Characterizing Important Dietary Exposure Sources of Perfluoroalkyl Acids in Inuit Youth and Adults in Nunavik Using a Feature Selection Tool

BACKGROUND

Previous studies have identified the consumption of country foods (hunted/harvested foods from the land) as the primary exposure source of perfluoroalkyl acids (PFAA) in Arctic communities. However, identifying the specific foods associated with PFAA exposures is complicated due to correlation between country foods that are commonly consumed together.

METHODS

We used venous blood sample data and food frequency questionnaire data from the Qanuilirpitaa? ("How are we now?") 2017 (Q2017) survey of Inuit individuals ≥ 16 y of age residing in Nunavik (n = 1,193 ). Adaptive elastic net, a machine learning technique, identified the most important food items for predicting PFAA biomarker levels while accounting for the correlation among the food items. We used generalized linear regression models to quantify the association between the most predictive food items and six plasma PFAA biomarker levels. The estimates were converted to percent changes in a specific PFAA biomarker level per standard deviation increase in the consumption of a food item. Models were also stratified by food type (market or country foods).

RESULTS

Perfluorooctanesulfonic acid (PFOS), perfluorodecanoic acid (PFDA), and perfluoroundecanoic acid (PFUnDA) were associated with frequent consumption of beluga misirak (rendered fat) [14.6%; 95% confidence interval (CI): 10.3%, 18.9%; 14.6% (95% CI: 10.1%, 19.0%)], seal liver [9.3% (95% CI: 5.0%, 13.7%); 8.1% (95% CI: 3.5%, 12.6%)], and suuvalik (fish roe mixed with berries and fat) [6.0% (95% CI: 1.3%, 10.7%); 7.5% (95% CI: 2.7%, 12.3%)]. Beluga misirak was also associated with higher concentrations of perfluorohexanesulphonic acid (PFHxS) and perfluorononanoic acid (PFNA), albeit with lower percentage changes. PFHxS, perfluorooctanoic acid (PFOA), and PFNA followed some similar patterns, with higher levels associated with frequent consumption of ptarmigan [6.1% (95% CI: 3.2%, 9.0%); 5.1% (95% CI: 1.1%, 9.1%); 5.4% (95% CI: 1.8%, 9.0%)]. Among market foods, frequent consumption of processed meat and popcorn was consistently associated with lower PFAA exposure.

CONCLUSIONS

Our study identifies specific food items contributing to environmental contaminant exposure in Indigenous or small communities relying on local subsistence foods using adaptive elastic net to prioritize responses from a complex food frequency questionnaire. In Nunavik, higher PFAA biomarker levels were primarily related to increased consumption of country foods, particularly beluga misirak, seal liver, suuvalik, and ptarmigan. Our results support policies regulating PFAA production and use to limit the contamination of Arctic species through long-range transport. https://doi.org/10.1289/EHP13556.

Analysis of the Stage Performance Effect of Environmental Protection Music and Dance Drama Based on Artificial Intelligence Technology

There are a lot of environmental protection musicals and dances in public entertainment life to support the concept of environmental protection and to entice people to support the cause of environmental protection. As we all know, in a musical performance, the distance between the audience and the actors, the design of the stage environment, the training of the actors, the costumes of the actors, the makeup of the actors, and the musical accompaniment will all have more or less influence on the stage performance effects. In order to make environmental protection music and dance dramas profoundly meaningful to social production and human activities, it is especially important to analyze the stage performance of such music and dance dramas. The analysis of stage performance effects is not only beneficial to making the ideas spread by the music and dance drama penetrate into people's hearts but can also provide guiding suggestions for the production of music and dance drama. Therefore, this paper proposes a linear regression algorithm based on artificial intelligence to deeply explore and analyze the influence of the above six factors on the stage performance effects of environmental protection music and dance drama. In our method, first we preprocess the data collected by classification to remove the odd values from the data so that the various types of data conform to a normal distribution. Secondly, we obtained linear fit plots of the six factors with the stage performance effect scores by using a linear regression algorithm to deeply analyze the correlation between various types of data and the stage performance effect scores. Finally, through numerical calculations, we found that the distance between the audience and the actors, the training of the actors, and the musical accompaniment have a greater influence on the performance effect of the musical cabaret in the environmental protection category. Meanwhile, the costumes of the actors, the makeup of the actors, and the design of the stage environment have less influence on the performance effect of the musical cabaret. Therefore, in the production and performance of environmental protection music and dance dramas, producers and performers should pay more attention to the distance between the audience and the actors, the design of musical accompaniment, and the training of actors. To sum up, this paper has made a scientific, detailed, and reasonable analysis of the performance effect of environmental protection music and dance dramas on stage, contributing to the dissemination of environmental protection ideas.

Feature Selection to Predict LED Light Energy Consumption with Specific Light Recipes in Closed Plant Production Systems

The use of closed growth environments, such as greenhouses, plant factories, and vertical farms, represents a sustainable alternative for fresh food production. Closed plant production systems (CPPSs) allow growing of any plant variety, no matter the year’s season. Artificial lighting plays an essential role in CPPSs as it promotes growth by providing optimal conditions for plant development. Nevertheless, it is a model with a high demand for electricity, which is required for artificial radiation systems to enhance the developing plants. A high percentage (40% to 50%) of the costs in CPPSs point to artificial lighting systems. Due to this, lighting strategies are essential to improve sustainability and profitability in closed plant production systems. However, no tools have been applied in the literature to contribute to energy savings in LED-type artificial radiation systems through the configuration of light recipes (wavelengths combination. For CPPS to be cost-effective and sustainable, a pre-evaluation of energy consumption for plant cultivation must consider. Artificial intelligence (AI) methods integrated into the prediction crucial variables such as each input-variable light color or specific wavelengths like red, green, blue, and white along with light intensity (quantity), frequency (pulsed light), and duty cycle. This paper focuses on the feature-selection stage, in which a regression model is trained to predict energy consumption in LED lights with specific light recipes in CPPSs. This stage is critical because it identifies the most representative features for training the model, and the other stages depend on it. These tools can enable further in-depth analysis of the energy savings that can be obtained with light recipes and pulsed and continuous operation light modes in artificial LED lighting systems.