Animal pose estimation (APE) is utilized in preclinical research settings for various neurological disorders such as Parkinson's disease (PD), Huntington's disease (HD) and multiple sclerosis. The technique includes real-time scoring of impairment in the animals during testing or video recording. This is a time-consuming operation prone to errors due to visual fatigue. To overcome these shortcomings, APE automation by deep learning has been studied. The field of APE has gone through significant development backed by improvements in deep learning techniques. These developments have improved 2D and 3D pose estimation, 3D mesh reconstruction and behavior prediction capabilities. As a result, there are numerous sophisticated tools and datasets available today. Despite these developments, APE still lags behind human observer scoring with respect to accuracy and flexibility under complex scenarios. In this project, two critical challenges are being addressed within the context of neurological research focusing on PD. The first challenge is about the lack of comprehensive diverse datasets necessary for accurate training as well as for fine-tuning deep learning models. This is compounded by the inherent difficulty in working with uncooperative rodent subjects, whose unpredictable behaviors often impede reliable data collection. The second challenge focuses on reduction in variation of scores that result from being scored by different evaluators. This will also involve tackling bias and reducing human error for the purpose of reliable and accurate assessments. In order to address these issues, systematic data collection and deep learning in APE have been utilized to automate manual scoring procedures. This project will contribute to neurological research, particularly in understanding and treating disorders like PD. The goal is to improve methods used in assessing rodent behavior which could aid in developing effective therapeutics. The successful implementation of an automated scoring mechanism could set a new standard in neurological research, offering insights and methodologies that are more accurate and reliable.

Technologies capable of characterizing the full breadth of cellular systems need to be able to measure millions of proteins, isoforms, and complexes simultaneously. We describe an approach that fulfils this criterion: Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT). ADAPT employs an enriched library of single-stranded oligodeoxynucleotides (ssODNs) to profile complex biological samples, thus achieving an unprecedented coverage of system-wide, native biomolecules. We used ADAPT as a highly specific profiling tool that distinguishes women with or without breast cancer based on circulating exosomes in their blood. To develop ADAPT, we enriched a library of ~10[superscript 11] ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 10[superscript 6] enriched ssODNs were then profiled against plasma from independent groups of healthy and breast cancer-positive women. ssODN-mediated affinity purification and mass spectrometry identified low-abundance exosome-associated proteins and protein complexes, some with known significance in both normal homeostasis and disease. Sequencing of the recovered ssODNs provided quantitative measures that were used to build highly accurate multi-analyte signatures for patient classification. Probing plasma from 500 subjects with a smaller subset of 2000 resynthesized ssODNs stratified healthy, breast biopsy-negative, and -positive women. An AUC of 0.73 was obtained when comparing healthy donors with biopsy-positive patients.