Robust Biosensors for Healthcare Applications: from High-Content Screening to Point-of-Care Testing

Efficient detection of proteins, mammalian cells, microorganisms and other biological systems in complex mixture is essential in disease diagnosis and environmental health. Therefore, technological platforms that provide sensors of high sensitivity, selectivity and stability are greatly desired. Recently, the ‘chemical-nose’ sensing approach has proved to be an effective strategy for profiling bio-relevant targets in complex mixtures. Detecting analytes in complex mixture is a challenge that conventional specificity-based sensors are still trying to solve due to the requirement of prior knowledge of the analyte, which is unknown in many cases. This thesis focuses on how to develop simple and robust chemical-nose sensors for complex mixtures using supramolecular interactions between nanoparticles, fluorescent proteins, enzymes, and fluorescent polymers. We have successfully developed effective sensors for many healthcare applications including chemotherapeutic drug profiling, cancer diagnostics, environmental toxicity and bacterial detection. Throughout this dissertation, there is an emphasis on moving from high-content screening to point-of-care testing, especially in cancer diagnostics. Overall, the chemical-nose sensors provide a simple generic tool for bio-relevant analyte profiling, avoiding additional processing steps prior to screening as seen in traditional methods. More importantly, chemical-nose sensors hold great promise for addressing the needs in personalized screening of disease states and environmental toxicology

Retrieval of interatomic separations of molecules from laser-induced high-order harmonic spectra

We illustrate an iterative method for retrieving the internuclear separations of N$_2$, O$_2$ and CO$_2$ molecules using the high-order harmonics generated from these molecules by intense infrared laser pulses. We show that accurate results can be retrieved with a small set of harmonics and with one or few alignment angles of the molecules. For linear molecules the internuclear separations can also be retrieved from harmonics generated using isotropically distributed molecules. By extracting the transition dipole moment from the high-order harmonic spectra, we further demonstrated that it is preferable to retrieve the interatomic separation iteratively by fitting the extracted dipole moment. Our results show that time-resolved chemical imaging of molecules using infrared laser pulses with femtosecond temporal resolutions is possible.Comment: 14 pages, 9 figure

Modeling the Response of Monterey Bay to Diurnal

LONG TERM GOALS: Long-term goals of this project are to improve high-resolution numerical models of the ocean circulation for the regions with complex bottom topography, coastlines and multi-scale physical fields using enhanced grid technology, nested open boundary and, ultimately, data assimilation of new observational data type like current maps from High Frequency (HF) radar installations.Award number: N0001497WR30065TER

A metric learning-based method for biomedical entity linking

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here

Retrieval of material properties of monolayer transition-metal dichalcogenides from magnetoexciton energy spectra

Reduced exciton mass, polarizability, and dielectric constant of the surrounding medium are essential properties for semiconduction materials, and they can be extracted recently from the magnetoexciton energies. However, the acceptable accuracy of the previously suggested method requires very high magnetic intensity. Therefore, in the present paper, we propose an alternative method of extracting these material properties from recently available experimental magnetoexciton s-state energies in monolayer transition-metal dichalcogenides (TMDCs). The method is based on the high sensitivity of exciton energies to the material parameters in the Rytova-Keldysh model. It allows us to vary the considered material parameters to get the best fit of the theoretical calculation to the experimental exciton energies for the $1s$, $2s$, and $3s$ states. This procedure gives values of the exciton reduced mass and 2D polarizability. Then, the experimental magnetoexciton spectra compared to the theoretical calculation gives also the average dielectric constant. Concrete applications are presented only for monolayers WSe$_2$ and WS$_2$ from the recently available experimental data. However, the presented approach is universal and can be applied to other monolayer TMDCs. The mentioned fitting procedure requires a fast and effective method of solving the Schr\"{o}dinger of an exciton in monolayer TMDCs with a magnetic field. Therefore, we also develop such a method in this study for highly accurate magnetoexciton energies.Comment: 8 pages, 4 figures, 4 table

Improving Pareto Front Learning via Multi-Sample Hypernetworks

Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.Comment: Accepted to AAAI-2

Modeling and Observations of Surface Waves in Monterey Bay

N0001499WR3018

Modeling and Observations of Surface Waves in Monterey Bay

The long-range goals of this project are to develop an improved turbulence closure model that takes explicit account of surface wave effects and to quantify the effect of Stokes drift on the surface current signature of High Frequency (HF) radar systems.N00014-00-WR2009

A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference vector and a Pareto optimal solution is still ambiguous, rendering its results. This study demonstrates the convergence and completion aspects of solving MOO with pseudoconvex scalarization functions and combines them into Hypernetwork in order to offer a comprehensive framework for PFL, called Controllable Pareto Front Learning. Extensive experiments demonstrate that our approach is highly accurate and significantly less computationally expensive than prior methods in term of inference time.Comment: Under Review at Neural Networks Journa

Studying Magnetic Fields and Dust in M17 Using Polarized Thermal Dust Emission Observed by SOFIA/HAWC

We report on the highest spatial resolution measurement to date of magnetic fields (B-fields) in M17 using thermal dust polarization measurements taken by SOFIA/HAWC+ centered at a wavelength of 154 μm. Using the Davis–Chandrasekhar–Fermi method, in which the polarization angle dispersion calculated using the structure function technique is the quantity directly observed by SOFIA/HAWC+, we found the presence of strong B-fields of 980 ± 230 and 1665 ± 885 μG in the lower-density M17-N and higher-density M17-S regions, respectively. The B-field morphology in M17-N possibly mimics the fields in gravitationally collapsing molecular cores, while in M17-S the fields run perpendicular to the density structure. M17-S also displays a pillar feature and an asymmetric large-scale hourglass-shaped field. We use the mean B-field strengths to determine Alfvénic Mach numbers for both regions, finding that B-fields dominate over turbulence. We calculate the mass-to-flux ratio, λ, finding λ = 0.07 for M17-N and 0.28 for M17-S. These subcritical λ values are consistent with the lack of massive stars formed in M17. To study dust physics, we analyze the relationship between dust polarization fraction, p, emission intensity, I, gas column density, N(H2), polarization angle dispersion function, S, and dust temperature, T d. p decreases with intensity as I −α with α = 0.51. p tends to first increase with T d, but then decreases at higher T d. The latter feature, seen in M17-N at high T d when N(H2) and S decrease, is evidence of the radiative torque disruption effect