Construction of a Chinese traditional instrumental music dataset: A validated set of naturalistic affective music excerpts.

Music is omnipresent among human cultures and moves us both physically and emotionally. The perception of emotions in music is influenced by both psychophysical and cultural factors. Chinese traditional instrumental music differs significantly from Western music in cultural origin and music elements. However, previous studies on music emotion perception are based almost exclusively on Western music. Therefore, the construction of a dataset of Chinese traditional instrumental music is important for exploring the perception of music emotions in the context of Chinese culture. The present dataset included 273 10-second naturalistic music excerpts. We provided rating data for each excerpt on ten variables: familiarity, dimensional emotions (valence and arousal), and discrete emotions (anger, gentleness, happiness, peacefulness, sadness, solemnness, and transcendence). The excerpts were rated by a total of 168 participants on a seven-point Likert scale for the ten variables. Three labels for the excerpts were obtained: familiarity, discrete emotion, and cluster. Our dataset demonstrates good reliability, and we believe it could contribute to cross-cultural studies on emotional responses to music.

Microplastics in infant milk powder.

Microplastics can be found almost everywhere and extensively expose to human. Infants are vulnerable, and the potential risks of microplastics may be greater for infants, however, infants' microplastic exposure is still poorly understood. Since milk powder makes up a significant portion of many infants' diets, we investigated microplastic pollution in infant milk powder and calculated the microplastic exposure from milk powder, feeding bottles and milk powder preparation. On one hand, we studied 13 different types of milk powder with different packaging, processing systems, and milk sources. The boxed milk powder (7 ± 3 items/100 g) was more polluted with microplastics than the canned milk powder (4 ± 3 items/100 g). The inner packaging of the boxed milk powder was plastic and aluminum foil laminated, it emitted 8 ± 2 to 17 ± 1 items/100 g of microplastics, and might be a main source of microplastics in boxed milk powder. On the other hand, we found that when infants consume milk powder, the microplastic exposure from milk powder itself is little, exposure from feeding bottles is 6.8 times higher than that from milk powder and milk powder preparation is 1.7 times higher.

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake.

There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (CODMn), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R2 value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.

Metabolic impacts of polystyrene microplastics on the freshwater microalga Microcystis aeruginosa.

Microplastics (plastic particles < 5 mm; MPs) are ubiquitous in aquatic environments but their potential adverse ecological effects on biota remain poorly understood. This is in part because in typical ecotoxicology tests the toxic effects of MPs were found to be limited. To capture the potential find-scale effects of MPs on freshwater organisms, we employed ultra-performance liquid chromatography-tandem mass spectrometry based untargeted metabolomics to investigate the metabolic impact of polystyrene microbeads microplastics (PS-MPs) of different sizes (0.1, 1, 10, 100 µm) and concentrations (1, 10, 100 mg/L) on a common freshwater microalga, Microcystis aeruginosa, after a 96-h exposure test. The phenotype-based results illustrated that while PS-MPs had no discernible effects on microalgal growth and photosynthesis, both oxidative stress and microcystin production were slightly increased. Metabolomics analysis revealed that the PS-MPs altered the global metabolic profile of the microalga. Specially, PS-MPs of larger size and higher concentration induced a larger number of differentially expressed metabolites. The PS-MPs significantly disturbed metabolisms involved in amino acid synthesis, membrane formation, nitrogen storage, and antioxidant defense of the microalga, consistent with the phenotypic observations. These results suggested several perturbed metabolic pathways, especially arginine-related pathways, as the mechanism. Our study showed that the insights provided by metabolomics-based approaches can enhance assessments of the ecological impacts of MPs on freshwater organisms.

Spatiotemporal dynamics of microplastics in an urban river network area.

Microplastics contamination in the environment is a global problem, but little is known about their dynamics in urban river networks, an important site of microplastics occurrence and harboring complex transport pathways. In this study, we investigated the spatiotemporal dynamic of microplastics in a typical urban river network in eastern China from December 2018 to September 2019. microplastics abundance (mean ± standard deviation) in the river network ranged from 2.3 ± 1.2 to 104.6 ± 5.6 particles/L and was significantly higher during the wet than during the dry season. The distribution of microplastics in the upper, middle, and lower reaches of the river network did not significantly differ, nor did the abundance of microplastics in the surface water vs. the bottom water. However, high abundances were determined in commercial and industrial areas, at a wastewater treatment plant outlet, in an urban canal, and in an urban-rural fringe area. The seasonal dynamics of the overall abundance of microplastics could be explained by the hysteresis effect of urban plastic production and the variation in regional precipitation. 78.2% of the microplastics were < 330 µm in size; the most common colors were blue and black, and the most common shapes were fragments and fibers. The polymer types of the microplastics were assessed using laser direct infrared (LDIR), a novel chemical imaging system that identified silicone, rubber, polytetrafluoroethylene, and polypropylene as the main components of the microplastics. A non-metric multidimensional scaling analysis (NMDS) based on the abundance of the polymer components across samples showed aggregations of sampling sites, that indicated the possible sources of the microplastics. Our study provides insights into the spatiotemporal dynamics of microplastics in an urban river network and suggests the potential of LDIR in the accurate quantitative analysis of microplastics in the environment.

Distribution and sedimentation of microplastics in Taihu Lake.

Microplastics have been reported in environmental media for decades, but gaps in our knowledge about them still remain. We investigated the third biggest freshwater lake in China - Taihu Lake - and the 30 major rivers around it. Microplastics were detected in lake water and sediment, and in river water, at abundances varying from 1.7 to 8.5 items/L, 460 to 1380 items/kg and 1.8 to 18.2 items/L, respectively. Inflow rivers were more polluted with microplastics than outflow rivers. The most common shape was fragment. Microplastic sizes of <100 µm dominated in inflow rivers, 100-200 µm dominated in lake water and outflow rivers. The average size of microplastics in outflow rivers (200.4 µm) was larger than that in inflow rivers (166.2 µm). Microplastics of <100 µm only accounted for 28% in the lake surface water but were as high as 70% in the sediment, indicating that smaller microplastics may more easily settle in the lake. The main components of the microplastics were identified as being polyvinyl chloride and polyethylene. There were about 1.2E06 items/s microplastics entered Taihu Lake. Four main rivers located at northwestern lake accounted for 79% of the total inflow microplastic fluxes.