Practically speaking, most communications channels are imperfect; noise will interfere with the communications and corrupt transmitted data. In order to combat this, many communications systems utilize feedback- the practice of relaying information regarding received data back to the transmitter- in order to efficiently transmit and decode messages. Research in this field, arguably set in motion by Michael Horstein in 1963, studies this phenomena and ways to mitigate interference. This research builds off an existing algorithm created by Amaael Antonini and Rita Gimelshein, which uses causal encoding over the the BSC (Binary Symmetric Channel), a channel through which binary messages can be transmitted with an equal crossover probability of zeros and ones. It modifies the algorithm to utilize sparse feedback instead of bitwise feedback- sending feedback after a specially determined number of bits have been sent through the channel, rather than after every bit, aiming to increase efficiency without loss in performance.

The loss of editing by the isoleucyl- and phenylalanyl-tRNA synthetase in Escherichia coli and by valyl-tRNA synthetase in zebrafish are known to increase mutation frequency and DNA damage, respectively. However, how translational errors lead to DNA damage and a higher level of mutations is unknown. One hypothesis is that the DNA-damage response, known as the SOS response in bacteria, is induced in the editing-defective cells, leading to error-prone DNA polymerases. To test this hypothesis, we will determine if a higher mutation frequency is observed for an E. coli strain expressing an editing-defective aaRS and a constitutive repressor that prevents induction of the SOS response. Our approach includes using P1 transduction to create the necessary strains to test the role of LexA during mutagenesis in both wild-type and editing-defective PheRS E. coli strain. For the P1 transduction, we have prepared a P1 lysate on E. coli and titered the lysate. In the long term, we hope to reveal the mechanism by which mistranslation can lead to heritable changes in the genome.

Encoders and decoders in communications systems are critical for the accurate and efficient transmission of information over noisy channels. Our research is focused on encoders and decoders for tail-biting convolutional codes used in conjunction with cyclic redundancy check (CRC) codes. We implement encoders and decoders that correct errors in the received message when possible. Often, when an error cannot be corrected, the CRC informs the decoder that the selected codeword is unreliable. In our research, we extend the work of Liang et al., who demonstrated that the use of distance spectrum optimal cyclic redundancy checks (DSO CRCs), along with list decoding, offered significant improvements in signal to noise ratio (SNR) with minimal additional computational cost for low rate convolutional codes of the form 1/n, which have n output bits for every 1 input bit. Our research applies this approach to high rate convolutional codes of the form (n − 1)/n, which have n output bits for every n − 1 input bits. Specifically, we implemented the decoder for a rate-3/4 tail-biting convolutional encoder, and used the dual trellis approach proposed by Yamada et al. for efficient decoding, along with the tree-trellis list decoding algorithm proposed by Roder and Hamzaoui. By implementing this system in C++, we have the ability to simulate its performance at low frame error rates and compare it to both the random coding union bound and the performance of a standard maximum likelihood decoder.

Analysis of the relative orderings of the differences between model predictions as opposed to a quantitative method is often required in cases such as customers expressing their preferences instead of giving numerical scores. The purpose of this research project is to use an algorithm based on the alternating direction method of multipliers (ADMM) to solve large-scale non-metric multidimensional scaling (NMDS) problems. The NMDS problem seeks to optimize the Gram matrix of the calculated position vectors by minimizing violations of the inequality constraints that express the ordering relations of their pairwise distances. ADMM is a method for large-scale optimization which splits variable x into two parts and performs alternating optimizations over each part. The problem is coded using Python and Matlab, allowing us to see what fraction of the ordering of the original distances is preserved. We are working with randomly generated datasets. We are also working with more interesting data, including Swiss Roll and S curve data generated using Python, and real-world data such as sets of related images. For our initial results, which did not include any code for ADMM, the fraction of the ordering of distances preserved was quite high, indicating that the relative ordering of the original distances was preserved overall.

