Herbal medicines:physician's recommendation and clinical evaluation of St.John's Wort for depression

Why some physicians recommend herbal medicines while others do not is not well understood. We undertook a survey designed to identify factors, which predict recommendation of herbal medicines by physicians in Malaysia. About a third (206 out of 626) of the physicians working at the University of Malaya Medical Centre ' were interviewed face-to-face, using a structured questionnaire. Physicians were asked about their personal use of, recommendation of, perceived interest in and, usefulness and safety of herbal medicines. Using logistic regression modelling we identified personal use, general interest, interest in receiving training, race and higher level of medical training as significant predictors of recommendation. St. John's wort is one of the most widely used herbal remedies. It is also probably the most widely evaluated herbal remedy with no fewer than 57 randomised controlled trials. Evidence from the depression trials suggests that St. John's wort is more effective than placebo while its comparative efficacy to conventional antidepressants is not well established. We updated previous meta-analyses of St. John's wort, described the characteristics of the included trials, applied methods of data imputation and transformation for incomplete trial data and examined sources of heterogeneity in the design and results of those trials. Thirty randomised controlled trials, which were heterogeneous in design, were identified. Our meta-analysis showed that St. John's wort was significantly more effective than placebo [pooled RR 1.90 (1.54-2.35)] and [Pooled WMD 4.09 (2.33 to 5.84)]. However, the remedy was similar to conventional antidepressant in its efficacy [Pooled RR I. 0 I (0.93 -1.10)] and [Pooled WMD 0.18 (- 0.66 to 1.02). Subgroup analyses of the placebo-controlled trials suggested that use of different diagnostic classifications at the inclusion stage led to different estimates of effect. Similarly a significant difference in the estimates of efficacy was observed when trials were categorised according to length of follow-up. Confounding between the variables, diagnostic classification and length of trial was shown by loglinear analysis. Despite extensive study, there is still no consensus on how effective St. lohn's wort is in depression. However, most experts would agree that it has some effect. Our meta-analysis highlights the problems associated with the clinical evaluation of herbal medicines when the active ingredients are poorly defined or unknown. The problem is compounded when the target disease (e.g. depression) is also difficult to define and different instruments are available to diagnose and evaluate it.

A low-cost remote sensing system for agricultural applications

This research develops a low cost remote sensing system for use in agricultural applications. The important features of the system are that it monitors the near infrared and it incorporates position and attitude measuring equipment allowing for geo-rectified images to be produced without the use of ground control points. The equipment is designed to be hand held and hence requires no structural modification to the aircraft. The portable remote sensing system consists of an inertia measurement unit (IMU), which is accelerometer based, a low-cost GPS device and a small format false colour composite digital camera. The total cost of producing such a system is below GBP 3000, which is far cheaper than equivalent existing systems. The design of the portable remote sensing device has eliminated bore sight misalignment errors from the direct geo-referencing process. A new processing technique has been introduced for the data obtained from these low-cost devices, and it is found that using this technique the image can be matched (overlaid) onto Ordnance Survey Master Maps at an accuracy compatible with precision agriculture requirements. The direct geo-referencing has also been improved by introducing an algorithm capable of correcting oblique images directly. This algorithm alters the pixels value, hence it is advised that image analysis is performed before image georectification. The drawback of this research is that the low-cost GPS device experienced bad checksum errors, which resulted in missing data. The Wide Area Augmented System (WAAS) correction could not be employed because the satellites could not be locked onto whilst flying. The best GPS data were obtained from the Garmin eTrex (15 m kinematic and 2 m static) instruments which have a highsensitivity receiver with good lock on capability. The limitation of this GPS device is the inability to effectively receive the P-Code wavelength, which is needed to gain the best accuracy when undertaking differential GPS processing. Pairing the carrier phase L1 with the pseudorange C/A-Code received, in order to determine the image coordinates by the differential technique, is still under investigation. To improve the position accuracy, it is recommended that a GPS base station should be established near the survey area, instead of using a permanent GPS base station established by the Ordnance Survey.

