295 research outputs found
Correction: Corrigendum: Rapid generation of hypomorphic mutations
No full textNature Communications 8: Article number: 14112 (2017); Published: 20 January 2017; Updated: 16 February 2017 The original version of this Article contained a typographical error in the spelling of the Author Preetam Janakirama, which was incorrectly given as Preetam Jankirama. This has now been corrected in both the PDF and HTML versions of the Article.</jats:p
Parametrically upscaled coupled constitutive damage model for piezoelectric composites
No full textPiezoelectric composites, known for their strong electromechanical coupling capabilities, are used in various engineering applications requiring sensing and actuation functions. These composites offer improved performance compared to monolithic piezoelectric ceramics. However, owing to heterogeneous microstructures, these composites are susceptible to different failure modes at the micro-scale, ultimately impacting their electromechanical performance at the macro-scale. Consequently, damage in these composites is a multi-scale phenomenon and their macroscopic response is strongly influenced by the underlying microstructure and the physics governing its deformation. Phenomenological constitutive models used for macroscopic analysis often ignore the effect of the underlying material microstructure and its physics, resulting in inaccuracies. Although hierarchical multi-scale models are available, they are computationally expensive for nonlinear electromechanical damage problems. To overcome these shortcomings, a computationally efficient microstructure-sensitive parametrically upscaled coupled constitutive damage model (PUCCDM) for piezoelectric composites is developed in this dissertation.
To realize the effects of the microstructure at macro-scale, it is imperative to understand different failure mechanisms in heterogeneous piezoelectric composites at the micro-scale. Towards this goal, the first half of the dissertation develops a finite deformation cohesive zone enhanced phase field model to simulate electromechanical fracture in these composite microstructures. Fracture in these composites is driven by complex mechanical and electrical mechanisms governed by a set of strongly coupled nonlinear governing equations. The model incorporates cohesive traction-separation laws at material interfaces via an auxiliary phase field order parameter. A Gibbs free energy density function, considering the anisotropic elastic stiffness of piezoelectric materials is proposed. Numerical simulations showcasing different failure mechanisms are carried out to demonstrate the model's efficacy.
The later part of the dissertation concentrates on the development of the PUCCDM, a higher-scale multi-physics damage model explicitly accounting for the influence of material microstructure. The microstructure comprises of nonuniformly distributed unidirectional piezoelectric fibers embedded in an epoxy matrix. Key microstructural features influencing macroscopic responses are identified and expressed as representative aggregated microstructural parameters (RAMPs), and statistically equivalent representative volume elements (SERVEs) are constructed. A database of SERVEs is created and simulations are performed under different loading conditions to calibrate the PUCCDM constitutive parameters as functions of RAMPs using machine learning tools. The developed PUCCDM exhibits computational efficiency, making it an indispensable tool for conducting multi-physics and multi-scale analysis of damage in multifunctional composites thereby enabling the material-by-design process
Parametrically upscaled coupled constitutive damage model for piezoelectric composites
No full textPiezoelectric composites, known for their strong electromechanical coupling capabilities, are used in various engineering applications requiring sensing and actuation functions. These composites offer improved performance compared to monolithic piezoelectric ceramics. However, owing to heterogeneous microstructures, these composites are susceptible to different failure modes at the micro-scale, ultimately impacting their electromechanical performance at the macro-scale. Consequently, damage in these composites is a multi-scale phenomenon and their macroscopic response is strongly influenced by the underlying microstructure and the physics governing its deformation. Phenomenological constitutive models used for macroscopic analysis often ignore the effect of the underlying material microstructure and its physics, resulting in inaccuracies. Although hierarchical multi-scale models are available, they are computationally expensive for nonlinear electromechanical damage problems. To overcome these shortcomings, a computationally efficient microstructure-sensitive parametrically upscaled coupled constitutive damage model (PUCCDM) for piezoelectric composites is developed in this dissertation.
