bluuebunny/crossref_metadata_2025 huggingface dataset:
The impact of genetic analysis on the early detection of colorectal cancer
Authors: Agrawal Nikita , Jafari Haddadian Esmael
Published: September 2023

Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer. First, the patient's exome sequence and the reference exome sequence were repeatedly aligned to identify the regions of similarity. The alignment between the two sequences that resulted in the most similar regions (optimal alignment) was selected. Next, we performed variant calling and identified variants in the patient's exome sequence. We applied a quality control check to assess sequencing data quality and filtered out the variants that did not pass the quality control check. Finally, the remaining selected variants were annotated with biologically pertinent information and explored for their potential roles in human disease by cross-referencing databases. A variant in the FGFR4 gene, known to cause accelerated cancer progression and tumor cell motility, was found in the patient's exome sequence. Studies suggest that this variant corresponds with an increased risk of colorectal cancer, supporting the usefulness of this procedure in early detection of colorectal cancer. This research was expanded to demonstrate that exome sequencing methods are capable of identifying other genetic variants. With higher computational power, one can produce more accurate alignment readings, detecting even the smallest of deviations from the reference exome sequence and thus enhancing the ability to evaluate genetic risk of disease.
The effect of viscous drag on damped simple harmonic motion
Authors: Michael Powell Richard , Michael Powell Richard
Published: September 2023

Dynamic viscosity is a quantity that describes the magnitude of a fluid’s internal friction or thickness. Traditionally, scientists measure this quantity by either calculating the terminal velocity of a falling sphere or the time a liquid takes to flow through a capillary tube. However, they have yet to conduct much research on finding this quantity through viscous damped simple harmonic motion. This phenomenon occurs when the movement of an oscillator experiencing Hooke’s law dissipates at a rate defined by the damping coefficient due to a fluid’s external drag force. The present study hypothesized that the relationship between the dynamic viscosity and the damping coefficient is positively correlated. To test this hypothesis, we computed the dynamic viscosities of glycerol-water mixtures of varying mass concentrations and filmed the oscillations of a vertical-spring mass system in each of these mixtures. We then determined the system’s damping coefficients through Vernier’s Logger Pro video analysis feature and implemented inferential statistics on a regression between these damping coefficients and the mixtures’ dynamic viscosities. The results through these methods demonstrated not only compelling evidence in favor of this hypothesis but also an alternative way to estimate a fluid’s dynamic viscosity through a least-squares regression line. With future experimentation, these results can help prepare shock absorbers for vehicle and structural engineering practices.
Sex differences in linear polyubiquitination in the entorhinal cortex during fear memory formation
Authors: Gollapudi Yashwanth , Martin Kiley , Farrell Kayla , Jarome Timothy
Published: September 2023

Post-traumatic stress disorder (PTSD) is a major anxiety disorder that has a higher incidence rate in women than men, despite that women do not report experiencing more traumatic events. Understanding the neurobiological mechanisms supporting the formation of fear memories that underlie PTSD and how they vary by sex is important for responding to the etiology of this disorder. We recently reported that linear polyubiquitination, a noncanonical form of ubiquitination that is independent of proteasomal-mediated protein degradation, is increased in the amygdala in a sex-specific manner following contextual fear conditioning in rodents. However, it is unknown if linear polyubiquitination is changed in other parts of the fear circuit following training and if this varies between sexes. Here, we found that contextual fear conditioning results in decreased levels of linear polyubiquitination in the entorhinal cortex (EC) of male, but not female, rats. Further, females had lower resting levels of linear polyubiquitination in the EC, suggesting that sex differences already existed at baseline in this brain region. Conversely, neither sex nor training altered linear polyubiquitination levels in the dorsal hippocampus (DH), prefrontal cortex (PFC) or retrosplenial cortex (RSC). Together, these data provide the first evidence that linear polyubiquitination is altered in a sex and brain region-specific manner during fear memory formation. Our data have important implications for understanding the sex differences that exist in PTSD.
Analyzing aerosol variation during the COVID-19 pandemic lockdown using satellite data
Authors: Wang Arthur , Sharbaugh Janine
Published: September 2023

The COVID-19 pandemic caused lockdowns worldwide throughout many cities, implementing numerous restrictions on human activity. Previous studies have shown that various anthropogenic pollutants have decreased in many regions from these lockdowns, which should translate into aerosol levels, measured by aerosol optical depth (AOD), also having a large reduction. However, AOD levels over metropolitan areas during the lockdown periods have not been sufficiently documented. Using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data, we analyzed AOD level variances during the pandemic over 13 selected metropolitan areas in Europe, India, and China. We hypothesized that reduced human activities from the pandemic lockdowns would also result in a large decrease in AOD levels in metropolitan areas. So, we compared the average AOD levels during the lockdown to the previous five-year average during the city’s corresponding lockdown dates in order to investigate the statistical significance of variations. For European cities, there were few changes in AOD levels observed during the pandemic lockdown periods. As for the Indian cities, three showed slight decreases in AOD levels (~0.5%–7.4%) and one showed an 11% increase. However, compared to Indian and European cities, all of the cities in China showed noticeable reductions in AOD levels during the lockdown (~11%–30%). Statistical analysis did not show statistical significance for most of the cities due to the large variation of AOD, suggesting that the AOD level decrease attributed to the reduction of human activities does not exceed the range of natural AOD level variation.
Maximizing anaerobic biogas production using temperature variance
Authors: Verma Kushagra , Sharma Utkarsh , Agrawal Suruchi
Published: September 2023

