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This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The implementation is available on github https://github.com/jacob6/ENIQA.

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.

The success of transformers in computer vision has led to several attempts to adapt them for mobile devices, but their performance remains unsatisfactory in some real-world applications. To address this issue, we propose PP-MobileSeg, a semantic segmentation model that achieves state-of-the-art performance on mobile devices. PP-MobileSeg comprises three novel parts: the StrideFormer backbone, the Aggregated Attention Module (AAM), and the Valid Interpolate Module (VIM). The four-stage StrideFormer backbone is built with MV3 blocks and strided SEA attention, and it is able to extract rich semantic and detailed features with minimal parameter overhead. The AAM first filters the detailed features through semantic feature ensemble voting and then combines them with semantic features to enhance the semantic information. Furthermore, we proposed VIM to upsample the downsampled feature to the resolution of the input image. It significantly reduces model latency by only interpolating classes present in the final prediction, which is the most significant contributor to overall model latency. Extensive experiments show that PP-MobileSeg achieves a superior tradeoff between accuracy, model size, and latency compared to other methods. On the ADE20K dataset, PP-MobileSeg achieves 1.57% higher accuracy in mIoU than SeaFormer-Base with 32.9% fewer parameters and 42.3% faster acceleration on Qualcomm Snapdragon 855. Source codes are available at https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.8.

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between the color channels and the two-dimensional entropy are calculated. In the frequency domain, the two-dimensional entropy and the mutual information of the filtered sub-band images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The source code is available on github https://github.com/jacob6/ENIQA.

Objective We sought to investigate somatic symptoms detected by the Somatic Self-rating Scale and to evaluate whether they were associated with the psychological symptoms of anxiety and depression in patients with cardiac neurosis. Methods A total of 180 patients with cardiac neurosis at the Third Xiangya Hospital, Changsha, China, were surveyed from January 2017 to July 2018. Participants completed a general information questionnaire, the Somatic Self-rating Scale, the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder Scale-7. Results The mean (±standard deviation) somatic symptom score in patients with cardiac neurosis was 40.83 ± 7.12. The most severe symptoms were cardiovascular symptoms, fatigue and muscle soreness. A total of 90 patients (46.4%) had anxiety and 80 (50.0%) had depression. Multiple stepwise regression analysis showed that somatic symptoms in patients with cardiac neurosis were associated with both anxiety and depression. Conclusion Somatic symptoms in patients with cardiac neurosis were associated with both anxiety and depression. Therefore, it is important to provide effective emotional interventions to promote patient rehabilitation.

Rosacea is a chronic inflammatory dermatosis that involves dysregulation of innate and adaptive immune systems. Osteopontin (OPN) is a phosphorylated glycoprotein produced by a broad range of immune cells such as macrophages, keratinocytes, and T cells. However, the role of OPN in rosacea remains to be elucidated. In this study, it was found that OPN expression was significantly upregulated in rosacea patients and LL37-induced rosacea-like skin inflammation. Transcriptome sequencing results indicated that OPN regulated pro-inflammatory cytokines and promoted macrophage polarization towards M1 phenotype in rosacea-like skin inflammation. In vitro , it was demonstrated that intracellular OPN (iOPN) promoted LL37-induced IL1B production through ERK1/2 and JNK pathways in keratinocytes. Moreover, secreted OPN (sOPN) played an important role in keratinocyte-macrophage crosstalk. In conclusion, sOPN and iOPN were identified as key regulators of the innate immune system and played different roles in the pathogenesis of rosacea.

[This corrects the article DOI: 10.3389/fimmu.2023.1285951.].

Objective. We investigated whether and how diazoxide can attenuate brain injury after cardiopulmonary resuscitation (CPR) by selective opening of mitochondrial ATP-sensitive potassium (mitoKATP) channels. Methods. Adult male Sprague-Dawley rats with induced cerebral ischemia (n=10 per group) received an intraperitoneal injection of 0.1% dimethyl sulfoxide (1 mL; vehicle group), diazoxide (10 mg/kg; DZ group), or diazoxide (10 mg/kg) plus 5-hydroxydecanoate (5 mg/kg; DZ + 5-HD group) 30 min after CPR. The control group (sham group, n=5) underwent sham operation, without cardiac arrest. Mitochondrial respiratory control rate (RCR) was determined. Brain cell apoptosis was assessed using TUNEL staining. Expression of Bcl-2, Bax, and protein kinase C epsilon (PKCε) in the cerebral cortex was determined by Western blotting and immunohistochemistry. Results. The neurological deficit scores (NDS) in the vehicle group decreased significantly at 24 h and 48 h after CPR. Diazoxide significantly improved NDS and mitochondrial RCR after CPR at both time points; 5-HD cotreatment abolished these effects. Diazoxide decreased TUNEL-positive cells following CPR, upregulated Bcl-2 and PKCε, downregulated Bax, and increased the Bcl-2/Bax ratio; 5-HD cotreatment reversed these effects. Conclusions. Diazoxide attenuates postresuscitation brain injury, protects mitochondrial function, inhibits brain cell apoptosis, and activates the PKC pathway by opening mitoKATP channels.