To realize the effects of the microstructure at macro-scale, it is imperative to understand different failure mechanisms in heterogeneous piezoelectric composites at the micro-scale. Towards this goal, the first half of the dissertation develops a finite deformation cohesive zone enhanced phase field model to simulate electromechanical fracture in these composite microstructures. Fracture in these composites is driven by complex mechanical and electrical mechanisms governed by a set of strongly coupled nonlinear governing equations. The model incorporates cohesive traction-separation laws at material interfaces via an auxiliary phase field order parameter. A Gibbs free energy density function, considering the anisotropic elastic stiffness of piezoelectric materials is proposed. Numerical simulations showcasing different failure mechanisms are carried out to demonstrate the model's efficacy.
The later part of the dissertation concentrates on the development of the PUCCDM, a higher-scale multi-physics damage model explicitly accounting for the influence of material microstructure. The microstructure comprises of nonuniformly distributed unidirectional piezoelectric fibers embedded in an epoxy matrix. Key microstructural features influencing macroscopic responses are identified and expressed as representative aggregated microstructural parameters (RAMPs), and statistically equivalent representative volume elements (SERVEs) are constructed. A database of SERVEs is created and simulations are performed under different loading conditions to calibrate the PUCCDM constitutive parameters as functions of RAMPs using machine learning tools. The developed PUCCDM exhibits computational efficiency, making it an indispensable tool for conducting multi-physics and multi-scale analysis of damage in multifunctional composites thereby enabling the material-by-design process
Stochastic Models For In-silico Event-based Biological Network Simulation
No full textThe multi-scale biological system model is a new research direction to capture the dynamic measurements of complex biological systems. The current statistical thermodynamic models can not scale to this challenge due to the explosion of state-spaces of the system, where a biological organ may have billions of cells, each with millions of molecule types and each type may have a few million molecules. We seek to propose a phenomenological theory that will require a smaller number of state variables to address this multi-scaling problem. Discrete Markov statistical process is used to understand the system dynamics in the networking community for a long time. In this dissertation, we focus more specifically on a composite system by combining the state variables in the time-space domain as events, and determine the immediate dynamics between the events by using statistical analysis or simulation methods. In our approach the space-time behavior of the cell dynamics is captured by discrete state variables, where an event is a combined process of a large number of state transitions between a set of state variables. The execution time of these state transitions to manifest the event outcome is a random variable called event-holding time. The underlying assumption is that it will be possible to segregate the complete system state-space into a disjoint set of independent events and events can be executed simultaneously without any interaction once the execution conditions are satisfied (removal of resource bottleneck, collision). In this dissertation, we present the event-time models for some biological functions that will be incorporated in the discrete-event based stochastic simulator. In particular, we present analytical models for the molecular transport event in cells considering charged/non-charged macromolecules. We show, that molecular transport event completion time can be approximated by an exponential distribution. Next we present stochastic models for biochemical reactions in the cell (that can be extended to reactions occurring in the cell cytoplasm, membrane or nucleus). We show that the reaction completion time follows an exponential distribution when one of the reactant molecules enter the cell one at a time, whereas, it follows a gamma distribution when a batch of the reactant molecules enter the cell. We also present stochastic models for the protein-DNA binding and protein-ligand docking events and show that both these events have an exponentially distributed event completion time. We also validate each of the models presented in the dissertation with experimental findings reported in the literature. Finally, we present a markov chain based stochastic biochemical system simulator which can give us the dynamics of more complex events and can be used to improve the scalability of the discrete-event based stochastic simulator. We propose to successfully demonstrate this technique by modeling the complete dynamics of one Salmonella cell
Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking.
