Browse Articles

The authors looked at which measurements from the NFL combine were the most predictive of success for defensemen in the NFL.

Classical financial forecasting models often fail to capture the complex, nonlinear dynamics of the stock market. This study demonstrates that incorporating a single variable to represent the 'geometric curvature' of a time series dramatically improves the accuracy of standard econometric forecasts. Our findings highlight that geometric properties are a significant predictive factor, opening new avenues for more powerful financial modeling.

Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.

A common form of Acne is caused by a species of bacterium called Cutibacterium acnes. By using a predictive algorithm and structural analysis, the authors identified 5 small molecules with high affinity to growth factors in Catibacterium acnes. This has potential implications for supplemental skincare products.

Tertiary lymphoid structures (TLS) are lymph node-like structures that form at sites of inflammation, and their presence in cancer patients is predictive of a better clinical outcome. One significant obstacle to TLS formation is reduced immune cell infiltration into the tumor microenvironment (TME). Recent studies have shown that vasculature normalizing (VN) agents may override this defect to improve tissue perfusion and increased immune cell entry into the TME. However, their effects on immune cell and tumor cell phenotype remain understudied. Here the authors investigate whether treating tumor cells with VN would reduce their immunosuppressive phenotype and promote production of chemokine that recruit immune cells and foster TLS formation.

Kalirin is a guanine nucleotide exchange factor (GEF) for the GTPase RAC1, linked to schizophrenia and Alzheimer’s Disease. It plays a crucial role in synaptic plasticity by regulating dendritic spine formation and actin cytoskeleton remodeling, which are essential for creating new synapses. Authors developed two novel compounds targeting kalirin, confirming that predictive modeling can indicate biological activity.

Asthma is one of the most prevalent chronic conditions in the United States. But not all people experience asthma equally, with factors like healthcare access and environmental pollution impacting whether children are likely to be hospitalized for asthma's effects. Li, Li, and Ruffolo investigate what demographic and environmental factors are predictive of childhood asthma hospitalization rates across Pennsylvania.

One common age-related health problem is the loss of bone mineral density (BMD), which can lead to a variety of negative health outcomes, including increased risk of spinal fracture. In this study, the authors investigate risk factors that may be predictive of an individual's risk of spinal fracture. Their findings provide valuable information that clinicians can use in patient evaluations.

The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.

This study examined behavioral and psychological predictors of burnout among high school and university students in Pakistan using survey data and machine-learning models. Shorter sleep and greater mental fatigue—especially fatigue—were associated with higher burnout risk, while a Random Forest model successfully identified students at risk of burnout.