IEC-18 cells are a non-transformed, immortal cell line derived from juvenile rat ileal crypt cells. They may have experimental advantages over tumor-derived gastrointestinal lineages, including preservation of phenotype, normal endocrine responses and retention of differentiation potential. However, their proclivity for spontaneous differentiation / transformation may be stereotypical and could represent a more profound experimental confounder than previously realized. We hypothesized that IEC-18 cells spontaneously diverge towards a uniform mixture of epigenetic fates, with corresponding phenotypes, rather than persist as a single progenitor lineage.IEC-18 cells were cultured for 72 hours in serum free media (SFM), with and without various insulin-like growth factor agonists to differentially boost the basal rate of proliferation. A strategy was employed to identify constitutive genes as markers of divergent fates through gene array analysis by cross-referencing fold-change trends for individual genes against crypt cell abundance in each treatment. We then confirmed the cell-specific phenotype by immunolocalization of proteins corresponding to those genes. The majority of IEC-18 cells in SFM alone had a loss in expression of the adenomatous polyposis coli (APC) gene at the mRNA and protein levels, consistent with adenoma-like transformation. In addition, a small subset of cells expressed the serotonin receptor 2A gene and had neuroendocrine-like morphology.IEC-18 cells commonly undergo a change in cell fate prior to reaching confluence. The most common fate switch that we were able to detect correlates with a down regulation of the APC gene and transformation into an adenoma-like phenotype. In the last quarter century, more than five hundred published manuscripts contained experiments using the popular rat crypt cell lines IEC-6 and IEC-18 originally established by Quaroni and Isselbacher in 1978 and 1981 . These lOne of the more exciting aspects of IEC-18 cells is their capacity to retain the crypt cell phenotype as well as the continued potential for differentiation/maturation into enterocytes -5. HowevThis is a fairly crucial experimental question because serum-restricted conditions allow the tightest experimental rigor but may drive IECs towards altered cell fates, potentially introducing uncontrolled experimental confounders. Until recently, we did not have the necessary tools with which to determine more subtle epigenetic divergences in IECs. Our current investigation uses a novel strategy to demonstrate that spontaneous cell fate divergence is not only highly prevalent in IEC-18 cells, but it is fairly uniform and therefore a predictable confounder for gene array studies in this cell line.We have recently discovered a novel antigen localization pattern by using anti-carboxyl IGF binding protein-2 (IGFBP-2) antibody  to demonstrate a multivesicular pattern in some IEC-18 cells but not in others that are immediately adjacent . Both stress fibers and microvilli are heavily stained and appear to have high turnover rates in cells that have C2 staining, but not in those without it Figure . We noteWhen IEC-18 cells were plated at variable densities in 10% fetal bovine serum (FBS), we saw that all crypt cells were initially positive for C2 staining, but with increasing density there were increasing numbers of C2 negative cells Figure . We alsoTo determine if the C2 positive and negative IEC-18 cells were each capable of phenotype preservation during proliferation, IGFBP-2 stained cells on coverslips were searched for mitotic cells in the stage of cytokinesis and their images were captured by digital photomicroscopy (n = 6 cell pairs for each phenotype). In each case, all daughter cells had the same phenotype as their sister, confirming that both the C2 positive and C2 negative phenotypes are conserved in subsequent cycles of mitosis  while none of the treatments significantly altered the abundance of C2 negative cells. This strategy allowed us to systematically skew the cell composition and use gene array analysis to determine whether C2 positive cells were epigenetically divergent.Because IEC-18 cells grow best in the presence of high dose insulin, we suspected that crypt cell proliferation was dependent upon IGF receptor stimulation. In general, differentiated and benignly transformed epithelial cells are less likely to proliferate upon reaching confluence, so we sought to preferentially drive crypt cell proliferation in a graded fashion using different IGF receptor agonists Figure . IGF-II Eleven genes met our criteria for a significant fold change, four were positively correlated with crypt cell abundance and seven were inversely correlated Figure . Of thesWestern blots using antibodies against APC and 5-HT2A in IEC-18 cell lysates revealed staining of the appropriate sized band for each Figure . In the Immunolocalization for APC revealed that C2 positive cells also had intense APC staining whereas C2 negative cells either had limited or no APC staining Figure . This fiin vivo [Immunolocalization for 5-HT2A demonstrated a second cell phenotype Figure  and 5D. in vivo . The celDouble immunolabeling with overlay technique (using C2 and actin antibodies) demonstrated that C2 negative cells have a paucity of microvilli in comparison to C2 positive cells Figure . AdditioIn this manuscript we have taken advantage of an IEC immunolocalization marker that we call C2 to demonstrate two forms of cell fate divergence within IEC-18 culture. Using a combination of gene array screening and immunolocalization, we found that C2 is lost in over half the cells by the time of confluence and its loss is also associated with a down regulation of the APC gene, decreased APC protein abundance, decreased actin filament turn over, and reduced microvillar density. In short, there is an adenoma-like phenotype that fits with this genotype and this fits well with what is known about the function of the APC protein -15. In aWhile we think our findings have important implications for the existing IEC literature, the more important aspect of this manuscript may be in the methodology we have piloted. In many ways, cell culture, whether primary or immortalized, transformed or not, is by definition a model in flux. The progenitor lineage that a researcher starts with is rarely the hodge podge they end up with after a limited number of passages and it is common, if not expected that epigenetic drift will occur with each cell culture passage. However, what we describe in this manuscript is different in that IEC-18 cells are displaying a uniform trend in cell fate divergence – a trend that can be modulated with IGF. Many if not most gut epithelial cell lines are IGF (or high dose insulin) dependent for proliferation and thus are potentially vulnerable to this biological confounder. It will remain to be seen if other epithelial cell types have similar behaviors when examined in this fashion. Conversely, there is also a positive light to our findings; IEC-18 cells could be a compelling model for spontaneous adenomatous transformation because these adenoma-like cells are arising from a genetically competent progenitor prior to reaching confluence. To our knowledge, no such model with this property has been previously defined.Our study has notable weaknesses and strengths. First, we have identified two divergent cell fates but have only partially characterized them because we were focused on developing a viable screening methodology (hence the ubiquitous use of the word "like" in this manuscript). Second, we are using a rat gene array chip that has approximately 9000 non-EST genes per chip. This is not an exhaustive survey of the rat genome and it is possible that there are other cell fates present in IEC-18 culture that we did not detect. Third, our phenotype assays are based on immunolocalization, which is a semi-quantitative technique with regards to assessing protein abundance. However, in this case we are actually combining cell-to-cell differences in protein abundance with distinguishing morphologic characteristics  to delineate the phenotypes between adjacent cells. In short, what we are quantifying, in the case of adenoma-like cells, is the percentage of cells with a given phenotype. For this purpose, blinded immunolocalization is simple, quantitative and exceedingly efficient. The combined methodologies we chose result in a highly accurate technique for assessing divergence and their specificity can be bolstered by comparative studies of co-divergent markers (as we demonstrated with C2 and APC). As for other positives, the methodology is relatively rapid and can detect low prevalence phenotypes (as demonstrated by the anti-5-HT2A antibody). Additionally, we have demonstrated that a transcriptional marker is not required to create an effective screen. What is required, and what should probably prompt a researcher to employ this methodology, is a probe, a phenotype, or a pleiotropism that results in a consistently heterogeneous and quantifiable pattern (as C2 proved to be for us).In closing, we point out one last caveat. Gene array investigation is an evolving science but there remain three potential pitfalls for every new application: experimental design flaws, data integrity issues and biological misassumptions -20. In tWe have demonstrated a novel methodology for detecting and characterizing cell fate divergence in cell cultures derived from a common progenitor. The majority of IEC-18 cells are transformed into adenoma-like cells in SFM. IGF agonists reduce the rate of transformation by driving proliferation of the progenitor phenotype but do not prevent it. We also detected a very low incidence of differentiation toward a neuroendocrine-like cell type in these same cultures.