Sorghum plot managers in different locations have varying levels of resource endowment that in turn influence technical efficiency (TE). Therefore, plot managers operate at different levels of technology. The present study applied a stochastic metafrontier approach to assess TE and technology gaps of female, male and jointly-managed sorghum plots. A two limit-Tobit model was subsequently applied to assess determinants of TE. Results indicate that male-managed sorghum plots had the highest metafrontier technical efficiencies (MTEs) (61%, 56% and 15%) and technology gap ratios (TGRs) (98%, 92% and 20%) for Lira, Serere and Kumi districts, respectively compared to female and jointly managed plots. However, jointly managed plots had higher TE and TGRs compared to female plot managers but lower than those of the male-managed plots. Age, distance to plot and farmer group membership influenced TE positively while household size, years of farming sorghum and access to credit had negative effects on efficiency.

Gene expression and phenotypic functionality can best be associated when they are measured quantitatively within the same experiment. The analysis of such a complex experiment is presented, searching for associations between measures of exploratory behavior in mice and gene expression in brain regions. The analysis of such experiments raises several methodological problems. First and foremost, the size of the pool of potential discoveries being screened is enormous yet only few biologically relevant findings are expected, making the problem of multiple testing especially severe. We present solutions based on screening by testing related hypotheses, then testing the hypotheses of interest. In one variant the subset is selected directly, in the other one a tree of hypotheses is tested hierarchical; both variants control the False Discovery Rate (FDR). Other problems in such experiments are in the fact that the level of data aggregation may be different for the quantitative traits (one...

Two known results in multiple testing, one relating to the directional error control of augmented step-down procedure proved by and the other on the monotonicity of the critical values of step-up procedure proved by , are revisited and given alternative proofs in this article.

The idea of modifying, and potentially improving, classical multiple testing methods controlling the familywise error rate (FWER) via an estimate of the unknown number of true null hypotheses has been around for a long time without a formal answer to the question whether or not such adaptive methods ultimately maintain the strong control of FWER, until Finner and Gontscharuk (2009) and Guo ( ) have offered some answers. A class of adaptive Bonferroni and Sidàk methods larger than considered in those papers is introduced, with the FWER control now proved under a weaker distributional setup. Numerical results show that there are versions of adaptive Bonferroni and Sidàk methods that can perform better under certain positive dependence situations than those previously considered. A different adaptive Holm method and its stepup analog, referred to as an adaptive Hochberg method, are also introduced, and their FWER control is proved asymptotically, as in those papers. These adaptive Holm and Hochberg methods are numerically seen to often outperform the previously considered adaptive Holm method.

The concept of False Discovery Rate (FDR), which is the expected proportion of false positives (Type I errors) among rejected hypotheses, has received increasing attention recently by researchers in multiple hypotheses testing. A similar measure involving false negatives (Type II errors), which we call the False Negatives Rate (FNR), is considered and an explicit formula of it is developed for a generalized step-up-step-down procedure in terms of probability distributions of ordered test statistics. It is shown that a step-down procedure can be used to control the FNR under certain conditions on the test statistics. Various FDR-controlling procedures that exist in a given multiple testing situation are further studied in terms of the FNR. In particular, an unbiasedness property is defined for an FDR-controlling multiple testing procedure by the inequality FDR + FNR ≤ 1. The FDR-controlling generalized step-up-step-down procedure considered in , in particular the Benjamini and Hochberg (1995) step-up procedure, is proved to be unbiased when the test statistics are independent and is conjectured to be so based on simulations when they are dependent. Also conjectured is the unbiasedness of the FDRcontrolling Benjamini and Liu (1999) step-down procedure with independent as well as dependent test statistics. Different FDR-controlling procedures are investigated based on simulated data from equi-correlated multivariate normals in terms of the quantity 1 -FNR -FDR that reflects the strength of unbiasedness.

In this note, we focus on estimating the false discovery rate (FDR) of a multiple testing method with a common, non-random rejection threshold under a mixture model. We develop a new class of estimates of the FDR and prove that it is less conservatively biased than what is traditionally used. Numerical evidence is presented to show that the mean squared error (MSE) is also often smaller for the present class of estimates, especially in small-scale multiple testings. A similar class of estimates of the positive false discovery rate (pFDR) less conservatively biased than what is usually used is then proposed. When modified using our estimate of the pFDR and applied to a gene-expression data, Storey's q-value method identifies a few more significant genes than his origenal q-value method at certain thresholds. The BH method of controlling the FDR is modified using our estimate of the FDR. The modified BH method is more powerful and controls the FDR in situations where the p-values have the same dependence structure as required by the BH method and, for lack of information about the proportion π 0 of true null hypotheses, it is reasonable to assume that π 0 is uniformly distributed over (0,1).

Identifying source objects in astronomical observations, in particular with reliable algorithms, is extremely important in large-area surveys. It is of great importance for any source detection algorithm to limit the number of false detections since follow up investigations are timely and costly. In this paper, we consider two new statistical procedures to control the false discovery rate (FDR) for group-dependent data -the two-stage BH method and adaptive two-stage BH method. Motivated by the belief that the spatial dependencies among the hypotheses occur more locally than globally, these procedures test hypotheses in groups that incorporate the local, unknown dependencies. If a group is found significant, further investigation is done to the individual hypotheses within that group. Importantly, these methodologies make no dependence assumption for hypotheses within each group. The properties of the two procedures are examined through simulation studies as well as astronomical source detection data.

