Stochastic Loss Research Articles

Stochastic Assessment of Predictions and Uncertainties for Reflectance Losses Based on Experimental Data for Three Australian Sites

A stochastic reflectance loss model is applied to extended datasets of experimental data collected at three sites in Australia, each representative of a different environment: urban, rural, and remote outback. The three sites are analysed in terms of TSP (Total Suspended Particles) or PM10 (Particulate Matter below 10µm in diameter), depending on the available dust sampler deployed at each location. Assessment of seasonal and daily patterns are also performed for further understanding of local phenomena likely to affect soiling in the area. Airborne dust concentration data are exploited to provide density distributions of expected daily reflectance losses. These mean losses for the three sites are 0.31 pp/day, 0.72 pp/day, and 0.77pp/day for the outback, rural, and urban location, respectively. These values and their distributions are paramount for evaluation of a prospective plant profitability, planning for operating plants cleaning scheduling, and assessment of a prospective CSP location at site selection phase. The developed methodology is capable of providing highly valuable information based on easily measurable airborne dust concentration data only, hence becoming a critical step for de-risking CSP plants financing and deployment.

Mechanisms that clear mutations drive field cancerization in mammary tissue

Oncogenic mutations are abundant in the tissues of healthy individuals, but rarely form tumours1–3. Yet, the underlying protection mechanisms are largely unknown. To resolve these mechanisms in mouse mammary tissue, we use lineage tracing to map the fate of wild-type and Brca1−/−;Trp53−/− cells, and find that both follow a similar pattern of loss and spread within ducts. Clonal analysis reveals that ducts consist of small repetitive units of self-renewing cells that give rise to short-lived descendants. This offers a first layer of protection as any descendants, including oncogenic mutant cells, are constantly lost, thereby limiting the spread of mutations to a single stem cell-descendant unit. Local tissue remodelling during consecutive oestrous cycles leads to the cooperative and stochastic loss and replacement of self-renewing cells. This process provides a second layer of protection, leading to the elimination of most mutant clones while enabling the minority that by chance survive to expand beyond the stem cell-descendant unit. This leads to fields of mutant cells spanning large parts of the epithelial network, predisposing it for transformation. Eventually, clone expansion becomes restrained by the geometry of the ducts, providing a third layer of protection. Together, these mechanisms act to eliminate most cells that acquire somatic mutations at the expense of driving the accelerated expansion of a minority of cells, which can colonize large areas, leading to field cancerization.

Longer healthy life, but for how many? A stochastic analysis of healthy lifespan inequality

AbstractOver the past 150 years, life expectancy doubled and healthy life expectancy increased. Expectations reveal nothing about variability, so we present a stochastic analysis to investigate changes over time, age and gender of variation, among individuals, in healthy lifespan, for different levels of country income. To complement health-adjusted life expectancy (HALE) data from the Global Burden of Disease Study, we use a stochastic model to compute the standard deviation of healthy life (SDHL). The model is a finite-state absorbing Markov chain with rewards. It includes stochastic survival, mortality, and loss of good health status. An individual surviving from one age to the next gains, as a "reward," a year of good health. This method provides all the moments of healthy longevity. The mean healthy longevity is exactly the HALE. As a measure of variation, here we focus on the standard deviation of healthy longevity. From 1990 to 2019, HALE increased, with greater increases at younger ages. At the same time, SDHL at younger ages decreased and at older ages increased. The most significant changes at birth occurred in low- and lower-middle-income countries. High- and upper-middle-income countries saw notable increases at old ages. Women generally have longer HALE and higher SDHL, but the overall HALE increase was greater for men. The reduction in SDHL over time suggests that more individuals benefit from increased longevity, particularly in low-income countries closing the gap with high-income countries. However, improvements in healthy survival at older ages appear unevenly distributed among individuals in high-income countries.