-6M NBI 31772 , as a means of boosting crypt cell proliferation. We point out that crypt cells can be driven to proliferate despite confluence whereas more differentiated epithelial cell types are resistant to IGF-driven proliferation upon reaching confluence. Alternatively, IEC-18 cells were diluted to 1/8, 1/4 and 1/2 the original seeding density and plated in DMEM with 10% FBS for 24 hours as a means to evaluate cell fate divergence in the presence of serum.Rat ileal epithelial cells  from aliquots of passage numbers 6–8 were grown to confluence in DMEM with 10% fetal bovine serum (FBS) and .1% insulin. The time period for complete epithelial cell confluence was 24–48 hours. Confluent cells were incubated for 72 hours in DMEM to establish a serum free period either alone or with 10iego, CA ), 0.5 × Cell lysate samples were collected from 150 mm dishes, washed with PBS and recovered in 700 uL of lysis buffer by scraping the dish. Cells were spun down for 10 minutes in a pre-chilled centrifuge. The supernatant was diluted 4:1 with loading buffer and run on 12 or 15% acrylamide gels. After the gel had been transferred onto a nitrocellulose membrane, 0.5% Ponceau S stain was applied to confirm equitable protein transfer across all lanes.Western blot membranes were incubated in 5% milk block for 1 hour at RT. Goat polyclonal primary antibody for APC and 5-HT2A were applied for 1 hour at RT. Primary antibody was washed off with TBS-Tween and secondary antibody was placed on the membranes for one hour, then washed again. ABC  was placed on the membrane for an hour and then washed and visualized with SuperSignal chemiluminescent substrate , each was prepared per the manufacturer's instructions.ell Culture methods and then utilized to screen for specific cell fate markers. Total RNA was harvested for each treatment condition in triplicate experiments and then used for gene chip analysis per the manufacturer's protocol . All available non-EST tags were searched within the R3-IGF-I data set and those with significantly different fold changes when compared to SFM cells were selected as potential candidates for further analysis . To further refine the list of potential candidates, the fold changes for NBI 31772 and IGF-II analog, which had step-wise reductions in effects upon crypt cell proliferation, were used to determine if there was a positive or inverse trend between a selected gene's mRNA abundance and crypt cell abundance. In this way, we sought to select constitutively expressed genes, whose primary differences in abundance would be due to differences in cell composition. Our statistical test for determining significance was a two-tailed paired t-test .Immunolocalization in IEC-18 cells was performed for IGFBP-2 (C2), f-actin, IGF receptor type 2 and villin as well as two proteins that were screened out by our gene array analysis (APC and 5-HT2A) in a minimum of three separate experiments for each. In brief, the cells were fixed in formalin overnight, washed in DIG buffer  5 times then put in blocking solution (1% wt/vol BSA in DIG buffer) for 1 hour at room temperature. This was followed by a one-hour incubation with goat polyclonal primary antibody  in antibody diluent solution . Primary antibody was washed off with DIG × 5 and secondary antibody  was placed on the slides for one hour. ABC  was prepared and applied per the manufacturer's instructions, washed as above and visualized with DAB solution  in parallel. The slides were counterstained with hematoxylin, cover-slipped, photomicrographed and representative images were chosen for publication. With respect to the actin visualization, detection was performed such that colored precipitate was observed under the microscope and stopped when stress fibers and microvillus cores were evident and before the high background of the total cell f-actin began to stain efficiently. In the crypt cell quantification experiments, six 10X images were taken at random, printed on color paper at maximum size, blinded, and all cells were assessed as C2 positive or C2 negative and their means and standard deviations calculated for each treatment condition.To objectively test our observations that C2 positive cells have a distinctive colocalization pattern when compared to C2 negative cells, C2 double labeling experiments with f-actin and with APC were performed as overlay experiments using digital microscopy. In brief, C2 immunolocalization was performed as described above except that a grid was drawn on the wet mount slide, allowing digital photomicrographs to be taken at 40X of C2 staining while still covered with stop solution and then a second antibody, either for f-actin or APC was applied and the same process repeated before being counter-stained with hematoxylin and cover-slipped. The same exact images were then re-mapped using the grid and the stored digital images were used for precise position verification. The overlaid double-labeled image was then taken and contrasted to the original image to allow comparison of differential localization of the two antigens.IGF, insulin-like growth factor; IGFBP-2, IGF binding protein-2; IGF-II analog, synthetic truncated IGF-II; R3-IGF-I, synthetic long arginine IGF-I; NBI 31772, an alpha-numeric designation for a non-biologic compound that best displaced IGFs from IGF binding proteins in a large bioassay screen; SFM, serum-free media; APC, adenomatous polyposis coli; 5-HT2A, serotonin receptor 2A; C2, IGFBP-2 carboxyl fragmentPG designed the study, participated in the immunolocalization studies, and performed data analyses. JP carried out the immunolocalization, cell count, and Western blot studies. NF maintained the IEC cells lines and expertly provided uniformly confluent cells. PG and JP produced the figures and drafted the manuscript. All authors read and approved the final manuscript.


Human T-cell leukemia virus type I (HTLV-I) Tax protein is a transcriptional regulator of viral and cellular genes. In this study we have examined in detail the determinants for Tax-mediated transcriptional activation.Whereas previously the LTR enhancer elements were thought to be the sole Tax-targets, herein, we find that the core HTLV-I TATAA motif also provides specific responsiveness not seen with either the SV40 or the E1b TATAA boxes. When enhancer elements which can mediate Tax-responsiveness were compared, the authentic HTLV-I 21-bp repeats were found to be the most effective. Related bZIP factors such as CREB, ATF4, c-Jun and LZIP are often thought to recognize the 21-bp repeats equivalently. However, amongst bZIP factors, we found that CREB, by far, is preferred by Tax for activation. When LTR transcription was reconstituted by substituting either κB or serum response elements in place of the 21-bp repeats, Tax activated these surrogate motifs using surfaces which are different from that utilized for CREB interaction. Finally, we employed artificial recruitment of TATA-binding protein to the HTLV-I promoter in "bypass" experiments to show for the first time that Tax has transcriptional activity subsequent to the assembly of an initiation complex at the promoter.Optimal activation of the HTLV-I LTR by Tax specifically requires the core HTLV-I TATAA promoter, CREB and the 21-bp repeats. In addition, we also provide the first evidence for transcriptional activity of Tax after the recruitment of TATA-binding protein to the promoter. In eukaryotes, transcription by RNA polymerase II requires the orderly recruitment of basal transcription factors and activators to the core promoter and enhancers, respectively ,2. The cHuman T-cell leukemia virus type I (HTLV-I) Tax protein is a unique transcriptional regulator . Tax canTo date, the molecular mechanisms for Tax trans-activation have been well studied. Due to its pleiotropic activities, there are likely nuances to Tax's activity which remain unrevealed. Currently, we understand Tax to harbor a minimal activation domain , to inteIn mammalian cells, the artificial recruitment of TBP sufficiently activates transcription from some promoters -24. It iHere, we have constructed a series of chimeric enhancer-TATAA reporters to analyze the functional roles of these transcription elements in Tax-mediated activation. We observed that Tax activates the HTLV-I 21-bp repeats more potently than other enhancer elements. Analysis of ten mutants of Tax revealed that Tax utilizes different domains to target different cellular factors. We also found that multiple bZIP transcription factors including the newly-identified LZIP are involved in Tax activation of HTLV-I LTR. Finally, two other salient findings are that optimal Tax-responsiveness is specified by the HTLV-I-specific TATAA element, and that Tax synergizes with artificially recruited, DNA-tethered, TBP in a phase of transcription after the assembly of an initiation complex at the promoter.Tax can activate transcription through 21-bp repeats, CRE, κB site or SRE -9. HowevEach reporter contains two copies of enhancer motifs  and a minimal HTLV-I TATAA promoter Fig. . BecauseWhen the reporters were tested in the presence of Tax, a different pattern emerged. Transcription from the 21-bp repeats was stimulated approximately 70-fold  of Tax function with respect to TBP recruitment, we constructed a series of reporter plasmids Fig.  with twoHere, we have delineated functional requirements for both the TATAA promoter and the 21-bp enhancer elements in HTLV-I Tax mediated activation of the viral LTR. To date Tax has been considered solely to initiate transcription. Our study shows for the first time that Tax has a transcriptional role after assembly of an initiation complex at the promoter.HTLV-I is etiologically associated with adult T-cell leukemia ,39. ExprII28 [Beyond the requirement for the 21-bp enhancer, our experiments revealed that the HTLV-I TATAA is also specifically preferred by Tax Fig. . This fiII28  might prA provocative notion which emerges from our study is that Tax can further activate a promoter at which TBP has already been artificially tethered Fig. . ExperimAll the transcriptional assays in the present study were based on transiently transfected reporters. We noted that transiently transfected and stably integrated promoters might behave differently ,56. ObviChloramphenicol acetyltransferase (CAT) reporter plasmid pG5CAT was from Clontech. CAT plasmid pU3RCAT containing the HTLV-I LTR has been previously described . Other CSequences of canonical CRE, Sp1, AP1 and κB motifs in the reporter plasmids have been described ,58,59. HExpression plasmids for wild type and mutant Tax have been described elsewhere ,61. pIEXExpression plasmid pRSV-KCREB for the dominant-negative CREB protein KCREB  was kind5 cells/well into six-well culture plates and transfected using calcium phosphate method as described [14C-labeled chloramphenicol (Amersham) and acetyl coenzyme A  for CAT reaction. CAT activities were detected using thin-layer chromatography and quantified by phosphorimager (Molecular Dynamics). For transfection of cells, each well received the same dose of plasmids. The empty vector or pUC19 was added to compensate for the different amounts of plasmids when necessary.HeLa cells were grown in Dulbecco's modified Eagle's medium supplemented with fetal calf serum and antibiotics, seeded at 5 × 10escribed . Jurkat escribed . BrieflyNone declared.


