From 1963 to 1971, 117 babies with open spina bifida were treated non-selectively from birth. In 2002 we reviewed all the survivors by postal questionnaire and telephone call. The aims were to find out how many were living independently in the community or were in open employment or drove a car. In addition to these achievements we recorded health, medication and admissions to hospital and asked how much daily help they needed.Ascertainment was 100%. There had been 63 deaths, mainly of the most severely affected. The mean age of the 54 survivors was 35 years. The outcome in terms of disability ranged from apparent normality to total dependency. It reflected both the neurological deficit, which had been recorded in infancy in terms of sensory level, and events in the CSF shunt history. Overall about 2 in 5 of the survivors lived independently in the community, 2 in 5 drove a car, 1 in 5 was in competitive employment and 1 in 5 could walk 50 metres.Although those who survived to age 35 years tended to be less disabled, 2 in 5 continued to need daily care. Neurosurgical intervention in babies with open spina bifida had dramatic results in terms of survival. However, the disability and the complications of the survivors were often severe -5. Many In 1963 the Regional Neurosurgical Unit at Addenbrooke's Hospital, Cambridge, England offered treatment to all cases of open spina bifida, without any attempt at selection. Between 1963 and 1971, after a detailed neurological examination, 117 babies  had their open spinal defects closed within 48 hours of birth. A ventriculo-atrial cerebrospinal fluid (CSF) shunt was inserted for hydrocephalus when required.In 2002 all survivors were surveyed by confidential questionnaire and telephone interview. They were asked about health, disability and achievements in terms of living independently, driving a car and working in open employment. Causes of death for those who had died were obtained from medical records and from the Office of National Statistics. The study was approved by the Cambridge Local Research Ethics Committee.2.When first surveyed at the mean age 4 years, the cohort had been classified into four groups according to sensory level to pin prick recorded in infancy [Ascertainment was 100%. Twenty (37%) of the 54 survivors responded to the questionnaire and all survivors or a carer or relative were interviewed by telephone.Figure Of the original 117 babies there were 54 survivors (46%) age range 31–38 years. Of these 24 were male and 29 female and there was one who had undergone a gender change from male to female. The disability of the survivors ranged from blindness with paraplegia and double incontinence to apparent normality. Table Only 16 (30%) remained community walkers defined as being able to walk ≥50 metres with or without aids. Ten of the 16 could walk at least a kilometre. Table Of the 54 survivors, eight (15%) never had a shunt, seven of whom had little or no disability and a sensory level below L3. The remaining 46 had had a ventriculo-atrial shunt inserted. In 16 the shunt had never been revised. The other 30 had had a total of 104 revisions: for shunt insufficiency (65), infection (15), detachment (14), extrusion or leaking wound of back (5), unknown (5). In 9 patients revisions were done only before the age of 2 (mean 1.3 revisions, range 1–3), and in 21 between the ages of 2 and 35 (mean 3.1 revisions, range 1–14). Elective revisions were not performed; and shunts were inserted or revised only in response to definite clinical need. Of those who had revisions, 75% had had symptoms of raised intracranial pressure.Nearly half of the survivors had been in hospital during the previous 5 years. The main reasons were urological (7 patients), neurosurgical (3), and sepsis (7). Pressure sores were responsible for four of the admissions for sepsis, and 12 patients were currently being treated at home for pressure sores. Only 11 patients (20%) were fully continent of bladder and bowel without the use of catheters or appliances. Two patients needed nocturnal respiratory support, two were totally blind following shunt dysfunction and four others had severe visual defects. Endocrine conditions were common: two patients had diabetes mellitus, one had adrenal hyperplasia, one had primary azoospermia and six had had precocious puberty. Twenty-four patients were on long-term therapy: antihypertensives (12), anticonvulsants (10), antibacterials (10) and antidepressants (4). Eight patients needed regular analgesics for musculo-skeletal pain, mainly backache.Seven women and two men had become parents. One man had minimal disability and no detectable sensory loss; the other had undergone percutaneous epididymal sperm aspiration followed by intracytoplasmic sperm injection. None of the 13 children had visible spina bifida.Twenty-two individuals (41%) lived independently in the community, 11 of them used wheelchairs. A further 12 (22%) were personally independent but had supervision and help when required. The remaining 20 37%) needed help daily for dressing, shaving, toilet or nursing care (mainly pressure sores). Ten of these still lived with a parent now aged 52–77, two women were in the care of their partners, five were in residential establishments and three lived in the community with help from social services (Table 7% neededTwenty-nine (54%) of the survivors had passed the driving test, but 9 had discontinued driving. Eleven others were unfit to drive on account of poor sight (3), epilepsy (3) or severe cognitive or perceptual defects (5).Nine men and four women were in open employment. All had an IQ ≥80 and five used wheelchairs. Three did clerical work, three were teachers, two were unskilled manual workers and the remainder were a business executive, accountant, engineer, van driver and builder. Three were studying in addition to working full time. Three men and two women were in sheltered employment.Twenty-seven survivors 50%) had one or more achievements in terms of living completely independently in the community (22), driving a car (20) or working in open employment 13). Achievements were related to sensory level in infancy and to shunt history  are unlikely to be seriously disabled and could be achievers in adulthood. Babies who cry during a routine heel prick have a sensory level of S1 or below and are likely to remain community walkers in adulthood. Those with sensation to pin prick in the saddle area (S2, 3, 4) are likely to have bladder and bowel control.Events in the history of the CSF shunt also had a profound effect on outcome and achievement. Revisions of shunt were associated with poor achievement particularly when the revisions were needed after the age of 2. The cranial sutures have usually fused by the age of 2 after which the skull is less expansile rendering the brain more susceptible to pressure . Table 5The main strength of the study is the community basis, which provides social as well as clinical data enabling the realities of adulthood to be seen against the optimistic forecasts of the early years . Less thAlthough outcome in childhood of early operated spina bifida has been widely reported -5,11, thThese data may help health professionals counselling parents of a child with spina bifida. They show a range of possible outcomes in adulthood when parents may no longer be able or willing to look after their child -16. Two The authors declare that they have no competing interests.


Mood disorders are a major public health problem in the United States as well as globally. Less information exists however, about the health burden resulting from subsyndromal levels of depressive symptomatology, such as feeling sad, blue or depressed, among the general U.S. population.As part of an optional Quality of Life survey module added to the U.S. Behavioral Risk Factor Surveillance System, between 1995–2000 a total of 166,564 BRFSS respondents answered the question, "During the past 30 days, for about how many days have you felt sad, blue, or depressed?" Means and 95% confidence intervals for sad, blue, depressed days (SBDD) and other health-related quality of life (HRQOL) measures were calculated using SUDAAN to account for the BRFSS's complex sample survey design.Respondents reported a mean of 3.0 (95% CI = 2.9–3.1) SBDD in the previous 30 days. Women (M = 3.5, 95% CI = 3.4–3.6) reported a higher number of SBDD than did men (M = 2.4, 95% CI = 2.2–2.5). Young adults aged 18–24 years reported the highest number of SBDD, whereas older adults aged 60–84 reported the fewest number. The gap in mean SBDD between men and women decreased with increasing age. SBDD was associated with an increased prevalence of behaviors risky to health, extremes of body mass index, less access to health care, and worse self-rated health status. Mean SBDD increased with progressively higher levels of physically unhealthy days, mentally unhealthy days, unhealthy days, activity limitation days, anxiety days, pain days, and sleepless days.Use of this measure of sad, blue or depressed days along with other valid mental health measures and community indicators can help to assess the burden of mental distress among the U.S. population, identify subgroups with unmet mental health needs, inform the development of targeted interventions, and monitor changes in population levels of mental distress over time. Mood disorders are a major public health problem in the United States as well as globally, imposing a substantial burden of disability, impaired quality of life, and death if they remain untreated -3. NatioMental health disorders due to depression, anxiety and substance use are not only burdensome by themselves, but they can complicate existing physical disorders and also increase risk for other physical comorbidity ,12,13. FFor at least one year since 1995, more than one-third of state health departments have assessed the number of recent days that adults "felt sad, blue or depressed" using the Behavioral Risk Factor Surveillance System (BRFSS). This measure has good construct validity when compared with other BRFSS health-related quality-of-life (HRQOL) domains related to mental health , and hasUsing a large multi-state sample, this study is the first to focus on the prevalence of self-reported "sad, blue or depressed" days (SBDD) overall and in sociodemographic subgroups in the United States. It also examines the construct validity of the measure.The BRFSS, which is designed to monitor behavioral health risks in the United States, is an annual random-digit-dialed telephone survey of the non-institutionalized civilian population aged 18 years or older conducted in all states and the District of Columbia . As part2), normal weight (18.5< 25.0 kg/m2), overweight (25.0 to < 30.0 kg/m2), or obese (≥ 30.0 kg/m2) [Respondents answered standard BRFSS questions about age, race/ethnicity, education, employment, income, marital status, health status, physical health, mental health, activity limitation, access to care, and the presence of certain health conditions such as hypertension and diabetes. Respondents also answered questions about how often they engaged in behaviors risky to health, such as smoking and binge drinking, and in health promoting behaviors, such as using a seat belt and exercising. Each respondent's body mass index (BMI), (weight in kilograms divided by the square of height in meters), was classified according to the National Institutes of Health criteria as either underweight (<18.5 kg/m0 kg/m2) .Individual responses were weighted to reflect the age and sex distribution of each state's population during each survey year. To account for the BRFSS's complex sample survey design, means (M) and 95% confidence intervals (CI) for SBDD and other HRQOL measures were calculated using SUDAAN (Research Triangle, release 