Jiangsu University of Technology

Ultrastrong, smooth, and shiny graphene paper with a layered structure (see figure) is prepared by vacuum filtration of a well-dispersed graphene dispersion. Moderate thermal annealing further enhances its mechanical properties and electrical conductivity. A combination of exceptional mechanical strength, thermal stability, high electrical conductivity, and biocompability makes this unique material promising for many technological applications. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

As one of the most popular computational approaches in modern structure-based drug design, molecular docking can be used not only to identify the correct conformation of a ligand within the target binding pocket but also to estimate the strength of the interaction between a target and a ligand. Nowadays, as a variety of docking programs are available for the scientific community, a comprehensive understanding of the advantages and limitations of each docking program is fundamentally important to conduct more reasonable docking studies and docking-based virtual screening. In the present study, based on an extensive dataset of 2002 protein-ligand complexes from the PDBbind database (version 2014), the performance of ten docking programs, including five commercial programs (LigandFit, Glide, GOLD, MOE Dock, and Surflex-Dock) and five academic programs (AutoDock, AutoDock Vina, LeDock, rDock, and UCSF DOCK), was systematically evaluated by examining the accuracies of binding pose prediction (sampling power) and binding affinity estimation (scoring power). Our results showed that GOLD and LeDock had the best sampling power (GOLD: 59.8% accuracy for the top scored poses; LeDock: 80.8% accuracy for the best poses) and AutoDock Vina had the best scoring power (rp/rs of 0.564/0.580 and 0.569/0.584 for the top scored poses and best poses), suggesting that the commercial programs did not show the expected better performance than the academic ones. Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs. However, for some types of protein families, relatively high linear correlations between docking scores and experimental binding affinities could be achieved. To our knowledge, this study has been the most extensive evaluation of popular molecular docking programs in the last five years. It is expected that our work can offer useful information for the successful application of these docking tools to different requirements and targets.

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

PURPOSE: Radiofrequency ablation (RFA) has been shown to elicit tumor-specific T-cell immune responses, but is not sufficient to prevent cancer progression. Here, we investigated immune-suppressive mechanisms limiting the efficacy of RFA. EXPERIMENTAL DESIGN: We performed a retrospective case-controlled study on patients with synchronous colorectal cancer liver metastases who had received primary tumor resection with or without preoperative RFA for liver metastases. Tumor-infiltrating T cells and tumoral PD-L1 expression in human colorectal cancer tissues were analyzed by immunohistochemistry. T-cell immune responses and PD-1/PD-L1 expression were also characterized in an RFA mouse model. In addition, the combined effect of RAF and PD-1 blockade was evaluated in the mouse RFA model. RESULTS: We found that RFA treatment of liver metastases increased not only T-cell infiltration, but also PD-L1 expression in primary human colorectal tumors. Using mouse tumor models, we demonstrated that RFA treatment of one tumor initially enhanced a strong T-cell-mediated immune response in tumor. Nevertheless, tumor quickly overcame the immune responses by inhibiting the function of CD8(+) and CD4(+) T cells, driving a shift to higher regulatory T-cell to Teff ratio, and upregulating PD-L1/PD-1 expression. Furthermore, we established that the combined therapy of RFA and anti-PD-1 antibodies significantly enhanced T-cell immune responses, resulting in stronger antitumor immunity and prolonged survival. CONCLUSIONS: The PD-L1-PD-1 axis plays a critical role in dampening RFA-induced antitumor immune responses, and this study provides a strong rationale for combining RFA and the PD-L1/PD-1 blockade in the clinical setting.

Abstract Digital transformation, board characteristics, and environmental performance are increasingly important in the field of corporate sustainability. However, despite the growing literature on digital transformation, there is a paucity of literature that considers board characteristics. This paper aims to fill this gap by exploring the relationship between digital transformation and environmental performance from the perspective of board characteristics. Chinese listed companies from 2010 to 2019 were taken as the original data, the moderating effect of the board characteristics was tested by using the moderating effect model. We find that digital transformation can significantly improve corporate environmental performance. A low willingness to digital transformation was showed in the board of directors with age diversity, nationality diversity, shareholding concentration, and political connections. In contrast, digital transformation strategies were preferred in the boards with more female directors and higher educational backgrounds. Our study is of significant value for companies undergoing digital transformation.

All-inorganic trihalide perovskite nanocrystals (NCs) are emerging as a new class of superstar semiconductors with excellent optoelectronic properties and great potential for a broad range of applications in lighting, lasing, photon detection, and photovoltaics. This article provides an up-to-date review on the developments of fully-inorganic trihalide perovskite NCs by emphasizing their controllable solution fabrication strategies, structural phase transformation, tunable optoelectronic properties, stability, as well as their photovoltaic and optoelectronic applications. Among the properties to be surveyed, particular focus is on the size-, shape-, and composition-dependent photoluminescence properties. Finally, by identifying new challenges, suggestions are provided for further research and potential development of this area.

