ikatkov/journal-of-student-research-hs-articles huggingface dataset:
Treating Dilated Cardiomyopathy with Methylene Blue Using the Drosophila melanogaster Heart Model
Authors: Tiffany Zhang, Aiden Pan; Nicole Spinelli
Date: 08-31-2022

Dilated cardiomyopathy (DCM), the most common cardiomyopathy, is characterized by ventricular dilation and impaired heart contractility. Past studies found that the inhibition of the ubiquitin-proteasome system (UPS), a crucial protein degradation system that removes dysfunctional proteins, plays a key role in the pathogenesis of DCM. Since treatments for DCM only aim at alleviating heart failure symptoms, a new therapeutic was sought. Methylene blue (MB) was selected because of its cardioprotective properties and ability to increase proteasome activity, potentially allowing it to revert the impact of an impaired UPS. The Drosophila melanogaster strain wupA, which presents DCM symptoms, was used and treated with MB (30 μM). The flies’ lifespans and negative geotaxis were assessed, and dissections were conducted to analyze heart rates (HR), heart diameters, and fractional shortening (FS) with ImageJ. The log-rank test and t-tests were used to analyze statistical significance. Results showed that DCM increased the heart diameters (p&lt;0.01) and decreased the HR (p&lt;0.05), FS (p&lt;0.01), and negative geotaxis (p&lt;0.01). MB fully restored the dilated heart diameters and impaired FS as there was no significant difference between control and experimental groups (p&gt;0.05), exhibiting potential in treating DCM. However, MB didn't affect the impaired HR and negative geotaxis of flies with DCM. Hence, future studies should investigate supplemental treatments to fully restore those properties.
Denoising Speech Signals with Hifi-Coulomb-GANs
Authors: Anirudh Satheesh; Karthick Muthu-Manivannan
Date: 08-31-2022

Recorded speech signals often contain noise that affects the quality of the signal and reduces intelligibility. Several studies have used Generative Adversarial Networks (GANs) to remove noise artifacts and improve speech intelligibility. However, GANs can suffer from gradient vanishing or gradient explosion that can reduce their effectiveness in denoising. To mitigate gradient vanishing, we applied the CoulombGAN architecture to speech denoising using a model structure similar to Hifi-GAN, the current state of the art speech denoiser. We call this new model Hifi-CoGAN. We used a WaveNet generator to denoise signals, a PostNet for general cleanup, and a Multi-Resolution Discriminator to evaluate the signal quality relative to the clean signal. Our results show that Hifi-CoGAN was able to outperform Hifi-GAN in many of the narrowband signals (signals with a limited range of frequencies) in terms of the Short-Term Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) metrics. However, the model did not perform as well as Hifi-GAN with wideband noise signals (signals with a wider range of frequencies) such as white noise, so future work must be done to improve the model for these noise signals.
Machine Learning for Policy Guidance
Authors: Justin Chae; Timothy Raines
Date: 08-31-2022

This paper leverages machine learning algorithms and techniques to create models that can assist in a country's policy guidance. The machine learning process used to conduct research is discussed with steps such as preprocessing, feature selection, model selection, and model interpretation. Specifically, using datasets from the CIA's World Factbook and the United Nations' Human Development Index (HDI), machine learning models are created that use select features from several counties (e.g., real gross domestic product (GDP), population, and area). Then, the models make predictions on the countries' HDI scores. Model interpretation methods are used to find the most important features in predicting a country's score. This paper argues that important features can be derived through machine learning and guide government policy relevant to human development. Supply-side policies are discussed based on the results from the machine learning models. The use of machine learning with other indexes is also explored.
Ursula Le Guin’s Refutation of Gendered Traits in ‘The Left Hand of Darkness’
Authors: Avni Bansal
Date: 08-31-2022

In her novel ‘The Left Hand of Darkness’, Ursula Le Guin challenges the idea that certain human behaviors are fundamentally feminine or masculine, or that gendered traits exist. Firstly, she cleaves gender and personality apart by setting the novel on a planet of androgynes. Secondly, she creates complex characters that exhibit both stereotypically masculine and feminine behaviors. Thirdly, she presents an allegory that criticizes the idea of gendered traits as irrational and unnecessary. Finally, the novel is peppered with subversive images like that of a pregnant king, which are designed to challenge gender roles.&nbsp;
The Move Towards Sustainable Aviation
Authors: Ishaan Jain; Dr. Mritunjay Sharma
Date: 08-31-2022

