Although the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%.The prediction of human protein-protein interactions was investigated by combining orthogonal protein features within a probabilistic framework. The features include co-expression, orthology to known interacting proteins and the full-Bayesian combination of subcellular localization, co-occurrence of domains and post-translational modifications. A novel scoring function for local network topology was also investigated. This topology feature greatly enhanced the predictions and together with the full-Bayes combined features, made the largest contribution to the predictions. Using a conservative threshold, our most accurate predictor identifies 37606 human interactions, 32892 (80%) of which are not present in other publicly available large human interaction datasets, thus substantially increasing the coverage of the human interaction map. A subset of the 32892 novel predicted interactions have been independently validated. Comparison of the prediction dataset to other available human interaction datasets estimates the false positive rate of the new method to be below 80% which is competitive with other methods. Since the new method scores and ranks all human protein pairs, smaller subsets of higher quality can be generated thus leading to even lower false positive prediction rates.The set of interactions predicted in this work increases the coverage of the human interaction map and will help determine the highest confidence human interactions. Protein-protein interactions perform and regulate fundamental cellular processes. The comprehensive study of such interactions on a genome-wide scale will lead to a clearer understanding of diverse cellular processes and of the molecular mechanisms of disease. Although the determination of interactions by small-scale laboratory techniques is impractical for a complete proteome on the grounds of cost and time, several experimental techniques now exist to determine protein-protein interactions in a high-throughput manner . High-thThe low coverage and variable quality of the experimental interaction datasets have prompted many groups to investigate computational methods to predict interactions or to determine the most likely interactions seen in the high-throughput datasets. The different approaches to predict interactions can be grouped into five main categories:1) Predictors based on sequence and structure exploit the observation that some pairs of sequence motifs, domains and structural families tend to interact preferentially. Some methods predict interaction from sequence-motifs found to be over-represented in interacting protein pairs , or by c2) Predictors based on comparative genomics have been exploited primarily in prokaryotes. They consider the physical location of genes, as well as their pattern of occurrence and evolutionary rate, to predict interactions or functional relationships between protein pairs. Some predictors make use of the observation that neighboring genes whose relative location is conserved across several prokaryotic organisms are likely to interact . Other p-10, it has been shown that 16–30% of yeast interactions can be transferred to the worm  and PTM[j] are distinct PTMs and I is the set of all interacting proteins that were used to train the predictor. The annotations of PTMs in human proteins were downloaded from UniProt  and HPRDcombined module.The localization, co-occurrence of domains, and PTMs were considered simultaneously to measure their predictive power in assessing the likelihood of protein interaction. To do this, all possible combinations of the 4 localization bins, 5 chi-square domain-co-occurrence bins and 4 PTM_score bins were investigated and are referred to as the It has been suggested that unstructured regions of proteins are often involved in binding interactions, particularly in the case of transient interactions . ProteinGroup A modules. Likelihood ratios for each of the Group A modules are illustrated in Figure The Expression, Orthology, Combined and Disorder modules are referred collectively as the The transitive module works on the premise that a pair of proteins is more likely to interact if it shares interacting partners. It does this by considering the local topology of the network predicted by the integration of the Group A modules as depicted in Figure c is the set of edges that connect proteins i and j to their common interactors, Ei is the set of edges that involve protein i, se is the score (likelihood ratio) of edge e and Ei\Ec refers to the set difference of Ei and Ec. For a given set of neighbors, T increases as the interactions with these neighbors become more likely (as the sum of se increases). Additionally, the topology score T of a pair of proteins increases as the proportion of likely interactors that these two proteins share increases. The topology scores were grouped into 5 bins of increasing value. It should be noted that the neighborhood topology score calculated for a given protein pair does not consider the preliminary score assigned to that protein pair. It only considers the preliminary scores of its neighbors and so is truly based on the local network topology around that protein pair. Accordingly, the likelihood ratio the transitive module outputs for a given protein pair is independent of the likelihood ratio calculated by the Group A modules for this same protein pair.where EThe Pearson correlation between pairs of modules was estimated by taking 150 samples of 10000 protein pairs each and calculating the Pearson correlation of the likelihood ratios for the two modules considered, for each sample. The reported correlation values are the average of the 150 experiments. Samples of the protein pair space were taken instead of considering the whole space as this was more computationally tractable.The accuracy of the predictors was measured by performing five-fold cross validation experiments in which the datasets were randomly divided into five non-overlapping sets, four of which were used to train the predictor while the fifth was used to test the prediction accuracy. The accuracy reported is the average measured for all combinations of training and testing sets using these five sets. Testing was done by predicting the total likelihood scores for all protein pairs in the test set using the models computed in the training phase and then counting the number of pairs that were well predicted. We used the area under partial ROC curves as a measure of accuracy. Receiver operator characteristic (ROC) curves plot the true positive rate versus the false positive rate over their full range of possible values. In some circumstances, it is more informative to use partial ROC curves (ROCn curves) which illustrate the number of true positives identified by the predictor that score higher than the n highest scoring negatives, plotted for all values from 0 to n. There are many more negatives than positives in our datasets and this is also thought to be true for the full protein interaction networks we are modeling. Since the aim is to identify the largest number of true interacting pairs while leaving out as many non-interacting pairs as possible, it is most informative to measure the performance of the predictor under conditions of very low false-positive rates. Accordingly, ROC50 and ROC100 curves were analyzed because given the size of the datasets, these curves consider all the protein pairs predicted to have a posterior odds ratio above 1.0, for all the predictors investigated. The area under ROC curves is often used as a summary measure of accuracy. For ROCn curves, it can be calculated asi is the number of positives that score higher than the ith highest scoring negative.where i takes on values from 1 to n, T is the total number of positives in the test set and TMSS conceived and designed the study, created and implemented the predictor, analyzed the predictions and drafted the manuscript. GJB participated in the design of the study, the analysis of the predictions and the writing of the manuscript. Both authors read and approved the final manuscript.High scoring InterPro domain pairs. List of InterPro domain pairs that achieve highest chi-square score of co-occurrence in our set of positive interactors.Click here for fileHigh scoring post-translational modification pairs. List of high-scoring post-translational modification pairs.Click here for fileAll LR400 predicted interactions ranked. All human protein pairs predicted to have a likelihood ratio of interaction greater than 400 and thus a posterior odds ratio of interaction greater than 1.Click here for fileAdditional methods. In depth description of the calculation of likelihood ratios for the modules.Click here for file


Although experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. For a considerable fraction of proteins whose structures will not be determined experimentally, computational methods can provide valuable information. The value of structural models in biological research depends critically on their quality. Development of high-accuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling.Large sets of structural decoys have been generated using reduced conformational space protein modeling tool CABS. Subsequently, the reduced models were subject to all-atom reconstruction. Then, the resulting detailed models were energy-minimized using state-of-the-art all-atom force field, assuming fixed positions of the alpha carbons. It has been shown that a very short minimization leads to the proper ranking of the quality of the models (distance from the native structure), when the all-atom energy is used as the ranking criterion. Additionally, we performed test on medium and low accuracy decoys built via classical methods of comparative modeling. The test placed our model evaluation procedure among the state-of-the-art protein model assessment methods.These test computations show that a large scale high resolution protein structure prediction is possible, not only for small but also for large protein domains, and that it should be based on a hierarchical approach to the modeling protocol. We employed Molecular Mechanics with fixed alpha carbons to rank-order the all-atom models built on the scaffolds of the reduced models. Our tests show that a physic-based approach, usually considered computationally too demanding for large-scale applications, can be effectively used in such studies. Reliable high-resolution prediction of protein structure remains a formidable challenge and it becomes more and more evident that we are entering the era in which high-resolution predictions and molecular designs will make increasingly important contributions to biology and medicine ,2. The hDetermining and properly quantifying the properties that are characteristic for protein native structures are of primary importance for the construction of an accurate tool for the model quality assessment. Several different approaches to the optimal model selection have been proposed – such as