Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. As a consequence of increased computational power in the last decades, evolutionary search algorithms emerged as important heuristic optimization techniques in the early eighties. Evolutionary optimization techniques have demonstrated their potential across a broad spectrum of areas such as transportation, machine learning or industry. Based on the development of current high-throughput data capturing devices in biotechnology, a wide range of high-dimensional optimization problems surfaced in the field of bioinformatics and computational biology over the last decade. Because classic optimization techniques only explore a limited portion of the solution space, researchers soon realized that sequential search engines that try to improve a single solution are clearly insufficient to move through these huge search spaces. The use of population-based, randomized search engines was proposed as an alternative that would overcome these limitations and be better able to explore the vast solution space. Evolutionary optimization techniques, of which genetic algorithms (GAs) are the most well known class of techniques, have thus been the method of choice for many of these bioinformatics problems.Estimation of distribution algorithms (EDAs) are a novel class of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms in the last decade. The principal advantages of EDAs over genetic algorithms are the absence of multiple parameters to be tuned (e.g. crossover and mutation probabilities) and the expressiveness and transparency of the probabilistic model that guides the search process. In addition, EDAs have been proven to be better suited to some applications than GAs, while achieving competitive and robust results in the majority of tackled problems. In this review, we focus on a group of pioneering papers that have shown the power of the EDA paradigm in a set of recent bioinformatic, mainly genomic and proteomic, tasks. For each problem, we give a brief description, the EDA used, and the associated literature references. The solution representation and the cardinality of the search space are also discussed in some cases. Before discussing these problems, the next section presents what an EDA is and how it works, sets out a detailed taxonomy based on their main features and what potential they have within the bioinformatic discipline.Estimation of distribution algorithms -5 are evThen, a probabilistic model of the selected solutions is built, and a new set of points is sampled from the model. The process is iterated until the optimum has been found or another termination criterion is fulfilled.For more details, Table Essentially EDAs assume that it is possible to build a model of the promising areas of the search space, and use this model to guide the search for the optimum. In EDAs, modeling is achieved by building a probabilistic graphical model that represents a condensed representation of the features shared by the selected solutions. Such a model can capture different patterns of interactions between subsets of the problem variables, and can conveniently use this knowledge to sample new solutions.Probabilistic modeling gives EDAs an advantage over other evolutionary algorithms that do not employ models, such as GAs. These algorithms are generally unable to deal with problems where there are important interactions among the problems' components. This, together with EDAs' capacity to solve different types of problems in a robust and scalable manner ,5, has lSince several EDAs have been proposed with a variety of models and learning algorithms, the selection of the best EDA to deal with a given optimization problem is not always straightforward. One criterion that could be followed in this choice is to trade off the complexity of the probabilistic model against the computational cost of storing and learning the selected model. Both issues are also related to the problem dimensionality (i.e. number of variables) and to the type of representation .Researchers should be aware that simple models generally have minimal storage requirements, and are easy to learn. However, they have a limited capacity to represent higher-order interactions. On the other hand, more complex models, which are able to represent more involved relationships, may require sophisticated data structures and costly learning algorithms. The impact that the choice between simple and more complex models has in the search efficiency will depend on the addressed optimization problem. In some cases, a simple model can help to reach non-optimal but acceptable solutions in a short time. In other situations, e.g. deceptive problems, an EDA that uses a simple model could move the search away from the area of promising solutions.Another criterion that should be taken into consideration to choose an EDA is whether there is any previous knowledge about the problem structure, and which kind of probabilistic model is best suited to represent this knowledge. The following classification of EDAs is intended to help the bioinformatic researcher to find a suitable algorithm for his or her application.EDAs can be broadly divided according to the complexity of the probabilistic models used to capture the interdependencies between the variables: univariate, bivariate or multivariate approaches. Univariate EDAs, such as PBIL , cGA 1010 and UMThe bivariate models can represent low order dependencies between the variables and be learnt using fast algorithms. MIMIC , the bivMultivariate EDAs factorize the joint probability distribution using statistics of order greater than two. Figure • The (Factorized Distribution Algorithm) FDA  is appli• In , a facto• The Bayesian optimization algorithm (BOA)  is also • The extended compact Genetic Algorithm (EcGA) proposed in  is an alThere are alternatives to the use of Bayesian networks for representing higher order interactions in EDAs. Markov network-based EDAs -21 couldan a priori given model. The first class is denoted as structure+parameter learning, whereas the second is known as parameter learning. Both approaches need to induce the parameters of their models, but the first approach's need for structural learning makes it more time consuming. By contrast, parameter learning is dependent on the fixed model, whereas structure+parameter learning exhibits a greater power of generalization.In addition to the order of complexity encoded by the probability model, there is another key feature when dealing with an EDA algorithm: the way that model is learned. There are two alternatives: induce the model structure and its associated parameters, or induce just the set of parameters for Population-based incremental learning (PBIL) , the comSo as to have a graphical taxonomy of the subdivisions presented through this section, Table Evolutionary algorithms, and GAs in particular, have been widely and successfully applied in bioinformatics. It is reasonable to expect that the improvements in EDA efficiency and scalability can contribute to expanding the use of these algorithms, particularly for difficult problems where other evolutionary algorithms fail ,27.priori in such a way that they represent previous knowledge about the structure of the optimization problem. Even the use of incomplete or partial information about the problem domain can considerably reduce the computational cost of the search. Similarly, practitioners can manipulate the probabilistic models to favor solutions with certain pre-established partial configurations. This way they can test particular hypotheses about the configuration of the optimal solution.There are other situations where the use of EDAs can be very useful for solving bioinformatics problems. For instance, probabilistic models used by EDAs can be set up a EDAs have another advantage, also associated with the capacity to model key features of the search space. The models generated during the search can be mined to reveal previously unknown information about the problem -30.Furthermore, recent results of applying EDAs to problems from other domains  have shoDue to advances in modern high-throughput biotechnology devices, large and high-dimensional data sets are obtained from analyzed genomes and tissues. The heuristic scheme provided by EDAs has proved to be effective and efficient, in a variety of NP-hard genomic problems. Because of the huge cardinality of the solution spaces of most of these problems, researchers are aware of the need for an efficient optimization algorithm. In this way, authors have preferred simple EDA schemes that assume that the variables are independent. These schemes have obtained accurate and robust solutions in reasonable CPU times. Together with a brief definition of each tackled genomic problem, we describe the main characteristics of each EDA scheme are described, with a special emphasis on the codification used to represent the search individuals.As genomes are being sequenced at an increasing pace, the need for automatic procedures for annotating new genomes is becoming more and more important. A first and important step in the annotation of a new genome is the location of the genes in the genome, as well as their correct structure. As a gene may contain many different parts, the problem of gene structure prediction can be seen as a segmentation or parsing problem. To solve this problem automatically, pattern recognition and machine learning techniques are often used to build a model of what a gene looks like. This model can then be used to automatically locate potential genes in a genome ,33.A gene prediction framework consists of different components, where each component (often modeled as a classifier) aims at identifying a particular structural element of the gene. Important structural elements include the start of the gene (start codon), the end of a gene (stop codon) and the transitions between the coding and non-coding parts of the gene (splice sites).The exact mechanisms that the cell uses to recognize genes and their structural elements are still under research. As this knowledge is missing, one major problem in this context is to define adequate features to train the classifiers for each structural element. Consequently, large sets of sequence features are extracted in the hope that these sets will contain the key features. However, it is known that not all of these features will be important for