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Faculty Publications

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Full-Text Articles in Physical Sciences and Mathematics

Disorder By Design: A Data-Driven Approach To Amorphous Semiconductors Without Total-Energy Functionals, Dil K. Limbu, Stephen R. Elliott, Raymond Atta-Fynn, Parthapratim Biswas May 2020

Disorder By Design: A Data-Driven Approach To Amorphous Semiconductors Without Total-Energy Functionals, Dil K. Limbu, Stephen R. Elliott, Raymond Atta-Fynn, Parthapratim Biswas

Faculty Publications

X-ray diffraction, Amorphous silicon, Multi-objective optimization, Monte Carlo methods. This paper addresses a difficult inverse problem that involves the reconstruction of a three-dimensional model of tetrahedral amorphous semiconductors via inversion of diffraction data. By posing the material-structure determination as a multiobjective optimization program, it has been shown that the problem can be solved accurately using a few structural constraints, but no total-energy functionals/forces, which describe the local chemistry of amorphous networks. The approach yields highly realistic models of amorphous silicon, with no or only a few coordination defects (≤1%), a narrow bond-angle distribution of width 9–11.5°, and ...


Zenneck Waves In Decision Agriculture: An Empirical Verification And Application In Em-Based Underground Wireless Power Transfer, Usman Raza, Abdul Salam May 2020

Zenneck Waves In Decision Agriculture: An Empirical Verification And Application In Em-Based Underground Wireless Power Transfer, Usman Raza, Abdul Salam

Faculty Publications

In this article, the results of experiments for the observation of Zenneck surface waves in sub GHz frequency range using dipole antennas are presented. Experiments are conducted over three different soils for communications distances of up to 1 m. This empirical analysis confirms the existence of Zenneck waves over the soil surface. Through the power delay profile (PDP) analysis, it has been shown that other subsurface components exhibit rapid decay as compared to the Zenneck waves. A potential application of the Zenneck waves for energy transmission in the area of decision agriculture is explored. Accordingly, a novel wireless through-the-soil power ...


Legendrian Dga Representations And The Colored Kauffman Polynomial, Justin Murray, Dan Rutherford May 2020

Legendrian Dga Representations And The Colored Kauffman Polynomial, Justin Murray, Dan Rutherford

Faculty Publications

For any Legendrian knot K in standard contact R-3 we relate counts of ungraded (1-graded) representations of the Legendrian contact homology DG-algebra (A(K), partial derivative) with the n-colored Kauffman polynomial. To do this, we introduce an ungraded n-colored ru-ling polynomial, R-n,K(1)(q), as a linear combination of reduced ruling polynomials of positive permutation braids and show that (i) R-n,K(1)(q) arises as a specialization F-n,F-K(a, q)vertical bar(a-1) = 0 of the n-colored Kauffman polynomial and (ii) when q is a power of two R-n,K(1)(q) agrees with the total ungraded ...


Using Taint Analysis And Reinforcement Learning (Tarl) To Repair Autonomous Robot Software, Damian Lyons, Saba Zahra May 2020

Using Taint Analysis And Reinforcement Learning (Tarl) To Repair Autonomous Robot Software, Damian Lyons, Saba Zahra

Faculty Publications

It is important to be able to establish formal performance bounds for autonomous systems. However, formal verification techniques require a model of the environment in which the system operates; a challenge for autonomous systems, especially those expected to operate over longer timescales. This paper describes work in progress to automate the monitor and repair of ROS-based autonomous robot software written for an a-priori partially known and possibly incorrect environment model. A taint analysis method is used to automatically extract the data-flow sequence from input topic to publish topic, and instrument that code. A unique reinforcement learning approximation of MDP utility ...


On-Site And External Energy Harvesting In Underground Wireless, Usman Raza, Abdul Salam Apr 2020

On-Site And External Energy Harvesting In Underground Wireless, Usman Raza, Abdul Salam

Faculty Publications

Energy efficiency is vital for uninterrupted long-term operation of wireless underground communication nodes in the field of decision agriculture. In this paper, energy harvesting and wireless power transfer techniques are discussed with applications in underground wireless communications (UWC). Various external wireless power transfer techniques are explored. Moreover, key energy harvesting technologies are presented that utilize available energy sources in the field such as vibration, solar, and wind. In this regard, the Electromagnetic(EM)- and Magnetic Induction(MI)-based approaches are explained. Furthermore, the vibration-based energy harvesting models are reviewed as well. These energy harvesting approaches lead to design of an ...


