Occurrences and distribution characteristics of organophosphate ester flame retardants and plasticizers in the sediments of the Bohai and Yellow Seas, China

Concentrations and distribution characteristics of organophosphate esters (OPEs) in surface sediment samples were analyzed and discussed for the first time in the open Bohai Sea (BS) and YellowSea (YS). Three halogenated OPEs [ tris-(2-chloroethyl) phosphate (TCEP), tris-(1-chloro-2-propyl) phosphate (TCPP), and tris-(1,3-dichloro2- propyl) phosphate (TDCPP)] and five non-halogenated OPEs [ tri-isobutyl phosphate (TiBP), tri-n-butyl phosphate (TnBP), tripentyl phosphate (TPeP), triphenyl phosphate (TPhP) and tris-(2-ethylhexyl) phosphate (TEHP)] were detected in this region. The concentrations of eight OPEs in total (Sigma 8OPEs) ranged from 83 to 4552 pg g(-1) dry weight (dw). The halogenated OPEs showed higher abundances than the non-halogenated ones did, with TCEP, TCPP, and TEHP the main compounds. Generally, concentrations of OPEs in the BS were higher than those in the YS. Riverine input (mainly the Changjiang DilutedWater (CDW)) and deposition effect in the mud areas might have influenced the spatial distributions of OPEs. Correlation between OPE concentrations and total organic carbon (TOC) indicated TOC was an effective indicator for the distribution of OPEs. Inventory analysis of OPEs implied that sea sediment might not be the major reservoir of these compounds. (C) 2017 Elsevier B.V. All rights reserved.</p

Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation

The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image. This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM), which introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Technically, we design a fine-grained cross-modal alignment module to well explore the sequence similarity between the two modalities for learning robust feature representations. To improve the generalization ability, we propose a contrast-augmented diffusion model to explicitly explore positive and negative samples by maximizing a novel contrastive variational objective, which is mathematically inferred to provide a tighter bound for the model's optimization. Moreover, the context-aware cross attention module is developed to capture the contextual information within markup language during the denoising process, yielding better noise prediction results. Extensive experiments are conducted on four benchmark datasets from different domains, and the experimental results demonstrate the effectiveness of the proposed components in FSA-CDM, significantly exceeding state-of-the-art performance by about 2%-12% DTW improvements. The code will be released at https://github.com/zgj77/FSACDM.Comment: Accepted to ACM MM 2023. The code will be released at https://github.com/zgj77/FSACD

Dynamic control of spin wave spectra using spin-polarized currents

We describe a method of controlling the spin wave spectra dynamically in a uniform nanostripe waveguide through spin-polarized currents. A stable periodic magnetization structure is observed when the current flows vertically through the center of nanostripe waveguide. After being excited, the spin wave is transmitted at the sides of the waveguide. Numerical simulations of spin-wave transmission and dispersion curves reveal a single, pronounced band gap. Moreover, the periodic magnetization structure can be turned on and off by the spin-polarized current. The switching process from full rejection to full transmission takes place within less than 3ns. Thus, this type magnonic waveguide can be utilized for low-dissipation spin wave based filters.Comment: 10 pages, 5 figures, submitted to AP

COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization

Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary is always evaluated from summary-level which leads to the inconsistency in training and evaluation. In this paper, we propose a Contrastive Learning based re-ranking framework for one-stage summarization called COLO. By modeling a contrastive objective, we show that the summarization model is able to directly generate summaries according to the summary-level score without additional modules and parameters. Extensive experiments demonstrate that COLO boosts the extractive and abstractive results of one-stage systems on CNN/DailyMail benchmark to 44.58 and 46.33 ROUGE-1 score while preserving the parameter efficiency and inference efficiency. Compared with state-of-the-art multi-stage systems, we save more than 100 GPU training hours and obtaining 3~8 speed-up ratio during inference while maintaining comparable results.Comment: Accepted by COLING 202

Association of rs10811656 on 9P21.3 with the risk of coronary artery disease in a Chinese population

BACKGROUND: Genome-wide association studies have reported that the 9p21.3 locus confers risk for coronary artery disease (CAD). However, it is not known whether rs10811656 is linked with CAD in a Chinese population. Thus, the purpose of this study was to investigate the potential association between rs10811656 and the risk of CAD in a Chinese population. METHODS: We conducted a hospital-based, case–control study with 251 CAD patients and 304 controls to examine the potential association of rs10811656 with CAD. RESULTS: The frequencies of the TT genotypes in CAD cases were significantly different from those in controls (adjusted OR: 1.96, 95 % CI: 1.09–3.505, P = 0.024). Compared to controls, rs10811656 was significantly associated with the stable angina pectoris (adjusted OR: 1.42, 95 % CI: 1.06–1.90, P = 0.017), but not with acute coronary syndrome. There was also a highly significant association of rs10811656 with double-vessel and triple-vessel disease when patients were divided into subgroups based on the number of diseased vessels (adjusted OR: 1.68 and 1.60, 95 % CI: 1.14–2.44 and 1.10–2.33, P = 0.009 and 0.02, respectively). CONCLUSION: Our results suggest that the rs10811656 locus might be associated with CAD in a Chinese Han population