Epigenetic Down-Regulation of Sirt 1 via DNA Methylation and Oxidative Stress Signaling Contributes to the Gestational Diabetes Mellitus-Induced Fetal Programming of Heart Ischemia-Sensitive Phenotype in Late Life.

Rationale: The incidence of gestational diabetes mellitus (GDM) is increasing worldwide. However, whether and how GDM exposure induces fetal programming of adult cardiac dysfunctional phenotype, especially the underlying epigenetic molecular mechanisms and theranostics remain unclear. To address this problem, we developed a late GDM rat model. Methods: Pregnant rats were made diabetic on day 12 of gestation by streptozotocin (STZ). Experiments were conducted in 6 weeks old offspring. Results: There were significant increases in ischemia-induced cardiac infarction and gender-dependent left ventricular (LV) dysfunction in male offspring in GDM group as compared to controls. Exposure to GDM enhanced ROS level and caused a global DNA methylation in offspring cardiomyocytes. GDM attenuated cardiac Sirt 1 protein and p-Akt/Akt levels, but enhanced autophagy-related proteins expression (Atg 5 and LC3 II/LC3 I) as compared to controls. Ex-vivo treatment of DNA methylation inhibitor, 5-Aza directly inhibited Dnmt3A and enhanced Sirt 1 protein expression in fetal hearts. Furthermore, treatment with antioxidant, N-acetyl-cysteine (NAC) in offspring reversed GDM-mediated DNA hypermethylation, Sirt1 repression and autophagy-related gene protein overexpression in the hearts, and rescued GDM-induced deterioration in heart ischemic injury and LV dysfunction. Conclusion: Our data indicated that exposure to GDM induced offspring cardiac oxidative stress and DNA hypermethylation, resulting in an epigenetic down-regulation of Sirt1 gene and aberrant development of heart ischemia-sensitive phenotype, which suggests that Sirt 1-mediated signaling is the potential therapeutic target for the heart ischemic disease in offspring

Fully solvable lower dimensional dynamics of Cartesian product of Kuramoto models

Implementing a positive correlation between the natural frequencies of nodes and their connectivity on a single star graph leads to a pronounced explosive transition to synchronization, additionally presenting hysteresis behavior. From the viewpoint of network connectivity, a star has been considered as a building motif to generate a big graph by graph operations. On the other hand, we propose to construct complex synchronization dynamics by applying the Cartesian product of two Kuramoto models on two star networks. On the product model, the lower dimensional equations describing the ensemble dynamics in terms of collective order parameters are fully solved by the Watanabe-Strogatz method. Different graph parameter choices lead to three different interacting scenarios of the hysteresis areas of two individual factor graphs, which further change the basins of attraction of multiple fixed points. Furthermore, we obtain coupling regimes where cluster synchronization states are often present on the product graph and the number of clusters is fully controlled. More specifically, oscillators on one star graph are synchronized while those on the other star are not synchronized, which induces clustered state on the product model. The numerical results agree perfectly with the theoretic predictions. © 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft

Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction

In scenarios involving the grasping of multiple targets, the learning of stacking relationships between objects is fundamental for robots to execute safely and efficiently. However, current methods lack subdivision for the hierarchy of stacking relationship types. In scenes where objects are mostly stacked in an orderly manner, they are incapable of performing human-like and high-efficient grasping decisions. This paper proposes a perception-planning method to distinguish different stacking types between objects and generate prioritized manipulation order decisions based on given target designations. We utilize a Hierarchical Stacking Relationship Network (HSRN) to discriminate the hierarchy of stacking and generate a refined Stacking Relationship Tree (SRT) for relationship description. Considering that objects with high stacking stability can be grasped together if necessary, we introduce an elaborate decision-making planner based on the Partially Observable Markov Decision Process (POMDP), which leverages observations and generates the least grasp-consuming decision chain with robustness and is suitable for simultaneously specifying multiple targets. To verify our work, we set the scene to the dining table and augment the REGRAD dataset with a set of common tableware models for network training. Experiments show that our method effectively generates grasping decisions that conform to human requirements, and improves the implementation efficiency compared with existing methods on the basis of guaranteeing the success rate.Comment: 8 pages, 8 figure

Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma

Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC).Methods: One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: n = 88 and validation cohort: n = 38) were retrospectively recruited. Basic information was obtained from medical records. Preoperative multi-phase CE-MRI images were reviewed, and the 3D volumes of interest (VOIs) of the whole tumor were delineated on non-contrast T1-weighted imaging (T1), arterial phase (AP), portal venous phase (PVP), delayed phase (DP), and hepatobiliary phase (HBP). One hundred and seven original radiomics features were extracted from each phase, and delta-radiomics features were calculated. After a two-step feature selection strategy, radiomics models were built using two classification algorithms. A nomogram was constructed by combining the best radiomics model and clinical risk factors.Results: Serum alpha-fetoprotein (AFP) (p = 0.013) was significantly related to GPC3-positive HCC. The optimal radiomics model is composed of eight delta-radiomics features with the AUC of 0.805 and 0.857 in the training and validation cohorts, respectively. The nomogram integrated the radiomics score, and AFP performed excellently (training cohort: AUC = 0.844 and validation cohort: AUC = 0.862). The calibration curve showed good agreement between the nomogram-predicted probabilities and GPC3 actual expression in both training and validation cohorts. Decision curve analysis further demonstrates the clinical practicality of the nomogram.Conclusion: Multi-phase CE-MRI based on the delta-radiomics model can non-invasively predict GPC3-positive HCC and can be a useful method for individualized diagnosis and treatment

Detection of rubidium and samarium in the atmosphere of the ultra-hot Jupiter MASCARA-4b

Ultra-hot Jupiters (UHJs) possess the most extreme environments among various types of exoplanets, making them ideal laboratories to study the chemical composition and kinetics properties of exoplanet atmosphere with high-resolution spectroscopy (HRS). It has the advantage of resolving the tiny Doppler shift and weak signal from exoplanet atmosphere and has helped to detect dozens of heavy elements in UHJs including KELT-9b, WASP-76b, WASP-121b. MASCARA-4b is a 2.8-day UHJ with an equilibrium temperature of $\sim2250$ K, which is expected to contain heavy elements detectable with VLT. In this letter, we present a survey of atoms/ions in the atmosphere of the MASCARA-4b, using the two VLT/ESPRESSO transits data. Cross-correlation analyses are performed on the obtained transmission spectra at each exposure with the template spectra generated by petitRADTRANS for atoms/ions from element Li to U. We confirm the previous detection of Mg, Ca, Cr and Fe and report the detection of Rb, Sm, Ti+ and Ba+ with peak signal-to-noise ratios (SNRs) $>$ 5. We report a tentative detection of Sc+, with peak SNRs $\sim$6 but deviating from the estimated position. The most interesting discovery is the first-time detection of elements Rb and Sm in an exoplanet. Rb is an alkaline element like Na and K, while Sm is the first lanthanide series element and is by far the heaviest one detected in exoplanets. Detailed modeling and acquiring more data are required to yield abundance ratios of the heavy elements and to understand better the common presence of them in UHJ's atmospheres.Comment: 11 pages, 7 figures, Accepted to A

GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment

Due to the subjective nature of image quality assessment (IQA), assessing which image has better quality among a sequence of images is more reliable than assigning an absolute mean opinion score for an image. Thus, IQA models are evaluated by global correlation consistency (GCC) metrics like PLCC and SROCC, rather than mean opinion consistency (MOC) metrics like MAE and MSE. However, most existing methods adopt MOC metrics to define their loss functions, due to the infeasible computation of GCC metrics during training. In this work, we construct a novel loss function and network to exploit Global-correlation and Mean-opinion Consistency, forming a GMC-IQA framework. Specifically, we propose a novel GCC loss by defining a pairwise preference-based rank estimation to solve the non-differentiable problem of SROCC and introducing a queue mechanism to reserve previous data to approximate the global results of the whole data. Moreover, we propose a mean-opinion network, which integrates diverse opinion features to alleviate the randomness of weight learning and enhance the model robustness. Experiments indicate that our method outperforms SOTA methods on multiple authentic datasets with higher accuracy and generalization. We also adapt the proposed loss to various networks, which brings better performance and more stable training