Population Model Research Articles

Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.

Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions. In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatmentdays. Data from 151 abdominal cancer patients treated at a 0.35T MR-Linac (151 planning and 215 fraction MRIs) were included. Population baseline models (BMs) were trained on 107 planning MRIs for one-class segmentation of the aorta, bowel, duodenum, kidneys, liver, spinal canal, and stomach. PS models were obtained by fine-tuning the BMs using the planning MRI ( ). Maximal improvement by continuously updating the PS models was investigated by adding the first four out of five fraction MRIs ( ). Similarly, PS models without BM were trained ( and ). All hyperparameters were optimized using 23 patients, and the methods were tested on the remaining 21 patients. Evaluation involved Dice similarity coefficient (DSC), average ( ) and the 95th percentile (HD95) Hausdorff distance. A qualitative contour assessment by a radiation oncologist was performed for BM, , and . and networks had the best geometric performance. and BMs showed similar DSC and HDs values, however models outperformed BMs. predictions scored the best in the qualitative evaluation, followed by the BMs and models. Personalized auto-segmentation models outperformed the population BMs. In most cases, delineations were judged to be directly usable for treatment adaptation without further corrections, suggesting a potential time saving during fractionatedtreatment.

Spatiotemporal variation in population dynamics of a narrow endemic, Ranunculus austro-oreganus.

Understanding how population dynamics vary in space and time is critical for understanding the basic life history and conservation needs of a species, especially for narrow endemic species whose populations are often in similar environments and therefore at increased risk of extinction under climate change. Here, we investigated the spatial and temporal variation in population dynamics of Ranunculus austro-oreganus, a perennial buttercup endemic to fragmented prairie habitat in one county in southern Oregon. We performed demographic surveys of three populations of R. austro-oreganus over 4 years (2015-2018). We used size-structured population models and life table response experiments to investigate vital rates driving spatiotemporal variation in population growth. Overall, R. austro-oreganus had positive or stable stochastic population growth rates, though individual vital rates and overall population growth varied substantially among sites and years. All populations had their greatest growth in the same year, suggesting potential synchrony associated with climate conditions. Differences in survival contributed most to spatial variation in population growth, while differences in reproduction contributed most to temporal variation in population growth. Populations of this extremely narrow endemic appear stable, with positive growth during our study window. These results suggest that populations of R. austro-oreganus are able to persist if their habitat is not eliminated by land-use change. Nonetheless, its narrow distribution and synchronous population dynamics suggest the need for continued monitoring, particularly with ongoing habitat loss and climate change.

Optimizing dental students' use of feedback: Validation of a theoretical model.

A model describing the factors that influence the use of feedback by dental students from three UK dental schools has been proposed. Seven factors that influence feedback utilization were identified. This project aims to test the validity of this model in a larger student population from two UK dental schools in Newcastle upon Tyne and Cardiff. An electronic questionnaire was created and circulated to two dental schools. Data from schools were combined and analyzed using SPSS software. A total of 304 responses were analyzed using principal component analysis resulting in the extraction of nine components which were identified as "most important." The Kruskal-Wallis test was used to analyze the correlations of these components with the demographic characteristics of participants. The nine components were feedback utility, feedback value, credibility, learning approach, the learner's understanding of the feedback's purpose, and its processes, accessibility, future applicability, and institutional processes. According to the Kruskal-Wallis analysis, seven of the components (but not credibility or institutional processes) were found to be influenced by differences in the gender, nationality, and ethnicity of students as well as their previous educational experience. However, no evidence was found that variables such as age, student year group, or religious beliefs affected any of these components. Some demographic characteristics are more engaged with one or more components than others. Understanding that will help optimizing this model and ultimately benefits students and institutions. The main project's goal was met by the research in validating the previously developed model.

A physically modelled selection function for compact binary mergers in the LIGO-Virgo O3 run and beyond

Abstract Despite the observation of nearly 100 compact binary coalescence (CBC) events up to the end of the Advanced gravitational-wave (GW) detectors' third observing run (O3), there remain fundamental open questions regarding their astrophysical formation mechanisms and environments. Population analysis should yield insights into these questions, but requires careful control of uncertainties and biases. GW observations have a strong selection bias: this is due first to the dependence of the signal amplitude on the source's (intrinsic and extrinsic) parameters, and second to the complicated nature of detector noise and of current detection methods. In this work, we introduce a new physically-motivated model of the sensitivity of GW searches for CBC events, aimed at enhancing the accuracy and efficiency of population reconstructions. In contrast to current methods which rely on re-weighting simulated signals (injections) via importance sampling, we model the probability of detection of binary black hole (BBH) mergers as a smooth, analytic function of source masses, orbit-aligned spins, and distance, fitted to accurately match injection results. The estimate can thus be used for population models whose signal distribution over parameter space differs significantly from the injection distribution. Our method has already been used in population studies such as reconstructing the BBH merger rate dependence on redshift.

Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review

BackgroundTraditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission.ObjectivesTo better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb.Search methodsWe conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580.Selection criteriaOur review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data.ResultsOur search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency.Authors’ conclusionsABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB.

Initial Mass Functions of Young Stellar Clusters from the Gemini Spectroscopic Survey of Nearby Galaxies. I. Young Massive Clusters in the Antennae Galaxies

The stellar initial mass function (IMF) is a key parameter to understand the star formation process and the integrated properties of stellar populations in remote galaxies. We present a spectroscopic study of young massive clusters (YMCs) in the starburst galaxies NGC 4038/9. The integrated spectra of seven YMCs obtained with GMOS-S attached to the 8.1 m Gemini South telescope reveal the spectral features associated with stellar ages and the underlying IMFs. We constrain the ages of the YMCs using the absorption lines and strong emission bands from Wolf–Rayet stars. The internal reddening is also estimated from the strength of the Na i D absorption lines. Based on these constraints, the observed spectra are matched with the synthetic spectra generated from a simple stellar population model. Several parameters of the clusters including age, reddening, cluster mass, and the underlying IMF are derived from the spectral matching. The ages of the YMCs range from 2.5 to 6.5 Myr, and these clusters contain stellar masses ranging from 1.6 × 105 M ☉ to 7.9 × 107 M ☉. The underlying IMFs appear to differ from the universal form of the Salpeter/Kroupa IMF. Interestingly, massive clusters tend to have the bottom-heavy IMFs, although the masses of some clusters are overestimated due to the crowding effect. Based on this, our results suggest that the universal form of the IMF is not always valid when analyzing integrated light from unresolved stellar systems. However, further study with a larger sample size is required to reach a definite conclusion.

Estimation of population affinity using cranial measurements acquired in multidetector computed tomography images of Japanese and Malay individuals.

It is imperative in a forensic investigation to determine the identity of an unidentified corpse, for which a crucial starting point is to establish population affinity as part of the biological profile supplied by the forensic anthropologist. The present study investigates the feasibility of using multidetector computed tomography (MDCT) images to quantify craniometric variation between Japanese and Malay populations relative to the estimation of population affinity in a forensic context. The Japanese and Malay samples comprise MDCT scans of 252 (122 female; 130 male) and 182 (84 female; 98 male) adult individuals, respectively. A total of 18 measurements were acquired, and two machine learning methods (random forest modeling, RFM; support vector machine, SVM) were applied to classify population affinity. The accuracy of the two-way pooled-sex model was 88.0% for RFM and 94.5% for SVM, respectively. The four-way population and sex model produced an overall classification accuracy of 81.3% for RFM and 91.7% for SVM. The sex-specific models of population affinity showed correct rates of classification of more than 90% in both females (90.8% for RFM and 97.6% for SVM) and males (91.2% for RFM and 97.4% for SVM). Our findings clearly indicate that the cranial measurements acquired in MDCT images can be used for the forensic classification of Japanese and Malay individuals and thus serve as a reference for forensic anthropologists attempting to identify unidentified remains.