the classification task at hand, and many will be irrelevant or redundant.n possible subsets for evaluation (given n features), population-based heuristic search methods are an interesting engine for driving the search through the space of possible feature subsets. Each solution in the population decodes a feature subset as a binary string: features having a value of 1 are included in the subset, whereas the ones having a value of 0 are discarded.To find the most relevant features for recognizing gene structural elements, feature subset selection (FSS) techniques can be used. These techniques try to select a subset of relevant features from the original set of features ,35. As tAs a natural alternative to genetic algorithms, the use of EDAs for FSS was initiated in  for clasThe application of EDA-based FSS techniques in gene structure prediction was pioneered for the most important gene prediction components in . Its mosIn addition to better models, an UMDA-based approach was also used to get more insight into the selected features. This led to both the identification of new characteristics, as well as the confirmation of important previously known characteristics .et al.'s pioneering work [The quantitative and qualitative DNA analysis is one of the most important areas of modern biomedical research. DNA microarrays can simultaneously measure the expression level or activity level of thousands of genes under a set of conditions. Microarray technology has become a popular option for partial DNA analysis since Golub ing work .The starting point of this analysis is the so called gene expression matrix, where rows represent genes, columns represent experimental conditions (or samples), and the values at each position of the matrix characterize the expression level of the particular gene under the particular experimental condition. Additional biological information about the genes and the experimental conditions can be added to the matrix in the form of gene and/or sample annotation. Depending on how we treat the annotation, gene expression data analysis can be either supervised or unsupervised. When sample annotation is used to split the set of samples into two or more classes or phenotypes , supervised analysis (or class prediction) tries to find patterns that are characteristic of each of the classes. On the other hand, unsupervised analysis (or class discovery) ignores any annotation. Examples of such analysis are gene clustering, sample clustering and gene expression data biclustering.The FSS paradigm has taken a leading role due to the challenge posed by the huge dimension of DNA microarray studies , small sample sizes (gene expression studies with more than a hundred hybridizations are not common) and the notable influence of different sources of noise and variability. Thus, the application of dimensionality reduction techniques has become a must for any gene expression analysis.It is broadly assumed that a limited number of genes can cause the onset of a disease. Within this scenario biologists demand a reduction in the number of genes. In addition, the application of a FSS technique to microarray datasets is an essential step to achieve an accurate classification performance for any base classifier.n possible gene subsets, where n is the number of initial genes). Two research groups have proven that the EDA paradigm is useful for this challenging problem. Both groups have implemented efficient algorithms that have achieved accuracy levels comparable to the most effective state-of-the-art optimization techniques:Although univariate gene ranking procedures are very popular for differential gene expression detection, the multivariate selection of a subset of relevant and non-redundant genes has borrowed from the field of heuristic search engines to guide the exploration of the huge solution space . The first array represents each gene of the microarray, where the size is the number of genes. The second array represents each condition, with a size equal to the number of conditions. A value of 1 in the ith position of the first array shows that the ith gene has been selected for inclusion in the bicluster. Likewise, a value of 1 in the jth position of the second array indicates that the jth condition has been selected for inclusion in the bicluster. This codification results in a space of 2n+m possible biclusters.An example is the work by Palacios et al. , which aThe inference of gene-gene interactions from gene expression data is a powerful tool for understanding the system behaviour of living organisms .This promising research area is now of much interest for biomedical practitioners, and a few papers have even applied EDAs to this domain. One of these early works uses Bayesian networks as the paradigm for modeling the interactions among genes, while an UMDA approach explores the search space to find the candidate interactions . The subThe objective of protein structure prediction is to predict the native structure of a protein from its sequence. In protein design, the goal is to create new proteins that satisfy some given structural or functional constraints. Frequently, both problems are addressed using function optimization. As the possible solution space is usually huge, complex and contains many local optima, heuristic optimization methods are needed. The efficiency of the optimization algorithm plays a crucial role in the process. In this section, we review applications of EDAs to different variants of protein structure prediction and protein design problems.We start by reviewing some important concepts related to protein models and energy functions in optimization. Then, we propose an initial general classification of EDA applications to protein problems according to how sophisticated and detailed the protein models used are. Subsequently, we give a more detailed classification based on the specificities of the protein problems.Protein structure prediction and protein design are usually addressed by minimizing an energy function in the candidate solution space. Two essential issues in the application of EDAs and other optimization algorithms to these problems are the type of protein representation employed and the energy function of choice.There are many factors that influence the stability of proteins and have to be taken into account to evaluate candidate structures. The native state is thought to be at the global free energy minimum of the protein. Electrostatic interactions, including hydrogen bonds, van der Waals interactions, intrinsic propensities of the amino acids to take up certain structures, hydrophobic interactions and conformational entropy contribute to free energy. Determining to what extent the function can represent all of these factors, as well as how to weight each one are difficult questions that have to be solved before applying the optimization method.Simplified protein models omit some of these factors and are a first problem-solving approximation. For example, the approximate fold of a protein is influenced by the sequence of hydrophobic and hydrophilic residues, irrespective of what the actual amino acids in that sequence are . TherefoDepending on how sophisticated and detailed the protein model used is, EDAs can be divided into two groups: EDAs applying a simplified model -60 and E• Protein structure prediction in simplified models ,60.• Protein side chain placement ,63.• Design of protein peptide ligands .• Protein design by minimization of contact potentials ,64.• Aminoacid alphabet reduction for protein structure prediction .• Using EDAs as a simulation tool to investigate the influence of different protein features in the protein folding process .k-order Markov models (MK-EDAk) [In -60, EDAsMK-EDAk) ).2. Due to the particular topology of this instance, other evolutionary algorithms consistently fail to find the optimal solution [The results achieved outperform other evolutionary algorithms. For example, the configuration shown in Figure solution .Side chain placement problems are dealt with using UMDA with discrete representation in ,63. The et al. [Belda et al.  use diffet al.  and the et al.  are compet al. .r . Combining probabilistic models able to represent probabilistic dependencies with information about residue interactions in the protein contact graph is shown to improve the search efficiency for the evaluated problems. In [In , three dlems. In , EDAs thThe alphabet reduction problem is addressed in  using thResults of using EDAs and the HP model to simulate the protein folding process are presented in . Some ofThroughout this paper, we reviewed the state-of-the-art of EDA applications in bioinformatics. As soon as researchers realized the need to apply a randomized, population-based, heuristic search, EDAs emerged as a natural alternative to commonly used genetic algorithms. Since the possible solution space is huge for most of the addressed problems, researchers have made use of efficient EDA implementations.A group of interesting papers demonstrate the efficiency and the competitive accuracy of this novel search paradigm in a set of challenging NP-hard genomic and proteomic bioinformatic tasks. As the number of EDA application papers in bioinformatics is modest and the number and variety of problems is constantly growing, there is room for new EDA applications in the field.An interesting opportunity for future research is the adaptation and application of multivariate EDA models that can efficiently deal with the huge dimensionality of current bioinformatic problems. Going further than simple univariate models, bio-experts could explicitly inspect the probabilistic relationships among problem variables for each generation of the evolutionary process. This would create opportunities for improved accuracy. These probabilistic relationships induced from the evolutionary model are an attractive way of proposing novel biological hypotheses to be further tested by bio-experts.The authors declare that they have no competing interests.RA, II, and PL conceived of the manuscript. II, YS, JLF, RB, VR and CB participated in writting the genomics section. The proteomics section was designed and written by RS and JAL. The introduction to EDAs was carried out by RA, RS and YS. RA was in charge of the writing and coordination process. II, YVP and PL helped to write and correct the manuscript draft. All authors read and approved the final manuscript.


