Random Time Points Research Articles

Cardiovascular Risk Profile Among Patients With Inflammatory Bowel Disease: A Population-based Study of More Than 100 000 Individuals.

Patients with inflammatory bowel disease have increased risks of cardiovascular diseases, but the role of traditional and non-traditional cardiovascular risk factors remains unclear. We investigated if the cardiovascular risk profile differs between patients with inflammatory bowel disease and individuals in the general population. We included a population of 108789 participants from the Copenhagen General Population Study of individuals of Danish descent aged 20-100 years. The population included 1203 individuals with prevalent inflammatory bowel disease [347 with Crohn's disease and 856 with ulcerative colitis]. The cardiovascular risk profile was assessed by traditional risk factors [plasma lipids and glucose, body composition measures, and blood pressure] and non-traditional risk factors [inflammatory markers and biomarkers of liver and pancreas function]. Even though patients with inflammatory bowel disease more frequently are diagnosed with cardiovascular diseases, traditional cardiovascular risk factors were not increased. Indeed, patients with inflammatory bowel disease had slightly lower plasma levels of total cholesterol and low-density lipoprotein cholesterol. Levels of inflammatory markers, particularly high-sensitivity C-reactive protein, were higher in individuals with versus without a diagnosis of inflammatory bowel disease, when assessed at a random point in time during the disease course. The increased risk of cardiovascular diseases in patients with inflammatory bowel disease may be linked to chronic systemic inflammation rather than to traditional cardiovascular risk factors. Further studies need to examine whether cardiovascular-preventive strategies should focus on optimising management of inflammation in patients with inflammatory bowel disease rather than focusing on traditional cardiovascular risk factors.

Sample average approximation for a two-echelon inventory system with service-level constraints

We propose a sample-average-approximation-based simulation optimisation approach for solving a two-echelon inventory problem containing a total cost objective function and multiple service-level constraints. Some necessary parameter settings and conditions are provided to achieve the stopping condition in practice and the convergence to the optimal solution. The approach takes into account the stochastic nature of the objective and constraint performance measures, and allows the customer demands to occur with a random size and all events to occur at random points in time (including the stochastic lead times). Experimental studies are performed to evaluate the efficiency of the developed algorithms and other existing solution approaches.

Influence of Sleep Stage on LH Pulse Initiation in the Normal Late Follicular Phase and in Polycystic Ovary Syndrome

Objective: During the early follicular phase, sleep-related luteinizing hormone (LH) pulse initiation is positively associated with brief awakenings but negatively associated with rapid eye movement (REM) sleep. The relationship between sleep architecture and LH pulse initiation has not been assessed in other cycle stages or in women with polycystic ovary syndrome (PCOS). Design and Methods: We performed concomitant frequent blood sampling (LH pulse analysis) and polysomnography on 8 normal women (cycle day 7–11) and 7 women with PCOS (at least cycle day 7). Results: In the normal women, the 5 min preceding LH pulses contained more wake epochs and fewer REM epochs than the 5 min preceding randomly determined time points (wake: 22.3 vs. 9.1%, p = 0.0111; REM: 4.4 vs. 18.8%, p = 0.0162). However, LH pulse initiation was not related to wake or REM epochs in PCOS; instead, the 5 min preceding LH pulses contained more slow-wave sleep (SWS) than the 5 min before random time points (20.9 vs. 6.7%, p = 0.0089). Compared to the normal subjects, the women with PCOS exhibited a higher REM-associated LH pulse frequency (p = 0.0443) and a lower proportion of wake epochs 0–5 min before LH pulses (p = 0.0205). Conclusions: Sleep-related inhibition of LH pulse generation during the later follicular phase is normally weakened by brief awakenings and strengthened by REM sleep. In women with PCOS, LH pulse initiation is not appropriately discouraged by REM sleep and may be encouraged by SWS; these abnormalities may contribute to a high sleep-related LH pulse frequency in PCOS.

Phenotypic Heterogeneity in Sugar Utilization by E. coli Is Generated by Stochastic Dispersal of the General PTS Protein EI from Polar Clusters.

