Time-domain ptychography

Through dedicated measurements in the optical regime we demonstrate that ptychography can be applied to reconstruct complex-valued object functions that vary with time from a sequence of spectral measurements. A probe pulse of approximately 1 ps duration, time delayed in increments of 0.25 ps, is shown to recover dynamics on a ten times faster time scale with an experimental limit of approximately 5 fs.

Real-time computer-based visual feedback improves visual acuity in downbeat nystagmus - a pilot study.

BACKGROUND Patients with downbeat nystagmus syndrome suffer from oscillopsia, which leads to an unstable visual perception and therefore impaired visual acuity. The aim of this study was to use real-time computer-based visual feedback to compensate for the destabilizing slow phase eye movements. METHODS The patients were sitting in front of a computer screen with the head fixed on a chin rest. The eye movements were recorded by an eye tracking system (EyeSeeCam®). We tested the visual acuity with a fixed Landolt C (static) and during real-time feedback driven condition (dynamic) in gaze straight ahead and (20°) sideward gaze. In the dynamic condition, the Landolt C moved according to the slow phase eye velocity of the downbeat nystagmus. The Shapiro-Wilk test was used to test for normal distribution and one-way ANOVA for comparison. RESULTS Ten patients with downbeat nystagmus were included in the study. Median age was 76 years and the median duration of symptoms was 6.3 years (SD +/- 3.1y). The mean slow phase velocity was moderate during gaze straight ahead (1.44°/s, SD +/- 1.18°/s) and increased significantly in sideward gaze (mean left 3.36°/s; right 3.58°/s). In gaze straight ahead, we found no difference between the static and feedback driven condition. In sideward gaze, visual acuity improved in five out of ten subjects during the feedback-driven condition (p = 0.043). CONCLUSIONS This study provides proof of concept that non-invasive real-time computer-based visual feedback compensates for the SPV in DBN. Therefore, real-time visual feedback may be a promising aid for patients suffering from oscillopsia and impaired text reading on screen. Recent technological advances in the area of virtual reality displays might soon render this approach feasible in fully mobile settings.

Visual-auditory differences in duration discrimination of intervals in the subsecond and second range

A common finding in time psychophysics is that temporal acuity is much better for auditory than for visual stimuli. The present study aimed to examine modality-specific differences in duration discrimination within the conceptual framework of the Distinct Timing Hypothesis. This theoretical account proposes that durations in the lower milliseconds range are processed automatically while longer durations are processed by a cognitive mechanism. A sample of 46 participants performed two auditory and visual duration discrimination tasks with extremely brief (50-ms standard duration) and longer (1000-ms standard duration) intervals. Better discrimination performance for auditory compared to visual intervals could be established for extremely brief and longer intervals. However, when performance on duration discrimination of longer intervals in the 1-s range was controlled for modality-specific input from the sensory-automatic timing mechanism, the visual-auditory difference disappeared completely as indicated by virtually identical Weber fractions for both sensory modalities. These findings support the idea of a sensory-automatic mechanism underlying the observed visual-auditory differences in duration discrimination of extremely brief intervals in the millisecond range and longer intervals in the 1-s range. Our data are consistent with the notion of a gradual transition from a purely modality-specific, sensory-automatic to a more cognitive, amodal timing mechanism. Within this transition zone, both mechanisms appear to operate simultaneously but the influence of the sensory-automatic timing mechanism is expected to continuously decrease with increasing interval duration.

Estimates of in situ Larval Development Time for the Lobster, Homarus Americanus

Larval development time is a critical factor in assessing the potential for larval transport, mortality. and subsequently, the connectivity of marine populations through larval exchange. Most estimates of larval duration are based on laboratory studies and may not reflect development times in nature. For larvae of the American lobster (Homarus americanus), temperature-dependent development times have been established in previous laboratory studies. Here, we used the timing of seasonal abundance curves for newly hatched larvae (stage 1) and the final plankonic instar (postlarva), coupled with a model of temperature-dependent development to assess development time in the field. We were unable to reproduce the timing of the seasonal abundance curves using laboratory development rates in our model. Our results suggest that larval development in situ may be twice as fast as reported laboratory rates. This will result in reduced estimates of larval transport potential, and increased estimates of instantaneous mortality rate and production.

Time-Limited Property Rights and Investment Incentives

Although it is axiomatic that property rights of infinite duration are necessary for owners to make efficient long term investments in their property, time limits on property rights are pervasive in the law. This paper provides an economic justification for such limits by arguing that they actually enhance property values in the presence of various sorts of market failure. The analysis offers a coherent approach for understanding what otherwise appear to be unrelated doctrines in the law.

