A systematic review on distribution, sources and sorption of perfluoroalkyl acids (PFAAs) in soil and their plant uptake.

Perfluoroalkyl acids (PFAAs) are ubiquitous in environment, which have attracted increasing concerns in recent years. This study collected the data on PFAAs concentrations in 1042 soil samples from 15 countries and comprehensively reviewed the spatial distribution, sources, sorption mechanisms of PFAAs in soil and their plant uptake. PFAAs are widely detected in soils from many countries worldwide and their distribution is related to the emission of the fluorine-containing organic industry. Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are found to be the predominant PFAAs in soil. Industrial emission is the main source of PFAAs contributing 49.9% of the total concentrations of PFAAs (Æ© PFAAs) in soil, followed by activated sludge treated by wastewater treatment plants (WWTPs) (19.9%) and irrigation of effluents from WWTPs, usage of aqueous film-forming foam (AFFFs) and leaching of leachate from landfill (30.2%). The adsorption of PFAAs by soil is mainly influenced by soil pH, ionic strength, soil organic matter and minerals. The concentrations of perfluoroalkyl carboxylic acids (PFCAs) in soil are negatively correlated with the length of carbon chain, log Kow, and log Koc. The carbon chain lengths of PFAAs are negatively correlated with the root-soil concentration factors (RCFs) and shoot-soil concentration factors (SCFs). The uptake of PFAAs by plant is influenced by physicochemical properties of PFAAs, plant physiology and soil environment. Further studies should be conducted to make up the inadequacy of existing knowledge on the behavior and fate of PFAAs in soil-plant system.

Fully automatic tumor segmentation of breast ultrasound images with deep learning.

BACKGROUND: Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis systems. While the majority of clinical BUS scans are normal ones without tumors, segmentation approaches such as U-Net often predict mass regions for these images. Such false-positive problem becomes serious if a fully automatic artificial intelligence system is used for routine screening. METHODS: In this study, we proposed a novel model which is more suitable for routine BUS screening. The model contains a classification branch that determines whether the image is normal or with tumors, and a segmentation branch that outlines tumors. Two branches share the same encoder network. We also built a new dataset that contains 1600 BUS images from 625 patients for training and a testing dataset with 130 images from 120 patients for testing. The dataset is the largest one with pixel-wise masks manually segmented by experienced radiologists. Our code is available at https://github.com/szhangNJU/BUS_segmentation. RESULTS: The area under the receiver operating characteristic curve (AUC) for classifying images into normal/abnormal categories was 0.991. The dice similarity coefficient (DSC) for segmentation of mass regions was 0.898, better than the state-of-the-art models. Testing on an external dataset gave a similar performance, demonstrating a good transferability of our model. Moreover, we simulated the use of the model in actual clinic practice by processing videos recorded during BUS scans; the model gave very low false-positive predictions on normal images without sacrificing sensitivities for images with tumors. CONCLUSIONS: Our model achieved better segmentation performance than the state-of-the-art models and showed a good transferability on an external test set. The proposed deep learning architecture holds potential for use in fully automatic BUS health screening.

Heavy metals in paired samples of hair and nails in China: occurrence, sources and health risk assessment.

The occurrence of heavy metals including chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd) and lead (Pb) was investigated in paired samples of hair and nails collected from 121 volunteers in 16 cities, China. Results showed that the mean concentrations of Zn, Cu, As, Pb, Cr, Ni and Cd were 205, 18.0, 7.79, 6.18, 3.54, 2.02, 0.533 µg g-1 in hair and 103, 8.09, 0.760, 7.27, 6.07, 8.81, 0.485 µg g-1 in nails, respectively. The concentrations of Zn, Ni, Cr, Cd and Pb were positively correlated in paired samples of hair and nails, whereas a negative correlation was found for Cu and As between hair and nails. Higher concentrations of heavy metals were found in northern China than southern China. The multivariate analysis of variance revealed that dwelling environment was the dominant factor influencing the levels of Cd in hair (p < 0.05), while age was the dominant factor influencing the levels of Cr in nails (p < 0.05). Moreover, industrial pollution and smoking were also the important factors leading to the accumulation of heavy metals in human body. Principal component analysis (PCA) showed that industrial pollution and decoration material immersion were the main factors for the high concentrations of Cr and Ni in hair, accounting for 62.9% of the total variation; As in hair was dominantly related to groundwater pollution. The concentrations of heavy metals were within the recommended ranges in nails from this study. However, the mean levels of Cr, Ni and As in hair exceeded their recommended reference values, indicating potential health risks from heavy metals for residents in China.

A nationwide survey of polycyclic aromatic hydrocarbons (PAHs) in household dust in China: spatial distribution, sources, and health risk assessment.