This paper discusses how the aviation industry has been shaped by sustainability and examines how the aviation industry is progressing towards a more sustainable future. It compares the fuel usage of similar aircraft between aircraft manufacturers, Airbus and Boeing. The research also looks at how aircraft designs have evolved over time to reduce fuel usage and cut down carbon emissions. This can be seen in the fuel usage of aircraft. For example, if you compare the fuel usage of the Airbus A340 to the Airbus A350, the Airbus A350 uses significantly less fuel as it has improved engines and aerodynamics, allowing it to transport a similar number of passengers over a similar range. Airbus managed to make a better and far more fuel-efficient aircraft within 20 years.  &nbsp;  The aim of this research study was to examine how the commercial aviation industry can promote sustainable aviation in the future through a mixed-method approach. First, by using a quantitative approach, the impact of all the aircraft designs of Boeing and Airbus for commercial aircraft carrying 40 passengers and above on fuel usage and CO2 emissions would be compared, followed by a qualitative analysis explaining the varying fuel usage of similar aircraft and comparing which aircraft manufacturer holds the upper hand going into the future.&nbsp;
Identifying the difference in bacterial composition and life history strategies between high versus low nutrient soils
Authors: Felix Tuchscherer
Date: 08-31-2022

In this study, we aim to identify bacterial composition in soils treated with and without wood chips, as well as high levels and low levels of nitrogen. In order to do this, we used both culture dependent methods including plating, observing, and GenIII plates; as well as culture independent methods which include microbiome sequencing and EcoPlates. By doing this, we concluded that the bacterial communities differ between unamended and wood-chipped treated soils. Multiple differences were identified: rate of carbon consumption, bacterial diversity, rRNA gene copy number, and colony morphologies. Identifying the bacteria in wood-chipped soils will allow us to understand their effect on biogeochemical cycles, plant pathogens, and relationship with the trees (symbiotic/pathogen). Additionally, understanding the effect lower nitrogen levels have on the bacterial composition of soils can lead to reduction of synthetic fertilizer use, therefore lessening the environmental impact of nitrogen.
Bioartificial Liver Manufacturing Methodologies in Comparison to Hepatogenesis
Authors: Aanya Roy; Coach Jo
Date: 08-31-2022

End-stage organ failure is a major global issue, with the liver being the second-highest transplanted organ due to lifestyle choices or other conditions. In the field of biomedical engineering, artificial organ manufacturing has been a possible alternative to organ transplants by aiming to achieve less immune rejection, more efficient production, and higher accessibility. Of the biological manufacturing methods today, two up-and-coming technologies for the liver include 3D bioprinting and decellularized organ regeneration. By analyzing general stages of 3D bioprinting and decellularization for liver development and comparing it to the cellular and genetic stages of hepatic development in the embryo, dilemmas and successes in bioartificial manufacturing can be identified. Overall, neither artificial methodology can replicate the genetic influence or non-liver based influence of hepatogenesis. 3D bioprinting is similar to hepatogenesis in cell development and construction, but contains shortcomings in vascularization, which can be addressed using the vasculogenesis and angiogenesis processes in hepatic development. Decellularization allows for cell differentiation, although it is unable to instill natural cellular distribution and gradual ECM development. This may cause blockages for clinical use in the future. Both methods have potential for bioartificial liver development and clinical application, but 3D bioprinting technology is more aligned with the stages of hepatic development, which has proven to induce fewer errors in the physiological nature of the manufactured liver. In the future, modification and research must be conducted on the specific stages of these methodologies for either to create an operational bioartificial liver for clinical use.
Does the U.S. Federal Reserve Consider the Credit Cycle Theory When Trying to Predict Recession?
Authors: Tommy Heaton; Niklas Schmitz
Date: 08-31-2022

The credit cycle theory states that credit build-ups and their subsequent crashes are the common cause of recessions. If true, this theory could be used to both predict and prevent future recessions. However, it is unclear if policymakers do in fact take the theory into account when crafting monetary policy. Using the U.S. Federal Reserve (the Fed) as a case study, this paper seeks to answer if policymakers consider the credit cycle theory when attempting to predict recessions and determine optimal monetary policy. This paper analyzes Federal Open Market Committee minutes from meetings prior to four previous recessions: the Savings and Loan Crisis, the Dot-Com Bubble, the Great Recession, and the Covid Recession. The results indicate that the Fed did not begin to consider the credit cycle theory when implementing monetary policy until after the Great Recession. Credit was not addressed prior to the Savings and Loan Crisis and was only substantially mentioned on the eve of the Dot-Com Bubble and the Great Recession. This indicates that instead of continually discussing credit, the Fed was only concerned with credit when it began to tighten. While it did mention credit build-up, especially before the Great Recession, the Fed did not adjust its monetary policy accordingly. Instead, it primarily adjusted policy to manage inflation. However, after the Great Recession, the Fed began adhering more closely to the credit cycle theory both in terms of what was discussed in each meeting, as it consistently discussed credit, and how it implemented monetary policy.&nbsp;
Effects Of Online Tutoring On Test Scores, Confidence, and Future Career Paths During The Pandemic
Authors: Grace Gao; Rodion Kosovsky
Date: 08-31-2022