the use of empirical or knowledge-based potentials ,7 deriveIt is widely believed that the native conformation of a protein corresponds to the global minimum of the free energy surface defined by the protein's conformational space and the molecular interactions. A straightforward protein modeling by the all-atom energy minimization remains impractical due to the high complexity of the interactions and astronomical size of the conformational space to be searched. Thus, most approaches used for exploring the protein's energy surface have resorted to essential simplifications in the description of the polypeptide chain geometry and definition of molecular interactions. Properly designed reduced models make possible very effective search of the protein's conformational space. Model simplifications, while beneficial in filtering out the majority of unrealistic structures, limit the degree of accuracy that can be achieved. In most contemporary approaches to protein structure prediction large sets of alternative models are built. Proper selection of the best model is in many cases as difficult as obtaining very good models .Even in the simplest case of protein structure prediction – comparative modeling, the exact structure of a target protein differs from its nearest structural template used in modeling. Such deviations can not be corrected on the low-resolution modeling level, a more detailed representation of the protein and more realistic force field are needed. Unfortunately, more complex energy functions produce more rough energy landscapes, which consequently makes sampling much more difficult. Thus, it seems reasonable to split the modeling process into two stages: fold assembly (in a simplified representation) followed by the model refinement/selection procedure, using a more detailed representation  and a more exact interaction scheme.De novo models produced by Rosetta were also subjected to molecular dynamics simulations performed in explicit water [The first attempts at using the all-atom modeling as a final stage of hierarchical approach were applied to GCN4 leucine zipper – a very simple homodimer coiled-coil consisting of two 33-mer monomers . Howeverit water . RMSD vait water , who manVery interesting hierarchical approach to protein folding was developed by Levitt group . First, Currently, probably the most successful refinement procedures use the all atom force-field that focuses on the short range interactions and Monte Carlo minimization. Unfortunately, the methods consume a lot of computer power and can be used only for small protein domains . The autHere we show that by using a combination of a relatively high resolution sampling in a reduced conformational space, with the model selection by an all-atom detailed potentials and a high performance computing, the high resolution structure prediction can be achieved (less than 1.0 Å from native). To get such result the reduced models need to be diverse enough to cover the near-native subset of the conformational space. CABS (CA-CB-Side chain) modeling tool was employed for this purpose ,18. CABSIn the present work we have demonstrated that a short, all-atom minimization with fixed Cα positions can properly rank-order large sets of near native decoys generated by CABS. In this context it becomes apparent that critical for the high-resolution protein structure prediction is ability to generate sets of models that contain some near-native structures. In comparative modeling with CABS, it could be achieved by using restraints extracted from various templates with alternative alignments in the uncertain regions. To our knowledge, that's the first approach enabling a meaningful refinement of large protein domains. The procedure proposed here may also work for small proteins in the template-free modeling. In such cases very large and diverse sets of decoys need to be generated and properly clustered before the all-atom based model selection. To further evaluate the proposed method for model assessment and ranking, we also performed tests on models generated by MODELLER  – probabWe tested the proposed model ranking protocol on large sets of near-native decoys. We constructed a benchmark of 7 proteins , which ade novo structure predictions [Several studies have utilized different energy functions to discriminate the native structure among sets of decoys built in different ways ,22-25. Tdictions .de novo structure prediction. As can be seen from the example given in Figure In this work, long Monte Carlo simulation with the CABS model  were perThe main objectives of the use of Molecular Mechanics force field after the coarse-grained stage modeling in our work are: improving filtering of the crude models, providing better correlation with similarity to the native, and then identification of the best model (closest to the native). To reliably verify the correlation between the Molecular Mechanics energy and RMSD we decided to divide each protein subset  onto 30 bins, using RMSD from the native structure as a criterion for the classification . From each bin, 30 models or less if there weren't as many, were randomly selected. In this manner approximately 800 decoys were selected for each protein, with a broad spectrum of the quality of models (in the sense of similarity to the native structure).de novo structure prediction, various comparative modeling techniques, or hybrid methods utilizing different kinds restraints from experimental data) brings the necessity of reconstruction of the reduced models to the all-atom representation, compatible with the classical all-atom modeling tools [Employing simplified protein representation for exploration of the vast conformational space , from a low-accuracy models we used MOULDER decoys' set, evaluated by Eramian et al.  using 24Of the 20 targets subsets, only 7 contain models better than 3 Å (for RMSD range and median RMSD see Table The performance of the methods with the MOULDER decoys' set were measured by average RMSD difference (ΔRMSD) between the model identified as the best of the set and the model with the lowest RMSD. Each of the sets of 300 models was split into 2000 randomly populated smaller sets of 75 models. The purpose of this division was to reduce the impact of individual target sets on the final ranking and to increase the robustness of the benchmark. For each 75 model set, the model with the lowest Cα RMSD (after superposition with the native structure) was used as a reference to calculate the ΔRMSD measure. We followed the same rules, despite the fact that the average ΔRMSD value of the 75 model set is not suited for evaluating of our procedure, which is aimed at assessing much larger sets due to its characteristics. Our method narrows down the number of models in the testing set rejecting the fraction of them , due to their small inaccuracies. Sensitivity for the small displacements is the price for the high discriminatory power . AdditioTwo worst cases, which are the two largest proteins from the set, illustrate the effect of narrowing down the number of models and insufficient number of good models in the set. The former situation is observed for the subset of 2fjbL models, where only one third had been the subject to the 1000 iteration minimization, while the rest was rejected after 200 step minimization (E>0). The latter could be observed in the case of the 2cmd subset of models, which is the subset with the smallest number of models better than 3 Å (16 out of 300).de novo high-resolution small protein structure prediction [Omitting these two worst cases, our method performed similarly to the Rosetta scoring function which is apparently the most successful in ediction . ΔRMSD vediction . The autThe ability of the all tested methods to identify native-like models greatly varied across different targets . The mosAs we said earlier, the minimization with frozen Cα has to be performed on a sufficient number of models. The number of models in the studied MOULDER subsets (300) seems to be enough Table . Obtainede novo structure prediction can be achieved for small proteins by using an all-atom refinement procedure in the last stage of prediction. The cost of application of the high resolution refinement for large proteins was estimated by authors to require orders of magnitude more computing power, than the 150 CPU days required for small proteins [In summary, the proposed simulation protocol makes possible fast and reliable assessment of high resolution structures for relatively large proteins. Bradley et al.  have shoproteins . A singlde novo modeling) and the selection of the best models from a set of good models . The results of this work apply mostly to the model selection in comparative modeling. Recent CASPs test have shown that the best comparative models are built with a lot of human intervention using an assortment of well known modeling tools [Protein model filtering in the endgame of protein structure prediction protocols faces the following two challenges: fold identification  led to a new category of model assessments – Model Quality Assessment Programs (MQAPs). Although performance of currently available MQAPs indicate that some of these methods may be useful for new automated procedures, high false positive rates are observed, and the MQAP methods were suggested to be used as additional elements of the prediction protocols rather than as a simple post-filter .Our method gives surprisingly consistent correlation of the all-atom energies with RMSD distance from the native structure. Decoys within 3 Å from native were examined and no false positive cases were noted. As mentioned before, the correlation of the CABS energy with RMSD in this range is often insignificant. The starting energy of the all-atom structures are uncorrelated with the CABS energy and are extremely high, mostly due to the overlaps of the side chains. The Molecular Mechanics energy decreases rapidly during the initial stage of minimization, mainly due to the improvement of side-chain rotamers position. For a fraction of decoys  with frozen alpha carbons using implemented in Sybyl Amber7 ff99 force field, Amber charges, dielectric constant equal 1.0 and Powell minimization method, without initial optimization.Amber7 ff99 energy expression:total = Estr+ Ebend + Etor + Evdw/ele, EMaximum number of iterations was set to 5000, however the simulations longer than 1000 iterations were not necessary. To speed up maximally the calculations, a sufficient number of iterations (resulting in model ranking similar to the unlimited minimization) should be range of 500–1000, or even shorter, depending on the accuracy of models.CABS and Bioshell package can be downloaded from our website .SK performed the backbone rebuilding procedures, Molecular Mechanics, statistical analysis, assembled figures and wrote a draft of the manuscript. DG constructed the CABS decoys' set. AK conceived the study, coordinated it and wrote the final manuscript. All authors participated in the design of the study, read and approved the final manuscript.