8.0.0, Research Triangle Park, NC: 2001). Because mean SBDD varied by five-year age groups, the analyses were directly standardized to the age distribution of adults aged 18 years or older from the 2000 U.S. Census to control for confounding by age.Respondents reported a mean of 3.0 (95% CI = 2.9–3.1) SBDD in the previous 30 days. About 43.4% of respondents reported one or more SBDD including 7.9% who reported 14 or more SBDD. Women (M = 3.5, 95% CI = 3.4–3.6) reported a higher number of SBDD than did men (M = 2.4, 95% CI = 2.2–2.5) (Table Asians/Pacific Islanders reported the fewest SBDD (M = 2.0, 95% CI = 1.7–2.3), whereas Hispanics, Blacks, American Indians and Alaska Natives, and non-whites of another race/ethnicity reported about 4–5 SBDD; in the last three of these groups, the gaps between men and women were larger. Adults with more education reported fewer SBDD, with the gap between men and women diminishing with more education. Respondents who were unemployed or unable to work reported more SBDD than the employed. The number of SBDD decreased with increasing levels of annual household income. Widowed or separated adults reported about 6 SBDD, whereas respondents who were currently married reported the fewest number of SBDD (M = 2.5); the gap between men and women was least among those who had never married.SBDD was associated with an increased prevalence of behaviors risky to health, extremes of BMI, less access to health care, and worse self-rated health status. Respondents who reported physical inactivity, binge drinking, seldom-use or nonuse of seatbelt, or any or more cigarette smoking reported substantially higher numbers of SBDD than those who did not report engaging in these risky behaviors Table . SubjectSBDD were associated with other physical and mental HRQOL domains in expected ways. Mean SBDD increased with progressively higher levels (i.e., 0, 1–2, 3–13, 14–29, and 30 days) of physically unhealthy days, mentally unhealthy days, unhealthy days, activity limitation days, anxiety days, pain days, and sleepless days  associated with engaging in healthful behaviors . PerceivOur findings support the construct validity of the SBDD measure in this study because SBDD were associated with other physical and mental HRQOL domains in expected ways. Groups with progressively higher numbers of physically unhealthy days, activity limitation days, and pain days reported a higher number of SBDD. Moreover, these associations were more pronounced with mentally unhealthy days and anxiety days, than with physically unhealthy days, activity limitation days, and pain days. We found an exception to the linear relationship between SBDD and HRQOL measures with our measure for sleeplessness. Adults who reported 1–2 days of sleeplessness reported fewer SBDD than those who reported no days of sleeplessness. Sleep disturbance, both insomnia and hypersomnia are symptoms of depression. Those reporting no days of sleeplessness, but more SBDD, might be those with hypersomnia. Additional studies are warranted to examine this hypothesis.Besides the cross-sectional design, this study has other limitations. Only 38 states and the District of Columbia included the HRQOL supplemental module that assessed SBDD. All states and the District of Columbia, however examined mentally unhealthy days–the number of days respondents experienced poor mental health due to stress, depression or problems with emotions. Mean mentally unhealthy days in the states that assessed SBDD with the HRQOL supplemental module did not differ significantly from that in states that did not. Given the positive correlation between mentally unhealthy days and SBDD (r = 0.6), states that did not assess SBDD would most likely report similar SBDD as states that did include this measure, suggesting similar study results had all states assessed SBDD. Second, BRFSS excludes people who do not have telephones, live in institutions, and persons younger than 18 years. Third, BRFSS may under represent the severely impaired because functional capacity is required to participate in BRFSS. Including this group however, would probably only strengthen the associations we found because the variability of SBDD would increase because the severely impaired would be more likely to report more SBDD. Finally, because our findings on SBDD are based on respondents' self-reports rather than on professionally administered psychiatric evaluations, people who experience SBDD may differ from people with clinical depression.The 1999 Surgeon General's report states that mental health and mental illness "are not polar opposites but may be thought of as points on a continuum" . Use of Future research might examine in more detail the associations among SBDD, anxiety, vitality, and sleeplessness and their ability to assess mood, anxiety, and sleep disorders. It would also be useful to examine the prevalence and demographic characteristics of those who report 14 or more SBDD, and the criterion validity of this measure with other screening instruments and clinical assessments. While SBDD does not provide a strict measure of diagnosable depression as would validated screening and diagnostic assessments, SBDD and other measures such as activity limitations, alcohol or substance abuse, physical inactivity, and employment status can be useful community indicators for addressing the prevention and treatment of depressive symptoms and associated comorbidity .SBDD sad, blue or depressed daysHRQOL health-related quality of lifeBRFSS Behavioral Risk Factor Surveillance SystemCDC Centers for Disease Control and Prevention


Tracheal tumors are often misdiagnosed as asthma and are treated with inhaled steroids and bronchodilators without resolution.Here, a patient with tracheal adenoid cystic carcinoma who had been previously diagnosed with difficult asthma was reported. The possibility of the presence of localized airway obstruction was raised when the flow-volume curve suggesting fixed airway obstruction, was obtained.The presenting case report emphasizes the fact that not all wheezes are asthma. It is critical to bear in mind that if a patient does not respond to appropriate anti-asthma therapy, localized obstructions should be ruled out before establishing the diagnosis of asthma. Tracheal tumors are uncommon and often overlooked until they reach to an advanced stage. The presenting symptoms are typically prolonged cough and wheezing that can be misdiagnosed as asthma . TherefoHere, a patient with tracheal adenoid cystic carcinoma who had been previously diagnosed with difficult asthma was reported.A 29 year-old man was referred to our hospital with a 2-year history of paroxysmal attacks of dyspnea, dry cough and wheezing. He had smoked 2 packs/day cigarettes for 3 years and has been ex-smoker for 5 years. He experienced frequent sudden-onset coughing episodes followed by the development of dyspnea and wheezing a year ago. He was previously diagnosed with difficult asthma and treated with high dose inhaled corticosteroids (1600 μg budesonide) and bronchodilators. Since he was unresponsive to the therapy, he has applied to several institutions for multiple times to seek medical attention.th centimeter distal to larynx. Histopathological diagnosis was adenoid cystic carcinoma of the trachea.On admission, no stridor, wheezing and cyanosis were present and the general appearance was good. Vital signs were as follows: temperature 37°C, respiratory rate 20/min, pulse 96 beats/min, blood pressure 140/70 mmHg. The chest examination was unremarkable. The results of the routine laboratory analysis, including complete blood cell count, chemistry, arterial blood gas, urinalysis and chest x-ray were within normal ranges. On spirometric examination, flow-volume curve displayed suggestive fixed airway obstruction. Forced vital capacity (FVC) was 122 % of predicted, forced expiratory volume in one second (FEV1) was 31 % of predicted and FEV1/FVC was 21 % Figure . FOB revThe patient underwent resection surgery. At the operational site, there were severe adhesions between the mediastinal surface and the trachea. Therefore, a conservative surgery was performed. The tumor was seen on the right anterolateral wall of the trachea being expanded submucosally from the carina to the proximal end of the trachea.The patient underwent adjuvant radiation therapy after the operation. CT scan of the neck revealed resolution of the tumor Figure . Now, 3 Primary tracheal tumors are rare with the incidence of less than 0.1 % ,3. The mClinical manifestations of tracheal tumors are developed as a consequence of tumor bulk and location. Patients with tracheal tumor often have exertional shortness of breath, prolonged cough or a new onset of wheezing, which is frequently misdiagnosed as asthma. Patients are usually initially managed accordingly. However, tumor may occlude 75 % of the lumen before leading symptoms. In the literature, most of the reports highlight that there is always a remarkable delay of establishing accurate diagnosis as a result of misdiagnosis of asthma ,5,7. PeaAnother diagnostic challenge of tracheal tumor is the fact that it can occasionally be visualized by plain chest X-ray. CT scan of the chest or magnetic resonance imaging may yield more valuable data on the site and length of the tracheal lesion . The corIn the presenting report, the patient had been mistakenly diagnosed with difficult asthma because of the presence of uncontrolled asthmatic symptoms and poor lung functions despite the use of high doses corticosteroids. Nevertheless, the flow-volume curve typically displayed localized fixed obstruction of central airways. This led us to have a work-up of CT scan and FOB to rule out upper airway obstructions.Adenoid cystic carcinomas are smooth, firm, and well-circumscribed lesions. These tumors grow extremely slowly. Patients have been known to survive for 10 to 15 years with multiple lung metastases. Spread tends to occur submucosally [The optimal therapeutic approach is surgical resection and reconstruction. The surgeon should be aware of the fact that the apparent gross margin of the tumor is usually still involved with the tumor cells so that the resection should be done at least 1 cm beyond the gross tumor margin. Postoperative irradiation was recommended by most of the authors . Long-teIn this case, postoperative irradiation on curative doses has been applied for a month. Control bronchoscopic examination revealed near-complete remission.The presenting case report emphasizes the fact that not all wheezes are asthma. It is critical to bear in mind that if a patient does not respond to appropriate anti-asthma therapy, localized airway obstructions should be rule out before establishing the diagnosis of asthma.The author(s) declare that they have no competing interests.HT has seen the patient and made the diagnosis of tracheal obstruction, participated in the design of the manuscript. NK has seen the patient in the clinical, followed the patient and drafted the manuscript. SD and CK have performed the surgery and followed the patient. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Cognitive traits derived from neuropsychological test data are considered to be potential endophenotypes of schizophrenia. Previously, these traits have been found to form a valid basis for clustering samples of schizophrenia patients into homogeneous subgroups. We set out to identify such clusters, but apart from previous studies, we included both schizophrenia patients and family members into the cluster analysis. The aim of the study was to detect family clusters with similar cognitive test performance.Test scores from 54 randomly selected families comprising at least two siblings with schizophrenia spectrum disorders, and at least two unaffected family members were included in a complete-linkage cluster analysis with interactive data visualization.A well-performing, an impaired, and an intermediate family cluster emerged from the analysis. While the neuropsychological test scores differed significantly between the clusters, only minor differences were observed in the clinical variables.The visually aided clustering algorithm was successful in identifying family clusters comprising both schizophrenia patients and their relatives. The present classification method may serve as a basis for selecting phenotypically more homogeneous groups of families in subsequent genetic analyses. Schizophrenia is a severe mental illness which tends to run in families. Moreover, schizophrenia is a complex disorder with multiple environmental as well as genetic predisposing effects. Previous studies have shown that many neuropsychological functions are impaired in schizophrenia patients, and, to a lesser degree, also in their unaffected relatives -3. ConseRecently, cluster analysis of verbal learning and memory tests was used to divide patients with schizophrenia into subtypes. Categorization by these cognitive traits resulted in meaningful subgroups of schizophrenia . In anotIn the present study, we report a new visually aided clustering approach aimed at identifying clusters of multiply affected families with schizophrenia on the basis of performance in neuropsychological tests. In the clustering process, each family was represented by the test scores of its affected and unaffected members, and the closeness the families was defined by the maximum pairwise distance between the members of the families. To our knowledge, this is the first study in which the clustering has been applied to families instead of solely to affected subjects with schizophrenia.From a general population cohort of people born between 1940 and 1976 inclusive in Finland, a northern European country with approximately 5 million inhabitants, we identified 33,731 individuals with a diagnosis of schizophrenia, schizoaffective disorder or schizophreniform disorder. Data on the diagnosis were derived from three nation-wide computerized health care registers covering the years 1969 to 1998: the Hospital Discharge Register, the Free Medicine Register, and the Pension Register. Linking the personal identification numbers of the affected subjects to the National Population Register database allowed us to identify their family members and to construct pedigrees.Information on families with at least two members with schizophrenia, schizoaffective disorder or schizophreniform disorder, and at least two members with no diagnosis of psychiatric disorder was received from the aforementioned registers for 895 families from the whole of Finland. A blood sample for subsequent genetic analyses was drawn from 2295 subjects of 643 families. All available case note records were collected for those with a diagnosis of schizophrenia, schizoaffective disorder or schizophreniform disorder in any of the three registers. Two psychiatrists independently assessed the lifetime diagnoses for each case, according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) . One of n = 6), having a current substance use diagnosis (n = 6), or being mentally retarded (n = 4). Of the family members to whom no psychiatric diagnosis was assigned for their lifetime, 6 were excluded because of high age, or for a defect in vision or hearing. The final sample thus comprised 165 subjects with a psychiatric diagnosis and 94 unaffected family members from 54 families. Of the 165 subjects with a diagnosis, altogether 82 subjects had schizophrenia, while 13 subjects suffered from schizoaffective disorder, 10 from schizophreniform disorder and 12 from bipolar disorder. A nonpsychotic disorder was assigned to 48 individuals. The 94 unaffected subjects did not get any current or lifetime psychiatric diagnosis. In 51 families, at least one of the patients included in the analysis suffered from pure schizophrenia. In the remaining three families, at least one subject with schizoaffective or schizophreniform disorder was included. All families from which the subjects for the present study were drawn, represent familial schizophrenia, as in each of them there were at least one sibling with a diagnosis of pure schizophrenia, plus at least one other sibling with schizophrenia, schizoaffective disorder or schizophreniform disorder.Of those multiply affected families who already had given the blood samples, a subsample was targeted for collection of more detailed phenotypic information. This sample was selected randomly based on the data from the registers and the OPCRIT process. All subjects from the families gave a written informed consent for the study protocol comprising a diagnostic research interview and neuropsychological testing. Both patients and their family members were interviewed using the Structured Clinical Interview for DSM-IV (SCID-I for axis I disorders and SCID-II for axis II disorders) . All theA neuropsychological test battery was administered to all subjects in fixed order by well-trained examiners either after the interview during the same day, or the following day. All examiners were psychologists or advanced psychiatric nurses extensively trained and supervised with the test battery. Experienced psychologists scored all the tests.Auditory attention was assessed with the Digit Span Forward task, and verbal working memory with the Digit Span Backward task of the Wechsler Memory Scale-Revised (WMS-R) . AccordiThe Visual Span forward subtest of the WMS-R  was usedVerbal learning and memory were assessed with the California Verbal Learning Test (CVLT)  which exControlled Oral Word Association test (COWA)  was usedFour subtests of the Wechsler Adult Intelligence Test – Revised (WAIS-R)  were useM = 19 variables and N = 259 subjects, the data formed an M × N matrix x = (xik), where xik is the the value of the ith variable for the kth subject. However, there were 85 (1.7 %) missing values as not all test results were obtained for all subjects. The missing values were handled by the following procedure, which replaces an individual's missing value with an estimate obtained from a linear fit between the test with the missing value and the test that correlates with it most and that also has the individual's test result available.The variables used in cluster analysis included 17 neuropsychological test variables together with the age and the sex of the subjects. With a total of i and j by cij.1. Pairwise correlations were calculated between all test variables using only subjects with results available for both tests. We denote such correlation between the tests i for the subject k, we found the test j = j0 which had the highest value of |cij| among the tests with the value xjk available and set2. Given a missing value in the test b were found by computing linear regression of the test j0 on the test i using only subjects with results available for both tests.where the coefficients a and xk = (xk1,...,xMk) the normalized data for subject k and define the distance between two clusters Cr and Cs byThe families were clustered using a complete-linkage clustering algorithm. Each variable was normalized by subtracting the mean value and dividing by the standard deviation. The normalization was done to ensure that each variable contributes equally to the clustering procedure. Denote by drs = max{||xk - xl|| : xk ∈ Cr and xl ∈ Cs},drs is the maximum pairwise distance between members of the two clusters. Here ||·|| denotes the euclidean distance, . In the sense of this distance measure, two clusters are close when all subjects in both clusters are close.that is, Clustering was carried out using the following algorithm.1. Initial clusters are defined by the families.drs are merged into one larger cluster.2. The two clusters with the smallest inter-cluster distance 3. Steps 2 and 3 are repeated until a desired number of clusters remains.drs is shown by the solid red line. The inter-cluster distance between the two remaining clusters is shown by the dashed red line. Note that by using a different inter-cluster distance measure, such as the minimum pairwise distance, a different merging order would result (see the Discussion).In Figure We introduce a visualization technique that helps in identifying candidate clusters and also gives an overall picture of the main differences between the produced clusters as measured by all variables simultaneously. The method gives information about the dynamics of the clustering process and the characteristics of the candidate clusters. The upper part of Figure xi = (xi1,...,xiN) and the pairwise distance between two clusters Cr and Cs was defined asTo further improve the visual impression of the clusters, the neuropsychological test variables (the rows of the data image) were ordered using essentially the same procedure that was used in clustering the families. The initial clusters were now the individual variable vectors drs = max{1/|cij| : i ∈ Cr, j ∈ Cs},cij denotes the correlation between the variables i and j. Thus, at each step, the algorithm merged clusters with the highest correlating variables.where The lower part of Figure BWr,s = Br,s/(Ws + Wr), where Br,s is the mean distance between subjects from clusters r and s  and Wr is the mean distance between subjects within cluster r. The statistic BWr,s takes on a large value if the distance between the subjects from the different clusters is large compared to the distance between the subjects within the clusters themselves indicating that the two clusters are separated in the 19-dimensional space defined by the variables used. If the values of BWr,s for the proposed three clusters are significantly higher than for a random partition we take this as evidence that the clusters found indeed constitute meaningful groups also at the level of individual subjects.The clusters were obtained by treating families as single objects whose dissimilarity was measured by the pairwise test performance differences between the family members. One may therefore ask whether the clusters found still appear to be distinct groups when viewed simply as sets of individual subjects. We examined this question by dividing repeatedly the 54 families into three random clusters that had the same number of families as in the proposed three cluster solution and by computing, for each of the three pairs of the generated clusters, the ratio a priori, came from the same families. Thus, family was included as a random effect in all models with age and sex as the fixed effects. In addition, post hoc models were conducted with education years as an added fixed effect, a known confounder for cognitive functions. In all these analyses, the probability level < 0.05 indicated statistical significance. Analyses were performed using the S-Plus statistical software, version 3.4 [Further, after the cluster analysis, the proposed family clusters were examined for differences on demographic and neuropsychological measures. In addition, the patients included in the clusters were examined for the differences in clinical variables as evaluated by the OPCRIT  of the disorder. In comparing the demographic and clinical variables, the Chi-square test, or t-test, both two-tailed, were applied. The differences in the quantitative neuropsychological measures were analyzed using the linear mixed effects (LME) model, which takes into account the dependence between the subjects, who, sion 3.4 .Three clusters of families were successfully identified from the study sample. The first cluster comprised 94 subjects from 17 families, the second cluster 50 subjects from 12 families, and the third cluster 115 and 25. Adding more neuropsychological test variables or leaving out