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey, we examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines.

Continuous memristor has been widely used in chaotic oscillating circuits and neuromorphic computing systems. However, discrete memristor and its coupling discrete map have not been noticed yet. This article presents a discrete memristor and constructs a general two-dimensional memristive map model by coupling the discrete memristor with an existing discrete map. The pinched hysteresis loops of the discrete memristor are demonstrated. Four examples of memristive discrete maps are provided and their coupling strength-relied and memristor initial-boosted complex dynamics are investigated using numerical measures. The evaluation results manifest that the discrete memristor can enhance the chaos complexity and its coupling maps can generate hyperchaos. Particularly, the hyperchaotic sequences can nondestructively be controlled by memristor initial state and the initial-controlled hyperchaos is robust, which is applicable to many chaos-based applications. Additionally, we develop a hardware platform to implement the memristive maps and acquire the four-channel hyperchaotic sequences. We also apply the memristive maps to the application of secure communication and the experiments show that the memristive maps display better performance than some existing discrete maps.

Abstract Over the past few decades, flexible sensors have been developed from the “electronic” level to the “iontronic” level, and gradually to the “ionic” level. Ionic flexible sensors (IFS) are one kind of advanced sensors that are based on the concept of ion migration. Compared to conventional electronic sensors, IFS can not only replicate the topological structures of human skin, but also are capable of achieving tactile perception functions similar to that of human skin, which provide effective tools and methods for narrowing the gap between conventional electronics and biological interfaces. In this review, the latest research and developments on several typical sensing mechanisms, compositions, structural design, and applications of IFS are comprehensively reviewed. Particularly, the development of novel ionic materials, structural designs, and biomimetic approaches has resulted in the development of a wide range of novel and exciting IFS, which can effectively sense pressure, strain, and humidity with high sensitivity and reliability, and exhibit self‐powered, self‐healing, biodegradability, and other properties of the human skin. Furthermore, the typical applications of IFS in artificial skin, human‐interactive technologies, wearable health monitors, and other related fields are reviewed. Finally, the perspectives on the current challenges and future directions of IFS are presented.

Abstract Thermal quenching of photoluminescence represents a significant obstacle to practical applications such as lighting, display, and photovoltaics. Herein, a novel strategy is established to enhance upconversion luminescence at elevated temperatures based on the use of negative thermal expansion host materials. Lanthanide‐doped orthorhombic Yb 2 W 3 O 12 crystals are synthesized and characterized by in situ X‐ray diffraction and photoluminescence spectroscopy. The thermally induced contraction and distortion of the host lattice is demonstrated to enhance the collection of excitation energy by activator ions. When the temperature is increased from 303 to 573 K, a 29‐fold enhancement of green upconversion luminescence in Er 3+ activators is achieved. Moreover, the temperature dependence of the upconversion luminescence is reversible. The thermally enhanced upconversion is developed as a sensitive ratiometric thermometer by referring to a thermally quenched upconversion.

The median filtering algorithm has good noise-reducing effects, but its time complexity is not desirable. The paper proposed an improved median filtering algorithm. The algorithm uses the correlation of the image to process the features of the filtering mask over the image. It can adaptively resize the mask according to noise levels of the mask. The statistical histogram is also introduced in the searching process of the median value. Experimental results show that the algorithm reduces the noise and retains the details of the image. The complexity of the algorithm is decreased to O (N), and the performance of noise reduction has effectively improved.

PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. METHODS: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, for example, few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. RESULTS: Based on the state-of-the-art network, we provide more than 40 pretrained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. CONCLUSIONS: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.

Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-dimensional graph representations limit their capability to learn the generalized molecular interactions in 3D space. Here, we proposed a novel deep graph representation learning framework named InteractionGraphNet (IGN) to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. In IGN, two independent graph convolution modules were stacked to sequentially learn the intramolecular and intermolecular interactions, and the learned intermolecular interactions can be efficiently used for subsequent tasks. Extensive binding affinity prediction, large-scale structure-based virtual screening, and pose prediction experiments demonstrated that IGN achieved better or competitive performance against other state-of-the-art ML-based baselines and docking programs. More importantly, such state-of-the-art performance was proven from the successful learning of the key features in protein-ligand interactions instead of just memorizing certain biased patterns from data.

This dataset contains 20 labeled COVID-19 CT scans. Left lung, right lung, and infections are labeled by two radiologists and verified by an experienced radiologist. <br> To promote the studies of annotation-efficient deep learning methods, we set up three segmentation benchmark tasks based on this dataset https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark. In particular, we focus on learning to segment left lung, right lung, and infections using pure but limited COVID-19 CT scans; existing labeled lung CT dataset from other non-COVID-19 lung diseases; heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.