The "real" effect size of a medical therapy is constant over time. In contrast, the effect size reported in randomised controlled trials (RCTs) may change over time because the sum of all kinds of bias influencing the reported effectiveness is not necessarily constant. As this would affect the validity of meta-analyses, we tested the hypothesis that the reported effect size decreases over time. Furthermore, we tested three hypotheses that would explain a possible change.Because of well established outcome measures, the lipid-lowering drugs Pravastatin and Atorvastatin  and the anti-glaucoma drugs Timolol and Latanoprost  were chosen for this investigation. Studies were identified by a standardized MEDLINE search. RCTs investigating the above identified medications administered as monotherapy, and in defined dosages, were included. Publication year, baseline  and post intervention means, number of patients and the assignment to experimental or control group were extracted for each study.A total of 625 citations were screened; 206 met the inclusion criteria. The reported effect size of Pravastatin , Timolol  and Latanoprost  decreased over time, while there was no significant change for Atorvastatin . Multiple regression analysis showed that baseline values were the most important influencing factor; study size or treatment group did not play a significant role.fading of reported effectiveness". Under this condition the validity of a meta-analysis may be impaired. Therefore we propose to observe this phenomenon in future meta-analyses in order to guarantee a maximum of transparency.The effectiveness of medical therapies reported in RCTs decreases over time in three of the four investigated pharmaceuticals, caused mainly by baseline differences. We call this phenomenon " Recently, meta-analyses have become an instrument that is fundamental to the idea of best medical care. Meta-analyses combine the results of a high number of randomised controlled trials (RCTs) to a special topic in order to gain more significant results. Should the reported effect size of RCTs change with time, the result of a meta-analysis would depend on when it was performed. Thus, the validity of a meta-analysis could be impaired.Nevertheless, an extensive literature search on this topic yielded no results in the medical field, but we identified one relevant study in biology. Jennions and Møller recently examined 44 meta-analyses covering topics like animal behaviour, parasitism and plant growth . They foThe objective of our longitudinal meta-analysis was to determine if the effect size of medical therapies, as reported in RCTs, changes with time. Further we aimed to identify reasons for any possible change. The unit of analysis was the individual study and not the individual trial participant.But why should the effect size of a medical intervention change with time? Our hypothesis is based on the assumption that we have to distinguish between the "real" effectiveness of a medical therapy and the effectiveness reported in RCTs, and that the latter may change with time.(1) The true biological effect size of a medical therapy is constant over time, meaning it should be possible to obtain similar results when a trial is repeated at a later date under identical conditions.(2) The sum of all kinds of bias influencing the reported effect size of a medical therapy is not necessarily constant over time. In the course of time social, political and economic circumstances for medical research and its publication change. Therefore, it can be assumed that the impact of the various potential sources of bias changes with time in a highly dynamic way. In consequence, the effect size of a medical therapy, as reported in RCTs, may change over time.As we presumed that, if there was any change at all, the reported effectiveness of medical therapies was more likely to decrease than to increase, we tested three hypotheses that might explain a decrease in the reported effect size:The problem of Publication Bias is well known in the medical literature -5. StudiNew medical interventions tend to be studied in severely ill patients where significant benefits can be expected. After a therapy is established, physicians tend to broaden its use and prescribe it to a wide range of patients, including a high number of less sick patients. In addition, specific treatment goals have been developed in recent years for several diseases such as hypertension, diabetes mellitus and glaucoma; patients who might not have been treated a decade ago, today receive therapy. In less sick patients, less improvement of the study parameter can be expected. Over time the effectiveness of the therapy seems to diminish.Although the studies were conducted as RCTs, expectations of patients, physicians and study authors may play a role, favouring the therapy used in the experimental treatment group. Over time, medical therapies originally considered as innovative therapies become established, and, in later studies, are no longer innovative and therefore implemented as control therapies. This may lead to a decrease in the reported effect size over time.To answer our questions we examined data from a large number of RCTs dealing with the effectiveness of four different pharmaceuticals. The primary outcome measure was the reported effect size; secondary outcome measures were the publication year, the study size, the mean pre-intervention level of the investigated parameter and the treatment assignment to experimental or control group.The pharmaceuticals to be investigated in this experiment had to comply with the following requirements: (1) Their effectiveness was measurable in terms of commonly accepted quantitative parameters that are reported in most studies; (2) the pharmaceuticals were administered as a monotherapy, and in a fixed dosage in order to obtain a high number of studies with comparable results; (3) the therapies were of clinical importance and of general interest. For our investigation, we arbitrarily chose the lipid-lowering drugs Pravastatin and Atorvastatin  and the anti-glaucoma drugs Timolol and Latanoprost .A standardized literature search was performed with emphasis on transparency and repeatability rather than on completeness. Our MEDLINE search strategy included the following text strings : "PravasA study was included if it met the following criteria: 1) baseline value and post-intervention value of the parameter of interest were reported, i. e. low-density lipoprotein cholesterol for Pravastatin and Atorvastatin, intraocular pressure for Timolol and Latanoprost; (2) the pharmaceutical was administered as monotherapy and after a wash-out period; (3) the pharmaceutical was administered in the most commonly used dosage, i. e. Pravastatin 40 mg once daily, Atorvastatin 10 mg once daily, Timolol 0.5 % twice daily, and Latanoprost 0.005 % once daily; (4) the study was conducted as a randomised controlled trial. One investigator (BTG) reviewed 625 citations and selected appropriate studies. 274 studies were considered for more detailed evaluation. Eventually, 206 studies were deemed appropriate for inclusion  extracted the following data for each study: publication year, study size , pre- and post-intervention mean values of low-density lipoprotein cholesterol/intraocular pressure , and assignment to experimental or control group.Where necessary, means were approximated from figures in the manuscripts or calculated from individual patient data. In each study, the effect size for the intervention was calculated by the difference between the means of the treatment group of interest before and after intervention. In some studies, more than one post-intervention mean was reported; e. g., for different follow-up visits or for different hours of the day. In these cases, the arithmetic mean of the given means was calculated instead of choosing one of the given means arbitrarily. Unfortunately, standard deviations of effect sizes could not generally be reported as they were not included in all original papers. For the same reason it was not possible to perform a weighted analysis.A study was given the designation "control group", if the pharmaceutical of interest was compared with at least one newer pharmaceutical. The designation "experimental group" was chosen if the pharmaceutical of interest was compared with older pharmaceuticals, or if no other pharmaceuticals were involved in the study .We realized that it was difficult to find independent studies published over a period of 10 years which investigated exactly the same treatment in "exactly" the same patients. Unfortunately, it was not possible to find studies that used – in addition to the above criteria – the same comparative treatments. We found many consecutive studies but the comparative treatments used in these studies varied from trial to trial. As a consequence the other treatments that varied from study to study were not included in our analysis.Statistical analysis was performed with SAS software . For each of the variables  arithmetic means and standard deviations were calculated. The reported effect size was measured in the most commonly reported dimension . As the variable treatment group is dichotomous with the two possibilities experimental and control group, exact frequencies are reported. In addition, we calculated the arithmetic mean of the publication year of those studies coded as "control" versus those coded as "experimental".We examined the association between year and effect size and the association between year and other study characteristics. The primary outcome variable (reported effect size) and the secondary outcome variables  were regressed against the publication year. For the variable treatment group, point biserial correlation was used . For every correlation the equation of the regression line and the limits of its 95 percent confidence intervals (CI) were calculated . With that, the mean change of every variable during an interval of five years (± 95 % CI) was calculated .Pearson correlation coefficients and P-values were calculated for all variables that may influence the effect size. We used a standard approach for statistical significance (α = 0.05). The funnel plot technique was used to detect publication bias -9: DiagrFurthermore we quantified the impact of the different variables on the reported effect size. For this, we performed a multiple regression analysis with reported effect size as the outcome variable and publication year, baseline, study size, and treatment group as possible predictors. Up to two predictors were entered into the model.We investigated if measuring the primary outcome variable in absolute or relative dimensions changes the significance levels of the results. Bivariate qualitative analysis and multiple regression analysis were performed with reported effect size measured in absolute terms  and in relative terms (change of outcome variable in percentages).We could not investigate the change of reported effectiveness in the control therapies of the pharmaceutical of interest, because the control therapies were different in almost any of the studies. The chance to find a study comparing exactly the same control and experimental group several years later is rather low.A total of 206 studies were included in the final analysis Figure . 64 of tTable Over time, the reported effect size decreased significantly for three of the four investigated pharmaceuticals Figure  and 3. PMost of the other investigated variables changed over time as well Table  and 3. TFor Pravastatin, Timolol and Latanoprost the reported effect size correlated significantly with the baseline values of the variable of interest Table . The repwas significant for Atorvastatin (P < .0001 vs. P = 0.4045), (2)the relation between reported effect size and treatment group was not any more significant for Atorvastatin (P = 0.3731 vs. P = 0.0092), and (3) the relation between reported effect size and study size was significant for Pravastatin (P = 0.0139 vs. P = 0.1327). For Timolol and Latanoprost the results were not altered if the reported effect size was measured in relative terms and not in absolute terms.Some results of the lipid lowering drugs were altered when the reported effect size was measured in absolute and not in relative terms. When measuring the change of LDL-C in mg/dl and not in percentages, (1) the relation between reported effect size and baseline value 2; Table The results of the multiple regression analysis differed if the effect size was measured in absolute or in relative terms. If measured in absolute terms the baseline was the most reliable predictor and alone explained 80.37 percent of the variability of the reported effect size of Pravastatin ; of Latanoprost, the "baseline value" (R2 = .5030).If measuring the effect size in relative terms the results of the multiple regression analysis were less homogenous, but overall the year of publication was the most important predictor for the reported effect size. For Pravastatin and Timolol the variable "publication year" alone explained 28.73/19.58 percent of the effect size variability  by an average of 8.17 mmHg  A meta-analysis investigating the effectiveness of one single medical therapy that was conducted when the therapy was relatively new may estimate higher treatment effects than a meta-analysis that was conducted later.