Browse Articles

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

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.

Here, the authors compared financial advice output by chat-GPT to actual Reddit comments from the "r/Financial Planning" subreddit. By assessing the model's response content, length, and advice they found that while artificial intelligence can deliver information, it failed in its delivery, clarity, and decisiveness.

Here the authors investigated the motivation of a short squeeze of GameStop stock where users of the internet forum Reddit drove a sudden increase in GameStop stock price during the start of 2021. They relied on both qualitative and quantitative analyses where they tracked activity on the r/WallStreetBets subreddit in relation to mentions of GameStop. With these methods they found that while initially the short squeeze was driven by financial motivations, later on emotional motivations became more important. They suggest that social phenomena can be dynamic and evolve necessitating mixed method approaches to study them.

Invasive species pose a significant threat to many ecosystems, whether by outcompeting native species and disturbing food webs, or through increasing risks of natural disasters like flooding and wildfires. The ornamental grass species Pennisetum villosum R. Br. was previously identified by the California Invasive Plant Council as being potentially invasive; this experiment was conducted to determine if P. villosum displays characteristics of an invasive species when grown in a California chaparral environment. Reults found that in both conditions, the two species had similar germination rates, and that P. villosum grew significantly larger than N. pulchra for around 95 days.

Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

Iridescent materials reflect different colored depending on viewing angle. This specific effect can be achieved by biomimetic photonic materials. This project models the quantitative relationship between these material’s coloring and its nanostructure to facilitate personalized design of art materials.

This study examined how social determinants of health (SDH) relate to vision loss by analyzing publicly available data from 18 northern and southern U.S. states and using Bayesian correlation analysis.

The authors looked at using computer vision to evaluate the degree of exotropia in individuals with strabismus.

Analysis of the Sloan green and red photometry of the Type Ia supernova 2024neh