As a carrier of toxic substances, household dust has a great impact on human health. Here we collected 73 household dust samples from 27 provinces and 1 municipality in China to investigate the levels, spatial distribution, sources, and carcinogenic risk of 16 polycyclic aromatic hydrocarbons (PAHs). The total concentrations of 14 detected PAHs (∑14 PAHs) ranged from 3.72 to 60,885 ng g-1. High ∑14 PAHs were found in Northeast and Southwest China. High molecular weights (HMW) PAHs (4-6 rings) were predominant PAHs in most dust samples, accounting for 93.6% of ∑14 PAHs. Household fuel, cooking frequency, air conditioning, and smoking were the main factors influencing PAH concentrations in household dust. Principal component analysis model indicated that fossil combustion (81.5%) and biomass combustion and vehicle exhaust (8.1%) are the primary sources of PAHs. Positive matrix factorization model suggested that household cooking and heating contributed about 70% of ∑14 PAHs, and smoking contributed another 30%. The values of benzo[a]pyrene equivalent in rural dust were found to be higher than those in urban dust. The sum of toxic equivalents (TEQs) of 14 PAHs were in range of 0.372-7241 ng g-1, in which 7 HMW PAHs accounted for 98.0 ± 1.98% of the total TEQs. Monte Carlo Simulation showed a low to moderate potential carcinogenic risk of PAHs in household dusts. This study documents comprehensive information on human exposure to PAHs in household dust at a national-scale.

Heavy metals in road dust across China: occurrence, sources and health risk assessment.

We investigated the occurrence of Cd, Cr, Cu, Ni, Pb and Zn in 28 road dust samples collected across China from June to August, 2020. The mean concentrations of Cd, Cr, Cu, Ni, Pb and Zn were 3.16, 24.2, 27.4, 10.4, 49.8 and 608 mg·kg- 1, respectively. The mean levels of Cd and Zn exceeded the Chinese background values by 32.6- and 8.2- fold. Cd, Ni mainly distributed in southern China, whereas Cu, Pb and Zn mainly distributed in central China. Higher concentrations of Cd, Cr, Cu and Pb were found in road dusts from urban areas than those from rural areas. Cu and Ni mainly came from natural sources; Pb and Cd mainly originated from industrial emissions and vehicle exhaust. Hand-mouth ingestion was the most common exposure pathway for both adults and children, followed by dermal contact and inhalation. Pb was found to be the highest risk element via ingestion. No significant non-carcinogenic risks and carcinogenic risks were found for local residents.

Comparison of phthalate esters (PAEs) in freshwater and marine food webs: Occurrence, bioaccumulation, and trophodynamics.

Phthalate esters (PAEs) have received widespread attentions due to their ubiquity in various kinds of matrices and potential biotoxicity. This study systematically compared the concentrations, bioaccumulation, trophodynamics and health risk of PAEs in 25 species (n = 225) collected from a marine (Bohai Bay, BHB) and freshwater environment (Songhua River, SHR), China. Results showed that di-(2-ethylhexyl) phthalate and di-n-butyl phthalate were the predominant PAEs in the organisms from the two aquatic environments. The total concentrations of 6 PAEs in algae and fish from SHR were significantly higher than those from BHB. Two food webs were constructed in BHB and SHR based on the abundance of 15N in the organisms. All the PAEs except dimethyl phthalate exhibited trophic dilution with the trophic magnification factors less than 1. Moreover, an obvious biodilution of PAEs was observed in marine food web compared to freshwater food web. A low health risk of PAEs was found in organisms from both BHB and SHR. However, di-(2-ethylhexyl) phthalate exhibited a potential carcinogenic risk by consumption of some benthos in BHB and fish in SHR. This study provides a valuable perspective for understanding the trophodynamics and health risk of PAEs in marine and freshwater environments.

Trophodynamics and bioaccumulation of polycyclic aromatic hydrocarbons (PAHs) in marine food web from Laizhou Bay, China.

Here, we collected 16 species (n = 298) from Laizhou Bay, China to investigate the trophodynamics, bioaccumulation and cancer risks of polycyclic aromatic hydrocarbons (PAHs). Results demonstrated that naphthalene was the most abundant PAH, followed by phenanthrene and fluorene in the marine organisms. The sum of 16 PAHs concentrations (Æ©16PAHs) ranked with algae (19,435 ng·g-1 lipid weight, lw) > benthonic animals (6599 ng·g-1 lw) > fish (1760 ng·g-1 lw). Combustion and oil spill are two primary sources, contributing 60.3 % and 39.7 % of Æ©16PAHs, respectively. High values of log BAF were found for 4-6 rings PAHs. Algae and benthonic animals showed a high ability to accumulate 2-4 rings PAHs and 5-6 rings PAHs, respectively. A biodilution pattern for PAHs was found in the marine food web. The carcinogenic risks of some benthos and fish were higher than 1 × 10-6, threatening resident health by consumption of these seafoods.

