Markov Chain Monte Carlo Model Research Articles

Refining Credit Risk Analysis- Integrating Bayesian MCMC with Hamiltonian Monte Carlo

The accurate prediction of loan defaults is paramount for financial institutions to enhance decision-making processes, optimize loan approval rates, and mitigate associated risks. This study develops a predictive model utilizing Bayesian Markov Chain Monte Carlo (MCMC) techniques to forecast potential loan defaults. Employing a comprehensive dataset of 255,000 borrower profiles, which include detailed borrower characteristics and loan information, the model integrates advanced statistical methods to assess and interpret the factors influencing loan defaults. The Bayesian framework allows for robust uncertainty quantification and model complexity management, making it particularly suitable for the nuanced nature of credit risk assessment. Results from the model demonstrate a compelling accuracy rate, substantially aligning with industry benchmarks while providing deeper insights into the probability of default as influenced by various borrower attributes. This research underscores the efficacy of Bayesian MCMC modelling in financial risk management and offers a scalable approach for financial institutions aiming to refine their credit evaluation strategies.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

The Efficacy and Safety of Different Targeted Drugs for the Treatment of Generalized Myasthenia Gravis: A Systematic Review and Bayesian Network Meta-analysis.

The treatment of generalized myasthenia gravis (gMG) has been transformed by the development and approval of new targeted therapies. This analysis aimed to rank and compare the new therapies for gMG using efficacy and safety data from randomized controlled trials (RCTs). We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov (up to November 2022) for RCTs of targeted drugs for gMG. We used a Bayesian random-effects network meta-analysis (NMA) model and a Markov chain Monte Carlo (MCMC) model for statistical analysis. The primary outcome was the change in quantitative myasthenia gravis score (QMGS) from baseline, while the secondary outcome was the risk ratio (RR) of adverse events (AEs) during treatment. The surface under the cumulative ranking curve (SUCRA) was used to rank these targeted drugs, with higher SUCRA values indicating better efficacy or lower likelihood of AEs. In total, 13 studies (872 subjects) were included in this analysis evaluating 10 targeted drugs (batoclimab, belimumab, CFZ533, eculizumab, efgartigimod, nipocalimab, rituximab, ravulizumab, rozanolixizumab, and zilucoplan). With regards to the primary outcome, batoclimab [standardized mean difference (SMD), - 1.61; 95% credible interval (CrI), - 2.78, - 0.43] significantly reduced QMGS in patients with gMG when compared with placebo and was ranked as the most efficacious drug. Ranked second and third were eculizumab (SMD, - 0.67; 95% CrI, 1.43, 0.01) and zilucoplan (SMD, - 0.54; 95% CrI, - 1.56, 0.46), respectively. Nipoclimab (SMD, - 0.02; 95% CrI, - 1.04, 1.00) had the worst efficacy and ranked last among all targeted drugs. In our study, except for batoclimab, there was no statistically significant difference in the reduction of patient QMGS for the remaining targeted agents compared with placebo. With regards to the secondary outcomes, only batoclimab (RR, 0.19; 95% CrI, 0, 0.97) led to a significant reduction in the incidence of AEs when compared with the placebo. Belimumab (RR, 0.85; 95% CrI, 0.57, 1.19), CFZ533 (RR, 0.95; 95% CrI, 0.72, 1.25), eculizumab (RR, 0.99; 95% CrI, 0.85, 1.21), and efgartigimod (RR, 0.93; 95% CrI, 0.76, 1.15) also led to a lower incidence of AEs, although these effects were not significantly different from the placebo. Batoclimab had the best efficacy and safety for the treatment of gMG and was ranked first out of the 10 targeted drugs included in this study. Eculizumab was ranked second, and nipocalimab had the worst efficacy. With the exception of batoclimab,the incidence of AEs for the remaining drugs was not statistically significantly different from placebo. We note, however, that wide CrIs reflect the uncertainty in this analysis owing to the small number of available studies and low numbers of study participants; moreover, batoclimab had the widest CrI of all drugs in this analysis. More well-designed studies with long-term follow-up are needed to further evaluate and compare the efficacy and safety of these drugs in the future.

