Comprehensive Computational Framework Research Articles

Multifunctionality can be promoted by increasing agriculture-dominated heterogeneous landscapes in an agro-forestry interlacing zone in northeast China

The assessment of landscape multifunctionality is critical for making informed decisions about landscape resource allocation, optimization, and planning. However, accurately estimating multifunctionality has been a challenging issue, and whether heterogeneous landscapes can promote multifunctionality is still unclear. In our study, we developed a comprehensive computational framework for assessing landscape multifunctionality in agro-forestry interlacing zones. Our study considered both the quality and quantity of multifunctionality by using an improved incremental thresholds method. Compared to homogeneous landscapes, heterogeneous landscapes have greater potential to maintain high levels of multifunctionality in terms of both quality and quantity. Moreover, we have conducted an analysis of the peak multifunctionality performance to establish the optimal threshold, with the aim of striking a balance between the quality and quantity of multifunctionality. Specifically, the optimal thresholds for agro-forest landscapes were 60% and 55%, respectively. Our study showed the distinct combinatorial arrangements of functions observed across various thresholds, thereby discerning the potentially dominant function in the landscape. The potential dominant function of agro-forestry landscapes was carbon sequestration, which could significantly contribute to achieving carbon neutrality goals. These findings are instructive for land managers and policy formulators working to achieve “win–win” sustainable development.

Abstract 6614: Spatial transcriptomic driven mechanistic model to investigate and predict pathogenesis of oligodendroglioma

Abstract Rationale: Oligodendrogliomas represent the second most common glioma in adults after glioblastoma. Recent studies conjecture that oligodendrocyte progenitor cells (OPCs) are precursors for glioma. The etiology of OPC derived oligodendrogliomas, the corresponding genetic program and genetic aberrations, and the underlying mechanisms leading to the pathogenesis of oligodendroglioma is yet to be explored. We hypothesize that genetic mechanisms mediating oligodendrogenesis (the process of OPCs maturing to oligodendrocytes (OL)) are temporally and spatially coordinated. In this study, we spearhead a comprehensive computational framework that utilizes a) Machine learning (ML) to interpret spatial transcriptomic (ST) images of a normal whole mouse brain dataset obtained from Vizgen’s MERFISH Mouse Brain Receptor Map and b) mathematical modeling to perturb the cellular mechanisms in the normal brain to understand the abnormalities leading to the pathogenesis of oligodendroglioma. Methods: Using ML for clustering and trajectory inference, we create a comprehensive spatial catalog of cell types. The cell types are validated using canonical genes from the scRNAseq mouse brain data from CZI Biohub’s Tabula Muris Consortium. From the cell types, we extract potential OL pseudotime differentiation (from immature to mature OLs) trajectories. These trajectories are used to develop a phenotype structured partial differentiation equation (PDE) model of oligodendrogenesis. This can be used to simulate both spatial and phenotypic interactions, differentiation status, and the pathogenesis of oligodendroglioma by mathematically perturbing the process of cellular proliferation and differentiation using genes known to cause oligodendroglioma: PDGFRA, OLIG1, MAS1. Results: We mathematically perturb cell differentiation lineages in normal oligodendrogenesis to simulate, and predict the effects of perturbation and how this leads to abnormal (cancer aiding) cell states. Our PDE simulations reinforce that OPC proliferation and OL differentiation occur more in the white matter in the normal mouse brain, and further postulate that OPC and OL differentiation occur more in gray matter in malignant mouse brains. Conclusions: Our study is one of the earliest attempts to integrate qualitative and scalable ML techniques with quantitative and interpretable mathematical modeling at the cellular, omics, and organ level using ST imaging and sequencing data. We develop and calibrate PDE mathematical models for robustly simulating, predicting, and studying the effects of perturbation from sparse, noisy single cell spatial measurements to forecast abnormal behavior in OLs based on spatial and (pseudo)temporal changes. In future work, we will apply our framework to jointly perturb differentiation trajectories of multiple cell types to understand their cumulative effects in tumor progression. Citation Format: Sandhya Prabhakaran, Heyrim Cho, Maximilian Strobl, Russell Rockne, Alexander R. Anderson. Spatial transcriptomic driven mechanistic model to investigate and predict pathogenesis of oligodendroglioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6614.