The Expanded View Of Individualism And Collectivism: One, Two, Or Four Dimensions?, Jennifer L. Priestley, Kamal Fatehi, Gita Taasoobshirazi Apr 2020

The Expanded View Of Individualism And Collectivism: One, Two, Or Four Dimensions?, Jennifer L. Priestley, Kamal Fatehi, Gita Taasoobshirazi

Faculty Publications

Recent research to analyze and discuss cultural differences has employed a combination of five major dimensions of individualism–collectivism, power distance, uncertainty avoidance, femininity– masculinity (gender role differentiation), and long-term orientation. Among these dimensions, individualism–collectivism has received the most attention. Chronologically, this cultural attribute has been regarded as one, then two, and more recently, four dimensions of horizontal and vertical individualism and collectivism. However, research on this issue has not been conclusive and some have argued against this expansion. The current study attempts to explain and clarify this discussion by using a shortened version of the scale developed by ...


Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing, Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola Apr 2020

Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing, Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola

Faculty Publications

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed ...


Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: I. Using Steady-State Simulations, Mark F. Spencer Mar 2020

Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: I. Using Steady-State Simulations, Mark F. Spencer

Faculty Publications

Part I of this two-part paper uses wave-optics simulations to look at the Monte Carlo averages associated with turbulence and steady-state thermal blooming (SSTB). The goal is to investigate turbulence thermal blooming interaction (TTBI). At wavelengths near 1  μm, TTBI increases the amount of constructive and destructive interference (i.e., scintillation) that results from high-power laser beam propagation through distributed-volume atmospheric aberrations. As a result, we use the spherical-wave Rytov number and the distortion number to gauge the strength of the simulated turbulence and SSTB. These parameters simplify greatly given propagation paths with constant atmospheric conditions. In addition, we use ...


Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: Ii. Using Time-Dependent Simulations, Mark F. Spencer Mar 2020

Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: Ii. Using Time-Dependent Simulations, Mark F. Spencer

Faculty Publications

Part II of this two-part paper uses wave-optics simulations to look at the Monte Carlo averages associated with turbulence and time-dependent thermal blooming (TDTB). The goal is to investigate turbulence thermal blooming interaction (TTBI). At wavelengths near 1  μm, TTBI increases the amount of constructive and destructive interference (i.e., scintillation) that results from high-power laser beam propagation through distributed-volume atmospheric aberrations. As a result, we use the spherical-wave Rytov number, the number of wind-clearing periods, and the distortion number to gauge the strength of the simulated turbulence and TDTB. These parameters simply greatly given propagation paths with constant atmospheric ...


Optimizing The Environmental And Economic Sustainability Of Remote Community Infrastructure, Jamie E. Filer, Justin D. Delorit, Andrew J. Hoisington, Steven J. Schuldt Mar 2020

Optimizing The Environmental And Economic Sustainability Of Remote Community Infrastructure, Jamie E. Filer, Justin D. Delorit, Andrew J. Hoisington, Steven J. Schuldt

Faculty Publications

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions ...


A New Ectotherm 3d Tracking And Behavior Analytics System Using A Depth-Based Approach With Color Validation, With Preliminary Data On Kihansi Spray Toad (Nectophrynoides Asperginis) Activity, Philip Bal, Damian Lyons, Avishai Shuter Mar 2020

A New Ectotherm 3d Tracking And Behavior Analytics System Using A Depth-Based Approach With Color Validation, With Preliminary Data On Kihansi Spray Toad (Nectophrynoides Asperginis) Activity, Philip Bal, Damian Lyons, Avishai Shuter

Faculty Publications

The Kihansi spray toad (Nectophrynoides asperginis), classified as Extinct in the Wild by the IUCN, is being bred at the Wildlife Conservation Society’s (WCS) Bronx Zoo as part of an effort to successfully reintroduce the species into the wild. Thousands of toads live at the Bronx Zoo presenting an opportunity to learn more about their behaviors for the first time, at scale. It is impractical to perform manual observations for long periods of time. This paper reports on the development of a RGB-D tracking and analytics approach that allows researchers to accurately and efficiently gather information about the toads ...


Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple Feb 2020

Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple

Faculty Publications

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and ...


