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Publications of Maria-Veronica Ciocanel    :chronological  alphabetical  combined listing:

%% Papers Published   
@article{fds355297,
   Author = {Mallory, K and Rubin Abrams and J and Schwartz, A and Ciocanel, M-V and Volkening, A and Sandstede, B},
   Title = {Influenza spread on context-specific networks lifted from
             interaction-based diary data.},
   Journal = {Royal Society Open Science},
   Volume = {8},
   Number = {1},
   Pages = {191876},
   Publisher = {The Royal Society},
   Year = {2021},
   Month = {January},
   url = {http://dx.doi.org/10.1098/rsos.191876},
   Abstract = {Studying the spread of infections is an important tool in
             limiting or preventing future outbreaks. A first step in
             understanding disease dynamics is constructing networks that
             reproduce features of real-world interactions. In this
             paper, we generate networks that maintain some features of
             the partial interaction networks that were recorded in an
             existing diary-based survey at the University of Warwick. To
             preserve realistic structure in our artificial networks, we
             use a context-specific approach. In particular, we propose
             different algorithms for producing larger home, work and
             social networks. Our networks are able to maintain much of
             the interaction structure in the original diary-based survey
             and provide a means of accounting for the interactions of
             survey participants with non-participants. Simulating a
             discrete susceptible-infected-recovered model on the full
             network produces epidemic behaviour which shares
             characteristics with previous influenza seasons. Our
             approach allows us to explore how disease transmission and
             dynamic responses to infection differ depending on
             interaction context. We find that, while social interactions
             may be the first to be reduced after influenza infection,
             limiting work and school encounters may be significantly
             more effective in controlling the overall severity of the
             epidemic.},
   Doi = {10.1098/rsos.191876},
   Key = {fds355297}
}

@article{fds353550,
   Author = {Ciocanel, M-V and Topaz, CM and Santorella, R and Sen, S and Smith, CM and Hufstetler, A},
   Title = {JUSTFAIR: Judicial System Transparency through Federal
             Archive Inferred Records.},
   Journal = {Plos One},
   Volume = {15},
   Number = {10},
   Pages = {e0241381-e0241381},
   Year = {2020},
   Month = {October},
   url = {http://dx.doi.org/10.1371/journal.pone.0241381},
   Abstract = {In the United States, the public has a constitutional right
             to access criminal trial proceedings. In practice, it can be
             difficult or impossible for the public to exercise this
             right. We present JUSTFAIR: Judicial System Transparency
             through Federal Archive Inferred Records, a database of
             criminal sentencing decisions made in federal district
             courts. We have compiled this data set from public sources
             including the United States Sentencing Commission, the
             Federal Judicial Center, the Public Access to Court
             Electronic Records system, and Wikipedia. With nearly
             600,000 records from the years 2001-2018, JUSTFAIR is the
             first large scale, free, public database that links
             information about defendants and their demographic
             characteristics with information about their federal crimes,
             their sentences, and, crucially, the identity of the
             sentencing judge.},
   Doi = {10.1371/journal.pone.0241381},
   Key = {fds353550}
}

@article{fds353551,
   Author = {Ciocanel, M-V and Fricks, J and Kramer, PR and McKinley,
             SA},
   Title = {Renewal Reward Perspective on Linear Switching Diffusion
             Systems in Models of Intracellular Transport.},
   Journal = {Bulletin of Mathematical Biology},
   Volume = {82},
   Number = {10},
   Pages = {126},
   Year = {2020},
   Month = {September},
   url = {http://dx.doi.org/10.1007/s11538-020-00797-w},
   Abstract = {In many biological systems, the movement of individual
             agents is characterized having multiple qualitatively
             distinct behaviors that arise from a variety of biophysical
             states. For example, in cells the movement of vesicles,
             organelles, and other intracellular cargo is affected by
             their binding to and unbinding from cytoskeletal filaments
             such as microtubules through molecular motor proteins. A
             typical goal of theoretical or numerical analysis of models
             of such systems is to investigate effective transport
             properties and their dependence on model parameters. While
             the effective velocity of particles undergoing switching
             diffusion dynamics is often easily characterized in terms of
             the long-time fraction of time that particles spend in each
             state, the calculation of the effective diffusivity is more
             complicated because it cannot be expressed simply in terms
             of a statistical average of the particle transport state at
             one moment of time. However, it is common that these systems
             are regenerative, in the sense that they can be decomposed
             into independent cycles marked by returns to a base state.
             Using decompositions of this kind, we calculate effective
             transport properties by computing the moments of the
             dynamics within each cycle and then applying renewal reward
             theory. This method provides a useful alternative large-time
             analysis to direct homogenization for linear
             advection-reaction-diffusion partial differential equation
             models. Moreover, it applies to a general class of
             semi-Markov processes and certain stochastic differential
             equations that arise in models of intracellular transport.
             Applications of the proposed renewal reward framework are
             illustrated for several case studies such as mRNA transport
             in developing oocytes and processive cargo movement by teams
             of molecular motor proteins.},
   Doi = {10.1007/s11538-020-00797-w},
   Key = {fds353551}
}

