General Computational Framework Research Articles

MMFA-DTA: Multimodal Feature Attention Fusion Network for Drug-Target Affinity Prediction for Drug Repurposing Against SARS-CoV-2.

The continuous emergence of novel infectious diseases poses a significant threat to global public health security, necessitating the development of small-molecule inhibitors that directly target pathogens. The RNA-dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2 have been validated as potential key antiviral drug targets for the treatment of COVID-19. However, the conventional new drug R&D cycle takes 10-15 years, failing to meet the urgent needs during epidemics. Here, we propose a general multimodal deep learning framework for drug repurposing, MMFA-DTA, to enable rapid virtual screening of known drugs and significantly improve discovery efficiency. By extracting graph topological and sequence features from both small molecules and proteins, we design attention mechanisms to achieve dynamic fusion across modalities. Results demonstrate the superior performance of MMFA-DTA in drug-target affinity prediction over several state-of-the-art baseline methods on Davis and KIBA data sets, validating the benefits of heterogeneous information integration for representation learning and interaction modeling. Further fine-tuning on COVID-19-relevant bioactivity data enhances model predictions for critical SARS-CoV-2 enzymes. Case studies screening the FDA-approved drug library successfully identify etacrynic acid as the potential lead compound against both RdRp and Mpro. Molecular dynamics simulations further confirm the stability and binding affinity of etacrynic acid to these targets. This study proves the great potential and advantages of deep learning and drug repurposing strategies in supporting antiviral drug discovery. The proposed general and rapid response computational framework holds significance for preparedness against future public health events.

Phase-Field Model of SEI Formation in Li-Metal Batteries

The formation of solid electrolyte interphase (SEI) is critical to the overall performance of lithium batteries. Here we have developed a rather comprehensive phase-field model for predicting the formation and growth of SEI, taking into account of multiple electrochemical/chemical reaction pathways leading to multiple SEI reaction products, diffusion of ionic and molecular species, and electron tunneling. All the SEI products are treated stoichiometric compounds without the typical approximation of parabolic composition dependence in almost all existing phase-field models involving stoichiometric compounds, allowing the direct connection between the phase-field model and atomistic calculations without further approximation. As a model system, we consider a 1-D prototypical half-cell with Li metal anode, liquid ethylene carbonate (EC) electrolyte with 1 molar (M) LiPF6, and SEI products Li2BDC and Li2CO3 under the open-circuit condition. The reduction potentials and activation barriers of the reactions are determined using atomistic simulations while the diffusivities of Li+ and EC in the bulk electrolyte are obtained by performing molecular dynamics simulations. The phase-field simulation is capable of predicting the evolution behavior of SEI products within a wide range of time scales from nanoseconds to a few seconds. It reveals that: (i) a porous Li2BDC layer forms at the anode/electrolyte interface and grows to a stable thickness of several tens of nanometers within ~20 μs; (ii) a porous Li2CO3 layer forms between anode and Li2BDC and grows to ~10 nm by partially consuming the existing Li2BDC layer within ~33 μs; and (iii) Li2CO3 forms a dense layer by filling the pores within ~20 ms. The stage (i) and (ii) are dominated by Li+ diffusion kinetics whereas stage (iii) is controlled by the reaction kinetics. The general computational framework can be extended and applied to the simultaneous evolution of Li-metal deposition and stripping and the SEI evolution.

A Computational Model of the Secondary Hemostasis Pathway in Reaction Systems

Reaction Systems (RSs) are a computational framework inspired by biochemical mechanisms. An RS defines a finite set of reactions over a finite set of entities (molecules, proteins, etc). Starting from an initial set of entities (the initial state), a computation is performed by applying all reactions to a state in order to produce the following state, giving rise to a sequence of sets of entities. RSs have shown to be a general computational framework whose application ranges from the modeling of biological phenomena to molecular chemistry and computer science. In this paper, we contribute to research on the application of RSs for modeling biological systems. We consider the problem of modeling hemostasis, for which several models have been defined, starting from the 1960s. Previous models are based on sets of ordinary differential equations, while we develop a discrete model in RSs for pathways of the secondary hemostasis. Then, we implement our model in BioReSolve, a computational framework for RSs that we have previously defined which provides tools for the specification and verification of properties. By using the tools in BioReSolve we derive important observations on the model behaviour for hemostasis, and in particular, we study the role of three important inhibitors, verifying that their presence or absence leads to phenomena such as thrombophilia, or thromboembolism, or excessive coagulation, etc. We can also study computationally the causality relations between the molecules involved in the reactions showing which entities play a fundamental role, thus contributing to the design of more effective and specialized drugs. Our work can hence help to show how to model complex biological systems in RSs and derive computationally and biologically relevant properties of the systems.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy.