the sex or the age of the subjects had little effect on the solution.The data image Figure  indicateA three cluster solution is supported by the homogeneity of the within-cluster test performance patterns of the proposed groups Figure . As showIn Figure BWr,s the randomly generated clustering solution almost always had a smaller value than the proposed clustering solution . This lends support to the visual impression that the three clusters are separate groups when viewed as subsets of individual subjects. Results were similar when the family structure was ignored and the random clusters were generated allowing subjects from the same family to be assigned to different clusters.In each of the three pairwise comparisons of the statistic p <0.001 in contrasts versus both other clusters). Overall, the clusters did not differ in clinical characteristics, except that the well-performing cluster showed better premorbid adaptation than the intermediate cluster (p = 0.04). The age of onset did not differ between the clusters . The impaired cluster did not include any patients with schizoaffective disorder, bipolar disorder or other affective psychotic disorders, while in the well-performing and intermediate clusters, these diagnoses were assigned to 14% and 11% of the subjects, respectively. About 36% of family members in all three clusters were unaffected.The demographic characteristics of the clusters of families are shown in Table The impaired cluster scored lowest in all measured neuropsychological variables, and the intermediate cluster showed consistently worse performance than the well-performing one Table . The difAs the clusters differed significantly from each other in education years, we conducted post hoc linear mixed effects models with family as the fixed effect, and age, sex and education years as the random effects (data not shown). This did not eliminate the significant differences in cognitive functioning between the well-performing and the impaired cluster. In contrasts between the well-performing and intermediate cluster, all other differences remained significant, except in the scores of Visual immediate recall, Digit Symbol, and Verbal fluency, which lost their significance. Between the intermediate and the impaired cluster, scores in Vocabulary and Digit Symbol were no longer significantly different after controlling for education years.We report on the application of a visually aided clustering algorithm to data based on performance in a set of neuropsychological test measures, these being potential endophenotypic traits in schizophrenia. We were able to successfully detect three separate family clusters comprising both schizophrenia patients and their family members. In the impaired cluster, the families scored significantly worse than those in the other two. The well-performing cluster received the highest scores in each cognitive test, and the intermediate cluster scored consistently between the other two. However, the clusters of families did not differ from each other in age, sex distribution, and, regarding the affected subjects, in the age of onset, or in most of the other clinical features. The well-performing cluster was significantly more educated than the two others, but controlling for education years did not change the main results.We tested the differences in the diagnostic class distributions (including those with no diagnosis), and although the differences did not reach statistical significance, we find it interesting that none of the subjects with schizoaffective disorder, bipolar disorder, or other affective psychotic disorders ended up into the impaired cluster. We consider this as supporting the validity of particularly the poor cluster, which seems to represent a subsample of core schizophrenia with the most defected cognitive functioning. This cluster included the same proportion of unaffected subjects than the other two clusters, and based on the clustering algorithm, these family members without any psychiatric diagnoses during their lifetime performed generally poorly, too.Global verbal memory, including the story recall from the WMS-R  and verbOur results suggest that molecular genetic analyses could benefit from prior appliance of our method, revealing meaningful family subgroups in a representative sample of familial schizophrenia. It would allow the resources to be targeted primarily for gene hunting projects among more homogeneous groups of families. Our new approach to combining data visualization and clustering appears to offer a valuable tool for identifying clusters in family-based data. Applying hierarchical clustering and the data image interactively helps to identify a reasonable value for the number of clusters in the cluster solution. By ordering the variables in the data image suitably, one gains useful insight into the test performance characteristics of the subjects in the clusters.As suggested in Palmer et al. , there mex post facto. Furthermore, our set of neuropsychological measures did not cover all those cognitive domains that previous studies have suggested as valid cognitive endophenotypes. However, it has been demonstrated in twin and in family studies [The present study is the first one in which cluster analysis of neuropsychological test variables has been conducted among a representative sample of familial schizophrenia comprising both affected and unaffected family members. The sample of the present study was randomly selected from a nationwide familial schizophrenia cohort. However, the results may not be generalizable to families with only one patient with the disorder. The similar patterns in neuropsychological performance in the clusters may be due to a variety of familial environmental effects, which are difficult to define  studies ,7,34, thIn the absence of a control sample, the present study could not test the possibility that the same clustering solution would emerge in normal families from the population. However, to our knowledge, such family clustering studies have not been conducted. In a study by Horan and Goldstein , a clustClearly, the choice of the inter-cluster distance measure can greatly influence the merging process and hence the cluster solutions obtained. The maximum pairwise distance between subjects adopted in our analysis assigns a small distance between clusters only if all subjects in the clusters are close to each other in their test performance. We also experimented with the minimum pairwise distance but the results were poor. An explanation for this emerges by studying the minimum distance measure along the first few principal component directions of the normalized test results (the directions of largest variance). It turns out that, along these directions, most families have a member with nearly average performance and whose test results therefore closely match those of many members of other families. The variance of the distribution of the pairwise minimum distances is small and modest changes in the test results can lead to significantly different cluster solutions. The mean of pairwise distances between two clusters would be a compromise between the maximum and the minimum distances, but it turned out to behave much like the minimum distance and was therefore not used.The new approach which combines clustering and data visualization was effective in identifying homogeneous subgroups of schizophrenia families with convergent cognitive test performance. Our results emerging from a sample of familial schizophrenia patients are in line with previous studies in which two extreme clusters have consistently emerged, characterized by a well-performing and a dysfunctional group of subjects, and at least one intermediate ,12,37. ONone declared.FH and LH made the cluster analysis and wrote the corresponding sections, ATH supervised the neuropsychological test data collection, performed the differences testing analyses and drafted the manuscript, TP was the clinical and diagnostic study leader and commented on the text, JH designed the models for differences testing, JL conceived the research project.The pre-publication history for this paper can be accessed here:Comparison of Cluster 1 (well-performing) and Cluster 2 (impaired). Theoriginal variables  are visualizedusing the parallel coordinate plot. The subjects classified aswell-performing are colored green and those classified as impaired arecolored red. Yellow indicates overlap of green and red. There is overlap inall the original variables.The above figure was produced using the Crystal Vision software. Thesoftware uses a so-called grand tour technique to systematically (and in acontinous manner) go through all possible rotations in the data space. Thetour can be visually monitored using the parallel coordinate plot.Click here for fileSmaller version of Additional file 1.Comparison of Cluster 1 (well-performing) and Cluster 2 (impaired). Theoriginal variables  are visualizedusing the parallel coordinate plot. The subjects classified aswell-performing are colored green and those classified as impaired arecolored red. Yellow indicates overlap of green and red. There is overlap inall the original variables.The above figure was produced using the Crystal Vision software. Thesoftware uses a so-called grand tour technique to systematically (and in acontinous manner) go through all possible rotations in the data space. Thetour can be visually monitored using the parallel coordinate plot.Click here for fileA parallel coordinate plot of the data after a rotation transformation inthe data space. The axes correspond now to linear combinations of theoriginal variables. The 6th axis from the top shows an interestingone-dimensional projection of the data. In this direction the two clustersare well separated.The above figure was produced using the Crystal Vision software. Thesoftware uses a so-called grand tour technique to systematically (and in acontinous manner) go through all possible rotations in the data space. Thetour can be visually monitored using the parallel coordinate plot.Click here for fileSmaller version of Additional file 3.A parallel coordinate plot of the data after a rotation transformation inthe data space. The axes correspond now to linear combinations of theoriginal variables. The 6th axis from the top shows an interestingone-dimensional projection of the data. In this direction the two clustersare well separated.The above figure was produced using the Crystal Vision software. Thesoftware uses a so-called grand tour technique to systematically (and in acontinous manner) go through all possible rotations in the data space. Thetour can be visually monitored using the parallel coordinate plot.Click here for fileReference for Crystal Vision softwareCrystalVision, copyright (c) 2000 by Crystal Data Technologies (Qiang Luo,Edward J. Wegman, and Xiaodong Fu), is a Windows 95/98/NT package for Wintelcomputers. A demonstration version of CrystalVision is available at A movie which demonstrates the use of the software can be downloaded here.Smaller resolution CrystalVision.mpg(0.86 Mb). A higer resolution version ofthis movie is available at Click here for file