A retrospective cohort study of the duration of hepatitis B vaccine immunity and associated factors in pediatric dialysis patients

Dialysis patients are at high risk for hepatitis B infection, which is a serious but preventable disease. Prevention strategies include the administration of the hepatitis B vaccine. Dialysis patients have been noted to have a poor immune response to the vaccine and lose immunity more rapidly. The long term immunogenicity of the hepatitis B vaccine has not been well defined in pediatric dialysis patients especially if administered during infancy as a routine childhood immunization.^ Purpose. The aim of this study was to determine the median duration of hepatitis B immunity and to study the effect of vaccination timing and other cofactors on the duration of hepatitis B immunity in pediatric dialysis patients.^ Methods. Duration of hepatitis B immunity was determined by Kaplan-Meier survival analysis. Comparison of stratified survival analysis was performed using log-rank analysis. Multivariate analysis by Cox regression was used to estimate hazard ratios for the effect of timing of vaccine administration and other covariates on the duration of hepatitis B immunity.^ Results. 193 patients (163 incident patients) had complete data available for analysis. Mean age was 11.2±5.8 years and mean ESRD duration was 59.3±97.8 months. Kaplan-Meier analysis showed that the total median overall duration of immunity (since the time of the primary vaccine series) was 112.7 months (95% CI: 96.6, 124.4), whereas the median overall duration of immunity for incident patients was 106.3 months (95% CI: 93.93, 124.44). Incident patients had a median dialysis duration of hepatitis B immunity equal to 37.1 months (95% CI: 24.16, 72.26). Multivariate adjusted analysis showed that there was a significant difference between patients based on the timing of hepatitis B vaccination administration (p<0.001). Patients immunized after the start of dialysis had a hazard ratio of 6.13 (2.87, 13.08) for loss of hepatitis B immunity compared to patients immunized as infants (p<0.001).^ Conclusion. This study confirms that patients immunized after dialysis onset have an overall shorter duration of hepatitis B immunity as measured by hepatitis B antibody titers and after the start of dialysis, protective antibody titer levels in pediatric dialysis patients wane rapidly compared to healthy children.^

Impact of real time polymerase chain reaction technology on the therapeutic approach to treatment of staphylococcal infections

Can the early identification of the species of staphylococcus responsible for infection by the use of Real Time PCR technology influence the approach to the treatment of these infections? ^ This study was a retrospective cohort study in which two groups of patients were compared. The first group, ‘Physician Aware’ consisted of patients in whom physicians were informed of specific staphylococcal species and antibiotic sensitivity (using RT-PCR) at the time of notification of the gram stain. The second group, ‘Physician Unaware’ consisted of patients in whom treating physicians received the same information 24–72 hours later as a result of blood culture and antibiotic sensitivity determination. ^ The approach to treatment was compared between ‘Physician Aware’ and ‘Physician Unaware’ groups for three different microbiological diagnoses—namely MRSA, MSSA and no-SA (or coagulase negative Staphylococcus). ^ For a diagnosis of MRSA, the mean time interval to the initiation of Vancomycin therapy was 1.08 hours in the ‘Physician Aware’ group as compared to 5.84 hours in the ‘Physician Unaware’ group (p=0.34). ^ For a diagnosis of MSSA, the mean time interval to the initiation of specific anti-MSSA therapy with Nafcillin was 5.18 hours in the ‘Physician Aware’ group as compared to 49.8 hours in the ‘Physician Unaware’ group (p=0.007). Also, for the same diagnosis, the mean duration of empiric therapy in the ‘Physician Aware’ group was 19.68 hours as compared to 80.75 hours in the ‘Physician Unaware’ group (p=0.003) ^ For a diagnosis of no-SA or coagulase negative staphylococcus, the mean duration of empiric therapy was 35.65 hours in the ‘Physician Aware’ group as compared to 44.38 hours in the ‘Physician Unaware’ group (p=0.07). However, when treatment was considered a categorical variable and after exclusion of all cases where anti-MRS therapy was used for unrelated conditions, only 20 of 72 cases in the ‘Physician Aware’ group received treatment as compared to 48 of 106 cases in the ‘Physician Unaware’ group. ^ Conclusions. Earlier diagnosis of MRSA may not alter final treatment outcomes. However, earlier identification may lead to the earlier institution of measures to limit the spread of infection. The early diagnosis of MSSA infection, does lead to treatment with specific antibiotic therapy at an earlier stage of treatment. Also, the duration of empiric therapy is greatly reduced by early diagnosis. The early diagnosis of coagulase negative staphylococcal infection leads to a lower rate of unnecessary treatment for these infections as they are commonly considered contaminants. ^

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.

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^