Robotic swarms are groups of autonomous robots that work together in order to perform tasks through cooperative behavior and interactivity with their environments. In order for robotic swarms to collaborate, they must have a reliable way of communicating with one another. Thus, we created a robust network infrastructure to allow autonomous robots to work together through peer-to-peer communication even in difficult environments. As a means of doing so, we used the painlessMesh Arduino library to implement a multi-hop mesh network, or a network topology that allows each node to relay signals to nodes too far away by routing packets across intermediate nodes. Additionally, we added 433 MHz packet radios onto our hardware stack and sent each message between robots over multiple, distinct frequencies; this keeps interference on one frequency band from preventing successful communication. Through this infrastructure, we enabled our robots to successfully communicate with any other robot. We also saw that for certain messages, when one frequency became unusable, our diversity maintained the infrastructure’s ability to communicate reliably. In the future, this work can be used to bring robotic swarms one step closer to becoming ubiquitous and useful in daily life, improving their communication so that together they can accomplish tasks previously and independently impossible.

Communications systems are crucial to modern everyday life – whether it be Wi-Fi, satellite communications, or storing and sharing documents digitally. However, imperfect communication channels can result in noise distorting transmitted data. Error correcting codes seek to identify and correct distorted data. Error correction comes at the cost of efficiency – this project’s rate-⅓ trellis encoder outputs three encoded bits per every one information bit, making it three times as inefficient as only sending the original data. By puncturing – omitting certain bits in transmission – higher efficiency can be attained, but the chance of receiving the correctly decoded information decreases. Previous literature has investigated the characteristics of a rate-⅓ 64-state 8PSK-modulated trellis encoder under puncturing and used these characteristics to develop a bit error rate (BER) union bound on the data. By running BER data simulations using C++ and the Hoffman2 Cluster and comparing them to theoretical union bound plots in MATLAB, this project has confirmed the results found in previous literature. Additionally, this project will extend the BER union bound methods to develop a union bound for the frame error rate (FER), as well as simulating the FER performance for this specific encoder and various puncturing patterns of interest. Whether in satellite transmissions, self-driving cars, streaming, Wi-Fi, memory storage hard drives, 5G, or GPS, our research has countless applications in the modern, digital world.

Exorbitant CEO pay as a contributor to earnings inequality has garnered much attention among scholars and policy-makers alike. In March 2016, the Israeli government enacted the Israeli Compensation Law, which capped executive compensation and within-firm earnings gaps. Specifically, the law limited the annual salaries of all C-level executives (e.g., CEO, CFO, COO) in financial firms to a maximum of 2.5 million NIS. Moreover, a firm's highest salary cannot exceed 35 times the salary of its lowest-paid worker, including contract employees. We will compare the outcomes of workers in financial (treated) and non-financial (control) firms using difference-in-differences. First, we will examine impacts on the C-level executives that the policy targeted--specifically, their wage and non-wage compensation and job separations. Second, we will examine effects on within-firm wage dispersion, estimating impacts on wage and non-wage compensation of non-C-level workers (who might experience earnings losses) and the firms' lowest-paid workers (who might experience earnings increases). Lastly, we will quantify impacts on firm outcomes and financial statements. This is important because previous literature suggests that regulatory interventions like the Israeli Compensation Law are doomed to fail and have unintended consequences by imposing enormous costs and causing an exodus of high-ability executives from regulated firms (Dittman, Maug, and Zhang 2011; Murphy and Jensen 2018). We are currently gathering data from the Tel Aviv 125 index from the Tel Aviv Stock Exchange on firm evaluation and firm financial statements. Future directions will consist of cleaning data from the financial sector of the Tel Aviv Stock Exchange.

Wireless signal classification plays an important role in the security of a wireless communication system since it can be used for transmitter authorization, the process by which authorized transmitters are distinguished from non-authorized transmitters based on transmitter-specific traits in their signals. Current systems have only investigated authorization for a closed set scenario, where the algorithm classifies among a finite set of known transmitters. This has several limitations, the most significant of which is that transmitters outside of the known set may be misclassified. In this project, we attempt to remedy this issue by performing authentication in an open set scenario, where the number of transmitters is not known. To do this, we generated and transmitted signals from eleven different ADALM Pluto Software Defined Radios using MATLAB software. We also simulated 5 unique transmitters by artificially adding different I/Q imbalance impairments to the signals. The signals were transmitted in the form of packets (active transmission separated by idle moments), which were then extracted using pre-written code. Finally, the extracted packets were inputted into an existing deep learning algorithm called One Vs. All, where the algorithm was tested and modified until a satisfactory accuracy was found. With this algorithm, wireless communication security can be significantly improved, since it minimizes the risk of misclassification by solving the previous weakness of authorization only under closed set conditions.

