Macrobrachium rosenbergii is the most important cultured freshwater prawn in the world and it is now farmed on a large scale in many countries. Generally, freshwater prawn is considered to be tolerant to diseases but a disease of viral origin is responsible for severe mortalities in larval, post-larval and juvenile stages of prawn. This viral infection namely white tail disease (WTD) was reported in the island of Guadeloupe in 1995 and later in Martinique (FrenchWest Indies) in Taiwan, the People's Republic of China, India, Thailand, Australia and Malaysia. Two viruses, Macrobrachium rosenbergii nodavirus (MrNV) and extra small virus-like particle (XSV) have been identified as causative agents of WTD. MrNV is a small icosahedral non-enveloped particle, 26-27 nm in diameter, identified in the cytoplasm of connective cells. XSV is also an icosahedral virus and 15 nm in diameter. Clinical signs observed in the infected animals include lethargy, opaqueness of the abdominal muscle, degeneration of the telson and uropods, and up to 100 % within 4 days. The available diagnostic methods to detect WTD include RT-PCR, dot-blot hybridization, in situ hybridization and ELISA. In experimental infection, these viruses caused 100 % mortality in post-larvae but failed to cause mortality in adult prawns. The reported hosts for these viruses include marine shrimp, Artemia and aquatic insects. Experiments were carried out to determine the possibility of vertical transmission of MrNV and XSV in M. rosenbergii. The results indicate that WTD may be transferred from infected brooders to their offspring during spawning. Replication of MrNV and XSV was investigated in apparently healthy C6/36 Aedes albopictus and SSN-1 cell lines. The results revealed that C6/36 and SSN-1cells were susceptible to these viruses. No work has been carried out on control and prevention of WTD and dsRNA against protein B2 produced RNAi that was able to functionally prevent and reduce mortality in WTD-infected redclaw crayfish.
Although plastic induces environmental damages, almost the consumption of poly(vinyl chloride) never stops increasing. Therefore, this work abstracted by two parts, first, synthesis of Schiff bases 1-4 compounds through the reaction of amino group with appropriate aromatic aldehyde, reaction of PVC with Schiff bases compounds 1-4 in THF to form a new modified PVC-1, PVC-2, PVC-3, and PVC-4. The structures of Schiff bases 1-4 and the modified PVC-1, PVC-2, PVC-3, and PVC-4 have been characterized by different spectroscopic analyses. Second, the influence of introducing 4-amino-1,2,4-triazole as a pendent groups into PVC chain investigated on photostability rules of tests. The modified polymers photostability investigated by observing indices (ICO, Ipo, and IOH), weight loss, UV and morphological studies, and all results obtained indicated that PVC-1, PVC-2, PVC-3 and PVC-4 gave lower growth rate of ICO, IPO, and IOH through UV exposure time. The photostability are given as PVC-4
The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources.