We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of identifying wind-driven shells and bubbles using data from magneto-hydrodynamic simulations of turbulent molecular clouds with embedded stellar sources. The network is applied to two tasks, dense regression and segmentation, on two varieties of data, simulated density and synthetic 12 CO observations. Our Convolutional Approach for Shell Identification (CASI) is able to obtain a true positive rate greater than 90%, while maintaining a false positive rate of 1%, on two segmentation tasks and also performs well on related regression tasks. The source code for CASI is available on GitLab.
Antimicrobial peptides (AMPs) are peptides with promising applications for healthcare, veterinary, and agriculture industries. Despite prior success in AMP design using physics- or knowledge-based approaches, there is still a critical need to create new methodologies to design peptides with a low false positive rate and high AMP activity and selectivity. Toward this goal, we invented a cost-effective approach which utilizes a generative model to produce AMP-like sequences and molecular simulations to select peptides based on their structures and interactions. For a proof of concept, we curated a dataset that comprises 500,000 non-AMP peptide sequences and 8,000 labeled AMP sequences to train the generative model, which generated novel and diverse AMP candidates to potentially target a wide variety of microbes. Following a screening process to select peptides that are cationic and likely helical, we assessed 12 candidates by simulating their membrane-binding tendency to a lipid bilayer model. With the umbrella sampling technique, we determined the free energy change during transfer from the solution to the membrane environments for each peptide. Accordingly, we selected the six peptides with the best membrane-binding tendency, synthesized them, and characterized through spectroscopies and biological assays. Three novel peptides were validated with activity to inhibit bacterial growth. In aggregate, the combination of AMP generator and molecular simulations afford an enhanced accuracy in AMP design. Towards future precision AMP design, our methodology and results demonstrate the viability to design novel AMP-like peptides to target selected pathogens and mechanisms.
Herbert Simon's classic rich-get-richer model is one of the simplest empirically supported mechanisms capable of generating heavy-tail size distributions for complex systems. Simon argued analytically that a population of flavored elements growing by either adding a novel element or randomly replicating an existing one would afford a distribution of group sizes with a power-law tail. Here, we show that, in fact, Simon's model does not produce a simple power law size distribution as the initial element has a dominant first-mover advantage, and will be overrepresented by a factor proportional to the inverse of the innovation probability. The first group's size discrepancy cannot be explained away as a transient of the model, and may therefore be many orders of magnitude greater than expected. We demonstrate how Simon's analysis was correct but incomplete, and expand our alternate analysis to quantify the variability of long term rankings for all groups. We find that the expected time for a first replication is infinite, and show how an incipient group must break the mechanism to improve their odds of success. We present an example of citation counts for a specific field that demonstrates a first-mover advantage consistent with our revised view of the rich-getricher mechanism. Our findings call for a reexamination of preceding work invoking Simon's model and provide an expanded understanding going forward.
Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in calendar year 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity marketplace are relatively common and persist for longer than what physical constraints may suggest. Information feeds reported different prices for the same equity more than 120 million times, with almost 64 million dislocation segments featuring meaningfully longer duration and higher magnitude. During this period, roughly 22% of all trades occurred while the SIP and aggregated direct feeds were dislocated. The current market configuration resulted in a realized opportunity cost totaling over $160 million, a conservative estimate that does not take into account intra-day offsetting events. * Corresponding authors: Brian Tivnan (btivnan@mitre.org) and Christopher Danforth (chris.danforth@uvm.edu).
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