Many human neurodegenerative diseases are characterized by the progressive accumulations of abnormal protein aggregates within the brain, resulting in cellular dysfunction and brain damage. However, these diseases are often difficult to study due to inaccessibility of human brain tissue and higher levels of complexity required than possible in existing cell culture systems. Recently, 3D culture systems like cerebral organoids have emerged as promising tools in investigating brain development and modeling diseases and can further serve as a viable platform for drug discovery and validation. Here, we are evaluating the utility of brain organoids in modeling and developing treatments for Alzheimer’s disease. To prolong the long-term culture of brain organoids, we endeavor to develop a slice culture preparation in which the human brain tissue can be sustained for extended lengths of time, which is crucial for evaluating the progressive pathology associated with the disease. We first aim to determine the optimal age to slice the organoids that yields the best structure and cell populations. We are also seeking to use gene editing approaches in hiPSCs to introduce early onset and high penetrance Alzheimer’s disease associated gene mutations into the organoids and assess the resultant effects on brain development and long-term health and survival. Currently, we are generating constructs to perform gene editing within the organoids and testing a variety of methods for gene transfer. Utilizing the cerebral brain organoid model system will further expand our understanding of Alzheimer’s disease and progress towards developing efficient, personalized treatments.

The design and development of robotic devices remains limited to those with considerable time, funds, and technical expertise. Our goal is to increase the accessibility of robotics so that the average person can design and create their own robotic boats. I set out to provide a variety of boat hull morphologies with three new actuation capabilities: a propeller, paddlewheel, and rudder. After designing the 2D layout of these origami-inspired boats, I implemented them in the Robot Compiler (RoCo) framework for generating foldable robotic designs, thus allowing a user to add any actuation capability to any boat hull by setting parameters in code. I then used a paper cutter to cut out inexpensive thin plastic sheets into foldable boats with actuators driven by continuous rotation servos and DC motors. I found that the propeller produces fast linear motion, the paddlewheel enables motion that is linear and rotational, and the rudder adds more precise steering capabilities. In order to test my boats’ autonomous capabilities such as following a colored object, I integrated my boats with OpenMV cameras and inertial measurement units (IMUs), which provided basic feedback-controlled movement. I found that given this relatively small set of tools and materials, it’s possible to generate a wide variety of robotic boats. This work establishes a foundation for the community at large to rapidly, easily, and inexpensively create novel types of robotic boats.

Gene editing is a promising approach to correct monogenic diseases, like X-linked agammaglobulinemia (XLA), first by gene manipulation of a patient’s hematopoietic stem and progenitor cells (HSPCs), then re-infusing back into the patient. XLA, characterized by low production of immunoglobulins and recurrent/severe infections, is caused by a mutation in Bruton’s tyrosine kinase (BTK) gene, which incapacitates B-lymphocyte development. This project is a preliminary study in editing BTK using CRISPR-Cas9 for site-specific integration of a BTK corrective-DNA (cDNA) downstream from the gene’s promoter for physiologic expression of lineage-negative (lin-) cells, as seen in prior studies with human HSPCs. Lin- cells are then transplanted into BTK/TEC double-knockout mice, as prior studies indicate that transplanting BTK gene-corrected stem cells may be therapeutic for XLA patients. This project’s specific goal is to identify which guide RNAs (gRNAs) results in the highest levels of allelic-disruption without harming viability and fold-expansion 24-hours post-electroporation. These preliminary optimizations were performed in 32Ds as a surrogate model for lin- cells. Several gRNAs designed to target BTK were electroporated in 32Ds with CRISPR-Cas9. Assessments include viability 24-hours post-electroporation and allelic-disruption efficiency five days post-electroporation by Sanger-sequencing. This preliminary data confirming which gRNAs result in a high percentage of disruption allows for future optimization experiments in co-electroporating 32Ds with the most efficient gRNAs and an AAV6 donor-template harboring BTK cDNA. Integration efficiency into the endogenous gene will be assessed via droplet-digital PCR. The results of these experiments are extremely promising towards the development of lasting treatments for XLA.

