Funding grant proposals for scientific research : retrospective analysis of scores by members of grant review panel

Objectives: To quantify randomness and cost when choosing health and medical research projects for funding. Design: Analysis of retrospective data from grant review panels. Setting: The National Health & Medical Research Council of Australia. Participants/Data: All panel members’ scores for grant proposals submitted in 2009. Main outcome measure: The proportion of grant proposals that were always, sometimes and never funded after accounting for random variability arising from variation in panel members’ scores; the cost-effectiveness of different size assessment panels. Results: 59% of 620 funded grants were sometimes not funded when random variability was accounted for. Only 9% of grant proposals were always funded, 61% were never funded and 29% were sometimes funded. The extra cost per grant effectively funded from the most effective system was $18,541. Conclusions: Allocating funding for scientific research in health and medicine is costly and somewhat random. There are many useful research questions to be addressed that could improve current processes.

Analysis of tapping test results in a test battery for advanced PD patients

Objective: To compare results from various tapping tests with diary responses in advanced PD. Background: A home environment test battery for assessing patient state in advanced PD, consisting of diary assessments and motor tests was constructed for a hand computer with touch screen and mobile communication. The diary questions: 1. walking, 2. time in off , on and dyskinetic states, 3. off at worst, 4. dyskinetic at worst, 5. cramps, and 6. satisfied with function, relate to the recent past. Question 7, self-assessment, allows seven steps from -3 ( very off ) to +3 ( very dyskinetic ) and relate to right now. Tapping tests outline: 8. Alternately tapping two fields (un-cued) with right hand 9. Same as 8 but using left hand 10. Tapping an active field (out of two) following a system-generated rhythm (increasing speed) with the dominant hand 11. Tapping an active field (out of four) that randomly changes location when tapped using the dominant hand Methods: 65 patients (currently on Duodopa, or candidates for this treatment) entered diary responses and performed tapping tests four times per day during one to six periods of seven days length. In total there were 224 test periods and 6039 test occasions. Speed for tapping test 10 was discardedand tests 8 and 9 were combined by taking means. Descriptive statistics were used to present the variation of the test variables in relation to self assessment (question 7). Pearson correlation coefficients between speed and accuracy (percent correct) in tapping tests and diary responses were calculated. Results: Mean compliance (percentage completed test occasions per test period) was 83% and the median was 93%. There were large differences in both mean tapping speed and accuracy between the different self-assessed states. Correlations between diary responses and tapping results were small (-0.2 to 0.3, negative values for off-time and dyskinetic-time that had opposite scale directions). Correlations between tapping results were all positive (0.1 to 0.6). Conclusions: The diary responses and tapping results provided different information. The low correlations can partly be explained by the fact that questions related to the past and by random variability, which could be reduced by taking means over test periods. Both tapping speed and accuracy reflect the motor function of the patient to a large extent.

Não-extensividade no contexto da atividade magnética solar: o problema da assimetria hemisférica

Solar activity indicators, each as sunspot numbers, sunspot area and flares, over the Sun’s photosphere are not considered to be symmetric between the northern and southern hemispheres of the Sun. This behavior is also known as the North-South Asymmetry of the different solar indices. Among the different conclusions obtained by several authors, we can point that the N-S asymmetry is a real and systematic phenomenon and is not due to random variability. In the present work, the probability distributions from the Marshall Space Flight Centre (MSFC) database are investigated using a statistical tool arises from well-known Non-Extensive Statistical Mechanics proposed by C. Tsallis in 1988. We present our results and discuss their physical implications with the help of theoretical model and observations. We obtained that there is a strong dependence between the nonextensive entropic parameter q and long-term solar variability presents in the sunspot area data. Among the most important results, we highlight that the asymmetry index q reveals the dominance of the North against the South. This behavior has been discussed and confirmed by several authors, but in no time they have given such behavior to a statistical model property. Thus, we conclude that this parameter can be considered as an effective measure for diagnosing long-term variations of solar dynamo. Finally, our dissertation opens a new approach for investigating time series in astrophysics from the perspective of non-extensivity.

