Geographic context-aware text mining:enhance social media message classification for situational awareness by integrating spatial and temporal features

To find disaster relevant social media messages,current approaches utilize natural language processing methods or machine learning algorithms relying on text only,which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when posted.Meanwhile,a disaster relevant social media message is highly sensitive to its posting location and time.However,limited studies exist to explore what spatial features and the extent of how temporal,and especially spatial features can aid text classification.This paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets,along with the text information,for classifying disaster relevant social media posts.This work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data,and then used to enhance text mining.The deep learning-based method and commonly used machine learning algorithms,assessed the accuracy of the enhanced text-mining method.The performance results of different classification models generated by various combinations of textual,spatial,and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification.

Three-dimensional ATUM-SEM reconstruction and analysis of hepatic endoplasmic reticulum-organelle interactions

The endoplasmic reticulum(ER)is a contiguous and complicated membrane network in eukaryotic cells,and membrane contact sites(MCSs)between the ER and other organelles perform vital cellular functions,including lipid homeostasis,metabolite exchange,calcium level regulation,and organelle division.Here,we establish a whole pipeline to reconstruct all ER,mitochondria,lipid droplets,lysosomes,peroxisomes,and nuclei by automated tape-collecting ultramicrotome scanning electron microscopy and deep learning techniques,which generates an unprecedented 3D model for mapping liver samples.Furthermore,the morphology of various organelles and the MCSs between the ER and other organelles are systematically analyzed.We found that the ER presents with predominantly flat cisternae and is knitted tightly all throughout the intracellular space and around other organelles.In addition,the ER has a smaller volume-to-membrane surface area ratio than other organelles,which suggests that the ER could be more suited for functions that require a large membrane surface area.Our data also indicate that ER-mitochondria contacts are particularly abundant,especially for branched mitochondria.Our study provides 3D reconstructions of various organelles in liver samples together with important fundamental information for biochemical and functional studies in the liver.