Activation of inflammatory pathways plays an important contributory role in coronary plaque instability and subsequent rupture, which can lead to the development of acute coronary syndrome (ACS). Elevated levels of serum inflammatory markers such as C-reactive protein (CRP) represent independent risk factors for further cardiovascular events. Recent evidence indicates that in addition to lowering cholesterol levels, statins also decrease levels of inflammatory markers. Previous controlled clinical trials reporting the positive effects of statins in participants with ACS were designed for very early secondary prevention. To our knowledge, no controlled trials have evaluated the potential benefits of statin therapy, beginning immediately at the time of hospital admission. A previous pilot study performed by our group focused on early initiation of cerivastatin therapy. We demonstrated a highly significant reduction in levels of inflammatory markers (CRP and interleukin-6). Based on these preliminary findings, we are conducting a clinical trial to evaluate the efficacy of another statin, fluvastatin, as an early intervention in patients with ACS.The FACS-trial (Fluvastatin in the therapy of Acute Coronary Syndrome) is a multicenter, randomized, double-blind, placebo-controlled study evaluating the effects of fluvastatin therapy initiated at the time of hospital admission. The study will enroll 1,000 participants admitted to hospital for ACS (both with and without ST elevation). The primary endpoint for the study is the influence of fluvastatin therapy on levels of inflammatory markers (CRP and interleukin-6) and on pregnancy associated plasma protein A (PAPP-A). A combined secondary endpoint is 30-day and one-year occurrence of death, nonfatal myocardial infarction, recurrent symptomatic ischemia, urgent revascularization, and cardiac arrest.The primary objective of the FACS trial is to demonstrate that statin therapy, when started immediately after hospital admission for ACS, results in reduction of inflammation and improvement of prognosis. This study may contribute to new knowledge regarding therapeutic strategies for patients suffering from ACS and may offer additional clinical indications for the use of statins. During the past decade, inflammation has often been cited as a major factor in the pathogenesis of atherosclerosis and its clinical sequelae, including ischemic heart disease. It was found that traditional risk factors such as hypertension, hypercholesterolemia, diabetes, and smoking could not fully account for the development of coronary stenosis in all patients suffering from ischemic heart disease. Intensive study of the pathogenesis of coronary plaque development and rupture led to the hypothesis that inflammatory factors contribute to this process. For example, T-lymphocytes and monocytes/macrophages have been repeatedly identified in plaque lesions; elevated levels of acute phase proteins , cytokines , and adhesive molecules (ICAM-1) correlate with the worse prognoses in patients with ischemic heart disease . FurtherActivation of the immune reaction in acute ischemic heart disease likely derives from: (i) pathological events occurring in the arterial wall where the lesion develops, leading to plaque rupture and the subsequent clinical consequences and (ii) myocardial necrosis, which triggers processes involved in removal of the necrotic mass and replacement with scar tissue. Whereas activation of plaque inflammation (as noted above) serves as a marker for plaque instability, elevation of inflammatory factors from the second source correlates with the extent of myocardial necrosis.Statins, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, lower cholesterol levels by decreasing the production of low-density lipoproteins (LDL) and up-regulating the expression of the LDL-receptor. These drugs are widely used in patients with hypercholesterolemia for primary and secondary prevention of coronary artery disease because of their efficacy in reducing cardiovascular morbidity and mortality -12. SurpA number of large clinical trials have been designed to investigate the effect of statins in treating acute coronary syndromes (ACS) -30 BecauLittle information is available on the efficacy of statins in treating ACS at an earlier phase, i.e., at the time of hospital admission. Recently published data from experimental projects -35 and fThe objectives of the FACS trial are to determine:(i) Whether initiation of fluvastatin therapy in patients with ACS immediately after hospital admission decreases levels of CRP, IL-6, and pregnancy-associated plasma protein A/ proform eosinophilic major basic protein (PAPP-A/proMBP), which represent indirect markers of plaque instability and indicators of poor prognosis; and(ii) Whether initiation of fluvastatin therapy decreases the occurrence of ischemic events  in patients with ACS.This is a prospective, 30-day, multicenter, randomized, double-blind, placebo-controlled study in 1,000 patients with ACS. Patients are enrolled from 10 sites in the Czech Republic and Slovakia. At each institution, the protocol and the informed consent form are reviewed and approved by the institutional ethics committee before study initiation. Eligible patients are randomized to one of two treatment groups immediately after hospital admission (within one hour). One group is assigned 80 mg/day fluvastatin (Lescol XL), with the other group receiving placebo. Participants are followed on an intention-to-treat basis. The primary endpoint relates to levels of CRP, IL-6, and PAPP-A/proMBP. The secondary endpoint is the occurrence of an ischemic event, defined as death, nonfatal myocardial infarction (MI), recurrent symptomatic myocardial ischemia, cardiac arrest with resuscitation, and urgent revascularization.This study will enroll high-risk patients admitted to the hospital for ACS. Eligible patients with ST elevation ACS must have resting chest pain less than 12 hours before admission and either ≥ 1 mm ST-segment elevation in 2 or more continuous leads or new left bundle branch block on ECG. Those with non-ST elevation ACS must have resting chest pain during the previous 48 hours and either ≥ 1 mm ST segment depression or negative T waves in 2 or more continuous leads.Subjects are excluded from study participation if they are <18 years of age or if they have concomitant active liver disease or persistent elevation of transaminases , a history of lipid-lowering therapy less than 30 days before the index event or a known allergy to fluvastatin or to any additives present in the drug. Other exclusions include inability to ingest oral medication, unwillingness to be followed for the duration of the study, muscle disease , and creatine kinase ≥ 5 times the upper limit of normal due to conditions other than myocardial infarction. Women of childbearing potential who are pregnant, nursing or who are not using effective contraception will also be excluded.After obtaining informed consent, blood samples are taken from patients for examination of inflammatory markers . Patients are then randomized to 80 mg fluvastatin (Lescol XL) or to placebo immediately p.o. Medical history and physical examination, standard 12-lead ECG, blood lipid profile, and liver function tests are performed as part of participants' routine admission care. Fluvastatin 80 mg or placebo are then taken once daily for 30 days. Follow-up measurement of inflammatory markers  is performed on day 2 and day 30. Follow-up visits are scheduled at pre-discharge, day 30, 90, 180 and 360. Blood liver function and creatine kinase tests are done at pre-discharge and at the 30-day visits. At day 30, the lipid profile is also examined, and study medication is withdrawn. All visits include assessment of ischemic events and recent medical history since the last follow-up visit, including use of concomitant medications  with respect to the primary endpoint (30-day decrease of CRP and IL-6) and the combined secondary endpoint . With 500 patients randomized to 80 mg fluvastatin (Lescol XL) and 500 patients randomized to placebo, the trial will have more than 80 % power to detect a decrease in CRP level by 1.36 μg/L and a decrease in IL-6 level by 1.09 ng/L. Calculations are based on a two-sample The FACS trial is the first multicenter, randomized, double-blind, placebo-controlled trial investigating the effects of fluvastatin therapy started immediately after hospital admission in patients with ACS.The author(s) declare that they have no competing interests.PO has made substantial contributions to the concept and design of the trial and has drafted the manuscript. DA, PH, JVej, JK, MMat, and JVes have made substantial contributions to the concept and design of the study and have been involved in revising the manuscript. PB, MW, OA, JS, EN, FH, MR, and JC have been involved in the acquisition and analysis of data. MMac and MK have given final approval of the version to be published.


