Eric Sembrat's Test Bonanza

The progress in neutrino physics over the past fifteen years has been tremendous: we have learned that neutrinos have mass and change flavor. This discovery won the 2015 Nobel Prize. I will pick out one of the threads of the story-- the measurement of flavor oscillation in neutrinos produced by cosmic ray showers in the atmosphere, and further measurements by long-baseline beam experiments. In this talk, I will present the latest results from the Super-Kamiokande and T2K (Tokai to Kamioka) long-baseline experiments, and will discuss how the next generation of high-intensity beam experiments will address some of the remaining puzzles.

Outermost occupied electron shells of chemical elements resemble monopoles, dipoles, quadrupoles, and octupoles corresponding to filled s-, p-, d-, and f-atomic orbitals. Theoretically, elements with hexadecapolar outer shells could also exist, but none of the known stable elements have filled g-orbitals. On the other hand, the research paradigm of “colloidal atoms” displays complexity of physical behavior of colloidal particles exceeding that of their atomic counterparts, allowing for switching between colloidal elastic dipole and quadrupole configurations using weak external stimuli. This lecture will describe colloidal elastic hexadecapoles formed by polymer microspheres dispersed in a liquid crystal, a nematic fluid of orientationally ordered molecular rods. The solid microspheres locally perturb the uniform molecular alignment of the nematic host, inducing hexadecapolar and other elastic multipoles that drive highly anisotropic colloidal interactions. We uncover physical underpinnings behind the spontaneous formation of colloidal elastic hexadecapoles and describe the ensuing particle bonding inaccessible to colloids studied previously. The lecture will conclude with discussion of practical applications that can be enabled by combining unique properties of metal and semiconductor nanoparticles with facile switching of self-assembled ordered superstructures that they exhibit in nematic hosts.

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Louisa Barama’s PhD defense on November 18^th in room ES&T L1255 at 3:00 pm (Zoom Meeting : https://gatech.zoom.us/j/97517526199).

Advanced Methods for Real-time Identification and Determination of seismic events

Natural disasters pose an indistinguishable threat to populations all around the world, affecting ~200 hundred million every year, with earthquakes being the most deadly. Global seismic monitoring allows for robust real-time analysis to provide useful information about an event to assist in earthquake emergency response. Additionally it is an essential tool for monitoring anthropogenic seismic sources like nuclear weapons tests, the use of which can have disastrous effects on human life, ecological environments and public health, ramifications that can last for generations.  The focus of this thesis is on characterizing and identifying unique seismic events in near-real-time using the waveforms of initial seismic phase arrivals from teleseismic stations, their derivative products like radiated earthquake energy and rupture duration, and machine learning (ML). This thesis is a compilation of several works addressing novel methods for seismic event identification of: global tsunamigenic earthquakes, uncharacteristically high-energy tsunami earthquakes, deep earthquakes, and underground nuclear explosions (UNE).  First, I present the current Real-Time Earthquake Energy and Rupture Duration Determinations (RTerg) products and methodology applied to a case study of fast-rupturing tsunami earthquakes in the Solomon Islands, testing the robustness of the RTerg derivative waveform products and Tsunami Earthquake (TsE) discriminant threshold used for real-time analysis. Second, I show how peaks in RTerg energy flux curves from teleseismic stations and their differences in broadband and high frequency bandwidths can be associated with depth phase arrivals (P, pP, sP) to identify deep earthquakes, highlighting the potential for real-time depth determinations using first derivative waveform products without additional processing of waveforms. Next, I introduce nuclear explosion monitoring from a global network of stations, starting with the compilation of the first openly available and comprehensive UNE seismic waveform and event catalog termed GTUNE (Georgia Tech Underground Nuclear Explosions). GTUNE seismic records are sourced from declassified nuclear tests, previously published datasets and openly available waveforms and were assembled into a user‐friendly format compatible with most python‐based ML packages. The next contribution to this thesis is the development of a global UNE classifier using labeled P-wave seismograms from GTUNE. I trained a Convolutional Neural Network (CNN) to identify three classes: earthquake P-wave, nuclear P-wave, and noise. I found that the model can accurately characterize most events, finding over 90% of the signals in the validation set, even with limited training data. Lastly, I combine the thesis works described thus far and applied similar ML methodology to classify/predict deep earthquakes, using both a CNN and a Deep Neural Net (DNN), trained on both physical features of the energy flux time series (prominence and peak density) as well as the original waveforms.  Results show better single station predictions using the original waveforms.  By contrast, for full network determinations, the energy flux products perform the best, despite the smaller training dataset.  We anticipate that ML models like our UNE and deep earthquake classifiers can have broad application for other “small data” seismic signals including volcanic and non-volcanic tremor, anomalous earthquakes, tsunami earthquakes, ice-quakes or landslide-quakes.

Committee:  Dr. Andrew V Newman (advisor), Dr. Zhigang Peng, Dr. Jesse Williams, Dr. Winnie Chu, Dr. Samer Naif

Title:

Combinatorial assembly of primary metabolites in animal model systems

Abstract:

The widespread availability of high-resolution mass spectrometry has resulted in the detection of a vast space of unannotated metabolites in animal model systems, but it is unclear whether these represent functional products of specific biosynthetic pathways or simply biochemical “noise”. Our ongoing characterization efforts in C. elegans mouse have revealed combinatorial assembly of primary metabolic building blocks as a new strategy to generate structural and functional diversity. Examples include highly modified nucleosides and modular glycosides that integrate diverse building blocks from amino acid and fatty acid metabolism. These modular metabolites are involved in virtually every aspect of C. elegans life history, regulating e.g. development, aging, and behavior. Ongoing complementary analyses of mammalian metabolomes have started to reveal metabolites of similar biogenetic origin.

Genome-wide association studies revealed carboxylesterase (cest) homologs as the key enzymes mediating modular assembly in C. elegans. This unexpected finding suggested that modular metabolite biosynthesis originates from “hijacking” of conserved detoxification mechanisms. Modular metabolites thus represent a distinct biosynthetic strategy for generating structural and functional diversity in nematodes, complementing the primarily PKS- and NRPS-derived universe of microbial natural products. While many aspects of modular metabolite biosynthesis and function remain to be elucidated, their identification demonstrates how phenotype-driven compound discovery, untargeted metabolomics, and genomic approaches can synergize to facilitate annotation of metabolic dark matter.

Are you a grad student or early-career researcher looking for a toolkit of easily available indicators to help find vetted, trustworthy sources?

Gain familiarity with some of the tools for measuring scholarship and outputs of published research​ in this helpful workshop. This course is an introduction to common, readily available indicators of research impact as a means of source validation. This includes traditional measures such as journal impact and article metrics, and how these are calculated in sources such as Journal Citation Reports (JCR) and Google Scholar. It is not meant to establish a standard of success with research using only these indicators.

The course also covers emerging metrics/altmetrics, researcher profiles and open access scholarship.