A human health risk assessment of rare earth elements in soil and vegetables from a mining area in Fujian Province, Southeast China

AbstractContaminated food through dietary intake has become the main potential risk impacts on human health. This study investigated concentrations of rare earth elements (REEs) in soil, vegetables, human hair and blood, and assessed human health risk through vegetables consumption in the vicinity of a large-scale mining area located in Hetian Town of Changting County, Fujian Province, Southeast China. The results of the study included the following mean concentrations for total and bio-available REEs of 242.92±68.98 (135.85–327.56)μgg−1 and 118.59±38.49 (57.89–158.96)μgg−1 dry weight (dw) in agricultural soil, respectively, and total REEs of 3.58±5.28 (0.07–64.42)μgg−1 dw in vegetable samples. Concentrations of total REEs in blood and hair collected from the local residents ranged from 424.76 to 1274.80μgL−1 with an average of 689.74±254.25μgL−1 and from 0.06 to 1.59μgg−1 with an average of 0.48±0.59μgg−1 of the study, respectively. In addition, a significant correlation was observed between REEs in blood and corresponding soil samples (R2=0.6556, p<0.05), however there was no correlation between REEs in hair and corresponding soils (p>0.05). Mean concentrations of REEs of 2.85 (0.59–10.24)μgL−1 in well water from the local households was 53-fold than that in the drinking water of Fuzhou city (0.054μgL−1). The health risk assessment indicated that vegetable consumption would not result in exceeding the safe values of estimate daily intake (EDI) REEs (100−110μgkg−1d−1) for adults and children, but attention should be paid to monitoring human beings health in such rare earth mining areas due to long-term exposure to high dose REEs from food consumptions

Multi-Channel Deep Networks for Block-Based Image Compressive Sensing

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions recently. As deep network approaches learn the inverse mapping directly from the CS measurements, a number of models have to be trained, each of which corresponds to a sampling rate. This may potentially degrade the performance of image CS, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement of the model is attributed to block-based sampling rates allocation and model-level removal of blocking artifacts. Specifically, the image blocks with a variety of sampling rates can be reconstructed in a single model by exploiting inter-block correlation. At the same time, the initially reconstructed blocks are reassembled into a full image to remove blocking artifacts within the network by unrolling a hand-designed block-based CS algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics, PSNR, SSIM, and subjective visual quality.Comment: 12 pages, 8 figure

Fast Exact NPN Classification with Influence-aided Canonical Form

NPN classification has many applications in the synthesis and verification of digital circuits. The canonical-form-based method is the most common approach, designing a canonical form as representative for the NPN equivalence class first and then computing the transformation function according to the canonical form. Most works use variable symmetries and several signatures, mainly based on the cofactor, to simplify the canonical form construction and computation. This paper describes a novel canonical form and its computation algorithm by introducing Boolean influence to NPN classification, which is a basic concept in analysis of Boolean functions. We show that influence is input-negation-independent, input-permutation-dependent, and has other structural information than previous signatures for NPN classification. Therefore, it is a significant ingredient in speeding up NPN classification. Experimental results prove that influence plays an important role in reducing the transformation enumeration in computing the canonical form. Compared with the state-of-the-art algorithm implemented in ABC, our influence-aided canonical form for exact NPN classification gains up to 5.5x speedup.Comment: To be appeared in ICCAD'2

Restoring Wnt/β-catenin signaling is a promising therapeutic strategy for Alzheimer's disease.