[formula omitted] control of uncertain T–S fuzzy networked control systems with actuator faults under stochastic packet loss-dependent event-triggered schemes

In this paper, the H∞ control problem of uncertain T–S fuzzy networked control systems (NCSs) with actuator faults under stochastic packet loss-dependent event-triggered schemes (SPDETS) is investigated. First, in order to improve the efficiency of communication networks, an innovative SPDETS is proposed, which relies on stochastic packet losses during data transmission and event-triggered conditions. Second, a packet loss-dependent switching-like controller is designed to maintain the control performance of the system. Third, some potential actuator faults are taken into account, besides considering system uncertainty and network-induced delay. Using the Lyapunov–Krasovskii theorem, sufficient conditions to guarantee the system stability for a specified performance index are presented, and design conditions are given for a switching-like fault-tolerant controller that compensates for the actuator efficiency loss. Finally, the effectiveness of the proposed control scheme is verified through the CSTR simulation example.

A stochastic loss given default model of Hungarian residential mortgages

AbstractThe paper analyses the Loss Given Default (LGD) rates of residential mortgages, using a model based on stochastic collateral value. The implementation of the model is based on exponential Ornstein-Uhlenbeck processes fitted to the Hungarian regions' house price indices. According to the model results, in case of a mortgage with a 80% loan-to-value ratio at origination, the expected LGD is around 30–40%, depending on the region. The highest LGD rates are estimated for villages, while the lowest rates are expected in Budapest and cities in the middle of the country. The range of the regional differences can reach 7 percentage points. According to the LGD Risk index based on the aggregated model, the LGD risk profile of recently issued mortgages has improved significantly since 2009 in Hungary.Due to the strong negative relation between the house prices and mortgage default rates, the expected return on defaulted collateral value tends to be low. The results could be relevant for credit institutions in their mortgage origination decisions and enhance analysis of lending processes and the associated risks.

Ensemble distributional forecasting for insurance loss reserving

ABSTRACT Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. In this paper, we propose a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework contains two main innovations compared to existing literature and practice. Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate – which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensembling techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). The framework developed in this paper can be implemented thanks to an R package, ADLP, which is available from CRAN.

RNASEH2B loss and PARP inhibition in advanced prostate cancer.

Clinical trials have suggested antitumor activity from PARP inhibition beyond homologous recombination deficiency (HRD). RNASEH2B loss is unrelated to HRD and preclinically sensitizes to PARP inhibition. The current study reports on RNASEH2B protein loss in advanced prostate cancer and its association with RB1 protein loss, clinical outcome and clonal dynamics during treatment with PARP inhibition in a prospective clinical trial. Whole tumor biopsies from multiple cohorts of patients with advanced prostate cancer were interrogated using whole-exome sequencing (WES), RNA sequencing (bulk and single nucleus) and immunohistochemistry (IHC) for RNASEH2B and RB1. Biopsies from patients treated with olaparib in the TOPARP-A and TOPARP-B clinical trials were used to evaluate RNASEH2B clonal selection during olaparib treatment. Shallow co-deletion of RNASEH2B and adjacent RB1, co-located at chromosome 13q14, was common, deep co-deletion infrequent, and gene loss associated with lower mRNA expression. In castration-resistant PC (CRPC) biopsies, RNASEH2B and RB1 mRNA expression correlated, but single nucleus RNA sequencing indicated discordant loss of expression. IHC studies showed that loss of the two proteins often occurred independently, arguably due to stochastic second allele loss. Pre- and post-treatment metastatic CRPC (mCRPC) biopsy studies from BRCA1/2 wildtype tumors, treated on the TOPARP phase II trial, indicated that olaparib eradicates RNASEH2B-loss tumor subclones. PARP inhibition may benefit men suffering from mCRPC by eradicating tumor subclones with RNASEH2B loss. gov NCT01682772FUNDING. AstraZeneca; Cancer Research UK; Medical Research Council; Cancer Research UK; Prostate Cancer UK; Movember Foundation; Prostate Cancer Foundation.

Cooperative Adaptive Cruise Control in the Presence of Communication and Radar Stochastic Data Loss

Cooperative Adaptive Cruise Control in the Presence of Communication and Radar Stochastic Data Loss

Stochasticity, determinism, and contingency shape genome evolution of endosymbiotic bacteria

Evolution results from the interaction of stochastic and deterministic processes that create a web of historical contingency, shaping gene content and organismal function. To understand the scope of this interaction, we examine the relative contributions of stochasticity, determinism, and contingency in shaping gene inactivation in 34 lineages of endosymbiotic bacteria, Sodalis, found in parasitic lice, Columbicola, that are independently undergoing genome degeneration. Here we show that the process of genome degeneration in this system is largely deterministic: genes involved in amino acid biosynthesis are lost while those involved in providing B-vitamins to the host are retained. In contrast, many genes encoding redundant functions, including components of the respiratory chain and DNA repair pathways, are subject to stochastic loss, yielding historical contingencies that constrain subsequent losses. Thus, while selection results in functional convergence between symbiont lineages, stochastic mutations initiate distinct evolutionary trajectories, generating diverse gene inventories that lack the functional redundancy typically found in free-living relatives.

