bluuebunny/crossref_metadata_2025 huggingface dataset:
Survey of medication disposal: Patient views and awareness
Authors: Choi Colin , Yang Payton , Sun Patrick Y
Published: September 2023

The improper disposal of medication poses potential environmental and health problems. When medications are disposed of improperly, they can enter the water supply and other parts of the environment. This can lead to unintended ecological and health consequences. Proper disposal is one of the best ways of preventing this. In this study, we characterized the attitudes and preexisting knowledge of proper disposal of unused medications in endocrinology patients in Southern California. Our hypothesis was that people would be willing to drive to properly dispose of medications if they were aware of proper medication disposal practices. The respondents overwhelmingly indicated that they would be willing to properly dispose of unused medication but reported that they were unaware of the procedures, and would be willing to properly dispose of their unused medications given the appropriate education. These results have applications for future efforts such as increased education or awareness of medication disposal procedures to limit improper drug disposal.
Managing CO2 levels through precipitation-based capture from seawater and electrochemical conversion
Authors: Ka Emma , Fields Jacob , Mubeen Syed
Published: September 2023

With the rise in global greenhouse gas emissions, many studies have sought methods to reduce the accumulation of carbon dioxide (CO2) in the atmosphere. Existing methodologies are primarily based on amine solvents such as monoethanolamine (MEA) and 2-amino-2-methylpropanol (AMP) due to their high carbon absorption capacity and good recyclability. As such, they are used for the capture of CO2 from sequestration sites and its subsequent electrochemical transformation. However, because amines have large enthalpies of reaction, this leads to expensive and energy-intensive processes that limit their capacity for large-scale application. Thus, this study seeks to develop an electrochemical device that can sustain efficient CO2 capture and conversion through precipitation. The dissolved inorganic divalent cation calcium (Ca,2+) reacts with carbon to form carbonate (CO32-) compounds that flow through a thin membrane, facilitating the filtration of the precipitate. We hypothesized that higher alkalinity will produce a greater quantity of CO2 capture, as measured by the change in seawater pH and the mass of precipitate formed. The CO32- compound is, in turn, converted to the industrial fuel formic acid (HCOOH). Through our experimentation, we found a statistically significant difference between the measured pH of seawater before and after CO2 capture (p < 0.001), thus suggesting this setup as a proof of concept for the effective reduction of ocean acidification. Additionally, we observed an upwards trend as higher concentrations of sodium hydroxide (NaOH) were generally associated with greater changes in pH.
Effect of environment factors on the expression of soluble PDE8A1 in E. coli
Authors: Jiang Yinming , Huang Ya-Dan
Published: September 2023

PDE8, a type of phosphodiesterase (PDE), is proven to be crucial in various cellular activities and physiological activities by influencing second messenger systems. It is involved in a wide range of diseases, including Alzheimer’s disease and various heart diseases. However, there is limited information about PDE8 selective inhibitors. The lack of studies is mainly due to the difficulty of obtaining large amounts of active, soluble enzymes, as only a limited amount of PDE8 is expressed by cells. Hence, this work aimed to improve the solubility and yield of PDE8 in the supernatant by exploring suitable culture conditions, including temperatures and different additives. We hypothesized that the presence of additives including glycerol, betaine, L-arginine, and L-glutamate, as well as different culture temperatures can positively affect the concentration of soluble PDE8A1 expressed by cultures of Escherichia coli. We conclude that PDE8A1 catalytic domain, the only splice variant of PDE8A with a complete functional domain, expression is favored at higher temperatures, at about 16oC. A glycerol concentration of 8% promotes the solubility of PDE8A1 expressed greatly. Betaine did not increase the concentration of soluble PDE8A1, but could potentially increase the its stability. L-glutamate and L-arginine had similar effects on the expression of soluble protein and were able to evidently increase the solubility of PDE8A1.
The novel function of PMS2 mutation on ovarian cancer proliferation
Authors: Cho Yoonseo , Lee Woo Rin
Published: September 2023

