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ObjectivesQuantitatively assess the severity and predict the mortality of interstitial lung disease (ILD) associated with Rheumatoid arthritis (RA) was a challenge for clinicians. This study aimed to construct a radiomics nomogram based on chest computed tomography (CT) imaging by using the ILD-GAP (gender, age, and pulmonary physiology) index system for clinical management.MethodsChest CT images of patients with RA-ILD were retrospectively analyzed and staged using the ILD-GAP index system. The balanced dataset was then divided into training and testing cohorts at a 7:3 ratio. A clinical factor model was created using demographic and serum analysis data, and a radiomics signature was developed from radiomics features extracted from the CT images. Combined with the radiomics signature and independent clinical factors, a nomogram model was established based on the Rad-score and clinical factors. The model capabilities were measured by operating characteristic curves, calibration curves and decision curves analyses.ResultsA total of 177 patients were divided into two groups (Group I, n = 107; Group II, n = 63). Krebs von den Lungen-6, and nineteen radiomics features were used to build the nomogram, which showed favorable calibration and discrimination in the training cohort [AUC, 0.948 (95% CI: 0.910–0.986)] and the testing validation cohort [AUC, 0.923 (95% CI: 0.853–0.993)]. Decision curve analysis demonstrated that the nomogram performed well in terms of clinical usefulness.ConclusionThe CT-based radiomics nomogram model achieved favorable efficacy in predicting low-risk RA-ILD patients.

Thick clouds reduce the useful information in visible remote sensing and restrict the usefulness of remote sensing images. Many scholars have proposed time-based strategies to solve these restrictions. However, these approaches still suffered from difficulties, such as requiring paired cloud mask pictures as additional auxiliary images, having artefacts or image distortions, and model training difffculties. For thick clouds in multitemporal remote sensing, this research offers an implicit multitemporal remote sensing thick cloud removal network (IRNet). As the name suggests, IRNet does not need cloud masks as external auxiliary data inputs to the network. To attenuate the temporal difference problem between multitemporal remote sensing images with model training difffculties, multiscale feature extraction block (MFEB) is designed in IRNet. In addition, the designed upsampling and convolution blocks are utilized in the designed high-fidelity reconstruction block (HFRB) to reconstruct the missing information under the cloud to generate highly preserved cloud-free images. By utilizing two readily datasets for removing multitemporal thick clouds, we have evaluated our method against the most effective existing techniques. Our findings consistently demonstrate that our method improves the restoration of texture detail and spatial information. [ABSTRACT FROM AUTHOR]

Objective: This study is aimed at investigating diagnostic biomarkers associated with lipotoxicity and the molecular mechanisms underlying diabetic nephropathy (DN). Methods: The GSE96804 dataset from the Gene Expression Omnibus (GEO) database was utilized to identify differentially expressed genes (DEGs) in DN patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the DEGs. A protein–protein interaction (PPI) network was established to identify key genes linked to lipotoxicity in DN. Immune infiltration analysis was employed to identify immune cells with differential expression in DN and to assess the correlation between these immune cells and lipotoxicity-related hub genes. The findings were validated using the external dataset GSE104954. ROC analysis was performed to assess the diagnostic performance of the hub genes. The Gene set enrichment analysis (GSEA) enrichment method was utilized to analyze the key genes associated with lipotoxicity as mentioned above. Result: In this study, a total of 544 DEGs were identified. Among them, extracellular matrix (ECM), fatty acid metabolism, AGE-RAGE, and PI3K-Akt signaling pathways were significantly enriched. Combining the PPI network and lipotoxicity-related genes (LRGS), LUM and ALB were identified as lipotoxicity-related diagnostic biomarkers for DN. ROC analysis showed that the AUC values for LUM and ALB were 0.882 and 0.885, respectively. The AUC values for LUM and ALB validated in external datasets were 0.98 and 0.82, respectively. Immune infiltration analysis revealed significant changes in various immune cells during disease progression. Macrophages M2, mast cells activated, and neutrophils were significantly associated with all lipotoxicity-related hub genes. These key genes were enriched in fatty acid metabolism and extracellular matrix-related pathways. Conclusion: The identified lipotoxicity-related hub genes provide a deeper understanding of the development mechanisms of DN, potentially offering new theoretical foundations for the development of diagnostic biomarkers and therapeutic targets related to lipotoxicity in DN. [ABSTRACT FROM AUTHOR]

