NIFK, a Potential Prognostic and Immunological Biomarker in Pan-- Cancer Analysis, Significantly Regulates Proliferation and Metastasis of Colorectal Cancer.

BACKGROUND: As a binding protein of Ki67, NIFK plays an important role in the mitosis of cells and is closely related to the progression of specific types of tumors. However, there is still a lack of systematic analysis of NIFK in pan-cancer and insufficient research to explore its role in human tumors. METHODS: We systematically evaluated the pan-cancer expression and mutation of NIFK in human cancers using data from The Cancer Genome Atlas (TCGA) through large-scale bioinformatics analysis. In addition, we explored the pan-cancer immunological characteristics of NIFK, especially in colorectal adenocarcinoma (COAD). Furthermore, we used single-cell sequencing to analyze the expression of NIFK in different cells of COAD tissues and performed GO, KEGG, and gene set enrichment analysis of NIFK in COAD. Lastly, we evaluated the effects of NIFK knockdown on the colorectal cancer cell lines in in vitro experiment. RESULTS: We found that NIFK was overexpressed in almost all types of tumors and showed significant prognostic efficacy. Additionally, correlations between NIFK and specific immune features, such as immune cell infiltration, immune checkpoint genes, TMB, and MSI, suggest that NIFK may be used to guide immunotherapy. Subsequently, it was found that the expression of NIFK was significantly upregulated in tumor cells through single-cell sequencing analysis, and the NIFK gene was closely associated with tumor progression and immune therapy response. Finally, we further elucidated the role of NIFK in colorectal cancer and found that downregulation of NIFK expression could inhibit the proliferation, migration, and invasion ability of colorectal cancer cells. CONCLUSION: The results of this study demonstrated that NIFK, as a member of the pan-cancer genes, will serve as a biomarker and a potential therapeutic target for a range of cancer types, providing new insight into precision medicine.

Ref-MEF: Reference-Guided Flexible Gated Image Reconstruction Network for Multi-Exposure Image Fusion.

Multi-exposure image fusion (MEF) is a computational approach that amalgamates multiple images, each captured at varying exposure levels, into a singular, high-quality image that faithfully encapsulates the visual information from all the contributing images. Deep learning-based MEF methodologies often confront obstacles due to the inherent inflexibilities of neural network structures, presenting difficulties in dynamically handling an unpredictable amount of exposure inputs. In response to this challenge, we introduce Ref-MEF, a method for color image multi-exposure fusion guided by a reference image designed to deal with an uncertain amount of inputs. We establish a reference-guided exposure correction (REC) module based on channel attention and spatial attention, which can correct input features and enhance pre-extraction features. The exposure-guided feature fusion (EGFF) module combines original image information and uses Gaussian filter weights for feature fusion while keeping the feature dimensions constant. The image reconstruction is completed through a gated context aggregation network (GCAN) and global residual learning GRL. Our refined loss function incorporates gradient fidelity, producing high dynamic range images that are rich in detail and demonstrate superior visual quality. In evaluation metrics focused on image features, our method exhibits significant superiority and leads in holistic assessments as well. It is worth emphasizing that as the number of input images increases, our algorithm exhibits notable computational efficiency.

Semi-TMS: an efficient regularization-oriented triple-teacher semi-supervised medical image segmentation model.

Objective. Although convolutional neural networks (CNN) and Transformers have performed well in many medical image segmentation tasks, they rely on large amounts of labeled data for training. The annotation of medical image data is expensive and time-consuming, so it is common to use semi-supervised learning methods that use a small amount of labeled data and a large amount of unlabeled data to improve the performance of medical imaging segmentation.Approach. This work aims to enhance the segmentation performance of medical images using a triple-teacher cross-learning semi-supervised medical image segmentation with shape perception and multi-scale consistency regularization. To effectively leverage the information from unlabeled data, we design a multi-scale semi-supervised method for three-teacher cross-learning based on shape perception, called Semi-TMS. The three teacher models engage in cross-learning with each other, where Teacher A and Teacher C utilize a CNN architecture, while Teacher B employs a transformer model. The cross-learning module consisting of Teacher A and Teacher C captures local and global information, generates pseudo-labels, and performs cross-learning using prediction results. Multi-scale consistency regularization is applied separately to the CNN and Transformer to improve accuracy. Furthermore, the low uncertainty output probabilities from Teacher A or Teacher C are utilized as input to Teacher B, enhancing the utilization of prior knowledge and overall segmentation robustness.Main results. Experimental evaluations on two public datasets demonstrate that the proposed method outperforms some existing semi-segmentation models, implicitly capturing shape information and effectively improving the utilization and accuracy of unlabeled data through multi-scale consistency.Significance. With the widespread utilization of medical imaging in clinical diagnosis, our method is expected to be a potential auxiliary tool, assisting clinicians and medical researchers in their diagnoses.

MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion.

In order to solve the problems of infrared target detection (i.e., the large models and numerous parameters), a lightweight detection network, MSIA-Net, is proposed. Firstly, a feature extraction module named MSIA, which is based on asymmetric convolution, is proposed, and it can greatly reduce the number of parameters and improve the detection performance by reusing information. In addition, we propose a down-sampling module named DPP to reduce the information loss caused by pooling down-sampling. Finally, we propose a feature fusion structure named LIR-FPN that can shorten the information transmission path and effectively reduce the noise in the process of feature fusion. In order to improve the ability of the network to focus on the target, we introduce coordinate attention (CA) into the LIR-FPN; this integrates the location information of the target into the channel so as to obtain more expressive feature information. Finally, a comparative experiment with other SOTA methods was completed on the FLIR on-board infrared image dataset, which proved the powerful detection performance of MSIA-Net.

River water influenced by shale gas wastewater discharge for paddy irrigation has limited effects on soil properties and microbial communities.

The processes of hydraulic fracturing to extract shale gas generate a large amount of wastewater, and the potential impacts of wastewater discharge after treatment are concerning. In this field study, we investigated the effects of the irrigation of paddy fields for 2 consecutive years by river water that has been influenced by shale gas wastewater discharge on soil physicochemical properties, microbial community structure and function, and rice grain quality. The results showed that conductivity, chloride and sulfate ions in paddy soils downstream of the outfall showed an accumulative trend after two years of irrigation, but these changes occurred on a small scale (<500 m). Two-year irrigation did not cause the accumulation of trace metals (barium, cadmium, chromium, copper, lead, strontium, zinc, nickel, and uranium) in soil and rice grains. Among all soil parameters, the accumulation of chloride ions was the most pronounced, with concentrations in the paddy soil at the discharge site 13.3 times higher than at the upstream control site. The use of influenced river water for paddy irrigation positively increased the soil microbial diversity, but these changes occurred after two years of irrigation and did not occur after one year of irrigation. Overall, the use of river water affected by shale gas wastewater discharge for agricultural irrigation has limited effects on agroecosystems over a short period. Nevertheless, the possible negative effects of contaminant accumulation in soil and rice caused by longer-term irrigation should be seriously considered.

Transformer-Based Model with Dynamic Attention Pyramid Head for Semantic Segmentation of VHR Remote Sensing Imagery.

Convolutional neural networks have long dominated semantic segmentation of very-high-resolution (VHR) remote sensing (RS) images. However, restricted by the fixed receptive field of convolution operation, convolution-based models cannot directly obtain contextual information. Meanwhile, Swin Transformer possesses great potential in modeling long-range dependencies. Nevertheless, Swin Transformer breaks images into patches that are single-dimension sequences without considering the position loss problem inside patches. Therefore, Inspired by Swin Transformer and Unet, we propose SUD-Net (Swin transformer-based Unet-like with Dynamic attention pyramid head Network), a new U-shaped architecture composed of Swin Transformer blocks and convolution layers simultaneously through a dual encoder and an upsampling decoder with a Dynamic Attention Pyramid Head (DAPH) attached to the backbone. First, we propose a dual encoder structure combining Swin Transformer blocks and reslayers in reverse order to complement global semantics with detailed representations. Second, aiming at the spatial loss problem inside each patch, we design a Multi-Path Fusion Model (MPFM) with specially devised Patch Attention (PA) to encode position information of patches and adaptively fuse features of different scales through attention mechanisms. Third, a Dynamic Attention Pyramid Head is constructed with deformable convolution to dynamically aggregate effective and important semantic information. SUD-Net achieves exceptional results on ISPRS Potsdam and Vaihingen datasets with 92.51%mF1, 86.4%mIoU, 92.98%OA, 89.49%mF1, 81.26%mIoU, and 90.95%OA, respectively.

Spatial and temporal variations in anti-androgenic activity and environmental risk in a small river.

