Stochastic Changes Research Articles

Abstract B30: Three-dimensional organoid model for acquired drug resistance in non-small cell lung cancer

Abstract Background: Targeted therapies against specific driver mutations of cancer progression have been used to improve survival of lung adenocarcinoma patients. In KRAS mutant NSCLC specifically, however, after some initial improvement in lung cancer patients, targeted therapies often fail due to acquired drug resistance. To uncover mechanisms of resistance and to discover new drivers, genome-scale sequencing of lung cancers has identified candidate genes, but these data have not rapidly translated in preclinical validation. A major obstacle in lung cancer research has been the deficiencies of standard in vitro models. Methods: To address the deficiencies within standard models we have developed an in vitro 3-dimensional, KRAS-mutated “organoid” model of lung adenocarcinoma that surpasses both in vitro and in vivo models by possessing the tractability of cell lines and the 3-dimensional architecture and morphology of animal models. We have engineered a p53 knockout and KRAS mutation on top of normal wild-type lung epithelium to achieve oncogenicity. Result: Through an optimized growth period in the presence of drug, an organoid model of resistance has been developed through which de novo genetic events underlying acquired resistance can be studied. Conclusion: The highly defined genetic background of the KRAS-mutated 3-D organoid model serves as a tabula rasa upon which stochastic secondary genetic and epigenetic changes can be identified and mechanistically studied by forward and reverse genetics approaches in order to rapidly identify mechanisms of acquired drug resistance and validate therapeutic options. Citation Format: Navika D. Shukla, Ameen A. Salahudeen, Sukhmani K. Padda, Joel W. Neal, Heather A. Wakelee, Calvin J. Kuo. Three-dimensional organoid model for acquired drug resistance in non-small cell lung cancer [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr B30.

Обгрунтування робочої міри ефективності електротехнологічної водоочистки

The purpose of research – substantiation of the operational efficiency of sewage treatment systems and methods of its use in production. Materials and methods of research. This is primarily due to the significant interdependence of quality indicators of runoff and constant stochastic changes in the parameters of production technology; with the measurement taking from several tens of minutes to several hours, even days (oxygen bio-consumption, Coli-index). In the case of volumes of hazardous industrial discharges from 500 m 3 /day to 10 000 m 3 /day, inefficiencies and long-term measurements, in terms of environmental safety, are unacceptable - especially in the context of the potential impact of unusual situations (US) on production and, accordingly, volley increases in the values of pollutants and / or wastewater costs. The situation is complicated by the lack of or low accuracy and speed of modern means of automatic measurement of water composition (there are temperature, pressure, turbidity, flow rates, pH, oxidation, chlorine content, ionic composition). That is, in reality, it is not possible to operate a water treatment system in real time based on modern measuring instruments, as there will be one of the negative scenarios: 1. Insufficient water purification and pollution of the environment; 2. Inappropriate use (over-utilization) of resources (electricity, reagents, etc.). In such the layout, the working measure efficiency electrotechnological water cleaning makes it possible to set up industrial water treatment systems that use the following basic methods: - biological: through the calculation of supply by the compressor of oxygen and known parameters of its oxidizing action on organic pollutants; - physical: evaluating the filtration through the sorption filter; - chemical: by establishing the degree of oxidation in oxidants and the efficiency of coagulation in the reactor vessel; - physico-chemical: assessing the work of the electrolyzer-pH correction and oxidizing agents. Accordingly, the working measure efficiency electrotechnological water cleaning can be used to correct the dependencies of the normative documents, implementing the object-oriented concept of designing water treatment systems on the basis of, for example, methods of dominant dynamic pollutant (DDP) and cross-pollutant impacts (CPI). Recalculation from energy consumption indicators is performed in accordance with the dependencies SNiP 2.04.03-85 "Design of sewage treatment facilities" and other basic studies. In the proposed scheme, the working measure efficiency electrotechnological water cleaning performs the tasks: - priority treatment of wastewater (part of the treated water no more than 1% of the total volume) with the choice of effective regimes for the operation of the main treatment facilities; - constant correction (adaptation) of the modes of the main treatment plants by comparing the quality of the effluents upon their exit with the quality of the waste after passing (processing) the working measure efficiency electrotechnological water cleaning. Conclusions 1. Based on modern instrumental means it is practically impossible in real time to operate a water treatment system based on modern means of measuring techniques, as there will be either poor water purification and pollution of the environment or inappropriate use of resources (electricity, reagents, etc.). 2. The grounded and created the working measure efficiency electrotechnological water cleaning allows to provide designing and real-time use of combined wastewater treatment systems of multipurpose objects. Key words : electrotechnological complex, ecological safety, measurement, working conditions, unpredictability, water purification

