Idiopathic Scoliosis and Scheuermann kyphosis) accounts for more than 80% of all spinal curvatures and imposes a substantial healthcare cost through bracing, hospitalizations, surgery, and chronic back pain. In aquaculture, the prevalence of heritable spinal curvature can reach as high as 80% of a stock, and thus imposes a substantial cost through production losses. The genetic basis of heritable spinal curvature is unknown and so the objective of this work is to identify quantitative trait loci (QTL) affecting heritable spinal curvature in the curveback guppy. Prior work with curveback has demonstrated phenotypic parallels to human idiopathic-type scoliosis, suggesting shared biological pathways for the deformity.Understanding the genetic basis of heritable spinal curvature would benefit medicine and aquaculture. Heritable spinal curvature among otherwise healthy children (A major effect QTL that acts in a recessive manner and accounts for curve susceptibility was detected in an initial mapping cross on LG 14. In a second cross, we confirmed this susceptibility locus and fine mapped it to a 5 cM region that explains 82.6% of the total phenotypic variance.curveback guppy has the potential to elucidate the biological basis of spinal curvature among humans and economically important teleosts.We identify a major QTL that controls susceptibility to curvature. This locus contains over 100 genes, including MTNR1B, a candidate gene for human idiopathic scoliosis. The identification of genes associated with heritable spinal curvature in the  Idiopathic-type spinal curvature is a heritable condition that occurs during development without other structural malformations that would be indicative of a congenital defect. The deformity is observed among humans and teleosts, but not among quadrupedal animals . The pheIdiopathic-type spinal curvature is the primary defect in the Idiopathic Scoliosis syndrome (IS) and Scheuermann kyphosis, and is also associated with other heritable disorders such as Prader-Willi syndrome and Turner syndrome. The extent to which etiological factors for curvature are shared between these developmental syndromes is unknown. Scheuermann kyphosis and IS may share genetic factors, based on familial clustering of the two pathologies , and/or The global prevalence of IS among children is 0.5-10% (reviewed in . NeitherPoecilia reticulata) curveback lineage has demonstrated morphological and developmental similarities to the human IS syndrome and Scheuermann kyphosis [Recent work with the guppy  and aquaculture  stocks. Among aquaculture stocks, spinal curvature can detract from the value of fish, and has prevented aquaculture from achieving optimal production [Spinal curvature is the most common morphological deformity among teleosts and can be caused by a variety of influences including nutritional, environmental, and genetic factors (reviewed in ). Heritaoduction -16. The oduction . Althougoduction ,16,18-20curveback guppy lineage has a phenotype that has been extensively characterized, consisting of a heritable, idiopathic-type curve that has a propensity to increase in magnitude with growth [curveback lineage, curvature begins after birth and is generally stable by sexual maturity (approximately a month after birth). The curveback phenotype occurs primarily on the sagittal plane of the fish as an anterior lordosis and a posterior kyphosis, both of variable magnitude. In addition, some individuals exhibit coronal deviation of the spine  progeny, 48% had curves of variable magnitude whereas in the 286 F2 progeny, 22% showed curves of varying magnitude. The percent curved fish among BC and F2 progeny suggests that curvature is caused by a major recessive locus that is inherited in a Mendelian recessive manner. However, variation for curve magnitude among curved progeny suggests the presence of modifier alleles. The distribution of curve magnitude among BC and F2 offspring is given in et al., 2009 [Table Of the 376 SNPs tested on BC offspring of Cross 1, 168 markers (44.6%) were polymorphic, 151 of which were assigned to 23 guppy linkage groups by Tripathi, l., 2009  . Therefore, curved individuals that were not homozygous for the allele from the curved parent at a given marker excluded that marker from the QTL region. In this way, we were able to narrow the QTL region further than by interval mapping . In Cross 2, Marker 0289 at 13 cM) is homozygous for the curved allele in all curved individuals , whereas flanking Markers 0635 and 0381(at 9.35 and 14.5 cM) are heterozygous in one or more curved individuals and so delimit the QTL region , confirm that the inheritance of curve susceptibility in curveback is recessive.To identify genomic regions that are associated with idiopathic-type spinal curvature in the i.e., comparing curved vs. non-curved individuals), 82.6% of the phenotypic variation in curve severity , and 54% of the difference between the parents. The estimated difference in curvature between curved homozygotes and heterozygotes at the LG14 QTL is slightly higher in Cross 2 (2.16) than in Cross 1 (1.39), but this difference could be due to different genetic backgrounds in the two crosses.Localization of the susceptibility QTL on LG14 using additional F2 mapping cross progeny from the second cross identified a 5 cM interval containing a marker whose genotypes explain 100% of the susceptibility for curvature (http://www.ncbi.nlm.nih.gov/pubmed/) to the medaka genome using the BLAST algorithm (Ensembl release 57: http://www.ensembl.org). We found that the gene order for 17 out of 20 markers is conserved between guppy and medaka on LG 14, demonstrating chromosome-wide synteny between these species. Therefore, we searched the medaka region corresponding to the 5 cM Guppy QTL for candidate genes named in human studies of IS. We found that the melatonin 1B receptor gene (MTNR1B), which has been implicated as a candidate for curve predisposition in human IS, is contained in the QTL [Based on synteny between the guppy and medaka genomes, we estimate that there are 100-150 genes in the QTL interval. Synteny between the guppy and medaka genomes has been established by Tripathi et al., (2009), using the same guppy markers as this QTL study  in the United States alone is over $3 billion. This cost estimate does not consider Scheuermann kyphosis, or adults with idiopathic-type spinal curvature who suffer from chronic back pain, contributing to the estimated $849 billion cost of treatment and lost wages associated with musculoskeletal disease . Among tcurveback phenotype has been extensively characterized so that the lineage can be applied as a model for understanding the biological context of heritable spinal curvature [Despite the prevalence and impact of this type of deformity, the genetic architecture and specific genes involved are unknown. The current view is that human idiopathic-type scoliosis is a complex genetic disorder with multiple genes segregating in the population exhibiting complex genotype by environment interactions -30. In aurvature ,7-9. Futurvature ,32, rathcurveback guppy, and identify this as a susceptibility locus. The locus acts in a recessive manner and accounts for a large portion of phenotypic variance. Fine mapping of the susceptibility locus has identified a 5 cM region that contains over 100 genes among which is the human candidate for IS, MTNR1B. The identification of genes for spinal curvature in curveback will determine the molecular pathway(s) leading to spinal curvature in the guppy, and these findings will provide insights into the etiopathogenesis of spinal curvature in humans and other teleost species.We detected a major QTL associated with idiopathic-type spinal curvature in the P. reticulata, is a live-bearing teleost native to streams in northeast South America that has been used as a genetic model since the 1920's. The curveback lineage was established from laboratory guppies that are derived from a wild population caught in Central Cumaná, Venezuela and raised under standardized conditions since 2000. The curveback lineage originated from a curved male crossed to a non-curved, unrelated female in 2003, followed by full-sib mating and backcrossing. Breeding pairs are maintained in 4L plastic aquaria, and offspring are separated into individual 600 ml plastic containers after birth. Laboratory fish are kept under standardized conditions as described in Gorman et al., (2007), in compliance with protocols approved by the Simon Fraser University Animal Care Facility and the Canadian Council on Animal Care (#763B-05).The guppy, The guppy spine is visible without magnification. Fish were scored from the side  and above  while in a plastic view tank 4"long × 2"wide × 3"high. Since all affected individuals exhibit sagittal curvature, the degree of lordosis is used as a standard for comparison of curve magnitude. Evaluation of curve magnitude is based on scores defined by Gorman et al., (2007) in which 0 denotes no curvature and scores of 1 though 4 reflect increasing curve magnitudes. Fish were scored for curve magnitude after birth and then once a month until 3 months of age . To maintain consistency and precision in scores, the same individual evaluated all fish.At 3 months past birth fish were culled and photographed on a light table with a digital camera  under 3X magnification. As described in Gorman and Breden (2010), all fish were positioned on their side and photographs were taken from above so that the camera looked down on the sagittal profile of the fish. The qualitative adult score 0-4 was used for analysis . Adult fcurveback lineage mated with a non-curved and unrelated female from a lineage having no prior incidence of spinal curvature . The maternal lineage is a mixture of laboratory fish derived from Central Cumaná, Venezuela and a pet store fancy guppy stock, and has been inbred (via sib mating) for 7 generations as part of another experiment. According to the qualitative scale for curve magnitude  from the curveback female  crossed to a non-curved male from the QUL lineage . The QUL lineage is derived from the lower Quare River in Trinidad and was chosen as the outcross lineage because it exhibits a high rate of polymorphism in molecular markers compared to the Central Cumaná strain [curveback lineage was derived. The Pfemale2 is derived from the same curveback lineage as Pmale1, but is the result of further generations of inbreeding. From Cross 2, 27 non-curved F1offspring were produced, from which we made four crosses to generate a total of 259 F2 mapping progeny.For confirmation of the QTL detected in Cross1 and for fine mapping, additional offspring were generated from a second mapping cross. Cross 2 is an extremely curved á strain  from whiet al., (2009), with the goal of creating a map with 10 cM resolution.The guppy genome consists of 23 haploid chromosomes , with a http://www.gridqtl.org.uk/[To detect QTL that are associated with curvature in Cross 1, and to confirm the location of the major QTL in Cross 2, we performed interval mapping using GridQTL at l.org.uk/,36. Gridl.org.uk/,38. ThisF-statistic under the null hypothesis that no QTL was segregating on that chromosome [We first conducted analyses under the single locus model, and then under the two QTL model. For putative QTL, the substitution effect  was estimated in Cross 1 and additive and dominance effects were estimated in Cross 2. Sex was included as a fixed effect, and we also tested for an interaction between putative QTL and sex. To determine significance thresholds, one thousand permutations per chromosome (linkage group) were carried out for each trait to determine the distribution of the romosome . For marThe initial genome screen of Cross 1 detected a major effect QTL on LG14 (see Results) and so we genotyped 9 additional SNP markers in this region  by eitheKFG and FB participated in the project conception and oversight. KFG was also in charge of mapping crosses and phenotyping. JP and RA were in charge of DNA extraction, and JP performed all PCR and RFLP procedures and sequence analysis for Cross 2. CD and DW provided the guppy specific markers. KFG, FB, and JKC performed statistical analyses. KFG and JKC drafted the manuscript and FB contributed to revisions for the final draft. All authors have read and approved the final manuscript.Polymorphic markers used for mapping. The supplemental file provides the markers that were polymorphic in curveback mapping crosses. All marker numbers and genetic locations are from the guppy linkage map . Markers in bold were genotyped by either direct sequencing or by RFLP of PCR products. File is in Excel 97-2004 format.Click here for file