Although the list of proteins that localize to the bacterial cell poles is constantly growing, little is known about their temporal behavior. EI, a major protein of the phosphotransferase system (PTS) that regulates sugar uptake and metabolism in bacteria, was shown to form clusters at the Escherichia coli cell poles. We monitored the localization of EI clusters, as well as diffuse molecules, in space and time during the lifetime of E. coli cells. We show that EI distribution and cluster dynamics varies among cells in a population, and that the cluster speed inversely correlates with cluster size. In growing cells, EI is not assembled into clusters in almost 40% of the cells, and the clusters in most remaining cells dynamically relocate within the pole region or between the poles. In non-growing cells, the fraction of cells that contain EI clusters is significantly higher, and dispersal of these clusters is often observed shortly after exiting quiescence. Later, during growth, EI clusters stochastically re-form by assembly of pre-existing dispersed molecules at random time points. Using a fluorescent glucose analog, we found that EI function inversely correlates with clustering and with cluster size. Thus, activity is exerted by dispersed EI molecules, whereas the polar clusters serve as a reservoir of molecules ready to act when needed. Taken together our findings highlight the spatiotemporal distribution of EI as a novel layer of regulation that contributes to the population phenotypic heterogeneity with regard to sugar metabolism, seemingly conferring a survival benefit.

An Options Approach to Cybersecurity Investment

Cybersecurity has become a key factor that determines the success or failure of companies that rely on information systems. Therefore, investment in cybersecurity is an important financial and operational decision. Typical information technology investments aim to create value, whereas cybersecurity investments aim to minimize loss incurred by cyber attacks. Admittedly, cybersecurity investment has become an increasingly complex one, since information systems are typically subject to frequent attacks, whose arrival and impact fluctuate stochastically. Furthermore, cybersecurity measures and improvements, such as patches, become available at random points in time making investment decisions even more challenging. We propose and develop an analytical real options framework that incorporates major components relevant to cybersecurity practice, and analyze how optimal cybersecurity investment decisions perform for a private firm. The novelty of this paper is that it provides analytical solutions that lend themselves to intuitive interpretations regarding the effect of timing and cybersecurity risk on investment behavior using real options theory. Such aspects are frequently not implemented within economic models that support policy initiatives. However, if these are not properly understood, security controls will not be properly set resulting in a dynamic inefficiency reflected in cycles of over or under investment, and, in turn, increased cybersecurity risk following corrective policy actions. Results indicate that greater uncertainty over the cost of cybersecurity attacks raises the value of an embedded option to invest in cybersecurity. This increases the incentive to suspend operations temporarily in order to install a cybersecurity patch that will make the firm more resilient to cybersecurity breaches. Similarly, greater likelihood associated with the availability of a cybersecurity patch increases the value of the option to invest in cybersecurity. However, the absence of an embedded investment option increases the incentive to delay the permanent abandonment of the company’s operation due to the irreversible nature of the decision.

Inventory model for non-instantaneous deteriorating item with random pre-deterioration period

The paper studies an inventory model for non-instantaneous deteriorating item where the deterioration of the item is initiated at a random time point. It is assumed that no shortages are allowed and demand occurs uniformly but at different rates during pre- and post-deterioration periods. The optimum order quantity and reorder intervals are determined so as to minimise the total expected cost per unit length of an inventory cycle. Numerical examples are cited and a sensitivity analysis is carried out to study the effect of model parameters on the optimum policy.

40 ED crowding: the acceptability of dysfunction

BackgroundCrowding in the Emergency Department is internationally recognised as one of the greatest challenges to healthcare provision. Numerous studies have highlighted the ill-effects of crowding, including increased length of stay,...

Pharmacokinetics of S-ketamine during prolonged sedation at the pediatric intensive care unit.