A general framework for time-changed Markov processes and applications

In this paper, we propose a general approximation framework for the valuation of (path-dependent) options under time-changed Markov processes. The underlying background process is assumed to be a general Markov process, and we consider the case when the stochastic time change is constructed from either discrete or continuous additive functionals of another independent Markov process. We first approximate the underlying Markov process by a continuous time Markov chain (CTMC), and derive the functional equation characterizing the double transforms of the transition matrix of the resulting time-changed CTMC. Then we develop a two-layer approximation scheme by further approximating the driving process in constructing the time change using an independent CTMC. We obtain a single Laplace transform expression. Our framework incorporates existing time-changed Markov models in the literature as special cases, such as the time-changed diffusion process and the time-changed Lévy process. Numerical experiments illustrate the accuracy of our method.

Impact of demography on extinction/fixation events.

In this article we consider diffusion processes modeling the dynamics of multiple allelic proportions (with fixed and varying population size). We are interested in the way alleles extinctions and fixations occur. We first prove that for the Wright-Fisher diffusion process with selection, alleles get extinct successively (and not simultaneously), until the fixation of one last allele. Then we introduce a very general model with selection, competition and Mendelian reproduction, derived from the rescaling of a discrete individual-based dynamics. This multi-dimensional diffusion process describes the dynamics of the population size as well as the proportion of each type in the population. We prove first that alleles extinctions occur successively and second that depending on population size dynamics near extinction, fixation can occur either before extinction almost surely, or not. The proofs of these different results rely on stochastic time changes, integrability of one-dimensional diffusion processes paths and multi-dimensional Girsanov's tranform.

A General Framework for Time-Changed Markov Processes and Applications

In this paper, we propose a general approximation framework for the valuation of (path-dependent) options under time-changed Markov processes. The underlying background process is assumed to be a general Markov process, and we consider the case when the stochastic time change is constructed from either discrete or continuous additive functionals of another independent Markov process. We first approximate the underlying Markov process by a continuous time Markov chain (CTMC) and derive the functional equation characterizing the double transforms of the transition matrix of the resulting time-changed CTMC. Then we develop a two-layer approximation scheme by further approximating the driving process in constructing the time change using an independent CTMC. We obtain a single Laplace transform expression. Our framework incorporates existing time-changed Markov models in the literature as special cases, such as the time-changed diffusion process and the time-changed Levy process. Numerical experiments illustrate the accuracy of our method.

СПОСОБ УПРАВЛЕНИЯ ПОДАЧЕЙ МАТЕРИАЛА И ВОЗДУХА В ПНЕВМОСЕПАРАЦИОННЫЙ КАНАЛ ЗЕРНООЧИСТИТЕЛЬНОЙ МАШИНЫ

The article substantiates the practical application of ultrasonic range finders for studying and controlling the operation of the pneumatic separation channel of a grain cleaning machine. A new technological scheme of the installation is described, as well as the results of experimental studies aimed at finding parameters that have the greatest correlation with the supply of material and air flow to the pneumatic separation channel of the grain cleaning machine. It has been experimentally determined that the speed of the feeding roller regulating the supply of material to the pneumatic separation channel in order to fully load it and provide maximum output has a strong inverse correlation (-0.89) with the difference in the averaged values ​​of the lengths of the paths of ultrasonic waves piercing across the moving grain layer , obtained from ultrasonic range finders at the beginning and end of the pneumatic separation channel. The rotor speed of the fan regulating the air supply to the pneumatic separation channel also has a strong inverse correlation (-0.82) with the difference in the averaged values ​​of the path lengths of ultrasonic waves piercing across the moving grain layer obtained from ultrasonic range finders in the middle and end of the air separation channel. It is proved that at a frequency of polling the sensors 1200 ms, the quality of the evaluation of the state of the stochastic layer is the best. The developed method for controlling the supply of material and air to the air separation channel of the grain cleaning machine can be used as the basis for the automation of the grain cleaning process, it will allow to react in a timely manner to the stochastic change in the variable state of the process of cleaning the grain heaps, and as a consequence, to improve its quality and to reduce specific energy costs.

