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Abstract Glial cells are known to influence neuronal functions through glia–neuron communication. The present study aims to elucidate the mechanism behind peroxisome-mediated glia–neuron communication using Drosophila neuromuscular junction (NMJ) as a model system. We observe a high abundance of peroxisomes in the abdominal NMJ of adult Drosophila. Interestingly, glia-specific knockdown of peroxisome import receptor protein, Pex5, significantly increases axonal area and volume and leads to axon swelling. The enlarged axonal structure is likely deleterious, as the flies with glia-specific knockdown of Pex5 exhibit age-dependent locomotion defects. In addition, impaired peroxisomal ether lipid biosynthesis in glial cells also induces axon swelling. Consistent with our previous work, defective peroxisomal import function upregulates pro-inflammatory cytokine upd3 in glial cells, while glia-specific overexpression of upd3 induces axonal swelling. Furthermore, motor neuron-specific activation of the JAK-STAT pathway through hop overexpression results in axon swelling. Our findings demonstrated that impairment of glial peroxisomes alters axonal morphology, neuroinflammation, and motor neuron function. 

Measuring dietary intake is a major challenge in the management of chronic diseases. Current methods rely on self-report measures, which are cumbersome to obtain and often unreliable. This article presents an approach to estimate dietary intake automatically by analyzing the post-prandial glucose response (PPGR) of a meal, as measured with continuous glucose monitors. In particular, we propose a sparse-coding technique that can be used to estimate the amounts of macronutrients (carbohydrates, protein, fat) in a meal from the meal’s PPGR. We use Lasso regularization to represent the PPGR of a new meal as a sparse combination of PPGRs in a dictionary, then combine the sparse weights with the macronutrient amounts in the dictionary’s meals to estimate the macronutrients in the new meal. We evaluate the approach on a dataset containing nine standardized meals and their corresponding PPGRs, consumed by fifteen participants. The proposed technique consistently outperforms two baseline systems based on ridge regression and nearest-neighbors, in terms of correlation and normalized root mean square error of the predictions. 

Diet monitoring is an important component of interventions in type 2 diabetes, but is time intensive and often inaccurate. To address this issue, we describe an approach to monitor diet automatically, by analyzing fluctuations in glucose after a meal is consumed. In particular, we evaluate three standardization techniques (baseline correction, feature normalization, and model personalization) that can be used to compensate for the large individual differences that exist in food metabolism. Then, we build machine learning models to predict the amounts of macronutrients in a meal from the associated glucose responses. We evaluate the approach on a dataset containing glucose responses for 15 participants who consumed 9 meals. Three techniques improve the accuracy of the models: subtracting the baseline glucose, performing z-score normalization, and scaling the amount of macronutrients by each individuals’ body mass index.