S-ketamine is the S(+)-enantiomer of the racemic mixture ketamine, an anesthetic drug providing both sedation and analgesia. In clinical practice, significant interpatient variability in drug effect of S-ketamine is observed during long-term sedation. The aim of this study was to evaluate the pharmacokinetic variability of S-ketamine in children aged 0-18years during long-term sedation. Twenty-five children (median age: 0.42years, range: 0.02-12.5) received continuous intravenous administrations of 0.3-3.6mg/kg/h S-ketamine for sedation during mechanical ventilation. Infusion rates were adjusted to the desired level of sedation and analgesia based on the COMFORT-B score and Visual Analog Scale. Blood samples were drawn once daily at random time-points, and at 1 and 4hours after discontinuation of S-ketamine infusion. Time profiles of plasma concentrations of S-ketamine and active metabolite S-norketamine were analyzed using nonlinear mixed-effects modeling software. Clearance and volume of distribution were allometrically scaled using the ¾ power model. A total of 86 blood samples were collected. A 2-compartment and 1-compartment model adequately described the PK of S-ketamine and S-norketamine, respectively. The typical parameter estimates for clearance and central and peripheral volumes of distribution were: CLS-KETAMINE =112L/h/70kg, V1S-KETAMINE =7.7L/70kg, V2S-KETAMINE =545L/70kg, QS-kETAMINE =196L/h/70kg, and CLS-NORKETAMINE =53L/h/70kg. Interpatient variability of CLS-KETAMINE and CLS-NORKETAMINE was considerable with values of 40% and 104%, respectively, leading to marked variability in steady-state plasma concentrations. Substantial interpatient variability in pharmacokinetics in children complicates the development of adequate dosage regimen for continuous sedation.

A Markov decision process model for equitable distribution of supplies under uncertainty

Many individuals suffering from food insecurity obtain assistance from governmental programs and nonprofit agencies such as food banks. Much of the food distributed by food banks come from donations which are received from various sources in uncertain quantities at random points in time. This paper presents a model that can assist food banks in distributing these uncertain supplies equitably and measure the performance of their distribution efforts. We formulate this decision problem as a discrete-time, discrete state Markov decision process that considers stochastic supply, deterministic demand and an equity-based objective. We investigate three different allocation rules and describe the optimal policy as a function of available inventory. We also provide county level estimates of unmet need and determine the probability distribution associated with the number of underserved counties. A numerical study is performed to show how the allocation policy and unmet need are impacted by uncertain supply and deterministic, time-varying demand. We also compare different allocation rules in terms of equity and effectiveness.

The experience sampling method as an mHealth tool to support self-monitoring, self-insight, and personalized health care in clinical practice.

The experience sampling method (ESM) builds an intensive time series of experiences and contexts in the flow of daily life, typically consisting of around 70 reports, collected at 8-10 random time points per day over a period of up to 10 days. With the advent of widespread smartphone use, ESM can be used in routine clinical practice. Multiple examples of ESM data collections across different patient groups and settings are shown and discussed, varying from an ESM evaluation of a 6-week randomized trial of mindfulness, to a twin study on emotion dynamics in daily life. Research shows that ESM-based self-monitoring and feedback can enhance resilience by strengthening the capacity to use natural rewards. Personalized trajectories of starting or stopping medication can be more easily initiated and predicted if sensitive feedback data are available in real time. In addition, personalized trajectories of symptoms, cognitive abilities, symptoms impacting on other symptoms, the capacity of the dynamic system of mental health to "bounce back" from disturbance, and patterns of environmental reactivity yield uniquely personal data to support shared decision making and prediction in clinical practice. Finally, ESM makes it possible to develop insight into previous implicit patterns of thought, experience, and behavior, particularly if rapid personalized feedback is available. ESM enhances clinical practice and research. It is empowering, providing co-ownership of the process of diagnosis, treatment evaluation, and routine outcome measurement. Blended care, based on a mix of face-to-face and ESM-based outside-the-office treatment, may reduce costs and improve outcomes.

Irregularly observed time series – some asymptotics and the block bootstrap

ABSTRACTWe consider time series being observed at random time points. In addition to Parzen's classical modelling by amplitude modulating sequences, we state another modelling using an integer-valued sequence as the observation times. Limiting results are presented for the sample mean and are generalized to the class of functions of smooth means. Motivated by the complicated limiting behaviour, (moving) block bootstrap possibilities are investigated. Conditional on the used modelling for the irregular spacings, one is lead to different interpretations for the block length and hence bootstrap approaches. The block length either can be interpreted as the time (resulting in an observation string of fixed length containing a random number of observations) or as the number of observations (resulting in an observation string of variable length containing a fixed number of values). Both bootstrap approaches are shown to be asymptotically valid for the sample mean. Numerical examples and an application to real-world ozone data conclude the study.