Shape design of an artificial pump-lung using high-resolution hemodynamic simulation with high-performance computing

Accurate and fast prediction of the hemodynamics of the artificial pump-lung is critical in the design process. In this study, a comprehensive computational framework, including a sliding mesh method, a coupled free flow and porous media flow model, a hemolysis prediction method, a k−ω shear stress transport turbulence model, and solution algorithms, is introduced to accurately predict the velocity field, pressure heads, and hemolysis. The framework is used to do the shape design of an artificial pump-lung on a supercomputer. High-resolution hemodynamics simulation results are obtained and analyzed, and the parallel performance of the algorithm is studied. The numerical results indicate that the proposed framework is capable of accurately predicting the velocity field, pressure heads, and hemolysis, and the performance of the designed artificial pump-lung meets the biocompatibility requirements. Additionally, the parallel performance results demonstrate the potential of the framework to efficiently perform the design of artificial pump-lungs using a large number of processors.

A quasi-implicit and coupled multiscale scheme for simulating combustion of pellets of core-shell structured intermetallic particles

A comprehensive theoretical and computational framework is developed to simulate combustion and flame propagation in a packed pellet of core-shell structured intermetallic energetic particles. A coupled multiscale approach is adopted, in which the atomic diffusion process in micron-scale particles is strongly linked to the macro-scale energy transport in the pellet. Species transport equations are solved for the core-shell structured particle, with the diffusivity treated as a temperature-dependent parameter. The energy equation is solved for the pellet, with the source term governed by the evolution of species concentrations in the core-shell structured particle. A quasi-implicit numerical scheme is developed to ensure stability and a generalized Crank-Nicholson scheme is adopted to tune the degree of implicitness and control the order of temporal accuracy. Further, the temperature dependence of properties and phase change of materials are considered. The theoretical and computational framework is applied to simulate combustion of pellets with Ni/Al core-shell particles of diameters in the range of 10–100 µm. An attempt is made to simulate experiments as closely as possible and the predictions are compared with the available experimental data. Suitable diffusivity model parameters that best describes the experimental data are identified. Parametric studies are conducted to study the effects of particle size, interstitial gas, and pressure on the combustion velocity. Results provide novel insights on the combustion process and directions for future studies.

A computational framework for uncertain locally resonant metamaterial structures

Locally resonant metamaterial structures, i.e., structures artificially engineered with periodic arrays of small resonators, are attracting a growing interest among scientists. The main reason lies in their remarkable attenuation properties of elastic waves over finite frequency ranges, named band gaps, resulting from periodicity and local resonance. Yet, very little attention has been paid to investigate whether and to which extent the behavior of these structures may be affected by uncertainties. This is indeed the purpose of our study, which proposes a comprehensive computational framework to calculate the frequency response of uncertain locally resonant structures, assuming an interval model for all the relevant parameters of the resonators: mass, stiffness and damping.The computational framework is conceived for finite-element models of the locally resonant structure and standard mass-spring-dashpot models of the resonators. The key steps are an exact dynamic condensation of the degrees of freedom within the resonators, the derivation of an exact and elegant expression for the transfer matrix of the locally resonant structure via the Sherman-Morrison-Woodbury formula, the calculation of the interval frequency response via either a sensitivity-based method or a global optimization method, the choice being driven by a preliminary monotonicity test. Considering two typical locally resonant structures, a beam and a plate, the computational framework proves easy to implement, accurate and robust as compared with the standard Monte Carlo method.

Modeling tumour heterogeneity of PD-L1 expression in tumour progression and adaptive therapy.

Although PD-1/PD-L1 inhibitors show potent and durable anti-tumour effects in some refractory tumours, the response rate in overall patients is unsatisfactory, which in part due to the inherent heterogeneity of PD-L1. In order to establish an approach for predicting and estimating the dynamic alternation of PD-L1 heterogeneity during cancer progression and treatment, this study establishes a comprehensive modelling and computational framework based on a mathematical model of cancer cell evolution in the tumour-immune microenvironment, and in combination with epigenetic data and overall survival data of clinical patients from The Cancer Genome Atlas. Through PD-L1 heterogeneous virtual patients obtained by the computational framework, we explore the adaptive therapy of administering anti-PD-L1 according to the dynamic of PD-L1 state among cancer cells. Our results show that in contrast to the continuous maximum tolerated dose treatment, adaptive therapy is more effective for PD-L1 positive patients, in that it prolongs the survival of patients by administration of drugs at lower dosage.

A Comprehensive conceptual and computational dynamics framework for autonomous regeneration of form and function in biological organisms.