Wireless Underground Communications In Sewer And Stormwater Overflow Monitoring: Radio Waves Through Soil And Asphalt Medium, Usman Raza, Abdul Salam Feb 2020

Wireless Underground Communications In Sewer And Stormwater Overflow Monitoring: Radio Waves Through Soil And Asphalt Medium, Usman Raza, Abdul Salam

Faculty Publications

Storm drains and sanitary sewers are prone to backups and overflows due to extra amount wastewater entering the pipes. To prevent that, it is imperative to efficiently monitor the urban underground infrastructure. The combination of sensors system and wireless underground communication system can be used to realize urban underground IoT applications, e.g., storm water and wastewater overflow monitoring systems. The aim of this article is to establish a feasibility of the use of wireless underground communications techniques, and wave propagation through the subsurface soil and asphalt layers, in an underground pavement system for storm water and sewer overflow monitoring ...


Geology Of Nacogdoches, Texas: Texas Academy Of Science March 1, 2020, R. Larell Nielson, Mike Read, Mindy Faulkner, Hannah C. Chambers, Jessica O'Neal Feb 2020

Geology Of Nacogdoches, Texas: Texas Academy Of Science March 1, 2020, R. Larell Nielson, Mike Read, Mindy Faulkner, Hannah C. Chambers, Jessica O'Neal

Faculty Publications

No abstract provided.


Making The Error Bar Overlap Myth A Reality: Comparative Confidence Intervals, Frank Corotto Feb 2020

Making The Error Bar Overlap Myth A Reality: Comparative Confidence Intervals, Frank Corotto

Faculty Publications

Many interpret error bars to mean that if they do not overlap the difference is statistically significant. This overlap rule is really an overlap myth; the rule does not hold true for any conventional type of error bar. There are rules of thumb for estimating P values, but it would be better to show error bars for which the overlap rule holds true. Here I explain how to calculate what I call comparative confidence intervals which, when plotted as error bars, let us judge significance based on overlap or separation. Others have published on these intervals (the mathematical basis goes ...


Effects Of Optical Turbulence And Density Gradients On Particle Image Velocimetry, Silvia Matt, Gero Nootz, Samuel Hellman, Weilin Hou Feb 2020

Effects Of Optical Turbulence And Density Gradients On Particle Image Velocimetry, Silvia Matt, Gero Nootz, Samuel Hellman, Weilin Hou

Faculty Publications

Particle image velocimetry (PIV) is a well-established tool to collect high-resolution velocity and turbulence data in the laboratory, in both air and water. Laboratory experiments are often performed under conditions of constant temperature or salinity or in flows with only small gradients of these properties. At larger temperature or salinity variations, the changes in the index of refraction of water or air due to turbulent microstructure can lead to so-called optical turbulence. We observed a marked influence of optical turbulence on particle imaging in PIV. The effect of index of refraction variations on PIV has been described in air for ...


An Ultra-Sparse Approximation Of Kinetic Solutions To Spatially Homogeneous Flows Of Non-Continuum Gas, Alexander Alekseenko, Amy Grandilli, Aihua W. Wood Feb 2020

An Ultra-Sparse Approximation Of Kinetic Solutions To Spatially Homogeneous Flows Of Non-Continuum Gas, Alexander Alekseenko, Amy Grandilli, Aihua W. Wood

Faculty Publications

We consider a compact approximation of the kinetic velocity distribution function by a sum of isotropic Gaussian densities in the problem of spatially homogeneous relaxation. Derivatives of the macroscopic parameters of the approximating Gaussians are obtained as solutions to a linear least squares problem derived from the Boltzmann equation with full collision integral. Our model performs well for flows obtained by mixing upstream and downstream conditions of normal shock wave with Mach number 3. The model was applied to explore the process of approaching equilibrium in a spatially homogeneous flow of gas. Convergence of solutions with respect to the model ...


A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela Feb 2020

A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela

Faculty Publications

We propose a novel approach to the ’reality gap’ problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions ...


An Explicit Construction Of Mechanically Correct Lagrangians For Systems With Linear Nonholonomic Constraints, Piotr W. Hebda Ph.D., Beata Hebda Dr. Jan 2020

An Explicit Construction Of Mechanically Correct Lagrangians For Systems With Linear Nonholonomic Constraints, Piotr W. Hebda Ph.D., Beata Hebda Dr.

Faculty Publications

Starting with an unconstrained mechanical system that is governed by an initial unconstrained Lagrangian, subsequently modified by nonholonomic, linear in velocities, constraints, an explicit construction for a Lagrangian that will produce mechanically correct equations of motion for that constrained nonholonomic system is given. Obtaining a Hamiltonian from that Lagrangian is briefly discussed.


