As one of the most promising cathodes for rechargeable sodium‐ion batteries (SIBs), O3‐type layered transition metal oxides commonly suffer from inevitably complicated phase transitions and sluggish ...kinetics. Here, a NaLi0.05Ni0.3Mn0.5Cu0.1Mg0.05O2 cathode material with the exposed {010} active facets by multiple‐layer oriented stacking nanosheets is presented. Owing to reasonable geometrical structure design and chemical substitution, the electrode delivers outstanding rate performance (71.8 mAh g−1 and 16.9 kW kg−1 at 50C), remarkable cycling stability (91.9% capacity retention after 600 cycles at 5C), and excellent compatibility with hard carbon anode. Based on the combined analyses of cyclic voltammograms, ex situ X‐ray absorption spectroscopy, and operando X‐ray diffraction, the reaction mechanisms behind the superior electrochemical performance are clearly articulated. Surprisingly, Ni2+/Ni3+ and Cu2+/Cu3+ redox couples are simultaneously involved in the charge compensation with a highly reversible O3–P3 phase transition during charge/discharge process and the Na+ storage is governed by a capacitive mechanism via quantitative kinetics analysis. This optimal bifunctional regulation strategy may offer new insights into the rational design of high‐performance cathode materials for SIBs.
An O3‐type NaLi0.05Ni0.3Mn0.5Cu0.1Mg0.05O2 cathode material with exposed {010} active facets by multiple‐layer oriented stacking nanosheets is successfully constructed via reasonable structure design and chemical substitution. An optimal bifunctional regulation is demonstrated to be an efficient strategy to restrain the unfavorable multiphase transformation and greatly improve Na+ transport kinetics resulting in excellent performance for sodium‐ion batteries.
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•We identified 8 MVI preoperative risk factors in HCC, including radiomic features.•Radiomic features do not provide significant added value to radiologist scores.•A model integrating ...clinic-radiologic and radiomic features demonstrates good performance for predicting MVI.
Microvascular invasion (MVI) impairs surgical outcomes in patients with hepatocellular carcinoma (HCC). As there is no single highly reliable factor to preoperatively predict MVI, we developed a computational approach integrating large-scale clinical and imaging modalities, especially radiomic features from contrast-enhanced CT, to predict MVI and clinical outcomes in patients with HCC.
In total, 495 surgically resected patients were retrospectively included. MVI-related radiomic scores (R-scores) were built from 7,260 radiomic features in 6 target volumes. Six R-scores, 15 clinical factors, and 12 radiographic scores were integrated into a predictive model, the radiographic-radiomic (RR) model, with multivariate logistic regression.
Radiomics related to tumor size and intratumoral heterogeneity were the top-ranked MVI predicting features. The related R-scores showed significant differences according to MVI status (p <0.001). Regression analysis identified 8 MVI risk factors, including 5 radiographic features and an R-score. The R-score (odds ratio OR 2.34) was less important than tumor capsule (OR 5.12), tumor margin (OR4.20), and peritumoral enhancement (OR 3.03). The RR model using these predictors achieved an area under the curve (AUC) of 0.909 in training/validation and 0.889 in the test set. Progression-free survival (PFS) and overall survival (OS) were significantly different between the RR-predicted MVI-absent and MVI-present groups (median PFS: 49.5 vs. 12.9 months; median OS: 76.3 vs. 47.3 months). RR-computed MVI probability, histologic MVI, tumor size, and Edmondson-Steiner grade were independently associated with disease-specific recurrence and mortality.
The computational approach, integrating large-scale clinico-radiologic and radiomic features, demonstrates good performance for predicting MVI and clinical outcomes. However, radiomics with current CT imaging analysis protocols do not provide statistically significant added value to radiographic scores.
The most effective treatment for hepatocellular carcinoma (HCC) is surgical removal of the tumor but often recurrence occurs, partly due to the presence of microvascular invasion (MVI). Lacking a single highly reliable factor able to preoperatively predict MVI, we developed a computational approach to predict MVI and the long-term clinical outcome of patients with HCC. In particular, the added value of radiomics, a newly emerging form of radiography, was comprehensively investigated. This computational method can enhance the communication with the patient about the likely success of the treatment and guide clinical management, with the aim of finding drugs that reduce the risk of recurrence.
•Deep learning based semantic segmentation method with fully convolutional network is proposed.•Defect images are captured via a self-developed image acquisition equipment (MTI-200a).•The proposed ...method can rapidly and accurately recognize defects for metro shield tunnels.