Density Increases in Clearcuts Restored with Fire in the Largest Remaining Population of Florida Scrub-Jays

Abstract Managers often use mechanical disturbance in conjunction with, or in place of, prescribed fire to maintain pyrogenic plant communities in the southeastern United States. However, information on the effects of mechanical fire-surrogates on disturbance-dependent wildlife is lacking. The Florida scrub-jay Aphelocoma coerulescens is an endemic bird species reliant on Florida scrub, a pyrogenic shrubland plant community narrowly distributed on sandy ridges in peninsular Florida. Ocala National Forest in north-central Florida supports the largest remaining population of Florida scrub-jays, and historically, most scrub habitat at the site was maintained through the commercial harvest (clearcutting) of mature sand pines Pinus clausa. Recently, Scrub-Jay Management Areas were established as part of a habitat restoration program, in which clearcut stands were converted to scrub maintained in an early-successional condition by prescribed fire. We studied Florida scrub-jays in Scrub-Jay Management Areas (2016‒2023) to better understand how family-group density changed over time since fire management compared to time since harvest in clearcut stands. In scrub treated with prescribed fire, Florida scrub-jay family-group density increased more rapidly post-disturbance and was consistently higher than in clearcut stands. Based on model predictions, the maximum mean density of family groups occurred at 8.5 years post-fire (11.8 family groups/41 ha) in Scrub-Jay Management Areas and 6.2 years post-harvest (5.5 family groups/41 ha) in clearcut stands. Our study provides the first quantitative data on the response of Florida scrub-jay populations to new forest management practices in this large and critically important population. These data provide an essential component for developing population models for Ocala National Forest under current conditions and for modeling the potential effects of future management decisions.

Use of integrated population models for assessing density-dependence and juvenile survival in Northern Bobwhites (Colinus virginianus).

Management of wildlife populations is most effective with a thorough understanding of the interplay among vital rates, population growth, and density-dependent feedback; however, measuring all relevant vital rates and assessing density-dependence can prove challenging. Integrated population models have been proposed as a method to address these issues, as they allow for direct modeling of density-dependent pathways and inference on parameters without direct data. We developed integrated population models from a 25-year demography dataset of Northern Bobwhites (Colinus virginianus) from southern Georgia, USA, to assess the demographic drivers of population growth rates and to estimate the strength of multiple density-dependent processes simultaneously. Furthermore, we utilize a novel approach combining breeding productivity and post-breeding abundance and age-and-sex ratio data to infer juvenile survival. Population abundance was relatively stable for the first 14 years of the study but began growing after 2012, showing that bobwhite populations may be stable or exhibit positive population growth in areas of intensive management. Variation in breeding and non-breeding survival drove changes in population growth in a few years; however, population growth rates were most affected by productivity across the entire study duration. A similar pattern was observed for density-dependence, with relatively stronger negative effects of density on productivity than on survival. Our novel modeling approach required an informative prior but was successful at updating the prior distribution for juvenile survival. Our results show that integrated population models provide an attractive and flexible method for directly modeling all relevant density-dependent processes and for combining breeding and post-breeding data to estimate juvenile survival in the absence of direct data.

A Methodology of Structural Incorporation for Model Transfer and Construction: The Case of Malthus’ and Darwin’s Models of Population

Abstract This paper proposes a methodology to investigate how the conceptual structure of a model may have borrowed from an already existing model in a different field. From a reverse perspective, it amounts to hypothesizing that an already existing model was somehow transferred to a new field and incorporated into a newly constructed model with the purpose of explaining some additional phenomena. We use two well-known theoretic models, Robert Malthus’ model of human population and Darwin’s model of organism population included in the theory of evolution by natural selection, to exemplify this “incorporation of a conceptual structure” methodology. By highlighting how the conceptual structure of Malthus’ model actually sits within that of Darwin’s model, we show how Darwin’s model may have been constructed, on the one hand, and how Malthus’ model may have been transferred, on the other hand. In order to show how the construction and transfer occur, we outline five epistemic strategies that could be underlying incorporation. Overall, this methodology can be viewed as a diadic and bijective perspective on models that can be applied to other similar cases, using “construction” and “transfer” as complementary handles to probe the available conceptual, structural, and historiographical material.

Utilizing an Opportunistic Clinical Study and Population-Based Pharmacokinetic Models to Identify Rational Empiric Dosing Regimens for Piperacillin-Tazobactam in Critically Ill Patients.

