Search for: All records

We present a mathematical model based on ordinary differential equations to investigate the spatially homogeneous state of tumor growth under virotherapy. The model emphasizes the interaction among the tumor cells, the oncolytic viruses, and the host immune system that generates both innate and adaptive immune responses. We conduct a rigorous equilibrium analysis and derive threshold conditions that determine the growth or decay of the tumor under various scenarios. Numerical simulation results verify our analytical predictions and provide additional insight into the tumor growth dynamics. 

Dataset discovery from data lakes is essential in many real application scenarios. In this paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes (with table union search as the main use case). Our proposed framework features a contrastive learning method to train column encoders from pre-trained language models in a fully unsupervised manner. The column encoder of Starmie captures the rich contextual semantic information within tables by leveraging a contrastive multi-column pre-training strategy. We utilize the cosine similarity between column embedding vectors as the column unionability score and propose a filter-and-verification framework that allows exploring a variety of design choices to compute the unionability score between two tables accordingly. Empirical results on real table benchmarks show that Starmie outperforms the best-known solutions in the effectiveness of table union search by 6.8 in MAP and recall. Moreover, Starmie is the first to employ the HNSW (Hierarchical Navigable Small World) index to accelerate query processing of table union search which provides a 3,000X performance gain over the linear scan baseline and a 400X performance gain over an LSH index (the state-of-the-art solution for data lake indexing). 

Complex systems can exhibit sudden transitions or regime shifts from one stable state to another, typically referred to as critical transitions. It becomes a great challenge to identify a robust warning sufficiently early that action can be taken to avert a regime shift. We employ landscape-flux theory from nonequilibrium statistical mechanics as a general framework to quantify the global stability of ecological systems and provide warning signals for critical transitions. We quantify the average flux as the nonequilibrium driving force and the dynamical origin of the nonequilibrium transition while the entropy production rate as the nonequilibrium thermodynamic cost and thermodynamic origin of the nonequilibrium transition. Average flux, entropy production, nonequilibrium free energy, and time irreversibility quantified by the difference in cross-correlation functions forward and backward in time can serve as early warning signals for critical transitions much earlier than other conventional predictors. We utilize a classical shallow lake model as an exemplar for our early warning prediction. Our proposed method is general and can be readily applied to assess the resilience of many other ecological systems. The early warning signals proposed here can potentially predict critical transitions earlier than established methods and perhaps even sufficiently early to avert catastrophic shifts. 

Pressure swing adsorption (PSA) is a widely used technology to separate a gas product from impurities in a variety of fields. Due to the complexity of PSA operations, process and instrument faults can occur at different parts and/or steps of the process. Thus, effective process monitoring is critical for ensuring efficient and safe operations of PSA systems. However, multi-bed PSA processes present several major challenges to process monitoring. First, a PSA process is operated in a periodic or cyclic fashion and never reaches a steady state; Second, the duration of different operation cycles is dynamically controlled in response to various disturbances, which results in a wide range of normal operation trajectories. Third, there is limited data for process monitoring, and bed pressure is usually the only measured variable for process monitoring. These key characteristics of the PSA operation make process monitoring, especially early fault detection, significantly more challenging than that for a continuous process operated at a steady state. To address these challenges, we propose a feature-based statistical process monitoring (SPM) framework for PSA processes, namely feature space monitoring (FSM). Through feature engineering and feature selection, we show that FSM can naturally handle the key challenges in PSA process monitoring and achieve early detection of subtle faults from a wide range of normal operating conditions. The performance of FSM is compared to the conventional SPM methods using both simulated and real faults from an industrial PSA process. The results demonstrate FSM’s superior performance in fault detection and fault diagnosis compared to the traditional SPM methods. In particular, the robust monitoring performance from FSM is achieved without any data preprocessing, trajectory alignment or synchronization required by the conventional SPM methods. 

Although microtubules in plant cells have been extensively studied, the mechanisms that regulate the spatial organization of microtubules are poorly understood. We hypothesize that the interaction between microtubules and cytoplasmic flow plays an important role in the assembly and orientation of microtubules. To test this hypothesis, we developed a new computational modeling framework for microtubules based on theory and methods from the fluid–structure interaction. We employed the immersed boundary method to track the movement of microtubules in cytoplasmic flow. We also incorporated details of the encounter dynamics when two microtubules collide with each other. We verified our computational model through several numerical tests before applying it to the simulation of the microtubule–cytoplasm interaction in a growing plant cell. Our computational investigation demonstrated that microtubules are primarily oriented in the direction orthogonal to the axis of cell elongation. We validated the simulation results through a comparison with the measurement from laboratory experiments. We found that our computational model, with further calibration, was capable of generating microtubule orientation patterns that were qualitatively and quantitatively consistent with the experimental results. The computational model proposed in this study can be naturally extended to many other cellular systems that involve the interaction between microstructures and the intracellular fluid.

 

Pepper (Capsicumspp.) is one of the earliest cultivated crops and includes five domesticated species,C. annuumvar.annuum,C. chinense,C. frutescens,C. baccatumvar.pendulumandC. pubescens. Here, we report a pepper graph pan-genome and a genome variation map of 500 accessions from the five domesticatedCapsicumspecies and close wild relatives. We identify highly differentiated genomic regions among the domesticated peppers that underlie their natural variations in flowering time, characteristic flavors, and unique resistances to biotic and abiotic stresses. Domestication sweeps detected inC. annuumvar.annuumandC. baccatumvar.pendulumare mostly different, and the common domestication traits, including fruit size, shape and pungency, are achieved mainly through the selection of distinct genomic regions between these two cultivated species. Introgressions fromC. baccatumintoC. chinenseandC. frutescensare detected, including those providing genetic sources for various biotic and abiotic stress tolerances.