Currently in the U.S. it is recommended that tuberculosis screening and treatment programs be targeted at high-risk populations. While a strategy of targeted testing and treatment of persons most likely to develop tuberculosis is attractive, it is uncertain how best to accomplish this goal. In this study we seek to identify geographical areas where on-going tuberculosis transmission is occurring by linking Geographic Information Systems (GIS) technology with molecular surveillance.This cross-sectional analysis was performed on data collected on persons newly diagnosed with culture positive tuberculosis at the Tarrant County Health Department (TCHD) between January 1, 1993 and December 31, 2000. Clinical isolates were molecularly characterized using IS6110-based RFLP analysis and spoligotyping methods to identify patients infected with the same strain. Residential addresses at the time of diagnosis of tuberculosis were geocoded and mapped according to strain characterization. Generalized estimating equations (GEE) analysis models were used to identify risk factors involved in clustering.Evaluation of the spatial distribution of cases within zip-code boundaries identified distinct areas of geographical distribution of same strain disease. We identified these geographical areas as having increased likelihood of on-going transmission. Based on this evidence we plan to perform geographically based screening and treatment programs.Using GIS analysis combined with molecular epidemiological surveillance may be an effective method for identifying instances of local transmission. These methods can be used to enhance targeted screening and control efforts, with the goal of interruption of disease transmission and ultimately incidence reduction. Mycobacterium tuberculosis strains (TB), in combination with traditional surveillance, has yielded insights into tuberculosis transmission [The application of molecular analysis to identify specific smission . These ismission ,3. The Ismission . While aPersons with molecularly clustered tuberculosis isolates are assumed to be in the same chain of recent tuberculosis transmission ,6. LimitThis cross-sectional analysis was performed on data collected on all persons newly diagnosed with culture positive tuberculosis at the Tarrant County Health Department (TCHD) between January 1, 1993 and December 31, 2000. The TCHD serves the western portion of the Fort Worth-Dallas metropolitan area and includes a population of approximately 1.5 million . The ForM. tuberculosis strains were typed by the Texas Department of Health Mycobacteriology Laboratory were analyzed. Clinical isolate IS6110-based RFLP analysis and spoligotyping analyses were utilized to identify patients infected with the same strain using published methods [6110 RFLP is a powerful tool for discerning one strain of M. tuberculosis from another when there are greater than 6 copies of IS6110 however, a secondary typing method is needed to help differentiate strains with 6 or fewer IS6110 copies [6110 patterns containing seven or more bands, or they had identical IS6110 patterns containing six or fewer bands and identical spoligotypes. A geographic cluster was defined as two or more patients with molecularly related TB strains living in Tarrant County, TX. The proportion of cases due to ongoing transmission was estimated allowing one source case per cluster (i.e. n-1 method) [All positive isolates obtained from persons residing in Tarrant County were sent to the Texas Department of Health (TDH) for DNA fingerprinting. Only persons whose  methods ,17. RFLP0 copies . For thi method) .6110 performed on their M. tuberculosis isolate, and/or did not live within Tarrant County at the time of collection was excluded from the geographical analysis. Each eligible patient participated in a standard interview as part of their routine initial medical evaluation. Interview data collected included current and past employment, housing, alcohol and illicit drug use, incarceration history, sexual orientation, and psychiatric history. Persons paid daily for work were considered sporadically employed; others were employed or unemployed. Homelessness was defined as being without a permanent address for more than 3 days since 1991. If persons had a history of homelessness, paid rent by the day, or lived with a non-spousal roommate without paying rent, they were considered unstably housed. Alcoholism was defined by admission of daily consumption of three or more ounces of an alcoholic beverage; a history of alcohol-related conditions including cirrhosis, hepatitis, alcohol withdrawal seizures; or incarceration for alcohol use. Illicit drug use was defined by admission of use or documentation of being under the influence of an illicit drug. Persons were classified as having a history of incarceration if they had spent more than 24 hours in any criminal justice facility since 1991. Patients born in the U.S. or one of its territories were considered American-born; all others were considered foreign-born. All patients received HIV testing and counseling as part of standard clinical practice at the time of diagnosis. HIV status was determined from these tests.Any patient who did not have both spoligotyping and RFLP analysis of ISResidential address at the time of diagnosis of tuberculosis, including zip code, were geocoded using ArcView, 4.0, Geographic Information System Software, (ESRI, Redlands, CA). After geocoding, automatically and interactively, 94% of the cases were correctly matched. The numbers of cases were then aggregated by zip code and, for each zip code, an average of the total population reported for the US Census 2000 and US Census 1990 was used to calculate incidence. Population information was retrieved from the US Census Bureau . The US p with p > 0. For p = 1, the interpolated function is "cone-like" in the vicinity of the data points [The three-dimensional analysis was performed using Inverse Distance Weighting (IDW) . Interpoa points . The resM. tuberculosis, and to derive maximum likelihood odds ratio and 95% confidence intervals for all variables. Age was the only continuous variable. Age statistics were analyzed by comparing the means between groups. A 95% confidence interval for the mean age difference was calculated by using the normal approximation, and an independent sample two-tailed student's t-test was used to assess the statistical significance of the mean age difference. The institutional review board of the University of North Texas Health Science Center at Fort Worth approved this investigation.Statistical analysis was performed utilizing SAS V.8 statistical software (SAS Institute, Cary, NC). Patients with genotypically clustered and unique strains (non-clustered) were compared regarding each categorical risk variable by using odds ratio as a measure of association. Risk categories included patient demographics, and tuberculosis risk factors such as homelessness, HIV-infection, incarceration, and foreign birth. Because members of the clustered cases are assumed to be related, generalized estimating equations (GEE) analysis  was perfM. tuberculosis was isolated from 828 (83.6%) cases. Of the 828 cases with a positive culture, 527 (63.6%) had viable clinical isolates for molecular analysis. Persons excluded because of no viable clinical isolation of M. tuberculosis did not differ statistically from those with those with viable isolates by age (p = 0.49), gender (p = 0.57), or location (p = 0.64). Two hundred and ninety-two (55.4%) patients met the criteria for molecular clustering. These patients were categorized into 48 clusters varying from 2 to 95 patients per group. In nine instances (1.7%), patients had an identical low copy RFLP pattern but did not have spoligotyping conducted, and were therefore classified as missing data , 44.1 ± 16.6 (SD) for those genotypically clustered, and 48.5 ± 17.6 (SD) for those with unique strains. Patients that were genotypically clustered differ significantly with age when compared to patients with unique strains, [p = 0.005]. One hundred and seventy-one (32.4%) patients were African-American, 165 (31.3%) were Caucasian, 109 (20.7%) were Hispanic, and 82 (15.6%) were Asian. African-Americans with tuberculosis were significantly more likely to have a clustered strain [OR = 2.7, 95% CI = 1.8, 4.0]. Alternatively, Asians [OR = 3.9, 95% CI = 2.3, 6.0], and Hispanics [OR = 1.9, 95% CI = 1.2, 2.9] were significantly more likely to have a unique strain. Three hundred and twenty-nine (67.4%) of the patients were males, and of these, 214 (65.0%) had clustered strains; 78 of 159 females (49.1%) had clustered strains. Males were more likely than females to have a strain that matched at least one other person in Tarrant County [OR = 1.9, 95% CI = 1.2, 2.8]. Persons with previous experience of homelessness were strongly associated with clustering suggesting a high rate of on-going transmission among this population. [OR = 12.4, 95% CI = 2.9, 52.1] (Table Three hundred and twenty-one (65.7%) patients were born in the United States. Of those, 235 (73.2%) had clinical isolates that matched the isolate from at least one other person living in Tarrant County. One hundred and sixty-seven patients were born outside of the United States. Of those, 57 (34.1%) clinical isolates that matched the isolate from at least one other person living in Tarrant County. U.S. born individuals were significantly more likely to be genotypically clustered than foreign-born counterparts [OR = 5.3, 95% CI 3.5, 7.9]. The birth country of foreign-born patients varied. Of those born outside of the U.S, 77 (46.1%) were born in Latin America, 47 (28.1%) in Southeast Asia, 14 (8.4%) in Sub-Saharan Africa, 12 (7.2%) in Pacific Asia, 11 (6.6%) in South Asia, and 6 (3.6%) in Europe.Evaluation of the spatial distribution of number of cases within zip-code boundaries displayed a distinct geographical distribution of disease. The average incidence for the entire county during the study period was 5.9 cases per 100,000. Zip code 1 recorded the highest incidence of 94.3 cases per 100,000 populations, followed by zip code 2 with an average incidence of 55.2 cases per 100,000 population  occurred in the same zip code with the highest incidence. Similarly, zip code 2 on the southeast border of zip code 1, recorded the second highest proportion of persons with molecular clustered TB isolates with 76.6% of all reported cases clustered. Cases reported in zip code 1 were more than six times as likely [OR = 6.2, 95% CI = 2.4, 16.1] than any other zip code to have isolates that match at least one other person living in Tarrant County.In zip code 3, we observed a morbidity that was more than triple the county average (22.3 cases per 100,000). Unlike other high morbidity areas, zip code 3 had a strong preponderance of unique strain distribution. In this zip code, 17 out of 26 (65.4%) patients had isolates that did not match any other patient in Tarrant County. Cases reported in this zip code were 70% less likely [OR = 0.3, 95% CI = 0.1, 0.7] to have a clustered strain, suggesting that the high rates of tuberculosis did not result from local on-going transmission.The number of cases in the United States is at its lowest point in history, with 15,075 cases reported in 2002 . The rolThe use of molecular strain characterization methods in conjunction with traditional surveillance has led to the recognition of a number of risk factors associated with on-going transmission, and has identified numerous outbreaks of tuberculosis undetected by conventional approaches -27. ThesThese finding are similar to those reported in Los Angeles where locations, specifically homeless shelters were identified as important sites of tuberculosis transmission . SimilarWe identified that 55% of our patients were clustered and 47% attributable to ongoing community transmission. This differs from a study conducted in a high incidence area of South Africa, where 72% of cases were clustered and 58% attributable to ongoing community transmission . Our lowAlthough the majority of the tuberculosis morbidity within the developed world is strongly influenced by imported tuberculosis from high prevalence countries ,36, the There are some limitations to this research approach. This is based on secondary data, which includes variables collected from a cross-sectional period of time. Although each case is an incident case at the time of diagnosis, under