Possible methods for distinguishing receptor binding models and analysing their parameters are considered.The conjugate gradients method is shown to be optimal for solving problems of the kind considered. Convergence with experimental data is rapidly achieved with the appropriate model but not with alternative models.Lack of convergence using the conjugate gradients method can be taken to indicate inconsistency between the receptor binding model and the experimental data. Thus, the conjugate gradients method can be used to distinguish among receptor binding models. Most medicinal preparations and biologically active substances do not penetrate into cells and must therefore exert their influence on intracellular processes by interaction with specific protein molecules at the cell surface -3, for wNon-cooperative interaction between ligand and receptor:R is the receptor molecule, L is the ligand molecule, RL is the ligand-receptor complex, and k+1 and k-1 are respectively the kinetic constants of formation and dissociation of the complex.where Cooperative interaction between ligand and receptorInteraction of one ligand with N types of binding sitesRL] are determined. The investigator has to solve two basic interrelated problems  , and = [R0] - [RL], [L] = [L0] - [RL].But [So equation (4) can be rewritten:Rikkatty equations. It can be solved analytically with the help of a special substitution [R] = [R0] - [RL], [L] = [L0] - [RL] do not generate analytically soluble equations. Therefore, all equations of this form were solved numerically using the Runge-Kutta method u. Random error assuming the normal distribution law was superimposed on the magnitude of [RL]u, and was calculated at 5, 10, 20 or 100 points.Numerical solution of equations (5–7) was carried out to determine [RT]m was calculated using parameters other than [RL]u from models (1–3). These parameters were applied to the determination of [RL]u by the following functional minimization:The magnitude [RL]u - [RL]m)2.     (8)Φ = , Newton's method and its variants (the conjugate gradients method and coordinate descent method in various modifications) were used -17. The R0] and Kd cannot be <10-15 M or >10-5 M. Hence the iteration procedure could be improved by re-scaling these parameters logarithmically, making 10-15 M equivalent to -1 on the new scale and 10-5 M equivalent to 1.It is clear from the literature  that [R0R0] Kd can be seen. Therefore the magnification of the random error in evaluating the magnitude of [RL]u displaces the functional (8) global maximum from its true values. In a sufficiently large neighbourhood of the global maximum, the functional magnitude (8) is practically invariant. However, this modification becomes more essential for evaluating the ratio of the functional (8) to basis vector of values [RL]u. Therefore this ratio was used with the inhibiting criterion choice.The functional (8) contour plots are shown in fig. The Newton method converges only in the close neighbourhood of the global maximum. However, modifications of the Newton method using second derivatives allow convergence to the global maximum after 1–2 iterations  using the conjugate gradients method, whereas this method required >10 points for identifying the parameters in a more complicated model. The Newton methods required >7 and 12 points respectively, and the coordinate descent method required >10 and 18 points.RL]m using an incorrect binding model. In particular  behaviour was analysed with respect to the evaluation of [see fig. , the funThus, the modification of the functional (8) contour plot from the type in fig. It appears that when an incorrect choice of the receptor binding model has been made, the conjugate gradients method does not lead to convergence, whereas in some cases the Newton method converges to one of the local minima. Therefore, lack of convergence using the conjugate gradients method suggests an incorrect choice of receptor binding model.even when the application of graphical methods is difficult or impossible. As seen here, lack of convergence in the conjugate gradients method indicates that an incorrect choice of model has been made. It is also shown that for the defining the parameters of the correct model, 5–10 data points are sufficient.Possible methods have been explored for discriminating among models for receptor binding model and for defining the relevant parameters. The procedure devised allows one to determine the receptor binding model and its parameters,


Scleromyxoedema is a rare chronic skin disease of obscure origin, which may often be associated with severe internal co-morbidity. Even though different casuistic treatment modalities have been described, to date, curing still seems to be impossible.+ and anti-mastcell-tryptase+ cells. Bone marrow biopsies confirmed a monoclonal gammopathy of undetermined significance without morphological characteristics of plasmocytoma; immunofixation unveiled the presence of IgG-kappa paraproteins.We report a 44-year-old Caucasian female presenting with remarkable circumscribed, erythematous to skin-coloured, indurated skin eruptions at the forehead, arms, shoulders, legs and the gluteal region. Routine histology and Alcian blue labelling confirmed a massive deposition of acid mucopolysaccharides. Immunohistochemical investigations revealed proliferating fibroblasts and a discrete lymphocytic infiltration as well as increased dermal expression of MIB-1Taking all data into account, our patient exhibited a complex form of lichen mxyoedematosus, which could most likely be linked a variant of scleromyxoedema. Experimental treatment with methotrexate resulted in a stabilisation of clinical symptoms but no improvement after five months of therapy. A subsequent therapeutic attempt by the use of medium-dose ultraviolet A1 cold-light photomonotherapy led to a further stabilisation of clinical symptoms, but could not induce a sustained amelioration of skin condition. Lichen myxoedematosus (LM) represents a rare chronic skin disorder of unknown aetiology, which may often be accompanied by severe internal co-morbidity such as haematological involvement including paraproteinaemia, neurologic syndromes, gastrointestinal complications or cardiac abnormalities -4. CliniThe oncoming case presentation focuses on a progressive variant of SCL as referred to clinical, immunohistochemical and laboratory investigations, followed by low-dose methotrexate (MTX) and subsequent medium-dose ultraviolet A1 (UVA1) cold-light treatment.We report a 44-year-old Caucasian woman who initially presented in 2003 with a multitude of progressive lichenoid 2–4 mm papules starting two years ago, particularly marked on the forehead right above both eyebrows, on the dorsal aspects of the forearms, shoulders, legs as well as on the gluteal region, accompanied by severe pruritus Fig. . ClinicaSkin biopsies were taken from the left forearm/wrist, both legs and the left shoulder. Routine histological examination including haematoxylin-eosin, PAS labelling and Alcian blue staining revealed marked mucinous deposition within the upper and mid dermis beside an increased appearance of fibroblasts, collagen bundles and a discrete inflammatory infiltration.+ (T helper cells), CD68+ (macrophages), anti-mastcell-tryptase+ (mastcells), decorin+ (collagen fibril stability protein), MIB-1+ (Ki-67+ proliferating cells), CD20+ (B lymphocytes), and FGF-R+ (fibroblast growth factor receptor bearing cells) cells taking consecutive sections . Exact results of all immunohistochemical stainings are detailed in Table Additionally, immunohistochemical investigations were performed in order to enumerate CD4lls Fig. , morphol-1, lymphocytes 13.2%, IgG 2000 mg/dl. There was no increase in B cell count. Immunoelectrophoresis disclosed albumin 51.4%, alpha-2 globuline 10.5%, gamma globuline 22.9%. Cranial x-ray, x-ray of the thorax, ultrasound of the abdominal organs, electrocardiography and urinary investigations were unremarkable. Blood smear cytological evaluation revealed beginning qualitative but still no quantitative changes as defined by leukocytic aberrations pointing towards a leftward shift. Serum immunofixation demonstrated an IgG-kappa paraproteinaemia. No elevation of the IgG-lambda paraprotein was assessed. Bone marrow biopsies displaying reactive lymphoid infiltration including minimal extension of plasma cells with monoclonal immunoglobuline production provided evidence for monoclonal gammopathy of undetermined significance (MGUS) without distinct morphological characteristics of a plasmocytic plasmocytoma or plasmoblasts.Complete laboratory measurements unveiled the following pathological results: leucocytes 10870 μLInitially, 20 MHz ultrasound scanning producing cross section images of the skin was established in order to measure both structure and thickness of the skin at the dorsal aspects of the left wrist and the right forearm. The total thickness of the skin was measured from the entrance echo to the border between the dermis and the subcutaneous tissue. A cutaneous diameter of 2291 μm at the left wrist  and of 1106 μm at the right forearm  could be assessed.2 single-dose UVA1  was performed four times a week for three weeks followed by two times a week for further two weeks resulting in a cumulative dose of 800 J/cm2 after five weeks. Meanwhile, the skin status again remained stable, whereas no improvement could be observed. Therefore, our patient broke up phototherapy. To date, skin condition has slightly worsened without any current treatment modality.In our unit, an experimental treatment modality using oral MTX 12.5 mg once per week followed by a subsequent folic acid application on the following day for a 6-months-period was subsequently initiated. MTX was well tolerated by our patient. After the first three months, the continuous progress of skin lesions during the last two