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While the TME's impact on immunotherapy has been extensively studied, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), a generic computational framework leveraging cellular deconvolution of bulk transcriptomics to associate the gene expression of individual cell types in the TME with clinical response. Employing DECODEM to analyze the gene expression of breast cancer (BC) patients treated with neoadjuvant chemotherapy, we find that the gene expression of specific immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, CAFs) are highly predictive of chemotherapy response, going beyond that of the malignant cells. These findings are further tested and validated in a single-cell cohort of triple negative breast cancer. To investigate the possible role of immune cell-cell interactions (CCIs) in mediating chemotherapy response, we extended DECODEM to DECODEMi to identify such CCIs, validated in single-cell data. Our findings highlight the importance of active pre-treatment immune infiltration for chemotherapy success. The tools developed here are made publicly available and are applicable for studying the role of the TME in mediating response from readily available bulk tumor expression in a wide range of cancer treatments and indications.

A general computational framework for Lagrangian hydrodynamic scheme. I: Unification of staggered‐grid and cell‐centered methods

AbstractThis paper focuses on a general computational framework to unify both Lagrangian staggered‐grid hydrodynamic (SGH) and cell‐centered hydrodynamic (CCH) methods. One challenge is that artificial viscosity has contained empirical parameters in the SGH method for seven decades. To address this challenge, a new relationship between pressure and velocity is constructed using specific volume as a medium. Another challenge is that entropy is increasing in isentropic flows for the CCH method. To overcome this second challenge, the forces acting on a target cell are split into linear and quadratic terms in the CCH method. The numerical results of the two methods are almost identical. The scheme is more general than both existing SGH and CCH methods.

A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters

We consider state and parameter estimation for compartmental models having both time-varying and time-invariant parameters. In this manuscript, we first detail a general Bayesian computational framework as a continuation of our previous work. Subsequently, this framework is specifically tailored to the susceptible-infectious-removed (SIR) model which describes a basic mechanism for the spread of infectious diseases through a system of coupled nonlinear differential equations. The SIR model consists of three states, namely, the susceptible, infectious, and removed compartments. The coupling among these states is controlled by two parameters, the infection rate and the recovery rate. The simplicity of the SIR model and similar compartmental models make them applicable to many classes of infectious diseases. However, the combined assumption of a deterministic model and time-invariance among the model parameters are two significant impediments which critically limit their use for long-term predictions. The tendency of certain model parameters to vary in time due to seasonal trends, non-pharmaceutical interventions, and other random effects necessitates a model that structurally permits the incorporation of such time-varying effects. Complementary to this, is the need for a robust mechanism for the estimation of the parameters of the resulting model from data. To this end, we consider an augmented state vector, which appends the time-varying parameters to the original system states whereby the time evolution of the time-varying parameters are driven by an artificial noise process in a standard manner. Distinguishing between time-varying and time-invariant parameters in this fashion limits the introduction of artificial dynamics into the system, and provides a robust, fully Bayesian approach for estimating the time-invariant system parameters as well as the elements of the process noise covariance matrix. This computational framework is implemented by leveraging the robustness of the Markov chain Monte Carlo algorithm permits the estimation of time-invariant parameters while nested nonlinear filters concurrently perform the joint estimation of the system states and time-varying parameters. We demonstrate performance of the framework by first considering a series of examples using synthetic data, followed by an exposition on public health data collected in the province of Ontario.