Modeling ants’ walks in patrolling multiple resources using stochastic approximation partial momentum refreshment

In the realm of ant foraging studies, researchers commonly map the movement of ant colonies to Markov Chain Monte Carlo (MCMC) models based on the probability matching strategy, aiming to achieve optimal foraging outcomes. When faced with multiple resources, ants often exhibit remarkable flexibility by patrolling over the foraging area and maximizing resource utilization. However, the regular MCMC models face challenges when resources are multimodally distributed as they struggle to efficiently explore the state space, particularly when modes are distantly separated. Building upon the existing partial momentum refreshment model, we propose a stochastic approximation partial momentum refreshment (SAPMR) model that not only performs equally well as regular MCMC models in bimodal distributions featuring two closely located modes but also overcomes energy barriers associated with multimodal distributions characterized by distantly separated modes. The synthetic data generated using SAPMR exhibits characteristics reminiscent of ants’ behavior such as Lévy-like patterns and maintaining a constant scaling function (≈1) when examining the relationship between the rescaled event speed and the rescaled time.

Nitrate budget of a terrestrial-to-marine continuum in South China: Insights from isotopes and a Markov chain Monte Carlo model

Anthropogenic nitrate (NO3−) production has been increasing and is exported to the ocean via river networks, causing eutrophication and ecological damage. While studies have focused on river NO3− pollution, what has been lacking is the quantification of the sources of NO3− in coastal rivers. This study applied the dual isotopes (δ15N/δ18O-NO3−) to quantify the sources and their fluxes of NO3− in two inflow rivers of the Qinzhou Bay. By adding our results to the NO3− source apportionment in Qinzhou Bay, we, for the first time, established the NO3− budgets of the terrestrial-to-marine continuum in both high- and low-flow seasons. We quantitatively showed the direct and indirect roles (e.g., the stimulation of nitrification by sewage ammonium-NH4+) of terrestrial sources in driving the high NO3− loading in the estuary. The results highlighted the necessity to consider coastal rivers and estuary as a whole, which could shed light on the effective reduction of NO3− pollution in coastal environments.

The Apparent Tidal Decay of WASP-4 b Can Be Explained by the Rømer Effect

Tidal orbital decay plays a vital role in the evolution of hot Jupiter systems. As of now, this has only been observationally confirmed for the WASP-12 system. There are a few other candidates, including WASP-4 b, but no conclusive result could be obtained for these systems as of yet. In this study, we present an analysis of new TESS data of WASP-4 b together with archival data, taking the light–time effect (LTE) induced by the second planetary companion into account as well. We make use of three different Markov chain Monte Carlo models: a circular orbit with a constant orbital period, a circular orbit with a decaying orbit, and an elliptical orbit with apsidal precession. This analysis is repeated for four cases. The first case features no LTE correction, with the remaining three cases featuring three different timing correction approaches because of the large uncertainties of the ephemeris of planet c. Comparison of these models yields no conclusive answer to the cause of WASP-4 b’s apparent transit timing variations. A broad range of values of the orbital decay and apsidal precession parameters are possible, depending on the LTE correction. However, the LTE caused by planet c can explain on its own—in full—the observed transit timing variations of planet b, with no orbital decay or apsidal precession being required at all. This work highlights the importance of continued photometric and spectroscopic monitoring of hot Jupiters.

Validating AU Microscopii d with Transit Timing Variations

AU Mic is a young (22 Myr), nearby exoplanetary system that exhibits excess transit timing variations (TTVs) that cannot be accounted for by the two known transiting planets nor stellar activity. We present the statistical “validation” of the tentative planet AU Mic d (even though there are examples of “confirmed” planets with ambiguous orbital periods). We add 18 new transits and nine midpoint times in an updated TTV analysis to prior work. We perform the joint modeling of transit light curves using EXOFASTv2 and extract the transit midpoint times. Next, we construct an O−C diagram and use Exo-Striker to model the TTVs. We generate TTV log-likelihood periodograms to explore possible solutions for d’s period, then follow those up with detailed TTV and radial velocity Markov Chain Monte Carlo modeling and stability tests. We find several candidate periods for AU Mic d, all of which are near resonances with AU Mic b and c of varying order. Based on our model comparisons, the most-favored orbital period of AU Mic d is 12.73596 ± 0.00793 days (T C,d = 2458340.55781 ± 0.11641 BJD), which puts the three planets near 4:6:9 mean-motion resonance. The mass for d is 1.053 ± 0.511 M ⊕, making this planet Earth-like in mass. If confirmed, AU Mic d would be the first known Earth-mass planet orbiting a young star and would provide a valuable opportunity in probing a young terrestrial planet’s atmosphere. Additional TTV observations of the AU Mic system are needed to further constrain the planetary masses, search for possible transits of AU Mic d, and detect possible additional planets beyond AU Mic c.