Alzheimer’s disease (AD) is an aging-related neurological disorder characterized by synaptic loss and dementia. Wnt/β-catenin signaling is an essential signal transduction pathway that regulates numerous cellular processes including cell survival. In brain, Wnt/β-catenin signaling is not only crucial for neuronal survival and neurogenesis, but it plays important roles in regulating synaptic plasticity and blood-brain barrier integrity and function. Moreover, activation of Wnt/β-catenin signaling inhibits amyloid-β production and tau protein hyperphosphorylation in the brain. Critically, Wnt/β-catenin signaling is greatly suppressed in AD brain via multiple pathogenic mechanisms. As such, restoring Wnt/β-catenin signaling represents a unique opportunity for the rational design of novel AD therapies

R-spondin1 synergizes with Wnt3A in inducing osteoblast differentiation and osteoprotegerin expression

AbstractR-spondins are a new group of Wnt/β-catenin signaling agonists, however, the role of these proteins in bone remains unclear. We reported herein that R-sponin1 (Rspo1) acted synergistically with Wnt3A to activate Wnt/β-catenin signaling in the uncommitted mesenchymal C2C12 cells. Furthermore, we found that Rspo1 at concentrations as low as 10ng/ml synergized strongly with Wnt3A to induce C2C12 osteoblastic differentiation and osteoprotegerin expression. These events were blocked by Wnt/β-catenin signaling antagonist Dickkopf-1. Finally, we demonstrated that Rspo1 synergized with Wnt3A to induce primary mouse osteoblast differentiation. Together, these findings suggest that Rpos1 may play an important role in bone remodeling

Revisiting Solvent Additives for the Fabrication of Polymer:Fullerene Solar Cells: Exploring a Series of Benzaldehydes

The power conversion efficiencies of organic solar cells delicately depend on the morphology of the light-harvesting bulk heterojunctions (BHJ). Upon deposition from solution, the formation of tailored bicontinuous networks of polymers and fullerenes is often achieved using combinations of solvents and solvent additives. Common wisdom infers that best solar cell performances are achieved when the solvent additives exhibit excellent fullerene solubility. Herein, this concept is revisited based on the investigation of a series of structurally similar, substituted benzaldehydes. It is concluded that the solvent additives do not only have to feature the commonly accepted good fullerene solubility, but must also exhibit lowest polymer solubility to suppress liquid–liquid demixing and hence achieve best solar cell performance. Thus, this study adds an important item to the list of selection criteria of solvent additives toward the production of polymer:fullerene solar cells with optimized power conversion efficiencies. The microscopic picture of the resulting domain configurations within the light-harvesting layers is developed around comprehensive multiscale investigations of the BHJ morphology, using atomic force microscopy, scanning transmission electron microscopy, and nano-infrared microscopy. The latter is operated in two complementary modes, one of which is more bulk sensitive, whereas the other mode is surface sensitive

Breast cancer-derived Dickkopf1 inhibits osteoblast differentiation and osteoprotegerin expression: Implication for breast cancer osteolytic bone metastases

Most breast cancer metastases in bone form osteolytic lesions, but the mechanisms of tumor-induced bone resorption and destruction are not fully understood. Although it is well recognized that Wnt/Β-catenin signaling is important for breast cancer tumorigenesis, the role of this pathway in breast cancer bone metastasis is unclear. Dickkopf1 (Dkk1) is a secreted Wnt/Β-catenin antagonist. In the present study, we demonstrated that activation of Wnt/Β-catenin signaling enhanced Dkk1 expression in breast cancer cells and that Dkk1 overexpression is a frequent event in breast cancer. We also found that human breast cancer cell lines that preferentially form osteolytic bone metastases exhibited increased levels of Wnt/Β-catenin signaling and Dkk1 expression. Moreover, we showed that breast cancer cell-produced Dkk1 blocked Wnt3A-induced osteoblastic differentiation and osteoprotegerin (OPG) expression of osteoblast precursor C2C12 cells and that these effects could be neutralized by a specific anti-Dkk1 antibody. In addition, we found that breast cancer cell conditioned media were able to block Wnt3A-induced NF-kappaB ligand reduction in C2C12 cells. Finally, we demonstrated that conditioned media from breast cancer cells in which Dkk1 expression had been silenced via RNAi were unable to block Wnt3A-induced C2C12 osteoblastic differentiation and OPG expression. Taken together, these results suggest that breast cancer-produced Dkk1 may be an important mechanistic link between primary breast tumors and secondary osteolytic bone metastases. © 2008 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/60217/1/23625_ftp.pd