We conducted this research as our start-up's research that addresses the problem of biogas production in cow-dense regions like India. We wanted to evaluate variables which would enable us to increase biogas production and ultimately increase the efficiency of our system. Through our prior knowledge of anaerobic digestion, we knew that temperature could be used as a variable to collect distinct data points and pursue our research. We hypothesized that the thermophilic temperature (45-60oC) would increase biogas production. This phenomenon happens primarily because, at this range, Methanobacteria (responsible for producing methane from slurry) multiply and digest the slurry (mixture of cow dung and water) at an optimum rate. Mesophilic temperature (<45oC) doesn't yield this optimum growth rate. Hence, the production process is much faster and more abundant at temperatures around 55-60oC. Our methods included trapping biogas produced inside a tire and measuring the change in the tire’s mass to predict the quantity of biogas produced. We measured the gas production in grams for the ease of experimenting using the available apparatus. Through our experiment, we concluded that this difference in production was almost two-fold. At mesophilic temperature, 121 g of biogas was produced in 15 days. At thermophilic temperature, the biogas production was about 247 g in the same period.
Mathematical modeling of plant community composition for urban greenery plans
Authors: Fang Yueqi , Zhang Weibo
Published: September 2023

Urban green space is critical for humans and other organisms living in cities. Researchers have noticed that urban green space planning has been focusing more on ornamental and public management purposes but less on its ecological purpose, which has a negative impact on eco-sustainability. We hypothesized that an urban greenery management plan with high eco-sustainability can be achieved by calculating the composition of a plant community through mathematical modeling. To test our hypothesis, we created a nonlinear programming function that generated a plant community composition aiming for high plant fitness, high biodiversity, and low managing cost. Green fields in a Beijing city park, comprising different combinations of plant species were used to optimize our model. Although the outputs of the optimized model do not match completely with the composition of testing fields, they illustrate possible patterns for plant community development. By applying this model, urban green space can be finely designed to become both ecologically and economically sustainable.
Pressure and temperature influence the efficacy of metal-organic frameworks for carbon capture and conversion
Authors: Lin Taohan , Webster Thomas J
Published: September 2023

Carbon dioxide (CO2) is a major contributor to global warming, which has led to irreversible environmental and socio-economic damage. Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO2 to other useful carbon products, such as ethanol, methanol, and cyclic carbonates. However, the large-scale production of MOFs for industrial use is limited because of their high porosity and customizability making their development, synthesis, and use expensive. The purpose of our research was to use computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO2. We used Python programming to examine current data presented in the literature, encompassing various MOFs with a range of pore sizes and different substrates to determine the optimal conditions for MOFs to capture and convert CO2 into useful products. Our results showed that pressure was significantly correlated in convex quadratic models with the selective capture of carbon and temperature was significantly correlated in a concave quadratic model with carbon conversion. Optimal reaction conditions were approximately 80°C (R² = 0.376) and 0-1 atm or 11-12 atm of pressure (R² = 0.210). In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO2 emissions.
Effects of various alkaline carbonic solutions on the growth of the freshwater algae Chlorophyceae
Authors: Jani Shaan A , Genet Ephratah , Jani Ashesh B , Kinsey Jennifer
Published: September 2023

Modern day fossil fuels are prone to polluting our environment, which can provide major habitat loss to many animals in our ecosystems. Algae-based biofuels have become an increasingly popular alternative to fossil fuels because of their sustainability, effectiveness, and environmentally-friendly nature. To encourage algae growth and solidify its role as an emerging biofuel, we tested basic (in terms of pH) solutions on pond water to determine which solution is most efficient in inducing the growth of algae. We hypothesized that a calcium carbonate solution would induce the most algae bloom, because calcium is a catalyst in photosynthesis. Alkaline carbonate solutions were used to create an optimal environment for the growth of algae; three carbonic alkaline solutions were tested in samples of pond water with algae measured through visible light absorbance using a spectrophotometer. As hypothesized, calcium carbonate induced the most algae growth compared to the control sample, potassium carbonate, and sodium bicarbonate. In conclusion, calcium-containing solutions stimulated algae growth the most, likely due to this solution having properties to stimulate the photosynthetic process by enhancing the transcription and translation process for genes related to chloroplast proteins and enzymes. Our results suggest that calcium is important in enhancing algae growth, which may be important as alternatives for fossil fuels are developed.
Evaluating the feasibility of SMILES-based autoencoders for drug discovery
Authors: Zhang Colin , Govani Kristine
Published: September 2023