The PMS2 gene encodes a protein that plays an essential role in repairing DNA. Recent studies have shown that the S815L PMS2 mutation, rarely found in ovarian cancer (3%), causes a reduction in the PMS2 protein’s mismatch repair activity in vitro. However, no studies have focused on how this mutation affects cancer progression. Although occurrences of patients with cancers associated with the PMS2 mutation are rare, the lack of studies published on this mutation still negatively impacts their treatment. Our study aimed to discover the novel effect of this mutation in ovarian cancer cells. We hypothesized that overexpressing the wild-type PMS2 gene would have tumor-suppressive effects while overexpressing S815L PMS2 mutant would negatively affect ovarian cancer cell proliferation. We tested three transfection conditions: pcDNA3 as a negative control variant, wild-type pcDNA3-PMS2, and pcDNA3-PMS2 mutated vectors. Using RT-PCR analysis, we optimized the transfection conditions for overexpression of both the wild-type PMS2 and mutant genes in OVCAR-3 cells. We acquired microscopic images to observe cell morphology and colonization. Also, we performed a Prestoblue assay to quantify the number of live cells in each condition. Results showed that the pcDNA3- PMS2 mutated vector has a higher cell proliferation than the wild-type vectors. As hypothesized, overexpressing the wild-type PMS2 gene had tumor-suppressive effects. This observation shows that the pcDNA3-PMS2 mutation has a tumor-progressive impact on the OVCAR-3 cells. Overall, we conclude that the PMS2 mutation has tumor-progressive effects in ovarian cancer cells while the unmutated version of the gene does not.
More efficient sources of water distribution for agricultural and general usage
Authors: Jung Luke , Jung Heidi , Killeen Heather
Published: September 2023

Current irrigation systems that disperse water above ground waste an enormous amount of fresh water. Since fresh water is a limited resource, new irrigation systems are desperately needed to make agriculture more sustainable. To investigate this issue, we tested two new watering systems for water efficiency and successful plant growth compared to traditional overhead sprinklers, systems that spray droplets of water (similar to rainfall) from above, with sesame plants. One system utilizes an underground pipe to distribute water intermittently; the other utilizes an underground cloth saturated with water to provide a continuous and gradual diffusion of water to plants. We hypothesized that both underground methods would be more efficient than the sprinkler method, saving more water and resulting in comparable plant growth. Given that sesame plants have been shown to prefer well-drained soil (and do not do well in standing water), we suspected that the cloth method would be more effective at delivering the minimal amount of water needed for these plants. We found that the underground cloth-based water diffusion method saved more water compared to the other distribution systems; however, it also resulted in a slight reduction of the yield (plant growth). The underground pipe-based water distribution system outperformed the sprinkler system (used less water); however, it still produced the same yield. Based on these experimental results, we propose substitution of the conventional overhead sprinkler watering system with underground methods such as a pipe- based water distribution system or cloth-based water diffusion system to conserve water.
Modular mimics of neuroactive alkaloids - design, synthesis, and cholinesterase inhibitory activity of rivastigmine analogs
Authors: Yu Erika , Raghavan Shloka , Anand Shreya , Xu Harrison , Nepani Tvisha , Nambiar Niharika , Vu Julia , Zhang Alivia , Babu Suhani , Brierley-Green Elena , Peer Anushka , Finkelstein Alice , Ferguson Adrienne , Avadhani Udbhav , Nittala Sanhita , Njoo Edward
Published: September 2023

The treatment of neurological diseases has evolved to include neuroactive alkaloids isolated from naturally occurring phytochemical sources. While some of these compounds have gone on to clinical use themselves, others have inspired the development of synthetic analogs, which might possess greater potency or better pharmacological features than the natural product itself. One such naturally occurring alkaloid, physostigmine, which is found in the Calabar bean plant Physostigma venenosum, has been demonstrated to be a potent cholinesterase inhibitor. However, some of physostigmine's characteristics limit its therapeutic potential, prompting the development of rivastigmine, a similarly structured synthetic compound. The research in our group focused on the synthetic optimization of rivastigmine and its analogs, utilizing computer modeling and biological assays to determine the most favorable analog for inhibition of acetylcholinesterase (AChE), the enzyme that breaks down the neurotransmitter acetylcholine (ACh) to terminate neuronal transmission and signaling between synapses. Patients with Alzheimer’s Disease have lower levels of ACh, which has been associated with symptoms like memory impairment and confusion. The inhibition of AChE allows for ACh accumulation and continued signaling in parasympathetic nervous system. Through our studies, we determined that rivastigmine and its analogs were less effective at inhibiting AChE than physostigmine, and their biological activity is governed by sterics.
Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis
Authors: Sirohi Aditya , Moore Jason H
Published: September 2023

