CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT.

The progressive growth of today's digital world has made news spread exponentially faster on social media platforms like Twitter, Facebook, and Weibo. Unverified news is often disseminated in the form of multimedia content like text, picture, audio, or video. The dissemination of such false news deceives the public and leads to protests and creates troubles for the public and the government. Hence, it is essential to verify the authenticity of the news at an early stage before sharing it with the public. Earlier fake news detection (FND) approaches combined textual and visual features, but the semantic correlations between words were not addressed and many informative visual features were lost. To address this issue, an automated fake news detection system is proposed, which fuses textual and visual features to create a multimodal feature vector with high information content. The proposed work incorporates the bidirectional encoder representations from transformers (BERT) model to extract the textual features, which preserves the semantic relationships between words. Unlike the convolutional neural network (CNN), the proposed capsule neural network (CapsNet) model captures the most informative visual features from an image. These features are combined to obtain a richer data representation that helps to determine whether the news is fake or real. We investigated the performance of our model against different baselines using two publicly accessible datasets, Politifact and Gossipcop. Our proposed model achieves significantly better classification accuracy of 93% and 92% for the Politifact and Gossipcop datasets, respectively, compared to 84.6% and 85.6% for the SpotFake+ model.

Mycoremediation of Congo red dye by filamentous fungi

Azo, anthroquinone and triphenylmethane dyes are the major classes of synthetic colourants, which are difficult to degrade and have received considerable attention. Congo red, a diazo dye, is considered as a xenobiotic compound, and is recalcitrant to biodegradative processes. Nevertheless, during the last few years it has been demonstrated that several fungi, under certain environmental conditions, are able to transfer azo dyes to non toxic products using laccases. The aim of this work was to study the factors influencing mycoremediation of Congo red. Several basidiomycetes and deuteromycetes species were tested for the decolourisation of Congo red (0.05 g/l) in a semi synthetic broth at static and shaking conditions. Poor decolourisation was observed when the dye acted as the sole source of nitrogen, whereas semi synthetic broth supplemented with fertilizer resulted in better decolourisation. Decolourisation of Congo red was checked in the presence of salts of heavy metals such as mercuric chloride, lead acetate and zinc sulphate. Decolourisation parameters such as temperature, pH, and rpm were optimized and the decolourisation obtained at optimized conditions varied between 29.25- 97.28 percent at static condition and 82.1- 100 percent at shaking condition. Sodium dodecyl sulphate polyacrylamide gel electrophoretic analysis revealed bands with molecular weights ranging between 66.5 to 71 kDa, a characteristic of the fungal laccases. High efficiency decolourisation of Congo red makes these fungal forms a promising choice in biological treatment of waste water containing Congo red.

Mycoremediation of congo red dye by filamentous fungi.

Azo, anthroquinone and triphenylmethane dyes are the major classes of synthetic colourants, which are difficult to degrade and have received considerable attention. Congo red, a diazo dye, is considered as a xenobiotic compound, and is recalcitrant to biodegradative processes. Nevertheless, during the last few years it has been demonstrated that several fungi, under certain environmental conditions, are able to transfer azo dyes to non toxic products using laccases. The aim of this work was to study the factors influencing mycoremediation of Congo red. Several basidiomycetes and deuteromycetes species were tested for the decolourisation of Congo red (0.05 g/l) in a semi synthetic broth at static and shaking conditions. Poor decolourisation was observed when the dye acted as the sole source of nitrogen, whereas semi synthetic broth supplemented with fertilizer resulted in better decolourisation. Decolourisation of Congo red was checked in the presence of salts of heavy metals such as mercuric chloride, lead acetate and zinc sulphate. Decolourisation parameters such as temperature, pH, and rpm were optimized and the decolourisation obtained at optimized conditions varied between 29.25- 97.28% at static condition and 82.1- 100% at shaking condition. Sodium dodecyl sulphate polyacrylamide gel electrophoretic analysis revealed bands with molecular weights ranging between 66.5 to 71 kDa, a characteristic of the fungal laccases. High efficiency decolourisation of Congo red makes these fungal forms a promising choice in biological treatment of waste water containing Congo red.