FoxG1 promotes neuritogenesis and the formation of dendritic spines - a potential mechanism for West syndrome

Foxg1 is an ancient transcription factor gene specifically expressed in the developing rostral brain. Here it is implicated in genetic control of multiple aspects of cerebral cortex morphogenesis, including early distinction between pallial and subpallial fields, dorsoventral patterning of the pallium, regulation of the balance between neural proliferation and differentiation, neocortical layering and tuning of astrogenesis rates. Proper Foxg1 allele dosage is crucial to normal brain morphogenesis and function. Rare microduplications of chromosome 14 fragments including Foxg1 are associated to a variant of the West Syndrome (WS), namely a devastating infantile pathological entity, characterized by seizures, abnormal interictal EEG activity, and a profound damage of cognitive abilities, persisting beyond the attenuation of EEG anomalies which often occurs around the third year of life. Aim of this thesis was to explore basic histological mechanisms possibly linking exaggerated Foxg1 expression levels by neocortical projection neurons to WS. Three were the main findings of this work. First, I found that, upon transient overexpression of Foxg1 within the pallial neuronogenic lineage, neurons originating from the engineered proliferating pool - more numerous than in controls - retain the glutamatergic phenotype and, upon transplantation into neonatal neocortex, they survive at rates comparable with wild type controls. This suggests that an increased ratio between excitatory neocortical neurons and astrocytes occurring in WS patients may jeopardize the removal of ions and metabolites released in the extracellular space upon neuronal hyperactivity. Second, I discovered that neurite overgrowth triggered by Foxg1, previously documented in vitro, takes also place in vivo, upon transplantation of engineered neurons into neonatal neocortex, regardless of neuron birthdate. Moreover, I found that the neuritic overgrowth triggered by Foxg1 was mainly restricted to dendrites. There was also an increase in axonal length and branching, however this did not reach statistical significance. Remarkably these cytoarchitectonic abnormalities may ultimately result into larger afferent basins impinging on excitatory neurons, which can ease neuronal synchronization over larger distances and contribute to EEG abnormalities of WS patients. Third, I discovered that neuronal overexpression of Foxg1 elicits a considerable increase of spines on proximal dendrites and that this effect is exacerbated upon stimulation of neuronal hyperactivity. These findings will be the starting point of an ad hoc follow-up study, aimed at unveiling molecular mechanisms which connect Foxg1 overexpression with the development of such histological anomalies. Hopefully, they will be of help for rationale design of novel therapeutic approaches aimed at alleviating and limiting the neurological damages triggered by Foxg1 duplications

Scalable factorization model to discover implicit and explicit similarities across domains

University of Technology Sydney. Faculty of Engineering and Information Technology.E-commerce businesses increasingly depend on recommendation systems to introduce personalized services and products to their target customers. Achieving accurate recommendations requires a sufficient understanding of user preferences and item characteristics. Given the current innovations on the Web, coupled datasets are abundantly available across domains. An analysis of these datasets can provide a broader knowledge to understand the underlying relationship between users and items. This thorough understanding results in more collaborative filtering power and leads to a higher recommendation accuracy. However, how to effectively use this knowledge for recommendation is still a challenging problem. In this research, we propose to exploit both explicit and implicit similarities extracted from latent factors across domains with matrix tri-factorization. On the coupled dimensions, common parts of the coupled factors across domains are shared among them. At the same time, their domain-specific parts are preserved. We show that such a configuration of both common and domain-specific parts benefits cross-domain recommendations significantly. Moreover, on the non-coupled dimensions, the middle factor of the tri-factorization is proposed to use to match the closely related clusters across datasets and align the matched ones to transfer cross-domain implicit similarities, further improving the recommendation. Furthermore, when dealing with data coupled from different sources, the scalability of the analytical method is another significant concern. We design a distributed factorization model that can scale up as the observed data across domains increases. Our data parallelism, based on Apache Spark, enables the model to have the smallest communication cost. Also, the model is equipped with an optimized solver that converges faster. We demonstrate that these key features stabilize our model’s performance when the data grows. Validated on real-world datasets, our developed model outperforms the existing algorithms regarding recommendation accuracy and scalability. These empirical results illustrate the potential of our research in exploiting both explicit and implicit similarities across domains for improving recommendation performance

Persistent Test-time Adaptation in Episodic Testing Scenarios

Current test-time adaptation (TTA) approaches aim to adapt to environments that change continuously. Yet, when the environments not only change but also recur in a correlated manner over time, such as in the case of day-night surveillance cameras, it is unclear whether the adaptability of these methods is sustained after a long run. This study aims to examine the error accumulation of TTA models when they are repeatedly exposed to previous testing environments, proposing a novel testing setting called episodic TTA. To study this phenomenon, we design a simulation of TTA process on a simple yet representative $\epsilon$-perturbed Gaussian Mixture Model Classifier and derive the theoretical findings revealing the dataset- and algorithm-dependent factors that contribute to the gradual degeneration of TTA methods through time. Our investigation has led us to propose a method, named persistent TTA (PeTTA). PeTTA senses the model divergence towards a collapsing and adjusts the adaptation strategy of TTA, striking a balance between two primary objectives: adaptation and preventing model collapse. The stability of PeTTA in the face of episodic TTA scenarios has been demonstrated through a set of comprehensive experiments on various benchmarks

INITIAL MODEL FOR SEDIMENT TRANSPORT AND TOPOGRAPHICAL CHANGE AT THE RED RIVER MOUTH, NORTHERN VIETNAM

Joint Research on Environmental Science and Technology for the Eart