years could be stopped and our patient experienced subjectively an improvement and objectively a stable clinical outcome without new lesions. Subjective impression of amelioration could not be confirmed by means of ultrasound measurement. Within the following two months, no further improvement could be evaluated, whereas no further progression such as formation of new lesions or increase of stiffness could be observed. Due to the unsatisfying clinical results and declining acceptance by our patient, MTX treatment was stopped and a subsequent therapeutic attempt with medium-dose UVA1 cold-light phototherapy was initiated. Irradiation consisting of 50 J/cmThe population prevalence of SCL is known to be extremely low. Skin lesions are characterised by an increased deposition of acid mucopolysaccharides within the papillary and upper reticular dermis . Even toBeside typical skin eruptions, LM might also be associated with severe internal and neurological abnormalities such as cardiac irregularities, paralysis, hemiparesis or even progress to coma ,4,13. De+ and occasional FGF-R+ fibroblasts in immunohistochemistry. Ki-67 (MIB-1) and fibroblast growth factors are involved in a variety of mitogen and proliferative activities [+ and CD20+ dermal inflammatory lymphocytes as well as anti-human mast cell tryptase+ cells, which may profoundly contribute to mucinosis formation [In our patient, Alcian blue staining disclosed a remarkable deposition of mucinous material within the upper dermal layers combined with an increased appearance of proliferating MIB-1tivities . Thus, ttivities . Here, aormation . Unfortu+ and FGF-R+ cells within the upper dermis beside a massive mucinous deposition.By considering clinical appearance, laboratory findings, immunofixation, bone marrow biopsy and histological evaluation, our patient presumably exhibited a complex variant of SCL. Fibroblast activity was supposed to be increased reflected by a corresponding high number of MIB-1MTX therapy is approved in the treatment of malignant lymphoma. Additionally, low-dose MTX therapy has been established as a potent regimen in the treatment of T cell related skin diseases associated with a subsequent elevated fibroblast activation status (e.g. progressive systemic sclerosis) . As in oHowever, in order to interrupt the clinical progression or even therapy-resistance as reflected by our case presentation and by considering the stable skin conditions following UVA1 phototherapy, we are currently discussing a new therapeutic attempt applying extracorporeal photochemotherapy, as proposed by Krasagakis et al. , in ordeLM: Lichen myxoedematosus; SCL: Scleromyxoedema; MTX: methotrexate; UVA1: ultraviolet A1; MGUS: monoclonal gammopathy of undetermined significanceNone declared.F.B. participated in the design of the study, carried out the immunohistochemistry, performed the statistical analysis and drafted the manuscript. S.R. carried out 20 MHz ultrasound scanning. A.K. conceived of the study. M.F., M.S. and P.A. participated in histology,  design and coordination.All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


While physical health has improved considerably over recent decades in Finland, the disease burden of mental health, especially that of depression, has become increasingly demanding. However, we lack long-term data on the natural course of subjective well-being in the general population. The aim of this study was to investigate the long-term course of self-reported life satisfaction.This was a 15-year prospective cohort study on a nationwide sample of adult Finnish twins (N = 9679), aged 18–45 and healthy at baseline, who responded to postal questionnaires in 1975, 1981 and 1990 including a 4-item life satisfaction scale (happiness/easiness/interest in life and feelings of loneliness). Life satisfaction score (range: 4–20) was classified into three categories: satisfied (4–6), intermediate (7–11) and dissatisfied group (12–20). The associations between life satisfaction scores during the follow-up were studied with linear/logistic regression.Moderate stability and only a slight effect of age or birth-cohort on mean life satisfaction score (LS) were detected. In 1990, 56% of all and 31% of the dissatisfied remained in the same LS category as at baseline. Only 5.9% of the study subjects changed from being satisfied to dissatisfied or vice versa. Correlations between continuous scores  were 0.3–0.4. Baseline dissatisfaction (compared to satisfaction) predicted dissatisfaction in 1981  and 1990 . Multiple adjustments decreased the risk only slightly.Life satisfaction in adult Finns was moderately stable during 15 years. Among an identifiable group (i.e. the dissatisfied) life dissatisfaction may become persistent, which places them at a greater risk of adverse health outcomes. While physical health has improved considerably during recent decades in Finland, the disease burden of mental health – especially that of depression – has become increasingly demanding for the health care services and society. When the global disease burden – including both fatal and non-fatal outcomes – has been assessed, major depression has been shown to be one of its leading causes . HoweverLife satisfaction and happiness are some of the concepts that have previously been viewed as indicators of subjective well-being ,8. Life However, good mental health is also an area that should be studied in psychiatry. It is something more than the absence of symptoms. It is a mental state that is objectively desirable, indicating for example maturity, emotional and social intelligence, resilience and subjective well-being according to Vaillant . While tSubjective well-being has mainly been investigated in cross-sectional settings and among the elderly, while follow-ups may have been brief and data from the general population have been sparse. Even if subjective well-being in general population has been suggested to be quite stable ,17-23, iMental health policy plays an increasingly recognized role in society, but it needs both epidemiological data as well as experts on the field of mental health to monitor the populations and trends . Thus, iThis prospective cohort study with a follow-up from 1975 to 1990 was based on the Finnish Twin Cohort, a nationwide sample of all Finnish same-sex twin pairs born before 1958 with both members alive in 1975. A baseline health questionnaire was sent in 1975 to twin candidates . The folThe questionnaires included a four-question scale for life satisfaction, which was modified from a questionnaire developed for measuring the quality of life for research purposes in Nordic countries . It has increasing scores indicating a decrease in life satisfaction. On the basis of the distribution of the sum score (LS), subjects were categorized into the satisfied (LS:4–6), the intermediate group (LS:7–11) and the dissatisfied (LS:12–20) .S:12–20) . At baseThe criteria for inclusion in the present study were the availability of baseline life satisfaction data, an age of 18–45 years on 1 January 1976 and being a twin , since only twins were eligible to receive follow-up questionnaires, as well as being healthy at baseline . Moreover, the questionnaire was sent in 1990 only to those whose twin partner was alive. Thus, from these eligible subjects, study subjects were those with all three life satisfaction scores available (N = 9679). They consisted of 4466 (46.1%) male and 5213 (53.9%) female twins  and three nationwide registries: the Hospital Discharge Registry (H), the Registry of Specially Refunded Medication (M) and the Cancer Registry (C). Thus, those with symptoms or diseases covering cardiovascular disease, diabetes, chronic obstructive pulmonary disease or malignant cancer, those who used medications for 37 selected chronic somatic or psychiatric diseases, as well as those who were on a work disability pension due to any cause or had an inpatient admission between 1972 and April 1976, were excluded . The speIt has previously been reported that 4-item life satisfaction is associated with a lower age, female sex, cohabiting, an upper social class, non-smoking, lower alcohol consumption and physical activity ,12,13. TData analysis was carried out using STATA (version 7.0). Since a study subject could be an age- and sex-matched twin sibling of another study subject, not all the observations were necessarily independent. Therefore, correct standard errors were computed by treating each pair of twins as a single unit (i.e. cluster sampling). The statistical significance of differences was tested by estimates of means (SVYMEAN and SVYLC procedure) for continuous variables and by the chi-squared test for categorical variables (SVYTAB procedure), corrected for clustered data and converted into F-statistics. The stability of life satisfaction over time was examined by computing Pearsonian correlation coefficients between continuous variables. To study how former life dissatisfaction predicts later life dissatisfaction, linear and logistic regression for clustered data was used.The baseline characteristics of the study population and those whose follow-up data on life satisfaction was not available at all three data collection times is presented in Table In the study population with complete follow-up data on life satisfaction, no marked differences were observed in mean LS scores between age or gender groups or measurement times Table . There wIn terms of 3-category LS scores  and highest among those aged 36–45 . For the total study population the annual auto-correlation was estimated as 0.92 during 1975–90.Baseline life dissatisfaction predicted future life dissatisfaction Table . This waLife satisfaction was moderately stable in healthy adult Finns during a 15 year period. Age or the birth-cohort had only a slight effect on mean life satisfaction. One third of those dissatisfied at baseline remained