The vast majority of molecules with desirable drug-like properties have not yet been discovered. With the advent of machine learning for de novo molecular generation, the process of designing these molecules has become increasingly efficient. However, to what extent are these machine learning models actually learning chemical properties versus memorizing the syntax of a training set? In this project, we trained a Simplified Molecular Input Line Entry System (SMILES)-based generative autoencoder for up to 200 epochs to investigate whether the latent space can separate molecules based on five chemical properties (partition coefficient, molecular weight, topological polar surface area, number of hydrogen bond donors, and number of hydrogen bond acceptors) and how generated molecules compare to the training set. We hypothesized that the model would preferentially encode molecular weight and that generated molecules would be similar to the training set. Consistent with our hypothesis, the model quickly learned to distinguish molecules primarily by their molecular weight, while other properties were considered to a lesser extent. Moreover, generated molecules were very similar to the training set both in terms of structure and properties. These results suggested that the model overfits the training set. In particular, the model best learns chemical properties that directly depend on atomic composition while it is difficult for the model to encode higher-level properties that rely on connectivity and structure. Our results may represent fundamental limitations of SMILES-based generative models and could assist in development of new research to mitigate these issues.
Testing antimicrobial properties of common household spices in a real-world scenario
Authors: Raza Syed , Ahmed Zara , Stephens Hunter , Kim Choah , Kelberman Michael A
Published: September 2023

Proper disinfection of household items is essential to prevent illness, especially with respect to kitchen items. However, many currently used household cleaners are toxic. Accidental poisonings from common chemical cleaners thus represent a significant threat to humans and have been exacerbated by the pandemic. Thus, there is a pressing need to find safer alternatives to these cleaning products. Spices could be useful in this respect, given their relative safety when ingested, wide availability, and potent antimicrobial properties. To date, studies have tested antimicrobial properties of a variety of spices in conditions that are different from how they would be implemented as cleaners. We therefore sought to test antimicrobial properties of spices in a real-world scenario, by quantifying microbial growth before and after cleaning a cutting board with garlic, nutmeg, or paprika. These spices were found to be relatively ineffective at cleaning cutting boards, as evidenced by similar levels of microbial growth before and after cleaning, though some of these effects appeared to be microbe and spice dependent. Overall, we demonstrate caution with the currently described method of repurposing spices for cleaning purposes but encourage further work to this end.
Development of anti-cancer bionanoparticles isolated from corn for bone cancer treatment
Authors: Richardson Michael , Lee Woo Rin
Published: September 2023

Nanoparticles (NPs) are particles ranging from 1 to 100 nm in size. NPs can be derived from various sources such as copper, gold, and even plants such as kiwis and corn. Previous studies have shown that nanoparticles derived from corn (cNPs) displayed anticancer properties, but their effects were not tested on bone cancer samples. Current bone cancer treatments are limited as anticancer drugs often either cannot reach the tumor or have little effect on it. Therefore, in this study, we hypothesize that cNPs may have anti-proliferative effects on bone cancer and metastasized bone cancer. First, we found that SAOS-2 (human osteosarcoma) cell viability decreased to 0% when treated with a 20% cNP concentration, and NCI-H526 (human lung carcinoma metastasized to bone marrow) cell viability decreased to 0% when treated with only a 15% cNP concentration. These results indicate that NCI-H526 is more sensitive to cNPs than SAOS-2. Secondly, we found that cNP treatment reduced the expression of BCL2, a gene that prevents apoptosis, suggesting that the cNP treatment induced BCL2-dependent apoptosis. Overall, these results indicate that cNPs have anti-proliferative effects on bone cancer cells and cancer cells that metastasize to the bone. Therefore, our study suggests that cNPs may be applied to the development of bone cancer treatment.
The effect of neuroinflammation and oxidative stress on the recovery time of seizures
Authors: Kantipudi Shrika , Caulkins Juliana , Joykutty Leya
Published: September 2023

Epilepsy is a neurological disorder in which neuronal activity in the brain is disturbed resulting in recurrent seizures and is the second most common neurological disorder. Epilepsy has large impacts on patients as well as the healthcare system. Neuroinflammation and oxidative stress are both known to play a role in the occurrence and severity of seizures. When neuroinflammation occurs, oxidative stress occurs simultaneously. Oxidative stress has been shown to accelerate aging and reduce reproductivity in Caenorhabditis elegans. We tested effects of oxidative stress from seizures by evaluating the longevity, egg-laying, and electroshock resilience of C. elegans. Pseudomonas aeruginosa was used as an oxidative stressor in this experiment, resulting in neuroinflammation in C. elegans. When neuroinflammation occurs, three anti-inflammatory genes, pmk-1, elt-2, and skn-1 are activated in order to help reduce inflammation. We blocked the translation of the anti-inflammatory genes using RNAi, to assess oxidative stress without the C. elegans anti-inflammatory response when neuroinflammation was induced by adding P. aeruginosa into the C. elegans diet. Our study revealed that oxidative stress and neuroinflammation diminish longevity and reproductivity while also increasing recovery time after seizures in C. elegans. Further study backs up these findings, indicating that neuroinflammation and oxidative stress both worsen seizure severity. This research can help lead to future studies and may also lead to finding new therapeutics for epilepsy.
Flight paths over greenspace in major United States airports
Authors: Lee Anthony , Shaikh Adiba
Published: September 2023