Students who fail to thrive on the Nottingham undergraduate medical course frequently suffer from anxiety, depression or other mental health problems. These difficulties may be the cause, or the result of, academic struggling. Early detection of vulnerable students might direct pastoral care and remedial support to where it is needed. We investigated the use of the short-form General Health Questionnaire (GHQ-12) as a possible screening tool.Two consecutive cohorts (2006 and 2007) were invited to complete the GHQ-12. The questionnaire was administered online, during the second semester (after semester 1 exams) for the 2006 cohort and during the first semester for the 2007 cohort. All data were held securely and confidentially. At the end of the course, GHQ scores were examined in relation to course progress.251 students entered the course in 2006 and 254 in 2007; 164 (65%) and 160 (63%), respectively, completed the GHQ-12. In both cohorts, the study and non-study groups were very similar in terms of pre-admission socio-demographic characteristics and overall course marks. In the 2006 study group, the GHQ Likert score obtained part-way through the first year was negatively correlated with exam marks during Years 1 and 2, but the average exam mark in semester 1 was the sole independent predictor of marks in semester 2 and Year 2. No correlations were found for the 2007 study group but the GHQ score was a weak positive predictor of marks in semester 2, with semester 1 average exam mark again being the strongest predictor. A post-hoc moderated-mediation analysis suggested that significant negative associations of GHQ scores with semester 1 and 2 exams applied only to those who completed the GHQ after their semester 1 exams. Students who were identified as GHQ ‘cases’ in the 2006 group were statistically less likely to complete the course on time . There was a non-significant trend in the same direction in the 2007 group.Results from two cohorts provide insufficient evidence to recommend the routine use of the GHQ-12 as a screening tool. The timing of administration could have a critical influence on the results, and the theoretical and practical implications of this finding are discussed. Low marks in semester 1 examinations seem be the best single indicator of students at risk for subsequent poor performance. In 2006 we introduced the concept of the ‘struggling’ medical student . We defiThe literature includes a number of studies which suggest that medical students, both in the UK and elsewhere, are potentially susceptible to stress and anxiety -8. ContrWe chose the12-item General Health Questionnaire (GHQ-12) as an investigative tool. This was developed originally as a 60-item questionnaire  and lateWe invited two consecutive cohorts of undergraduate medical students to complete the GHQ-12 in their first year of study. The main research questions that we wished to answer were:•What proportion of the students could be considered as ‘cases’?•Were high GHQ scores or ‘caseness’ associated with poorer examination marks in the early course and/or with course disruption?•Could the GHQ-12 be used as an early screening tool for those potentially at risk?The 2006 and 2007 entry cohorts were invited to complete the GHQ online. The Director of Medical Education (DJ) spoke to each cohort in advance of the study, describing its purpose, its voluntary nature, and giving reassurance that data would be fully confidential and not revealed or used outside the setting of the specific research project. Students were then sent an email with a link to full participant information  or June 2012 (2007 cohort). Separate databases were constructed for each cohort on course completion, with their available pre-admission data  and course progress data (Faculty databases), together with the GHQ scores. Age at course entry was collapsed into mature (21 and over) or not (< 21); ethnicity was grouped as White vs non-White; Fee status was used as a proxy for Domicile and grouped into UK vs EU or overseas; and pre-admission qualifications were grouped into standard A-levels (including Scottish Highers) vs all others.The course progress files of all students who had not graduated on time were inspected to determine why, and brief comments added to the database. Personal identifiers were removed from the databases on completion to provide anonymity.χ2 test.Data were analysed in IBM SPSS v19 and v20. A preliminary analysis was used to determine whether the two cohorts, and the study and non-study groups within each cohort, were similar. We compared pre-admission characteristics, expressed as bivariate categories, using the We also checked whether course performance was similar in the study and non-study groups of each cohort, using t-tests for average exam marks at key stages of the course . We then explored the data with hierarchical multivariate linear regression. The outcome variable was the average exam mark at either the end of semester 2 or Year 2. Explanatory variables were sex, ethnicity, domicile (as fee status), maturity, and standard A-level qualifications in block 1, the GHQ Likert score in block 2, and the semester 1 average in block 3.We also examined whether those in the study group who were ‘cases’ were more likely to have failed to complete the course on time.Ethical approval was obtained from the UoN Medical School Research Ethics Committee (ref A/02/2007).In October 2006, 251 students commenced the course, of whom 168 (67%) completed the GHQ-12 in April 2007. However, four of these did not indicate their consent to participate in the research, and therefore the study group with GHQ data for 2006 refers to 164 students (65%) who did give their consent. In October 2007, a further 254 students commenced the course, of whom 160 (63%) gave consent and completed the GHQ-12 in November 2007.Table 2 = 4.44, OR 0.22, 95%CI 0.05 to 1.03, p = 0.035), there were no other significant differences between them.We also compared the pre-admission characteristics of the two study groups (as shown in Table t-test). Relevant data are shown in Additional file Mean marks post-admission for the study and non-study groups were compared at each of the key stages of the course, for each cohort. Although the marks for the study groups were marginally higher than for the non-study groups in both cohorts , these differences were not statistically significant  in the 2006 cohort, and five items (from four students) in the 2007 cohort. These missing responses were scored low as recommended by the questionnaire distributors .The Likert scores for both cohorts at T1 followed a near-normal distribution . For the 2006 cohort the mean Likert score was 13.39, SD 5.77, and for the 2007 cohort the mean was slightly lower, 11.48, SD 5.03. This difference was significant on t-testing  and suggests a lower level of anxiety overall.χ2 test). However, the proportions with a high score of 8 or more were 21/164 (13%) in 2006 and 10/160 (6%) in 2007, which is just statistically significant .The Likert scores were converted to the 0-0-1-1 format to create scores of 0–12 and hence identify ‘cases’ with a score of 4 or more. Figure Correlations matrices between the GHQ Likert scores and early exam marks  for each cohort are shown in Table We calculated the average marks for semester 1 (S1) and semester 2 (S2) separately. This calculation excluded marks for two modules in Theme D, Early Clinical and Professional Development, and Communication Skills, which are examined largely via essays, coursework and practicals throughout the year. Average marks for the two semesters were closely correlated in the 2006 group . The GHQ correlated retrospectively with the semester 1 mark and prospectively with semester 2 .When the same analysis was conducted with the 2007 group, again there was a strong correlation between semesters 1 and 2 , but there was no significant correlation between either semester exam average and the GHQ score.Data were examined using multivariate hierarchical linear regression, firstly with the 2006 cohort and the semester 2 average mark as the outcome variable. There were no predictors in block 1 (pre-admission characteristics). In block 2, the GHQ Likert score was a significant negative predictor , but this effect disappeared in block 3 when the semester 1 average became the sole predictor . Full data are tabulated in Additional file For the 2007 group, multivariate linear regression for Year 1 indicated that the GHQ score was not predictive in block 2 but had a mild positive predictive effect in block 3 . Again the S1 mark predominated  may activate the expression of GHQ-anxiety (indeed the mean Likert score is higher in this group) in a way that is not observed when the questionnaire is administered earlier (2007 cohort). This activation of anxiety may then be linked to exam performance in semester 2. We therefore carried out a ert, ref  with theχ2 = NS). In 2007 the comparable figures were 147/160 (92%) and 84/94 (89%) (χ2 = NS).We flagged all students on the databases as successful , or less successful (left prematurely or suffered performance-related course disruption). In 2006, 147/164 (90%) of those in the study group completed successfully, compared to 76/87 (87%) of the non-study group , but correlations with performance were not provided . A compaThe question remains as to why the relationships between the GHQ scores and early course performance differed in the two cohorts. The pre-admission characteristics that we measured were broadly similar and it is unlikely that the few differences observed account for contrasting outcomes. One inherent change was that the admissions procedure in 2007 included scores from the UKCAT (United Kingdom Admissions Test). As this is a test of general intellectual ability, and has been shown not to influence national admissions very markedly , nor to The other possible explanation lies in the timing of the questionnaire administration. The 2006 cohort had completed the semester 1 exams in January and the students would be taking their semester 2 exams in 4–6 weeks. It is quite possible that some were experiencing exam-related stress, especially those who had performed less well in January. The 2007 cohort, on the other hand, had been on the course for only 6–8 weeks, well before the first exams. Those who were GHQ ‘cases’ may have been affected by anxiety secondary to settling in to University life, living away from home and having to make new friends, but probably unrelated to exams. The wording of the GHQ may also be a factor in the interpretation of these results. It asks questions in terms of comparison with ‘usual’ feelings, so may be sensitive to stressful events such as imminent examinations. Indeed, there is some evidence that context can cue responding to questionnaires ,22.Current thinking on personality theory within the context of behavioural reaction norms ,24 and sThe small but significant positive relationship between the GHQ and S2 marks in the 2007 cohort may reflect the “approach” nature of anxiety, which may help people to move towards and explore threatening contexts, in order to identify ways to deal with them ,29.This study concerns two year groups at one medical school so would not necessarily be generalizable to other schools with different intakes and curricula. In addition, only 63–65% of the intakes responded, so an underlying response bias could have affected results. However, there were