Abstract LB242: Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome

Abstract Introduction: The tumor microenvironment (TME) is a complex and dynamic ecosystem that plays critical roles in tumor development and clinical outcome. While the multifarious cellular interactions within the TME have been extensively studied with a significant emphasis on immunotherapy, understanding its role in chemotherapy outcome remains less explored. To this end, we present a novel generic computational framework named DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning). DECODEM facilitates the exploration of the association of cell-type-specific gene expression profiles with the effectiveness of neoadjuvant chemotherapy (NAC) in treating breast cancer (BC). This approach holds promise for elucidating the nuanced ways in which the TME can modulate therapeutic efficacy, potentially paving the way for more personalized and effective cancer treatments. Methods: DECODEM employs cellular deconvolution of bulk transcriptomics to extract cell-type-specific gene expression profiles within the TME, and subsequently harnesses machine learning to construct cell-type-specific predictors of clinical outcomes. Utilizing DECODEM, we analyzed three publicly available cohorts of HER2-negative BC patients who received NAC. We assessed the predictive capabilities of the individual cell types as well as their collective influence, benchmarking against a state-of-the-art predictor that employs both clinical and transcriptomic features. We corroborated our findings in a single-cell (SC) transcriptomics cohort of patients with triple negative breast cancer (TNBC) undergoing chemotherapy. Further, we expanded DECODEM to DECODEMi, which accounts for cell-cell interactions (CCIs) among the predominant cell types, offering potential insights into the mechanistic processes governing clinical responses. Results: Our analysis indicated that the heterogeneous composition of the TME is crucial for chemotherapy response prediction in HER2-negative BC, beyond the influence of malignant cells alone. The key insights from our study are as follows:1. Gene expression of immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, cancer-associated fibroblasts) displayed comparable-to-superior predictive powers over both bulk expression and the state-of-the-art predictor.2. The predictive accuracy is further augmented by multi-cell-type ensembles, with a triad ensemble comprising endothelial, myeloid, and plasmablast cells achieving the highest predictive accuracy across multiple cohorts.3. DECODEMi can effectively capture the regulatory impact of CCIs (validated in a SC dataset sourced from the SC-TNBC cohort) and has identified novel CCIs likely to influence chemotherapy response in HER2-negative BC. Conclusion: We provide an unprecedented computational methodology to quantitatively assess the cell-type-specific influences of the TME on clinical outcome. Our analysis offers insights into the complex interactions among TME cells and their correlation with chemotherapy efficacy in HER2-negative BC, thereby enriching the knowledge base of BC treatment effectiveness. Citation Format: Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, Di Wu, Yael Schmidt, Eldad Shulman, Sanju Sinha, Stephen-John Sammut, Carlos Caldas, Kun Wang, Eytan Ruppin. Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB242.

A unified Bayesian inversion approach for a class of tumor growth models with different pressure laws

Abstract. In this paper, we use the Bayesian inversion approach to study the data assimilation problem for a family of tumor growth models described by porous-medium type equations. The models contain uncertain parameters and are indexed by a physical parameter m, which characterizes the constitutive relation between density and pressure. Based on these models, we employ the Bayesian inversion framework to infer parametric and nonparametric unknowns that affect tumor growth from noisy observations of tumor cell density. We establish the well-posedness and the stability theories for the Bayesian inversion problem and further prove the convergence of the posterior distribution in the so-called incompressible limit, m →∞. Since the posterior distribution across the index regime m ∈ [2,∞) can thus be treated in a unified manner, such theoretical results also guide the design of the numerical inference for the unknown. We propose a generic computational framework for such inverse problems, which consists of a typical sampling algorithm and an asymptotic preserving solver for the forward problem. With extensive numerical tests, we demonstrate that the proposed method achieves satisfactory accuracy in the Bayesian inference of the tumor growth models, which is uniform with respect to the constitutive relation.

A general computational framework and a hybrid algorithm for large-scale data envelopment analysis

This paper develops a new algorithm to accelerate DEA computation for large-scale datasets. We first provide a general DEA computation framework that employs a simple small-size linear program (LP). This LP can obtain all the critical outcomes simultaneously for accelerating DEA computation in the literature. Based on the general computational framework, we propose a new algorithm (called hybrid algorithm) that uses a hybrid strategy of density-increasing mechanism and reference set selection. The hybrid algorithm continuously solves the simple small-size LP to either identify an extreme efficient DMU or directly obtain the efficiency of the DMU under evaluation. To ensure the LPs solved are always in a small size, the hybrid algorithm selects the data of only a small subsample of the identified extreme efficient DMUs into the LPs’ coefficient matrix each time when a DMU is evaluated. A new subsample selection technique is also suggested. The numerical experiment shows that the new technique can select subsample of extreme efficient DMUs more effectively compared with the previous subsample selection technique. Consequently, the hybrid algorithm solves only one or a minuscule number of small-size LPs to obtain each DMU’s efficiency. Therefore, the hybrid algorithm ensures that the size and number of LPs solved for each DMU are small. The computational experiment on large datasets shows that the hybrid algorithm performs more than an order of magnitude faster than the existing representative algorithms.