Target Displacements during Eye Blinks Trigger Automatic Recalibration of Gaze Direction

Eye blinks cause disruptions to visual input and areaccompanied by rotations of the eyeball [1]. Like every motor action, these eye movements are subject to noise and introduce instabilities in gaze direction across blinks [2]. Accumulating errors across repeated blinks would be debilitatingfor visual performance. Here, we show that the oculomotor system constantly recalibrates gaze direction during blinks to counteract gaze instability. Observers were instructed to fixate a visual target while gaze direction was recorded and blinks were detected in real time. With every spontaneous blink-while eyelids were closed-the target was displaced laterally by 0.5° (or 1.0°). Most observers reported being unaware of displacements during blinks. After adapting for ∼35 blinks, gaze positions after blinks showed significant biases toward the new target position. Automatic eye movements accompanied each blink, and an aftereffect persisted for a few blinks after target displacements were eliminated. No adaptive gaze shift occurred when blinks were simulated with shutter glasses at random time points or actively triggered by observers, or when target displacements were masked by a distracting stimulus. Visual signals during blinks are suppressed by inhibitory mechanisms [3-6], so that small changes across blinks are generally not noticed [7, 8]. Additionally, target displacements during blinks can trigger automatic gaze recalibration, similar to the well-known saccadic adaptation effect [9-11]. This novel mechanism might be specific to the maintenance of gaze direction across blinks or might depend on a more general oculomotor recalibration mechanism adapting gaze position during intrinsically generated disruptions to visual input.

Dynamical Modeling of Clonal Evolution in Paired Primary and Relapsed Follicular Lymphoma Predicts a Link Between Cell Migration and Evolutionary Heterogeneity

Background: Recently, data of paired primary and relapsed t(14;18) positive follicular lymphoma (FL) revealed a large heterogeneity in clonal evolution. In these studies, ongoing somatic hypermutation (SHM) in immunoglobulin heavy chain V gene (IGHV) rearrangements was used as a "flight recorder" of evolution providing phylogenetic trees. Pasqualucci et al. [Cell Rep. 2014;6(1):130-40] observed that FL transformation to DLBCL is associated with "divergent evolution", i.e. primary and relapse tumors arise from a common precursor. Loeffler et al. [Genomic and epigenomic co-evolution in follicular lymphomas. Leukemia. 2015;29(2):456-63] reported a broader evolutionary spectrum in relapsed FL, ranging from "divergent" to "sequential" (relapse sequences emerge out of primary clones) and "no evolution" (shared IGHV sequences in primary and relapse tumors).Currently, there is no conclusive concept to explain these different types of evolution. Furthermore, the consequences of this heterogeneity for clinical decision-making are yet unclear. To answer these questions, a mathematical modeling approach was used.Methods: We developed a single-cell model of the physiological germinal center (GC) reaction to describe the dynamics of GC expansion and B-cell affinity maturation in silico. Two different growth compartments (dark and light zone within a GC) are considered. GCs are simulated over time, based on cell-cell and cell-microenvironment interactions. To capture mechanisms such as SHM, we incorporated an artificial genome into each model cell. While IGHV mutations influence antigen affinities, driver mutations are reflected by parameter changes of proliferation and apoptosis behavior in affected cells. Additionally, we permitted cell migration between follicles. Using our model, we simulated primary FL emergence and relapse and compared the results to phylogenetic trees reconstructed from IGHV sequences of paired tumor samples.Results: We identified a set of parameter changes in single cells (driver mutations) which permit an FL-cell population to take over a normal GC, creating a primary tumor in silico. Importantly, our model only provides a consistent explanation for the different types of evolution following tumor initiation if interfollicular cell migration is taken into account. Modulating 2 critical parameters (timepoint of migration and number of migrating cells), the heterogeneity of clonal evolution can be fully reproduced. While "divergent evolution" is only obtained with early migration (i.e., shortly after GC formation) and few migrating FL cells, "sequential evolution" is found in cases with late migration. In contrast, "no evolution" can only be explained if migration of multiple cells is assumed.Our model results are also in agreement with quantitative measurements such as clone sizes and the ratio of replacement and silent IGHV mutations. Additionally, our model is consistent with the observed co-evolution of driver mutations.Finally, we can make biologically and clinically testable model predictions: Upon clonal dominance of FL cells within a GC we predict antigen affinity-independent growth regulation of B-cells and complete cessation of SHM. Furthermore, we predict a pivotal role of cell migration in FL evolution and the existence of extrafollicular niche-like environments in which FL cells lie dormant for extended periods of time. Our simulations indicate that there are no distinct types of evolution. We rather propose to view evolutionary heterogeneity as a continuum, predicting the possibility of co-existence of different patterns of evolution within a patient. This notably also includes the option that the evolutionary sequence of primary and relapse tumor appears reversed.Conclusions: Our work represents the first comprehensive dynamical in silico lymphoma model, providing a consistent explanation for evolutionary heterogeneity. We conclude that the type of evolution is not predictable based on profiling of tumor cells (e.g., with respect to driver mutations) but is rather stochastic (i.e., based on random timepoints and the frequency of cell migration). We therefore caution to draw clinically relevant conclusions from evolutionary profiles. As migration of FL cells within the lymphatic system appears to be a key event in disease evolution, inhibition of cell migration may be a therapeutic target.Acknowledgment: BMBF/PTJ 031 6166 DisclosuresNo relevant conflicts of interest to declare.