Estimating the orientations of remote sensing images is a very important step in remote sensing image matching and is now gradually receiving widespread attention. However, due to the inability to explicitly define the standard orientations of feature points, the current methods still produce feature point orientation estimation errors, resulting in reduced matching accuracy. In this letter, we propose a multiscale unsupervised orientation estimation method with transformers, in which we use a multiscale feature extraction module to aggregate rich semantic features and a transformer-based attention mechanism module to address robust feature extraction in weakly textured regions while predicting the orientations of feature points through a carefully designed loss function. We set up image matching experiments on remote sensing images in different scenes for comparison purposes, and the experimental results show that our proposed method achieves substantially improved orientation estimation accuracy and improved image matching performance. [ABSTRACT FROM AUTHOR]

The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%. [ABSTRACT FROM AUTHOR]

Due to the differences in radiation and geometric characteristics of optical and synthetic aperture radar (SAR) images, there is still a huge challenge for accurate matching. In this paper, we propose a patch-matching network (PM-Net) to improve the matching performance of optical and SAR images. First, a multi-level keypoints detector (MKD) with fused high-level and low-level features is presented to extract more robust keypoints from optical and SAR images. Second, we use a two-channel network structure to improve the image patch matching performance. Benefiting from this design, the proposed method can directly learn the similarity between optical and SAR image patches without manually designing features and descriptors. Finally, the MKD and two-channel net-work are trained separately on GL3D and QXS-SAROPT data sets, and the PM-Net is tested on multiple pairs of optical and SAR images. The experimental results demonstrate that the proposed method outperforms four advanced image matching networks on qualitative and quantitative assessments. The quantitative experiment results show that using our method correct matching points numbers are increased by more than 1.15 times, the value of F1-measure is raised by an average of 7.4% and the root mean squared error (RMSE) is reduced by more than 15.3%. The advantages of MKD and the two-channel network are also verified through ablation experiments. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G communication, crossly-polarized transmitting and receiving scalable channels have received wide attention. We conduct an in-depth analysis of the 5.8 GHz horizontal and vertical crossly-polarized electromagnetic (EM) wave propagation and propose a novel test scheme for the feature of the particular EM wave propagation. The scheme acquires the channel characteristics and the interchannel coupling interference strength by transmitting and receiving pseudorandom codes and analyzing the fuzzy function of the corresponding noise-like signals. We then optimize the pseudorandom codes with parallel pseudorandom sequence generation method based on Hash function, greatly reducing calculation time. The transceiver’s mode adopts time division duplexing and chooses the pseudorandom codes as the system transceiver’s pseudorandom codes. The fuzzy function analysis and processing are performed by a MATLAB code and we can clearly obtain pin-shaped fuzzy images and coupling interference between crossly-polarized channels. [ABSTRACT FROM AUTHOR]

