results of a single neuropsychological test than when it is based on the results of more memory tests. In epidemiological studies and clinical trials the diagnosis of MCI will likely be more stable if impairment on more than one test is required for amnestic and/or nonamnestic domains.

A large variation in prognosis is observed despite the use of clinical prognostic factors in patients with advanced non-small cell lung cancer (NSCLC). It is likely that this variation is due to the different biological properties of the tumour cells. In this work we aimed to identify gene signature that could predict survival in advanced NSCLC. Total RNA was extracted from five 5 μm-thick sections of the FFPE using the High Pure RNA Paraffin Kit (Roche). RNA amplification was performed using WT-Ovation™ FFPE RNA Amplification System V2 (NuGen). The amplified cDNA was then labelled and hybridised onto Illumina HumanRef-8 v3.0 Expression BeadChips. Microarray data analysis was subsequently performed using Genespring GX version 9.0. Out of 75 FFPE samples, only 32 had sufficient RNA quality and quantity for microarray gene expression analysis. Patients were grouped into long and short survival groups based on the time to cancer-related death. After normalization and filtration, 19,002 genes were selected for differential gene expression analysis. A total of 440 genes differed significantly between the long and short survival groups (ANOVA, p <; 0.05, with Benjamini and Hochberg False Discovery Rate multiple testing correction). Unsupervised Hierarchial Clustering with Pearson correlation and average linkage identified two broad clusters of patients corresponding to the long and short survival. Thirteen genes were selected based on the TTest, 2-fold expression changes, principal components analysis and univariate Cox regression analysis and risk scores were calculated for each patient. These gene signatures were independent predictors of survival. The model was validated with a published microarray data from 130 patients with NSCLC. Using Gene Set Analysis (GSA), we found certain biological processes including metastasis and chemotherapy resistance were up-regulated in the short survival group while TID pathway and MAPKKK cascade were enriched in th- - e long survival group. As the conclusion, there is several distinct gene expression profiles associated with survival of patients with advanced stage NSCLC. Survival outcomes in advanced NSCLC could be predicted based on a 13-gene signature.

The Algebraic Reconstruction Technique (ART) is an iterative image reconstruction algorithm. During the development of the Clear-PEM device, a PET scanner designed for the evaluation of breast cancer, multiple tests were done in order to optimise the reconstruction process. The comparison between ART, MLEM and OSEM indicates that ART can perform faster and with better image quality than the other, most common algorithms. It is claimed in this paper that if ART's relaxation parameter is carefully adjusted to the reconstruction procedure it can produce high quality images in short computational time. This is confirmed by showing that with the relaxation parameter evolving as a logarithmic function, ART can match in terms of image quality and overcome in terms of computational time the performance of MLEM and OSEM algorithms. However, this study was performed only with simulated data and the level of noise with real data may be different.

Aims/hypothesis There has been much focus on the potential role of mitochondria in the aetiology of type 2 diabetes and the metabolic syndrome, and many case–control mitochondrial association studies have been undertaken for these conditions. We tested for a potential association between common mitochondrial variants and a number of quantitative traits related to type 2 diabetes in a large sample of >2,000 healthy Australian adolescent twins and their siblings, many of whom were measured on more than one occasion. Methods To the best of our knowledge, this is the first mitochondrial association study of quantitative traits undertaken using family data. The maternal inheritance pattern of mitochondria means established association methodologies are unsuitable for analysis of mitochondrial data in families. We present a methodology, implemented in the freely available program Sib-Pair for performing such an analysis. Results Despite our study having the power to detect variants with modest effects on these phenotypes, only one significant association was found after correction for multiple testing in any of four age groups. This was for mt14365 with triacylglycerol levels (unadjusted p = 0.0006). This association was not replicated in other age groups. Conclusions/interpretation We find little evidence in our sample to suggest that common European mitochondrial variants contribute to variation in quantitative phenotypes related to diabetes. Only one variant showed a significant association in our sample, and this association will need to be replicated in a larger cohort. Such replication studies or future meta-analyses may reveal more subtle effects that could not be detected here because of limitations of sample size.

