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Background: Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are used simultaneously. DDIs act as an important link in both drug development and clinical treatment. Since it is not possible to study the interactions of such a large number of drugs using experimental means, a computer-based deep learning solution is always worth investigating. We propose a deep learning-based model that uses twin convolutional neural networks to learn representations from multimodal drug data and to make predictions about the possible types of drug effects. Results: In this paper, we propose a novel convolutional neural network algorithm using a Siamese network architecture called CNN-Siam. CNN-Siam uses a convolutional neural network (CNN) as a backbone network in the form of a twin network architecture to learn the feature representation of drug pairs from multimodal data of drugs (including chemical substructures, targets and enzymes). Moreover, this network is used to predict the types of drug interactions with the best optimization algorithms available (RAdam and LookAhead). The experimental data show that the CNN-Siam achieves an area under the precision-recall (AUPR) curve score of 0.96 on the benchmark dataset and a correct rate of 92%. These results are significant improvements compared to the state-of-the-art method (from 86 to 92%) and demonstrate the robustness of the CNN-Siam and the superiority of the new optimization algorithm through ablation experiments. Conclusion: The experimental results show that our multimodal siamese convolutional neural network can accurately predict DDIs, and the Siamese network architecture is able to learn the feature representation of drug pairs better than individual networks. CNN-Siam outperforms other state-of-the-art algorithms with the combination of data enhancement and better optimizers. But at the same time, CNN-Siam has some drawbacks, longer training time, generalization needs to be improved, and poorer classification results on some classes. [ABSTRACT FROM AUTHOR]

Abstract: Flooding in general is insignificant event worldwide and also in Thailand. The Central plain, the Northern plain and the northeast of Thailand are frequently flooded areas, caused by yearly monsoons. The Thai government has extra expenditure to provide disaster relief and for the restoration of flood affected structures, persons, livestock, etc. Current flood detection in real time or near real time has become a challenge in the flood emergency response. In this paper, an automatic instant time flood detection approach consisting of a data retrieval service, flood sensor observation service (SOS), flood detection web processing service (WPS) under a sensor web environment, is presented to generate dynamically real-time flood maps. A scenario of a RADARSAT and MODIS sensor web data service for flood detection cover of the Thailand Central plain is used to test the feasibility of the proposed framework. MODIS data are used to overview the wide area, while RADARSAT data are used to classify the flood area. The proposed framework using the transactional web coverage service (WCS-T) for instant flood detection processes dynamic real-time remote sensing observations and generates instant flood maps. The results show that the proposed approach is feasible for automatic instant flood detection. [Copyright &y& Elsevier]

Background: The Asian tiger mosquito, Aedes albopictus, has spread around the world. The migration was mainly mediated by maritime transportations. This species is known as an efficient vector for arboviruses, and it was responsible for the recent dengue outbreak in Tokyo, Japan. As the vector competence varies among geographical populations, and insecticide resistant populations have emerged, it is important to reveal their movements. The present study uses molecular techniques to search for a sign of introduction of an exotic population in three major international seaports on Kyushu Island., Methodology/principal Findings: Adults of Ae. albopictus were sampled around the international seaports of Fukuoka, Kitakyushu, and Nagasaki. Pairwise fixation indexes were estimated between the sampled populations based on 13 microsatellite markers. There was no clear genetic differentiation between distant and port populations in Kitakyushu and Nagasaki. However, the analysis found one distinct group near the container terminal in Fukuoka, which handles international freight containers mainly from adjacent countries. DNA samples were also obtained from Goto, Tsushima, Honshu, Ryukyu, Thailand, and the Philippines; and a cluster analysis and discriminant analysis revealed that the distinct group in Fukuoka did not belong to these groups. Combined with the results of phylogenetic analysis based on CO1, these results implied that this group originated from one Asian temperate region outside of Japan. Neutrality test and mismatch distribution analysis suggested that the establishment of this group was not recent., Conclusions/significance: The present study found a sign of Ae. albopictus introduction from a temperate region of Asia through maritime freight container transportation. The genetically distinct group found in Fukuoka likely originated from a temperate region outside of Japan. Maritime container transportation may introduce to Japan mosquitoes with greater vector competence/insecticide resistance. This is the first study to describe the spatial population structure of Ae. albopictus in Japan using molecular techniques., Competing Interests: The authors have declared that no competing interests exist.