Growth parameters such as leaf area (LA), total dry mass (TDM) production, crop growth rate (CGR), relative growth rate (RGR), and net assimilation rate (NAR) were compared in six varieties of mungbean under subtropical condition (24°8′ N 90°0′ E) to identify limiting growth characters for the efficient application of physiology breeding for higher yields. Results revealed that a relatively smaller portion of TDM was produced before flower initiation and the bulk of it after anthesis. The maximum CGR was observed during pod filling stage in all the varieties due to maximum leaf area (LA) development at this stage. Two plant characters such as LA and CGR contributed to the higher TDM production. Results indicated that high yielding mungbean varieties should possess larger LA, higher TDM production ability, superior CGR at all growth stages, and high relative growth rate and net assimilation rate at vegetative stage which would result in superior yield components. Mungbean is one of the most important pulse crops for protein supplement in subtropical zones of the world. It is widely grown in Indian subcontinent as a short duration catch crop between two principal crops. Mungbean contains 51% carbohydrate, 24–26% protein, 4% mineral, and 3% vitamins [In spite of the best efforts for improving the mungbean varieties, the yield of this crop remains low. Several studies have been made to understand their performances which mainly include the contribution of various yield components towards yield –6. The y−1, respectively at the time of final land preparation. Seeds were sown on 10 and 14 March for the year of 2010 and 2011, respectively. A unit plot size of 20 m2 (5 m × 4 m) with plant spacing of 30 cm × 10 cm was used. Cultural practices were the same in both the seasons. Seeds were sown in line, and two weeks after germination, the plants were thinned to a density of 30 plants m−2. First weeding was done followed by thinning at about 20 days after sowing (DAS). A single irrigation was given at 21 DAS at both years. Insecticide (Ripcord 50 EC at 0.025%) was sprayed at flowering and fruiting stage (45 and 55 DAS) to control shoot and fruit borer. The experiments were carried out at the experimental field of Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh (24°8′ N 90°0′ E), Bangladesh in Kharif-I (February-May) season of 2010 and 2011. Six mungbean varieties of which three high  and three low  yielding varieties were used as planting material. The soil of the experimental area is silty loam having a total of 0.065% nitrogen, 1.17% organic matter, 18.5 ppm available phosphorus, 0.30 meq/100g exchangeable potassium, 20 ppm sulphur and 6.8 pH. The experiments were laid out in a randomized complete block design with three replicates in both the years. Urea, triple superphosphate, muriate of potash, and gypsum were used as a source of nitrogen, phosphorus, potassium, and sulphur at the rate of 40, 120, 80, and 30 kg ha2 in two adjacent rows of 0.5 m (10 plants) was randomly selected from each plot and uprooted for collecting necessary parameters. The plants were separated into roots, stems, leaves, and pods, and the corresponding dry weight was recorded after oven-drying at 80 ± 2°C for 72 hours. The leaf area of each sample was measured by automatic leaf area meter . The growth analyses like crop growth rate (CGR), relative growth rate (RGR), and net assimilation rate (NAR) were carried out following the formulae of Hunt [−1 was recorded from 15 randomly selected pants, that is, five from each plot. The opened flowers were counted daily. Reproductive efficiency (per cent pod set to opened flowers) was calculated as follows: (number of pods plant−1 ÷ number of opened flowers plant−1) × 100. The yield contributing characters were recorded at harvest from ten competitive plants of each plot. The seed yield was recorded from five rows of each plot (1.50 m × 3.0 m) and converted into seed weight plant−1 by dividing the plant number. Harvest index was calculated from the collected data using formula:  × 100. To study ontogenetic growth characteristics, a total of five harvests were taken in both years. The second and third rows of each plot were used for sampling. The first crop sampling was done at 25 DAS, and continued at an interval of ten days up to 65 DAS, that is, till attaining physiological maturity. From each sampling, a unit area of 0.3 m of Hunt . The numData were analyzed statistically as per the design used following the analysis of variance (ANOVA) technique and the mean differences were adjusted with DMRT at 5% level of significance using the statistical computer package programme, MSTAT-C following Russell . The differences among the varieties for leaf area (LA) and total dry mass (TDM) were significantly different at all growth stages Figures  and 2. TThe crop growth rate (CGR) in four varieties  tended to increase with the advancement of stage till 55–65 DAS in 2010 and similar trend was observed in one variety of BINAmung-7 in 2011 . The othThe relative growth rate (RGR) declined with age in both years . In 2010The pattern of net assimilation rate (NAR) in both seasons was not similar . The cha−1), phenological (flowering duration and days to maturity), and reproductive characters (number of opened flowers plant−1 and reproductive efficiency) differed significantly among the studied cultivars (−1) than the low yielding ones (range 38.2–40.3 cm for plant height and 0.69–0.72 plant−1 for branch number). The flowering duration, number of opened flowers plant−1, and days to maturity were also higher in high yielding varieties than the low yielding ones but the reverse trend was observed in case of reproductive efficiency. It seems that flowering duration and number of open flower have positive relation with seed yield in mungbean. The present result is consistent with Mondal et al. [The morphological (plant height and branch number plantultivars . The effl et al.  who repo−1 (The three varieties, BARImung-4, BINAmung-7, and BUmung-1, produced higher grain yield than the other three varieties, BARImung-6, BINAmung-6, and BUmung-2, due to production of higher number of pods plant−1 . AlthougFrom the results, it appeared that, for getting superior characters of yield components, a high yielding mungbean genotype should posses a relatively larger leaf area with superior growth parameters.


Inverted takotsubo cardiomyopathy (TTC), a variant of stress-induced cardiomyopathy, features transient myocardial dysfunction characterized by a hyperdynamic left ventricular apex and akinesia of the base. Herein, we describe a 38-year-old primigravida with severe preeclampsia who had active labour for 4 h followed by an emergency caesarean delivery. She developed postpartum haemorrhage due to uterine atony complicated by pulmonary oedema, which was managed with large-volume infusion and hysterectomy. Her haemodynamic instability was associated with cardiac biomarkers indicative of diffuse myocardial injury and echocardiographic findings of an “inverted” TTC. The patient was almost fully recovered one month later. Our case shows that a reversible inverted TTC may result from a prolonged painful labour. TTC should be listed in the differential diagnosis of the patient presenting with pulmonary oedema of unknown origin, especially in patients with severe preeclampsia. Stress-induced cardiomyopathy, also called takotsubo cardiomyopathy (TTC), is a transient stunned myocardium characterized by suspicion of an acute myocardial infarction in patients who have no angiographic stenosis upon coronary angiography , 2. It hLabour pain may cause stress and emotional responses in a parturient . In addi+3 weeks of gestation with oedema of both legs lasting the last 2 weeks. She had no significant past medical history except for chronic hepatitis B. No coronary risk factors were reported. On admission, her blood pressure was 150/90 mmHg and urinary protein level was as high as 500 mg/dL. Her blood tests revealed haemoglobin 11.9 g/dL, platelets count 106 × 109/L, prothrombin time 11.0 s , international normalized ratio of 1.11, and activated partial thromboplastin time 47.7 s . Severe preeclampsia was diagnosed and MgSO4 4 g i.v. plus 10 g intramuscularly was given initially as a loading dose, followed by 1 g/h as infusion for seizure prophylaxis.A 38-year-old primigravida  presented for elective induction of labour at 40After 6 h in hospital, labour was induced with insertion of extraovular catheter through the cervical canal of the uterus and spontaneous uterine contraction began 5 hours thereafter. When the cervix was 2.5 cm dilated, an infusion of oxytocin was started to augment labour and the membranes were ruptured 3 hours thereafter. Throughout the labour course including 4 h of active, her arterial pressure was stable in the range of 140–160 mmHg systolic, 90–100 mmHg diastolic. She declined epidural labour analgesia because of possible aggravation of the backache she had had.Twenty-one hours after labour onset, when the cervix was 10 cm dilated, the foetal vertex did not descend past 1 station (arrest disorder) and the foetus developed deceleration (90 beats/min). The patient became dyspneic and diaphoretic and complained of sudden onset of severe left-sided chest pain. Her blood pressure was 160/90 mmHg. On electrocardiogram (ECG), a sinus tachycardia (100 beats/min) was noted with no ST-segment changes. She had been tolerating oral fluids but had taken nothing by mouth for more than seven hours. The decision was made to perform category-1 caesarean delivery. The potential risks and benefits of general, spinal, or epidural anaesthesia were explained to the patient, who accepted the anaesthetist's inclination towards general anaesthesia.2) 96% on air. After preoxygenation, a rapid sequence technique with remifentanil 1 μg/kg, thiopentone 300 mg, and suxamethonium 75 mg was employed and cricoid pressure was applied. After endotracheal intubation, anaesthesia was maintained with 1.2% sevoflurane  and 50% nitrous oxide in oxygen, with vecuronium 6 mg for neuromuscular block. A female infant weighing 4000 g was born through a low transverse uterine incision with Apgar scores of 5 and 7 at one and five minutes, respectively, and the baby was transferred to the neonatal ICU for observation. After cord clamping, an oxytocin infusion of 40 units in 500 mL of normal saline was commenced at 125 mL/h. In addition, i.v. 0.15 mg/kg morphine, cefazolin 1 g, and midazolam 3 mg were given while nitrous oxide was increased to 70% and end-tidal sevoflurane concentration was reduced to 0.8%. Approximately 5 min after delivery, the uterus was found to be atonic and bleeding diffusely. The oxytocin infusion rate was increased to 240 mL/h after an i.v. bolus of carbetocin 100 μg with continuous bimanual compression of the uterus. The fluid infusion rates were increased by pressurizing the infusion bags. Additional peripheral venous and central (left subclavian vein) venous assess was established with initial central venous pressure (CVP) value of 5.5 cm H2O. As the bleeding continued, bimanual uterine compression was combined with i.v. ergometrine 250 μg and blood transfusion. Nevertheless, the uterus was still atonic with blood spurting from the lax uterus. There were no vaginal or cervical lacerations. The patient lost more than 2500 mL within 30 min and rapidly became haemodynamically unstable. Additional oxytocics including intramyometrial carboprost (1 mg in divided doses), and repeated doses of i.v. oxytocin 5 units with warmed fluids and blood products were given. However, this was inadequate to address the clinical situation and finally, decision was made to proceed to hysterectomy.The patient received 30 mL of 0.3 M sodium citrate before induction of anaesthesia. Upon arrival in the operating theatre, the patient was positioned supine with left lateral tilt and routine monitoring devices including continuous EKG, noninvasive blood pressure, temperature, and pulse oximetry were applied. A 20-gauge catheter was placed into a radial artery and connected to a pressure transducer to measure blood pressure. Initial recordings showed a heart rate of 103 beats/min, blood pressure 170/110 mmHg, respiratory rate 25 breaths/min, and peripheral oxygen saturation (SpOμg/kg/min). Her SpO2 remained between 92% and 98% at FiO2 0.5 during the operation. On physical examination, rales were detected in both lungs and diuresis was started with furosemide. Her haematocrit level dropped below 15%. A total of 6700 mL crystalloid, 2,000 mL colloid, 9 units packed red blood cells, 3 units fresh frozen plasma, and 8 units cryoprecipitate were administered during the 4.5 h surgical procedure. Intraoperative blood loss was estimated to be 5,800 mL and urine output was about 260 mL in the operating theatre. Arterial blood gas analysis on an FiO2 of 0.5 revealed a pH of 7.25, PaO2 11.89 kPa (89 mmHg), PaCO2 5.60 kPa (42 mmHg), HCO3 18.4 mmol/L, and base excess −8.8 mmol/L at the end of operation . Tentative diagnosis of pulmonary oedema related to fluid overload was made.During the surgery, the systolic blood pressure was decreased to 70–90 mmHg and the pulse was tachycardic at 122–130 beats/min, despite the massive intravenous fluids and blood products combined with inotropic support (dopamine at 2 to 5 2O with dopamine infusion at 5 μg/kg/min. Because of the tachycardia dopamine was switched to dobutamine (3–5 μg/kg/min) which was continued until the weaning of inotropic support. The patient was mechanically ventilated with a tidal volume of 8 mL/kg at an assisted control rate of 12 breaths/min and a 10 cm H2O positive end-expiratory pressure with supportive care for pulmonary oedema, including oxygen, furosemide (20 mg q 4 hour), and fluid restriction. A chest X-ray taken immediately after ICU admission showed extensive bilateral pulmonary infiltrates in both hilar areas and pleural effusion in both lungs with normal lung volume and normal heart size. On the postoperative day 2, the patient became haemodynamically stable and was weaned off inotropic support. On the day of admission, urine output was about 80 mL/h but her renal function continued to deteriorate, and oliguria (creatinine 369.6 μmol/L) set in by day 4. Continuous renal replacement therapy (CRRT) was initiated to treat the acute pulmonary oedema as well as acute renal failure. Over the next 3 days, renal function gradually improved, and CRRT was discontinued on the third day, and the patient was weaned off the ventilator on the next day. However, she was still tachypneic (22–30 breaths/min) with peripheral oxygen saturation of 94–98% on 5 L/min of oxygen via nasal prongs over the following 4 days. Therefore, a cardiac workup was initiated, and an initial transthoracic echocardiogram (TTE) on the 12th day revealed severe left ventricular (LV) systolic dysfunction and ejection fraction of 23.5% with extensive akinesia of the LV base extending to the midcavity of the heart, together with preserved apical function  for further management. Initial recordings on arrival in the ICU were heart rate 132 beats/min, blood pressure 125/76 mmHg, and CVP 18.5 cm H Figures . ECG on  Figures . The folWe present a case of unrecognized inverted TTC resulting in pulmonary oedema in a severe pre-eclamptic woman who had caesarean delivery following a prolonged painful labour complicated by massive postpartum haemorrhage secondary to uterine atony. She experienced a severe sudden onset of substernal chest pain during the active labour accompanied by mild dyspnea and palpitations. The correct diagnosis was delayed by the initial belief that the symptoms were due to fluid overload. The patient was then treated with diuretics, mechanical ventilation, and fluid restriction. Our misdiagnosis was in fact encouraged by high risk of pulmonary oedema in patients with severe preeclampsia  and mass In the perioperative period, pulmonary oedema has been observed after a variety of inciting events, including anaphylaxis , severe A severe preeclampsia alone can cause pulmonary oedema due to increased capillary permeability, increased sympathetic tone, decreased colloid oncotic pressure, and an elevated afterload . TherefoMonitoring of CVP and pulmonary capillary artery occlusion pressure (PAOP) has been also advised in the pre-eclamptic patient for aggressive fluid resuscitation and pulmonary oedema. However, the use of CVP to estimate PAOP is limited by poor correlation between the values in women with preeclampsia . It has The mechanisms underlying the transient severe myocardial dysfunction (TTC) should be multifactorial. Among others, a catecholamine-mediated myocardial stunning has been highly favoured, since myocardial function returns to normal within a few days to a few weeks , 15. IndTTC is being increasingly recognized and even reported in relatively young women in the peripartum period , 17, altμg/kg to prevent a pressor response to tracheal intubation which is our routine in parturients with severe preeclampsia, since an abrupt increase in arterial pressure, although transient, can lead to cerebral oedema and haemorrhage, and cardiac failure with pulmonary oedema [Neuraxial anaesthesia is the standard for elective caesarean delivery in many countries and has become a preferred technique to provide anaesthesia for caesarean delivery even among women with severe preeclampsia . Howevery oedema . Remifeny oedema . Indeed,y oedema .4 for seizure prophylaxis and control has been well admitted in severe preeclampsia [4 in high doses may prolong the effect of nondepolarizing muscle relaxants and thus monitoring of neuromuscular function is essential in patients given large dose of MgSO4 [The effectiveness of MgSOclampsia . Howeverclampsia . One mayof MgSO4 .In conclusion, a pulmonary oedema following caesarean delivery, particularly when associated with increased CVP, requires immediate investigation with TTE in patients with severe preeclampsia. TTC should be listed in the differential diagnosis.


