SAR-to-Optical Image Translation via Thermodynamics-inspired Network

Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret in human visual perception. Recently, SAR-to-optical (S2O) image conversion methods have provided a prospective solution for interpretation. However, since there is a huge domain difference between optical and SAR images, they suffer from low image quality and geometric distortion in the produced optical images. Motivated by the analogy between pixels during the S2O image translation and molecules in a heat field, Thermodynamics-inspired Network for SAR-to-Optical Image Translation (S2O-TDN) is proposed in this paper. Specifically, we design a Third-order Finite Difference (TFD) residual structure in light of the TFD equation of thermodynamics, which allows us to efficiently extract inter-domain invariant features and facilitate the learning of the nonlinear translation mapping. In addition, we exploit the first law of thermodynamics (FLT) to devise an FLT-guided branch that promotes the state transition of the feature values from the unstable diffusion state to the stable one, aiming to regularize the feature diffusion and preserve image structures during S2O image translation. S2O-TDN follows an explicit design principle derived from thermodynamic theory and enjoys the advantage of explainability. Experiments on the public SEN1-2 dataset show the advantages of the proposed S2O-TDN over the current methods with more delicate textures and higher quantitative results

Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play a key role in regulating various biological processes such as participating in the post-transcriptional pathway and affecting the stability and/or the translation of mRNA. Current methods have extracted feature information at different levels, among which the characteristic stem-loop structure makes the greatest contribution to the prediction of putative miRNA precursor (pre-miRNA). We find that none of these features alone is capable of identifying new pre-miRNA accurately.</p> <p>Results</p> <p>In the present work, a pre-miRNA stem-loop secondary structure is translated to a network, which provides a novel perspective for its structural analysis. Network parameters are used to construct prediction model, achieving an area under the receiver operating curves (AUC) value of 0.956. Moreover, by repeating the same method on two independent datasets, accuracies of 0.976 and 0.913 are achieved, respectively.</p> <p>Conclusions</p> <p>Network parameters effectively characterize pre-miRNA secondary structure, which improves our prediction model in both prediction ability and computation efficiency. Additionally, as a complement to feature extraction methods in previous studies, these multifaceted features can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA.</p

The global/local (limited to some regions) effect of cesarean delivery on the risk of pediatric allergic rhinitis: a systematic review and meta-analysis

BackgroundAllergic rhinitis is a chronic and refractory disease that can be affected by a variety of factors. Studies have shown an association between cesarean section and the risk of pediatric allergic rhinitis.MethodsThe PubMed, Springer, Embase, Cochrane Library, and Web of Science databases were searched to retrieve all studies published from January 2000 to November 2022, focusing on the relationship between cesarean section and the risk of pediatric allergic rhinitis. A meta-analysis was conducted to find a correlation between cesarean section and the risk of pediatric allergic rhinitis. A subgroup analysis was performed, considering the region and family history of allergy, after adjusting for confounding factors. Pooled odds ratios (ORs) were calculated, publication bias was assessed using a funnel plot, and heterogeneity between study-specific relative risks was taken into account.ResultsThe results showed that cesarean section was significantly associated with an increased risk of pediatric allergic rhinitis (OR: 1.27, 95% CI: 1.20–1.35). Subgroup analysis stratified by region indicated that cesarean section increased the risk of pediatric allergic rhinitis, with the highest increase in South America (OR: 1.67, 95% CI: 1.10–2.52) and the lowest in Europe (OR: 1.13, 95% CI: 1.02–1.25). The results of the subgroup analysis stratified by family history of allergy indicate that family history of allergy was not associated with the risk of pediatric allergic rhinitis.ConclusionAn association exists between cesarean section as the mode of delivery and the increased risk of pediatric allergic rhinitis, and cesarean section is a risk factor for allergic rhinitis

Fingolimod exerts in vitro anticancer activity against hepatocellular carcinoma cell lines via YAP/TAZ suppression

Hepatocellular carcinoma (HCC) remains a notably global health challenge with high mortality rates and poor prognosis. The deregulation of the Hippo signalling pathway, especially the overexpression and activation of downstream effector Yes-associated protein (YAP), has been demonstrated to result in the rapid malignant evolution of HCC. In this context, multiple efforts have been dedicated to targeting YAP for HCC therapy, but effective YAP inhibitors are still lacking. In this study, through a YAP-TEAD (8×GTIIC) luciferase reporter assay, we identified fingolimod, an immunomodulatory drug approved for the treatment of multiple sclerosis, as a novel YAP inhibitor. Fingolimod suppressed the proliferation of HCC cell lines by downregulating the protein levels as well as the transactivating function of YAP. Overall, our current study not only identifies fingolimod as a novel YAP-targeting inhibitor, but also indicates that this clinically-approved drug could be utilized as a potential and feasible therapeutic drug for HCC

RealDex: Towards Human-like Grasping for Robotic Dexterous Hand

In this paper, we introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns, enriched by multi-view and multimodal visual data. Utilizing a teleoperation system, we seamlessly synchronize human-robot hand poses in real time. This collection of human-like motions is crucial for training dexterous hands to mimic human movements more naturally and precisely. RealDex holds immense promise in advancing humanoid robot for automated perception, cognition, and manipulation in real-world scenarios. Moreover, we introduce a cutting-edge dexterous grasping motion generation framework, which aligns with human experience and enhances real-world applicability through effectively utilizing Multimodal Large Language Models. Extensive experiments have demonstrated the superior performance of our method on RealDex and other open datasets. The complete dataset and code will be made available upon the publication of this work

Ultrasonic and CT scanning analysis of coal-rock mass under the primary bedding structure

In order to study the influence of the primary bedding structure on wave velocity and mechanical properties of coal-rock mass, multilayer and multi-directional ultrasonic and CT scanning tests were carried out in laboratory. It also aims to improve the accuracy, rapidity and convenience of obtaining the wave velocity and mechanical parameters of coal-rock mass in laboratory. Based on the ultrasonic test, the characteristics of wave velocity and wave velocity ratio at different layers of coal-rock mass under the primary bedding dip angle were obtained. Combined with CT scanning and 3D reconstruction technology, the calculation method of CT gray mean value in different layers was proposed by using the data of the gray frequency of coal and rock. And the variation law of gray value and coal and rock content of coal-rock mass in different layers under different primary bedding dip angle was obtained. Meanwhile, the relationships between CT gray mean value and wave velocity, mechanical parameters of coal-rock mass were established. Considering the effect of bedding dip angle and the content of coal and rock, the calculation model of longitudinal wave velocity of coal-rock mass was constructed. Then the correctness of the model is verified by comparing the test data. The results show that: ① the wave velocity and wave velocity ratio of coal-rock mass with primary bedding are linearly related to bedding dip angle; with the increase of bedding dip angle, the longitudinal wave velocity of coal-rock mass decreases linearly, while the distribution range of wave velocity ratio expands; ② the mean wave velocity of CT is linearly correlated under different bedding dip angles, and the wave velocity of coal-rock mass increases linearly with the increases of CT gray mean value; ③ the density of coal-rock mass increases linearly with the increase of CT gray mean value, the dynamic elastic modulus and shear modulus of coal-rock mass have third order polynomial relationships with the mean value of CT gray level, and they tend to increase with the increase of the mean value of CT gray level; ④ compared with the bedding dip angle, the wave velocity of coal-rock mass is more sensitive to the coal and rock content, and the wave velocity changes largest during the content of coal and rock similar