The Diaphanous Autoinhibitory Domains (DAD, or tail) of mammalian formins functions in autoinhibition, increasing actin nucleation, and increasing formin processivity. A potential mechanism for controlling these functions is post-translational modifications. DAD sequences from mammalian formins include putative consensus sites for PRMT7 methylation. FHOD3L and FHOD1 contain RXR sites for PRMT7 methylation. Building upon preliminary unpublished research by the Steven Clarke lab that methylated proteolytic fragments from FHOD3L are from its tail, the goal of this project is to identify the site of PRMT7 methylation. Here, we mutated these sites on the FHOD3L tail such that PRMT7 RXR consensus is abolished to determine if PRMT7 recognizes both sites or only one site. Site-directed mutagenesis was performed to generate two mutant MBP-tagged tail DNA constructs that had one of the methylation sites knocked out via removal of the RXR consensus. Expression and purification were performed to generate ample protein amounts for each tail construct. PRMT7 methylation assays were done with radioactive methyl sources to test for tail methylation. Methylation was confirmed on tail constructs with RTRSR knocked out, while it was not for RERKRSR site knockouts. Future experiments will involve characterizing the specific residues on the RERKRSR site, as well as determining the interplay and effects methylation has on formin function and regulation.

Laser spectroscopy has been utilized to advance studies and efficiency in various fields such as energy, environment, and aerospace by providing species-specific measurements of molecular temperature, pressure, and composition. For accurate measurements of those quantities, measured spectral data are compared with standard calibrated databases. SpectraPlot is a web-based application that allows users to simulate spectra and obtain calculations by sourcing from various databases. These databases have their own respective libraries of data regarding a range of gaseous species at different conditions. The project objective is to create a relational database server with various spectroscopic databases and develop application capabilities for broadband spectral simulations and line surveys. MySql was utilized as an efficient tool to manage databases with interconnected data tables and various data types. In MySql, the server was configured, data was then uploaded into its respective database within the server. When connected to the server, the Python IDE was used to fetch data remotely and perform spectral simulations and surveys. Fetching data from the relational database is shown to be roughly four times faster (twenty times after establishing a connection), than reading data directly from text files. Broadband line surveys were then conducted using data fetched from the server and linestrengths of different molecules within the wavenumber range of interest were plotted to provide visualization of results. Along with the line surveys, a hardware search on lasers was performed within the same wavenumber range of interest. The present work will help researchers target specific spectral regions of various species.

Mitochondria are dynamic organelles capable of oxidizing different nutrients and providing energy for various processes in the cell. There are two different subpopulations of mitochondria that are found in adipose tissue: peridroplet mitochondria and cytoplasmic mitochondria. Between these two mitochondrial populations, there have been three major distinctions: differences in size, metabolic preferences, and function. Peridroplet mitochondria (PDM) are bound to lipid droplets (LDs) via hook proteins while cytoplasmic mitochondria (CM) are found throughout the cell’s cytoplasm, interacting with other organelles. Interestingly, CM are capable of fusion and fission dynamics, while PDM do not have these dynamic interactions. Between PDM and CM, PDM have been found to be larger than CM, which impacts both function and dynamics. Additionally, the two populations have different fuel preferences: pyruvate for PDM and free fatty acids for CM. In terms of their functional differences, PDM produce ATP required during lipid droplet expansion while CM produce heat and CO2. Interestingly, preliminary data suggests PDM are attached in greater quantities to smaller LDs than to larger LDs. However, it remains to be determined whether PDM attached to small LDs are different from PDM attached to larger LDS. Thus, I hypothesize that PDM attached to the smaller LDs are more metabolically active than PDM that are attached to larger LDs.

The controllable nature of coacervate polyelectrolyte complexes holds immense potential as an encapsulation and protection mechanism for proteins, drugs, and hereditary material in the human body. Two oppositely charged polyelectrolytes interact to make a complex in an aqueous solution and at high salt concentrations these complexes take the form of coacervate liquid-phase droplets. The droplets tend to coalesce and sink to the bottom of the solution forming a polymer dense macro-phase. To increase the level of interaction of biological molecules with the complexes, the droplets must be stabilized and micro-phase separated in solution. Previously, weak polyelectrolyte complexes have been stabilized through the use of a graft polymer that interacts with the polyelectrolytes to give the complex an overall neutral charge. This experiment tested the same experimental design using strong polyelectrolytes, taking a closer look at the mechanisms behind complexation such as the effect of increasing salt concentration, varying the charge ratio of polyelectrolytes, shortening the polyelectrolyte chain length, and adding a graft polymer. In order to do so, the study employed PSS-Na, and PDADMAC, two oppositely charged polyelectrolytes. Salt was utilized to alter the level of electrostatic attraction to either promote or discourage precipitate complexation of the polyelectrolytes. The addition of a graft polymer with neutral offshoots was found to interact with the polyelectrolytes and disperse the complexes throughout the solution. The shortening of the polyelectrolyte chain weakened electrostatic interactions and lowered the degree of complexation, while charge ratio variation resulted in minimal changes to complexation tendencies. With an increased degree of control over polyelectrolyte complexation, the optimal concentration of polyelectrolytes, salt, and graft polymer were combined to yield a stabilized solution of polyelectrolyte complex coacervate droplets. This study demonstrates a method for stabilizing strong polyelectrolyte complex coacervates, and the success of the aforementioned experimental approach makes it viable for future stabilization attempts with precipitate and polyelectrolyte-protein complexes