An example of proactive control is the usage of informative cues to prepare for an upcoming task. Here the authors will present data from a series of three experiments, showing that positive affect along with low arousal reduces proactive control in form of a reduced reliance on informative cues. In three affect groups, neutral or positive affective picture stimuli with low and high arousal preceded every trial. In Experiments 1 and 2, using a simple response cueing paradigm with informative cues , a reduced cue validity effect (CVE) was found under positive affect with low arousal. To test the robustness of the effect and to see whether reactive control is also modulated by positive affect, Experiment 3 used a cued task switching paradigm with predicitive cues . As expected, a reduced CVE was again found specifically in the positive affect condition with low arousal, but only for task repetitions. Furthermore, there was no difference in switch costs between affect groups (with and without task cues). Taken together, the reduced CVE indicates that positive affect with low arousal reduces proactive control, while comparable switch costs suggest that there is no influence of positive affect on reactive control. Studying issues of cognitive control is of major interest for the understanding of human cognition and action. The dual mechanisms of control (DMC) framework by Braver and colleagues  was compared to a neutral control group and another positive affect group with higher arousal levels (positivehigh hereafter). In Experiments 1 and 2, we used a spatial response cueing task with spatially congruent target response mappings. A bias in favor of a proactive control strategy was induced by using informative cues, that is, the probability of validly cued trials was more than 50% but less than 100%. In this response cueing task, a peripheral, informative cue indicated a possible target location and thereby primed the congruent response. Furthermore, the higher probability of valid cues (66%) should promote the usage of a proactive control strategy resulting in a reliable CVE, that is, faster responses and fewer errors in validly cued trials. A reduction of proactive control should consequently reduce the CVE, because less usage of the cues would minimize the benefits in validly cued trials as well as the costs in invalidly cued trials.In sum, following the results of the previous study from our lab  and proactive control (as measured by the CVE) in a single experiment.low group as compared to the neutral group in the response cueing task with informative cues of Experiment 1. Because arousal differences were not considered in previous studiesa priori expectations concerning different outcomes in the positivelow and positivehigh group.Following previous results . All participants signed informed consent and were debriefed after the session.Sixty-six undergraduate students of Regensburg University participated in the experiment for course credit or 5 Euro. Sixty-two subjects (see Results for exclusion criteria) were included into the final data analysis . Participants were assigned randomly to the three affect groups , running E-Prime 2.0  was used for experiment presentation and data acquisition. Viewing distance was held constant at 50 cm by using a chin rest. Responses were collected via a QWERTZ-keyboard, with the y- and m-key serving as left and right response keys.M = 4.99), and low arousal levels (M = 2.45), whereas both positive picture sets were high in valence  but differed in arousal levels . Neutral pictures included household objects like plates or cups, positivelow pictures showed babies and families, and in the positivehigh group sport and adventure pictures were displayed. It should be noted, that no erotica were used in the positivehigh group to prevent different gender influences, and because erotica seem to be a special category with effects differing from non-sexual positive, highly arousing pictures  and the cue (the “§”-symbol) appeared 8.64 cm to the left or right of the fixation cross in font size 55 pt.Each trial started with the presentation of the fixation cross for 500 ms, followed by an IAPS picture for 350 ms. After another short fixation period (200 ms) the cue was presented left or right of the fixation cross for 200 ms. The target appeared after a variable inter stimulus interval of 50 or 150 ms, which was included to reduce premature responses to the cue, and remained visible until the participant pressed the spatially congruent response key. Participants were instructed to react as fast as possible while avoiding errors. In case of an error, the German word for error (“Fehler”) was presented for 1000 ms as feedback.To assure that all participants started with a similar mood, all participants passed a 5-min relaxation exercise – comprised of relaxing music and spoken instructions for muscle relaxation – prior to the actual experiment. These instructions were standardized mp3-files presented via stereo headphones. Subsequently, 12 practice trials without IAPS pictures enabled the participants to get used to the cueing task. These practice trials were followed by two experimental blocks, in which an IAPS picture preceded every trial. Both blocks consisted of 120 trials , separated by a short break. The trial procedure within each block was pseudo-random: Each block consisted of 10 sequences of 12 trials and within these 12 trials the only constraint was that cues and targets appeared equally often on the left and the right side. Affective pictures were drawn from the set of the picture pool at random without replacement until all pictures had been presented once and then the procedure started all over again.low vs. positivehigh) × 2  mixed factors design was used. Affect was manipulated between, and Cue validity varied within participants.A 3  in comparison to mean RTs of his affect group (M positivelow = 344 ms), and the other participant got continuously slower throughout the experiment and also had high mean RTs . Of the remaining data, mean RTs and error rates of each design cell (see Table low vs. positivehigh) × 2  mixed factors analysis of variance (ANOVA).The practice trials as well as the first trial of each experimental block were excluded from analyses. In addition, error trials, trials following an error, and trials with reaction times (RT) below 150 ms or above 1500 ms were excluded (4.31% of the data). Furthermore, RTs differing more than 3 SD from individual means were considered as outliers and also removed prior analysis . The data of two participants were excluded from further analyses, because of too many errors . Another two subjects had to be excluded due to untypical RTs throughout the experiment. One was exceptionally slow  = 90.35, p < 0.001, F = 2.68, p = 0.077, F = 3.45, p < 0.05,low group compared to the positivehigh group . The CVE in the neutral group was descriptively between both positive groups, but did not differ significantly from either group . The overall error rate was 2.23% (SD = 1.85).The overall ANOVA for the error data brought up a main effect of Cue validity, F = 88.86, p < 0.001, F < 2.08, all p > 0.133). Even though we did not find a significant interaction of Affect × Cue validity in the RT analysis, the descriptive data resembles the results found in error rates , intermediate in the neutral group (35 ms), and largest in the positivehigh group (43 ms). Because the neutral group was more of a descriptive baseline – it differed on both valance and arousal levels from the positive groups – we conducted an additional analysis without the neutral group to search more directly for a possible arousal effect on proactive control.We found a significant main effect of Cue validity, F, 59 = 88low vs. positivehigh) × 2  mixed factors ANOVA revealed a significant main effect of Cue validity, F = 54.19, p < 0.001, F = 3.74, p = 0.059, low and positivehigh group. The main effect of Arousal did not prove reliable .A 2  and a low overall error rate (2.23%). Therefore, marginally significant differences between groups might be due to a floor effect. To increase variance and thereby provide room for affective modulations, we increased task difficulty in Experiment 2. To assure that both experiments were still comparable we used the same cueing task with informative cues  in combination with a concurrent math task. Based on the results of Experiment 1 we expected to find a reduced CVE in the positivelow group, but an increased CVE in the positivehigh group, compared to the neutral group.We used a very simple response cueing task in Experiment 1, which resulted in very fast overall RTs . All participants signed informed consent and were debriefed after the session.Another 60 students of Regensburg University participated in the experiment for course credit or 5 Euro. Fifty-five subjects (see Results for exclusion criteria) were included into the final data analysis . Participants were assigned randomly to the three Affect groups . Second, the actual experiment was preceded by a math test to assure that the Affect groups did not differ according to their calculating skills. To this end, we used a subtest of the Leistungsprüfsystem  × 2  mixed factors design was used. Affect was manipulated between, whereas Cue validity varied within participants.A 3 , and the second made too many errors in the cueing task . Finally, two participants of the positivehigh group were excluded from further analysis, because they were exceptionally slow . Of the remaining data, mean RTs and error rates of each design cell (see Table low vs. positivehigh) × 2  mixed factors ANOVA.We checked for group differences in calculating skills before the experiment and during the experiment with an ANOVA on performance in the L-P-S subtest as well as in the additional math task. For analysis of error rates and RTs in the cueing task, trials with math task responses differing more than two from the correct result were excluded from analysis (6.31% of the data).F = 2.62, p = 0.082, F < 1, p = 0.395).There were no differences in the performance in the L-P-S subtest between Affect groups before the experiment, F = 36.63, p < 0.001, Fs < 1.37, ps > 0.263).The overall error rate was 1.3% (SD = 1.5), and individual mean error rates were below 7.5% for all subjects. The overall ANOVA for the error data brought up a main effect of Cue validity, F = 142.39, p < 0.001, F = 3.51, p < 0.05, low group (41 ms) as compared to the neutral group  and the positivehigh group . There was no significant difference between neutral group and positivehigh group . Also, the main effect of Affect was not significant .The ANOVA yielded a significant main effect of Cue validity, F1, 5 = 142.39low group compared to the neutral and the positivehigh group. This suggests that specifically positive affect with low arousal reduces proactive control in form of a reduced reliance on informative cues. In contrast to a proactive control strategy, participants in the positivelow group show behavioral costs in expected events  and benefits in unexpected events . A problem in our simple cueing paradigm is, however, that we cannot completely rule out that the reduced CVE might also be a sign of increased reactive control: participants in the positivelow group might have used the cues just as the other affect groups but they might have been better able to overcome the pre-activated response in invalidly cued trials. This would be in line with the results by van Wouwe et al.  = 474.0, p < 0.001, no affective modulation thereof . JZS-Bayes factors for CVE comparisons between the affect groups  ranged from 1.66 to 3.75, which means the null hypothesis – no difference in CVE – was indeed more likely. So, in sum the results of the response cueing experiments speak in favor of an affective modulation of proactive control only, with a reduced reliance on informative cues under positive affect with low arousal. However, it would be even better proof, if we could show that the affective modulation of the CVE is restricted to proactive control and is not present for reactive control in a unique experiment. Therefore, we conducted Experiment 3.An increase in mean RTs from Experiment 1 to 2 (345 vs. 437 ms) indicates that we succeeded in increasing task difficulty. With this adapted paradigm we found clear-cut evidence of a reduced CVE in the positivee et al.  that shoThe main aim of Experiment 3 was to gather more direct evidence that specifically proactive control and not reactive control is influenced by positive affect. Furthermore, we wanted to know, whether the affective modulation of proactive control can also be found for task cues . To address these issues we employed a task switching paradigm. Task switching  participated in the experiment for course credit or 5 Euro. Participants were assigned randomly to the three affect groups . All participants signed informed consent and were debriefed after the session. Because we were interested in a possible modulation of the switch costs, participants with negative switch costs were excluded and replaced .Sixty undergraduate students from the Regensburg University  written in green and eight letters  written in purple served as target stimuli and were presented at the center of the screen in font size 52. The color coding of the digit and letter task was counter balanced across participants. Odd numbers and vowels were always assigned to one response key, even numbers and consonants to the other, while response mapping to the left and right response key (y- and m-key on a QWERTZ-keyboard) was also counterbalanced between participants. In experimental blocks 2–4, a color coded fixation cross (purple or green) served as informative task cue.The experiment comprised one task switching block without task cues followed by three blocks including informative task cues. In the first block each trial started with an IAPS picture (350 ms) followed by a blank screen (150 ms) and a black fixation cross (1000 ms). Then the target stimulus appeared and remained on screen until the participant responded. Subjects had to decide whether a number was odd or even (digit task) or whether a letter was a vowel or consonant (letter task). Participants were instructed to react as fast as possible while avoiding errors. Feedback was given for errors only (2000 ms), each trial ended with an intertrial interval of 500 ms. Procedure in the following blocks with informative task cues was the same as in the first block except that the fixation cross was now color coded and served as a task cue for the following task. In valid trials  the colored fixation cross was followed by a target stimulus in the same color, thereby enabling the preparation of the upcoming task in a proactive manner. In contrast, in invalid trials  the fixation color incorrectly predicted the upcoming target color, and can therefore mislead to prepare the wrong task.The experiment started with the same relaxation exercise that was used before in Experiments 1 and 2. Subsequently, 16 practice trials  without IAPS pictures enabled the participants to get used to the task switching procedure. This practice block was followed by 64 trials with an IAPS picture preceding every trial. Data acquisition took place in the following four experimental blocks – the first without informative task cues – with 128 trials each. Each block contained 64 digit tasks (4 × 8 numbers) and 64 letter tasks (4 × 8 letters). Stimulus presentation was pseudo-randomized with the following constraints: repeat and switch trials were evenly distributed. Immediate repetitions of target stimuli or IAPS pictures were not allowed. Task cues  were counterbalanced across all trial types.low vs. positivehigh) × 2  design with affect as between and Trial type as within factor was used in the first block without task cues. The experimental blocks including informative task cues had a 3 (Affect) × 3 (Block: 2 vs. 3 vs. 4) × 2  × 2  repeated measures design.A 3 . Separate analyses were conducted for task switching performance (mean error rates and RTs) in the first experimental block without task cues and for performance in experimental blocks 2–4 with informative task cues.low vs. positivehigh) × 2  mixed factors ANOVA. We found a significant main effect of Trial type, F = 106.45, p < 0.001, F < 1.97, all p > 0.150). The same analysis for mean error rates  = 26.82, p < 0.001, F < 1.19, all p > 0.31). JZS-Bayes factors for differences in switch costs between the Affect groups ranged from 2.95 to 4.04, which means that it is more likely that there are indeed equal switch costs in all three groups.Mean RTs (see Table 655 vs. 73 ms. Thelow vs. positivehigh) × 3 (Block: 2 vs. 3 vs. 4) × 2  × 2  mixed factors ANOVA for the three experimental blocks with informative task cues  = 8.65, p < 0.001, F = 37.34, p < 0.001, F = 4.40, p < 0.05, F = 4.19, p < 0.05, F = 7.97, p < 0.01) and Block 4  = 14.20, p < 0.001). Blocks 3 and 4 did not differ significantly . Cue validity had no significant influence an error rates in task repetitions , but there was a significant negative CVE in task switches  = 6.41, p < 0.05) with more errors in valid trials (4.30 vs. 3.27%). The interaction of Block and Trial type did not prove reliable . There was no significant main effect of Affect or significant interactions with affect . In the RT analysis we found significant main effects for Block, F = 19.83, p < 0.001, F = 98.88, p < 0.001, F = 19.53, p < 0.001, F = 11.28, p < 0.001, F = 2.54, p < 0.001 . Further analysis of Block 2 showed a significant CVE with faster RTs after valid cues in repeat trials  = 32.28, p < 0.001), but not in switch trials . So, there was a strong cueing effect only in the first block with informative task cues, and specifically in repeat trials. The main effect of Affect as well as all other interactions did not prove reliable . With respect to our hypotheses, also in these blocks with informative task cues the Affect groups did not differ significantly in switch costs . JZS-Bayes factors for single comparisons of switch costs ranged from 2.46 to 4.24, which further supports that switch costs were indeed comparable in all three groups. Since we were interested in the affective modulation of the CVE, we reran the analysis, this time only including Block 2 , the only block where the CVE was significant.To check the effectiveness of the cues over time, we conducted a 3  × 2  × 2  mixed factors ANOVA revealed significant main effects for Trial type, F = 39.46, p < 0.001, F = 18.07, p < 0.001, F = 22.54, p < 0.001, F = 32.28, p < 0.001), but not in switch trials . Most important with respect to our hypothesis, there was a significant interaction of Affect × Trial type × Cue validity, F = 3.08, p = 0.05, low compared to the positivehigh group  = 6.32, p < 0.05). The CVE in the neutral group (45 ms) was descriptively between both positive groups but did not differ significantly from either group . The main effect Affect and all other interactions did not prove reliable . The same analysis for mean error rates resulted only in a significant main effect of Trial type, F = 25.06, p < 0.001, F < 1, all p > 0.47).A 3  by showing that switch costs were not manipulated by affect. Taken together, the results of Experiments 1–3 support the assumption that specifically positive affect with low arousal leads to a reduction in proactive control.Purpose of the present study was to investigate the influence of positive affect on processes of proactive control under different arousal conditions. According to the DMC framework  as well as the cued task switching paradigm (Experiment 3), while the CVE in the neutral group was roughly between both positive groups . This lack of significant differences might be a byproduct of our procedure: each experiment started with a short relaxation exercise to create a similar baseline mood in all participants. This procedure, however, might already have resulted in a mild positive affect induction, thereby possibly reducing the differences between the neutral group and the positive group especially with low arousal. Admittedly, what speaks against this assumption is that in Experiment 2, the CVE of the neutral group actually resembled the positivehigh group. It is, however, conceivable that the higher task demands due to the additional math task have counteracted the relaxed mood in the neutral group. Thus, the significant difference found in Experiment 2 might in fact be closer to the actual difference between neutral affect and positive affect with low arousal. Also, it can be assumed that everyday mood is generally rather mildly positive than truly neutral. Therefore, it might not be too surprising that differences between mild positive affect and neutral affect are not easily detected. However, with these constraints in mind, the observed differences in the CVE between positive affect with low arousal and neutral and positive affect with high arousal provide sufficient evidence for the conclusion that positive affect with low arousal decreases proactive control, while positive affect with high arousal seems to increase proactive control compared to neutral affect.The CVE was significantly smaller in the positivelow group converges with findings from previous studies by Compton et al. (proactive control (this study; Compton et al., reactive control (Kuhl and Kazen, The reduced CVE in the positiven et al.  and Drein et al. . Comptonn et al.  investign et al.  – and pen et al.  used then et al.  a recentn et al.  – also un et al.  that uselow group cannot be answered based on behavioral results alone. But nonetheless, it remains an interesting question why positive affect in combination with low arousal reduces proactive control, whereas positive affect along with high arousal seems to increase proactive control. Reduced proactive control under positive affect with low arousal seems to converge with our everyday experience: When being in a relaxed, mildly positive mood one tends to enjoy the moment without looking ahead. This would also be in line with Carver’s (low group apply less effort in sustained task preparation, and instead rely on reactive control alone as soon as the target appears. This might also explain why the effects of reduced proactive control were restricted to the positive affect group with low arousal and were not found with high arousal. Obviously, coasting might not be a reasonable strategy under high arousal as any high arousal signal might rather serve as a warning or alertness signal. For example, Fuentes and Campoy (The fact, that we found a reliable difference in the CVE between the positive groups with low and high arousal, demonstrates that it is most important to consider both dimensions of affect – valence and arousal (cf., Russell, Carver’s  coastingCarver’s . Thus, ad Campoy  showed id Campoy . ArousalThe DMC framework (Braver et al., 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.


Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics.Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes.Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs. In recent decades, Bayesian inference has been increasingly used for analysis of complex statistical models, in part because of increased availability and performance of personal computers and workstations. However, such models are generally not analytically tractable and, hence, computationally demanding numerical techniques are inevitably required. This is especially true of Bayesian computation for genome-enabled prediction and selection, which aims at using whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes . TypicalParallel computing operates on the principle that a large problem can be split into smaller components and solved concurrently , each on a separate processor (or CPU core) . An instParallel MCMC methods have recently been adopted in statistics and informatics ,9 and inIn this paper, we present a technical description of parallel MCMC methods in the context of animal breeding and genetics. These algorithms typically fall into two categories: running multiple MCMC chains or parallelization within a single chain; some variants of these algorithms are discussed as well. A major purpose of this paper is to advocate the use of parallel MCMC methods, hence infusing high-performance computing technologies into animal selection programs in the post-genome era.We start with parallel Monte Carlo methods, as a prelude to parallel MCMC. In practice, many statistical problems involve integrating over hundreds or even thousands of dimensions but usually these problems are not analytically tractable. Instead, Monte Carlo simulation methods  can be uTo begin, consider the following integral θ with density θ can be sampled independently from for some high-dimensional Simple Monte Carlo algorithms proceed by averaging large numbers of values that are generated independently of each other. Obviously, Monte Carlo simulation is parallel in computing because it can be conducted concurrently. By parallel computing, the entire set of samples can be divided among the available CPU cores and then each core generates a portion and summarizes its local samples. After all processors have finished their tasks, a master program summarizes all the partial data and outputs the final result.K CPU cores that generate a total of T samples, each handling an equal portion of these samples. For simplicity, we assume that T is divisible by K, such that the quotient (m = T/K) is an integer. Then, parallel Monte Carlo simulation proceeds as follows [Suppose that there are  follows :➔ Process 0 (master process):m to each process.(a) computes and passes j):➔ Each (slave) process  simulates , and passes Sj back to the master program.(b) computes ➔ Process 0 (master program):Sj and generates the final sum (a) sums (b) computes the Monte Carlo estimate as K cores.Note that, in this example, the master process does not involve computing the sum of a portion of the data but it actually can. Also, note that each process is given the same number of samples. This works well if all CPU cores process the data at the same speed or approximately so. In practice, however, clock frequencies  can vary markedly among processors. Hence, it can be more effective for each processor (or CPU core) to process a different number of samples, roughly proportional to its computing speed, and then let the master program compute the weighted average of all samples obtained from the μ and unknown variance y of n identically independently distributed (iid) observations is: A single-parameter model can serve as a building block for Bayesian modeling . Considewhere it can be shown that the posterior density of  freedom : K parallel processes are executed, each generating a portion of the posterior samples of Hence, the posterior mean of To show why the algorithm of parallel Markov chain simulation applies to parallel computing of a single-parameter model, consider equation (1). For this single-parameter normal model, for example, the marginal posterior expectation of n samples of Clearly, (5) implies a similar Monte Carlo implementation: if Many models involve more than one unknown. Although many parameters are involved, conclusions are often drawn about one or only a few parameters at a time. In Bayesian analysis, the aim is to obtain the marginal posterior distribution of each parameter of interest. Often, we can construct the joint posterior distribution of all unknowns and then integrate this distribution over the unknowns that are not of immediate interest, leading to the desired marginal distribution of the parameter of interest.μ and Now, consider the normal distribution (2) but with both mean and variance unknown. Assuming prior independence of location and scale parameters, a vague prior density for Then, it can be shown that the marginal posterior distribution of μ can be obtained by integrating the joint posterior density over , leading to a student-t density:where μ can be generated independently from the following marginal posterior distributions, for Therefore, posterior samples for ➔ Sampling ➔ Sampling π equal to some distribution of interest, such as a posterior distribution in a Bayesian analysis [π but if the chain is constructed properly, then d means convergence in distribution) and, under certain conditions, an estimator t indexes the order of MCMC iterations. This introduces a difficulty to parallelization of a Markov chain.Analytical solutions are not always available for most multiple-parameter models. Instead, MCMC simulation can be used to draw samples from the joint posterior distribution and then evaluate sampled values for the parameter(s) of interest while ignoring the values of other unknowns. MCMC methods are a variant of Monte Carlo schemes in which a Markov chain analysis . TypicalA naive yet natural approach to parallel MCMC is simply to generate several independent Markov chains on different processors and then combine results appropriately ,18. GiveX cannot be drawn directly from , it can be shown that the ensemble is ergodic (tending in the limit) to Assume that we want to estimate some target distributionit) to pX.ρ of steps, for , must be removed as burn-in from each chain, then the maximum speedup in computing through parallelization is :An appealing advantage of running multiple chains is that these processes can be conducted concurrently with minimal coordination among tasks, as in the case of parallel Monte Carlo simulation. However, unlike parallel Monte Carlo simulation, a major concern with running multiple MCMC is that the overall reduction in runtime from parallelism can be limited by the portion of each chain to be discarded in the beginning of MCMC sampling for convergence purposes . If every chain has to spend a significant proportion of its time in burn-in, this would place serious limitations on the performance of the algorithm, because it would not scale well with an increasing number of processors . Accordin is the number of iterations after burn-in. Thus, parallel MCMC computing by virtue of running multiple chains is rewarding only when ρ is small. However, if ρ is large, the gain in computation through running multiple chains instead of a single long chain can be very disappointing.where p(X) than using a single long chain. If several chains are drifting to disparate modes, they will tend to be strongly influenced by the chains that they confine, because the weights will not necessarily be proportional to their relative masses.Although running multiple Markov chains is theoretically straightforward, chains are not necessarily ergodic. Hence, some variant multiple MCMC methods have been proposed. For example, samples from multiple Markov chains may be confined to isolated modes if the target distribution is multi-modal, or the chains may mix poorly when there are strong correlations between variables. Unfortunately, the latter is a common problem of Gibbs samplers . Hence, Several strategies have been proposed for handling the aforementioned issues for single chains, such as adaptive MCMC algorithms ,22 and tBy running multiple Markov chains, we often observe that samplers mix poorly and each chain may require a very long burn-in time. Hence, it would be preferable to develop parallelism within a single chain, instead of running multiple chains. As mentioned in the previous section, Markov chain simulation is an iterative procedure, in the sense that simulation of the next value of the chain depends on the current value. This creates difficulty for delivering parallelism for a single Markov chain. Nevertheless, we will show that a single chain can be parallelized, subject to assumptions of conditional independence in the model. The key is to identify such steps that can be implemented in parallel.p scale parameters p can be equal to 1 in some cases, and q location parametersσ is very fast (given some sufficient statistics regarding the current state of θ) but updating θ is highly time-consuming. This is typical of a multivariate normal distribution with a common scale parameter  and a large number (say a few hundreds or thousands) of location parameters. In these cases, it would be preferable to parallelize the update steps for θ in order to gain speed up in computing. In theory, parallelization of the update of θ depends crucially on the conditional independence structure of the model. First, assume the simplest possible case, where θ to the K CPU cores. For illustrative purpose, let there be only one σ but manyConsider a Bayesian model with ➔ Master program:σ, given realization of θ and the data y, and(a) samples a new σ to each process.(b) distributes the new k):➔ Each process  updates a subset of (b) computes summary statistics for the updated (c) passes the summary statistics back to the master program.θ are all independent of one another, given σ and y. In practice, however, such independence may not necessarily hold and strategies must be developed to deliver efficient parallel MCMC algorithms given specific dependence between elements [Often, the above algorithm works quite well when the elements .μ and knownn iid observations, the likelihood is:Consider a normal distribution model with unknown If a normal prior is assumed, that is,μ is also normal [where o normal :θ is expressed as a weighted average of the prior mean , 16G memory, and cache size 6144KB.The example data are average body weight daily gains (ADG) measured on 7670 Angus cattle. The kernel density of ADG is shown in Figure The posterior mean was estimated to be 3.394, which corresponded very well to the sample mean of ADG of the 7670 Angus cattle Table  because The purpose of this example was to show parallel computing using the MPI (Message Passing Interface) library. The change in computing time for this example was, however, almost insignificant because sampling from a normal distribution is very quick. In addition, with parallel simulation, inter-process communication requires some extra time as overhead, which offset gains from parallel computing.MPI is a language-independent communication protocol used to program parallel computers that is extensively used for high-performance computing. More specifically, MPI is a library of routines for creating parallel programs e.g., in C or Fortran 77, that allow users to create programs that can run on most parallel computer architectures.  In the example code, the MPI library was used to handle inter-process communications in the C program. With MPI, each task can have its own local memory during computation . Typically, tasks exchange data by sending and receiving messages but data transfer usually requires cooperation among processors, that is, a “send” operation must have a matching “receive” operation.http://apps.nrbook.com/c/index.html), with process ID used as the random number seed. Preferably, one can use a parallel random number generator, such as the Scalable Parallel Random Number Generators (SPRNG) Library (http://sprng.cs.fsu.edu/).A few details about this program in the Appendix are described in the following. MPI_Comm_rand is used to find out the ID of all participating processors and MPI_Comm_size is used to get the number of participating processors. A common pattern of interaction among parallel processors is to use MPI_Send and MPI_Receive to allocate work among them. In the present example, however, this was done in a slightly different manner. MPI_Bcast is used to send common parameter values  to all participating processors. Then, after each processor has finished its work, the subroutine MPI_Reduction is used to sum up the posterior values from all processors. Subroutine MPI_Reduction collects data from all processors, reduces the data to a single value , and then stores the results on the master process . There are several predefined operations that MPI_Reduction can provide. In addition to summation, it can also conduct multiplication, and find minimum or maximum values. Finally, the master processor computes the means and standard deviation  for the mean of the normal model. Note that, in this illustration, we used sequential functions to generate random numbers  regression model ,27. In ty is a vector of phenotypes, μ is an effect common to all observations, β is a vector of unknown marker effects, X is an incidence matrix, and e is a vector of residuals. The prior specification follows de los Campos et al. [where s et al.  but withs et al. .The dataset used here consisted of 147 Angus cattle, each genotyped for 37 892 polymorphic SNP (Single Nucleotide Polymorphisms) markers and with estimated breeding values (EBV) for marbling score as response variable. In addition to running a single long chain of 100 000 iterations (after a burn-in of 1000 iterations), we also ran 10 chains, each consisting of 10 000 iterations (after a burn-in of 1000 iterations). All jobs were submitted and run on a Condor cluster at the University of Wisconsin – Madison . This clFor the multiple chains approach, each started with over-dispersed initial values (and with different seeds for the random number generator), and the chains converged after a certain number of iterations. Markov chain Monte Carlo convergence was examined using posterior samples of the residual variance collected from the first 4500 iterations of each chain. Trace plots of posterior samples of the residual variance showed that most chains tended to stabilize after 1000 iterations, and all approached 0.0034 Figure a, which n method  suggesteThe parallel computing took between 141 and 178 min to complete each of the 10 processes. The differences were due to varying CPU speeds and workloads on these computer nodes. In contrast, running a single chain with 100 000 iterations (after a burn-in of 1000 iterations) on a Linux workstation with similar specifications took 1386 min . Assuming each iteration takes the same amount of processing time, the reduction in runtime is given by:When running multiple chains, the reduction in runtime is limited by the time required for burn-in. Let K treated as unknown. Ideally, if K-fold. However, when b is a significant proportion of n, the actual speedup falls well short of its potential.The above is an alternative form of (10) with n = 10b, which is a useful rule-of-thumb in most practical situations. Then, equation (15) depends only on parameter K. Hence, we have K = 8 and K = 16. As Practically, burn-in time is related to the mixing rate of the chain, which is related to the total length of the Markov chain. Let S is a function of t and K, as depicted in Figure n and b, the speedup will reach a plateau after the number of CPU cores reaches a certain level.Thus, when running multiple chains, each with a significant length of burn-in, the speedup does not scale well with the number of available CPU cores. Let Finally, we show a real application of parallel computing on genome-enabled prediction in beef cattle. The computing was implemented with a high-throughput computing pipeline called parallel-BayesCpC . This isV Figure b. This pV Figure c. In ConFor simplicity of illustration, consider a linear model with only the overall mean and marker effects:ith animal; µ is the overall mean; aj is the substitution effect associated with the jth SNP  for the ith individual, and A priori, the BayesCπ model assumes that the effect of an SNP is null with probability where π is unknown and needs to be inferred, given the prior distribution of π that is taken as uniform between 0 and 1, Here, therwise . Posteritherwise .π model, then the next iteration assumes that all the k selected SNP have non-null effects on the quantitative trait. The above defines BayesC model with p replaced by k. Posterior inference in BayesC π is fixed at zero and sampling indicator variables is no longer relevant.With a subset of, say K-fold CV is often used to evaluate predictive models, in which the whole dataset is divided into K portions of approximately equal size. The model is then trained in the set of K-1 portions of the data and predicted in the remaining one portion. Portioning of training and testing sets is then rotated K times in each CV experiment. Furthermore, each CV experiment can be randomly replicated a number of times in order to increase the stability of model evaluation (but we did not do that in the present study).Typically, As an example, we used the parallel BayesCpC package to select the optimal number of SNP to predict rib eye area in a beef cattle population. The data consisted of 2919 animals each with estimated breeding values for rib eye area and genotypes obtained from the Illumina 50K SNP Beadchip. After data editing and data quality control, 46 723 polymorphic SNP markers were used. The optimal panel search started with the top 50 SNP markers according to the posterior model probability for having a non-zero effect for this marker, and then increased from the top 100 to the top 3000 markers at an increment of 100 markers each time. We did not exhaust all possible panels beyond 3K because the prediction accuracy showed a constant trend of decrease after the panel reached 1000 SNP.In the parallel-BayesCpC package, MPI is used for data quality control and parallel MCMC is employed by both FS and CV. In our example, distributed jobs were submitted to a local Condor pool with 128 cores. These are Linux workstations, with Intel® Xeon® CPU (2.67 GHz), > 8G memory per slot and a cache size of 12 288 KB. The submit machine is also a Linux workstation with Intel® Xeon® CPU (3.00 GHz), 16G memory and 6144 KB cache size. Each parallel MCMC chain for feature selection consisted of 10 000 iterations after a burn-in of 2000 iterations, thinned every one-tenth. Each parallel MCMC for CV consisted of 25 000 iterations, after a burn-in of 5000 iterations, thinned every one-tenth.We examined three computing strategies to search for an optimal panel size for predicting genetic merit using SNP markers. The first strategy executed 30 meta-jobs in parallel, each consisting of one round of parallel FS jobs using all SNP markers and one round of parallel post-FS inference and CV for a specific panel . In the second strategy, one round of parallel FS jobs was executed, followed by 30 rounds of parallel post-FS inference and CV jobs conducted sequentially for all panel sizes. Parallel CV jobs for the 500-SNP panel started only when parallel CV jobs for the 400-SNP jobs had finished. The third strategy consisted of 30 meta-jobs executed in series, with each meta-job consisting of one round of parallel FS jobs using all SNP markers and one round of parallel post-FS inference and CV for a specific panel. The difference between the second and the third strategy is that different optimal panel sizes were selected based on the same set of FS results with the second computing strategy but each panel was selected based on a different set of FS results with the third computing strategy.There were very significant differences in computing time between the three computing strategies Figure a. The fiDespite the differences in computing times, predictions obtained with the three computing scenarios were highly comparable. The correlation between estimated breeding values for rib eye area and their fitted values in the training set (referred to as fitting accuracy hereafter) increased almost monotonically with panel size, until it plateaued with a panel size of around 2000 SNP Figure b. Howeveπ model postulates that a portion π of all SNP have zero effect. In a high-density SNP panel, π is typically expected to be large, meaning that the portion of “signal” SNP, 1-π in the training set did not correspond to the number of SNP in the optimal SNP panel for prediction. Hence, we postulate that parameter π in a BayesC π model may not provide information on the size of an optimal SNP panel for prediction for small panels but could be informative for a higher density of markers.In the Bayes CpC procedure, the BayesC l Figure , so the With the Bayesian regression models explored here, feature selection may be important since a model with all SNP does not necessarily give the best predictions. This situation is unlike ridge regression best linear unbiased prediction  or prediTo date, almost all statistical software packages for animal breeding and genetics have been developed for serial computation. In such programs, only one instruction is executed at a time and after that instruction is finished, the next instruction begins. Hence, serial computing performance depends heavily on the speed (clock frequency) of CPU, and the runtime of a program is approximately equal to the number of instructions multiplied by the average time per instruction. Keeping everything else