Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs. Our method takes the topology of the input graphs and the specificity and relevance of associations between nodes into consideration. We apply our method to the extraction of associations between biomedical ontologies in an extensive use-case. Through a manual and an automatic evaluation, we show that our tests discover biologically relevant relations. The suite of statistical tests we develop for this purpose is implemented and freely available for download. An increasing number of discoveries, particularly in biomedicine, are facilitated by statistical analyses of data annotated to biomedical ontologies Statistical tests that utilize a single graph can only consider the given domain. However, entities from different domain are linked via biomedical relations We evaluated our statistical method through an extensive use-case in which we applied our tests to the detection of strong semantic associations between the Gene Ontology http://bioonto.de/pmwiki.php/Main/ExtractingBiologicalRelations.An implementation of the six novel statistical tests to identify associations between directed acyclic graphs is available as free software from our project webpage at Our approach to the computation of the strength of the association between two graphs relies on approaches for capturing the semantic similarity between categories in ontologies and for propagating these similarities within DAGs. In the following, we give a brief overview of methods for computing the similarity of categories .Within this section, let the score between the difference between the difference between “Being high” and “being low” are captured using the values of the cumulative distribution functions (CDFs) obtained by the Formally, let For each child All other tests are extensions of the first test. The second test, The first two tests We define three new distributions for the variances and choose the The tests To verify whether the tests we designed yield reasonable results, we applied our method to the detection of significant co-occurrences between ontological categories in natural language texts, as a precursor to the detection of relations between ontological categories. For this purpose, we make the following assumptions:A term occurs in a portion of text if it is an exact substring of this portion of text.occurrence ofTerms can designate ontological categories; the terms that designate the same category are henceforth called the category's synset. Every occurrence of an element of the category If To test our method, we used the Gene Ontology (GO) Based on the input requirements of our test, we constructed synsets from the synonyms attached to each category in the input ontologies, and counted the occurrences and co-occurrences of the categories based on two contexts: single sentences and sentences in documents. The second context refers to whole documents, but co-occurrence is based on single sentences. Therefore, when two terms co-occur in two or more sentences within one document, their co-occurrence is only counted once. The functions that assign the occurrence and co-occurrence count values to a synset of a category for each context are called We used exact string matching to identify terms in text. Our evaluation was conducted using a 2.2 GB text corpus containing 60143 fulltext articles from Open Access journals listed in Pubmed Central. The aim of our method is to test for significant co-occurrences between categories.First, we counted the number of occurrences and co-occurrences of the terms contained in synsets of categories from the input ontologies. The text processing yielded, for each category The first component in our method implements the assumption that the input graph decorations are transitive over the DAG structure. In the case of ontologies, this implements the assumption that occurrence and co-occurrence between categories is transitive over the We assumed that when two categories For all categories We applied the tests to the biological process (BP) branch of the GO and the CL. To recognize the categories in text, we used the identifier of the category, the name and all exact synonyms of the category. On average, every category had 2.1 synonyms. Using exact matching, we identified 3,751 out of BP's 14,542 (26%) categories in our text corpus. We found 491 of 754 (65%) categories from the CL. Categories from the BP co-occurred 70,967 times with CL categories.Using our method, we identified a total number of 202,627 co-occurrences between categories. After applying our tests, 157,894 co-occurrences produced test values distinct from We found that the tests using the minimum instead of the geometric mean of has-participant relation is defined in the OBO Relationship Ontology (RO) Participates-in relation as a relation between two categories: Participates-inparticipates-in is the primitive participation relation between individuals as defined in the RO. We extend the definition of located-in in the RO to a relation Located-in between processes and objects, which holds when all participants of a process are located-in a structure during the entire duration of the process.xanthine transport and erythrocyte are closely related according to xanthine transport refers to the inter- and intracellular level of granularity, while erythrocytes transport nutrients between organs. Second, some categories are indirectly related via another category. For example, osteoclasts and lymph node development are related via the protein RANK. Third, when cells have closely related functions, we sometimes identify too specific or too generic cell types as in the case of the association between basophil degranulation and mast cell. Finally, In our sample, We were not able to compute precision or recall for our method due to the absence of a gold standard. However, we compared our method with the GO-CL crossproducts available from the OBO Foundry. The dataset contains manually verified relations between categories from the GO and the CL that have been extracted using pattern matching on category names The GO-CL crossproduct contains 396 relations between GO and CL categories. From these 396, we identified 73 that co-occurred in our text corpus. myoepithelial cell (cells located in the mammary gland) and milk ejection.As our method relies exclusively on the distribution of terms and not on their syntactic structure, it permits the recognition of associations between categories that cannot be recognized using syntactic patterns. An example of such an association is is-a or part-of relation, then any annotation of Important potential applications for our tests arise from the fact that annotations of a large set of biomedical ontologies satisfy the conditions for our tests. Annotations satisfy the True Path Rule Another potential application of our tests lies in the field of relation extraction. The evaluation of our tests with the GO and CL reveals that we are able to detect biologically relevant associations between these ontologies. Our method is designed for the detection of associations between two DAGs. However, it can be generalized to test for associations between We developed a family of novel statistical tests for associations between two directed acyclic graphs. The tests account for the graphs' topologies and test for relevance and specificity of associations. The tests are suitable for the detection of associations between categories from two biomedical ontologies, in particular those which comply with the OBO criteria In an extensive use-case, we applied our tests to the discovery of associations between categories from the Gene Ontology and the Celltype Ontology that were decorated with the number of occurrences and co-occurrences of the categories' labels in a large corpus of full-text articles. Our results show that a large proportion of the associations discovered by our tests are biologically relevant relations.http://bioonto.de/pmwiki.php/Main/ExtractingBiologicalRelations.The family of tests is implemented in a Java library, which is available as free software from our project webpage at Supplement S1Statistical tests for associations between two directed acyclic graphs and their application to biomedical ontologies.(0.14 MB PDF)Click here for additional data file.


