Background. Medical school is recognized as a stressful environment that often has a negative effect on students' academic performance, physical health, and psychosocial well-being. Previous studies have not identified differences between depressed and nondepressed and anxious and nonanxious medical students' experiences of stress or their reactions to stressors. The present study aimed to identify the prevalence of depression and anxiety among a sample of 358 medical students attending a private university in Malaysia and to examine differences according to participants' gender, year of study, and stage of training . Additionally, this study examined the extent to which stress predicts depression and anxiety, differences between depressed and nondepressed medical students' experiences of and reactions to stressors, and differences between anxious and nonanxious medical students' experiences of and reactions to stressors. Methods. The Student Life Stress Inventory was used to measure stress and reaction to stressors and the Depression, Anxiety, and Stress Scale was used to measure depression and anxiety. Results. The results showed that 44% (n = 158) of the students were anxious and 34.9% (n = 125) were depressed. More female students exhibited anxiety compared to male students. Stress is a predictor for depression and anxiety. A significant difference was found between depressed and nondepressed and anxious and nonanxious students' experience of stressors due to frustration, change, and their emotional reaction to stressors. Conclusion. Overall, depressed and anxious students were found to experience more stress and react differently to stressors compared to nondepressed and nonanxious students. The mental health of university students is an area of increasing worldwide concern as this population has been shown to be particularly prone to depression, anxiety, and stress due to factors that include academic pressures, obstacles to their goal achievement, environmental changes, and life challenges such as transition from school to university and the change in role from student to knowledgeable physician , 2.Medical students have been found to experience higher levels of depression and anxiety compared to the general population and to their same age peers , 4. DiffThe prevalence of anxiety among medical students attending public universities has been estimated to be 43.7% in Pakistan , 54.5% iEvidence for a relationship between medical students' gender, year of study, and stage of training  and the development of depression and anxiety is equivocal. Some studies of medical students from public universities have reported that female students experience more depression, anxiety, and stress compared to male students , 19, whiGiven the inconsistent findings regarding the relationship between levels of depression and anxiety and gender, year of study, and stage of training  and the fact that studies have typically been undertaken in public universities in Malaysia and other countries, there is a need to further investigate these relationships and to investigate them among students in medical schools in private universities in Malaysia.Stress has been found to correlate with depression and anxiety . PreviouThe recent rapid increase in the number of medical schools in Malaysia from 21 (10 public and 11 private) in 2007  makes Maprevalence of depression and anxiety,relationship between demographic variables  and levels of depression and anxiety,extent to which stressors and reactions to stressors predict depression and anxiety,extent to which stressors  and reactions to stressors  vary between students with depression and anxiety and students without depression and anxiety.Therefore, among a sample of medical students attending a private medical university in Malaysia, the present study aimed to identify theIn this cross-sectional study, participants were 358 medical students who were attending a private university in Malaysia and who had completed at least six months of their medical degree ; and a measure of depression anxiety .The SSI is a 51-item inventory designed to assess students' perceived academic stress and reactions to stress. Responses are provided using a Likert scale that ranges from 1 = never true to 5 = always true. The inventory assesses five categories of academic stressors  and four categories describing reactions to stressors . Validity and reliability of the instrument have been found to be adequate and reported internal consistency estimates ranging from 0.69 to 0.82 across the nine categories .The DASS-21 was used in the present study; however, only the depression and anxiety scores were considered because stress was more comprehensively measured by the SSI. The DASS-21 is reported to have very good Cronbach's alpha values for depression and anxiety  .Following the granting of ethical approval from the university to conduct the study, medical students in years 1–5 were contacted after the conclusion of one of their lectures and were invited to participate in the study. Students who agreed in writing to participate were each given a questionnaire package to complete and return to the researcher before leaving the lecture room.Data were analyzed using PASW version 18.0  .The prevalence level of depression and anxiety among the sample is shown in χ2 = 10.3 (1), P = 0.01) and a nonsignificant relationship between year of study or stage of training  and anxiety.β = 2.11, t(355) = 4.06, P < 0.001) and anxiety  =  3.87, P < 0.001) and predicts 44%  = 16.51, P < 0.001) of depression and 40%  = 14.97, P < 0.001) of anxiety.For the regression model, this study used total stress score as a predictor variable and total anxiety and depression score as criterion variables. t(356) = −2.714, P = 0.007, d = 0.28) and total reaction to stressors score (t(356) = −2.80, P = 0.005, d = 0.38). Significant differences are shown between anxious and nonanxious students' responses to three of the five stressor subscales: frustration (t(356) = −2.406, P = 0.018, d = 0.25), pressure (t(356) = −2.402, P = 0.017, d = 0.25), and changes (t(356) = −2.230, P = 0.026, d = −.24). Significant differences are shown between anxious and nonanxious students' physiological (t(356) = −2.895, P = 0.004, d = 0.30) and emotional (t(356) = −2.285, P = 0.023, d = 0.24) response to stressors. The total mean values of stressors (M = 71.59) and reaction to stressors (M = 70.76) are higher for anxious students compared to nonanxious students' mean value of stressors (M = 67.99) and reaction to stressors (M = 65.72). The Cohen effect size value (d) suggested a smaller to medium significance between anxious and nonanxious students' stressors and reactions to stressors.t(356) = −2.12, P = 0.034, d = 0.23); however, no significant difference exists between depressed and nondepressed students' total reaction to stressors score (t(356) = −1.64, P = 0.101). A significant difference exists between depressed and nondepressed student's responses to two of the stressor subscales: frustration (t(356) = −2.056, P = 0.044, d = 0.22) and changes (t(356) = −2.57, P = 0.010, d = 0.29). A significant difference exists between depressed and nondepressed students' emotional reaction to stressors (t(356) = −2.03, P = 0.043, d = 0.22) but not to their physiological, behavioural, or cognitive reaction to stressors. The total mean value of depressed students' stressors level is higher (M = 71.52) compared to the total mean value (M = 68.54) of nondepressed students. The Cohen effect size value (d) suggested a smaller to medium significance between depressed and nondepressed students' stressors and reaction to stressors.International research has identified concern about the high levels of depression and anxiety among medical students attending either public or private medical schools. While some research indicates similarly high levels among students attending Malaysian public medical schools, little is known about the situation in Malaysian private medical schools. Given the recent considerable increase in the number of private medical schools in Malaysia, the current research investigated the prevalence of depression and anxiety in a Malaysian private medical school and examined relationships between gender, year of study, and depression and anxiety. In addition, this research has investigated differences between students with and without depression and anxiety regarding their stressors and reactions to stressors. Finally, stress as a predictor of depression and anxiety was examined.Prevalence of Depression and Anxiety. The prevalence of depression among the sample of medical students in the present study was 34.9%, a prevalence that is higher than was found in a study of medical students attending an American private university (19%) [ty 19%)  but lowety (19%)  and Pakity (19%) . Comparety (19%) , USA (15ty (19%) , Malaysity (19%) , UK (24%ty (19%) , and Ind%  but loThe prevalence of anxiety among students in the present study was 44%, a prevalence rate that lower than is found in private medical colleges in a number of countries, for example, India 56%)  and Paki6%  and PRelationships between Gender, Year of Study, and Depression and Anxiety. The results of this study show no significant relationship between gender and depression, a result also reported by a previous study [us study . The treus study .A significant relationship, however, was found in the present study between gender and anxiety where more females than males experienced anxiety. This result is in contrast to those reported for students in some other private medical schools , 13 but Regarding relationships between year of study stage of training , and depression and anxiety, results from the present study indicate no statistically significant relationships; however, a trend can be seen that suggests depression and anxiety increases as students progress through their medical training. The only exception to this trend occurs in the first year where the prevalence of depression is greater than in any other year. This exception may be due to a number of unique stressors facing first year students that relate to the transition from secondary school to university, homesickness, unfamiliarity with academic procedures and demands, time management, the process of making new friends, and increased expectations from family and faculty. The increase in the prevalence of both depression and anxiety as students progress through their programme is generally noted in the literature along with the identification of associated physical and mental health implications of anxiety and depression , 35, 36.Stressors and Reactions to Stressors. Significant differences were found in the present study between the types of stressors experienced by anxious and nonanxious students and depressed and nondepressed students. Anxious students experienced significantly more frustrations , pressures , and changes (rapid and too many occurring at the same time) than did nonanxious students. Their reactions to stressors were also significantly different. Anxious students reacted more physiologically  and emotionally  than did nonanxious students. These results are consistent with previous findings that indicate medical students who are anxious experience major stressors such as lack of concentration, heavy workload, many assignments, too many test activities, frequent strain, and inability to make decisions and inability to answer for lecturers [ecturers , 19, 37.In this study depressed students experienced significantly more stressors due to frustration and too many changes occurring at the same time than nondepressed students. In addition, depressed students exhibit more emotional reaction of stress than nondepressed students. This study did not find significant differences on conflict, pressure, and self-imposed stressors and physiological, behavioural, and cognitive reactions to stressors among depressed medical students. Previous research has, however, suggested that medical students who are depressed experience more affective, cognitive, and somatic problems . The resPrivate universities expect their students to be more competitive and, to achieve their targets, students have to work hard. Private university students exhibit more psychological problems and stress due to the new study environment, financial indebtedness, homesickness, and greater degree of workload with obligations to succeed. Due to the workload, they often experience a lack of sleep, leisure activities, and exercise. In addition, parents may pressurise their children to work hard as the tuition fees are higher at private universities  and highStress as a Predictor of Depression and Anxiety. Stressors were the significant predictor of anxiety and depression found in this study. Previous studies also found that examination criteria, too many test activities, lack of leisure time, work pressure, and overworkload are the major stressors for the development of anxiety and depression among medical students [students , 37.Universities and students counsellors are to focus on stressors such as frustration, pressure, and changes as these stressors are existing more among depressed students compared to nondepressed students. Identifying these stressors at the early stage in the medical programme will prevent students from suffering from depression and anxiety. Conducting stress management workshop, providing awareness about stressors, and availability of mental health services during orientation day will be beneficial for the students to have insight about stress, depression, and anxiety. Scholastic related workload in the field of medical education is unavoidable and universities have to teach time management and study management skills for the students to manage their work pressure and frustration.This study has a few limitations. One of the limitations is that this study did not focus on other causes for depression and anxiety such as personality, demographic information, and family status. Second limitation is that the findings of this study may not be generalized as the results are based upon one private university in Malaysia. Future study would focus on what coping skills used by students with depression and anxiety and without depression and anxiety are. In addition future study would be conducted on providing opportunity to participate in stress management and study skill management workshop or individual training in the first semester of the medical program prevent the anxiety and depression or not.This study concluded that female students experienced more anxiety compared to male students. Stressors were the predictor of depression and anxiety. Universities have to consider the stressors due to frustration, pressure, and conflict and their physiological and emotional reactions to stressors.


