Delay in diagnosis of eosinophilic esophagitis increases risk for stricture formation in a time-dependent manner

BACKGROUND & AIMS Development of strictures is a major concern for patients with eosinophilic esophagitis (EoE). At diagnosis, EoE can present with an inflammatory phenotype (characterized by whitish exudates, furrows, and edema), a stricturing phenotype (characterized by rings and stenosis), or a combination of these. Little is known about progression of stricture formation; we evaluated stricture development over time in the absence of treatment and investigated risk factors for stricture formation. METHODS We performed a retrospective study using the Swiss EoE Database, collecting data on 200 patients with symptomatic EoE (153 men; mean age at diagnosis, 39 ± 15 years old). Stricture severity was graded based on the degree of difficulty associated with passing of the standard adult endoscope. RESULTS The median delay in diagnosis of EoE was 6 years (interquartile range, 2-12 years). With increasing duration of delay in diagnosis, the prevalence of fibrotic features of EoE, based on endoscopy, increased from 46.5% (diagnostic delay, 0-2 years) to 87.5% (diagnostic delay, >20 years; P = .020). Similarly, the prevalence of esophageal strictures increased with duration of diagnostic delay, from 17.2% (diagnostic delay, 0-2 years) to 70.8% (diagnostic delay, >20 years; P < .001). Diagnostic delay was the only risk factor for strictures at the time of EoE diagnosis (odds ratio = 1.08; 95% confidence interval: 1.040-1.122; P < .001). CONCLUSIONS The prevalence of esophageal strictures correlates with the duration of untreated disease. These findings indicate the need to minimize delay in diagnosis of EoE.

The long-term stability of self-esteem: Its time-dependent decay and nonzero asymptote

How stable are individual differences in self-esteem? We examined the time-dependent decay of rank-order stability of self-esteem and tested whether stability asymptotically approaches zero or a nonzero value across long test–retest intervals. Analyses were based on 6 assessments across a 29-year period of a sample of 3,180 individuals aged 14 to 102 years. The results indicated that, as test–retest intervals increased, stability exponentially decayed and asymptotically approached a nonzero value (estimated as .43). The exponential decay function explained a large proportion of variance in observed stability coefficients, provided a better fit than alternative functions, and held across gender and for all age groups from adolescence to old age. Moreover, structural equation modeling of the individual-level data suggested that a perfectly stable trait component underlies stability of self-esteem. The findings suggest that the stability of self-esteem is relatively large, even across very long periods, and that self-esteem is a trait-like characteristic.

Time-dependent and somatically acquired mitochondrial DNA mutagenesis and respiratory chain dysfunction in a scleroderma model of lung fibrosis.

Reactive oxygen species (ROS) have been implemented in the etiology of pulmonary fibrosis (PF) in systemic sclerosis. In the bleomycin model, we evaluated the role of acquired mutations in mitochondrial DNA (mtDNA) and respiratory chain defects as a trigger of ROS formation and fibrogenesis. Adult male Wistar rats received a single intratracheal instillation of bleomycin and their lungs were examined at different time points. Ashcroft scores, collagen and TGFβ1 levels documented a delayed onset of PF by day 14. In contrast, increased malon dialdehyde as a marker of ROS formation was detectable as early as 24 hours after bleomycin instillation and continued to increase. At day 7, lung tissue acquired significant amounts of mtDNA deletions, translating into a significant dysfunction of mtDNA-encoded, but not nucleus-encoded respiratory chain subunits. mtDNA deletions and markers of mtDNA-encoded respiratory chain dysfunction significantly correlated with pulmonary TGFβ1 concentrations and predicted PF in a multivariate model.

Time-series analysis of Campylobacter incidence in Switzerland

SUMMARY Campylobacteriosis has been the most common food-associated notifiable infectious disease in Switzerland since 1995. Contact with and ingestion of raw or undercooked broilers are considered the dominant risk factors for infection. In this study, we investigated the temporal relationship between the disease incidence in humans and the prevalence of Campylobacter in broilers in Switzerland from 2008 to 2012. We use a time-series approach to describe the pattern of the disease by incorporating seasonal effects and autocorrelation. The analysis shows that prevalence of Campylobacter in broilers, with a 2-week lag, has a significant impact on disease incidence in humans. Therefore Campylobacter cases in humans can be partly explained by contagion through broiler meat. We also found a strong autoregressive effect in human illness, and a significant increase of illness during Christmas and New Year's holidays. In a final analysis, we corrected for the sampling error of prevalence in broilers and the results gave similar conclusions.