Although syndromic surveillance systems are gaining acceptance as useful tools in public health, doubts remain about whether the anticipated early warning benefits exist. Many assessments of this question do not adequately account for the confounding effects of autocorrelation and trend when comparing surveillance time series and few compare the syndromic data stream against a continuous laboratory-based standard. We used time series methods to assess whether monitoring of daily counts of Emergency Department (ED) visits assigned a clinical diagnosis of influenza could offer earlier warning of increased incidence of viral influenza in the population compared with surveillance of daily counts of positive influenza test results from laboratories.For the five-year period 2001 to 2005, time series were assembled of ED visits assigned a provisional ED diagnosis of influenza and of laboratory-confirmed influenza cases in New South Wales (NSW), Australia. Poisson regression models were fitted to both time series to minimise the confounding effects of trend and autocorrelation and to control for other calendar influences. To assess the relative timeliness of the two series, cross-correlation analysis was performed on the model residuals. Modelling and cross-correlation analysis were repeated for each individual year.Using the full five-year time series, short-term changes in the ED time series were estimated to precede changes in the laboratory series by three days. For individual years, the estimate was between three and 18 days. The time advantage estimated for the individual years 2003–2005 was consistently between three and four days.Monitoring time series of ED visits clinically diagnosed with influenza could potentially provide three days early warning compared with surveillance of laboratory-confirmed influenza. When current laboratory processing and reporting delays are taken into account this time advantage is even greater. Early detection is crucial for achieving effective control of outbreaks and epidemics of influenza. Monitoring of clinical and other health data streams which are available electronically in real-time or near real-time is now recognised internationally, nationally and locally as an essential complement to established mechanisms for public health surveillance -4. SeverWhile some studies have evaluated syndromic surveillance against continuous laboratory series ,21, noneIn 2001, the State of New South Wales (NSW), Australia, which has a population of approximately seven million, mandated the reporting of positive laboratory test results for influenza virus to the Department of Health . In 2003Amongst the many well-established analytical techniques for time series, cross-correlation analysis is frequently used to quantify the temporal relationship between two time series and to assess the statistical significance of their correlation at various time lags or offsets. However, before undertaking such analysis, care must be taken to remove both long-term trend and autocorrelation from the time series, lest they give rise to spurious evidence of a temporal relationship between data series ,25. AutoThe purpose of this paper is to investigate, with adequate control of temporal confounders, whether daily counts of ED influenza visits provided by a near real-time syndromic surveillance system offers the potential for earlier warning of changes in influenza activity in the community than traditional laboratory-based surveillance, and to estimate the magnitude of any time advantage.For the period 1 January 2001 to 31 December 2005, we obtained from the NSW Emergency Department Data Collection (EDDC)  the timeFrom 1 January 2001 in NSW, it became mandatory that all public and private laboratories notify the health department of all positive influenza test results. Public laboratories receive specimens from public hospitals whereas private laboratories receive them from general practitioners, private hospitals and other private health care facilities, such as aged care facilities. Notifications from private laboratories constitute the majority of the influenza notifications. For the same period as the ED time series, we obtained from the NSW notifiable disease surveillance system the time series of daily counts of laboratory-confirmed influenza cases, aggregated by date of disease onset. For the majority (98.5%) of influenza notifications, the date of disease onset is the collection date of the laboratory specimen. All sub-types of influenza virus were included. These results include direct detection of viral antigen from nasopharyngeal swabs or aspirates by direct immunofluorescence, detection of viral nucleic acid by polymerase chain reaction, serological tests and isolation of virus by culture. Multiple notifications for the same case of influenza were counted only once.Descriptive analyses were performed using SAS version 8.02  and timea priori decision was made to choose the minimum degree of smoothing that would adequately remove autocorrelation and trend from the two five-year time series. To ensure comparability of results between years, we applied the same degree of smoothing when fitting models to the individual one-year time series. The final model had the form:In order to remove long-term trend and autocorrelation from the ED and laboratory time series of daily counts, we fitted generalised linear regression models to each series for the full five-year period as well as each individual year. We assumed a "quasi-Poisson" error distribution for the model, which is appropriate for count data exhibiting over-dispersion (greater variance than would be expected in Poisson-distributed data). Because both ED visits and specimen collection are likely to be affected by holidays, weekends and other factors that vary by day of the week, we included terms in the models for weekday, public holidays, and school holidays. "Natural cubic smoothing splines" are recognised as an effective means of controlling for long-term trends in time series of counts and have been used in studies of the short-term effects of air-pollution -35. ThesFitted values obtained from the model were then subtracted from the original time series to leave two sufficiently "stationary" and "whitened" residual time series that allowed valid inferences to be drawn from cross-correlation analysis ,25. The Cross-correlation analysis calculates a series of correlation coefficients between two time series by overlaying and temporally shifting the two series over a range of successive time lags. This allows determination of the time lag that maximises the strength of the correlation between the two time series. R software automatically chooses between 20 and 30 positive and negative lags in its cross-correlation function, depending on the number of observations. Statistical significance was defined as a correlation coefficient greater than twice the standard error.The requirement for a de-trended (stationary), non-autocorrelated time series for the cross-correlation analysis meant that we were unable to use statistical methods to compare longer-term trends between the ED and laboratory time series. However, a visual comparison of both the observed time series counts and the fitted spline component of the ED and laboratory models nevertheless allows an impression to be gained of the relative location, shape and magnitude of peak influenza activity between the two series.To assess the plausibility of the observed correlation, we plotted the 7-day moving averages of the raw ED series and the laboratory series for the most distinct influenza season in the five-year period, which was 2003.In a separate analysis, we used the NSW notifiable disease surveillance system to calculate the median lag in days between the date when the specimen was taken from the patient and the date the laboratory reported the positive test results to local health authorities.This study used de-identified epidemiological information and therefore ethical approval was not required.Visual inspection reveals broad similarity between the ED and laboratory daily series in terms of both the relative size and timing of seasonal peaks Figure . The lowThe specified model with eleven degrees of freedom per year  was the minimum degree of smoothing that adequately limited autocorrelation within the two five-year time series. Plots of raw daily count series of both ED and laboratory data and the fitted values obtained from the Poisson regression models are shown in Figure For the full five-year period, the strongest statistically significant positive correlation  for the residuals of the ED model relative to the residuals of the laboratory model occurred at a lag of negative three days. This suggests that short-term changes in the ED time series preceded those in the laboratory series by three days on average Figure . A clustVisual comparison of the logarithm of the smoothing spline component  of the fitted models for each year showed a strong similarity in the timing and relative magnitude of influenza peaks, and a broad similarity in the overall shape of peaks between the two data sources Figure . OutsideVisual comparison of the 7-day moving averages of ED visits and laboratory results showed that during the influenza season from July to September 2003, the ED activity consistently rose, peaked and then declined earlier than the laboratory results. However, at the end of the season, the laboratory results tailed off more quickly than the ED visits Figure .For the period 2001 to 2005, the median delay for the reporting of positive laboratory results for influenza to local public health authorities was four days after the date of specimen collection.Through use of time series methods with adequate control of temporal confounders we found that monitoring short-term changes in the incidence of ED-diagnosed influenza could provide at least three days early warning of changing influenza activity in the population relative to monitoring short-term changes in laboratory-based surveillance data. Despite the small residual correlation coefficients, the consistency of this result across each of the most recent years and the whole five years suggests that this finding did not occur by chance. The unclear correlations in the two earlier years may have arisen from the immaturity of laboratory-based surveillance at that time. The time lag between the two data sources was clearly evident when plotting the smoothed raw incidence for the year with the most distinct seasonal influenza epidemic (2003). The close similarities between the longer-term trends within each time series provides further evidence that counts of ED visits diagnosed with influenza closely mirror the circulation of influenza virus in the population. This time advantage could be exploited through, for example, the use of statistical process control techniques, such as cumulative sum (CUSUM) or exponentially weighted moving average techniques that respond to short-term variation in the ED time series .If both time series accurately represented actual incidence of influenza infection in the population then we would expect the two time series to be perfectly aligned in time. We believe, however, that the observed lag arises from the different aspects of the disease process that predominate in each data source. Respiratory specimens are more likely to be taken from patients with more advanced or more serious illness or those who are experiencing secondary complications of infection such as pneumonia. In the ED, on the other hand, otherwise healthy patients with a classic influenza-like syndrome at the early stage of illness are more likely to be assigned the provisional influenza diagnosis and are also unlikely to have a specimen taken. This would also partly explain the low correlation coefficients observed, because the exact time lag between incidence of uncomplicated and more severe illness would not be fixed but would vary according to some distribution. This is supported by the observation of a cluster of small positive correlations around the most significant lag. Other reasons for the small correlations could be the loss of information arising from the pre-whitening process, and the fact that the two data sources are drawn from quite different sources and neither can provide complete coverage of all influenza infections in the population; the ED data is from public hospital EDs, while the laboratory results could be from specimens taken in general or specialist medical practices, private or public hospitals (admitted or non-admitted patients), and nursing