These data represent multiple aspects of a biological system and need to be investigated as a whole to shed light on the mechanisms which underpin the system functionality.Present-day The gathering and convergence of data generated by high-throughput technologies, the effective integration of different data-sources and the analysis of the information content based on comparative approaches are key methods for meaningful biological interpretations.In the frame of the International Solanaceae Genome Project, we propose here ISOLA, an Italian SOLAnaceae genomics resource.) represents a trial platform and it is conceived as a multi-level computational environment.ISOLA .ISOLA currently consists of two main levels: the genome and the expression level. The cornerstone of the genome level is represented by the The cross-talk between the genome and the expression levels is based on data source sharing and on tools that enhance data quality, that extract information content from the levels' under parts and produce value-added biological knowledge.‘-omics’ data based on effective integration to acquire biological knowledge and to approach a systems biology view. Beyond providing experimental biologists with a preliminary annotation of the tomato genome, this effort aims to produce a trial computational environment where different aspects and details are maintained as they are relevant for the analysis of the organization, the functionality and the evolution of the Solanaceae family.ISOLA is the result of a bioinformatics effort that addresses the challenges of the post-genomics era. It is designed to exploit  The Solanaceae family comprises about 95 genera and at least 2,400 species. Many of these species have considerable economic importance as food , drug (tobacco) and ornamental plants (petunia). The Solanaceae species show a wide morphological variability and occupy various ecological niches though they share high genome conservation. The need of increasing the knowledge on the genetic mechanisms which determine Solanaceae diversification and adaptation has brought the scientific efforts to be gathered into the International Solanaceae (SOL) Genome Project .Solanum lycopersicum, is the plant chosen by the SOL initiative for a BAC-by-BAC sequencing of the genome euchromatin portion, which is considered the gene rich region and represents the 25% of the entire genome [The cultivated tomato, The long term goal is to exploit the information generated by the Tomato Genome Sequencing Consortium  for the The major bioinformatics effort within the international project currently aims to provide a high quality and homogeneous annotation of the tomato genome. To this end, the international Tomato Annotation Group (iTAG) organized an annotation pipeline which is parcelled out among different members of the consortium and which will result in the release of the annotated tomato genome.As members of the iTAG, we are committed to collect and manage Expressed Sequence Tag (EST) data from different Solanaceae species and to generate alignments of native and non-native ESTs to the tomato genome.‘-omics’ approaches are being generated to address key questions risen by the SOL vision. Raw data are hardly useful as they stand and need to be converted into biologically meaningful information. Therefore, bioinformatics approaches become pre-eminent, though their results may be far from being exhaustive and complete. The success of bioinformatics is directly dependent on the efficiency of integration, which in turn is determined by the diversity of data sources, the quality of their annotation and the level of details of the information produced. This remarks the need of designing computational platforms to support research in the post-genome era. The Solanaceae family represents a suitable context to face these challenges and to optimize and tune up the existing bioinformatics methods. To this end, we designed a trial platform, ISOLA , for exploiting Solanaceae ‘-omics’ data to address different and complex biological questions crossing the ‘-omics’ barrier for exploring the data as a whole.Furthermore, large amounts of data from different In addition, such an effort is going with the iTAG tomato genome annotation, and will maintain details and features that will be reconciled into, or will be discarded by, the official annotation, but that may still highlight useful information. Meanwhile the tomato genome initiative is under way, this platform provides a preliminary annotation of the tomato genome and makes available to the plant community tools for its structural, functional and comparative analyses.Here we present ISOLA, an Italian SOLAnaceae genomics resource available at . ISOLA iISOLA is currently organized into two main levels: the genome and expression levels Figure . The cor‘Basic’ tools are designed and included into the multi-level environment for enhancing data quality and increasing data information content. ‘Subsidiary’ tools lay over the existing multi-level environment exploiting the synergy between the levels.Each level can be independently accessed through specific Web applications which allow user-driven data investigation and permit overall as well as detailed views of specific information Figure . A non-sHerein, we describe the organization and the maintenance of the platform which has been designed in order to be extended, through pre-defined entry points, to the proteome and metabolome levels.S. lycopersicum BAC sequences from the GenBank repository, which are used to feed the genome annotation process. The current collection (May 2007) comprises 129 BAC sequences.An automated pipeline has been implemented in order to ensure a daily retrieval of new S. lycopersicum genome sequences.The BAC annotation process aims to identify coding regions and other genetic elements along the ‘gene finding’ process is exclusively based on the EST spliced-alignments to the genome sequences. To accomplish this task, ESTs from different plant sources (Solanaceae and Rubiaceae species), and the corresponding tentative consensus sequences (TCs), which have been generated by assembling ESTs in a cluster [ESTs/TCs mapped) report the number of ESTs and TCs per species, aligned to the 129 BAC sequences. ESTs of non-native origin  are included in the analysis so to improve detection of coding regions which lack source-native EST evidence and support comparative approaches.The protein coding  cluster , are useloci. They represent 10 distinct RNA types whose occurrence and distribution are reported in table Non-coding RNAs (ncRNAs) from the Rfam collection  are aligS. lycopersicum genome.The TIGR Solanaceae Repeats database  is the rloci. All the genomic regions identified, unless the one detected on the BAC AC171733 (26388::27727) and labelled as unclassified, corresponds to the transposable element (TEs) superclass. Among these 66 are retrotransposons, while the remaining 4 are members of the transposon class. Considering the retrotransposon class, 6 of them are unclassified, 20 are Ty1-copia and 40 are Ty3-gypsy. As usual in plants genomes [The repeats identified on the 129 BAC sequences are listed in Additional file  genomes , the traThe BAC sequences collected in ISOLA are annotated and released to the scientific community through the Gbrowse  Web applArabidopsis thaliana is a cornerstone for plant biology [S. lycopersicum BACs.The availability of the full genome sequence of  biology ,13. We aloci. The sole AT3G08520 sequence, annotated as “a structural constituent of ribosome”, overlaps S. lycopersicum ESTs in correspondence of two different BACs assigned to the chromosome 7. This indicates that, given the large phylogenetic distance between tomato and Arabidopsis, mRNA sequences are hardly identified when the RNA to genome alignments are filtered out with 90% identity and 80% coverage (See Methods).The majority of the RNA sequences (324) do not overlap any genome region covered by Solanaceae ESTs. The RNA sequences are annotated as tRNAs and locate 24 distinct gene S. lycopersicum transcripts [The Tomato Genome Array is designed specifically to monitor gene expression in tomato and other Solanaceae species . The comnscripts .To date, 4,445 out of 112,528 probes are mapped to the tomato genome. Because some probes are aligned to BAC sequences more than one time, the number of the matches identified  is higher than the number of the distinct probes aligned.S. lycopersicum EST. On the other hand 197 probes are in genomic regions where non-native ESTs have been aligned: S. tuberosum (164 probes), S. habrochaites (12 probes), S. chacoense (12 probes) and both S. habrochaites and S. tuberosum (9 probes). 