Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

Generally, facial expressions could be classified into two categories: static facial expressions and micro-expressions. There are many promising applications of facial expression recognition, such as pain detection, lie detection, and babysitting. Traditional convolutional neural network (CNN)-based methods suffer from two critical problems when they are adopted to recognize micro-expressions. First, they are usually dependent on very deep architectures that overfit on small datasets. However, reliable expressions are relatively difficult to collect and relevant datasets are usually relatively small. Second, for micro-expressions, these methods usually neglect the temporal redundancy of micro-expressions which could be utilized to reduce the temporal complexity. In this paper, we propose a shallow CNN (SHCNN) architecture with only three layers to classify static expressions and micro-expressions simultaneously without big training datasets. To better explain the functionality of our SHCNN architecture, we improve the saliency maps by introducing a shrinkage factor after studying the vanishing gradient problem of existing saliency maps. Experiments are conducted on five open datasets: FER2013, FERPlus, CASME, CASME II, and SAMM. To the best of our knowledge, by comparing with other methods offering source code (or pseudo code), we believe that our method would be the best on FERPlus, CASME, and CASME II and competitive on FER2013 and SAMM.

In recent years, there has been a considerable research focus on integrating cancer cell imaging and therapeutic functions into single nanoscale platforms for better treatment of cancer. This task could often be achieved by incorporating multiple components into a hybrid nanosystem. In this minireview, we highlight different types of silica-based hybrid nanosystems and their recent applications as integrated multifunctional platforms for cancer imaging and treatment. The discussions are divided into several sections focusing on various types of materials employed to integrate with silica, which include silica-metallic nanoparticle based hybrid nanocarriers, silica-gold nanoparticle based hybrid nanocarriers, silica-quantum dot based hybrid nanocarriers, silica-upconversion nanoparticle based hybrid nanocarriers, silica-carbon based hybrid nanocarriers, and organosilica nanocarriers. Therapeutic agents loaded in such hybrids include chemodrugs, proteins, DNA/RNA and photosensitizers. For targeted delivery into tumor sites, targeting ligands such as antibodies, peptides, aptamers, and other small molecules are grafted on the surface of the nanocarriers. At the end of the review, a brief summary and research outlook are presented. This minireview aims to provide a quick update of recent research achievements in the field.