the same after the 15-year follow-up. The ability of baseline life satisfaction to predict future life satisfaction was strong, but decreased during the follow-up period, which is a trend that has also been suggested previously .Previous studies have indicated that depression and depressive symptoms may have a chronic course -6. On thIn Finland the number of new work disability pensions due to depression has strongly increased . HoweverOur results with respect to predictions seemed not to be overestimations. Those subjects whose dissatisfaction might have led to the most adverse result, i.e. death, were excluded from our study. The response to follow-up surveys was somewhat lower among the dissatisfied, but the observed risks among the study subjects did not differ statistically significantly from those obtained from the subjects with incomplete follow-up data. Similarly, when adjusting for follow-up health behavior instead of baseline health behavior variables, these predictions strengthened slightly, but not significantly.The 4-item life satisfaction scale is easily administered and well accepted. Its sum score was available for 96% (with imputed scores for 99%) of all respondents at baseline ,13. ThisTo prevent a process from leading to more adverse outcomes, subjects should acknowledge their situation and use their own personal resources, if available. According to a panel of experts, the concept of mental health can be regarded as a developmental process providing an individual or a group with the necessary resources to cope with the demands of life without the simultaneous appearance of negatively experienced moods of longer duration . SocietyOur large nationwide sample with a high response rate and a long follow-up period enabled an examination of the long-term course of life satisfaction. The exclusion criteria for baseline health disorders were comprehensive and based on both self-reports and several national registries with high coverage and validity -41. AlthLife satisfaction among healthy adult Finns was moderately stable in a 15-year follow-up. Since the dissatisfied, one third of whom consistently rated themselves as dissatisfied, can be identified from the general population, and since dissatisfaction places them at a risk, and repeatedly reported dissatisfaction at even greater risk, of adverse health outcomes, assessing subjective well-being should be encouraged both in surveys and in clinical practice in order to identify those in need of further evaluation of their mental health.The author(s) declare that they have no competing interest.KJ participated in composing the Finnish Twin Cohort data, in planning, commenting on, revising and approving the final manuscript.KM participated in composing the Finnish Twin Cohort data, in planning, commenting on, revising and approving the final manuscript.HR participated in planning, commenting on, revising and approving the final manuscript.HV participated in planning, commenting on, revising and approving the final manuscript.KHH planned the study, performed the statistical analyses and was the main author.The pre-publication history for this paper can be accessed here:


CytoJournal! We would like to introduce you to your journal, one that is run by and for the scientific cytopathology community with incontestable benefits of Open Access, and support from Cytopathology Foundation, Inc. CytoJournal is a peer-reviewed, PubMed indexed, online journal, publishing research in the field of cytopathology and related areas, with world wide free access. Authors submitting to CytoJournal retain the copyright to their hard earned work.Welcome to  CytoJournal is a peer-reviewed cytopathology journal is owned and supported by Cytopathology Fondation, Inc.  It is a PubMed indexed, online journal, publishing research in the field of cytopathology and related areas with world wide free access. Authors submitting to CytoJournal retain the copyright to their hard earned work.Welcome to CytoJournal One could argue whether we need more journals in cytopathology, but without a shadow of doubt there is a global need for greater access to scientific information in this field. As an Open Access journal CytoJournal will meet this need, by removing subscription barriers.Communication in general has been revolutionized in the last decade. With the emergence of the internet, entire libraries of scientific information are potentially just a mouse click away. Open Access to quality controlled, scientific information to the general public and scientific community alike is extremely valuable for harvesting the fruits of hard work by academicians.However, to date little has been done to realize the potential of this technological revolution. It is now affordable to make our hard earned scientific information available to a much wider audience. Millions of students, teachers, physicians, scientists, general public, and other potential readers can have free access to the gold mine of this scientific information.The traditional model for journal publication has performed an admirable job of disseminating research and advancing science. Unfortunately, it neither can fully utilize recent technology nor extend its benefits to the scientific community and the general public. Principally, why should scientific information generated by academicians and published without charge, not be freely available? Giving away the copyrights to the original research reports and then paying for access of the same material is paradoxically anachronistic.CytoJournal with increasing support to open access philosophy by some of the existing leading journals.With the advent of the internet and image digitization, the time has come for the development and implementation of a new model – a model beneficial to the scientific community and general public, alike. The recent adoption of an Open Access model by the Public Library of Science (PLoS)  and its The benefits of an online journal can include rapid turnaround time, real time publication, significant cost savings, and a reduction in the environmental burden engendered in the production and disposal of a print publication. Presumably in the future, the majority of research publications will be of this type .Free flow of scientific information is crucial for advancements in diagnosis and management of diseases in both the developed and developing world. Simply providing a conduit of information is not enough. There is one matter involving the human element that must be dealt with – peer review. We do not see a future where authors post their manuscripts on the web without peer-review. Science requires that publications be properly vetted (peer-reviewed) and experimental findings logically presented.In brief, standards must be maintained! This critical role is played by peer review. Acting on behalf of the scientific community, it is peer review that helps the scientific community distinguish between legitimate scientific work and quackery -7. Thus,The peer-review process at CytoJournal combines the expertise of both professional editors, who are available to survey the broad landscape of cytopathology, and academic editors, who understand deeply the strengths and limitations of their specific area of research. Every article that is published in CytoJournal will be reviewed carefully by selected reviewers, in addition to evaluation by a managing/academic editor and professional editor, who work together throughout the editorial process. A summary of your peer review process is illustrated in Figure CytoJournal's Open Access policy changes the way in which articles are published. First, all articles become freely and universally accessible online, and so an author's work can be read by anyone, free of charge. Second, the authors hold copyright for their work and grant anyone the right to reproduce and disseminate the article, provided that it is correctly cited and no errors are introduced [CytoJournal's articles are archived in PubMed Central [troduced . Third,  Central , the US  Central  in Germa Central  in Franc Central .Open Access has four broad benefits for science and the general public. First, authors are assured that their work is disseminated to the widest possible audience, given that there are no barriers to their work. This is accentuated by the authors being free to reproduce and distribute their work, for example by placing it on their institution's website. It has been suggested that free online articles are more highly cited because of their easier availability . Second,Open Access facilitates the transformation of scientific literature from rows of printed journals on library shelves to an instantly searchable archive of data. Liberating scientific literature from the vestiges of paper publication introduces the potential of various opportunities such as navigating, integrating, mining, annotating, and mapping connections in the high-dimensional space of scientific knowledge.CytoJournal will use a new business model. Our editorial expenses  will be supported by corporate sponsors through Cytopathology Foundation, Inc.  and honorary pro bono services by the academicians (Figure To provide Open Access, s Figure . The pubCytoJournal and it's editorial and publication decisions. However, they will be acknowledged and their links will be displayed on the home page of CytoJournal as a thank you note.Our sponsors do not influence in any way the content of . We never want our publication charges to be a barrier to publication and are committed to publishing any manuscript that our editors and reviewers deem to be appropriate for the journal; we will treat the costs of handling these papers as a fundamental expense of running a high-quality journal through support from Cytopathology Foundation Inc.We understand that there are many scientists who might wish to publish in our journal but do not have access to grant funds or institutional support. For such authors, a decision can be made by the Editors-in-Chief to waive publication fees. Furthermore, the fee will automatically be waived for authors from institutes that are members of BioMed Central CytoJournal is your journal and you can lead the way. However, we have to face and overcome a few traditional obstacles. First, an unfamiliarity to the Open Access model. Second, lack of the benefits of an established "brand name" aura. 