Learning nonequilibrium statistical mechanics and dynamical phase transitions

Nonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint distribution of a large number of configurations, and also poses new challenges as the distribution evolves over time. Characterizing dynamical phase transitions as an emergent behavior further requires tracking nonequilibrium systems under a control parameter. While a number of methods have been proposed, such as tensor networks for one-dimensional lattices, we lack a method for arbitrary time beyond the steady state and for higher dimensions. Here, we develop a general computational framework to study the time evolution of nonequilibrium systems in statistical mechanics by leveraging variational autoregressive networks, which offer an efficient computation on the dynamical partition function, a central quantity for discovering the phase transition. We apply the approach to prototype models of nonequilibrium statistical mechanics, including the kinetically constrained models of structural glasses up to three dimensions. The approach uncovers the active-inactive phase transition of spin flips, the dynamical phase diagram, as well as new scaling relations. The result highlights the potential of machine learning dynamical phase transitions in nonequilibrium systems.

Motion-resolved, reference-free holographic imaging via spatiotemporally regularized inversion

Holography is a powerful technique that records the amplitude and phase of an optical field simultaneously, enabling a variety of applications such as label-free biomedical analysis and coherent diffraction imaging. Holographic recording without a reference wave has been long pursued because it obviates the high experimental requirements of conventional interferometric methods. However, due to the ill-posed nature of the underlying phase retrieval problem, reference-free holographic imaging is faced with an inherent tradeoff between imaging fidelity and temporal resolution. Here, we propose a general computational framework, termed spatiotemporally regularized inversion (STRIVER), to achieve motion-resolved, reference-free holographic imaging with high fidelity. Specifically, STRIVER leverages signal priors in the spatiotemporal domain to jointly eliminate phase ambiguities and motion artifacts, and, when combined with diversity measurement schemes, produces a physically reliable, time-resolved holographic video from a series of intensity-only measurements. We experimentally demonstrate STRIVER in near-field ptychography, where dynamic holographic imaging of freely swimming paramecia is performed at a framerate-limited speed of 112 fps. The proposed method can be potentially extended to other measurement schemes, spectral regimes, and computational imaging modalities, pushing the temporal resolution toward higher limits.

Optimization of Data Processing Schemes for Locating Low-Frequency Oscillation Sources

The organization of data processing for measurement data arrays is one of the key factors for the effective application of synchronized phasor measurement technology (PMU) within a Wide Area Monitoring System (WAMS). This encompasses not only ensuring high computational performance and scalability but also the openness of solutions to the continually evolving data analysis methodologies. In both the scientific community and current operational-dispatching practice, there is a growing interest in the issue of power system oscillatory stability. This interest is driven by the increasing complexity of modern power systems and enhanced observability of processes due to the deployment of WAMS. One of the emerging tasks is the real-time detection of low-frequency oscillation (LFO) sources using PMU data. The dissipating energy flow (DEF) method has shown high effectiveness in addressing this task by enabling the assessment of the magnitude, speed, and direction of oscillation energy propagation. It has been previously demonstrated that, while maintaining the general computational framework of the DEF method, the time required to reach a stable decision about the LFO source depends on the choice of the combination of subtask algorithms and their parameters. This study discusses a prototype computational platform for optimizing generalized data processing schemes based on PMU data. Using the DEF method and a dataset of actual LFOs in the Russian power system as an example, the study explores mechanisms for expanding the solution space and the measurement data base. The paper presents experimental results that provide an estimation of the expected time from the onset of oscillations to the identification of their source.