Projection and interpolation based techniques for structured and impulsive noise filtering

In this chapter we present the relevant mathematical background to address two well defined signal and image processing problems. Namely, the problem of structured noise filtering and the problem of interpolation of missing data. The former is addressed by recourse to oblique projection based techniques whilst the latter, which can be considered equivalent to impulsive noise filtering, is tackled by appropriate interpolation methods.

Description and preliminary results from a structured specialist behavioural weight management group intervention:Specialist Lifestyle Management (SLiM) programme

Background -  Specialist Lifestyle Management (SLiM) is a structured patient education and self-management group weight management programme. Each session is run monthly over a 6-month period providing a less intensive long-term approach. The groups are patient-centred incorporating educational, motivational, behavioural and cognitive elements. The theoretical background, programme structure and preliminary results of SLiM are presented. Subjects/methods - The study was a pragmatic service evaluation of obese patients with a body mass index (BMI) ≥35 kg/m2 with comorbidity or ≥40 kg/m2 without comorbidity referred to a specialist weight management service in the West Midlands, UK. 828 patients were enrolled within SLiM over a 48-month period. Trained facilitators delivered the programme. Preliminary anonymised data were analysed using the intention-to-treat principle. The primary outcome measure was weight loss at 3 and 6 months with comparisons between completers and non-completers performed. The last observation carried forward was used for missing data. Results - Of the 828 enrolled within SLiM, 464 completed the programme (56%). The mean baseline weight was 135 kg (BMI=49.1 kg/m2) with 87.2% of patients having a BMI≥40 kg/m2 and 12.4% with BMI≥60 kg/m2. The mean weight change of all patients enrolled was −4.1 kg (95% CI −3.6 to −4.6 kg, p=0.0001) at the end of SLiM, with completers (n=464) achieving −5.5 kg (95% CI −4.2 to −6.2 kg, p=0.0001) and non-completers achieving −2.3 kg (p=0.0001). The majority (78.6%) who attended the 6-month programme achieved weight loss with 32.3% achieving a ≥5% weight loss. Conclusions - The SLiM programme is an effective group intervention for the management of severe and complex obesity.

Properties of the Bellman Gamma Distribution

2000 Mathematics Subject Classification: 62H10.

Interactive Stepwise Discriminant Analysis in Matlab

2000 Mathematics Subject Classification: 62-04, 62H30, 62J20

A Generalized Quasi-Likelihood Estimator for Nonstationary Stochastic Processes−Asymptotic Properties and Examples

2010 Mathematics Subject Classification: 62F12, 62M05, 62M09, 62M10, 60G42.

Concurrent codes:a holographic-type encoding robust against noise and loss

Concurrent coding is an encoding scheme with 'holographic' type properties that are shown here to be robust against a significant amount of noise and signal loss. This single encoding scheme is able to correct for random errors and burst errors simultaneously, but does not rely on cyclic codes. A simple and practical scheme has been tested that displays perfect decoding when the signal to noise ratio is of order -18dB. The same scheme also displays perfect reconstruction when a contiguous block of 40% of the transmission is missing. In addition this scheme is 50% more efficient in terms of transmitted power requirements than equivalent cyclic codes. A simple model is presented that describes the process of decoding and can determine the computational load that would be expected, as well as describing the critical levels of noise and missing data at which false messages begin to be generated.

Handwritten and machine-printed text discrimination using a template matching approach

We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark.

