Snapshot Data Research Articles

MT-BCS-Based DoA and Bandwidth Estimation of Unknown Signals through Multiple Snapshots Data

The Direction-of-Arrival (DoA) and bandwidth (BW) estimation strategy impinging on a linear array using multiple snapshots data is addressed within the multitask Bayesian Compressive Sensing (MT-BCS). The DoA estimation is used as the reconstruction of sparse signal constrained by the Laplace prior through multitask Bayesian Compressive Sensing. Receiving wideband signal data through linear array, the space is divided into I parts according to the equal interval. The data of interest are assumed to be represented as I-dimensional vector, and the wideband signal can be reconstructed accurately using only a small number M. The receiving antenna operates in the frequency range fmin,fmax. Starting from the voltages measured at the output of the array elements at a multiple time instants at fp=fmin+Δf,p=1,…,P, the retrieval of the DoAs is addressed by means of a customized strategy based on MT-BCS in order to correlate the solutions obtained over different frequency samples. The bandwidth of the signals is obtained as a byproduct by identifying at which frequencies the MT-BCS estimations include a signal along the ith (i = 1,…, I) sampling direction. From the outputs of different frequencies, we can know the DoA and BW of signals. A preliminary numerical result is reported to show the behavior of the proposed approach in multiple snapshots data.

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the \emph{a priori} settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs multilayer feedforward artificial neural network (ANN) with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network (CNN) fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. We detail the hyperparameters optimization of these models using the grid search algorithm.

Inference of the statistics of a modulated promoter process from population snapshot gene expression data

In previous work, we have developed mathematical tools for the analysis of single-cell gene expression data from population snapshots, and an inference algorithm for the estimation of stationary statistics of promoter activation. In this work, we address the inference problem in the nonstationary case of modulated processes. This is of special relevance to control scenarios, where an exogenous input modulates the time evolution of promoter activation. We provide an effective method for the computation of the output statistics of a reaction network with a nonstationary, causal input process of modulated form. Based on this we devise and demonstrate an algorithm for the reconstruction of the promoter (input) process statistics from snapshot data.

Location big data differential privacy dynamic partition release method

Aiming at the privacy protection requirements in real-time statistical publishing process of location big data, a dynamic partition method is proposed based on differential privacy mechanism. The temporal redundancy between adjacent data snapshots has been eliminated by sampling and differential processing of dynamic location big data, and the spatial redundancy of location big data has been reduced by adaptive density meshing and uniformity heuristic quadtree partitioning. Differential privacy protection has been realised by adjusting partition structures of the current dataset on the spatial structure of previous moment and adding Laplace noise. Experiments carried out on the cloud computing platform and real location big datasets show that the proposed algorithm can meet the dynamic partition release requirements of real-time location big data, and the query precision of single-released location big data is better than other similar methods.

Pitfalls and requirements in quantifying asymmetric mitotic segregation.

The asymmetric inheritance of NUMB during mitosis determines future daughter cell fates in multiple model organisms. NUMB asymmetric inheritance has also been postulated for hematopoietic stem cell (HSC) divisions but remained controversial until recently. To reconcile conflicting reports, we revisited the evidence for asymmetric inheritance of NUMB during HSC divisions. We demonstrate that previously used strategies to identify dividing cells in fixed samples suffer from multiple systematic errors. Nonmitotic cells in close proximity are frequently mistaken as dividing cells, while mitotic cells are not detected. Furthermore, microtubule depolymerization by either nocodazole or low temperatures prevents the reliable detection of mitosis and introduces mitotic artifacts. Without artificial microtubule depolymerization and by the use of reliable mitotic markers, we find NUMB differences in daughter cells to be reduced and restricted to cells with low NUMB expression and thus low signal over background. This bias fits the expected random distribution of simulated noise data, suggesting that the putative asymmetric inheritance of NUMB in HSCs could be merely technical noise. We conclude that functionally relevant asymmetric inheritance of NUMB and other factors in mitotic HSCs and other cells cannot be conclusively demonstrated using snapshot data and requires alternative approaches, such as continuous quantitative single-cell analysis.

Evolutionary Clustering via Message Passing

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this article we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.

