Fungi in Landfill Leachate Treatment Process

The landfill leachate has high concentration of COD, ammonia and other recalcitrant composition compounds. The amount of eachwhich is mainly largely dependent on the age of the landfill. The conventional leachate treatments can be classified as chemical-physical treatments and biological treatments. Using fungi to treat leachate is an emerging research topic. Fungi, with their excellent recalcitrant compound degradability, have been used to treat industrial wastewater that contains toxic or recalcitrant compound. Due to the complex composition and toxicity of landfill leachate, fungi have showed shown better removal efficiency in terms of COD, toxicity and color removal than the conventional leachate treatment. White rot fungi species and yeast are so far the two species that have been studied in treating landfill leachate. Future research should be extended to the other fungi species as well asand also on the impact of ammonia in landfill leachate on the fungi treatment process

The Thought and Educational Practices of Hu Shih: The Influence of Dewey

John Dewey was an American philosopher, psychologist, and educational reformer whose ideas have been influential on education in Japan and China. Hu Shih made a notable contribution to this influence through his introduction of John Dewey to modern China (the period of the Republic of China on the mainland, 1912–1949). Hu studied philosophy at the Teachers College at Columbia University from 1915 to 1917, during which time he was greatly influenced by his professor, Dewey. Hu was a famous Chinese thinker, philosopher, essayist, and diplomat who was influential in the May Fourth Movement and one of the leaders of China’s New Culture Movement. In this paper, I clarify the relationship between the thought of Hu and Dewey by referencing Hu’s career and achievements. Specifically, this paper aims to clarify Hu’s ideological characteristics during various periods in his life. Through this process of exploration, we see how, in the great tide of times, Hu, a traditional intellectual, accepted and renewed Dewey’s ideas. Specifically, I consider, in detail, Hu’s ideas at each stage of his career (before, during, and after studying abroad) and the connection between his ideas, his educational practices, and Dewey’s thought

Raman Solitons in Nanoscale Optical Waveguides, with Metamaterials, Having Polynomial Law Nonlinearity Using Collective Variables

A mathematical analysis is conducted to illustrate the controllability of the Raman soliton self-frequency shift with polynomial nonlinearity in metamaterials by using collective variable method. The polynomial nonlinearity is due to the expanding nonlinear polarization P NL in a series over the field E up to the seventh order. Gaussian assumption is selected to these pulses on a generalized mode. The numerical simulation of soliton parameter variation is given for the Gaussian pulse parameters

Acceptor side effects on the electron transfer at cryogenic temperatures in intact photosystem II

AbstractIn intact PSII, both the secondary electron donor (TyrZ) and side-path electron donors (Car/ChlZ/Cytb559) can be oxidized by P680+ at cryogenic temperatures. In this paper, the effects of acceptor side, especially the redox state of the non-heme iron, on the donor side electron transfer induced by visible light at cryogenic temperatures were studied by EPR spectroscopy. We found that the formation and decay of the S1TyrZ EPR signal were independent of the treatment of K3Fe(CN)6, whereas formation and decay of the Car+/ChlZ+ EPR signal correlated with the reduction and recovery of the Fe3+ EPR signal of the non-heme iron in K3Fe(CN)6 pre-treated PSII, respectively. Based on the observed correlation between Car/ChlZ oxidation and Fe3+ reduction, the oxidation of non-heme iron by K3Fe(CN)6 at 0 °C was quantified, which showed that around 50–60% fractions of the reaction centers gave rise to the Fe3+ EPR signal. In addition, we found that the presence of phenyl-p-benzoquinone significantly enhanced the yield of TyrZ oxidation. These results indicate that the electron transfer at the donor side can be significantly modified by changes at the acceptor side, and indicate that two types of reaction centers are present in intact PSII, namely, one contains unoxidizable non-heme iron and another one contains oxidizable non-heme iron. TyrZ oxidation and side-path reaction occur separately in these two types of reaction centers, instead of competition with each other in the same reaction centers. In addition, our results show that the non-heme iron has different properties in active and inactive PSII. The oxidation of non-heme iron by K3Fe(CN)6 takes place only in inactive PSII, which implies that the Fe3+ state is probably not the intermediate species for the turnover of quinone reduction

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. The multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Furthermore, we combine the reverse diffusion procedure to further optimize the shallow output and generate the most realistic image. The proposed method is compared with ten state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. The superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible.Comment: To appear in MICCAI 2023. Code availability: https://github.com/longbai1006/LLCap

Chemical composition of the essential oil of whole plant of Elsholtizia dense Benth and its anti-tumor effect on human hepatoma cells

Purpose: To determine the chemical components of the essential oil of Elsholtizia dense in Sichuan Province and evaluate the effect of the oil on human hepatoma cells (SMMC-7721) in vitro.Methods: The essential oil was extracted using the modified steam-distillation  extraction method, and its chemical components were determined by gas  chromatography-mass spectrometry (GC-MS). The effect of the essential oil on proliferation of SMMC-7721 cells was studied by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay, with L02 and HeLa cells serving as control groups.Results: GC-MS results show that the essential oil of E. dense contains 40  components. Thirty seven components were identified and accounted for 98.39 % of the essential oil. The two main components were rosefuran epoxide (53.12 %) and 2-ethyl imidazole (29.8 %). The oil significantly inhibited cell proliferation in a concentration- and time-dependent manner (p < 0.05). SMMC-7721 cells were more inhibited than L02 and HeLa cells by the oil, with half maximal inhibitory concentration (IC50) values of 26.23 and 25.46 μg/mL after 8-h and 24-h treatments, respectively.Conclusion: Out of the 40 chemical components of the essential oil of E. dense, rosefuran epoxide and 2-ethyl imidazole were the most abundant. The oil has a significant anti-tumor effect on SMMC-7721 cells, and thus has a potential to be developed as an anti-liver cancer drug.Keywords: Medicinal herb, Elsholtizia dense Benth, Essential oil, Rosefuran epoxide, 2-Ethyl imidazole, Anti-tumor activit

VR-GNN: Variational Relation Vector Graph Neural Network for Modeling both Homophily and Heterophily

Graph Neural Networks (GNNs) have achieved remarkable success in diverse real-world applications. Traditional GNNs are designed based on homophily, which leads to poor performance under heterophily scenarios. Current solutions deal with heterophily mainly by mixing high-order neighbors or passing signed messages. However, mixing high-order neighbors destroys the original graph structure and passing signed messages utilizes an inflexible message-passing mechanism, which is prone to producing unsatisfactory effects. To overcome the above problems, we propose a novel GNN model based on relation vector translation named Variational Relation Vector Graph Neural Network (VR-GNN). VR-GNN models relation generation and graph aggregation into an end-to-end model based on Variational Auto-Encoder. The encoder utilizes the structure, feature and label to generate a proper relation vector. The decoder achieves superior node representation by incorporating the relation translation into the message-passing framework. VR-GNN can fully capture the homophily and heterophily between nodes due to the great flexibility of relation translation in modeling neighbor relationships. We conduct extensive experiments on eight real-world datasets with different homophily-heterophily properties to verify the effectiveness of our model. The experimental results show that VR-GNN gains consistent and significant improvements against state-of-the-art GNN methods under heterophily, and competitive performance under homophily