Determining an effective dosing regimen for piperacillin-tazobactam in critically ill patients is challenging due to substantial pharmacokinetic variability caused by complex pathophysiological changes. To address this need, a prospective clinical study was conducted, which enrolled 112 critically ill patients and employed an opportunistic sampling strategy. Population modeling and simulation were performed to characterize the pharmacokinetics (PK) and probability of target attainment (PTA) of piperacillin-tazobactam under various dosing regimens. Both piperacillin and tazobactam final models were one-compartment models with zero-order input and first-order elimination. Significant covariates included lean body weight for piperacillin and creatinine clearance along with continuous renal replacement therapy (CRRT) for both drugs. Monte Carlo simulations demonstrated that continuous infusion can achieve higher PTA than intermittent and extended infusions. When considering the minimum inhibitory concentration (MIC) of 16 mg/L for Pseudomonas aeruginosa (a frequently encountered bacterial pathogen among critically ill patients) and a PK/PD target of 100% fT >MIC, continuous infusion of 6 g/day is recommended for critically ill patients with a CLcr <60 mL/min, 9 g/day for patients with CLcr in the range of 60 to 129 mL/min, and 12 g/day for patients with a CLcr ≥130 mL/min. In addition, extended infusion represents a good alternative, especially the 3 g q6h or 4 g q6h regimens which can achieve the designated European Committee on Antimicrobial Susceptibility Testing (EUCAST) non-species-related PK/PD breakpoint of 8 mg/L. Our study provided valuable insight into PTA outcomes, which, together with individual renal function of future patients and institution-specific piperacillin susceptibility patterns, may assist physicians when making dosing decisions.

Aotearoa New Zealand Tongan residents’ attitudes to chronic cough and access to healthcare

Little is known about the unique attitudes of Aotearoa New Zealand Tongan residents to chronic cough and healthcare access. Chronic cough is synonymous with respiratory conditions and delayed assessment and management may result in detrimental effects on quality of life, hospital admission, and mortality. Talanoa were undertaken with seven Tongan adults to explore attitudes to chronic cough and healthcare access that may facilitate or inhibit diagnosis and management. Three key themes were constructed representing disruption to dimensions of the Fonua model of health and an imbalance between the interconnectivity of life’s dimensions: (1) “feeling the cold” and the “warmth of remedies”; (2) the multidimensional impact of cough and action/inaction taken; and (3) discrepancies between understanding and accessing cough care, including respiratory physiotherapy. Study findings highlight the importance of increased community understanding of chronic cough and why and how to better access care pathways. Appreciation of the unique cultural nuances and health models of diverse patient populations, including Tongan, is essential to enhance engagement and ensure culturally responsive practice is provided. The promotion and marketing of respiratory physiotherapy in cough management is also required so that people understand, access, and engage with therapies to optimise their respiratory health.

Gas dynamics in an AGN-host galaxy at z~2.6: Regular rotation, noncircular motions, and mass models

The gas dynamics of galaxies provide critical insights into the evolution of both baryons and dark matter (DM) across cosmic time. In this context, galaxies at cosmic noon, that is, in the period characterized by the most intense star formation and black hole activities, are particularly significant. We present an analysis of the gas dynamics of This galaxy lies at $z and hosts a radio-loud active galactic nucleus (AGN). We used new ALMA observations of the line at a spatial resolution of 0.18$''$ (sim 1.5 kpc). We found that (1) the molecular gas forms a rotation-supported disk with rot v and displays a flat rotation curve out to 3.3 kpc; (2) there are several noncircular components, including a kinematically anomalous structure near the galaxy center, a gas tail to the southwest, and possibly a second weaker tail to the east; and finally, (3) the dynamical estimates of the gas and stellar masses from fitting the rotation curve are inconsistent with photometric estimates made using standard gas conversion factors and stellar population models, respectively. These discrepancies may be due to systematic uncertainties in the photometric masses or in the dynamical masses, or they might be caused by a more massive radio-loud AGN-host galaxy that is hidden behind the gas-rich emitting starburst galaxy along the line of sight. Our work shows that in-depth investigations of 3D line cubes are crucial for revealing the complexity of gas dynamics in high-$z$ galaxies, in which regular rotation may coexist with noncircular motions and possibly tidal structures.

