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AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.

The authors test how the size-segregation theory applies to the behavior of hollow and irregular-shaped objects.

Berberine is a natural product isoquinoline alkaloid derived from plants of the genus Berberis. When exposed to photoirradiation, it produces singlet oxygen through photosensitization of triplet oxygen. Through qNMR analysis of ¹H NMR spectra gathered through kinetic experiments, we were able to track the generation of a product between singlet oxygen and alpha terpinene, allowing us to quantitatively measure the photosensitizing properties of our scaffolds.

This study applies Taft linear free-energy relationships to study kinetic trends in the enzymatic hydrolysis of sterically hindered substrates.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

Mammographic screening is a common diagnostic tool for breast cancer among average-risk women. The authors 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. The 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.

Overwatering and underwatering grass are widespread issues with environmental and financial consequences. This study developed an accessible method to assess grass water use efficiency (WUE) combining smartphone imaging with open access color unmixing analysis. The method can be applied in automated irrigation systems or apps, providing grass WUE assessment for regular consumer use.

Here, recognizing the widespread use of hand sanitizers, the authors investigated their effectiveness in relation to storage temperature. They applied hand sanitizer before and after touching a cell phone and used LB-agar plates to monitor the growth of bacteria following this process. They found that 70% ethyl-alcohol-based sanitizers are least effective at temperatures above 107.27 °F and most effective at 96.17 °F.

Machine learning algorithms are becoming increasingly popular for data crunching across a vast area of scientific disciplines. Here, the authors compare two machine learning algorithms with respect to accuracy and user-friendliness and find that random forest algorithms outperform logistic regression when applied to the same dataset.