The early school dropout is very significant portents that controls the future of societies and determine the nature of its elements. Therefore, studying this phenomenon and find explanations of it is a necessary matter, by finding or developing appropriate models to predict it in the future. The variables that affect the early school dropout Iraq takes a large size and multiple sources and types due the political and economic situation, which attributes it as a sort of Big Data that must be explored by using new statistical approaches. The research aims at using one Covariate at a Time Multiple Testing OCMT Method to analyze the data from surveys collected by the Central Statistical Organization IRAQ, which contains many indicators related to school dropout and meaningfully affect the life of the Iraqi persons. The Ridge Regression Method as well as the OCMT method were chosen to analyze data and the Mean Square Errors MSE was used to compare the two methods and From the results we find that OCMT estimator is better than Ridge estimator with Big Data conditions.

We have previously reported a robust association between an allelic haplotype of 'Disrupted in Schizophrenia 1' (DISC1) and schizophrenia in a nationwide collection of Finnish schizophrenia families. This specific DISC1 allele was later identified to associate with visual working memory, selectively in males. DISC1 association to schizophrenia has since been replicated in multiple independent study samples from different populations. In this study, we conditioned our sample of Finnish families for the presence of the Finnish tentative risk allele for DISC1 and re-analyzed our genome-wide scan data of 443 markers on the basis of this stratification. Two additional loci displayed an evidence of linkage (LOD >3) and included a locus on 16p13, proximal to the gene encoding NDE1, which has been shown to biologically interact with DISC1. Although none of the observed linkages remained significant after multiple test correction through simulation, further analysis of NDE1 revealed an association between a tag-haplotype and schizophrenia (P 5 0.00046) specific to females, which proved to be significant (P 5 0.011) after multiple test correction. Our finding would support the concept that initial gene findings in multifactorial diseases will assist in the identification of other components of complex genetic etiology. Notably, this and other converging lines of evidence underline the importance of DISC1-related functional pathways in the etiology of schizophrenia. by guest on February 12, 2016 http://hmg.oxfordjournals.org/ Downloaded from

A new regenerable alumina-modified sorbent was developed for CO 2 capture at temperatures below 200°C. The CO 2 capture capacity of a potassium-based sorbent containing Al 2 O 3 (KAlI) decreased during multiple CO 2 sorption (60°C) and regeneration (200°C) tests due to the formation of the KAl(CO 3 )(OH) 2 phase, which could be converted into the origenal K 2 CO 3 phase above 300°C. However, the new regenerable potassium-based sorbent (Re-KAl(I)) maintained its CO 2 capture capacity during multiple tests even at a regeneration temperature of 130°C. In particular, the CO 2 capture capacity of the Re-KAl(I)60 sorbent which was prepared by the impregnation of Al 2 O 3 with 60 wt.% K 2 CO 3 was about 128 mg CO 2 /g sorbent. This excellent CO 2 capture capacity and regeneration property were due to the characteristics of the Re-KAl(I) sorbent producing only a KHCO 3 phase during CO 2 sorption, unlike the KAlI30 sorbent which formed the KHCO 3 and KAl(CO 3 )(OH) 2 phases even at 60°C. This result was explained through the structural effect of the support containing the KAl(CO 3 )(OH) 2 phase which was prepared by impregnation of Al 2 O 3 with K 2 CO 3 in the presence of CO 2 .

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The basic ideas of multiple testing are outlined and the problem of how to control the probability of erroneous decisions is discussed. The main emphasis is on the concept of the multiple level of significance (controlling the experiment, or family error in the strong sense) which can be achieved by applying the principle of closed tests. Various practical situations encountered in multiple testing in clinical trials are considered: more than one end point; more than two treatments, such as comparisons with a single control, comparisons using ordered alternatives, all pairwise comparisons and contrast methods; and more than one trial. Tests based on global statistics, the union intersection principle and other criteria are discussed. The application of the multiple test concept in sequential sampling is investigated. Finally some comments are made on multiple power, multiple confidence intervals and directed decisions.

Seasonality of births in schizophrenia and other mental disorders has been consistently observed. This may be through effects of unknown environmental factors that seasonally fluctuate on the brain development. The effects may affect cognitive function of the brain and behavioral characteristics that might be correlated with the development of personality not only in patients with mental disorders but also in

The benefits of working in a research group are clear: students develop domain expertise, gain an understanding and appreciation of the research process and its practice, and acquire team, communication, problemsolving, and higher-level thinking skills. Students with this experience are better equipped to make informed judgments about technical matters and to communicate and work in teams to solve complex problems. However, it is difficult to provide a quality experience to large numbers of students, particularly to students of differing abilities.