Since the outbreak of COVID-19 in 2020, online learning has rapidly begun to replace in-person learning and demand for online tutoring has grown. The switch to remote learning has also exacerbated learning inequality, as shown by students’ test scores. The paper examines the efficacy of ManhattanACE’s mathematics online tutoring model to provide a template for creating a valuable online tutoring experience.  To understand the main aspects of online learning and its effects on students’ learning outcomes, an electronic survey was created and sent to the students. The survey contained both Yes/No questions and scaled 1-5 questions. Analysis of the survey responses shows that through a combination of factors such as passionate tutors and a responsive learning environment that feels like a community, online learning can boost both students’ grades in math class and the effectiveness of virtual learning, while fostering students’ confidence in their skills and future career choices. These three outcomes can, in turn, work to minimize educational and resource inequality.
Melanoma Skin Cancer Classification via Region-Aware Hierarchical Feature Aggregation
Authors: Kyungryun Kim
Date: 08-31-2022

Melanoma is a type of skin cancer with the highest risk of death. It is critical to identify these early, as the chances of survival significantly drop after stage 2 melanoma. The use of many technologies has lessened this risk but is still limited when correctly identifying malignant ones. Classifying malignant skin cancers is difficult for the following reasons: the shapes and sizes of melanomas are irregular. It is also challenging to visually distinguish between melanomas and non-melanoma regions.&nbsp;The performance of the previous research is based on the depth of the networks. This has a significant trade-off as using more layers adds to the computational cost of the process. Also, their methods use melanoma segmentation as an auxiliary input to the classification network. Thus, the performance of the classification significantly drops when the segmentation task fails.&nbsp;To address this issue, I propose a novel melanoma classification network that uses hierarchical feature aggregation with an attention mechanism. The overall architecture of the proposed network is as follows: The melanoma feature extractor takes the melanoma image as input and produces the image feature related to melanoma. The second module, the attention network, takes the same melanoma image and outputs the attention map which provides the melanoma feature extractor with feature-level regions of interest. The proposed network achieves an accuracy of 82.7% on the melanoma detection dataset which is publicly available online. Throughout the experiments, I have shown that the proposed method outperforms the previous state-of-the-art methods.
Nuclear Energy as a Highly Viable Source to Mitigate Rapid Warming
Authors: Huaxu Zhang; Joel Morrissey
Date: 08-31-2022

The longstanding issue of global warming has taken its toll on countries around the world. Global warming has become one of the major issues that humans need to face together in the 21st century. A highly viable solution to this issue is adopting nuclear energy as one of the world’s major energy sources. Nuclear energy is more reliable and consistent than renewable energy and cost-efficient in energy generation in the long run, making it a suitable replacement for acquiring energy from traditional fossil fuels. In addition, nuclear power and renewable energy should both be considered as possible solutions for mitigating global warming, rather than as rivals contending for superiority. Currently, the two major challenges of using nuclear energy include the high short-term cost of building nuclear power plants and nuclear fear by the general public. At the current rate of warming, it is necessary to use every viable means to slow down the process of warming enough for better solutions to climate change to be devised.
Machine Learning Methods for Breast Cancer Diagnosis
Authors: Matthew Lee; Zhaonan Sun
Date: 08-31-2022

As many modern diseases begin to surface especially as of late, such as the Ebola and COVID-19 epidemics, scientists have begun developing new and innovative tactics to combat them. While new medicine and vaccines may be developed, one area that needs special attention is the diagnosis of diseases – this is because without a proper and speedy diagnosis, scientists wouldn’t be able to detect diseases, rendering treatment ineffective. Scientists have begun using machine learning algorithms to help ensure an accurate and speedy diagnosis. One specific disease that has seen frequent testing around machine learning diagnosis is breast cancer. Breast cancer is one of the deadliest and common cancers around the world for women, and due to its effects, the doctrine of speed in diagnosis is essential. This study will attempt to find out, out of three machine learning algorithms (neural networks, logistic regression and K-nearest neighbours), which one is the most effective at diagnosing breast cancer using the Wisconsin Breast Cancer Dataset. Results suggest that neural networks perform the best in diagnosing breast cancer, however only by a small margin compared to other results.
Predicting the Chance of Heart Attack with a Machine Learning Approach – Supervised Learning
Authors: Lanting Zhu; Guillermo Goldsztein
Date: 08-31-2022