483 oligonucleotide probes do not overlap any EST.In particular, 680 oligonucleotide probes do not overlap any We collected all the EST data from Solanaceae species available in dbEST  Table 11. In parbasic tool has been designed to remove over-represented EST sequences from each of the 16 collections in order to clip the original datasets and produce non-redundant EST collections (Table tentative consensus sequences (TCs) per putative transcript and ii) determine a preliminary functional annotation of both ESTs and TCs.A specific S. lycopersicum and S. tuberosum.The EST database architecture is relational. The database stores raw EST sequences and library details; clustering information as well as all the features which describes EST-alignments within clusters. The EST set in a cluster can be assembled into multiple TCs  so that S. lycopersicum species are compared to i) the Affymetrix Tomato Genome Array probe-sets [ESTs from obe-sets  and ii) obe-sets .Of the 112,528 probes from the Affymetrix Tomato Genome Array, 101,743 have at least one match with an EST sequence. However, all the matches accounts for 735,124 hits.Considering the 17,015 TOM1 cDNA clones, 9,696 are the number of those sequences that align at least to one EST sequence. The total number of hits is 1,057,491.subsidiary tool in ISOLA.The higher quality collections obtained by the local data processing are organized into dedicated repositories and are provided to the community as value-added data both through Web applications and FTP services. ISOLA provides: i) the collections of non redundant Solanaceae ESTs and the corresponding computationally defined transcripts, used to sample the species-specific transcriptome space; ii) the definition of the Solanaceae proteomes, including functional annotations and the open reading frame detection, enriching the still poor collection of Solanaceae proteins available from general databases; iii) gene models from tomato, necessary to increase the meagre gene information available to build a training set for gene predictors -22. In oexpressed’ loci are considered to select highly confident gene models. Furthermore, the software exploits the TC functional annotations stored in the EST databases, to check if the ‘expressed’ loci represent full-length products. Overlapping TCs are explored as they could represent alternative transcripts but are neglected from the selection of gene models because they provide ambiguous information.GeneModelEST requires the genome coordinates of the spliced-alignments of ESTs and TCs which have been independently aligned along the tomato genome draft sequences. Hence, GeneModelEST detects non-overlapping TC sequences which are consistently supported by EST alignments and therefore are evidence of expressed genome regions. These ‘S. lycopersicum TCs have been selected because consistently supported by EST evidence [loci are located. The number accordingly increases to 262 loci if the potato TC sequences are also evaluated.In the current update of ISOLA, 339 evidence  and are Only the TCs covering the 95% of the length of the matching protein are selected as reliable gene models for the training of gene predictors. They account for a total of 111 gene models.The success of bioinformatics approaches is directly dependent on the efficiency of data integration and on the value added information which it produces. This is, in turn, determined by the diversity of data sources and by the quality of the annotation they are endowed with.In the light of these significant issues and in order to approach a systems biology view, we designed a trial computational environment in the framework of the International Solanaceae Genome Project.‘-omics’ technologies are producing in a quest to investigate multiple aspects of a biological system and tune up bioinformatics methods for novel challenges.ISOLA represents an Italian resource for the genomics of the SOLAnaceae family. It is conceived to collect the overwhelming amount of data that the ISOLA consists of data and tools, and aims to meet the need to create a core collection of information from worldwide efforts in order to transform experimental data into biological knowledge. This implies the major effort of enhancing data quality efficiently and of exploiting data integration.ISOLA is conceived as a ‘data warehouse’ which summarizes information collected periodically from other data systems as well as value-added information generated by the local data processing. The ‘data warehouse’ connects to the external databases from which data are selectively downloaded Figure .The biological semantic is represented in ISOLA through a multi-level environment which currently consists of two main levels: the genome and the expression levels.basic tool ParPEST and provide an estimation of the species-specific transcriptome space. The protein-based EST annotation together with the open reading frame detection represents an entry point to plague into the platform data collected from proteomics efforts.The expression level is mainly represented by EST data collected from libraries of 16 different plant species. These collections are processed by the The EST-based collections are organized so to offer the possibility to investigate on different libraries from different tissues and at different developmental stages. The data/tools design permits the study of species-specific expression patterns and their time course, in normal or pathological conditions and/or under specific biotic or abiotic stimula, exploiting the large amount of cDNA libraries today available for tomato, potato and other Solanaceae species. In addition, the link to the KEGG metabolic pathways let metabolome data be integrated in ISOLA so to further support the definition of expression patterns.Furthermore the genome level is enriched with the annotation of all the gene types (mRNA and non protein coding RNAs), the repeats (simple and complex ones) and other relevant features. In particular, the integration with the wealth collection of pre-annotated data derived from ESTs represents a valuable resource for an efficient analysis and functional annotation of the daily uploaded BAC sequences. In addition, it permits genome-based comparative analyses of the Solanaceae transcriptomes.ab initio gene predictors. Gene models represent a value-added information that can be elicited from the effective integration of the main levels of the platform. As an example, GeneModelEST, a subsidiary tool in the platform, collects putative alternative transcripts from the ESTs/TCs aligned to the genome and automatically selects high reliable gene models. The availability in ISOLA of expressed sequence collections from other Solanaceae species also contributes to enrich the number of possible coding regions in the tomato genome and to support comparative genomic studies.To provide a well-founded genome annotation, we are considering only evidence from experimental approaches or from reference databases, neglecting results from gene predictions. Indeed, gene predictors still need to be trained on a consistent set of tomato gene models. The definition of a good quality and representative data set of gene models is one of the tasks of the international Tomato Annotation Group and a preliminary requirement for the training of The mapping of oligonucleotide probes to the tomato genome as well as to the transcriptome is an entry point to plague into the platform data from expression profiling arrays. This corroborates gene expression studies and supports as the cross-validation of expression patterns derived from microarray analysis, as the detection of network of co-expressed genes derived from EST-based investigations.The probes-to-genome mapping provides also support for their evaluation. Indeed results demonstrate the non-specificity of some probes and also highlight the presence of probes which are not confirmed by experimentally defined transcripts.ISOLA can be accessed through two different gateways: the Genome Browser gateway which permits investigations on the BAC sequences and the EST databases gateway for exploring the EST/transcript resources by friendly and flexible interfaces. Both gateways provide access points to the respective levels which are, in turn, cross-linked to support the Web-based navigation Figure .One of the major efforts in ISOLA is to maintain details of information from all the analytical processes that are part of the platform. This with the purpose to further support the user-driven investigations and the biological knowledge discovery. As an example, in the expression level, raw EST sequence data are maintained to provide the user with information concerning the quality of the cDNA libraries. In order to permit investigations on putative alternative transcripts, the organization of each cluster (made up of ESTs which tag the same gene) into multiple TCs is available. This provides added value that will further support genome investigations.ISOLA is based on the collection of the large amount of data produced in the frame of the Solanaceae Genome Project. It represents an Italian effort to integrate heterogeneous data at the semantic as well as at the data source levels. It aims to enhance the quality of the data gathered and to self feed by the value-added information produced.The platform is becoming a reference within the Solanaceae Genome Project.ISOLA is daily accessed from scientists from different countries because it provides a preliminary annotation of the tomato genome, while awaiting for the official annotation by the international Tomato Annotation Group. Furthermore, the platform collects and distributes the Solanaceae transcripts, provides their functional annotation and classification, and allows investigations on genome functionalities on the basis of EST supported expression pattern analysis.Since ISOLA is designed as a multi-level computational environment, it is thought to be flexible and to easily evolve in consideration of the continuous production of new data and novel methods. In addition, ISOLA meets the need to collect, integrate and explore high-throughput biological data in the context of various experiments from multiple organisms. This surely will support successful analysis based on comparative approaches.Among different platforms for plant genomics, ISOLA represents a novel effort where EST-based functional information is cross-linked to genome data and vice versa, and details are maintained because they are relevant in revealing still hidden biological aspects. Therefore, we believe that the approach here proposed, aimed to support investigations on the structure, the function and the evolution of the Solanaceae genomes, could represent a suitable test bench for similar challenges.1. BAC sequences were downloaded from the GenBank repository.2. EST sequences were downloaded from the dbEST division of the NCBI.. The collection includes 45,644 sequences.3. FASTA sequences of Rfam members (version 8.0) were downloaded at . The file is a collection of 252 repetitive sequences reporting transposable, telomere-related, rDNAs and unclassified elements. No centromere-related repeats are included in the collection.4. The file TIGR_Solanaceae_Repeats.v2 was downloaded from the ftp server . The collection includes 112,528 oligonucleotide probes.5. The Tomato Affymetrix probe sequence file was retrieved from 6. The TOM1 cDNA microarray sequences data were kindly provided by Jim Giovannoni on March 2006. The