Over the last few decades, childhood stunting has persisted as a major global challenge. More than 154 million children under five are stunted across the world, with 95% of them residing in Asia and Africa alone. Apart from the small stature, stunting poses additional impediments including delayed cognitive development and health complications. Furthermore, the involvement of several demographic factors and limited timeframe of intervention effectiveness poses challenges to existing treatments. Thus, understanding the impact of demographic conditions on stunting and developing predictive methods is vital for advancing future research developing predictive models. In this study, we hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. The TPOT pipelines, for data sets from Kenya and Bangladesh, outscored previously reported ML models with most performance metrics improving by 0.1 or more. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. Additionally, the machine learning models trained by TPOT resulted in strong overall performances with the best metrics scoring over 0.8. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.
Association of agenesis of the corpus callosum with epilepsy and anticonvulsant drug treatment
Authors: Steger Ava A , Sipple LeAnn F , Steger Michael F
Published: September 2023

Agenesis of the Corpus Callosum (ACC) is a birth defect where an infant’s corpus callosum, the structure linking the brain’s two hemispheres to allow interhemispheric communication, fails to develop in a typical manner during pregnancy. Existing research on the connection between ACC and epilepsy leaves significant gaps, due to the lack of focused investigation. One important gap is the degree to which ACC may impact the course of epilepsy treatment and outcomes. The present study was conducted to test the hypotheses that epilepsy is highly prevalent among individuals with ACC, and that those with both ACC and epilepsy have a lower response rate to anticonvulsant drugs than other patients treated with anticonvulsant drugs. A weighted average of epilepsy rates was calculated from a review of existing literature, which supported the hypothesis that epilepsy was more common among individuals with ACC (25.11%) than in the general population (1.2%). An empirical survey administered to 57 subjects or parents of subjects showed that rate of intractable epilepsy among study subjects with both ACC and epilepsy was substantially higher than the rate found in the general population, indicating that individuals with both conditions had a lower response rate to the anticonvulsant drugs. This study contributes novel results regarding the potential for concurrence of ACC and epilepsy to interfere with anticonvulsant drug treatment. We also discuss implications for how medical professionals may use the findings of this study to add depth to their treatment decisions.
Using economic indicators to create an empirical model of inflation
Authors: Kasera Jai , Powers Howard
Published: September 2023

Inflation affects all aspects of the economy, and it is important for traders, economists, and monetary authorities to understand the behavior of inflation to predict economic growth in the future. In our research, we investigated and calculated the correlation between various economic indicators and inflation over history. We then used the correlation information to develop a rolling linear regression model to predict inflation on an out-of-sample basis. For the measure of inflation, we used the Month over Month Consumer Price Index Seasonally Adjusted (CPI), released by the U.S. Bureau of Labor Statistics (BLS) every month. CPI measures inflation by calculating the change in the price of a basket of goods that consumers pay for. We chose 50 of the most important economic indicators followed by the market, and we hypothesized that by using out-of-sample data, the CPI of the next month could be reasonably predicted by using a regression model of a subset of economic indicators. We concluded that the average gasoline price, U.S. import price index, and 5-year market expected inflation have the most significant correlation with CPI. By using these indicators, we predicted CPI using a linear regression with a mean absolute error of about a tenth of a unit of CPI, where CPI is measured as a percentage. Furthermore, we discuss the possible public policy implications of our study and how inflation may be reduced by focusing on the three economic indicators highly correlated with it.
Inflated scores on the online exams during the COVID-19 pandemic school lockdown
Authors: Hwang Christina , Lee Soonnam
Published: September 2023

Due to the COVID-19 pandemic, colleges and universities in the US had to provide students with online-only instruction and assessment in the middle of the Spring 2020 semester. Since this was neither adequately planned nor thoroughly prepared due to little time, its effects on student academic performance are unknown. This study tested a hypothesis that students’ test scores were significantly higher on online exams during the COVID-19 school lockdown than those of the in-person exams before the lockdown. We compared the scores of the same students between one in-person exam and two online exams in a fundamental engineering class in the Spring 2020 semester. We further compared the scores between the students who took three in-person exams in the 2019 semesters and those in the Spring 2020 semester. The same instructor taught the class in both cases, and all exams had similar difficulties. We found that students achieved significantly inflated test scores when taking online exams, with the increase being in the range of 20% to 50%. Therefore, we have proven the hypothesis. Score inflation also depended on the level of prevention of student collaboration on online exams, with a loosely-controlled online exam’s scores increasing by 50% compared to exams with strict measures to prevent cheating. In comparison, students showed a similar performance at three in- person exams in the 2019 semesters, with differences ranging 6% to 8%. The current study could help show how the COVID-19 pandemic influences long-term academic performance of institutions, instructors, and more importantly, students.
Uncovering mirror neurons’ molecular identity by single cell transcriptomics and microarray analysis
Authors: Zhou YiFei , Varty Steven
Published: September 2023