Longitudinal vibration control strategy for long-span suspension bridges under operational and extreme excitations using eddy current dampers

A full-floating or a semi-floating system is conventionally employed in long-span suspension bridges (LSSBs) to reduce the excessive internal forces of main girders when subjected to various ambient loads/effects. However, such a system may induce appreciable back-and-forth longitudinal displacements of the main girder and result in the deterioration of auxiliary structural components (e.g., expansion joints). In light of this, this paper presents a vibration control strategy for mitigating the longitudinal girder vibrations of LSSBs under primary operational loads (i.e., wind and traffic) and extreme seismic effects using eddy current dampers (ECDs). A general computational framework of the seismic-wind-traffic-bridge system is first developed, whereby the longitudinal girder vibration characteristics of a prototype LSSB with and without ECD installations under various loading scenarios are investigated. Particularly, a novel multi-attribute decision-making (MADM) method is introduced to address a common challenge in developing optimal vibration control designs, i.e., the optimal vibration control design for one objective might adversely affect the others, which is especially critical from a life-cycle bridge management perspective. By using the MADM, typical bridge performance indices under various loading scenarios can be rationally involved in the associated optimization. The results show that different excitations can induce longitudinal girder vibrations with different dominant frequencies, which can be effectively mitigated by the ECD. However, for different bridge performance indices and different loading scenarios, the optimal ECD parameters may vary significantly. The proposed MADM method can help develop a balanced vibration control design that can ensure excellent bridge safety and serviceability simultaneously.

Spatially-explicit absolute life cycle assessment by multi-regional hybrid modeling: Computational framework

Human activities, environmental impacts, and the provision of ecosystem services vary by region. The existing regional environmental sustainability assessment approaches addressed such regional heterogeneity of inventory data at a single scale (i.e., either process or economy scale). However, depending on regional data availability and the assessment scope, a multiscale modeling approach that integrates existing data and models at various scales needs to be employed. This work introduces a general computational framework for spatially-explicit environmental sustainability assessment based on a multi-regional hybrid modeling approach. The developed framework accounts for regional activities at multiple scales by integrating the existing regional approaches at each scale. Also, the framework integrates the supply of ecosystem services, which vary by region as well, at multiple scales to perform spatially-explicit absolute environmental sustainability assessment. The proposed framework is demonstrated using an illustrative example of corn production in two regions. The results are discussed in comparison to the conventional approaches.

Predicting Bone Adaptation in Astronauts during and after Spaceflight.

A method was previously developed to identify participant-specific parameters in a model of trabecular bone adaptation from longitudinal computed tomography (CT) imaging. In this study, we use these numerical methods to estimate changes in astronaut bone health during the distinct phases of spaceflight and recovery on Earth. Astronauts (N = 16) received high-resolution peripheral quantitative CT (HR-pQCT) scans of their distal tibia prior to launch (L), upon their return from an approximately six-month stay on the international space station (R+0), and after six (R+6) and 12 (R+12) months of recovery. To model trabecular bone adaptation, we determined participant-specific parameters at each time interval and estimated their bone structure at R+0, R+6, and R+12. To assess the fit of our model to this population, we compared static and dynamic bone morphometry as well as the Dice coefficient and symmetric distance at each measurement. In general, modeled and observed static morphometry were highly correlated (R2> 0.94) and statistically different (p < 0.0001) but with errors close to HR-pQCT precision limits. Dynamic morphometry, which captures rates of bone adaptation, was poorly estimated by our model (p < 0.0001). The Dice coefficient and symmetric distance indicated a reasonable local fit between observed and predicted bone volumes. This work applies a general and versatile computational framework to test bone adaptation models. Future work can explore and test increasingly sophisticated models (e.g., those including load or physiological factors) on a participant-specific basis.

Complexity of The Systems of Linear Restrictions over a Finite Field

This paper continues the results obtained in [1]. In the previous paper, we formulated the problem of the unknown vector recovering from linear dependencies with this vector, which act as constraints on it. The next step, after finding out some algebraic and combinatorial properties, is to give basic estimates of complexity for the main problem as well as for related problems. Such related problems can be obtained by fixing some parameters of the main problem or applying constraints on the number of restrictions in the system. Such an analysis makes possible to arrange the problem of recovering an unknown vector based on partial information into the general computational complexity framework in order to approach existing theoretical results to its solution. The obtained theoretical results can be used in algebraic cryptanalysis of stream ciphers and cryptosystems based on linear codes.