Abstract The unstable feature of the widely employed organic hole‐transporting materials (HTMs) (e.g., spiro‐MeOTAD) significantly limits the practical application of perovskite solar cells (PSCs). Therefore, it is desirable to design new structured PSCs with stable HTMs presenting excellent carrier extraction and transfer properties. This work demonstrates a new inverted PSC configuration. The new PSC has a graded band alignment and bilayered inorganic HTMs (i.e., compact NiO x and mesoporous CuGaO 2 ). In comparison with planar‐structured PSCs, the mesoporous CuGaO 2 can effectively extract holes from perovskite due to the increased contact area of the perovskite/HTM. The graded energy alignment constructed in the ultrathin compact NiO x , mesoporous CuGaO 2 , and perovskite can facilitate carrier transfer and depress charge recombination. As a result, the champion device based on the newly designed mesoscopic PSCs yields a stabilized efficiency of ≈20%, which is considered one of the best results for inverted PSCs with inorganic HTMs. Additionally, the unencapsulated PSC device retains more than 80% of its original efficiency when subjected to thermal aging at 85 °C for 1000 h in a nitrogen atmosphere, thus demonstrating superior thermal stability of the device. This study may pave a new avenue to rational design of highly efficient and stable PSCs.

Ras mediates cell proliferation, survival and differentiation. Mutations in K-Ras4B are predominant at residues G12, G13 and Q61. Even though all impair GAP-assisted GTP → GDP hydrolysis, the mutation frequencies of K-Ras4B in human cancers vary. Here we aim to figure out their mechanisms and differential oncogenicity. In total, we performed 6.4 μs molecular dynamics simulations on the wild-type K-Ras4B (K-Ras4B(WT)-GTP/GDP) catalytic domain, the K-Ras4B(WT)-GTP-GAP complex, and the mutants (K-Ras4B(G12C/G12D/G12V)-GTP/GDP, K-Ras4B(G13D)-GTP/GDP, K-Ras4B(Q61H)-GTP/GDP) and their complexes with GAP. In addition, we simulated 'exchanged' nucleotide states. These comprehensive simulations reveal that in solution K-Ras4B(WT)-GTP exists in two, active and inactive, conformations. Oncogenic mutations differentially elicit an inactive-to-active conformational transition in K-Ras4B-GTP; in K-Ras4B(G12C/G12D)-GDP they expose the bound nucleotide which facilitates the GDP-to-GTP exchange. These mechanisms may help elucidate the differential mutational statistics in K-Ras4B-driven cancers. Exchanged nucleotide simulations reveal that the conformational transition is more accessible in the GTP-to-GDP than in the GDP-to-GTP exchange. Importantly, GAP not only donates its R789 arginine finger, but stabilizes the catalytically-competent conformation and pre-organizes catalytic residue Q61; mutations disturb the R789/Q61 organization, impairing GAP-mediated GTP hydrolysis. Together, our simulations help provide a mechanistic explanation of key mutational events in one of the most oncogenic proteins in cancer.

When chaotic sequences are used in engineering applications, their oscillating amplitudes need to be adjusted nondestructively. To accommodate this issue, this article presents a simple 2-D sine map. It can not only generate the chaotic sequences with high complexity, but also boost the oscillating amplitudes by switching their initial states. To show the complex dynamics of the sine map, this article investigates its control parameters-related dynamical behaviors and initials-boosted coexisting bifurcations using numerical methods. The results demonstrate that the oscillating amplitudes of chaotic sequences generated by the sine map can be nondestructively controlled by switching their initial states. This makes the sine map more suitable for many chaos-based engineering applications. Furthermore, we develop a microcontroller-hardware test platform to implement the sine map. The experimental results show that the platform synchronously outputs multichannel initials-controlled chaotic sequences. We also design a pseudorandom number generator to explore the application of the sine map.

This paper is devoted to study the Razumikhin stability theorem for a class of impulsive stochastic delay differential systems. By developing a new lemma, stochastic analysis technique, and Razumikhin approach, several novel criteria of the pth moment exponential stability are derived for the related systems. The key feature of the criteria is that time-derivatives of the Razumikhin functions are allowed to be indefinite, which loosens the constraints of the existing results greatly. Finally, two examples are given to illustrate the usefulness and significance of the theoretical results.

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By replacing Chua&apos;s diode in the canonical Chua&apos;s oscillator with a smooth flux-controlled memristor, a memristor based oscillator is presented. The memristor oscillator generates a steady periodic orbit and has a transition from transient chaotic to steady periodic behaviour. The complicated dynamical behaviour is extremely dependent on the initial condition of the memristor.