Objective: To screen for immune genes that play a major role in Kawasaki disease and to investigate the pathogenesis of Kawasaki disease through bioinformatics analysis. Methods: Kawasaki disease‐related datasets GSE18606, GSE68004, and GSE73461 were downloaded from the Gene Expression Omnibus database. Three microarrays were integrated and standardized to include 173 Kawasaki disease samples and 101 normal samples. The samples were analyzed using CIBERSORT to obtain the infiltration of 22 immune cells and analyze the differential immune cells in the samples and correlations. The distribution of the samples was analyzed using principal component analysis (PCA). Immune‐related genes were downloaded, extracted from the screened samples and analyzed for differential analysis (different expression genes [DEG]) and weighted gene co‐expression network analysis (WGCNA). We constructed coexpression networks, and used the cytohobbe tool in Cytoscape to analyze the coexpression networks and select the immune genes that played a key role in them. Results: Immune cell infiltration analysis showed that B cells naive, T cells CD8, natural killer (NK) cells activated, and so forth were highly expressed in normal samples. T cells CD4 memory activated, monocytes, neutrophils, and so forth were highly expressed in Kawasaki disease samples. PCA results showed a significant difference in the distribution of normal and Kawasaki disease samples. From the screened samples, 97 upregulated and 103 downregulated immune‐related genes were extracted. WGCNA analysis of DEG yielded 10 gene modules, of which the three most relevant to Kawasaki disease were red, yellow, and gray modules. They were associated with cytokine regulation, T‐cell activation, presentation of T‐cell receptor signaling pathways, and NK cell‐mediated cytotoxicity. CXCL8, CCL5, CCR7, CXCR3, and CCR1 were identified as key genes by constructing a coexpression network. Conclusion: Our study shows that we can distinguish normal samples from Kawasaki disease samples based on the infiltration of immune cells, and that CXCL8, CCL5, CCR7, CXCR3, and CCR1 may play important roles in the development of Kawasaki disease. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is a high incidence, high degree of malignancy, and high mortality in the digestive system tumor. The incidence of pancreatic cancer in China has increased nearly six folds in the past 20 years, ranking fifth in the mortality rate of malignant tumors, so it is particularly important to actively explore clinical indicators with better diagnostic significance for pancreatic cancer. LncRNA performs an essential regulatory function in the occurrence, development, and metastasis of many kinds of tumors, playing both a carcinogenic role and a tumor suppressor gene. Here, we demonstrated the function and mechanism of LncRNA-XLOC_012370 in the development of pancreatic cancer. In our research, the abnormal upregulation of XLOC_012370 was observed in pancreatic cancer patients' tumor tissues. XLOC_012370 was related to tumor stage, lymph node metastasis, and overall survival. Silencing of XLOC_012370 prevented the proliferation, migration, and invasion via the NF-κB signal pathway. Further, miR-140-5p was identified as the target and downstream of XLOC_012370 and involved in pancreatic cancer progression. In vivo , knockdown of XLOC_012370 inhibited tumor growth via the NF-κB signal pathway. In conclusion, lncRNA-XLOC_012370 is closely related to some malignant clinicopathological features and prognosis of pancreatic cancer. Thus the miR-140-5p/NF-κB signal pathway might represent a promising treatment strategy to combat pancreatic cancer. [ABSTRACT FROM AUTHOR]