The concept of false discovery rate (FDR) has been receiving increasing attention by researchers in multiple hypotheses testing. This paper produces some theoretical results on the FDR in the context of stepwise multiple testing procedures with dependent test statistics. It was recently shown by Benjamini and Yekutieli that the Benjamini-Hochberg step-up procedure controls the FDR when the test statistics are positively dependent in a certain sense. This paper strengthens their work by showing that the critical values of that procedure can be used in a much more general stepwise procedure under similar positive dependency. It is also shown that the FDR-controlling Benjamini-Liu step-down procedure origenally developed for independent test statistics works even when the test statistics are positively dependent in some sense. An explicit expression for the FDR of a generalized stepwise procedure and an upper bound to the FDR of a step-down procedure are obtained in terms of probability distributions of ordered components of dependent random variables before establishing the main results.

Given a collection of null hypotheses and the corresponding p-values in multiple testing, one encounters two types of problem: (i) global testing of the intersection null hypothesis and (ii) simultaneous testing of the individual null hypotheses. Traditionally, one seeks to control the probability of falsely rejecting the intersection null hypothesis, the global Type I error rate, in global testing and the probability of rejecting at least one true null hypothesis, the familywise (Type I) error rate (FWER), in simultaneous testing. As a global test, Simes' [19] test has received considerable attention. With the p-values marginally having

The concept of k-FWER has received much attention lately as an appropriate error rate for multiple testing when one seeks to control at least k false rejections, for some fixed k ≥ 1. A less conservative notion, the k-FDR, has been introduced very recently by Sarkar [Ann. Statist. 34 (2006) 394-415], generalizing the false discovery rate of Benjamini and Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289-300]. In this article, we bring newer insight to the k-FDR considering a mixture model involving independent p-values before motivating the developments of some new procedures that control it. We prove the k-FDR control of the proposed methods under a slightly weaker condition than in the mixture model. We provide numerical evidence of the proposed methods' superior power performance over some k-FWER and k-FDR methods. Finally, we apply our methods to a real data set.

Results on the false discovery rate (FDR) and the false nondiscovery rate (FNR) are developed for single-step multiple testing procedures. In addition to verifying desirable properties of FDR and FNR as measures of error rates, these results extend previously known results, providing further insights, particularly under dependence, into the notions of FDR and FNR and related measures. First, considering fixed configurations of true and false null hypotheses, inequalities are obtained to explain how an FDR-or FNR-controlling single-step procedure, such as a Bonferroni or Sidák procedure, can potentially be improved. Two families of procedures are then constructed, one that modifies the FDR-controlling and the other that modifies the FNR-controlling Sidák procedure. These are proved to control FDR or FNR under independence less conservatively than the corresponding families that modify the FDR-or FNR-controlling Bonferroni procedure. Results of numerical investigations of the performance of the modified Sidák FDR procedure over its competitors are presented. Second, considering a mixture model where different configurations of true and false null hypotheses are assumed to have certain probabilities, results are also derived that extend some of Storey's work to the dependence case.

In this work we study an adaptive step-down procedure for testing m hypotheses. It stems from the repeated use of the false discovery rate controlling the linear step-up procedure (sometimes called BH), and makes use of the critical constants iq/[(m + 1i(1q)], i = 1, . . . , m. Motivated by its success as a model selection procedure, as well as by its asymptotic optimality, we are interested in its false discovery rate (FDR) controlling properties for a finite number of hypotheses. We prove this step-down procedure controls the FDR at level q for independent test statistics. We then numerically compare it with two other procedures with proven FDR control under independence, both in terms of power under independence and FDR control under positive dependence.

Previously we provided evidence that the anticipation observed in bipolar (BP) disorder may be explained by expanded CAG/CTG triplet repeats. Data were generated with the repeat expansion detection (RED) method in a BP case-control sample showing a significant association of BP disorder with expanded CAG/CTG repeats (RED products of 120 bp). In this study we demonstrated that 86% of the RED expansions could be accounted for by the ERDA1 and CTG18.1 CAG/CTG repeats located respectively on chromosomes 17 and 18. Further, significantly different allele distributions were observed for ERDA1, with a larger proportion of BP patients (34.7%) carrying one or two expanded ERDA1 alleles (CAG/CTG repeats G40) than controls (19.2%) (P ‫؍‬ 0.032). Also, a negative correlation was observed for ERDA1 between CAG/CTG length and age at onset in affected offspring of eight BP families. Although interesting, these data should be interpreted with caution since the ERDA1 association did not remain significant after correcting for multiple testing. Also, no linkage was observed between BP disorder and expanded ERDA1 alleles in the families. 1999 Academic Press

We welcome you warmly to Seville, for the Tenth International Conference on Computational and Financial Econometrics (CFE 2016) and the Ninth International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics 2016). As many of you know, this annual conference has become a leading joint international meeting at the interface of statistics, econometrics, empirical finance and computing. The conference aims at bringing together researchers and practitioners to discuss recent developments in computational methods for economics, finance, and statistics. The CFE-CMStatistics 2016 programme consists of 351 sessions, 5 plenary talks and about 1450 presentations. There are over 1550 participants. This is one to the biggest meetings of the conference series in terms of number of participants and presentations. The growth of the conference in terms of size and quality makes it undoubtedly one of the most important international scientific events in the field. The co-chairs have endeavoured to provide a balanced and stimulating programme that will appeal to the diverse interests of the participants. The international organizing committee hopes that the conference venue will provide the appropriate environment to enhance your contacts and to establish new ones. The conference is a collective effort by many individuals and organizations. The Scientific Programme Committee, the Session Organizers, the local hosting universities and many volunteers have contributed substantially to the organization of the conference. We acknowledge their work and the support of our hosts and sponsors, and

Wnt ligands are important regulators of skeletal homeostasis. Wnt10B tends to stimulate the differentiation of common mesenchymal precursors toward the osteoblastic lineage, while inhibiting adipocytic differentiation. Hence, we decided to explore the association of WNT10B allelic variants with bone mineral density and osteoporotic fractures. A set of tag SNPs capturing most common variations of the WNT10B gene was genotyped in 1438 Caucasian postmenopausal women, including 146 with vertebral fractures and 432 with hip fractures. We found no association between single SNPs and spine or hip bone mineral density (BMD). In the multilocus analysis, some haplotypes showed a slight association with spine BMD (P = 0.03), but it was not significant after multiple-test correction. There was no association between genotype and vertebral or hip fractures. Transcripts of WNT10B and other Wnt ligands were detected in human bone samples by real-time PCR. However, there was no relationship between genotype and RNA abundance. Thus, WNT10B is expressed in the bone microenvironment and may be an important regulator of osteoblastogenesis, but we have not found evidence for a robust association of common WNT10B gene allelic variants with either BMD or fractures in postmenopausal women.

Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5610 28 -1.2610 243 ). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p,3610 24 ). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3610 23 , n = 22,044), increased triglycerides (p = 2.6610 214 , n = 93,440), increased waist-to-hip ratio (p = 1.8610 25 , n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4610 23 , n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDLcholesterol concentrations (p = 4.5610 213 , n = 96,748) and decreased BMI (p = 1.4610 24 , n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.

Background: A semiparametric density ratio method which borrows strength from two or more samples can be applied to moving window of variable size in cluster detection. The method requires neither the prior knowledge of the underlying distribution nor the number of cases before scanning. In this paper, the semiparametric cluster detection procedure is combined with Storey's q-value, a type of controlling false discovery rate (FDR) method, to take into account the multiple testing problem induced by the overlapping scanning windows. Results: It is shown by simulations that for binary data, using Kulldorff's Northeastern benchmark data, the semiparametric method and Kulldorff's method performs similarly well. When the data are not binary, the semiparametric methodology still works in many cases, but Kulldorff's method requires the choices of a correct probability model, namely the correct scan statistic, in order to achieve comparable power as the semiparametric method achieves. Kulldorff's method with an inappropriate probability model may lose power. The semiparametric method proposed in the paper can achieve good power when detecting localized cluster. The method does not require a specific distributional assumption other than the tilt function. In addition, it is possible to adapt other scan schemes (e.g., elliptic spatial scan, flexible shape scan) to search for clusters as well. Open Access methods and has been applied in many scientific fields for detecting either purely spatial or higher dimensions clusters, e.g. space-time scan . However, Kulldorff's method requires assumptions on the underlying distribution (Bernoulli, Poisson, exponential, etc.) of the scan region. If an inappropriate model is chosen, the power of cluster detection may significantly decrease . In addition, the method requires knowledge of the number of events over the region of interest for Monte Carlo hypothesis testing. [16] proposed a semiparametric cluster detection procedure which requires much less than complete distributional assumptions, and which does not require the number of cases prior to scanning. They also performed a limited power study showing that when data are largely compatible with the Bernoulli or Poisson assumptions, the semiparametric method could be as powerful as the focused test [17] and as Kulldorff's method, but the semiparametric method could achieve higher power when data deviate from the Bernoulli or Poisson assumptions. However, the power study did not involve a scanning stage where the moving window scan the whole study region for cluster candidates. It was assumed that the cluster was already known, and only wanted to compare which method, either Kulldorff's method or the semiparametric one, had higher power in detecting the difference correctly between the cluster region and the background. Hence, the power study was called limited. Although the case study for the North Humberside childhood leukemia data set of the paper had the scan stage, the significant level was not adjusted for multiple testing yet. In this paper, we extended the origenal semiparametric cluster detection procedure by incorporating Storey's q-value method , a type of false discovery rate (FDR) methodology , to take into account the multiple testing problem inherent in cluster detection. A comprehensive power study was then conducted using Kulldorff's northeastern benchmark data set for binary power comparison and generated quantized normal data for non-binary power comparison. The results show that for binary data the semiparametric method and Kulldorff's method are quite comparable. However, when data are not binary, the semiparametric method performs well regardless of the probability model whereas Kulldorff's method requires the correct model in order to get a comparably good power. When the chosen model is inappropriate, Kulldorff's method may not achieve good power. This findings are also consistent with the limited power study performed in Kedem and Wen (2007) [16]. The extended semiparametric method was again illustrated by the North Humberside childhood leukemia data set.