no major differences in measurable criteria between cohorts or study groups, and our data for the percentage of students being classified as ‘cases’ was similar to that obtained elsewhere.As mentioned in the Discussion above, the GHQ tends to identify ‘state anxiety’ (by asking how respondents have been feeling over the last few weeks) rather than ‘trait anxiety’ (how they feel most of the time). It is therefore unsurprising that the results could be dependent on the exact timing of administration. Repeated administration of the questionnaire at different times during the academic year would be required to clarify this aspect.The results from two cohorts provide insufficient evidence to recommend the routine use of the GHQ-12 as a screening tool. The timing of administration may have a critical influence on the results, and the theoretical and practical implications of this finding are discussed. Low marks in semester 1 examinations appear to be the best indicator of students at risk for subsequent poor performance.The authors declare that they have no competing interests.All authors contributed to devising the study, interpreting the data, and writing the paper. JY collected and analysed the data and wrote the first draft of the paper and EF performed the path modelling. All authors approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6920/13/48/prepubResearch project. General health in medical students: is there a relationship with course progress? Participant Information. Participant information as approved by the Ethics Committee and provided to the students.Click here for fileExam performance of study and non-study groups in both cohorts. This Table shows the average exam data (mean and SD) throughout the course for the study and non-study groups in both cohorts.Click here for fileRegression Tables for GHQ and Exam marks. Tables 3.1 and 3.2 show the multivariate hierarchical regression data for both cohorts, using semester 2 exam data as the dependent variable. Tables 3.3 and 3.4 show the equivalent data when the Year 2 average is the outcome variable.Click here for fileModerated-mediation Analysis. This file explains the statistical modelling technique of moderated-mediation as applied to the data. It provides tabulated coefficients (Table 4.1), and a visual model of the effects seen , together with relevant references.Click here for file


Sequencing-by-ligation (SBL) is one of several next-generation sequencing methods that has been developed for massive sequencing of DNA immobilized on arrayed beads . SBL has the advantage of being easy to implement and accessible to all because it can be performed with off-the-shelf reagents. However, SBL has the limitation of very short read lengths.cyclic SBL (cSBL), to resequence this region. Using our method, we were able to read thirteen contiguous bases in the 3' - 5' direction.To overcome the read length limitation, research groups have developed complex library preparation processes, which can be time-consuming, difficult, and result in low complexity libraries. Herein we describe a variation on traditional SBL protocols that extends the number of sequential bases that can be sequenced by using Endonuclease V to nick a query primer, thus leaving a ligatable end extended into the unknown sequence for further SBL cycles. To demonstrate the protocol, we constructed a known DNA sequence and utilized our SBL variation, Combining this read length with sequencing in the 5' - 3' direction would allow a read length of over twenty bases on a single tage. Implementing mate-paired tags and this SBL variation could enable > 95% coverage of the genome. Following the completion of the human genome project it is anticipated that genome sequencing of an individual will be an aspect of routine treatment for a number of diseases and illnesses, truly ushering in the era of personalized medicine. However, the reality of implementing genome sequencing as a medical tool depends on the cost of sequencing technology . The priSanger sequencing remains the gold standard today for accurate DNA sequencing. Sanger sequencing can reach read lengths of up to roughly 1,000 base pairs, dwarfing most current next-generation methods that average fewer than 100 base pairs . What neThere are multiple sequencing methods that are utilized in next-generation methods. The two most common can be broadly categorized as Sequencing By Synthesis (SBS) -21 and Sanchor primer is hybridized to one of these known regions, and a ligatable end (3' or 5' depending on the direction of desired sequencing) is available. An oligo, called a query primer, is then ligated to the end of the anchor primer. The query primer is a mix of oligos that are degenerate for all positions except a single position that is being sequenced, which allows the sequencing of a single position based on the design of the query primer. After sequencing a single position, the query primer and anchor primer are stripped from the DNA template, effectively resetting the sequencing. The process begins again, sequencing a different position by using a different query primer, and repeating until the entire sequence of the tag has been determined [SBL is a straightforward enzymatic method of sequencing DNA. SBL uses known, universal sequences that flank an unknown genomic tag as anchor primer sites . An anchtermined . Increastermined .Currently, the number of sequential bases that SBL-based approaches can sequence is limited by loss of specificity of base pair hybridization at any distance away from the site of ligation. Errors in the first six base pairs adjacent to the site of ligation are rare due to the destabilizing effect of mismatches. However, at a distance of about seven base pairs, the specificity of the SBL reaction is reduced Figure . Therefocyclic SBL or cSBL. Our approach is conceptually similar to the ABI SOLiD method of SBL, which uses a chemical cleavage of the query primer to get extensions of read lengths. However, in contrast, our method utilizes an enzymatic cleavage using completely off-the-shelf reagents. Deoxyinosine is a universal base [nd and 3rd phosphodiester bond 3' from the deoxyinosine site [In this manuscript, we describe a variation on SBL that utilizes a deoxyinosine in the query primer that can be cleaved by Endonuclease V  to incresal base  that is ine site . Cyclic Three cycles of cSBL were performed, giving accurate signal for the first 13 positions of the Test Template. There was a slight increase in non-specific signal with each cycle, but the third cycle still had clearly correct signal with an acceptable signal to noise ratio Figure .th position and beyond using the cSBL strategy. In order to determine the possible cause of this, we performed a series of tests to explore whether the template DNA had been digested by the Endonuclease V treatment, since this seemed the most likely problem. After the beads had undergone cSBL and stripping of the sequencing strand of DNA, we hybridized a fluorescent probe to the 3' end of the DNA loaded onto the beads and confirmed that the Test Template was still present on the bead.We were unable to sequence the 14M) of secondary structures were performed assuming 50 mM Na+ and 10 mM Mg++. This simulated the highest folding TM at 31.5 degrees, and the fold as modeled by the software was not located near the 14th base pair.We also ruled out the issue of secondary structure causing the 3' end of our Test Template to become inaccessible. We performed folding calculations using IDTDNA's Oligo Analyzer software (29) when constructing our Test Template specifically in order to avoid secondary-structure problems. Calculations for melting temperatures , which has a higher optimal temperature, but could not obtain the 14et al., to be submitted, 2011). Human genome coverage was simulated using mate-paired data ranging from twenty-six bases to  to forty bases .To demonstrate the feasibility of a cSBL approach to genome sequencing and calculate gains in using cSBL over traditional SBL methods, we utilized the SawTooth resequencing code developed at the University of New Mexico . 30 uL of beads were washed three times in Bind and Wash Buffer  and collected using a magnetic particle collector. The beads were then resuspended in 120 uL of BW Buffer and 1.2 uL of 1 mM Test Template sequence  was added incubated at room temperature in a rotisserie for forty-five minutes. Finally, the beads were washed times and resuspended in 60 ul of Wash 1E .2PO4, and 1 mM EDTA pH 7.4). The beads were then resuspended in 150 uL 1 × SSPE with 2 uL of 1 mM anchor primer . The solution was incubated at 50°C for 15 minutes and then cooled to room temperature for ten minutes. Lastly, the beads were washed in Wash 1E three times and immediately used in the Query Primer Ligation.The beads were washed in Wash 1E , then washed once in a 1 × SSPE , with a query primer concentration of 3 uM each, and T4 DNA Ligase . The ligation reaction was incubated at 30°C for 45 minutes on a rotisserie. Following the reaction the beads were washed three times in Wash 1E and resuspended in Wash 1E. The fluorescent signal was verified using a fluorescent microscope.The exposure and gain for each fluorescent filter was adjusted with all positions present for each cycle. Camera settings were optimized each cycle of cSBL as signal dropped from one cycle to the next. The individual populations of beads were examined separately with the same settings, and then scored using NIS-Elements Basic Research imaging software  Figure .NIS-Elements Basic Research 3.0  was used to determine the pixel intensities in the Cy3, Cy5, FITC, and TxRed channels. Individual channel intensity values ranged from 1-16,383. One-hundred pixels were averaged in each channel and compared. This gave a metric for estimating sequencing accuracy, as the correct signal was known for each position.A saturation step was performed to fully saturate all Anchor Primers sites not extended during the Query Primer ligation cycle. The ligation was performed in a 1 × T4 DNA Ligase Buffer, with a Saturation Primer concentration of 10 uM and T4 DNA Ligase (2 U/mL), at 30°C for forty-five minutes on a rotisserie.The beads were washed three times and resuspended in 1 × NEB4  with 100 ug/mL BSA and Endonuclease V at a 2 U/mL concentration. The endonuclease V digestion was incubated at 37 degrees on a rotisserie for ten minutes. Removal of the fluorescence was confirmed visually using a fluorescent microscope. Specific digestion and negligible non-specific Endonuclease V digestion was confirmed by an overnight incubation with Endonuclease V with test-template bound beads. The overnight digestion resulted in no detectable non-specific endonuclease activity when gauged by hybridizing a probe to the distal region of the Test Template.Following the Endonuclease V digestion, the beads were extensively washed to remove all Endonuclease V. Enzyme carry forward could cause phasing problems, therefore, a guanidine wash was also performed to inactivate residual enzyme. The bead solution was washed in a 3 M Guanidine solution at room temperature. Following the guanidine wash, the beads were washed three time and resuspended in Wash 1E.After Endonuclease V deactivation, the template DNA has been sequenced in one position, but now the anchor primer is effectively lengthened. In traditional SBL, the sequencing strand would be stripped to repeat the sequencing process for a different position. With cSBL, the sequencing of additional bases is dependent upon the preservation of the hybridized sequencing strand of DNA. The process therefore begins again with query primer ligation, and is repeated until the signal to noise ratio is too low to effectively continue sequencing by SBL. At that point, the entire sequencing strand can be stripped and a different length anchor primer can be used to sequence different bases, as in traditional SBL Figure .AH, MM, SW, SRA, and JE have all contributed to and participated in drafting this manuscript. All authors read and approved the final manuscript.