Erratum: Acceleration and escape processes of high-energy particles in turbulence inside hot accretion flows

We investigate acceleration and propagation processes of high-energy particles inside hot accretion flows. The magnetorotational instability (MRI) creates turbulence inside accretion flows, which triggers magnetic reconnection and may produce non-thermal particles. They can be further accelerated stochastically by the turbulence. To probe the properties of such relativistic particles, we perform magnetohydrodynamic simulations to obtain the turbulent fields generated by the MRI, and calculate orbits of the high-energy particles using snapshot data of the MRI turbulence. We find that the particle acceleration is described by a diffusion phenomenon in energy space with a diffusion coefficient of the hard-sphere type: $D_\epsilon\propto \epsilon^2$, where $\epsilon$ is the particle energy. Eddies in the largest scale of the turbulence play a dominant role in the acceleration process. On the other hand, the stochastic behaviour in configuration space is not usual diffusion but superdiffusion: the radial displacement increases with time faster than that in the normal diffusion. Also, the magnetic field configuration in the hot accretion flow creates outward bulk motion of high-energy particles. This bulk motion is more effective than the diffusive motion for higher energy particles. Our results imply that typical active galactic nuclei that host hot accretion flows can accelerate CRs up to $\epsilon\sim 0.1-10$ PeV.

Seasonal microplastics variation in nival and pluvial stretches of the Rhine River – From the Swiss catchment towards the North Sea

Rivers are pivotal carriers of microplastic (MP) towards the oceans. Investigative data on MP pollution in rivers at specific timepoints is continuously compiled. However, such snapshot data can only roughly indicate the long-term extent of contamination and particle fluxes; modelling studies informed by this limited data are prone to large uncertainties. The present study sought to narrow this knowledge gap by examining the differences in MP concentrations, loads and compositions at three nival tributaries and the Rhine River in Basel, Switzerland, as well as two downstream pluvial Lower Rhine River locations in Germany over four seasons throughout 2016–2017. MP concentrations (>0.3 mm) correlated positively with average water discharge and catchment size of the evaluated stream locations and MP concentrations were significantly higher at the downstream pluvial than upstream nival sites. There was no coherent pattern in MP concentration fluctuations between seasons across the six sites investigated, and no correlation with recent precipitation. These findings suggest that temporal variations in MP fluxes towards the North Sea through the year are dominated by the different discharge regimes along the river course. This study also corroborates theoretical models that predict the highest MP loads move downstream the Rhine River during the European winter months.

Nonintrusive reduced order modeling framework for quasigeostrophic turbulence.

In this study, we present a nonintrusive reduced order modeling (ROM) framework for large-scale quasistationary systems. The framework proposed herein exploits the time series prediction capability of long short-term memory (LSTM) recurrent neural network architecture such that (1) in the training phase, the LSTM model is trained on the modal coefficients extracted from the high-resolution data snapshots using proper orthogonal decomposition (POD) transform, and (2) in the testing phase, the trained model predicts the modal coefficients for the total time recursively based on the initial time history. Hence, no prior information about the underlying governing equations is required to generate the ROM. To illustrate the predictive performance of the proposed framework, the mean flow fields and time series response of the field values are reconstructed from the predicted modal coefficients by using an inverse POD transform. As a representative benchmark test case, we consider a two-dimensional quasigeostrophic ocean circulation model which, in general, displays an enormous range of fluctuating spatial and temporal scales. We first demonstrate that the conventional Galerkin projection-based reduced order modeling of such systems requires a high number of POD modes to obtain a stable flow physics. In addition, ROM-Galerkin projection (ROM-GP) does not seem to capture the intermittent bursts appearing in the dynamics of the first few most energetic modes. However, the proposed nonintrusive ROM framework based on LSTM (ROM-LSTM) yields a stable solution even for a small number of POD modes. We also observe that the ROM-LSTM model is able to capture quasiperiodic intermittent bursts accurately, and yields a stable and accurate mean flow dynamics using the time history of a few previous time states, denoted as the lookback time window in this paper. We show several features of ROM-LSTM framework such as significantly higher accuracy than ROM-GP, and faster performance using larger time step size. Throughout the paper, we demonstrate our findings in terms of time series evolution of the field values and mean flow patterns, which suggest that the proposed fully nonintrusive ROM framework is robust and capable of predicting chaotic nonlinear fluid flows in an extremely efficient way compared to the conventional projection-based ROM framework.