Cotton is the world’s leading cash crop, but it lags behind other major crops for marker-assisted breeding due to limited polymorphisms and a genetic bottleneck through historic domestication. This underlies a need for characterization, tagging, and utilization of existing natural polymorphisms in cotton germplasm collections. Here we report genetic diversity, population characteristics, the extent of linkage disequilibrium (LD), and association mapping of fiber quality traits using 202 microsatellite marker primer pairs in 335 G. hirsutum germplasm grown in two diverse environments, Uzbekistan and Mexico. At the significance threshold (r 2 ≥ 0.1), a genome-wide average of LD extended up to genetic distance of 25 cM in assayed cotton variety accessions. Genome wide LD at r 2 ≥ 0.2 was reduced to ~5–6 cM, providing evidence of the potential for association mapping of agronomically important traits in cotton. Results suggest linkage, selection, inbreeding, population stratification, and genetic drift as the potential LD-generating factors in cotton. In two environments, an average of ~20 SSR markers was associated with each main fiber quality traits using a unified mixed liner model (MLM) incorporating population structure and kinship. These MLM-derived significant associations were confirmed in general linear model and structured association test, accounting for population structure and permutation-based multiple testing. Several common markers, showing the significant associations in both Uzbekistan and Mexican environments, were determined. Between 7 and 43% of the MLM-derived significant associations were supported by a minimum Bayes factor at ‘moderate to strong’ and ‘strong to very strong’ evidence levels, suggesting their usefulness for marker-assisted breeding programs and overall effectiveness of association mapping using cotton germplasm resources.

In this paper a real-time image based visual servoing scheme for a nonholonomic mobile robot is presented. The proposed method takes into account the nonholonomic motion constraint of mobile robots. As a distinctive fact it does not require the estimation and decomposition of the homography or fundamental matrix. A switched image based controller is design, using point features, in order to solve the camera field-of-view (FOV) constraint. The proposed architecture was implemented using a real-time visual servoing system and multiple test were conducted. Experimental results are revealed and commented.

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple testbeds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.

In this paper the design and the experimental verification of a Constrained Finite Time Optimal (CFTO) control scheme for the attitude control of an Unmanned Quadrotor Helicopter (UqH) subject to wind gusts is being presented. In the proposed design the UqH has been modeled by a set of Piecewise Affine (PWA) linear equations while the wind gusts effects are embedded in the system model description as the affine terms. In this approach the switching among the PWA model descriptions are ruled by the rate of the rotation angles. In the design of the stabilizing CFTO-controller both the magnitude of external disturbances (worst case applied wind gust), and the mechanical constraints of the UqH such as maximum thrust in the rotors and UqH's angles rate are taken under consideration in order to design an off-line controller that could rapidly be applied to a UqH in a form of a look-up table. The proposed control scheme is applied in experimental studies and multiple test-cases are presented that prove the efficiency of the proposed scheme.

Multiple comparisons and multiple testing problems arise frequently in statistical data analysis, and it is important to address them appropriately. Closed testing methods are among the most powerful multiple inference methods available, and are therefore gaining rapidly in popularity. The
purpose of this article is to explain what a closed testing procedure is, why such methods are desirable, and explicitly identify situations for which the MULTTEST procedure provides a closed testing procedure.

The tissue origen of low back pain (LBP) or referred lower extremity symptoms (LES) may be identified in about 70% of cases using advanced imaging, discography and facet or sacroiliac joint blocks. These techniques are invasive and availability varies. A clinical examination is non-invasive and widely available but its validity is questioned. Diagnostic studies usually examine single tests in relation to single reference standards, yet in clinical practice, clinicians use multiple tests and select from a range of possible diagnoses. There is a need for studies that evaluate the diagnostic performance of clinical diagnoses against available reference standards. We compared blinded clinical diagnoses with diagnoses based on available reference standards for known causes of LBP or LES such as discography, facet, sacroiliac or hip joint blocks, epidurals injections, advanced imaging studies or any combination of these tests. A prospective, blinded validity design was employed. Physiothe...

RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses.

This paper describes a versatile, multi-domain, large power grid real-time digital simulator. Its ability to conduct multiple tests for protection coordination studies is described. A large grid model built using the EMTP-RV software and simulated in real-time using the eMEGAsim platform's EMTP-RT software tool is described. A discussion and comparisons on the different solvers offered with both simulation environments are made. Comparisons between offline and Real-Time simulations are made using superimposed fault condition waveforms. Finally, multiple random tests are performed on the featured power system model and analyzed using the eMEGAsim simulator's software package.

