ikatkov/journal-of-student-research-hs-articles huggingface dataset:
High Throughput in Silico Identification of α-Syn Aggregation Inhibitors for Parkinson's Disease
Authors: Amrutha Srivatsav; Palos Verdes Peninsula High School Faculty
Date: 11-30-2021

Parkinson’s Disease (PD) is caused by the depletion of dopamine as a result of aggregates formed by a protein called alpha-synuclein (a-syn), which are toxic and kill dopamine neurons in the substantia nigra pars compacta brain region. PD currently only has some symptomatic treatments that lose effectiveness over time. The purpose of this study was to identify drugs that bind to the fibril forming segment of the non-amyloid beta component (NAC) domain of neuronal membrane bound a-syn. These drugs will inhibit other a-syn monomers from binding and prevent further a-syn aggregation, slowing the progression of PD.  Molecular docking was used to run a high throughput screening of 646 FDA approved drugs. The interactions between each drug and a-syn were analyzed and compared to those of Baicalein and SynuClean-D, two drugs that can inhibit a-syn aggregation by binding with the NAC domain but are not FDA approved.   13 drugs were identified that can potentially be used to inhibit membrane bound a-syn aggregation and were almost 6 times more potent than Baicalein and 10 times more potent than SynuClean-D at binding with a-syn’s NAC domain. The Blood Brain Barrier (BBB) penetrability of these top 13 drugs was assessed, indicating the drugs with a higher likelihood of penetrating the BBB. The results of this study appoint Telmisartan and Ibrutinib as FDA approved, BBB penetrable drugs that could inhibit membrane bound a-syn aggregation, slowing the progression of PD. Verifying these results through in vitro experiments can result in identifying a promising treatment for PD.
Applications of Feature Engineering and Logistic Regression to Analyze Microchip Performance  Using key features of machine learning to calculate, generate, and enhance a nonlinear decision boundary
Authors: Agranya Ketha; Dr. Guillermo Goldsztein
Date: 11-30-2021

Machine learning is an adaptive concept that has applications in several other industries. Businesses can use it to create real-time and predictive analyses of corporate data to identify consumer trends, calculate product success rates, visualizing short- and long-term business models, and more. This paper takes machine learning algorithms to analyze microchip performance. Beginning with an introduction of fundamental concepts of machine learning — including feature, labels, and classification vs regression models — the paper’s example is introduced with a scatterplot visualization, a definition of the loss function binary cross entropy, and the parameters of the dataset. The third and fourth sections explain the critical function of feature engineering and its utility in refining the decision boundary to best separate the data classes with minimal error. The final section reiterates the main idea that machine learning is ubiquitously applicable and, like the example in this paper, has many cross-industrial functions.
The Response of 3D Printed Breast Cancer Cells to Chemotherapy Drugs
Authors: Saanvi Garg; M. Hunt
Date: 11-30-2021

3D culture of breast cancer cells gives researchers a better understanding of how cells behave in the&nbsp; human body and a better representation of their response to cancer therapy compared to 2D. Breast&nbsp; cancer cells also interact with other cell types in the body, including endothelial cells in the blood vessels.&nbsp; While endothelial cells have been co-cultured with breast cells to study tissue growth/development,&nbsp; cancer metastasis, and angiogenesis, 3D breast structures have not been used to study cancer&nbsp; metabolism- accomplished with this project. The purpose is to determine how cell&nbsp; metabolism changes in A) 2D vs. 3D culture; B) monoculture vs. co-culture; and C) no treatment vs. Paclitaxel treatment. I hypothesize that Paclitaxel will have a greater effect on cell&nbsp; metabolism when breast cells are co-cultured with endothelial cells. Data was collected with three&nbsp; varying techniques, 2D Monolayer Printing, Manual 3D Hydrogel, and 3D Printing. The 3D Printed data displayed increased efficiency between trials of the same cell type and could be used to develop new&nbsp; drugs in a low-cost and efficient manner. The most important finding is with both 3D techniques, the absorbance had a further decrease for the Human Umbilical Vein Endothelial Cells (HUVEC) co-culture with cancerous cells than the 2D data. The metabolism of cancer cells is approximately eight times greater than normal cells, so decreasing the absorbance in cancer cells over noncancerous cells has a greater impact. 3D cell structures are essential to create co-cultures to study and develop drugs with cell interactions using endothelial cells.
Health Habits and Attitudes of Adolescents during the COVID-19 Pandemic
Authors: Eugene Reicher; Dr. Jessica Ricci
Date: 11-30-2021