Environmental Factors Modify the Severity of Acute DSS Colitis in Caspase-11-Deficient Mice.

Human and mouse studies implicate the inflammasome in the pathogenesis of inflammatory bowel diseases, though the effects in mice are variable. The noncanonical inflammasome activator caspase-11 (Casp11) reportedly attenuates acute dextran sodium sulfate (DSS) colitis in mice. However, the effects of Casp11 on chronic experimental colitis and factors that influence the impact of Casp11 on acute DSS colitis are unknown. We studied the role of Casp11 in Il10-/- mice and acute and chronic DSS colitis mouse models. We quantified colonic Casp11 mRNA using quantative polymerase chain reaction and colitis using weight loss, blinded histological scoring, IL-12/23p40 secretion by colonic explants, and fecal lipocalin-2. We determined fecal microbial composition using 16S amplicon sequencing. We detected increased colonic Casp11 mRNA in Il10-/- mice with chronic colitis, but not in mice with DSS colitis. The presence of Casp11 did not alter the severity of chronic colitis in DSS-treated or Il10-/- mice. Contrary to prior reports, we initially observed that Casp11 exacerbates acute DSS colitis. Subsequent experiments in the same animal facility revealed no effect of Casp11 on acute DSS colitis. There were pronounced stochastic changes in the fecal microbiome over this time. The majority of bacterial taxa that changed over time in wild-type vs Casp11-/- mice belong to the Clostridiales. Casp11 does not impact chronic experimental colitis, and its effects on acute DSS colitis vary with environmental factors including the microbiota, particularly Clostridiales. Stochastic drifts in intestinal microbiota composition, even in mice in the same housing facility, should be considered when interpreting studies of acute DSS colitis models.

Development of the PID-neurocontroller to compensate for the impact of damages and degradation of induction motor on operation of the electric drive system

In order to synthesize adaptive control systems over asynchronous electric drive that includes a motor with defects or degradation, we proposed a structure and developed an algorithm for training a PID-neurocontroller based on a multilayer feedforward neural network. Such an approach makes it possible to operatively respond to a change in the characteristics of control object that occurs as a result of the emergence and development of damage and degradation of the motor. This, in turn, makes it possible to improve controllability of the motor, and, consequently, to prolong its operation life cycle and to enhance the energy efficiency of its operation. The proposed solutions, in contrast to the traditional, do not require the use of additional equipment for implementation. It is only needed to change a control program for the frequency converter based on the constructed algorithm. To implement the proposed solutions in practice, we synthesized an algorithm for training a neural network of the PID-neurocontroller with self-tuning. It enables the calculation of weights of the neurons that could be in the future used as the basis of software for a physical control system with the PID-neurocontroller. We mathematically modeled the operation of IM with breaks of the rotor bars and the short-circuited turns in the stator windings when using the proposed controller. An analysis of modeling results showed that the proposed approach to control the electric drive with a damaged IM makes it possible to decrease the amplitude and the number of non-basic harmonics of current and power signals of IM while maintaining the preset parameters of the technological process. Thus, our paper demonstrates the effectiveness of applying the proposed approach to the tasks on maintaining the predefined parameters of the technological process for the case of a stochastic change in the characteristics of control object

Molecular edge effects in the Endangered golden-brown mouse lemurMicrocebus ravelobensis

