300 research outputs found
2017 GREAT Day Program
SUNY Geneseoâs Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
2023- The Twenty-seventh Annual Symposium of Student Scholars
The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp
A Framework for Investigating Random Ensembles of Structured Ecosystems and Quantifying Their Emergent Coarse-grainability
The interface between statistical physics and theoretical ecology has a long history, employing powerful concepts such as ensemble approaches and typicality to study emergent properties of ecosystems. This of course raises the question of what ensembles are useful to describe the typical behaviors of evolution and ecology, but so far, the traditional context of high-diversity ecology has considered ensembles of random, unstructured ecosystems. Although much insight has been gained in this regime, one naturally wonders how representative are random ensembles of real, natural ecosystems that are arguably atypical and highly structured by evolution. Moreover, the question of coarse-graining ecosystems has yet to be addressed because the very ingredient responsible for predictive coarse-grained descriptions â ecosystem structure â is explicitly absent from the current theoretical framework. This dissertation investigates the coarse-grainability of ecosystems within minimal models that intend to capture the atypicality generated by evolution, aiming to establish a conceptual language from which a general theoretical framework can be built.
In the first two chapters, I review the applications of statistical physics in classical models of ecology, moving on to then explore the evolutionary consequences of the atypicality that arises from evolution. In Chapter 3, I present a model for investigating random, structured ecosystems, enabling me to begin studying the emergent coarse-grainability of microbial ecosystems. In particular, I develop the hypothesis that a high strain diversity, despite being nominally more complex, may in fact facilitate coarse-grainability, which is maximized when an ecosystem is assembled in its native environment. Building on this framework in Chapter 4, I provide a more principled approach for defining coarse-grainability by systematically mapping the prediction power versus information content of coarse-grained descriptions of ecosystem composition. Applying this framework to experimental data, I confirm the diversity-enhanced coarse-grainability hypothesis and discuss how this effect cannot be reproduced in standard ecological models parameterized using random ensembles. Finally, I link these results to the theoretical concept of functional attractors of diverse ecosystems
Inferring ecological interactions from dynamics in phage-bacteria communities
Characterizing how viruses interact with microbial hosts is critical to understanding microbial community structure and function. However, existing methods for quantifying bacteria-phage interactions are not widely applicable to natural communities. First, many bacteria are not culturable, preventing direct experimental testing. Second, â-omicsâ based methods, while high in accuracy and specificity, have been shown to be extremely low in power. Third, inference methods based on time-series or co-occurrence data, while promising, have for the most part not been rigorously tested. This thesis work focuses on this final category of quantification strategies: inference methods.
In this thesis, we further our understanding of both the potential and limitations of several inference methods, focusing primarily on time-series data with high time resolution. We emphasize the quantification of efficacy by using time-series data from multi-strain bacteria-phage communities with known infection networks. We employ both in silico simulated bacteria-phage communities as well as an in vitro community experiment. We review existing correlation-based inference methods, extend theory and characterize tradeoffs for model-based inference which uses convex optimization, characterize pairwise interactions in a 5x5 virus-microbe community experiment using Markov chain Monte Carlo, and present analytic tools for microbiome time-series analysis when a dynamical model is unknown. Together, these chapters bridge gaps in existing literature in inference of ecological interactions from time-series data.Ph.D
Path-based splitting methods for SDEs and machine learning for battery lifetime prognostics
In the first half of this Thesis, we present the numerical analysis of splitting methods for
stochastic differential equations (SDEs) using a novel path-based approach. The application
of splitting methods to SDEs can be viewed as replacing the driving Brownian-time path
with a piecewise linear path, producing a âcontrolled-differential-equationâ (CDE). By Taylor
expansion of the SDE and resulting CDE, we show that the global strong and weak errors of
splitting schemes can be obtained by comparison of the iterated integrals in each. Matching
all integrals up to order p+1 in expectation will produce a weak order p+0.5 scheme, and in
addition matching the integrals up to order p+0.5 strongly will produce a strong order p
scheme. In addition, we present new splitting methods utilising the âspace-timeâ LÂŽevy area
of Brownian motion which obtain global strong Oph1.5q and Oph2q weak errors for a class
of SDEs satisfying a commutativity condition. We then present several numerical examples
including Multilevel Monte Carlo.
In the second half of this Thesis, we present a series of papers focusing on lifetime prognostics
for lithium-ion batteries. Lithium-ion batteries are fuelling the advancing renewable-energy
based world. At the core of transformational developments in battery design, modelling and
management is data. We start with a comprehensive review of publicly available datasets.
This is followed by a study which explores the evolution of internal resistance (IR) in cells,
introducing the original concept of âelbowsâ for IR. The IR of cells increases as a cell degrades
and this often happens in a non-linear fashion: where early degradation is linear until an
inflection point (the elbow) is reached followed by increased rapid degradation. As a follow up
to the exploration of IR, we present a model able to predict the full IR and capacity evolution
of a cell from one charge/discharge cycle. At the time of publication, this represented a
significant reduction (100x) in the number of cycles required for prediction. The published
paper was the first to show that such results were possible.
