MOESM1 of LIN28B is highly expressed in atypical teratoid/rhabdoid tumor (AT/RT) and suppressed through the restoration of SMARCB1

Additional file 1: Table S1. mRNA/miRNA expression after LIN28 knockdown

Additional file 1: of M1 macrophage recruitment correlates with worse outcome in SHH Medulloblastomas

Figure S1. Macrophage recruitment in human tonsil FFPE tissue. Figure S2. Expression heatmap of 22 subgroup-specific signature genes in 48 study patients by the nanoString nCounter System. Figure S3. TAM recruitment and prognostic outcomes in the whole patient cohort. Figure S4. TAM recruitment and prognostic outcomes in SHH MB from Yonsei University. (PPTX 3883 kb

Additional file 2: of M1 macrophage recruitment correlates with worse outcome in SHH Medulloblastomas

Table S1. List of antibodies used for immunohistochemistry and immunofluorescence assay Table S2. Correlation between TAM and other prognostic factors estimated with a logistic regression in SHH MB. (DOCX 17 kb

Taste Bud-Inspired Single-Drop Multitaste Sensing for Comprehensive Flavor Analysis with Deep Learning Algorithms

The electronic tongue (E-tongue) system has emerged as a significant innovation, aiming to replicate the complexity of human taste perception. In spite of the advancements in E-tongue technologies, two primary challenges remain to be addressed. First, evaluating the actual taste is complex due to interactions between taste and substances, such as synergistic and suppressive effects. Second, ensuring reliable outcomes in dynamic conditions, particularly when faced with high deviation error data, presents a significant challenge. The present study introduces a bioinspired artificial E-tongue system that mimics the gustatory system by integrating multiple arrays of taste sensors to emulate taste buds in the human tongue and incorporating a customized deep-learning algorithm for taste interpretation. The developed E-tongue system is capable of detecting four distinct tastes in a single drop of dietary compounds, such as saltiness, sourness, astringency, and sweetness, demonstrating notable reversibility and selectivity. The taste profiles of six different wines are obtained by the E-tongue system and demonstrated similarities in taste trends between the E-tongue system and user reviews from online, although some disparities still exist. To mitigate these disparities, a prototype-based classifier with soft voting is devised and implemented for the artificial E-tongue system. The artificial E-tongue system achieved a high classification accuracy of ∼95% in distinguishing among six different wines and ∼90% accuracy even in an environment where more than 1/3 of the data contained errors. Moreover, by harnessing the capabilities of deep learning technology, a recommendation system was demonstrated to enhance the user experience