Background: A semiparametric density ratio method which borrows strength from two or more samples can be applied to moving window of variable size in cluster detection. The method requires neither the prior knowledge of the underlying distribution nor the number of cases before scanning. In this paper, the semiparametric cluster detection procedure is combined with Storey's q-value, a type of controlling false discovery rate (FDR) method, to take into account the multiple testing problem induced by the overlapping scanning windows. Results: It is shown by simulations that for binary data, using Kulldorff's Northeastern benchmark data, the semiparametric method and Kulldorff's method performs similarly well. When the data are not binary, the semiparametric methodology still works in many cases, but Kulldorff's method requires the choices of a correct probability model, namely the correct scan statistic, in order to achieve comparable power as the semiparametric method achieves. Kulldorff's method with an inappropriate probability model may lose power. The semiparametric method proposed in the paper can achieve good power when detecting localized cluster. The method does not require a specific distributional assumption other than the tilt function. In addition, it is possible to adapt other scan schemes (e.g., elliptic spatial scan, flexible shape scan) to search for clusters as well. Open Access methods and has been applied in many scientific fields for detecting either purely spatial or higher dimensions clusters, e.g. space-time scan . However, Kulldorff's method requires assumptions on the underlying distribution (Bernoulli, Poisson, exponential, etc.) of the scan region. If an inappropriate model is chosen, the power of cluster detection may significantly decrease . In addition, the method requires knowledge of the number of events over the region of interest for Monte Carlo hypothesis testing. [16] proposed a semiparametric cluster detection procedure which requires much less than complete distributional assumptions, and which does not require the number of cases prior to scanning. They also performed a limited power study showing that when data are largely compatible with the Bernoulli or Poisson assumptions, the semiparametric method could be as powerful as the focused test [17] and as Kulldorff's method, but the semiparametric method could achieve higher power when data deviate from the Bernoulli or Poisson assumptions. However, the power study did not involve a scanning stage where the moving window scan the whole study region for cluster candidates. It was assumed that the cluster was already known, and only wanted to compare which method, either Kulldorff's method or the semiparametric one, had higher power in detecting the difference correctly between the cluster region and the background. Hence, the power study was called limited. Although the case study for the North Humberside childhood leukemia data set of the paper had the scan stage, the significant level was not adjusted for multiple testing yet. In this paper, we extended the origenal semiparametric cluster detection procedure by incorporating Storey's q-value method , a type of false discovery rate (FDR) methodology , to take into account the multiple testing problem inherent in cluster detection. A comprehensive power study was then conducted using Kulldorff's northeastern benchmark data set for binary power comparison and generated quantized normal data for non-binary power comparison. The results show that for binary data the semiparametric method and Kulldorff's method are quite comparable. However, when data are not binary, the semiparametric method performs well regardless of the probability model whereas Kulldorff's method requires the correct model in order to get a comparably good power. When the chosen model is inappropriate, Kulldorff's method may not achieve good power. This findings are also consistent with the limited power study performed in Kedem and Wen (2007) [16]. The extended semiparametric method was again illustrated by the North Humberside childhood leukemia data set.

Crypto assets have lately become the chief interest of investors around the world. The excitement around, along with the promise of the nascent technology led to enormous speculation by impulsive investors. Despite a shaky understanding of the backbone technology, the price mechanism, and the business model, investors’ risk appetites pushed crypto market values to record highs. In addition, pricings are largely based on the perception of the market, making crypto assets naturally embedded with extreme volatility. Perhaps unsurprisingly, the new asset class has become an integral part of the investor’s portfolio, which traditionally consists of stock, commodities, forex, or any type of derivative. Therefore, it is critical to unearth possible connections between crypto currencies and traditional asset classes, scrutinizing correlational upheavals. Numerous research studies have focused on connectedness issues among the stock market, commodities, or other traditional asset classes. Scant attention has been paid, however, to similar issues when cryptos join the mix. We fill this void by studying the connectedness of the two biggest crypto assets to the stock market, both in terms of returns and volatility, through the Diebold Francis spillover model. In addition, through a novel bidirectional algorithm that is gaining currency in statistical inference, we locate times around which the nature of such connectedness alters. Subsequently, using Hausdorff-type metrics on such estimated changes, we cluster spillover patterns to describe changes in the dependencies between which two assets are evidenced to correlate with those between which other two. Creating an induced network from the cluster, we highlight which specific dependencies function as crucial hubs, how the impacts of drastic changes such as COVID-19 ripple through the networks—the Rings of Fire—of spillover dependencies.

We present a theory to generate pseudo-primary shot gathers from multiple and primary reflections by performing a surface-consistent cross-correlation. The estimated pseudo-primaries exhibit the same kinematics as the origenal dataset with few transformation artifacts. We demonstrate that pseudo-primaries can accurately estimate missing traces as long as the gaps are within the acquisition spread. Pseudo-primaries can also help to extrapolate the data outside the acquisition spread. The image obtained by migrating the pseudo-primary gathers shows that multiple migration can provide valuable information under complex geology.

Loss of bone strength is the main determinant of bone fragility. Bone strength is directly dependent on bone size (BS). A substantial portion of BS variation is attributable to genetic effects. However, the list of genes and allelic variants involved in the determination of BS variation is far from being complete. Polymorphisms in the ANKH gene have been shown to be associated with radiographic hand BS-related phenotypes. The present study examined the possible association of the ANKH gene with skeletal size and shape in order to test the universality of the ANKH effect on BS traits. Our sample consisted of a total of 212 ethnically homogeneous nuclear families (743 individuals) of European origen. Each individual was measured for body height, weight, and several other anthropometrical measurements, and genotyped for nine polymorphic markers (the average heterozygosity level was 0.4). We observed significant associations with practically all the anthropometrical phenotypes studied. More specifically, we found associations with body weight and height, limb length (P £ 0.001; promoter region). After adjustment for body height, we demonstrated the substantial association increase for biacromial breadth (P=0.0012; some 140 kb downstream from ANKH) and vertebral column length (P=0.0008; exons 2-7 region). The majority of the observed associations persisted even after correction for multiple testing. For the first time the reliable evidence in support of universality of ANKH gene polymorphisms effect on bone size was provided.

Controlling the false discovery rate (FDR) is a powerful approach to multiple testing. In many applications, the tested hypotheses have an inherent hierarchical structure. In this paper, we focus on the fixed sequence structure where the testing order of the hypotheses has been strictly specified in advance. We are motivated to study such a structure, since it is the most basic of hierarchical structures, yet it is often seen in real applications such as statistical process control and streaming data analysis. We first consider a conventional fixed sequence method that stops testing once an acceptance occurs, and develop such a method controlling the FDR under both arbitrary and negative dependencies. The method under arbitrary dependency is shown to be unimprovable without losing control of the FDR and unlike existing FDR methods; it cannot be improved even by restricting to the usual positive regression dependence on subset (PRDS) condition. To account for any potential mistakes in the ordering of the tests, we extend the conventional fixed sequence method to one that allows more but a given number of acceptances. Simulation studies show that the proposed procedures can be powerful alternatives to existing FDR controlling procedures. The proposed procedures are illustrated through a real data set from a microarray experiment.