In biology, regeneration is a mysterious phenomenon that has inspired self-repairing systems, robots, and biobots. It is a collective computational process whereby cells communicate to achieve an anatomical set point and restore original function in regenerated tissue or the whole organism. Despite decades of research, the mechanisms involved in this process are still poorly understood. Likewise, the current algorithms are insufficient to overcome this knowledge barrier and enable advances in regenerative medicine, synthetic biology, and living machines/biobots. We propose a comprehensive conceptual framework for the engine of regeneration with hypotheses for the mechanisms and algorithms of stem cell-mediated regeneration that enables a system like the planarian flatworm to fully restore anatomical (form) and bioelectric (function) homeostasis from any small- or large-scale damage. The framework extends the available regeneration knowledge with novel hypotheses to propose collective intelligent self-repair machines with multi-level feedback neural control systems driven by somatic and stem cells. We computationally implemented the framework to demonstrate the robust recovery of both form and function (anatomical and bioelectric homeostasis) in an in silico worm that, in a simple way, resembles the planarian. In the absence of complete regeneration knowledge, the framework contributes to understanding and generating hypotheses for stem cell mediated form and function regeneration, which may help advance regenerative medicine and synthetic biology. Further, as our framework is a bio-inspired and bio-computing self-repair machine, it may be useful for building self-repair robots/biobots and artificial self-repair systems.

KAS-Analyzer: a novel computational framework for exploring KAS-seq data.

Kethoxal-assisted ssDNA sequencing (KAS-seq) is rapidly gaining popularity as a robust and effective approach to study the nascent dynamics of transcriptionally engaged RNA polymerases through profiling of genome-wide single-stranded DNA (ssDNA). Its latest variant, spKAS-seq, a strand-specific version of KAS-seq, has been developed to map genome-wide R-loop structures by detecting imbalances of ssDNA on two strands. However, user-friendly, open-source computational tools tailored for KAS-seq data are still lacking. Here, we introduce KAS-Analyzer, the first comprehensive computational framework aimed at streamlining and enhancing the analysis and interpretation of KAS-seq and spKAS-seq data. In addition to standard analyses, KAS-Analyzer offers many novel tools specifically designed for KAS-seq data, including, but not limited to: calculation of transcription-related metrics, identification of single-stranded transcribing (SST) enhancers, high-resolution mapping of R-loops, and differential RNA polymerase activity analysis. We provided a detailed overview of KAS-seq data and its diverse applications through the implementation of KAS-Analyzer. Using the example time-course KAS-seq datasets, we further showcase the robust capabilities of KAS-Analyzer for investigating dynamic transcriptional regulatory programs in response to UVB radiation. KAS-Analyzer is available at https://github.com/Ruitulyu/KAS-Analyzer.

MUG: A mutation overview of GPCR subfamily A17 receptors

G protein-coupled receptors (GPCRs) mediate several signaling pathways through a general mechanism that involves their activation, upholding a chain of events that lead to the release of molecules responsible for cytoplasmic action and further regulation. These physiological functions can be severely altered by mutations in GPCR genes. GPCRs subfamily A17 (dopamine, serotonin, adrenergic and trace amine receptors) are directly related with neurodegenerative diseases, and as such it is crucial to explore known mutations on these systems and their impact in structure and function. A comprehensive and detailed computational framework - MUG (Mutations Understanding GPCRs) - was constructed, illustrating key reported mutations and their effect on receptors of the subfamily A17 of GPCRs. We explored the type of mutations occurring overall and in the different families of subfamily A17, as well their localization within the receptor and potential effects on receptor functionality. The mutated residues were further analyzed considering their pathogenicity. The results reveal a high diversity of mutations in the GPCR subfamily A17 structures, drawing attention to the considerable number of mutations in conserved residues and domains. Mutated residues were typically hydrophobic residues enriched at the ligand binding pocket and known activating microdomains, which may lead to disruption of receptor function. MUG as an interactive web application is available for the management and visualization of this dataset. We expect that this interactive database helps the exploration of GPCR mutations, their influence, and their familywise and receptor-specific effects, constituting the first step in elucidating their structures and molecules at the atomic level.

A Uniquely Stable Trimeric Model of SARS-CoV-2 Spike Transmembrane Domain.