homes, for example. Note that nearly all EDs in our state are in public hospitals. The different age structures confirm the limited overlap between the two data sources.If the early warning potential of ED-diagnosed influenza could be exploited using a sufficiently sensitive and specific signalling technique, the public health benefits would be manifold. It is unlikely that public health professionals would treat influenza virus as the certain cause of such a signal. However, the "situational awareness" of increased influenza-like illness activity could trigger a cascade of activities aimed at excluding more serious public health threats. Those threats could include the emergence of a mutated strain of influenza, introduction into the population of an agent, by natural or intentional means such as bioterrorism that mimics influenza in its prodromal stage, or seasonal vaccine failure. Influenza's short incubation period (around two days) and its capricious propensity to mutate underline the importance of early warning systems. The activities that might follow a signal might includes increasing the number of tests ordered for patients with influenza-like illness, applying a wider battery of tests than would otherwise be performed, and then using that information to guide subsequent public health prevention and control efforts. These activities would be particularly important outside of the influenza season when there is a low index of suspicion for influenza but when outbreaks have occurred . In manyBecause our analysis is effectively based on the date of specimen collection for the laboratory time series, it does not reflect the inevitable real-life delays involved in transporting specimens to laboratories, performing the tests and reporting the results to health authorities responsible for surveillance. At present in NSW, the median delay for the reporting of positive laboratory results for influenza is four days after the date of specimen collection. In the NSW ED surveillance system, where data capture is near real-time, more than three-quarters of all ED diagnoses are available for analysis within one day of the patient's arrival at the ED . Thus thThis study differs in several respects from previously published assessments of the timeliness of syndromic data streams. To our knowledge, no previous timeliness studies have compared daily ED visits for influenza with a continuous population-based laboratory standard. The use of daily count data in this study also allows for a more fine-grained estimation of time lags between data sources. Many syndromic surveillance systems analyse data on a daily basis, thereby giving our analysis direct relevance. Another advantage of this study is that the seasonality, longer-term trends and autocorrelation, which are inherent in population-based count data for a readily communicable disease such as influenza, were appropriately removed prior to undertaking the cross-correlation analysis. This essential step permitted valid statistical inferences to be drawn from the results, although for the ED series in 2002 and the laboratory series in 2005, autocorrelation could not be adequately controlled using our modelling strategy. We believe that autocorrelation in our data and in infectious disease time series generally derives largely from the communicability of the organism, resulting in successive incidence observations being serially correlated as the infection spreads through the population. In 2002, there was a somewhat prolonged period of seasonal influenza and this may have influenced the overall autocorrelation for the ED data for that year. This may not have been as apparent in the laboratory series because of incomplete reporting in the early years of mandatory laboratory notification. In the laboratory series for 2005, the incomplete control of autocorrelation may have arisen from a period of false positive results that are known to have been reported by one laboratory in that year.et al. reported time advantages of up to 50 days for ED-identified seasonal respiratory activity against a pneumonia and influenza mortality standard [et al. compared daily counts of over-the-counter sales of common cold medications to a reference standard of a national ambulatory care-based influenza database and found that the medication data provided no time advantage [Other analyses using daily count data have been conducted. Brownstein standard . Howeverstandard -42. The dvantage .et al. compared count data from a health advice telephone service to counts from a general practice-based sentinel surveillance system for influenza-like illness [Other studies have used weekly counts to evaluate syndromic data streams for the surveillance of influenza. Using correct control of temporal confounders, Doroshenko  illness  and foun illness ,11,12.The data sources we used have some limitations. ED provisional diagnoses are not coded by trained clinical coders but instead are selected by ED medical and nursing staff in the course of their work. Coding practice may vary between hospitals and staff and may change over time. It is known that not all people with influenza infections are assigned the ICD codes for influenza, and there are a number of other valid but less specific codes that can be used, such as 'unspecified viral infection' or 'viraemia'. Conversely, some ED patients infected by other organisms but presenting with influenza-like symptoms could have been assigned the influenza diagnosis. Although this may have contaminated the ED series, it would be unlikely that other organisms would cause such a consistent correlation across individual years when compared with a laboratory standard. Also, Bourgeois et al.  demonstrA limitation of the laboratory diagnoses may be that they are influenced by changes in the accuracy of laboratory tests over time. Also, the propensity of clinicians to order tests may vary according to their knowledge of whether influenza is circulating or not. However, the NSW laboratory notification system is a passive surveillance system and active sampling and sentinel surveillance has declined in our state over recent years. On the other hand if clinicians know that influenza is circulating they may not choose to test because influenza has a higher probability of being the cause of illness. Also, during off-season, adults with ILI may be more likely to be tested because influenza as a cause is unexpected. Under-reporting in 2001, and, as mentioned earlier, some false positive test results from a laboratory in 2005 are known problems in the laboratory series.One aspect of the ED surveillance data not evaluated by this study is the added surveillance dimension available from other variables captured and analysed along with ED diagnoses. For example, in NSW we include routine sub-analyses by age, sex, triage (urgency) category and discharge status variables in our daily reports. These give valuable clues to the epidemiology of the syndrome and the urgency and severity of illness in presenting patients . FurtherOur findings demonstrate that ED-based syndromic data streams can provide more timely detection of unusual influenza activity in the population. Although syndromic surveillance cannot replace laboratory surveillance, the earlier warning it provides can signal the need to increase vigilance for influenza or pathogens with an influenza-like prodrome. Even just a few days early warning may be crucial in allowing public health authorities to gain a head start in implementing interventions designed to reduce disease transmission, and delay or curtail a local outbreak or seasonal epidemic of influenza, or, potentially, the start of a pandemic caused by a novel strain of virus.The author(s) declare that they have no competing interests.DM conceived of the study and supervised the data analysis. WZ and RA conducted the data analysis. WZ and DM drafted the manuscript, which RA and TC edited. All of the authors read and approved the manuscript.The pre-publication history for this paper can be accessed here:


Tissue microarray technology (TMA) is a relatively new approach for efficiently and economically assessing protein and gene expression across large ensembles of tissue specimens. Tissue microarray technology holds great potential for reducing the time and cost associated with conducting research in tissue banking, proteomics, and outcome studies. However, the sheer volume of images and other data generated from even limited studies involving tissue microarrays quickly approach the processing capacity and resources of a division or department. This challenge is compounded by the fact that large-scale projects in several areas of modern research rely upon multi-institutional efforts in which investigators and resources are spread out over multiple campuses, cities, and states. To address some of the data management issues several leading institutions have begun to develop their own “in-house” systems, independently, but such data will be only minimally useful if it isn’t accessible to others in the scientific community. Investigators at different institutions studying the same or related disorders might benefit from the synergy of sharing results. To facilitate sharing of TMA data across different database implementations, the Technical Standards Committee of the Association for Pathology Informatics organized workshops in efforts to establish a standardized TMA data exchange specification. The focus of our research does not relate to the establishment of standards for exchange, but rather builds on these efforts and concentrates on the design, development and deployment of a decentralized collaboratory for the unsupervised characterization, and seamless and secure discovery and sharing of TMA data. Specifically, we present a self-organizing, peer-to-peer indexing and discovery infrastructure for quantitatively assessing digitized TMA’s. The system utilizes a novel, optimized decentralized search engine that supports flexible querying, while guaranteeing that once information has been stored in the system, it will be found with bounded costs. Tissue microarray (TMA) technique enables researchers to extract small cylinders of tissue from histological sections and arrange them in a matrix configuration on a recipient paraffin block such that hundreds can be analyzed simultaneously . This neOne of the advantages of TMA technology is that it serves to amplify limited tissue resources by generating large numbers of small core biopsies, rather than a single section. Another attractive feature of the TMA technique is that specimens from different donor tissue blocks are treated in an identical manner in terms of incubation times, temperatures and washing conditions making comparison of expression among the discs comprising a given array feasible. Currently, the primary methods used to evaluate the arrays involve manual, interactive review of TMA samples under the microscope while they are subjectively evaluated and scored. An alternate, but less utilized strategy is to sequentially and manually digitize specimens for subsequent semi-quantitative assessment . Both prTwo recent studies highlight this point. In a recent study on HER2 expression in breast cancer tissue, it was found through the use of TMA’s that higher levels of HER2 protein correlated with poorer clinical outcomes . In an eunsupervised quantitative analysis.Tissue microarray technology holds tremendous potential for reducing the time and cost associated with conducting research in tissue banking, proteomics, and outcome studies, however, analyzing, sharing, and managing the data that TMA’s generate creates a number of significant challenges. To address some of the data management issues several leading institutions have begun to develop their own functional systems “in-house”, but such data will be only minimally useful if it isn’t accessible to others in the scientific community. Researchers at different institutions studying the same or related diseases might potentially benefit from the synergy of sharing results. The Technical Standards Committee of the Association for Pathology Informatics is working to establish a TMA data exchange specification which is free and non-proprietary . In spitAt the same time, there exists a real need for reliable tools which enable individuals to dynamically acquire, share and assess imaged specimens and correlated data. The focus of our research is not on the establishment of standards for exchange, but rather builds on these efforts and concentrates on the design, development and deployment of a decentralized