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Abstract Background: Smoking has not been an established risk factor for prostate cancer (PCa), and has not been emphasized in PCa prevention. However, recent studies have shown increasing evidence that there is a higher risk of biochemical recurrence, PCa mortality, and metastasis among current smokers, presenting an urgent need in re-evaluating the association between smoking and aggressive PCa. This study aimed to determine whether smoking increase the likelihood of developing a more aggressive prostate cancer. Methods: Equal numbers of African Americans (AAs) and European Americans (EAs) by smoking status (never/former/current) matched with PCa aggressiveness, BMI, 5-year age group, and year of baseline recruitment, totaling 480 participants, were included in the metabolomics study. For metabolomics analysis, fold change and BH-adjusted p-value from t-test adjusted for age for univariate analysis, and PCA adjusted for age and PLS-DA supervised statistical analysis for multivariate analysis were employed to decipher the underlying metabolomic patterns, and identify significantly dysregulated metabolites for the variables of interest. Results: AA participants were significantly younger (mean=61.4, SD=7.7) compared with EAs (mean=63.5, SD=7.5). Current smokers had a 2.4 times higher risk of high aggressive PCa. When stratified by race, the risk diminished for EAs but increased for AAs. Global metabolic profiles detected a total of 1,487 compounds of known identity. After excluding metabolites with missing values in more than 20% of the samples and with small standard variation, we observed a distinct cluster of participants from AA aggressive PCa patients and current smokers that were separated from EAs and never smokers. With BH-adjusted p-value < 0.05 and fold change > 2, we identified 10 significantly dysregulated metabolites between AA and EA among high aggressive PCa and current smokers. Further, 36 metabolites between current and never smokers among AA high aggressive PCa were significantly dysregulated, but none of them are annotated as tobacco metabolites. Conclusion: Our study presented distinctive metabolomics profiles specific to AA current smokers who had high aggressive PCa. Furthermore, the distinctive patterns were not driven by the tobacco metabolites, with the potential to identify metabolites that might help to understand the relationships between smoking and aggressive PCa in AA. Citation Format: Se-Ran Jun, L. Joseph Su, Eryn Matich, Ping-Ching Hsu. Distinctive metabolomics profiles associated with African American current smokers who have high aggressive prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3680. 

This article considers resilient cooperative state estimation in unreliable multiagent networks. A network of agents aim to collaboratively estimate the value of an unknown vector parameter, while an unknown subset of agents suffer Byzantine faults. We refer to the faulty agents as Byzantine agents. Byzantine agents malfunction arbitrarily and may send out highly unstructured messages to other agents in the network. As opposed to fault-free networks, reaching agreement in the presence of Byzantine agents is far from trivial. In this article, we propose a computationally efficient algorithm that is provably robust to Byzantine agents. At each iteration of the algorithm, a good agent performs a gradient descent update based on noisy local measurements, exchanges its update with other agents in its neighborhood, and robustly aggregates the received messages using coordinate-wise trimmed means. Under mild technical assumptions, we establish that good agents learn the true parameter asymptotically in almost sure sense. We further complement our analysis by proving (high probability) finite-time convergence rate, encapsulating network characteristics. 

We consider the problem of multiagent optimization wherein an unknown subset of agents suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a local cost function fi , and the overarching goal of the good agents is to collaboratively minimize a global objective that properly aggregates these local cost functions. To the best of our knowledge, we are among the first to study Byzantine-resilient optimization where no central coordinating agent exists, and we are the first to characterize the structures of the convex coefficients of the achievable global objectives. Dealing with Byzantine faults is very challenging. For example, in contrast to fault-free networks, reaching Byzantine-resilient agreement even in the simplest setting is far from trivial. We take a step toward solving the proposed Byzantine-resilient multiagent optimization problem by focusing on scalar local cost functions. Our results might provide useful insights for the general local cost functions. 

We investigate the importance of quorum sensing in the success of house-hunting of emigrating Temnothorax ant colonies. Specifically, we show that the absence of the quorum sensing mechanism leads to failure of consensus during emigrations. We tackle this problem through the lens of distributed computing by viewing it as a natural distributed consensus algorithm. We develop an agent-based model of the house-hunting process, and use mathematical tools such as conditional probability, concentration bounds and Markov mixing time to rigorously prove the negative impact of not employing the quorum sensing mechanism on emigration outcomes. Our main result is a high probability bound for failure of consensus without quorum sensing in a two-new-nest environment, which we further extend to the general multiple-new-nest environments. We also show preliminary evidence that appropriate quorum sizes indeed help with consensus during emigrations. Our work provides theoretical foundations to analyze why Temnothorax ants evolved to utilize the quorum rule in their house-hunting process. 

Organic compounds in the atmosphere vary widely in their molecular composition and chemical properties, so no single instrument can reasonably measure the entire range of ambient compounds. Over the past decade, a new generation of in situ , field-deployable mass spectrometers has dramatically improved our ability to detect, identify, and quantify these organic compounds, but no systematic approach has been developed to assess the extent to which currently available tools capture the entire space of chemical identity and properties that is expected in the atmosphere. Reduced-parameter frameworks that have been developed to describe atmospheric mixtures are exploited here to characterize the range of chemical properties accessed by a suite of instruments. Multiple chemical spaces ( e.g. oxidation state of carbon vs. volatility, and oxygen number vs. carbon number) were populated with ions measured by several mass spectrometers, with gas- and particle-phase α-pinene oxidation products serving as the test mixture of organic compounds. Few gaps are observed in the coverage of the parameter spaces by the instruments employed in this work, though the full extent to which comprehensive measurement was achieved is difficult to assess due to uncertainty in the composition of the mixture. Overlaps between individual ions and regions in parameter space were identified, both between gas- and particle-phase measurements, and within each phase. These overlaps were conservatively found to account for little (<10%) of the measured mass. However, challenges in identifying overlaps and in accurately converting molecular formulas into chemical properties (such as volatility or reactivity) highlight a continued need to incorporate structural information into atmospheric measurements.