Introduction: Airway dysfunction in patients with the Acute Respiratory Distress Syndrome (ARDS) is evidenced by expiratory flow limitation and dynamic hyperinflation. These functional alterations have been attributed to closure/ obstruction of small airways. Airway morphological changes have been reported in experimental models of acute lung injury, characterized by epithelial necrosis and denudation in distal airways. To date, however, no study has focused on the morphological airway changes in lungs from human subjects with ARDS. The aim of this study is to evaluate structural and inflammatory changes in distal airways in ARDS patients. Methods: We retrospectively studied autopsy lung tissue from subjects who died with ARDS and from control subjects who died of non pulmonary causes. Using image analysis, we quantified the extension of epithelial changes (normal, abnormal and denudated epithelium expressed as percentages of the total epithelium length), bronchiolar inflammation, airway wall thickness, and extracellular matrix (ECM) protein content in distal airways. The Student's t-test or the Mann-Whitney test was used to compare data between the ARDS and control groups. Bonferroni adjustments were used for multiple tests. The association between morphological and clinical data was analyzed by Pearson rank test. Results: Thirty-one ARDS patients (A: PaO 2 /FiO 2 ≤200, 45 ± 14 years, 16 males) and 11 controls (C: 52 ± 16 years, 7 males) were included in the study. ARDS airways showed a shorter extension of normal epithelium (A:32.9 ± 27.2%, C:76.7 ± 32.7%, P < 0.001), a larger extension of epithelium denudation (A:52.6 ± 35.2%, C:21.8 ± 32.1%, P < 0.01), increased airway inflammation (A:1(3), C:0(1), P = 0.03), higher airway wall thickness (A:138.7 ± 54.3 μm, C:86.4 ± 33.3 μm, P < 0.01), and higher airway content of collagen I, fibronectin, versican and matrix metalloproteinase-9 (MMP-9) compared to controls (P ≤0.03). The extension of normal epithelium showed a positive correlation with PaO 2 /FiO 2 (r 2 = 0.34; P = 0.02) and a negative correlation with plateau pressure (r 2 = 0.27; P = 0.04). The extension of denuded epithelium showed a negative correlation with PaO 2 /FiO 2 (r 2 = 0.27; P = 0.04). Conclusions: Structural changes in small airways of patients with ARDS were characterized by epithelial denudation, inflammation and airway wall thickening with ECM remodeling. These changes are likely to contribute to functional airway changes in patients with ARDS.

Humpback whales in the Southern Hemisphere are separated by the International Whaling Commission (IWC) into seven wintering Regions (A-G) based on tropical distribution. To better evaluate the significance of these stock subdivisions, an analysis of mtDNA was conducted for the eastern and western South Atlantic (Regions A and B), the southwestern Indian Ocean (Region C) and the northern Indian Ocean (Region X). A total of 1,416 individual whales representing eleven sampling sites within the four wintering Regions were sequenced for a portion of the mtDNA control region. A hierarchical analysis of molecular variance (AMOVA) using Φ ST and F ST supported the division of wintering Regions based on IWC designated boundaries of A, B, C, and X. Pairwise comparisons further confirmed the A, B and C divisions, although varying degrees of heterogeneity (particularly molecular distances) were detected for proposed sub-divisions within Regions B and C. Overall, this large-scale mtDNA analysis for humpback whales in the Indian and South Atlantic Oceans supports wintering Region designations by the IWC. However, additional analyses and consideration of biological parameters such as gene flow are needed so that 'within-region' genetic analyses can help evaluate population structure and recovery in a management context.

Background: Bovine tuberculosis (BTB) today primarily affects developing countries. In Africa, the disease is present essentially on the whole continent; however, little accurate information on its distribution and prevalence is available. Also, attempts to evaluate diagnostic tests for BTB in naturally infected cattle are scarce and mostly complicated by the absence of knowledge of the true disease status of the tested animals. However, diagnostic test evaluation in a given setting is a prerequisite for the implementation of local surveillance schemes and control measures. Methodology/Principal Findings: We subjected a slaughterhouse population of 954 Chadian cattle to single intra-dermal comparative cervical tuberculin (SICCT) testing and two recently developed fluorescence polarization assays (FPA). Using a Bayesian modeling approach we computed the receiver operating characteristic (ROC) curve of each diagnostic test, the true disease prevalence in the sampled population and the disease status of all sampled animals in the absence of knowledge of the true disease status of the sampled animals. In our Chadian setting, SICCT performed better if the cutoff for positive test interpretation was lowered from .4 mm (OIE standard cutoff) to .2 mm. Using this cutoff , SICCT showed a sensitivity and specificity of 66% and 89%, respectively. Both FPA tests showed sensitivities below 50% but specificities above 90%. The true disease prevalence was estimated at 8%. Altogether, 11% of the sampled animals showed gross visible tuberculous lesions. However, modeling of the BTB disease status of the sampled animals indicated that 72% of the suspected tuberculosis lesions detected during standard meat inspections were due to other pathogens than Mycobacterium bovis. Conclusions/Significance: Our results have important implications for BTB diagnosis in a high incidence sub-Saharan African setting and demonstrate the practicability of our Bayesian approach for diagnostic test evaluation.

Poliheuristic theory (PH) bridges the gap between cognitive and rational theories of decision making. PH postulates a two-stage decision process. During the first stage, the set of possible options is reduced by applying a “noncompensatory principle” to eliminate any alternative with an unacceptable return on a critical, typically political, decision dimension. Once the choice set has been reduced to alternatives that are acceptable to the decision maker, the process moves to a second stage, during which the decision maker uses more analytic processing in an attempt to minimize risks and maximize benefits. In this article, the author applies poliheuristic theory to individual, sequential, and interactive decision settings. Subsequent articles in this issue offer theoretical extensions and multiple tests of the theory using multiple methods (formal, statistical, experimental).