Time-Efficiency Analysis Comparing Digital and Conventional Workflows for Implant Crowns: A Prospective Clinical Crossover Trial

PURPOSE To compare time-efficiency in the production of implant crowns using a digital workflow versus the conventional pathway. MATERIALS AND METHODS This prospective clinical study used a crossover design that included 20 study participants receiving single-tooth replacements in posterior sites. Each patient received a customized titanium abutment plus a computer-aided design/computer-assisted manufacture (CAD/CAM) zirconia suprastructure (for those in the test group, using digital workflow) and a standardized titanium abutment plus a porcelain-fused-to-metal crown (for those in the control group, using a conventional pathway). The start of the implant prosthetic treatment was established as the baseline. Time-efficiency analysis was defined as the primary outcome, and was measured for every single clinical and laboratory work step in minutes. Statistical analysis was calculated with the Wilcoxon rank sum test. RESULTS All crowns could be provided within two clinical appointments, independent of the manufacturing process. The mean total production time, as the sum of clinical plus laboratory work steps, was significantly different. The mean ± standard deviation (SD) time was 185.4 ± 17.9 minutes for the digital workflow process and 223.0 ± 26.2 minutes for the conventional pathway (P = .0001). Therefore, digital processing for overall treatment was 16% faster. Detailed analysis for the clinical treatment revealed a significantly reduced mean ± SD chair time of 27.3 ± 3.4 minutes for the test group compared with 33.2 ± 4.9 minutes for the control group (P = .0001). Similar results were found for the mean laboratory work time, with a significant decrease of 158.1 ± 17.2 minutes for the test group vs 189.8 ± 25.3 minutes for the control group (P = .0001). CONCLUSION Only a few studies have investigated efficiency parameters of digital workflows compared with conventional pathways in implant dental medicine. This investigation shows that the digital workflow seems to be more time-efficient than the established conventional production pathway for fixed implant-supported crowns. Both clinical chair time and laboratory manufacturing steps could be effectively shortened with the digital process of intraoral scanning plus CAD/CAM technology.

Is sensitization to stimulants time dependent and mediated via NMDA receptors?

Chronic administration of psychomotor stimulants has been reported to produce behavioral sensitization to its effects on motor activity. This adaptation may be related to the pathophysiology of recurrent psychiatric disorders. Since disturbances in circadian rhythms are also found in many of these disorders, the relationship between sensitization and chronobiological factors became of interest. Therefore, a computerized monitoring system investigated the following: whether repeated exposure to methylphenidate (MPD) and amphetamine (AMP) could produce sensitization to its locomotor effects in the rat; whether sensitization to MPD and AMP was dependent on the circadian time of drug administration; whether the baseline levels of locomotor activity would be effected by repeated exposure to MPD and AMP; whether the expression of a sensitized response could be affected by the photoperiod; and whether MK-801, a non-competitive NMDA antagonist, could disrupt the development of sensitization to MPD. Dawley rats were housed in test cages and motor activity was recorded continuously for 16 days. The first 2 days served as baseline for each rat, and on day 3 each rat received a saline injection. The locomotor response to 0.6, 2.5, or 10 mg/kg of MPD was tested on day 4, followed by five days of single injections of 2.5 mg/kg MPD (days 5–9). After five days without injection (days 10–14) rats were re-challenged (day 15) with the same doses they received on day 4. There were three separate dose groups ran at four different times of administration, 08:00, 14:00, 20:00, or 02:00 (i.e. 12 groups). The same protocol was conducted with AMP with the doses of 0.3, 0.6, and 1.2 mg/kg given on day 4 and 15, and 0.6 mg/kg AMP as the repeated dose on days 5 to 9. In the second set of experiments only sensitization to MPD was investigated. The expression of the sensitized response was dose-dependent and mainly observed with challenge of the lower dose groups. The development of sensitization to MPD and ANT was differentially time-dependent. For MPD, the most robust sensitization occurred during the light phase, with no sensitization during the middle of the dark phase. (Abstract shortened by UMI.) ^

The Moderating Role of Electoral Pressure on Congressional Ideological Polarization: A Cross-Sectional Time-Series Analysis

Reelection and self-interest are recurring themes in the study of our congressional leaders. To date, many studies have already been done on the trends between elections, party affiliation, and voting behavior in Congress. However, because a plethora of data has been collected on both elections and congressional voting, the ability to draw a connection between the two provides a very reasonable prospect. This project analyzes whether voting shifts in congressional elections have an effect on congressional voting. Will a congressman become ideologically more polarized when his electoral margins increase? Essentially, this paper assumes that all congressmen are ideologically polarized, and it is elections which serve to reel congressmen back toward the ideological middle. The election and ideological data for this study, which spans from the 56th to the 107th Congress, finds statistically significant relationships between these two variables. In fact, congressman pay attention to election returns when voting in Congress. When broken down by party, Democrats are more exhibitive of this phenomenon, which suggest that Democrats may be more likely to intrinsically follow the popular model of representation. Meanwhile, it can be hypothesized that insignificant results for Republicans indicate that Republicans may follow a trustee model of representation.