constant, higher clock frequency leads to faster computing speed and thus decreased runtime for all computation-bounded programs . This waPi and Pj be two program jobs, where i < j indexes the order of execution. Bernstein’s conditions [Ii and Oi be the input variables and output variables, respectively, of Pi. Likewise, the same definitions hold for Ij and Oj of Pj . Then, Pi and Pj are independent if they satisfy (1) Pi) are used by the second job (Pj). Violation of condition (2) introduces an anti-dependency, because Pj would overwrite a variable needed by Pi . The third condition represents an output dependency: when two statements write to the same location, the final result is determined by the last executed statement.Parallel computing uses multiple processing elements concurrently to solve a problem. To implement parallel computing, one first needs to break the problem into discrete “chunks” of work, so that they can be distributed to run on multiple processors. This is known as task decomposition or partitioning. The next fundamental step in designing the parallel algorithm involves identifying independencies that are assumed explicitly in the model. Without loss of generality, let nditions  can be unditions . Let Ii MCMC algorithms have revolutionized the application of Bayesian inference, because it tackles a large range of complex inferential problems that were previously not considered possible, tractable. In the meantime, statisticians are becoming ever more ambitious in the range (complexity) of models they consider and MCMC algorithms for large complex models often require enormous amounts of computing power. Consequently, effective exploitation of parallel algorithms is of high relevance to Bayesian computation. A difficulty, however, is that MCMC algorithms are serial by nature and do not easily migrate onto a parallel system. Nevertheless, various strategies can be used to design parallel MCMC methods. The key is to identify steps with data independence or conditional independence on which parallelism can reside. Several algorithms or strategies exist for running parallel MCMC, which is straightforward and it requires minimal inter-process communication. However, poorly mixing MCMC algorithms with long burn-in periods are not ideally suited to this situation, because a long period of burn-in must be repeated on every available CPU core. Thus, it is often desirable to explore strategies for parallelism within single chains.The actual performance of parallel MCMC depends on several issues. Among them, inter-process communication is a primary factor. Many parallel applications require processes to share data with each other, which is known as inter-task communication and implies overhead. Inter-task communication can offset the gain in computing speed from parallel computing, because it frequently requires some type of synchronization between tasks, causing processes to spend time “waiting”. In the worst case, competing communication traffic can saturate the available network bandwidth, leading to poor parallel computing performance. Thus, there is always a need to balance the distribution of work among tasks. There is no simple rule for this type of load balancing. Ideally, all tasks are to be kept busy all of the time, so that task idle time is minimized ,10.https://computing.llnl.gov/tutorials/parallel_comp/). On one hand, a low computation to communication ratio  facilitates load balancing, as relatively small amounts of computational work are done between communication events but this can imply high communication overhead and less opportunity for performance enhancement, because communication and synchronization between tasks may take longer than the computation. On the other hand, a high computation to communication ratio  allows more opportunity for performance enhancement; because relatively large amounts of computational work are done between communication and synchronization events. However, it is difficult to balance loads efficiently with coarse-grain parallelism and computing time may differ dramatically between computer cores. Therefore, there is a tradeoff between computing and communication and the optimal granularity depends on the problem at hand. In most parallel MCMC problems, it is advantageous to have coarse granularity because the overhead associated with communication and synchronization is high relative to execution speed, but fine-grain parallelism can sometimes help reduce overhead due to load imbalance.The ratio of computation to communication is qualitatively measured by the concept of granularity , each on a separate processor (or CPU core). In the context of parallel MCMC, two basic algorithms exist: running multiple chains and parallelism within a single chain, yet some variants can be useful as well. In principle, all Bayesian models can be parallelized in computing but the associated algorithms and strategies may differ, leading to varied computing efficiencies. Although many technical details have yet to be explored, we expect that the use of parallel MCMC methods will revolutionize computational tools for research and breeding programs for animals in the post-genome era.(a) Example C code using MPI for parallel simulation of a single-parameter normal model with unknown mean and known variance/* This is an example C program using MPI for parallel computing of ** a single-parameter normal model with unkown mean and known ** variance. For illustration purpose, the step for data input is omitted. ** Instead, the sample mean and standard deviation, as well as the prior ** mean and standard are used. ** Contact: X-L Wu, nick.wu@ansci.wisc.edu; UW-Madison, 09-12-2011 */#include < stdio.h>#include < math.h>#include < mpi.h>#include “ranNum.h”main{int proc_id, root_process, nprocs, ierr, niters, i;double xi, xi2, sum, psum, sum_xi2, psum_xi2;double tar0, tar1, sdn, tarn, varn, mun, mumu, sdmu;MPI_Status status;/* Prior mean and standard deviation */double mu0 = 4.000;double sd0 = 1.000;/* sample size, mean and variance */int nind = 7670;double mu1 = 3.394;double sd1 = 0.580;/* compute posterior statistics */tar0 = 1.0/(sd0*sd0);tar1 = (1.0*nind)/(sd1*sd1);varn = 1.0/(tar0 + tar1);sdn = sqrt(varn);mun = varn * (tar0*mu0 + tar1*mu1);/* Parallel simulation begins, starting from here *//* process 0 as the root process. */root_process = 0;/* Replicate this process to create parallel processes. */ierr = MPI_Init;/* Allocate memory for random seed variable*/long* idum;MPI_Alloc_mem(sizeof(long), MPI_INFO_NULL, &idum);/* Find out process ID and number of participating processes. */ierr = MPI_Comm_rank;ierr = MPI_Comm_size;/* Root process gets the number of simulation steps */if(proc_id == root_process) {printf(“Please enter the number of simulation steps: ”);scanf;}/* Broadcast the number of simulation steps to all participating processes */ierr = MPI_Bcast;/*process id as the random number seed */*idum = proc_id;/* Each process computes a partial sum of simulated values */psum = 0;psum_xi2 = 0;for {xi = mun + sdn * randomnormal(idum);xi2 = xi * xi;psum = psum + xi;psum_xi2 = psum_xi2 + xi2;}printfpsum);/* Do a reduction in which all partial sums are combined into the grand sum */ierr = MPI_Reduce;ierr = MPI_Reduce;/* Finally, the root process prints posterior mean and standard error of mu. */if(proc_id == root_process) {mumu = sum / niters;sdmu = sqrt((sum_xi2 - niters*mumu*mumu)/(niters-1));printfmumu,(float)sdmu);}/* Close down this processes. */ierr = MPI_Finalize;}(b) Condor job batch files # Condor DAGMan jobJOB input job_InputDataJOB selection job_SelectionSCRIPT POST selection /usr/local/wgse_beta/V2/cmd_postscript_SelectionSummaryJOB validation job_ValidationSCRIPT POST validation /usr/local/wgse_beta/V2/cmd_postscript_OutputResultsPARENT input CHILD selectionPARENT selection CHILD validation# Data input and quality controlUniverse = vanillaExecutable = /usr/local/wgse_beta/V2/rungeneric.plArguments = ‐‐new ‐‐type = R ‐‐tarball = built-sl5-R-2.10.1.tar.gz ‐‐installfrom = R-2.10.1 ‐‐cmdtorun = p1_data_input_n_processing.R ‐‐unique = inputLog = step_input.logOutput = step_input.outError = step_input.errornotification = NEVERshould_transfer_files = YESwhen_to_transfer_output = ON_EXITtransfer_input_files =(data path and files are omitted), /home/nickwu/BayesCpC/V2_UW/data/datafile.csv, /usr/local/wgse_beta/V2/rungeneric.pl, /usr/local/wgse_beta/V2/SLIBS.tar.gz, /usr/local/wgse_beta/V2/cmd_data_input_n_processing, /usr/local/wgse_beta/V2/p1_data_input_n_processing.R,Queue# Feature selectionUniverse = vanillaExecutable = /usr/local/wgse_beta/V2/rungeneric.plArguments = ‐‐new ‐‐type = R ‐‐tarball = built-sl5-R-2.10.1.tar.gz ‐‐installfrom = R-2.10.1 ‐‐cmdtorun = p4_BayesCpC_validation.R ‐‐unique = validation_1 1Log = step_validation.$(process).logOutput = step_validation.$(process).outError = step_validation.$(process).errornotification = NEVERshould_transfer_files = YESwhen_to_transfer_output = ON_EXITtransfer_input_files = .linkPar, /usr/local/wgse_beta/V2/rungeneric.pl, /usr/local/wgse_beta/V2/SLIBS.tar.gz, /usr/local/wgse_beta/V2/cmd_cross_validation, /usr/local/wgse_beta/V2/p4_BayesCpC_validation.R,QueueArguments = ‐‐new ‐‐type = R ‐‐tarball = built-sl5-R-2.10.1.tar.gz ‐‐installfrom = R-2.10.1 ‐‐cmdtorun = p4_BayesCpC_validation.R ‐‐unique = validation_2 2QueueArguments = ‐‐new ‐‐type = R ‐‐tarball = built-sl5-R-2.10.1.tar.gz ‐‐installfrom = R-2.10.1 ‐‐cmdtorun = p4_BayesCpC_validation.R ‐‐unique = validation_3 3Queue# Post-FS inference and cross-validationUniverse = vanillaExecutable = /usr/local/wgse_beta/V2/rungeneric.plArguments = ‐‐new ‐‐type = R ‐‐tarball = built-sl5-R-2.10.1.tar.gz ‐‐installfrom = R-2.10.1 ‐‐cmdtorun = p4_BayesCpC_validation.R ‐‐unique = validation_1 1Log = step_validation.$(process).logOutput = step_validation.$(process).outError = step_validation.$(process).errornotification = NEVERshould_transfer_files = YESwhen_to_transfer_output = ON_EXITtransfer_input_files = .linkPar, /usr/local/wgse_beta/V2/rungeneric.pl, /usr/local/wgse_beta/V2/SLIBS.tar.gz, /usr/local/wgse_beta/V2/cmd_cross_validation, /usr/local/wgse_beta/V2/p4_BayesCpC_validation.R,QueueArguments = ‐‐new ‐‐type = R ‐‐tarball = built-sl5-R-2.10.1.tar.gz ‐‐installfrom = R-2.10.1 ‐‐cmdtorun = p4_BayesCpC_validation.R ‐‐unique = validation_2 2QueueArguments = ‐‐new ‐‐type = R ‐‐tarball = built-sl5-R-2.10.1.tar.gz ‐‐installfrom = R-2.10.1 ‐‐cmdtorun = p4_BayesCpC_validation.R ‐‐unique = validation_3 3QueueThe authors declare that they have no competing interests.XLW worked out the conception and design of this study, developed the pipeline, analyzed the results and wrote the article. CS and TMB helped with programming, data analysis and proof-read the article. KAW, GJMR, NDLG, and DG were involved in the design of this study, discussion of the results, and proof-reading of this article. All authors read and approved the final manuscript.


