An Asymptotic-Preserving and Energy-Conserving Particle-In-Cell Method for Vlasov-Maxwell Equations

In this paper, we develop an asymptotic-preserving and energy-conserving (APEC) Particle-In-Cell (PIC) algorithm for the Vlasov-Maxwell system. This algorithm not only guarantees that the asymptotic limiting of the discrete scheme is a consistent and stable discretization of the quasi-neutral limit of the continuous model, but also preserves Gauss's law and energy conservation at the same time, thus it is promising to provide stable simulations of complex plasma systems even in the quasi-neutral regime. The key ingredients for achieving these properties include the generalized Ohm's law for electric field such that the asymptotic-preserving discretization can be achieved, and a proper decomposition of the effects of the electromagnetic fields such that a Lagrange multiplier method can be appropriately employed for correcting the kinetic energy. We investigate the performance of the APEC method with three benchmark tests in one dimension, including the linear Landau damping, the bump-on-tail problem and the two-stream instability. Detailed comparisons are conducted by including the results from the classical explicit leapfrog and the previously developed asymptotic-preserving PIC schemes. Our numerical experiments show that the proposed APEC scheme can give accurate and stable simulations both kinetic and quasi-neutral regimes, demonstrating the attractive properties of the method crossing scales.Comment: 21 pages, 30 figure

Systematic evaluation of AML-associated antigens identifies anti-U5 SNRNP200 therapeutic antibodies for the treatment of acute myeloid leukemia.

Despite recent advances in the treatment of acute myeloid leukemia (AML), there has been limited success in targeting surface antigens in AML, in part due to shared expression across malignant and normal cells. Here, high-density immunophenotyping of AML coupled with proteogenomics identified unique expression of a variety of antigens, including the RNA helicase U5 snRNP200, on the surface of AML cells but not on normal hematopoietic precursors and skewed Fc receptor distribution in the AML immune microenvironment. Cell membrane localization of U5 snRNP200 was linked to surface expression of the Fcγ receptor IIIA (FcγIIIA, also known as CD32A) and correlated with expression of interferon-regulated immune response genes. Anti-U5 snRNP200 antibodies engaging activating Fcγ receptors were efficacious across immunocompetent AML models and were augmented by combination with azacitidine. These data provide a roadmap of AML-associated antigens with Fc receptor distribution in AML and highlight the potential for targeting the AML cell surface using Fc-optimized therapeutics

Harnessing Transcriptionally driven chromosomal instability adaptation to target therapy-refractory lethal prostate cancer.

Metastatic prostate cancer (PCa) inevitably acquires resistance to standard therapy preceding lethality. Here, we unveil a chromosomal instability (CIN) tolerance mechanism as a therapeutic vulnerability of therapy-refractory lethal PCa. Through genomic and transcriptomic analysis of patient datasets, we find that castration and chemotherapy-resistant tumors display the highest CIN and mitotic kinase levels. Functional genomics screening coupled with quantitative phosphoproteomics identify MASTL kinase as a survival vulnerability specific of chemotherapy-resistant PCa cells. Mechanistically, MASTL upregulation is driven by transcriptional rewiring mechanisms involving the non-canonical transcription factors androgen receptor splice variant 7 and E2F7 in a circuitry that restrains deleterious CIN and prevents cell death selectively in metastatic therapy-resistant PCa cells. Notably, MASTL pharmacological inhibition re-sensitizes tumors to standard therapy and improves survival of pre-clinical models. These results uncover a targetable mechanism promoting high CIN adaptation and survival of lethal PCa

US Soybean Market Forecasting Using Statistics & Machine Learning Techniques

The agricultural product stock market is very stochastic and difficult to predict. The market is especially affected due to different political and economic policies. This year, the soybean trading market has been affected the most due to the trade war between the U.S. and China. According to USDA, 17% of the U.S. agriculture produce exports to China and 62% of those products were soybeans. Thus, the soybean market has a remarkable change from previous years. In this study, Long-Short Term Memory (LSTM), Time Series Regression model and GARCH model are explored to analyze the soybean market. Google trend and other factors are evaluated as important indicators to the market

Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax

Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modelled human mobility via macro indicators (e.g., average daily travel distance) and then study the effectiveness of NPIs. In this work, we focus on mobility modelling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information). To address these challenges and jointly model variables including a geometric graph, a set of diffusions and a set of locations, we propose a model named Deep Graph Diffusion Infomax (DGDI). We show the maximization of DGDI can be bounded by two tractable components: a univariate Mutual Information (MI) between geometric graph and diffusion representation, and a univariate MI between diffusion representation and location representation. To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods