Distribution and health risk of chromium in wheat grains at the national scale in China.

Chromium (Cr) pollution may threaten food safety in China. In this study, the concentration, pollution level, distribution, and non-cancer risk of Cr in wheat grains grown in 186 areas across 28 provinces in China were investigated. Results indicated that mean concentration of Cr was 0.28 ± 2.5 mg/kg, dry mass (dm). Of the samples, 7.5 % were found to be polluted with Cr. The mean concentrations were in the following order: Northwest > Northeast > South > East > North > Southwest > Central China. Based on deterministic models, mean hazard quotient (HQ) values for adult males, adult females, and children were 0.11 ± 3.4, 0.11 ± 3.4, and 0.13 ± 3.5, respectively with < 6 % of HQ values ≥ 1. Eleven sites in northern China were identified as hotspots, whereas Gansu Province and Northwestern China were labeled as priority provinces and regions for risk control. The mean HQ values estimated by probabilistic risk assessment were two times greater than those estimated using deterministic models. The risk probabilities for adult males, adult females, and children were 4.81 %, 3.78 %, and 6.55 %, respectively. This study provides valuable information on Cr pollution in wheat grains and its risks at a national scale in China.

Self-Supervised Motion Perception for Spatiotemporal Representation Learning.

In this study, we propose a novel pretext task and a self-supervised motion perception (SMP) method for spatiotemporal representation learning. The pretext task is defined as video playback rate perception, which utilizes temporal dilated sampling to augment video clips to multiple duplicates of different temporal resolutions. The SMP method is built upon discriminative and generative motion perception models, which capture representations related to motion dynamics and appearance from video clips of multiple temporal resolutions in a collaborative fashion. To enhance the collaboration, we further propose difference and convolution motion attention (MA), which drives the generative model focusing on motion-related appearance, and leverage multiple granularity perception (MG) to extract accurate motion dynamics. Extensive experiments demonstrate SMP's effectiveness for video motion perception and state-of-the-art performance of self-supervised representation models upon target tasks, including action recognition and video retrieval. Code for SMP is available at github.com/yuanyao366/SMP.