Herpes simplex virus type 1 is a neurotropic herpesvirus that establishes latency within sensory neurones. Following primary infection, the virus replicates productively within mucosal epithelial cells and enters sensory neurones via nerve termini. The virus is then transported to neuronal cell bodies where latency can be established. Periodically, the virus can reactivate to resume its normal lytic cycle gene expression programme and result in the generation of new virus progeny that are transported axonally back to the periphery. The ability to establish lifelong latency within the host and to periodically reactivate to facilitate dissemination is central to the survival strategy of this virus. Although incompletely understood, this review will focus on the mechanisms involved in the regulation of latency that centre on the functions of the virus-encoded latency-associated transcripts (LATs), epigenetic regulation of the latent virus genome and the molecular events that precipitate reactivation.This review considers current knowledge and hypotheses relating to the mechanisms involved in the establishment, maintenance and reactivation herpes simplex virus latency. Herpes simplex virus type 1 (HSV-1) and type 2 (HSV-2) are closely related ancient human pathogens responsible for a number of diseases of minor, moderate and severe pathology including oral and genital ulceration, virally induced blindness, viral encephalitis and disseminated infection of neonates. HSV-1 is typically transmitted during childhood, whilst HSV-2 is the cause of most genital herpes . WorldwiHSV is a neurotropic virus and it is within sensory neurons that latent infection is established. Upon primary HSV replication at the oral or genital mucosa, the virus is able to infect the neuronal dendrites of sensory ganglia that innervate these tissues, and after retrograde microtubule-associated transport to the nerve cell body, the virus encounters a ‘choice' of gene expression programmes that dictates the fate of the neuron: lytic replication or latent infection. The latent virus genome is stably retained within sensory neurones and is characterized by repression of all viral lytic genes. In response to a variety of diverse stimuli, the virus can periodically reactivate to resume virus replication and produce infectious virus, which is transported anterograde back to the periphery to facilitate epithelial cell infection and consequent transmission.in vivo, for example, by transient induction of hyperthermia  expressed and can the virus be easily reactivated.1 or γ-1), DNA replication and finally late (L2 or γ-2) gene expression  genes and then proceeds through early (E or β), ‘leaky-late’  class 1 evasion gene), all of the IE proteins are involved in gene regulation. Two of these proteins, ICP0 and ICP4, are both major transactivators of gene expression. ICP0 does not bind DNA and is a promiscuous transactivator of gene expression . Althoug+ T cell recognition via ICP47-mediated TAP inhibition  or RT-PCR methods  of viral DNA . In particular, virus-encoded miRNAs are found in the majority of herpesviruses studied to date . MiR-H1 et al. utilized cell lines that encoded, a region of LAT in which no miRNA proposed to target ICP0 or ICP4 is found. However, within this region, two sRNA species have recently been discovered  epitope and not IE proteins. gB is a leaky-late gene and, the fact that the reactivating neuron was recognized by a gB-specific CD8+ T cell indicates that IE and early genes are likely to be expressed prior to a CD8+ T cell-mediated block in reactivation. These data indicate that, not only are neurons able to survive high viral genome inputs, they can also survive extensive viral gene expression  and each is flanked by inverted repeats , these preferential sites swapped between viruses, defining the LAT as the viral determinant of this specificity . Given that approximately 50% of all neurones latently infected with HSV-1 are A5+  reactivate and are antigen-positive 22-h poststimulus  responsible for the initiation of the lytic gene expression and exit from latency. ICP0 has always been regarded as the most likely candidate owing to its ability to target ND10 domains for degradation and its capacity to act as a promiscuous activator of gene expression  treatments from latently infected PC12 cells revealed that only mutants lacking functional VP16 failed to reactivate (A recent publication by Thompson and Sawtell (nfection  in up tonfection . Recent nfection . Experim+ T cell immunosurveillance may influence both the efficiency and mechanism by which reactivation can occur (Reactivation from latency is a critical phase of HSV life cycle; therefore, it is not surprising that it is highly regulated. It seems that only a small proportion of latently infected neurones are able to respond to a given reactivation stimulus at any given time. The responsiveness of latent virus genomes may be related to the nonuniform nature of the latent state. Thus, multiple factors such as latent DNA copy number, level of LAT expression, neuronal type, degree of genome repression and CD8an occur . Given t


In breast carcinoma, the stromal loss of CD34 expression and acquisition of SMA myofibroblastic features may constitute a prerequisite for tumor invasiveness. However, this hypothesis remains controversial, with some authors describing the loss of CD34 fibrocytes in the absence of SMA myofibroblastic-like cells in the stroma of invasive carcinoma. Others have also described the disappearance of CD34 fibrocytes from in situ carcinoma. To clarify this issue, we compared the distribution of CD34 fibrocytes and SMA reactive myofibroblasts between stromal areas of tumor-free mammary tissue, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). In addition to 28 IDC, 300 normal duct–lobular units and 600 ducts with DCIS  were scored. The relationships between staining patterns and different histological features (grade of DCIS and presence or absence of necrosis) were compared. Loss of CD34 expression and acquisition of SMA expression were more frequent in high-grade in situ lesions than in intermediate and low-grade lesions (p<0.001). When necrosis was found in association with grade 2 or 3 DCIS, the decrease in CD34 expression was higher than in lesions without necrosis and that independently of the grade of DCIS (p<0.05). Necrosis did not appear to play a significant role in the expression of SMA (p = 0.35). In all cases, the stroma of invasive carcinomas showed a complete loss of CD34 fibrocytes. Future research on both CD34 fibrocytes and mechanisms stromal changes are essential in the future and may potentially lead to new treatment approaches. Epithelial-mesenchymal interactions are critical for normal mammary gland development and for breast tumorigenesis th gestational week. In this developmental phase they make up the majority of stromal cells Normal mammary stroma harbor huge numbers of CD34 fibrocytes, which can first be detected during the 10In breast carcinoma, recent data suggest that CD34 fibrocytes undergo morphologic and phenotypic alterations characterized by the adoption of a plump myofibroblast-like appearance and loss of CD34 expression, accompanied by the acquisition of α-smooth muscle actin (SMA) expression The study protocol was approved by the institutional ethics and research review boards at Erasme Hospital. People sign a written informed consent to admission to the hospital. Consent requires that physicians have the right to use the surplus biological material. The material that has not been used for diagnosis can be used for research. Consent has been established by the local ethics committee and is in accordance with Belgian and International law. We used a computer database from the Department of Pathology to identify 48 consecutive patients diagnosed between January 2010 and June 2012. All patients were female. The present retrospective study included 20 pure DCIS, 12 DCIS associated with IDC and 16 pure IDC . For each case of ductal carcinoma in situ (DCIS), all the foci were graded and analyzed separately representing a total of 600 foci of DCIS. In the majority of cases (27/32), all the foci of DCIS showed the same grade, but in 5 cases, a mixture of different grades was observed .The specimens were fixed in histology-grade 4% buffered formalin. Paraffin sections were stained with hematoxylin and eosin and immunohistochemical detection was performed according to the manufacturer’s protocols . We usedWe compared the distribution of CD34 fibrocytes and SMA reactive myofibroblasts between stromal areas located within the tumor with areas of tumor-free mammary tissue surrounding the carcinoma. Because endothelial cells expressed CD34 and myoepithelial cells expressed SMA, CD31 and p63 were also assessed to facilitate the semi-quantitative evaluation of CD34 and SMA in stromal tissue. The immunoreactivity of CD34 and SMA was assessed semi-quantitatively in the tumor-free tissue and the tumor. The percentage of stromal cells expressing each antigen was graded as “0”, “+”, “++” and “+++” when up to 5%, more than 5% and up to 25%, more than 25% and up to 50% or more than 50% of stromal cells, disclosed immunoreactivity, respectively. Percentages were assessed by two independent observers, assuming that a high-power microscopic field  harbored 100 stromal cells (range: 75–150). Because many of the sections contained ducts and lobules showing a range of histopathological features, the relationship between staining patterns and pathology was interpreted for each duct-lobular unit. Thus, in addition to 28 IDC, 300 normal duct-lobular units and 600 ducts with DCIS  were scored. When foci of DCIS were associated with invasive lesions, we considered them separately at the periphery of invasive areas and at the center of invasive areas. DCIS lesions located in the centers of invasive areas were not included in statistical tests. We also took into account the presence or absence of necrosis associated with grade 2 or 3 carcinoma in situ. The relationship between the staining patterns and different histological features (grade of DCIS and presence or absence of necrosis) was compared using a Chi-squared test, and a p-value <0.05 was considered statistically significant.In normal mammary tissue, muscular blood vessels, glandular ducts, and acinii were surrounded by a dense concentric network of CD34 fibrocytes. Slight CD34 staining was noted on small-caliber blood vessels within the stroma, and was confirmed by the expression of the vascular antigen CD31. Fibrocytes were predominantly found in the intralobular parenchyma, and the extralobular stroma harbored few CD34 fibrocytes, which mainly surrounded thick-walled arteries. These cells revealed slender elongated dendrite-like processes with a bipolar arrangement; the nuclei were small and inconspicuous. No CD34 reactivity was observed in epithelial cells. SMA was detected in the wall of muscular vessels and in myoepithelia lining the ductal and acinar basement membranes, whereas SMA-reactive myofibroblasts were not detected in the stroma of normal breast tissue .The most variable pattern of expression was seen in fibroblasts surrounding ducts with DCIS. A statistically significant difference in CD34 and SMA expression was found depending on the grade of DCIS. Indeed, loss of CD34 expression and acquisition of SMA expression were more frequent in high-grade lesions than in intermediate lesions or low-grade lesions (p<0.001). In high grade DCIS, fibroblast CD34 expression was reduced in 87% of cases compared with 20% of intermediate and 0% of low-grade cases of DCIS ; Fig. 1.In contrast with normal mammary tissue, the number of CD34 fibrocytes was reduced in the stroma surrounding ducts harboring carcinoma in situ, although capillaries with CD34 reactive endothelia were still detectable adjacent to these ducts. In contrast, ducts with DCIS were encircled by a rim of densely packed SMA myofibroblasts, which were not detected in normal breast tissue.We assessed whether the presence of necrosis associated with grade 2 or 3 in situ lesions could influence the expression of CD34 and SMA. When necrosis was present, the decrease in CD34 expression was higher than in lesions without necrosis and that independently of the grade of DCIS (p<0.05). However, necrosis did not appear to play a significant role in SMA expression (p = 0.35).In all cases investigated (100%), the stroma of invasive carcinomas showed a complete loss of CD34 fibrocytes, while the surrounding mammary tumor-free tissue disclosed a normal distribution of this cell population . Twenty-th week of gestation and that there is strong expression of CD34 fibrocytes in the normal breast. This distribution of CD34 fibrocytes is similar to that of normal skin, and we know that the mammary gland develops from solid epithelial cords growing from the epidermal layer into the underlying mesenchyme The DCIS to IDC transition is a clinically important, yet poorly understood, step in breast tumorigenesis. The importance of changes in the microenvironment during tumor progression has been increasingly recognized Several studies have shown the loss of CD34 fibrocytes to be a feature of stromal alterations associated with invasive carcinomas of the breast However, the mechanism leading to the loss of CD34 fibrocytes in the stroma of carcinomas are far from being understood. Breast cancer cells have been shown to be capable of factor secretion We believe that several mechanisms may explain the promotion of tumor invasion that induces the transformation of CD34 fibrocytes to SMA myofibroblasts. First, CD34 fibrocytes are potent antigen-presenting cells capable of priming naïve T-cells, and might be involved in specific immune surveillance Therefore, future research on both CD34 fibrocytes and mechanisms stromal changes are essential in the future and may potentially lead to new treatment approaches. Such studies are now in progress.