The ongoing COVID-19 pandemic has significantly impacted adolescent health and lifestyle. This study aims to evaluate adolescent physical and mental health, and behaviors and attitudes related to the COVID-19 pandemic. We surveyed 7-12th grade students in one Los Angeles independent suburban school (Sierra Canyon School) and statistically analyzed their anonymous responses. Two surveys were administered: Survey 1 in March 2021, when the school had been closed to in-person learning for approximately one year, and Survey 2 in May 2021, about six weeks after the school had opened for voluntary in-person learning. Survey 2 included additional demographic questions about gender, political affiliation, and race/ethnicity to evaluate the correlation between these factors and COVID-related issues. We used linear regression to analyze correlations between parameters characterizing various aspects of health habits, attitudes, and behaviors and evaluated the statistical significance of the differences using Student’s t-test. We were most interested in analyzing the impact of school reopening by comparing the data in Survey 1 to Survey 2. The main result is that the return to in-school learning had a pronounced positive effect not only on the education quality but also on a number of various aspects of adolescent health and behavior. In addition, the survey showed interesting, sometimes surprising, correlations between distinct parameters, for example, the effects of gender on physical and mental health; or political affiliation on COVID-related attitudes and behavior.
Loving Wisely: Gender, Wisdom, and Ourania in Xenophon's Symposium
Authors: Elizabeth Casey; Namrata Narula
Date: 11-30-2021

Xenophon’s Symposium details a dinner party including Socrates in a manner like Plato’s Symposium. Also similar to Plato’s Symposium, Xenophon’s Socrates speaks on the nature of love, and the different types of love; Ourania, heavenly, and Pandemos, common. How are gender and wisdom linked to the idea of Ourania, or heavenly love between souls, as depicted in Xenophon’s Symposium? This paper will discuss whether women are capable of Ourania, which is traditionally reserved for only men. To answer this question, this paper will first show how wisdom is inherently tied to Ourania. This is because the love of souls involves knowledge of the soul, which is wisdom, or sophia. Then, it goes on to argue that virtues, including wisdom, can be taught. Finally, women are reintroduced, and it is argued that women can be taught wisdom. Consequently, this paperconcludes that women are capable of Ourania, for their souls can be wise.
Optimization of a Convolutional Neural Network for the Automated Diagnosis of Melanoma
Authors: Kemka Ihemelandu; Chukwuemeka Ihemelandu
Date: 11-27-2021

The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.
The Improvement of Total Rewards and Working Condition for Taekwondo Instructor in the COVID-19 Era  A Comparative Study between the U.S. and Korea
Authors: Hyunjun Ju; Eileen Lee
Date: 11-25-2021

The purpose of this study is to identify the overall difficulties of the Taekwondo industry due to the COVID-19 situation and to find ways to improve the total rewards and working condition so that Taekwondo instructors can play their roles while maintaining their self-esteem. The method of this study was a comparative study of the U.S. and Korea, which is the home country of Taekwondo. For this study, 12 American and Korean instructors were interviewed and additional surveys were conducted. As a result, both American and Korean Taekwondo instructors were playing their roles with high self-esteem. However, there were differences in job classification, allowances pay, incentives, and self-development opportunities. In U.S., Taekwondo instructors are clearly defined their tasks based on the job classification system and are applied well-designed overtime pay, incentive system and vacation plans. On the other hand, Korean instructors were working overtime and overworking without additional compensation to maintain long-term vision and employment relations. Comparative studies show that both countries have improvements to learn from each other. Taekwondo instructors in the U.S. need to increase their self-esteem in taekwondo while providing instructors with a vision for long-term careers. Taekwondo instructors in Korea need to improve total rewards and develop new skills such as mental training, counseling, and online lessons. The study will contribute to the overall improvement of total rewards, working condition and career development of taekwondo instructors in the U.S. and Korea.
Identification and Analysis of Plant Growth Promoting Bacteria in LEAF Community Garden Soil
Authors: Pooja Ramadas, Dhruv Pathak; Prabhjeet Kaur
Date: 11-23-2021

In today’s largely populated modern world, crop yield is becoming increasingly important. To increase crop yield, new modern technologies for farming are continuously being innovated. The aim of this study is the identification of Plant Growth Promoting Bacteria (PGPBs) and their properties. In order to conduct the experiment, soil samples were collected from the community garden LEAF (Local Ecology and Agriculture Fremont). These samples were grown in Luria Bertani agar plates, and the two bacterial strains that grew from them were analyzed to determine the species of the bacteria. Using a DNA extraction kit, DNA was extracted from the bacteria and then amplified versions were sent to RF Biotech for DNA sequencing. The DNA sequences were then used to determine that the two bacterial species in question are Bacillus cereus and Morganella morganii. Afterwards, multiple assays were used to measure the efficiency of each bacterial species to absorb various substances that would be helpful for plant growth. The aim of this research is to better understand which bacterial strains are beneficial for plants, and which are harmful. Through having greater zones of inhibition, the bacterial species M. morganii proved to be more efficient in the siderophore and phosphate solubilization assays. In contrast, the bacterial species B. cereus proved to be more efficient in the cellulase and amylase production assays. These results will assist LEAF in enriching their soil in order to increase their crop yields by creating an increase in concentration of advantageous bacteria and decrease that of detrimental bacteria.
Was there any widespread fraud in 2020 presidential election? What does Benford's Law say?
Authors: Deeya Datta; Dr. David Banks
Date: 11-22-2021

