Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles

Deep-learning-based super-resolution photoacoustic angiography (PAA) is a powerful tool that restores blood vessel images from under-sampled images to facilitate disease diagnosis. Nonetheless, due to the scarcity of training samples, PAA super-resolution models often exhibit inadequate generalization capabilities, particularly in the context of continuous monitoring tasks. To address this challenge, we propose a novel approach that employs a super-resolution PAA method trained with forged PAA images. We start by generating realistic PAA images of human lips from hand-drawn curves using a diffusion-based image generation model. Subsequently, we train a self-similarity-based super-resolution model with these forged PAA images. Experimental results show that our method outperforms the super-resolution model trained with authentic PAA images in both original-domain and cross-domain tests. Specially, our approach boosts the quality of super-resolution reconstruction using the images forged by the deep learning model, indicating that the collaboration between deep learning models can facilitate generalization, despite limited initial dataset. This approach shows promising potential for exploring zero-shot learning neural networks for vision tasks.Comment: 12 pages, 6 figures, journa

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.Comment: 14 pages, 13 figures, accepted by IEEE Transactions on Wireless Communication

Impact of Ocean Acidification on the Energy Metabolism and Antioxidant Responses of the Yesso Scallop (Patinopecten yessoensis)

Ocean acidification (OA), which is caused by increasing levels of dissolved CO2 in the ocean, is a major threat to marine ecosystems. Multiple lines of scientific evidence show that marine bivalves, including scallops, are vulnerable to OA due to their poor capacities to regulate extracellular ions and acid-based status. However, the physiological mechanisms of scallops responding to OA are not well understood. In this study, we evaluated the effects of 45 days of exposure to OA (pH 7.5) on the energy metabolism and antioxidant capability of Yesso scallops. Some biochemical markers related to energy metabolism (e.g., content of glycogen and ATP, activity of ATPase, lactate dehydrogenase, glutamate oxaloacetate transaminase, and glutamate-pyruvate transaminase), antioxidant capacity (e.g., reactive oxygen species level, activity of superoxide dismutase, and catalase) and cellular damage (e.g., lipid peroxidation level) were measured. Our results demonstrate that the effects of the reduced pH (7.5) on scallops are varied in different tissues. The energy reserves are mainly accumulated in the adductor muscle and hepatopancreas. Yesso scallops exhibit energy modulation by increasing lactate dehydrogenase activities to stimulate anaerobic metabolism. The highly active Na+/K+-ATPase and massive ATP consumption in the mantle and gill indicate that a large amount of energy was allocated for the ion regulation process to maintain the acid-base balance in the reduced-pH environment. Moreover, the increase in the reactive oxygen species level and the superoxide dismutase and catalase activities in the gill and adductor muscle, indicate that oxidative stress was induced after long-term exposure to the reduced-pH environment. Our findings indicate that the effects of OA are tissue-specific, and physiological homeostasis could be modulated through different mechanisms for Yesso scallops

Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors

PurposeTo develop a point-based scoring system (PSS) based on contrast-enhanced computed tomography (CT) qualitative and quantitative features to differentiate gastric schwannomas (GSs) from gastrointestinal stromal tumors (GISTs).MethodsThis retrospective study included 51 consecutive GS patients and 147 GIST patients. Clinical and CT features of the tumors were collected and compared. Univariate and multivariate logistic regression analyses using the stepwise forward method were used to determine the risk factors for GSs and create a PSS. Area under the receiver operating characteristic curve (AUC) analysis was performed to evaluate the diagnostic efficiency of PSS.ResultsThe CT attenuation value of tumors in venous phase images, tumor-to-spleen ratio in venous phase images, tumor location, growth pattern, and tumor surface ulceration were identified as predictors for GSs and were assigned scores based on the PSS. Within the PSS, GS prediction probability ranged from 0.60% to 100% and increased as the total risk scores increased. The AUC of PSS in differentiating GSs from GISTs was 0.915 (95% CI: 0.874–0.957) with a total cutoff score of 3.0, accuracy of 0.848, sensitivity of 0.843, and specificity of 0.850.ConclusionsThe PSS of both qualitative and quantitative CT features can provide an easy tool for radiologists to successfully differentiate GS from GIST prior to surgery