Approximately 13,000 people die of an abdominal aortic aneurysm (AAA) every year. This study aimed to identify the immune response-related genes that play important roles in AAA using bioinformatics approaches. We downloaded the GSE57691 and GSE98278 datasets related to AAA from the Gene Expression Omnibus database, which included 80 AAA and 10 normal vascular samples. CIBERSORT was used to analyze the samples and detect the infiltration of 22 types of immune cells and their differences and correlations. The principal component analysis showed significant differences in the infiltration of immune cells between normal vascular and AAA samples. High proportions of CD4+ T cells, activated mast cells, resting natural killer cells, and 12 other types of immune cells were found in normal vascular tissues, whereas high proportions of macrophages, CD8+ T cells, resting mast cells, and six other types of immune cells were found in AAA tissues. In the selected samples, we identified 39 upregulated (involved in growth factor activity, hormone receptor binding, and cytokine receptor activity) and 133 downregulated genes (involved in T cell activation, cell chemotaxis, and regulation of immune response mediators). The key differentially expressed immune response-related genes were screened using the STRING database and Cytoscape software. Two downregulated genes, PI3 and MAP2K1 , and three upregulated genes, SSTR1, GPER1 , and CCR10 , were identified by constructing a protein–protein interaction network. Functional enrichment of the differentially expressed genes was analyzed, and the expression of the five key genes in AAA samples was verified using quantitative polymerase chain reaction, which revealed that MAP2K1 was downregulated in AAA, whereas SSTR1, GEPR1 , and CCR10 were upregulated; there was no significant difference in PI3 expression. Our study shows that normal vascular and AAA samples can be distinguished via the infiltration of immune cells. Five genes, PI3, MAP2K1, SSTR1, GPER1 , and CCR10 , may play important roles in the development, diagnosis, and treatment of AAA. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is one of the most lethal malignancies. With the promising prospects conveyed by immunotherapy in cancers, we aimed to construct an immune‐related gene pairs (IRGPs) signature to predict the prognosis of pancreatic cancer patients. We downloaded clinical and transcriptional data of pancreatic cancer patients from The Cancer Genome Atlas data set as the training group and GSE57495 data set as the verification group. We filtered immune‐related transcriptional data by IMMPORT. With the assistance of lasso penalized Cox regression, we constructed our prognostic IRGPs signature and divided all samples into high‐/low‐risk groups by receiver operating characteristic curve for further comparisons. The comparisons between high‐ and low‐risk groups including survival rate, multivariate, and univariate Cox proportional‐hazards analysis, infiltration of immune cells, and Gene Set Enrichment Analysis (GSEA). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) are facilitated to analyze the proceedings in which our IRGPs signature may involve in. The results revealed that 18 IRGPs were defined as our prognostic signature. The prognostic value of this IRGPs signature was verified from the GSE57495 data set. We further demonstrated the independent prognostic value of this IRGPs signature. The contents of six immune cells between high‐/low‐risk groups were different, which was associated with the progression of diverse cancers. Results from GO, KEGG, and GSEA revealed that this IRGPs signature was involved in extracellular space, immune response, cancer pathways, cation channel, and gated channel activities. Evidently, this IRGPs signature will provide remarkable value for the therapy of pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

Objective: To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC). Methods: We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared. Results: By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis. Conclusion: LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert–Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect. The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal. For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states. The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm. [ABSTRACT FROM AUTHOR]

The coordinated development of smart cities has become the goal of world urban development, and the railway network plays an important role in this progress. This paper proposes a solution that integrates data acquisition, storage, GIS visualization, deep learning, and statistical correlation analysis to deeply analyze the distribution data of companies collected in the past 40 years in the Yangtze River Delta. Through deep learning, we predict the spatial distribution of the company after the opening of the train stations. Through statistical and correlation analysis of the company's registered capital and quantity, the urban development relationship under the influence of the opening of the railway is explored. Going forward, the use and application of such analysis can be tested for use and application in the context of other smart cities for specific aspects or scale. [ABSTRACT FROM AUTHOR]

Objective: To explore the mechanism of plasma circulating miRNA-126 and miRNA-28-3p in diabetes mellitus (DM) patients, and to identify the related bioinformatics analysis. Methods: Randomly selected 120 DM patients as the observation group and 120 non- DM patients as the control group. The plasma circulating miRNA-126 and miRNA-28-3p were analyzed by qRT-PCR, and their target genes, biological information, related lncRNA and circRNA were predicted. Results: The circulating miRNA-126 (0.1162 ± 0.0236 vs. 0.0018 ± 0.0862) and miRNA-28-3p (0.1378 ± 0.0268 vs. 0.0006 ± 0.0167) levels in the observation group were significantly higher than those in the control group, and differences were statistically significant (P < 0.01). The Pearson correlation coefficient of miRNA-126 and miRNA- 28-3p was 0.4337 (P < 0.01). ROC curve analysis of miRNA-126 and miRNA-28-3p showed that the differences of the area under curve were statistically significant between the two groups (P < 0.01). Bioinformatics prediction showed that miRNA-126 and miRNA-28-3p may be involved in regulation of the insulin signaling pathway, insulin receptor signaling pathway, insulin/insulin growth factor signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway and angiogenesis. Moreover, it may be associated with a variety of lncRNA and cir-cRNA. Conclusion: Circulating miRNA-126 and miRNA-28-3p can be a potential biomarker of DM and it may play an important role in the DM by regulating insulin or insulin growth factor related signaling pathways. [ABSTRACT FROM AUTHOR]