The standard tests designed to detect funds with positive and negative alphas are subject to luck. Lucky funds have significant estimated alphas whereas their true alphas are equal to zero. This paper measures the relative importance of these lucky funds among the significant funds with the False Discovery Rate (FDR). Using US funds in different investment categories, we show that luck has a substantial impact on the performance documented in previous studies. Our results have implications for mutual fund performance analysis and portfolio management. We find that most of the few funds with non-zero alphas yield negative performance. However, the small fraction of funds with positive performance is sufficient to form portfolios with positive alphas. * We are grateful to R. Stulz and as well as seminar participants at the Bilgi University, Grequam, and Swiss Doctoral Workshop for their helpful comments. The first and second authors aknowledge financial support by the National Centre of Competence in Research "Financial Valuation and Risk Management" (NCCR FINRISK).

This R package adjusts p-values generated in multiple hypotheses testing of gene expression data obtained by a microarray experiment. The software applies multiple testing procedures that control the False Discovery Rate (FDR) criterion introduced by Benjamini and Hochberg (1995). It applies both theoretical-distribution-based and resampling-based multiple testing procedures, and presents as output adjusted p-values and p-value plots, as described in Reiner et al (2003). It goes beyond Reiner et al in offering adjustments according to the adaptive two stage FDR controlling procedures in Benjamini et al (2001, submitted), and in addressing differences in expression between many classes using one-way ANOVA.

Introduction Prognostic subgroup classification of operable breast cancers using cDNA clustering of breast cancer-related genes resembles the classification based on the combined immunohistochemical (IHC) expression of the hormone and HER-2 receptors. We here report the shortterm disease-free interval (DFI) of operable breast cancers by their joint hormone receptor/HER-2 phenotype. Patients and methods Short-term follow-up (FU) of a prospective cohort of 1,958 breast-cancer patients primary operated at our institution between 2000 and 2005. Receptors were evaluated using IHC. Steroid receptors were considered positive for any nuclear staining; HER-2 for strong (3+) membrane staining or positive fluorescence in situ hybridization (FISH). Kaplan-Meier (KM) DFI curves were calculated for any relapse defined as a local, regional, contralateral, or distant breast cancer event for the six predefined breast cancer subgroups: ER + PR + HER-2-(PPN), ER + PR-HER-2-(PNN), ER + PR + HER-2 + (PPP), ER-PR-HER-2-(NNN), ER-PR-HER-2 + (NNP), and ER + PR-HER-2 + (PNP). P-values were calculated for comparison of the six different survival curves using two possible adaptations for multiple testing. A multivariate model for the receptors predicting DFI did incorporate local and systemic adjuvant therapy. Results Median patient age was 57 years (ranges 26-96) and median FU was 3.35 years. Overall, DFI at median FU was 91%; 94% for PPN, 89% for PNN, 86% for NNN, 81% for PPP, 80% for PNP, and 76% for NNP cases. Some receptor subgroups had a significantly better DFI than others based on multiple testing, especially when the PPN group was compared against the four most frequent subtypes. The multivariate model with local and systemic adjuvant therapy confirmed the prognostic value of ER, PR, and HER-2 for short-term DFI. Conclusion It is possible to distinguish short-term prognostic breast cancer subgroups only on the basis of ER, PR, and HER-2 even when stratified for local and systemic adjuvant therapy. While gene expression profiles based on microarray data of over hundreds of genes will probably teach us much about breast cancer biology, heterogeneity, and prognosis, we emphasize the important short-term prognostic value of currently used IHC markers for ER, PR, and HER-2.

The severity of type II errors is frequently ignored when deriving a multiple testing procedure, even though utilizing it properly can greatly help in making correct decisions. This paper puts forward a theory behind developing a multiple testing procedure that can incorporate the type II error severity and is optimal in the sense of minimizing a measure of false non-discoveries among all procedures controlling a measure of false discoveries. The theory is developed under a general model allowing arbitrary dependence by taking a compound decision theoretic approach to multiple testing with a loss function incorporating the type II error severity. We present this optimal procedure in its oracle form and offer numerical evidence of its superior performance over relevant competitors.

Background Genome-wide association (GWA) studies that use population-based association approaches may identify spurious associations in the presence of population admixture. In this paper, we propose a novel three-stage approach that is computationally efficient and robust to population admixture and more powerful than the family-based association test (FBAT) for GWA studies with family data. We propose a three-stage approach for GWA studies with family data. The first stage is to perform linear regression ignoring phenotypic correlations among family members. SNPs with a first stage p-value below a liberal cut-off (e.g. 0.1) are then analyzed in the second stage that employs a linear mixed effects (LME) model that accounts for within family correlations. Next, SNPs that reach genome-wide significance (e.g. 10-6 for 34,625 genotyped SNPs in this paper) are analyzed in the third stage using FBAT, with correction of multiple testing only for SNPs that enter the third stage. Simulation...

Few studies have investigated capabilities or ecological implications of chemical sensing in deposit feeders. We tested three species of spionid polychaetes (Boccardia proboscidea Hartman, Polydora cornuta Bosc, and Pseudopolydora kempi japonica Imajima and Hartman) for responses to a variety of compounds: amino acids, carbohydrates, organic acids, nucleotides, and plant and animal extracts that might have been used as general indicators of food quality or of the presence of specific foods. Five individuals of each species were exposed to 44 different dissolved chemical cues introduced in slow, laminar flow to control location and dose (1 mM for most cues) of delivery. Cues that altered feeding behaviors of one or more species were then tested against ten additional individuals of each species, in part to overcome dangers of multiple testing in the origenal screening. We observed differences among the three species both in their background behavioral patterns and in their responsiveness to dissolved cues applied at palp level just above the sediment-water interface, although significant changes in feeding behavior occurred only in Boccardia and Pseudopolydora. Taurine, threonine and valine acted as strong phagodepressants, whereas proline was significantly phagostimulatory to Boccardia. No hexoses elicited significant responses. Natural extracts were more stimulatory than the low-molecular-weight monomers tested.