The international spill over effect of American economy on China's macro-economy based on MCMC-Gibbs sampling algorithm.

China's export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China's macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China's financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China's macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China's and the United States' economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model's sampling results are good. China's pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China's pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market.

Significant influence of water diversion and anthropogenic input on riverine sulfate based on sulfur and oxygen isotopes

The transportation of dissolved sulfate (SO42-) to the ocean via river systems plays a critical role in the global sulfur cycle. However, the increasing anthropogenic input has made it challenging to quantify the various pristine contributions accurately. The riverine sulfur and oxygen isotopes of sulfates (δ34SSO4 and δ18OSO4) and the advantages of the Markov-Chain Monte Carlo model were used to distinguish the anthropogenic input in Fenhe River, a tributary of the Yellow River. The SO42- concentrations and δ34SSO4 and δ18OSO4 values of the mainstream river were normally distributed with minor seasonal variations (P > 0.05). However, the upstream riverine SO42- concentrations were significantly altered by the water diversion from the Yellow River. The midstream and downstream sections exhibited a continuous increase in the riverine SO42- concentration and δ34SSO4 and δ18OSO4 values during both seasons. The coal mining drainage significantly impacted the tributaries near coal mining activities, and chemical fertilizers and sewage contributed more sulfate in the midstream and downstream sections. The yearly sulfate flux of the Fenhe River accounted for 4.2% of that of Yellow River, among which over 60% was from anthropogenic sources. The average δ34SSO4 and δ18OSO4 values had elevated to 11.0‰ and 6.0‰, respectively, due to the extensive input of anthropogenic sulfate. Our findings confirm the utility of δ34SSO4 and δ18OSO4 for tracing anthropogenic input and underscore its impacts on river systems and the global sulfur cycle.

Eruption history of the Columbia River Basalt Group constrained by high-precision U-Pb and 40Ar/39Ar geochronology

Large igneous province volcanism of the Columbia River Basalt Group (CRBG) has been suggested to play a causal role in elevated global temperatures and atmospheric carbon dioxide levels of the Miocene Climate Optimum (MCO). However, assessing the connection between volcanism and warming is dependent upon an accurate and precise chronology for the timing and duration of CRBG emplacement. Building on our previous work (Kasbohm and Schoene, 2018), we present fifteen new high-precision ages, using CA-ID-TIMS U-Pb on zircon and multi-collector 40Ar/39Ar on basaltic groundmass, to provide a detailed dual-chronometer timeline for CRBG eruptions. We use both sets of new ages and precise stratigraphic positions of our samples in an integrated Markov Chain Monte Carlo model to calculate average long-term emplacement rates for main-phase CRBG volcanism of 0.2-0.9 km3/a, with a high likelihood of one prominent hiatus of 60-120 kyr duration occurring after main-phase emplacement. We analyzed trace elements and hafnium isotopes of each dated zircon from CRBG interbeds. The compositions are consistent with both Cascades subduction volcanism and evolved syn-CRBG volcanism proximal to the depositional area. Our age model also yields ages for all magnetic field reversals during the main phase of CRBG emplacement, which can be used to improve calibrations of Miocene paleoclimate records. We find that main-phase CRBG emplacement is coincident with the greatest sustained warmth of the MCO in astronomically-tuned records. Our work shows the power of using both U-Pb and 40Ar/39Ar geochronology in an integrated stratigraphic context to assess data reliability and develop the most robust age model possible for large igneous province emplacement.