(2) A meta-analysis comparing the effectiveness of two or more different medical therapies may come to distorted results favouring the newer therapies.We conclude that the validity of a meta-analysis where the described phenomenon is present may be impaired. In order to establish a maximum of transparency, we propose to include a test for this phenomenon in future meta-analyses. In our view it would be sufficient to plot effect size against publication year, as shown in Figure In conclusion, the current meta-analysis suggests that the effectiveness of medical therapies, as reported in randomised controlled trials, may decrease over time. We call this phenomenon "fading of reported effectiveness". Baseline differences could be identified as the main factor contributing to this effect; changes in study size or treatment group did not play a significant role. As the validity of a meta-analysis where the fading of reported effectiveness is present may be undermined, we propose to consider this problem in future meta-analyses.The author(s) declare that they have no competing interests.BTG participated in the design of the study, collected the raw data and drafted the manuscript. CW provided statistical expertise and performed the statistical analysis. FP generated the study question, supervised the study, and participated in the design of the study. All authors participated in analysis and interpretation of data and read and approved the final manuscript.None.The pre-publication history for this paper can be accessed here:Appendix I: Index of studies included in the meta-analysisClick here for fileAppendix II: Raw data of the studies included in the meta-analysisClick here for file


Annotators can evaluate gene structure evidence derived from multiple sources to create gene structure annotations. Administrators regulate the acceptance of annotations into published gene sets. yrGATE is designed to facilitate rapid and accurate annotation of emerging genomes as well as to confirm, refine, or correct currently published annotations. yrGATE is highly portable and supports different standard input and output formats. The yrGATE software and usage cases are available at  Complete and accurate gene structure annotation is a prerequisite for the success of many types of genomic projects. For example, gene expression studies based on gene probes would be misleading unless the gene probes uniquely labelled distinct genes. Identification of potential transcription signals relies on correct determination of transcriptional start and termination sites. Characterization of orthologs or paralogs and other studies of molecular phylogeny are also compromised by incomplete or inaccurate gene structure annotation.Gene structure determination is particularly difficult for eukaryotic genomes. Here, we focus on protein-coding genes. In higher eukaryotes, most of these genes contain introns, and a large fraction of the genes appear to permit alternative splicing -3. High-A policy of 'open annotation', using the internet as the forum for annotation, and bringing annotation into the mainstream has been suggested as a means to eliminate the restraints of manual annotation and to develop high quality gene annotation -15. SeveThe yrGATE package consists of a web-based Annotation Tool for gene structure annotation creation and Community Utilities for regulating the acceptance of the annotations into a community gene set. The yrGATE Annotation Tool can be used without the Community Utilities for analysis of gene loci independent of a community. The Annotation Tool presents pre-calculated exon evidence in several summaries with different selection mechanisms and provides other methods for specifying custom exons, allowing thorough analysis and quick annotation of loci. Annotators access the tool over the web, where they create an annotation, decide to save the annotation in their personal account, or submit the annotation for review for acceptance into the community gene set. The online nature of yrGATE permits a large and nonexclusive group of annotators, ranging in expertise from professional curators to students . This alThe Annotation Tool of the yrGATE package is a web-based utility for creating gene structure annotations. The inputs and outputs of the Annotation Tool are depicted in Figure ab initio predictions, or a combination of sources. The evidence is filtered by stringent thresholds to provide exons suggestive of authentic genes. User-defined exons are exons not contained in the pre-defined evidence and are individually specified by the user. Annotators have several channels to designate both categories of exons.Defining a gene's exon-intron structure is the central step in creating a eukaryotic gene annotation. The Annotation Tool provides two general categories to specify exons: pre-defined evidence-supported exons and novel user-defined exons. Pre-defined exons are provided by the Annotation Tool from prior computations and are supported by evidence derived from spliced alignments of expressed sequence tags (ESTs) and cDNAs, The Annotation Tool contains three representations of the evidence: the Evidence Plot, the Evidence Table, and links to evidence reference files. The Evidence Plot is a clickable graphic that presents evidence in a color-coded schematic (8 in Figure User-defined exons are specified through portals to exon-generating programs or through entry of the genomic coordinates of an exon. As these exons are defined, they are listed in the User Defined Exons Table (2 in Figure As an additional channel provided for designating gene structures, the tool allows pasting a coordinate structure into the mRNA structure field (6 in Figure To document the annotator's procedure and parameters, the Exon Origins attribute of an annotation record automatically stores information about the source of each exon. The following information is stored: the method of exon-generation, a score associated with the method and exon, sequence identifiers used in the method, unique database identifiers to the specific output file or record, and a hyperlink to the program output yielding the exon. Exon Origins allows for complete re-creation of the gene structure annotation and for analysis of manual annotation procedures that could aid in future manual annotation efforts and techniques.After a gene structure has been defined, a user can specify the protein coding region of the annotation through entry of genomic coordinates (4 in Figure Coordinately with gene structure and protein coding region designation and edits, the mRNA and protein sequence fields are updated (3 and 5 in Figure For cases in which genomic sequence requires editing, such as correction of sequencing errors or annotation of genes undergoing mRNA editing, the Sequence Editor Tool (7 in Figure At the conclusion of a gene annotation session, an annotator decides the outcome of their annotation record (1 in Figure The yrGATE package includes community annotation utilities for sharing annotations among a public or private community. These utilities form a process for annotation management and review (diagrammed in Figure A typical annotation submission begins with an annotator logging in to their private account, which contains all of the annotations created by the annotator. Then, the annotator creates a new annotation using the Annotation Tool and decides to submit the annotation to the community.This newly submitted annotation is listed in the Administration Tool, where an administrator can 'check out' this annotation for review, so that other administrators do not review this annotation concurrently. The administrator accesses the 'checked-out' annotation in a review version of the Annotation Tool. Then, the administrator reviews the annotation and is able to edit any attributes of the record. When satisfied with their analysis, the administrator accepts or rejects the annotation. If a decision cannot be reached, the annotation is returned to the to-be-reviewed group. Accepted annotations are added to the public community gene annotation database, where they are presented through the Community Annotation Central and Annotation Record facilities. Rejected annotations can be edited by the annotator to be resubmitted for review.For specific implementations, the described community annotation process can be adjusted by dropping any of the steps, such as eliminating the user log in or eliminating the review process so that all submitted annotations are published. New steps can also be added to the review process, such as a voting utility for submitted annotations.The yrGATE package can be implemented in different configurations depending on the input and output Figure  and on tArabidopsis, ZmGDB [PlantGDB includes a family of species-specific databases: AtGDB ,27 for As, ZmGDB  for maizs, ZmGDB  for rices, ZmGDB . EvidencArabidopsis protein NP_190282. These proteins provided a putative functional assignment of 'sugar transporter' for the annotation. The annotator was satisfied with the annotation and submitted it for review. Administrators reviewed the annotation and accepted it because it was novel and of good quality. The annotation, ZM-yrGATE-sugar_transporter, is now accessible from the ZmGDB Community Annotation Central [The first case study is a novel maize annotation using the ZmGDB yrGATE implementation. An unannotated genome region, 158659-162032 of BAC 51315585, was chosen by the annotator using the genome browsing function of ZmGDB. A screenshot of the Annotation Tool shows the completed annotation Figure  with no Arabidopsis gene model using the yrGATE implementation at AtGDB. A screenshot of the transcript view of AtGDB presents two accepted community annotations (green structures in interior window, Figure The second PlantGDB case study concerns alternative splicing and correction of an inaccurate published annotation of an DAS servers provide sequence and annotation information that can be queried and is in a standard format ,33. The Figure The primary evidence also suggests an annotation on the reverse strand that contains the angiopoietin-2 gene within one of its introns. However, current annotations on the reverse strand are inaccurate and incomplete based on mRNA and EST evidence (3 in Figure Links to these case study annotations are provided on the yrGATE website .The Annotation Tool was designed with emphasis on usability for annotators. Annotators can immediately select from high quality evidence that has a high likelihood of yielding an accurate annotation and can specify new custom evidence for cases where the evidence is inadequate. The two categories provide for a good annotation process where high quality evidence is first examined and then additional evidence is checked, which is completed in a minimal amount of mouse clicks and screen display, achieved by the tool's design.The main components of the tool are contained in one standard 1,024 × 768 resolution screen. The tool is loaded once per genomic region, and the form fields are dynamically updated, which allows annotators to quickly evaluate the impact of different exon variants and combinations of exons on the gene structure, mRNA sequence, and protein sequence. yrGATE is compatible with several major operating systems, including Linux, Windows and Macintosh, on several web browsers, of which Mozilla Firefox has the best performance in terms of speed.yrGATE is available for download . The pacyrGATE opens gene structure annotation to a large, nonexclusive community. The characteristics of yrGATE contribute to its potential for user appeal and community adoption. Among other applications, it is particularly useful for annotating emerging genomes and for correcting inaccurate published annotations. yrGATE is easily adaptable to different input data and can support a community using the Community Utilities.