collaboratory for the unsupervised characterization and seamless and secure discovery and sharing of TMA data. Specifically, we present a self-organizing, peer-to-peer indexing and discovery infrastructure for quantitatively assessing digitized TMA’s.The rich diversity and large volumes of TMA data that makes indexing, cataloging and sharing non-trivial and renders centralized solutions infeasible. Today, TMAs can contain from tens to hundreds of samples (0.6 to 2mm in diameter) arranged on a single slide. A digitized TMA specimen containing just 400 discs can easily approach 18GB in size. Given the increasing number of institutions and investigators utilizing TMA technology it is likely that modern facilities may easily generate tens of thousands of entries and terabytes of data. Clearly archiving, indexing and cataloging and mining this data across the TMA research community is a significant challenge. Further, the increasing popularity of TMA has lead to more and more medical and research institutions being interested and conducting research in this area. While the exact focus of the research conducted by each of these groups may differ in terms of the patient group, the type of cancer, and/or the nature of the staining, being able to share data and meta-data has many advantages. Sharing experimental results and clinical outcomes data could lead to huge benefits in drug discovery and therapy planning. While some leading institutions are developing data management systems for TMA data, these systems are only minimally useful if the data isn’t accessible to others in the scientific community. However, the size of the data involved as well as issues of ownership can quickly limit the scalability and feasibility of this approach.This paper presents the design, development and evaluation of a prototype peer-to-peer collaboratory for imaging, analyzing, and seamlessly sharing tissue microarrays (TMA), correlated clinical data, and experimental results across a consortium of distributed clinical and research sites. Key components of the collaboratory addressed in this paper include:A key requirement for effective sharing of TMA data and metadata is the definition of semantic schemas for describing the TMA sample, the patient parameters, the evaluations conducted and the observed results. We propose an XML schema that builds on emerging metadata standards and is sufficiently rich to capture these dimensions and can be effectively parsed and presented using conventional technologies.As mentioned above, current procedures for TMA analysis ultimately involve the interactive evaluation of TMA samples which is a slow, tedious process that is prone to error. Recent studies showed that having a pathologist score the specimens yields results that are subjective, difficult to reproduce, and do not reflect subtleties. Reliable quantitative measurements will allow investigators to make accurate predictions about patient outcomes and response to therapy. But for the most part, the promise of TMAs remains unrealized because scientists lack methods of high throughput, automated quantitative evaluation.We have already reported the development of a web-based prototype for automatically imaging, analyzing, and archiving tissue microarrays. The software is both platform- and operating system-independent and with minor modifications to the configuration file can interface with any commercially available robotic microscopy equipment. The software is developed with a modular design to facilitate future integration with automatic slide scanners as well. The system utilizes a combination of sophisticated image processing and pattern recognition strategies to co-register specimens while the software directs a robotic microscope to systematically image specimens at multiple optical magnifications, extract spectral and spatial signatures of the specimen and populate local or distributed relational databases with the resulting data including pointers to imaged arrays. The prototype features both stand-alone and network modes. A visually intuitive interface was developed to enable local and remote users to manipulate the digitized arrays in order to facilitate the organization of specimens for new experiments and to provide a means for data assimilation .In addition to the algorithmic and software development that is required for analyzing tissue microarrays, reliable tools are also needed to enable individual groups to dynamically acquire and seamlessly share imaged specimens and correlated metadata. However scalable information discovery in the absence of global knowledge of naming conventions remains a fundamental problem in large, decentralized, distributed environments. This is due to the heterogeneous nature and large volume of data and resources, their dynamism and the dynamism of the sharing environment. As a result, an information indexing and discovery system has to be efficient, fault-tolerant and self-organizing. Further, in the case of TMA data, security as well as the ability of each research group to maintain ownership as well as access control capabilities to their data is critical.As a part of the TMA collaboratory we present Squid, a P2P information indexing and discovery infrastructure. Each peer (e.g. research institution) in this system maintains ownership of its data and only publishes (in a controlled manner) metadata describing its data, which can then be discovered and search externally. The key innovation is a dimension reducing indexing scheme that effectively maps the multidimensional metadata information space to physical peers. Note that access to TMA data in this system is always controlled by the owner of the data.A key requirement for the TMA collaboratory is the ability to flexibly and efficiently search TMA data and metadata across peer site using keywords, partial keywords, wildcards and ranges. Further, the underlying query engine should guarantee that all existing data elements that match a query are found with bounded costs. The Squid query engine supports such complex queries and guarantees that all existing data elements that match a query will be found with bounded costs in terms of number of messages and number of nodes involved.The grid-like organization of tissue microarrays lends itself to quantitative analysis. Although some DNA microarray readers are capable of reading tissue array slides, automatic imaging and evaluation of tissue microarray samples presents several technical challenges. For example, the content of each well on a cDNA microarray chip can generally be considered homogeneous, and hence it is sufficient to describe the expression outcomes by numerical expression levels, which can be generated by relatively straightforward image processing protocols. On the contrary, tissue microarrays are quite heterogeneous in their composition. Depending on the type of tumor or tissue section analyzed, the area of interest may represent nearly the entire disc or only a small percentage thereof. For example, a pancreatic carcinoma or lobular carcinoma of the breast with substantial desmoplastic response may show stromal tissue representing a large percentage of the total area of the disc. If the goal of the assay is to determine epithelial cell expression of a given marker, a protocol must be used that evaluates only that region of the disc. The protocol must not only be able to select the region of interest but also be able to normalize it so that the expression level read from any given disc can be compared with that of other discs.Our group has made significant progress towards performing unsupervised quantitation of protein expression. Proof of concept of the color decomposition approach using breast cancer tissue array stained with anti-Smad antibodies was recently described . A new aunsupervised fashion. The auto-focusing protocol utilizes Shannon’s entropy , the probability that a photon falls into pixel i is computed as,Imaging of the TMAs benefit from the development of a Computer-Assisted Microscopy module (CAM). One of the features of CAM is an auto-focusing module which was developed to enable scanning and imaging processes to operate in  entropy  as detaiand image entropy is defined as,N pixels, a uniform image, which we consider completely out-of-focus, has maximum entropy, S0 = log(N), where S0 is solely dependent upon image size N.Of all possible images of S0. The same panel illustrates the use of entropy as monotonic indicator of focus. The algorithm directs the robotics and imaging device to acquire a series of images while varying Z positions under a fixed magnification, without horizontal movement. Entropy values are automatically generated.A focus curve can be generated by computing the image entropy across the Z-axis for any given microscopic field. As shown in the left panel of focus curve is kept to a minimum.Due to slight variations in image complexity and sample thickness each microscopic field exhibits a specific set of curve characteristics, however, the overall inverse-bell-shape of each respective curve is generally preserved. Using image entropy as a monotonic indicator of focus, we consider the task of auto-focusing as a function minimization problem while sampling of the Utilizing a modified golden section search algorithm in one dimension the system reliably delivers an in-focus or nearly-in-focus image each time that a different objective lens is selected. The auto-focus algorithms have been tested using peripheral blood smears, liver biopsies, and breast cancer tissue arrays.unsupervised imaging and analysis steps during the course of an active Distributed Telemicroscopy (DT) session. The system automatically performs registration of the specimen, generates the spectral and spatial signature for each disc, and serializes and streams the corresponding images to any number of mirror sites. At this time, the system prompts the user for the name of any correlated clinical reports. A Perl-based program is being developed to automatically process flat-ASCII version of the correlated clinical reports by stripping off salient fields while omitting any patient identifiers such as .The Distributed Telemicroscopy subsystem  and TissThe de-identified data along with the corresponding feature vector housing the image metrics generated during analysis can be transferred to a server-side JAVA application which automatically checks for potential conflicts of new entries, and populates the database with the new set of image metrics and correlated data while inserting an entry into the database to indicate the location of the digitized cancer specimens. The data and identifiers will be developed to meet all HIPAA requirements for sharing data anonymized for research by adapting to the evolving data exchange specification .Distributed databases are organized as shown in In addition to the algorithmic and software development that is required for analyzing tissue microarrays, reliable tools are also needed to enable individual groups to dynamically acquire and seamlessly share imaged specimens and correlated metadata. In spite of some of the progress that has been made in organizing and standardizing data for exchange, scalable information discovery is quite limited in the absence of global knowledge of naming conventions. This remains a fundamental problem for most applications which must operate in large, decentralized, distributed environments. This is due to the heterogeneous nature and large volume of data and resources, their dynamism and the dynamism of the sharing environment (with nodes joining and leaving). As a result, an information indexing and discovery system has to be efficient, fault-tolerant and self-organizing. In the case of TMA data, the ability of each research group to maintain ownership of their own data while providing access control capabilities is critical.Recent years have seen increasing interest in peer-to-peer (P2P) information sharing environments. In the P2P computing paradigm, entities at the edges of the network can directly interact as equals (or peers) and share information, services and resources without centralized servers. Key characteristics of these systems include decentralization, self-organization, dynamism and fault-tolerance, which make them naturally scalable and attractive solutions for information storage and discovery applications. As a part of our feasibility studies a prototype TMA collaboratory has been developed utilizing Squid , a P2P idata gathering module collects the data that is processed and shared. There are four major types of data: the experimental