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The decline in caries has slowed and this may be indicative of variation in the susceptibility of differing teeth to caries. This study tests the hypothesis that in children, there are groups of tooth sites that exhibit differences in caries susceptibility.Probit analysis of caries data collected from a 4-year longitudinal study of 20,000 schoolchildren aged between 5 and 16 years in 10 differing locations in the United States.The development of dental caries within the mouth followed a fixed hierarchy indicating that tooth surfaces show variation in caries susceptibility. Certain teeth and tooth sites have similar susceptibilities and can be grouped, the sizes of the groups vary. The most susceptible group consists of six tooth surfaces: the buccal pits and occlusal fissured surfaces of the first molar teeth. The second group consisted of 12 sites on the second molar and premolar teeth. The group formed by the least susceptible sites included the largest number of tooth surfaces and consists of the majority of the lower anterior teeth and canines.Variation in the caries susceptibility of tooth surfaces exists. Surfaces can be grouped according to caries susceptibility. An effect that reduces the cariogenic challenge of one of the sites within a group is likely to affect all the other sites within the particular group. The decline in caries that has occurred in industrialized countries over the past 30 years has been accompanied by major changes in the pattern of caries within the mouth. While the absolute levels of disease have declined, a relatively higher proportion of pit and fissured surfaces and lower proportion of approximal and smooth surfaces are involved. An additional feature in the pattern of dental caries is the existence of a surface hierarchy in susceptibility to caries -4. TheseThere is also a reported degree of symmetry both between the upper and lower jaws in the posterior sextants  and leftFor a given DMF-T or S, there is a specific pattern of caries within a population. A working rule is that 'As caries in the population declines, caries in the least susceptible surfaces  decreases considerably more than in the most susceptible surfaces (pits and fissures)'. This pattern is independent of the presence of fluoride .Furthermore, changes in mean DMF scores are not linear, but 'stepped' . This stThis study used the data from the National Preventive Dentistry Demonstration Programme (NPDDP) in the United States . The NPDz score of 0.To test the hypothesis probit analysis was used. Probit analysis is a method for examining any dose-response relationship where the dependent variable, i.e. caries, is dichotomous (caries/no caries). Since all tooth surfaces may not 'respond' in a similar manner, the problem must be formulated in terms of the proportion responding (diagnosed as caries) at each level of challenge. With probit analysis, any changes are constant in proportion so that changes on a log scale will also be constant. In a probit transformation each of the observed proportions were translated into the value of the standard normal curve below which the observed proportion of the area was found. For example, if half the subjects in a caries trial had one particular site carious, the probit value would be 0, since half the area in a standard normal distribution falls below a z score of 0. When the constant is added, the transformed value for the proportion becomes 5. If the observed proportion of individuals in whom the site was carious was 0.84, the probit value would be 0.34, i.e. a z score of 1, which would give a transformed value of 6. The actual proportion of each of the tooth sites recorded carious at each DMF-S level was calculated and replaced with the value of the standard normal curve below which the observed proportion of the area below the curve was found.When using standard normal values, negative scores can occur. To overcome this, the constant 5 was added. For example, if a particular surface scored 1 and half the subjects studied had caries, the probit value would be 0, since half of the area in a standard normal curve falls below a The logarithmic transformation of the data provides a linear relationship between the probability of an event occurring for any given value. The proportion of each surface within a population diagnosed as carious at a particular DMF-S score can be calculated. For example, take the occlusal surface of the first right lower molar. At a DMF of 1, a certain proportion of this site within a population would be carious. This proportion would increase for a DMF of 2, and so on.For each DMF-S score, the percentage of tooth surfaces exhibiting caries was calculated to give a probability score of between 0 and 1. Subsequently, the log transformation of the probabilities for each DMF-S against the actual DMF-S score was plotted for every surface. The common reference value, of 0.5 outlined above was used to establish the susceptibility of each surface to a given caries challenge. As some random variation can be expected, the susceptibilities are grouped within bands rather than as individual sites.To establish whether a hierarchy for caries susceptibility exists, the probability of finding each site carious for a given DMF-S score was calculated. The probability was calculated by adopting a common reference value for the proportion of the tooth sites that become carious. In the present study 0.5 was used to provide the most accurate value, although any value of proportion can be used. The question can then be phrased in terms of the value of the DMF-S at which 50% of the sites or teeth would be expected to have become carious. The probability scores derived are then aggregated to produce an overall picture of tooth and site susceptibilities.The probability of an event occurring ranges from zero to 1. As the overall DMF score rises, the probabilities of any site being carious will change. For example, if a group of 128 individuals, each having a DMF-S score of 1 with all sites exhibiting the same propensity for caries. The distribution of sites affected within the mouth should be random, the probability for a particular site being carious would then be 1/128. If, however, the group all had a DMF-S of 128, the relative probability of finding a particular site carious remains the same, but this time the absolute probability changes from 1/128 to 1.As the DMF score increases the probabilities alter. However, once a probability of 1 has been reached no further increase is possible. Thus, when examining changes in the distribution of caries at different DMF-S scores, the ratio between individual probability scores is unimportant. The crucial factor is the overall ranking exhibited by the probabilities for each site. The order of susceptibly will be determined by the relative values of the probabilities. Whether an individual site is twice as likely to become carious as another cannot be determined using this approach. However, certain sites may exhibit similar probabilities. For example, a particular site on the left hand side of the mouth may have a similar mean probability as the corresponding site on the right hand side. Other factors may influence the distribution of probabilities. For example, it has been suggested that fluoride has a more beneficial effect on approximal sites when compared to occlusal.The probabilities derived are for each site for each individual and are then aggregated for the sample population. The aggregation of probabilities gives rise to a distribution, approximately normal in character, and the mean of this distribution is subsequently reported and used in the analyses.Data analyses were performed using SPSS. The data files of the NPDDP were supplied by the Rand Corporation in ASCII format and subsequently read onto the mainframe system. Two of the five data files were utilised in this project: the master file containing the demographic information of each individual and the clinical file containing the status of each tooth site.Figure The emerging pattern indicates a left:right side symmetry with the propensity of attack similar for the two sides of the mouth. Furthermore, there is a degree of symmetry between the upper and lower jaws in the posterior sextants. For the anterior sextants, teeth in the upper jaw are more prone to attack than those in the lower jaw.An important finding is the relative sizes of the groups by the probability of susceptible sites having experienced caries. The group with most susceptible sites consisted of six tooth surfaces: the pit and fissured surfaces of the first molar teeth. The second group consists of 12 sites on the second molar and premolar teeth. The fifth group, and least susceptible group, is the largest and consists of the majority of the lower anterior teeth and canines.The groups, in order of susceptibility, allowing for some left:right asymmetry, were:1. occlusal surfaces of 1st molars and buccal pits of lower 1st molars;2. occlusal surfaces of 2nd molars and buccal surfaces of lower 2nd molars and occlusal surfaces of all 2nd premolars;3. occlusal surfaces of 1st premolars, palatal surfaces of upper lateral incisors, approximal surfaces of 1st molars, lingual surfaces of lower 1st molars and buccal surfaces of upper 1st molars and palatal surfaces of upper 2nd molars;4. all approximal surfaces of 2nd