Deep vein thrombosis (DVT) is a common and lethal complication of surgery. In the clinic, thrombolytic drugs are primarily used for treating DVT. However, the utilization of thrombolytic drugs is limited due to the risk of urokinase (UK)-related hemorrhagic complications. In this paper, a binary eutectic phase-change fatty acid composed of lauric acid and stearic acid was used to block the pores of gold-mesoporous silica core–shell nanoparticles, so as to deliver thrombolytic drugs. The eutectic mixture has a well-defined melting point at 39.2 °C, which can be used as a biocompatible phase-change material for hyperthermia-triggered drug release. The prepared system presents remarkable photothermal effects due to the gold nanoparticles and quick drug release in response to near-infrared irradiation (NIR). In addition, localized hyperthermia could also enhance the lysis of the thrombus. The thrombolytic effect of this system was evaluated in vitro and in vivo. Herein, a rabbit femoral vein thrombosis model was first built for imitating thrombolysis in vivo. The B-ultrasound was then used to monitor the changes in the thrombus after treatment. The results indicated that the reported system could be potentially used to deliver thrombotic drugs in the clinic. [ABSTRACT FROM AUTHOR]

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person's brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients. [ABSTRACT FROM AUTHOR]

How to automatically generate a pool of keywords used by potential consumers is a challenging issue for advertisers in sponsored search advertising (SSA). Such a keyword pool serves as the base for market research and determines the feasible space of consequent keyword related decisions. This article presents a novel method for keyword generation with Wikipedia as a corpus of the source text (WIKG). Starting with a few seed keywords, the WIKG supports flexible keywords generation by taking advantage of Wikipedia's rich link structure to construct a graph of entry articles in an iterative way. The termination condition is determined by a threshold reflecting the tradeoff between coverage of the generated keyword set and its relevance to seed keywords. Experimental results show that the WIKG outperforms three baselines derived from the extant literature, in terms of both coverage and relevance. [ABSTRACT FROM AUTHOR]

Abstract: Blue organic luminescent materials play a crucial role in full‐color display and white lighting but efficient ones meeting commercial demands are very rare. Herein, the design and synthesis of tailor‐made bipolar blue luminogens with an anthracene core and various functional groups are reported. The thermal stabilities, photophysical properties, electronic structures, electrochemical behaviors, carrier transport abilities, and electroluminescence performances are systematically investigated. The luminogen TPE‐TAPBI containing a tetraphenylethene moiety shows aggregation‐induced emission, while another luminogen TriPE‐TAPBI bearing a triphenylethene unit exhibits light aggregation‐caused quenching. In comparison with TriPE‐TAPBI, TPE‐TAPBI has stronger blue emission in neat film and functions more efficiently in nondoped organic light‐emitting diodes (OLEDs). High maxima current, power, and external quantum efficiencies of 7.21 cd A−1, 6.78 lm W−1, and 5.73%, respectively, are attained by the nondoped blue OLED of TPE‐TAPBI (CIEx,y = 0.15, 0.16). Moreover, efficient two‐color hybrid warm white OLEDs (CIEx,y = 0.457, 0.470) are achieved using TPE‐TAPBI neat film as the blue‐emitting component, which provide total current, power, external quantum efficiencies of up to 70.5 lm W−1, 76.0 cd A−1, and 28% at 1000 cd m−2, respectively. These blue and white OLEDs are among the most efficient devices with similar colors in the literature. [ABSTRACT FROM AUTHOR]