The biological effects of multiple compounds have been widely investigated in aquatic environments. However, investigations of spatial and temporal variations in biological effects are rarely performed because they are time-consuming and labor-intensive. In this study, the variability of the anti-androgen, receptor-mediated activity of surface water samples was observed over 3 years using in vitro bioassays. Large-volume water samples were collected at one site upstream (Wer site) and two sites downstream (Sil and Nien sites) of a wastewater treatment plant (WWTP) outfall in the Holtemme River. Anti-AR activity was persistently present in all surface water samples over the three years. Large spatial variations in anti-androgenic activity were observed, with the lowest activity at the Wer site (mean concentration of 9.5 ± 7.2 µg flutamide equivalents/L) and the highest activity at the Sil site (mean concentration of 31.1 ± 12.0 µg flutamide equivalents/L) directly influenced by WWTP effluents. On the temporal scale, no distinct trend for anti-AR activity was observed among the seasons in all three years. The anti-androgenic activity at the upstream Wer site showed a decreasing trend from 2014 to 2016, indicating improved water quality. A novel bioanalytical-equivalent-based risk assessment method considering the frequency of risk occurrence was developed and then utilized to assess the environmental risk of anti-androgenic activity in the Holtemme River. The results revealed that the highest risk was present at the Sil site, while the risk was considerably reduced at the Nien site. The risk at the upstream Wer site was the lowest.

BRefine: Achieving High-Quality Instance Segmentation.

Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone network and the ROIAlign layer loses much detailed information, especially from large targets. The sawtooth effect of the edge mask is caused by the lower resolution. A lesser percentage of boundary pixels leads to not-fine segmentation. In this paper, we propose a new method called Boundary Refine (BRefine) that achieves high-quality segmentation. This approach uses FCN as the foundation segmentation architecture, and forms a multistage fusion mask head with multistage fusion detail features to improve mask resolution. However, the FCN architecture causes inconsistencies in multiscale segmentation. BRank and sort loss (BR and S loss) is proposed to solve the problems of segmentation inconsistency and the difficulty of boundary segmentation. It is combined with rank and sort loss, and boundary region loss. BRefine can handle hard-to-partition boundaries and output high-quality masks. On the COCO, LVIS, and Cityscapes datasets, BRefine outperformed Mask R-CNN by 3.0, 4.2, and 3.5 AP, respectively. Furthermore, on the COCO dataset, the large objects improved by 5.0 AP.

Combining eDNA and morphological approaches to reveal the impacts of long-term discharges of shale gas wastewaters on receiving waters.

The potential threats of shale gas wastewater discharges to receiving waters is of great concern. In this study, chemical analyses and biomonitoring were performed three times in a small river that received treated wastewater over a two-year period. The results of chemical analyses showed that the concentrations of chloride, conductivity, barium, and strontium increased at the discharge site, but their concentrations decreased considerably farther downstream (≥500 m). The concentrations of toxic organic compounds (16 US EPA priority polycyclic aromatic hydrocarbons and 6 priority phthalates), trace metals (strontium, arsenic, zinc, copper, chromium, lead, cadmium, nickel, and neodymium), and natural radionuclides (40K, 238U, 226Ra, and 232Th) were comparable to the corresponding background values or did not exhibit obvious accumulation in sediments with continued discharge. Morphological and environmental DNA approaches were used to reveal the potential effects of wastewater discharges on aquatic ecosystems. The results showed that the community structure of benthic invertebrates was not altered by the long-term discharges of shale gas wastewaters. However, the biodiversity indices (richness and Shannon) from the two approaches showed inconsistencies, which were caused by multiple reasons, and that substrates had a strong influence on the morphological biodiversity indices. A multimetric index was proposed to further analyze morphological and environmental DNA data, and the results showed no significant difference between the upstream and downstream sites. Generally, the chemical and biological results both demonstrated that the discharges of shale gas wastewaters had limited impacts on river ecosystems within two years.

3D visualization model construction based on generative adversarial networks.

The development of computer vision technology is rapid, which supports the automatic quality control of precision components efficiently and reliably. This paper focuses on the application of computer vision technology in manufacturing quality control. A new deep learning algorithm is presented, Multi-angle projective Generative Adversarial Networks (MapGANs), to automatically generate 3D visualization models of products and components. The generated 3D visualization models can intuitively and accurately display the product parameters and indicators. Based on these indicators, our model can accurately determine whether the product meets the standard. The working principle of the MapGANs algorithm is to automatically infer the basic three-dimensional shape distribution through the product's projection module, while using multiple angles and multiple views to improve the fineness and accuracy of the three-dimensional visualization model. The experimental results prove that MapGANs can effectively reconstruct two-dimensional images into three-dimensional visualization models, and meanwhile accurately predict whether the quality of the product meets the standard.

Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization.

In the traditional particle swarm optimization algorithm, the particles always choose to learn from the well-behaved particles in the population during the population iteration. Nevertheless, according to the principles of particle swarm optimization, we know that the motion of each particle has an impact on other individuals, and even poorly behaved particles can provide valuable information. Based on this consideration, we propose Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization, called LFIACL-PSO. In the LFIACL-PSO algorithm, First, when the particle is trapped in the local optimum and cannot jump out, inverse learning is used, and the learning step size is obtained through the Lévy flight. Second, to increase the diversity of the algorithm and prevent it from prematurely converging, a comprehensive learning strategy and Ring-type topology are used as part of the learning paradigm. In addition, use the adaptive update to update the acceleration coefficients for each learning paradigm. Finally, the comprehensive performance of LFIACL-PSO is measured using 16 benchmark functions and a real engineering application problem and compared with seven other classical particle swarm optimization algorithms. Experimental comparison results show that the comprehensive performance of the LFIACL-PSO outperforms comparative PSO variants.

Dynamic gesture recognition based on 2D convolutional neural network and feature fusion.

Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and accuracy. Therefore, proposing a dynamic gesture recognition algorithm that balances efficiency and accuracy is still a meaningful work. Currently, most of the commonly used dynamic gesture recognition algorithms are based on 3D convolutional neural networks. Although 3D convolutional neural networks consider both spatial and temporal features, the networks are too complex, which is the main reason for the low efficiency of the algorithms. To improve this problem, we propose a recognition method based on a strategy combining 2D convolutional neural networks with feature fusion. The original keyframes and optical flow keyframes are used to represent spatial and temporal features respectively, which are then sent to the 2D convolutional neural network for feature fusion and final recognition. To ensure the quality of the extracted optical flow graph without increasing the complexity of the network, we use the fractional-order method to extract the optical flow graph, creatively combine fractional calculus and deep learning. Finally, we use Cambridge Hand Gesture dataset and Northwestern University Hand Gesture dataset to verify the effectiveness of our algorithm. The experimental results show that our algorithm has a high accuracy while ensuring low network complexity.

Characterization of microbial communities and functions in shale gas wastewaters and sludge: Implications for pretreatment.

As hydraulic fracturing (HF) practices keep expanding in China, a comparative understanding of biological characteristics of flowback and produced waters (FPW) and sludge in impoundments for FPW reserve will help propose appropriate treatment strategies. Therefore, in this study, the microbial communities and functions in impoundments that collected wastewaters from dozens of wells were characterized. The results showed that microbial richness and diversity were significantly increased in sludge compared with those in FPW. The vast majority of microorganisms found in FPW and sludge are organic degraders, providing the possibility of using these indigenous microorganisms to biodegrade organic compounds. Our laboratory findings first show that wastewater pretreatment using these microorganisms was effective, and organic compounds in FPW from different shale formations were removed by 35-68% within 72 h in a wide temperature range (8 - 30 â„ƒ). Meanwhile, highly toxic compounds such as phthalate esters (PAEs), polycyclic aromatic hydrocarbons (PAHs), and petroleum hydrocarbons were effectively eliminated in reactors. The main microorganisms, key functional genes, and putative pathways for alkanes, PAHs, and PAEs degradation were also identified.

Risk assessment of pollutants in flowback and produced waters and sludge in impoundments.

Due to the growing hydraulic fracturing (HF) practices in China, the environmental risks of pollutants in flowback and produced waters (FPW) and sludge in impoundments for FPW reserves have drawn increasing attention. In this context, we first characterized the comparative geochemical characteristics of the FPW and the sludge in impoundments that collected FPW from 75 shale gas wells, and then the risks associated with the pollutants were assessed. The results demonstrated that four organic compounds detected in the FPW, naphthalene, acenaphthene, dibutyl phthalate, and bis(2-ethylhexyl)phthalate, were potential threats to surface waters. The concentrations of trace metals (copper, cadmium, manganese, chromium, nickel, zinc, arsenic, and lead) in the FPW and sludge were low; however, those of iron, barium, and strontium were high. The accumulation of chromium, nickel, zinc, and lead in the sludge became more evident as the depth increased. The environmental risks from heavy metals in the one-year precipitated sludge were comparable to those reported in the environment. However, the radium equivalent activities were 10-41 times higher than the recommended value for human health safety, indicating potential radiation risks. Although hydrophobic organic compounds, such as high-molecular-weight polycyclic aromatic hydrocarbons (PAHs), phthalate esters (PAEs), benzene, ethylbenzene, toluene, and xylene (BTEX), tended to accumulate in the sludge, their environmental risks were within tolerable ranges after proper treatment. Multiple antibiotic resistance genes (ARGs), such as those for macrolide, lincosamide, streptogramin (MLS), tetracycline, and multidrug resistances, were detected in the shale gas wastewaters and sludge. Therefore, the environmental risks of these emerging pollutants upon being discharged or leaked into surface waters require further attention.

