Crystal Nucleation of Tolbutamide in Solution: Relationship to Solvent, Solute Conformation, and Solution Structure.

The influence of the solvent in nucleation of tolbutamide, a medium-sized, flexible and polymorphic organic molecule, has been explored by measuring nucleation induction times, estimating solvent-solute interaction enthalpies using molecular modelling and calorimetric data, probing interactions and clustering with spectroscopy, and modelling solvent-dependence of molecular conformation in solution. The nucleation driving force required to reach the same induction time is strongly solvent-dependent, increasing in the order: acetonitrile

Repeatability analysis of airborne electromagnetic surveys.

PURPOSE: We provide methods for determining the repeatability of airborne electromagnetic surveys when conducted at different altitudes over a number of repeated flights. Our data arise from the TELLUS project carried out by the Geological Surveys of Ireland and Northern Ireland and we examine the repeatability of the apparent resistivity at different frequencies. METHODS: After considering a number of issues with the data, we propose two different models from the functional data analysis literature; a Weiner process with random effects, and a penalised spline smoother. RESULTS: Both methods arrive at the same conclusion regarding repeatability of the data; results obtained are more repeatable for flights at lower altitudes. CONCLUSIONS: The target altitude for aircraft carrying out airborne electromagnetic surveys should be as low as possible.

Near-infrared monitoring of roller compacted ribbon density: Investigating sources of variation contributing to noisy spectral data.

The aim of this study was to highlight how variability in roller compacted ribbon quality can impact on NIR spectral measurement and to propose a simple method of data selection to remove erroneous spectra. The use of NIR spectroscopy for monitoring ribbon envelope density has been previously demonstrated, however to date there has been limited discussion as to how spectral data sets can contain erroneous outliers due to poor sample presentation to the NIR probes. In this study compacted ribbon of variable quality was produced from three separate blends of microcrystalline cellulose (MCC)/lactose/magnesium stearate at 8 Roll Force settings (2-16kN/cm). The three blends differed only in the storage conditions of MCC prior to blending and compaction. MCC sublots were stored at ambient (41% RH/20°C), low humidity (11% RH/20°C) and high humidity (75% RH/40°C) conditions prior to blending. Ribbon envelope density was measured and ribbon NIR spectral data was acquired at line using a multi-probe spectrometer (MultiEye™ NIR). Initial inspection of the at-line NIR spectral data set showed a large degree of variability which indicated that some form of data cleaning was required. The source of variability in spectral measurements was investigated by subjective visual examination and by statistical analysis. Spectral variability was noted due to the storage conditions of MCC prior to compaction, Roll Force settings and between individual ribbon samples sampled at a set Roll Force/Blend combination. Variability was also caused by ribbon presentation to probes, such as differences in the presentation of broken, curved and flat intact ribbons. Based on the subjective visual examination of data, a Visual Discard method was applied and was found to be particularly successful for blends containing MCC stored at ambient and low humidity. However the Visual Discard method of spectra cleaning is subjective and therefore a non-subjective method capable of screening for erroneous probe readings was developed. For this data set a Trimmed Mean method was applied to set a limit on how data is cleaned from the data set allowing for the removal of a faulty probe reading (25% of data) or a poor sample (33% of data). The 33% Trimmed Mean reduced the impact of spectral variation or misreads between samples or probes and was found to be as successful as the Visual Discard method at cleaning the data set prior to development of the calibration equation.

Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences.

