Robust Texture-Aware Computer-Generated Image Forensic: Benchmark and Algorithm.

With advances in rendering techniques and generative adversarial networks, computer-generated (CG) images tend to be indistinguishable from photographic (PG) images. Revisiting previous works towards CG image forensic, we observed that existing datasets are constructed years ago and limited in both quantity and diversity. Besides, current algorithms only consider the global visual features for forensic, ignoring finer differences between CG and PG images. To mitigate these problems, we first contribute a Large-Scale CG images Benchmark (LSCGB), and then propose a simple yet strong baseline model to address the forensic task. On the one hand, the introduced benchmark has three superior properties, 1) large-scale: the benchmark contains 71168 CG and 71168 PG images with the corresponding expert-annotated labels. It is orders of magnitude bigger than previous datasets. 2) high diversity: we collect CG images from 4 different scenes generated by various rendering techniques. The PG images are varied in terms of image content, camera models, and photographer styles. 3) small bias: we carefully filter the collected images to ensure that the distributions of color, brightness, tone and saturation between CG and PG images are close. Furthermore, inspired by an empirical study on texture difference between CG and PG images, an effective texture-aware network is proposed to improve forensic accuracy. Concretely, we first strengthen texture information of multilevel features extracted from a backbone. Then, the relations among feature channels are explored by learning its gram matrix. Each feature channel represents a specific texture pattern. The gram matrix is thus able to embed the finer texture differences. Experimental results demonstrate that this baseline surpasses the existing methods. The benchmark is publically available at https://github.com/wmbai/LSCGB.

Assessment of heavy metal contamination in surface sediments from the nearshore zone, southern Jiangsu Province, China.

We determined the heavy metal concentrations and evaluated the pollution status of 213 surface sediment samples collected from the nearshore zone of southern Jiangsu Province, China. The distributions of lead (Pb) and zinc (Zn) were similar, and their concentrations were highest in tidal creeks dominated by fine-grained sediments, such as the Dawanhong and Wangcanghong creeks and the outer part of Lanshayang Creek. The spatial distributions of chromium (Cr) and cadmium (Cd) were similar, with high concentrations south of Yangkou Port and Haozhi Port. Copper (Cu) concentrations were high off the coast of Rudong. The sediments were not polluted by Cu, but were weakly polluted by Cd, Cr, Pb, and Zn. The results of principal component analysis indicated that Pb and Zn were mainly from natural sources, Cd and Cr were from anthropogenic sources, and Cu was from a mixture of natural and anthropogenic sources.

Two-dimensional strain ultrasound speckle tracking as a novel approach for the evaluation of right hemidiaphragmatic longitudinal deformation.

The diaphragm is an important respiratory organ. The aim of this study was to quantitatively evaluate the longitudinal deformation of the right hemidiaphragm in normal subjects using two-dimensional strain ultrasound speckle tracking. Twenty-one healthy subjects were enrolled in this study. GE Healthcare Vivid E9 equipment with M5S probe and Q-analysis software were used. Negative strain values first appeared in the zone of apposition and then in the crura of the right hemidiaphragm in the inspiratory phase; the dome of the diaphragm was observed to be passively stretched. The longitudinal strain of the right hemidiaphragm in the zone of apposition was higher than that in the crura in forced breathing (P=0.024). The strains of the whole diaphragm and the zone of apposition changed significantly in quiet (P=0.000) and forced breathing (P=0.005). Ultrasound strain imaging may quantitatively assess diaphragm deformation and provide another useful modality for evaluating diaphragm kinetics.

Vasodilator effect of gaseous sulfur dioxide and regulation of its level by Ach in rat vascular tissues.

To explore the toxicological and physiological role of gaseous SO(2) on vascular contractility and its level in vascular tissues, a vasodilation study of isolated rat thoracic aortic rings by gaseous SO(2) was carried out. The level of SO(2) in vascular tissue was assayed using a modified high-performance liquid chromatographic method with fluorescence detection (HPLC-FD). The results show that gaseous SO(2) (from 1 microM to 2000 microM) relaxed rat thoracic aortic rings in a dose-dependent manner. The physiological concentrations of SO(2) in thoracic aortic tissues and plasma in rats were 127.76 +/- 31.34 microM and 16.77+/-8.24 microM, respectively; The vasorelaxant effect of gaseous SO(2) at physiological and low concentrations (<450 microM) was endothelium dependent, and at high concentrations (>500 microM) was endothelium independent. The results also show that SO(2) could be endogenously generated in vascular tissues, and mainly in vascular endothelial cells; acetylcholine (Ach) increased the SO(2) level in vascular tissue, and noradrenaline (NE) decreased the SO(2) level. These findings demonstrate that gaseous SO(2) is a vasorelaxant substance, and the vasorelaxant effect of gaseous SO(2) is much stronger than that of its derivatives sulfite and bisulfite, which result from the inactivation process of SO(2) gas transmitter by which SO(2) is hydrated to form sulfite, and the latter is enzymatically oxidized to form sulfate. These findings also demonstrate that endogenous SO(2) level in vascular tissue may be regulated by Ach and NE.