A hallmark of cancer is metabolic reprogramming, characterized by the upregulation of glycolysis to fuel cancer cells’ energy demand. Targeting altered cancer metabolism is an emerging strategy for developing new chemotherapeutics. Phenolic compounds, plant-derived natural products, are potential metabolism modulators with their range of interactions with enzymes and proteins. However, the mechanisms by which phenolic compounds affect cancer metabolism remain unclear. To address this research gap, we treated a human non-small-cell lung cancer H1299 cell line with 50 µM of gallic acid, a dietary phenolic compound, for 3 h. Gallic acid-induced metabolite changes in H1299 were determined via liquid chromatography-high-resolution mass spectrometry and stable-isotope tracing using [1,2-¹³C] glucose. After 3-h gallic acid exposure, increased glucose uptake and accumulation of intracellular glycolysis intermediates were observed. Citrate, an intermediate of the tricarboxylic acid (TCA) cycle interconnected to glycolysis, were accumulated in gallic acid-treated cells while remaining downstream metabolites from the same pathway were depleted. These results may indicate that glycolysis is upregulated to feed increased cells' energy need after gallic acid treatment, possibly due to gallic acid-induced oxidative stress and blockage of metabolic pathways. Upon feeding cells with [1,2-¹³C] glucose, we observed higher enrichment in M+2 isotopic fraction in intracellular pyruvate and extracellular alanine in gallic acid-treated cells than that of the controls, further suggesting upregulated glycolysis. These results taken together suggest gallic acid may be a promising anticancer agent for the pursuit of developing new chemotherapeutics by altering H1299 metabolism by affecting downstream glycolysis and TCA pathways, consequently reducing energy supply.

We explored the magneto-optic Kerr effect (MOKE), which refers to the changes in light reflected from a magnetized surface. In MOKE, the incident circular polarized light becomes elliptically polarized and its axis of polarization rotates after reflection. These changes are termed Kerr ellipticity and Kerr rotation angle, respectively. Our objective is to achieve milli-radian Kerr rotation angle readout resolution on ferro/ferrimagnetic materials, such as CoFeB and GdFeCo, with a MOKE setup at near-normal incidence. By modulating the incident light with a photoelastic modulator (PEM-100), magnetizing the sample by placing it between two solenoids, and extracting voltage signals from a photo-detector with a lock-in amplifier (SR830) and multimeter (Keithley 2000), a magnetic hysteresis loop relating applied magnetic field strength and Kerr rotation angle was generated using MATLAB. The CoFeB sample was a wedge, meaning its thickness changed linearly from 0.4 to 1.4 nm. For this CoFeB wedge, we observed many hysteresis loops along the wedge to find both quantitative Kerr rotation angle and perpendicular magnetic anisotropy at various thicknesses. The GdFeCo film was of uniform thickness, but of a nonuniform makeup as it was a composition gradient from one side being Gd-rich and the other being FeCo-rich. For the GdFeCo film, the loops generated near the Gd-rich side neared but did not reach indication of a magnetization compensation point (where the net magnetic moment is 0). Points around the middle of the film were dominated by out-of-plane anisotropy, and the FeCo-rich side displayed predominantly in-plane anisotropy.

The composition of the gut microbiome has been linked to health-related concerns, including obesity altered gastrointestinal immunity. Probiotic microorganisms may impact the gut microbiome and provide health benefits upon consumption. Escherichia coli Nissle strain (EcN), one of the best-characterized probiotics, has been found to inhibit the growth of pathogenic organisms. However, there is little consensus regarding the mechanism by which EcN improves health. To determine whether EcN modulates the gut microbiome, in a pilot study, the Li Lab characterized the gut microbiome composition in dogs before and after EcN consumption. The stool samples from 3 healthy dogs were collected at day 0 (before treatment) and days 3 and 7 (after treatment). 16S rRNA sequencing analysis was performed on these samples. There were no significant differences in alpha diversity (variance within a particular sample) among treatment days or individual dogs. There were also no significant differences in beta diversity (how samples vary against each other) among treatment days. However, there was a significant difference in beta diversity between individual dogs. Several genera were differentially abundant among individual dogs. Based on this limited dataset, it appears that EcN does not drastically alter the composition of a healthy gut microbiome, and differences in taxonomic composition among individual dogs are much greater than those between treatment days. Future studies may consider applying alternative methods to capture the impact of EcN on the microbiome at species and strain levels, as well as testing in dogs with GI conditions.