AbstractHabitat fragmentation creates habitat edges, and ecological edge effects can cause major changes in the ecology and distribution of many taxa. However, these ecological changes may in turn influence animal movements and lead to molecular edge effects and edge-related genetic structure, matters that are largely unexplored. This study aims to infer molecular edge effects and to test three possible underlying mechanisms in the Endangered golden-brown mouse lemurMicrocebus ravelobensis, a nocturnal species in the dry deciduous forest of the Ankarafantsika National Park in north-western Madagascar. Mouse lemurs were sampled in one edge and two interior habitats in close proximity to each other (500–1,400 m) in a continuous forest. A total of 41 mouse lemur samples were genotyped with seven nuclear microsatellites, and a fragment of the mitochondrial control region was sequenced for all samples. The overall genetic diversity (allelic richness, heterozygosity, haplotype richness, nucleotide diversity) was lower in the edge habitat compared to the two interior sites and all subpopulations showed signals of relatively low genetic exchange and significant genetic differentiation between them despite the short geographical distances, supporting the local preference model. These findings can be interpreted as preliminary signals of a molecular edge effect and suggest the potential for local adaptation. They are highly relevant for the conservation of fragmented populations, because a further subdivision of already small populations may increase their vulnerability to stochastic demographic changes and collapse.

Battle of the Mondrians: Investigating the Role of Unpredictability in Continuous Flash Suppression.

In continuous flash suppression (CFS), a dynamic sequence of Mondrian patterns presented to one eye suppresses a static target in the other eye for several seconds at a time. Its effectiveness has been linked to low-level properties such as spatial frequency and orientation, but the role of higher order influences remains unstudied. Here, using a tracking paradigm, we asked if the spatial and temporal predictability of the Mondrian sequence affects CFS dynamics. Predictable temporal sequences were regularly updated every 100 ms or modulated sinusoidally in pixel luminance at 2 Hz. Unpredictable temporal sequences were irregularly updated or had stochastic pixel luminance changes across time. To vary spatial predictability, sequences were either updated with different Mondrian patterns or had a fixed spatial pattern. We found a modest effect of spatial uncertainty when the target modulation was low (0.125 Hz) but not temporal uncertainty, which had no significant effects regardless of target modulation. Similar results were obtained when we pitted the standard Mondrian sequence against sequences with a fixed spatial pattern and temporally low-pass filtered sequences in a binocular rivalry paradigm. Thus, not only was the effect of information predictability was modest and spatial, but it was also dependent on the presence of higher temporal frequencies. Together, the results demonstrate the significance of low-level properties in affecting CFS dynamics and the possible involvement of pattern structure masking in CFS.

Abstract 1185: Synergistic drug combinations promote expansion of partially-resistant subpopulations in computational modeling of cancer heterogeneity and graded plasticity

Abstract Introduction: Synergistic drug combinations are widely sought as anti-cancer strategies because they yield higher therapeutic efficacy than additive or antagonistic combinations, enabling reduced doses. The effect of synergistic drugs (versus non-synergistic drugs) on the process of drug resistance evolution is not well understood, particularly for heterogeneous cell populations and for gradual phenotype transitions toward resistance. In this work, we use computational modeling of cancer cell numbers to interrogate the dynamics of resistance evolution during treatment with two-drug combinations. Methods: We developed computational models of cancer evolution under two-drug combination therapy, to study the development of drug resistance in cancers with varying heterogeneity or graded plasticity of resistance. A large population of cells was simulated to undergo infrequent but stochastic phenotype change over time, subject to a pharmacological dosing model of combination effects. Results: When synergistic and non-synergistic combinations were administered at equally effective doses in silico, drug resistance evolved more rapidly under the synergistic drugs. When we modeled graded plasticity by simulating multiple levels of drug resistance, results were similar except with additional bottlenecks before fully resistant cells could emerge. Meanwhile, as heterogeneity increased, synergism increased the speed of forming resistant subclones with higher fitness. Because synergistic treatments by nature exploit the simultaneous action of both drugs for their efficacy, the development of partial or single-drug resistance caused a disproportionate escape from drug effects and faster clonal expansion under synergistic therapy, compared with additive or non-synergistic therapies. Our simulations consistently showed that pairs of drugs with less synergism were able to prolong the time until double-resistance arose, provided the comparison combinations were dosed for equal initial efficacy. Conclusion: We identify dosing criteria whereby synergistic pairs of drugs actually have worse performance than additive or non-synergistic pairs, for delaying the onset of double-drug resistance. Dosing criteria are also shown for graded resistance and variable levels of population heterogeneity. Future work must examine non-heritable contributions to resistance, such as micro-environmental effects. Our theoretical work shows the need for further study of the divergence between short-term and long-term drug efficacy in the presence of clonal selection, and suggests increased attention to non-synergistic drug combinations as therapeutic candidates. Citation Format: Elysia C. Saputra, Lu Huang, Lisa Tucker-Kellogg. Synergistic drug combinations promote expansion of partially-resistant subpopulations in computational modeling of cancer heterogeneity and graded plasticity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1185.

Dynamic modeling and energy distribution analysis in a hydroelectric generating system considering the stochastic turbine flow

This study focuses the energy distribution of a hydroelectric generating system. Understanding the way energy distribution is important for researchers because it determines many of the macroscopic attributes, such as conversion efficiency, dynamic characteristics, et al. Historically, energy modeling has been split into two directions, focusing on stability analysis and its control, and has not yet been studied from the viewpoint for the stochastic turbine flow. Here we established a novel stochastic model to analyze the energy distribution of the hydroelectric generating system. We verified this model with the monitored data from Nazixia hydropower station of China. We also show how the stochastic turbine flow affects the energy losses of the system. The result shows that the one with the highest influence is the hydraulic impact losses in spiral case (more than 60%), and the hydraulic friction loss first keeps 19% as the stochastic intensity changes from 0.01 to 0.5, then increases to 20% when the stochastic intensity reaches 0.6.

Trait-based predictions and responses from laboratory mite populations to harvesting in stochastic environments.

Predictions on population responses to perturbations are often derived from trait‐based approaches like integral projection models (IPMs), but are rarely tested. IPMs are constructed from functions that describe survival, growth and reproduction in relation to the traits of individuals and their environment. Although these functions comprise biologically non‐informative statistical coefficients within standard IPMs, model parameters of the recently developed dynamic energy budget IPM (DEB‐IPM) are life‐history traits like “length at maturation” and “maximum reproduction rate”. Testing predictions from mechanistic IPMs against empirical observations can therefore provide functional insights into the links between individual life history, the environment and population dynamics.Here, we compared the population dynamics of the bulb mite (Rhizoglyphus robini) predicted by a DEB‐IPM with those observed in an experiment where populations experienced daily food rations that were either positively correlated over time (red noise), negatively (blue noise) or uncorrelated (white noise). We also selectively harvested large adults in half of these populations. The model failed to generate detailed predictions of population structure as juvenile numbers were overestimated; likely because juvenile–adult interference competition was underestimated. The model performed well at the population level as, for both harvested and unharvested populations, simulations matched the observed, long‐term stochastic growth rate λs.We next generalised the model to investigate how stochastic change affects mite λs, which correlated well with the frequency f of experiencing periods of good environment, but, due to the relationship between f and noise colour ρ, did not correlate well with shifts in ρ. The sensitivity of λs to perturbations in life‐history parameters depended on the type of stochastic change, as well as population growth.Our findings show that responses to differential mortality depend on individual life‐history traits, environmental characteristics and population growth. As long‐term climate change causes ever greater environmental fluctuations, trait‐based approaches will be increasingly important in predicting population responses to change. We therefore conclude by illustrating what questions can be examined with mechanistic trait‐based models like the DEB‐IPM, the answers to which will advance our knowledge of the functional links between individual traits, the environment and population dynamics.

Lower bounds on the run time of the Univariate Marginal Distribution Algorithm on OneMax

The Univariate Marginal Distribution Algorithm (UMDA) – a popular estimation-of-distribution algorithm – is studied from a run time perspective. On the classical OneMax benchmark function on bit strings of length n, a lower bound of Ω(λ+μn+nlog⁡n), where μ and λ are algorithm-specific parameters, on its expected run time is proved. This is the first direct lower bound on the run time of UMDA. It is stronger than the bounds that follow from general black-box complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation-of-distribution algorithms in general.

Latent Growth and Dynamic Structural Equation Models.

Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

Marginal consistent dependence modelling using weak subordination for Brownian motions

We present an approach for modelling dependencies in exponential Lévy market models with arbitrary margins originated from time changed Brownian motions. Using weak subordination of Buchmann et al. [Bernoulli, 2017], we face a new layer of dependencies, superior to traditional approaches based on pathwise subordination, since weakly subordinated processes are not required to have independent components considering multivariate stochastic time changes. We apply a subordinator being able to incorporate any joint or idiosyncratic information arrivals. We emphasize multivariate variance gamma and normal inverse Gaussian processes and state explicit formulae for the Lévy characteristics. Using maximum likelihood, we estimate multivariate variance gamma models on various market data and show that these models are highly preferable to traditional approaches. Consistent values of basket-options under given marginal pricing models are achieved using the Esscher transform, generating a non-flat implied correlation surface.

Marginal Consistent Dependence Modeling Using Weak Subordination for Brownian Motions

We present an approach for modeling dependencies in exponential Levy market models with arbitrary margins originated from time changed Brownian motions. Using weak subordination of Buchmann et al. (2016), we face a new layer of dependencies, superior to traditional approaches based on pathwise subordination, since weakly subordinated processes are not required to have independent components considering multivariate stochastic time changes. We apply a subordinator being able to incorporate any joint or idiosyncratic information arrivals. We emphasize multivariate variance gamma and normal inverse Gaussian processes and state explicit formulae for the Levy characteristics. Using maximum likelihood, we estimate a multivariate variance gamma model on various market data and show that the model is highly preferable to traditional approaches. Consistent values of basket-options under given marginal pricing models are achieved using Esscher transform, generating a non-flat implied correlation surface.

How likely am I to find parking? – A practical model-based framework for predicting parking availability

Parking availability information (or occupancy of parking facility) is highly valued by travelers, and is one of the most important inputs to many parking models. This paper proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility. While the underlying queuing model can be any reasonable model, we demonstrate the framework with the well-established continuous-time Markov M\\M\\C\\C queue in this paper. The possibility of adopting a different queuing model that can potentially incorporate the parking-searching process is also discussed. The parameter estimation module and the occupancy prediction module are both built on the underlying queuing model. To apply the estimation and prediction methods in real world, a few practical considerations are accounted for in the framework with methods to handle variations of arrival and departure patterns from day to day and within a day, including special events. The proposed framework and models are validated using both simulated and real data. Our San Francisco case studies demonstrate that the parameters estimated offline can lead to accurate predictions of parking facility occupancy both with and without real-time update. We also performed extensive numerical experiments to compare the proposed framework and methods with several pure machine-learning methods in recent literature. It is found that our approach delivers equal or better performance, but requires a computation time that is orders of magnitude less to tune and train the model. Additionally, our approach can predict for any time in the future with one training process, while pure machine-learning methods have to train a specific model for a different prediction interval to achieve the same level of accuracy.

Dynamic Speed Harmonization in Connected Urban Street Networks

AbstractConnected vehicle technology, the Internet of Things, and other advanced communication technologies create possibilities to facilitate the movement of vehicles through transportation networks and reduce their travel time. Harmonizing the speed of vehicles in different network links not only yields a more efficient network capacity utilization, but also regulates the movement of vehicles to achieve a “smoother” flow of traffic. This study develops a mathematical nonlinear formulation for dynamic speed harmonization in urban street networks aiming at improving traffic operations. We have converted the nonlinear problem into a linear program utilizing the fundamental flow–density relationship and developed a model predictive control approach to account for stochastic changes in traffic demand and further improve the efficiency of the developed solution algorithm. Results showed that the algorithm efficiently found dynamic optimal advisory speeds on various network links, and speed harmonization significantly reduced the travel time (up to 5.4%), speed variance (19.8%–29.4%), and the number of stops (8.3%–18.5%), while increasing the average speed (up to 5.9%) and the number of completed trips (up to 4%) in our case study network under all tested demand patterns.

Methylating patterns

Xie et al. show that senescence-associated DNA methylation changes evolve independently of stochastic tumour-associated methylation changes and that a subset of commonly methylated genes with the highest gains are associated with early tumorigenesis and ageing.