Understanding fusion mechanisms employed by SARS-CoV-2 spike protein entails realistic transmembrane domain (TMD) models, while no reliable approaches towards predicting the 3D structure of transmembrane (TM) trimers exist. Here, we propose a comprehensive computational framework to model the spike TMD only based on its primary structure. We performed amino acid sequence pattern matching and compared the molecular hydrophobicity potential (MHP) distribution on the helix surface against TM homotrimers with known 3D structures and selected an appropriate template for homology modeling. We then iteratively built a model of spike TMD, adjusting “dynamic MHP portraits” and residue variability motifs. The stability of this model, with and without palmitoyl modifications downstream of the TMD, and several alternative configurations (including a recent NMR structure), was tested in all-atom molecular dynamics simulations in a POPC bilayer mimicking the viral envelope. Our model demonstrated unique stability under the conditions applied and conforms to known basic principles of TM helix packing. The original computational framework looks promising and could potentially be employed in the construction of 3D models of TM trimers for a wide range of membrane proteins.

TidyMass an object-oriented reproducible analysis framework for LC–MS data

Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.

Mathematical modelling of endovascular drug delivery: Balloons versus stents

The most common treatment for obstructive coronary artery disease (CAD) is the implantation of a permanent drug-eluting stent (DES). Not only has this permanency been associated with delayed healing of the artery, but it also poses challenges when treating subsequent re-narrowing due to in-stent restenosis (ISR). Drug-coated balloons (DCBs) provide a potential solution to each of these issues. While their use has been primarily limited to treating ISR, in recent years, DCBs have emerged as an attractive potential alternative to DESs for the treatment of certain de novo lesions. However, there remain a number of concerns related to the safety and efficacy of these devices. Firstly, unlike DESs, DCBs necessitate a very short drug delivery window, favouring a higher drug loading. Secondly, while the majority of coronary DCBs in Europe are coated with paclitaxel, the potential mortality signal raised with paclitaxel DCBs in peripheral interventions has shifted efforts towards the development of limus-eluting balloons. The purpose of this paper is to provide a computational model that allows drug delivery from DCBs and DESs to be investigated and compared. We present a comprehensive computational framework that employs a 2D-axisymmetric geometry, incorporates two nonlinear phases of drug binding (specific and non-specific) and includes the influence of diffusion and advection, within a multilayer arterial wall. We utilise this framework to (i) simulate drug delivery from different types of balloon platform; (ii) explore the influence of DCB application time; (iii) elucidate the importance on release kinetics of elevated pressure during DCB application; (iv) compare DCB delivery of two different drugs (sirolimus and paclitaxel) and; (v) compare simulations of DESs versus DCBs. Key measures of comparison are related to safety (drug content in tissue, DC) and efficacy (specific binding site saturation, %SBSS) markers. Our results highlight the pros and cons of each device in terms of DC and %SBSS levels achieved and, moreover, indicate the potential for designing a DCB that gives rise to sufficiently similar safety and efficacy indicators as current commercial DESs.

Stage-differentiated ensemble modeling of DNA methylation landscapes uncovers salient biomarkers and prognostic signatures in colorectal cancer progression

Aberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, contributing to cancer etiology. A gap in our understanding concerns the epigenomics of stagewise cancer progression. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC). The methylation β-matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using an ensemble of approaches involving stage-differentiated modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2), and then filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by matching with clinical data and performing Kaplan-Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis. We found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. The stage-differentiated models yielded the following consensus salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were significantly hypermethylated in the promoter regions, indicating down-regulation of expression and implying a putative CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1 survived all the analytical filters, and represents a novel early-stage epigenetic biomarker / target. We have designed and executed a workflow for stage-differentiated epigenomic analysis of colorectal cancer progression, and identified several stage-salient diagnostic biomarkers, and an early-stage prognostic biomarker panel. The study has led to the discovery of an alternative CIMP-like signature in colorectal cancer, reinforcing the role of CIMP drivers in tumor pathophysiology.