Data On Gc/Ms Elution Profile, 1h And 13c Nmr Spectra Of 1-, 3-, And 6-Nitrobenzo[A]Pyrenes, Kefa Karimu Onchoke Jan 2020

Data On Gc/Ms Elution Profile, 1h And 13c Nmr Spectra Of 1-, 3-, And 6-Nitrobenzo[A]Pyrenes, Kefa Karimu Onchoke

Faculty Publications

The data presented in this article is related to the research article entitled, “13C NMR Chemical Shift Assignments of Nitrated Benzo[a]pyrenes based on Two-dimensional Techniques and DFT/GIAO Calculations”, Kefa K. Onchoke, PeerJ., . The NMR spectral profiles of nitrated benzo[a]pyrenes is presented. Further, the article describes elution profiles of 1-, 3- and 6-NBaP, the acquisition of 1H and 13C NMR data and the J-Coupling constants (which are useful for the assignment of peaks via 2D HMQC and HMBC techniques). The data presented is useful for developing structure-activity relationships for other nitrated polycyclic aromatic ...


Heterogeneous Multi-Layered Network Model For Omics Data Integration And Analysis, Bohyun Lee, Shuo Zhang, Aleksandar Poleksic, Lei Xie Jan 2020

Heterogeneous Multi-Layered Network Model For Omics Data Integration And Analysis, Bohyun Lee, Shuo Zhang, Aleksandar Poleksic, Lei Xie

Faculty Publications

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven ...


Digital Holography Experiments With Degraded Temporal Coherence, Douglas E. Thornton, Davin Mao, Mark F. Spencer, Christopher A. Rice, Glen P. Perram Jan 2020

Digital Holography Experiments With Degraded Temporal Coherence, Douglas E. Thornton, Davin Mao, Mark F. Spencer, Christopher A. Rice, Glen P. Perram

Faculty Publications

To simulate the effects of multiple-longitudinal modes and rapid fluctuations in center frequency, we use sinusoidal phase modulation and linewidth broadening, respectively. These effects allow us to degrade the temporal coherence of our master-oscillator laser, which we then use to conduct digital holography experiments. In turn, our results show that the coherence efficiency decreases quadratically with fringe visibility and that our measurements agree with our models to within 1.8% for sinusoidal phase modulation and 6.9% for linewidth broadening.


Stochastic Complex Transmittance Screens For Synthesizing General Partially Coherent Sources, Milo W. Hyde Iv Jan 2020

Stochastic Complex Transmittance Screens For Synthesizing General Partially Coherent Sources, Milo W. Hyde Iv

Faculty Publications

We develop a method to synthesize any partially coherent source (PCS) with a genuine cross-spectral density (CSD) function using complex transmittance screens. Prior work concerning PCS synthesis with complex transmittance screens has focused on generating Schell-model (uniformly correlated) sources. Here, using the necessary and sufficient condition for a genuine CSD function, we derive an expression, in the form of a superposition integral, that produces stochastic complex screen realizations. The sample autocorrelation of the screens is equal to the complex correlation function of the desired PCS. We validate our work by generating, in simulation, three PCSs from the literature—none has ...


Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chu, Weihua Zhou Dec 2019

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chu, Weihua Zhou

Faculty Publications

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

Results: Compared with the seven conventional machine learning algorithms, the ...


Cell Velocity Is Asymptotically Independent Of Force: A Differential Equation Model With Random Switching., J. C. Dallon, Emily J. Evans, Christopher P. Grant, William V. Smith Dec 2019

Cell Velocity Is Asymptotically Independent Of Force: A Differential Equation Model With Random Switching., J. C. Dallon, Emily J. Evans, Christopher P. Grant, William V. Smith

Faculty Publications

Numerical simulations suggest that average velocity of a biological cell depends largely on attachment dynamics and less on the forces exerted by the cell. We determine the relationship between two models of cell motion, one based on finite spring constants modeling attachment properties (a randomly switched differential equation) and a limiting case (a centroid model-a generalized random walk) where spring constants are infinite. We prove the main result of this paper, the Expected Velocity Relationship theorem. This result shows that the expected value of the difference between cell locations in the differential equation model at the initial time and at ...