Both arms received approximately 750 min of intervention in a small-group setting over a 3–5 week period. Youth in the CBT/MT arm also logged the amount of out-of-session time spent practicing MT exercises. The Attention Network Test was used to index attentional task performance at baseline and 4 months post-baseline. Overall, task performance degraded over time in all participants. The magnitude of performance degradation was significantly less in the CBT/MT vs. control arm. Further, within the CBT/MT arm, performance degraded over time in those with no outside-of-class practice time, but remained stable over time in those who practiced mindfulness exercises outside of the session meetings. Thus, these findings suggest that sufficient CBT/MT practice may protect against functional attentional impairments associated with high-stress intervals.We investigated the impact of cognitive behavioral therapy and mindfulness training (CBT/MT) on attentional task performance in incarcerated adolescents. Attention is a cognitive system necessary for managing cognitive demands and regulating emotions. Yet persistent and intensive demands, such as those experienced during high-stress intervals like incarceration and the events leading to incarceration, may deplete attention resulting in cognitive failures, emotional disturbances, and impulsive behavior. We hypothesized that CBT/MT may mitigate these deleterious effects of high stress and protect against degradation in attention over the high-stress interval of incarceration. Using a quasi-experimental, group randomized controlled trial design, we randomly assigned dormitories of incarcerated youth, ages 16–18, to a CBT/MT intervention (youth  On any given day, over 100,000 youth are detained in prisons, jails, and juvenile detention centers across the United States . The overwhelming majority of these youth have experienced significant early psychosocial adversity which often has deleterious effects on brain development, particularly in areas of cognitive control responsible for regulating emotions  has garnered the greatest empirical support as a treatment modality for youth involved in the criminal justice system, although the effects of these interventions tend to be small to moderate in magnitude  resulted in improved attentional capabilities among adolescents incarcerated in a high-stress, high-security correctional facility in comparison to an active control intervention. Four main questions were examined: (1) Does performance degrade over time among detained youth, as might be expected given the stressful context? (2) If so, does CBT/MT protect against performance degradation? (3) If MT is helpful, is participation in intervention sessions alone sufficient or are there added benefits to engaging in MT practice outside of sessions? (4) Does being released from incarceration impact the magnitude of potential benefits in attentional performance that may come from CBT/MT?M = 17.4 years; SD = 0.71, range 16–18) from two buildings within a large urban correctional complex that houses mainly adults. Within each building, youth dormitories were assigned at random to receive either a CBT/MT intervention or an active control intervention. Only a subset of participants completed both pre- and post-training assessments (n = 201) due to the following reasons. As per the study protocol, participants (n = 24) who were transferred or released after the T1 assessment but before intervention activities began were not contacted for follow-up assessment. Participants (n = 28) who were later transferred to a facility where study activities were prohibited by correction officials could not be contacted for a follow-up assessment. In addition, 4 participants refused to complete the T2 assessment, 9 computer files were corrupted, and 1 participant was deported out of the country. Finally, an additional 10 participants were missing release dates, thus, a subset (n = 191) were included in the analyses. Between Time 1  and Time 2  testing sessions, 123 participants were not released and 68 participants were released. The mean length of stay was 106 days .We recruited 267 incarcerated male youth (age The majority of the participants (98%) were Black or Latino, reflecting the racial/ethnic composition of all youth in the facility. Self-report of offending behavior indicated that 74% of participants reported ever engaging in non-violent offenses  and over half (54%) of participants reported engaging in violent offenses . Official reports of offending behavior were not available from the correctional facility for the majority of youth.Power Source (PS) is a group-based cognitive-behavioral/mindfulness meditation intervention for youth involved in the criminal justice system  or an active control intervention (youth n = 117 in 3 dormitories). The four CBT/MT dormitories contained 20, 24, 33, and 70 participants, respectively. The three active control intervention dormitories contained 17, 26, and 74 participants, respectively.Between August 2009 and April 2011, we recruited 267 incarcerated male youth from a large, urban correctional complex populated primarily by adult prisoners. Two buildings in the complex that contained dormitories for youth, ages 16–18, were used for the current study. The first building (2 dormitories) housed sentenced youth who were serving short-term sentences (up to 12 months) while the second building (5 dormitories) housed youth who were awaiting trial or sentencing. Within each building youth dormitories were assigned at random to receive either the Power Source cognitive behavior/mindfulness training (CBT/MT) intervention , participants reported demographic and background including age, race/ethnicity, and offense history. The Self Report of Offending  for correct trials and accuracy (% correct) scores. In addition to overall task performance, the efficiency of each attentional network  was examined separately via paired RT subtractions across subsets of conditions. These analyses considered only trials on which the target was flanked by arrows, excluding the neutral target condition. Alerting was indexed by the difference between RTs on double-cue trials and no-cue trials (collapsed across target congruency condition). Orienting was indexed by the difference between RTs on spatial-cue trials and center-cue trials (collapsed across target congruency). Conflict monitoring was indexed by the difference between RTs on congruent target trials and those on incongruent target trials (collapsed across cue-type). The results of these paired subtractions will be referred to as subsystem scores. This method of analysis has been used extensively with the ANT and has been reported in detail elsewhere  to confirm basic task effects and compare effects to prior ANT findings in this age-range .t-tests were conducted to examine if there were group differences (CBT/MT vs. Control) at T1 on each system score .Independent t(197.58) = −0.57, p = 0.57] or % correct  scores. See Figure The contrast between the CBT/MT group and the control group revealed no significant difference between groups in either RT  and no significant differences in % correct difference scores . See Figure There was a marginally significant difference between CBT/MT and control groups in RT difference  or % correct difference  scores. See Figure The CBT/MT group and the control group did not differ in either RT difference  or RT .In addition to the system scores, experimental group and release status differences were examined via bivariate analyses. Treatment and control groups did not differ in overall % correct , no main effect of group , and no interaction of time by group .For Alerting, analyses of RT and % correct revealed no main effect of time , no main effect of group , and no interactions of time and group .For Orienting, analyses of RT and % correct revealed a main effect of time for RT only , a main effect of group for RT only , and no interaction of time and group . At post-intervention testing, there were no differences between the CBT/MT and Control groups on any of the attentional subsystem scores.Finally, for RT and % correct analyses of Conflict Monitoring, there was a main effect of time for RT only , no main effect of group , and a significant interaction of group by time . At T2, overall % correct was lower for both groups but higher for the CBT/MT than control group . Analyses of RT revealed a significant main effect of time  but no effect of group  and no interaction of group by time . See Figure To determine if overall % correct and RT were influenced by training group over time, repeated measures ANOVAs were conducted. Examination of % correct scores revealed a significant main effect of time , no main effect of group , and an interaction of time and group . At T2, ICV was lower (responses were more stable) in the CBT/MT group compared to the control group, . See Table To examine response variability, we conducted a repeated measures ANOVA with factors of time and intervention group. Results revealed a main effect of time  and RT  such that the CBT/MT participants who did not practice had the lowest accuracy and the fastest RT  when compared to the participants in the control group  and the CBT/MT participants who practiced .To examine the effect of those who engaged in out-of-session MT practice and those who did not, we looked at participants in the control arm  = 6.59, p < 0.01, partial η2 = 0.06], such that those who practiced had better accuracy (92%) at T2 than those who did not practice  and controls . No significant effects of group were observed for RT . See Figure Because of these baseline differences between the practice groups, two One-Way ANCOVAs were conducted with T1 overall % correct and T1 RT as covariates. There was a main effect of practice group for % correct , no main effect of practice , and an interaction of time and group . Post-hoc tests (LSD) showed significant differences between control participants and participants who practiced (p = 0.05) with lower variability in those who practiced  than in controls  at T2. See Figure We also examined the effects of out-of-session practice over time on the ICV coefficient. These analyses showed a main effect of time with higher variability at T2 , no main effect of group , and an interaction of time and group . For RT, there was a main effect of time , no main effect of group , and no interaction of time and group . These results directly parallel the intervention patterns found in the group as a whole.An ANOVA for % correct with time (T1 vs. T2) and intervention arm (CBT/MT vs. control) revealed a main effect of time , no effect of release