collection includes 17,015 sequences.Arabidopsis thaliana. A total of 31,249 RNA sequences were retrieved.7. The files NC_003070.frn, NC_003071.frn, NC_003074.frn, NC_003075.frn and NC_003076.frn were downloaded from the genome session of the NCBI ftp server in order to collect all the RNA from The automated pipeline, that downloads and annotates the BACs which are released to GenBank from the Tomato Genome Sequencing Consortium, is composed of several Perl scripts. Each script enables a specific task inside the pipeline. Using BioPerl an Entrez query is formulated to the NCBI web services system. The query is daily performed to select all the tomato BAC sequences to be retrieved as they represent novel submissions to GenBank or modified records. The pipeline checks, via BAC identifier (i.e. accession.version), if the sequences are already annotated and deposited into the local Genome Browser Database. The retrieved BACs are used to feed into the annotation pipeline.The mapping of ESTs/TCs, cDNAs, RNAs and expression array probes is performed by GenomeThreader . The parThe identification of simple and complex repeats is performed by RepeatMasker  using thWe used ParPEST  to pre-pESTs and TCs are collected into dedicated repositories whose features are described in . The annISOLA is available at The authors declare that they have no competing interests.MLC conceived the project, directed its design and implementation, coordinated the different efforts and wrote the manuscript; NDA was mainly involved in the development, the organization and the maintenance of the EST databases and its basic tools and contributed to write the manuscript; AT was mainly involved in the development and maintenance of the Genome Browser database and its basic tools; CL contributed in the data integration effort; ER contributed in the implementation of some tools; MA supported the organization of the EST database and the technical aspects of the platform; LF and LM contributed to the design and the realization of the project. All authors read and approved the final manuscript.Title: Occurrence and distribution of repetitive DNA in the 129 tomato BACs. Description: The Table includes the chromosome number the BAC GenBank accession numbers the start/end positions for the BAC regions covered by TIGR Repeats the TIGR Repeat class and sub-class and the total number of repetitive DNA sequences per BAC region.Click here for file


Rates of hospital-acquired infections, such as methicillin-resistant Staphylococcus aureus (MRSA), are increasingly used as quality indicators for hospital hygiene. Alternatively, these rates may vary between hospitals, because hospitals differ in admission and referral of potentially colonized patients. We assessed if different referral patterns between hospitals in health care networks can influence rates of hospital-acquired infections like MRSA. We used the Dutch medical registration of 2004 to measure the connectedness between hospitals. This allowed us to reconstruct the network of hospitals in the Netherlands. We used mathematical models to assess the effect of different patient referral patterns on the potential spread of hospital-acquired infections between hospitals, and between categories of hospitals . University hospitals have a higher number of shared patients than teaching or general hospitals, and are therefore more likely to be among the first to receive colonized patients. Moreover, as the network is directional towards university hospitals, they have a higher prevalence, even when infection control measures are equally effective in all hospitals. Patient referral patterns have a profound effect on the spread of health care-associated infections like hospital-acquired MRSA. The MRSA prevalence therefore differs between hospitals with the position of each hospital within the health care network. Any comparison of MRSA rates between hospitals, as a benchmark for hospital hygiene, should therefore take the position of a hospital within the network into account. The prevalence of hospital acquired infections is widely believed to reflect the quality of health care in individual hospitals, and is therefore often used as a benchmark. Intuitively, the idea is that infections spread more easily in hospitals with a poor quality of health care. This assumes that the rate at which admitted patients introduce new infections is the same for all hospitals. In this article, we show that this assumption is unlikely to be correct. Using national data on patient admissions, we are able to reconstruct the entire hospital network consisting of patients referred between hospitals. This network reveals that university hospitals admit more patients that recently stayed in other hospitals. Consequently, they are more likely to admit patients that still carry pathogens acquired during their previous hospital stay. Therefore, the prevalence of infections does not only reflect the quality of health care but also the connectedness to hospitals from which patients are referred. This phenomenon is missed at the single hospital level; our study is the first to address the connectedness between hospitals in explaining the prevalence of hospital acquired infections. Our findings imply that interventions should focus on hospitals that are central in the network of patient referrals. Pathogens that typically cause hospital-acquired infections have an opportunistic nature. These organisms are usually part of the normal bacterial flora of humans and only cause disease when reaching body sites that are normally free from bacterial colonization e.g. when anatomical barriers are breached due to trauma or medical/surgical interventions. For this reason, severe problems with nosocomial pathogens are mainly seen in the very young and elderly and most frequently in institutions such as hospitals and long-term care facilities where patients are treated for acute or chronic conditions.Staphylococcus aureus (MRSA) is an antimicrobial resistant variant of S. aureus, a common bacteria frequently colonizing healthy humans and animals. Emergence of MRSA is due to the acquisition of a large DNA fragment, which seems to be rare Methicillin-resistant S. aureus causing bloodstream infections in the UK is resistant to methicillin, against only 1% in the Netherlands and Scandinavian countries The prevalence of the MRSA differs considerably within and between countries Patients can carry MRSA, asymptomatically, for a long time In 2004, hospital care in the Netherlands was provided through 71 general hospitals, 19 top clinical hospitals and 8 university medical centres . During The individual-based model emulated the dynamics of patient referrals and allows us to assess the spread of hospital-acquired infections. Colonized patients from one hospital spread the pathogen to nearby hospitals within days, but it takes more time –5 to 10 years– before all hospitals encounter it . The medWe reconstructed the Dutch national network of hospitals  with resIn order to determine the effect of the directionality of the network, we repeated the analysis of the individual-based model using a dataset with alternative direction. We created a dataset in which all referral probabilities to hospitals were set equal. In the resulting network, both the indegree and outdegree of the UMCs were higher than the other hospital categories, but the outdegree is now higher than the indegree . The relWe also used two other networks with alternative directions, to test if our observation holds under different conditions. First, we reversed the direction of the network by reversing time in the original dataset, the patients who first visited a general hospital and then a UMC now do the opposite. In this dataset the UMCs still have a higher relative indegree, compared to the general hospitals, although their outdegree is now higher than their indegree . These sSecond, we increased the reversed direction in order to decrease the relative indegree of the UMCs to a level below the relative indegree of the general hospital, while keeping both the absolute degree of the UMCs (both indegree and outdegree) above the degree of the general hospitals. These simulations resulted in a lower prevalence in the university medical centres compared to the hospitals of other care categories, whereby the top clinical hospitals had the highest prevalence, reflecting their highest relative indegree . This reThis study sets a precedent by using data about all hospital admissions obtained from the National Medical Register LMR  to exploIn the Netherlands, 98 hospitals provide various forms of specialist care. Within the category of general hospitals, there are considerable differences from hospital to hospital, with some smaller hospitals providing only basic hospital care. Therefore, patients who need advanced medical treatment need to be referred to so-called top clinical hospitals or university medical centres. Top clinical hospitals are large institutions that provide a wide range of clinical specialities and are involved in specialists training and education of doctors and other health care workers. In contrast to university medial centres they are not affiliated with universities and do not include the same comprehensive spectrum of specialities. Within the health care system, the university medical centres occupy a special place as leading hospitals with advanced specialist and final referral functions.In the Netherlands the hospital admission rate is rather low compared to international standards with 15.6 admissions per 100 inhabitants The individual-based model which emulates the referral characteristics recorded in the LMR, describes the spread of nosocomial infections among hospitals on an individual patient level. It shows that patients who are admitted only two or three times contribute significantly to the inter-hospital spread of the infection and suggests that the prevalence is directly related to the referral level of different hospital categories. This model is, however, unable to provide a mechanistical explanation for the predicted differences in