Although computer-aided diagnosis (CAD) have shown excellent performance in Breast cancer (BC) histopathological image, it commonly requires a high-level network, and the recognition efficiency is frequently unsatisfied due to the complex structure of histopathological image. In this study, a ten-layers convolutional neural network (CNN) model called "ColorDeep" is used to extract color features corresponding to the different tissue parts in cell, pure color image slices obtained by a three-channel separation and reconstruction method are used as model input. Two models are tested on the BreaKHis dataset show that images under four magnifications achieved the recognition accuracy of 96.89%-99.67% at the image level, which is better than many state-of-the-art methods. The characteristics contained by the B channel have the largest effect on BC recognition, and compared to other research results, the proposed model improves the recognition speed on a single image by about 0.1s. More importantly, instead of using large histopathological images to input into the model for BC diagnosis, and instead of segmenting the nuclei, only the reconstructed B-channel features containing the nuclei region in the stained BC image need to be input into the model to enable accurate diagnosis of BC.

Initialization by a novel clustering for wavelet neural network as time series predictor.

The architecture and parameter initialization of wavelet neural network are discussed and a novel initialization method is proposed. The new approach can be regarded as a dynamic clustering procedure which will derive the neuron number as well as the initial value of translation and dilation parameters according to the input patterns and the activating wavelets functions. Three simulation examples are given to examine the performance of our method as well as Zhang's heuristic initialization approach. The results show that the new approach not only can decide the WNN structure automatically, but also provides superior initial parameter values that make the optimization process more stable and quickly.

Experimental and theoretical study on molecular structure and FT-IR, Raman, NMR spectra of 4,4'-dibromodiphenyl ether.

In this work, both experimental and theoretical study on the FT-IR and Raman spectra as well as (1)H NMR and (13)C NMR chemical shifts of 4,4'-dibromodiphenyl ether have been carried out. The optimized geometry was obtained by using both HF and density functional B3LYP method with the 6-31G(d) and 6-311+G(d, p) basis sets. The calculated bond lengths and dihedral angles for both methods on 6-31G(d) level show the best agreement with the experimental data, while the dihedral angles of C(1')-O-C(1)-C(6) and C(1')-O-C(1)-C(2), critical geometry parameters for conformers in the ground state, indicates significant deviation of HF results from the experimental information. The harmonic vibration frequencies and intensities in IR and Raman spectra and chemical shifts of the molecule were calculated on the B3LYP/6-31G(d) and B3LYP/6-311+G(d, p) levels. The scaled theoretical vibration frequencies present good agreement with the experimental values. The larger basis set makes no significant improvement in the accuracy of the vibration frequencies. Besides, chemical shifts of hydrogen and carbon computed on B3LYP/6-31G(d) level agree well with the observations.