Mirror neurons (MNs) fire action potentials both when performing and seeing particular actions, ranging from grasping an object to observing a simple smile. During neuroimaging, the active cortical regions in both execution and observation are referred to as the mirror neuron system. It is composed of the superior temporal sulcus, posterior parietal cortex, and inferior frontal gyrus in humans. We aimed to find MNs’ neuronal identity, or the cell cluster to which MN belongs. Such identification lays the groundwork for future MN analysis and understanding of the mechanism of action. Due to the uniqueness of the human MN system, we performed bioinformatics analysis instead of experiments on animal models. High-expression genes throughout the MN system became candidate genes derived from differential gene analysis, or microarray. Ten cell clusters fit the MN’s trait as a layer three pyramidal excitatory neuron in the Allen Brain Atlas, human cortical single cell sequencing database. The cluster that had the highest sum of relative expression of candidate genes was cluster 85 with markers THEMIS and UBE2F. With MNs’ likely molecular identity in cluster 85 identified, we further explored MNs’ functional aspects. We gathered and analyzed data from single-cell sequencing and STRING (a functional protein association network) to uncover possible connections between typical neural degenerative diseases and psychiatric disorders with MN impairment. We found moebius facial syndrome, autism spectrum disorder, and amyotrophic lateral sclerosis to be potentially related to MN impairment.
Innovative Treatment for Reducing Senescence and Revitalizing Aging Cells through Gene Silencing
Authors: Kim Jason , Lee Woo Rin
Published: September 2023

Many studies explore whether harnessing a cellular state known as senescence holds the solution to revitalizing aging tissues. Senescent cells are unique in that they stop cell division but are resistant to apoptosis, a programmed cell death. Senescent cells continue to release chemicals that can trigger inflammation. mTOR inhibitors are currently the only known pharmacological interventions that increase lifespan and revitalize aging tissues in animal models. However, the side effect profiles of mTOR inhibitors are a major cause of concern because mTOR inhibitors inhibit the genes that regulate cell proliferation and immune cell differentiation. To explore a possible solution to the side effects of an mTOR inhibitor, we specifically targeted two mTOR downstream genes, RPS6KB1 (G1 cell cycle inducer) and PPARGC1A (mitochondria energy metabolism regulator) using a small interfering RNA (siRNA) to inhibit gene expression. We hypothesized that inhibiting expression of RPS6KB1 and PPARGC1A would decrease senescence caused by hydrogen peroxide (H2O2). We used human skin cells (Detroit 551) to test our hypothesis and induced senescence by H2O2 treatment. Our result indicates that when both genes were knocked down by siRNA on Detroit 551 cells pretreated with H2O2, the number of senescent cells significantly decreased compared to scrambled siRNA transfected control cells. Our results suggest that inhibiting gene expression of both RPS6KB1 and PPARGC1A may inhibit senescence. Our results may support the development of novel treatments for revitalizing aging tissues and inhibiting inflammation triggered by senescent cells.
Increased carmine red exposure periods yields a higher number of vacuoles formed in Tetrahymena pyriformis
Authors: Shah Nina , Sullivan Laura , Edgar Michael
Published: September 2023

Tetrahymena pyriformis (T. pyriformis) are single celled eukaryotes that use the process of phagocytosis to ingest food by forming vacuoles. Vacuoles are compartments in the cytoplasm of a cell which store the essential nutrients for the cell. T. pyriformis can use phagocytosis to create vacuoles of carmine red, a dye which is made using crushed insects and is full of nutrients (1). Establishing a relationship between vacuole formation and duration of exposure to food can demonstrate how phagocytosis occurs in T. pyriformis. We hypothesized that if T. pyriformis was incubated in a carmine red solution, then more vacuoles would form over time in each cell. This was because with increasing amounts of time, T. pyriformis would have a longer period to create new vacuoles. Our results revealed that during the 2 and 5-minute incubation periods, the number of vacuoles increased exponentially, and vacuole formation increased linearly after 5 minutes. The correlation between elongated carmine red exposure and the number of vacuoles formed suggests that the effectiveness of phagocytosis may depend on exposure to the dye, which is used to simulate the particles that they ingest. This may shed light on how other phagocytes manage their consumption of food for energy storage and other metabolic processes.
How CAFOs affect Escherichia coli contents in surrounding water sources
Authors: Lieberman Henry , DuPont Golda Stephanie
Published: September 2023

