INQUIRY

INQUIRY • Volume 19, 2015
spectroscopy and UV-Visible absorption spectroscopy.
The crystals will be studied in the lab’s spectroscopic
system to elucidate the femtosecond electronic dynamics
of singlet fission.
Small Amplitude Excitations in the Gauge-Higgs
Interaction Model
Gordon Chavez, Mathematics
Sponsor: Professor Daniel Zwanziger, Physics
This project is a gauge theoretical study of the Higgs
mechanism and resulting physics. Essentially, this study
examines the interaction between light and the Higgs
field and shows how electromagnetic waves acquire
mass-energy from their interaction with the Higgs field. A
Lagrangian was used that was originally formulated as a
phenomenological model of superconductivity, where the
gauge field was the electromagnetic field and the scalar
field was the superconducting electron-pair condensate.
However, the model can be applied to the study of many
physical systems. The study found propagating wave
solutions and instabilities with an Einstein-form (E=mc^2)
dispersion relation. This study is made more interesting
and relevant given the 2012 discovery of the Higgs particle
at CERN. The model used is indeed the model for
Abelian gauge-Higgs interaction, where the gauge field is
electromagnetism and the scalar field represents the Higgs
field. This model’s solutions can impart an understanding
of the physics generated by the Higgs.
The Molecular Role of E-cadherin in Contact-Mediated
Cell Polarization
Kimberly Chen, Biology
Sponsor: Professor Jeremy Nance, Cell Biology, NYU
School of Medicine
Polarization is an essential process for key developmental
events. Caenorhabditis elegans embryos polarize
radially by excluding the polarity protein PAR-6 specifically
from contact sites. This restriction is possible due to
the transmembrane protein HMR-1/E-cadherin. HMR-1
polarizes cells by recruiting the RhoGAP PAC-1 to cellcontacts.
This results in the inactivation of Rho GTPase
CDC-42, the protein responsible for localizing PAR-6, at
cell-contacts. In hmr-1 mutant embryos, PAC-1 is recruited
to cell-contacts by other factors but fails to function, and
thus cells remain unpolarized. It is unknown how HMR-1
regulates PAC-1 function. By ectopically expressing
PAC-1 to contact-free surfaces and producing cells that fail
to polarize, it was shown that PAC-1 cannot function without
HMR-1. The results, obtained through immunostaining
and structure-function analysis, suggest that HMR-1 and/or
a component of the cadherin-catenin complex are required
to activate PAC-1. Determining the role HMR-1 plays in C.
elegans cell polarization provides insight into E-cadherin
homologs of other biological systems. Furthermore, studying
polarity defects in relation to cell-cell adhesion may
lead to better understanding of cancer metastasis, which
requires a loss of polarity.
Learning Distributed Representations from Temporal
Relational Graphs
Youngduck Choi, Computer Science, Mathematics
Sponsor: Professor David Sontag, Computer Science
Distributed representations (embeddings) of concepts
are a powerful tool for machine learning, summarization
and information retrieval. For example, in natural language
processing, using word embeddings as the input for
deep learning of convolutional neural networks results in
state-of-the-art accuracy on tasks ranging from sentiment
analysis to part-of-speech tagging. However, it is less
clear how to learn embeddings from non-textual data such
as medical records of diagnoses and medications across
time or the products viewed and purchased by customers
of an e-commerce website. Two strategies for learning
distributed representations are presented: one takes as
input a weighted graph derived from co-occurrence counts
across time, while the other directly uses the temporal data.
Using these, this study shows how to learn distributed
representations for all of medicine including diseases,
medications, procedures and lab test results. It is believed
these embeddings will be broadly useful across medical
informatics. This study introduces several new benchmarks
and uses them to perform a comprehensive evaluation of
the learned semantics of these embeddings, comparing
them to embeddings learned from medical text. Finally,
this study demonstrates how to use the embeddings within
a supervised prediction task of early detection of Type 2
diabetes.
Analysis of the “Euglenoid” Motion: Locomotion by
Shape Deformations
Olivia J. Chu, Mathematics
Sponsor: Professor Trushant Majmudar, Mathematics
Unicellular microorganisms typically swim using their
flagella, constantly needing to push or pull to move forward.
Some microorganisms, such as the unicellular protist
Euglena, have developed an alternate strategy for locomotion
known as “euglenoid movement,” or “metaboly,” in
which the contour of the organism’s surface changes in
a wave-like pattern. Currently, euglenoid movement is
widely recognized but not well understood. Fundamental
questions such as why or when this strategy of motion is
activated and the hydrodynamic efficiency of the strokes
remain unanswered. When in water, Euglena exhibits
conventional flagellum-driven motion. However, when the
79