As gene expression profile data from DNA microarrays accumulate rapidly, there is a natural need to compare data across labs and platforms. Comparisons of microarray data can be quite challenging due to data complexity and variability. Different labs may adopt different technology platforms. One may ask about the degree of agreement we can expect from different labs and different platforms. To address this question, we conducted a study of inter-lab and inter-platform agreement of microarray data across three platforms and three labs. The statistical measures of consistency and agreement used in this paper are the Pearson correlation, intraclass correlation, kappa coefficients, and a measure of intra-transcript correlation. The three platforms used in the present paper were Affymetrix GeneChip, custom cDNA arrays, and custom oligo arrays. Using the within-platform variability as a benchmark, we found that these technology platforms exhibited an acceptable level of agreement, but the agreement between two technologies within the same lab was greater than that between two labs using the same technology. The consistency of replicates in each experiment varies from lab to lab. When there is high consistency among replicates, different technologies show good agreement within and across labs using the same RNA samples. On the other hand, the lab effect, especially when confounded with the RNA sample effect, plays a bigger role than the platform effect on data agreement. Diversity of microarray data poses some unique and interesting questions on cross-experiment comparisons and the analysis tools needed for such comparisons. Since the invention of the microarray technology in 1995 , many ofPossible incompatibility of results between similar microarray experiments is a major challenge that needs to be addressed, even though the data produced within a single experiment may be consistent and easy to analyze. Different labs produce microarray data in different ways using different technology platforms, such as Affymetrix GeneChip, spotted cDNA array, and spotted oligo array. Affymetrix GeneChip uses one fluorescent dye while the spotted array uses two fluorescent dyes in the experiments. Direct comparison of raw data obtained from different technologies may not be meaningful. Instead, the final form of the data is often presented as relative expression levels, mostly ratios of intensities, after some statistical treatments including filtering, normalization and model-based estimation. Experiments using different technologies require different protocols for analyzing the raw data to derive the ratios. Scientists have published microarray data in a variety of formats including raw intensities and ratios of intensities. Does it make a difference which technology platform is chosen? Can we make use of the studies from different platforms and labs? To answer these questions and provide some guidance for platform comparisons, we report on a comparative study of three different platforms. The experiment is a simple two-tissue comparison between mouse liver and spleen. We used previously published data sets from two different sources as well as new data sets produced in house. There are several similar studies published in recent years -8. A not. Two data sets were downloaded from the NCBI Gene Expression Omnibus , which were generated by Choi et al. at California Institute of Technology  using Agilent oligo (GEO accession: GSE334) and cDNA arrays (GEO accession: GSE330), respectively. Two other data sets were generated at the Functional Genomics Unit at the W. M. Keck Center for Comparative and Functional Genomics at the University of Illinois using an in-house printed cDNA mouse array and Affymetrix mouse expression set 430A, and the data sets are available at . Another data set was downloaded from  and it was generated using Affymetrix Murine Genome set U74Av2. The data set names  given in Table As summarized in Table One indication of data reliability is the consistency of replicates in a particular data set. We used kappa coefficients as well as the Pearson correlation coefficients and intraclass correlation coefficients on the replicates within each data set. Those measures set a benchmark against which the reliability of different platforms can be assessed; see Figure For the KC data, we can see that two pairwise comparisons gave slightly higher agreement than the other four. It is, we believe, due to the double spots of each gene on the array. The two comparisons with higher agreement are the comparisons between the replicates within the slides.Using the matched genes by common UniGene IDs, we compared different data sets in this study. Table From Figure In a recent study, Jarvinen et al.  comparedSince we only used a subset of genes in the study due to the gene matching problem, we asked whether the gene-to-gene variation was impacted by the choice of the subset. A comparison of the box plots for the full data set and for the subset used in the overall comparison showed that the variation in the subset of genes is similar to that of the full data set. For example, Figure UniGene has been widely used to match genes on different microarrays. Using UniGene and sequence similarity, Thompson, et al.  reportedFrom the present study, we showed that the GNF data set had the lowest agreement with the other data sets. This difference is compounded with the facts that the consistency of replicates within the GNF data set is the lowest and the sample used to generate the data is different from those used by other labs. We believe that the technology platform plays a relatively minor role in the disagreement, but the variation introduced by sample differences is one of the major factors. It has been shown that the expression level can vary significantly between genetically identical mice . VariatiIn this study, we also found that the lab effect can be greater than the platform effect. As shown in Figure Obviously, the present study has limitations. The results were generated from a very limited number of data sets. Using UniGene IDs for gene matching across data sets can also be questioned. Further research is clearly needed to address these limitations.In this paper, we aim to address several issues in comparing microarray data across different platforms and different labs. We demonstrated that the consistency of replicates in each experiment varied from lab to lab. With high consistency among replicates, different technologies seemed to show good agreement within and across labs using the same RNA samples. A closer look at the results indicated that the variability between two labs using the same technology was higher than that between two technologies within the same lab. The source of RNA samples can make a difference in microarray data, however in our present study we do not show conclusive results pertaining to possible sample or lab effects, because we did not have data collected from two different samples within one lab.For the spotted arrays , we used raw intensity data from both Cy5 and Cy3. We filtered out the non-expressive data points using median plus three times median absolute deviation  of the affy of Bioconductor using the average difference (AV) between Perfect Match (PM) and Mis-Match (MM) probe pairs, and the Robust Multi-Array Average (RMA) expression measure developed by Irizarry et al. [For the in-house Affymetrix data, we used three summarization methods to generate the probe set level signals. KAV and KRMA are based on the R package y et al. , and KLWy et al. . The GNFTo stabilize the variances of data across the full range of gene expressions, we also performed the generalized log transformation for all the data sets following Durbin et al. .There are five different data sets in this study. The origins of the genes vary in those datasets. In order to study inter-lab agreement, we have to first identify common genes represented in different arrays. Based on the annotation of each data set, we found that we could maximize the number of cross-matched genes using the mouse UniGene IDs (Build 107). Based on the common UniGene IDs, we found 551 common genes across all five different data sets. But in the pairwise comparisons, the number of common genes ranged from 1,374 to 5,018  cell, ni+ is the sum of the i th row, nj + is the sum of the j th column, and n is the total number of subjects.An intuitive measurement of agreement is to count the percentage of genes falling in the same categories . However, this percentage can be high even if the data obtained from different platforms are not so compatible. Usually the ratios for the great majority of genes do not show a two-fold change, and the percentage of agreement can be high just due to chance. To adjust for this excess agreement expected by chance, we prefer to use the kappa coefficient, which is a popular measure of inter-rater agreement in many other areas of science. The kappa coefficient was first proposed by Cohen  for analFor example, the kappa coefficient between KC and KAV is 0.523. Table , divided by the total variance. A high ICC (close to 1) means that the inter-rater ratios vary little relative to the overall variability in the data. In computing the ICC for the replicates,  equals , where  is the variance within UniGene IDs. If we consider lab as a random effect in the overall comparison, the total variance  will equal , where  is the variance between labs. The ICC incorporates both the association between raters and the rater differences, while the Pearson correlation is insensitive to the latter.We used three measures of correlation to compare the ratios from different data sets: Pearson correlation coefficient, intraclass correlation coefficient (ICC) and intra-transcript correlation coefficient (ITC). ICC measures the inter-rater reliability relative to the total variability of the ratios. Here, a rater could be a replicate or a technology platform. ICC is the variance of different ratios between UniGene IDs,i, we defined ρi to be the square root of the ratio of within dataset sum of squares (SSW) and the total sum of squares (TSS). A common SSW was used in comparing lab pairs to avoid the problem of having seemingly higher ITC's due to unusually large within-lab variability at some lab. We applied logit transformation to each ρi to get γi, and then calculated the average γ. Converting γ back to the correlation scale, we obtained .We introduced the ITC for pairwise comparisons as described below. For each gene HW and LL collected data from public resources. MB and CW generated in-house data. HW and LL processed the collected data. HW and XH did statistical analyses. All authors read and approved the manuscript.


The immune system, which is a complex machinery, is based on the highly coordinated expression of a wide array of genes and proteins. The evolutionary history of the human immune system is not well characterised. Although several studies related to the development and evolution of immunological processes have been published, a full-scale genome-based analysis is still missing. A database focused on the evolutionary relationships of immune related genes would contribute to and facilitate research on immunology and evolutionary biology. was constructed for studying the evolution and evolutionary trees of the human immune system. ImmTree contains information about orthologs in 80 species collected from the HomoloGene, OrthoMCL and EGO databases. In addition to phylogenetic trees, the service provides data for the comparison of human-mouse ortholog pairs, including synonymous and non-synonymous mutation rates, Z values, and Ka/Ks quotients. A versatile search engine allows complex queries from the database. Currently, data is available for 847 human immune system related genes and proteins.An Internet resource called ImmTree ImmTree provides a unique data set of genes and proteins from the human immune system, their phylogenetics, and information for comparisons of human-mouse ortholog pairs, synonymous and non-synonymous mutation rates, as well as other statistical information. The immune system is a very complex machinery thet has evolved and diversified over time. Numerous processes are necessary for mounting adaptive and innate immune responses to protect an individual from invading organisms and molecules. Acquired and congenital problems in almost any part of the immune system can lead to diseases, many of which are very severe or even life threatening. The different processes and pathways of the immune system have evolved gradually and become increasingly complex. More ancient innate or intrinsic immunity has been further complemented by adaptive processes, which provide a specific response when required.Although intensively studied, the evolutionary history of this system is not well known. The evolution of certain immunological protein groups of the human immunome have already been studied. For example, five gene groups of the NF-κB signaling pathway in vertebrates and insects , or the We collected from articles, textbooks and electronic sources altogether 847 human genes that are involved in immunology related processes, or which are essential for the life of immunological cells and organs  and EuroThe nucleotide sequences of ortholog groups were taken from EGO and the protein sequences from HomoloGene and OrthoMCL. The sequences were aligned using ClustalW  with theFor a general overview of the ortholog groups, we generated a fourth tree. This tree represents protein sequences from all the species in any of the three datasets. Moreover, each species is represented by just one sequence, preventing the accumulation of identical sequences from multiple data sources. This way the large paralog groups from the OrthoMCL database are represented by just a single sequence.The nucleotide sequences from the EGO database were translated to amino acids to align the representative protein sequences from the three databases. The translation was done in all six frames, and all six transcripts were aligned with the human protein sequence using bl2seq from the BLAST package . Only ths or dS) and of non-synonymous mutations per non-synonymous sites (Ka or dN) values were calculated [a/Ks quotients describe the conservation of given genes since the human-mouse divergence.In 603 cases orthologs were present both in the mouse and human genome in the HomoloGene database. These pairs were further analysed in detail. The cDNA sequences of the human and mouse genes were translated to protein sequences and then aligned using the blast2seq program. The corresponding cDNA sequences were aligned based on the amino acid sequence alignment with proprietary Perl scripts, some of which utilize modules from the Bioperl Project . The estlculated . Z valueThe ImmTree database can be accessed online . The serThe second group of search options helps to identify features common for ortholog groups. The most basic option is to search for organisms within an ortholog group either by NCBI's Taxonomy  id or bya/Ks quotient value or by the Z value. Both these parameters refer to the conservation of sequences [a/Ks value less than 0.15' (5 result groups) or 'genes with the keyword lectin which have a Ka/Ks value greater than 0.6' (4 result groups).The third type of search option is based on the statistical information of human-mouse ortholog pair comparisons. Gene pairs can be found by the Kequences ,23. Is is, Ka), their quotient value (Ka/Ks) and Z value, are presented. The evolutionary trees for the combined, EGO, HomoloGene and OrthoMCL datasets are in the third section. Links for the trees for the four datasets are also provided. The multiple sequence alignments and the evolutionary trees are available in nexus format [All the search results are displayed in an interactive list from which one can investigate details for each of the identified ortholog groups Fig . Similars format  for downs format .Figure ImmTree is a new and unique data resource for exploring the molecular evolution of the immune system. Although excellent databases, such as The Adaptive Evolution Database (TAED)  or the DThe ImmTree database is freely available for academic use from the URL: The author(s) declare that they have no competing interests.CO and MS collected the sequences of the immunome genes. CO carried out the phylogenetic analysis and MS collected the identification numbers connected to the immunome genes. MV designed and coordinated the project and compiled the list of genes and proteins. All authors drafted the manuscript and approved its content.