Commercial Concentrated Animal Feeding Operations (CAFOs) produce large quantities of waste material from the animals being housed in them. These feedlots found across the United States contain livestock that produce waste that results in hazardous runoff. Water sources surrounding CAFOs become contaminated making the water unusable for humans, livestock, and uninhabitable for aquatic life. This study examines how CAFOs affect water sources by testing for Escherichia Coli (E. coli) content in bodies of water near CAFOs. We hypothesized that the level of E. coli content in water samples near CAFO sites will be higher than in water samples not near CAFOs sites. To test our hypothesis, private drinking water bacteriological tests were performed on water samples sourced within one mile of a CAFO and then compared to water samples sourced more than five miles from the nearest CAFO. We found an increase in the E. coli concentration in water source samples closer to CAFOs. This research is crucial to understanding the impact that CAFOs have on the environment. It also provides important information for those living near CAFOs so that they can make informed decisions regarding their use of nearby water sources.
The study of technology and the use of individual cognitive effort
Authors: Neravetla Arushi , Steen Matthew , Neravetla Aravind
Published: September 2023

A trial study was performed in 2021 to investigate the link between technology and transactive memory. Transactive memory is shared knowledge in which members share the responsibility to encode, store, and retrieve certain tasks or assignments, leading to a successful and collective performance. In general, the internet has a become a popular database for individuals to use because of its ability to encode, store, and retrieve files of information for our own purposes, so our experiment wanted to test the cognitive ability of retrieving information through our memory without relying on technology. We hypothesize that a participants’ expected access to an external source affects the recall rate and retrieval of information. We asked high school students to read 20 trivia statements and observed the difference in recall rate while typing on their computers to see their conditional response by saying either the statements were saved on the computer or deleted. The use of computers is significant in testing participants who were told that the trivia statements would be deleted were more likely to remember the information than participants who were told the statements would be saved, although this result was statistically non-significant.
An optimal pacing approach for track distance events
Authors: Lee Kaitlyn , Lee Adrian
Published: September 2023

Track athletes aiming to compete at the highest level often must meet specific qualifying standards for middle and high school state championships, national collegiate championships, and ultimately, the Olympic Games. In this project, we aimed to address the best approach for achieving personal best times in track distance events. We used an existing mathematical model based on physiological attributes of world-record-setting elite runners to yield an optimal pacing approach. We then confirmed the validation of this energy depletion model using elite men’s and women’s gold medal performances at the Tokyo 2020 Olympic Games. We hypothesized that the average pace of a field of high school athletes competing in 800 m, 1600 m, and 3200 m distance events at a championship track meet does not follow the optimal pacing profile. Instead, we believed many runners begin with a fast pace to stay within close range of the race leaders, while others start conservatively to save energy for the final stage of the race. We used official timing data to test our hypothesis against computational simulations. We found that the average pace could deviate from the theoretical optimal pace by as much as 4%, translating to a difference of 1-2 seconds every 200 meters. Our analysis helps middle and high school athletes understand how pacing can improve their personal best times and what training can be performed to improve their physiological capabilities.
Cleaning up the world’s oceans with underwater laser imaging
Authors: Gurbuz Mustafa Alperen , Kim Seongsin
Published: September 2023

It is estimated that there are 5.25 trillion pieces of plastic waste in the oceans. Of this plastic, roughly 70% is not visible because it is underwater. This plastic is harmful to animals as they can get entangled in the litter and ingestion of plastic toxins can cause disease. Although cameras and hyperspectral imagers have been used to find plastics in the ocean, their efficacy is limited to visible garbage on the ocean surface. Ocean cleaning up requires retrieval of not just visible garbage, but underwater garbage as well. For this task, both sonar and Light Detection and Ranging (LIDAR) can be utilized; however, sonar can be harmful to marine animals by disrupting their echolocation capabilities. LIDAR generates high resolution point clouds with which recognition of small objects may be difficult. Therefore, we investigated the ability of lasers to image and identify underwater objects. Laser imaging can penetrate underwater, providing informative images at depths where there is no ambient light. This study examined the influence of various factors on underwater laser image quality: polarization, depth, turbidity, and object material. We conducted experiments by illuminating objects in water with a laser and recording illuminated objects using a camera. We utilized various image processing techniques to enhance image quality. Our results show that deconvolution was a more effective method than alternatives for reducing blurriness and that laser imaging is a viable method for detecting underwater garbage.
Analyzing honey’s ability to inhibit the growth of Rhizopus stolonifer
Authors: Johnecheck Camden , Petersburg Alexander , Rasmus Joe
Published: September 2023