Power Law Condition for Stability of Poisson Hail

The Poisson hail model is a space-time stochastic system introduced by Baccelli and Foss (J Appl Prob 48A:343–366, 2011) whose stability condition is nonobvious owing to the fact that it is spatially infinite. Hailstones arrive at random points of time and are placed in random positions of space. Upon arrival, if not prevented by previously accumulated stones, a stone starts melting at unit rate. When the stone sizes have exponential tails, then stability conditions exist. In this paper, we look at heavy tailed stone sizes and prove that the system can be stabilized when the rate of arrivals is sufficiently small. We also show that the stability condition is, in a weak sense, optimal. We use techniques and ideas from greedy lattice animals.

Identifiability issues in dynamic stress–strength modeling

In many real-life scenarios, system reliability depends on dynamic stress–strength interference, where strength degrades and stress accumulates concurrently over time. In some other cases, shocks appear at random time points, causing damage which is only effective at the instant of shock arrival. In this paper, we consider the identifiability problem of a system under deterministic strength degradation and stochastic damage due to shocks arriving according to a homogeneous Poisson process. We provide conditions under which the models are identifiable with respect to lifetime data only. We also consider current status data and suggest to collect additional information and discuss the issues of model identifiability under different data configurations.

Spectral estimation for diffusions with random sampling times

The nonparametric estimation of the volatility and the drift coefficient of a scalar diffusion is studied when the process is observed at random time points. The constructed estimator generalizes the spectral method by Gobet et al. (2006). The estimation procedure is optimal in the minimax sense and adaptive with respect to the sampling time distribution and the regularity of the coefficients. The proofs are based on the eigenvalue problem for the generalized transition operator. The finite sample performance is illustrated in a numerical example.

A Campus Community-based Mobility Model for Routing in Opportunistic Networks

Mobility models are invaluable for determining the performance of routing protocols in opportunistic networks. The movement of nodes has a significant influence on the topological structure and data transmission in networks. In this paper, we propose a new mobility model called the campus-based community mobility model (CBCNM) that closely reflects the daily life pattern of students on a real campus. Consequent on a discovery that the pause time of nodes in their community follows a power law distribution, instead of a classical exponential distribution, we abstract the semi-Markov model from the movement of the campus nodes and analyze its rationality. Then, using the semi-Markov algorithm to switch the movement of the nodes between communities, we infer the steady-state probability of node distribution at random time points. We verified the proposed CBCNM via numerical simulations and compared all the parameters with real data in several aspects, including the nodes’ contact and inter-contact times. The results obtained indicate that the CBCNM is highly adaptive to an actual campus scenario. Further, the model is shown to have better data transmission network performance than conventional models under various routing strategies.