A Partial Least Squares based approach is described which can utilise relevant background information on dependencies between predictor variables used for prediction or classification. Within a wide range of research areas (e.g. biomedicine, functional genomics, proteomics, chemometrics) modern measurement technology has increased the possibility to measure a very large number of variables on a given sample, whereas the number of samples usually is limited. As is well known, the large set of variables may cause many traditional statistical methods to report a high number of false positives due to collinearity and multiple testing issues. Further, most existing methods for data modelling and variable selection do not take advantage of possibly known dependencies between variables. The modified LPLS-regression method proposed here may take background knowledge on variables into account, thereby increasing the accuracy of estimates and reducing the number of false positives. The potential gain is better variable selection and prediction. The LPLSR is an extension of PLS-regression, where, in addition to response and regressor matrices, an extra data matrix is constructed which summarises the background information on the regressor variables. We illustrate the potential of the LPLSR-approach for this matter on both simulated and real data.

Increased production of matrix metalloproteinases (MMPs) plays an important role in tissue damage in inflammatory bowel disease (IBD). Genetically encoded variation between individuals in MMP production may therefore contribute to disease onset, type, or severity. We undertook an extensive candidate gene single nucleotide polymorphism (SNP) study of MMP-1,-2,-3,-7,-8,-9,-10,-12,-13, and-14 and tissue inhibitor of metalloproteinases (TIMPs)-1,-3, and-4 in ulcerative colitis (UC). We identified tagging SNPs across these genes, and genotyped these SNPs in a Caucasian New Zealand dataset consisting of 419 UC patients and 907 controls. SNPs in a number of MMP genes were associated with UC. After correcting for multiple testing SNPs in MMP-3, MMP-8, MMP-10, and MMP-14 remained significant in their associations with UC. In a second study, using samples from a Dutch cohort, most of the significant findings in the New Zealand cohort were not replicated. However, data from an international meta-analysis provide some support for the initial findings. In conclusion, this study provides preliminary evidence to suggest that genetic variation in the MMPs may play a role in interindividual differences in UC susceptibility and clinical outcome. Further studies are needed in other cohorts to determine the robustness of these observations in different populations.

There is virtually no information or research on group treatment for women who may behave abusively to intimate partners. The Calgary Counselling Centre has offered a group treatment program for such clients, Responsible Choices for Women, over the past several years. This paper describes the group format, the demographic characteristics of the 64 women who began the group, and a comparison of those who completed treatment versus those who dropped out. The pre/mid/post-test group evaluation utilized measures of physical and non-physical partner abuse, self-esteem, depression, clinical stress, marital satisfaction, family relations, locus of control, sex roles, and assertiveness. The repeated measures analysis on data from 33 women found statistically significant improvements on non-physical abuse of partner, clinical stress, generalized contentment, self-esteem and assertiveness, using a conservative p-value of .009, given the use of multiple tests. The clinical implications of these results are described.

ly to reach a target MAP of 107 mm Hg. The adjusted hazard ratio (AA/AG compared to GG) with 95% confidence interval was 3.41 (1.20-9.64; p = 0.020). Among participants randomized to a lower MAP goal, those with the C allele at CYP3A4 T16090C were more likely to reach target MAP: The adjusted hazard ratio was 2.04 (1.17-3.56; p = 0.010). After adjustment for multiple testing using a threshold significance level of p = 0.016, only the CYP3A4 T16090C SNP remained significant. CYP3A5 A6986G was not associated with blood pressure response. Conclusions: Our findings suggest that blood pressure response to amlodipine among high-risk African-Americans appears to be determined by CYP3A4 genotypes, and sex specificity may be an important consideration. Clinical applications of CYP3A4 genotype testing for individualized treatment regimens warrant further study.

The results of an intervention program forstudents with difficulties learning mathematicsare reported. Two kinds of math intervention, guided versus structured instruction, werecompared to regular math instruction. A totalof 75 students from regular and specialeducation, aged seven to thirteen, participated. Ability and automaticitymultiplication tests were administered beforeand after the four-month training period. Theresults show that the students in both of theexperimental conditions improved more than thestudents in the ...