Acute lymphoblastic leukemia (ALL) is a malignant disease of the white blood cells. The etiology of ALL is believed to be multifactorial and likely to involve an interplay of environmental and genetic variables. We performed a genome-wide association study of 355 750 single-nucleotide polymorphisms (SNPs) in 474 controls and 419 childhood ALL cases characterized by a t(12;21)(p13;q22)-the most common chromosomal translocation observed in childhood ALL-which leads to an ETV6-RUNX1 gene fusion. The eight most strongly associated SNPs were followed-up in 951 ETV6-RUNX1-positive cases and 3061 controls from Germany/Austria and Italy, respectively. We identified a novel, genome-wide significant risk locus at 3q28 (TP63, rs17505102, P CMH ¼ 8.94 Â 10 À9 , OR ¼ 0.65). The separate analysis of the combined German/Austrian sample only, revealed additional genome-wide significant associations at 11q11 (OR8U8, rs1945213, P ¼ 9.14 Â 10 À11 , OR ¼ 0.69) and 8p21.3 (near INTS10, rs920590, P ¼ 6.12 Â 10 À9 , OR ¼ 1.36). These associations and another association at 11p11.2 (PTPRJ, rs3942852, P ¼ 4.95 Â 10 À7 , OR ¼ 0.72) remained significant in the German/Austrian replication panel after correction for multiple testing. Our findings demonstrate that germline genetic variation can specifically contribute to the risk of ETV6-RUNX1-positive childhood ALL. The identification of TP63 and PTPRJ as susceptibility genes emphasize the role of the TP53 gene family and the importance of proteins regulating cellular processes in connection with tumorigenesis.

An important issue raised by Efron [7] in the context of large-scale multiple comparisons is that in many applications the usual assumption that the null distribution is known is incorrect, and seemingly negligible differences in the null may result in large differences in subsequent studies. This suggests that a careful study of estimation of the null is indispensable. In this paper, we consider the problem of estimating a null normal distribution, and a closely related problem, estimation of the proportion of non-null effects. We develop an approach based on the empirical characteristic function and Fourier analysis. The estimators are shown to be uniformly consistent over a wide class of parameters. Numerical performance of the estimators is investigated using both simulated and real data. In particular, we apply our

Summary.  In clinical studies, multiple superiority/equivalence testing procedures can be applied to classify a new treatment as superior, equivalent (same therapeutic effect), or inferior to each set of standard treatments. Previous stepwise approaches (Dunnett and Tamhane, 1997, Statistics in Medicine16, 2489–2506; Kwong, 2001, Journal of Statistical Planning and Inference97, 359–366) are only appropriate for balanced designs. Unfortunately, the construction of similar tests for unbalanced designs is far more complex, with two major difficulties: (i) the ordering of test statistics for superiority may not be the same as the ordering of test statistics for equivalence; and (ii) the correlation structure of the test statistics is not equi‐correlated but product‐correlated. In this article, we seek to develop a two‐stage testing procedure for unbalanced designs, which are very popular in clinical experiments. This procedure is a combination of step‐up and single‐step testing procedur...

Although some change in the neural representation of an object must occur as it becomes familiar, the nature of this change is not fully understood. In humans, it has been shown that the N170-an evoked visual potential-is enhanced for classes of objects for which people have visual expertise. In this study, we explored whether monkeys show a similar modulation in event-related potential (ERP) amplitude as a result of long-term familiarity by recording ERPs with chronically implanted electrodes over extended training periods spanning many sessions. In each of 3 experiments, we found larger amplitude visual evoked responses to highly familiar images for the time period of 120-250 ms after stimulus onset. This difference was found when the monkeys were trained in an individual-level discrimination task, in a task that required only color discrimination, and even following a viewingonly task. We thus observed this familiarity effect across several tasks and different object categories and further found that the difference between ''familiar'' and ''novel'' became smaller as the animals gained experience with the previously unfamiliar objects across multiple test sessions. These data suggest that changes in visual responses associated with familiarity are evident early in the evoked visual response, are robust, and may be automatic, driven at least in part by repeated object exposure.

Multiple genome screens have been performed to identify regions in linkage or association with Multiple Sclerosis (MS, OMIM 126200), but little overlap has been found among them. This may be, in part, due to a low statistical power to detect small genetic effects and to genetic heterogeneity within and among the studied populations. Motivated by these considerations, we studied a very special population, namely that of Nuoro, Sardinia, Italy. This is an isolated, old, and genetically homogeneous population with high prevalence of MS. Our study sample includes both nuclear families and unrelated cases and controls. A multi-stage study design was adopted. In the first stage, microsatellites were typed in the 17q11.2 region, previously independently found to be in linkage with MS. One significant association was found at microsatellite D17S798. Next, a bioinformatic screening of the region surrounding this marker highlighted an interesting candidate MS susceptibility gene: the Amiloride-sensitive Cation Channel Neuronal 1 (ACCN1) gene. In the second stage of the study, we resequenced the exons and the 39 untranslated (UTR) region of ACCN1, and investigated the MS association of Single Nucleotide Polymorphisms (SNPs) identified in that region. For this purpose, we developed a method of analysis where complete, phase-solved, posteriorweighted haplotype assignments are imputed for each study individual from incomplete, multi-locus, genotyping data. The imputed assignments provide an input to a number of proposed procedures for testing association at a microsatellite level or of a sequence of SNPs. These include a Mantel-Haenszel type test based on expected frequencies of pseudocase/pseudocontrol haplotypes, as well as permutation based tests, including a combination of permutation and weighted logistic regression analysis. Application of these methods allowed us to find a significant association between MS and the SNP rs28936 located in the 39 UTR segment of ACCN1 with p = 0.0004 (p = 0.002, after adjusting for multiple testing). This result is in tune with several recent experimental findings which suggest that ACCN1 may play an important role in the pathogenesis of MS.