A naturalistic examination of negative affect and disorder-related rumination in anorexia nervosa.

In anorexia nervosa (AN), volitional inhibition of rewarding behaviors, such as eating, involves a conflict between the desire to suppress appetite and the inherent motive to consume. This conflict is thought to have costs that carry over into daily life, e.g., triggering negative affect and/or recurring ruminations, which may ultimately impact long term outcome. Hence, increasing research effort is being dedicated to understand the link between emotional and ruminative processes in the etiology and maintenance of AN. We investigated whether affective states influence disorder-related rumination in AN applying "ecological momentary assessment", a method which allows the experimenter to gain insight into psychological processes in the natural environment and assess data in real time. Participants (AN=37, healthy controls=33) were given a smartphone for 14days. A ringtone signaled at six random time-points each day to fill in a questionnaire, which gauged disorder-typical thoughts about food and weight as well as affective state. Analyses, applying hierarchical linear models confirmed that AN patients spend more time thinking about food, body shape and weight than controls (p<0.001). Additionally, the results support the hypothesis that momentary negative affect (but not baseline depression (p=0.56) or anxiety symptoms (p=0.60) are positively associated with a higher amount of disorder-related rumination in patients (p<0.001). Our findings are in line with theories which claim that ruminative thinking induces a vulnerability to negative stimuli which, in turn, fosters heightened negative affect. Thus, therapeutic interventions could be improved by implementing modules that specifically target disorder-related rumination.

Time-course evaluation of survival and treatment in FIRE-3 (AIO KRK0306, first-line therapy of KRAS wild-type metastatic colorectal cancer with FOLFIRI plus cetuximab or bevacizumab).

617 Background: FIRE-3 reported overall survival (OS) difference in favour of arm A (FOLFIRI plus cetuximab, N= 297 pts.) compared to arm B (FOLFIRI plus bevacizumab, N= 295 pts.) in the absence of significant differences in progression-free survival and response rate. Methods: We subdivided the study into six-month time intervals, starting at the time point of randomisation and evaluated OS in a time-wise fashion. Within each interval, OS was analyzed by Cox regression. Furthermore, systemic treatment and local interventions were investigated in the respective time-frames. Results: The time-course hazard ratios of OS are summarized in the Table. Pronounced differences in overall survival by time-interval analysis are observed between 24 and 30 months and between 30 and 36 months. Within the interval of 24-30 months, 24.6% (73/297) of pts in arm A and 25.1% (74/295) of pts in arm B received systemic therapy (any treatment-line). In the subsequent interval of 30-36 months, systemic anti-tumor treatment decreased to 18.9% (56/297) of pts in arm A and 16.3% (48/295) of pts in arm B. Median time on treatment was less than 2 months in the intervals of 24-30 months and 30-36 months. Updated data including RAS wild-type population will be presented at the meeting. Conclusions: The differences in OS observed in FIRE-3 are pronounced in time intervals between 24-30 months as well as 30-36 months after randomisation into the study. A small number of patients received rather short courses of anti-tumor therapy in these observation units. The significant difference in OS that manifests in time-frames between 24 and 30 months as well as between 30 and 36 months suggests that underlying effects on OS in FIRE-3 may include multiple factors and cannot be explained by the number of patients on active treatment alone. Clinical trial information: NCT00433927. [Table: see text]

Are You Happy While You Work?

Using a new data source permitting individuals to record their well-being via a smartphone, we explore within-person variance in individuals’ well-being measured momentarily at random points in time. We find paid work is ranked lower than any of the other 39 activities individuals can report engaging in, with the exception of being sick in bed. Precisely how unhappy one is while working varies significantly with where you work; whether you are combining work with other activities; whether you are alone or with others; and the time of day or night you are working.