Divergently paired genes (DPGs), also known as bidirectional (head-to-head positioned) genes, are conserved across species and lineages, and thus deemed to be exceptional in genomic organization and functional regulation. Despite previous investigations on the features of their conservation and gene organization, the functional relationship among DPGs in a given species and lineage has not been thoroughly clarified. Here we report a network-based comprehensive analysis on human DPGs and our results indicate that the two members of the DPGs tend to participate in different biological processes while enforcing related functions as modules. Comparing to randomly paired genes as a control, the DPG pairs have a tendency to be clustered in similar “cellular components” and involved in similar “molecular functions”. The functional network bridged by DPGs consists of three major modules. The largest module includes many house-keeping genes involved in core cellular activities. This module also shows low variation in expression in both CNS  and non-CNS tissues. Based on analyses of disease transcriptome data, we further suggest that this particular module may play crucial roles in HIV infection and its disease mechanism. Divergently paired genes (DPGs) are also known as bi-directionally expressed and head-to-head oriented genes, which can be further defined based on their TSS (transcription start site) distances, such as within or beyond 1 kb between the gene pairs Several investigations have been conducted on DPGs in recent years, especially on those of human http://www.geneontology.org/) annotations and regulatory roles are examined based on functional interactions or networking linked by the DPG pairs. We also compare DPG gene expression patterns in 65 normal human tissues and several common disease samples. We use HIV as an example to illustrate the role of DPGs played in immune defense mechanisms.Here we report an examination of human DPGs and the differences between DPGs and random gene pairs in their functional connections and regulatory roles in normal tissues and disease processes. Functional similarity of DPGs is tested based on Gene Ontology . When considering both gene density and chromosome length, we found that DPGs were enriched on chromosomes 1 and 2. We also found that DPGs was absent on chromosome Y, which might be due to the fact that the small number of genes still present on it. Chromosome Y is known to be fast evolving, and it poses great challenge to preserve the genomic structure of DPGs on this chromosome There are 1,063 pairs of human DPGs recorded in the LCGbase Next, we conducted GO term-based functional enrichment analysis on 1,728 DPGs using hypergeometric test. On the one hand, consistent with previous studies As physically neighbouring pairs, the functional similarity within a DPG is of great interest. We calculated the GO similarity score of gene pairs of DPGs (details in Methods) and compared the distribution of these scores to random pairing using Kolmogorov-Simirnov test. Our main findings are four folds. First, DPGs are not significantly different from random gene pairs in biological process  but significantly different in cell component  and molecular function . In details, we observed the left shift of the distribution line of BP and the right shift of those of CC and MF, in comparison to the background BG; Figure 1Our analysis indicates that DPG pairs are not formed randomly as the two genes within a DPG pair tend to be involved in similar “cellular component” and “molecular function” but become divergent in “biological process”. We therefore propose that the genomic arrangement of DPGs facilitate the regulation of two biological functions that might be related. For example, none of the 134 DPGs involved in RNA process fells in the same gene pair, and the functions of the paired genes are mainly related to protein localization, phosphorylation and transcription, and preferably associated with mitochondrion, ribosome, and nucleolus. Only two of the 73 DPGs involved in DNA repair formed a pair (SMC6 and GEN1) and the functions of the paired genes are enriched in energy metabolism, RNA processing, and protein localization, and again preferably associated with mitochondrion, ribosome, and nucleolus. In addition, this observation is further supported when we examine individual DPG pairs and their functional networking.de novo purine nucleotide biosynthetic pathway, respectively; PRKDC and MCM4 participate in cell cycle, where PRKDC regulates DNA double-strand break repair and recombination, and MCM4 acts as a DNA unwinding enzyme and controls the initiation of eukaryotic genome replication. The rest of the DPG pairs annotated in KEGG are all involved in different pathways. Here we used “spliceosome” and “cell cycle” as examples to show the relationships between the gene pair of a DPG. Three DPGs were involved in the two pathways, including ORC1-PRPF38A, CDC26-PRPF4, and THOC4-APC11 , among which 120 DPGs formed 60 pairs. If excluding 12 pairs of histone genes and 6 pairs of gene families, we only had 2 DPGs or 4 genes involved in the same KEGG pathway; PPAT and PAICS are enzymes that regulate the step 1 and step 6/7 of C4-APC11 Figure 2One previous study by Li et al indicated that DPGs prefer similar biological process, but using only 267 annotated DPGs (21.15%) and Resnik's method S2). The overlap map is based on the overlap of DPGs between a pair of functional gene sets, whereas the interaction map is based on the bridging of a pair of functional gene sets by a DPG gene pair. When the two maps are compared, only the DNA packaging and the cancer signaling modules show consistency. The DNA packaging module is mainly attributed to the histone gene pairs as described earlier, whereas the functional sets in the cancer signaling module are mostly small gene sets and connected by only one DPG pair. Other than the two modules, we observed that many cellular functions are coupled by DPG pairs despite the lack of connection on the overlap map.To clearly demonstrate relationships among cellular functions connected by DPGs, we first constructed two network maps, an overlap map and an interaction map , all of which reside in the densely connected regions.We organized the interaction map in a clear layout Figure 3Table S2). Module 1, including 7 functional gene sets and 162 DPGs, is formed by 17 DPG pairs, among which 12 pairs are histone genes. Module 2 is connected by 303 DPG pairs and includes 15 functional gene sets and 1,068 DPGs. Module 3 is joined by only one DPG pair, and therefore is not included in further statistical analysis.To better understand the functional roles of DPGs, we used Cytoscape plugin “ClusterOne” and found 3 modules in the network We evaluated the average correlation value of DPGs within the two main modules. Similar to what's shown in Since DPGs and HKGs are both involved in the basic cellular functions, a thorough examination of their relationship becomes necessary at this point. We compared DPGs with a HKG list curated previously based on microarray-based gene expression profiling data To further characterize tissue-specific functions of DPGs, we examined the expression pattern of DPGs among different tissues. The major microarray dataset used in this study is GSE3526, which contains 20 CNS tissues and 45 non-CNS tissues. This dataset was chosen because of the wide coverage of different tissue types within one dataset for a fair comparison. Within the 20 CNS tissues, we used cerebellum as control and other 19 CNS tissues as cases. For the 45 non-CNS tissues, we used skeletal muscle as control and other 44 tissues as cases. We also compared the CNS tissues with skeletal muscle or all of the non-CNS tissues, and compared cerebellum with skeletal muscle to reveal the difference. The selection of controls was based on the hierarchical clustering result by Roth et al Figure S4). The most significant difference is found in corpus callosum, medulla, and spinal cord, all of which are at the connection zone between CNS and peripheral tissues (data not shown). For non-CNS tissues, much higher between-tissue difference is found . However, module 1 remains as the least variable, indicating the fundamental role of chromosome maintenance in all the tissues. The most variable functional sets among non-CNS tissues include energy metabolism and cancer signaling gene sets, neither is significantly variable among CNS tissues, suggesting the critical role of energy homeostasis and tight control of cell proliferation in CNS tissues Among the CNS tissues, the variance of gene expression is mostly small in modules 1 and 2, and larger variance is seen mostly in the functional sets outside of the two main modules  and cancers . We found that DPGs in cancers display much higher differential expression than that in infection diseases (data not shown). More importantly, we found an interesting dysregulation pattern of DPGs in HIV. The main findings based on the absolute pathway score are as follows. First, significant differential expression is found in both CD4+ and CD8+ T cells when comparing the acute and chronic stages with the negative controls. The observed change of gene expression may reflect the action of the host defense mechanism. Second, there are no significant differences between early infection and chronic stage in CD4+ T cell, which are attributable to the early established HIV-1 infection S7). Although there is no significant difference on DEGs between the acute and chronic stages, DPGs seem to play more active role in many functional categories. First, DPGs display no significant difference from non-DPGs in module 2 for both CD4+ and CD8+ T cells, indicating coordinated perturbations of all the genes (both DPGs and non-DPGs) in module 2. Second, DPGs display higher perturbation relative to non-DPGs at the acute stage in CD8+ T cells, while they display higher perturbation at the chronic stage in CD4+ T cells. This different perturbation pattern of DPGs between CD4+ and CD8+ T cell is likely due to their different roles in the immune response. Almeida et al suggested that CD8+ T cells function as the superior control of DNA replication of HIV-1 virus in CD4+ T cells Distinct contribution of DPGs can be revealed based on the relative pathway score and the changes of perturbation during infection process can be observed  and HGNC databases (http://www.genenames.org)http://david.abcc.ncifcrf.gov/conversion.jsp). The dataset contains 864 DPGs.The human DPG information was obtained from LCGbase The evaluate of GO similarity was based on the R package GOSemSim, which implements four information content (IC)- and a graph-based methods, and made the comparison among the methods easily http://www.biocarta.com/, and KEGG or Kyoto Encyclopedia of Genes and Genomes http://www.genome.jp/kegg/Table S2).We clustered 1,728 (864 paris) human DPGs into functional categories based on the Biochart analysis by DAVID In order to reproducibly build functional network connected by DPGs, we first constructed two network maps: an overlap map and an interaction map. Since most genes were annotated by more than one term, overlaps were common among the functional gene sets and were displayed in the overlap map. Similarly, DPG pairs tended to be separated into two related pathways, so they can be organized by the interaction map. When the two types of maps led to different network topologies, only the interaction map was kept. The interaction rate >0.07 was chosen as the cutoff to retain as many functional sets as possible . The modules on the network were automatically generated by the Cytoscape plugin “ClusterONE”http://www.ncbi.nlm.nih.gov/gds). We retrieved datasets for normal tissue (GSE3526) Datasets for gene expression profiling were downloaded from GEO database  of DPG genes in a pathway.We defined the categories with the score less than 1.301 as no perturbation, corresponding to the –log, while scores at 1.301–2, 2–3, 3–4, 4–6, and 6– were assigned to level 1 to 5 differential expressions, corresponding to the p value of 0.05–0.01, 0.01–1E-3, 1E-3–1E-4, 1E-4–1E-6, and 1E-6–, respectively.The relative score was focused on the differential perturbation patterns between DPGs and all genes, which reflected the difference between DPGs and non-DPGs. The details were similar to previous study p depended on the frequency of comparing with randomly sampled genes. Then we calculated a score for every category (score  =  frequency/permutation), which was further converted to level 0 to 5 differential expressions .The significant of SFigure S1The overlap pattern of 114 functional DPG sets. Only overlap rate >30% is included to leave the layout cleaner. The edge color depends on the number of overlaps between sets and the edge width on the overlap rate between sets. The overlap and interaction patterns are both considered when laying out these 114 sets. The linkages with both higher overlap and interaction rates are circled in red.