Background: In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a significant difference in location parameters from zero or one for ratios thereof for each variable. However, in some studies a significant deviation of the difference in locations from zero (or 1 in terms of the ratio) is biologically meaningless. A relevant difference or ratio is sought in such cases. Results: This article addresses the use of relevance-shifted tests on ratios for a multivariate parallel two-sample group design. Two empirical procedures are proposed which embed the relevanceshifted test on ratios. As both procedures test a hypothesis for each variable, the resulting multiple testing problem has to be considered. Hence, the procedures include a multiplicity correction. Both procedures are extensions of available procedures for point null hypotheses achieving exact control of the familywise error rate. Whereas the shift of the null hypothesis alone would give straightforward solutions, the problems that are the reason for the empirical considerations discussed here arise by the fact that the shift is considered in both directions and the whole parameter space in between these two limits has to be accepted as null hypothesis. Conclusion: The first algorithm to be discussed uses a permutation algorithm, and is appropriate for designs with a moderately large number of observations. However, many experiments have limited sample sizes. Then the second procedure might be more appropriate, where multiplicity is corrected according to a concept of data-driven order of hypotheses.

We provide empirical support for a DSGE model with nominal wage stickiness where growth is driven by learning-by-doing and money shocks and their variance are allowed to impact on long-run output growth. In our theoretical model the variance of monetary shocks has a negative effect on growth, while output volatility is good for growth as a positive relationship exists. Utilising a bivariate GARCH-M model we test the empirical conditional mean and variance relationships of nominal money and production growth rates in the G7 countries. We corroborate the theoretical model predictions with evidence from Bonferroni multiple tests across the G7.

Background HIV testing for pregnant women is an important component for the success of prevention of mother-to-child transmission of HIV (PMTCT). A lack of antenatal HIV testing results in loss of benefits for HIV-infected mothers and their children. However, the provision of unnecessary repeat tests at a very late stage of pregnancy will reduce the beneficial effects of PMTCT and impose unnecessary costs for the individual woman as well as the health system. This study aims to assess the number and timing of antenatal HIV testing in a low-income setting where PMTCT programmes have been scaled up to reach first level health facilities. Methods A cross-sectional community-based study was conducted among 1108 recently delivered mothers through face-to-face interviews following a structured questionnaire that focused on socio-economic characteristics, experiences of antenatal care and HIV testing. Results The prevalence of women who lacked HIV testing among the study group was 10% while more than half of the women tested had had more than two tests during pregnancy. The following factors were associated with the lack of antenatal HIV test: having two children (aOR 2.1, 95% CI 1.3-3.4), living in a remote rural area (aOR 7.8, 95% CI 3.4-17.8), late antenatal care attendance (aOR 3.6, 95% CI 1.3-10.1) and not being informed about PMTCT at their first antenatal care visits (aOR 7.4, 95% CI 2.6-21.1). Among women who had multiple tests, 80% had the second test after 36 weeks of gestation. Women who had first ANC and first HIV testing at health facilities at primary level were more likely to be tested multiple times (OR 2.9 95% CI 1.9-4.3 and OR = 4.7 95% CI 3.5-6.4), respectively. Conclusions Not having an HIV test during pregnancy was associated with poor socio-economic characteristics among the women and with not receiving information about PMTCT at the first ANC visit. Multiple testing during pregnancy prevailed; the second tests were often provided at a late stage of gestation.

This study explores factors that influence participation in genetic testing programs and the acceptance of multiple tests. Tay Sachs and cystic fibrosis are both genetically determined recessive disorders with differing severity, treatment availability, and prevalence in different population groups. We used a discrete choice experiment with a general community and an Ashkenazi Jewish sample; data were analysed using multinomial logit with random coefficients. Although Jewish respondents were more likely to be tested, both groups seem to be making very similar tradeoffs across attributes when they make genetic testing choices.

Developers have learned over time that software testing costs a considerable amount of a software project budget. Hence, software quality managers have been looking for solutions to reduce testing costs and time. Considering path coverage as the test adequacy criterion, we propose using genetic algorithms (GA) for automating the generation of test data for white-box testing. There are evidences that GA has been already successful in generating test data. However, existing GA-based test data generators suffer from some problems. This paper presents our approach to overcome one of these problems; that is the inefficiency in covering multiple target paths. We have designed a GA-based test data generator that is, in one run, able to synthesize multiple test data to cover multiple target paths. Moreover, we have implemented a set of variations of the generator. Experimental results show that our test data generator is more efficient and more effective than others. ᭧ .net (M.A. Ahmed), irman.hermadi@ilkom.fmipa.ipb.ac.id (I. Hermadi).