Fair elections free of any interference are integral tenets of any functioning democracy, and widespread election fraud is undoubtedly a serious threat to a free republic. While instances of electoral fraud are much more prevalent in countries with illiberal democracies, the U.S has recently faced such an accusation. Although he was unable to provide any concrete evidence, the former U.S. President Donald Trump accused his opponent, Joe Biden, now president, of electoral fraud after the presidential election. Fortunately, election forensics are often successful in investigating the validity of such fraud allegations. In this paper, I applied Benford’s law, a rule that should stand up to any large set of natural numbers, such as un-tampered electoral data. Using this law and basic statistical analysis of votes of U.S. counties for candidates of the two major parties, I completed a forensic analysis to investigate Mr. Trump’s allegation. My comprehensive investigation does not find any evidence supporting his allegation.
Predicting Flying Robot Dynamics with Deep Learning
Authors: Brian Li; Nathan Lambert
Date: 11-22-2021

With the rapid increase in the power of computing and technological advances in robotics, research in the field of robotics has rapidly become very expansive. Being able to accurately predict movements of a robot is vital to many applications within this field, allowing for more precise simulation and prototyping as well as more accurate control of robotic systems. In this paper, we present an adaptable neural network that accurately predicts the movement of quadcopter robotic agents which can be expanded to encompass many more robots and applications given the requisite data, producing accurate results within a small margin for error.
To determine the effect of healthcare policy implementation on the COVID-19 pandemic.
Authors: Aaroosh Mishra; Dr. Ravi Mishra
Date: 11-21-2021

As the Delta variant of SARS-CoV-2 has spread across the USA, some states have been impacted in different ways than others. This is due to factors such as the implementation of public health guidelines, primarily mask usage, and vaccination rates. With the Delta variant already causing much damage and with newer variants mutating, it is imperative to understand the spread of the Delta variant of SARS-CoV-2.  The study examined five states - Minnesota, Iowa, Missouri, Arkansas, and Louisiana - and their respective COVID-19 cases. Data on these states were collected from the US Census Bureau and the CDC. The data was then compared between each state as well as to the USA. Finally, the data were analyzed and visualized using statistics software.  First, COVID-19 cases were normalized by dividing by population in millions to get a standard measure, Daily Cases Per Million (DPCM), to compare the five states. Next, we used the CDC data to create a timeline, which was used to compare case data between states. Additionally, the CDC data was used to compare states concerning non-communicable disorders.  Our analysis showed that the vaccination rate reduced while the masking mandates were interrupted for more than two months, with a rapid rise in delta variant of COVID-19 virus. Thus, from the study, it can be concluded that vaccines and mask usage are the most critical factors in preventing COVID-19 transmission.
Simulation of Excited Molecule Propagation to Determine Plasma Sterilization Effectiveness in Lumens
Authors: Clair Koo; Andy Poggio, Tarn Wilson
Date: 11-20-2021

Sterilizing or disinfecting medical narrow-lumen instruments such as endoscopes is a highly difficult process. Although plasma sterilization is effective against a broad spectrum of microorganisms, its effectiveness is dependent on the density of plasma-generated, excited particles that attack the microorganisms. The lifetime of the excited particles is not long, and their survival is a function of operating condition and geometry. The current study employs a numerical method to investigate the propagation and density of the excited nitrogen molecules in a narrow lumen and to identify the conditions where the plasma sterilization is effective. The plasma gas properties are calculated using the gas kinetic theory, and different lifetimes are assumed for excited molecules. The simulation results show that the survival of excited particles in a lumen increases more than 10 times when the pressure is reduced by a factor of 10. Thus, it is highly recommended that the plasma be operated at lower pressure to increase the sterilization efficiency of narrow lumen devices.
Heuristic Oncological Prognosis Evaluator (HOPE): Deep-Learning Framework to Detect Multiple Cancers
Authors: Anu Iyer; Lee Conrad, Fred Prior
Date: 11-20-2021

