Effect of changing data collection parameters on statistical motor unit number estimates

The effect of number of samples and selection of data for analysis on the calculation of surface motor unit potential (SMUP) size in the statistical method of motor unit number estimates (MUNE) was determined in 10 normal subjects and 10 with amyotrophic lateral sclerosis (ALS). We recorded 500 sequential compound muscle action potentials (CMAPs) at three different stable stimulus intensities (10–50% of maximal CMAP). Estimated mean SMUP sizes were calculated using Poisson statistical assumptions from the variance of 500 sequential CMAP obtained at each stimulus intensity. The results with the 500 data points were compared with smaller subsets from the same data set. The results using a range of 50–80% of the 500 data points were compared with the full 500. The effect of restricting analysis to data between 5–20% of the CMAP and to standard deviation limits was also assessed. No differences in mean SMUP size were found with stimulus intensity or use of different ranges of data. Consistency was improved with a greater sample number. Data within 5% of CMAP size gave both increased consistency and reduced mean SMUP size in many subjects, but excluded valid responses present at that stimulus intensity. These changes were more prominent in ALS patients in whom the presence of isolated SMUP responses was a striking difference from normal subjects. Noise, spurious data, and large SMUP limited the Poisson assumptions. When these factors are considered, consistent statistical MUNE can be calculated from a continuous sequence of data points. A 2 to 2.5 SD or 10% window are reasonable methods of limiting data for analysis. Muscle Nerve 27: 320–331, 2003

Remotely sensed data for ecosystem analyses: Combining hierarchy and scene models

Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales; however, a limited range of satellite imaging systems often. constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem; (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost-benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.

Tissue-specific gene expression in soybean (Glycine max) detected by cDNA microarray analysis

We have constructed cDNA microarrays for soybean (Glycine max L. Merrill), containing approximately 4,100 Unigene ESTs derived from axenic roots, to evaluate their application and utility for functional genomics of organ differentiation in legumes. We assessed microarray technology by conducting studies to evaluate the accuracy of microarray data and have found them to be both reliable and reproducible in repeat hybridisations. Several ESTs showed high levels (>50 fold) of differential expression in either root or shoot tissue of soybean. A small number of physiologically interesting, and differentially expressed sequences found by microarray analysis were verified by both quantitative real-time RT-PCR and Northern blot analysis. There was a linear correlation (r(2) = 0.99, over 5 orders of magnitude) between microarray and quantitative real-time RT-PCR data. Microarray analysis of soybean has enormous potential not only for the discovery of new genes involved in tissue differentiation and function, but also to study the expression of previously characterised genes, gene networks and gene interactions in wild-type, mutant or transgenic; plants.

The importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data

As for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, intersample variation in the proportion of linked families ( a) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage. findings obtained using allele- sharing LOD scores ( LODexp) - which assume homogeneity - and heterogeneity LOD scores ( HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LODexp and two different heterogeneity statistics. One of these ( HLOD- P) estimates the heterogeneity parameter a only in aggregate data, while the second ( HLOD- S) determines a separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LODexp. Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD- S, but not HLOD- P. Using HLOD- S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.

Serum triglycerides as a risk factor for cardiovascular diseases in the Asia-Pacific Region

Background-The importance of serum triglyceride levels as a risk factor for cardiovascular diseases is uncertain. Methods and Results-We performed an individual participant data meta-analysis of prospective studies conducted in the Asia-Pacific region. Cox models were applied to the combined data from 26 studies to estimate the overall and region-, sex-, and age-specific hazard ratios for major cardiovascular diseases by fifths of triglyceride values. During 796 671 person-years of follow-up among 96 224 individuals, 670 and 667 deaths as a result of coronary heart disease (CHD) and stroke, respectively, were recorded. After adjustment for major cardiovascular risk factors, participants grouped in the highest fifth of triglyceride levels had a 70% (95% CI, 47 to 96) greater risk of CHD death, an 80% (95% CI, 49 to 119) higher risk of fatal or nonfatal CHD, and a 50% (95% CI, 29% to 76%) increased risk of fatal or nonfatal stroke compared with those belonging to the lowest fifth. The association between triglycerides and CHD death was similar across subgroups defined by ethnicity, age, and sex. Conclusions-Serum triglycerides are an important and independent predictor of CHD and stroke risk in the Asia-Pacific region. These results may have clinical implications for cardiovascular risk prediction and the use of lipid-lowering therapy.