Rhizopus stolonifer is a mold commonly found growing on bread that can cause many negative health effects when consumed. Preservatives are the well-known answer to this problem; however, many preservatives are not naturally found in food, and some have negative health effects of their own. We focused on honey as a possible solution because of its natural origin and self-preservation ability. We hypothesized that honey would decrease the growth rate of R. stolonifer . We evaluated the honey with a zone of inhibition (ZOI) test on agar plates. Sabouraud dextrose agar was mixed with differing volumes of honey to generate concentrations between 10.0% and 30.0%. These plates were then inoculated with a solution of spores collected from the mold. The ZOI was measured to determine antifungal effectiveness. A statistically significant difference was found between the means of all concentrations except for 20.0% and 22.5%. Our findings support the hypothesis as we showed a positive correlation between the honey concentration and growth rate of mold. By using this data, progress could be made on an all-natural, honey-based preservative.
Differential privacy in machine learning for traffic forecasting
Authors: Vinay Sunny , Redburg Rachel
Published: September 2023

Time series data has many applications to understand real-world phenomena. A common application is traffic congestion forecasting. Preserving privacy with traffic data is also essential, and an emerging solution is differential privacy, which causes a tradeoff in utility. In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.
Cytotoxicity evaluation of Amaranthus extracts compared with AS20 on MCF-7 cancer cells
Authors: Shah Mahek , Vijaykumar Kasture Pooja , N V Bhavana , Rao Jyothsna , Rao Gururaj
Published: September 2023

Advancement in oncology research is striving to find new and effective therapies for treating cancer, limiting the drawbacks of conventional treatments like chemotherapy, radiotherapy, etc. Many plant-derived active ingredients such as saponins, tannins, alkaloids, and polyphenols are used in cancer treatments which are FDA approved. Amaranthus is a traditional Indian medicinal plant used to treat a variety of diseases. It was previously found to contain phenols, tannins, flavonoids, and alkaloids among other bioactive phytochemicals. In the present study, we hypothesized that AS20, a polyherbal formulation derived from Amaranthus leaves and inflorescence has higher anticancer properties than individual extracts such as leaf methanol and inflorescence acetone extract on MCF-7 breast cancer cells. We also hypothesized that AS20 would induce higher levels of apoptosis in MCF-7 breast cancer cells. We discovered that AS20 had a considerably lesser maximal inhibitory concentration (IC50) compared to individual plant extracts of Amaranthus spinosus using 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay. AS20 had a lower IC50 than leaf methanol and inflorescence acetone extract respectively. Paclitaxel (a chemotherapeutic drug which can be used in cancer treatment) was used as a positive control and showed similar IC50 value like AS20. These findings indicated that AS20 did not show a significantly lower IC50 value compared to the chemotherapeutic drug Paclitaxel. Fluorescence staining methods such as Hoechst, 4′,6-diamidino-2-phenylindole (DAPI), and acridine orange with propidium iodide (dual staining) showed that AS20 caused 51% cell death in MCF-7 cells compared to 4% cell death in untreated MCF-7 cells after 48 hours of exposure, indicating successful drug activity in inducing apoptosis.
Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules
Authors: Sikdar Diptanshu , Cui Max , Chau Adelina , Bhamra Arjun , Prasanna Sathvik , McMahan Larry
Published: September 2023

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. These generative models can extract salient features that characterize the molecules. However, they often suffer from increased time complexity. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules. There are two parts in the QGAN: a quantum generator that creates molecules based on the probability distributions of likely combinations and a classical discriminator that differentiates real molecules from the generated ones. We hypothesized that a quantum generator would be more impactful, offering a runtime speedup and increasing generated molecule possibilities, because we could use quantum mechanical phenomena superposition, entanglement, and interference to analyze more atom-bond combinations than a classical generator. The PyTorch-PennyLane implementation of the QGAN generated seven chemically stable molecules out of 300, a 2.3% success rate. Although the QGAN is a work in progress, it has demonstrated a path towards more efficient drug development, which would accelerate the development of medicines and reduce costs for the whole R&D process.
Optimizing airfoil shape for small, low speed, unmanned gliders: A homemade investigation
Authors: Lara Teo , Galvan Adriana
Published: September 2023