A multi-sample particle swarm optimization algorithm based on electric field force.

Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.

An image super-resolution reconstruction model based on fractional-order anisotropic diffusion equation.

The image denoising model based on anisotropic diffusion equation often appears the staircase effect while image denoising, and the traditional super-resolution reconstruction algorithm can not effectively suppress the noise in the image in the case of blur and serious noise. To tackle this problem, a novel model is proposed in this paper. Based on the original diffusion equation, we propose a new method for calculating the adaptive fidelity term and its coefficients, which is based on the relationship between the image gradient and the diffusion function. It is realized that the diffusion speed can be slowed down by adaptively changing the coefficient of the fidelity term, and it is proved mathematically that the proposed fractional adaptive fidelity term will not change the existence and uniqueness of the solution of the original model. At the same time, washout filter is introduced as the control item of the model, and a new model of image super-resolution reconstruction and image denoising is constructed. In the proposed model, the order of fractional differential will be determined adaptively by the local variance of the image. And we give the numerical calculation method of the new model in the frequency domain by the method of Fourier transform. The experimental results show that the proposed algorithm can better prevent the staircase effect and achieve better visual effect. And by introducing washout filter to act as the control of the model, the stability of the system can be improved and the system can converge to a stable state quickly.

Evidence of increased estrogenicity upon metabolism of Bisphenol F - Elucidation of the key metabolites.

The increasing concern over bisphenol A (BPA) has directed much attention toward bisphenol F (BPF) and bisphenol S (BPS) as BPA alternatives for the development of "BPA-free" products. Consequently, BPS and BPF were frequently detected in surface water, sediment, sewage effluent, indoor dust, and even in food and biological fluids in humans. Thus, environmental researches start to focus on the potential environmental risks of BPA alternatives. While the estrogenically active metabolites and the specific estrogenically active structure are still unknown. In this study, the MTT assay on acute cytotoxicity and the recombinant transactivation assay were carried out to determine whether BPF and BPS are suitable alternatives to BPA. Our results show that the cytotoxic and estrogenic activities of BPS and BPF are lower than those of BPA. However, after the addition of a rat liver homogenate to simulate mammal metabolism, BPF exhibited higher estrogenic activity than BPA. To identify the chemical structures and estrogen receptor binding affinities of active estrogenic metabolites, LC-MS, MetaPrint2D(-React), and VirtualToxLab were integrated. The observed results indicated that the para-hydroxylated BPF and BPF-OCH3 might have strong ER binding affinities. These results demonstrate that metabolization is important to consider upon investigating endocrine disruption of chemicals getting into contact with humans, such as in dental sealing or food packaging. Alternatives to potentially hazardous substances should be thoroughly tested prior to use.

Nitric oxide-dependent biodegradation of phenanthrene and fluoranthene: The co-occurrence of anaerobic and intra-aerobic pathways.

Polycyclic aromatic hydrocarbons (PAHs) pollution as well as the emissions of nitric oxide (NO) and greenhouse gas nitrous oxide (N2O) in denitrification processes are currently two environmental issues of great concern. Although bioremediation of PAHs under denitrification is considered a promising approach, denitrification was an important contributor to N2O and NO emissions. This long-term study confirmed for the first time that microorganisms could utilize NO to efficiently degrade phenanthrene and fluoranthene. When the two systems of NO-dependent phenanthrene and fluoranthene degradation were stable, the first-order rate constants of phenanthrene and fluoranthene in the two systems (0.1940 and 0.0825 day-1, respectively) were close to those values (0.2290 and 0.1085 day-1, respectively) observed at nitrate-reducing conditions. Further analysis of functional genes revealed that phenanthrene and fluoranthene might be degraded under the combined action of the anaerobic pathway mediated by NO reduction and intra-aerobic pathway mediated by NO dismutation. The genomic analysis showed that Nod genes had high diversity and most of them were similar to aquifer cluster group in the two systems. Microbial community structure analysis indicated that Pseudomonas and Ochrobactrum might be key participants in NO-dependent phenanthrene degradation system, and Azoarcus, Alicycliphilus and Moheibacter might play vital roles in NO-dependent fluoranthene degradation system. This study provides new perspective for anaerobic remediation of PAH pollution and simultaneously reducing NO and N2O emissions during bioprocesses, which has important ecological significance for amending sediment and soil PAHs contamination and potential application for the removal of PAHs in flue gas.