Abstract: Non‐doped organic light‐emitting diodes (OLEDs) possess merits of higher stability and easier fabrication than doped devices. However, luminescent materials with high exciton use are generally unsuitable for non‐doped OLEDs because of severe emission quenching and exciton annihilation in neat films. Herein, we wish to report a novel molecular design of integrating aggregation‐induced delayed fluorescence (AIDF) moiety within host materials to explore efficient luminogens for non‐doped OLEDs. By grafting 4‐(phenoxazin‐10‐yl)benzoyl to common host materials, we develop a series of new luminescent materials with prominent AIDF property. Their neat films fluoresce strongly and can fully harvest both singlet and triplet excitons with suppressed exciton annihilation. Non‐doped OLEDs of these AIDF luminogens exhibit excellent luminance (ca. 100000 cd m−2), outstanding external quantum efficiencies (21.4–22.6 %), negligible efficiency roll‐off and improved operational stability. To the best of our knowledge, these are the most efficient non‐doped OLEDs reported so far. This convenient and versatile molecular design is of high significance for the advance of non‐doped OLEDs. [ABSTRACT FROM AUTHOR]

In order to fabricate high-performance, non-doped and simplified OLEDs, excellent p/n-type light emitters with both high solid-state emission efficiencies and good carrier-transporting abilities are urgently needed, especially efficient n-type light emitters. Herein, we designed and synthesized four new silole derivatives, (PBI)2DMTPS, (PBI)2MPPS, (PPI)2DMTPS, and (PPI)2MPPS, consisting of the 2,3,4,5-tetraphenylsilole unit and 1-phenyl-1H-benzo[d]imidazole (PBI) or 1-phenyl-1H-phenanthro[9,10-d]imidazole (PPI) groups. These novel siloles are aggregation-induce emission (AIE)-active and highly emissive in solid films, and show good thermal stabilities and low LUMO energy levels. It is noteworthy that the amorphous films of (PBI)2DMTPS and (PBI)2MPPS present high electron mobilities of 1.24 × 10−4 and 3.42 × 10−4 cm2 V−1 s−1, respectively, at an electric field of 5.5 × 105 V cm−1, which are superior to that of 1,3,5-tris(phenyl-2-benzimidazolyl)benzene (TPBi). The non-doped trilayer OLED of (PPI)2DMTPS exhibits an outstanding electroluminescence (EL) performance with efficiencies of 15.06 cd A−1, 16.24 lm W−1 and 4.84%. More importantly, an efficient double-layer OLED is achieved based on (PBI)2DMTPS, offering comparable EL efficiencies (13.30 cd A−1, 14.51 lm W−1 and 4.25%) with respect to those of its trilayer OLED (13.50 cd A−1, 12.58 lm W−1 and 4.22%). These results demonstrate an effective approach towards n-type light emitters for efficient, simplified and low-cost OLEDs. [ABSTRACT FROM AUTHOR]

Through-space conjugation is an important noncovalent interaction in artificial materials and biomacromolecules. Establishing relationships between geometry and property is of high significance to provide deeper insights into this phenomenon. In this work, we have focused on the through-space conjugation in a new class of foldamers with a folded tetraphenylethene core. We have studied its impact on the photophysical properties through experimental measurements and theoretical calculations. It is found that the through-space conjugation makes significant contributions to the short-wavelength absorption in these foldamers. Moreover, these foldamers exhibit aggregation-enhanced emission (AEE) and apparent blue shifts in their emission spectra on going from solution to aggregates. The structural changes are smaller in the aggregated state than those in the isolated state during the excited-state relaxation process, which results in lower re-organization energies. This accounts for the blueshifted and enhanced emissions in the aggregates. [ABSTRACT FROM AUTHOR]