Cancer is the common name used to categorize a collection of diseases. In the United States, there were an estimated 1.8 million new cancer cases and 600,000 cancer deaths in 2020. Though it has been proven that an early diagnosis can significantly reduce cancer mortality, cancer screening is inaccessible to much of the world’s population. Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. A literature search with the Google Scholar and PubMed databases from January 2020 to June 2021 determined that currently, no machine learning model (n=0/417) has an accuracy of 90% or higher in diagnosing multiple cancers. We propose our model HOPE, the Heuristic Oncological Prognosis Evaluator, a transfer learning diagnostic tool for the screening of patients with common cancers. By applying this approach to magnetic resonance (MRI) and digital whole slide pathology images, HOPE 2.0 demonstrates an overall accuracy of 95.52% in classifying brain, breast, colorectal, and lung cancer. HOPE 2.0 is a unique state-of-the-art model, as it possesses the ability to analyze multiple types of image data (radiology and pathology) and has an accuracy higher than existing models. HOPE 2.0 may ultimately aid in accelerating the diagnosis of multiple cancer types, resulting in improved clinical outcomes compared to previous research that focused on singular cancer diagnosis.
Yoga Pose Perfection using Deep Learning  An Algorithm to Estimate the Error in Yogic Poses
Authors: Satyam Goyal; Animesh Jain
Date: 11-20-2021

Abstract  Even with lots of attention and work in the computer vision and artificial intelligence field, human body pose detection is still a daunting task. The application of human pose detection is wide-ranging from health monitoring to public security. This paper focuses on the application in yoga, an art that has been performed for over a millennium. In modern society yoga has become a common method of exercise and there-in arises a demand for instructions on how to do yoga properly. Doing certain yoga postures improperly may lead to injuries and fatigue and hence the presence of a trainer becomes important. As many people don’t have the resources to have a yoga instructor or guide, artificial intelligence can act as a substitute and advise people on their poses. Currently, the research surrounding pose estimation for yoga mainly discusses the classification of yogic poses. In this work, we propose a method, using the Tensorflow MoveNet Thunder model, that allows real-time pose estimation to detect the error in a person's pose, thereby allowing them to correct it.
Machine Learning Application on Prediction of Male Breast Cancer with PLCO Dataset
Authors: Juntao Li; Dr. Ganesh Mani
Date: 11-20-2021

The objective of the paper is to explore and examine the applicability of machine learning models on Male Breast Cancer with PLCO dataset. People who are unaware of the potential danger of getting breast cancer like males would not have the medical awareness beforehand for predictions. Therefore, the PLCO trials dataset consisting of ages, prostate status, marriage status etc. from National Institute of Cancer is used in this research for detection. The main purpose of using PLCO test is to discover the potential risk of getting an Male Breast Cancer (MBC) as soon as possible with low cost and easy collection. It is the rarity of MBC that imposes the threat for males who are unaware of the danger. To explore the relatively most suitable models to use for detecting MBC using non-traditional PLCO test dataset, different existing models including decision tree, random forest, DBSCAN, One Class SVM and so on were used to fit the data. Due to its extremity of imbalance, evaluation comes from the combination of standard accuracy and Area Under the Receiver Operating Characteristics(AUROC) for the overall accuracy of those models mentioned above. K-means and Logistic Regression models performed best with the AUC score of 0.62 and 0.67. Results suggested that more efficient approaches for common male breast cancer diagnosis or more advanced models and algorithms are needed in further study.
Sports at Play in American Politics
Authors: Jonathan Shen; Courtenay Shrimpton
Date: 11-18-2021

Sports have been a vital element to American entertainment for decades, which are only gaining popularity. Various sport events allow Americans to temporarily escape the stress associated with their social lives and the divisiveness of partisan politics; however, a closer look at the numerous features of sport games reveal an intricate connection between American sports and politics. With the mandatory playing of the national anthem and the integration of sports and political terminology, sport games in America have become a platform to impose various political ideals. This paper will first introduce how the usage of sport terminologies in politics can simplify a complex process into a game of “winners and losers,” which can further political polarization and disincentivize bipartisan collaboration. It will then aim to demonstrate how the imposition of novel rituals that stem from nationalistic and militaristic values can silent dissenting opinions and enforce a homogenous yet unjustified “American Identity.” However, the final part of this paper aims to showcase the alternate impacts that sports can have on politics, especially in the realm of sports-driven activism. This paper does not aim to take a stance on the exact impact that sports can have on American politics, as it is mostly likely to be multi-dimensional, but to unveil to the reader how sports, an entity that is seemingly designed as a form of escape from political agendas, can in reality have substantial impacts on America’s political atmosphere.&nbsp;