group , no effect of training group , and no interaction of time, release group, and training group . For RT, there was a significant effect of time , no main effect of either release status or training group , and no interaction of time, release group, and training group .To determine if the release of participants from incarceration during the study period interacted with the intervention arm, an ANOVA was conducted with factors of time (T1 vs. T2), intervention arm (CBT/MT vs. control), and release group (not-released vs. released). For % correct, there was a significant effect of time  or T1 ICV , but there was an effect of facility on T1 overall RT . Sentenced participants were faster than those who were detained. Thus, for those measures in which T1 differences were present, ANCOVAs were conducted instead of ANOVAs to account for these baseline group differences. For those measures on which no T1 differences as a function of facility were present, ANOVAs were conducted.Detained youth (those awaiting sentencing) and sentenced youth were held in two different buildings. To determine whether these two groups differed at baseline, we completed three univariate ANOVAs with overall % correct, overall RT, and ICV. There was no effect of facility on T1 overall % correct , no main effect of group , a main effect of facility , a significant interaction of time and group , and no significant interaction of time, group, and facility . For ICV, there was a main effect of time , no main effect of group , a main effect of facility , a significant interaction of time and group , and no significant interaction of time, group, and facility . Finally, for overall RT, there was no main effect of facility  and no main effect of group . These patterns, namely the time by group interactions, are consistent with the intervention-related changes over time and demonstrate that facility or adjudication statuses compound the effects of the CBT/MT.Next, to examine the impact of facility over time on the control and CBT/MT arms, we conducted 2-by-2 ANOVAs for % correct and ICV and a 2-by-2 ANCOVA (with T1 RT as a covariate) for RT with facility (detained and sentenced) and group (CBT/MT and Control). For overall % correct, there was a main effect of time [k is the total number of clusters, and mi is the number of participants in each cluster. Because dormitory and treatment condition effects cannot be disentangled after treatment began, we only consider dorm effects at T1, before treatment.To test for possible effects due to individual dormitories, we calculated intra-class correlation coefficients (ICC) for overall performance and ANT system scores at T1 to investigate the variability within dormitories vs. between dormitories using the following formula which accounts for variable cluster sizes , with one ICC approximating a medium size ICC (0.10), as reported by  decision making are increasingly unavailable. As this depletion may result in a downward spiral for high-risk youth, it is important that we investigate viable intervention strategies for this group. Since earlier research demonstrated the beneficial effects of MT on cognitive task performance in adults  at T2 than those who did not practice or did not receive training. Finally, some members of the CBT/MT and control groups were released during the period of the project, but there was no significant effect of release on the pattern of results described above.This project investigated whether an intervention program designed to bolster core cognitive control and emotion-regulation resources in individuals experiencing a protracted period of high stress might curb the degree of degradation in cognitive control. Our findings support this; however, the mechanisms of action are yet unclear. It may have been that the CBT/MT group was more motivated to engage in the task, and this basic motivation (not MT-related improvements in present-moment focus) could have produced the group-wise effects. Motivation may differ in the groups due to the content differences between the CBT/MT and control intervention. The control intervention was a cognitive-perception intervention designed to raise awareness of expectancies related to violence and substance use. In contrast, the CBT/MT intervention included emotionally-laden exercises focused on exploring familial histories of maltreatment and offending, accepting responsibility for offending behavior, and taking the perspective of the victim in their offense. Exploring these painful topics with two trusting, empathic adults who were not correctional staff may have therapeutic value that increased motivation for the task. However, we did not find differences between the CBT/MT and the control group on other tasks requiring similar levels of motivation.Alternatively, performance in the CBT/MT group may have differed from the control group because of training in different attention skills. The CBT/MT intervention taught attention-related metacognitive skills such as reappraisal and identification of triggers for offending behavior. Training of these metacognitive skills involved repeated discussion and practice of specific ways of attending to affective, cognitive, and behavioral stimuli that place youth at risk for offending behavior. Training in mindfulness meditation involved engaging youth in discussions about their practice immediately after each sitting meditation, thereby cultivating meta-awareness of their process of attending, including when their attention wanders and their capacity to repeatedly return attention back to the breath. The intervention clinicians were experienced meditation practitioners who provided tips for maintaining engagement in meditation both in intervention sessions and outside of class. Thus, MT may have set the stage for the adoption of attention-related metacognitive skills through CBT training. Further studies should fractionate the differing possible contributions of motivation, specific intervention-related elements, and psychosocial support as well as include a direct comparison of CBT alone to the combined CBT/MT intervention with this population.A necessary practical constraint of the study is that the group randomization was carried out at the level of the dormitories. One concern is that individuals within the same cluster might be more similar to each other than to individuals from different clusters, which could undermine standard error of the ANOVAs. To investigate such cluster variability, intraclass correlations at T1 were conducted. ICCs for most of the variables of analysis were small, with one ICC approximating a medium size for Overall RT. However, there were no significant group effects or interaction between group and time for this variable. Moreover, a small ICC is reported for variables in which we report a significant effect. Thus, it is unlikely that the pattern of findings is a result of intraclass correlations at the level of the dormitory. Therefore, the findings from the current study are well-suited to provide estimates of effect sizes and T1 ICC estimates that are essential to planning a larger cluster-randomized study.for all trials relative to controls, and this deliberate and voluntary general attention to the task may have resulted in better performance in that group. Thus, further research is required to better understand the mechanisms by which this program was beneficial.Although our results are in line with the view that MT may be protective, there is a lack of specificity in effects on the subsystems of attention. Prior studies suggest that MT improves selective attention more so than receptive attention in novice practitioners (see Jha et al., Nonetheless, better performance in the training group suggests that offering this CBT/MT intervention to incarcerated youth may be beneficial, and the degree of benefit does not seem to differ based on release status over the course of the program. It is notable that the degradation in performance did not remit once youth were released, highlighting the long-lasting, harmful effects of incarceration on youth. If these results are due to improvements in present-moment focus tied to the MT practices that were engaged during the intervention, the benefits of training may go far beyond our laboratory measures of attentional task performance. Many studies have reported that greater attentional capacity and WMC are tied to improved rational decision making, better emotion-regulation, behavioral inhibition, reduced impulsivity, and better psychological health (see Schmeichel et al., In sum, the current study suggests that a multi-session CBT/MT intervention exerted a protective effect on offending youths' functional attentional impairments during incarceration in a high-security urban jail. Though performance on an attention control task degraded over time among youth in both the CBT/MT and control groups, the magnitude of performance degradation was significantly less in the CBT/MT condition. Moreover, within the CBT/MT group, performance degraded over time in those with no outside-of-session practice time, but remained stable over time among youth who practiced mindfulness outside of intervention sessions. Although this was a quasi-experimental study, to our knowledge, this is the first active-controlled study of the effects of CBT/MT for youth involved in the criminal justice system and it adds to the small but expanding body of research on the protective aspects of contemplative practice for youth in highly stressful situations.