@article{fds353552,
   Author = {Topaz, CM and Ciocanel, V and Cohen, P and Ott, M and Rodriguez,
             N},
   Title = {Institute for the Quantitative Study of Inclusion,
             Diversity, and Equity (QSIDE)},
   Journal = {Notices of the American Mathematical Society},
   Volume = {67},
   Number = {02},
   Pages = {1-1},
   Publisher = {American Mathematical Society (AMS)},
   Year = {2020},
   Month = {February},
   url = {http://dx.doi.org/10.1090/noti2019},
   Doi = {10.1090/noti2019},
   Key = {fds353552}
}

@article{fds353553,
   Author = {Ciocanel, M-V and Jung, P and Brown, A},
   Title = {A Mechanism for Neurofilament Transport Acceleration through
             Nodes of Ranvier},
   Journal = {Cell Regulation},
   Volume = {31},
   Number = {7},
   Publisher = {American Society for Cell Biology},
   Year = {2020},
   Month = {January},
   url = {http://dx.doi.org/10.1101/806786},
   Abstract = {<jats:title><jats:bold>Abstract</jats:bold></jats:title><jats:p>Neurofilaments
             are abundant space-filling cytoskeletal polymers in axons
             that are transported along microtubule tracks. Neurofilament
             transport is accelerated at nodes of Ranvier, where axons
             are locally constricted. Strikingly, these constrictions are
             accompanied by a sharp decrease in neurofilament number but
             no decrease in microtubule number, bringing neurofilaments
             closer to their microtubule tracks. We hypothesize this
             leads to an increase in the proportion of the time that the
             filaments spend moving and that this can explain the local
             acceleration. To test this, we developed a stochastic model
             of neurofilament transport that tracks their number, kinetic
             state and proximity to nearby microtubules in space and
             time. The model assumes that the probability of a
             neurofilament moving is dependent on its distance from the
             nearest available microtubule track. Taking into account
             experimentally reported numbers and densities for
             neurofilaments and microtubules in nodes and internodes, we
             show that the model is sufficient to explain the local
             acceleration of neurofilaments across nodes of Ranvier. This
             suggests that proximity to microtubule tracks may be a key
             regulator of neurofilament transport in axons, which has
             implications for the mechanism of neurofilament accumulation
             in development and disease.</jats:p>},
   Doi = {10.1101/806786},
   Key = {fds353553}
}

@article{fds353554,
   Author = {Adams, H and Ciocanel, M-V and Topaz, C and Ziegelmeier,
             L},
   Title = {Topological Data Analysis of Collective Motion},
   Journal = {Siam News},
   Publisher = {SIAM News},
   Year = {2020},
   Month = {January},
   Key = {fds353554}
}

@article{fds353555,
   Author = {Panaggio, MJ and Ciocanel, M-V and Lazarus, L and Topaz, CM and Xu,
             B},
   Title = {Model reconstruction from temporal data for coupled
             oscillator networks.},
   Journal = {Chaos (Woodbury, N.Y.)},
   Volume = {29},
   Number = {10},
   Pages = {103116},
   Year = {2019},
   Month = {October},
   url = {http://dx.doi.org/10.1063/1.5120784},
   Abstract = {In a complex system, the interactions between individual
             agents often lead to emergent collective behavior such as
             spontaneous synchronization, swarming, and pattern
             formation. Beyond the intrinsic properties of the agents,
             the topology of the network of interactions can have a
             dramatic influence over the dynamics. In many studies,
             researchers start with a specific model for both the
             intrinsic dynamics of each agent and the interaction network
             and attempt to learn about the dynamics of the model. Here,
             we consider the inverse problem: given data from a system,
             can one learn about the model and the underlying network? We
             investigate arbitrary networks of coupled phase oscillators
             that can exhibit both synchronous and asynchronous dynamics.
             We demonstrate that, given sufficient observational data on
             the transient evolution of each oscillator, machine learning
             can reconstruct the interaction network and identify the
             intrinsic dynamics.},
   Doi = {10.1063/1.5120784},
   Key = {fds353555}
}

