[Pollution Characteristics and Risk Assessment of Heavy Metals in Surface Dusts and Surrounding Green Land Soils from Yellow River Custom Tourist Line in Lanzhou].

In order to investigate the contamination levels of dust and its surrounding green land soil heavy metal pollution and potential ecological and health risks in the scenic areas of urban waterfront parks, the gardens, squares, and theme parks of the Yellow River Custom Tourist Line in Lanzhou were selected as the research area, using 27 dust samples and 26 soil samples from its surrounding green lands. The contamination characteristics and potential ecological risks of eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb) were evaluated using the geo-accumulation index (Igeo), single-factor pollution index (Pi), Nemerow integrated pollution index (PN), and improved potential ecological risk index (RI). The human health risk assessment was also evaluated using the exposure risk model. The results showed that the average concentrations of the other heavy metals in the surface dusts were higher than the background values of Gansu Province and Lanzhou City, except that the As mean concentrations in the surface dusts and the surrounding green land soils were slightly lower than the Gansu Province background values. For its surrounding green land soils, the mean concentrations of the other heavy metals (Cu, Zn, Cd, Hg, and Pb) exceeded the soil background values of Gansu Province and Lanzhou City, whereas the Cr and Ni mean concentrations were lower than their corresponding soil background values of Gansu Province and Lanzhou City. The geo-accumulation and single-factor pollution indices demonstrated that a slight to moderate pollution of Cr, Cu, Zn, Cd, Hg, and Pb occurred in surface dusts, and Cu, Zn, Cd, Hg, and Pb appeared in varying degrees of contamination levels in its surrounding green land soils. The Nemerow integrated pollution index analysis manifested that the overall contamination status of the study areas was between slightly and heavily polluted. The potential ecological risk index suggested that Cd and Hg were recognized as significant pollutant elements and that the RI of the other heavy metals were all below 40, presenting slight ecological risk. The health risk assessment indicated that ingestion was the dominant exposure pathway for heavy metals from the surface dusts and the surrounding green land soils, and no carcinogenic and noncarcinogenic risks posed threats to adults and children.

Low-Cost Gel Polymer Electrolyte for High-Performance Aluminum-Ion Batteries.

The classical AlCl3/imidazole-chloride-salt ionic liquid electrolytes are expensive, corrosive, and environmentally sensitive, which limit the large-scale application of aluminum-ion batteries. Herein, a gel polymer electrolyte is prepared through a facile process using a low-cost AlCl3/Et3NHCl ionic liquid as the plasticizer and polyamide as the polymer matrix. The gel polymer electrolyte achieves a decent ionic conductivity of 3.86 × 10-3 S cm-1, a wide electrochemical stability window of 2.6 V (vs Al), and long-term interfacial stability at room temperature. The assembled Al//graphite battery delivers considerable rate capability and excellent cycling performance. Besides, the gel polymer electrolyte can alleviate both moisture sensitivity and leakage corrosion issues owing to the full encapsulation of the ionic liquid by polyamide polymeric matrix. The gel polymer electrolyte should offer great potential for aluminum-ion battery applications.

Multiple metal(loid)s bioaccessibility from cooked seafood and health risk assessment.

Seafood has been generally considered to be the main diet exposure source of metal(loid)s. We evaluated health risk of mercury (Hg), arsenic (As), cadmium (Cd), lead (Pb), chromium (Cr), nickel (Ni), copper (Cu), and zinc (Zn) through consumption of cooked seafood based on bioaccessibility, which was obtained by physiologically based extraction test method. Results showed that cooking practices could decrease metal(loid)s concentration from seafood (by 6.0-45.7%). Metal(loid)s release from seafood in this study followed the descending order of Hg > Zn > Ni > Cd > Pb > As > Cu > Cr. On average, cooking lowered the bioaccessibility of As, Hg, Cd, Pb, Ni, Cr, Cu, and Zn by 15.2, 26.1, 30.9, 30.7, 25.7, 31.2, 17.6, and 22.4%, respectively. Health risk calculation results showed that Cr, Ni, and Zn in seafood species in this study were within the human health benefits range. Hg, Cd, Pb, and Cu exposure from cooked seafood was within the safe dose. However, we found that there is a potential of having cancer (especially bladder and lung cancer) for people exposure to iAs from seafood based on bioaccessible contents the first time.

Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment.

Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC.

Predictive models of resting state networks for assessment of altered functional connectivity in MCI.

Due to the difficulties in establishing accurate correspondences of brain network nodes across individual subjects, systematic elucidation of possible functional connectivity (FC) alterations in mild cognitive impairment (MCI) compared with normal controls (NC) is a challenging problem. To address this challenge, in this paper, we develop and apply novel predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and DTI data to assess large-scale FC alterations in MCI. Our rationale is that some RSNs in MCI are substantially altered and can hardly be directly compared with those in NC. Instead, structural landmarks derived from DTI data are much more consistent and correspondent across MCI/NC brains, and therefore can be employed to encode RSNs in NC and serve as the predictive models of RSNs for MCI. To derive these predictive models, RSNs in NC are constructed by group-wise ICA clustering and employed to functionally annotate corresponding structural landmarks. Afterwards, these functionally-annotated structural landmarks are predicted in MCI based on DTI data and used to assess FC alterations in MCI. Experimental results demonstrated that the predictive models of RSNs are effective and can comprehensively reveal widespread FC alterations in MCI.