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Synechocystis sp. PCC 6803, requires SQDG for growth: its mutant (SD1) with the sqdB gene for SQDG synthesis disrupted can grow with external supplementation of SQDG. However, upon removal of SQDG from the medium, its growth is retarded, with a decrease in the cellular content of SQDG throughout cell division, and finally ceases. Concomitantly with the decrease in SQDG, the maximal activity of photosynthesis at high-light intensity is repressed by 40%.Sulfoquinovosyl diacylglycerol (SQDG) is present in the membranes of cyanobacteria and their postulated progeny, plastids, in plants. A cyanobacterium, Synechocystis with the use of SD1. SD1 cells defective in SQDG exhibited normal photosynthesis at low-light intensity as on culturing. Meanwhile, SD1 cells defective in SQDG were impaired in light-activated heterotrophic growth as well as in photoautotrophic growth. Flow cytometric analysis of the photoautotrophically growing cells gave similar cell size histograms for the wild type and SD1 supplemented with SQDG. However, the profile of SD1 defective in SQDG changed such that large part of the cell population was increased in size. Of particular interest was the microscopic observation that the mitotic index, i.e., population of dumbbell-like cells with a septum, increased from 14 to 29% in the SD1 culture without SQDG. Flow cytometric analysis also showed that the enlarged cells of SD1 defective in SQDG contained high levels of Chl, however, the DNA content was low.We investigated effects of SQDG-defect on physiological aspects in Our experiments strongly support the idea that photosynthesis is not the limiting factor for the growth of SD1 defective in SQDG, and that SQDG is responsible for some physiologically fundamental process common to both photoautotrophic and light-activated heterotrophic growth. Our findings suggest that the SQDG-defect allows construction of the photosynthetic machinery at an elevated level for an increase in cell mass, but represses DNA synthesis. SQDG may be essential for normal replication of chromosomal DNA for completion of the cell cycle. Synechocystis sp. PCC 6803, and a green alga, Chlamydomonas reinhardtii, through characterization of their respective mutants deficient in SQDG synthesis [Thermosynechococcus elongatus, observed on X-ray analysis of the complex [Synechococcus sp. PCC 7942, and a seed plant, Arabidopsis thaliana, showed no remarkable impairment in photosynthesis when the genes for SQDG synthesis were disrupted [C. reinhardtii, which degrades SQDG to ensure a major internal sulfur-source for protein synthesis [C. reinhardtii to maintain SQDG at 5% of the initial level throughout the period of sulfur-starvation [A. thaliana does not positively degrade SQDG when exposed to S-deficient stress [Synechocystis, but not for that of either Synechococcus, C. reinhardtii or A. thaliana [The membranes of cyanobacteria and plastids in plants are similar to include three glycolipids, monogalactosyl diacylglycerol (MGDG), digalactosyl diacylglycerol (DGDG) and SQDG, and a phospholipid, phosphatidylglycerol (PG) . We haveynthesis ,3. First complex . In contisrupted ,6. Distit stress . Of partthaliana .A. thaliana showed almost complete loss of both MGDG and DGDG, disruption of photosynthetic membranes, and impairment in photoautotrophic growth of the seedling [Synechocystis mutants with the genes for PG synthesis disrupted were lethal, and required supplementation of PG in the culture for the growth [A. thaliana was also shown for growth, through characterization of the mutants defective in the genes for PG synthesis [Indispensability of galactolipids or PG for normal growth has been reported in some photosynthetic organisms. A knockout mutant of MGDG synthase 1 isolated from seedling . These lseedling . Synechoe growth ,13. PG we growth ,15. Indiynthesis ,17. PG synthesis ,17. AlthSynechocystis, by examining the effects of the SQDG-defect on photosynthesis and respiration on culturing, and also on cell size, and the Chl and DNA contents of individual cells with use of a flow cytometer. From the results obtained, we proposed the importance of SQDG for DNA synthesis and eventually for completion of the cell cycle.We here explored the role of SQDG that is essential for growth of 2·s) for 5 min each day [Synechocystis sp. PCC 6803 was sonicated in the growth medium for liposome production (5 mM) and filtered for sterilization. The amount of SQDG that was added to the culture medium was 20 μM. Growth of the cells was monitored by measurement of the optical density at 730 nm.Wild type of Synechocystis sp. PCC 6803 and its mutant, SD1, deficient in SQDG synthesis were grown photoautotrophically in BG-11 medium, as we previously reported [each day . SQDG pr2 evolution (net photosynthesis) or respiratory O2 uptake was measured at 30°C for intact cells equivalent to 2.5 μg Chl·ml-1 in the culture medium containing 10 mM NaHCO3, with the use of a Clark-type oxygen electrode , according to [Photosynthetic Ohttp://rsb.info.nih.gov/ij/) was used for size analysis of cells in the images.Cells were observed under a Nomarski microscope  equipped with an image-capture camera . ImageJ software version 1.44 provided with NIH  was performed for determination of the cell size, chlorophyll autofluorescence intensity and DNA content. Forward scattering (FSC), side scattering (SSC), and fluorescence (FL) were sdetected by irradiation with a laser at 488 nm. FSC and SSC reflect the size and complexity of a cell, respectively. FL was detected as FL1 (530 ± 15 nm), FL2 (585 ± 21 nm), and FL3 (over 650 nm). FL3 reflects chlorophyll autofluorescence. As size standards, polystyrene beads  were used. For determination of the DNA content, collected cells were fixed with 100% MeOH, passed through a 25 gauge syringe (in order to prevent aggregation), and then washed twice with phosphate-buffered saline. The suspension of fixed cells was treated with RNase  for more than 30 min at room temperature, and thereafter stained with 50 μg/ml  propidium iodide (PI) [http://facs.scripps.edu/software.html, The Scripps Research Institute, CA, USA) for 2D color density plot analyses.ide (PI) . FL2 wasSynechocystis, which has the sqdB gene for SQDG synthesis disrupted, requires external supplementation of SQDG for its growth. However, even after a shift to SQDG-free medium, the SD1 cells that had incorporated SQDG in advance could continue to grow for four days although at a much reduced rate. Eventually, the OD730 value became double the initial level , which we adopted for the culturing, the photosynthetic activity was similar for SQDG-replete and -defective SD1 cells. Moreover, the rates of respiration in the dark were also indistinguishable (0 μEin/m2·s). These results implied that the SQDG-defective mutation has no deleterious effect on the functioning of the photosynthetic machinery under low-light conditions or the respiratory machinery, and that the arrest of the cell growth is therefore not due to a defect in energy production or carbon fixation.The SD1 mutant of l Figure . The maxSynechocystis exhibits light-activated heterotrophic growth (LAHG), i.e., the cells grow heterotrophically in a BG11 medium supplemented with 10 mM glucose, when illuminated with a light-pulse for 5 min each day. We then examined whether or not SQDG is required for LAHG  was subjected to microscopic analysis. No obvious effect was found on the cell morphology  thus seemed to synthesize DNA during the whole cell cycle, as in the case of vegetative cells of Anabaena sp. PCC 7120 [Figure PCC 7120 . The cytoriC and multimerizes in an ATP-bound form, thereby opening up the duplex DNA so that other proteins can enter [Escherichia coli, acidic phospholipids in lipid bilayers promote the exchange of ADP for ATP in DnaA for generation of replicatively active ATP-DnaA [Synechocystis.We here reported two novel discoveries concerning the roles of SQDG. One is the responsibility of SQDG for DNA synthesis. In bacteria, initiation of chromosomal DNA replication depends on the DnaA protein, which binds to an enter . In EschATP-DnaA ,25. One sn-1 position in Synechocystis [Synehocystis, no matter whether its action is direct or indirect.In contrast, acidic phospholipids also have an inhibitory effect on DnaA-binding, which was proposed to prevent re-initiation of DNA replication until the proper time, and cardiolipin or synthetic PG with unsaturated fatty acids is effective . SD1 celSynechocystis. A future work will be to specify whether it is a defect in SQDG or a concomitant increase in PG that impairs DNA synthesis and cell division. One possible strategy to answer this question may be to investigate how the DNA content and cell cycle progression are affected in SD1 when the unsaturation level of PG is lowered by disruption of the desaturase genes [The other novel discovery is the responsibility of SQDG for the progression of cell division, which we judged from the increase in cell size as well as in the mitotic index, in SD1 cells defective in SQDG. Evidence has accumulated for bacteria that membrane lipids participate in cell division by interacting with proteins in the cell division machinery such as MinD . Apart fse genes .E. coli, in which phospholipids predominate. The characteristic lipid composition of the oxygenic photosynthetic organisms seems reasonable in view of their physiological and ecological aspects, i.e. their ability to ensure sugar by photosynthesis and limited availability of phosphorus-source in their environment. The regulatory mechanism for the cell cycle might have evolved to show quite a novel dependency on SQDG in Synechocystis, in accordance with its unique lipid composition.The membrane lipids of cyanobacteria, similar to plastids, are composed mainly of three glycolipids and PG , thus beGloeobacter violaceus PCC 7421, which is regarded as the most primitive type of cyanobacterium, does not contain SQDG, cyanobacteria seem to have acquired an SQDG synthetic system through evolution within cyanobacteria [Synechocystis postulated above do not hold true for Synechococcus, and thus at least two mechanisms for progression of the cell cycle might have evolved in cyanobacteria, with respect to the dependency on SQDG. Our novel discovery of involvement of SQDG in progression of the cell cycle in Synechocystis will greatly facilitate understanding of how the regulatory mechanism for the cell cycle has developed, especially, how SQDG has been integrated into it, through evolution within cyanobacteria, and how the cell-cycle dependency on SQDG has been attained through the evolution of cyanobacteria into plastids in plants. Moreover, our study will provide an opportunity for researchers to examine whether or not membrane lipids other than SQDG participate in cell cycle progression of cyanobacteria.Since Synechocystis for physiologically fundamental process that is not dependent on photosynthesis, but is common to both photoautotrophic and light-activated heterotrophic growth. On the basis of our flow-cytometric analysis, it is tempting to speculate that SQDG is involved in DNA replication, and eventually, in cell cycle progression. However, it will be necessary to unravel the elementary process during DNA replication, in which SQDG participates, and the corresponding protein(s) SQDG interact with, in order to specify the role of SQDG at the molecular level.We have found strong supportive evidence for responsibility of SQDG in The authors declare that they have no competing interests.MA measured photosynthesis and respiration, and carried out microscopic and flow-cytometric analysis. MA, MT, and NS conceived of the study, participated in its design, and drafted the manuscript. All authors read and approved the final manuscript.