@article{fds353556,
   Author = {Ciocanel, M-V and Docken, SS and Gasper, RE and Dean, C and Carlson, BE and Olufsen, MS},
   Title = {Cardiovascular regulation in response to multiple
             hemorrhages: analysis and parameter estimation.},
   Journal = {Biological Cybernetics},
   Volume = {113},
   Number = {1-2},
   Pages = {105-120},
   Year = {2019},
   Month = {April},
   url = {http://dx.doi.org/10.1007/s00422-018-0781-y},
   Abstract = {Mathematical models can provide useful insights explaining
             behavior observed in experimental data; however, rigorous
             analysis is needed to select a subset of model parameters
             that can be informed by available data. Here we present a
             method to estimate an identifiable set of parameters based
             on baseline left ventricular pressure and volume time series
             data. From this identifiable subset, we then select, based
             on current understanding of cardiovascular control,
             parameters that vary in time in response to blood
             withdrawal, and estimate these parameters over a series of
             blood withdrawals. These time-varying parameters are first
             estimated using piecewise linear splines minimizing the mean
             squared error between measured and computed left ventricular
             pressure and volume data over four consecutive blood
             withdrawals. As a final step, the trends in these splines
             are fit with empirical functional expressions selected to
             describe cardiovascular regulation during blood withdrawal.
             Our analysis at baseline found parameters representing
             timing of cardiac contraction, systemic vascular resistance,
             and cardiac contractility to be identifiable. Of these
             parameters, vascular resistance and cardiac contractility
             were varied in time. Data used for this study were measured
             in a control Sprague-Dawley rat. To our knowledge, this is
             the first study to analyze the response to multiple blood
             withdrawals both experimentally and theoretically, as most
             previous studies focus on analyzing the response to one
             large blood withdrawal. Results show that during each blood
             withdrawal both systemic vascular resistance and
             contractility decrease acutely and partially recover, and
             they decrease chronically across the series of blood
             withdrawals.},
   Doi = {10.1007/s00422-018-0781-y},
   Key = {fds353556}
}

@article{fds353557,
   Author = {Ciocanel, MV and Stepien, TL and Sgouralis, I and Layton,
             AT},
   Title = {A multicellular vascular model of the renal myogenic
             response},
   Journal = {Processes},
   Volume = {6},
   Number = {7},
   Year = {2018},
   Month = {July},
   url = {http://dx.doi.org/10.3390/PR6070089},
   Abstract = {The myogenic response is a key autoregulatory mechanism in
             the mammalian kidney. Triggered by blood pressure
             perturbations, it is well established that the myogenic
             response is initiated in the renal afferent arteriole and
             mediated by alterations in muscle tone and vascular diameter
             that counterbalance hemodynamic perturbations. The entire
             process involves several subcellular, cellular, and vascular
             mechanisms whose interactions remain poorly understood.
             Here, we model and investigate the myogenic response of a
             multicellular segment of an afferent arteriole. Extending
             existing work, we focus on providing an accurate-but still
             computationally tractable-representation of the coupling
             among the involved levels. For individual muscle cells, we
             include detailed Ca2+ signaling, transmembrane transport of
             ions, kinetics of myosin light chain phosphorylation, and
             contraction mechanics. Intercellular interactions are
             mediated by gap junctions between muscle or endothelial
             cells. Additional interactions are mediated by hemodynamics.
             Simulations of time-independent pressure changes reveal
             regular vasoresponses throughout the model segment and
             stabilization of a physiological range of blood pressures
             (80-180 mmHg) in agreement with other modeling and
             experimental studies that assess steady autoregulation.
             Simulations of time-dependent perturbations reveal irregular
             vasoresponses and complex dynamics that may contribute to
             the complexity of dynamic autoregulation observed in vivo.
             The ability of the developed model to represent the myogenic
             response in a multiscale and realistic fashion, under
             feasible computational load, suggests that it can be
             incorporated as a key component into larger models of
             integrated renal hemodynamic regulation.},
   Doi = {10.3390/PR6070089},
   Key = {fds353557}
}