Tools for Cre-Mediated Conditional Deletion of Floxed Alleles from Developing Cerebellar Purkinje Cells.

The Cre-lox system is an indispensable tool in neuroscience research for targeting gene deletions to specific cellular populations. Here we assess the utility of several transgenic Cre lines, along with a viral approach, for targeting cerebellar Purkinje cells (PCs) in mice. Using a combination of a fluorescent reporter line (Ai14) to indicate Cre-mediated recombination and a floxed Dystroglycan line (Dag1flox ), we show that reporter expression does not always align precisely with loss of protein. The commonly used Pcp2Cre line exhibits a gradual mosaic pattern of Cre recombination in PCs from Postnatal Day 7 (P7) to P14, while loss of Dag1 protein is not complete until P30. Ptf1aCre drives recombination in precursor cells that give rise to GABAergic neurons in the embryonic cerebellum, including PCs and molecular layer interneurons. However, due to its transient expression in precursors, Ptf1aCre results in stochastic loss of Dag1 protein in these neurons. NestinCre , which is often described as a "pan-neuronal" Cre line for the central nervous system, does not drive Cre-mediated recombination in PCs. We identify a Calb1Cre line that drives efficient and complete recombination in embryonic PCs, resulting in loss of Dag1 protein before the period of synaptogenesis. AAV8-mediated delivery of Cre at P0 results in gradual transduction of PCs during the second postnatal week, with loss of Dag1 protein not reaching appreciable levels until P35. These results characterize several tools for targeting conditional deletions in cerebellar PCs at different developmental stages and illustrate the importance of validating the loss of protein following recombination.

Improvements in charged lepton and photon propagation for the software PROPOSAL

Accurate particle simulations are essential for the next generation of experiments in astroparticle physics. The Monte Carlo simulation library PROPOSAL is a flexible tool to efficiently propagate high-energy leptons and photons through large volumes of media, for example in the context of underground observatories. It is written as a C++ library, including a Python interface. In this paper, the most recent updates of PROPOSAL are described, including the addition of electron, positron, and photon propagation, for which new interaction types have been implemented. This allows the usage of PROPOSAL to simulate electromagnetic particle cascades, for example in the context of air shower simulations. The precision of the propagation has been improved by including rare interaction processes, new photonuclear parametrizations, deflections in stochastic interactions, and the possibility of propagating in inhomogeneous density distributions. Additional technical improvements regarding the interpolation routine and the propagation algorithm are described. New version program summaryProgram Title: PROPOSAL.CPC Library link to program files:https://doi.org/10.17632/g478pjdcxy.2.Developer's repository link:https://github.com/tudo-astroparticlephysics/PROPOSAL.Licensing provisions: LGPL.Programming language: C++, Python.Journal reference of previous version: Comput. Phys. Commun. 242 (2019) 132.Does the new version supersede the previous version?: Yes.Reasons for the new version: Substantial addition of features. Various bugfixes.Summary of revisions: The library now also treats photons and has the corresponding processes implemented. New parametrizations for photonuclear interaction have been implemented. The angular deflection in stochastic energy losses has been implemented in addition to the already existing multiple scattering implementation, which has been improved to reduce the runtime. The implementation of the Landau-Pomeranchuk-Migdal effect has been corrected. The propagation algorithm has been improved, including the support of inhomogeneous density distributions.Nature of problem: Three-dimensional propagation of charged leptons and photons through different media. Particles lose energy stochastically by ionization, bremsstrahlung, pair production, and photonuclear interaction for charged leptons (including annihilation with atomic electrons for positrons) and Compton scattering, pair production, photoelectric effect and photohadronic interaction for photons. Additionally, they are deflected while propagating through the medium due to both multiple elastic Coulomb scattering as well as deflections in individual stochastic interactions. Unstable particles eventually decay, producing secondary particles.Solution method: Monte-Carlo simulation. The library samples the next interaction point, the type of interaction process, the energy lost in this interaction process, and the energy lost until this point. Particles are propagated until they decay, lose all their kinetic energy (for photons: reach a lower energy limit defined by the validity of the underlying cross sections), or until a user-defined termination criterion is reached. For each propagation step, the angular deflection and endpoint shift due to multiple scattering is calculated.To improve the performance and deal with the divergence of the bremsstrahlung cross section for small photon energies, energy losses below a predefined relative or absolute energy threshold are treated continuously. The computation time is improved by the use of interpolation tables.