(TIF)Click here for additional data file.Figure S2The interaction pattern of 114 functional DPG sets. Only interaction rate >7% is included to make the layout cleaner. The edge color depends on the number of interacting gene pairs between the sets and the edge width on the interaction rate between the sets. The overlap and interaction pattern are both considered when laying out the 114 sets. The linkages with both higher overlap and interaction rates are circled in red.(TIF)Click here for additional data file.Figure S3The distribution of top 10 correlated functional sets. Different node colors are used to distinguish the correlation, and the nodes with same color indicate higher correlation.(TIF)Click here for additional data file.Figure S4Variance of DPG expression among CNS tissues. Different colors represent different range of variance .(TIF)Click here for additional data file.Figure S5Variance of DPG expression among non-CNS tissues. Different colors represent different range of variance .(TIF)Click here for additional data file.Figure S6The change of perturbation pattern of functional set during HIV process in CD4 T cell. The relative pathway score and perturbation level are used. The different colors represent the change direction of the set; the color deepness is related to the intensity of the change. The infection process includes acute stage, chronic stage and long-term nonprogressor stage.(TIF)Click here for additional data file.Figure S7The change of perturbation pattern during the HIV infection process in CD8+ T cell. The relative pathway score is used. Different colors represent different patterns; the color deepness is related to the intensity of the change. The infection processes include acute, chronic, and long-term nonprogressor stages.(TIF)Click here for additional data file.Table S1Information on Human DPGs.(XLS)Click here for additional data file.Table S2Detailed information on the functional categories of gene networks.(XLS)Click here for additional data file.


The criteria for deciding who should be admitted first from a waiting list to a forensic secure hospital are not necessarily the same as those for assessing need. Criteria were drafted qualitatively and tested in a prospective 'real life' observational study over a 6-month period.A researcher rated all those presented at the weekly referrals meeting using the DUNDRUM-1 triage security scale and the DUNDRUM-2 triage urgency scale. The key outcome measure was whether or not the individual was admitted.Inter-rater reliability and internal consistency for the DUNDRUM-2 were acceptable. The DUNDRUM-1 triage security score and the DUNDRUM-2 triage urgency score correlated r = 0.663. At the time of admission, after a mean of 23.9 (SD35.9) days on the waiting list, those admitted had higher scores on the DUNDRUM-2 triage urgency scale than those not admitted, with no significant difference between locations  at the time of admission. Those admitted also had higher DUNDRUM-1 triage security scores. At baseline the receiver operating characteristic area under the curve for a combined score was the best predictor of admission while at the time of admission the DUNDRUM-2 triage urgency score had the largest AUC .The triage urgency items and scale add predictive power to the decision to admit. This is particularly true in maintaining equitability between those referred from different locations. We have shown that a structured professional judgment instrument for allocating patients to the appropriate level of therapeutic security has good psychometric properties and can distinguish those admitted from a remand prison to various levels of therapeutic security with good receiver operating characteristics, sensitivity and specificity in a retrospective-cohort study ,2. The sSimilar instruments for selecting and allocating to waiting lists have been devised for interventional cardiology -7, transBased on our clinical experience, we hypothesised that when organising a waiting list, two decisions must be made. The first decision is to assess whether the patient referred for admission needs this level of service - in this case, admission to a high and medium secure hospital; the second decision concerns who amongst those who have qualified to go on the waiting list should be prioritised. This second decision is not always explicitly formulated and it is not clear that it is based on the same criteria as the first. Prioritisation, unlike assessment of need, is likely to change from week to week, and should be a dynamic rather than a static assessment, repeated as often as appropriate. The interaction between the relatively static assessment of need for therapeutic security and the dynamic assessment of the urgency of need is also unclear. It could be argued that those who meet criteria for admission to a given level of therapeutic security should be prioritised over those who do not meet the criteria. This is implicit when those who do not meet criteria are excluded from a waiting list. However the urgency of need when prioritising those who are accepted onto a waiting list may be determined by many other criteria. We set out to draft and validate a set of criteria for decision making regarding the prioritisation of the waiting list for the Central Mental Hospital, the only forensic mental health facility in the Republic of Ireland. We have followed the criteria suggested for validation of risk assessment instruments, though this is not itself a risk assessment instrument .nd revision of this draft. It forms part of a suite of structured professional judgment instruments [This study consisted of three phases. The first was an iterative drafting process followed by an observational study of decision making in practice at the weekly referrals meeting when all referrals are discussed, accepted for admission or dealt with in some other way, and those accepted are prioritised. This has been described elsewhere . The strtruments  along witruments , and twotruments .The third phase was a prospective study in which all those on the waiting list over a six month period from January to June 2010 were rated using the DUNDRUM-1 and DUNDRUM-2 each week based on pre-admission written and oral assessment information presented at the weekly referrals meeting, supplemented where necessary by further information obtained from the clinicians presenting the referrals. The consultant psychiatrists chairing the meeting and the clinical director whose role was to resolve any impasses were blind to the ratings at the time when prioritisation decisions were made.The Central Mental Hospital provides high, medium and low therapeutic security and community follow-up services for a population of 4.4 million. At the time of the study there were 93 in-patient beds at varying levels of therapeutic security. The service also provides extensive mental health in-reach services to the busiest remand and sentenced committals prisons (prisons that accept new admissions to prison) in the state, and to the other prisons. Patients can be admitted to the hospital from the prisons under the Criminal Law (Insanity) Act 2006 if medically certified. They can also be admitted from the courts if found unfit to stand trial or not guilty by reason of insanity. We have previously described the process of diversion from prison custody [The DUNDRUM-2 triage urgency scale was rated by GF based on the pre-admission assessments presented at the weekly meeting. GF also rated the DUNDRUM-1 triage security items. Data were collected over a six month period from January to June 2010.The DUNDRUM-2 triage urgency scale is intended to be used only for those who have been accepted onto the waiting list. The criteria for being accepted on the waiting list are covered by the DUNDRUM-1 triage security instrument . The DUNWe have trained mental health professionals from psychiatry, nursing, psychology, social work and occupational therapy to use the DUNDRUM-1 and DUNDRUM-2 by guiding the use of the handbook followed by joint ratings of three prepared vignettes. This training takes two and a half hours and forms part of the induction for all new clinical staff, with optional top up training every six months.The key outcome measure is whether or not the person was admitted to the Central Mental Hospital. Not all those accepted onto the waiting list are admitted. Over time their clinical state may improve, they may be released by the courts on bail, come to the end of a sentence or they may be diverted via the courts to some other less secure hospital or community placement. Of the 16 remand prisoners initially placed on the waiting list but not eventually admitted, 14 were diverted to other less secure hospitals or community mental health teams via the courts. Accordingly the rating for the DUNDRUM-2 was compared for the time when the person was admitted or for the last week when they were removed from the waiting list. Comparisons were also made for when the person was first placed on the waiting list and when they were removed either because they were admitted to the Central Mental Hospital or were diverted or released or recovered.2 tests. Change over time for individual items was examined with paired t-tests.All data was entered in SPSS-16. Inter-rater reliability for scale scores was calculated using exact probability calculations for agreement for each item. Internal consistency was examined using factor analysis and Cronbach's alpha statistic. Cross validation was examined using Spearman's rank correlation coefficient, a non-parametric test. The relationship between the DUNDRUM-1 score and admission as outcome was examined using univariate general linear modelling with admission as dependent variable, the DUNDRUM-2 score as fixed factor and location prior to admission as co-variant. One-way analysis of variance was then