Neuroscience of depression and anxiety related disorders
RNA-seq and bioinformatic analysis
A guide to how I share code for scientific work.
Intrinsic and extrinsic calibration examples from 3D photogrammetry.
Golgi-Cox, immunofluorescence.
Raspberry Pi cameras for high quality results.
Simple code for turning DeepLabCut outputs into meaningful results and publishable figures.
Levaraging deep learning for studying mouse gait
Supporting researchers developing coding skills at Carleton University and the University of Ottawa
PCA, t-SNE, UMAP, Fourier, and other concepts applied to neuroscience data.
Time series applications of Deep Learning
Current pharmacotherapies for depression and anxiety have problematic disadvantages, the most important being high resistance in the population. Novel treatments such as the hallucinogens (I.E. LSD, Psilocybin (Psilocin), Ketamine), while robust, possess their own weaknesses such as the potential for abuse and the strain of hallucinogenic side-effects on treatment facility infrastructure. In recent years there has been a push to find novel, so-called next-generational psychedelic treatments that retain the positive anti-depression or anxiolytic properties with out the hallucinogenic side-effects. In the lab of Dr. Argel Aguilar Valles, we work with REDACTED, a non-hallucinogenic analogue of REDACTED which is showing promise as a novel therapeutic for REDACTED.
Currently, we use chronic variable stress assays, next-generational sequencing, and immunofluorescent imaging to investigate the effects of next-generation psychedelics on depression-like behavioural and neurogenesis in a mouse model of depression.
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