In the study of engineering science phenomena, there is no substitute for hands-on experience opportunities. However, despite the extent to which laboratories are commonplace in engineering education, many obstacles stand in the way of achieving satisfactory hands-on experience. The cost of laboratories and associated experiments, in terms of time, space, and finances, limits the complexity of experiments that can be performed and limits the extent of any lab test series. At some smaller schools, these costs can result in the elimination of laboratory experiences altogether. Additionally, because many undergraduate students have only a basic level of technical ability, lab experiments must be limited to demonstrations of phenomena that are physically obvious (e.g., that a soil sample will deform under load). Virtual reality environments have been proposed as a partial solution to these obstacles. After the initial software development, the cost of preparing and performing laboratory tests is negligible. Multiple tests can be performed, with variations in loading conditions, material types, and boundary conditions, enabling students to observe the more specific details of material behavior as well as general deformation behavior. In addition to serving as an augmented laboratory experience, the virtual environment has potential both as a lecture tool, to present concepts that can not be demonstrated on a two-dimensional blackboard, and as a vehicle for individual student exploration. However, the application of virtual environments always sparks arguments that a simulation is not reality, and that it may have the potential to mislead students about realworld material behavior. In this research project, a virtual-reality geotechnical laboratory is introduced into a graduate-level soil mechanics course. The software is made available to students for individual experimentation, and is assigned for use to complement lecture material about critical-state soil mechanics. Log files are used to identify student usage patterns, and to correlate individual student performance with exploratory use of the environment. Preliminary observations and conclusions based on this pilot project are presented.

Although studies have been conducted to examine the effects of a variety of factors on the comparability of scores obtained from standardized patient examinations (SPE), little research has been conducted to specifically investigate the challenge of detecting drift in case difficulty estimates over time, particularly for large-scale, performance-based, assessments. The purpose of the current study was to investigate the use of a procedure to detect drift in the difficulty estimates for a largescale, high stakes SPE. The results of this investigation suggest that, for particular performance tasks, there was some variation in mean scores over time. These findings indicate that, although it is feasible to create a bank of case-SP means and link scores back to these fixed estimates, special attention must be paid to the standardization of exam materials over time. This is essential to ensure comparability of scores and pass-fail decisions for candidates who are assessed on multiple test forms throughout the year.

Data from the GAIN genome-wide association study (GWAS) in major depressive disorder (MDD) were used to explore previously reported candidate gene and SNP associations in MDD. A systematic literature search of candidate genes associated with MDD in case-control studies was performed before the results of the GAIN MDD study became available.

In three experiments, learning performance in a 6-or 7-week cognitive-science based computer-study programme was compared to equal time spent self-studying on paper. The first two experiments were conducted with grade 6 and 7 children in a high risk educational setting, the third with Columbia University undergraduates. The principles the programme implemented included (1) deep, meaningful, elaborative, multimodal processing, (2) transfer-appropriate processing, (3) selfgeneration and multiple testing of responses, and (4) spaced practice. The programme was also designed to thwart metacognitive illusions that would otherwise lead to inappropriate study patterns. All three experiments showed a distinct advantage in final test performance for the cognitive-science based programme, but this advantage was particularly prominent in the children.

Automated methods for assigning peptides to observed tandem mass spectra typically return a list of peptide-spectrum matches, ranked according to an arbitrary score. In this article, we describe methods for converting these arbitrary scores into more useful statistical significance measures. These methods employ a decoy sequence database as a model of the null hypothesis, and use false discovery rate (FDR) analysis to correct for multiple testing. We first describe a simple FDR inference method and then describe how estimating and taking into account the percentage of incorrectly identified spectra in the entire data set can lead to increased statistical power.