Accurate identification of circRNA landscape and complexity reveals their pivotal roles in human oligodendroglia differentiation

BackgroundCircular RNAs (circRNAs), a novel class of poorly conserved non-coding RNAs that regulate gene expression, are highly enriched in the human brain. Despite increasing discoveries of circRNA function in human neurons, the circRNA landscape and function in developing human oligodendroglia, the myelinating cells that govern neuronal conductance, remains unexplored. Meanwhile, improved experimental and computational tools for the accurate identification of circRNAs are needed.ResultsWe adopt a published experimental approach for circRNA enrichment and develop CARP (CircRNA identification using A-tailing RNase R approach and Pseudo-reference alignment), a comprehensive 21-module computational framework for accurate circRNA identification and quantification. Using CARP, we identify developmentally programmed human oligodendroglia circRNA landscapes in the HOG oligodendroglioma cell line, distinct from neuronal circRNA landscapes. Numerous circRNAs display oligodendroglia-specific regulation upon differentiation, among which a subclass is regulated independently from their parental mRNAs. We find that circRNA flanking introns often contain cis-regulatory elements for RNA editing and are predicted to bind differentiation-regulated splicing factors. In addition, we discover novel oligodendroglia-specific circRNAs that are predicted to sponge microRNAs, which co-operatively promote oligodendroglia development. Furthermore, we identify circRNA clusters derived from differentiation-regulated alternative circularization events within the same gene, each containing a common circular exon, achieving additive sponging effects that promote human oligodendroglia differentiation.ConclusionsOur results reveal dynamic regulation of human oligodendroglia circRNA landscapes during early differentiation and suggest critical roles of the circRNA-miRNA-mRNA axis in advancing human oligodendroglia development.

Computational risk assessment framework for the hazard analysis of bisphenols and quinone metabolites

Bisphenol A (BPA) is a widely used chemical in plastics but its proven harmful effects has led to the replacement and production of its analogs that might also induce hazard as well as associated risks. To elucidate the adverse impact of the BPA analogs, a comprehensive computational framework is developed which applies toxicogenomics aligned with Density Functional Theory (DFT) and Molecular Dynamics (MD) based approaches to understand the toxic potential of quinone metabolites of Bisphenol F (BPF) and 3,3′-dimethylbisphenol A (DMBPA). The obtained results indicate a similar chemical reactivity profile for these metabolites of bisphenols to BPA metabolite. MD simulation revealed that the quinone metabolites tend to interact with the DNA comprising hydrogen bonding, van der Waals forces, and electrostatic interactions as an onset for covalent binding to adduct formation. Structural analysis suggests that interactions with DC9, DG10, DG16, DA17, DA18, and DT19 play a crucial role in stabilizing the quinone metabolite in the interactive pocket of DNA. These observations are demonstrating that BPF and DMBPA have the potential to impose genotoxicity via forming the quinone metabolite adducts. Combination of DFT and MD-based computational approaches providing a structure-activity-toxicity spectrum of chemicals can serve for the purpose of risk assessment.

Towards the Development of a Substance Abuse Index (SEI) through Informatics.

Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other external influences (e.g., pharmaceutical advertising campaigns). However, currently there is no comprehensive computational or machine learning framework that allows systematic studies of substance abuse and its factors with majority of the works using subjective surveys questionnaires and focusing on classification techniques. Lacking standardized methods and/or measures to prescribe medication and to study substance abuse makes it difficult to advance through collective research efforts. Thus, in this paper, we propose to test the feasibility of developing a, objective substance effect index, SEI, that can measure the tendency of an individual towards substance abuse. To that end, we combine the benefits of Electronics Medical Records (EMR) with machine learning technology by defining SEI as a function of EMR data and using logistics regression to obtain a closed form expression for SEI. We conduct various evaluations to validate the proposed model, and the results show that further work towards the development of SEI will not only provide researchers with standard computational measure for substance abuse, but may also allow them to study certain attribute interactions to gain further insights into substance abuse tendencies.

Computationally Reconstructed Interactome of Bradyrhizobium diazoefficiens USDA110 Reveals Novel Functional Modules and Protein Hubs for Symbiotic Nitrogen Fixation.

Symbiotic nitrogen fixation is an important part of the nitrogen biogeochemical cycles and the main nitrogen source of the biosphere. As a classical model system for symbiotic nitrogen fixation, rhizobium-legume systems have been studied elaborately for decades. Details about the molecular mechanisms of the communication and coordination between rhizobia and host plants is becoming clearer. For more systematic insights, there is an increasing demand for new studies integrating multiomics information. Here, we present a comprehensive computational framework integrating the reconstructed protein interactome of B. diazoefficiens USDA110 with its transcriptome and proteome data to study the complex protein-protein interaction (PPI) network involved in the symbiosis system. We reconstructed the interactome of B. diazoefficiens USDA110 by computational approaches. Based on the comparison of interactomes between B. diazoefficiens USDA110 and other rhizobia, we inferred that the slow growth of B. diazoefficiens USDA110 may be due to the requirement of more protein modifications, and we further identified 36 conserved functional PPI modules. Integrated with transcriptome and proteome data, interactomes representing free-living cell and symbiotic nitrogen-fixing (SNF) bacteroid were obtained. Based on the SNF interactome, a core-sub-PPI-network for symbiotic nitrogen fixation was determined and nine novel functional modules and eleven key protein hubs playing key roles in symbiosis were identified. The reconstructed interactome of B. diazoefficiens USDA110 may serve as a valuable reference for studying the mechanism underlying the SNF system of rhizobia and legumes.