In the final paper, we consider
experimental design for battery testing. Where we focus on the important question of how
many cells are required to accurately capture statistical variation
A matter of timing : A modelling-based investigation of the dynamic behaviour of reproductive hormones in girls and women
Hypothalamus-hypofyse-gonade aksen er en del av det kvinnelige endokrine systemet, og regulerer evnen til reproduksjon. Hormoner produsert og utskilt fra tre kjertler (hypotalamus, hypofysen, eggstokkene) pÄvirker hverandre via tilbakemeldingsinteraksjoner, som er nÞdvendige for Ä etablere en regelmessig menstruasjonssyklus hos kvinner. Matematiske modeller som forutsier utviklingen av slike hormonkonsentrasjoner og modning av eggstokkfollikler er nyttige verktÞy for Ä forstÄ menstruasjonssyklusens dynamiske oppfÞrsel. Slike modeller kan for eksempel hjelpe oss med Ä undersÞke patologiske tilstander som endometriose og polycystisk ovariesyndrom. Videre kan de brukes til systematiske undersÞkelser av effekten av medikamenter pÄ det kvinnelige endokrine systemet. Derfor kan vi potensielt bruke slike menstruasjonsyklusmodeller som kliniske beslutningsstÞttessystemer.
Vi trenger modeller som forutsier hormonkonsentrasjoner sammen med modningen av eggstokkfollikler hos enkeltindivider gjennom pÄfÞlgende sykluser. Dette for Ä kunne simulere hormonelle behandlinger som stimulerer vekst av eggstokkfolliklene (eggstokkstimuleringsprotokoller). Her legger jeg fram et forslag til en matematisk menstruasjonsyklusmodell og viser modellens evne til Ä forutsi resultatet av eggstokkstimuleringsprotokoller.
For Ă„ kalibrere denne typen modell trenges individuelle tidsseriedata. Innsamling av slike data er tidskrevende, og forutsetter hĂžy grad av engasjement fra deltakerne i studien. Det er derfor viktig Ă„ finne brukbare datatyper som er mindre tid- og ressurskrevende Ă„ samle inn, og som likevel kan brukes til modellkalibrering. En type data som er enklere Ă„ samle inn er tversnittdata. I denne avhandlingen har jeg utviklet en prosedyre for Ă„ bruke tversnittpopulasjonsdata i modellens kalibreringsprosess, og viser hvordan en modell kalibrert med tversnittdata kan brukes til Ă„ forutsi individuelle resultater ved oppdatering av en del av modellens parametere.
I tillegg til det vitenskapelige bidraget, hÄper jeg at avhandlingen min skaper oppmerksomhet rundt viktigheten av forskning pÄ kvinners reproduktive helse, og at avhandlingen underbygger verdien av matematiske modeller i forskning pÄ kvinnehelse.The hypothalamic-pituitary-gonadal axis (HPG axis), a part of the human endocrine system, regulates the female reproductive function. Feedback interactions between hormones secreted from the glands forming the HPG axis are essential for establishing a regular menstrual cycle. Mathematical models predicting the time evolution of hormone concentrations and the maturation of ovarian follicles are useful tools for understanding the dynamic behaviour of the menstrual cycle. Such models can, for example, help us to investigate pathological conditions, such as endometriosis or Polycystic Ovary Syndrome. Furthermore, they can be used to systematically study the effects of drugs on the endocrine system. In doing so, menstrual cycle models could potentially be integrated into clinical routines as clinical decision support systems.
For the simulation-based investigation of hormonal treatments aiming to stimulate the growth of ovarian follicles (Controlled Ovarian Stimulation (COS)), we need models that predict hormone concentrations and the maturation of ovarian follicles in biological units throughout consecutive cycles. Here, I propose such a mechanistic menstrual cycle model. I also demonstrate its capability to predict the outcome of COS.
Individual time series data is usually used to calibrate mechanistic models having clinical implications. Collecting these data, however, is time-consuming and requires a high commitment from study participants. Therefore, integrating different data sets into the model calibration process is of interest. One type of data that is often more feasible to collect than individual time series is cross-sectional data. As part of my thesis, I developed a workflow based on Bayesian updating to integrate cross-sectional data into the model calibration process. I demonstrate the workflow using a mechanistic model describing the time evolution of reproductive hormones during puberty in girls. Exemplary, I show that a model calibrated with cross-sectional data can be used to predict individual dynamics after updating a subset of model parameters.
In addition to the scientific contributions of this thesis, I hope that it creates attention for the importance of research in the area of women's reproductive health and underpins the value of mathematical modelling for this field.Doktorgradsavhandlin
New Frontiers in the Application of Stable Isotopes to Ecological and Ecophysiological Research
This Research Topic aims to present cutting-edge applications of stable isotope methods to animal and plant ecology and ecophysiology.https://digitalcommons.odu.edu/biology_books/1020/thumbnail.jp
- âŠ