The performance of traditional visual inspection by handcrafted features for crack and leakage defects of metro shield tunnel is hardly satisfactory nowadays because it is low-efficient to distinguish defects from some interference such as segmental joints, bolt holes, cables and manual marks. Based on deep learning (DL), this paper proposes a novel image recognition algorithm for semantic segmentation of crack and leakage defects of metro shield tunnel using hierarchies of features extracted by fully convolutional network (FCN). The defect images in training dataset and testing dataset are captured via a self-developed image acquisition equipment named Moving Tunnel Inspection (MTI-200a). After the establishment of image datasets, FCN models of crack and leakage are separately trained through several iterations of forward inference and backward learning. Semantic segmentation of defect images is implemented via the corresponding FCN models using two-stream algorithm, i.e. one stream is used to recognize the crack by sliding-window-assembling operation and the other is adopted for the leakage by resizing-interpolation operation. Compared with two frequently-used traditional methods, i.e. region growing algorithm (RGA) and adaptive thresholding algorithm (ATA), great superiority of the proposed method in terms of recognition results, inference time and error rates is shown based on four typical types of defect images which are crack-only image, leakage-only image, two-defect-nonoverlapping (TDN) image, two-defect-overlapping (TDO) image. The proposed method using DL can be employed to rapidly and accurately recognize defects for structure health monitoring and maintenance of metro shield tunnels.
Background
Deep learning is the most promising methodology for automatic computer‐aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp‐MRI).
Purpose
To develop an automatic approach ...based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp‐MRI.
Study Type
Retrospective.
Subjects
In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively.
Sequence
T2‐weighted, diffusion‐weighted, and apparent diffusion coefficient images.
Assessment
A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI‐RADS) scores for each region. Inspired by VGG‐Net, we designed a patch‐based DCNN model to distinguish between PCa and NCs based on a combination of mp‐MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI‐RADS score to evaluate its clinical value using decision curve analysis.
Statistical Test
Two‐sided Wilcoxon signed‐rank test with statistical significance set at 0.05.
Results
The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876–0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI‐RADS and DCNN provided additional net benefits compared with the DCNN model and the PI‐RADS scheme.
Data Conclusion
The proposed DCNN‐based model with enhanced prediction yielded high performance in statistical analysis, suggesting that DCNN could be used in computer‐aided diagnosis (CAD) for PCa classification.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;48:1570–1577
Phylogenetic relationships in Rosaceae have long been problematic because of frequent hybridisation, apomixis and presumed rapid radiation, and their historical diversification has not been ...clarified.
With 87 genera representing all subfamilies and tribes of Rosaceae and six of the other eight families of Rosales (outgroups), we analysed 130 newly sequenced plastomes together with 12 from GenBank in an attempt to reconstruct deep relationships and reveal temporal diversification of this family.
Our results highlight the importance of improving sequence alignment and the use of appropriate substitution models in plastid phylogenomics. Three subfamilies and 16 tribes (as previously delimited) were strongly supported as monophyletic, and their relationships were fully resolved and strongly supported at most nodes. Rosaceae were estimated to have originated during the Late Cretaceous with evidence for rapid diversification events during several geological periods. The major lineages rapidly diversified in warm and wet habits during the Late Cretaceous, and the rapid diversification of genera from the early Oligocene onwards occurred in colder and drier environments.
Plastid phylogenomics offers new and important insights into deep phylogenetic relationships and the diversification history of Rosaceae. The robust phylogenetic backbone and time estimates we provide establish a framework for future comparative studies on rosaceous evolution.
Fruit category identification is important in factories, supermarkets, and other fields. Current computer vision systems used handcrafted features, and did not get good results. In this study, our ...team designed a 13-layer convolutional neural network (CNN). Three types of data augmentation method was used: image rotation, Gamma correction, and noise injection. We also compared max pooling with average pooling. The stochastic gradient descent with momentum was used to train the CNN with minibatch size of 128. The overall accuracy of our method is 94.94%, at least 5 percentage points higher than state-of-the-art approaches. We validated this 13-layer is the optimal structure. The GPU can achieve a 177× acceleration on training data, and a 175× acceleration on test data. We observed using data augmentation can increase the overall accuracy. Our method is effective in image-based fruit classification.
Summary
Aims
Baicalin (BAI), a flavonoid compound isolated from the root of Scutellaria baicalensis Georgi, has been established to have potent anti‐inflammation and neuroprotective properties; ...however, its effects during Alzheimer's disease (AD) treatment have not been well studied. This study aimed to investigate the effects of BAI pretreatment on cognitive impairment and neuronal protection against microglia‐induced neuroinflammation and to explore the mechanisms underlying its anti‐inflammation effects.
Methods
To determine whether BAI plays a positive role in ameliorating the memory and cognition deficits in APP (amyloid beta precursor protein)/PS1 (presenilin‐1) mice, behavioral experiments were conducted. We assessed the effects of BAI on microglial activation, the production of proinflammatory cytokines, and neuroinflammation‐mediated neuron apoptosis in vivo and in vitro using Western blot, RT‐PCR, ELISA, immunohistochemistry, and immunofluorescence. Finally, to elucidate the anti‐inflammation mechanisms underlying the effects of BAI, the protein expression of NLRP3 inflammasomes and the expression of proteins involved in the TLR4/NF‐κB signaling pathway were measured using Western blot and immunofluorescence.