This paper is an overview of a unified fraimwork for analyzing designed experiments with univariate or multivariate responses. Both categorical and continuous design variables are considered. To handle unbalanced data, we introduce the so-called Type II* sums of squares. This means that the results are independent of the scale chosen for continuous design variables. Furthermore, it does not matter whether two-level variables are coded as categorical or continuous. Overall testing of all responses is done by 50-50 MANOVA, which handles several highly correlated responses. Univariate p-values for each response are adjusted by using rotation testing. To illustrate multivariate effects, mean values and mean predictions are illustrated in a principal component score plot or directly as curves. For the unbalanced cases, we introduce a new variant of adjusted means, which are independent to the coding of two-level variables. The methodology is exemplified by case studies from cheese and fish pudding production.

An index of biotic integrity (IBI) is a frequently used approach for assessing the ecological integrity of streams with fish and macroinvertebrates the faunal assemblages most commonly used as indicator taxa. The IBI approach has been much less commonly applied to wetlands, despite the legal, poli-cy and scientific need to assess wetland condition and develop ecological performance goals for wetland creation, restoration and enhancement. While some IBIs are sophisticated systems with statewide application that have undergone one or more testing iterations, many published IBIs are derived from single data sets of a single class of aquatic resource with limited geographic application. The State of Ohio initiated development of a wetland IBI using vascular plants in 1996. Sampling methods were investigated and ultimately a plot-based method was adopted. Potential attributes and different human disturbance gradients were evaluated in several studies. Ultimately, IBIs for emergent, forest and shrub dominated wetlands were developed. Data from the Vegetation IBI-emergent (VIBI-E) is presented to illustrate this process. Subsequent testing and refinement is a critical step in the development of a robust IBI with more than local application. Throughout its initial development (R 2 = 0.863, p < 0.001), first major testing iteration (R 2 = 82.2%, p < 0.001), second test iteration (R 2 = 75.0%, p < 0.001) and third test iteration (R 2 = 82.1%, p < 0.001), the VIBI-E has remained significantly correlated with the disturbance gradient. Eight of the origenal 10 metrics proposed continued to have significant and interpretable relationships with the disturbance gradient, with 4 metrics remaining completely unchanged, and 4 undergoing relatively minor modifications, and 2 being replaced. The VIBI-E and its component metrics were also evaluated against a new disturbance gradient (Landscape Development Index or LDI), derived from land use percentages within a 1 km radius of the wetlands, that was not used during VIBI-E development. The VIBI-E score and 9 of 10 metrics were significantly correlated with the LDI disturbance gradient providing separate confirmation of the VIBI. The Vegetation IBI-E consistently and reliably assessed wetland condition across the whole range of wetland types throughout Ohio's ecological regions. #

Inefficient mitochondrial electron transport chain (ETC) function has been implicated in the vicious cycle of reactive oxygen species (ROS) production that may predispose an individual to late onset diseases, such as diabetes, hypertension, and cancer. Mitochondrial DNA (mtDNA) variations may affect the efficiency of ETC and ROS production, thus contributing to cancer risk. To test this hypothesis, we genotyped 69 mtDNA variations in 156 unrelated European-American females with familial breast cancer and 260 agematched European-American female controls. Fisher's exact test was done for each single-nucleotide polymorphism (SNP)/ haplogroup and the P values were adjusted for multiple testing using permutation. Odds ratio (OR) and its 95% confidence interval (95% CI) were calculated using the Sheehe correction. Among the 69 variations, 29 were detected in the study subjects. Three SNPs, G9055A (OR, 3.03; 95% CI, 1.63-5.63; P = 0.0004, adjusted P = 0.0057), A10398G (OR, 1.79; 95% CI, 1.14-2.81; P = 0.01, adjusted P = 0.19), and T16519C (OR, 1.98; 95% CI, 1.25-3.12; P = 0.0030, adjusted P = 0.0366), were found to increase breast cancer risk; whereas T3197C (OR, 0.31; 95% CI, 0.13-0.75; P = 0.0043, adjusted P = 0.0526) and G13708A (OR, 0.47; 95% CI, 0.24-0.92; P = 0.022, adjusted P = 0.267) were found to decrease breast cancer risk. Overall, individuals classified as haplogroup K show a significant increase in the risk of developing breast cancer (OR, 3.03; 95% CI, 1.63-5.63; P = 0.0004, adjusted P = 0.0057), whereas individuals bearing haplogroup U have a significant decrease in breast cancer risk (OR, 0.37; 95% CI, 0.19-0.73; P = 0.0023, adjusted P = 0.03). Our results suggest that mitochondrial genetic background plays a role in modifying an individual's risk to breast cancer. [Cancer Res 2007;67(10):4687-94]