To determine the prevalence, types and demographic features of child labour among school children in Nigeria.A cross-sectional interview study of 1675 randomly selected public primary and secondary school pupils aged 5 to less than 18 years was conducted in the Sagamu Local Government Area of Ogun State, Nigeria from October 1998 to September 1999.The overall prevalence of child labour was 64.5%: 68.6% among primary and 50.3% among secondary school pupils. Major economic activities included street trading (43.6%), selling in kiosks and shops (25.4%) and farming (23.6%). No child was involved in bonded labour or prostitution. Girls were more often involved in labour activities than boys : this difference was most obvious with street trading (p = 0.0004). Most of the children (82.2%) involved in labour activities did so on the instruction of one or both parents in order to contribute to family income. Children of parents with low socio-economic status or of poorly educated parents were significantly involved in labour activities (p = 0.01 and p = 0.001 respectively). Child labour was also significantly associated with increasing number of children in the family size (p = 0.002). A higher prevalence rate of child labour was observed among children living with parents and relations than among those living with unrelated guardians.It is concluded that smaller family size, parental education and family economic enhancement would reduce the pressure on parents to engage their children in labour activities. Child labour covers all economic activities carried out by children regardless of their occupational status . It has Most of child labour takes place in Asia, the Pacifics and Africa -9. In moThis presentation is part of a broader study on child labour among school children in public primary and junior secondary schools in Sagamu Local Government Area of Ogun State, Nigeria. The study was conducted between October 1998 and September 1999 (inclusive). It was approved by the Ethical Committee of the Olabisi Onabanjo University Teaching Hospital Sagamu (OOUTH).Sagamu Local Government Area (SLGA) is one of the 15 Local Government Areas in the State. It is predominantly rural and semi-urban with a land space of 68.4 sq km. The estimated population for the year 1997 is 177,514. Administratively, SLGA is made up of 11 wards. The total school enrolment in the 50 primary and 16 junior secondary schools (JSS) for the year 1997 was 30,597, comprising 21,476 primary and 9,121 JSS pupils. Excluding 6,762 new entrants who did not have academic records for the preceding year left 23,835 pupils . Thus the ratio of primary to junior secondary school pupil population was about 3 to 1. This ratio was considered while selecting the subjects for the study.The survey sample was drawn randomly from all 50 public primary and 16 secondary schools in the LGA. Using ballot papers, one primary and one secondary school were randomly selected from each administrative ward. However, one ward had no primary school and four wards had no secondary school. Thus, a total of ten primary and seven secondary schools were selected. There were five classes in each primary school and two in each junior secondary after excluding primary 1 and JSS1 pupils because they were new entrants in their respective schools and therefore did not have academic records for the preceding year. Each class was made up of at least two arms.One arm of each eligible class in the selected schools was randomly selected, also using ballot papers. Altogether, 64 classes were selected: 50 classes from 10 primary schools and 14 classes from 7 secondary schools. Thirty pupils were randomly selected from each class to form the study school. The pupils were interviewed one at a time between the hours of 8.00 am and 2.00 pm on Monday through Friday using a structured, close-ended questionnaire. The interviews were conducted by author MBF with the help of previously trained research assistants. Relevant background information on age, sex, family background, involvement or otherwise in after-school economic activities were obtained. Information on parental education, occupation/income was obtained through requests accompanying letters to parents and guardians asking for their consent. The occupation/income and educational attainments of the parents were used to determine socio-economic index scores of the children using modified criteria described by Oyedeji .Each parent was given two index scores: one for occupation/income and the other for educational attainment. The scores were rated on a scale of 1 to 5, from the more educated and more highly placed occupation/income groups to the least advantaged. The mean of the four scores for both parents was calculated and the value became the assigned socioeconomic group of the family/child. Where the calculated mean was not a whole number, the next higher integer was used.Child labour was defined as any type of economic task, paid, unpaid, or exploitative, engaged in by a child less than 18 years of age, which places the interests of the beneficiary well above those of the child and is detrimental to the physical mental, social, educational and moral development of the child [For the purpose of analysis the study children were grouped into two; those involved in child labour and those who were not. Chi-square analysis was used to compare proportions and probability; (p) values less than 5% (0.05) were accepted as statistically significant.A total of 1675 day pupils were recruited into the study. The male/female ratio was 1:1.02. The majority (86.4%) of respondents were of Yoruba extract. Their ages ranged between 5 and less than 18 years. Those in primary schools were 1299 while 376 were in junior secondary schools as shown in Table As shown in Figure 2 = 5.22, p <0.025). The prevalence of child labour did not vary significantly among children living with different types of relatives (p > 0.05). A significant rising trend of involvement in child labour with increasing number of children in the family  was observed.Over 80% of the study children lived with one or both parents, 15.2% with other relatives and very few (0.3%) with unrelated guardians. Children living with parents (both or single) and relatives had higher prevalence rates of child labour. The number of children living with unrelated guardians was relatively small but this group had less than 50% the prevalence of child labour of their counterparts living with relatives  declare that they have no competing interests.MBF conceived of the study, participated in its design, collection, statistical analysis and interpretation of data. FON contributed to the statistical analysis, interpretation of the data and has been involved in drafting and revising the manuscript.AOO participated in coordination of the study and revision of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


When the number of groups involved is more than two, however, a more general approach is needed.Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample We describe how logistic regression with overdispersion supplies this generalization, carrying with it the framework for incorporating other covariates into the model as a byproduct. This approach has the advantage that logistic regression routines are available in several common statistical packages.The described method provides an easily implemented tool for analyzing SAGE data that correctly handles multiple types of variation and allows for more flexible modelling. The Serial Analysis of Gene Expression (SAGE) methodology introduced by Velculescu et al.  is a seqBriefly, mRNA transcripts are converted to cDNA and then processed so as to isolate a specific subsequence; starting from the poly-A tail, the subsequence is the 10  or 14 (long SAGE) bp immediately preceding the first occurrence of a cleavage site for a common restriction enzyme. Ideally, this subsequence, or "tag" is sufficiently specific to uniquely identify the mRNA from which it was derived. Tags are sampled, concatenated and sequenced, and a table consisting of the tag sequences and their frequency of occurrence is assembled. The complete table derived from a given biological sample is referred to as a SAGE "library". As most tags are sparse within the entire sample, most libraries contain numbers of tags in the tens of thousands to allow the expression levels to be estimated. Due to the current costs of sequencing, however, the total number of libraries assembled for a given experiment is typically small: often in the single digits and occasionally in the tens.While the type of information, gene expression, being investigated in a SAGE experiment is the same as that in a cDNA or oligonucleotide microarray experiment, there are some qualitative differences in the approaches. First, SAGE uses sequencing as opposed to competitive hybridization. Second, while the expression value reported for an array experiment is a measure of fluourescence and is loosely continuous, SAGE supplies data on gene expression in the form of counts, potentially allowing for a different type of "quantitative" comparison. Third, SAGE is an "open" technology in that it can provide information about all of the genes in the sample. Microarrays, by contrast, are "closed" in that we will only get information about the genes that have been printed on the array.Y, the number of counts of that tag in the library, and n, the total number of tags in the library. In analyzing SAGE data across a series