Coupled Experimental and Computational Investigation of the Interplay between Discrete and Continuous Reinforcement in Ultrahigh Performance Concrete Beams. II: Mesoscale Modeling

The first experimental campaign presented in the preceding Part I of this study is used to calibrate and validate a comprehensive computational framework called the lattice discrete particle model for fiber-reinforced concrete (LDPM-F). The model is then used to design the second experimental campaign that was also presented in the preceding Part I so that all beams fail in shear. Finally, the model is used to investigate and explain the observed failure modes, validate the fiber/reinforcement interplay effects postulated in Part I, and to analyze comprehensively the load-transfer mechanisms in the reinforced ultra-high performance concrete (R-UHPC) beams in both shear and flexural failure. This two-part study proves the effectiveness of coupling experimental analysis with comprehensive computational modeling to understand the behavior of structural members made from complex materials. Using this coupled understanding, detailed explanations of load-transfer mechanisms in shallow and deep beam shear failure as well as flexural failure are discussed and compared to simplified sectional analysis models showing the places of needed improvement in such models. These detailed discussions show the ability of the presented coupled approach to accurately predict these failure mechanisms and their dependence on fiber/reinforcement contents and their interplay. The presented accurate probing of different load-transfer mechanisms within the structural elements and how they vary during failure progression paves the road towards developing rigorous design formulations based on fundamental understanding of the complex mechanical behavior of these structural members.

Mathematical Modeling and Analysis of Spatial Neuron Dynamics: Dendritic Integration and Beyond

Neurons compute by integrating spatiotemporal excitatory (E) and inhibitory (I) synaptic inputs received from the dendrites. The investigation of dendritic integration is crucial for understanding neuronal information processing. Yet quantitative rules of dendritic integration and their mathematical modeling remain to be fully elucidated. Here neuronal dendritic integration is investigated by using theoretical and computational approaches. Based on the passive cable theory, a PDE‐based cable neuron model with spatially branched dendritic structure is introduced to describe the neuronal subthreshold membrane potential dynamics, and the analytical solutions in response to conductance‐based synaptic inputs are derived. Using the analytical solutions, a bilinear dendritic integration rule is identified, and it characterizes the change of somatic membrane potential when receiving multiple spatiotemporal synaptic inputs from the dendrites. In addition, the PDE‐based cable neuron model is reduced to an ODE‐based point‐neuron model with the feature of bilinear dendritic integration inherited, thus providing an efficient computational framework of neuronal simulation incorporating certain important dendritic functions. The above results are further extended to active dendrites by numerical verification in realistic neuron simulations. Our work provides a comprehensive and systematic theoretical and computational framework for the study of spatial neuron dynamics. © 2021 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.

Computational optimization of solar thermal generation with energy storage

Integrating renewable energy resources into power systems is essential for achieving sustainability targets. Concentrated solar power can incorporate thermal energy storage, which can provide larger storage capacities than other technologies. In this study, a comprehensive computational framework is developed for the modeling and optimization of a parabolic trough plant with storage. A Particle Swarm Optimization – Mesh Adaptive Direct Search hybrid scheme is applied to optimize hourly plant operations. A 16.5 MWe parabolic trough plant with thermal energy storage, simulated to be located in Daggett, California, is modeled and optimized. The optimal storage duration is four hours with a net present value of $37.40 million. The results show that scenarios including higher peaks in electricity prices yield an optimal storage duration of six hours and net present value of $63.70 million. Importantly, a plant operating under a Purchase Power Agreement without storage results in a 25% decrease in net present value, indicating that it is beneficial to utilize storage and sell at market prices rather than enter into a Power Purchase Agreement at fixed prices. Computational capabilities of the type developed in this study are essential for assessing the impact of future price scenarios on optimal plant design and operations.