used to distinguish between sub-groups. The receiver operating characteristic (ROC) area under the curve (AUC) was calculated with 95% confidence intervals taking as the null hypothesis that AUC = 0.5. For item to outcome tests of criterion validity, those admitted were compared with those not admitted using univariate analysis of variance and X2 = 0.03, NS, Fisher's exact probability test = 1.0, NS).During the six month observation period 65 individuals were placed on the waiting list and 38 were eventually admitted, some after the end of the six month observation period. There were 10 women placed on the waiting list of whom 6 were admitted and 56 men of whom 32 were admitted. Women were no more likely to be admitted than men (XTwenty four patients were rated independently by a second clinician (CO'N). Mean scores were 12.4 (SD 2.9) and 12.5 (SD 3.7) for the two raters . The kappa statistic can only be calculated for items rated 0/1. Each item of the DUNDRUM-2 triage urgency scale is rated from '0' to '4' so that any two raters may agree randomly in 20% of cases. There was exact agreement in the rating for items 1 'location' in 11/24 ; item 2 'mental health' 10 , item 3 'suicide prevention' 9 , item 4 'humanitarian considerations' 12 , item 5 'systemic considerations' 24  and item 6 'legal urgency' 12 . The two raters differed by no more than one point (random probability 52%) in 18/24  for item 1, 22  for item 2, 19 (79% p = 0.006) for item 3, 18 (75% p = 0.019) for item 4, 21 (88% p < 0.001) for item 5 and 23 (96% p < 0.001) for item 6.The first item of the DUNDRUM-2 (current location) was defined separately for each of five possible locations  but was treated as a single item for this initial analysis. An initial factor analysis of the six items rated in the DUNDRUM-2 yielded two components with Eigenvalue greater than 1. The first component had an Eigenvalue = 3.2 accounting for 50% of the variance and the second component had Eigenvalue = 1.3 accounting for 21% of the variance. Principle components analysis showed that the first five items loaded positively on the first component  with only the sixth item 'legal urgency' loading negatively. The second component loaded positively on the first item 'location' and the sixth item 'legal urgency' with the remaining items loading negatively. Further inspection showed that 'legal urgency' correlated with the 'current location' rating for remand prisoners but not for other locations.Cronbach's alpha is a measure of internal consistency. For all 65 patients on the waiting list, treating the first item 'current location' as a single item yielded alpha = 0.723. Each of the first five items if deleted lead to small decreases in the alpha statistic and the sixth item 'legal urgency' if deleted lead to an increase in Cronbach's alpha from 0.723 to 0.814. Cronbach's alpha calculated for the 39 persons in custody on remand was alpha = 0.754, while deletion of any item lead to negligible declines in the alpha statistic except for the sixth item 'legal urgency' which if deleted lead to an increase in alpha to 0.802. Calculating Cronbach's alpha for the 21 persons serving prison sentences yielded alpha = 0.681. As before deletion of any one item lead to very small declines in the alpha statistic except for the sixth item 'legal urgency' deletion of which improved the alpha statistic to 0.811. Similarly for the seven patients referred from less secure psychiatric hospitals, Cronbach's alpha = 0.821. Deleting items lead to very small falls in the alpha statistic except for the third item (suicide risk) deletion of which caused alpha to increase to 0.830, a very small increase, and the sixth item 'legal urgency' deletion of which increased alpha to 0.888.For the 65 patients on the waiting list, the DUNDRUM-2 triage urgency score correlated positively with the DUNDRUM-1 triage security score . Factor analysis of the six items of the DUNDRUM-2 combined with the eleven items of the DUNDRUM-1 for all 65 patients generated five components with Eigenvalue greater than 1. The first had Eigenvalue = 6.9 accounting for 40.8% of the variance and all items loaded positively on it except triage urgency item 6 'legal urgency'. The second component had Eigenvalue = 2.6 accounting for 15.2% of the variance and loaded strongly positively on triage urgency item 3 'suicide prevention', triage security item 2 'seriousness of self harm' and triage security item 4 'immediacy of risk of suicide/self harm'. The third component had an Eigenvalue of 1.6 accounting for 9.4% of the variance and loaded negatively on Triage urgency items 2 'mental health' and 4 'humanitarian issues' and loaded positively on triage security items 2 'seriousness of deliberate self harm' and 8 'victim issues'. This component appears to lack any coherent theme. The fourth component had an Eigenvalue of 1.2 accounting for 7.3% of the variance and loaded positively on triage urgency item 6 'legal urgency' and triage security item 11 'legal procedure'. The fifth component with Eigenvalue 1.1 accounting for 6.3% of the variance had no coherent theme. Because the first component appears to include almost all items and the remaining components are thematically subsets such as 'suicide management' and 'legal considerations' it would appear that there is no factorial distinction between the DUNDRUM-1 and DUNDRUM-2 items. In keeping with this, testing the internal consistency of a combined scale of 17 items has Cronbach's alpha = 0.861. Omitting any one item lead to small decreases in the alpha statistic (lowest 0.849) except for triage urgency item 6 'legal urgency' omission of which lead to an increase of the alpha statistic to 0.876, a negligible increase.2 = 0.1, df = 2, NS).Table In an overall estimate of effect sizes using general linear modelling with admission as the dependent outcome variable and location prior to admission as the independent variable, significant effects were found for admission  and location  and the interaction between admission outcome and location . Table A different pattern emerged when the DUNDRUM-2 score was examined at the time when the person left the waiting list (a mean of 23.9 days (S.D. 35.9) after first being placed on the waiting list). People left the waiting list either because they were admitted to the Central Mental Hospital or were thought to no longer need admission to the hospital. Table The DUNDRUM-1 triage security items may also influence the decision to admit. Table for location  and the interaction between admission outcome and location was significant .When the DUNDRUM-1 triage security scores are compared at the time of leaving the waiting list however Table , signifiThe mean time spent on the waiting list before being either admitted or removed for some other reason was 23.9 days (S.D. 35.9). Those admitted spent a mean of 21.4 days on the waiting list (SD 40.7) while those not admitted spent a mean of 27.3 days on the waiting list (SD 29.2), a difference that was not statistically significant.The DUNDRUM-2 triage urgency score changed while on the waiting list, from a mean score of 10.6 (S.D. 4.4) when first placed on the waiting list to 9.5 (S.D. 5.0) by the time the person was removed from the waiting list, either because they were admitted, diverted or no longer needed admission . The DUNDRUM-1 triage security score changed relatively less over the same period from a mean of 22.6 (S.D. 7.6) to 21.8 (S.D. 8.4) which did not reach statistical significance (mean difference 0.8 (S.D. 3.4) paired t = 1.9, df = 64, NS).To clarify the differences over time, Table The receiver operating characteristic (ROC) was calculated for the DUNDRUM-2 triage urgency score where the outcome is admission, and also for the DUNDRUM-1 triage security score and the combined score which is the sum of the two. This was done for the ratings at the time when patients were placed on the waiting list, and also at the time when patients were removed from the waiting list, either because they had been admitted or no longer needed admission to the Central Mental Hospital. Table Because the first item of the DUNDRUM-1 triage urgency scale is defined separately according to the location of the patient while on the waiting list, the receiver operating characteristic was calculated separately according to the location of the patient for the ratings at the time the patient left the waiting list. Table Table Concerning change over time, paired t tests showed significant falls in mean scores for DUNDRUM-2 item 1 'location' , item 2 'mental health' , item 4 'humanitarian issues'  and item 5 'systemic' . Item 3 'self harm' and item 6 'legal urgency' had declines which did not reach significance. For the DUNDRUM-1 triage security items there were significant declines only for item 3 'immediacy of violence'  and item 11 'legal procedure' .The DUNDRUM-2 triage urgency scale has good psychometric properties. It has good inter-rater reliability and high internal consistency although the legal urgency item is the least cohesive of the six items. The DUNDRUM-2 triage urgency score correlated with the DUNDRUM-1 triage security score. It appears for practical purposes that there is only a single underlying 'factor' amongst all six items of the DUNDRUM-2 triage urgency scale and the eleven items of the DUNDRUM-1 triage security scale. However both the content and the 'dynamic' change sensitive nature of the DUNDRUM-2 triage urgency items provide sufficient reason for distinguishing the DUNDRUM-1 from the DUNDRUM-2 scale. This is because the DUNDRUM-1 has distinct content oriented towards assessing need rather than urgency. It is interesting to note that although in this study there were some falls over time in the DUNDRUM-1 score, this arose only due to changes in the items regarding immediacy of risk of violence and legal urgency.When predicting admission from the waiting list the DUNDRUM-2 triage urgency score generally has a better area under the curve of the receiver operating characteristic compared to the DUNDRUM-1 triage security score and the combined score Tables  and 8. TEach item of the DUNDRUM-1 triage security scale and the DUNDRUM-2 triage urgency scale was tested for its relationship to outcome (admitted or not admitted from the waiting list). Table In the course of this study it emerged that there is an anomaly concerning patients referred from hospitals at lower levels of security. Those accepted onto the waiting list from less secure hospitals had higher scores on the DUNDRUM-1 triage security scale than those accepted from other locations Table  and thosThere is a clinical justification for having a lower admission threshold for those in prison compared to those already in another hospital. Those in another hospital are in receipt of mental health care, they have the protections of treatment under mental health legislation where necessary and they are not in the challenging and anti-therapeutic environment of a prison. These are all 'urgency' factors rather than factors concerning need for a particular level of therapeutic security. However it may be that there is reluctance to admit patients from less secure or other hospitals for another reason. Such patients commonly have much longer lengths of stay in conditions of high or medium therapeutic security. Clinicians and bed managers may be reluctant to admit those who are likely to become long-stay as they will reduce the