A comparison of lipid variables as predictors of cardiovascular disease in the Asia Pacific Region

PURPOSE: Many guidelines advocate measurement of total or low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL), and triglycerides (TG) to determine treatment recommendations for preventing coronary heart disease (CHD) and cardiovascular disease (CVD). This analysis is a comparison of lipid variables as predictors of cardiovascular disease. METHODS: Hazard ratios for coronary and cardiovascular deaths by fourths of total cholesterol (TC), LDL, HDL, TG, non-HDL, TC/HDL, and TG/HDL values, and for a one standard deviation change in these variables, were derived in an individual participant data meta-analysis of 32 cohort studies conducted in the Asia-Pacific region. The predictive value of each lipid variable was assessed using the likelihood ratio statistic. RESULTS: Adjusting for confounders and regression dilution, each lipid variable had a positive (negative for HDL) log-linear association with fatal CHD and CVD. Individuals in the highest fourth of each lipid variable had approximately twice the risk of CHD compared with those with lowest levels. TG and HDL were each better predictors of CHD and CVD risk compared with TC alone, with test statistics similar to TC/HDL and TG/HDL ratios. Calculated LDL was a relatively poor predictor. CONCLUSIONS: While LDL reduction remains the main target of intervention for lipid-lowering, these data support the potential use of TG or lipid ratios for CHD risk prediction. (c) 2005 Elsevier Inc. All rights reserved.

The phylogeography and connectivity of the latitudinally widespread scleractinian coral Plesiastrea versipora in the Western Pacific

Whereas terrestrial animal populations might show genetic connectivity within a continent, marine species, such as hermatypic corals, may have connectivity stretching to all corners of the planet. We quantified the genetic variability within and among populations of the widespread scleractinian coral, Plesiastrea versipora along the eastern Australian seaboard (4145 km) and the Ryukyu Archipelago (Japan, 681 km) using sequences of internal transcribed spacers (ITS1-2) from ribosomal DNA. Geographic patterns in genetic variability were deduced from a nested clade analysis (NCA) performed on a parsimony network haplotype. This analysis allowed the establishment of geographical associations in the distribution of haplotypes within the network cladogram, therefore allowing us to deduce phylogeographical patterns based under models of restricted gene flow, fragmentation and range expansion. No significant structure was found among Ryukyu Archipelago populations. The lack of an association between the positions of haplotypes in the cladogram with geographical location of these populations may be accounted for by a high level of gene flow of P. versipora within this region, probably due to the strong Kuroshio Current. In contrast, strong geographical associations were apparent among populations of P. versipora along the south-east coast of Australia. This pattern of restricted genetic connectivity among populations of P. versipora on the eastern seaboard of Australia seems to be associated with the present surface ocean current (the East Australian Current) on this side of the south-western Pacific Ocean.

Genetic diversity and population structure of Tasmanian devils, the largest marsupial carnivore

Genetic diversity and population structure were investigated across the core range of Tasmanian devils (Sarcophilus laniarius; Dasyuridae), a wide-ranging marsupial carnivore restricted to the island of Tasmania. Heterozygosity (0.386-0.467) and allelic diversity (2.7-3.3) were low in all subpopulations and allelic size ranges were small and almost continuous, consistent with a founder effect. Island effects and repeated periods of low population density may also have contributed to the low variation. Within continuous habitat, gene flow appears extensive up to 50 km (high assignment rates to source or close neighbour populations; nonsignificant values of pairwise F-ST), in agreement with movement data. At larger scales (150-250 km), gene flow is reduced (significant pairwise F-ST) but there is no evidence for isolation by distance. The most substantial genetic structuring was observed for comparisons spanning unsuitable habitat, implying limited dispersal of devils between the well-connected, eastern populations and a smaller northwestern population. The genetic distinctiveness of the northwestern population was reflected in all analyses: unique alleles; multivariate analyses of gene frequency (multidimensional scaling, minimum spanning tree, nearest neighbour); high self-assignment (95%); two distinct populations for Tasmania were detected in isolation by distance and in Bayesian model-based clustering analyses. Marsupial carnivores appear to have stronger population subdivisions than their placental counterparts.