Greenspaces refers to urban and wetlands that contain vegetation, essentially spaces that are green. Because greenspaces are beneficial to reducing pollution, we investigated whether if there is a trend where planes tend to fly over greenspace near airports. We used flight data to generate the flight paths and satellite images and then processed them to find if there was an observable pattern between the location of greenspaces and flight paths. Our hypothesis was that each airport would show a different percentage of planes that fly over greenspace; we expected airports in the suburbs to have higher values, whereas airports near the cities to have lower values. To test the hypothesis, we used image processing functions such as Binarize and SetAlphaChannel from Wolfram Mathematica and statistical analysis in Mathematica. We observed that generally near airports, around 10–15% of total flight paths go over greenspaces. From our observations, we believe that increasing greenspaces near airports would reduce noise pollution and having more trees will absorb the carbon dioxide from planes, resulting in less pollution near airports.
Developing a neural network to model the mechanical properties of 13-8 PH stainless steel alloy
Authors: Zeng Wesley , Killeen Heather
Published: September 2023

We systematically evaluated the effects of raw material composition, heat treatment, and mechanical properties on 13-8PH stainless steel alloy. 13-8PH is widely used in the aerospace industry for various structural components. Little is known about the predictive modeling of mechanical properties for 13-8 PH per heat treatment. To achieve the desired properties, multiple back-and-forth heat treatments and mechanical tests are often required which utilize significant amounts of resources. Thus, we hypothesized that no specific heat treatment temperature can be used to achieve the desired property ranges across all 13-8PH compositions. We heat treated 13-8PH alloy through solution treatment, cryogenic treatment, and final elevated temperature aging. We conducted double shear testing to measure this mechanical property. Consistent with expectations, experimental data confirmed that no single specific aging heat treatment temperature could be used to achieve desired property range for all compositions. A unique aging heat treatment temperature is required and there is a need to develop a model to predict heat treatment response for each composition. The results of the neural network models were in agreement with our experimental results and aided in the evaluation of the effects of aging temperature on double shear strength. The data suggests that this model can be used to determine the appropriate 13-8PH alloy aging temperature needed to achieve the desired mechanical properties, eliminating the need for many costly trials and errors through re-heat treatments.
An explainable model for content moderation
Authors: Cao Steven , Johnson Matt
Published: September 2023

The spread of fake news on social media has eroded trust in traditional news outlets and institutions. In response, social media platforms have incorporated machine learning, algorithms that can learn patterns in data without explicit programming, into their content moderation systems to help remove fake posts. However, these algorithms often misinterpret language and make poor moderation decisions. We need to better understand how machine learning algorithms interpret language. Explainability is a challenge with neural networks, a popular machine-learning model inspired by how neurons communicate in the brain. In this study, we sought to develop an explainable model for content moderation comparable in accuracy to a traditional neural network, focusing on classifying real and fake news articles. We hypothesized that by identifying keywords and quantifying their contribution to an article’s credibility, we could predict credibility by summing the contributions of all the keywords in an article. We trained a convolutional neural network to classify articles using a 3,000-keyword vocabulary, achieving 85.8% accuracy. We used the Shapley additive explanations algorithm to calculate each keyword's median contribution to the model’s predictions. We created a linear model that summed the median contributions of keywords in an article, achieving a comparable 81.0% accuracy. We then examined keywords with the largest median contributions. Clickbait and COVID-19 terms correlated with fake news. Legal and political terminology correlated with real news. Our results demonstrate the potential for explainable models to improve our understanding of content moderation algorithms and fake news linguistics.
Evaluating TensorFlow image classification in classifying proton collision images for particle colliders
Authors: Nakra Rohan , Vansomphone Arin , Kimes Ryan
Published: September 2023

The Large Hadron Collider at the European Organization for Nuclear Research (CERN) is planned to increase the number of proton collisions 10-fold by 2025. To keep up with this rapid pace, novel and more efficient particle collision classification methods must be developed, which many physicists believe can be achieved via machine learning. Since particle collisions and trajectories are mapped onto images, we chose TensorFlow’s image classification model to analyze and predict particle classes from these collision images. We aimed to evaluate the accuracy at which image classification could correctly identify a particle’s category from its trajectory image. We hypothesized that image classification would improve its accuracy with larger training datasets and more training epochs. To test our hypotheses, we randomly partitioned 100 images for testing and reserved 3,400 for training. We ran numerous trials using different training set sizes, different training epoch values, and binary classification. Our results show that the model’s accuracy increases with more training samples, and it increases its accuracy and consistency with more training epochs. In binary classification, the model distinguishes the electron class the best, with a 95.2% mean accuracy. We conclude that image classification has remarkable implementation potential in classifying particle collision outcomes because of its ability to improve with training sample size and in binary classification. Our work highlights one method collision classification algorithms should utilize, which can work effectively with the increased data output by particle colliders and give physicists a vital tool in accelerating their field.
Osmotic characteristics of water retention structures of Bursera microphylla in relation to soil salinity
Authors: Groom Evelyn , White Courtney , Perez Mauricio Tamayo , Miller Kaitlyn , Michael David
Published: September 2023