prevalence between hospital categories. For this reason, a simplified model of the hospital network was created. This model weights the contact pattern between hospitals on the basis of average patient referrals between any two hospitals without taking individual referrals and catchment populations into consideration. Despite being a coarse simplification, the hospital network model provides excellent heuristic value as it is able to demonstrate the directionality of the entire network, which is the driving force behind the difference in prevalence between different hospital categories.Our methods rely on three key assumptions that should be addressed. First, all of our methods do not take account of transmission outside of the hospitals. If community transmission of hospital-acquired infections become a significant factor, the dynamics of the epidemic will ultimately change and the effect of patient referrals between hospitals will be diluted. Community transmission of MRSA is mainly seen in families Second, we have assumed a specific measure of connectedness to create the network. However, the construction of hospital networks can be done based on other measures than the one we used, like weighting the contact between two hospitals by the number of patients these hospitals share, or by taking only subsequent admissions into account. These measures would slightly alter the difference in connectedness between the hospital types, but the differences between referral levels would remain (data not shown). However, we feel that exclusion of data about the length of stay and time between admissions would disguise the true utilization patterns that govern the spread of HA-MRSA.Third, both the individual based model and the measure of connectedness assume homogeneous mixing within the hospital and leave out any ward structure. However, because the medical condition of a patient determines both the ward of admission and his/her health-care use, patients with a certain utilization pattern may mainly meet patients with comparable utilization patterns. This assortative behavior of patients A higher prevalence of health care-associated infections has been repeatedly demonstrated for tertiary referral centres such as university and teaching hospitals, which also witness the majority of outbreaks of these types of infections. As a conventional explanation, the severity of underlying conditions, more invasive diagnostic and therapeutic procedures and higher rates of antibiotic prescription have been incriminated for this difference. Our model predictions based on the observed admission pattern in the Netherlands, however, suggest a more parsimonious explanation. In the Dutch health care network, the university medical centres admit a large number of referred patients from other hospitals, much more than the top clinical hospitals . Each unThese observations can have important implications concerning hospital infection control. When hospital infection control fails within a single hospital, hospital-acquired infections will start to spread between hospitals, with the most connected ones at the highest risk of both acquiring and spreading the disease. Differentiation of intervention measures over hospital categories, for instance by making the university medical centres the focal point, could then be considered. The exact implementation of such a differentiation is, however, beyond the scope of this paper and should be the focus of further research. Furthermore, our results suggest that differences in prevalence of nosocomial infections between hospitals do not necessarily reflect the success of the hospital infection control measures of individual hospitals. Direct comparisons of infection rates between hospitals may therefore give a distorted view of hospital standards, if national  health care utilization patterns are not considered. The use of such comparisons, for benchmarking, may therefore lead to a false conclusion about a hospitals effort to reduce nosocomial infections.In summary we predict that (1) Hospital-acquired infections can spread rapidly from index hospitals to the next referral level. (2) Secondary and tertiary referral hospitals must be prepared for rapid response. (3) High connectedness and the directionality in the health care network towards the university medical centres cause a local build-up of nosocomial pathogens, such as MRSA, and thus a higher prevalence in these hospitals. This should be taken into consideration for benchmarking and the design of national control strategies.We used the Dutch national medical register from 2004  was recorded. For each index hospital we perform 200 simulations, sequentially repeating these sets of simulations for each 98 hospitals as index hospital, thus performing a total of 19600 simulations. In further analysis, we only include simulation runs resulting in an outbreak larger than a threshold of 1000 colonized persons, to exclude runs that resulted only in small local outbreaks. The results are not sensitive to the exact value of this threshold.In order to reduce the complexity inherent to the individual-based model, we created a hospital network model assuming transmission parameters between hospitals. All transmission parameters were based on the patient characteristics as observed in the LMR. Thereby, we calculated the infection rate, For each admission we calculate the probability that the patient transmits the infection from the referring hospital to the admitting one, The probability of being colonized depends on the length of stay in each referring hospital, The probability of introduction in the admitting hospital, The probability that a patient is still colonized upon readmission, The degree with which hospitals connect with the rest of the hospital network through referrals of patients can be divided into two parts. These consist of the indegree In order to determine the effect of the difference between inward and outward degree of connectedness, we created a number of datasets with alternative directions. One of these has no direction, the other two have reversed directions. In all three alternatives the university medical centers still have a high degree of connectedness, consistent with the LMR-based network, but a higher outdegree than indegree, contrary to the LMR based network.We first created a dataset without direction, by setting all referral probabilities in the referral matrix equal, but leaving all other parameters the same as the original simulated dataset. We then created a reverse dataset by reversing the time of the original simulated dataset. The new date of admission of a patient, In order to reverse the direction of the dataset even further, we created another dataset in the same way as the generated dataset with the characteristics of the LMR, in which we set all referral probabilities to university medical centers, in the referral matrix, to zero. This, however, also lowered the overall degree of connectedness of these hospitals. In order to raise the degree we increased the size of the university medical centers 7 fold. The university medical centers now have a higher outdegree than indegree, while their indegree is still higher than the outdegree of the top clinical hospitals.Furthermore, we created a number of small datasets of only five hospitals, in which we varied network properties such as directionality and hospital size See .Text S1Analysis of networks consisting of five hospitals in which network properties, such as directionality and hospital size, are varied.(7.71 MB PDF)Click here for additional data file.


Many epidemiologic studies report the odds ratio as a measure of association for cross-sectional studies with common outcomes. In such cases, the prevalence ratios may not be inferred from the estimated odds ratios. This paper overviews the most commonly used procedures to obtain adjusted prevalence ratios and extends the discussion to the analysis of clustered cross-sectional studies.Prevalence ratios(PR) were estimated using logistic models with random effects. Their 95% confidence intervals were obtained using delta method and clustered bootstrap. The performance of these approaches was evaluated through simulation studies. Using data from two studies with health-related outcomes in children, we discuss the interpretation of the measures of association and their implications.The results from data analysis highlighted major differences between estimated OR and PR. Results from simulation studies indicate an improved performance of delta method compared to bootstrap when there are small number of clusters.We recommend the use of logistic model with random effects for analysis of clustered data. The choice of method to estimate confidence intervals for PR (delta or bootstrap method) should be based on study design. While the odds ratio (OR) is one of the most frequently used measures of association between a risk factor and an outcome in epidemiology, the risk ratio(RR) and prevalence ratio (PR) are important indices to quantify the strength of association between a given disease and a suspected risk factor . The maiThe use of odds ratios in cross-sectional studies, a common practice among epidemiologists, has been criticized because prevalence odds ratios are good estimates of prevalence ratios only under specific circumstances -14. MoreTrichuris infection between children living in treated and non treated communities.The evaluation of these methods has been motivated by data from the SCAALA  studies in Brazil  and EcuaIn this paper, we have evaluated modeling strategies for the estimation of adjusted PRs in cross-sectional studies with common outcomes through simulation studies in the settings of clustered data.OR = exp(β1), where β1 denotes this effect). The estimation of the PR, however, requires a more complicated mathematical expression that relates the effects and the values of the factors of interest. For example, suppose it is of interest to evaluate the effect of an exposure (X1) on the occurrence of an outcome while controlling for k - 1 confounders . In such