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  1. State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity, but also the underlying degradation modes which cause capacity fade because these modes give further insight into maximizing cell usage. There have been some successes in estimating cell degradation modes, however, these methods either require long-term degradation data, are demonstrated solely on artificially constructed cells, or exhibit high error in estimating late-life degradation. To address these shortfalls and alleviate the need for long-term cycling data, we propose a method for estimating the capacity of a battery cell and diagnosing its primary degradation mechanisms using limited early-life degradation data. The proposed method uses simulation data from a physics-based half-cell model and early-life degradation data from 16 cells cycled under two temperatures and C rates to train a machine learning model. Results obtained from a four-fold cross validation study indicate that the proposed physics-informed machine learning method trained with only 60 early life data (five data from each of the 12 training cells) and 30 high-degradation simulated data can decrease estimation error by up to a total of 9.77 root mean square error % when compared to models which were trained only on the early-life experimental data. 
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  3. The adenine, cytosine, and guanine bases of DNA are susceptible to alkylation by the aldehyde products of lipid peroxidation and by the metabolic byproducts of vinyl chloride pollutants. The resulting adducts spontaneously cyclize to form harmful etheno lesions. Cells employ a variety of DNA repair pathways to protect themselves from these pro-mutagenic modifications. Human alkyladenine DNA glycosylase (AAG) is thought to initiate base excision repair of both 1, N 6 -ethenoadenine (ϵA) and 1, N 2 -ethenoguanine (ϵG). However, it is not clear how AAG might accommodate ϵG in an active site that is complementary to ϵA. This prompted a thorough investigation of AAG-catalyzed excision of ϵG from several relevant contexts. Using single-turnover and multiple-turnover kinetic analyses, we found that ϵG in its natural ϵG·C context is very poorly recognized relative to ϵA·T. Bulged and mispaired ϵG contexts, which can form during DNA replication, were similarly poor substrates for AAG. Furthermore, AAG could not recognize an ϵG site in competition with excess undamaged DNA sites. Guided by previous structural studies, we hypothesized that Asn-169, a conserved residue in the AAG active-site pocket, contributes to discrimination against ϵG. Consistent with this model, the N169S variant of AAG was 7-fold more active for excision of ϵG compared with the wildtype (WT) enzyme. Taken together, these findings suggest that ϵG is not a primary substrate of AAG, and that current models for etheno lesion repair in humans should be revised. We propose that other repair and tolerance mechanisms operate in the case of ϵG lesions. 
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