TRIPBASE: a database for identifying the human genomic DNA and lncRNA triplexes.

Long-non-coding RNAs (lncRNAs) are defined as RNA sequences which are >200 nt with no coding capacity. These lncRNAs participate in various biological mechanisms, and are widely abundant in a diversity of species. There is well-documented evidence that lncRNAs can interact with genomic DNAs by forming triple helices (triplexes). Previously, several computational methods have been designed based on the Hoogsteen base-pair rule to find theoretical RNA-DNA:DNA triplexes. While powerful, these methods suffer from a high false-positive rate between the predicted triplexes and the biological experiments. To address this issue, we first collected the experimental data of genomic RNA-DNA triplexes from antisense oligonucleotide (ASO)-mediated capture assays and used Triplexator, the most widely used tool for lncRNA-DNA interaction, to reveal the intrinsic information on true triplex binding potential. Based on the analysis, we proposed six computational attributes as filters to improve the in-silico triplex prediction by removing most false positives. Further, we have built a new database, TRIPBASE, as the first comprehensive collection of genome-wide triplex predictions of human lncRNAs. In TRIPBASE, the user interface allows scientists to apply customized filtering criteria to access the potential triplexes of human lncRNAs in the cis-regulatory regions of the human genome. TRIPBASE can be accessed at https://tripbase.iis.sinica.edu.tw/.

NK cell receptor and ligand composition influences the clearance of SARS-CoV-2.

To explore how the immune system controls clearance of SARS-CoV-2, we used a single-cell, mass cytometry-based proteomics platform to profile the immune systems of 21 patients who had recovered from SARS-CoV-2 infection without need for admission to an intensive care unit or for mechanical ventilation. We focused on receptors involved in interactions between immune cells and virus-infected cells. We found that the diversity of receptor repertoires on natural killer (NK) cells was negatively correlated with the viral clearance rate. In addition, NK subsets expressing the receptor DNAM1 were increased in patients who more rapidly recovered from infection. Ex vivo functional studies revealed that NK subpopulations with high DNAM1 expression had cytolytic activities in response to target cell stimulation. We also found that SARS-CoV-2 infection induced the expression of CD155 and nectin-4, ligands of DNAM1 and its paired coinhibitory receptor TIGIT, which counterbalanced the cytolytic activities of NK cells. Collectively, our results link the cytolytic immune responses of NK cells to the clearance of SARS-CoV-2 and show that the DNAM1 pathway modulates host-pathogen interactions during SARS-CoV-2 infection.

Termite Pest Identification Method Based on Deep Convolution Neural Networks.

Several species of drywood termites, subterranean termites, and fungus-growing termites cause extensive economic losses annually worldwide. Because no universal method is available for controlling all termites, correct species identification is crucial for termite management. Despite deep neural network technologies' promising performance in pest recognition, a method for automatic termite recognition remains lacking. To develop an automated deep learning classifier for termite image recognition suitable for mobile applications, we used smartphones to acquire 18,000 original images each of four termite pest species: Kalotermitidae: Cryptotermes domesticus (Haviland); Rhinotermitidae: Coptotermes formosanus Shiraki and Reticulitermes flaviceps (Oshima); and Termitidae: Odontotermes formosanus (Shiraki). Each original image included multiple individuals, and we applied five image segmentation techniques for capturing individual termites. We used 24,000 individual-termite images (4 species × 2 castes × 3 groups × 1,000 images) for model development and testing. We implemented a termite classification system by using a deep learning-based model, MobileNetV2. Our models achieved high accuracy scores of 0.947, 0.946, and 0.929 for identifying soldiers, workers, and both castes, respectively, which is not significantly different from human expert performance. We further applied image augmentation techniques, including geometrical transformations and intensity transformations, to individual-termite images. The results revealed that the same classification accuracy can be achieved by using 1,000 augmented images derived from only 200 individual-termite images, thus facilitating further model development on the basis of many fewer original images. Our image-based identification system can enable the selection of termite control tools for pest management professionals or homeowners.