Chengdu University

Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.

The review summarizes the recent progress in the synthesis, fundamental properties, morphology, photocatalytic applications and challenges of CdS-based photocatalysts.

The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists' judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

Covalent organic frameworks (COFs), which are constructed from organic linkers, are a new class of crystalline porous materials comprising periodically extended and covalently bound network structures. The intrinsic structures and the tailorable organic linkers endow COFs with a low density, large surface area, tunable pore size and structure, and facilely-tailored functionality, attracting increasing interests in different fields including membrane separations. Exciting research activities ranging from fabrication strategies to separation applications of COF-based membranes have appeared. This review analyzes the synthesis and applications of diverse continuous/discontinuous COF membranes, such as COF-based mixed matrix membranes (MMMs), COF-based thin film nanocomposite (TFN) membranes, and free-standing COF films. Special attention was given to pore size, stability, hydrophilicity/hydrophobicity and surface charge of COFs in view of determining proper COFs for membrane fabrication, along with the approaches to fabricate COF-based membranes, such as blending, in situ growth, layer-by-layer stacking and interfacial polymerization (IP). Moreover, applications of COF-based membranes in gas separation, water treatment (deaslination and dye removal), organic solvent nanofiltration (OSN), pervaporation and fuel cell are disscussed. Finally, we illustrate the advantages and disadvantages of COF-based membranes through a comparison with MOF-based membranes, and the remaining challenges and future opportunities in this field.

Transition metal nitrides (TMNs), by virtue of their unique electronic structure, high electrical conductivity, superior chemical stability, and excellent mechanical robustness, have triggered tremendous research interest over the past decade, and showed great potential for electrochemical energy conversion and storage. However, bulk TMNs usually suffer from limited numbers of active sites and sluggish ionic kinetics, and eventually ordinary electrochemical performance. Designing nanostructured TMNs with tailored morphology and good dispersity has proved an effective strategy to address these issues, which provides a larger specific surface area, more abundant active sites, and shorter ion and mass transport distances over the bulk counterparts. Herein, the most up-to-date progress on TMN-based nanomaterials is comprehensively reviewed, focusing on geometric-structure design, electronic-structure engineering, and applications in electrochemical energy conversion and storage, including electrocatalysis, supercapacitors, and rechargeable batteries. Finally, we outline the future challenges of TMN-based nanomaterials and their possible research directions beyond electrochemical energy applications.

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

Convolutional neural networks (CNNs) have gained remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For the most state-of-the-art CNNs, their architectures are often manually designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this article, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its "automatic" characteristic that users do not need domain knowledge of CNNs when using the proposed algorithm, while they can still obtain a promising CNN architecture for the given images. The proposed algorithm is validated on widely used benchmark image classification datasets, compared to the state-of-the-art peer competitors covering eight manually designed CNNs, seven automatic + manually tuning, and five automatic CNN architecture design algorithms. The experimental results indicate the proposed algorithm outperforms the existing automatic CNN architecture design algorithms in terms of classification accuracy, parameter numbers, and consumed computational resources. The proposed algorithm also shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.

Jasmonic acid (JA) and its precursors and dervatives, referred as jasmonates (JAs) are important molecules in the regulation of many physiological processes in plant growth and development, and especially the mediation of plant responses to biotic and abiotic stresses. JAs biosynthesis, perception, transport, signal transduction and action have been extensively investigated. In this review, we will discuss the initiation of JA signaling with a focus on environmental signal perception and transduction, JA biosynthesis and metabolism, transport of signaling molecules (local transmission, vascular bundle transmission, and airborne transportation), and biological function (JA signal receptors, regulated transcription factors, and biological processes involved).

A comprehensive review on designs and mechanisms of semiconducting metal oxides with various nanostructures for room-temperature gas sensor applications.

The urgent need for ecofriendly, stable, long-lifetime power sources is driving the booming market for miniaturized and integrated electronics, including wearable and medical implantable devices. Flexible thermoelectric materials and devices are receiving increasing attention, due to their capability to convert heat into electricity directly by conformably attaching them onto heat sources. Polymer-based flexible thermoelectric materials are particularly fascinating because of their intrinsic flexibility, affordability, and low toxicity. There are other promising alternatives including inorganic-based flexible thermoelectrics that have high energy-conversion efficiency, large power output, and stability at relatively high temperature. Herein, the state-of-the-art in the development of flexible thermoelectric materials and devices is summarized, including exploring the fundamentals behind the performance of flexible thermoelectric materials and devices by relating materials chemistry and physics to properties. By taking insights from carrier and phonon transport, the limitations of high-performance flexible thermoelectric materials and the underlying mechanisms associated with each optimization strategy are highlighted. Finally, the remaining challenges in flexible thermoelectric materials are discussed in conclusion, and suggestions and a framework to guide future development are provided, which may pave the way for a bright future for flexible thermoelectric devices in the energy market.

