Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

The gut microbiome from patients with schizophrenia modulates the glutamate-glutamine-GABA cycle and schizophrenia-relevant behaviors in mice

Abstract: Schizophrenia (SCZ) is a devastating mental disorder with poorly defined underlying molecular mechanisms. The gut microbiome can modulate brain function and behaviors through the microbiota-gut-brain axis. Here, we found that unmedicated and medicated patients with SCZ had a decreased microbiome α-diversity index and marked disturbances of gut microbial composition versus healthy controls (HCs). Several unique bacterial taxa (e.g., Veillonellaceae and Lachnospiraceae) were associated with SCZ severity. A specific microbial panel (Aerococcaceae, Bifidobacteriaceae, Brucellaceae, Pasteurellaceae, and Rikenellaceae) enabled discriminating patients with SCZ from HCs with 0.769 area under the curve. Compared to HCs, germ-free mice receiving SCZ microbiome fecal transplants had lower glutamate and higher glutamine and GABA in the hippocampus and displayed SCZ-relevant behaviors similar to other mouse models of SCZ involving glutamatergic hypofunction. Together, our findings suggest that the SCZ microbiome itself can alter neurochemistry and neurologic function in ways that may be relevant to SCZ pathology.

A deep learning approach to detection of splicing and copy-move forgeries in images

Abstract: In this paper, we present a new image forgery detection method based on deep learning technique, which utilizes a convolutional neural network (CNN) to automatically learn hierarchical representations from the input RGB color images. The proposed CNN is specifically designed for image splicing and copy-move detection applications. Rather than a random strategy, the weights at the first layer of our network are initialized with the basic high-pass filter set used in calculation of residual maps in spatial rich model (SRM), which serves as a regularizer to efficiently suppress the effect of image contents and capture the subtle artifacts introduced by the tampering operations. The pre-trained CNN is used as patch descriptor to extract dense features from the test images, and a feature fusion technique is then explored to obtain the final discriminative features for SVM classification. The experimental results on several public datasets show that the proposed CNN based model outperforms some state-of-the-art methods.