With a worldwide increase in soil salinity, as well as the climate crisis raising global temperatures, it is important to understand the effects of soil salinity on plant survival. One way to understand how plants may evolve to survive in these xeric desert environments is to gain a further understanding of existing halophytic (live in saline environments) and/or xerophytic (live in dry, arid environments) plants. Bursera microphylla in particular is quite interesting because it is both a halophyte and a xerophyte. One reason it is able to survive in dry environments is due to its bulbs at the base of the trunk and major limbs of the tree, which have been shown to store water during droughts. To facilitate mineral uptake, plants use ATP to transport NaCl ions into the plant, which facilitates water uptake. Because observed field conditions in the Baja California Peninsula were extremely xeric, most plants there have distinct evolutionary traits to facilitate water storage or retention. The Elephant Tree (Bursera microphylla) gets its name from the abnormal proportions of the base of its stem, which is much wider than much of the tree, and studies show that this disproportionately wide stem can store water for use during times of drought, which are common in the Baja California Peninsula. Our hypothesis was that sodium chloride was taken up through the plant’s root structure to facilitate this water transportation, and that this sodium chloride accumulation was directly proportional to the soil salinity. We tested this hypothesis via light microscopy of plant tissues to observe cell tonicity under different concentrations of saline solution. soil salinity was measured using saturated extraction. The results of this experiment show that most cells within the “bulb” structures were isotonic at a concentration approximately twice as high as that of root tissue and ambient soil salinity, therefore supporting the presented hypothesis.
A cost-effective IoT-based intelligent indoor air quality monitoring
Authors: Heengama Sanithu , Heengama Kosala
Published: September 2023

Air quality monitoring has become a critical issue for human beings as it is related to living quality. Poor air quality harms human health, worsening respiratory diseases, increasing heart risks, impacting brain health, and endangering vulnerable populations. Various countries legislate air quality norms related to air monitoring and measurement standards. Nonetheless, most of the monitoring stations for air pollution are set up outdoors and monitor sources of air pollution and greenhouse gases in larger regions, neglecting the importance of indoor air quality (IAQ). Thus, monitoring the IAQ for better healthcare has become necessary. However, low-cost sensors are often associated with design compromises that hamper data reliability. Although scientists have extensively studied IAQ, the practical application of low-cost devices in indoor environments has yet to be investigated. We explored the effectiveness of a low- cost IAQ experimental monitoring device by studying the quality of the results gathered from the reference monitor unit. We hypothesized that the advanced technology and sensors would provide accurate and valid data to make the low-cost IAQ monitor feasible for widespread usage. Our goal was to provide an intelligent, movable, and cost-effective indoor air quality monitoring unit which has been designed to simulate users' indoor routes and detect air quality in real-time. Since, the main factor affecting the accuracy and validity of the measurements results from its orthogonal design. Our results revealed a low-cost monitoring device can be used with efficient design and by using sensors that are highly accurate which are low-cost for accurate and valid readings.
Breast cancer mammographic screening by different guidelines among women of different races/ethnicities
Authors: Wang Richard C , Lipin Daniel I , Swoboda Thomas K
Published: September 2023

Mammographic screening is a common diagnostic tool for breast cancer among average-risk women. However, breast cancer screening guidelines differ between societies and organizations. Appropriate adherence to screening guidelines helps early diagnosis and treatment of breast cancer, which subsequently improves breast cancer survival. We used three national guidelines to define adherence recommended by US Preventive Services Task Force (USPSTF), American Cancer Society (ACS), and American College of Radiology (ACR), which included different criteria for age and frequency. (USPSTF: 50–74, biennial; ACS: 45–54, yearly, 55+ biennial; ACR: 40+, yearly) We hypothesized that adherence rates for mammographic screening may be lower among minorities (non-Hispanic black (NHB) and Hispanic/Latino) than among non-Hispanic whites (NHW) regardless of the guideline applied. We conducted a cross-sectional study using nationally representative survey data from the 2020 Health Information National Trends Survey (HINTS). Adherence to mammographic screening among NHW, NHB, and Hispanic/Latino populations were calculated. A total of 1,751 survey samples were used in the final analysis. Adherence rates of each guideline for average-risk woman in this study were as follows: USPSTF - 77.37%, ACS - 68.41%, and ACR - 57.71%. Additionally, our results demonstrated that the adherence rate was higher among NHB when compared with NHW women regardless of which guideline was applied. Multivariate logistic regression analysis indicated that both NHB ethnicity and insurance coverage exhibited strong positive associations with adherence to breast cancer screening. Our findings support other studies’ results that different racial/ethnic and socio-demographic factors can affect screening adherence. Therefore, healthcare providers should promote breast cancer screening especially among NHW/Hispanic women and women lacking insurance coverage.
A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Authors: Adami Evan C , Pandit Ayush
Published: September 2023