Pendekatan Matematika yang Digunakan pada Biologi

Despite their apparent differences, mathematics and biology are increasingly being used in tandem to model and analyze a wide range of biological processes. This article addresses the use of multiple methods, including population modeling, differential equations, mathematical genetics, and epidemiology, to the study of biology, a field known as biomathematics. Genetic dynamics and the behavior of biological systems, such as population expansion and disease transmission, are understood through mathematical modeling. SIR and SEIR models are used in epidemiology to describe how infectious diseases spread throughout a population. This journal emphasizes the value of mathematical approaches in comprehending intricate biological systems and the necessity of interdisciplinary research between mathematics and biology to address both theoretical and practical issues through a review of the literature. The purpose of this article is to explain the relationship between mathematics and biology and the application of mathematical models in the analysis of biological phenomena. This article uses a literature study writing method by recording research problems, analyzing, and collaborating different thoughts.

Mitigating Acute Climate Change Threats to Reintroduced Migratory Northern Bald Ibises

For the past 20 years, reintroduction efforts have been underway to re-establish a migratory population of Northern Bald Ibises (Geronticus eremita) in Central Europe, which now consists of more than 250 birds. They breed both north and south of the Alps and migrate to a common wintering ground in Tuscany. Recently, the start of autumn migration has been increasingly delayed, which correlates with extended warm periods in autumn. Later in the year, however, the birds no longer find sufficient thermals to cross the Alps and remain in the northern Alpine foothills. In order to save their lives, we had to capture the affected birds before the onset of winter, which is not a sustainable solution. A new approach to solving the problem is the establishment of a second migration route to a wintering area in Andalusia, Spain, connecting our population with a sedentary population there. The new migration route bypasses mountain barriers and also allows the birds to reach the wintering grounds later in the year. The modelling of a pan-European population will provide the birds with high ecological and spatial flexibility. Our project exemplifies the consequences of advancing global warming for animal populations and the associated challenges for conservation projects.

Comparing E-MOSAICS predictions of high-redshift proto-globular clusters with JWST observations in lensed galaxies

ABSTRACT High-resolution imaging and strong gravitational lensing of high-redshift galaxies have enabled the detection of compact sources with properties similar to nearby massive star clusters. Often found to be very young, these sources may be globular clusters detected in their earliest stages. In this work, we compare predictions of high-redshift ($z \sim 1$–10) star cluster properties from the E-MOSAICS simulation of galaxy and star cluster formation with those of the star cluster candidates in strongly lensed galaxies from JWST and Hubble Space Telescope (HST) imaging. We select galaxies in the simulation that match the luminosities of the majority of lensed galaxies with star cluster candidates observed with JWST. We find that the luminosities, ages, and masses of the brightest star cluster candidates in the high-redshift galaxies are consistent with the E-MOSAICS model. In particular, the brightest cluster ages are in excellent agreement. The results suggest that star clusters in both low- and high-redshift galaxies may form via common mechanisms. However, the brightest clusters in the lensed galaxies tend to be $\approx 1$–$1.5 \, \rm {mag}$ brighter and $\approx 0.5$ dex more massive than the median E-MOSAICS predictions. We discuss the large number of effects that could explain the discrepancy, including simulation and observational limitations, stellar population models, cluster detection biases, and nuclear star clusters. Understanding these limitations would enable stronger tests of globular cluster formation models.

Population growth of the grey-headed albatross population on Marion Island inferred using three analysis methods

Abstract Globally, the grey-headed albatross Thalassarche chrysostoma is listed as Endangered due to decreasing populations at its major breeding colonies. We analysed the population trend at Marion Island using annual counts of incubating pairs and fledglings from 1984 to 2021 and three methods: TRends and Indices for Monitoring data (TRIM), a generalized additive model and a state-space model. In contrast to rapid decreases at most other colonies, all three methods indicated a local population increase at an average annual rate of 1.3%. The latter two models indicated a decreasing trend from 1997 to 2002, presumably related to mortality on longlines set for Patagonian toothfish Dissostichus eleginoides around the island, which peaked from 1996 to 1998. Grey-headed albatrosses exhibit greater variation in annual counts than other seabird species at Marion Island, possibly linked to interannual variation in breeding success because they are biennial breeders. However, breeding success alone was not significant in any of the models, presumably because it cannot capture the complexities of both population processes and environmental variation. Although all three models predicted the overall trends well, integrated population models, which can account for demographic processes, might be more appropriate to model long-term population trends.