circumstances, the PR between exposed and unexposed subjects could be expressed as:Logistic regression is the most popular model used for the analysis of binary outcomes to estimate adjusted odds ratios. These can be expressed in terms of the estimated effect of the factor of interest on the outcome, or more simply as the exponential of the factor's coefficient , and one continuous covariate, X2. Using the conditional method, the adjusted PR is given by X2. For the marginal method the adjusted PR is given by n individuals. A similar expression is used in the stratified method, with k, the number of strata formed by categorizing X2, replacing n and with the use of weights Wk chosen from a reference populationSeveral standardization procedures for epidemiologic measures of effect based on regression models have been proposed. Wilcosky and Chambless  followinAn alternative approach for conditional standardization is by specifying a reference value for each covariate rather than using their mean values . This apFlanders and Rhodes  provide Yij be a dichotomous outcome at cluster j for subject i and Xij 1and Xij 2two covariates. The random effects logistic regression model can be written asThere are two large families of statistical models that account for the correlation in different ways, leading to estimated parameters that have different interpretations, which are denoted as marginal models and random effects models . We willuoj ~ N  represents a cluster specific random effect, leading to a random intercept logistic regression model, which is the simplest example of a generalized linear mixed model (GLMM). This model describes the combined effect of all omitted subject-specific covariates that cause some subject to be more prone to disease (for example) than others. It is appealing to model unobserved heterogeneity in the same way as observed heterogeneity by simply adding the random intercept to the equation.where β's) of covariates on the outcome obtained with the random effects logistic model, we can choose a standardization procedure and estimate PR using the formulae presented previously relating the values of covariates and their effects to the prevalence ratio. Several investigators interpret the regression coefficients (or the odds ratios) obtained from the logistic model with random effects in the same way as in the usual logistic regression model, by conditioning on the random effects .The results for the comparison of logistic and Poisson random effects models are presented in Table A major advantage of the odds ratio is that it can be estimated for all study types. However, investigators should avoid interpreting odds ratios as an approximation to prevalence ratios when the prevalence of the event of interest is high (greater than 10%). In such situations, the odds ratio generally overestimates the prevalence ratio. The importance of differences in the interpretation of the OR compared to PR/RR, particularly when prevalence is high, has been discussed by others ,11,16.If the adjusted prevalence ratio (PR) is the measure of interest, logistic regression is one of the approaches that can be used for its estimation ,16. HoweTrichuris infection, the estimated PR using logistic model with random effects represents the ratio of the probability of a given child having Trichuris infection if he/she receives ivermectin compared to the probability that the same child having Trichuris infection if he/she does not receive treatment. In this way the PR is adjusted for unobserved individual characteristics.There is also no consensus about the the best way to interpret regression coefficients in the the context of random effects models. Some authors interpret the fixed regression coefficients similarly to the usual logistic regression model, conditioning on the random effects -29. WhenAlternatively, population-averaged estimates for the regression coefficients can be obtained using approximate formulae as suggested by Zeger and colleagues (1988), which can be interpreted in terms of the response averaged over the population . In someThe confidence intervals for prevalence ratio using logistic regression should be defined using appropriate approaches, such as delta and bootstrap methods. Other methods discussed in the literature, such as the substitution method , have beOther model-based approaches that have been commonly used to estimate PR are the Poisson and log-binomial models ,10,16-18All previous discussions about the estimation of PRs has been done in the context of independent observations. In this paper we have extended this discussion to include clustered design studies, in which the dependence between observations is taken into account. We used random effects logistic models to deal with intracluster correlation. We evaluated the performance of methods for defining confidence intervals through simulation studies with several levels of correlation between observations in the same cluster. For the scenarios considered here the delta method outperformed the clustered bootstrap method when there are data for a small number of clusters. However, for situations where size and number of clusters are large, they show equivalent performance. We also noticed a poorer performance of the Poisson model with random effects, especially with increasing level of clustering and number of clusters, and there were problems with convergence when the number of clusters was small.We illustrated the estimation of prevalence ratios using data from two studies with health-related outcomes in children and we observed major differences between estimated PR and OR in these studies. Therefore, we highlight the importance of avoiding interpreting odds ratios as prevalence ratios in many situations, particularly when the outcome is not rare. Based on the results of the simulation studies, we recommend the use of the logistic model with random effects for analysis of clustered data when there are at least 30 clusters of size greater or equal to 10. The choice of estimation method for the calculation of confidence intervals for PRs – delta or clustered bootstrap methods – should be based on study design.The authors declare that they have no competing interests.Studies concept and design: MLB, LR, PJC. Analysis and interpretation: MBBC, ALM, SC, CAT, LDA. Development and implementation of statistical methodology: CAT, RLF, NFO, LDA. Simulation Studies: CAT, RLF, NFO, LDA. Drafting of the manuscript: CAT, RLF, NFO, LDA. All authors critically revised and approved the final manuscript.The pre-publication history for this paper can be accessed here:


The Perceived Impact of Problem Profile (PIPP) was developed to provide a tool for measuring the impact of a health condition from the individual's perspective, using the ICF model as a framework. One of the aims of the ICF is to enable the comparison of data across countries, however, relatively little is known about the subjective experience of disability in middle and low-income countries. The aim of this study was to assess the validity of the Perceived Impact of Problem Profile (PIPP) for use among adults with a disability in Thailand using Rasch analysis.A total of 210 adults with mobility impairment from the urban, rural and remote areas of northeast Thailand completed the PIPP, which contains 23 items assessing both impact and distress across five key domains . Rasch analysis, using RUMM2020, was conducted to assess the internal validity and psychometric properties of the PIPP Impact subscales. Validation of the PIPP Impact scales was conducted by comparing scores across the different response levels of the EQ5D items.Rasch analysis indicated that participants did not clearly differentiate between 'impact' and 'distress,' the two aspects assessed by the PIPP. Further analyses were therefore limited to the PIPP Impact subscales. These showed adequate psychometric properties, demonstrating fit to the Rasch model and good person separation reliability. Preliminary validity testing using the EQ5D items provided support for the PIPP Impact subscales.The results provide further support for the psychometric properties of the PIPP Impact scales and indicate that it is a suitable tool for use among adults with a locomotor disability in Thailand. Further research is needed to validate the PIPP across different cultural contexts and health conditions and to assess the usefulness of separate Impact and Distress subscales. The International Classification of Functioning, Disability and Health (ICF) was developed by the World Health Organization (WHO)  to proviThe first is that until recently, much of the research worldwide has focused on etiology, treatment and epidemiology. An advancement of the ICF, compared with previous classification tools, was to incorporate contextual factors, including physical and social environmental factors as well as personal factors , into a model of functioning and disability. Consequently the ICF adopted a biopsychosocial approach, integrating conventional medical and social models. However, limited attention has been given to non-clinical, particularly social and personal aspects of health, disability and illness .In addition to the need to further elaborate on these contextual factors , there iThe second gap in the literature is that research on non-communicable and chronic disease has been conducted primarily in Western Europe and North America, and relatively little is known about the subjective experiences of disability in middle and low-income countries. Concern has been raised about the application of Western notions of well-being, illness and disability across different cultures, including the adaptation of health and health-related quality of life measures -8. CultuREsearch into Social Inclusion, Locomotor Impairment and Empowerment through Networking, Collaboration and Education) was a large interdisciplinary, multi-country project which considered the contextual factors which impact upon the subjective experience of physical impairments in Australia and Southeast Asia. Both qualitative and quantitative research methods