this cross-sectional design, exposure and disease outcomes are assessed simultaneously. In addition patients with tuberculosis may have moved shortly before their diagnosis. However, this should not cause systematic error (bias) or result in an association of clustering with specific locations, because these events would be expected to produce a random misclassification. Also persons exposed within certain zip codes may go on to reside elsewhere and later develop the disease, and result in an underestimate of the morbidity and that may be reflected in calculating associations. Finally, genotyping results were not available for a proportion of TB cases in this study. Some unique isolates might have clustered if some of the missing isolates had been available or if other cases with the same strain moved or are located outside the study area . We therWhen using this approach TB control programs must select the appropriate geographical boundary to examine transmission in their area. For example, using zip codes may be too large a boundary in very populated metropolitan areas. Census block groups may provide greater resolution in determining localized transmission.6110 RFLP is recognized as the most discriminatory method for genotyping M. tuberculosis isolates, the discriminatory ability of the technique decreases when there are fewer than 6 IS6110 insertions in the genome. In this case, spoligotyping was used for further strain discrimination. However, it is still possible that some isolates classified as being the same strain based on identical genotypes may represent distantly related, but distinct, strains. Moreover, demonstration that particular patients have the same strain supports, but does not irrefutably prove, direct transmission between these patients as opposed to another source of infection. Conversely, strains continue to evolve, and the resulting genotypic differences over time can result in assigning isolates from cases of direct transmission to distinct strain lineages. Given that a small minority of the isolates had fewer than 6 IS6110 bands (18.2%) or differed by the presence or absence of one band in an otherwise conserved pattern (3.7%), we believe that estimates of the degree of clustering and the size of clusters are conservative.Nor are the molecular techniques used without limitation. Patients are clustered according to their isolates having the same genotype. While ISUsing GIS analysis combined with molecular epidemiological surveillance can be an effective method for identifying tuberculosis transmission not identified during standard contact tracing methods. The application of these methods can be utilized in countries where contact tracing is routinely performed. These methods can enhance targeted screening and control efforts, with the goal of interruption of disease transmission and ultimately incidence reduction. This study demonstrates that using existing health data, GIS can identify previously undetected TB transmission. These results were used to design new targeted screening efforts . StudiesCI Confidence IntervalGEE Generalized Estimating EquationsGIS Geographic Information SystemsHIV Human Immuno-deficiency VirusIDW Inverse Distance WeightingNCTCG North Central Texas Council of GovernmentsOR Odds RatioRFLP Restriction Fragment Length PolymorphismTB TuberculosisTCHD Tarrant County Health DepartmentTDH Texas Department of HealthStudy concept and design: PM, MB, SWAcquisition of data: PM, TQ, KJ, DD, GB, SWAnalysis and interpretation of data: PM, MB, TQ, JO, SWDrafting of the manuscript: PM, SWCritical revision of the manuscript for important intellectual content: SW, PM, JO, TNStatistical expertise: KS, MB, PMObtained funding: TQ, SWAdministrative, technical or material support: GBThis work was supported in part by the Centers for Disease Control and Prevention, National Tuberculosis Genotyping and Surveillance Network Cooperative Agreement U52/CCU600497-18, and Tuberculosis Epidemiologic Studies Consortium 200-2001-00084.


Escherichia coli and other bacteria capable of rapid growth is commonly attributed to selection for high translational power. Conversely, the absence of such a correlation in some slowly growing microbes has been interpreted as the absence of selection for translational power. Because codon bias caused by translational selection varies between rapidly growing and slowly growing microbes, we investigated whether observed differences in translational power among microbes could be explained entirely by differences in the degree of codon bias. Although the data are not available to estimate the effect of codon bias in other species, we developed an empirically-based mathematical model to compare the translation rate of E. coli to the translation rate of a hypothetical strain which differs from E. coli only by lacking codon bias.Translational power is the cellular rate of protein synthesis normalized to the biomass invested in translational machinery. Published data suggest a previously unrecognized pattern: translational power is higher among rapidly growing microbes, and lower among slowly growing microbes. One factor known to affect translational power is biased use of synonymous codons. The correlation within an organism between expression level and degree of codon bias among genes of E. coli and slowly growing microbial species. Using empirical codon-specific in vivo translation rates for 29 codons, and several scenarios for extrapolating from these data to estimates over all codons, we find that codon bias cannot account for more than a doubling of the translation rate in E. coli, even with unrealistic simplifying assumptions that exaggerate the effect of codon bias. With more realistic assumptions, our best estimate is that codon bias accelerates translation in E. coli by no more than 60% in comparison to microbes with very little codon bias.Our reanalysis of data from the scientific literature suggests that translational power can differ by a factor of 5 or more between While codon bias confers a substantial benefit of faster translation and hence greater translational power, the magnitude of this effect is insufficient to explain observed differences in translational power among bacterial and archaeal species, particularly the differences between slowly growing and rapidly growing species. Hence, large differences in translational power suggest that the translational apparatus itself differs among microbes in ways that influence translational performance. Translational power is the rate of protein synthesis of a cell or culture, normalized to the amount of biomass invested in the protein synthesis machinery. We are introducing the term 'translational power' to describe precisely the same concept (and the same quantitative parameter, see Methods) that was originally defined as 'ribosome efficiency' -3. In rerate at which a process occurs. Physicists and engineers use a distinct term, 'power,' to refer to the rate of energy consumption or the rate at which work is performed [According to these conventions, calculations of efficiency make no direct reference to the erformed . The semerformed . In biolerformed ,13 have erformed -16. Noneerformed -24. Amonerformed -28, as herformed ,30. Howeerformed -33. Even-1). We prefer the term 'translational power', which refers to the rate of protein synthesis of a cell or culture, normalized to the mass of the translational apparatus, in a manner that is more consistent with the connotations of 'power' and 'efficiency' derived from other areas of science and from colloquial usage. Translation rate  suggests that the association is robust.The concept and a quantitative metric of translational power were first introduced to facilitate comparisons of translational performance between different growth rates within a single bacterial strain . The iniE. coli ,4,35,36.[E. coli -40. In eE. coli and Bacillus subtilis turns out to be common: the majority of genes within an organism show a preference for the same subset of codons, but the degree of bias towards the preferred subset is correlated with the expression level of the gene [One factor capable of affecting translational power is the biased usage of synonymous alternative codons. In the standard translational code, 18 of the 20 amino acids are encoded by more than a single codon, but in many microorganisms, synonymous codons are not used with equal frequency. The pattern first found in the gene ,42. For the gene ,8. Codonthe gene -45. Thisthe gene ,8. Codonthe gene ,8.In contrast to the codon bias caused by translational selection, codon bias that is consistent in both magnitude and direction in genes that vary widely in expression level is explained most easily by mutational bias acting on DNA ,46. WhilTreponema pallidum [Helicobacter pylori [The absence of a correlation between codon usage and gene expression level has also been reported in some organisms with moderate G+C content, in particular the spirochete pallidum  and the r pylori . The lacr pylori ,55, consin vivo for each codon. Unfortunately, these data are incomplete even for E. coli, and are not available for other microbes. Therefore, we approach the issue by framing the following question: How much faster is the translation rate of E. coli than the expected translation rate of a hypothetical organism that has the same proteome composition and the same investment in translational machinery as E. coli, but which lacks codon bias? Here we report results from a simple mathematical model developed to address this question.If variation in the strength of selection for translational power leads to differences in the degree of codon bias between microbes , we wondered whether differences in codon bias could in turn explain the observed differences in translational power between microbes. An estimate of the effect of biased codon use on the overall rate of translation would depend on knowledge of absolute or relative translation rates E. coli-like organism with uniform use of synonymous alternative codons as 'Uni'. By 'same proteome composition', we mean that over a cell generation, each amino acid is incorporated into protein the same number of times in Uni and in E. coli, although for the 18 amino acids specified by multiple codons, the individual codons will differ in frequency. By 'same investment in translational machinery', we mean that the total biomass of the translational apparatus is the same in Uni and in E. coli, although ideally the allocation of that biomass among various components of the apparatus in Uni would be optimized for unbiased codon usage. However, in order to apply empirical codon-specific translation rate data, we will impose a more stringent requirement on Uni, that the abundance of each individual component of the translational apparatus will be unchanged in comparison to E. coli. Due to this restriction, and due to the incomplete data for codon-specific translation rates, we make no claim to be able to answer our question precisely. However, our approximations are adequate to conclude that differences in codon bias alone are unlikely to account for differences in translational power of the magnitude inferred from macromolecular analysis of slowly growing and rapidly growing microbes.For convenience, we will refer to the hypothetical E. coli and a single slowly growing strain [E. coli [E. coli data of Bremer and Dennis [E. coli cell volume is constant over a range of growth rates [E. coli and other strains. In addition, we applied the same comparative methodology to every microbial species for which we could find the requisite data in the literature. The list of species that could be included is surprisingly short; most studies reporting both the protein and RNA content of microbes growing at known rates