Interrogation of Astrophysical Laboratory Photoionized Plasmas with Experiments at the 1MA Zebra and 26MA Z Pulsed-Power Drivers

Due to the lack of spaceships with intergalactic capability, now and in the foreseeable future, we must find alternate methods to bring the universe to us. By studying laboratory photoionized plasmas we can further our understanding of highly energetic astrophysical environments, such as the accretion disks around black holes and neutron stars, giving rise to, active-galactic-nuclei and x-ray binary systems. These cosmic engines produce high-intensity broadband x-ray and UV radiation flux, which heats and ionizes the surrounding gas into a photoionized plasma. Laboratory data is a crucial aid in our interpretation of astrophysical observations and ability to test and validate astrophysical codes. We have used the 1MA Zebra and 26MA Z-machine pulsed-power drivers to experimentally study astrophysically relevant photoionized plasmas. On Zebra, the supersonic gas jet platform provides the first method for university-scale drivers to study such plasmas. The gas jet platform leverages the diverse diagnostic capability and robust shot rate of Zebra, providing perspectives inaccessible to that of large-scale drivers. This work has motivated the first broadband spectral characterization of Zebra’s radiation drive. Alternatively, on the Z-machine, the photoionized gas cell platform has enabled studies at the highest level of x-ray flux terrestrially possible. For the first time, we have integrated photon Doppler velocimetry into the gas cell, which has been used to answer the critical question of uniformity within the gas cell by observing spatially resolved electron number density time histories. In the broader context of Z diagnostics, this work has also demonstrated the feasibility of high-precision low-noise measurements in close proximity to the overwhelming x-ray flux of the Z-machine. These two laboratory photoionized plasma platforms each provide unique capabilities: Zebra experiments emphasize the role of L-shell atomic physics on heating and ionization of neon plasmas, while Z extends it to plasmas populated with K-shell ions. Hence, both experiments provide complementary astrophysically relevant data of photoionized plasmas

CRABS & ‘CROBES: THE TRIPARTITE RELATIONSHIP OF A HOST, PARASITE, AND THEIR RESPECTIVE MICROBIOMES

Growing evidence suggests that the associated microbiome of organisms (the holobiont) has been shaping the evolutionary pathways of macroorganisms for thousands of years, and that these tiny symbionts can influence species interactions. Yet, while studies have investigated host-parasites and microbiomes separately, how the two systems interact and influence each other has only begun to be explored. This relationship among hosts, parasites, and microbial communities changes the dynamic of host-parasite evolution from a more traditional co-evolution, to a tripartite evolutionary relationship. My research aimed to resolve questions about the community composition and diversity of microbial symbionts associated with host and parasite separately and when combined (parasitized hosts). My research also developed a methodology that can be used in future studies to manipulate microbiomes, in the pursuit of understanding how the loss or manipulation of the microbiome affects parasitism. Developing the methodology for future microbiome manipulation included determining a method for bacterial inhibition and testing the effects of inhibition on community diversity. For the first part of the research, infected and uninfected crab hosts were sampled from a coastal North Carolina oyster reef three times over a four-month period. Tissue samples were collected from four biological groups: uninfected crab viscera, infected crab viscera (i.e. host + parasite), the entire adult parasite externa, and parasite larvae. Microbial DNA was extracted from tissue samples and sequenced using the V6-V8 region of the 16S rRNA microbial gene to determine the community composition and diversity of the microbiome for each biological sample and across time. Microbial community analysis revealed that parasite externae and larvae had very similar microbiomes but were significantly different from the microbiomes of the crabs. Microbiomes of infected versus uninfected crabs were also significantly different. Both adult parasite externae and parasite larvae were found to have bacteria including Pseudoalteromonas species, which provide natural antimicrobial defenses, while uninfected crabs were mainly comprised of Rhodobacteraceae, commonly associated with photo- and chemoautotrophy. To develop the methodology for the second part of the research, modified Kirby-Bauer disk diffusion technique was used to determine the effects of biological sample, antibiotic type, antibiotic concentration, and time to inhibition of bacteria. Three broad spectrum antibiotics (ampicillin, chloramphenicol, and gentamicin) and their combinations (total of seven antibiotics and combinations) were tested across four concentrations against all four biological groups. Chloramphenicol, at 2mg/mL, was found to be the most effective antibiotic at inhibiting microbial growth across all four biological samples. To determine the effect of chloramphenicol on microbial community composition and diversity, this treatment (or a no treatment control) was applied to live infected and uninfected crabs for 24 hours. After which, microbial DNA was extracted from all four biological samples and will be sent for sequencing, thus the results of bacterial community diversity are pending. Understanding the microbial community composition of a host and parasite, and developing a methodology for manipulating those microbiomes, is an important step to beginning to understand the microbiome's role in the host-parasite relationship and determining how the tripartite relationship impacts coevolutionary processes

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources. The need for UQ is especially acute for neural models, which are becoming increasingly standard yet are challenging to interpret. While several approaches to UQ have been proposed in the literature, there is no clear consensus on the comparative performance of these models. In this paper, we study this question in the context of regression tasks. We systematically evaluate several methods on five benchmark datasets using multiple complementary performance metrics. Our experiments show that none of the methods we tested is unequivocally superior to all others, and none produces a particularly reliable ranking of errors across multiple datasets. While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others

Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport

Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction. Our approach employs optimal transport (OT) to find a minimal cost alignment between the inputs. However, directly applying OT often produces dense and therefore uninterpretable alignments. To overcome this limitation, we introduce novel constrained variants of the OT problem that result in highly sparse alignments with controllable sparsity. Our model is end-to-end differentiable using the Sinkhorn algorithm for OT and can be trained without any alignment annotations. We evaluate our model on the StackExchange, MultiNews, e-SNLI, and MultiRC datasets. Our model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models.Comment: To appear at ACL 202

Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network

Predicting the solubility of given molecules is an important task in the pharmaceutical industry, and consequently this is a well-studied topic. In this research, we revisited this problem with the advantage of modern computing resources. We applied two machine learning models, a linear regression model and a graph convolutional neural network model, on multiple experimental datasets. Both methods can make reasonable predictions while the GCNN model had the best performance. However, the current GCNN model is a black box, while feature importance analysis from the linear regression model offers more insights into the underlying chemical influences. Using the linear regression model, we show how each functional group affects the overall solubility. Ultimately, knowing how chemical structure influences chemical properties is crucial when designing new drugs. Future work should aim to combine the high performance of GCNNs with the interpretability of linear regression, unlocking new advances in next generation high throughput screening.Comment: 6 pages, 4 figures, 2 table