Despite the MMSE's known flaws, it is still used extensively as both a screening instrument for dementia and a population measure of cognitive ability. The aim of this paper is to provide data on the distribution of MMSE scores in a representative sample from the UK population and to compare it with an extended cognitive assessment (EMSE) which covers a wider range of cognitive domains and provides a wider range of difficulty levels.The MMSE and the EMSE were administered to over 12,000 participants at the screening stage of the MRC Cognitive Function and Ageing Study (MRC CFAS). MRC CFAS is a multi-centre population-based study in England and Wales with respondents aged 65 years and older.Normative values on the MMSE and EMSE are presented by age group, sex and level of education. There are very large differences between age groups, with smaller differences seen between the sexes and by level of education. The EMSE extends the scores at the high end of the ability range, but is no better than the MMSE at differentiating between dementia and non-dementia.Population-derived norms are valuable for comparing an individual's score to the score that would be expected among the general population, given the individual's specific demographic characteristics. The Mini-Mental State Examination (MMSE) was developed almost 30 years ago as a screen for dementia among hospital patients. It remaThe Modified Mini-Mental State (3MS) Examination  was deveIn September 1986, before the publication of the 3MS, the MRC convened an Alzheimer's Disease Workshop whose aiThe MMSE items and most of the additional cognitive items recommended by the MRC report were included at the screening stage of the multi-centre MRC Cognitive Function and Ageing Study and the The aim of this paper is to describe the population distribution of performance on the MMSE and an extended cognitive assessment (EMSE) in a representative UK population.The Medical Research Council Cognitive Function and Ageing Study (MRC CFAS) is a longitudinal population-based cohort study that involves six different study centres. The six centres were chosen because they represent the main national variation with regards to urban-rural differences, the north-south and east-west gradients, and variation in socio-economic levels and in known rates of chronic disease. Furthermore, all centres had existing researchers who were experienced in conducting population-based studies of the elderly. Urban sites included Liverpool, Newcastle, Nottingham, and Oxford, and rural sites included Cambridgeshire and Gwynedd, in North Wales. Liverpool was not included in this particular analysis because it was funded earlier than the other sites and had a different design without the same extended measurement of cognition. The full study design of the five identical centres is described in detail elsewhere and is explained briefly here.Random samples of subjects over the age of 65 were selected from the Family Health Service Authority lists, giving an interviewed sample of approximately 2,500 people in each centre, stratified for equal numbers aged 65–74 years and 75 years and above. The study is longitudinal and this analysis focuses on information obtained at baseline – the prevalence (first) wave of the study. There were two phases at the prevalence wave. The first, screening, stage was used to establish level of cognitive performance and baseline risk factors on all individuals. A median of three months later, 20% of the subjects had a more detailed assessment interview to establish dementia diagnosis using the Geriatric Mental State (GMS) Automated Geriatric Examination Computer Assisted Taxonomy (AGECAT) diagnosis . This grThe screening interview included the Mini-Mental State Examination (MMSE) in the version developed for field surveys. SpellinAt screen, subjects were assigned an organicity score using the organic symptoms component of the AGECAT computerised algorithm . The AGETwenty percent of the screened sample went on to the diagnostic assessment interview. This included the majority of those who had a screen AGECAT organicity score of O3 and above. Individuals with a score of O3 at screen could have had dementia, but they are a mixed group with mild organic symptoms that could relate to dementia, mild cognitive impairment or depression. Of this group, those who went on to receive a diagnosis of dementia at the assessment interview were regarded as demented for this analysis. All other individuals who scored O3 and the interviewer reported moderate to severe memory impairment were excluded from the analysis for non-dementia norms.When describing performance on the MMSE and the EMSE, age was grouped into five-year bands . Education was grouped into low level (9 years of schooling or less) and high level (greater than 9 years of full-time education). Those who had missing data about their educational attainment (n = 337) were placed in the low group.To derive normative data on the MMSE and the EMSE, individuals classified as demented at screen or at assessment were excluded (n = 627). Tables by age group and sex, as well as by age, sex, and education are presented.Items that may have been missed due to sensory or motor impairment, called physical items, were recoded to 0 (i.e. treated as an incorrect answer). Such items include those involving writing or drawing, or those involving visual object recognition . Other factors included dementia status, whether the subject appeared to be muddled, and whether the interview went into priority mode or had to be abandoned. Physical health was also analysed in relation to missing data, and included ADL impairment using the Townsend disability scale . InterviScores on the MMSE, and to a lesser degree the EMSE, do not follow a normal distribution. Hence, medians and other percentiles have been provided. For completeness, a logarithmic transformation (log (31-MMSE) or log(61- EMSE)) has been calculated and the estimate and reference ranges have been back-transformed to the original scale. Version 6.2 of the CFAS data has been used in this analysis. The analysis has been undertaken using STATAversion 8 .The total number of individuals screened was 13,004, representing a response rate of 80%. Table For the sample as a whole, the median MMSE score is 27 (interquartile range 24 to 28) and the median EMSE score is 52 (interquartile range 47 to 55). The distributions of the two scores are presented in Figures Logarithmic means, standard deviations, medians, and various percentiles for both the MMSE and the EMSE in non-demented individuals are reported in Tables th,10th) of the distribution, and for two standard deviations below the mean of the distributions. There is also a modest effect of gender, with women obtaining lower scores, which is particularly marked in the oldest age groups.Tables The effect of education on cognitive performance can be seen in Tables th percentile, the 10th percentile, 1 standard deviation of the mean, and 2 standard deviations of the mean for the MMSE and the EMSE. Furthermore, when comparing three tables, we find that adjusting for age and sex makes little difference to the percentage of demented subjects who fall below the given cut-points. Further adjusting for education seems to have no added benefit in this context. This suggests that the EMSE is primarily extending the description of higher functioning individuals rather than discriminating between the low functioning groups.Using selected cut-points from these normative tables, we examined how well these values were able to differentiate between demented and non-demented groups. These data are presented in Tables th percentile MMSE scores were at most one point higher. The EMSE median scores were not affected, however the 5th percentile was at most 2 points higher.Excluding the missing physical items responses from both the numerator and denominator caused little change in the results. The median and 5A comparison of the characteristics of those with complete MMSE scores, those with only complete EMSE scores and those with neither score complete has been undertaken. Those with no complete data or with only the MMSE complete were more likely to be older, female, in a manual occupation, and have a low level of education. They were also far more likely to have been classified as demented at screen. The vast majority of those who only had the MMSE complete (95%) went into priority mode during the screen interview. In general, those who completed both tests had fewer health problems than the other two groups.This paper presents the full normative values for the MMSE in a UK population sample aged 65 years and over, together with normative values for an extended cognitive assessment (EMSE), with a more complete coverage of cognitive domains than the MMSE and a wider difficulty range. The normative values have been calculated for the whole population sample, excluding those with probable or diagnosed dementia. The MMSE norms adds to the existing literature, in both English and other languages, providing norms based on the largest study to date for this age group -24.This paper also presents normative values for a new scale – the Extended Mental State Exam (EMSE) which combines the MMSE with additional items recommended by a MRC Alzheimer's Disease Workshop . ResultsWe have examined how well selected MMSE and EMSE values differentiate between individuals with and without dementia. Both perform moderately well for this purpose. Like the EMSE, the Modified Mini-Mental State (3MS) Examination ,25 extenThe values of the population norms were affected by level of education as in other studies ,22,26 hoOther researchers have investigated the use of the MMSE in individuals with physical impairments and suggested improvements , but in The results show that the EMSE is comparable to the MMSE in its ability to differentiate between individuals with and without dementia. However, as described above, the EMSE is superior at providing data for individuals at the high end of the performance range, in a similar way to other tests .The choice of a screening test for dementia depends on the type of population to be screened, and the aim of the screening procedure. If the population to be screened can be expected to perform poorly on a cognitive function measure , then we believe that the EMSE has few advantages over the shorter MMSE. This is also true if the aim of screening is to pick up definite cases of dementia. However if the purpose of the cognitive test is to examine whether individuals have early cognitive changes or mild cognitive impairment (MCI) or when the population to be measured includes many high performing individuals (e.g. population surveys), then the EMSE applied longitudinally has distinct advantages over the MMSE, and the additional 3 minutes of administration time may be regarded as worthwhile.The data reported here are from the first cross-sectional wave of the MRC Cognitive Function & Ageing Study . The EMSPopulation-derived norms are valuable for comparing an individual's score to the score that would be expected among the general population, given the individual's specific demographic characteristics.The funding bodies have had no influence on the paper or decision to publish.The author(s) declare that they have no competating interests.FH oversaw the clinical context of the paper, SC undertook the initial statistical analysis and co-wrote the paper, FM oversaw the analysis, undertook the final analysis and co-wrote the paper. MRC CFAS investigators undertook the fieldwork and oversaw the scientific integrity of the study and commented on the paper. All authors have seen and approved the final draft of the paper.The pre-publication history for this paper can be accessed here:


The cervical muscles are considered a potential site of whiplash injury, and there are many impact scenarios for whiplash injury. There is a need to understand the cervical muscle response under non-conventional whiplash impact scenarios, including variable head position and impact direction.2 peak acceleration, each with the head rotated to the left, then the head rotated to the right in a random order of impact severities. Bilateral electromyograms of the sternocleidomastoids, trapezii, and splenii capitis following impact were measured.Twenty healthy volunteers underwent right anterolateral impacts of 4.0, 7.6, 10.7, and 13.0 m/s2, with the head rotated to the right, the right trapezius generated 61% of its maximal voluntary contraction electromyogram (MVC EMG), while all other muscles generated 31% or less of this variable . The sternocleidomastoids muscles also tended to show an asymmetric EMG response, with the left sternocleidomastoid (the one responsible for head rotation to the right) generating a higher percentage (26%) of its MVC EMG than the left sternocleidomastoid (4%) (p < 0.05). When the head is rotated to the left, under these same conditions, the results are reversed even though the impact direction remains right anterolateral.At a peak acceleration of 13.0 m/sThe EMG response to a right anterolateral impact is highly dependent on the head position. The sternocleidomastoid responsible for the direction of head rotation and the trapezius ipsilateral to the direction of head rotation generate the most EMG activity. Although many diagnostic efforts over the decades have aimed at objectively identifying the acute whiplash injury that is often labelled as "soft tissue injury" or "neck sprain", with the exception of a few case reports and excluding spinal cord or bony injury, the pathology of the acute whiplash injury remains elusive . In the Frontal collisions thus require more investigative attention, and yet there are a number of variables to consider in terms of understanding how the cervical muscles respond to a whiplash-type frontal impact. First, not all collision victims have their head in the neutral (facing forward) position. We recently reported on the effect of head rotation in straight-on frontal impacts , and comThen there is the direction of impact. Frontal impacts are not always straight-on impacts. We have considered the example of a right anterolateral impact , and theThe question is whether head rotation in anterolateral impacts will increase or decrease the EMG activity, and how. We thus undertook a study to assess the cervical muscle response in right anterolateral impacts, but with the head rotated to either the left or right at the time of impact. This is part of a series of experiments to approach the more complex impact scenarios of varying directions and head positions.The methods for this study of offset frontal impacts are the same as that used previously for our previous right anterolateral and frontal impact studies -15. Twen2 generated in a random order by a pneumatic piston. To release the piston the solenoid of the pneumatic system was activated by an electronic impulse which was recorded for timing reference. Upon delivery of impact by the pneumatic piston, the sled moved on two parallel tracks mounted 60 cm. apart. The coefficient of friction of the tracks was 0.03 which allowed for smooth gliding of the sled on the rails. The opposite end of the track was equipped with non-linear springs and high density rubber stopper to prevent the subject from sliding off the platform. Each subject effectively underwent 4 levels of accelerative impacts under two conditions of head rotation, for one direction of impact . The head rotation itself did not place the head in a more forward position. Although the subjects are asked to rotate their head prior to impact, nothing was done to fix the position, and the head is free to move after impact. The accelerations involved in this experiment were low enough that injury was not expected. The acceleration was delivered in a way that mimicked the time course seen in motor vehicle collisions and occurred fast enough to produce eccentric muscle contractions. The acceleration impulse reached its peak value in 33 ms. Subjects were asked to report any headache or other aches they experienced in the days following the impacts.Active surface electrodes with 10 times on-site amplification were placed on the belly of the sternocleidomastoids, upper trapezius at C4 level, and splenius capitis in the triangle between sternocleidomastoids and trapezii bilaterally. The fully-isolated amplifier had additional gain settings up to 10, 000 times with frequency response DC-5 kHz and common mode rejection ratio of 92 dB. Before calibrating sled acceleration, the cervical strength of the volunteers was measured to develop force-EMG calibration factor ,17. The The data on the peak and average accelerations in all three axes of the sled, shoulder, and head for all four levels of accelerative impacts were measured. The gravity bias was eliminated by subtracting this value from the accelerometer readings. The onset of acceleration was measured by dropping the ascending slope line on the base line. The point of intersection of these lines was considered as onset of acceleration. In the analysis, the sample of volunteers was collapsed across gender because preliminary analysis showed no statistically significant differences in the EMG amplitudes between the men and women. The sled velocity and its acceleration subsequent to the pneumatic piston impact and the rubber stopper impact were measured. All timing data (time to onset of EMG and peak EMG) were referred to the solenoid of the piston firing. The time of the peak accelerations of sled and head were measured. Also, the time relations of the onset and peak of the EMG were measured and analyzed. The time to onset was determined when the EMG perturbation reached 2% of the peak EMG value to avoid false positives due to tonic activity. This method was chosen to avoid any false positives due to tonic EMG. This method was in agreement with projection of the line of slope on the baseline. EMG amplitudes were normalised against the subjects' maximal voluntary contraction electromyogram. The ratio percentage of the EMG amplitude versus the maximal contraction normalised EMG activity for that subject allowed us to determine the force equivalent generated due to the impact for each muscle.Statistical analysis was performed using the SPSS statistical package  to calculate descriptive statistics, correlation analysis between EMG and head acceleration, analysis of variance (ANOVA) of the time to EMG onset, time to peak EMG, average EMG, and the force equivalents. Additionally, a linear regression analysis was carried out for the kinematic variables of head displacement, head velocity and head acceleration and EMG variables on the peak of the sled acceleration. Initially, all regressions were carried out to the level of exposure and subsequently they were extrapolated to twice the level of acceleration used in the study. The purpose of the regression analysis was to see if using the acceleration of the sled – one could predict the head acceleration and EMG response. The regression analysis was carried out using linear and non-linear functions. The linear regression was found to be the best fit, perhaps because the input acceleration impulse was non-linear.The kinematic response of the head to the four levels of applied acceleration are shown in Fig. In a right anterolateral impact, with the head rotated 45 degrees to the right or left, the trapezius muscle ipsilateral to the direction of head rotation showed the greatest EMG response (p < 0.05). The sternocleidomastoid muscles responsible for the head rotation each showed more EMG response to the pertubation than their counterparts (p < 0.05).2, for example with the head rotated to the right, the right trapezius generated 61% of its maximal voluntary contraction electromyogram, while all other muscles generated 31% or less of this variable. Though they generated less EMG activity, the sternocleidomastoids muscles also tended to show an asymmetric EMG response, with the left sternocleidomastoid (the one responsible for head rotation to the right) generating a higher percentage (26%) of its maximal voluntary contraction electromyogram than the right sternocleidomastoid (4%) (p < 0.05). When the head is rotated to the left, under these same conditions, the EMG results are reversed even though the impact direction remains right anterolateral. When looking left, the left trapezius generated 51% of its maximal voluntary contraction electromyogram, with only 14% of the maximal voluntary contraction for the right trapezius, and less than 25% for the remaining muscles. The sternocleidomastoid muscles in this case still showed an asymmetric EMG response, with the right sternocleidomastoid (the one responsible for head rotation to the left) generating a higher percentage (22%) of its maximal voluntary contraction electromyogram than the left sternocleidomastoid (4%) (p < 0.05).At a peak acceleration of 13.0 m/sThe normalized EMG for the sternocleidomastoid (SCM), splenius capitis (SPL) and trapezius (TRP) muscles are shown in Fig. The time to onset of the sled, shoulder, and head acceleration onset in the z-axis  and the EMG signals of the six muscles examined for head rotated to the left or right are presented in Table The relationship between the force equivalent EMG response of each muscle and the head acceleration are shown in Table 2. Initially, regression analyses were performed only up to 13.0 m/s2 using a linear function. The kinematic variables of head displacement, velocity, and acceleration in response to applied acceleration were calculated  as shown in Table  as shownThe chief purpose of this study was to see what effect head rotation had on muscle responses in a right anterolateral impact. When the head was in neutral position in a previous study of right anterolateral impact , the lefAlthough one might predict this, the human response to impacts and the neck structure is seemingly complex enough that it cannot always be assumed to be as one predicts. Our study methodology allowed for direct testing of the response rather than assumptions. There is no direct way to measure forces exerted by muscles due to neck perturbation and subsequent muscle activity, examining the EMG activity generated allows one to compare this to EMG activity in voluntary contractions. This in turn allows one to relate the muscle responses to normal muscle forces in various