RELIBILITY ANALYSIS OF SIMPLE SLOPES AND SOIL-STRUCTURES WITH LINEAR LIMIT STATES

The first objective of this research was to develop closed-form and numerical probabilistic methods of analysis that can be applied to otherwise conventional methods of unreinforced and geosynthetic reinforced slopes and walls. These probabilistic methods explicitly include random variability of soil and reinforcement, spatial variability of the soil, and cross-correlation between soil input parameters on probability of failure. The quantitative impact of simultaneously considering the influence of random and/or spatial variability in soil properties in combination with cross-correlation in soil properties is investigated for the first time in the research literature. Depending on the magnitude of these statistical descriptors, margins of safety based on conventional notions of safety may be very different from margins of safety expressed in terms of probability of failure (or reliability index). The thesis work also shows that intuitive notions of margin of safety using conventional factor of safety and probability of failure can be brought into alignment when cross-correlation between soil properties is considered in a rigorous manner. The second objective of this thesis work was to develop a general closed-form solution to compute the true probability of failure (or reliability index) of a simple linear limit state function with one load term and one resistance term expressed first in general probabilistic terms and then migrated to a LRFD format for the purpose of LRFD calibration. The formulation considers contributions to probability of failure due to model type, uncertainty in bias values, bias dependencies, uncertainty in estimates of nominal values for correlated and uncorrelated load and resistance terms, and average margin of safety expressed as the operational factor of safety (OFS). Bias is defined as the ratio of measured to predicted value. Parametric analyses were carried out to show that ignoring possible correlations between random variables can lead to conservative (safe) values of resistance factor in some cases and in other cases to non-conservative (unsafe) values. Example LRFD calibrations were carried out using different load and resistance models for the pullout internal stability limit state of steel strip and geosynthetic reinforced soil walls together with matching bias data reported in the literature.

Influence of Spatial Variability on Pavement Responses Using Latin Hypercube Sampling on Two-Dimensional Random Fields

Although uncertainties in material properties have been addressed in the design of flexible pavements, most current modeling techniques assume that pavement layers are homogeneous. The paper addresses the influence of the spatial variability of the resilient moduli of pavement layers by evaluating the effect of the variance and correlation length on the pavement responses to loading. The integration of the spatially varying log-normal random field with the finite-difference method has been achieved through an exponential autocorrelation function. The variation in the correlation length was found to have a marginal effect on the mean values of the critical strains and a noticeable effect on the standard deviation which decreases with decreases in correlation length. This reduction in the variance arises because of the spatial averaging phenomenon over the softer and stiffer zones generated because of spatial variability. The increase in the mean value of critical strains with decreasing correlation length, although minor, illustrates that pavement performance is adversely affected by the presence of spatially varying layers. The study also confirmed that the higher the variability in the pavement layer moduli, introduced through a higher value of coefficient of variation (COV), the higher the variability in the pavement response. The study concludes that ignoring spatial variability by modeling the pavement layers as homogeneous that have very short correlation lengths can result in the underestimation of the critical strains and thus an inaccurate assessment of the pavement performance. (C) 2014 American Society of Civil Engineers.

Genetic variability analysis among clinical Candida spp. isolates using random amplified polymorphic DNA

The patterns of genetic variation of samples of Candida spp. isolated from patients infected with human immunodeficiency virus in Vitória, state of Espírito Santo, Brazil, were examined. Thirty-seven strains were isolated from different anatomical sites obtained from different infection episodes of 11 patients infected with the human immunodeficiency virus (HIV). These samples were subjected to randomly amplified polymorphic DNA (RAPD) analysis using 9 different primers. Reproducible and complex DNA banding patterns were obtained. The experiments indicated evidence of dynamic process of yeast colonization in HIV-infected patients, and also that certain primers are efficient in the identification of species of the Candida genus. Thus, we conclude that RAPD analysis may be useful in providing genotypic characters for Candida species typing in epidemiological investigations, and also for the rapid identification of pathogenic fungi.

Deterring drug drivers : a study into the initial impact of oral random roadside drug testing in Queensland

Objective: The global implementation of oral random roadside drug testing is relatively limited, and correspondingly, the literature that focuses on the effectiveness of this intervention is scant. This study aims to provide a preliminary indication of the impact of roadside drug testing in Queensland. Methods: A sample of Queensland motorists’ (N= 922) completed a self-report questionnaire to investigate their drug driving behaviour, as well as examine the perceived affect of legal sanctions (certainty, severity and swiftness) and knowledge of the countermeasure on their subsequent offending behaviour. Results: Analysis of the collected data revealed that approximately 20% of participants reported drug driving at least once in the last six months. Overall, there was considerable variability in respondent’s perceptions regarding the certainty, severity and swiftness of legal sanctions associated with the testing regime and a considerable proportion remained unaware of testing practices. In regards to predicting those who intended to drug driving again in the future, perceptions of apprehension certainty, more specifically low certainty of apprehension, were significantly associated with self-reported intentions to offend. Additionally, self-reported recent drug driving activity and frequent drug consumption were also identified as significant predictors, which indicates that in the current context, past behaviour is a prominent predictor of future behaviour. To a lesser extent, awareness of testing practices was a significant predictor of intending not to drug drive in the future. Conclusion: The results indicate that drug driving is relatively prevalent on Queensland roads, and a number of factors may influence such behaviour. Additionally, while the roadside testing initiative is beginning to have a deterrent impact, its success will likely be linked with targeted intelligence-led implementation in order to increase apprehension levels as well as the general deterrent effect.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study

Background Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival. Methods Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20–84 years diagnosed during 1997–2007 from Queensland, Australia. Results Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients. Conclusions With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings

Effect of soil variability on the bearing capacity of clay and in slope stability problems