@article{fds353558,
   Author = {Ciocanel, M-V and Sandstede, B and Jeschonek, SP and Mowry,
             KL},
   Title = {Modeling Microtubule-Based Transport and Anchoring of
             mRNA},
   Journal = {Siam Journal on Applied Dynamical Systems},
   Volume = {17},
   Number = {4},
   Pages = {2855-2881},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2018},
   Month = {January},
   url = {http://dx.doi.org/10.1137/18m1186083},
   Doi = {10.1137/18m1186083},
   Key = {fds353558}
}

@article{fds353559,
   Author = {Ciocanel, M-V and Stepien, T and Edwards, A and Layton,
             A},
   Title = {Modeling Autoregulation of the Afferent Arteriole of the Rat
             Kidney},
   Journal = {Association for Women in Mathematics Series},
   Volume = {8},
   Publisher = {Springer, Cham},
   Editor = {Miller, L},
   Year = {2017},
   Month = {August},
   url = {http://dx.doi.org/10.1007/978-3-319-60304-9_5},
   Abstract = {One of the key autoregulatory mechanisms that control blood
             flow in the kidney is the myogenic response. Subject to
             increased pressure, the renal afferent arteriole responds
             with an increase in muscle tone and a decrease in diameter.
             To investigate the myogenic response of an afferent
             arteriole segment of the rat kidney, we extend a
             mathematical model of an afferent arteriole cell. For each
             cell, we include detailed Ca2+ signaling, transmembrane
             transport of major ions, the kinetics of myosin light chain
             phosphorylation, as well as cellular contraction and wall
             mechanics. To model an afferent arteriole segment, a number
             of cell models are connected in series by gap junctions,
             which link the cytoplasm of neighboring cells. Blood flow
             through the afferent arteriole is modeled using Poiseuille
             flow. Simulation of an inflow pressure up-step leads to a
             decrease in the diameter for the proximal part of the vessel
             (vasoconstriction) and to an increase in proximal vessel
             diameter (vasodilation) for an inflow pressure down-step.
             Through its myogenic response, the afferent arteriole
             segment model yields approximately stable outflow pressure
             for a physiological range of inflow pressures
             (100–160 mmHg), consistent with experimental
             observations. The present model can be incorporated as a key
             component into models of integrated renal hemodynamic
             regulation.},
   Doi = {10.1007/978-3-319-60304-9_5},
   Key = {fds353559}
}

@article{fds353560,
   Author = {Ciocanel, M-V and Kreiling, JA and Gagnon, JA and Mowry, KL and Sandstede, B},
   Title = {Analysis of Active Transport by Fluorescence Recovery after
             Photobleaching},
   Journal = {Biophysical Journal},
   Volume = {112},
   Number = {8},
   Pages = {1714-1725},
   Publisher = {Elsevier BV},
   Year = {2017},
   Month = {April},
   url = {http://dx.doi.org/10.1016/j.bpj.2017.02.042},
   Doi = {10.1016/j.bpj.2017.02.042},
   Key = {fds353560}
}

@article{fds353561,
   Author = {Powrie, EA and Ciocanel, V and Kreiling, JA and Gagnon, JA and Sandstede, B and Mowry, KL},
   Title = {Using in vivo imaging to measure RNA mobility in Xenopus
             laevis oocytes},
   Journal = {Methods (San Diego, Calif.)},
   Volume = {98},
   Pages = {60-65},
   Publisher = {Elsevier BV},
   Year = {2016},
   Month = {April},
   url = {http://dx.doi.org/10.1016/j.ymeth.2015.11.003},
   Doi = {10.1016/j.ymeth.2015.11.003},
   Key = {fds353561}
}

@article{fds354092,
   Author = {Ciocanel, V},
   Title = {Modeling and Numerical Simulation of the Nonlinear Dynamics
             of the Parametrically Forced String Pendulum},
   Journal = {Siam Undergraduate Research Online},
   Volume = {5},
   Pages = {95-115},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2012},
   url = {http://dx.doi.org/10.1137/11s011444},
   Doi = {10.1137/11s011444},
   Key = {fds354092}
}

 

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