were used to explore the personal and social environmental factors that contributed to disability and disablement in the different country and social settings . One. OneREsengs (see ).Thailand has a national population of 63 million  , two thiAs part of the RESILIENCE project, we developed the Perceived Impact of Problem Profile (PIPP ) as a reWe have previously published our analysis of the PIPP, validating its use among adults with mobility impairment in Australia . OverallThe study was conducted in urban, rural and remote areas of Khon Kaen Province in the Northeast Region (Isaan), the setting of the Thai arm of the RESILIENCE study. The Isaan region, with a population of 19 million, is the largest of four regions in Thailand. The majority of the population are of Lao descent and ethnicity, and those living in rural areas are among the poorest in Thailand. Ethics clearance was granted by Khon Kaen University and The University of Melbourne.pai sai ma sai yak). A researcher then visited the persons identified, and during the visit, used a snowball technique to identify other persons having difficulty in movement. All persons who were invited to participate agreed to be involved in the study. The participant criteria included age (≥ 18 years), ability to communicate well enough to give informed consent, and willingness to participate as well as experience of a condition causing difficulty in movement. These difficulties were ones defined by the participants, reflecting lay understandings of impairment and ability, and did not use pre-selected clinical criteria. The recruitment area was extended to contiguous villages if a village was not large enough to identify seven suitable participants. The study was conducted between September 2003 and August 2004.A modified cluster sampling method was employed, following stratification into urban, sub-urban, rural and remote areas. In total, 38 villages were randomly identified. In each village, the headman was asked to list names of persons "having difficulty in movement"  was developed as a relatively short, self-report instrument to assess both the impact and the distress of health problems from the individual's perspective . The devParticipants were also asked to complete information regarding their socio-demographic background , health background , and current health status as measured by the EQ-5D . The EQ-The EQ-5D and PIPP were pretested with 20 people with a disability in periurban communities of Khon Kaen City to establish clarity of the questions and the sequence of the items.To assess the psychometric properties of each PIPP Impact subscale, the relevant items for each were subjected to Rasch analysis using the RUMM2020 software . Rasch aThe procedures adopted in this study are consistent with those conducted in the preliminary validation of the PIPP in an Australian sample  and PIPP Distress items (associated with feelings), despite linguistic differentiation. People tended to give the same value to each item for both impact and distress. For the purposes of this paper, therefore, we chose to evaluate only one set of subscales, those relating to impact of health problems. Rasch calibrated PIPP Impact subscales scores were exported to SPSS Version 12 for further statistical analysis to assess the construct validity of the subscales. Non-parametric techniques were used due to the non-normal distribution of scores for a number of the scales. Spearman correlation coefficients were generated to assess the intercorrelations among the PIPP Impact subscales. Mann-Whitney U tests and Kruskal-Wallis Tests were used to compare PIPP subscale scores across the various levels of responses to the EQ5D items.Preliminary inspection of the threshold map (not shown) for the PIPP Impact items indicated disordered thresholds for many of the items. This suggests that respondents experienced difficulty in utilizing the full 6-point response scale, but instead typically used only four response points. All items were therefore rescored by collapsing categories, with a change in scoring from 012345 to 011223.The four Self-care items showed adequate fit to the model after Bonferroni adjustment to the alpha level  with good person separation reliability (PSI = .89). No item showed misfit  and good person separation (PSI = .85).Good fit to the model was achieved for the five Mobility items ; however significant DIF for age was detected for items The four Relationship items showed good model fit  and adequate person separation reliability (PSI = .88). No items showed misfit Table  and therparticipation in family activities, with males showing a greater likelihood of endorsing this item than females. Removal of the item resulted in fit to the model, however the PSI value dropped from .79 to .72, indicating a undesirable reduction in the person separation reliability of the scale. Given that the DIF noted for the item was relatively minor, and that the original overall model fit was very good, it was decided to retain the item in the scale for further investigation.A non-significant overall item-trait interaction chi square was obtained for the five Participation items , suggesting good model fit. No items showed misfit  and good fit to the model . No items showed misfit  no problems (N = 129), and (2) some problems or unable to care for self (N = 81). Mann-Whitney tests revealed significant differences between the two groups on the PIPP Impact Self-care subscale . The mean rank scores on the PIPP Impact Self-care subscale was higher for the respondents classified as having self-care problems on the EQ-5D (149 vs 78), supporting the validity of the PIPP Impact Self-care subscale.Kruskal-Wallis tests were conducted to compare the PIPP Impact Mobility subscale scores with responses on the EQ-5D Mobility item , although the majority of participants indicated the middle category on the EQ-5D (i.e. 83%). There was a statistically significant difference . Mean ranks for each group were in the expected direction with those 'confined to bed' showing the highest PIPP Impact Mobility scores (154 vs 105 vs 56).Kruskal-Wallis tests were conducted to compare PIPP Impact Participation scores for respondents in each of the three response categories to the EQ-5D item 'Usual Activities' . There was a statistically significant difference , with mean ranks for each group in the expected direction (i.e. those 'unable to perform' showing the highest PIPP Participation impact scores: 138 vs 109 vs 68).To assess the construct validity of the PIPP Impact Psychological Well-being subscale, scores were compared to those obtained for the EQ-5D Anxiety/Depression item. Kruskal-Wallis tests revealed a statistically significant difference in scores . Mean ranks for each group were in the expected direction with those indicating extreme anxiety and/or depression on the EQ-5D also showing the highest PIPP Impact Psychological Well-being mean rank scores (127 vs 107 vs 73).took' or 'suffer', while impact is 'pon-kratop' or effect [The aim of this study was to validate the use of the PIPP among people with a disability in Thailand. The PIPP was initially developed as a multidimensional generic measure of the impact and distress of health conditions from the individual's perspective, and has been validated in an Australian sample . The inir effect ,34. ThesThe similarity of Impact and Distress responses from Thai participants contrasts with Australians, who were able to distinguish the direct impact (function) of their health condition and the distress  caused by it. For this paper, we decided that it would only be appropriate to attempt to validate the PIPP Impact subscales at this stage for use in the Thai sample, and not the Distress subscales. Further research is required to explore the understanding of differences between impact of health problems and the distress caused by this impact, in the Thai context.no impact, mild impact, moderate impact, extreme impact) to assist respondents to distinguish more clearly between response options This change in scale format, however, would require further psychometric testing.For all PIPP Impact subscales it was necessary to collapse the original 6-point response scale to a 4-point response scale. For most of the items disordered thresholds were detected which suggested that, while participants could consistently differentiate the two extreme response points, they were not able to reliably distinguish among the four response points in the centre of the scale. Although a number of alternatives were considered, the most suitable action was to recode all items from 012345 to 011223. It may be appropriate in future administrations of the scale for the four response points to be labeled  requiring removal from the subscale. No DIF was found for either gender or age, except for participation in family activities in the Participation subscale. Specifically, men indicated a greater likelihood of endorsing this item than women. Given that removal of the item would have resulted in an undesirable reduction in the person separation reliability of the scale, and that the DIF was relatively minor, the item was retained in the subscale. One possible explanation for the DIF for this item is the interpretations of the term 'family activity'. In traditional Thai settings, earning an income and providing for the family is considered an important responsibility of men and therefore, likely to be one of the major ways in which men evaluate their 'participation in family activities' [In the current Thai study the five PIPP Impact subscales showed adequate psychometric properties, with all demonstrating fit to the Rasch model. All subscales showed adequate person separation reliability. Only one misfitting item was identified  declare that they have no competing interests.The literature review was undertaken by RM, the statistical analyses were conducted by both RM and JP, the study was designed by LM, SC, and JP, and the data collection was managed by LM and SC. All authors contributed to the preparation of the article and approved the final manuscript.


Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability.Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment.We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms.dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the  Public databases of results from high-throughput experiments are abundant and extremely useful, but most biologists lack the training in computer programming to effectively explore and interact with the data. A solution to this problem is afforded by recent developments in information technology, which facilitate the development of web-based systems that support interaction and explorative data analysis. These systems require only basic web-surfing skills and modest computer power, but may deliver powerful data analysis capabilities to the biologist's fingertips. The major advantages of these systems over first-generation web applications is that they provide the look and feel of a desktop application within a web browser window, with intuitive visualization and the availability of helpful hints through techniques such as term-completion and tagging. Instead of the infamous "Submit" button and the corresponding switch from one web page to another, modern systems employ an interactive, single-screen interface, which adapts to the user's data and actions. Such interfaces are used in many popular applications, such as e-mail browsers, spreadsheets, words processors, and social networking websites, but the field of bioinformatics has yet to adopt these new technologies., that we consider to be a pioneering attempt at this change. This application is designed around a repository of gene expression data from microarray experiments in the social amoeba Dictyostelium discoideum. Dictyostelium is a popular organism in which individual cells aggregate upon starvation and differentiate into fruiting bodies that consist of two major cell types – spores and stalks. This organism is a convenient model system for the study of cell motility, chemotaxis, development, social behaviour and more, and the availability of global gene expression profiles greatly facilitates these studies. Until now, gene expression profiles of this popular system have been deposited in public databases and some of them are available as static graphs on the organism's central web site, dictyBase . These graphs depict individual gene expression profiles as they change during development of the wild type strain, but they do not allow curious biologists to explore gene expression in other strains, or interact with the data in any other way.In this paper, we report on the development of a gene expression database and its corresponding web application, dictyExpress Dictyostelium development. dictyExpress was developed to specifically address time, and to provide optimized procedures for retrieval, visualization and interactive analysis of time-series data. The result of this effort is a comprehensive, electronically-accessible database of all the Dictyostelium expression data published by the Functional Genomics Project at Baylor College of Medicine, featuring a web-based application that can query the database and perform sophisticated data mining tasks. The web interface can be easily expanded to include additional tools and adapted to the analysis of other public databases. In this paper, we provide the overall description of both the database and the website components of dictyExpress. We then focus on the web-based exploratory environment, which we regard as the major original contribution of our work.Dealing with time is central in , offers a number of data analysis procedures, mainly relying on an analytics server that either performs the analysis on the fly , or fetches stored results of previous analyses . The dictyExpress database server is freely accessible. In contrast, the dictyExpress analytics server, which also implements an HTTP-based programmatic interface, is available only to dictyExpress client-specific requests. The analytics server is powered by the Orange data mining suite [dictyExpress is composed of a MS SQL database, software for data management and retrieval, data analysis software and a client web-based application Figure . The datng suite ,3 to proA critical issue with dictyExpress is the performance of the analytics server, which carries all the computational load of the application and is a potential bottleneck in the system. We have developed a dedicated web application to estimate the response time under different loads of the server, and are monitoring its performance daily. An expansion to a server farm is planned if the current, multi-core server will become insufficient.dictyExpress provides access to over 1057 expression array experiments from the BCM Functional Genomics group. The web interface Figure  includesDictyostelium development. In most of the experiments, gene expression was observed at 13 different time points . A list of Dictyostelium strains, time points at which the data were recorded, and the number of replication measurements is provided on the application home page.As of March 2009, the dictyExpress database included data from over 1057 expression array experiments collected from wild type and from 24 mutant strains that were grown in HL5 liquid suspension and developed on nitrocellulose filters . All of Six different components make up the graphical user interface of the dictyExpress web application:Gene Expression Query is where the user selects the desired experiments and defines the genes of interest. Experiment filtering  and gene name completion are also supported. The names in the gene selection list are clickable and the links lead to the corresponding dictyBase gene home page.- Expression Profile displays gene expression profiles (time-series) for the given experiment and the set of selected genes. Any profile can be selected form the visualization, and may be followed by a request to search for genes with similar expression profiles. The user may also hand-draw a hypothetical profile using the 'freehand' option and search for genes with a similar behaviour.- Hierarchical Clustering considers a list of selected genes and displays a corresponding dendrogram with associated gene names, gene IDs and textual annotations. A heat map, representing the expression time-series, can also be visualized, thus providing a standard dendrogram-heat map visualization that researchers have become familiar with in recent years [- nt years . The denGene Ontology displays the results of a term-enrichment analysis in which the selected genes serve as the query and all the genes from the experiment serve as the reference set. The display follows the visual design proposed by the enrichment analysis application GOAT [p-values. Reports on each of the three Gene Ontology aspects  can be accessed separately.- ion GOAT , and proCo-expression Network considers a single reference gene and displays a neighbourhood network of the most similar genes. The application uses the same profile similarity measures as the gene clustering application. The networks are drawn so that the most distant gene in the network is up to 3 hops away from the target gene. Double-clicking on any gene makes it the reference component, and results in redrawing the network accordingly.- Experiment Comparison displays expression profiles of a reference gene in several (selected) experiments. Double-clicking on any of the profiles makes the corresponding experiment the current one and updates the content of all the other components, while retaining the current selection of genes.- The web-based interface of dictyExpress also implements a set of supporting functions that are available through the applications menu bar  or automatically .Another useful feature implemented in dictyExpress is an explicit search for similarly expressed genes Figure . The useAll of the features listed above contribute to the implementation of explorative data analysis, where the visual presentation of the data guides the user on which genes to focus next and what gene expression profiles to consider. This interactivity sets dictyExpress apart from other current web-based gene expression analysis applications.The data in the dictyExpress repository are available through a programmatic (HTTP) data access. The user can query for raw microarray data, normalized data, and time series data, obtain various mapping schemas, such as the one between microarray spot ID and gene ID, and access various annotations of the experiments. The following links are a few examples of HTTP queries in dictyExpress: returns a mapping from microarray spots to gene names and IDs-  lists all the biological samples in the data base-  returns the time series expression data for six genes (given are their repository IDs) for an experiment done with the mek1- knockout mutant where no treatment was applied and where the growth conditions are not specified.- .The detailed documentation on the syntax of HTTP dictyExpress queries, along with more examples that illustrate their use, is available on-line at Dictyostelium transcriptome, shuttling back and forth to dictyBase in order to explore the annotations of interesting genes.dictyExpress provides a public database of gene expression data for a popular model organism and an original web-based application for data exploration. The prototype of the system has been launched in September 2008. As of April 2009, dictyBase includes direct links to dictyExpress from the gene home pages. Following these links, dictyExpress displays the wild-type expression profile for a selected gene, and supports the user for further exploration. The users are able to test the expression of their gene of interest in the wild type and in the mutant strains, and use the application for exploring the dictyExpress has an enormous value to the entire community. Currently, it allows researchers to explore a large database of expression arrays without having knowledge in data mining or access to powerful computers and expensive software. Eventually, we anticipate that other research communities will adopt this approach so that public databases of gene expression arrays would become readily available to the public.. The software is platform-independent and runs in all major browsers with a Flash player plug-in. It has been tested within the Firefox, Internet Explorer and Safari web browsers.dictyExpress can be accessed at The pre-processing pipeline incorporates the LIMMA package  in R/BioGene expression profiles in dictyExpress are time sequences mainly consisting of 13 developmental time points that were analyzed with a custom cDNA array . Several analysis components, including the one for clustering, gene co-expression network construction and search for similar expression profiles rely on the scoring of a pair-wise profile distance. We use three alternative standard similarity measures: Euclidian distance (squared sum of squared per-point distances), Pearson's correlation (standard Pearson product-moment correlation coefficient) or Manhattan distance (squared sum of absolute per-point distances).Gene expression profiles are clustered using agglomerative hierarchical clustering , where tp-values and enrichment scores.The hypergeometric distribution is used to compute the enrichment of Gene Ontology terms  for the Co-expression networks are developed around a user-selected target gene and its neighbours with similar expression profiles. For reasons of obtaining reasonable response times, we use the following heuristics to define which genes to consider in the network. Network genes are gathered starting from the target gene, including its five most similar neighbours and the procedure is iteratively repeated on the newly added genes up to three times (according to a user-defined parameter). Only edges that link the most similar genes are shown in the visualization, and they are selected such that the average connectivity of the genes in the network is 5.0. The networks are developed on the analytics server from pre-processed data that includes a list of 20 closest neighbours for each similarity measure and each gene. This particular network construction algorithm is speed-efficient and heuristically constructs networks that are close to those we would obtain through extensive search and computation of the entire similarity matrix.BZ and GS conceived the project and the initial design. AP developed the dictyExpress database server and its HTTP interface. GR implemented the web-based interface and designed the corresponding web pages. Server-based data analysis scripts were implemented by TC, BZ and GR. GS and AK provided the test data and performed user testing. All the authors drafted the manuscript and read and approved its final version.