Site-specific geotechnical data are always random and variable in space. In the present study, a procedure for quantifying the variability in geotechnical characterization and design parameters is discussed using the site-specific cone tip resistance data (qc) obtained from static cone penetration test (SCPT). The parameters for the spatial variability modeling of geotechnical parameters i.e. (i) existing trend function in the in situ qc data; (ii) second moment statistics i.e. analysis of mean, variance, and auto-correlation structure of the soil strength and stiffness parameters; and (iii) inputs from the spatial correlation analysis, are utilized in the numerical modeling procedures using the finite difference numerical code FLAC 5.0. The influence of consideration of spatially variable soil parameters on the reliability-based geotechnical deign is studied for the two cases i.e. (a) bearing capacity analysis of a shallow foundation resting on a clayey soil, and (b) analysis of stability and deformation pattern of a cohesive-frictional soil slope. The study highlights the procedure for conducting a site-specific study using field test data such as SCPT in geotechnical analysis and demonstrates that a few additional computations involving soil variability provide a better insight into the role of variability in designs.

Influence of spatial variability of permeability property on steady state seepage flow and slope stability analysis

In recent years, spatial variability modeling of soil parameters using random field theory has gained distinct importance in geotechnical analysis. In the present Study, commercially available finite difference numerical code FLAC 5.0 is used for modeling the permeability parameter as spatially correlated log-normally distributed random variable and its influence on the steady state seepage flow and on the slope stability analysis are studied. Considering the case of a 5.0 m high cohesive-frictional soil slope of 30 degrees, a range of coefficients of variation (CoV%) from 60 to 90% in the permeability Values, and taking different values of correlation distance in the range of 0.5-15 m, parametric studies, using Monte Carlo simulations, are performed to study the following three aspects, i.e., (i) effect ostochastic soil permeability on the statistics of seepage flow in comparison to the analytic (Dupuit's) solution available for the uniformly constant permeability property; (ii) strain and deformation pattern, and (iii) stability of the given slope assessed in terms of factor of safety (FS). The results obtained in this study are useful to understand the role of permeability variations in slope stability analysis under different slope conditions and material properties. (C) 2009 Elsevier B.V. All rights reserved.

An efficient reduction algorithm for computation of interconnect delay variability for statistical timing analysis in clock tree planning

In this paper, we propose a novel and efficient algorithm for modelling sub-65 nm clock interconnect-networks in the presence of process variation. We develop a method for delay analysis of interconnects considering the impact of Gaussian metal process variations. The resistance and capacitance of a distributed RC line are expressed as correlated Gaussian random variables which are then used to compute the standard deviation of delay Probability Distribution Function (PDF) at all nodes in the interconnect network. Main objective is to find delay PDF at a cheaper cost. Convergence of this approach is in probability distribution but not in mean of delay. We validate our approach against SPICE based Monte Carlo simulations while the current method entails significantly lower computational cost.

Processes of 30-90 days sea surface temperature variability in the northern Indian Ocean during boreal summer

During summer, the northern Indian Ocean exhibits significant atmospheric intraseasonal variability associated with active and break phases of the monsoon in the 30-90 days band. In this paper, we investigate mechanisms of the Sea Surface Temperature (SST) signature of this atmospheric variability, using a combination of observational datasets and Ocean General Circulation Model sensitivity experiments. In addition to the previously-reported intraseasonal SST signature in the Bay of Bengal, observations show clear SST signals in the Arabian Sea related to the active/break cycle of the monsoon. As the atmospheric intraseasonal oscillation moves northward, SST variations appear first at the southern tip of India (day 0), then in the Somali upwelling region (day 10), northern Bay of Bengal (day 19) and finally in the Oman upwelling region (day 23). The Bay of Bengal and Oman signals are most clearly associated with the monsoon active/break index, whereas the relationship with signals near Somali upwelling and the southern tip of India is weaker. In agreement with previous studies, we find that heat flux variations drive most of the intraseasonal SST variability in the Bay of Bengal, both in our model (regression coefficient, 0.9, against similar to 0.25 for wind stress) and in observations (0.8 regression coefficient); similar to 60% of the heat flux variation is due do shortwave radiation and similar to 40% due to latent heat flux. On the other hand, both observations and model results indicate a prominent role of dynamical oceanic processes in the Arabian Sea. Wind-stress variations force about 70-100% of SST intraseasonal variations in the Arabian Sea, through modulation of oceanic processes (entrainment, mixing, Ekman pumping, lateral advection). Our similar to 100 km resolution model suggests that internal oceanic variability (i.e. eddies) contributes substantially to intraseasonal variability at small-scale in the Somali upwelling region, but does not contribute to large-scale intraseasonal SST variability due to its small spatial scale and random phase relation to the active-break monsoon cycle. The effect of oceanic eddies; however, remains to be explored at a higher spatial resolution.

Ancient DNA from Hunter-Gatherer and Farmer Groups from Northern Spain Supports a Random Dispersion Model for the Neolithic Expansion into Europe

10 p.