Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.
We are looking into natural ways to help hair grow better and stronger by studying keratin synthesis in human hair follicles. The reason for conducting this research was to have the ability to control hair growth through future innovations. We wanted to answer the question: How can we find natural ways to enhance hair growth by understanding the connection with natural resources, particularly keratin dynamics? The main focus of this experiment is understanding the promotion of keratin synthesis within human hair follicles, which is important for hair development and health. While keratin is essential for the growth and strength of body tissues, including skin and hair, our research hints at its specific synthesis within hair follicles. In our research utilizing castor oil, coconut oil, a turmeric and baking soda mixture, and a sugar, honey, and lemon mixture, we hypothesize that oils, specifically coconut oil and castor oil, will enhance keratin synthesis, whereas mixtures, such as the turmeric and baking soda mixture and the sugar, honey, and lemon mixture, will result in a decrease keratin synthesis. The methods used show how different natural substances influence keratin formation within the hair follicles. The experiment involved applying natural resources to hair strands and follicles, measuring their length under the microscope daily, and assessing their health and characteristics over seven days. In summary, our research helps us understand how hair grows better. We found that using natural items like essential oils effectively alters keratin growth within the hair follicles and hair strands.
The authors analyze political endorsement patterns and impacts from the 2018 and 2020 midterm elections and find that such endorsements may be predictable based on the ideological and demographic factors of the endorser.
Life requires many things, including a hospitable temperature, elements, and energy. Here the authors utilize Newton's laws of physics and information relating a star's luminosity and temperature to determine the minimum and maximum masses and luminosities of planets and stars that would support life as we know it. This work can be used to determine the likelihood of a planet being able to support life based on attributes we can measure from here on Earth.
Economic disruptions and housing instabilities have for long propelled a homelessness epidemic among adults and youth in the United States. The COVID-19 pandemic has accelerated this phenomenon with a 2.2% increase in the number of homeless individuals and more than 70% of Americans fearing this outcome for themselves. This study aimed to analyze the perception of homelessness in two groups: Those who have previously experienced and overcome homelessness and those who are at-risk for experiencing the same. The study analyzed publicly available Reddit posts by people in both groups and found that at-risk individuals tended to associate primarily fearful emotions with the event, and those who had overcome homelessness tended to view the event in a negative context. These results may encourage the establishment of resources to support the currently homeless and help them transition into society, and services to help them cope with negative emotions, as negative attitudes have been shown to decrease life expectancy.
In this study the authors looked at sustainable ways to clean up oil spills that harm marine life. Using water spangle leaves and milk week the authors looked at the ability to recovery oil from both fresh and salt water and the ability to reuse the organic material to clean up spills. Their results show promise to help find a sustainable, eco-friendly way to clean up oil spills and protect marine life and habitats.
The number of bacterial infections in humans is rising, and a major contributor is foodborne illnesses, which affect a large portion of the population and result in many hospitalizations and deaths. Common household cleaners are an effective strategy to combat foodborne illness, but they are often costly and contain harmful chemicals. Thus, the authors sought to test the antimicrobial effectiveness of spices (clove, nutmeg, astragalus, cinnamon, turmeric, and garlic) on microbes cultured from refrigerator handles and cutting boards. Results from this study demonstrate long-lasting, antimicrobial effects of multiple spices that support their use as alternatives to common household cleaners.
In the United States, there are currently 17.8 million affected by atopic dermatitis (AD), commonly known as eczema. It is characterized by itching and skin inflammation. AD patients are at higher risk for infections, depression, cancer, and suicide. Genetics, environment, and stress are some of the causes of the disease. With the rise of personalized medicine and the acceptance of gene-editing technologies, AD-related variations need to be identified for treatment. Genome-wide association studies (GWAS) have associated the Filaggrin (FLG) gene with AD but have not identified specific problematic single nucleotide polymorphisms (SNPs). This research aimed to refine known SNPs of FLG for gene editing technologies to establish a causal link between specific SNPs and the diseases and to target the polymorphisms. The research utilized R and its Bioconductor packages to refine data from the National Center for Biotechnology Information's (NCBI's) Variation Viewer. The algorithm filtered the dataset by coding regions and conserved domains. The algorithm also removed synonymous variations and treated non-synonymous, frameshift, and nonsense separately. The non-synonymous variations were refined and ordered by the BLOSUM62 substitution matrix. Overall, the analysis removed 96.65% of data, which was redundant or not the focus of the research and ordered the remaining relevant data by impact. The code for the project can also be repurposed as a tool for other diseases. The research can help solve GWAS's imprecise identification challenge. This research is the first step in providing the refined databases required for gene-editing treatment.
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. 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.
Gamma radiation can be produced by both natural and man-made sources and abnormally high exposure levels could lead to an increase in cell damage. In this study, gamma radiation was measured at different locations and any correlation with various geographic factors, such as distance from a city center, elevation and proximity to the nearest nuclear reactor, was determined.
