2021 BEETL competition: advancing transfer learning for subject independence & heterogenous EEG data sets

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because regular machine learning methods cannot generalise well across human subjects and handle learning from different, heterogeneously collected data sets, thus limiting the scale of training data available. On the other hand, the many developments in transfer- and meta-learning fields would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for all the things that make biosignal data analysis a hard problem. We design two transfer learning challenges around a. clinical diagnostics and b. neurotechnology. These two challenges are designed to probe algorithmic performance with all the challenges of biosignal data, such as low signal-to-noise ratios, major variability among subjects, differences in the data recording sessions and techniques, and even between the specific BCI tasks recorded in the dataset. Task 1 is centred on the field of medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets. The successful 2021 BEETL competition with its over 30 competing teams and its 3 winning entries brought attention to the potential of deep transfer learning and combinations of set theory and conventional machine learning techniques to overcome the challenges. The results set a new state-of-the-art for the real-world BEETL benchmarks

PIK3CA dependence and sensitivity to therapeutic targeting in urothelial carcinoma

Background Many urothelial carcinomas (UC) contain activating PIK3CA mutations. In telomerase-immortalized normal urothelial cells (TERT-NHUC), ectopic expression of mutant PIK3CA induces PI3K pathway activation, cell proliferation and cell migration. However, it is not clear whether advanced UC tumors are PIK3CA-dependent and whether PI3K pathway inhibition is a good therapeutic option in such cases. Methods We used retrovirus-mediated delivery of shRNA to knock down mutant PIK3CA in UC cell lines and assessed effects on pathway activation, cell proliferation, migration and tumorigenicity. The effect of the class I PI3K inhibitor GDC-0941 was assessed in a panel of UC cell lines with a range of known molecular alterations in the PI3K pathway. Results Specific knockdown of PIK3CA inhibited proliferation, migration, anchorage-independent growth and in vivo tumor growth of cells with PIK3CA mutations. Sensitivity to GDC-0941 was dependent on hotspot PIK3CA mutation status. Cells with rare PIK3CA mutations and co-occurring TSC1 or PTEN mutations were less sensitive. Furthermore, downstream PI3K pathway alterations in TSC1 or PTEN or co-occurring AKT1 and RAS gene mutations were associated with GDC-0941 resistance. Conclusions Mutant PIK3CA is a potent oncogenic driver in many UC cell lines and may represent a valuable therapeutic target in advanced bladder cancer

Marco activo de recursos de innovación docente: Madrid

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