Alzheimer’s disease (AD) is the leading cause of dementia worldwide. Mild cognitive impairment (MCI) is an early stage of mental decline that can precede the development of AD. Oftentimes, the progression of MCI to AD is difficult to predict. An AD diagnosis can involve invasive brain scans and spinal fluid tests. However, one promising biomarker for the diagnosis of AD is epigenetic data, specifically the methylation level of cytosine-phosphate-guanine (CpG) sites in the DNA. AD is linked to environmental factors, with some factors causing changes in these DNA methylation levels. We hypothesized that machine learning models can use blood DNA methylation levels, sex, and age to predict between cognitive normality, MCI, and AD with at least 50% accuracy. In this paper, we generated four machine learning models and two dataset dimensionality reduction methods in order to test this hypothesis. We trained the models on data from CpG sites from whole blood. When predicting if a patient would be cognitively normal, have MCI, or have AD, we achieved an accuracy of 53.33%, which is 20% greater than random guessing. We achieved this accuracy using a gradient boosting decision trees model in combination with a logistic regression method for feature selection. While this accuracy is low, the feature selection method that we developed may be useful in future research, as it identifies CpG sites that are most correlated with AD. Since peripheral blood is easily accessible through blood draw, our model represents a practical way to assist in the diagnosis of AD.
The effect of activation function choice on the performance of convolutional neural networks
Authors: Wang Shin-Hao , Sakk Eric
Published: September 2023

With the advance of technology, artificial intelligence (AI) is now applied widely in society. For example, natural language processing, speech recognition, and autopilot are all famous examples of how AI is changing our world. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes. Choosing the right neural activation function is crucial to prevent the loss of important trends and increase the efficiency of training time. Among all the different widely used activation functions, we hypothesized that a rectified linear unit (ReLU) would be the most efficient in training time and attain the highest accuracy on the image recognition task because ReLU has the advantage of avoiding the vanishing gradient problem and only requires light mathematical calculations. Having high accuracy and efficiency in the task of image classification is beneficial as this technique can be employed in many different real-world applications, such as diagnosing in healthcare, identifying potential threats in security, or developing an autonomous vehicle. Our results indicate that when the number of hidden layers is small, networks employing the ReLU performed similarly to networks using hyperbolic tangent, and both networks with ReLU and hyperbolic tangent outperformed networks using the sigmoid function.
Effects of Wi-Fi EMF on Drosophila melanogaster
Authors: Anand Mary , Anand Herm
Published: September 2023

Wi-Fi is commonplace in developed countries, and the number of people exposed to it is increasing. The health effects of Wi-Fi electromagnetic fields (EMF) are not well-documented. This research study engineered an apparatus, a “Box Setup,” to expose fruit flies, Drosophila melanogaster, to three concentrations of Wi-Fi EMF and a control. Wi-Fi EMF exposure produced a statistically significant decrease in the number of total progeny adults, and number of pupae. There was no significant effect on the progeny male:female ratio, nor the adult:pupa ratio. Only the highest Wi-Fi EMF concentration (100%) decreased the number of progeny adults, number of progeny females, and number of pupae; whereas even a low EMF concentration (5%) decreased the number of progeny males. This suggests that male D. melanogaster are more sensitive to Wi-Fi EMF than female D. melanogaster. Further research is needed to identify what aspects of fruit fly biology are affected by Wi-Fi EMF exposure, and why males are more sensitive than females.
TENDÊNCIA TEMPORAL DE SÍFILIS CONGÊNITA E SÍFILIS EM GESTANTES NO MUNICÍPIO DE RIO BRANCO ACRE, 2014 – 2020
Authors: Solidade Jeferson Soares , Leitão Francisco Naildo Cardoso , Lopez Alberto Grover Prado , Pereira Pedro  Omar Batista , Jastrow Juliana Maria Bello , Morais Mauro José de Deus , De Sousa Orivaldo Florencio
Published: October 2023

Objetivo: Avaliar a tendência temporal da sífilis em gestante e sífilis congênita no município de Rio Branco, Acre, entre o ano de 2014 a 2020. Método: Estudo de tendência temporal da taxa de detecção de sífilis em gestante e sífilis congênita no município de Rio Branco, Acre, com dados do Departamento de Informática do Sistema Único de Saúde (DATASUS) entre o ano de 2014 a 2020. Os dados foram analisados por meio da taxa de detecção e da regressão Prais-Winsten.  Resultados: No período de 2014 a 2020, foram identificados 342 casos de sífilis congênita e 1.724 casos de sífilis em gestantes em Rio Branco, capital do estado do Acre. Os resultados encontrados demonstram que a taxa de sífilis congênita, em crianças com até um ano de idade não apresentou variação, sendo 10.7 casos por 1.000 nascidos vivos a maior taxa entre o período compreendido uma taxa de incidência estacionária (p<0,04), enquanto em sífilis em gestante houve um aumento progressivo, sendo observado um pico entre 2017-2018 chegando a 47,5 casos por 1.000 habitantes, a taxa de incidência da sífilis em gestantes apresentou uma taxa de variação anual de 34,38% apresentando variação crescente (p>0,04). Conclusão: O estudo destaca o aumento expressivo de sífilis em gestantes em Rio Branco entre 2014 e 2020. Também houve a estabilidade nos casos de sífilis congênita entre 2014 e 2020, destacando o controle da transmissão vertical.
CLASES DE EQUIVALENCIA Y CONDUCTA VERBAL
Authors:   UNED (España), Pérez Fernández Vicente
Published: September 2023