The spin magnitude of stellar-mass black holes evolves with the mass

Aims. Using gravitational-wave (GW) data from the latest GW Transient Catalog (GWTC-3), we conduct a comprehensive investigation into the relationship between the masses and spin magnitudes (χ) of binary black holes (BBHs). Our focus is on identifying potential correlations between BBH masses and spin magnitudes, and exploring their astrophysical implications in terms of formation channels. Methods. We employed hierarchical Bayesian methods and new population models for spin-mass distributions to analyze the GW data. We further validated our results with several sanity checks. Results. Analyzing 59 GW signals, we find statistical evidence for an evolution of the spin magnitude of the BBHs as a function of the mass. We interpret the evolution in two ways. First, using a class of population models that parameterize the evolution of the spin distribution with mass, we observe a transition from a population of BBHs with lower spin magnitudes (χ ∼ 0.2) at lower masses to higher, but less constrained, spin magnitudes for higher masses. The transition between these two distinct distributions occurs around 45 M⊙ − 55 M⊙. Additionally, using population models built by mixing independent populations of BBHs, we find that the observed GW signals can be interpreted as consisting ∼98% of low-spin black holes with masses ≲40 M⊙ and ∼2% high-spin black holes with masses ≳40 M⊙. Conclusions. Using different prescriptions for the interplay between BBH spins and masses, we find evidence of a mass scale at 45 M⊙ − 55 M⊙, where the population distribution of spin magnitudes changes. We speculate that this result may support the hypothesis that a large fraction of low-mass, low-spin BBHs are formed through the evolution of isolated stellar binaries, whereas a smaller fraction of higher-mass, high-spin BBHs are likely formed through dynamical assembly or hierarchical mergers.

Integrating complexity in population modelling: From matrix to dynamic models

Matrix models are widely used in population ecology studies and are valuable for analysing population dynamics, although they are limited in the use of time-varying parameters. This limitation can be overcome by dynamic models. In this study, we revisit a previously published study on a matrix model of a population of the box jellyfish Carybdea marsupialis (L. 1758) in the Western Mediterranean. A dynamic model integrating the transition matrix of the original model is developed in STELLA Architect with the following improvements: (1) Sensitivity study of the reliability of the methodology for calculating the transition matrix and estimation of the errors of the fitting parameters; (2) Closure of the jellyfish life cycle by adding the polyp stage. This will make it possible to simulate scenarios of ecological interest over several years such as a decline in food supply, jellyfish removal strategies, changes in drift currents and changes in substrate availability for planulae to settle. (3) The inclusion of more biological reality. In particular, a temporal pattern of strobilation is added, which improves the fit of the model to the field data.

Generation of Shape Models of Calcified TAVR populations for Solid Mechanics Simulations by means of Deep Learning

Abstract Minimally invasive transcatheter aortic valve replacement (TAVR) procedure has become the preferred procedure for patients with aortic valve stenosis or insufficiency with high risk for conventional open surgery. The favorable clinical outcomes of high-risk patients led to an expansion of the cohort including intermediate and low-risk patients. A critical aspect of advancing TAVR procedures lies in preoperative planning, integrating patient-specific in-silico deployment simulation and post-deployment fluid mechanics assessments. This study introduces a novel approach to calcified TAVR patient shape modeling, addressing this problems. The model integrates an extended mesh generated by DeepCarve, encompassing the aortic arch, and a novel deep learning-based volumetric shape model of calcifications. The key innovation lies in the utilization of a conditional Convolutional Variational Autoencoder (cCVAE) to generate realistic calcification patterns, demonstrating promising preliminary results in matching actual cohort data. Future investigations should focus on data collection from diverse medical centers to validate and refine the proposed methodology. This study showcases significant progress in generating synthetic TAVR patient geometries, incorporating detailed anatomical structures such as the aortic root, valve, and arch, along with volumetric calcification patterns. These findings represent a crucial step towards enabling real-time preoperative TAVR planning, inclusive of patient- specific in-silico deployment simulation and comprehensive fluid mechanics assessments.