Inventory control for reusable express packaging with under sharing policy

AbstractOnline shopping and e‐commerce have contributed significantly to the environmental pollution caused by the generation of packaging waste. In response to this issue, reusable express packaging has emerged as a promising solution. However, the widespread adoption of reusable express packaging faces significant challenges, namely, the initial investment burden and the complexities associated with recycling. To address these challenges and enhance resource utilization efficiency while minimizing supply–demand discrepancies, inventory sharing has emerged as an effective measure. We first establish an inventory model for reusable express packaging, which takes into account stochastic loss rates and customer service level. We further extend our research to scenarios involving both inventory sharing and competition. We substantiate the value of inventory sharing and the impact of competition through comprehensive numerical analyses. Our findings demonstrate that inventory sharing contributes to enhancing the profits of express delivery companies, especially with a more pronounced effect in companies where competition exists.

Exact Characterization of the Jointly Optimal Restocking and Auditing Policy in Inventory Systems with Record Inaccuracy

We present a continuous-time stochastic model of an inventory system with record inaccuracy. In this formulation, demand is modeled by a point process and is observable only when it leads to sales. In addition to demand that can reduce the stock, an unobservable stochastic loss process can also reduce the stock. The retailer’s goal is to identify the restocking and auditing policy that minimizes the expected discounted cost of carrying a product over an infinite horizon. We analytically characterize the optimal restocking and jointly optimal auditing policy. We prove that the optimal restocking policy is a threshold policy. Our proof of this result is based on a coupling argument that is valid for any demand and loss model. Unlike the optimal restocking policy, the jointly optimal auditing policy is not of threshold type. We show that a complete proof of this statement cannot be obtained by solely resorting to the first-order stochastic dominance property of the Bayesian shelf stock distribution induced by the demand and loss process. Instead, our characterization of the jointly optimal auditing policy is based on proving that the dynamics of the shelf stock distribution constitute a (strictly) sign-regular kernel. To our knowledge, this is the first paper that characterizes the optimal policy of a complex control problem by establishing sign regularity of its underlying Markovian dynamics. Our theoretical results lead to a fast algorithm for computing the exact jointly optimal auditing/restocking policy over the problem’s entire state space. This enables comparative statics analysis, which allows us to determine how inventory record inaccuracy affects the economic significance of various cost drivers. This, in turn, allows us to determine when or, better, under what conditions auditing can be an effective tool for reducing the total cost.

Statistically Principled Deep Learning for SAR Image Segmentation

This paper proposes a novel approach for Synthetic Aperture Radar (SAR) image segmentation by incorporating known statistical properties of SAR into deep learning models. We generate synthetic data using the Generalized Gamma distribution, modify the U-Net architecture to encompass statistical moments, and employ stochastic distance losses for improved segmentation performance. Evaluation against traditional methods will reveal the potential of this approach to advance SAR image analysis, with broader applications in environmental monitoring and general image segmentation tasks.

Role of seasonal importation and genetic drift on selection for drug-resistant genotypes of Plasmodium falciparum in high-transmission settings.

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low-transmission settings before spreading to high-transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low-transmission season were (i) more susceptible to stochastic extinction due to the action of genetic drift, and (ii) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g. neighbouring countries with high levels of resistance) may produce a greater risk during low-transmission periods.