Microsimulation of activity generation, activity scheduling and shared travel choices within an activity-based travel demand modelling system

This study presents the microsimulation of activity generation, activity scheduling and shared travel choice processes within an activity-based shorter-term decisions simulator (SDS). Activity generation simulates individuals’ daily activities in an orderly fashion utilizing a Markov Chain Monte Carlo modelling technique within the SDS model. Activity scheduling is simulated as a process of activity agenda formation, destination location choice and shared travel choice decisions utilizing heuristics and econometric modelling (e.g. mixed logit) approaches. The proposed SDS model addresses multi-way interactions among different activity and travel decision components. It also accounts for individuals’ social interactions within the modelling framework. The model is implemented within C#.NET platform for Halifax, Canada from 2006 to 2036 on each year interval. The performance of the model is measured based on absolute percentage error (APE) values for the year 2011, and microsimulation results are validated with the 2016 Canadian Census information. The simulation results suggest that higher density of activity destinations is predicted within approximately 10 km distances from home locations in 2006. Interestingly, with time, the densities become more skewed to the left of 10 km. The highest percentage of non-shared travel (32%) is predicted for a travel length of less than 5 km in 2036, which decreases with distance. In contrast, the proportions of shared travel choice alternatives are predicted to increase as travel length increases. This paper offers critical insights on spatio-temporal evolution of activity and travel decisions, which will be useful for integrated transportation and land use policy making.

Analysis of Factors Affecting the Honeybee Colony Size from the Model-based Perspective

Bees and other important animals are vital to human survival on the earth. They not only produce delicious honey, but also play an indispensable role in plant pollination, making a unique contribution to the world. The bee population reduction crisis is known as a global problem of sustainable development goals. To consider the factors that can influence the size of honey bee populations and how these factors can affect the size of honey bee populations, we will use the most basic evaluation model. Given the need to accurately calculate the weight of each factor, we use the Analytic Hierarchy Process (AHP) model. In order to analyze the results of the AHP model, we will list our variable factors, apply the AHP model and use sensitivity analysis on it. Also, to predict how many honeybees hives you will need to support pollination of an 81,000 square meters parcel of land containing crops that benefit from pollination. we consider to use Markov Chain Monte Carlo (MCMC) model, which can indicate a stationary distribution to the attendance rate of the bees after n times.

Source apportionment of black carbon aerosols by isotopes (14C and 13C) and Bayesian modeling from two remote islands in east Asian outflow region

We report the results of source apportionment of combustion-derived black carbon (BC), in PM2.5 aerosols collected from the East Asian continental outflow region at two islands; Okinawa and Fukue during the period from October 2009 to May 2010. The 14C contents of BC from Okinawa (Cape Hedo) and Fukue islands (Fukue) varied from 24.4 to 34.2 pMC and from 18.1 to 43.0 pMC, respectively. Source apportionment of BC was conducted by using a simple 14C mass balance model and a dual-isotope Bayesian Markov chain Monte Carlo model. Biomass combustion contributions (e.g., wood fuel and crop residues) were 23–31 % (mean 27 ± 3 %) at Cape Hedo and 15–38 % (mean 27 ± 12 %) at Fukue. Both sites were equally affected by biomass combustion, whose levels are similar to those observed in the hotspot regions (megacities) in China. Fossil fuel contributions (e.g., oil, gasoline, and diesel) were 69–77 % (mean 74 ± 3 %) with 2–69 % from coal and 3–76 % from liquid fossil at Cape Hedo, and 55–85 % (mean 73 ± 12 %) at Fukue with 3 % from coal and 53–56 % from liquid fuel for the early winter. Those variations for both sites showed significantly different source region-specific trends and this trend was also consistent with the results of the air mass trajectory analysis with a higher contribution of coal-BC from North China including Beijing, and East China including Shanghai. In spring, we found a higher contribution of liquid fossil fuel combustion (e.g., traffic) over the East China Sea, including Pacific air masses, suggesting that the possible contribution of ship emissions was relatively high. Although our results for isotopes-based source apportionment of BC are preliminary, these findings suggest that long-term monitoring of BC aerosols, even in remote sites of Asian outflows, and source-specific descriptions of BC are important to improve climate models. We also recommend further measurements for implementing regionally tailored mitigation.

Cost-effectiveness of rhythm control strategy: Ablation versus antiarrhythmic drugs for treating atrial fibrillation in Korea based on real-world data.