capacity of the service to admit future incident cases including those who are in urgent need in prisons. We have not included an item to take account of likely length of stay as a 'systemic' consideration because we have insufficient evidence on which to base an operationalised rating scale that would accurately predict likely length of stay. Likely length of stay was included in earlier structured professional judgment instruments for assessing need for therapeutic security ,15 thougAnother reason for inconsistency in waiting list prioritisation which can be discounted in this study has been described  in complThe DUNDRUM-2 triage urgency items have been validated as a scale but it has been developed for use as a structured professional judgment instrument, not an actuarial scale. Structured professional judgment instruments are intended to guide and assist the decision makers, but they are not intended to bind the decision maker in the way an actuarial checklist score might appear to do. The advantage of using a structured professional judgment instrument over unstructured professional judgment should be the transparency of the decision making process and openness to audit at the systems level and review at the individual level, in keeping with recommendations for ethical rationing ,11,19. RThe numbers included in this prospective naturalistic outcome study are small when some sub-groups are considered, particularly for those waiting for admission from less secure hospitals. All other analyses had sufficient power to reach statistical significance and there does not appear to be any evidence of possible error due to lack of statistical power. Although the receiver operating characteristic is a property of the instrument not the population, this structured professional judgement instrument will have to be tested and validated in other jurisdictions where the organisation and structure of forensic mental health services may differ as may legal frameworks and processes. We believe however that the item content is likely to be generalisable.This naturalistic prospective observational study provides evidence that there is a distinction between the items assessing need for admission to various levels of therapeutic security such as the medium and high secure forensic hospital studied here and the items assessed to decide the prioritisation of those on a waiting list for admission to a medium or high secure forensic hospital.The authors declare that they have no competing interests.GF collected and entered data and coordinated the project. GF and CO'N rated the data and contributed to the item definitions in the manual. HGK produced the first draft of the manual, designed the study and carried out the data analysis. All contributed to the authorship of the paper and approved the final manuscript.DUNDRUM-2 triage urgency scale. This is an extract from the handbook of the DUNDRUM Quartet.Click here for file


Introduction: Motor imagery (MI) is the mental rehearsal of a motor first person action-representation. There is interest in using MI to access the motor network after stroke. Conventional fMRI modeling has shown that MI and executed movement (EM) activate similar cortical areas but it remains unknown whether they share cortical networks. Proving this is central to using MI to access the motor network and as a form of motor training. Here we use multivariate analysis  to map the array of neural networks involved during MI and EM.Methods: Fifteen right-handed healthy volunteers (mean-age 28.4 years) were recruited and screened for their ability to carry out MI (Chaotic MI Assessment). fMRI consisted of an auditory-paced (1 Hz) right hand finger-thumb opposition sequence  with two separate runs acquired (MI & rest and EM & rest: block design). No distinction was made between MI and EM until the final stage of processing. This allowed TICA to identify independent-components (IC) that are common or distinct to both tasks with no prior assumptions.Results: TICA defined 52 ICs. Non-significant ICs and those representing artifact were excluded. Components in which the subject scores were significantly different to zero (for either EM or MI) were included. Seven IC remained. There were IC's shared between EM and MI involving the contralateral BA4, PMd, parietal areas and SMA. IC's exclusive to EM involved the contralateral BA4, S1 and ipsilateral cerebellum whereas the IC related exclusively to MI involved ipsilateral BA4 and PMd.Conclusion: In addition to networks specific to each task indicating a degree of independence, we formally demonstrate here for the first time that MI and EM share cortical networks. This significantly strengthens the rationale for using MI to access the motor networks, but the results also highlight important differences. Athletes have used motor imagery (MI) for decades but recently there has been considerable interest in applying it to the patient population . In other words we make no prior assumptions as to the extent of overlap, if any, between the MI and EM. If the cognitive process of imagery and EM involve similar area but are actually distinct then the analysis will produce networks  that relate to either MI or EM but not both. However, given the extensive behavioral literatures we hypothesize that three categories of networks will be identified; first, those networks that are present during EM only, which will involve the contralateral primary motor cortex; second, networks that are common to both MI and EM, involving premotor and posterior parietal areas; and finally networks that involve MI only, involving the premotor areas . Subjects overlapped with those reported in  and asked to identify whether the picture is of a left or right hand (Component 1). A score below 75% correct indicates that the subject is unable to perform accurate MI. Second, subjects are asked to perform MI of a finger sequence task . The duration of the finger tapping exercise varied and the subject had to confirm their position at the end of each block. Third, subjects are required to perform the same finger taping sequence initially using EM and then using MI (Component 3). During both phases of this test the external auditory pacing rate, which starts at 40 beats/min is increased by 10 beats every 5 s. The break point is defined as the time when the subject is unable to perform the task accurately. Subjects are excluded if the break point is greater for MI than for EM. During all tasks requiring MI, subjects were given specific instructions to perform first person MI; not to view the scene from the 3rd person; and not to count or assign numbers or tones to each finger. Subjects were excluded if unable to perform MI adequately.The fMRI used an established block design .A 3-Tesla Brucker MRI scanner was used to acquire both T2-weighted and proton density anatomical images and T2www.fmrib.ox.ac.uk/fsl). The first 12 volumes were discarded to allow for T1 equilibration effects. Given our hypothesis and the identical temporal design of the MI and EM task, no distinction was made between tasks until the final stage of processing. As 14 subjects (one subject was excluded see below) performed 2 tasks, MI and EM, 28 “blinded” tasks were processed-we use the term blinded as no distinction was made between either imagery or EM during the generation of the IC.Analysis was carried out using Tensorial Independent Component Analysis  for the associated spatio-temporal process shown in the spatial map and the time course. An IC was considered to be involved in MI or EM if a one-way SD = 4.1%), fMRI simulation (Component 2) and Fitts law [mean break point 19% (SD = 14.2) less for MI than EM], as well as during the fMRI session. No subject failed to either suppress movement or showed evidence of non-compliance during the fMRI paradigm. Median post-MRI MI scores was 6 (range 4–7).One subject was excluded because of a failure to perform MI satisfactorily. The remaining 14 subjects performed adequately on all aspects of the hand rotation task  across task were excluded. Components in which the subject scores were significantly different to zero (for either EM or MI) were included. Seven IC remained.Fifty-two IC were defined by TICA. IC's that identified artifact recognized by previously published patterns and high frequency were excluded by visual inspection  and components that are exclusive to EM (subject score greater than zero for EM only) and to MI (subject score greater than zero for MI only). The whole brain activations and deactivations, time course, variance explained and subject score are shown in Figures Five components  and to a lesser degree the left parietal lobe  as well as the cerebellum (r7L) and BA44 and premotor areas. IC2 showed activation that was largely limited to the premotor areas bilaterally including PMd and SMA. IC3 showed activation that was predominantly localized to the left hemisphere including motor areas , premotor , somatosensory cortex and left parietal areas hIP2-3 and IPC. The activation patterns of IC8 were largely restricted to subcortical structures notable the thalamus  and cerebellum with limited involvement of the premotor areas.IC9 is notable as it is equally involved in MI and EM. This IC involves activation of the preSMA, SMA, BA44 and right IPC (PFm).IC1, IC2, and IC3 all contained areas of deactivation. This generally involved bilateral dorsal BA4. IC1 contained additional deactivation of the left caudate and SPL in IC3.One component, IC19 was significantly involved during EM only (2.06% of explained variance). Again this correlated with the motor tasks rather than rest. This involved activation of areas typically seen in movement; the contralateral motor cortex, somatosensory cortex and hIP2&3. IC19 involved deactivation of the left medial frontal gyrus.IC26 was significantly involved during MI only (1.55% of explained variance). This correlated with MI rather than rest. The activation was restricted to the right hemisphere and included the right BA4, premotor and area 3b.Here we use a data led method to report that MI and EM share cortical networks. The majority of the networks involved in the tasks appear to be shared . One network was exclusive to EM (accounting for 2.06% of the explained variance) and another was exclusive to MI (accounting for 1.55% of the explained variance). That being said a number of the shared networks are significantly more involved in EM than MI. This provides an important foundation for the use of MI as an alternative means to access the motor system in diseases that limit physical performance such as stroke . The SMA been implicated in motor planning and learning  and the ipsilateral PMd .TICA appears to be a useful tool in testing hypothesis that explore shared networks. It has its limitations, however. For instance a central assumption in this work is that the two motor tasks have the same temporal profile. It is entirely possible that cortical networks that have different temporal profiles have been overlooked by this method. However, if that were the case then one would expect those areas to have been highlighted by earlier mass-univariate fMRI studies. Furthermore, a recent report has highlighted TICA may not be as robust as Parallel Factor Analysis (PARAFAC) if there is a possible violation of the assumption of spatial independence (Helwig and Hong, The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.