Motivated by an interest to increase the efficiency of static airfoils, the objective of this study was to optimize airfoil lift generation based solely on airfoil shape. Enhancing the lift generated by airfoils without flaps can lead to longer flight times for unmanned gliders and possibly reduce fuel requirements for ground and air vehicles (1,2,3). We employed a Bernoullian model of airfoil lift generation to predict an optimized wing shape which was then constructed and compared with five others in a wind tunnel. We assembled airfoils from Styrofoam and conducted experiments in a small wind tunnel, measuring air speeds with a handheld anemometer. Our Bernoullian model related lift generation to the ratio of surface areas above and below an airfoil’s chord, and because this ratio was maximized in our optimal airfoil, we predicted that this wing would generate more lift force than the other tested wings with lower ratios. However, we identified a variety of confounding variables including imperfections in wing constructions, turbulent airflow and air resistance within the wind tunnel, and low resolution of the anemometer, all of which contributed to unpredicted and unreliable results. Moreover, we concluded that our mathematical model was not rigorous enough to be generalized to a wider set of experimental conditions. Despite these shortcomings, our results did point to a correlation between airfoil shape and lift generation, and will allow for more informed, less error prone future studies.
A comparative analysis of synthetic and natural fabrics
Authors: Stenzel Taylor , Rasmus Joe
Published: September 2023

A potential alternative to synthetic fabrics such as polyester is textile made of bamboo. Natural fabrics such as those made from bamboo are more beneficial for the planet because they do not shed microplastics into the water. We compared the different qualities of natural and synthetic fabrics to determine if natural bamboo fabric is more durable than synthetic fabric. To test this, we performed a wash/dry, color fade, and a loose thread test. These tests allowed us to determine if bamboo textile is more durable than polyester textile. We hypothesized that natural fabric is more durable than synthetic fabric due to synthetic fabric having fewer loose threads and less shrinkage. The synthetic fabric had significantly fewer loose threads compared to the natural fabric. However, there was not significant evidence to support our hypothesis for the total area shrinkage. Overall, our data does not suggest that bamboo fabric is more durable than polyester in terms of loose thread, color fade, and shrinkage (wash/dry) tests.
The peroxidase-like activity of papain colorimetrically detects H2O2 and glucose with high sensitivity
Authors: Lee Seo Yun , Lee Woo Rin
Published: September 2023

To date, several glucometer methods are available for patient-guided glycemic control, which diminishes the onset and severity of diabetic complications. In a dipstick urine glucose test, horseradish peroxidase (HRP) oxidizes tetramethylbenzidine (TMB) in the presence of hydrogen peroxide (H2O2) to quantitatively produce a colorimetric signal and predict glucose concentration. Papain, a sulfhydryl protease secreted by the latex of Carica papaya, displays HRP-like activity with high selectivity. However, the current papain-based glucometer method lacks sensitivity. We hypothesized that catalyzing the oxidation of papain under varying buffer conditions (e.g., papain, TMB, and acetic acid concentrations) would improve glucose detection sensitivity. To test this, we performed glucose oxidase (GOx)-mediated glucose oxidation and papain-mediated TMB oxidation under varying sample conditions in vitro and spontaneously coupled them. The aims of this study were to validate the coupling of GOx-mediated glucose oxidation and papain-mediated TMB oxidation and identify the optimized conditions that maximize catalytic activity. We confirmed that glucose oxidase quantitatively produces H2O2 (in a glucose concentration-dependent manner), the presence of which catalyzes papain-mediated production of a colorimetric signal. When two oxidation reactions were coupled, we identified that the sensitivity of papain’s glucose detection increased by ten-fold compared to previous studies with a markedly high correlation (R2 = 0.99). In particular, substituting sodium acetate-acetic acid (HAc-NaAc) buffer with acetic acid and increasing the sample incubation time and papain concentration markedly increased the sensitivity of colorimetric signals. The optimized protocols can be integrated into emerging hydrogel technology to develop a non-invasive glucometer patch predicting blood glucose from the patient’s produced sweat.
Collaboration beats heterogeneity: Improving federated learning-based waste classification
Authors: Chong Jiwon , Chong Jaehyok
Published: September 2023

Classifying waste is a simple but useful exercise to protect the environment. Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. As we attempted to make our very own model to address waste classification, we recognized a common issue that other researchers reported as well: the lack of training data. This is an especially big problem when attempting to train a model for a task that has not been commonly explored before. Further research led us to federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. However, instead of sending the data from the clients to the server for training, FL trains the model on the clients’ machines using the clients’ data, and then aggregates the trained models into the server’s model to protect the clients’ private information. This means that there is a higher chance that the data used to train local models are heterogeneous, decreasing the overall performance. To overcome this issue, we ran a multitude of tests to determine how to maximize performance. For these tests, we hypothesized that the impact of data heterogeneity on an FL framework can be diminished through increasing the client participation ratio. Then, we measured the accuracies of the models trained with varying data heterogeneity, participation ratio, and the number of clients. From the results, we discovered that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.
A novel in vitro blood-brain barrier model using 3D bioprinter: A pilot study
Authors: Kim Andrea , Lee Woo Rin
Published: September 2023

