133 research outputs found
E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification
We propose the use of group convolutional neural network architectures
(GCNNs) equivariant to the 2D Euclidean group, , for the task of galaxy
morphology classification by utilizing symmetries of the data present in galaxy
images as an inductive bias in the architecture. We conduct robustness studies
by introducing artificial perturbations via Poisson noise insertion and
one-pixel adversarial attacks to simulate the effects of limited observational
capabilities. We train, validate, and test GCNNs equivariant to discrete
subgroups of - the cyclic and dihedral groups of order - on the
Galaxy10 DECals dataset and find that GCNNs achieve higher classification
accuracy and are consistently more robust than their non-equivariant
counterparts, with an architecture equivariant to the group achieving
a test-set accuracy. We also find that the model loses
accuracy on a -noise dataset and all GCNNs are less susceptible to
one-pixel perturbations than an identically constructed CNN. Our code is
publicly available at https://github.com/snehjp2/GCNNMorphology.Comment: 10 pages, 4 figures, 3 tables, Accepted to the Machine Learning and
the Physical Sciences Workshop at NeurIPS 202
Synthetic Nanoparticles for Vaccines and Immunotherapy
The immune system plays a critical role in our health. No other component of human physiology plays a decisive role in as diverse an array of maladies, from deadly diseases with which we are all familiar to equally terrible esoteric conditions: HIV, malaria, pneumococcal and influenza infections; cancer; atherosclerosis; autoimmune diseases such
as lupus, diabetes, and multiple sclerosis. The importance of understanding the function of the immune system and learning how to modulate immunity to protect against or treat disease thus cannot be overstated. Fortunately, we are entering an exciting era where the
science of immunology is defining pathways for the rational manipulation of the immune system at the cellular and molecular level, and this understanding is leading to dramatic advances in the clinic that are transforming the future of medicine.1,2 These initial advances are being made primarily through biologic drugsâ recombinant proteins (especially antibodies) or patient-derived cell therapiesâ but exciting data from preclinical studies suggest that a marriage of approaches based in biotechnology with the materials science and chemistry of nanomaterials, especially nanoparticles, could enable more effective and safer immune engineering strategies. This review will examine these nanoparticle-based strategies to immune modulation in detail, and discuss the promise and outstanding challenges facing the field of immune engineering from a chemical biology/materials engineering perspectiveNational Institutes of Health (U.S.) (Grants AI111860, CA174795, CA172164, AI091693, and AI095109)United States. Department of Defense (W911NF-13-D-0001 and Awards W911NF-07-D-0004
Catalysing sustainable fuel and chemical synthesis
Concerns over the economics of proven fossil fuel reserves, in concert with government and public acceptance of the anthropogenic origin of rising CO2 emissions and associated climate change from such combustible carbon, are driving academic and commercial research into new sustainable routes to fuel and chemicals. The quest for such sustainable resources to meet the demands of a rapidly rising global population represents one of this centuryâs grand challenges. Here, we discuss catalytic solutions to the clean synthesis of biodiesel, the most readily implemented and low cost, alternative source of transportation fuels, and oxygenated organic molecules for the manufacture of fine and speciality chemicals to meet future societal demands
Communicating regulatory high-throughput sequencing data using BioCompute Objects
This project demonstrates the use of the IEEE 2791-2020 Standard (BioCompute Objects [BCO]) to enable the complete and concise communication of results from next generation sequencing (NGS) analysis. One arm of a clinical trial was replicated using synthetically generated data made to resemble real biological data and then two independent analyses were performed. The first simulated a pharmaceutical regulatory submission to the US Food and Drug Administration (FDA) including analysis of results and a BCO. The second simulated an FDA review that included an independent analysis of the submitted data. Of the 118 simulated patient samples generated, 117 (99.15%) were in agreement in the two analyses. This process exemplifies how a template BCO (tBCO), including a verification kit, facilitates transparency and reproducibility, thereby reinforcing confidence in the regulatory submission process
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