Long‐term demographic surveys reveal a consistent relationship between average occupancy and abundance within local populations of a butterfly metapopulation

Species distribution models are the tool of choice for large‐scale population monitoring, environmental association studies and predictions of range shifts under future environmental conditions. Available data and familiarity of the tools rather than the underlying population dynamics often dictate the choice of specific method – especially for the case of presence–absence data. Yet, for predictive purposes, the relationship between occupancy and abundance embodied in the models should reflect the actual population dynamics of the modelled species. To understand the relationship of occupancy and abundance in a heterogeneous landscape at the scale of local populations, we built a spatio‐temporal regression model of populations of the Glanville fritillary butterflyMelitaea cinxiain a Baltic Sea archipelago. Our data comprised nineteen years of habitat surveys and snapshot data of land use in the region. We used variance partitioning to quantify relative contributions of land use, habitat quality and metapopulation covariates. The model revealed a consistent and positive, but noisy relationship between average occupancy and mean abundance in local populations. Patterns of abundance were highly variable across years, with large uncorrelated random variation and strong local population stochasticity. In contrast, the spatio‐temporal random effect, habitat quality, population connectivity and patch size explained variation in occupancy, vindicating metapopulation theory as the basis for modelling occupancy patterns in fragmented landscapes. Previous abundance was an important predictor in the occupancy model, which points to a spillover of abundance into occupancy dynamics. While occupancy models can successfully model large‐scale population structure and average occupancy, extinction probability estimates for local populations derived from occupancy‐only models are overconfident, as extinction risk is dependent on actual, not average, abundance.

P4788One-year outcomes of elderly patients with atrial fibrillation: snapshot data from the global noninterventional program on edoxaban treatment in routine clinical practice in atrial fibrillation

Abstract Background As populations age, prevalence of atrial fibrillation (AF) and ensuing need for oral anticoagulation increase. Benefits and risks of nonvitamin K antagonist oral anticoagulants such as edoxaban in the frail, elderly population with AF in regular clinical care is of special interest. Purpose Data from Global ETNA-AF capturing almost 2ehz745.1164 patients treated with edoxaban in Europe, Japan, and Korea/Taiwan, was analyzed to compare outcomes in patients <75 years, elderly (≥75 years), and very elderly (≥85 years) patients. Methods Global ETNA-AF is a multinational, multicentre, prospective, noninterventional program (EU: NCT02944019, Japan: UMINehz745.116417011, Korea/Taiwan: NCT02951039). Demographics, baseline characteristics, and 1-year outcome data were reported for 19416 patients classified into 3 age categories. Results At 1-year follow-up, rates of major bleeding (including intracranial haemorrhage [ICH]) and ischaemic stroke were generally low. All-cause and CV mortality increased with age; CV mortality was a minor proportion of all-cause mortality in all age groups. Rates of major bleeding and ischaemic stroke increased slightly with age, but to a lesser extent than all-cause and CV mortality. There was no increase in the rate of ICH with age. <75 yrs (N=9725) ≥75 yrs (N=9687) ≥85 yrs (N=2186) Age, median (IQR) 68.0 (63.0, 72.0) 80.0 (77.0, 84.0) 87.0 (86.0, 89.0) Gender, male % 65.8 51.5 41.4 BMI, median (IQR) 25.6 (22.9, 29.0) 24.5 (21.9, 27.5) 23.4 (20.8, 26.1) Weight, median (IQR) kg 71.0 (60.0, 84.5) 62.5 (52.9, 75.0) 55.4 (47.6, 67.0) CHA2DS2-VASc, mean (SD) 2.4 (1.28) 4.1 (1.27) 4.4 (1.34) CrCl [mL/min], median (IQR) 78.4 (63.6, 95.9) 52.0 (41.1, 64.5) 40.3 (32.2, 49.4) Edoxaban 60/30 mg, % 64.0/36.0 34.3/65.7 15.8/84.2 1-year outcome, n (%/year) Major bleeding (ISTH) 57 (0.71) 93 (1.19) 25 (1.53) Intracranial hemorrhage 22 (0.27) 22 (0.28) 3 (0.18) Major GI* bleeding 18 (0.22) 36 (0.46) 16 (0.98) CRNMB** 126 (1.57) 212 (2.73) 56 (3.45) Ischaemic stroke 53 (0.66) 83 (1.06) 23 (1.41) All-cause/CV mortality 95 (1.18)/25 (0.31) 224 (2.86)/55 (0.70) 89 (5.44)/23 (1.41) *Gastrointestinal. **Clinically relevant nonmajor bleeding. Conclusion Global data from this set of unselected patients support the use of edoxaban as a safe and effective treatment in elderly and very elderly patients with AF in regular clinical care. Acknowledgement/Funding Daiichi Sankyo

Compressive two-dimensional beamforming for localization of tip vortex cavitation

