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It has been established that the early presence of Glial cell line-derived neurotrophic factor (GDNF) in the metanephric mesenchyme is essential for the outgrowth of the ureteric bud from the nephric duct. Also, GDNF in the cap mesenchyme promotes branching of the collecting duct throughout kidney development. We have shown that GNDF is expressed by self-renewing nephron progenitor cells (NPCs) at mouse embryonic day 10.5 (E10.5); however, single-cell RNAseq by Magella et al., demonstrated that Gdnf is strongly expressed by stromal progenitor cells at E14.5. To reconcile these seemingly opposed results, we used a transgenic GDNF-CreERT2 and R26R-tdTomato mouse model to conduct lineage tracing experiments activated through tamoxifen injections at different time points during development. We hypothesized that the expression of Gdnf was dynamic, with early expression in NPCs transitioning into stromal progenitors at later stages. Our lineage studies demonstrate that Gdnf is expressed primarily by NPCs at early stages (from E9.5 to E12.5) and primarily by stromal cells during the later stages of development (E13.5 and later). Unexpectedly, we revealed that Gdnf is also expressed by nephric duct (ND) progenitors at even earlier stages. Expression of Gdnf was transient in the ND, and quantification of recombination shows that expression in the ND progenitors starts as early as E7.5 and peaks around E8.5. Altogether, our results indicate that Gdnf is expressed by all kidney progenitor cells (ND progenitors, NPCs, and stromal progenitors) but at different time points during kidney development. The physiological relevance of these transitions remains to be elucidated.

The objective of machine-based binaural sound source localisation is to estimate the position of sound sources as accurately as possible from a binaural sound signal. A subset of machine-based binaural sound source localisation methods utilise the listener's measured head related transfer function (HRTF) dataset. These HRTF-based binaural sound source localisation methods provide opportunities for audio augmented reality and may also provide head mounted visualisations for the hard of hearing. In their original publications, the majority of HRTF-based binaural sound source localisation methods have been tested by synthetically generating their test data and training data using the exact same HRTF dataset. It has been shown that these methods are using the unique measurement noise present in the HRTFs in the dataset as a cue for localisation. This would not occur in a real life setting. Therefore, a comprehensive evaluation methodology is used in this thesis. The evaluation methodology is designed to test the methods' robustness to different sources of noise associated with HRTF-based binaural sound source localisation. A HRTF-based binaural sound source localisation method is proposed that uses interaural and spectral features for localisation of sound sources with any direction of arrival (DOA) on the full-sphere. The method is designed to be robust to the presence of reverberation, additive noise and different types of sounds. The method uses the interaural phase difference (IPD) for lateral angle localisation, then interaural and spectral features for polar angle localisation. For polar angle estimation, the method applies a different weighting to the likelihood distributions derived from the interaural and spectral features depending on the estimated lateral angle. In particular, only the spectral features are used for sound sources near or on the median plane. The proposed method outperforms the state of the art HRTF-based binaural sound source localisation methods in challenging testing conditions with the presence of measurement noise and reverberation.