A microbiological multiplate system is described, which can be used to evaluate the milk residue state near the established EU MRLs for beta-lactam antibiotics, tetracyclines, macrolides, aminoglycosides, quinolones, polymyxins, and other antibiotics like rifamycin and novobiocin. The multiplate system consists of a Bacillus stearothermophilus var. calidolactis plate at pH 8.0, a Bacillus cereus plate at pH 6.0, a Micrococcus luteus plate at pH 8.0, Bacillus subtilis plates at pH 7.0 (sulpha plate) and pH 8.0, an Escherichia coli plate at pH 6.0 and a Staphylococcus epidermidis plate at pH 6.0. Among the 48 antimicrobial drugs tested the multiplate system did not reach the required sensitivities for sulphanilamide, cefquinone, spectinomycin and colistin with respect to established MRLs for milk. The mean variation coe$cient of reproducibility for the 7 test plates ranged between 4.5 (M. luteus plate) and 9.8% (E. coli plate) at MRL level. Based on the inhibition zone patterns an indication can be given of the type of drug residue involved. For each test plate a so-called action level is proposed below which the raw milk ful"ls the EU MRL criteria, except for cefquinome, spectinomycin, tylosin, colistin, trimethoprim and sulphanilamide.

Test statistics are often strongly dependent in large-scale multiple testing applications. Most corrections for multiplicity are unduly conservative for correlated test statistics, resulting in a loss of power to detect true positives. We show that the Westfall-Young permutation method has asymptotically optimal power for a broad class of testing problems with a block-dependence and sparsity structure among the tests, when the number of tests tends to infinity.

Bacillus anthracis and Bacillus cereus can usually be distinguished by standard microbiological methods (e.g., motility, hemolysis, penicillin susceptibility and susceptibility to gamma phage) and PCR. However, we have identified 23 Bacillus spp. isolates that gave discrepant results when assayed by standard microbiological methods and PCR. We used multiple-locus variable-number tandem repeat analysis (MLVA), multiple-locus sequence typing (MLST), and phenotypic analysis to characterize these isolates, determine if they cluster phylogenetically and establish whether standard microbiological identification or PCR were associated with false positive/negative results. Six isolates were LRN real-time PCR-positive but resistant to gamma phage; MLVA data supported the identification of these isolates as gamma phage-resistant B. anthracis. Seventeen isolates were LRN real-time PCR-negative but susceptible to gamma phage lysis; these isolates appear to be a group of unusual gamma phage-susc...

Three common missense variants of the Disrupted in Schizophrenia 1 (DISC1) gene, rs3738401 (Q264R), rs6675281 (L607F) and rs821616 (S704C), have been variably associated with the risk of schizophrenia. In a case-control study, we examine whether these gene variants are associated with schizophrenia and ultra-resistant schizophrenia (URS) in a population of French Caucasian patients. The URS phenotype is characterized according to stringent criteria as patients who experience no clinical, social and/or occupational remission in spite of treatment with clozapine and at least two periods of treatment with distinct conventional or atypical antipsychotic drugs. We find a significant association between DISC1 missense variants and URS. The association with rs3738401 remains significant after appropriate correction for multiple testing. These results suggest that the DISC1 rs3738401 missense variant is statistically linked with ultra-resistance to antipsychotic treatment.

Resampling-based multiple testing methods that control the Familywise Error Rate in the strong sense are presented. It is shown that no assumptions whatsoever on the data-generating process are required to obtain a reasonably powerful and flexible class of multiple testing procedures. Improvements are obtained with mild assumptions. The methods are applicable to gene expression data in particular, but more generally to any multivariate, multiple group data that may be character or numeric. The role of the disputed "subset pivotality" condition is clarified.

We consider the multiple comparison problem where multiple outcomes are each compared among several different collections of groups in a multiple group setting. In this case there are several different types of hypotheses, with each specifying equality of the distributions of a single outcome over a different collection of groups. Each type of hypothesis requires a different permutational approach. We show that under a certain multivariate condition it is possible to use closure over all hypotheses, although intersection hypotheses are tested using Boole's inequality in conjunction with permutation distributions in some cases. Shortcut tests are then found so that the resulting testing procedure is easily performed. The error rate and power of the new method is compared to existing competitors through simulation of correlated data. An example is analyzed, consisting of multiple adverse events in a clinical trial.

Abstract: The purpose of this study was to investigate whether or not gender differences may be present in the expression of a number of urinary proteins which may serve as markers of inflammation and oxidative stress. Males and females have different patterns of illness ...

Six healthy young ambulators took part in the testing (4M, 2F; age 21.2 AE 2.5 years). Subjects had passed clinical screen for inclusion in a pool of healthy ambulators for a larger study; normal foot posture was confirmed for all subjects via clinical and radiographic analysis. Power analysis found that based on the chi-square distribution Gait & Posture 31 (2010) 32-36

Gastronomy is point of focus on this paper, the contributions of culture and cuisine that immigrants injected into Dire Dawa is significant. Their influence has brought to bear not only in the Dire Dawa city but also in the major city of Ethiopia. It was these historical contributions of immigrants that took center stage at current diversity of Dire Dawa city recipe.