Escherichia coli, is a widely documented sanitary problem. The objective of the present study was to isolate E. coli strains from the gills, muscle, and body surface of farmed Nile tilapias (Oreochromis niloticus) fresh-marketed in supermarkets in Fortaleza , to determine their susceptibility to antibiotics of different families , and to determine the nature of resistance by plasmid curing. Forty-four strains  were isolated, all of which were susceptible to amikacin, aztreonam, cefotaxime, ciprofloxacin, gentamicin, and imipenem. Gill and body surface samples yielded 11 isolates resistant to ampicillin, tetracycline, and sulfametoxazol-trimetoprim, 4 of which of plasmidial nature. The multiple antibiotic resistance index was higher for strains isolated from body surface than from gills. The overall high antibiotic susceptibility of E. coli strains isolated from fresh-marketed tilapia was satisfactory, although the occasional finding of plasmidial resistance points to the need for close microbiological surveillance of the farming, handling, and marketing conditions of aquaculture products.The contamination of seafood by bacteria of fecal origin, especially  Escherichia coli is widely used as indicator of the bacteriological condition of food and environments due to its almost exclusively fecal origin [ E. coli in fresh-marketed seafood indicates recent contamination and is usually attributed to infected handlers or storage on contaminated ice [The bacteriuml origin . The preated ice .The intensification of production and the consequent increase in stocking density have made fish farming more vulnerable to disease , 4. The  E. coli in farmed fish, but resistant strains may be selected due to the presence of antibiotics in the culture environment, leading to the dissemination by mobile genetic elements of resistance to potentially pathogenic bacteria [In fact, bacterial strains resistant to different families of antibiotics have been isolated from environmental samples by a number of researchers –10. Foodbacteria –17. E. coli in fresh-marketed Nile tilapia obtained from supermarkets in Fortaleza, Brazil, (b) establish the antibiotic susceptibility profile of E. coli strains isolated from Nile tilapia samples, (c) determine whether resistance was potentially chromosomal or plasmidial, and (d) determine the multiple antibiotic resistance index of strains isolated from gills, muscle, and body surface.Due to the importance of tilapia farming in Northeastern Brazil, the aim of the present study was to (a) investigate the presence ofOreochromis niloticus) were collected from twelve supermarkets in Fortaleza . The specimens were wrapped individually in plastic film and transported in ice-cooled isothermal boxes to the Laboratory of Seafood and Environmental Microbiology of the Marine Sciences Institute  for immediate bacteriological analysis.Thirty-six specimens of Nile tilapia . The samples were then placed in a bacteriological incubator at 35°C for 48 hours. Aliquots from positive LST tubes were seeded in 4 mL tubes containing EC broth and incubated in a water bath at 45°C for 48 hours. E. coli was isolated using eosin-methylene blue agar plates (Difco), from which 3–5 colonies suspected of E. coli were selectedand submitted to IMViC testing. Colonies were considered to be E. coli when positive in the indole and methyl-red test, negative in the Voges-Proskauer and citrate test, and Gram-negative with short rods in the Gram staining test [Theociation . Presumping test . E. coli ATCC 25922 was used as positive control [8 CFU·mL−1 (CLSI 2010). The susceptibility of the E. coli strains was tested in relation to several families of antibiotics, including the aminoglycoside family: amikacin  and gentamicin ; the carbapenem family: imipenem ; the cephalosporin family: cephalothin  and cefotaxime ; the fluoroquinolone family: ciprofloxacin ; the monobactam family: aztreonam ; the penicillin family: ampicillin ; the quinolone family: nalidixic acid ; the sulfonamide family: sulfametoxazol-trimetoprim ; and the tetracycline family: tetracycline . Using sterile tweezers, commercially available antibiotic disks (Laborclin) were placed individually on the surface of Mueller-Hinton agar. After 24 hours of incubation at 35°C, the strains were scored as “susceptible,” “intermediate,” or “resistant” to each antibiotic based on the measurement of the inhibition halo, as recommended by CLSI [The antibiogram was done with the disk diffusion method  using Mu control . InitialL−1 CLSI 10. The s E. coli strains from each type of tissue sampled  using the formula a/(b · c), where a is the total resistance score of the strains, b is the total number of families of antibiotics tested, and c is the number of strains from each type of tissue sampled [The multiple antibiotic resistance (MAR) index was determined for the total number of sampled . E. coli strains were submitted to plasmid curing using acridine orange dye at 100 μg·mL−1 (Sigma). Following exposure to the mutagen, the strains were rechallenged with the antibiotics to which they were initially resistant [Resistantesistant . E. coli, 25 (56.82%) of which were isolated from gills, 15 (34.09%) from the body surface, and 4 (9.09%) from muscle. Eleven E. coli strains isolated from gills and body surface were resistant to AMP, SUT, and TC, especially to last of these . On the other hand, strains isolated from muscle samples were susceptible to all the antibiotics tested , resistance to AMP (n = 4), resistance to SUT+TC (n = 1), and resistance to AMP+SUT+TC (n = 1) (Four resistance profiles were observed in this study: resistance to TC ( (n = 1) . The pro E. coli in the gut of farmed Nile tilapia, the isolated strains were susceptible to AMI, ATM, CTX, and GEN, matching our own findings and supporting the notion that these antibiotics are little used in aquaculture. E. coli strains resistant to AMP, SUT, and TC were also reported by Ryu et al. [ E. coli to TC were found by Wang et al. [ E. coli strains resistant to AMP, SUT, and TC revealed resistance to be plasmid-mediated in 4 cases and potentially chromosomal in 7.In a study by Jiao et al.  on the ou et al. . Likewisg et al. . The autAccording to Lamshöft et al. , sulfonaThe presence of mobile genetic elements of resistance, especially plasmids and integrons, poses a risk to public health, as evidenced by Koo and Woo . Not sur E. coli strains isolated from fresh-marketed Nile tilapia was satisfactory, although the occasional finding of plasmid-mediated resistance points to the need for close microbiological surveillance of the farming, handling, and marketing conditions of aquaculture products. Nevertheless, it is necessary to note the origin of marketed fish in order to evaluate the potential risk to the consumer.The overall high antibiotic susceptibility of


To understand the genetic architecture of complex traits and bridge the genotype-phenotype gap, it is useful to study intermediate -omics data, e.g. the transcriptome. The present study introduces a method for simultaneous quantification of the contributions from single nucleotide polymorphisms (SNPs) and transcript abundances in explaining phenotypic variance, using Bayesian whole-omics models. Bayesian mixed models and variable selection models were used and, based on parameter samples from the model posterior distributions, explained variances were further partitioned at the level of chromosomes and genome segments.2 population of 440 mice. The genomic variation was covered by 1806 tag SNPs, and transcript abundances were available from 23,698 probes measured in the liver. Explained variances were computed for models using pedigree, SNPs, transcripts, and combinations of these. Comparison of these models showed that for BW, a large part of the variation explained by SNPs could be covered by the liver transcript abundances; this was less true for FI and FE. For BW, the main quantitative trait loci (QTLs) are found on chromosomes 1, 2, 9, 10, and 11, and the QTLs on 1, 9, and 10 appear to be expression Quantitative Trait Locus (eQTLs) affecting gene expression in the liver. Chromosome 9 is the case of an apparent eQTL, showing that genomic variance disappears, and that a tri-modal distribution of genomic values collapses, when gene expressions are added to the model.We analyzed three growth-related traits: Body Weight (BW), Feed Intake (FI), and Feed Efficiency (FE), in an FWith increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. Partitioning of explained variances at the chromosome and genome-segment level clearly separated regulatory and structural genomic variation as the areas where SNP effects disappeared/remained after adding transcripts to the model. The models that include transcripts explained more phenotypic variance and were better at predicting phenotypes than a model using SNPs alone. The predictions from these Bayesian models are generally unbiased, validating the estimates of explained variances. Large amounts of genomic information generated from Single Nucleotide Polymorphism (SNP) microarrays have become available in recent years for many species-3. This However, in the eQTL approach, associations between SNPs, transcript level, and phenotypes are analyzed individually. This is likely to lead to “missing heritability”, becauseThe choice of Bayesian variable selection (BVS) models was due to its features to separate markers with large/moderate or small effects, and to locate the important regions in the genome or transcriptome which serves a better QTL mapping method because it produces clearer signals for QTL. FurtherThe aim of this study was to explore the contributions of various sources of variation, such as population structure, SNP variants, and gene expression levels, to a set of growth related traits  in mice. These traits are very important, both in terms of agricultural production and for obesity in humans. Bayesian mixed models and Bayesian variable selection models were applied to model pedigree, SNPs and/or gene expressions and to derive explained variances for these components. In addition, they were used to partition of SNPs and gene expression by chromosome and genome sections. To validate the estimates of explained variances, the predictive ability of these models was studied using cross validation.2 population of 440 mice was available with complete records for body weight at 8 weeks (BW) and 337 records for feed intake (FI) and feed efficiency (FE), measured during the period 3 weeks to 8 weeks[0 founder lines. Data was obtained in three batches and the sex of the animals was recorded. At the end of the experiment, the mice were sacrificed and liver tissue was extracted for genome-wide expression profiling. RNA isolation, cDNA synthesis, array hybridization, normalization of probe level intensity, and annotation of data were performed as described in detail by[2 population. Density functions of phenotypes are available in Additional filehttp://gbi.agrsci.dk/~pso/BIG_genome_transcriptome/).An M16 × ICR F 8 weeks. An addietail by. Genotypy  by an intercept μ, environmental effects of batch and sex b, a polygenic effect based on pedigree u, regressions on SNP covariates a, regressions on gene expression covariates g, and a model residual e, as:The most complete model used describes phenotypes X is the design matrix for batch and sex effects, Z is a design matrix that links polygenic effects to the observed records, W is a matrix with 1806 SNP covariates, and Q is a matrix with 23,698 gene expression covariates. The SNP and gene expression covariates were centered and scaled to unit variance.where u, a, g, and e in (1), i.e.,A is the numerator relationship matrix based on pedigree information,b, are assigned flat prior distributions, which is the Bayesian analog of fitting “fixed effects” (unshrunken) estimates. A Markov chain Monte Carlo (MCMC) algorithm was applied in the software bayz[u, a and g, requiring a correlation >0.999 from repeated MCMC runs, and by computing the effective sample sizes for the variance components using the R Coda package[Based on work of-22, the are bayz to obtaiview see. The Mon package.y from (1) is var(Zu) + var(Wa) + var(Qg) + var(e). To obtain posterior means (PMs) and posterior standard deviations (PSDs) on the explained variances for SNPs and gene expressions, var(Wa) and var(Qg) were evaluated based on the posterior samples for a and g from the MCMC, i.e., as the PM and PSD of var(Wat) values over MCMC cycles, where at is the posterior sample for a from MCMC cycle t. This procedure is not required for the polygenic variance, because Z is a design matrix, unlike W and Q, which are covariate matrices.The explained variance in a and g as follows:The second model used was a Bayesian variable selection model, where the approach of George and McCulloch was folla,g, anda and g, respectively, indicating whether the ith element in a or g, respectively, comes from the distribution with large or small variance. The varianceswhereribed in. This moribed in, and thea and g in the variable selection model, explained variances were computed and partitioned by chromosome and by genome section. The variable selection model is more suited to make such a partitioning, because unlike the mixed model version, it allows for different variance contributions per SNP. The explained variances were evaluated in the same way as for the mixed model, by evaluating var(Wat) and var(Qgt) over MCMC cycles t, except that the a and g samples are obtained under the mixture model prior assumptions. The same expressions can be straightforwardly evaluated for parts of the SNPs or gene expressions to obtain explained variances per chromosome and for small windows of SNPs within chromosomes. Variance within a chromosome was computed using a 5-SNP sliding window to obtain a genomic variance profile.From the posterior samples for 2 between adjacent SNPs was 0.55, and average R2 between SNPs two apart was 0.39, which we considered sufficiently high to warrant computation of variances in a 5-SNP window. To study the relative importance of family structure, SNPs, and gene expressions, six sub models and the complete model (1) were used. These were models that use only pedigree information (PED), only SNP data (SNP), only gene expression data (GEX), SNP + GEX, PED + GEX, PED + SNP, and the complete model PED + SNP + GEX. These models always included sex and batch effects.It is difficult to choose an optimal windows size as it depends on extend of LD, marker density and an arbitrary cut-off for what is considered important LD. In the data analyzed here, average Rth fold. All predictions from the 11-fold cross validation were collected to compute correlations between predicted and actual phenotypes, and regressions of predicted phenotypes on actual phenotypes, using the whole data set. The slope of the regression lines of predicted phenotypes on actual phenotypes are expected to be 1 if the model produces unbiased predictions, which would validate the estimates of explained variances. The University of Nebraska Institutional Animal Care and Use Committee approved all procedures and protocols.The predictive ability of the models was evaluated using an 11-fold cross-validation. For body weight, 440 records were divided randomly in 11 groups, each with 40 individuals. Feed intake and feed efficiency, with 337 records in total, were randomly divided in 10 groups of 30 records and one group of 37 records. The complete model, including all variance parameters, was re-estimated on each set of 10 folds and predictions were computed for the phenotypes in the remaining 11Table Overall, explained variances increase by adding gene expression information , i.e., in the most complete model (PED + SNP + GEX) explained variances were 88%, 75%, and 71% for BW, FI, and FE respectively. This confirms the assumption that gene expressions can explain a larger part of phenotypic variance than genetic or genomic information, by capturing environmental, and possibly non-additive, genetic effects through the gene expressions. InformaThis model shows that, for these traits, the liver transcriptome contributes a larger portion of explained variance. This is most pronounced for BW, with 18% of explained variance from the genome and 63% from the liver transcriptome. Thus, in this case, the predominant model is that SNPs regulate gene expressions to exert their effect on the phenotype.FigureThis method/approach is suitable for gene-level resolution. However, gene-level resolution is highly data dependent, i.e. it requires high marker density and a study population with LD blocks that span small genomic regions. In this work we have used F2 crosses from outbred lines, which has large LD blocks and this kind of data has limited resolution for fine-mapping of QTL.One may argue that the most complete model is more interesting to investigate genetic architecture and chromosomal/sub-chromosomal variance but as we have shown SNPs and pedigree are largely confounded and they explain about the same variance. This confounded explained variance is getting worse in the case that both Pedigree and SNPs are in one model (PED + SNP model) which is shown in higher confidence intervals of explained variance by pedigree. The model with only omics information (SNP + GEX) is therefore simpler, more accurate and as effective as the model that also uses pedigree information. This is interesting for future applications of omics technologies, because we expect that pedigree information often will be absent.Figures The prediction of phenotypes from these models, using cross-validation, is shown in Table With increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. We have developed a complementary approach to the univariate “eQTL” mapping, by considering Bayesian models that fit all genome-wide SNPs and transcript abundances in one model, and that estimate and partition explained variances by chromosome and genome segments. Our results show that, using gene expressions, more of the phenotypic variance can be explained and phenotypes can be better predicted. Predictions were also shown to be unbiased, which validates the assessed explained variances. The improvement of phenotype predictions using gene expression data will be useful for several applications in agriculture and medicine, although it should be assessed on a case-by-case basis as to whether a suitable tissue can be sampled for the gene expression measurements. Partitioning of the explained genomic variance at the level of chromosomes and genome segments showed clear examples of eQTL locations as regions where genomic variance disappears when gene expressions are added to the model. Our study used only gene expressions from the liver, and an obvious further extension is to include expressions from other tissues. The QTLs that did not disappear when transcripts are added to the model may be eQTLs that affect gene expression in a tissue other than liver. The Bayesian model is quite efficient for handling large sets of covariates, and extensions to include multiple sets of expressions will be feasible. We have not provided formal statistical tests in this model, but the Bayesian approach lends itself naturally to obtaining confidence intervals for (differences between) parameter estimates. The estimates of total explained variances from the Bayesian mixed model can also be obtained by a residual maximum likelihood (REML) approach. We verified this, and the Bayesian and REML estimates generally agree. However, using REML it is not feasible to utilize mixture priors to better discriminate between SNPs which contribute more or less variance, and to partition the variances at the sub-chromosome level, which is all straightforward in a Bayesian approach.Our approach can easily allow up scaling to higher-density arrays, even to whole-genome sequence data with the variance components analysis as it was for gene expression probes in this study.BW: Body Weight; FI: Feed Intake; FE: Feed Efficiency; SNPs: Single Nucleotide Polymorphisms; REML: Restricted maximum Likelihood; QTL: Quantitative trait loci; eQTL: Expression Quantitative trait loci.The authors declare that they have no competing interests.AE developed the data analysis pipeline, performed statistical analyses, interpreted the results and wrote the manuscript. PS and LJ were involved in project design, statistical analyses, interpretation of results and manuscript editing. DP and MA prepared the data for the analysis. All authors have read and approved the final manuscript.Figure S3. Distribution of phenotypes of traits Body Weight including 440 animals, Feed Intake and Feed Efficiency including 337 animals each.Click here for fileFigure S1. Decomposition of the proportion of variance explained by SNPs at the level of chromosomes and individual SNPs in two models: the independent model SNP and the conditional model SNP+GEX for Feed Intake. (a) explained variances from SNPs in SNP model (black) and SNP+GEX model (white) in each chromosome. (b) explained variance by individual SNPs in SNP model and (c) SNP+GEX model.Click here for fileFigure S2. Decomposition of the proportion of variance explained by SNPs at the level of chromosomes and individual SNPs in two models: the independent model SNP and the conditional model SNP+GEX for Feed Efficiency. (a) explained variances from SNPs in SNP model (black) and SNP+GEX model (white) in each chromosome. (b) explained variance by individual SNPs in SNP model and (c) SNP+GEX model.Click here for fileFigure S4. Comparison of predicted breeding values versus phenotypes in the models using pedigree information only (PED), SNPs information only (SNP) and gene expression information only (GEX) for three traits Body Weight, Feed Intake and Feed Efficiency according to correlation shown in TableClick here for file


Objective. We aimed to examine the independent contributions of physical health and cognitive function to disability among Chinese older adults living in two Asian metropolises and explore the potential influences of environment. Design and Participants. Cross-sectional analysis based on data from two population-based studies: the Shanghai Survey of Alzheimer's Disease and Dementia (n = 4639) and the Singapore Longitudinal Ageing Study (n = 2397). Disability was defined as needing help in at least one activity of daily living. Results. The prevalence of functional disability was higher in Shanghai sample (5%) than that in Singapore sample (1.8%). Number of chronic diseases, self-rated health status, cognitive function , and environment (Singapore versus Shanghai) significantly contributed to functional disability independent of each other. The adjusted Odds Ratio was 1.35 (95%CI 1.22–1.50), 2.85 (95% CI 2.36–3.43), 0.89 (95% CI 0.85–0.94), and 0.68 (95% CI 0.48–0.96), respectively. The strength of associations between health variables and disability appeared to be influenced by environment. Conclusion. Physical health and cognitive function independently contributed to functional disability. The associations are modulated by environmental factors. Disability prevalence increases with advanced age , and disThere is a big gap between the scale of the problem and the quantity and quality of available information. The first population-based report from China was published 16 years ago , and couIn this study, with large population-based data from Shanghai (the biggest city in China) and Singapore , we aimed to examine the independent contributions of physical and cognitive health to disability in Chinese older adults and explore the potential influences of environment.Participants of the present study were identified from two big population-based studies: the Shanghai Survey of Alzheimer's Disease and Dementia (SSADD) and the Singapore Longitudinal Ageing Study (SLAS). The SSADD was conducted in 1987 based on a probability sample of 5055 community-dwelling Chinese older adults from Jing'an district of Shanghai city. The SLAS was conducted among a total population sample of 2808 older adults from South East Region of Singapore. Baseline assessments of the SLAS were completed from September 2003 to December 2005. Details of the SSADD  and SLASFor the present analysis, we selected 4639 participants from the SSADD sample and 2397 participants from the SLAS cohort. All participants had complete data on demographic information, physical health, and cognitive function and obtained a Mini-Mental State Examination (MMSE) total score of at least 21 points. Face-to-face interviews were conducted by psychiatrists/psychiatric nurses (in Shanghai) or trained research nurses (in Singapore), and data were collected for a wide range of variables. For the present analysis, we extracted the following variables from the databases: age, sex, functional status, chronic diseases, self-rated health status, and MMSE total score. Functional status was assessed by the participant's level of dependency in performing 8 Activities of Daily Living (ADL): eating, grooming, dressing, transferring, walking, toileting, bathing, and climbing stairs. Disability was defined as needing help in at least one ADL.A list of 32 medical conditions was included in the SSADD interview schedule. The participants were asked “Do you have or not have any of the following illnesses or conditions at the present time?" In the SLAS, a list of 14 medical conditions was covered in the interview. The participants were asked “Do you have a history of this medical problem?” Medical conditions that were not included in the list were recorded under “any other problems.” We selected ten chronic diseases on which data were available from both samples: hypertension, diabetes, heart diseases , stroke (in Shanghai: effects of stroke), kidney disorder (in Singapore: kidney failure), chronic obstructive lung disease (in Shanghai: emphysema/bronchitis), asthma, arthritis (in Shanghai: arthritis or rheumatism), mental illness, and cancer . In statistical analysis, the number of chronic diseases was used as an objective measure of physical health. In Singapore, self-rated health status was assessed using a single question: “In general, would you say your health is: excellent, very good, good, fair, or poor?” In Shanghai, the same question was asked but there were four choices: excellent, good, fair, poor. We grouped “excellent” and “very good” together and created a new variable “self-rated health status”: 1 = excellent or very good, 2 = good, 3 = fair, and 4 = poor. This was used as a continuous variable in multivariate analyses . Cognitive function was assessed by Mini-Mental State Examination (MMSE) test . The test test (for continuous variables) was used to compare the difference regarding various characteristics between the two study samples. Multiple logistic regressions were used to examine the relationship between health factors and disability, and the influence from environment. In analysis based on the combined sample, age, sex, number of chronic diseases, self-rated health status, MMSE total score, and environment (Singapore versus Shanghai) were included in the regression model. The same set of variables (except environment) was used in the stratified analysis. All analyses were performed using SPSS version 18.0.Chi-square test (for dichotomous variables) or independent sample In multiple logistic model based on the combined sample, number of chronic diseases, self-rated health status, MMSE total score, and environment were significantly associated with disability . The OddResults from stratified analysis showed that the strength of associations between health variables and disability differed between the two samples . For exaBased on data from two population-based studies, we found that the number of chronic diseases, self-rated health status, cognitive function, and unmeasured environment factors represented by study sample were significantly associated with functional disability among Chinese older adults.The contributions of physical health , 18 and We found that environment was significantly associated with disability. Participants in the Singapore sample were 32% less likely to have disability compared with participants in the Shanghai sample. Participants from both studies were Chinese older adults living in big city, and a number of important covariates were adjusted in multiple regression models. It is less likely that the observed difference was caused by unmeasured factors at individual level. We did not collect data on objective indicators of environmental factors. However, a careful examination of the two most difficult ADL tasks (bathing and climbing stairs) provided us with plausible explanations. In 1987, most families in Shanghai had no bathroom and shower, and bathing was considered as a complicated task [Our study provided fresh evidence on the role of environmental factors in disablement process –23. As pStrengths of our study include concurrent measuring of various health factors and simultaneous inclusion of those factors in multivariate model. This made a cross-comparison of the strength of independent associations possible. However, no causal relationship could be drawn given cross-sectional nature of the study design. In conclusion, physical health and cognitive function were significantly associated with disability among Chinese older adults living in Asian metropolises. The association could be influenced by environment. A comprehensive approach should be adopted in disability prevention.There are neither financial nor dual commitments that represent potential conflict of interests.All the authors contributed substantially to the design, analysis and interpretation of the data and participated in drafting or revising the paper.