Results
The results indicated that BAI treatment attenuated spatial memory dysfunction in APP/PS1 mice, as assessed by the passive avoidance test and the Morris water maze test. Additionally, BAI administration effectively decreased the number of activated microglia and proinflammatory cytokines, as well as neuroinflammation‐mediated neuron apoptosis, in APP/PS1 mice and LPS (lipopolysaccharides)/Aβ‐stimulated BV2 microglial cells. Lastly, the molecular mechanistic study revealed that BAI inhibited microglia‐induced neuroinflammation via suppression of the activation of NLRP3 inflammasomes and the TLR4/NF‐κB signaling pathway.
Conclusion
Overall, the results of the present study indicated that BAI is a promising neuroprotective compound for use in the prevention and treatment of microglia‐mediated neuroinflammation during AD progression.
STING （also known as MITA） mediates the innate antiviral signaling and ubiquitination of STING is key to its function. However, the deubiquitination process of STING is unclear. Here we report that ...USP18 recruits USP20 to deconjugate K48-1inked ubiquitination chains from STING and promotes the stability of STING and the expression of type I IFNs and proinflammatory cytokines after DNA virus infection. USP18 deficiency or knockdown of USP20 resulted in enhanced K48-1inked ubiquitination and accelerated degradation of STING, and impaired activation of IRF3 and NF-κB as well as induction of downstream genes after infection with DNA virus HSV-1 or transfeetion of various DNA ligands. In addition, Uspl8-/- mice were more susceptible to HSV-1 infection compared with the wildtype littermates. USP18 did not deubiquitinate STING in vitro but facilitated USP20 to catalyze deubiquitination of STING in a manner independent of the enzymatic activity of USP18. In addition, reconstitution of STING into Uspl8-/- MEFs restored HSV-1-induced expression of downstream genes and cellular antiviral responses. Our findings thus uncover previously uncharacterized roles of USPI8 and USP20 in mediating virus-triggered signaling and contribute to the understanding of the complicated regulatory system of the innate antiviral responses.
Pancreatic cancer (PC) is one of the most fatal diseases with a very high rate of metastasis and low rate of survival. Despite the advances in understanding this devastating disease, PC still ...accounts for 3% of all cancers and causes almost 7% of death of cancer patients. Recent studies have demonstrated that the transcription factor nuclear factor-erythroid 2-related factor 2 (Nrf2) and its key negative regulator Kelch-like ECH-associated protein 1 (Keap1) are dysregulated in PC and the Keap1-Nrf2 pathway is an emerging target for PC prevention and therapy. Indeed, Nrf2 plays an either tumor-suppressive or promoting function in PC, which depends on the developmental stages of the disease and the cellular context. Several natural-product Nrf2 activators have been developed to prevent pancreatic carcinogenesis, while the Nrf2 inhibitors have been examined for their efficacy in inhibiting PC growth and metastasis and reversing chemoresistance. However, further preclinical and clinical studies for determining the effectiveness and safety of targeting the Keap1-Nrf2 pathway for PC prevention and therapy are warranted. In this review, we comprehensively discuss the dual roles of the Keap1-Nrf2 signaling pathway in PC as well as the current targeting strategies and known activators and inhibitors of Nrf2. We also propose new strategies that may be used to address the current issues and develop more specific and more effective Nrf2 activator/inhibitors for PC prevention and therapy.
Emerging artificial enzymes with reprogrammed and augmented catalytic activity and substrate selectivity have long been pursued with sustained efforts. The majority of current candidates have rather ...poor catalytic activity compared with natural molecules. To tackle this limitation, we design artificial enzymes based on a structurally well-defined Au
cluster, namely clusterzymes, which are endowed with intrinsic high catalytic activity and selectivity driven by single-atom substitutions with modulated bond lengths. Au
Cu
and Au
Cd
clusterzymes exhibit 137 and 160 times higher antioxidant capacities than natural trolox, respectively. Meanwhile, the clusterzymes demonstrate preferential enzyme-mimicking catalytic activities, with Au
, Au
Cu
and Au
Cd
displaying compelling selectivity in glutathione peroxidase-like (GPx-like), catalase-like (CAT-like) and superoxide dismutase-like (SOD-like) activities, respectively. Au
Cu
decreases peroxide in injured brain via catalytic reactions, while Au
Cd
preferentially uses superoxide and nitrogenous signal molecules as substrates, and significantly decreases inflammation factors, indicative of an important role in mitigating neuroinflammation.
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