of libraries, interest typically centers on assessing how the underlying true level of gene expression is changing as we move from one library to the next.Mathematically, the information pertaining to the abundance of a particular tag in a sample is summarized in two numbers: Yi} and the set of library sizes {ni}, where the subscript i denotes the specific library. Unless otherwise specified, we will restrict our assessment of differential expression to the case of a single tag. This approach is common to all of the procedures described below. In a real analysis the chosen test is applied to all tags individually and a list of those tags showing differential expression is reported. Different tests will provide altered assessments of significance for individual tags, and hence the list provided will depend on the test employed.When surveyed across a series of libraries, the sufficient statistics containing all of the information about the change in expression for a single tag are the set of counts {Xi describing properties of library i. The most common case involves comparing two groups of libraries, such as cancer and control. In this case the information Xi simply defines which group library i belongs to. If there are more than two groups, Xi can have more levels or can even be vector valued, but as before interest centers on assessing how and whether the expected proportion changes with X.In most problems of interest, there is also covariate information Much work has been done on the problem of comparing expression between two groups. Most of the approaches -9 deal wt-test used to compare two groups of samples in ,ni are the same. While approximate equality may suffice, even this assumption may be questionable for SAGE data, particularly if some of the libraries are drawn from experiments conducted at different times. Williams  should be more than adequate. Second, the presence of overdispersion means that pooling the samples underemphasizes the evidence of a small proportion being supplied by the zero variance of the observed proportions. While we could pursue a more optimal proportion, we choose in this case to simply use the simplistic bound noted above. Here, as the library sizes in the first group are 49610 and 48479, the proportion is 1/(49610 + 48479) and the faked counts are 0.506 = 49610/(49610 + 48479) and 0.494, respectively. Some reformatted results from this fit are shown in Table The second run of the fitting procedure takes the overdispersion parameter as given, and fits the data after replacing the zero proportions in a group with the same small nonzero proportion, giving us a hopefully conservative estimate of the fold change. This type of replacement is commonly used, and is most often justified via the assumption of a vague prior distribution for the proportions, with the point estimate being derived as the posterior mean or mode. A common assumption for a prior in dealing with proportions is the uniform distribution. The posterior mean after 0 successes are observed out of t-value  relies on the approximate normality of the likelihood function in the vicinity of the maximum, and this shape assumption breaks down severely if the number of counts in one group is small. Tests based on changes in the scaled deviance, corresponding to likelihood ratio tests, are better.The results for this fit are ridiculously "insignificant". The problem lies in the fact that the use of a The third run of the fitting procedure fits a simpler submodel, in this case a single proportion for all eight libraries, using the same overdispersion estimate so as to measure the change in deviance. The results of this fit are shown in Table Here, we cannot conclude (given the level of overdispersion) that the difference is real. Note that the degrees of freedom used in the denominator is 5; this follows from the fact that only 6 libraries were used to estimate the overdispersion parameter, and one of those 6 degrees of freedom was needed to estimate the proportion.In general, when any of the groups has very small counts, checking the change in deviance is a good idea.Logistic regression with overdispersion addresses three issues with SAGE data: simultaneously modelling multiple types of variance, dealing with multiple groups at once, and allowing for the incorporation of covariates. This procedure is widely implemented in available software. Further, and most importantly, viewing SAGE data in the logistic regression setting supplies the framework for thinking of models that describe such data.t-tests. The regression setting carries with it other benefits, such as a well-developed body of work regarding model checking, residual analysis, and detection of outliers. For example, the influence of any given library tag count on the overall analysis can be assessed, and methods can be made more robust by bounding these functions so that no single library drives the results.Dealing with multiple types of variance yields significance estimates we believe to be superior to those derived from pooled counts or from There are some areas in which we can identify difficulties and see room for improvement.First, the model that we are using for the error may be improved. For SAGE data, the proportion associated with a specific tag is rarely on the order of a percent, sopi) ≈ log(pi)logit = β0 + β1xi + εi,logit, where "mixed" refers to the fact that we have both fixed effects of interest, the changes with the covariates, and random shocks whose variance needs to be estimated and allowed for. Williams ;p.values <- 2 * pt,fit3$df.residual);}########################################### Next, we deal with three groups##########################################if(0){# We begin by focusing on gains# available when multiple groups are# present, even if the other groups are# not directly part of the contrast of# interest, due to the additional# information that the added groups can# provide about the scale of the# overdispersion.# Here, we use the data from the tag# TGCTGCCTGT, and this time we note that# there are 3 groups of libraries:# normals (libraries 1–2), primary# tumors (libraries 3–4), and cell lines# (libraries 5–8). If we are interested# in the contrast between normals and# primary tumors, we can fit this using# only the data from those two groups,# or using the data from all three.# First, fit the model as if there were# just two groups present.y <- c;n <- c;x <- c;fit1 <- glm ~x,family=binomial);fit2 <- glm.binomial.disp(fit1);# get the correct p-valuesfit2.t.values <- summary(fit2)$coefficients ;fit2.p.values <- 2 * pt, fit2$df.residual);# Next, fit the model assuming that# there are three groups. In this case,# we cannot use a single covariate# vector x, as this is not suited to# indicating 3 or more groups in an# unordered fashion # In general, if we have k groups, we# need to use k-1 covariate vectors.# Here, we use# x1 <- c;# x2 <- c;# The set of all 0s # corresponds to the first group, here# the normals, and the other groups are# defined by which one of the other# covariates is nonzero:# Group 2 (primaries), ,# Group 3 (cell lines), y <- c;n <- c;x1 <- c;x2 <- c;fit3 <- glm ~x1 + x2,family=binomial);fit4 <- glm.binomial.disp(fit3);# get the correct p-valuesfit4.t.values <- summary(fit4)$coefficients ;fit4.p.values <- 2*pt, fit4$df.residual);# The above approach has fit the model# with all of the covariates available,# but in order to perform an analysis of# deviance we want to fit various# submodels using the same estimate of# overdispersion as found here. In this# case, there are 3 submodels:fit5 <- glm ~x1,family=binomial,weights = fit4$disp.weights);fit6 <- glm ~x2,family=binomial,weights = fit4$disp.weights);fit7 <- glm ~1,family=binomial,weights = fit4$disp.weights);# alternatively, the anova function can# be used, but this only considers the# submodels obtained by adding terms# sequentially. Thus, we get the# deviances for beta_0 (the null model),# beta_0 + beta_1 (adding the x1# covariate only), and beta_0 + beta_1 +# beta_2 ;}########################################### Next, we deal with the case of other# covariates, possibly continuous.##########################################if(0){# Here, we are using the counts from the# GCGAAACCCT tag, but we are treating# the 8 libraries as coming from tissue# type 1 (libraries 1–4) and tissue type# 2 (libraries 5–8), with normal tissue# of both types  and# primary tumor of both types . In this hypothetical# example, we are able to partition the# changes into effects associated with# normal/primary differences (x1) or# tissue 1/tissue 2 differences (x2).y <- c;n <- c;x1 <- c;x2 <- c;fit1 <- glm ~x1 + x2,family=binomial);fit2 <- glm.binomial.disp(fit1);# get the correct p-valuesfit2.t.values <- summary(fit2)$coefficients ;fit2.p.values <- 2*pt, fit2$df.residual);# Next, again using the tag as above, we# posit that we also have access to the# levels of a biomarker potentially# predictive of survival, supplied as# the levels of another covariate x3.# The values supplied here were# generated as random draws from a# uniform  distributionx3 <- c;fit3 <- glm ~x1 + x2 + x3,family=binomial);fit4 <- glm.binomial.disp(fit3);# get the correct p-valuesfit4.t.values <- summary(fit4)$coefficients ;fit4.p.values <- 2*pt, fit2$df.residual);}KAB, LD and JSM developed the main ideas and the methodology; LD did most of the coding. CMA supplied SAGE data and provided practical feedback on aspects of earlier approaches found to be wanting, thus guiding further development.