The purpose of the study was to identify the style of parental bonding and the personality characteristics that might increase the risk of hyperventilation and adjustment disorder.A total of 917 males were recruited, 156 with adjustment disorder and hyperventilation syndrome (AD + HY), 273 with adjustment disorder without hyperventilation syndrome (AD–HY), and 488 healthy controls. All participants completed the Parental Bonding Instrument, Eysenck Personality Questionnaire, and Chinese Health Questionnaire.Analysis using structural equation models identified a pathway relationship in which parental bonding affected personality characteristics, personality characteristics affected mental health condition, and mental health condition affected the development of hyperventilation or adjustment disorder. Males with AD–HY perceived less paternal care, and those with AD + HY perceived more maternal protection than those with adjustment disorder and those in the control group. Participants with AD–HY were more neurotic and less extroverted than those with AD + HY. Both groups showed poorer mental health than the controls.Although some patients with hyperventilation syndrome demonstrated symptoms of adjustment disorder, there were different predisposing factors between the two groups in terms of parental bonding and personality characteristics. This finding is important for the early intervention and prevention of hyperventilation and adjustment disorder. Parental attachment and premorbid personality traits play an important role in shaping the developmental trajectory of an individual and influence their ability to adjust to stressful events -3. In paHyperventilation syndrome refers to various somatic and psychological symptoms that appear to be a consequence of episodes of hyperventilation with no known organic basis . It was The term adjustment disorder refers to patients who have developed clinically significant emotional or behavioral symptoms; these symptoms dissipate eventually, once the stressor has been terminated . It has It is thought that hyperventilation syndrome might be related to adjustment disorder. In addition to stress, both hyperventilation syndrome and adjustment disorder have been found to be associated with parental bonding and personality characteristics ,14,18. IHence, the purpose of the study reported herein was to identify the style of parental bonding and the personality characteristics that might increase the risk of hyperventilation syndrome and adjustment disorder. Patients with hyperventilation syndrome, those with adjustment disorder, and male controls were compared to determine the differences between the groups, to provide information for the development of early intervention and prevention programs.2)  [International Classification of Diseases, 10th Edition (ICD-10) [All participants in the adjustment disorder with hyperventilation (AD + HY) group and in the adjustment disorder without hyperventilation (AD–HY) group were admitted to a teaching hospital for at least one week to verify the diagnosis. The patients were interviewed by a senior psychiatrist with reference to the (DSM-IV) . All of (ICD-10)  was refeA control group composed of 488 male conscripts who were at the same military base as the case group and had no experience of hyperventilation syndrome or any other physical or psychiatric illness was also recruited.A self-report questionnaire, which included demographic information, the Parental Bonding Instrument (PBI), the Eysenck Personality Questionnaire (EPQ), and the Chinese Health Questionnaire (CHQ), was used. The demographic data collected included the patient’s age, marital status, and education.Parker, Tupling, and Brown developed the PBI in 1979 . The PBIEysenck and Eysenck developed the EPQ . It consCheng and Williams  designedThe data were analyzed using the software package SPSS 17.0 for Windows , and the structural equation model (SEM) was analyzed using the AMOS 7.0 statistical software package . All variables were analyzed using one-way analysis of variance (ANOVA) to compare the differences among the three groups. In addition, Bonferroni post hoc multiple comparisons were used to compare each pair group in terms of any statistically significant variables. The SEM techniques made use of all the information that was provided by the regression techniques in a path analysis. If the SEM resulted in a p value greater than 0.05, an adjusted goodness-of-fit index (AGFI) greater than 0.9, and a root mean square error of approximation (RMSEA) less than 0.08, this indicated that the null model corresponded to the real structure.p < 0.001) and level of education  among the three groups; in subsequent analyses, these factors were controlled for.The demographic distribution of the three groups – those with adjustment disorder and hyperventilation (AD + HY), those with adjustment disorder alone (AD–HY), and controls – was as follows. All participants were male; the mean age of the AD + HY group was 21.12 (SD = 1.98) years, for the AD–HY group it was 21.86 (SD = 2.36) years, and for the controls 22.61 (SD = 2.18) years. In terms of the average years of education received by the participants, all had graduated from high school; the AD + HY group had received a mean of 12.92 (SD = 2.29) years of education, the AD–HY group a mean of 13.56 (SD = 2.87) years, and the controls 13.94 (SD = .61) years. There were statistically significant differences with respect to age . For paternal care, in the AD + HY group the mean score was 19.07 (SD = 6.88), and in the AD–HY group it was 16.59 (SD = 7.48), compared with 22.86 (SD = 6.78) in the controls. For maternal care, in the AD + HY group the mean score was 22.75 (SD = 6.04), and in the AD–HY group it was 21.34 (SD = 7.63), compared with 25.42 (SD = 6.39) in the controls. The differences in paternal and maternal protection among the three groups were also statistically significant . For paternal protection in the AD + HY group the mean score was 14.47 (SD = 5.54), and in the AD–HY group it was 15.50 (SD = 6.20), compared with 10.68 (SD = 5.81) in the controls. For maternal protection in the AD + HY group the mean score was 14.72 (SD = 6.54), and in the AD–HY group it was 15.29 (SD = 6.89), compared with 10.92 (SD = 6.06) in the controls. Using Bonferroni post hoc tests, between-group comparisons showed that there were no significant differences between the AD + HY and AD–HY groups in terms of the parental bonding dimensions of maternal care, maternal protection, and paternal protection . The results are shown in Table The comparisons of parental bonding, personality characteristics, and mental health condition among the three groups are shown in Table extraversion = 155.53, p < 0.001; Fneuroticism = 473.74, p < 0.001).The mean score for extraversion in the AD + HY group, as assessed with the EPQ, was 6.08 (SD = 4.23), and in the AD–HY group it was 4.47 (SD = 3.94), compared with 9.31 (SD = 3.50) in the controls. The mean score for neuroticism in the AD + HY group was 7.86 (SD = 3.08), and in the AD–HY group it was 9.26 (SD = 2.34), compared with 3.00 (SD = 3.04) in the controls. The differences in extraversion and neuroticism among three groups were statistically significant (Fp < 0.001). Using Bonferroni post hoc tests, between-group comparisons showed that there were no significant differences between the AD + HY and AD–HY groups .Furthermore, the mean score for mental health in the AD + HY group, as assessed with the CHQ, was 6.62 (SD = 3.51), and in the AD–HY group it was 7.45 (SD = 3.49), compared with 1.00 (SD = 1.88) in the controls. The difference in mental health among the three groups was statistically significant , an AGFI of 0.987 , and an RMSEA of less than 0.001 , as shown in Figure p < 0.001; β = −0.21, p < 0.001; β = −0.07, p = 0.016, respectively). When parents were overprotective, their sons were less extroverted and more neurotic . In addition, participants who were less extroverted or more neurotic had poorer mental health and were more likely to develop adjustment disorder . Finally, participants with poorer mental health or those who were younger were more likely to develop adjustment disorder . Extroversion, neuroticism, mental health condition, and adjustment disorder accounted for 19%, 42%, 63%, and 66% of the variance, respectively.The first parsimonious model, which compared AD–HY and controls, resulted in a good fit, with a p value of 0.203, an AGFI of 0.971, and an RMSEA of 0.028, as shown in Figure p < 0.001; β = −0.12, p = 0.013; mother: β = −0.14, p = 0.006; β = 0.18, p = 0.001, respectively). In the case of fathers who were overprotective, their sons were more neurotic and had poorer mental health . In addition, participants who were less extroverted had poorer mental health , and those who were more neurotic had poorer mental health and a greater likelihood of developing adjustment disorder and hyperventilation . Those with poorer mental health were also at a higher risk of developing adjustment disorder and hyperventilation . Extroversion, neuroticism, mental health condition, and hyperventilation accounted for 12%, 35%, 57%, and 55% of the variance, respectively.The second model compared participants with AD + HY and the controls. The model resulted in a good fit, with a p value of 0.451, an AGFI of 0.982, and an RMSEA of less than 0.001, which indicated a good fit, as shown in Figure p < 0.001; β = −0.15, p = 0.003, respectively). When fathers were overprotective, their sons were more neurotic , and when mothers were overprotective, their sons were less extroverted . Compared with those with AD + HY, those with AD–HY were less extroverted and more neurotic . Extroversion, neuroticism, and adjustment disorder accounted for 8%, 18%, and 9% of the variance, respectively.The last model compared participants with AD + HY and those with AD–HY. The model resulted in a Use of one-way ANOVA showed significant differences in all dimensions among the three groups; however, mediating and moderating factors, and any causal effect, have been neglected. The paired group comparison SEMs for the AD + HY, AD–HY, and control groups all revealed a pathway relationship in which parental bonding affected personality characteristics, personality characteristics affected mental health condition, and mental health condition affected the development of hyperventilation or adjustment disorder. More specifically, with respect to parental bonding, males with AD–HY perceived less care from their fathers than those with AD + HY and the controls, and males with AD + HY perceived more maternal protection than those with AD–HY and the controls. Although participants with AD–HY and those with AD + HY were both more neurotic and less extroverted than the controls, those with AD–HY were even more neurotic and less extroverted than those with AD + HY. The two groups with adjustment disorder did not differ in their mental health condition; both showed worse mental health than the controls.The pathway relationship in which parental bonding affected personality characteristics, which in turn affected mental health condition and the development of hyperventilation syndrome or adjustment disorder, is consistent with previous studies on hyperventilation and adjustment disorders ,14,18. PAttachment style is an important factor in understanding the particular ways in which individuals feel and react when stressed by illness, and is important when assessing the utilization of healthcare ,34. WithInterestingly, although high neuroticism and low extroversion were found to increase the risk of adjustment disorder or hyperventilation syndrome, individuals with AD–HY showed even greater neuroticism and less extroversion than those with AD + HY. Extroversion had only an indirect effect on individuals with AD + HY when compared with the controls. Several studies have demonstrated that neuroticism and introversion might increase the risk of mental disorders such as anxiety, depression, and maladaptive behaviors -3,42,43.Few studies have investigated the differences between adjustment disorder and hyperventilation syndrome; however, criteria to assist in the differential diagnosis are required. Our study compared individuals with AD–HY, those with AD + HY, and controls, and identified differences among these three groups with respect to parental bonding, personality characteristics, and mental health. Self-report measures were used for the study, which might have resulted in problems of recall bias in terms of the rating of parental attachment, and this could have influenced the results. However, studies have demonstrated that the PBI has acceptable reliability and validity ,23. FurtThe study showed that, although some patients with hyperventilation syndrome demonstrated symptoms of adjustment disorder, different predisposing factors were found between the two groups of patients with respect to parental bonding and personality characteristics. Patients with AD–HY perceived a greater degree of paternal care, and those with AD + HY perceived a greater degree of maternal overprotection. Those with AD–HY also showed a higher degree of neuroticism and less extroversion than those with AD + HY, although both had poorer mental health than the controls. In addition, the comparison of the AD + HY group with the controls using SEM yielded high variances of 66% and 55%. With adjustment for age, sex, and level of education, this finding is potentially important for the development of early intervention and prevention programs for hyperventilation syndrome and adjustment disorder. Further studies are required to determine whether there is a gender difference in the etiology of hyperventilation syndrome and adjustment disorder, and thus whether this model can be generalized to female patients with hyperventilation syndrome or adjustment disorder.All authors have no conflict of interest to declare.FWL contributed to the conceptual design of the study and modified the manuscript. THL participated in the conceptual design, collected the data, data analysis of the study, and drafted the manuscript. MFH interpreted the data. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/12/56/prepub