Long Term Durable Responses in Relapsed/Refractory (r/r) ALL, DLBCL, and FL Patients Treated with Tisagenlecleucel and Its Association with Persistence of CAR T-Cells

Introduction: Monitoring of chimeric antigen receptor (CAR) transgene levels in peripheral blood post tisagenlecleucel (tisa-cel) infusion provides information on expansion and persistence of CAR T-cells. In adult diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL) patients (pts), the expansion of CAR T-cells was similar between responding and non-responding pts, while in pediatric and young adult acute lymphoblastic leukemia (ALL) pts, higher expansion in responding pts was observed. Based on long term follow up data from tisa-cel treated pts, analyses were performed to delineate the impact of CAR persistence and B cell aplasia on duration of remission (DOR). Methods: Transgene levels in blood measured by quantitative polymerase chain reaction were available from pivotal phase II studies in pts with r/r ALL (ELIANA [N=79], ENSIGN [N=64], NCT03123939 [N=69], NCT01626495 [N=60]), r/r DLBCL (JULIET [N=115]), and r/r FL (ELARA [N=97]), along with the long term follow up study (NCT02445222). The time related to loss of persistence of CAR T-cells (T loss) was defined as the time at which transgene levels first dropped below 50 transgene copies/µg DNA (approximates to lower limit of quantification (LLOQ) of the assay) after maximal expansion. Kaplan Meier analysis was performed to investigate the impact of duration of T loss and time corresponding to last quantifiable level (T last) on DOR/relapse. The impact of time to B cell recovery (>1% CD19+ B cells/WBCs or >3% CD19+ B cells/lymphocytes for ALL, and 80-616 cells/µL for DLBCL and FL), on DOR/relapse was also investigated. Results: Long term CAR persistence in tisa-cel treated pts has been observed for up to 9, 6, and 2.5 years for ALL, DLBCL, and FL pts, respectively, reflecting differing length of follow up based on initiation of trials in respective indications. Across indications, pts who lost transgene within 6 months or between 6-12 months had shorter DOR relative to pts with persistent transgene (Fig 1, ALL pts). In ALL, the median T loss was 27.4, 18.2, 9.7, and 18.0 months for ongoing complete response (CR) > 12 months, CR pts between 6-12 months, relapsed pts < 12 months, and relapsed pts > 12 months, respectively. However, of the pts who lost transgene within first 6 months, some pts maintained durable responses for ≥12 months (29%, 15%, and 50% in ALL, DLBCL, and FL, respectively). Among ALL relapsed pts, majority (17/26, 65%) of the pts with T loss < 6 months showed B cell recovery, however 6 pts had T loss at < 6 months as well as B-cell recovery but maintained response for ≥ 12 months. In ALL, Pulsipher et al.,(Blood Cancer Discov. 2022)have shown that NGS MRD within the first 6 months to 1 year post infusion may be a more reliable predictor of potential relapse than B cell recovery. The median time to B-cell recovery was 266 days in pts who relapsed/censored within 12 months but was not reached for pts with ongoing response at 12 months. On the contrary, B cell recovery seems to have no association with relapse in DLBCL or FL pts. Of the ALL pts with CAR persistence, pts with <50% morphologic blasts at any time prior to infusion of tisa-cel had longer DOR compared to pts with ≥50% blasts. This may also reflect a more resistant high risk ALL at the time of study entry or greater potential for stochastic loss of CD19 in pts with the higher disease burden. Conclusions: Long term sustained remission/response was observed in tisa-cel treated pts in the pivotal trials. The analyses demonstrate a positive association between CAR persistence and durable clinical responses across indications, however, some pts maintained durable responses despite early loss of transgene and/or early B cell recovery (< 6 months post infusion; Myers et al., J Clin Oncol. 2021). To note, in DLBCL and FL, the transgene levels in blood may not represent the levels at target sites including lymph nodes, moreover, T last and T loss are dependent on the duration of follow up, LLOQ etc. Of note, these analyses represent data from pivotal tisa-cel clinical studies with a heavily treated pt population post multiple lines of therapy including prior HSCT, and may not represent pts treated with commercial product, at earlier times post diagnosis in current studies with fewer lines of prior treatment, lower pre-infusion disease burden, and/or other CAR product. In pts with early loss of transgene, further research is needed to identify potential factors or patient characteristics resulting in durable responses.

Plant species identity and mycorrhizal type explain the root-associated fungal pathogen community assembly of seedlings based on functional traits in a subtropical forest.

As a crucial factor in determining ecosystem functioning, interaction between plants and soil-borne fungal pathogens deserves considerable attention. However, little attention has been paid into the determinants of root-associated fungal pathogens in subtropical seedlings, especially the influence of different mycorrhizal plants. Using high-throughput sequencing techniques, we analyzed the root-associated fungal pathogen community for 19 subtropical forest species, including 10 ectomycorrhizal plants and 9 arbuscular mycorrhizal plants. We identified the roles of different factors in determining the root-associated fungal pathogen community. Further, we identified the community assembly process at species and mycorrhizal level and managed to reveal the drivers underlying the community assembly. We found that plant species identity, plant habitat, and plant mycorrhizal type accounted for the variations in fungal pathogen community composition, with species identity and mycorrhizal type showing dominant effects. The relative importance of different community assembly processes, mainly, homogeneous selection and drift, varied with plant species identity. Interestingly, functional traits associated with acquisitive resource-use strategy tended to promote the relative importance of homogeneous selection, while traits associated with conservative resource-use strategy showed converse effect. Drift showed the opposite relationships with functional traits compared with homogeneous selection. Notably, the relative importance of different community assembly processes was not structured by plant phylogeny. Drift was stronger in the pathogen community for ectomycorrhizal plants with more conservative traits, suggesting the predominant role of stochastic gain and loss in the community assembly. Our work demonstrates the determinants of root-associated fungal pathogens, addressing the important roles of plant species identity and plant mycorrhizal type. Furthermore, we explored the community assembly mechanisms of root-associated pathogens and stressed the determinant roles of functional traits, especially leaf phosphorus content (LP), root nitrogen content (RN) and root tissue density (RTD), at species and mycorrhizal type levels, offering new perspectives on the microbial dynamics underlying ecosystem functioning.

Stochastic soiling loss models for heliostats in Concentrating Solar Power plants

Reflectance losses on solar mirrors due to soiling are a significant challenge for Concentrating Solar Power (CSP) plants. Soiling losses can vary significantly from site to site — with (absolute) reflectance losses varying from fractions of a percentage point up to several percentage points per day (pp/day), a fact that has motivated several studies in soiling predictive modelling. Yet, existing studies have so far neglected the characterization of statistical uncertainty in their parameters and predictions. In this paper, two reflectance loss models are proposed that model uncertainty: an extension of a previously developed physical model and a simplified model. A novel uncertainty characterization enables Maximum Likelihood Estimation techniques for parameter estimation for both models, and permits the estimation of parameter (and prediction) confidence intervals.The models are applied to data from ten soiling campaigns conducted at three Australian sites (Brisbane, Mount Isa, Wodonga). The simplified model produces high-quality predictions of soiling losses on novel data, while the semi-physical model performance is mixed. The statistical distributions of daily losses were estimated for different dust loadings. Under median conditions, the daily soiling losses for Brisbane, Mount Isa, and Wodonga are estimated as 0.53±0.66, 0.08±0.08, and 0.58±0.15 pp/day, respectively. Yet, higher observed dust loadings can drive average losses as high as 2 pp/day.Overall, the results suggest a relatively simple approach characterizing the statistical distributions of soiling losses using airborne dust measurements and short reflectance monitoring campaigns.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance.

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

Emergence of linkage between cooperative RNA replicators encoding replication and metabolic enzymes through experimental evolution.

The integration of individually replicating genes into a primitive chromosome is a key evolutionary transition in the development of life, allowing the simultaneous inheritance of genes. However, how this transition occurred is unclear because the extended size of primitive chromosomes replicate slower than unlinked genes. Theoretical studies have suggested that a primitive chromosome can evolve in the presence of cell-like compartments, as the physical linkage prevents the stochastic loss of essential genes upon division, but experimental support for this is lacking. Here, we demonstrate the evolution of a chromosome-like RNA from two cooperative RNA replicators encoding replication and metabolic enzymes. Through their long-term replication in cell-like compartments, linked RNAs emerged with the two cooperative RNAs connected end-to-end. The linked RNAs had different mutation patterns than the two unlinked RNAs, suggesting that they were maintained as partially distinct lineages in the population. Our results provide experimental evidence supporting the plausibility of the evolution of a primitive chromosome from unlinked gene fragments, an important step in the emergence of complex biological systems.