premolars, all approximal surfaces of upper 1st premolars, mesial and lingual surfaces of lower 2nd molars and distal and buccal surfaces of upper 2nd molars, approximal surfaces of upper central incisors, some approximal surfaces of upper and lower lateral incisors, all approximal surfaces of lower central incisors and distal approximals of upper canines, and approximal surfaces of 2nd molars; and5. all surfaces of lower canines, buccal and mesial and labial aspects of upper canines, all smooth and approximal surfaces of lower 1st premolars, smooth surfaces of lower central incisors, approximal surfaces of lateral incisors  declare that they have no competing interests.PAB conceived, undertook the design of the study, performed the statistical analysis and drafted the manuscript. AS participated in the study design and coordination and helped to draft the manuscript. Both authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Retroviruses are expert manipulators when it comes to co-opting their host's cellular resources. A great deal of human complexity stems from the vast repertoire of proteins and mechanisms dedicated to the business of regulating gene expression, and retroviruses like HIV have evolved myriad ways of redirecting that machinery to their own benefit.Humans and other eukaryotes have three types of RNA polymerases, each charged with transcribing different types of genetic elements. RNA polymerase II transcribes protein-coding genes. RNA polymerases join with so-called general transcription factors to form a pre-initiation complex (PIC) on the gene's promoter, where it binds to region rich in thymine (T) and adenine (A) named theTATA box. The first transcription factor to associate with theTATA box is called TFIID, a large protein complex containing a protein that binds theTATA box  and several cofactors called TBP-associated factors (TAFs). PIC assembly sometimes also requires activator proteins, which can enhance transcriptional activity by supporting proper elongation of nascent transcripts.Tat, an activator encoded in the HIV genome, is required for HIV gene activation and viral replication. It affects these processes, the current model holds, by stimulating transcript elongation and increasing RNA polymerase's processing efficiency. In a new study, Tamal Raha, Grace Cheng, and Michael Green work with human cell lines and find evidence that Tat can also stimulate PIC assembly.While most transcription factors bind to DNA, Tat binds to an area at the end of newly emerging viral RNA called the transactivation response element (TAR). Once bound, Tat recruits a cellular complex called P-TEFb (consisting of two subunits) to the HIV promoter, and enhances RNA polymerase's transcribing capacity. Previous studies in yeast had shown that activators appear to stimulate transcription complex assembly, leading the authors to ask whether Tat could play a similar role.To study this question in living human cells, Green and colleagues turned to chromatin immunoprecipitation, a technique that detects proteins bound (directly or indirectly) to DNA. Working with three well-known effectors of transcription—an activator , a transcriptional enhancer, and another viral activator called E1a—the authors show that what's true for yeast also holds for mammals, or at least for the human cell lines investigated here. Each effector was required for PIC assembly, which was in turn required to activate transcription.The big surprise came in the next round of experiments, which explored Tat's influence on transcription and PIC assembly on the HIV promoter. As expected, transcription factors were “virtually undetectable” at the core promoter in the absence of Tat. Adding Tat recruited all the usual transcription factors to the promoter and increased transcription. But none of the TAFs that normally associate with TFIID were found. When the authors used the activator Gal4-VP16 to initiate HIV transcription, every one of the 11 TAFs studied appeared. None of them did so in the presence of Tat, suggesting that Tat-mediated HIV transcription doesn't rely on TAFs. Green and colleagues confirmed this hypothesis in experiments showing that Tat-driven transcription proceeded as usual in cells lacking TAFs. And they demonstrated that it is Tat—along with its cofactor P-TEFb, which is normally bound to RNA through Tat—that recruits the TAF-deficient TBP.10.1371/journal.pbio.0030074] and “A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation” [DOI: 10.1371/journal.pbio.0030076].)Altogether, these results show a surprising new role for Tat in stimulating assembly of a transcription complex. What's more, the complex lacks the TAFs typically linked to TBP in mammalian cells. Because their experiments analyzed only transcription complex assembly, the authors are careful to note that Tat may well stimulate assembly in addition to promoting transcription elongation. And it may be this resourcefulness that makes Tat such a potent activator—and HIV so hard to control. (For more on Tat's role in HIV transcription, see “Novel Enzyme Shows Potential as an Anti-HIV Target” [DOI:


Saccharomyces cerevisiae. A computer program, PeakFinder, enables flexible, automated identification and annotation of cohesin binding peaks in ChIP chip data. Cohesin sites are highly conserved in meiosis and mitosis, suggesting that chromosomes share a common underlying structure during different developmental programs. These sites occur with a semiperiodic spacing of 11 kb that correlates with AT content. The number of sites correlates with chromosome size; however, binding to neighboring sites does not appear to be cooperative. We observed a very strong correlation between cohesin sites and regions between convergent transcription units. The apparent incompatibility between transcription and cohesin binding exists in both meiosis and mitosis. Further experiments reveal that transcript elongation into a cohesin-binding site removes cohesin. A negative correlation between cohesin sites and meiotic recombination sites suggests meiotic exchange is sensitive to the chromosome structure provided by cohesin. The genome-wide view of mitotic and meiotic cohesin binding provides an important framework for the exploration of cohesins and cohesion in other genomes.In eukaryotic cells, cohesin holds sister chromatids together until they separate into daughter cells during mitosis. We have used chromatin immunoprecipitation coupled with microarray analysis (ChIP chip) to produce a genome-wide description of cohesin binding to meiotic and mitotic chromosomes of  Chromatin immunoprecipitation reveals that cohesin sites -- those responsible for holding sister chromatids together until they separate into daughter cells -- are conserved in meiosis and mitosis Saccharomyces cerevisiae, since mutation results in the precocious dissociation of sister chromatids.Sister chromatid cohesion ensures that daughter cells inherit complete copies of their genome. Cohesion in eukaryotic cells is mediated by a multisubunit protein complex called cohesin. Cohesin consists of four proteins: Smc1, Smc3, Scc1/Mcd1, which is called kleisin and is the target of the protease separase, and Scc3. These proteins have recently been proposed to form a ring structure that encircles sister chromatids . AlternaCohesion serves at least three roles in the cell with respect to accurate genome transmission. Firstly, cohesion proximal to the centromere facilitates biorientation of chromosomes with respect to the spindle . Secondlcis determinants of cohesin binding. Cohesin-associated regions have been identified in yeast using chromatin immunoprecipitation. In these studies cohesin association with chromatin was followed at low resolution along the entire length of Chromosome III  or high resolution  at limited regions on Chromosome III, V, and XII  and microarray methods, often referred to as ChIP chip technology. To aid identification of peaks of cohesin binding we developed a program, PeakFinder, for extraction of peaks from raw ChIP data. We further used this approach to map all the cohesin-binding sites on an “ectopic chromosome,” a yeast artifical chromosome containing a 334-kb insert from human Chromosome VII. Information from a large number of sites greatly facilitates the assessment of cohesin distribution and of the significance of correlations with many local properties of the genome, such as base composition and coding content. Furthermore, it allows us to evaluate the impact of several factors, such as strain background, transcription, and developmental programs like meiosis, on cohesin binding, and to test the predictions by engineering individual cohesin sites.One approach to better understand cis determinants of cohesin sites  much of the analysis has to be done manually. A better approach would be to use the local parameters of cohesin binding to identify the peaks. To aid such a task, we have written a Windows program, PeakFinder, which discerns and filters the peaks from a variable local background and maps the tip of the peak to a single array element. The program is freely available (http://research.stowers-institute.org/jeg/2004/cohesin/peakfinder).We used the genome-wide approach of ChIP chip to identify and evaluate anaphase . We werehip data . A singlCEN3 is the highest for the chromosome, (2) the height of the peaks declines towards the ends of the chromosome, and (3) peaks correlate with AT content , dense region of binding in pericentric domains  (p < 0.0001) even though intergenic regions make up only 27% of the genome length. Intergenic regions in S. cerevisiae are more AT-rich than open reading frames (ORFs). Therefore, we tested whether the AT bias could be explained by the bias towards binding intergenic sequences by comparing the AT content of all intergenic regions with the AT content of intergenic regions associated with