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The silver pride of Bangladesh, migratory shad, Tenualosa ilisha (Hilsa), makes the highest contribution to the total fish production of Bangladesh. Despite its noteworthy contribution, a well-annotated transcriptome data is not available. Here we report a transcriptomic catalog of Hilsa, constructed by assembling RNA-Seq reads from different tissues of the fish including brain, gill, kidney, liver, and muscle. Hilsa fish were collected from different aquatic habitats (fresh, brackish, and sea water) and the sequencing was performed in the next generation sequencing (NGS) platform. De novo assembly of the sequences obtained from 46 cDNA libraries revealed 462,085 transcript isoforms that were subsequently annotated using the Universal Protein Resource Knowledgebase (UniPortKB) as a reference. Starting from the sampling to final annotation, all the steps along with the workflow are reported here. This study will provide a significant resource for ongoing and future research on Hilsa for transcriptome based expression profiling and identification of candidate genes. [ABSTRACT FROM AUTHOR]

The availability of images of events almost in real-time on social media has a prospect in many application developments. A humanitarian technology for disaster type and level assessment can be developed using the images and video available on social media. In this paper, we investigate the potential use of various available deep learning techniques to develop such an application. For our research, based on the use of publicly available image data, we have started collecting disaster images from various sources from South Asia. We created the South Asia Disaster (SAD) image dataset containing 493 images from various online news portals. Using the Keras as our framework to run our models: Visual Geometry Group (VGG-16 and VGG-19), Inception-V3 and Inception-ResNet-V2 (ResNet: Residual Network). However, to boost up the training speed, we dropped the fully connected layer and added a small, fully connected model. To identify the five different disasters: fire disaster, flood disaster, human disaster, infrastructure disaster, natural disaster; our proposed method with VGG-16 model's recognition accuracy was 84.51%, which is the highest accuracy on the SAD dataset. After performing the testing, we calculate the VGG-16 classifier's attention to visualize which part of the disaster images VGG-16 pays attention. [ABSTRACT FROM AUTHOR]