The surface antigen 2 (SAG2) gene of the protozoan parasite, Toxoplasma gondii, was cloned and extracellularly expressed in the yeast Pichia pastoris. The effectiveness of the secreted recombinant SAG2 (rSAG2-S) as a serodiagnosis reagent was assessed by western blots and ELISA. In the western blot assay, rSAG2-S reacted with all Toxoplasma-antibody positive human serum samples but not with Toxoplasma-negative samples. In the ELISA, rSAG2-S yielded sensitivity rates ranging from 80% (IgG negative, IgM positive) to 100% (IgG positive, IgM negative). In vivo experiments showed that serum from mice immunized with rSAG2-S reacted specifically with the native SAG2 of T. gondii. These mice were protected when challenged with live cells of T. gondii.
Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word embedding with various vector sizes on a large corpus composed of user questions. By comparing analysis, we conducted an experiment on deep learning architectures based on test and 10-cross fold validation accuracy. Experiment results were obtained to illustrate the effectiveness of various Word2vec techniques that have a considerable impact on the accuracy rate using different deep learning approaches. We attained an accuracy of 93.7% by using these techniques on the question dataset.
Dengue virus infects millions of people worldwide and there is no vaccine or anti-dengue therapeutic available. Screening large numbers of medicinal plants for anti-dengue activities is an alternative strategy in order to find the potent therapeutic compounds. Therefore, this study was designed to identify anti-dengue activities in nineteen medicinal plant extracts that are used in traditional medicine. Local medicinal plants Vernonia cinerea, Hemigraphis reptans, Hedyotis auricularia, Laurentia longiflora, Tridax procumbers and Senna angustifolia were used in this study. The highest inhibitory activates against dengue NS2B-NS3pro was observed in ethanolic extract of S. angustifolia leaves, methanolic extract of V. cinerea leaves and ethanol extract of T. procumbens stems. These findings were further verified by in vitro viral inhibition assay. Methanolic extract of V. cinerea leaves, ethanol extract of T. procumbens stems and at less extent ethanolic extract of S. angustifolia leaves were able to maintain the normal morphology of DENV2-infected Vero cells without causing much cytopathic effects (CPE). The percentage of viral inhibition of V. cinerea and T. procumbens extracts were significantly higher than S. angustifolia extract as measured by plaque formation assay and RT-qPCR. In conclusion, The outcome of this study showed that the methanolic extract of V. cinerea leaves and ethanol extract of T. procumbens stems possessed high inhibitory activates against dengue virus that worth more investigation.