Bacillus Calmette Guérin (BCG)-immunotherapy has a well-documented and successful clinical history in the treatment of bladder cancer. However, regularly observed side effects, a certain degree of nonresponders and restriction to superficial cancers remain a major obstacle. Therefore, alternative treatment strategies are intensively being explored.Mycobacterium tuberculosis for the treatment of bladder cancer. The phosphate transport protein PstS1 which represents the phosphate binding component of a mycobacterial phosphate uptake system is known to be a potent immunostimulatory antigen of M. tuberculosis. This preclinical study was designed to test the potential of recombinant PstS1 to serve as a non-viable and defined immunotherapeutic agent for intravesical bladder cancer therapy.We report a novel approach of using a well defined immunostimulatory component of Mononuclear cells (PBMCs) were isolated from human peripheral blood and stimulated with PstS1 for seven days. The activation of PBMCs was determined by chromium release assay, IFN-γ ELISA and measurement of lymphocyte proliferation. The potential of PstS1 to activate monocyte-derived human dendritic cells (DC) was determined by flow cytometric analysis of the marker molecules CD83 and CD86 as well as the release of the cytokines TNF-α and IL-12. Survival of presensitized and intravesically treated, tumor-bearing mice was analyzed by Kaplan-Meier curve and log rank test. Local and systemic immune response in PstS1-immunotherapy was investigated by anti-PstS1-specific ELISA, splenocyte proliferation assay and immunohistochemistry.in vitro experiments showed that PstS1 is able to stimulate cytotoxicity, IFN-γ release and proliferation of PBMCs. Further investigations showed the potential of PstS1 to activate monocyte-derived human dendritic cells (DC). In vivo studies in an orthotopic murine bladder cancer model demonstrated the therapeutic potential of intravesically applied PstS1. Immunohistochemical analysis and splenocyte restimulation assay revealed that local and systemic immune responses were triggered by intravesical PstS1-immunotherapy.Our in vitro activation of human immune cells by recombinant PstS1. In addition, intravesical PstS1 immunotherapy induced strong local and systemic immune responses together with substantial anti-tumor activity in a preclinical mouse model. Thus, we have identified recombinant PstS1 antigen as a potent immunotherapeutic drug for cancer therapy.Our results demonstrate profound  Urothelial carcinoma of the bladder accounts for about 4% of all cancer related death in man. The large majority of tumors (70–80%) is superficial at diagnosis and has a high rate of local recurrence (70%) and progression (30%) after local surgical therapy. Therefore, patients require lifelong medical follow-up examinations and effective prophylactic treatment to prevent recurrences and progression of the tumor.Mycobacterium bovis – has a well-documented and successful clinical history. Immunotherapy with BCG is performed by six weekly instillations of viable Mycobacteria (for induction course) into the bladder of patients after initial transurethral resection of the tumor. Until now various clinical trials have shown that this type of therapy is superior to topical chemotherapy and transurethral resection of the tumor alone to prevent recurrences and local progression especially in patients with high risk tumors [In this type of cancer, the immunotherapeutic use of Mycobacteria – specifically Bacillus Calmette-Guerin (BCG), a non-pathogenic strain of k tumors -3. Despik tumors . Furtherk tumors . BCG's ak tumors . Roughlyk tumors ,8. Thus,M. tuberculosis belonging to the family of ABC (ATP-binding cassette) transporters [In order to limit toxicity recent endeavors are therefore focused on the development of alternative non-viable products and some of those have already been tested in preclinical and clinical studies -12. The sporters ,15. It isporters ,17. Moresporters .in vitro system and in a previously described murine model of experimental bladder cancer therapy [We hypothesized that this highly immunogenic protein antigen could function as an effective biological response modifier in immunotherapy of bladder cancer. To test the tumortherapeutic potential of recombinantly expressed PstS1  we perfo therapy . Because therapy -23, we pThe data reported herein demonstrate that PstS1 is a potent activator of human tumor-cytotoxic MNCs, induces maturation and activation of human dendritic cells and most importantly is very effective in the treatment of experimental orthotopic bladder cancer. While local and systemic immune responses were observed in sensitized and non-sensitized mice, immunotherapy of cancer was only successful in non-sensitized animals.2 in RPMI 1640  containing 10% FCS , 1% glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin. The murine bladder tumor cell line MB-49 was cultured at 37°C and 5% CO2 in DMEM  containing 10% FCS , 1% L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin.The human bladder tumor cell line T-24 was cultured at 37°C and 5% CO6/ml in RPMI-1640 medium containing 5% human serum, 100 U/ml penicillin and 100 μg/ml streptomycin. Recombinant PstS1 in concentrations from 0.1 μg/ml – 100 μg/ml, BCG , or PBS (100 μl) was added and the cells were cultured for 7 days in 6-well microtiter plates at 37°C and 5% CO2.MNCs from heparinized blood of healthy human donors were obtained by discontinuous gradient centrifugation using Biocoll Separating Solution  and adjusted to a concentration of 1 × 10251CrO4  for 1 h at 37°C, washed and resuspended at 5 × 104 cells/ml. Effector cells were added to a total of 100 μl of target cells at an effector:target ratio of 40:1. The radioactive content of the supernatant was measured in a gamma-counter . The specific lysis was determined according to the formula: spec. lysis (%) = 100 × (Exp – Spo)/ (Max – Spo) where Exp is the experimental release, Spo the spontaneous release and Max the maximum release.Cytotoxicity was determined in a standard 4-hour chromium release assay. Target cells were labeled with NaSupernatants of PstS1, BCG and PBS stimulated PBMCs were recovered at days 2, 5 or 7 and examined for the presence of IFN-γ. Each experiment was carried out several times with different donors. Detection was performed with an anti human IFN-γ ELISA-Kit according to the manufacturers' instructions .5 cells / well of the stimulated PBMCs were cultured in a microwell plate and 1 μCi 3H-Thymidine (five wells per sample)  was added during the last 18 h of stimulation. DNA was harvested on filter membranes and thymidine incorporation was measured by liquid scintillation counting .PBMCs were stimulated for 2 to 7 days with PstS1, BCG, PBS or PHA (Sigma). 2 × 106 monocytes were cultured for seven days with 2 ml RPMI 1640, 10% FCS  1% penicillin/streptomycin and IL-4/GM-CSF (500 U/ml each)  in 24-well cell culture plates . Exchange of medium was carried out at day three and day five. This procedure resulted in full differentiation of monocytes with no undifferentiated monocytes present in the cultures after seven days. Immature DCs were stimulated for three days with 10 μg/ml PstS1 or BCG (MOI = 0.01) and subjected to flow cytometry. Debris was excluded from the analysis according to FSC/SSC gating.After separating peripheral blood mononuclear cells (PBMCs) from heparinized blood of healthy donors by Ficoll-Paque centrifugation, monocytes were elutriated by counterflow centrifugation. For generation of immature DCs, 2 × 10Expression of cell surface molecules was analyzed by flow cytometry  using phycoerythrin (PE) conjugated monoclonal antibodies (mAbs): anti-CD1a , anti-CD83 , anti-CD14 and anti-CD86 .5 DCs suspended in PBS containing 5% human serum and 0.1 % sodium acide were incubated with mAbs for 30 minutes on ice. Then cells were washed with PBS and resuspended in 400 μl 1.5 % paraformaldehyde in PBS.1 × 10Supernatants of PstS1, BCG and PBS stimulated DCs were recovered at day 3 after stimulation and examined for the presence of TNF-α and IL-12. Each experiment was carried out several times with different donors. TNF-α detection was performed with a quantitative ELISA, provided by Dr. H. Gallati .IL-12 detection was carried out with an ELISA-Kit from eBioscience (San Diego/USA).E. coli and purified by standard chromatography.PLG-particles (10 mg/ml)  were diluted 4:1 with PBS and the pH was adjusted to 7.0 with NaOH. PstS1 was recombinantly expressed in 250 μg of PLG-particles were loaded with PstS1 or BSA by coincubation with 60 μg of the respective protein for 15 h at RT on a shaker . Afterwards the particles were spun down and washed for 5 min with 100 μl PBS. The supernatant was removed and the particles were diluted in 100 μl of PBS. 10 μl of loaded particle solution were centrifuged and the pellet was treated for 30 min at RT with 0.1 M NaOH/ 10% SDS. After centrifugation in a minifuge, supernatant was removed and analyzed on a 10% SDS page. Using recombinant PstS1 as a calibration standard it was determined that 250 μg PLG-particles bound approximately 50 μg of PstS1 protein.4 MB-49 cells were instilled into the bladder. Intravesical immunotherapy with 100 μg PstS1 in 100 μl PBS was performed on days 1, 8, 15 and 22 after tumor implantation. Control groups were instilled with PBS (100 μl) alone. The viability status of the mice was checked daily. Surviving mice were sacrificed on day 70. Survival of mice was compared using Kaplan-Meier analysis and log-rank test. Trial II: Performance similar to trial I with the following differences. 