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Abstract Black flies (Diptera: Simuliidae) are reemerging as biting and nuisance pests in many southern states, presumably from improving water quality in creeks and rivers. Since 2009, entomologists at Mississippi State University and the Mississippi Department of Health have conducted surveys to ascertain what black fly species are present in the state as well as their geographic distribution and seasonality. These surveys revealed what appears to be a recurring, significant emergence of black flies every year around 25 December at one site in southern Mississippi. In this study, adult black flies were collected from 1 January 2018 to 31 December 2021 by hand netting in the exact same way each time at Okatoma Creek, Seminary, MS. Forty-eight collecting trips to the site over the 4-yr period yielded a total of 176 black flies, all morphologically identified as Simulium jenningsi Group Malloch. Molecular identification was successfully performed on 17 specimens collected during the December outbreaks. Of the 17 specimens analyzed, 10 and 7 specimens grouped with 100% bootstrap confidence inside clades comprising S. jenningsi or S. podostemi, respectively. 

Abstract The ability to accurately identify peptide ligands for a given major histocompatibility complex class I (MHC-I) molecule has immense value for targeted anticancer therapeutics. However, the highly polymorphic nature of the MHC-I protein makes universal prediction of peptide ligands challenging due to lack of experimental data describing most MHC-I variants. To address this challenge, we have developed a deep convolutional neural network, HLA-Inception, capable of predicting MHC-I peptide binding motifs using electrostatic properties of the MHC-I binding pocket. By approaching this immunological issue using molecular biophysics, we measure the impact of sidechain arrangement and topology on peptide binding, feature not captured by sequence-based MHC-I prediction methods. Through a combination of molecular modeling and simulation, 5821 MHC-I alleles were modeled, providing extensive coverage across human populations. Predicted peptide binding motifs fell into distinct clusters, each defined with different degrees of submotif heterogeneity. Peptide binding scores generated by HLA-Inception are strongly correlated with quantitative MHC-I binding data, indicating predicted peptides can be ranked, both within and between alleles. HLA-inception also showed high precision when predicting naturally presented peptides and can be used for rapid proteome-scale MHC-I peptide binding predictions. Finally, we show that the binding pocket diversity measured by HLA inception predicts response to checkpoint blockade. Citation Format: Eric A. Wilson, John Kevin Cava, Diego Chowell, Abhishek Singharoy, Karen S. Anderson. Protein structure-based modeling to improve MHC class I epitope predictions. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5376. 

Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models. 

Deep Learning (DL) models achieve great successes in many domains. However, DL models increasingly face safety and robustness concerns, including noisy labeling in the training stage and feature distribution shifts in the testing stage. Previous works made significant progress in addressing these problems, but the focus has largely been on developing solutions for only one problem at a time. For example, recent work has argued for the use of tunable robust loss functions to mitigate label noise, and data augmentation (e.g., AugMix) to combat distribution shifts. As a step towards addressing both problems simultaneously, we introduce AugLoss, a simple but effective methodology that achieves robustness against both train-time noisy labeling and test-time feature distribution shifts by unifying data augmentation and robust loss functions. We conduct comprehensive experiments in varied settings of real-world dataset corruption to showcase the gains achieved by AugLoss compared to previous state-of-the-art methods. Lastly, we hope this work will open new directions for designing more robust and reliable DL models under real-world corruptions.