data, the observed data, the simulation data and the archived data.A schematic overview of the overall architecture of the prototype collaboratory is presented in data processing module employs vision-based and feature-based techniques to extract relevant data from the images and data associated with the TMA slide (i.e. the TMA-AID system). The extracted data is stored in the database using the Array Archiving (AA) subsystem. The TMA-AID system can be used remotely using the Distributed Telemicroscopy (DT) subsystem. The data access module enables remote access to the data stored in the local database.The metadata extraction module extracts information describing the shared data from the local database. The metadata is published in Squid P2P storage and discovery system. Finally the collaboratory GUI allows users to flexibly search TMA data and metadata in Squid and access it through the Database Portal.The p% of the data, the number of nodes with matching data approaches p% of all nodes in the system  of metadata while allowing the peers to maintain ownership and locally control access to their data.Squid replicates the metadata at multiple nodes, for fault tolerance. Also, to avoid storing metadata for peers that left the system (voluntarily or due to failures), each metadata file indexed has an expiration date associated with it. If the owner of the metadata does not re-publish it before the expiration date, the entry is deleted from the system.Since peers typically run on dedicated machines, the machines will be likely to be robust and stay alive for longer periods of time, and the P2P system can become quite stable. While this property is not necessary for Squid, which can deal with peers joining, leaving and failing dynamically, it can be exploited to reduce the maintenance costs of the overlay network.The metadata extraction process is illustrated in Note that only the metadata is published in Squid, and not the actual data. In this way, the owners of the data may enforce access control on their data. However, the system does not provide access control on metadata.To support keyword searches, Squid uses the values of the XML tags as keywords. These values are the keywords used to store a metadata XML file in the overlay, and to search the system. Note that typically only a subset of the xml tags is used to construct an index. The idea is to construct indices using attributes that are frequently used in queries. Multiple indices may be created, using different sets of keys (possibly overlapping).n numbers, for example n can be 10 for numeric keywords or 26 for English words. An example of a 2-dimensional keyword space is shown in The keywords form a multidimensional keyword space, each axis (dimension) representing an XML tag. The metadata files are points in this space and the keywords are the coordinates. The keywords are base-Using the Hilbert SFC  from the same range. Squid uses the SFC mapping to create the data numerical indices. Each metadata file is mapped, based on its SFC index or key, to the first node whose identifier is equal to or follows the key in the identifier space. This node is called the successor of the key. An example of an overlay network with 5 nodes and an identifier space from 0 to 26–1 is shown in m neighbors, called fingers, in a finger table. The finger table is used for efficient routing. Chord essentially implements one operation, lookup(key), which routes the request to the peer responsible for storing the key. When a node receives a query for a key that is not stored locally, it routes the query to the node in its routing table that places the query closest to the destination. In Chord, a data lookup requires O (log N) hops, where N is the number of nodes in the system.In Chord each node has a unique identifier ranging from 0 to 2Publishing metadata in Squid consists of the following steps: (1) extract keywords from the metadata XML file; (2) use the SFC-mapping to construct the index from the keywords; and (3) using this index store the metadata at the appropriate node in the overlay. This node is located using the Chord’s lookup mechanism. The primary function of the query engine is to efficiently process user queries. The expected result of a query is the complete set of data elements that match the user’s query.The system is queried through a friendly graphical user interface (GUI). The user query is presented to Squid as an XML file. Squid parses the document, extracts the user query and resolves it. The results are presented to the user and consist of links to relevant data in databases maintained by hospitals, research centers, etc. The user can then contact the owners of the data to obtain required permission to access the data using the database portal. Note that the access to the data is outside Squid and is subject to the hospital’s (or research center’s) regulations. The queries can consist of a combination of keywords, partial keywords, or wildcards. An example of a possible user query is presented in If the query consists of complete keywords (no wildcards or ranges) it will be mapped to at most one point in the index space, and the node containing the matching data-element is located using the overlay’s lookup protocol. If the query contains partial keywords, wildcards and/or ranges, the query identifies a region in the keyword space, which corresponds to a set of points in the index space. For example, in Processing a query consists of two steps: Translating the keyword query to relevant clusters of the SFC-based index space, and querying the appropriate nodes in the overlay network for data-elements.Once the clusters associated with a query are identified, straightforward query processing consists of sending a query message for each cluster, using the lookup mechanism provided by Chord. This section presents an evaluation of Squid using both simulations and a prototype deployment. 1 The objectives of the experiments were to demonstrate that: (1) as the number of nodes in the system grows, the percentage of nodes with matches for a query approaches the percentage of the data matched, and (2) the optimization used by the query engine is successfully reducing the number of clusters that have to be generated for a query, and that the number of extra nodes involved in the process is small.This section presents simulation results that demonstrate the efficiency of the Squid query engine. Two sets of data were used in this evaluation: (1) synthetically generated data, uniformly distributed on the curve, and (2) real data consisting of world-wide publications collected by a search engine.The performance of Squid is evaluated using a simulator that implements the SFC-based mapping, the overlay network, the load-balancing steps, and the query engine with the query optimizations described earlier.All queries were issued on a load-balanced system where each node stored the same quantity of data. It was assumed that the system stores only the index , and not the actual data. Only unique data was used where each data element was described uniquely by keywords, and had a unique SFC index. Queries containing ranges, partial keywords and/or wild-cards were evaluated. The results were grouped by query coverage, i.e. the percentage of data matched, and the average was computed for each group.Note that the results presented here are independent of the overlay used. Only the nodes involved in query processing were measured. The additional nodes involved in routing the queries are overlay-dependant and are not presented here.24 cells and systems of sizes 103, 104, 105 and 106 nodes. The system was populated with 224 unique, synthetically generated data elements that completely populated the space and resulted in a uniform distribution.The first set of experiments used a 3-dimensional keyword space composed of 25 unique data elements. The experiments were carried out in a 3-dimensional keyword space, composed of 248 cells, and three system sizes with 103, 104 and 105 nodes.The second set of experiments was performed using real data. The system was populated with 4x1048 points, it is divided into 1000 bins in the plot. The x axis plots the bin number and the y axis plots the number of data elements in each bin. As the figure shows, the data is not uniformly distributed. Note that there are empty spaces on the curve, which are primarily due to the fact that the curve is in base 32 (the basic step of recursion is the base-2 Hilbert SFC refined 5 times) while the keywords using the English alphabet are in base 26. However, even if these empty spaces are ignored, the distribution is far from uniform.The distribution of the second set of data on the SFC curve is shown in x-axis plots the size of the system, and the y-axis plots the percentage of nodes with data matching the query. The plots show that the percentage of nodes queried decreases as the size of the system increases, and it approaches the percentage of data that the query matches.Three classes of queries were used in the evaluation providing coverage of 1%, 0.1% and 0.01% respectively. A query with a coverage of 1% matched 1% of the total data stored in the system. The same set of queries was used for each system size. The results are plotted in The experiments presented in this section evaluate the optimization strategies used by the query engine. Measurements included the number of clusters generated for a query and the number of nodes involved in resolving a query, with and without the optimization.4, only 0.35% of the clusters need to be generated for the first set of data  and Squid are implemented as event-driven JXTA services. The prototype system was deployed and evaluated on a Linux cluster consisting of 64 1.6 GHz Pentium IV machines and a 100Mbps Ethernet interconnection. Each of the 64 machines was running a peer in the overlay.The experiment measured the Squid overhead at a node. Three sets of queries were used, the first containing wildcards, the second containing ranges and the third containing both wildcards and ranges. The query processing overheads at the Squid layer were measured at each node and averaged. The results are plotted in Current research includes the development of an image-based metadata specification and combine these new feature measurements with existing and emerging data exchange specifications  for cancMuch of the difficulty in rendering consistent evaluation of expression patterns in cancer tissue microarrays is due to subjective impressions of observers and it has been shown that when characterizations are based upon computer-aided analysis, objectivity, reproducibility and sensitivity improve considerably. Advanced imaging and computational tools could potentially enable investigators to detect and track subtle changes in measurable parameters leading to the discovery of novel prognostic clues, which may not be apparent by human inspection alone. In modern cancer research large-scale projects involving tissue microarrays will inevitably involve multi-institutional efforts in which investigators and resources are spread out over multiple campuses, cities, and states. Using modern discovery tools it is now possible to enable individuals to automatically detect, locate and share experimental results and specimens from de-centralized sources distributed across the cancer research community. The distinguishing characteristics of the prototype system that we have described is that it can self-organize and through the implementation of a dimension reducing indexing scheme is able to effectively map multidimensional metadata information space to physical peers. Each peer (e.g. research institution) in this system maintains ownership of its own data and only publishes (in a controlled manner) metadata describing its data, which can then be discovered and searched externally. In the next phase of the project we will determine a definitive set of image-based feature measurements which are shown through robust statistical methods to best represent the under-lying pathology of the cancer tissues under study  and incorporate them with emerging TMA metadata standards. As part of our initial feasibility studies the TMA and Squid software was deployed at strategic sites at the University of Medicine & Dentistry of New Jersey (UMDNJ), Rutgers University (RU), and The Cancer Institute of New Jersey. In the next phase of experiments we plan to deploy and evaluate the TMA/Squid systems at strategic sites throughout UMDNJ, RU, CINJ and the University of Pennsylvania (UPenn), Arizona Cancer Center, and Penn State Cancer Institute.