have involved E. coli or closely related enteric bacteria. Table E. coli and all other species.We know of 4 studies that have made explicit comparisons of translational power between different microbial species; in each case, the comparison was made between g strain -40. One [E. coli ; the remd Dennis . Althougd Dennis , at leasd Dennis ,40 compath rates , which iE. coli are not always based the fastest E. coli growth rate, but rather on the growth rate at which E. coli makes a comparable investment in the translational apparatus as the comparison organism. A comparison at similar investment levels reflects the expectation that the selective pressure to maximize translational power increases with the biomass invested in the apparatus [E. coli growth rate , the disparity in translational power would be greater for most of the comparisons shown.The comparisons of translational power in Table pparatus ,58. If tE. coli and other rapidly growing organisms, it is lower in slowly growing organisms, ranging from less than 17% to 42% of the value for E. coli. Hence, if differences in the degree of codon bias are to explain these differences in translational power, we would expect codon bias to be capable of accelerating the rate of translation by 2.5-fold to 6-fold. In summarizing the comparisons of Table Sphingopyxis alaskensis and Rickettsia prowazekii are slow growers, Salmonella enterica and Enterobacter aerogenes are rapid growers), and on the number of copies of the ribosomal RNA (rrn) operon per genome. High rrn copy number is an adaptation permitting rapid growth [rrn copy number is characteristic of microbes adapted for slow growth [Even with the conservative comparisons displayed in Table d growth ,60, whilw growth ,61.E. coli as sbias, the fractional increase in the time required to replicate the E. coli proteome if the actual codon bias of E. coli were to be replaced with uniform use of synonymous codons (Equation (10) in Methods). Our estimates of sbias depend on the relative translation rates of individual codons in vivo, and on the frequency with which each codon is used in synthesizing the proteome. The sources we have used for these data, and the details of several adjustments made to the source data, are described in the Methods section. All data used in our estimates of sbias are presented in Table E. coli, we have explored 4 different scenarios (described in Methods) for extrapolating from the empirical rate data to obtain an estimate of sbias over all codons. Scenarios 1–4 are increasingly complex, and represent deliberate attempts to assign translation rates to the unmeasured codons in a way that increases sbias while remaining consistent with patterns found in the empirical data. Furthermore, in Scenario 5, we apply a theoretical approach [We define the translation rate benefit of codon bias in approach  for predsbias for all scenarios are presented in Figure sbias that are based on different assumptions regarding the relative duration of translocation and ternary complex selection. The white bars of Figure E. coli relative to the complete absence of codon bias range from 0.6 – 1.4 if translocation time is neglected, or from 0.4 – 1.1 with the more realistic assumption that translocation requires a short amount of time.Estimates of E. coli relative to the limited degree of codon bias that might be found in an actual low-bias organism, rather than making a comparison to the biologically unrealistic standard of strictly uniform synonymous codon use. We took T. pallidum as our example of a microbe with limited codon bias, since it is a slowly growing bacterium with little mutational bias (52.7% G+C) that has also been reported to lack translational selection [T. pallidum genome has the second-most uniform codon use over all predicted genes  among 1sbias can be applied over any subset of codons, in particular, it can be applied to the codons of each amino acid separately. While all amino acids with multiple codons except proline contribute positively to sbias in all scenarios, the magnitude of that contribution is highly variable between amino acids , the difference between using the non-preferred codon at 16% of asparagine residues in E. coli instead of at 50% of asparagine residues in Uni corresponds to only about a 3-fold acceleration of translation (sbias ≈ 2) for this amino acid. With more realistic disparities between the translation rates of preferred and non-preferred codons, the largest estimate of sbias for asparagine in any of our scenarios is less than 0.2. In other words, we estimate that codon bias in E. coli leads to no more than a 20% decrease in the time required to translate all asparagine codons in the proteome  The frequencies of the most and the least abundant synonyms for leucine, isoleucine and arginine differ by 74-fold, 83-fold, and 1460-fold, respectively. (The higher ratio for arginine reflects the extreme rarity of AGG, which is 17-fold less abundant than the second rarest E. coli codon, AUA encoding isoleucine.) Since the translation rates measured or assumed for the 6 rarest codons are quite slow, their increased abundance in Uni accounts for the much of the additional time required for replicating the Uni proteome. If these six codons remained as rare in Uni as they are in E. coli, while all other synonymous codons were used without bias in Uni, the translation rate benefit estimated under Scenario 4 (the scenario producing the largest estimate of sbias) would be reduced by almost half (data not shown). The influence of these 6 codons is such that the estimate of sbias is quite sensitive to the translation rates assigned to them, in contrast to the relative insensitivity of sbias to the exact translation rates assigned to most codons.The amino acids with the largest values of e Figure . AlthougE. coli. We approach this issue by its converse, calculating how much faster the proteome is replicated in E. coli than it would be in the complete absence of codon bias. If we take our estimates at face value, we would conclude that even during rapid growth when the proteome is most biased and translation is fastest, sbias is unlikely to be much larger than 1  may havE. coli necessarily produces the fastest possible translation rate for the E. coli codon frequency distribution, it is clear that selection for translational power has been a significant factor in the coevolution of codon frequencies and cognate tRNA abundances in E. coli. Thus, it is very unlikely that we have attained the maximum possible translation rate for Uni by matching the E. coli distribution of tRNA abundance values (in the form of a particular distribution of codon-specific translation rates) to the very different codon frequency distribution of Uni. For this reason, our estimates confound the translation rate benefit of codon bias in E. coli with the penalty of a suboptimal allocation of translational resources in Uni.At a larger scale, the correlation across all codons between frequency and cognate tRNA abundance ,70 is beE. coli in comparison to actual slow-growing organisms is that actual microbes are not completely devoid of codon bias. Assessing sbias in E. coli in comparison to a biologically plausible standard for low codon bias, instead of in comparison to the implausible standard of no codon bias whatsoever, reduces the estimated benefit in E. coli by about half . If codnot reflect their biased use of codons.The translation rate benefit of codon bias depends on matching preferred codons with cognate tRNAs that are abundant and/or that form 3 canonical base pairs ,8. Even Mycoplasma capricolum (25% G+C) [Micrococcus luteus (74% G+C) [M. capricolum, but not M. luteus, can be considered a constitutively slow-growing strain. As expected, cognate tRNA abundance in both organisms is correlated with codon frequency, both across all codons and within synonymous codon families [M. capricolum, this is accomplished largely without the tRNA gene dosage effects that are important for E. coli [B. subtilis [M. capricolum tRNA genes are present in only a single copy [sbias for E. coli compared to codon frequencies derived from T. pallidum will overstate the benefit that exists for E. coli relative to most other slowly growing microbes that have greater mutational bias.This prediction is confirmed by the only two studies we have found of tRNA abundance in microbes with extreme G+C content, involving 25% G+C)  and Micr74% G+C) . M. caprfamilies ,77. For  E. coli  and B. ssubtilis , since 2gle copy . These eE. coli is likely to be less than 0.6  reproduce the same pattern; we have found differences in translational power that are comparable in magnitude to those shown in Table Is it possible that the comparisons of translational power presented in Table Because codon bias influences translational power, and because the degree of codon bias due to translational selection may differ systematically between rapidly growing and slowly growing strains, we investigated the parsimonious hypothesis that observed differences in translational power between microbial species could be explained by differences in the degree of codon bias. However, based on the analysis reported here, such an explanation is not plausible. Instead, differences in translational power between rapidly growing and slowly growing species suggest that the translational apparatus itself has different performance characteristics in rapidly growing and slowly growing microbes.R) as an index of the biomass invested in the entire system. For a culture in balanced, exponential growth, the instantaneous rate of increase of any culture component is dX/dt = μX, where μ is the specific growth rate and X is the mass of the component present in the culture at that moment. Hence, μP is the rate of protein synthesis in a culture containing mass P of protein. Thus, our quantitative measure of translational power is:Conceptually, we define translational power as the rate of protein synthesis in a cell or culture, normalized to the biomass invested in the protein synthesis system. We intend the term to be synonymous with 'translational efficiency' ,5,8; ourThis quantitative measure of translational power will be consistent with the conceptual definition as long as RNA is a nearly constant fraction of the mass of the entire protein synthesis system.Translational power reflects both the average translation rate and the fraction of active ribosomes in a cell or culture, which we demonstrate as follows, using the approach of chapter 6 of reference . 'TranslμP. Units of protein mass can be converted to a number of amino acids by dividing the protein mass by the average mass of an amino acid:We know that the mass rate of protein synthesis in a culture in balanced growth is μP/(average mass of amino acid)     (3)number of amino acids polymerized per unit time = R of RNA can be found by multiplying R by the fraction of RNA that is ribosomal, and then dividing by the mass of RNA in a ribosome. However, only a fraction of these ribosomes are active at any given time. Thus:The number of ribosomes in a culture containing a mass Substituting Equations (3) and (4) into Equation (2) yields:After rearranging terms in Equation (5), we have:whereμP/R in Equation (6) is the quantitative measure of translational power from Equation (1) [The quantity Equation  1,3]. F. FμP/R iC, implying a constant? The two quantities in the numerator, the mass of RNA in a ribosome and the average mass of an amino acid, are indeed constant or nearly constant, both within a strain at different growth rates, and across strains. However, despite the constant ribosomal fraction of RNA reported in reference [E. coli from 1.7 hr-1 to 0.28 hr-1 [E. coli and a slowly growing strain. The rRNA fraction is reported as 84% for H. cutirubrum at specific growth rates of both 0.10 hr-1 and 0.05 hr-1, after the authors made the deliberately generous assumption that messenger RNA comprises 5% of the total RNA [R. prowazekii at a specific growth rate of ~0.07 hr-1, after a correction is made for 2–3% messenger RNA [What of the term we have labeled eference , other d.28 hr-1 , a resul.28 hr-1 ,65. Thisotal RNA . The rRNnger RNA . These dE. coli and all other bacterial and archaeal species for which we could find both the protein content and the RNA content of cultures growing at known rates. Throughout this work, E. coli is represented by the Bremer and Dennis data [E. coli in many other studies. Similarly, comparisons between E. coli and 2 closely related species of enteric bacteria, S. enterica and E. aerogenes, are made using only a single representative study for the latter strains, chosen from among several published reports. For the remaining species, only a single published study was available for comparison, except for one species represented by two studies, both of which are included. For strains not grown at 37°C, we assume that the growth rate, but not the macromolecular content, would be altered by growth in the same medium at a different temperature [E. coli, the comparison is mathematically identical whether the temperature correction is applied to E. coli or to the comparison strain.)Table nis data , which aperature . The groperature .  One of three measures was used to gauge the level of investment in the translational apparatus, depending on the quantity measured in the original study. The possible measures were the number of ribosomes per cell volume, the ratio of protein to ribosomal RNA, or the ratio of protein to total RNA. Values of these quantities for E. coli were interpolated between adjacent data points to estimate the growth rate at which E. coli made the same investment in the translational apparatus as the comparison organism. The translational power of the comparison organism at the fastest available growth rate was then expressed as a percentage of the translational power of E. coli at the 'same investment' growth rate. A comparison at similar investment levels reflects the expectation that the selective pressure to maximize translational power increases with the biomass invested in the apparatus [E. coli growth rate , the disparities in translational power would be greater for most of the comparisons shown.The comparisons in Table pparatus ,58. If tCi codons of type i are translated during a single cell generation, so that the sum over all sense codons C = ΣCi is the total number of codons translated during a cell generation.  If we define ci = Ci/C as the proportion of all codons of type i in the proteome and ri as the average translation rate of codons of type i, the total time required for replication of the proteome (i.e., the proteome generation time) will beConsider a cell in which a total of R# is the average number of ribosomes active in translation over the cell cycle and the sum is over all sense codons. Codon bias in favor of rapidly translated codons will reduce gp in comparison to uniform codon use. If a mutation changes the fitness of an organism from w to w', the benefit of the mutation is typically described as s, where w'/w = 1 + s. By analogy, and considering gp to be inversely related to fitness, we can express the translation rate benefit of codon bias aswhere C) is the same in Uni as in E. coli by hypothesis. With the restrictive condition that the abundance of each individual component of the translational apparatus is unchanged in Uni, ribosome content (R#) will be the same also. Hence, the C/R# term of gp in Equation (8) cancels from both the numerator and denominator of Equation (9) for sbias, leading toThe protein content  for both Uni and E. coli, again invoking the restrictive stipulation that the abundance of each individual tRNA species is unchanged. If rate constants for the interaction of each codon with each of its cognate tRNA species were known, we could calculate the optimal tRNA abundance distribution for the codon frequencies of Uni, and infer the resulting codon-specific translation rates [in vivo codon-specific translation rate data are available only as codon averages, including translation from all tRNA species cognate to each codon. Hence, rate constants specific to each codon-cognate tRNA pair cannot be calculated from the available data for the codons translated by multiple tRNA species, and thus we cannot calculate an optimal tRNA abundance distribution for Uni. Instead, we have constrained Uni to maintain the same tRNA distribution and codon-specific translation rates as E. coli. Insofar as the E. coli rates reflect an allocation of tRNA abundance that would be sub-optimal for Uni (as we argue in the Discussion section), our approach will tend to overestimate of the benefit of codon bias in E. coli, a conservative error for our purposes.We will use the same codon-specific translation rates  either with Uni having strictly uniform use of synonymous codons, or with Uni assigned a set of low bias codon frequencies Table . The lowpallidum . By relatRNA/Rshift) of Curran and Yarus [in vivo translational kinetics. The original publication transposed values reported for two arginine codons, CGC and CGA [subsequent to CGA [To represent codon-specific translation rates, we use the relative rate data . Following Björk, we assume that 40% of the tRNAs for glutamate, glutamine and lysine with uridine in the anticodon wobble position are modified to mnm5Se2U and thus read codons ending in A or G; the balance of these tRNA species are assumed to have mnm5S2U in the wobble position and read A-ending codons only [et al. [To calculate the total abundance of cognate tRNA for each codon, we assign cognate specificity largely according to Björk , and useet al. . We diffons only . The abu [et al. , since t [et al. . We showE. coli, assuming optimality  according to theory developed by Solomovici et al. [We address the incompleteness of codon-specific translation rate data in several ways. In Scenario 1, we assume that the effects of biased use of YNN codons on translation rate can be used to represent the effects of bias over all codons, without assigning particular translation rates to the unmeasured codons. However, since the YNN codons are almost half of all sense codons but only account for about a third of all expression Table , they mui et al. .sbias. Hence, in Scenario 1 we apply Equation (10) over YNN codons, with the calculated translation time for non-proline YNN codons weighted by a factor of 3.2, which scales the expression level of these codons to the expression level of all non-proline codons. In other words, we assume the effects of codon bias on translation rate among the 25 non-proline YNN sense codons are representative of the effects of codon bias among all 57 non-proline sense codons, whereas the translation rates measured for proline codons are applied only to themselves.The 29 YNN codons encode 10 amino acids, 9 of which have multiple codons. For 7 of these 9 amino acids, the most common synonym is the codon with the fastest translation rate. One of the remaining amino acids is serine, for which the two fastest-translated codons are the two most abundant, although in reverse order, with relatively small differences between the two in both rate and abundance. Only proline appears to be anomalous; the 2 most abundant codons encode over 90% of all proline residues in the proteome , but supet al. [2 = 0.56) of a quadratic relationship passing through the origin between the codon frequency and translation rate data of Table ci = 0.205 ri - 0.522 ri2. We use this equation to predict translation rates from codon frequency for all RNN codons, as shown in Table Curran and Yarus noted that among highly expressed genes, there is a significant tendency for rapidly-translated codons to be used frequently, although the relationship appears to be nonlinear . We obseet al.  at the hci = 0.352 ri - 1.611 ri2 (R2 = 0.81). Among the 10 preferred codons, we include UGG, the sole tryptophan codon, and UUG, the preferred leucine codon within the UUR split box although not the preferred leucine codon overall. We then apply this equation to predict translation rates from codon frequencies for 12 preferred RNN codons, including AUG, the sole methionine codon, and AGG and AGC, the preferred arginine and serine codons within their respective split boxes, although not the preferred codons overall. For non-preferred RNN codons, translation rate is predicted by multiplying the predicted rate for the preferred synonym (within the full or split box) by the ratio of the square roots of the codon frequencies for the non-preferred and preferred codons:The preceding scenario applied to the YNN codons tends to predict translation rates among synonymous alternatives that are not as disparate as those actually observed. Furthermore, the fit of a functional relationship between codon frequency and translation rate among YNN codons is better when only preferred codons are considered, instead of all codons. Hence, we fit a quadratic relationship passing through the origin to data from 10 preferred non-proline YNN codons, obtaining 2 = 0.57). The predicted rates for RNN codons and the measured rates for YNN codons  are assigned the slowest relative translation rate observed among YNN codons (ri = 0.6 for the rare leucine codon CUA). Second, the translation rates for preferred RNN codons within full or split boxes (except AGG) are estimated according to the regression equation described for Scenario 3. Finally, the translation rates for non-preferred codons (except AGA and AUA) are predicted from the preferred synonym using the ratios of the most disparate translation rates observed empirically among synonymous alternatives, treating split boxes and full boxes of the translational code separately. The most extreme ratio observed among translation rates in a split box is 3.375, for glutamate codons in the study of Sorensen and Pedersen [x ≤ y ≤ 24 in ratios of the form 1:x:y:24 failed to find any that greatly increased the estimated benefit beyond that using the leucine ratios, data not shown.) Although this scenario is based on extreme observations, applying these 3 rules to the non-proline YNN codons leads to a correlation of predicted and measured translation rates (R2 = 0.67) somewhat better than that obtained under Scenario 2 or Scenario 3. The predicted rates for RNN codons and the measured rates for YNN codons . However, the empirical codon frequencies of Dong et al. [2 = 0.70) than with the empirical relative rates of Curran and Yarus [2 = 0.31). A good correlation between the predicted translation rates and the empirical codon frequencies is expected, since the codon frequencies were used to generate the predictions. However, the poor correlation between predicted and empirical translation rates could reflect the inadequacies in any of 3 areas: 1) the assumptions of Solomovici et al. [et al. [E. coli is not perfectly optimized for maximal translation rates  the i et al. , and/or eference ), eitherNonetheless, the disparity between the relative rates of synonymous preferred and non-preferred codons for most amino acids are greater with the predicted rates of Scenario 5 than with the observed rates. Hence, Scenario 5 will generate a higher estimate of the translation rate benefit of codon bias than would a strict application of the empirical codon-specific translation rates.  The predicted translation rates for all codons Table  are usedLD conceived of the project, collected and analyzed the data, developed the mathematical model, and drafted the manuscript. TMS helped plan the project, critiqued the work as it progressed, and edited the manuscript.