Clinical significance of obstructive sleep apnea in patients with acute coronary syndrome in relation to diabetes status.

Objective: The prognostic significance of obstructive sleep apnea (OSA) in patients with acute coronary syndrome (ACS) according to diabetes mellitus (DM) status remains unclear. We aimed to elucidate the association of OSA with subsequent cardiovascular events in patients with ACS with or without DM. Research design and methods: In this prospective cohort study, consecutive eligible patients with ACS underwent cardiorespiratory polygraphy between June 2015 and May 2017. OSA was defined as an Apnea Hypopnea Index ≥15 events/hour. The primary end point was major adverse cardiovascular and cerebrovascular events (MACCEs), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. Results: Among 804 patients, 248 (30.8%) had DM and 403 (50.1%) had OSA. OSA was associated with 2.5 times the risk of 1 year MACCE in patients with DM (22.3% vs 7.1% in the non-OSA group; adjusted HR (HR)=2.49, 95% CI 1.16 to 5.35, p=0.019), but not in patients without DM (8.5% vs 7.7% in the non-OSA group, adjusted HR=0.94, 95% CI 0.51 to 1.75, p=0.85). Patients with DM without OSA had a similar 1 year MACCE rate as patients without DM. The increased risk of events was predominately isolated to patients with OSA with baseline glucose or hemoglobin A1c levels above the median. Combined OSA and longer hypoxia duration (time with arterial oxygen saturation22 min) further increased the MACCE rate to 31.0% in patients with DM. Conclusions: OSA was associated with increased risk of 1 year MACCE following ACS in patients with DM, but not in non-DM patients. Further trials exploring the efficacy of OSA treatment in high-risk patients with ACS and DM are warranted

Impact of multichannel river network on the plume dynamics in the Pearl River estuary

Author Posting. © American Geophysical Union, 2015. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 120 (2015): 5766–5789, doi:10.1002/2014JC010490.Impacts of the multichannel river network on plume dynamics in the Pearl River estuary were examined using a high-resolution 3-D circulation model. The results showed that during the dry season the plume was a distinct feature along the western coast of the estuary. The plume was defined as three water masses: (a) riverine water (22 psu), respectively. A significant amount of low-salinity water from Hengmen and Hongqimen was transported through a narrow channel between the QiAo Island and the mainland of the Pearl River delta during the ebb tide and formed a local salinity-gradient feature (hereafter referred to as a discharge plume). This discharge plume was a typical small-scale river plume with a Kelvin number K = 0.24 and a strong frontal boundary on its offshore side. With evidence of a significant impact on the distribution and variability of the salinity and flow over the West Shoal, this plume was thought to be a major feature of the Pearl River plume during the dry season. The upstream multichannel river network not only were the freshwater discharge sources but also played a role in establishing an estuarine-scale subtidal pressure gradient. This pressure gradient was one of the key dynamical processes controlling the water exchange between discharge and river plumes in the Pearl River estuary. This study clearly showed the role of the river network and estuary interaction on river plume dynamics.The research work was supported by the National Natural Science Foundation of China (grant 41206005), the Ocean Public Welfare Scientific Research Project, State Oceanic Administration of the People's Republic of China (grant 201305019-3) and the CAS Strategic Pilot Science and Technology (XDA11020205). Changsheng Chen's participation was supported by the International Center for Marine Studies, Shanghai Ocean University.2016-02-2

Downwelling wind, tides, and estuarine plume dynamics

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 121 (2016): 4245–4263, doi:10.1002/2015JC011475.The estuarine plume dynamics under a downwelling-favorable wind condition were examined in the windy dry season of the Pearl River Estuary (PRE) using the PRE primitive-equation Finite-Volume Community Ocean Model (FVCOM). The wind and tide-driven estuarine circulation had a significant influence on the plume dynamics on both local and remote scales. Specifically, the local effect of downwelling-favorable winds on the plume was similar to the theoretical descriptions of coastal plumes, narrowing the plume width, and setting up a vertically uniform downstream current at the plume edge. Tides tended to reduce these plume responses through local turbulent mixing and advection from upstream regions, resulting in an adjustment of the isohalines in the plume and a weakening of the vertically uniform downstream current. The remote effect of downwelling-favorable winds on the plume was due to the wind-induced estuarine sea surface height (SSH), which strengthened the estuarine circulation and enhanced the plume transport accordingly. Associated with these processes, tide-induced mixing tended to weaken the SSH gradient and thus the estuarine circulation over a remote influence scale. Overall, the typical features of downwelling-favorable wind-driven estuarine plumes revealed in this study enhanced our understanding of the estuarine plume dynamics under downwelling-favorable wind conditions.National Natural Science Foundation of China Grant Number: (41206005); Ocean Public Welfare Scientific Research Project, State Oceanic Administration of the People's Republic of China Grant Number: (201305019-3)2016-12-2

Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number

Grey target decision model for mixed attributes including real numbers, interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers is complex for its data processing in different ways and information distortion in handling fuzzy numbers. To solve these problems, the binary connection number proposed in set pair analysis is applied to unify different types of index values with their parameters’ average values and standard deviations as determinacy-uncertainty vectors. Then the target center index vectors are determined by the modules of index vectors of all alternatives under different attributes. So the similarity of each index vector and its target center index vector called nearness degree can be calculated. Following, all the nearness degrees are normalized in linear method in order to be compared with each other. Finally, the optimal alternative can be determined by the minimum of all integrated nearness degrees. Case study demonstrated that this approach can not only unify different types of numbers, and simplify the calculation but also reduce the information distortion in operating fuzzy numbers

Concomitant Retrograde Coronary Venous Infusion of Basic Fibroblast Growth Factor Enhances Engraftment and Differentiation of Bone Marrow Mesenchymal Stem Cells for Cardiac Repair after Myocardial Infarction.

AIM: Basic fibroblast growth factor (bFGF) increases the migration and viability of bone marrow mesenchymal stem cells (MSCs) in vitro. Retrograde coronary venous infusion can provide both increased regional bFGF concentrations and homogeneous cell dissemination. We determined whether retrograde delivery of bFGF enhances the potency of transplanted MSCs for cardiac repair in a canine infarct model. METHODS AND RESULTS: Under hypoxic conditions, cellular migration was significantly increased in MSCs co-cultured with bFGF compared to vascular endothelial growth factor or insulin-like growth factor, and bFGF promoted MSCs differentiation into a cardiomyocyte phenotype. A canine infarct model was employed by coronary ligation. One week later, animals were subjected to retrograde infusion of combination bFGF (200ng/mL) and MSCs (1×10(8) cells) (n=5), MSCs (1×10(8) cells, n=5), bFGF (200ng/mL, n=5), or placebo (phosphate-buffered saline, n=3). Four weeks after infusion, only the bFGF+MSCs therapy exhibited significantly increased left ventricular ejection fraction (LVEF) by echocardiography (p CONCLUSIONS: Retrograde coronary venous bFGF infusion augments engraftment and differentiation capacity of transplanted MSCs, recovering cardiac function and preventing adverse remodeling. This novel combined treatment and delivery method is a promising strategy for cardiac repair after ischemic injury

SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation

In this paper, we propose a novel model called SGFormer, Semantic Graph TransFormer for point cloud-based 3D scene graph generation. The task aims to parse a point cloud-based scene into a semantic structural graph, with the core challenge of modeling the complex global structure. Existing methods based on graph convolutional networks (GCNs) suffer from the over-smoothing dilemma and can only propagate information from limited neighboring nodes. In contrast, SGFormer uses Transformer layers as the base building block to allow global information passing, with two types of newly-designed layers tailored for the 3D scene graph generation task. Specifically, we introduce the graph embedding layer to best utilize the global information in graph edges while maintaining comparable computation costs. Furthermore, we propose the semantic injection layer to leverage linguistic knowledge from large-scale language model (i.e., ChatGPT), to enhance objects' visual features. We benchmark our SGFormer on the established 3DSSG dataset and achieve a 40.94% absolute improvement in relationship prediction's R@50 and an 88.36% boost on the subset with complex scenes over the state-of-the-art. Our analyses further show SGFormer's superiority in the long-tail and zero-shot scenarios. Our source code is available at https://github.com/Andy20178/SGFormer.Comment: To be published in Thirty-Eighth AAAI Conference on Artificial Intelligenc

Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning

We address the challenging problem of Long-Tailed Semi-Supervised Learning (LTSSL) where labeled data exhibit imbalanced class distribution and unlabeled data follow an unknown distribution. Unlike in balanced SSL, the generated pseudo-labels are skewed towards head classes, intensifying the training bias. Such a phenomenon is even amplified as more unlabeled data will be mislabeled as head classes when the class distribution of labeled and unlabeled datasets are mismatched. To solve this problem, we propose a novel method named ComPlementary Experts (CPE). Specifically, we train multiple experts to model various class distributions, each of them yielding high-quality pseudo-labels within one form of class distribution. Besides, we introduce Classwise Batch Normalization for CPE to avoid performance degradation caused by feature distribution mismatch between head and non-head classes. CPE achieves state-of-the-art performances on CIFAR-10-LT, CIFAR-100-LT, and STL-10-LT dataset benchmarks. For instance, on CIFAR-10-LT, CPE improves test accuracy by over 2.22% compared to baselines. Code is available at https://github.com/machengcheng2016/CPE-LTSSL.Comment: Accepted by AAAI202