Noises induced by propeller tip vortex cavitation (TVC) have sparse sources near the propeller tip. Localization of these noise sources with sensor arrays involves the direction-of-arrival (DOA) estimation. Provided the noise sources are sparsely located, compressive sensing (CS) is used for two-dimensional (2-D) DOA estimation, which estimates both azimuth and elevation angles of the sources. With a planar array configuration, the 2-D DOA estimation problem is formulated in the CS framework and CS has shown less ambiguity and higher resolution capabilities compared to traditional DOA estimation schemes even under a single snapshot data. The superior performance of CS is demonstrated on experimental data from cavitation tunnel experiments. Two experimental data are considered. For the first experiment with one known source location, the true DOA has been estimated using CS. The second case involves initially intended TVC noises, and CS has estimated DOAs near the upper downstream area of the propeller, where TVC noises are generally generated.

P4343Reconciling acute coronary syndrome diagnoses between linked administrative data and hospital medical records in medical research

Abstract Background/Introduction Administrative data incorporating the International Classification of Diseases 10th Revision (ICD-10) is commonly used in cardiac research. Using patient records, diagnoses are systematically coded by trained coders who have limited/no clinical experience. Therefore, it is important to understand how systematically coded cardiac diagnoses compare with clinically assessed diagnoses to better analyse and interpret studies that have used linked administrative data to adjudicate patient's diagnosis. Purpose To assess the agreement between the acute coronary syndrome (ACS) diagnoses according to linked data compared to those extracted from hospital medical records by clinicians participating in a national registry and determine the factors associated with diagnoses disagreement. Methods The rate of ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI) and unstable angina (UA) obtained from the medical records, from admission to discharge, for the nationwide SNAPSHOT ACS audit in 2012 were compared to the corresponding ICD-10 Australian Modification (ICD-10-AM) codes using linked data from 6 jurisdictions covering all Australian states (6) and territories (2). The proportions of the overall agreement (OA), the positive agreement (PA) and the Cohen's weighted kappa and the 95% confidence interval (CI) were derived using both data sources for STEMI, NSTEMI and UA individually, where kappa≥0.8 confers strong agreement and 0.6≤kappa<0.8 moderate agreement. The factors associated with the diagnostic disagreement were explored using multilevel multivariable logistic regression model (backward selection method), accounting for the hospital clustering effect. Results Overall, 3130 patients had both medical records and linked data available for comparison. The degree of agreement was greatest for STEMI and lowest for UA (STEMI: OA=97%, PA=85%, kappa (95% CI)=0.84 (0.81, 0.87); NSTEMI: OA=91%, PA=81%, kappa (95% CI)=0.76 (0.73,0.79); UA: OA = 81%, PA=53%, kappa (95% CI)=0.41 (0.38, 0.45)). Further, the independent factors associated with the disagreement between the medical records and the linked data were the diagnosis of UA (UA vs. STEMI (odds ratio (95% CI)): 6.85 (4.12, 11.40)), not receiving revascularisation (2.27 (1.69, 3.03)), and the state where the ICD-10-AM was coded (p=0.007) (see Figure). Figure 1 Conclusion This study suggests that the agreement between the systematically coded diagnoses from linked administrative data and the diagnosis from the clinical assessment is greater in patients who received revascularisation and worse in those with UA. Also, the degree of agreement varies between states. As the linked data and the ICD codes are being used more often in research to support the evidence-based policies and practice, more attention is needed in testing and improving the accuracy of the ICD-10 codes as well as the ICD-11 codes that are soon to be introduced. Acknowledgement/Funding KH is funded by Heart Foundation Postdoctoral Fellowship. SNAPSHOT data linkage project was funded by the NSW Heart Foundation CVRN Project Grant

What can observational data reveal about metacommunity processes?