Desde el primer estudio publicado por Sidman en 1971, y su posterior sistematización una década después, el fenómeno de la formación de clases de equivalencia ha demostrado ser una línea de trabajo altamente fructífera, tanto en el campo de la investigación básica como en el de la aplicada. A pesar de la enorme generalización que ha demostrado este fenómeno, tanto en características de los sujetos como en procedimientos de entrenamiento, su naturaleza es una cuestión sobre la que aún, más de cuarenta años después, no existe un amplio consenso. A lo largo de este tiempo se han desarrollado diferentes teorías explicativas, pero el auge que ha ido experimentando en los últimos veinte años la investigación sobre el lenguaje desde un punto de vista analítico-conductual ha contribuido a que se fortalezca la consideración de la conducta verbal como elemento necesario para la aparición de las relaciones derivadas que definen las clases de equivalencia. En el presente trabajo se revisan algunos de los hallazgos experimentales más relevantes que apoyan o rechazan dicha aproximación como argumento para defender que la etiqueta “clases de equivalencia” puede estar aplicándose a conductas que no son funcionalmente equiparables.
ORAL ALCOHOL SELF-ADMINISTRATION AND MAINTENANCE OF OPERANT BEHAVIOR IN RATS
Authors:   Instituto de Neurociencias. Universidad de Guadalajara. (México), Martínez Héctor , Espinoza Becerra Eder ,   Centro Universitario UTEG – Guadalajara. (México)
Published: September 2023

We evaluated the effects of two alcohol induction procedures on food-reinforced lever-pressing in rats. Nine outbreed Wistar rats were assigned to one of three groups. For all subjects a fixed-ratio (FR)11 food-reinforcement schedule was established. The induction group (IG) was exposed to alcohol at increasing concentrations over the course of 10 days up to a final concentration of 10%. The non-induction group (NG) received only the 10% alcohol dose during the same period. The control group (CG) received only water and never had alcohol. After induction, the responses in two groups (IG, NG) were food-reinforced for 10 days with free access to alcohol, followed by 15 days of food reinforcement when water was made available instead of alcohol. The alternating cycle of access to alcohol and water was repeated twice more to complete the experiment. Results were as follows: Rate of reinforcement per minute was lower, body weight increased, sessions duration increased, reduced food intake decreased, and alcohol consumption increased during induction and when ethanol was available. Results are discussed by comparing the behavioral effects of alcohol and water on operant behavior.
ENSEÑANZA DE TÉRMINOS CIENTÍFICOS: ESTUDIO EXPERIMENTAL DE LA TRANSFERENCIA ENTRE MODALIDADES LINGÜÍSTICAS
Authors:   FES Iztacala, UNAM (México), Rocha Hernández Edgar , Silva Victoria Héctor Octavio , Serrano de la Cruz Valeria Yazmin , Galindo Rodríguez Luis , Vargas Ávila Andrés Francisco , Galguera Rosales Ricardo
Published: September 2023

Se realizó un estudio con el objetivo de evaluar los efectos de variar la modalidad lingüística del entrenamiento sobre el desempeño efectivo en tareas de igualación a la muestra de segundo orden con términos metodológicos de la psicología experimental, tomando en consideración los trabajos realizados en el estudio de la transferencia entre modos lingüísticos y los problemas en torno a la enseñanza tradicional. Participaron 16 estudiantes de la carrera de psicología asignados en 4 grupos que difirieron entre sí por el modo lingüístico en el que se realizaron las condiciones del experimento. Se establecieron dos grupos de transferencia intramodal cuya modalidad lingüística en cada fase fue Textual o Auditiva –grupos TTT y AAA-, y dos más de transferencia extramodal: Textual-Auditivo-Textual y Auditivo-Textual-Auditivo, grupos TAT y ATA. Los resultados señalan que no existe evidencia suficiente para considerar que se lleve a cabo de manera efectiva la transferencia extramodal. En contraste, la consistencia de la modalidad en el grupo TTT muestra resultados satisfactorios en cuanto al cambio en la disposición interactiva mostrada en la Prueba de Transferencia. Se discute la necesidad de evaluar el efecto de variar las modalidades lingüísticas durante las interacciones didácticas, puesto que la modalidad auditiva, característica de la enseñanza tradicional, mostró ser insuficiente para la transferencia del desempeño efectivo.
Sosialisasi Alat Peraga Penjumlahan dan Pengurangan di SD Swasta Santo Thomas 2 Medan
Authors: Silaban Patri Janson , Malau Novia Elisabet , Sianipar Angelina Romaito Yoseva , Siahaan Martina Estepania Hinal , Sembiring Lola Ipana Br , Simamora Anastasya Apriliaty
Published: September 2023

Sosialisasi ini bertujuan untuk mengidentifikasi perkembangan alat peraga. Alat  peraga digunakan untuk membantu peserta didik dalam mempelajari konsep matematika. Penambahan berlapis matematika bertujuan untuk meningkatkan pemahaman konseptual jumlah siswa sekolah dasar. Alat peraga yang dapat mengatasi masalah tersebut adalah alat peraga blok pecahan dan alat peraga Penjumlahan dan pengurangan di SD Santo Thomas 2 Medan. Jumlah sempel pada Sosialisasi yang kami lakukan sebanyak 40 peserta didik yang ada di kelas 4 SD. Permasalahan yang terjadi dilapangan, masih banyak siswa yang belum memahami penjumlahan dan pengurangan bilangan bulat. Sosialisasi ini meningkatkan prestasi belajar matematika dalam berhitung dengan udah melalui media penjumlahan dan pengurangan pada siswa kelas 4 SD khususnya pada Kompetensi Dasar dan melatih siswa agar terampil menggunakan media. Kegiatan ini dilakukan dengan sekali pertemuan. Dengan adanya alat  peraga matematika yang berisi mengenai penjumlahan dan pengurangan, maka siswa dapat mudah memahami dan memecahkan soal mengenai penjumlahan dan penguranagn dengan menggunakan alat peraga.
Pelatihan Pendampingan Strategi Pemasaran UMKM Kota Dumai
Authors: Saputra Juni , Indrawan Surya , Rahmi Hanifatul , Sirlyana Sirlyana , Putra Sony Adiya
Published: September 2023