cohesin . Thus, AT content appears to be a major determinant for cohesin association.Cohesin peaks were strongly associated with AT-rich regions . We foun 0.0001) A. CohesiWe observed local oscillations of AT content in a 5-kb sliding window, which corresponded to cohesin-binding peaks in chromosome arms see , thus exThe semiregular spacing of cohesin and the correlation with local oscillations of base composition suggested that AT content and/or a measuring mechanism might control cohesin distribution on the chromosome. In order to test these possibilities, we used a nonessential ectopic yeast artificial chromosome (YAC). We used ChIP followed by quantitative PCR to map cohesin-binding sites in a YAC containing 334 kb of human DNA from Chromosome VII. The pericentric region on the right end of the YAC shows a broad (approximately 35 kb), intense association with cohesin. This is similar to the cohesin association with endogenous pericentric regions. However, the spacing of cohesin does not have the same periodic nature as on endogenous chromosomes. For example, the leftmost 83 kb of the YAC contains only one peak of cohesin binding, resulting in two large gaps for cohesin of 38 and 45 kb A. These To further test the contribution of sequence to cohesin binding, we took advantage of the fact that none of the human sequences were essential for yeast survival. When we replaced the region from 156 to 162 kb, which contains a cohesin-binding site, with the gene encoding for geneticin resistance, this region was no longer associated with cohesin B. Neighbp < 0.0001). In fact, 39% of the convergent intergenic regions in the genome have peaks of cohesin binding, and nearly half of all cohesin-binding sites are in convergent intergenic regions. These percentages approach the predictive power of consensus sequences for identifying binding sites of their cognate transcription factor  location within 25 kb of a centromere, (2) above average AT content, or (3) location in an intergenic region with converging transcription. This leaves 230 sites unexplained. Of these, 43 are intergenic. Most of these are simply difficult to assign to a transcriptional category. Interestingly, of the 230 “unexplained” sequences, 187 are in ORFs, which is more than half of all the ORFs associated with cohesin. These ORFs do not appear to have any unifying theme with regard to function, dubiousness, transcription level, or essentiality. The 187 peaks have similar height to other peaks. Thus, unlike the 5 (p = 10−65) and (GAN)10 (p = 10−76). The p values reflect the significance of these sequences as calculated based on a Monte Carlo simulation for the yeast genome. These sequences were rare in intergenic regions in yeast. These sequences were not present in the YAC. Further experiments will be required to determine whether these repeats are targeted by cohesin. Interestingly, human cohesin has been localized in Alu repeats . We carried out ChIP chip analysis in parallel with monitoring gene expression in the same cells using ORF arrays. Of the regions where gene expression changed 5-fold or more, only two regions were associated with cohesin. One peak of cohesin binding in glucose was associated with the promoter of the GAL2 gene . Expression of YDL218W was induced 11-fold in the presence of glucose compared to galactose. This ORF is associated with cohesin in the presence of galactose, but this association is reduced when glucose is present (unpublished data). The peaks surrounding both regions were unaffected. These results demonstrate that high levels of transcription are incompatible with cohesin binding. It also supports the results observed with the human DNA that neighboring cohesin sites behave independently.We analyzed whether changing the transcriptional program could change the association of cohesin with a locus. We grew cultures with either 2% glucose or 2% galactose as the carbon source and arrested them in metaphase using nocodazole. The main difference between metaphase arrest in the presence of nocodazole and in AL2 gene A, which AL2 gene B. The prThere are a number of mechanisms that could account for the incompatibility between transcription and cohesin binding. Transcription of a region during G1 may prevent new association of cohesin, or transcription may displace cohesin in G2. We explored the mechanistic link between transcription and cohesin association at the previously characterized cohesin sites cohesin-associated region on Chromosome III (C) (CARC1) and cohesin-associated region on Chromosome XII (L) (CARL2)  by inserThe displacement of cohesin may be due to a competition between RNA pol II/chromatin–remodeling factors and cohesin for DNA, or transcript elongation may remove cohesin. We tested if the incompatibility was dependent on transcript elongation. A culture was arrested with nocodazole, and galactose-responsive transcription was induced by the addition of galactose, as described above. Thiolutin was added to half of this culture immediately prior to the addition of galactose. Thiolutin inhibits transcript elongation but presumably does not inhibit the binding of RNA pol II . The effcis determinants of this complex has been reported. We expressed Rec8-3HA in SK1, a rapidly and synchronously sporulating strain, and analyzed the location of the cohesin–DNA complex in ChIP experiments . This suggests that binding of the meiotic complex, like the mitotic complex, is not compatible with transcription.ing site . Bindinging site A. The tring site . Other gWe compared the location of meiotic cohesin to the location of the double-strand breaks (DSBs) that initiate meiotic recombination . We founROK1, RRS1, and EBP2  locus.  The rDNA is encoded in an approximately 1–2-Mb region on the right arm of Chromosome XII consisting of 100–200 tandem copies of a 9.1-kb repeat. There is a peak of cohesin binding that localizes to the left border of the rDNA repeat  that is and EBP2 , are dowand EBP2 , suggestand EBP2 , 2000. Tcdc16-ts or nocodazole, galactose versus glucose as a carbon source, and induction of meiosis) and in three different yeast-strain backgrounds . Using PeakFinder, a program that assesses cohesin binding by comparison of signal to variable local background, we find that the majority of cohesin-binding sites are remarkably constant under these different circumstances. Distribution of cohesins throughout the genome appears to depend on a combination of base composition, sequence, and transcriptional activity. We find evidence for three types of cohesin sites in the genome: (1) the centromere and pericentric domain, (2) intergenic regions in chromosome arms, and (3) ORFs in chromosome arms. The association of cohesin with these three types of sites is subject to different genomic parameters. Cohesin at centromeres and pericentric regions is spread over a broad domain with an elevated “baseline” level and is not affected by the natural transcriptional and coding status. Much of the cohesin in chromosome arms is located in transcriptionally converging intergenic regions. ORFs in chromosome arms where cohesin is found are enriched for repetitive sequences. This suggests that there may be three mechanisms to load cohesin, consistent with what has been proposed for cohesin in meiotic chromosomes for S. pombe . If cohesin and transcript elongation were incompatible, then we would also expect to find sites biased towards intergenic regions, which we do. However, we find a strong bias towards intergenic regions with converging transcription, and a bias against intergenic regions with surrounding unidirectional transcription or diverging transcription, suggesting that intergenic regions with converging transcription may have especially low transcription. These regions may have evolved particularly strong transcriptional stops since they are quite short on average and the cell may need to avoid transcription from one side extending to the other to prevent the synthesis of antisense RNA. The protection of sequence elements important for the replication and segregation of eukaryotic chromosomes from transcription may be a general necessity for their proper function in vivo. For instance, transcription through an autonomous replicating sequence  “pushes” cohesin into place or strips cohesin from inappropriate locations in each cell cycle. Secondly, transcript elongation may be indirectly responsible for localizing cohesins, for example by accumulation of “nonpermissive” chromatin in transcribed regions and “permissive” chromatin in nontranscribed regions. This type of genome-wide demarcation of transcription units has been shown to occur in revisiae  and may revisiae  and histrevisiae . The prelization . The poslization .Despite the subunit difference between the meiotic and mitotic cohesin complex, we find that the association of cohesin with DNA in meiotic cells is similar to that in mitotic cells. In addition, we find that the constitutive peaks of meiotic cohesin binding are negatively correlated with DSB sites. This negative correlation is consistent with the model proposed by S. cerevisiae will serve as a useful framework upon which to explore attributes of cohesin localization in higher eukaryotes.Genome-wide studies of protein–DNA complexes afford a better understanding of the role of these complexes in the biology of an organism and its genome. In the process of analyzing the first genome-wide map of cohesin in any organism, we developed PeakFinder, a program able to sensitively identify binding sites of protein–DNA complexes in their local genomic environment, and potentially useful for analysis of any other genome-wide measurements. While budding yeast appears to have largely opted for placement of cohesin in AT-rich, transcriptionally inactive regions, other organisms with much longer and more complicated transcriptional units, different base composition