Organophosphate esters (OPEs) and novel brominated flame retardants (NBFRs) in indoor dust: A systematic review on concentration, spatial distribution, sources, and human exposure.

In recent years, the indoor exposure of organophosphate esters (OPEs) and novel brominated flame retardants (NBFRs) has received widespread attention worldwide. Using published data on 6 OPEs in 23 countries (n = 1437) and 2 NBFRs in 18 countries (n = 826) in indoor dust, this study systematically reviewed the concentrations, spatial distribution, sources and exposure risk of 8 flame retardants (FRs) worldwide. Tris(chloroisopropyl)phosphate (TCIPP) is the predominant FR with a median concentration of 1050 ng g-1 ΣCl-OPEs are significantly higher than Σnon-Cl-OPEs (p < 0.05). ΣOPEs in indoor dust from industrially-developed countries are higher than those from the countries lacking industrial development. Household appliances, electronics and plastic products are the main sources of non-Cl-OPEs and NBFRs, while interior decorations and materials contribute abundant Cl-OPEs in indoor dust. The mean hazard index (HI) of TCIPP for children is greater than 1, possibly posing non-cancer risk for children in some countries. The median ILCRs for 3 carcinogenic OPEs are all less than 10-6, suggesting no cancer risk induced by these compounds for both adults and children. This review helps to understand the composition, spatial pattern and human exposure risk of OPEs and NBFRs in indoor dust worldwide.

Heavy metals in marine food web from Laizhou Bay, China: Levels, trophic magnification, and health risk assessment.

Heavy metals in ocean may accumulate in seafood through food web and pose risks to human health. This study investigated the occurrence, trophic magnification, and health risks of 7 heavy metals in 20 marine organisms (n = 222) in Laizhou Bay (LZB), China. Results showed that Zn was the most abundant metal, followed by Cu, As, Cd, Cr, Ni and Pb. The total concentrations of 7 heavy metals in the organisms ranked in the order of crab ˃ shellfish ˃ algae ˃ fish ˃ starfish. Interspecific differences were found in the concentrations of Cr, Ni, Cu and Cd in marine organisms from LZB. Crab and shellfish showed much higher enrichment ability of heavy metals than that of algae, starfish and fish. Cd is the most biological accumulated element with the mean biota-sediment accumulation factor (BSAF) of 12.9. Stable isotope analysis showed a significant difference of δ15N among these five species (p < 0.01), and a food web was constructed accordingly. A biodilution pattern was found for Pb, As and Ni and no trophic interference in metal uptake was observed for Zn, Cu, Ni and Cr in the food web of LZB. The estimated daily intake (EDI) and target hazard quotients (THQs) of As and Cd indicated an adverse health effect on consumption of the seafood. The mean lifetime cancer risks (LCRs) for Cd and As suggested a potential carcinogenic effect on consumption of these seafood. This study provides a basis for health risk assessment of heavy metals in marine foods.

Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification.

Objective. Motor imagery (MI) electroencephalography (EEG) classification is regarded as a promising technology for brain-computer interface (BCI) systems, which help people to communicate with the outside world using neural activities. However, decoding human intent accurately is a challenging task because of its small signal-to-noise ratio and non-stationary characteristics. Methods that directly extract features from raw EEG signals ignores key frequency domain information. One of the challenges in MI classification tasks is finding a way to supplement the frequency domain information ignored by the raw EEG signal.Approach. In this study, we fuse different models using their complementary characteristics to develop a multiscale space-time-frequency feature-guided multitask learning convolutional neural network (CNN) architecture. The proposed method consists of four modules: the space-time feature-based representation module, time-frequency feature-based representation module, multimodal fused feature-guided generation module, and classification module. The proposed framework is based on multitask learning. The four modules are trained using three tasks simultaneously and jointly optimized.Results. The proposed method is evaluated using three public challenge datasets. Through quantitative analysis, we demonstrate that our proposed method outperforms most state-of-the-art machine learning and deep learning techniques for EEG classification, thereby demonstrating the robustness and effectiveness of our method. Moreover, the proposed method is employed to realize control of robot based on EEG signal, verifying its feasibility in real-time applications.Significance. To the best of our knowledge, a deep CNN architecture that fuses different input cases, which have complementary characteristics, has not been applied to BCI tasks. Because of the interaction of the three tasks in the multitask learning architecture, our method can improve the generalization and accuracy of subject-dependent and subject-independent methods with limited annotated data.