No full textBackgroundAfter claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures.MethodsWe process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making.FindingsPopulation density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance.InterpretationStates forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic
A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes
No full textEmerging diseases—and none as recently or devastatingly impactful toward humans as COVID-19—pose an immense challenge to researchers concerned with infectious disease. This study is tasked with expanding the computational probe of treatment regimes in a differential equations-based model of the SARS-CoV-2 host–virus interaction. Parameters within the model are tweaked to simulate dose specifications. Further, parametric variations are introduced in a timed manner to infer the importance of dose timing. Arming in silico testing, and eventually, clinical testing, with abundant information on simulated therapeutic regimes is the overall contribution of this pharmacodynamic model; thus, a wide range of dose and timing combinations are examined. Therapeutic interventions that block viral replication inhibit viral entry into host cells, and vaccination-induced antibodies are all studied alone and in combination. Especially during early detection, exhaustive parameter sweeps of well-suited within-host models are often the first step in the clinical response to a novel disease
A Comparative Study on Distancing, Mask and Vaccine Adoption Rates from Global Twitter Trends
No full textCOVID-19 is a global health emergency that has fundamentally altered human life. Public perception about COVID-19 greatly informs public policymaking and charts the course of present and future mitigation strategies. Existing approaches to gain insights into the evolving nature of public opinion has led to the application of natural language processing on public interaction data acquired from online surveys and social media. In this work, we apply supervised and unsupervised machine learning approaches on global Twitter data to learn the opinions about adoption of mitigation strategies such as social distancing, masks, and vaccination, as well as the effect of socioeconomic, demographic, political, and epidemiological features on perceptions. Our study reveals the uniform polarity in public sentiment on the basis of spatial proximity or COVID-19 infection rates. We show the reservation about the adoption of social distancing and vaccination across the world and also quantify the influence of airport traffic, homelessness, followed by old age and race on sentiment of netizens within the US
A Comparative Study on Distancing, Mask and Vaccine Adoption Rates from Global Twitter Trends
No full textCOVID-19 is a global health emergency that has fundamentally altered human life. Public perception about COVID-19 greatly informs public policymaking and charts the course of present and future mitigation strategies. Existing approaches to gain insights into the evolving nature of public opinion has led to the application of natural language processing on public interaction data acquired from online surveys and social media. In this work, we apply supervised and unsupervised machine learning approaches on global Twitter data to learn the opinions about adoption of mitigation strategies such as social distancing, masks, and vaccination, as well as the effect of socioeconomic, demographic, political, and epidemiological features on perceptions. Our study reveals the uniform polarity in public sentiment on the basis of spatial proximity or COVID-19 infection rates. We show the reservation about the adoption of social distancing and vaccination across the world and also quantify the influence of airport traffic, homelessness, followed by old age and race on sentiment of netizens within the US
Genomic Characterization of Multidrug-Resistant Escherichia coli BH100 Sub-strains
No full textThe rapid emergence of multidrug-resistant (MDR) bacteria is a global health problem. Mobile genetic elements like conjugative plasmids, transposons, and integrons are the major players in spreading resistance genes in uropathogenic Escherichia coli (UPEC) pathotype. The E. coli BH100 strain was isolated from the urinary tract of a Brazilian woman in 1974. This strain presents two plasmids carrying MDR cassettes, pBH100, and pAp, with conjugative and mobilization properties, respectively. However, its transposable elements have not been characterized. In this study, we attempted to unravel the factors involved in the mobilization of virulence and drug-resistance genes by assessing genomic rearrangements in four BH100 sub-strains (BH100 MG2014, BH100 MG2017, BH100L MG2017, and BH100N MG2017). Therefore, the complete genomes of the BH100 sub-strains were achieved through Next Generation Sequencing and submitted to comparative genomic analyses. Our data shows recombination events between the two plasmids in the sub-strain BH100 MG2017 and between pBH100 and the chromosome in BH100L MG2017. In both cases, IS3 and IS21 elements were detected upstream of Tn21 family transposons associated with MDR genes at the recombined region. These results integrated with Genomic island analysis suggest pBH100 might be involved in the spreading of drug resistance through the formation of resistance islands. Regarding pathogenicity, our results reveal that BH100 strain is closely related to UPEC strains and contains many IS3 and IS21-transposase-enriched genomic islands associated with virulence. This study concludes that those IS elements are vital for the evolution and adaptation of BH100 strain
Characterization of the first vaginal Lactobacillus crispatus genomes isolated in Brazil
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