Computational Screening Of New Perovskite Materials Using Transfer Learning And Deep Learning, Xiang Li, Yabo Dan, Rongzhi Dong, Zhuo Cao, Chengcheng Niu, Yuqi Song, Shaobo Li, Jianjun Hu Dec 2019

Computational Screening Of New Perovskite Materials Using Transfer Learning And Deep Learning, Xiang Li, Yabo Dan, Rongzhi Dong, Zhuo Cao, Chengcheng Niu, Yuqi Song, Shaobo Li, Jianjun Hu

Faculty Publications

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us ...


Comparison Of Charge Storage Properties Of Prussian Blue Analogues Containing Cobalt And Copper, Amanda Rensmo, Jennifer R. Hampton Dec 2019

Comparison Of Charge Storage Properties Of Prussian Blue Analogues Containing Cobalt And Copper, Amanda Rensmo, Jennifer R. Hampton

Faculty Publications

Prussian blue analogues are of great interest as alternative battery materials because of their long life cycle and potential use of earth-abundant elements. In this work, thin film mixed-metal hexacyanoferrates (HCFs) based on NiCo and NiCu alloys were fabricated in an all electrochemical process. The structure and composition of the samples were characterized, along with the charge storage capacity and kinetics of the charge transfer reaction. For both NiCo-HCF and NiCu-HCF samples, the total charge capacity increased with the substitution of Ni with more Co or Cu, and the increase was larger for Cu samples than for Co samples. On ...


Simultaneous Enhancement Of Near-Infrared Emission And Dye Photodegradation In A Racemic Aspartic Acid Compound Via Metal-Ion Modification, Frank R. Fronczek, Jian Xu Nov 2019

Simultaneous Enhancement Of Near-Infrared Emission And Dye Photodegradation In A Racemic Aspartic Acid Compound Via Metal-Ion Modification, Frank R. Fronczek, Jian Xu

Faculty Publications

Changing functionalities of materials using simple methods is an active area of research, as it is "green" and lowers the developing cost of new products for the enterprises. A new small molecule racemic N,N-dimethyl aspartic acid has been prepared. Its structure is determined by single-crystal X-ray diffraction. It is characterized by FTIR, XPS, 1 H NMR, and mass spectroscopy. Its near-infrared luminescence can be enhanced by the combination of metal ions, including Dy3+, Gd3+, Nd3+, Er3+, Sr3+, Y3+, Zn2+, Zr4+, Ho3+, Yb3+, La3+, Pr6+/Pr3+, and Sm3+ ions. An optical chemistry mechanism upon interaction between the sensitizer and activator ...


Simultaneous Enhancement Of Near-Infrared Emission And Dye Photodegradation In A Racemic Aspartic Acid Compound Via Metal-Ion Modification, Frank R. Fronczek, Jian Xu Nov 2019

Simultaneous Enhancement Of Near-Infrared Emission And Dye Photodegradation In A Racemic Aspartic Acid Compound Via Metal-Ion Modification, Frank R. Fronczek, Jian Xu

Faculty Publications

Changing functionalities of materials using simple methods is an active area of research, as it is "green" and lowers the developing cost of new products for the enterprises. A new small molecule racemic N,N-dimethyl aspartic acid has been prepared. Its structure is determined by single-crystal X-ray diffraction. It is characterized by FTIR, XPS, 1 H NMR, and mass spectroscopy. Its near-infrared luminescence can be enhanced by the combination of metal ions, including Dy3+, Gd3+, Nd3+, Er3+, Sr3+, Y3+, Zn2+, Zr4+, Ho3+, Yb3+, La3+, Pr6+/Pr3+, and Sm3+ ions. An optical chemistry mechanism upon interaction between the sensitizer and activator ...


Machine Learning To Quantitate Neutrophil Netosis, Laila Elsherif, Noah Sciaky, Carrington A. Metts, Md. Modasshir, Ioannis Rekleitis, Christine A. Burris, Joshua A. Walker, Nadeem Ramadan, Tina M. Leisner, Stephen P. Holly, Martis W. Cowles, Kenneth I. Ataga, Joshua N. Cooper, Leslie V. Parise Nov 2019

Machine Learning To Quantitate Neutrophil Netosis, Laila Elsherif, Noah Sciaky, Carrington A. Metts, Md. Modasshir, Ioannis Rekleitis, Christine A. Burris, Joshua A. Walker, Nadeem Ramadan, Tina M. Leisner, Stephen P. Holly, Martis W. Cowles, Kenneth I. Ataga, Joshua N. Cooper, Leslie V. Parise

Faculty Publications

We introduce machine learning (ML) to perform classifcation and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in diferentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for ...