Interagency collaboration between child protection and mental health services: Practices, attitudes and barriers

Objective: The aim of this paper is to examine some of the factors that facilitate and hinder interagency collaboration between child protection services and mental health services in cases where there is a parent with a mental illness and there are protection concerns for the child(ren). The paper reports on agency practices, worker attitudes and experiences, and barriers to effective collaboration. Method: A self-administered, cross-sectional survey was developed and distributed via direct mail or via line supervisors to workers in statutory child protection services, adult mental health services, child and youth mental health services, and Suspected Child Abuse and Neglect (SCAN) Teams. There were 232 completed questionnaires returned, with an overall response rate of 21%. Thirty-eight percent of respondents were statutory child protection workers. 39% were adult mental health workers, 16% were child and youth mental health workers, and 4% were SCAN Team medical officers (with 3% missing data). Results: Analysis revealed that workers were engaging in a moderate amount of interagency contact, but that they were unhappy with the support provided by their agency. Principle components analysis and multivariate analysis of variance (MANOVA) on items assessing attitudes toward other workers identified four factors, which differed in rates of endorsement: inadequate training, positive regard for child protection workers, positive regard for mental health workers, and mutual mistrust (from highest to lowest level of endorsement). The same procedure identified the relative endorsement of five factors extracted from items about potential barriers: inadequate resources, confidentiality, gaps in interagency processes, unrealistic expectations, and professional knowledge domains and boundaries. Conclusions: Mental health and child protection professionals believe that collaborative practice is necessary; however, their efforts are hindered by a lack of supportive structures and practices at the organizational level. (c) 2005 Published by Elsevier Ltd.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Onset of sheath extension and duration of lamina extension are major determinants of the response of maize lamina length to plant density

Background and Aims Plants regulate their architecture strongly in response to density, and there is evidence that this involves changes in the duration of leaf extension. This questions the approximation, central in crop models, that development follows a fixed thermal time schedule. The aim of this research is to investigate, using maize as a model, how the kinetics of extension of grass leaves change with density, and to propose directions for inclusion of this regulation in plant models. • Methods Periodic dissection of plants allowed the establishment of the kinetics of lamina and sheath extension for two contrasting sowing densities. The temperature of the growing zone was measured with thermocouples. Two-phase (exponential plus linear) models were fitted to the data, allowing analysis of the timing of the phase changes of extension, and the extension rate of sheaths and blades during both phases. • Key Results The duration of lamina extension dictated the variation in lamina length between treatments. The lower phytomers were longer at high density, with delayed onset of sheath extension allowing more time for the lamina to extend. In the upper phytomers—which were shorter at high density—the laminae had a lower relative extension rate (RER) in the exponential phase and delayed onset of linear extension, and less time available for extension since early sheath extension was not delayed. • Conclusions The relative timing of the onset of fast extension of the lamina with that of sheath development is the main determinant of the response of lamina length to density. Evidence is presented that the contrasting behaviour of lower and upper phytomers is related to differing regulation of sheath ontogeny before and after panicle initiation. A conceptual model is proposed to explain how the observed asynchrony between lamina and sheath development is regulated.

Equational Reasoning as a Tool for Data Analysis

A combination of deductive reasoning, clustering, and inductive learning is given as an example of a hybrid system for exploratory data analysis. Visualization is replaced by a dialogue with the data.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Data analysis in multiple-frequency bioelectrical impedance analysis

The performance of three analytical methods for multiple-frequency bioelectrical impedance analysis (MFBIA) data was assessed. The methods were the established method of Cole and Cole, the newly proposed method of Siconolfi and co-workers and a modification of this procedure. Method performance was assessed from the adequacy of the curve fitting techniques, as judged by the correlation coefficient and standard error of the estimate, and the accuracy of the different methods in determining the theoretical values of impedance parameters describing a set of model electrical circuits. The experimental data were well fitted by all curve-fitting procedures (r = 0.9 with SEE 0.3 to 3.5% or better for most circuit-procedure combinations). Cole-Cole modelling provided the most accurate estimates of circuit impedance values, generally within 1-2% of the theoretical values, followed by the Siconolfi procedure using a sixth-order polynomial regression (1-6% variation). None of the methods, however, accurately estimated circuit parameters when the measured impedances were low (<20 Omega) reflecting the electronic limits of the impedance meter used. These data suggest that Cole-Cole modelling remains the preferred method for the analysis of MFBIA data.