Ablation-based treatment has emerged as an alternative rhythm control strategy for symptomatic atrial fibrillation (AF). Recent studies have demonstrated the cost-effectiveness of ablation compared with medical therapy in various circumstances. We assessed the economic comparison between ablation and medical therapy based on a nationwide real-world population. For 192,345 patients with new-onset AF (age ≥ 18 years) identified between August 2015 and July 2018 from the Korean Health Insurance Review and Assessment Service (HIRA) database, medical resource use data were collected to compare AF patients that underwent ablation (N = 2,131) and those administered antiarrhythmic drugs (N = 8,048). Subsequently, a Markov chain Monte Carlo model was built. The patients had at least one risk factor for stroke, and the base-case used a 20-year time horizon, discounting at 4.5% annually. Transition probabilities and costs were estimated using the present data, and utilities were derived from literature review. The costs were converted to US $ (2019). Sensitivity analyses were performed using probabilistic and deterministic methods. The net costs and quality-adjusted life years (QALY) for antiarrhythmic drugs and ablation treatments were $37,421 and 8.8 QALYs and $39,820 and 9.3 QALYs, respectively. Compared with antiarrhythmic drugs, incremental cost-effectiveness ratio of ablation was $4,739/QALY, which is lower than the willingness-to-pay (WTP) threshold of $32,000/QALY. In symptomatic AF patients with a stroke risk under the age of 75 years, ablation-based rhythm control is potentially a more economically attractive option compared with antiarrhythmic drug-based rhythm control in Korea.

Impact Assessment of Wet and Dry Spell on Agriculture Productivity of Chhattisgarh, India

The impact of global warming on the hydrological cycle specifically changes in precipitation patterns and their impact on agriculture in the Chhattisgarh region, the study examines historical rainfall data from 1986 to 2021 and divides the area into 27 sub-divisions for a thorough examination. The study distinguishes between wet and dry spells by using a threshold value of 2.5 mm. To assess the likelihood of wet days, the Markov Chain Monte Carlo (MCMC) model is used, and a correlation analysis finds that a proba¬bility range of 55 to 65% correlates to the maximum agricultural productivity. According to the findings, the best time for the greatest crop yield in the study area is during the rainy season (MJJASO). Farmers are urged to start planting in the second or third week of June to coincide with the onset of rainfall, allowing crops to use rainwater from July through August. For the research area, the period from the end of June to the beginning of October is rated as the most favorable for agricultural operations. Overall, this study improves our understanding of climate conditions, particularly rainfall patterns, and its findings provide useful insights for the agricultural sector and other stakeholders.

A Three-Stage Stochastic Framework for Smart Electric Vehicle Charging

As one of the most significant part of carbon neutralisation, the rapid growth of electric vehicle (EV) market in past few years has greatly expedited the transport electrification, which, however, has brought in new challenges to power system including isolated distribution network for commercial and industrial set up. Stochastic and complex EV behaviours would violate network permissible operation region and increase costs for system operators. To address these problems, a chance-constrained smart EV charging strategy in a DC microgrid (DCMG) supporting large office complex is proposed to minimize system cost from distribution network and fleet battery degradation cost from EVs providing ancillary service to the DCMG. When dealing with uncertainties from EVs, a Markov Chain Monte Carlo (MCMC) model is built to couple different parameters in load profiles and characterize the time series of likelihood of charging and discharging. A state-of-charge (SOC) space random walk method is then proposed to solve the resultant massive recursive probabilistic charging requirements. Based on that, a three-stage optimization framework is established to illustrate the work flow in system level. Numerical results verifying the effectiveness of the proposed method are also presented.

FRAME-Monte Carlo model for evaluation of the stable isotope mixing and fractionation.

Bayesian stable isotope mixing models are widely used in geochemical and ecological studies for partitioning sources that contribute to various mixtures. However, none of the existing tools allows accounting for the influence of processes other than mixing, especially stable isotope fractionation. Bridging this gap, new software for the stable isotope Fractionation And Mixing Evaluation (FRAME) has been developed with a user-friendly graphical interface (malewick.github.io/frame). This calculation tool allows simultaneous sources partitioning and fractionation progress determination based on the stable isotope composition of sources/substrates and mixture/products. The mathematical algorithm applies the Markov-Chain Monte Carlo model to estimate the contribution of individual sources and processes, as well as the probability distributions of the calculated results. The performance of FRAME was comprehensively tested and practical applications of this modelling tool are presented with simple theoretical examples and stable isotope case studies for nitrates, nitrites, water and nitrous oxide. The open mathematical design, featuring custom distributions of source isotope signatures, allows for the implementation of additional processes that alternate the characteristics of the final mixture and its application for various range of studies.