properties, or different levels of condensation may employ different strategies for the placement of cohesin, which may in turn affect the stability of those genomes. The genome-wide analysis of cohesin in ChIPs were performed as previously described . Semiquacdc16-ts cells.For the meiotic timecourse, cultures were grown in YPA, then transferred to SPM. Timepoints were removed for ChIP at 4, 6, 8, 10, and 12 h after transfer to SPM. Nocodazole-mediated arrest was accomplished by adding nocodazole to a final concentration of 15 μg/ml to the media. All cultures were grown at 30 °C. Shifting cultures to 37 °C in prewarmed media induced metaphase arrest in http://microarrays.org). The meiotic experiments were done in the SK1 strain background and although two independent timecourses were performed, the results from a single representative timecourse were used for analysis. The resolution of these genome-wide maps is limited by (1) the shear size of the DNA fragments (range of 200–1000 bp) and (2) the size of the elements on our microarrays (mean of 0.9 kb). We do not expect these fragment-size distributions to introduce a significant bias in our mapping effort since the previously estimated size of a cohesin-binding region at an arm site is 0.8–1.0 kb . PeakFinder allows filtering of peaks based on the parameters of the peak. For example, cohesin peaks analyzed in the cdc16-ts dataset were identified using the following set of parameters: (1) sampling log2-transformed ratios at 100-bp intervals, (2) smoothing over eight rounds using a nine-point Gaussian-weighted moving average, and (3) filtering of peaks with a left and right rise of less than 0.1 and a height less than 0.4 (log2 space). These conditions identified all peaks mapped to high resolution on Chromosomes III and XII  4000B scanner and quantitated using GenePix 4.0. Results were stored in the AMAD database. Data were normalized and filtered by requiring intensity to be 200 or more, and spots to have a correlation coefficient of 0.5 or more. For analysis purposes, any feature with less than two measurements was excluded (with the exception of the meiotic timecourse). Data were analyzed using PeakFinder, a program developed specifically for finding peaks in ChIP data, but generally applicable for plotting any measurement against genomic coordinates, smoothing the curves, and annotating peaks on the basis of local properties of the curve. Extensive documentation for PeakFinder is available at  and XII . A curreURA3. Nucleotides 326,702—332,707 contain vector sequences from pYAC4 including the centromere, TRP1, and ARS1. The YAC was introduced into the strain 1377 A1 4B, two independent cultures were grown to exponential phase, and nocodazole was added. After 3 h of growth at 23 °C, more than 90% of cells were arrested in metaphase. Cultures were processed for ChIP as described previously.PCR primers were designed to amplify 150–300-bp sequences, at 1-kb intervals along the entire length of the 1572 YAC . Nucleot6/ml. Cultures were arrested in G1 with alpha factor, released from G1 in the presence of nocodazole, and grown for 3 h to generate an M phase arrest. Cultures were split in two, and one half received galactose to a final concentration of 4%. One hour after addition of galactose, cultures were fixed and processed for ChIP. Experiments with thiolutin were performed as described above except thiolutin was added to a final concentration of 3 μg/ml just prior to galactose addition. Primers were generated that amplified 5′ and 3′ regions of CARC1 and CARL2 in the endogenous locus and on pCM34 and pCM38. Reduction in cohesin binding was expressed as the ratio of (1) the amount of cohesin bound to the 5′ or 3′ ends of the CAR on the plasmids to (2) the amount of cohesin bound to the 5′ or 3′ regions of the CAR in the genome.0.8 kb from CARC1 and 1.4 kb from CARL2 were cloned into pUNI and then recombined with pYCE to form pCM34 and pCM38. This places the pGAL1-10 promoter immediately adjacent to cohesin-associated regions. pCM34 and pCM38 were introduced into 1377 A1 4B by transformation. Strains with pCM34 and pCM38 were initially grown in complete medium lacking uracil with raffinose as a carbon source. This medium selects for retention of the plasmids and prevents transcription from the Gal-inducible promoter. These cultures were diluted approximately 100-fold in YEP raffinose and grown to 7 × 10Dataset S1File cdc16_Mcd1-18Myc_W303.(483 KB TXT).Click here for additional data file.Dataset S2File cdc16_Mcd1-6HA_A364a.(957 KB TXT).Click here for additional data file.Dataset S3File cdc16_Smc3-6Myc_A364a.(701 KB TXT).Click here for additional data file.Dataset S4File Mcd1_18Myc_W303_NZgalCHIP.(406 KB TXT).Click here for additional data file.Dataset S5File Mcd1-18Myc_W303_NZgal_exp.(196 KB TXT).Click here for additional data file.Dataset S6File Mcd1-18Myc_W303_NZglu_exp.(221 KB TXT).Click here for additional data file.Dataset S7File Mcd1-18Myc_W303_NZgluChIP.(694 KB TXT).Click here for additional data file.Dataset S8File Mcd1-3HA_SK1_NZ.(339 KB TXT).Click here for additional data file.Dataset S9File Rec8-3HA_SK1.(710 KB TXT).Click here for additional data file.Dataset S10cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S11cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S12cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S13cdc16-ts Mcd1-6HA in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S14cdc16-ts Mcd1-6HA in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S15cdc16-ts Smc3-6Myc in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S16cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S17cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for Myc in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S18cdc16-ts Smc3-6Myc in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S19cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for HA in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S20cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for Myc in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S21cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.5 MB XLS).Click here for additional data file.Dataset S22cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.5 MB XLS).Click here for additional data file.Dataset S23Cy3 = ChIP 6h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S24Cy3 = ChIP 8h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.5 MB XLS).Click here for additional data file.Dataset S25Cy3 = ChIP 10h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S26Cy3 = ChIP 6h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S27Cy3 = ChIP 8h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S28Cy3 = genomic DNA; Cy5 = ChIP 12h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S29Cy3 = genomic DNA; Cy5 = ChIP 4h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S30cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S31cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S32cdc16-ts Mcd1-18Myc in W303; Cy5 = genomic DNA.Cy3 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S33cdc16-ts Mcd1-18Myc in W303; Cy5 = genomic DNA.Cy3 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S34Cy3 = genomic DNA; Cy5 = ChIP 4h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S35Cy3 = genomic DNA; Cy5 = ChIP 10h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S36Cy3 = genomic DNA; Cy5 = ChIP 12h Rec8-3HA in SK1.(5.6 MB XLS).Click here for additional data file.Dataset S37Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest Mcd1-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S38Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest Mcd1-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S39Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(2.7 MB XLS).Click here for additional data file.Dataset S40Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(2.7 MB XLS).Click here for additional data file.Dataset S41Cy3 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S42Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S43Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S44Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S45Cy3 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = genomic DNA.(4.4 MB XLS).Click here for additional data file.Dataset S46Cy3 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = genomic DNA.(4.4 MB XLS).Click here for additional data file.Dataset S47Cy3 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = genomic DNA.(4.4 MB XLS).Click here for additional data file.Dataset S48Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S49Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S50Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S51Cy3 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S52Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S53Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S54Cy3 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S55Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S56Cy3 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S57Cy3 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S58Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Protocol S1(61 KB DOC).Click here for additional data file.Protocol S2(37 KB DOC).Click here for additional data file.Saccharomyces Genome Database (http://www.yeastgenome.org/) accession numbers for the genes and gene products discussed in this paper are CDC16 (SGDID S0001505), EBP2 (SGDID S0001655), GAL2 (SGDID S0004071), Mif2 (SGDID S0001572), NCE102/YPR149W (SGDID S0006353), PCD1 (SGDID S0004141), Pds1p (SGDID S0002520), Rec8 (SGDID S0006211), ROK1 (SGDID S0003139), RRS1 (SGDID S0005820), Scc1/Mcd1 (SGDID S0002161), Scc3 (SGDID S0001288), Smc1 (SGDID S0001886), Smc3 (SGDID S0003610), YDL218W (SGDID S0002377), YDL238C (SGDID S0002397), YER179W (SGDID S0000981), and YPR006C (SGDID S0006210).The