In clinical settings, it is important for health care providers to measure different aspects of functioning in older adults with joint pain and comorbidity. Besides the use of distinct measures, it could also be attractive to have one general measure of functioning that incorporates several distinct measures, but provides one summary score to quantify overall level of functioning, for example for the identification of older adults at risk of poor functional outcome. Therefore, we selected four measures of functioning: Physical Functioning (PF), Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL) and participation, and tested the possibility to aggregate these measures into one general measure of functioning.A prospective cohort study of older adults (≥65 years) with joint pain and comorbidity provided baseline data (n = 407) consisting of PF , ADL , IADL  and participation . We tested two models with confirmatory factor analysis: first, a bifactor model with all four measures and second, a bifactor model with PF, ADL and IADL and a correlated but distinct subgroup factor for participation. Several model fit indexes and reliability coefficients, such as explained common variance (ECV) and omegas were computed for both models.ECV = 0.67, omega-t = 0.94, low omega-s = 0.18-0.22 on the subgroup factors and high omega of 0.82 on participation, which all were in favour of the second model.The first model fitted the data well, but the reliability analysis indicated multidimensionality and unique information in the subgroup factor participation. The second model showed similar model fits, but better reliability; The results indicate that PF, ADL and IADL could be aggregated into one general measure of functioning, whereas participation should be considered as a distinct measure. Joint pain is a common symptom in primary care, which often contributes to impaired functioning, especially among older adults ,2. In olactivities and participation capture levels of functioning at an individual and societal level, respectively. In clinical settings, it is important for health care providers to measure aspects of functioning that are incorporated in these two ICF domains, as this contributes to optimal management and treatment of joint pain and comorbidity [The International Classification of Functioning (ICF) model provides a framework to describe normal and abnormal functioning . The domorbidity . For theactivities and participation domains for assessment, because of their interrelatedness [activities and participation can be aggregated. However, other studies found impairments, activity limitations and participation restrictions to be only moderately related; these studies reported that impaired older adults, with limitations in activities, were often still capable to participate in social activities [Some researchers already have suggested combining the atedness . Furtheratedness . This intivities . Furthertivities ,13.The contradictory findings in literature denote that it remains challenging to find the best approach in how to use the different measures of functioning and how to optimize the identification of older adults at risk of poor functional outcome. Therefore, in the context of a larger research project aiming to develop a prediction model for poor functional outcome, in the present study we took a first step by exploring the possibility to aggregate four functional measures: Physical Functioning (PF), Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL) and participation. Such an overall score incorporates all measures, but enables a more general approach in the identification of older adults at risk of poor functional outcome.We used baseline data from a prospective cohort study on functional outcome in older adults with joint pain and comorbidity. Details about the study design, selection procedure and methods have been previously published . The MedPhysical Functioning was measured with the 10-item PF subscale of the RAND-36 Item Health Survey (RAND-36) [RAND-36) , which mADL was measured with a modified version of the KATZ index of Independence in Activities of Daily Living (ADL) [ng (ADL) , which aIADL was measured with the Lawton Instrumental Activities of Daily Living Index (IADL) [x (IADL) , which aParticipation was measured with the 11-item Keele Assessment of Participation (KAP) [on (KAP) , which mon (KAP) . Based oWe only studied participants with complete data. Descriptive statistics were used to describe the baseline characteristics of the sample and prevalence of limitations in PF, ADL, IADL and participation. Participants were classified as limited in PF if they scored below the cut-off score of 61   and limiWe reversed the PF scoring so that all four functional measures were scored in the same direction, with higher scores reflecting worse functioning. To examine the possibility to aggregate the four functional measures, i.e. physical functioning PF; 10 items), ADL , IADL  and participation , into one general measure for ‘functioning’ , we tested a bifactor model. This model is illustrated in Figure 0 items, Based on the findings from the above mentioned bifactor model analysis, we subsequently tested a second model that includes a bifactor model, based on three functional measures PF, ADL and IADL, and a separate but correlated factor participation, as illustrated in Figure 2/df, root mean squared error of approximation (RMSEA) and weighted root mean square residual (WRMSR) and two incremental close-fit indexes, i.e. comparative fit index (CFI) and Tucker-Lewis index (TLI) were used, according to Hu and Bentler [2/df <2, CFI and TLI >0.95, RMSEA <0.06 and WRMSR < 1.0 [Both models were tested with CFA for ordered categorical items , which uses polychoric correlations and the method of weighted least squares with mean and variance adjustment (WLSMV) to estimate the loadings . Factor  Bentler . The folSR < 1.0 ,23.omegas[omegas as sum score reliabilities, we transformed the fitted polychoric correlations back into the original score metric, yielding the fitted covariance matrix and calculated the reliability coefficients based on this matrix, by using a SAS program [omegas . Furthermore, the total score will be applied in clinical practice. Similar to Cronbach’s alpha, omega-total (omega-t) is an estimator for the reliability of the general factor , thus the proportion of test variance due to all the factors in the model , whereas the omega-hierarchical (omega-h) is the proportion of test variance that can be attributed to only the general factor. Next, we calculated the distinct omegas for all subgroup factors, in which subgroup-omegas yield the reliability of the subgroup factors, whereas omega-s indicates the residual reliability of the subgroup factors [omega-t, omega-h and subgroup-omegas, a coefficient omega of >0.70 corresponds with an appropriate model, whereas a low omega-s indicates that the subgroup factor does not contribute to the information provided by omega-t[We computed the proportion of explained common variance (ECV) and the model based reliability coefficients of all included items. The ECV is the ratio of the explained variance by the general factor divided by the variance explained by both the general and the subgroup factors, in which a higher ECV indicates unidimensionality . For relomegas. This isomegas. In orde program . This me factors . For omey omega-t.The patient characteristics are presented in Table Do you need help with eating?” Therefore, we removed this item from the model. Secondly, the residual variances of item PF8  and item I1 (using telephone) were negative, which indicated an error in the model. Therefore, we constrained the residual variance to 0.05 for these items. The final adjusted bifactor model fitted the data well. Fit statistics were as follows: ×2/df = 534/324 = 1.65, RMSEA = 0.040 (90% CI: 0.034-0.046), CFI = 0.987, TLI = 0.984. Only WRMR showed a minimal deviation compared to the guidelines (1.081).At first, we tested a bifactor model that includes all four measures Figure , model 1omega-s of the subgroup factors, in which we controlled for the general factor. The results showed values of 0.22, 0.20, 0.15 and 0.53 for PF, ADL, IADL and participation, respectively. In contrast to the first three subgroup factors, the high omega-s of the subgroup factor participation indicates that the majority of the reliability variance on the participation score is independent of the general factor and thus provides unique information over and above the general factor. This indicates that the subgroup factor participation should be assessed separately.The ECV (explained common variance) was 0.61, which indicates underlying factors in the general factor, and thus multidimensionality. This was further confirmed when we looked at the 2/df = 542/327 = 1.65, RMSEA = 0.040 (90% CI: 0.034-0.046), CFI = 0.986, TLI = 0.984, WRMW = 1.083.Based on the above findings, we further tested a combined model, which included a bifactor model with the subgroup factors PF, ADL and IADL and separate but correlated factor participation Figure , model 2omega-t was 0.94, whereas the omega-h was 0.79. This indicates that 0.79/0.94 = 84% of the reliability variance in the total score is due to the general factor for functioning. In other words, the interpretation of the total score was hardly confounded by the subgroup factors. Compared to model 1, the ECV increased to 0.67. The omega-s of the subgroup factors were all low (0.18 to 0.22), which indicates no unique information on the subgroup factors over and above the general factor. Furthermore, the omega of the subgroup factor participation was 0.82, which indicates good reliability.Firstly, we examined the degree to which the general factor was confounded by the subgroup factors. The functioning and unique information over and above the general factor in the subgroup factor participation. Therefore, we tested a second model that included a bifactor model  and separate but correlated factor participation. Compared to model 1, model 2 showed equal model fits, but better reliability. Thus, the results favour the use of an aggregated measure of PF, ADL and IADL and a separate measure for participation. For research studies, this means that full data should be collected for all four functional measures, but subsequently the researchers can calculate a summary score for PF, ADL and IADL, to assess overall level of functioning and to develop prediction models for poor functional outcome in older populations. Participation should be used as distinct measure.In this study, we explored the possibility to aggregate four measures of functioning: Physical Functioning (PF), Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL) and participation, into one general measure that quantifies overall level of functioning, by testing a bifactor model. The bifactor model fitted the data well. However, the reliability analysis indicated multidimensionality in the general factor performing self-care tasks. In the second measure ADL, participants are asked if they need help with their self-care. In the third measure participation, participants are asked if they are able to perform their self-care if and when they want to, despite possible help from devices or relatives. All three constructs could be related, but this is definitely not always the case. Someone with problems in performing a task does not necessary need help with performing this task. On top of these arguments, a correlation analyses with all items about the example self-care showed correlations between 0.21 and 0.61, which indicates no signs of collinearity (data not shown). This also accounts for other overlapping items in the four measurements.Two considerations should be made, when interpreting these results. Although the ECV increased in model 2, this increase was lower then expected. This could be explained by the low omega on the subgroup factor ADL (0.50), which indicates that this factor contributes to a lesser extent to the general construct functioning than the other subgroup factors. Thus, one could argue about including ADL as functional outcome in the general measure, as it provides no additional information over and above the measures PF and IADL, at least not in our primary care sample. Furthermore, there is some overlap between the included items within the four measures of functioning. For example, the item ‘self-care’ is assessed in the PF and ADL as well as in the participation measure. One would expect high correlations between these items, which could subsequently be an argument for exclusion of these items. However, we decided to maintain all items in the analyses for two reasons. First, these items are part of existing and validated questionnaires and all necessary to examine the constructs of interest. Second, the conceptual models behind the four measures differ substantially. This can be illustrated by the item ‘self-care’. In the measure PF, participants are asked if they are disabled in Until now, there is no consensus about the most optimal use and application of the ICF domains activities and participation to study level of disability in research. In the pre-final version of the ICF model, activities and participation were presented as two separate domains, but in the final version these two domains were again combined into one concept, because of the difficulty to distinguish between the two domains. The WHO stated that despite the combined concept, the two domains still have two distinct definitions and remain distinguishable. According to the ICF model, activity limitations are defined as difficulties in performing a task, whereas participation restrictions are defined as difficulties in engaging in life situations . SeveralOur study has several strengths. We used validated questionnaires to measure functioning and had full data available from almost all participants. Also, besides testing the bifactor model, we examined the reliability of the model, by investigating the omegas of both the general factor and subgroup factors. However, some limitations of the study should also be mentioned. Firstly, there are contradictory findings in the literature about the dimensionality of the PF subscale of the RAND-36. As intended, many studies found evidence for the PF subscale to measure a unidimensional construct . HoweverThe results of our analysis support the use of an aggregated functional measure for PF, ADL and IADL, whereas participation should be considered as a distinct measure, in research studies that have the broader aim to develop prediction models for poor functional outcome.The authors declare that they have no competing interests.LH: Study design; data collection; data analysis; interpretation; manuscript preparation. DLK: data analysis; interpretation, manuscript preparations. SL, MS, JD, HvdH: study design; interpretation; manuscript preparations. LH drafted the article. SL, DLK, MS, JD and HvdH discussed all versions of the manuscript. All authors revised and approved the final version of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2318/13/119/prepub