Estimating the Length of the Preclinical Detectable Phase for Open-Angle Glaucoma

A 50% reduction of glaucoma-related blindness has previously been demonstrated in a population that was screened for open-angle glaucoma. Ongoing screening trials of high-risk populations and forthcoming low-cost screening methods suggest that such screening may become more common in the future. One would then need to estimate a key component of the natural history of chronic disease, the mean preclinical detectable phase (PCDP). Knowledge of the PCDP is essential for the planning and early evaluation of screening programs and has been estimated for several types of cancer that are screened for. To estimate the mean PCDP for open-angle glaucoma. A large population-based screening for open-angle glaucoma was conducted from October 1992 to January 1997 in Malmö, Sweden, including 32 918 participants aged 57 to 77 years. A retrospective medical record review was conducted to assess the prevalence of newly detected cases at the screening, incidence of new cases after the screening, and the expected clinical incidence, ie, the number of new glaucoma cases expected to be detected without a screening. The latter was derived from incident cases in the screened age cohorts before the screening started and from older cohorts not invited to the screening. A total of 2029 patients were included in the current study. Data were analyzed from March 2020 to October 2021. The length of the mean PCDP was calculated by 2 different methods: first, by dividing the prevalence of screen-detected glaucoma with the clinical incidence, assuming that the screening sensitivity was 100% and second, by using a Markov chain Monte Carlo (MCMC) model simulation that simultaneously derived both the length of the mean PCDP and the sensitivity of the screening. Of 2029 included patients, 1352 (66.6%) were female. Of 1420 screened patients, the mean age at screening was 67.4 years (95% CI, 67.2-67.7). The mean length of the PCDP of the whole study population was 10.7 years (95% CI, 8.7-13.0) by the prevalence/incidence method and 10.1 years (95% credible interval, 8.9-11.2) by the MCMC method. The mean PCDP was similar for both methods of analysis, approximately 10 years. A mean PCDP of 10 years found in the current study allows for screening with reasonably long intervals, eg, 5 years.

A Markov chain Monte Carlo model of mechanical-feedback-driven progressive apical constrictions captures the fluctuating collective cell dynamics in the Drosophila embryo

Communication via mechanical stress feedback is believed to play an important role in the intercellular coordination of collective cellular movements. One such movement is ventral furrow formation (VFF) in the Drosophila melanogaster embryo. We previously introduced an active granular fluid (AGF) model, which demonstrated that cellular constriction chains observed during the initial phase of VFF are likely the result of intercellular coordination by tensile-stress feedback. Further observation of individual cellular dynamics motivated us to introduce progressive constrictions and Markov chain Monte Carlo based fluctuation of particle radii to our AGF model. We use a novel stress-based Voronoi tessellation method to translate the anisotropic network of highly polydisperse, axisymmetric force centers into a confluent cellular layer. This allows us to apply a similar means of analysis to both live and simulated embryos. We find that our enhanced AGF model, which combines tensile mechanical stress feedback and individual cellular fluctuations, successfully captures collective cell dynamics.

Dust reverberation mapping and light-curve modelling of Zw229-015

ABSTRACT Multiwavelength variability studies of active galactic nuclei can be used to probe their inner regions that are not directly resolvable. Dust reverberation mapping (DRM) estimates the size of the dust emitting region by measuring the delays between the infrared (IR) response to variability in the optical light curves. We measure DRM lags of Zw229-015 between optical ground-based and Kepler light curves and concurrent IR Spitzer 3.6 and 4.5 µm light curves from 2010 to 2015, finding an overall mean rest-frame lag of 18.3 ± 4.5 d. Each combination of optical and IR light curve returns lags that are consistent with each other within 1σ, which implies that the different wavelengths are dominated by the same hot dust emission. The lags measured for Zw229-015 are found to be consistently smaller than predictions using the lag–luminosity relationship. Also, the overall IR response to the optical emission actually depends on the geometry and structure of the dust emitting region as well, so we use Markov chain Monte Carlo modelling to simulate the dust distribution to further estimate these structural and geometrical properties. We find that a large increase in flux between the 2011–2012 observation seasons, which is more dramatic in the IR light curve, is not well simulated by a single dust component. When excluding this increase in flux, the modelling consistently suggests that the dust is distributed in an extended flat disc, and finds a mean inclination angle of 49$^{+3}_{-13}$ deg.