11 animals per group were subcutaneously injected with a) 250 μg empty PLG-particles b) 250 μg PLG-particles coated with 50 μg BSA c) 100μl of PBS. Intravesical treatments were performed with 100 μg PstS1 or PBS only.Female C57BL/6 mice were purchased from Charles-River Laboratories (Sülzfeld/ Germany) at the age of 6–8 weeks. To test efficacy of PstS1 therapy in C57/BL6 mice a published syngeneic, orthotopic bladder cancer model was used . Trial IC57/BL6 mice (5 per group) were treated as described in table Murine blood was obtained immediately after sacrificing mice by heart punctation and centrifuged in a microcentrifuge to obtain the serum for antibody analysis which was performed with the PstS1-ELISA-kit  according to the manufacturer's instructions.2O. Afterwards 2 × 105 cells / well were cultured in a microwell plate and stimulated with 10 μg/ml PstS1 for a period of five days (five wells per sample). Then 1μCi 3H-Thymidine  was added and cells were incubated for 15 h at 37°C and 5% CO2. Finally the DNA was harvested on filter membranes and 3H-Thymidine incorporation was measured by liquid scintillation counting .Splenocytes were isolated, washed with PBS and erythrocytes were lysed with Hin vitro systems. As in these systems the immunostimulatory mechanisms of whole BCG bacteria have been thoroughly studied in the past [To assess the immunostimulatory properties of our PstS1-preparation we analyzed several human PBMC the past ,25-27 weIn a first set of experiments the optimal concentration of PstS1 to stimulate human PBMC cytotoxicity Fig. , IFN-γ rAs depicted in figure Dendritic cells are central cellular mediators for the induction of anti-tumor immune responses. We tested the potential of PstS1 to induce activation and maturation of human monocyte-derived DCs.After 7 days of differentiation human monocyte-derived dendritic cells showed the typical immature phenotype with low or absent expression of the monocyte marker CD14 and the maturation markers CD83 and CD86 Fig. . At the in vitro we conducted a series of studies to test its immunotherapeutic potential in vivo. Prompted by the controversy about the role of prior exposure to mycobacterial antigens in BCG immunotherapy we wanted to assess the in vivo activity of PstS1 in sensitized and non-sensitized mice. For this purpose two independent in vivo experiments were carried out. PLG-particles have previously been described as useful agents for sensitization of mice to mycobacterial antigens and were used as such in our study [After we have shown profound immunostimulatory properties of PstS1 ur study .In a first series of experiments we analyzed mice which had been s.c. sensitized with empty control particles or with particles loaded with PstS1 antigen. Ten days later mice received inoculation of tumor cells and subsequent intravesical treatment with either PstS1 or PBS control solution (four weekly instillations). Mice which received s.c. PBS followed by intravesical PBS served as negative controls. Using this experimental set up we could show that pre-vaccination with empty particles and intravesical treatment with PstS1 protein significantly prolonged survival of mice and thus provided a clear therapeutic benefit in this model of experimental bladder cancer Fig. . When miAfter we had demonstrated successful immunotherapy of bladder cancer by local instillation of PstS1 we next analyzed the local and systemic immune response of the various treatment groups to obtain initial insight into the immunological basis of this novel immunotherapy. To achieve this we analyzed the systemic antibody response by a semiquantitative anti-PstS1 IgG ELISA, the systemic lymphocyte response by splenocyte restimulation assays and the local immune response in the bladder by immunohistology.in vitro stimulation with PstS1. In contrast, the four groups of mice which had been exposed to PstS1 either s.c. or intravesically or via both routes strongly responded to specific restimulation of their splenocytes. Again these data indicate that systemic immune responses to PstS1 can be achieved by both s.c. and intravesical challenge. While a s.c. sensitization of mice with PstS1 abrogated the anti-tumor effect of intravesical PstS1  declare that they have no competing interests.CS carried out most of the experiments, provided experimental protocols and prepared the initial version of the manuscript. ABu contributed to figure The pre-publication history for this paper can be accessed here:


The objective of this study was to demonstrate the use of the Heckman two-step method to assess and correct for bias due to missing health related quality of life (HRQL) surveys in a clinical study of acute coronary syndrome (ACS) patients.We analyzed data from 2,733 veterans with a confirmed diagnosis of acute coronary syndromes (ACS), including either acute myocardial infarction or unstable angina. HRQL outcomes were assessed by the Short-Form 36 (SF-36) health status survey which was mailed to all patients who were alive 7 months following ACS discharge. We created multivariable models of 7-month post-ACS physical and mental health status using data only from the 1,660 survey respondents. Then, using the Heckman method, we modeled survey non-response and incorporated this into our initial models to assess and correct for potential bias. We used logistic and ordinary least squares regression to estimate the multivariable selection models.We found that our model of 7-month mental health status was biased due to survey non-response, while the model for physical health status was not. A history of alcohol or substance abuse was no longer significantly associated with mental health status after controlling for bias due to non-response. Furthermore, the magnitude of the parameter estimates for several of the other predictor variables in the MCS model changed after accounting for bias due to survey non-response.Recognition and correction of bias due to survey non-response changed the factors that we concluded were associated with HRQL seven months following hospital admission for ACS as well as the magnitude of some associations. We conclude that the Heckman two-step method may be a valuable tool in the assessment and correction of selection bias in clinical studies of HRQL. The potential impact of missing survey responses is often ignored in health-related quality of life (HRQL) studies ,2. MissiNewer statistical techniques have been developed to assess and correct for bias resulting from missing HRQL surveys ,3. One tThe objective of this study was to demonstrate the use of the Heckman two-step method to assess and correct for bias due to missing HRQL surveys. To accomplish this goal, we evaluated HRQL outcomes in a cohort of patients with acute coronary syndromes (ACS).We analyzed data from the VA Access To Cardiology study, which was a multi-center prospective cohort study of 2,733 veterans with a confirmed diagnosis of acute coronary syndromes (ACS), including either acute myocardial infarction or unstable angina . BaselinThe outcome variables were the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores from the 7-month SF-36 health status survey. The PCS and MCS scores reflect a patient's overall physical and mental health status, respectively ,11. The Baseline characteristics of the patients who did and did not complete an SF-36 were compared using analysis of variance for continuous variables and chi-square for categorical variables Table . Then, aThere are two steps in the Heckman method. The first step is the development of a selection equation (i.e. a model of factors associated with survey non-response). This step includes derivation of a variable from the selection equation called the Inverse Mills Ratio (IMR). The second step of the Heckman method is the insertion of the IMR variable into the initial regression models  from a given study in order to assess for, and attempt to control for, selection bias.The first step in the Heckman method is to create the selection model, which estimates whether or not the quality of life survey was completed. The Heckman selection equation is usually estimated using a probit estimator ,21. The Once the Heckman selection equation is estimated, the residuals (error term) from this equation are used to form a new variable called the Inverse Mills Ratio (IMR). The formula to create the IMR variable depends on the distributional assumption in the outcome equation. In most HRQL applications, the quality of life score is the outcome of interest and is usually estimated using multivariable linear regression. In this case, the distributional assumption of the error term is the standard normal distribution, so that the ratio of the standard normal probability density function (pdf) and cumulative density function (cdf) applied to the residuals for each individual in the data set is created. The ratio of pdf/cdf is the IMR.Each individual in the study sample receives an individual value of the IMR based on the residual observed for that individual in the selection equation. In this study, the value of the IMR for each individual represented the predicted probability that they completed the 7-month SF-36 survey. It is important to note that the IMR is a function not only of observed or measured variables that are included in the selection equation, but also of unobserved or unmeasured variables. These are captured through the error term or residual in the selection equation, and included through the non-linear function used to estimate the IMR. As a result, adding the IMR into the outcome equation introduces a term that attempts to capture both observed and unobserved variables that affect selection, or non-response.We estimated the Heckman model using the maximum likelihood estimation method in Stata version 8.0. In this approach, the outcome and selection models are estimated jointly, which can result in slightly different selection models for different outcomes, in this case the PCS and MCS scores from the SF-36. However, for clarity of presentation of the Heckman process, we present only one table of selection equation results (the probit estimation of the probability of returning the SF36), assuming that the patient survived to the 7-month survey point.The second step of the Heckman method is to include the IMR as a separate predictor variable in the initial regression models. In this study, the IMR variable derived from our Heckman selection model was inserted into the initial PCS and MCS models. Once this variable is inserted, two factors can be evaluated to help determine whether there is significant bias from missing responses in the initial models. First, one can examine the significance of the IMR variable itself. If significant, it suggests there was significant bias in the initial model. However, one potential limitation of the Heckman method is that if the Heckman selection model is not well-specified, and the variables in the selection model do not predict response/non-response well, the IMR may be weaker than expected and the Heckman method may have limited power to detect bias. Therefore, a second factor to examine following the addition of the IMR variable into the initial outcome models is whether or not there have been significant changes in any of the parameter estimates of the other predictor variables in the model. While somewhat arbitrary, changes in parameter estimates of >10% may indicate that these estimates were biased due to missing surveys. Where possible, one should apply clinical judgment about whether changes in parameter estimates are 'biologically important' .With these factors taken together, the insertion of the IMR variable into the initial risk models allows the assessment of whether or not there was bias in the initial models, and suggests which initial predictors may have been most associated with this bias. Furthermore, by including the effect of unmeasured as well as measured variables from the selection equation, bias due to selection is controlled.Compared to patients that completed the 7-month SF-36 survey, patients who did not respond to the survey were older, more likely to be current smokers and more likely to have a history of alcohol or substance abuse, dementia, stroke, or depression Table . FurtherThe initial multivariable PCS and MCS models not accounting for potential selection bias from missing HRQL surveys are presented in Tables Variables significantly