Bioanalytical equivalents and relative potencies for predicting the biological effects of mixtures.

Bioanalytical equivalents (BEQs) of mixtures and environmental samples are widely used to reflect the potential threat of pollutants in the environment and can be obtained by bioassays or using chemical analysis combined with relative potencies (REPs). In this study, the relationships between bioassay-detected BEQs (Bio-BEQs) and chemically analyzed BEQs (Chem-BEQs) were studied. BEQs and REPs are correlated with effect level and the concentration-response curves of the reference standard and sample. Thus, effect level (e.g., EC10, EC25 and EC50) should be addressed for the BEQ values obtained from bioassays or chemical analyses. The previous prerequisites for REPs application (i.e., curves that are parallel and have the same maximum response) are redundant, and the use of REPs for the calculation of BEQs or in risk assessment should instead be based on the same effect level. For a complex mixture with many components, all active components can be regarded as dilutions of a standard compound for inducing a specific effect. Relative toxicity estimates based on EC50 ignore the contribution of weak-active components with maximum response below EC50 of the reference standard, especially in complex mixtures or environmental samples. REPs based on an effect level EC10 that can be clearly discriminated from background response are recommended for BEQ calculation. As an example, the aryl hydrocarbon receptor (AhR)-mediated activity of US EPA priority polycyclic aromatic hydrocarbons (PAHs) in RTL-W1 cells was used to assess the reliability of REPs for mixture toxicity prediction based on the effect level EC10.

Identification of 7-9 ring polycyclic aromatic hydrocarbons in coals and petrol coke using High performance liquid chromatography - Diode array detection coupled to Atmospheric pressure laser ionization - Mass spectrometry (HPLC-DAD-APLI-MS).

Polycyclic aromatic hydrocarbons containing at least 24 carbon atoms (≥C24-PAH) are often associated with pyrogenic processes such as combustion of fuel, wood or coal, and occur in the environment in diesel particulate matter, black carbon and coal tar. Some of the ≥C24-PAH, particularly the group of dibenzopyrenes (five isomers, six aromatic rings) are known to show high mutagenic and carcinogenic activita. Gas chromatography - mass spectrometry is a well-established method for the analysis of lower molecular weight PAH but is not optimally suited for the analysis of ≥C24-PAH due to their low vapor pressures. Also, hundreds of ≥C24-PAH isomers are possible but only a few compounds are commercially available as reference standards. Therefore, in this study, a combination of multidimensional liquid chromatography, UV-Vis diode array detection, PAH selective and highly sensitive atmospheric pressure laser ionization - mass spectrometry is used to detect and unequivocally identify PAH. For identification of PAH in two bituminous coals and one petrol coke sample, unique and compound specific UV-Vis spectra were acquired. It was possible to identify ten compounds (naphtho[1,2,3,4-ghi]perylene, dibenzo[b,ghi]perylene, dibenzo[e,ghi]perylene, dibenzo[cd,lm]perylene, benzo[a]coronene, phenanthrol[5,4,3,2-abcde]perylene, benzo[ghi]naphtho[8,1,2-bcd]perylene, benzo[pqr]naphtho[8,1,2-bcd]perylene, naphtho[8,1,2-abc]coronene and tribenzo[e,ghi,k]perylene) by comparison of acquired spectra with spectra from literature. Additionally, it was possible to detect similar distribution patterns in different samples and signals related to alkylated naphthopyrenes, naphthofluoranthenes or dibenzopyrenes. Subsequent effect-directed analysis of a bituminous coal sample using the microEROD (ethoxyresorufin-O-deethylase) bioassay showed high suitability and revealed lower EROD induction for the ≥C24-PAH (TEQ range 0.67-10.07 ng/g) than for the allover < C24-PAH containing fraction (TEQ 84.00 ng/g). Nevertheless, the toxicity of ≥C24-PAH has a significant impact compared with