The overconsumption of unsustainable energy sources have greatly contributed to the growing concern regarding global climate change. With the clear consequences of maintaining the abuse of non-renewable resources, it’s become increasingly important to expand the accessibility of clean energy by developing safe and affordable energy storage systems (ESS) to replace fossil-fuel equivalents in industrial complexes and transportation. To ensure ESS progression, the systems’ energy density and power density must be improved to support feasible operation runtime and faster charge and discharge of power. This project aims to find ways to combine the energy density potential of the lithium ion battery (LOB), with the power density capabilities of supercapacitors through the implementation of carbon nanodots (CNDs). Graphene oxide and activated carbon electrodes were doped with CND before a laser scribing technique was applied to the electrodes’ surface. The electrodes exhibited higher conductivity, pseudocapacitive sites, and porosity which all contribute to increased energy density. Preliminary data suggest that the laser scribing technique reduced crystallographic defects and decreased the formation of a dielectric dead layer that would’ve further impeded the supercapacitor’s energy density. Based on these initial findings, the application of CNDs in conjunction with laser scribing addresses considerable concerns regarding the energy density of ESS. We plan to create a LOB utilizing the CND electrodes to further investigate the system’s power density capabilities. Understanding how CND affects energy and power density allows us to extend the applications of ESS to contribute to a more widespread acceptance of clean energy and sustainable technologies.

Reinforcement learning (RL) has been a growing subset of machine learning with increasing success and promise - but it has just begun to be used in complex, multiplayer environments and games. Through trial and error, an agent begins from fully random trials and finishes with sophisticated actions. We apply RL to the imperfect information game known as Liar’s Dice, which presents a challenging mix of two-player dynamics and partial information to explore. The game forces players to call bluffs and doubt opponents while reading others’ potential actions. Implementing reinforcement learning to imperfect information games allows us to find successful strategies and models in dynamic Markov Decision Process (MDP) environments that require sequential decision making. We employed the popular Q-learning method of RL to train agents that begin with random actions or to use a combination of fixed strategies against others. An agent employing Q-learning improved its win rate from 50% to only 65% within 1,000,000 episodes against a simple agent. Another agent that made decisions based on various fixed strategies available increased its win rate from 11% to 77.7% with 100,000 episodes. These rates demonstrate the variability of Q-learning in a game with partial information. In future work, we may compare counterfactual regret minimization and more state-of-the-art RL algorithms, which would expand our understanding of various methods on partially observed, dynamic environments. By studying this game, we hope to broaden our results to the education space, a similar Markov process where individuals also make decisions sequentially.

Triboelectric Nanogenerators (TENG) collects energy from the static electricity that is formed from the triboelectric effect. There are several different modes of generators, and each with a specific function. One such mode is the Vertical Contact Separation mode. Which consists of two layers of opposing charge with a conductor and an electrode attached to it. When the two materials are brought together, the charges need to neutralize. Therefore, the electron travels from one electrode to the other which creates electricity. TENG requires very specific materials, and it is either layer stacked, or yarn intersected. The result shows that different materials paired can affect the overall output of the machine. For instance, silicon rubber and nylon has 42.9V while silver and PTFE only has 23.5 V. In addition, it was found that yarn intersection created more energy compared to layer stacked. By understanding the energy output of the material combinations and arrangements of TENG, it will further advance smart textile and self-powered wearables.