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The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

Glioblastoma Multiforme (GBM) is the most malignant brain tumor with the highest fraction of genome alterations (FGA), manifesting poor disease-free status (DFS) and overall survival (OS). We explored The Cancer Genome Atlas (TCGA) and cBioportal public dataset- Firehose legacy GBM to study DNA repair genes Activating Signal Cointegrator 1 Complex Subunit 3 (ASCC3) and Alpha-Ketoglutarate-Dependent Dioxygenase AlkB Homolog 3 (ALKBH3). To test our hypothesis that these genes have correlations with FGA and can better determine prognosis and survival, we sorted the dataset to arrive at 254 patients. Analyzing using RStudio, both ASCC3 and ALKBH3 demonstrated hypomethylation in 82.3% and 61.8% of patients, respectively. Interestingly, low mRNA expression was observed in both these genes. We further conducted correlation tests between both methylation and mRNA expression of these genes with FGA. ASCC3 was found to be negatively correlated, while ALKBH3 was found to be positively correlated, potentially indicating contrasting dysregulation of these two genes. Prognostic analysis showed the following: ASCC3 hypomethylation is significant with DFS and high ASCC3 mRNA expression to be significant with OS, demonstrating ASCC3’s potential as disease prediction marker.

In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and T_g is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's T_g, providing valuable improvements to this useful platform.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

The escalating crisis of climate change, driven by the accumulation of greenhouse gases from human activities, demands urgent and innovative solutions to curb rising global temperatures. Plasma-based methane (CH₄) and carbon dioxide (CO₂) reforming offers a promising pathway for carbon capture and the sustainable production of hydrogen fuel and syngas components. To advance this technology, particularly in terms of energy efficiency and selectivity, it is essential to enhance the conversion efficiencies of CO₂ and CH₄.

This study explores the factors that influence coffee quality ratings using data from the Coffee Quality Institute. Through a regression model based on gradient descent, the authors aimed to predict coffee ratings (total cup points) and hypothesized that sweetness and the coffee producer would be the most influential factors.

Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.

Droughts kill over 45,000 people yearly and affect the livelihoods of 55 million others worldwide, with climate change likely to worsen these effects. However, unlike other natural disasters (hurricanes, etc.), there is no early detection system that can predict droughts far enough in advance to be useful. Bora, Caulkins, and Joycutty tackle this issue by creating a drought prediction model.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

The authors looked at non-natural factors that influenced the spread rate of fire ants in multiple cities in China.