Although most patients with severe acute hepatitis are conservatively cured, some progress to acute liver failure (ALF) with a high rate of mortality. Based on the evidence that over-activation of macrophages, followed by disturbance of the hepatic microcirculation, plays a key role in ALF, we hypothesized that the production of serum lactate dehydrogenase (LDH) might increase in the liver under hypoxic conditions and could be an indicator to discriminate between conservative survivors and fatal patients at an early stage.To confirm this hypothesis, we developed a new parameter with serum alanine aminotransferase (ALT) and LDH: the ALT-LDH index = serum ALT/. We analyzed retrospectively 33 patients suffering acute liver injury  and evaluated the prognostic value of the ALT-LDH index, comparing data from the first 5 days of hospitalization with the Model for End-Stage Liver Disease (MELD) score. Patients whose symptoms had appeared more than 10 days before admission were excluded from this study. Among those included, 17 were conservative survivors, 9 underwent liver transplantation (LT) and 7 died waiting for LT. We found a rapid increase in the ALT-LDH index in conservative survivors but not in fatal patients. While the prognostic sensitivity and specificity of the ALT-LDH index was low on admission, at day 3 they were superior to the results of MELD.ALT-LDH index was useful to predict the prognosis of the patients with acute liver injury and should be helpful to begin preparation for LT soon after admission. Acute liver failure (ALF) or fulminant liver failure is a disease characterized by abrupt onset and high mortality. Liver transplantation (LT) is the only effective treatment for ALF and many patients die before undergoing LT because of rapid progression of the disease ,2. ThereThe difficulty in predicting the prognosis of ALF is mainly attributable to incomplete elucidation of its mechanism. The most characteristic pathological finding of ALF is massive necrosis without regeneration, which implies the involvement a disturbance of the hepatic circulation in the progression of the disease. Although this idea is not novel and has not been regarded as important, we believe that it should be revisited, considering recent reports of over-activation of macrophages in the liver, which is believed to cause hepatic hypoxia as a result of disturbance of the microcirculation -12. AlthComparing the backgrounds on admission between LT or death cases and the conservative survivors, the former were older and had a higher proportion of patients with ascites and hepatic encephalopathy Table . The labThe serum ALT levels on admission were over 1000 U/L in 27 patients, and decreased quickly (23 patients) or remained steady (10 patients) during the first three hospital days. This finding also indicated that the periods from the onset of the disease to admission of the patients were relatively short. There was one patient who showed re-elevation of serum ALT after the third hospital day, triggered by HBV. There was no particular tendency of ALT transition during the first five days in the conservative survivors or the LT or death cases. On the other hand, the transition of the ALT-LDH index during the same period differed between the two categories: the index increased quickly in most of the conservative survivors while it tended to remain low in the LT or death cases  over 1.5 were enrolled into this study, amounting to 33 patients . LDH was assayed using an enzymatic rate method with lactate as the substrate (lactate-pyruvate direction). ALT assay was performed without pyridoxal phosphate supplementation. The normal ranges of ALT and LDH were 6–30 U/L and 119–229 U/L, respectively. We aimed to evaluate the increase of these enzymes above normal levels and developed a new index calculated by following formula:In acute liver injury, both serum ALT and LDH commonly decrease after the peak observed in the acute phase regardless of the prognosis. However, in the patients with fatal prognosis, the decrease of serum LDH is expected to delay compared with that of serum ALT, which would be caused by microcirculation disturbance in liver. Therefore, if we use the serum LDH value as a predictive marker of ALF, it should be evaluated under connection with the serum ALT value. Although the simple ALT/LDH ratio seems to be acceptable in evaluation of the serum activity of LDH connecting to ALT, it could not reflect the degree of those enzymes' elevation from normal level when they are in relatively low levels because of the difference of their normal ranges. On the other hand, the value of ALT-LDH index distinctly increases when the serum LDH decreases close to the normal range.For the enrolled patients, this index was calculated for the first 5 days from their admission, comparing the changes in serum ALT activity during the same period. According to the normal range of our assay system, the median of the serum LDH was calculated as 174 U/L in this study.Differences in clinical backgrounds and laboratory data between conservative survivors and fatal patients, including those who underwent LT, were analyzed using the Χ2-test and Student t-test. The utilities of the ALT-LDH index and MELD score were evaluated using receiver operating characteristic (ROC) curves. The sensitivity, specificity, positive and negative predictive values (PPV and NPV), efficiency, and area under the ROC curve were calculated for each indicator.The authors declare that they have no competing interests.KK conceived the design of the study and prepared the manuscript. ME and MK participated in the study design. MK analyzed clinical data. MN and RT drafted the paper. All authors read and approved the final manuscript.


