NO2 Physical-to-Chemical Adsorption Transition on Janus WSSe Monolayers Realized by Defect Introduction.

As is well known, NO2 adsorption plays an important role in gas sensing and treatment because it expands the residence time of compounds to be treated in plasma-catalyst combination. In this work, the adsorption behaviors and mechanism of NO2 over pristine and Se-vacancy defect-engineered WSSe monolayers have been systematically investigated using density functional theory (DFT). The adsorption energy calculation reveals that introducing Se vacancy acould result in a physical-to-chemical adsorption transition for the system. The Se vacancy, the most possible point defect, could work as the optimum adsorption site, and it dramatically raises the transferred-electron quantities at the interface, creating an obviously electronic orbital hybridization between the adsorbate and substrate and greatly improving the chemical activity and sensing sensitivity of the WSSe monolayer. The physical-to-chemical adsorption transition could meet different acquirements of gas collection and gas treatment. Our work broadens the application filed of the Janus WSSe as NO2-gas-sensitive materials. In addition, it is found that both keeping the S-rich synthetic environments and applying compression strain could make the introduction of Se vacancy easier, which provides a promising path for industrial synthesis of Janus WSSe monolayer with Se vacancy.

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization.

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical applications. Deep convolutional neural networks have become the state-of-the-art in PET tumor segmentation. The normalization process is one of the key components for accelerating network training and improving the performance of the network. However, existing normalization methods either introduce batch noise into the instance PET image by calculating statistics on batch level or introduce background noise into every single pixel by sharing the same learnable parameters spatially. In this paper, we proposed an attentive transformation (AT)-based normalization method for PET tumor segmentation. We exploit the distinguishability of breast tumor in PET images and dynamically generate dedicated and pixel-dependent learnable parameters in normalization via the transformation on a combination of channel-wise and spatial-wise attentive responses. The attentive learnable parameters allow to re-calibrate features pixel-by-pixel to focus on the high-uptake area while attenuating the background noise of PET images. Our experimental results on two real clinical datasets show that the AT-based normalization method improves breast tumor segmentation performance when compared with the existing normalization methods.

Multimodal Fusion Network for Detecting Hyperplastic Parathyroid Glands in SPECT/CT Images.

In secondary hyperparathyroidism (SHPT) disease, preoperatively localizing hyperplastic parathyroid glands is crucial in the surgical procedure. These glands can be detected via the dual-modality imaging technique single-photon emission computed tomography/computed tomography (SPECT/CT) since it has high sensitivity and provides an accurate location. However, due to possible low-uptake glands in SPECT images, manually labeling glands is challenging, not to mention automatic label methods. In this work, we present a deep learning method with a novel fusion network to detect hyperplastic parathyroid glands in SPECT/CT images. Our proposed fusion network follows the convolutional neural network (CNN) with a three-pathway architecture that extracts modality-specific feature maps. The fusion network, composed of the channel attention module, the feature selection module, and the modality-specific spatial attention module, is designed to integrate complementary anatomical and functional information, especially for low-uptake glands. Experiments with patient data show that our fusion method improves performance in discerning low-uptake glands compared with current fusion strategies, achieving an average sensitivity of 0.822. Our results prove the effectiveness of the three-pathway architecture with our proposed fusion network for solving the glands detection task. To our knowledge, this is the first study to detect abnormal parathyroid glands in SHPT disease using SPECT/CT images, which promotes the application of preoperative glands localization.

Low-Complexity Error Resilient HEVC Video Coding: A Deep Learning Approach.

Intra/inter switching-based error resilient video coding effectively enhances the robustness of video streaming when transmitting over error-prone networks. But it has a high computation complexity, due to the detailed end-to-end distortion prediction and brute-force search for rate-distortion optimization. In this article, a Low Complexity Mode Switching based Error Resilient Encoding (LC-MSERE) method is proposed to reduce the complexity of the encoder through a deep learning approach. By designing and training multi-scale information fusion-based convolutional neural networks (CNN), intra and inter mode coding unit (CU) partitions can be predicted by the networks rapidly and accurately, instead of using brute-force search and a large number of end-to-end distortion estimations. In the intra CU partition prediction, we propose a spatial multi-scale information fusion based CNN (SMIF-Intra). In this network a shortcut convolution architecture is designed to learn the multi-scale and multi-grained image information, which is correlated with the CU partition. In the inter CU partition, we propose a spatial-temporal multi-scale information fusion-based CNN (STMIF-Inter), in which a two-stream convolution architecture is designed to learn the spatial-temporal image texture and the distortion propagation among frames. With information from the image, and coding and transmission parameters, the networks are able to accurately predict CU partitions for both intra and inter coding tree units (CTUs). Experiments show that our approach significantly reduces computation time for error resilient video encoding with acceptable quality decrement.

Robust Object Tracking Based on Motion Consistency.

Object tracking is an important research direction in computer vision and is widely used in video surveillance, security monitoring, video analysis and other fields. Conventional tracking algorithms perform poorly in specific scenes, such as a target with fast motion and occlusion. The candidate samples may lose the true target due to its fast motion. Moreover, the appearance of the target may change with movement. In this paper, we propose an object tracking algorithm based on motion consistency. In the state transition model, candidate samples are obtained by the target state, which is predicted according to the temporal correlation. In the appearance model, we define the position factor to represent the different importance of candidate samples in different positions using the double Gaussian probability model. The candidate sample with highest likelihood is selected as the tracking result by combining the holistic and local responses with the position factor. Moreover, an adaptive template updating scheme is proposed to adapt to the target's appearance changes, especially those caused by fast motion. The experimental results on a 2013 benchmark dataset demonstrate that the proposed algorithm performs better in scenes with fast motion and partial or full occlusion compared to the state-of-the-art algorithms.

Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters.

In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view.

Effects of naturally occurring charged mutations on the structure, stability, and binding of the Pin1 WW domain.

Pin1 is a peptidyl-prolyl cis-trans isomerase, whose WW domain specifically recognizes the pSer/Thr-Pro motif. Pin1 is involved in multiple phosphorylation events that regulate the activities of various substrates, and Pin1 deregulation has been reported in various diseases, including cancer and Alzheimer's disease. The WW domain of Pin1 has been used as a small model protein to investigate the folding mechanisms of the ß-sheet structure by studying the effect of mutations or its naturally occurring variants. However, only a few studies have investigated the structure and binding of Pin1 WW mutants. In the present work, two naturally occurring Pin1 WW variants, namely, G20D and S16R, derived from the cynomolgus monkey and African green monkey, respectively, were selected to investigate the influence of charge mutation on the structure, stability, and binding properties of the Pin1 WW domain. Analysis using a combination of nuclear magnetic resonance (NMR) and chemical shift-based calculations revealed that the G20D and S16R mutants had high structural similarity to the wild-type Pin1 WW domain. However, the presence of a charge mutation significantly decreased the stability of the Pin1 WW domain. Both the wild-type and G20D forms of the Pin1 WW domain utilized a three-site mode to bind to a phosphorylated Tau peptide, pT231, whereas the S16R mutant binds to the pT231 peptide either in a non-specific manner or through a totally different binding mechanism. Correspondingly, the wild-type and two mutant Pin1 WW domains showed different binding affinities to the Tau phosphopeptide. Considering that the WW domain participates in the catalytic activity of the Pin1 isomerase, our study represents a novel approach for studying Pin1 function through the analysis of its naturally occurring mutants.