Evolutionary paradigms have been successfully applied to neural network designs for two decades. Unfortunately, these 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 potentially 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 minimum that 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 designs in terms of classification error rate and the number of parameters (weights).

Compared to the available protein sequences of different organisms, the number of revealed protein-protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11,474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www.scucic.cn/Predict_PPI/index.htm.

Electrically conductive polymer composite-based smart strain sensors with different conductive fillers, phase morphology, and imperative features were reviewed.

The present work theoretically and experimentally provides an insight into the internal mechanism of Li⁺ transport within an artificial hybrid SEI layer consisting of lithium-antimony (Li₃Sb) alloy and lithium fluoride (LiF).

Superhydrophobic surfaces hold great prospects for extremely diverse applications owing to their water repellence property. The essential feature of superhydrophobicity is micro-/nano-scopic roughness to reserve a large portion of air under a liquid drop. However, the vulnerability of the delicate surface textures significantly impedes the practical applications of superhydrophobic surfaces. Robust superhydrophobicity is a must to meet the rigorous industrial requirements and standards for commercial products. In recent years, major advancements have been made in elucidating the mechanisms of wetting transitions, design strategies and fabrication techniques of superhydrophobicity. This review will first introduce the mechanisms of wetting transitions, including the thermodynamic stability of the Cassie state and its breakdown conditions. Then we highlight the development, current status and future prospects of robust superhydrophobicity, including characterization, design strategies and fabrication techniques. In particular, design strategies, which are classified into passive resistance and active regeneration for the first time, are proposed and discussed extensively.

The urgent need for efficient energy storage devices has stimulated a great deal of research on electrochemical double layer capacitors (EDLCs). This review aims at summarizing the recent progress in nanoporous carbons, as the most commonly used EDLC electrode materials in the field of capacitive energy storage, from the viewpoint of materials science and characterization techniques. We discuss the key advances in the fundamental understanding of the charge storage mechanism in nanoporous carbon-based electrodes, including the double layer formation in confined nanopores. Special attention will be also paid to the important development of advanced in situ analytical techniques as well as theoretical studies to better understand the carbon pore structure, electrolyte ion environment and ion fluxes in these confined pores. We also highlight the recent progress in advanced electrolytes for EDLCs. The better understanding of the charge storage mechanism of nanoporous carbon-based electrodes and the rational design of electrolytes should shed light on developing the next-generation of EDLCs.

A novel approach to effectively suppress the “polysulfide shuttle” in Li–S batteries is presented by designing a freestanding, three-dimensional graphene/1T MoS₂ (3DG/TM) heterostructure with highly efficient electrocatalysis properties for lithium polysulfides (LiPSs).

Lithium–sulfur (Li–S) batteries have been regarded as one of the most promising next-generation energy-storage devices, due to their low cost and high theoretical energy density (2600 W h kg⁻¹).

Protons play crucial roles in many physiological and pathological processes, such as receptor-mediated signal transduction, ion transport, endocytosis, homeostasis, cell proliferation, and apoptosis. The urgent demand for pH imaging and measurement in biological systems has incited the development of fluorescent pH probes. Numerous fluorescent probes have been reported, but many lack the abilities needed for biological applications. Hence, the development of new pH probes with better biocompatibility, sensitivity, and site-specificity is still indispensable. This review highlights the recent trends in the development of fluorescent materials as essential tools for tracing pH variations in the biological processes of diverse living systems.

Stimuli responsiveness has long been a fascinating feature of smart material design. Polydopamine (PDA), a nature inspired polymeric pigment, exhibits excellent photo-responsive properties and has active surface functionality for loading various responsive motifs, making it a promising candidate for the construction of stimuli-responsive smart functional materials. PDA has long been considered as a robust coating material, but its responsive feature has rarely been emphasized in the past reviews. Herein, we present the first effort to summarize recent advances in the design strategies, responsive mechanisms, and diverse applications of stimuli-responsive PDA-based smart materials; the stimuli include light, pH, chemicals, temperature, humidity, electric fields, mechanical force, magnetic fields, and ultrasound. Moreover, the current trends, challenges, and future directions of stimuli-responsive PDA-based materials are also elaborated.