tRNA‐derived RNA fragments (tRFs), non‐coding single‐stranded RNAs with 14–35 nt in length, were found to play important roles in gene regulation, even in carcinogenesis. In this study, we investigated the expression of tRF‐Leu‐CAG in human non‐small cell lung cancer (NSCLC) and its function in the cell proliferation and cell cycle of NSCLC. The expression level of tRF‐Leu‐CAG was detected in NSCLC tissues, cell lines, and sera. tRF‐Leu‐CAG RNA levels were higher in NSCLC tumor tissues than in normal tissues, and also upregulated in NSCLC cell lines. A significant relationship was observed between stage progression and tRF‐Leu‐CAG in NSCLC sera. We found that in H1299 cells, inhibition of tRF‐Leu‐CAG suppressed cell proliferation and impeded cell cycle. AURKA was also repressed with the knockdown of tRF‐Leu‐CAG. Thus, our study revealed that tRF‐Leu‐CAG may be involved in regulating AURKA and could be a new diagnostic marker and potential therapeutic target in NSCLC.

Time series anomaly detection has been a perennially important topic in data science, with papers dating back to the 1950s. However, in recent years there has been an explosion of interest in this topic, much of it driven by the success of deep learning in other domains and for other time series tasks. Most of these papers test on one or more of a handful of popular benchmark datasets, created by Yahoo, Numenta, NASA, etc. In this work we make a surprising claim. The majority of the individual exemplars in these datasets suffer from one or more of four flaws. Because of these four flaws, we believe that many published comparisons of anomaly detection algorithms may be unreliable, and more importantly, much of the apparent progress in recent years may be illusionary. In addition to demonstrating these claims, with this paper we introduce the UCR Time Series Anomaly Datasets. We believe that this resource will perform a similar role as the UCR Time Series Classification Archive, by providing the community with a benchmark that allows meaningful comparisons between approaches and a meaningful gauge of overall progress.

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Multispectral filter array (MSFA)-based imaging is a compact, practical technique for snapshot spectral image capturing and reconstruction. The imaging and reconstruction quality is highly influenced by the spectral sensitivities and spatial arrangement of channels on MSFAs, and the used reconstruction method. In order to design a MSFA with high imaging capacity, we propose a sparse representation based approach to optimize spectral sensitivities and spatial arrangement of MSFAs. The proposed approach first overall models the various errors associated with spectral reconstruction, and then uses a global heuristic searching method to optimize MSFAs via minimizing the estimated error of MSFAs. Our MSFA optimization method can select filters from off-the-shelf candidate filter sets while assigning the selected filters to the designed MSFA. Experimental results on three datasets show that the proposed method is more efficient, flexible, and can design MSFAs with lower spectral construction errors when compared with existing state-of-the-art methods. The MSFAs designed by our method show better performance than others even using different spectral reconstruction methods.