BACKGROUND: While statistics is increasingly taught as part of the medical curriculum, it can be an unpopular subject and feedback from students indicates that some find it more difficult than other subjects. Understanding attitudes towards statistics on entry to graduate entry medical programmes is particularly important, given that many students may have been exposed to quantitative courses in their previous degree and hence bring preconceptions of their ability and interest to their medical education programme. The aim of this study therefore is to explore, for the first time, attitudes towards statistics of graduate entry medical students from a variety of backgrounds and focus on understanding the role of prior learning experiences. METHODS: 121 first year graduate entry medical students completed the Survey of Attitudes toward Statistics instrument together with information on demographics and prior learning experiences. RESULTS: Students tended to appreciate the relevance of statistics in their professional life and be prepared to put effort into learning statistics. They had neutral to positive attitudes about their interest in statistics and their intellectual knowledge and skills when applied to it. Their feelings towards statistics were slightly less positive e.g. feelings of insecurity, stress, fear and frustration and they tended to view statistics as difficult. Even though 85% of students had taken a quantitative course in the past, only 24% of students described it as likely that they would take any course in statistics if the choice was theirs. How well students felt they had performed in mathematics in the past was a strong predictor of many of the components of attitudes. CONCLUSION: The teaching of statistics to medical students should start with addressing the association between students' past experiences in mathematics and their attitudes towards statistics and encouraging students to recognise the difference between the two disciplines. Addressing these issues may reduce students' anxiety and perception of difficulty at the start of their learning experience and encourage students to engage with statistics in their future careers.

Risk of several cancers is higher in urban areas after adjusting for socioeconomic status. Results from a two-country population-based study of 18 common cancers.

Some studies suggest that there are urban-rural variations in cancer incidence but whether these simply reflect urban-rural socioeconomic variation is unclear. We investigated whether there were urban-rural variations in the incidence of 18 cancers, after adjusting for socioeconomic status. Cancers diagnosed between 1995 and 2007 were extracted from the population-based National Cancer Registry Ireland and Northern Ireland Cancer Registry and categorised by urban-rural status, based on population density of area of residence at diagnosis (rural <1 person per hectare, intermediate 1-15 people per hectare, urban >15 people per hectare). Relative risks (RR) were calculated by negative binomial regression, adjusting for age, country and three area-based markers of socioeconomic status. Risks were significantly higher in both sexes in urban than rural residents with head and neck (males RR urban vs. rural = 1.53, 95 % CI 1.42-1.64; females RR = 1.29, 95 % CI 1.15-1.45), esophageal (males 1.21, 1.11-1.31; females 1.21, 1.08-1.35), stomach (males 1.36, 1.27-1.46; females 1.19, 1.08-1.30), colorectal (males 1.14, 1.09-1.18; females 1.04, 1.00-1.09), lung (males 1.54, 1.47-1.61; females 1.74, 1.65-1.84), non-melanoma skin (males 1.13, 1.10-1.17; females 1.23, 1.19-1.27) and bladder (males 1.30, 1.21-1.39; females 1.31, 1.17-1.46) cancers. Risks of breast, cervical, kidney and brain cancer were significantly higher in females in urban areas. Prostate cancer risk was higher in rural areas (0.94, 0.90-0.97). Other cancers showed no significant urban-rural differences. After adjusting for socioeconomic variation, urban-rural differences were evident for 12 of 18 cancers. Variations in healthcare utilization and known risk factors likely explain some of the observed associations. Explanations for others are unclear and, in the interests of equity, warrant further investigation.

Declining mortality rates despite increases in clinical coronary artery disease among US dialysis patients: a national registry study.

BACKGROUND/AIMS: Coronary artery disease (CAD) is a major risk factor for death on dialysis. The objective of this study was to compare prevalent trends and patterns of survival in successive national cohorts. METHODS: National data on 823,753 incident dialysis patients, aged 18 and over, were analyzed from the US Renal Data System from 1995 to 2004. The prevalence of CAD was compared across calendar years by sex and race categorized as; White, Black, Asian and Native American/Alaskan Native (Native Am). Two-year mortality rates were determined for annual cohorts and multivariable Cox regression compared hazard ratios (HR) and 95% confidence intervals. RESULTS: From 1995 to 2004, the annual prevalence of CAD increased significantly in men from 25.2 to 30.1% and in women from 22.1 to 25.3%, p < 0.001. For men, the rise in prevalence was largely due to increases among Black men and older White men. For women, the pattern was similar. During this period, death rates decreased significantly from 379 to 348 and from 396 to 357 per 1,000 person-years in men and women respectively. Multivariate analysis identified significant reductions in mortality with advancing calendar year for White (HR 0.98 (0.98-0.99)), Asian (HR 0.93 (0.91-0.96)), and Native Am men (HR 0.95 (0.90-0.99)), and for White (HR 0.99 (0.98-0.99)) and Native Am women (HR 0.93 (0.89-0.98)). No significant trends were observed for Black patients. CONCLUSIONS: Despite a rising burden of CAD among incident US dialysis patients, mortality rates have fallen for most groups. Substantial racial disparities remain.