physiological ranges of activity. Because one cannot test the higher accelerations for ethical reasons, the best one can do currently is to compare to the small volunteer studies that were done previously. Further studies with larger samples and perhaps somewhat higher accelerations  will allow to determine further how reasonable these extrapolations are. The projected values are hypothetical and likely to be affected by the ligaments and joint geometry in a manner different from that recorded in the experiment.In frontal impacts, the direction of impact, anterolateral or straight-on, determines the muscle response, but so too does the occupant's head position, rotated right or left, at the time of impact. Anecdotally at least, whiplash patients report both offset impacts and also may report head rotation to the left or right at the time of impact. These patients also tend to emphasize the unilateral nature of their neck pain, but it remains to be seen in epidemiological studies if this is true. The evidence from low-velocity impacts studies does point in the direction of differential injury risks to different muscles depending on the impact conditions. This is in keeping with other studies of the pattern of muscle activation. Gabriel et al. assessedWhether or not the pathology of the acute whiplash injury is known, measures to prevent this injury or understand its nature may well be advanced by understanding both the cervical muscle responses and the head kinematics in response to whiplash-type impacts. The difficulty is that besides individual subject characteristics, there are many collision parameters which may affect the pattern of response, including severity of impact, direction of impact, awareness of impending impact, head position, seat design and restraint systems. We have, however, begun the process of a larger series of investigations by showing what effect increasing acceleration, impact direction, head rotation and expectation has on muscle responses when other factors are held constant (i.e. seat and restraint type) ,13-15. FMVC ; EMG (Electromyogram); cm (Centrimetres); dB (decibels); C4 ; mV/g (Millivolts per gram); Hz (Hertz); kHz (kilohertz); g (acceleration due to gravity); m/s2 (metres per second per second); kg (kilograms); SCM (Sternocleidomstoid); TRP (Trapezius); SPL (Splenius capitis)The author(s) declare that they have no competing interests.SK made substantial contributions to conception and design, to acquisition of data, and analysis and interpretation of data, was involved in drafting the article and revising it critically for important intellectual content. RF made substantial contributions to analysis and interpretation of data, and was involved in drafting the article and revising it critically for important intellectual content. YN made substantial contributions to acquisition of data, and analysis and interpretation of data. All authors read and approved the final manuscript.


Urban birth or upbringing increase schizophrenia risk. Though unknown, the causes of these urban-rural differences have been hypothesized to include, e.g., infections, diet, toxic exposures, social class, or an artefact due to selective migration.We investigated the hypothesis that traffic related exposures affect schizophrenia risk and that this potential effect is responsible for the urban-rural differences. The geographical distance from place of residence to nearest major road was used as a proxy variable for traffic related exposures. We used a large population-based sample of the Danish population (1.89 million people) including information on all permanent addresses linked with geographical information on all roads and house numbers in Denmark. Schizophrenia in cohort members  was identified by linkage with the Danish Psychiatric Central Register.The geographical distance from place of residence to nearest major road had a significant effect. The highest risk was found in children living 500–1000 metres from nearest major road . However, when we accounted for the degree of urbanization, the geographical distance to nearest major road had no significant effect.The cause(s) or exposure(s) responsible for the urban-rural differences in schizophrenia risk were closer related to the degree of urbanization than to the geographical distance to nearest major road. Traffic related exposures might thus be less likely explanations for the urban-rural differences in schizophrenia risk. HoweTo reduce the number of candidates responsible for the urban-rural differences in schizophrenia risk and to replicate the association between air pollution from traffic and schizophrenia risk in a larger sample, we investigated the hypothesis that traffic related exposures affect schizophrenia risk, and that this potential effect is responsible for the urban-rural differences in schizophrenia risk. As, we had no direct measurements of traffic related exposures for the total Danish Population we used the geographical distance from place of residence to nearest major road as a proxy variable for traffic related exposures.We utilised a large population-based sample of the Danish population including information on all permanent addresses at which cohort members had lived since 1971 linked with a Geographical Information System containing geographical information on all roads and house numbers in Denmark .We evaluated whether the geographical distance from place of residence to the nearest major road influenced schizophrenia risk and whether this potential effect could explain the effect of the degree of urbanization at place of residence.We used data from the Danish Civil Registration System  to obtaiIndividual information on schizophrenia in cohort members and mental illness in their relatives was obtained by linkage to the Danish Psychiatric Central Register . The DanThe Danish address and road database  containsThe 275 municipalities in Denmark were classified according to the degree of urbanization : capitalUsing data from the Civil Registration System, for each person in the cohort we calculated the degree of urbanization, and the geographical distance from the place of residence to nearest major road. This information was calculated for each one-year age-point from birth to the 15th birthday. Throughout this paper by place of residence we refer to place of residence at the 15th birthday, and by degree of urbanization we refer to the degree of urbanization at place of residence at the 15th birthday.Note that, all information was independent of the disease status. A total of 1.89 million persons were followed from their 15th birthday or April 1, 1970 whichever came later, until the date of onset of schizophrenia, the date of death, the date of emigration from Denmark, or December 31, 2001, whichever came first.The relative risk of schizophrenia was estimated by log-linear Poisson regression  with theth birthday and 36,976 (1.9 percent) had unknown information on the geographical distance to nearest major road at the 15th birthday. Among people who lived more than 2000 metres from nearest major road, the lower the degree of urbanization the greater the percentage of people , 13,151 (0.7 percent) were not resident in Denmark at their 15A total of 10,755 persons  developed schizophrenia during the 32.6 million person-years at risk. Among those, 10,394 (96.6 percent) cases and 31.4 (96.4 percent) million person-years at risk were resident in Demark and had available information on place of residence and the geographical distance to nearest major road.The relative risk associated with the distance to nearest major road for each place of residence is shown in Table Table When the effect of geographical distance to nearest major road and the degree of urbanization were adjusted mutually  gave similar results. Neither the effect of the degree of urbanization, nor the effect of the distance to nearest major road depended significantly on birth year.We found no evidence that the distance to nearest major road had an impact on the risk of developing schizophrenia when we accounted for place of residence Tables  and 3. TIf short geographical distance to nearest major road was a proxy for traffic related exposures , our findings suggested that these factors were less likely candidates of explaining the urban-rural differences in schizophrenia risk. In contrast to the current findings, in a previous study  based onAlthough many studies identified urban-rural differences in the occurrence of schizophrenia there is no standardized measure of urbanization; some authors defined urbanization by the density of people , some asWe used the distance to nearest major road as a proxy for traffic related exposures. However, we had no data documenting whether the distance to nearest major road was a reasonable proxy for traffic related exposures. Such data are currently being collected for 10,000 selected people in the capital area in Denmark. However, although the distance to nearest major road almost certainly was a crude proxy for traffic related exposures and if traffic related exposures were responsible for the urban-rural differences in schizophrenia risk, it was unlikely that the degree of urbanization would be a better proxy for traffic related exposures than the distance to nearest major road. This study suggested that traffic related exposures were not responsible for the urban-rural differences in schizophrenia risk. Previously, Mortensen  hypothesThe strengths of this study were the use of the Danish national registers where we had information on all people living in Denmark, and that all information on exposures was measured independently of the disease.This is the first study to investigate whether geographical distance from residence to nearest major road influences schizophrenia risk, and it is also the first study to utilise detailed geographical information on the location of each household nation-wide. Methods utilising Geographical Information Systems may prove useful in identifying the underlying unknown causes explaining the urban-rural differences in schizophrenia risk.The results of this study do not support the hypothesis that the cause(s) or exposure(s) responsible for the urban-rural differences in schizophrenia risk are closer related to the geographical distance from residence to nearest major road than to the degree of urbanization of place of residence. Traffic related exposures may thus be less likely explanations for the urban-rural differences in schizophrenia risk.The author(s) declare that they have no competing interests.CBP designed the study, provided statistical expertise, analysed data and drafted the manuscript. Both authors acquired data on the Danish population used for this study, interpreted the results from the analysis, and revised the manuscript. PBM provided administrative support, and support in obtaining funding. Both authors approved the submitted manuscript.The pre-publication history for this paper can be accessed here:Map of Denmark showing the degree of urbanization and all major roads.Click here for fileEach of the 1.75 million addresses in Denmark coloured according to the calculated geographical distance to nearest major road.Click here for file