A key challenge for community ecology is to understand to what extent observational data can be used to infer the underlying community assembly processes. As different processes can lead to similar or even identical patterns, statistical analyses of non‐manipulative observational data never yield undisputable causal inference on the underlying processes. Still, most empirical studies in community ecology are based on observational data, and hence understanding under which circumstances such data can shed light on assembly processes is a central concern for community ecologists. We simulated a spatial agent‐based model that generates variation in metacommunity dynamics across multiple axes, including the four classic metacommunity paradigms as special cases. We further simulated a virtual ecologist who analysed snapshot data sampled from the simulations using eighteen output metrics derived from beta‐diversity and habitat variation indices, variation partitioning and joint species distribution modelling. Our results indicated two main axes of variation in the output metrics. The first axis of variation described whether the landscape has patchy or continuous variation, and thus was essentially independent of the properties of the species community. The second axis of variation related to the level of predictability of the metacommunity. The most predictable communities were niche‐based metacommunities inhabiting static landscapes with marked environmental heterogeneity, such as metacommunities following the species sorting paradigm or the mass effects paradigm. The most unpredictable communities were neutral‐based metacommunities inhabiting dynamics landscapes with little spatial heterogeneity, such as metacommunities following the neutral or patch sorting paradigms. The output metrics from joint species distribution modelling yielded generally the highest resolution to disentangle among the simulated scenarios. Yet, the different types of statistical approaches utilized in this study carried complementary information, and thus our results suggest that the most comprehensive evaluation of metacommunity structure can be obtained by combining them.

Matched steering\xa0vector searching based direction-of-arrival estimation using acoustic vector sensor array

The acoustic vector sensor (AVS) array is a powerful tool for underwater target’s direction-of-arrival (DOA) estimation without any bearing ambiguities. However, traditional DOA estimation algorithms generally suffer from low signal-to-noise ratio (SNR) as well as snapshot deficiency. By exploiting the properties of the minimum variance distortionless response (MVDR) beamformer, a new DOA estimation method basing on matched steering vector searching is proposed in this article. Firstly, attain the rough estimate of the desired DOA using the traditional algorithms. Secondly, set a small angular interval around the crudely estimated DOA. Thirdly, make the view direction vary in the view interval, and for each view direction, calculate the beam amplitude response of the MVDR beamformer, and find the minimum of the amplitude response. Finally, the pseudo-spatial spectrum is achieved, and the accurate estimate of the desired DOA can be obtained through peak searching. Computer simulations verify that the proposed method is efficient in DOA estimation, especially in low SNR and insufficient snapshot data scenarios.

Sparse feature map-based Markov models for nonlinear fluid flows

Data-driven Markov linear models of nonlinear fluid flows using maps of the state into a sparse feature space are explored in this article. The underlying principle of low-order models for fluid systems is identifying maps to a feature space where the system evolution (a) is simpler and efficient to model accurately and (b) the state can be recovered accurately from the features through inverse mapping. Such methods are useful when real-time models are needed for online decision making from sensor data. The Markov linear approximation is popular as it allows us to leverage the well established linear systems machinery. Examples include the Koopman operator approximation techniques and evolutionary kernel methods in machine learning. The success of these models in approximating nonlinear dynamical systems is tied to the effectiveness of the feature map in accomplishing both (a) and (b) above as long as the system provides a feasible prediction horizon using data. We assess this by performing an in-depth study of two different classes of sparse linear feature transformations of the state: (i) a pure data-driven POD-based projection that uses left singular vectors of the data snapshots – a staple of common Koopman approximation methods such as Dynamic Mode Decomposition (DMD) and its variants such as extended DMD; and (ii) a partially data-driven sparse Gaussian kernel (sGK) regression (a mean sparse Gaussian Process (sGP) predictor). The sGK/sGP regression equivalently represents a projection onto an infinite-dimensional basis characterized by a kernel in the inner product reproducing kernel Hilbert space (RKHS). We are particularly interested in the effectiveness of these linear feature maps for long-term prediction using limited data for three classes of fluid flows with escalating complexity (and decreasing prediction horizons) starting from a limit-cycle attractor in a cylinder wake followed by a transient wake evolution with a shift in the base flow and finally, a continuously evolving buoyant Boussinesq mixing flow with no well-defined base state. The results indicate that a purely data-driven POD map is good for full state reconstruction as long as the basis remains relevant to the predictions whereas the more generic sparse Gaussian Kernel (sGK) basis is less sensitive to the evolution of the dynamics but prone to reconstruction errors from lack of parsimony. Contrastingly, the sGK-maps outperform POD-based maps in learning the transient nonlinear evolution of the state for the same feature dimension in systems that contain a well-defined attractor(s). Consequently, both POD and sGK-maps require additional layer(s) to help mitigate these shortcomings. For example, POD-maps require nonlinear functional extensions for improved feature space predictions whereas sGK-maps require dimensionality reduction to balance the large feature dimension needed for accurate full state reconstruction. However, both classes of multilayer feature maps fail to predict the highly evolving buoyant mixing flow for very different reasons.

