The complement system is emerging as a new target for treating many diseases. For example, Eculizumab, a humanized monoclonal antibody against complement component 5 (C5), has been approved for paroxysmal nocturnal hemoglobinuria (PNH) in which patient erythrocytes are lysed by complement. In this study, we developed vaccines to elicit autologous anti-C5 antibody production in mice for complement inhibition. Immunization of mice with a conservative C5 xenoprotein raised high titers of IgGs against the xenogenous C5, but these antibodies did not reduce C5 activity in the blood. In contrast, an autologous mouse C5 vaccine containing multiple predicted epitopes together with a tolerance-breaking peptide was found to induce anti-C5 autoantibody production in vivo, resulting decreased hemolytic activity in the blood. We further validated a peptide epitope within this C5 vaccine and created recombinant virus-like particles (VLPs) displaying this epitope fused with the tolerance breaking peptide. Immunizing mice with these novel nanoparticles elicited strong humoral responses against recombinant mouse C5, reduced hemolytic activity, and protected the mice from complement-mediated intravascular hemolysis in a model of PNH. This proof-of-concept study demonstrated that autologous C5-based vaccines could be an effective alternative or supplement for treating complement-mediated diseases such as PNH.

Machine learning algorithms for medical diagnostics often require resourceintensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical for use in the field. We investigate the use of model quantization and GPU-acceleration for chest X-ray classification on edge devices. We employ 3 types of quantization (dynamic range, float-16, and full int8) which we tested on models trained on the Chest-XRay14 Dataset. We achieved a 2-4x reduction in model size, offset by small decreases in the mean AUC-ROC score of 0.0%-0.9%. On ARM architectures, integer quantization was shown to improve inference latency by up to 57%. However, we also observe significant increases in latency on x86 processors. GPU acceleration also improved inference latency, but this was outweighed by kernel launch overhead. We show that optimization of diagnostic models has the potential to expand their utility to day-to-day devices used by patients and healthcare workers; however, these improvements are context-and architecture-dependent and should be tested on the relevant devices before deployment in low-resource environments.

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Most human activity recognition datasets that are publicly available have data captured by using either smartphones or smartwatches, which are usually placed on the waist or the wrist, respectively. These devices obtain one set of acceleration and angular velocity in the x -, y -, and z -axis from the accelerometer and the gyroscope planted in these devices. The PLHI-MC10 dataset contains data obtained by using 3 BioStamp nPoint® sensors from 7 physically healthy adult test subjects performing different exercise activities. These sensors are the state-of-the-art biomedical sensors manufactured by MC10. Each of the three sensors was attached to the subject externally on three muscles-Extensor Digitorum (Posterior Forearm), Gastrocnemius (Calf), and Pectoralis (Chest)-giving us three sets of 3 axial acceleration, two sets of 3 axial angular velocities, and 1 set of voltage values from the heart. Using three different sensors instead of a single sensor improves precision. It helps distinguish between human activities as it simultaneously captures the movement and contractions of various muscles from separate parts of the human body. Each test subject performed five activities (stairs, jogging, skipping, lifting kettlebell, basketball throws) in a supervised environment. The data is cleaned, filtered, and synced.