The Impact of Adjustment for Socioeconomic Status on Comparisons of Cancer Incidence between Two European Countries.

Background. Cancer incidence rates vary considerably between countries and by socioeconomic status (SES). We investigate the impact of SES upon the relative cancer risk in two neighbouring countries. Methods. Data on 229,824 cases for 16 cancers diagnosed in 1995-2007 were extracted from the cancer registries in Northern Ireland (NI) and Republic of Ireland (RoI). Cancers in the two countries were compared using incidence rate ratios (IRRs) adjusted for age and age plus area-based SES. Results. Adjusting for SES in addition to age had a considerable impact on NI/RoI comparisons for cancers strongly related to SES. Before SES adjustment, lung cancer incidence rates were 11% higher for males and 7% higher for females in NI, while after adjustment, the IRR was not statistically significant. Cervical cancer rates were lower in NI than in RoI after adjustment for age (IRR: 0.90 (0.84-0.97)), with this difference increasing after adjustment for SES (IRR: 0.85 (0.79-0.92)). For cancers with a weak or nonexistent relationship to SES, adjustment for SES made little difference to the IRR. Conclusion. Socioeconomic factors explain some international variations but also obscure other crucial differences; thus, adjustment for these factors should not become part of international comparisons.

Geographical analysis of cancer incidence in Ireland: a comparison of two Bayesian spatial models.

AIMS: Our objective was to describe the geographical variation in cancer incidence using gastro-intestinal and non-melanoma skin cancer incidence data in Ireland using two different Bayesian spatial models and to compare the performance of these models. METHODS: Cases diagnosed between 1994 and 2003 were extracted from the National Cancer Registry of Ireland. Population data were estimated from census data. For each of 3401 electoral divisions (EDs), relative risk (RR) estimates were calculated and smoothed using a conditional autoregressive model (CAR) and a spatial partition model introduced by Hegarty and Barry using a product partition model (PPM). The results were compared by mapping the ratio of the two RR estimates and other mainly descriptive statistics. RESULTS: The two methods gave broadly similar results. For gastro-intestinal cancers the RRs were lower in a northwest/southeast band across the country with greater RRs around Dublin, Cork and in Donegal. Greater RR of non-melanoma skin cancer was observed in coastal areas. Median differences between the RR estimates were small (=0.01). The range of RRs was wider when estimated by the CAR model illustrating that the PPM smoothed the data to a greater extent than the CAR model. CONCLUSIONS: The two approaches gave similar results providing stronger evidence for the resulting geographical patterns. PPMs give a more global picture of the risk distribution whereas CAR models provide more local estimates. The observed patterns may reflect socio-demographic or geographic variations in risk factors or access to cancer services. By helping to identify those risks, these maps may help in the optimal allocation of scarce health resources.

Bayesian disease mapping using product partition models.

Our objective is to develop a model to estimate the relative risk of disease in each area, Ai, i=1, ... , n, of a region and to identify areas of unusually high or low risk. We use a product partition model (PPM) in which we assume that the true relative risks can be partitioned into a number of components or sets of areas where the relative risks are equal. The PPM allows the data to weight those partitions likely to hold and inference about particular parameters may be made by first conditioning on the partition and then averaging over all partitions. We develop Markov chain Monte Carlo (MCMC) techniques to approximate the posterior distributions of the partitions and the parameters. We first test the method in a simulation study and then apply it to data for two separate groups of different types of cancer in the Mid-Western Health Board region in Ireland. The results are compared with those obtained using the standardized mortality ratio method, an empirical Bayes method, a spatial scan method and a nonparametric Bayesian method.