HEMATOPOIETIC STEM AND PROGENITOR CELLS EXHIBIT CELL TYPE SPECIFIC NF-ΰB SIGNALING DYNAMICS UPON INFLAMMATORY STIMULATION

Cells sense external stimuli with receptors and further process these stimuli via a variety of signaling pathways consisting of complex biochemical reaction cascades. For decades, cellular signaling was measured at population average level with snapshot data, without taking into account changing signaling activity over time (e.g. signaling dynamics) and cell-to-cell heterogeneity. Recent studies however show the importance of signaling dynamics for controlling future cell fates. As a master regulator of immune responses, the transcription factor nuclear factor kappa-light-chain-enhancer of activated B cells (Nf-κB) coordinates the expression of hundreds of target genes including its own inhibitors NFKBIA and TNFAIP3 in many cell types. While a link between Nf-κB dynamics and future cell state was shown in cell lines, its role in relevant primary cell types remains elusive. Here, we use continuous single-cell quantitative time-lapse imaging of 10 different hematopoietic cell types, including stem and early progenitor cells, from a GFPp65/H2BmCherry reporter mouse line, in custom microfluidic devices, to observe the dynamics of the Nf-κB signaling pathway activity, and investigate how those dynamics influence their fates. We demonstrate that upon an inflammatory challenge by tumor necrosis factor alpha (TNFα), different HSPCs exhibit different and cell type specific Nf-κB dynamics including sustained, transient and oscillatory behaviors. RNA expression kinetics analyses identify molecular regulators responsible for these different dynamics. Additionally, Nf-κB signaling dynamics can identify novel progenitor cell subpopulations with distinct lineage potential. These data demonstrate that signaling dynamics can identify subpopulations of traditionally described cell populations, and suggest a role of Nf-κB signaling dynamics for lineage choice and cell fate control.

Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods

Learning reservoir flow dynamics is of primary importance in creating robust predictive models for reservoir management including hydraulic fracturing processes. Physics-based models are to a certain extent exact, but they entail heavy computational infrastructure for simulating a wide variety of parameters and production scenarios. Reduced-order models offer computational advantages without compromising solution accuracy, especially if they can assimilate large volumes of production data without having to reconstruct the original model (data-driven models). Dynamic mode decomposition (DMD) entails the extraction of relevant spatial structure (modes) based on data (snapshots) that can be used to predict the behavior of reservoir fluid flow in porous media. In this paper, we will further enhance the application of the DMD, by introducing sparse DMD and local DMD. The former is particularly useful when there is a limited number of sparse measurements as in the case of reservoir simulation, and the latter can improve the accuracy of developed DMD models when the process dynamics show a moving boundary behavior like hydraulic fracturing. For demonstration purposes, we first show the methodology applied to (flow only) single- and two-phase reservoir models using the SPE10 benchmark. Both online and offline processes will be used for evaluation. We observe that we only require a few DMD modes, which are determined by the sparse DMD structure, to capture the behavior of the reservoir models. Then, we applied the local DMDc for creating a proxy for application in a hydraulic fracturing process. We also assessed the trade-offs between problem size and computational time for each reservoir model. The novelty of our method is the application of sparse DMD and local DMDc, which is a data-driven technique for fast and accurate simulations.

Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology.

Recent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.

Equation Discovery Using Fast Function Extraction: a Deterministic Symbolic Regression Approach

Advances in machine learning (ML) coupled with increased computational power have enabled identification of patterns in data extracted from complex systems. ML algorithms are actively being sought in recovering physical models or mathematical equations from data. This is a highly valuable technique where models cannot be built using physical reasoning alone. In this paper, we investigate the application of fast function extraction (FFX), a fast, scalable, deterministic symbolic regression algorithm to recover partial differential equations (PDEs). FFX identifies active bases among a huge set of candidate basis functions and their corresponding coefficients from recorded snapshot data. This approach uses a sparsity-promoting technique from compressive sensing and sparse optimization called pathwise regularized learning to perform feature selection and parameter estimation. Furthermore, it recovers several models of varying complexity (number of basis terms). FFX finally filters out many identified models using non-dominated sorting and forms a Pareto front consisting of optimal models with respect to minimizing complexity and test accuracy. Numerical experiments are carried out to recover several ubiquitous PDEs such as wave and heat equations among linear PDEs and Burgers, Korteweg–de Vries (KdV), and Kawahara equations among higher-order nonlinear PDEs. Additional simulations are conducted on the same PDEs under noisy conditions to test the robustness of the proposed approach.