Sichuan University

About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.

Enzyme-Powered Three-Dimensional DNA Nanomachine for DNA Walking, Payload Release, and Biosensing

Abstract: Herein, we report a DNA nanomachine, built from a DNA-functionalized gold nanoparticle (DNA-AuNP), which moves a DNA walker along a three-dimensional (3-D) DNA-AuNP track and executes the task of releasing payloads. The movement of the DNA walker is powered by a nicking endonuclease that cleaves specific DNA substrates on the track. During the movement, each DNA walker cleaves multiple substrates, resulting in the rapid release of payloads (predesigned DNA sequences and their conjugates). The 3-D DNA nanomachine is highly efficient due to the high local effective concentrations of all DNA components that have been co-conjugated on the same AuNP. Moreover, the activity of the 3-D DNA nanomachine can be controlled by introducing a protecting DNA probe that can hybridize to or dehybridize from the DNA walker in a target-specific manner. This property allows us to tailor the DNA nanomachine into a DNA nanosensor that is able to achieve rapid, isothermal, and homogeneous signal amplification for specific nucleic acids in both buffer and a complicated biomatrix.

Evolving Deep Convolutional Neural Networks for Image Classification

Abstract: Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights. In this paper, we propose a new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems. In the proposed algorithm, an efficient variable-length gene encoding strategy is designed to represent the different building blocks and the unpredictable optimal depth in convolutional neural networks. In addition, a new representation scheme is developed for effectively initializing connection weights of deep convolutional neural networks, which is expected to avoid networks getting stuck into local minima which is typically a major issue in the backward gradient-based optimization. Furthermore, a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource. The proposed algorithm is examined and compared with 22 existing algorithms on nine widely used image classification tasks, including the state-of-the-art methods. The experimental results demonstrate the remarkable superiority of the proposed algorithm over the state-of-the-art algorithms in terms of classification error rate and the number of parameters (weights).

Resting-state functional connectivity in treatment-resistant depression.

Abstract: Objective: The authors used resting-state functional connectivity MRI to evaluate brain networks in patients with refractory and nonrefractory major depressive disorder

Facial Emotion Processing in Schizophrenia: A Meta-analysis of Functional Neuroimaging Data

Abstract: Background: People with schizophrenia have difficulty with emotion perception. Functional imaging studies indicate regional brain activation abnormalities in patients with schizophrenia when processing facial emotion. However, findings have not been entirely consistent across different studies. Methods: Activation likelihood estimation (ALE) meta-analyses were conducted to examine brain activation during facial emotion processing in patients with schizophrenia, controls, and patients compared with controls. Secondary meta-analyses were performed to assess the contribution of task design and illness chronicity to the results reported. Results: When processing facial expressions of emotions, both patients with schizophrenia and healthy controls activated the bilateral amygdala and right fusiform gyri. However, the extent of activation in these regions was generally much more limited in the schizophrenia samples. When directly compared with controls, the extent of activation in bilateral amygdala, parahippocampal gyrus and fusiform gyrus, right superior frontal gyrus, and lentiform nucleus was significantly less in patients. Patients with schizophrenia, but not controls, activated the left insula. A relative failure to recruit the amygdala in patients occurred regardless of whether the task design was explicit or implicit, while differences in fusiform activation were evident in explicit, not implicit, tasks. Restricting the analysis to patients with chronic illness did not substantially change the results. Conclusions: A marked underrecruitment of the amygdala, accompanied by a substantial limitation in activation throughout a ventral temporal-basal ganglia-prefrontal cortex "social brain" system may be central to the difficulties patients experience when processing facial emotion.