A prospective comparison of a noninvasive cardiac output monitor versus esophageal doppler monitor for goal-directed fluid therapy in colorectal surgery patients

Copyright © 2014 International Anesthesia Research Society.BACKGROUND: Goal-directed fluid therapy (GDFT) is associated with improved outcomes after surgery. The esophageal Doppler monitor (EDM) is widely used, but has several limitations. The NICOM, a completely noninvasive cardiac output monitor (Cheetah Medical), may be appropriate for guiding GDFT. No prospective studies have compared the NICOM and the EDM. We hypothesized that the NICOM is not significantly different from the EDM for monitoring during GDFT. METHODS: One hundred adult patients undergoing elective colorectal surgery participated in this study. Patients in phase I (n = 50) had intraoperative GDFT guided by the EDM while the NICOM was connected, and patients in phase II (n = 50) had intraoperative GDFT guided by the NICOM while the EDM was connected. Each patient's stroke volume was optimized using 250- mL colloid boluses. Agreement between the monitors was assessed, and patient outcomes (postoperative pain, nausea, and return of bowel function), complications (renal, pulmonary, infectious, and wound complications), and length of hospital stay (LOS) were compared. RESULTS: Using a 10% increase in stroke volume after fluid challenge, agreement between monitors was 60% at 5 minutes, 61% at 10 minutes, and 66% at 15 minutes, with no significant systematic disagreement (McNemar P > 0.05) at any time point. The EDM had significantly more missing data than the NICOM. No clinically significant differences were found in total LOS or other outcomes. The mean LOS was 6.56 ± 4.32 days in phase I and 6.07 ± 2.85 days in phase II, and 95% confidence limits for the difference were -0.96 to +1.95 days (P = 0.5016). CONCLUSIONS: The NICOM performs similarly to the EDM in guiding GDFT, with no clinically significant differences in outcomes, and offers increased ease of use as well as fewer missing data points. The NICOM may be a viable alternative monitor to guide GDFT.

Combining Information from Multiple Sources in Bayesian Modeling

Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.

This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.

The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new

individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the

refreshment sample itself. As we illustrate, nonignorable unit nonresponse

can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse

in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.

The second method incorporates informative prior beliefs about

marginal probabilities into Bayesian latent class models for categorical data.

The basic idea is to append synthetic observations to the original data such that

(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.

We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.

The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.

Non-Pharmacological Approaches for Pain Management in Sickle Cell Disease: Development of a Mindfulness-Based Intervention

Background: Sickle Cell Disease (SCD) is a genetic hematological disorder that affects more than 7 million people globally (NHLBI, 2009). It is estimated that 50% of adults with SCD experience pain on most days, with 1/3 experiencing chronic pain daily (Smith et al., 2008). Persons with SCD also experience higher levels of pain catastrophizing (feelings of helplessness, pain rumination and magnification) than other chronic pain conditions, which is associated with increases in pain intensity, pain behavior, analgesic consumption, frequency and duration of hospital visits, and with reduced daily activities (Sullivan, Bishop, & Pivik, 1995; Keefe et al., 2000; Gil et al., 1992 & 1993). Therefore effective interventions are needed that can successfully be used manage pain and pain-related outcomes (e.g., pain catastrophizing) in persons with SCD. A review of the literature demonstrated limited information regarding the feasibility and efficacy of non-pharmacological approaches for pain in persons with SCD, finding an average effect size of .33 on pain reduction across measurable non-pharmacological studies. Second, a prospective study on persons with SCD that received care for a vaso-occlusive crisis (VOC; N = 95) found: (1) high levels of patient reported depression (29%) and anxiety (34%), and (2) that unemployment was significantly associated with increased frequency of acute care encounters and hospital admissions per person. Research suggests that one promising category of non-pharmacological interventions for managing both physical and affective components of pain are Mindfulness-based Interventions (MBIs; Thompson et al., 2010; Cox et al., 2013). The primary goal of this dissertation was thus to develop and test the feasibility, acceptability, and efficacy of a telephonic MBI for pain catastrophizing in persons with SCD and chronic pain.