associated with better 7-month physical health status included ST-segment elevation MI on electrocardiogram and coronary revascularization during the index ACS hospital admission. Variables significantly associated with worse 7-month physical health status included older age, history of prior CABG surgery, chronic heart failure, arthritis, COPD, stroke, depression, diabetes, and elevated serum creatinine during index ACS admission.Variables significantly associated with better 7-month mental health status included older age and ST-segment elevation MI on electrocardiogram. Variables significantly associated with worse 7-month mental health status included a history of prior MI, alcohol and/or substance abuse, COPD, and arthritis.The Heckman selection model (modeling response to the SF-36) is presented in Table The final multivariable models for PCS and MCS (after addition of the IMR variables from the Heckman selection model) are presented in Tables By contrast, when the IMR variable was inserted into the initial MCS model, we found evidence of selection bias. In this case, the parameter estimate for history of alcohol or substance abuse changed from significant to insignificant with the introduction of the IMR variable. Therefore, it appears that a history of alcohol or substance abuse was associated with lower likelihood of responding to the survey, but not directly associated with mental health status. In addition, a number of parameter estimates changed quantitatively, with larger changes than those observed in the PCS findings. Finally, the coefficient on the IMR variable itself was significant, and was negatively associated with MCS, implying that unobserved variables in the selection equation appear to be associated with worse MCS scores.The goal of this study was to demonstrate the use of the Heckman two-step method to assess and correct for bias due to missing HRQL surveys in a clinical study of acute coronary syndrome patients. We created initial multivariable models of 7-month post-ACS physical and mental health status using data only from survey respondents. Then, using the Heckman method, we modeled survey non-response, derived an Inverse Mills Ratio variable for each patient that captured the likelihood of survey response, and incorporated this variable into our initial models to assess and correct for potential bias from survey non-response.We found that our initial model of 7-month physical health status was not biased due to survey non-response. In contrast, our initial model of 7-month mental health status was biased. After controlling for bias due to non-response, a history of alcohol or substance abuse was no longer associated with mental health status. Furthermore, the magnitude of the parameter estimates for several of the other predictor variables in the MCS model changed after accounting for bias due to survey non-response.Given these results, biased parameter estimates of the association between these variables and mental health status would have been reported if we had used the standard approach to evaluating the predictors of HRQL outcomes in this population. Furthermore, we might have concluded that alcohol/substance abuse was significantly associated with mental health status outcomes following ACS, and may therefore be an important target for interventions to improve post-ACS HRQL . While alcohol/substance abuse may be important for other reasons, it would have been incorrect to conclude that it was associated with HRQL in our study population. Rather, it was a marker for survey non-response. This analysis demonstrates the utility of the Heckman method in its application for assessing and correcting survey response bias in clinical studies of HRQL.Health-related quality of life data are usually not missing at random, and failure to account for missing HRQL assessments can bias estimates of associations and may lead to inappropriate conclusions about the determinants of HRQL outcomes -3. OftenThere are other statistical techniques, or approaches, to assess and correct for bias resulting from missing HRQL surveys, including index function models, propensity scores, instrumental variables, and multiple imputation methods -4,23. ThPropensity score approaches can be analogous to the Heckman method in that a multivariable model of survey non-response is developed and the probability of survey non-response is used to stratify the study population and/or the propensity score is used as an independent variable in the primary HRQL models. In other words, propensity scores are similar to the Heckman method in that the predicted probability of non-response is used as the basis for assessing the impact of missing data and controlling for it . Unlike Instrumental variable approaches are also used to address similar questions to those addressed by the Heckman method. In the full instrumental variable approach, a single exogenous variable  is used to stratify the full sample, removing the effect of the correlated error terms that lead to biased estimates . An instFinally, multiple imputation methods can be employed to address missing HRQL data . In contThe Heckman method has several limitations. First, the selection equation must have at least one variable that is associated with survey response but not the outcome of the study (i.e. HRQL). In some clinical applications, the inability to identify such a variable may make it difficult to use the full Heckman method to control bias. However, it is still possible to use the first step of estimating a selection equation to assess the degree to which selection bias may affect the parameter estimates in an outcome equation. If there are variables that are significant in both the selection equation and the outcomes equation, it is likely that there is bias due to selection effects in the outcome equation. Acknowledging this and commenting on the likely magnitude of effect may provide helpful guidance to clinicians and other researchers. Another limitation of the Heckman method is that this technique depends heavily on the quality of the data available for the selection equation. If the amount of variance explained is relatively low, then there is a possibility that selection bias in the outcomes equation may not be detected. In other words, the Heckman method can be under-powered for the detection of bias in some cases.Finally, the Heckman method is very sensitive to how the model is specified; in other words, omitting variables that are associated with either non-response or with the outcome of interest  can lead to inaccurate findings and biased estimates of the parameters in the final models. Careful attention to specifying the models, and ensuring that model specification follows what is known in the literature to be associated with the outcomes of interest is essential .This study demonstrated the use of the Heckman two-step method to assess and control for bias from missing HRQL surveys in a clinical study. We found that our mental health status model was significantly biased due to missing HRQL assessments. Recognition and correction of this bias changed the parameter estimates of association and the factors that we concluded were associated with HRQL seven months following hospital admission for an acute coronary syndrome. We conclude that the Heckman two-step method may be a valuable tool in the assessment and correction of selection bias in clinical studies of HRQL.AES conceived of the study, participated in design and coordination, conducted analyses, and drafted the manuscript; MEP conducted analyses and contributed to the manuscript; DJM and JAS reviewed and contributed to the manuscript; JSR participated in the design and coordination of the study and participated in the drafting and revision of the manuscript. All authors read and approved the final manuscript.


Amidst the hoopla over the exact number of genes we have in our genome—more than a fruitfly, fewer than a rice plant—a more fundamental genetic truth has often been obscured. The expression of 20,000–30,000 genes is under the control of an uncounted host of non-coding sequences, which bind transcription factors and thereby regulate when and where genes are expressed. Unlike coding sequences, whose signatures are easy to spot, the characteristic features of non-coding regulatory elements are largely unknown, making their discovery by simple sequence analysis difficult. In this issue, Greg Elgar and colleagues attack this problem by comparing the non-coding sequences of the human and the pufferfish.Since the last common ancestor of these two species existed 450 million years ago, the authors reasoned that non-coding sequences conserved between them are likely to be fundamental to vertebrate development. Through sequence alignment with increasingly strict criteria, they identified 1,373 highly conserved non-coding elements (CNEs), with an average length of about 200 base pairs. The average sequence match is 84%: not perfect, but much higher than for coding regions shared by humans and pufferfish. A quick check showed that virtually all the sequences also occurred in rodents, chickens, and zebrafish, but not in the nematode, fruitfly, or even the sea squirt, a primitive non-vertebrate chordate.CNEs are not spread uniformly throughout the genome. Instead, they are bunched together in fewer than 200 clusters, most of them in close proximity to genes implicated in transcriptional regulation or development. This clustering of CNEs suggests they may not only attract transcription factors, but may also influence the local topology of the DNA, thereby controlling access to their associated gene. Several clusters also appear in regions without any known genes—the identification of these clusters might lead to the discovery of new developmentally significant genes.While “in silico” discoveries such as this can be the jumping-off point for whole new areas of investigation, their validity must be tested “in aqua,” in the wet biology of real organisms. For this Elgar and colleagues chose the zebrafish, because its transparent embryo is ideal for observing developmental events. They injected individual CNEs into embryos, along with a green fluorescent protein (GFP) reporter. By day two of development, 23 out of 25 CNEs injected had upregulated GFP expression, indicating interaction of these sequences with endogenous transcription factors. Different CNEs caused different regional patterns of expression, in keeping with their presumed roles in distinct developmental processes.The discovery of these developmentally important sequences opens several avenues of new research. For example, analyzing the sequence and location of these CNEs may help point the way to other non-coding elements that remain undiscovered. It is also likely that mutations in these critical sequences cause human diseases. Studying how such mutations drive development astray may lead to better understanding not only of these diseases, which are likely to be rare, but also of normal human development.


Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming.This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain.This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution.Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%. Bio-signals are essentially non-stationary signals; they display a fractal like self-similarity. They may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random – in the time scale. However, to (study and) pinpoint anomalies in voluminous data collected over several hours is strenuous and time consuming. Therefore, computer based analytical tools for in-depth study and classification of data over day long intervals can be very useful in diagnostics.Electrocardiography deals with the electrical activity of the heart. Monitored by placing sensors at defined positions on chest and limb extremities of the subject, electrocardiogram (ECG) is a record of the origin and propagation of the electric action potential through cardiac muscle. It is considered a representative signal of cardiac physiology, useful in diagnosing cardiac disorders. The state of cardiac health is generally reflected in the shape of ECG waveform and heart rate. It may contain important pointers to the nature of diseases afflicting the heart. However, bio-signals being non-stationary signals, this reflection may occur at random in the time scale. Therefore, for effective diagnostics, the study of ECG pattern and heart rate variability signal (instantaneous heart rate against time axis) may have to be carried out over several hours. HRV is a useful signal for understanding the status of the autonomic nervous system (ANS).The interest in the analysis of heart rate variability (HRV), that is, the fluctuations of the heart beating in time, is not new. And much progress was achieved in this field with the advent of cheap and massive computational power, which provoked many recent advances.HRV is a non-invasive measurement of cardiovascular autonomic regulation. Specifically, HRV is a measurement of the interaction between sympathetic and parasympathetic activity in autonomic functioning. There are two main approaches for analysis: time domain analysis of HRV [for standard deviation of normal to normal intervals (SDNN)]; and frequency domain analysis [for power spectrum density (PSD)]. The latter provides high frequency (parasympathetic activity) and low frequency (sympathetic and parasympathetic activity) and total power  values -3. RecenThe cardiovascular system is a complex system that includes heart and vessels. ECG and HRV are two methods for study it. Hence, many attempts have been made to analyze these signals and extract information about the cardiovascular system. Most of the methods used are linear and it has been recognized that nonlinear methods may be more suitable for analyzing signals that originate from complex nonlinear living systems . Recent A complex system like cardiovascular system can not be linear in nature and by considering it as a nonlinear system can lead to better understanding of the system dynamics. Recent studies have also stressed the importance of nonlinear techniques to study HRV in both health and disease. The progress made in the field using measures of chaos has attracted scientific community applying these tools in studying physiological systems, and HRV is no exception. There have been several methods of estimating invariants from nonlinear dynamical systems reported in the literature. Recently, Fell et al and Radhakrishna et al have tried the nonlinear analysis of ECG and HRV signals respectively ,14. Alsopremature ventricular contraction (PVC), Complete Heart Block (CHB), Sick Sinus Syndrome (SSS), Congestive heart failure (CHF), Ishemic/Dilated cardiomyoapathy (ISCDIL), Atrial Fibrillation (AF), and ventricular fibrillation (VF).ECG data for the analysis was obtained from MIT-BIH arrhythmia database . Prior tr-r seconds) and the heart rate (beats per minute) is given as:Each dataset is taken consists of more than10,000 samples and the sampling frequency of the data is 360 Hz. The interval between two successive QRS complexes is defined as the RR interval (tr-r     (1)HR = 60/tX(k) in phase space. Then the dynamical behavior of the signal is reconstructed by succession of these points X(k) in the phase space. Phase space reconstructions are based on the analysis of dynamic systems by delay maps. The vectors X(k) in the multidimensional phase space are constructed by time delayed values of the time series, which determine the coordinates of the phase space plot.Let the signal be represented by the coordinates of a point X(k) = {x (k), x((k + τ), ...,x (k + (E-1)τ)} for k = 1,2,...,N - (E - 1)τ     (1)X(k) is one point of the trajectory in the phase space at time k, x(k + τ) are the coordinates in the phase space corresponding to the time delayed values of the time series, τ is the time delay between the points of the time series considered and E is the embedding dimension, which is the number of coordinates of the phase space plot. The attributes of the reconstructed phase space plot depend on the choice of value of τ. One way to choose τ is to take it as the time it takes the autocorrelation function of the data to decay to 1/e [τ is the best choice for ECG signals and 5 for HRV signals [where y to 1/e . Anothery to 1/e , which a signals .x (1), x(2), ..., x (N), a matrix AE is obtained asFrom the given signal E is the number of dimensions and M is related to N by the equation:where M = N - (E -1)τ     (3)A against column 1 (for the case E = 2), the phase space plot for two dimensions is obtained.By plotting column 2 of matrix A3 represent a phase space plot in three dimensions. Now, a normalized matrix BE is obtained by dividing each element of AE by xmax whereSimilarly, the first three columns of matrix xmax = max |x(k)| 1 ≤ k ≤ N     (5)B2 (in two dimensions) is hence represented asThe matrix R × R |R ∈ Real, 2/R ∈ Integer}. Then the number of grids in the normalized phase space is n = 2/R. A matrix C is now obtained with its elements c equal to the number of phase space points falling in a grid g. The matrix C is called the phase space matrix and its elements are divided by m, whereIn two dimensions, the phase space plot corresponding to the normalized matrix spans from -1 to +1 on either axis. The phase space area is now divided into small square areas of size {P, the probability of a phase space point falling in a grid g. A matrix Q is now formed by squaring each element of P to get q as the elements of Q. The sum of elements of matrix Q is calculated asThis division yields η is defined as:The spatial filling index η = s / n2     (9)η is used to quantify the degree of variability in the test signals.Now the value of There are three common approaches to generating the time-frequency (TF) plots. These are the short Time Fourier Transform; the Wigner-Ville based bilinear distributions and the Continuous Wavelet Transform. In this investigation the latter two were used.The Wigner-Ville distribution (WVD) is defined as:z(t) is the analytic signal and h(τ) is a window function. The results where obtained using a Hamming window. This window attenuates the interferences oscillating perpendicularly to the frequency axis. The WVD satisfies a large number of desirable mathematical properties. In particular, the WVD is always real-valued; it preserves time and frequency shifts and satisfies the marginal properties. Moreover, the WVD conserves the Energy of the signal. We obtain the Energy (Ex) by integrating the WVD of z all over the time frequency plane:where With the Energy conservation property the WVD can be interpreted in terms of probability density: expression (10) is the Fourier transform of an acceptable form of characteristic function for the distribution of energy. Therefore, the WVD can be used to obtain the information content of a signal; this thought is further extended in Section 4.3.'wavelet' implies a small wave of finite duration and finite energy, which is correlated with the signal to obtain the wavelet coefficients [mother wavelet, and the coefficients are evaluated for the entire range of dilation and translation factors [wavelet is shifted (translated) continually along the time scale for evaluating the set of coefficients at all instants of time. In the next phase, the wavelet is dilated for a different width – also normalized to contain the same amount of energy as the mother wavelet – and the process is repeated for the entire signal. The wavelet coefficients are real numbers usually shown by the intensity of a chosen color, against a two dimensional plane with y-axis representing the dilation  of the wavelet, and the x-axis, its translation . Thus the wavelet transform plot  can be seen as a color pattern against a two dimensional plane. In the CWT the wavelet coefficients are evaluated for infinitesimally small shifts of translation as well as scale factors. That is, the color intensity of each pixel in the scalogram is separately evaluated, and the resulting pattern contains information about the size and location of the 'event' occurring in the time domain [scalogram representation of even high frequency, low energy 'events' occurring in the time scale are more conspicuous than in the Fourier Transform. Thus the color patterns in the scalogram can be useful in highlighting the abnormalities specific to different types of disease. MATLAB version 6.1 is used to plot the various scalogram plots.A ficients . The ref factors . Initiale domain ,27. SincΨa,b(t), the coefficients are evaluated using Eq. (12):For a given wavelet  is energy normalizing coefficient, and Ψa,b(t) is the mother wavelet; a → scale factor ; b → translation factor.The wavelet, defined as ,...small wave of finite duration and finite energy...' has also zero mean value, Ex) is obtained by integrating over all scale and translation factors:Just like the WVD, the CWT representation preserves also the energy of the signal. The total energy - is a continuous and strictly monotonic function subclass of Kolmogorov-Nagumo functions. To satisfy the constraints of an information measurewhere I(pk)- any information measureSimplifying the above relation, we haveα = 3) is calculated from the WVD time-frequency representations as follows:The third order Renyi's entropy  is expressed in bits: If one elementary signal yields zero bit of information (20), then two well separated elementary singles will yield one bit of information (21), four well speared elementary singles will yield two bits of information (22), and so on. It shows that for different cardiac signals the Renyi's entropy in the time-frequency domain is different.The result produced by this measure • εij is a matrix of random disturbances.• The model posits that the columns of y are a constant plus a random disturbance. You want to know if the constants are all the same.η) defined in Equation (9). For WVD the parameter is the third order Renyi's entropy  defined in Equation (14). For CWT the parameter is also the third order Renyi's entropy  defined in Equation (15). For each dataset these three parameters  where calculated. Table N-1 where N is the sequence length) of these parameters for each of the data classes. The p-value, also shown in Table The result section compares the three different analyzing methods. Each of these methods results in a single parameter for each of the datasets. For Phase Space the parameter used for comparison is the spatial filling index  seems to provide very useful information for detection of abnormalities in the condition of the heart that is not available by conventional means. In this paper, a phase space and the time-frequency analysis of these cardiac signals using spatial filling index and Renyi's entropy has been proposed for detecting cardiac dysfunction. The ANOVA test was used to compare the different analyzing methods. The Renyi's entropy gives better result for the scalogram than the Wigner-Ville distribution. The evaluation of the proposed technique on a larger data set will improve the efficacy of the technique. It is left as future work to compare the different methods with more sophisticated statistical methods, such as post hoc comparisons. It is hoped that the graphical representation along with its corresponding analytical index and Renyi's entropy proposed here will find potential applications in computer analysis of cardiac patients' status in intensive care units.