Pelatihan Pendampingan Strategi Pemasaran UMKM Kota Dumai dengan studi bertujuan untuk membantu pemilik usaha dalam mengembangkan strategi pemasaran yang efektif untuk memperluas pangsa pasar dan meningkatkan keberhasilan usaha mereka. Studi kasus ini akan fokus pada Pabrik Tahu Kembar, sebuah UMKM yang bergerak di sektor makanan di Kota Dumai. Pelatihan ini akan memberikan pemahaman mendalam tentang konsep dasar pemasaran yang relevan untuk UMKM, dengan penekanan khusus pada industri makanan. Peserta akan diajarkan tentang pengenalan merek, analisis pasar, segmentasi pasar, dan strategi promosi yang tepat untuk produk tahu yang dihasilkan oleh Pabrik Tahu Kembar. Selain itu, peserta juga akan mempelajari pengelolaan distribusi dan pengaturan harga yang sesuai untuk memaksimalkan keuntungan. Melalui pendekatan praktis dan interaktif, peserta pelatihan akan dilibatkan dalam diskusi kelompok dan simulasi bisnis yang berfokus pada permasalahan yang dihadapi oleh Pabrik Tahu Kembar. Mereka akan diajak untuk mengidentifikasi peluang pemasaran, mengembangkan strategi branding, memanfaatkan media sosial dan platform digital, serta membangun hubungan yang kuat dengan pelanggan. Dengan melalui pelatihan ini, diharapkan pemilik usaha dan tim Pabrik Tahu Kembar dapat mengimplementasikan strategi pemasaran yang lebih efektif dan inovatif. Mereka akan mampu memahami dan mengenali target pasar yang potensial, serta mampu menghadapi persaingan dengan lebih baik. Pelatihan ini juga diharapkan dapat memberikan inspirasi kepada UMKM lain di Kota Dumai untuk mengembangkan strategi pemasaran yang lebih baik
Pelatihan Penyusunan Laporan Keuangan Dengan Menggunakan Aplikasi SI APIK Pada UMKM di Kelurahan Katilimbang Cibeber Kota Cilegon
Authors: Siregar Dina Khairuna , Maelani Puspita , Octafiani Venny , Pratama Dandy Yoga , Febriansyah Yoghi , Ismiyati Ismiyati , Hermawan M. Rudy , Zahratunnisa Evi
Published: September 2023

Perkembangan UMKM menuntut para pelaku usaha untuk terus beradaptasi demi mempertahankan keberlangsungan usahanya, termasuk pada pengelolaan keuangan, UMKM di kelurahan Katilimbang yang  memproduksi dan menjual keripik tauge Lasak terus meningkat namun belum tertib dalam pencatatan keuangan. Metode pelaksanaan terdapat tiga tahapan yaitu survey untuk mengidentifikasi permasalahan pada UMKM, kedua Perencanaan dalam menentukan materi, jadwal kegiatan dan kebutuhan lainnya, tahap terakhir adalah pelaksanaan pelatihan. Hasil kegiatan ini  adalah meningkatkan pengetahuan para pelaku UMKM dalam pengelolaan keuangan dan pencatatan keuangan dengan menggunakan aplikasi SIAPIK.
Implementasi Pembuatan Pojok Baca sebagai Upaya Peningkatan Literasi Siswa SD Muhammadiyah Sambeng
Authors: Santi Fitta Ummaya , Setyaningsih Nurita
Published: September 2023

Implementasi Pembuatan Pojok Baca ini dilaksanakan di SD Muhammadiyah Sambeng, Poncosari, Srandakan, Bantul. Pojok Baca yaitu pemanfaatan sudut ruang sebagai tempat untuk membaca siswa dan disediakan buku-buku untuk dibaca. Tujuan pelaksanaan program ini untuk meningkatkan literasi siswa khususnya kemampuan membaca dan menulis. Pojok baca memberikan peranan penting dalam meningkatkan literasi siswa yaitu memberikan ruang baca yang nyaman dan menarik serta menyediakan berbagai bahan bacaan. Metode pelaksanaan program ini yaitu diawali dengan membuat konsep pojok baca, kemudian implementasi pembuatan pojok baca, pengadaan buku bacaan, sosialisasi tata tertib pojok baca, dan yang terakhir launching pojok baca. Kegiatan ini dianggap dapat meningkatkan minat baca tulis siswa dan secara signifikan akan meningkakan kegiatan literasi di SD Muhammadiyah Sambeng.