Methods: First, a telephonic MBI was developed through an informal process that involved iterative feedback from patients, clinical experts in SCD and pain management, social workers, psychologists, and mindfulness clinicians. Through this process, relevant topics and skills were selected to adapt in each MBI session. Second, a pilot randomized controlled trial was conducted to test the feasibility, acceptability, and efficacy of the telephonic MBI for pain catastrophizing in persons with SCD and chronic pain. Acceptability and feasibility were determined by assessment of recruitment, attrition, dropout, and refusal rates (including refusal reasons), along with semi-structured interviews with nine randomly selected patients at the end of study. Participants completed assessments at baseline, Week 1, 3, and 6 to assess efficacy of the intervention on decreasing pain catastrophizing and other pain-related outcomes.

Results: A telephonic MBI is feasible and acceptable for persons with SCD and chronic pain. Seventy-eight patients with SCD and chronic pain were approached, and 76% (N = 60) were enrolled and randomized. The MBI attendance rate, approximately 57% of participants completing at least four mindfulness sessions, was deemed acceptable, and participants that received the telephonic MBI described it as acceptable, easy to access, and consume in post-intervention interviews. The amount of missing data was undesirable (MBI condition, 40%; control condition, 25%), but fell within the range of expected missing outcome data for a RCT with multiple follow-up assessments. Efficacy of the MBI on pain catastrophizing could not be determined due to small sample size and degree of missing data, but trajectory analyses conducted for the MBI condition only trended in the right direction and pain catastrophizing approached statistically significance.

Conclusion: Overall results showed that at telephonic group-based MBI is acceptable and feasible for persons with SCD and chronic pain. Though the study was not able to determine treatment efficacy nor powered to detect a statistically significant difference between conditions, participants (1) described the intervention as acceptable, and (2) the observed effect sizes for the MBI condition demonstrated large effects of the MBI on pain catastrophizing, mental health, and physical health. Replication of this MBI study with a larger sample size, active control group, and additional assessments at the end of each week (e.g., Week 1 through Week 6) is needed to determine treatment efficacy. Many lessons were learned that will guide the development of future studies including which MBI strategies were most helpful, methods to encourage continued participation, and how to improve data capture.

ROCADA: Romanian daily gridded climatic dataset (1961-2013) V1.0

Daily records of nine meteorological variables covering the interval 1961-2013 were used in order to create a state-of-the-art homogenized climatic dataset over Romania at a spatial resolution of 0.1°. All meteorological stations with full data records, as well as stations with up to 30 % missing data, were used for the following variables: air pressure (150 stations); minimum, maximum, and average air temperature (150 stations); soil temperature (127 stations); precipitation (188 stations); sunshine hours (135 stations); cloud cover (104 stations); relative humidity (150 stations). For each parameter, the data series were first homogenized with the software MASH (Multiple Analysis of Series for Homogenization); then, the data series were gridded by means of the software MISH (Meteorological Interpolation based on Surface Homogenized Data).

New statin use and left ventricular structure: estimating long-term associations in the Multi-Ethnic Study of Atherosclerosis (MESA)

Thesis (Master's)--University of Washington, 2016-08

A method for assessing ecological values to reconcile multiple land use needs

We present a new method for ecologically sustainable land use planning within multiple land use schemes. Our aims were (1) to develop a method that can be used to locate important areas based on their ecological values; (2) to evaluate the quality, quantity, availability, and usability of existing ecological data sets; and (3) to demonstrate the use of the method in Eastern Finland, where there are requirements for the simultaneous development of nature conservation, tourism, and recreation. We compiled all available ecological data sets from the study area, complemented the missing data using habitat suitability modeling, calculated the total ecological score (TES) for each 1 ha grid cell in the study area, and finally, demonstrated the use of TES in assessing the success of nature conservation in covering ecologically valuable areas and locating ecologically sustainable areas for tourism and recreational infrastructure. The method operated quite well at the level required for regional and local scale planning. The quality, quantity, availability, and usability of existing data sets were generally high, and they could be further complemented by modeling. There are still constraints that limit the use of the method in practical land use planning. However, as increasing data become available and open access, and modeling tools improve, the usability and applicability of the method will increase.