A deep learning model for efficient end-to-end stratification of thrombotic risk in left atrial appendage

Clot formation in the left atrial appendage (LAA) poses a high risk of ischemic strokes and systemic embolism to patients with atrial fibrillation (AF), the most common type of sustained heart arrhythmia that affects more than 35 million people worldwide. Hemodynamic metrics evaluated using computational fluid dynamics (CFD) have been employed to assess the risk of thrombosis in LAA, but its utilization in clinical settings is limited due to their cumbersome operations and high computational cost. To this end, we propose UDGCNN (U: U-net, DGCNN: Dynamic Graph Convolutional Neural Network) that can utilize data from the point cloud of patient-specific LAA geometries as inputs and predict multiple hemodynamic indexes for systematic assessment of the thrombotic risk. The novelty of the proposed model lies in introducing edge convolution layers to the PointNet structure to improve the model’s capacity to learn local features, which is essential for training and predicting complex patient-specific geometries. Moreover, the network structure of UDGCNN is optimized by integrating the Encoder-Decoder structure and skip-connection to extract hierarchical features from the point cloud. The accuracy and efficiency of the UDGCNN is examined by training and testing on 371 LAA geometries from patients with AF, the largest patient-specific dataset in the literature. Using mean absolute error and model inference time as metrics, we demonstrate that UDGCNN can provide an assessment of multiple hemodynamic metrics in 3D patient-specific geometries with prediction error ∼30% lower than those of the state-of-art PointNet model, whereas the inference time is 500-fold shorter compared to computational time CFD simulation. It is noted that UDGCNN is a general computational framework that can be extended to study various cardiovascular diseases. In summary, this study presents a new computational tool that enables end-to-end stratification of thrombotic risk in LAA based on bioimaging, thereby advancing the current screening approach in clinical practice.

PyStructureFactor: A Python code for the molecular structure factor in tunneling ionization rates

Tunneling ionization is at the core of strong-field and attosecond science. In this paper, we present PyStructureFactor — a general Python code towards the calculation of the structure factor in the tunneling ionization rate of common molecules under intense laser fields. The numerical implementation is based on the well-developed weak-field asymptotic theory in the integral representation. The information of the electronic structure of the molecules is obtained via the PySCF quantum chemistry package. PyStructureFactor is a general computational framework that can be utilized to compute the molecular structure factor of various types of molecules, including polar and nonpolar diatomic molecules, degenerate molecules, and open-shell molecules. Examples are given that are benchmarked against known results with good agreements. The present PyStructureFactor is implemented in an efficient manner and is easily applicable towards larger molecules. Program summaryProgram title: PyStructureFactorCPC Library link to program files:https://doi.org/10.17632/td88mhw2sg.1Developer's repository link:https://github.com/TheStarAlight/PyStructureFactorLicensing provisions: Apache-2.0Programming language: Python 3Nature of problem: The structure factor of a molecule in intense laser fields determines its orientation-dependent tunneling ionization rate, which is crucial for the studies of ultrafast molecular dynamics and its control. However, the strong-field community lacks an open-source code to calculate the molecular structure factor, and can only resort to known results of a few molecules.Solution method: We developed the PyStructureFactor program with the structure factor of arbitrary molecules calculated using the weak-field asymptotic theory in the integral representation. The underlying electronic structure necessary for the calculation is obtained using the PySCF quantum chemistry package.Additional comments including restrictions and unusual features: The accuracy of the molecular structure factor calculated by PyStructureFactor is restricted by the level of precision of the electronic structure information extracted from the PySCF package.The running time depends on the size of the molecule, the basis set of the calculation, the level of precision of the electronic structure calculations, and other parameters passed to the program. The example in Fig. 2 took 1.2 seconds to finish on an AMD Ryzen 9 7950X CPU on the WSL Ubuntu 22.04 LTS.

CAJAL enables analysis and integration of single-cell morphological data using metric geometry

High-resolution imaging has revolutionized the study of single cells in their spatial context. However, summarizing the great diversity of complex cell shapes found in tissues and inferring associations with other single-cell data remains a challenge. Here, we present CAJAL, a general computational framework for the analysis and integration of single-cell morphological data. By building upon metric geometry, CAJAL infers cell morphology latent spaces where distances between points indicate the amount of physical deformation required to change the morphology of one cell into that of another. We show that cell morphology spaces facilitate the integration of single-cell morphological data across technologies and the inference of relations with other data, such as single-cell transcriptomic data. We demonstrate the utility of CAJAL with several morphological datasets of neurons and glia and identify genes associated with neuronal plasticity in C. elegans. Our approach provides an effective strategy for integrating cell morphology data into single-cell omics analyses.