Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophage carbon load (MaCL) is a novel cytology method that quantifies lung deposition dose of black carbon, however it has limited feasibility in large-scale epidemiological study due to the labor-intensive manual counting. To assess the association between MaCL and episodic elevation of combustion particles; to develop artificial intelligence based counting algorithm for MaCL assay. Sputum slides were collected during episodic elevation of ambient PM2.5 (n = 49, daily PM2.5 > 10 µg/m3 for over 2 weeks due to wildfire smoke intrusion in summer and local wood burning in winter) and low PM2.5 period (n = 39, 30-day average PM2.5 < 4 µg/m3) from the Lovelace Smokers cohort. Over 98% individual carbon particles in macrophages had diameter <1 µm. MaCL levels scored manually were highly responsive to episodic elevation of ambient PM2.5 and also correlated with lung injury biomarker, plasma CC16. The association with CC16 became more robust when the assessment focused on macrophages with higher carbon load. A Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP) was developed based on the Mask Region-based Convolutional Neural Network. MacLEAP algorithm yielded excellent correlations with manual counting for number and area of the particles. The algorithm produced associations with ambient PM2.5 and plasma CC16 that were nearly identical in magnitude to those obtained through manual counting. Understanding lung black carbon deposition is crucial for comprehending health effects of combustion particles. We developed "Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP)", the first artificial intelligence algorithm for quantifying airway macrophage black carbon. Our study bolstered the algorithm with more training images and its first use in air pollution epidemiology. We revealed macrophage carbon load as a sensitive biomarker for heightened ambient combustion particles due to wildfires and residential wood burning.

Hairy Cell Leukemia is an infrequent leukemia that can be recognized both microscopically and flow cytometrically once the patient develops symptoms. We present a case where early diagnosis was achieved using flow cytometry long before the patient became symptomatic. This was achieved by focusing on a small percentage (0.9%) of total leukocytes that exhibited a higher side scatter and brighter CD19/CD20 than the remaining lymphocytes. A bone marrow aspirate three weeks later confirmed the presence of malignant B-cells. Shortly after, the patient presented splenomegaly and complained of fatigue.

Background: My personal journey to developing fluency in Spanish was through gaming, and so I felt strongly that this popular pastime would help my students, too. Ocarina of Time was a natural choice, as it is not only my favorite game, it is also dialogue-heavy and mimics real life speech, so I couldn’t pass up the opportunity to play it with my students! Aim: My main objective was to improve students’ Spanish reading comprehension. Other objectives included: developing an understanding of the difference between equivalent and literal translation, developing a more comprehensive understanding of Spanish sentence structure, learning the 100 most used Spanish verbs, and being able to understand perfect verbs and object pronouns when encountered in text. Methods: Engaging learners through play, student collaboration, ungraded practice, semantic and syntactic reading comprehension, spiraling curriculum, and gamification of rote memorization. Results: Students’ Spanish reading comprehension improved rapidly, and so did their confidence in performing this skill. Students also learned 90%+ of the 100 top used Spanish verbs, as well as a large variety of other vocabulary. Conclusion: Video games are a viable resource for teaching a foreign language.

Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model. The multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points. The major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.

IntroductionThe Southwestern United States (SWUS) has an extensive history of coal and metal mining, including uranium (U) mining. Lung diseases, including but not limited to, lung cancer and pulmonary fibrosis, have been studied extensively in miners due to occupational, dust-related exposures. However, high-throughput autoimmune biomarkers are largely understudied in miners, despite the fact that ore miners, such as U-miners, are at an increased risk for the development of autoimmune diseases such as systemic sclerosis and systemic lupus erythematosus (SLE). Additionally, there are current gaps in knowledge regarding which signaling pathways may play a role in occupational exposure-associated autoimmunity. MethodsMost current and former miners in the SWUS live close to their previous workplaces, in remote areas, with limited access to healthcare. In this pilot study, by leveraging a mobile clinical platform for patient care and clinical outreach, we recruited 44 miners who self-identified as either U (n = 10) or non-U miners (n = 34) and received health screenings. Serum IgG and IgM autoantibodies against 128 antigens were assessed using a high-throughput molecular technique, as a preliminary health screening opportunity. ResultsEven when adjusting for age as a covariate, there was a significant (p < 0.05) association between self-reported U-mining exposure and biomarkers including IgM alpha-actinin, histones H2B, and H4, myeloperoxidase (MPO) and myelin basic protein. However, adjusting for age did not result in significant associations for IgG autoantibody production in U-miners. Bioinformatic pathway analysis revealed several altered signaling pathways between IgM and IgG autoantibodies among both U and non-U miners. ConclusionsFurther research is warranted regarding the mechanistic connection between U-exposure and autoantibody development, especially regarding histone-related alterations and IgM autoantibody production.

The assigned protection factor (APF) for quarter-facepiece respirators is currently 5, based on fit test data from the 1970s with models no longer commercially available. The goal of this project was to evaluate the respirator fit capability of a NIOSH-approved N95 quarter-facepiece elastomeric respirator with a gel-based facial seal design (Envo Mask by Sleepnet Corporation). Human subjects were recruited from healthcare and the general population to satisfy a 25-member NIOSH bivariate panel. Subjects were fit tested with a fast fit protocol using a TSI Portacount Model 8038 in the N95 mode. Second-by-second measures of fit were then collected while subjects performed a 30-min series of simulated healthcare activities. Subjects completed a short comfort questionnaire. The median (5th, 95th percentile) fit factor was 188 (48, 201). Simulated workplace protection factors (SWPFs) had a median (5th, 95th percentile) of 181 (94, 199) (data truncated at 200) and 570 (153, 1508) (non-truncated data). Subjects ranked inhalation and exhalation as “easy” with average scores of 5.0/6.0 and 5.2/6.0, respectively. The facepiece was ranked between slightly comfortable and comfortable (4.8/6.0) and the harness as comfortable (5.0/6.0). Most users agreed (5.2/6.0) that the mask was stable on their faces. The 5th percentile SWPF of 95 supports an APF of at least 10 for this quarter-facepiece elastomeric respirator, similar to the APF for half-facepiece respirators. This study supports increasing the APF for quarter-facepiece respirators, a class that has been largely ignored by manufacturers for the past 40 years. A lightweight, low profile, reusable quarter-facepiece respirator is an effective option for healthcare and other worker protection during a pandemic and similar situations.

We introduce a new algorithm for the structural analysis of finite abstract simplicial complexes based on local homology. Through an iterative and top-down procedure, our algorithm computes a stratification π of the poset P of simplices of a simplicial complex K, such that for each strata Pπ=i⊂P, Pπ=i is maximal among all open subposets U⊂Pπ=i‾ in its closure such that the restriction of the local Z-homology sheaf of Pπ=i‾ to U is locally constant. Passage to the localization of P dictated by π then attaches a canonical stratified homotopy type to K.