Machine learning is a multidisciplinary field combining statistics, computer science and artificial intelligence. This research finds a way to use machine learning to predict the chance of heart attack based on information about the patient. There are 13 features collected about each patient which are age, sex, cholesterol, chest pain type, maximum heart rate achieved, resting blood pressure, resting electrocardiographic results, fasting blood sugar, exercise-induced angina, previous peak, slope, number of major blood vessels, and thalassemia. The information of all the patients is put into a dataset. The dataset is split into two sets, one for training and another for validation. A computer model using a supervised learning algorithm is developed and trained to predict the chance of heart attack. During training, the training set is used for training the model, while the validation set is used for evaluating the accuracy of the model.
An Analysis of COVID-19 Fiscal Policies in the US and Japan
Authors: Avinash Gogineni; Mr. Martin
Date: 08-31-2022

In 2020, the COVID-19 disease created an unprecedented impact on the world. It created a health crisis in many countries, causing a pandemic. Along with the health crisis, most countries fell into an immediate economic recession including the US and Japan. This paper focuses on the fiscal policies used in the US and Japan due to the COVID-19 pandemic-related economic recession in both countries. First, a detailed analysis of the US and Japanese fiscal policies is presented, analyzing their effectiveness. Subsequently, these policies were compared and contrasted to obtain a better understanding of fiscal responses around the world. Overall, this paper aims to provide a new global perspective on the implementation of fiscal policies while also aiding policy-makers in making more educated decisions for future recessions caused by COVID-19 or other pandemics.
NFTs: License to Own?
Authors: Jeremy Freeman
Date: 08-31-2022

In recent years, NFTs have burst onto the global scene, quickly going from an internet meme to a multi-billion dollar digital industry. These digital assets have everyone talking, but nobody seems to know what they are or whether you actually own anything when you buy one. This article uses extensive philosophical, legal, copyright, and empirical research to explore what is actually being purchased with an NFT. With everything from the morale words of John Locke to warnings from the district office of Manhattan, this article quantifies what NFT ownership entails, which is, essentially, nothing. Although governments acknowledge the possibility of legitimate ownership for NFTs, the lack of regulations and protections, as well as the unclear copyright laws and shady ownership claims make NFTs, for our current legal system at least, essentially worthless.&nbsp;
Deep Learning for MS2 Feature Detection in Liquid Chromatography Mass Spectrometry
Authors: Jonathan He, Olivia Liu; Xuan Guo
Date: 08-31-2022

Accuracy of peptide identification is crucial for LC-MS analysis to reveal information regarding many different aspects of proteins that aid in the discovery of biomarkers and profiling of complex proteomes. Preprocessing steps such as feature detection are crucial yet challenging; current feature detection tools are not robust enough to detect low-abundance, low-peak fragments of peptides found in MS2 data from tandem mass spectrometry. In this study, we developed a deep learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct accurate feature detection on MS2 data. Experimental results show that our model is able to produce more accurate values and identifications than existing feature detection tools. Therefore, we believe that our model can realize the full potential of neural networks in the field of bioinformatics and yields long-term benefits in the advancement of proteomic inquiry.
Profit and Stock Prices: New Evidence Contrasting the General Consensus of Their Relationship
Authors: Ziyang Zhong
Date: 08-31-2022

Ever since the early 17th century, the stock market has become one of the most important aspects of economy in modern society. Which is why is it important to clearly understand the stock market and invest in the companies that would bring profit. When profit and stock prices are discussed together, people tend to assume that there is a correlation between the two. This is because profit is the monetary representation of that company outside of the stock market, and stock prices are the monetary representation within the stock market. However, while there are countless articles proving why there are such relationships between the two, there are also articles disputing that there are any significant relationships. Due to this disagreement in the literature, this paper is going to investigate the relationship between profit and stock prices.
Increase in Stress and Post-Traumatic Stress During 6 Month Quarantine in Various Demographics
Authors: Claire Padilla
Date: 06-10-2022

Due to the Coronavirus, people have quarantined for periods of at least six months all over the world. In the United States, there have been over six hundred thousand Coronavirus deaths and over 33 million cases of COVID-19 as of May 2021(World O Meter 2021). When the pandemic first struck, 23 million people in the U. S. lost their jobs. Today's unemployment rate is higher than it was during the Great Depression, which lasted from the 1920s to the late 1930s. People are stressed about COVID, according to John Hopkins (Maguire 2020), and they encourage people to find coping mechanisms. When a person is stressed, they experience physical or emotional tension (MedlinePlus 2020). Since coping can have a detrimental effect on the immune system, coping in necessary (Maguire 2020). This study sought to determine whether people aged 18 and up experienced a rise in stress levels during a 6-month quarantine period. Then, based on the Impact of Event Scale - Revised&nbsp; (IES-R) score, this study would determine which coping mechanism, if any, is most successful in alleviating quarantine-induced stress. It was found that there was a significant increase in stress levels after a six-month quarantine period with a confidence level of 95% and a p-value of 0.00000031. The data also showed that there was not a significant difference in the stress levels between varying coping mechanisms with a p-value of 0.23. It is recommended to use a coping mechanism, all coping mechanisms are effective when matched properly to each individual.