There is a growing awareness of the need for easily administered, psychometrically sound screening tools to identify individuals with elevated levels of psychological distress. Although support has been found for the psychometric properties of the Depression, Anxiety and Stress Scales (DASS) using classical test theory approaches it has not been subjected to Rasch analysis. The aim of this study was to use Rasch analysis to assess the psychometric properties of the DASS-21 scales, using two different administration modes.The DASS-21 was administered to 420 participants with half the sample responding to a web-based version and the other half completing a traditional pencil-and-paper version. Conformity of DASS-21 scales to a Rasch partial credit model was assessed using the RUMM2020 software.To achieve adequate model fit it was necessary to remove one item from each of the DASS-21 subscales. The reduced scales showed adequate internal consistency reliability, unidimensionality and freedom from differential item functioning for sex, age and mode of administration. Analysis of all DASS-21 items combined did not support its use as a measure of general psychological distress. A scale combining the anxiety and stress items showed satisfactory fit to the Rasch model after removal of three items.The results provide support for the measurement properties, internal consistency reliability, and unidimensionality of three slightly modified DASS-21 scales, across two different administration methods. The further use of Rasch analysis on the DASS-21 in larger and broader samples is recommended to confirm the findings of the current study. According to the World Health Organisation (WHO) mental illness is prevalent, in all strata across all countries and societies . DisordeThe importance of recognising and treating depression and anxiety cannot be understated as these conditions can result in a substantial reduction in perceived quality of life. This may manifest as restricted participation in the workplace, reduction in general health and dissatisfaction in family or social life -6. IndivIn addition to the personal burden associated with depression and anxiety, there are also substantial financial costs to the community ,12. DireThe impact of untreated depression and anxiety on the ability to function is reported to be equal or greater than that of other common health problems such as heart disease or arthritis . Timely Several scales have been developed for the purpose of measuring depression and anxiety. These include the Beck Depression Inventory (BDI) , the BecSubsequent studies have used factor analytic techniques to investigate the underlying structure of the DASS. The results from confirmatory factor analytic studies indicate that the original three factor structure rarely meets current standards regarding good model fit. The fit statistics reported by Lovibond and colleagues  fall belThe psychometric evaluation of the DASS to date has been conducted within the framework of classical test theory. Over the last ten years however there has been a growing awareness in the health and psychological sciences of modern test theory approaches, such as those based on the Rasch measurement model. Rasch analysis allows a detailed investigation of many aspects of a scale including the response format, the fit of individual items and persons, dimensionality, targeting, and the detection of item bias. Testing for differential item functioning is particularly important as it allows researchers and clinicians to ensure that items function uniformly across age, gender or, for example, different scale administration methods, at all difficulty levels.The internet has been used to raise awareness of mental illness in the general public by providing self-administered tests online. As a result, scales such as Web-Based Depression and Anxiety Test  and the Research investigating the use of web surveys across a variety of disciplines has suggested that this method of data collection can be cost effective, decreases turn-around time for data collection  and provDespite these advantages, problems such as missing data, differences in mean scores between web-based and pencil and paper surveys and a potentially biased sample due to the need for internet access  have beeIn addition to the practical psychometric issues associated with online surveys, another concern raised in the literature is whether participant response styles vary between administration methods . Some auOnly one study thus far has used a web-based data collection method for the DASS-21 . Wong anWhile there have been a number of studies finding support for the psychometric properties of the DASS-21 no studies to date have used Rasch analysis. The first aim of this study was therefore to apply Rasch analysis to the three DASS-21 subscales to conduct a detailed assessment of the response format, item fit, dimensionality and targeting. The suitability of using all items of the DASS-21 as a measure of general psychological distress was also explored. Finally, Rasch analysis was used to determine whether the web-based version of the DASS-21, compared to a pencil-and-paper version, would introduce item bias due to the use of an alternative administration technique.Respondents were recruited to complete either a pen-and-paper version or a web-based version of the DASS-21 in a large stress resilience study of 745 participants. The web-based respondents were recruited from a variety of organizations  in Melbourne, Australia. The pen-and-paper sample was obtained by inviting the staff, students (and associated family members) of Swinburne University, Melbourne, to complete a questionnaire booklet. In this study 210 respondents completed a pencil and paper questionnaire and 535 respondents completed a web-based questionnaire. The present study consists of the 210 respondents who completed the pen and paper version of the DASS-21 and a random selection of 210 respondents from the original 535 respondents who completed the web-based version. This was done to ensure an equal number of respondents who had completed the pen-and-paper and web-based versions of the DASS-21 to comply with the requirements of the ANOVA based analysis for invariance of the items across groups. The questionnaires were anonymous and no financial remuneration was offered to participants. The study was approved by the Swinburne University Ethics Committee.The short version of the DASS (DASS-21) was used in this study . It consThis scale was developed by Lovibond and colleagues  and has Rasch analysis in this study was conducted using RUMM 2020 . The DASThe process of Rasch analysis is described in detail elsewhere ,50. Brieand function in the same way for each item [Initially, because the DASS-21 has polytomous response options, a likelihood ratio test was conducted for each subscale to determine whether it was more appropriate to use the Rating Scale version of the model  or the Pach item . Consequach item .Several tests of fit were used in the current study including overall summary tests of fit, as well as individual item and person tests. The main aim of these tests was to show that the responses do not deviate from Rasch model expectations. Thus a summary chi-square interaction fit statistic should be non significant, as should the individual item chi-square statistics after Bonferroni adjustment to the alpha level. This adjustment is required because multiple tests are performed, one for each chi-square statistic for each item . ConsequThe standard deviation of the summary residual statistic should also not deviate too much from 1 (perfect fit), and certainly should not be above 1.5. Individual item residuals should fall within the range ± 2.5 (99% confidence level). High positive fit residual values indicate misfit, while high negative fit residuals suggest item redundancy. Items were also examined for Differential Item Functioning (DIF) across subgroups within the sample  using analysis of variance with a Bonferroni adjusted alpha level.The three DASS-21 scales, and the DASS-21 as a measure of general psychological distress, were evaluated to determine how well targeted the items were for the sample and to assess whether the individual scales and the DASS-21 as a whole represented unidimensional constructs. Unidimensionality was tested using the approach suggested by Smith . Person The sample consisted of a total of 420 respondents with 210 (50%) completing the pencil and paper version of the DASS-21 and 210 (50%) completing the web-based version of the scale. In each group approximately one third of the sample were males  improved model fit (p = .03)  (see Table it p = .0 (Table 3I experienced breathing difficulty) and seven (I experienced trembling e.g. in the hands) displayed disordered thresholds however rescoring of these items did not improve model fit so original scoring was retained. Three items  had significant chi-square probability values and item 15 (I felt I was close to panic) had an extreme negative fit residual value (-2.57). The deletion of item 2 (I was aware of dryness of my mouth) resulted in an improved overall model fit (p = .02) and item fit residuals  (p = .01). Although item 12 recorded a significant individual chi square value (p = .007) removal of this item resulted in a decrease in overall model fit (p = .002), therefore it was retained    Table . No diff Table . Depression screening instruments are now widely used in both clinical practice and research. Increasingly the scales have been scrutinized by a mix of classical and modern psychometric approaches. The purpose of the present study was to use Rasch analysis to assess the psychometric properties of the DASS-21 scales, and specifically to evaluate two different administration methods . The possibility that the combined items of the DASS-21 could represent a valid measure of general psychological distress was also investigated.I found it difficult to work up the initiative to do things) from the Depression scale, item 2 (I was aware of dryness of my mouth) from the Anxiety scale and item 11 (I found myself getting agitated) from the Stress scale. All revised scales showed adequate internal consistency, and no evidence of multidimensionality. There was no differential item functioning by administration method for any of the scales showing that the items are invariant across mode of administration, and therefore comparable.Initially none of the three DASS-21 scales satisfied the criteria for fit to the Rasch model. To achieve satisfactory fit it was necessary to remove item 5  or presRemoving items from well established scales is contentious, particularly those which have established clinical cut points and thus where this would change with a reduced item set . This maReducing existing item sets, as in the case of the reduction of the DASS-42 to the DASS-21 is an important issue. Modern psychometric approaches may give a different view of item reduction, and thus different short-forms may emerge as a result. This has the potential for causing confusion for users. Unfortunately, where short forms are shown to fail modern psychometric standards this becomes problematic and decisions may have to be taken to reduce the item set further  or hope that the misfit to the model will not bias results of the original scale in any meaningful way. Given just one item was removed from each subscale, it would seem that at the present time the existing structure of the scale, while not ideal, may suffice in an ordinal scale format until such a time that further evidence accrues to suggest a revision of the item content. Where a transformation to interval scaling is required, then the current solution derived from the Rasch model is most appropriate. Another option would be to revisit the original full scale (DASS-42) and see if another short set of items would better satisfy modern standards.The proposition that the combined items of the DASS-21 could be used as a measure of general psychological distress  was not Although it was not found to be appropriate to combine all three subscales, additional analysis in the current study suggested that the anxiety and stress subscales could form an anxiety-stress continuum. While it was necessary to remove three items from this combined scale, the model showed adequate fit, good internal consistency and no evidence of multidimensionality. This proposal is not consistent with the structure proposed by Duffy and colleagues  who suggThere are limitations to the current study. The targeting of the sample was less than desirable, in that significant floor effects were observed for the anxiety and depression subscales. Although this may simply reflect the distribution of symptoms in the general population, and is not a problem for a screening instrument, it can affect the precision of the item estimates, particularly items representing severe levels of anxiety and depression, where relatively few respondents were found. However, Linacre  has showThis was the first study to undertake a rigorous examination of the psychometric properties of the DASS-21 using Rasch analysis and to assess item bias by mode of administration (pen and pencil versus web-based). The results provide support for the measurement properties, internal consistency reliability, targeting, and unidimensionality of the three DASS-21 scales. However it was necessary to remove one item from each of the scales to achieve fit to the Rasch model. No differential item functioning was found for sex, age, education or mode of administration. The summation of all items to form a total scale representing general psychological distress was not supported, however a scale combining anxiety and stress items showed adequate psychometric properties. Further examination of fit of data from the DASS-21 to the Rasch measurement model in larger and appropriately targeted samples is recommended to confirm the findings of the current study.DASS: Depression, Anxiety and Stress Scales; DIF: Differential Item Functioning; EFA: Exploratory Factor Analysis; PCA: Principal Components Analysis; PSI: Person Separation Index.The authors declare that they have no competing interests.TS conducted the literature review, performed the data analysis and prepared the first draft of the manuscript. AT participated in the data analysis and preparation of the manuscript. JP designed the study, collected the data, and supervised the data analysis and preparation of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: