Physical characteristics of microplastic particles and potential for global atmospheric transport: A meta-analysis.

Recent interest in microplastic pollution of natural environments has brought forth samples which confirm the pollutant's omnipresence in a variety of ecosystems. This includes locations furthest removed from human activity. Atmospheric transport and deposition are suspected as the primary transport pathway to these remote locations. The factors most influential on participation in atmospheric transport are yet to be determined. This meta-analysis aims to identify patterns that exist between physical characteristics of microplastic particles and their potential for atmospheric transport. Our review addresses the following questions: Which characteristics of microplastic particles promote atmospheric transport and deposition into remote regions, and how significant are these factors in determining distance transported from their sources? This article analyzes commonly reported physical attributes-- shape, polymer composition and color-- from studies in urban and remote areas. The analysis of 68 studies, composed of data from 2078 samples, shows higher occurrence of microplastic particles in remote samples with fiber shapes, polyester compositions, and red, blue, and transparent colors. This meta-analysis is the first to identify patterns between physical properties of microplastic particles and extent of their participation in atmospheric transport to global remote locations.

Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases.

A substantial portion of dementia risk can be attributed to modifiable risk factors that can be affected by lifestyle changes. Identifying the contributors to dementia risk could prove valuable. Recently, machine learning methods have been increasingly applied to healthcare data. Several studies have attempted to predict dementia progression by using such techniques. This study aimed to compare the performance of different machine-learning methods in modeling associations between known cognitive risk factors and future dementia cases. A subset of the AGES-Reykjavik Study dataset was analyzed using three machine-learning methods: logistic regression, random forest, and neural networks. Data were collected twice, approximately five years apart. The dataset included information from 1,491 older adults who underwent a cognitive screening process and were considered to have healthy cognition at baseline. Cognitive risk factors included in the models were based on demographics, MRI data, and other health-related data. At follow-up, participants were re-evaluated for dementia using the same cognitive screening process. Various performance metrics for all three machine learning algorithms were assessed. The study results indicate that a random forest algorithm performed better than neural networks and logistic regression in predicting the association between cognitive risk factors and dementia. Compared to more traditional statistical analyses, machine-learning methods have the potential to provide more accurate predictions about which individuals are more likely to develop dementia than others.