3D bioprinters allow the creation of functional tissues using various cell lines. The blood-brain barrier (BBB) is crucial for maintaining homeostasis in the brain, acting as a protective interface between the cerebral blood vessels and the brain parenchyma. Advancements in tissue engineering have led to the development of in vitro BBB models. However, model accuracy and reproducibility still require improvements. Thus, we hypothesized that producing BBB models using a 3D bioprinter is a novel method to overcome these limitations. We tested the maintainability of habitable conditions for cells and the durability of the 3D-bioprinted BBB model. We printed four different BBB models using a bio-ink composed of Matrigel base and CPA47 endothelial cells: single- and double-layered square and circular scaffolds. The double-layered square scaffold fostered optimal BBB functions as it most effectively maintained a fixed shape and initial cell positions. When A172 glioblastoma cells were placed in the empty square spaces of the square BBB scaffold, most cells were alive and healthy after 96 hours. Although A172 cells remained in their initial cell positions until the first 48 hours, we observed movement across the Matrigel barrier when checked at 96 hours. At 48 hours, A172 cells showed no movement from their initial positions; however, by 96 hours, we observed clear movement of A172 cells across the Matrigel barrier. Due to its relative cost-effectiveness and high reproducibility, our BBB model has several applications, especially for studying brain cancer metastasis past the BBB and developing potential corresponding clinical therapeutics.
The effect of nicotine and lead on neuron morphology, function, and ɑ-Synuclein levels in a C. elegans model
Authors: Sharma Ambica , Corso John , Sammi Shreesh Raj
Published: September 2023

An electronic cigarette (e-cigarette) is a device that simulates cigarette-tobacco smoking by atomizing a liquid into an inhalable vapor. It is often billed as a smoking cessation device that is safer than cigarette smoking, however, these devices are feared to work as a gateway to cigarette smoking through nicotine addiction. In addition to e-cigarette injury and death, there are also neurological implications to using an e-cigarette. Our research aimed to determine the effect of chemicals commonly found in e-cigarette aerosol, specifically nicotine and lead, on dopaminergic neuron morphology and ɑ-synuclein levels using the model organism Caenorhabditis elegans. Our hypothesis stated that should exposure to these chemicals have an effect, then we would observe negative neurological implications such as changes in dopamine neuron morphology and possible neurodegeneration. We used transgenic C. elegans to assess the effects of nicotine and lead on ɑ-synuclein levels and neuron morphology. C. elegans were treated with different doses of lead, nicotine, or a mixture of to simulate the neurological effects of e-cigarette use. We determined dopamine and acetylcholine-dependent behavior using a 1-nonanol assay and an aldicarb assay, respectively. We observed a significant dopaminergic deterioration upon the treatment of lead and nicotine. At higher concentration of each treatment (lead, nicotine, and the combination), we observed C. elegans(NL5901 and BZ555) strains were smaller with an increase in protein aggregation. Our findings indicate that using e-cigarettes may induce morphological changes that are not favorable for normal brain function and could lead to a decrease in individuals’ e-cigarette use.
Tomato disease identification with shallow convolutional neural networks
Authors: Trinh Minh , Jung Edward , Trinh Linh
Published: September 2023

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. Successful disease management requires precise and timely disease identification methods, but slow and inconsistent diagnoses from human experts or laboratory testing prevents farmers from applying appropriate treatments before the disease takes its toll. As an alternative diagnostic method, a mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. Previous research has achieved high accuracy in diagnoses using “deep” convolutional neural networks (CNNs) that have a large number of processing layers and thus a large computational requirement difficult to satisfy on most mobile devices. To enable a practical mobile solution, a “shallow” CNN with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible. We hypothesized that shallow CNNs would be capable of achieving even higher accuracy than deep CNNs because of shallow CNNs’ relatively low inherent dimensionality reduction, allowing it to preserve more distinguishing features between photos of leaves with different diseases. We compared the performance of a shallow CNN we built against conventional deep CNNs. The shallow CNN outperformed several deep CNNs, confirming our hypothesis. This finding enabled us to develop PlantDoctor, a mobile app that can accurately identify plant diseases via leaf images captured by the phone camera in real-time and suggest appropriate treatments.