Implementation of active surveillance cultures and associated infection control practices for the reduction of methicillin resistant Staphylococcus aureus in the healthcare setting: An interrupted time series analysis

OBJECTIVE. To determine the effectiveness of active surveillance cultures and associated infection control practices on the incidence of methicillin resistant Staphylococcus aureus (MRSA) in the acute care setting. DESIGN. A historical analysis of existing clinical data utilizing an interrupted time series design. ^ SETTING AND PARTICIPANTS. Patients admitted to a 260-bed tertiary care facility in Houston, TX between January 2005 through December 2010. ^ INTERVENTION. Infection control practices, including enhanced barrier precautions, compulsive hand hygiene, disinfection and environmental cleaning, and executive ownership and education, were simultaneously introduced during a 5-month intervention implementation period culminating with the implementation of active surveillance screening. Beginning June 2007, all high risk patients were cultured for MRSA nasal carriage within 48 hours of admission. Segmented Poisson regression was used to test the significance of the difference in incidence of healthcare-associated MRSA during the 29-month pre-intervention period compared to the 43-month post-intervention period. ^ RESULTS. A total of 9,957 of 11,095 high-risk patients (89.7%) were screened for MRSA carriage during the intervention period. Active surveillance cultures identified 1,330 MRSA-positive patients (13.4%) contributing to an admission prevalence of 17.5% in high-risk patients. The mean rate of healthcare-associated MRSA infection and colonization decreased from 1.1 per 1,000 patient-days in the pre-intervention period to 0.36 per 1,000 patient-days in the post-intervention period (P<0.001). The effect of the intervention in association with the percentage of S. aureus isolates susceptible to oxicillin were shown to be statistically significantly associated with the incidence of MRSA infection and colonization (IRR = 0.50, 95% CI = 0.31-0.80 and IRR = 0.004, 95% CI = 0.00003-0.40, respectively). ^ CONCLUSIONS. It can be concluded that aggressively targeting patients at high risk for colonization of MRSA with active surveillance cultures and associated infection control practices as part of a multifaceted, hospital-wide intervention is effective in reducing the incidence of healthcare-associated MRSA.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

Susceptibility and time-dependent magnetization of basalts at DSDP Hole 76-534A

Basalts from Hole 534A are among the oldest recovered from the ocean bottom, dating from the opening of the Atlantic 155 Ma. Upon exposure to a 1-Oe field for one week, these basalts acquire a viscous remanent magnetization (VRM), which ranges from 4 to 223% of their natural remanent magnetization (NRM). A magnetic field of similar magnitude is observed in the paleomagnetic lab of the Glomar Challenger, and it is therefore doubtful if accurate measurements of magnetic moment in such rocks can be made on board unless the paleomagnetic area is magnetically shielded. No correlation is observed between the Konigsberger ratio (beta), which is usually less than 3, and the ability to acquire a VRM. The VRM shows both a log t dependence and a Richter aftereffect. Both of these, but especially the log t dependence, will cause the susceptibility measurements (made by applying a magnetic field for a very short time) to be minimum values. The susceptibility and derived Q should therefore be used cautiously for magnetic anomaly interpretation, because they can cause the importance of the induced magnetization to be underestimated.

Seagrass time-series analysis products, in Moreton Bay, Australia, with links to GeoTIFFs

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (~200 km**2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

Lagrangian back-tracing results for Kohnen station; output of time dependent numerical experiment with a 3D nested Antarctic ice sheet model

A nested ice flow model was developed for eastern Dronning Maud Land to assist with the dating and interpretation of the EDML deep ice core. The model consists of a high-resolution higher-order ice dynamic flow model that was nested into a comprehensive 3-D thermomechanical model of the whole Antarctic ice sheet. As the drill site is on a flank position the calculations specifically take into account the effects of horizontal advection as deeper ice in the core originated from higher inland. First the regional velocity field and ice sheet geometry is obtained from a forward experiment over the last 8 glacial cycles. The result is subsequently employed in a Lagrangian backtracing algorithm to provide particle paths back to their time and place of deposition. The procedure directly yields the depth-age distribution, surface conditions at particle origin, and a suite of relevant parameters such as initial annual layer thickness. This paper discusses the method and the main results of the experiment, including the ice core chronology, the non-climatic corrections needed to extract the climatic part of the signal, and the thinning function. The focus is on the upper 89% of the ice core (appr. 170 kyears) as the dating below that is increasingly less robust owing to the unknown value of the geothermal heat flux. It is found that the temperature biases resulting from variations of surface elevation are up to half of the magnitude of the climatic changes themselves.