A pathway for mitotic chromosome formation

Mitotic chromosomes fold as compact arrays of chromatin loops. To identify the pathway of mitotic chromosome formation, we combined imaging and Hi-C analysis of synchronous DT40 cell cultures with polymer simulations. Here we show that in prophase, the interphase organization is rapidly lost in a condensin-dependent manner, and arrays of consecutive 60-kilobase (kb) loops are formed. During prometaphase, ~80-kb inner loops are nested within ~400-kb outer loops. The loop array acquires a helical arrangement with consecutive loops emanating from a central spiral staircase condensin scaffold. The size of helical turns progressively increases to ~12 megabases during prometaphase. Acute depletion of condensin I or II shows that nested loops form by differential action of the two condensins, whereas condensin II is required for helical winding

Structural, item, and test generalizability of the psychopathology checklist - revised to offenders with intellectual disabilities

The Psychopathy Checklist–Revised (PCL-R) is the most widely used measure of psychopathy in forensic clinical practice, but the generalizability of the measure to offenders with intellectual disabilities (ID) has not been clearly established. This study examined the structural equivalence and scalar equivalence of the PCL-R in a sample of 185 male offenders with ID in forensic mental health settings, as compared with a sample of 1,212 male prisoners without ID. Three models of the PCL-R’s factor structure were evaluated with confirmatory factor analysis. The 3-factor hierarchical model of psychopathy was found to be a good fit to the ID PCL-R data, whereas neither the 4-factor model nor the traditional 2-factor model fitted. There were no cross-group differences in the factor structure, providing evidence of structural equivalence. However, item response theory analyses indicated metric differences in the ratings of psychopathy symptoms between the ID group and the comparison prisoner group. This finding has potential implications for the interpretation of PCL-R scores obtained with people with ID in forensic psychiatric settings

Fission yeast 26S proteasome mutants are multi-drug resistant due to stabilization of the pap1 transcription factor

Here we report the result of a genetic screen for mutants resistant to the microtubule poison methyl benzimidazol-2-yl carbamate (MBC) that were also temperature sensitive for growth. In total the isolated mutants were distributed in ten complementation groups. Cloning experiments revealed that most of the mutants were in essential genes encoding various 26S proteasome subunits. We found that the proteasome mutants are multi-drug resistant due to stabilization of the stress-activated transcription factor Pap1. We show that the ubiquitylation and ultimately the degradation of Pap1 depend on the Rhp6/Ubc2 E2 ubiquitin conjugating enzyme and the Ubr1 E3 ubiquitin-protein ligase. Accordingly, mutants lacking Rhp6 or Ubr1 display drug-resistant phenotypes

An IRT Analysis of Motive Questionnaires: The Unified Motive Scales

Multiple inventories claiming to assess the same explicit motive (achievement, power, or affiliation) show only mediocre convergent validity. In three studies (N = 1685) the structure, nomological net, and content coverage of multiple existing motive scales was investigated with exploratory factor analyses. The analyses revealed four approach factors (achievement, power, affiliation, and intimacy) and a general avoidance factor with a facet structure. New scales (the Unified Motive Scales; UMS) were developed using IRT, reflecting these underlying dimensions. In comparison to existing questionnaires, the UMS have the highest measurement precision and provide short (6-item) and ultra-short (3-item) scales. In a fourth study (N = 96), the UMS demonstrated incremental validity over existing motive scales with respect to several outcome criteria

Motor Preparatory Activity in Posterior Parietal Cortex is Modulated by Subjective Absolute Value

For optimal response selection, the consequences associated with behavioral success or failure must be appraised. To determine how monetary consequences influence the neural representations of motor preparation, human brain activity was scanned with fMRI while subjects performed a complex spatial visuomotor task. At the beginning of each trial, reward context cues indicated the potential gain and loss imposed for correct or incorrect trial completion. FMRI-activity in canonical reward structures reflected the expected value related to the context. In contrast, motor preparatory activity in posterior parietal and premotor cortex peaked in high “absolute value” (high gain or loss) conditions: being highest for large gains in subjects who believed they performed well while being highest for large losses in those who believed they performed poorly. These results suggest that the neural activity preceding goal-directed actions incorporates the absolute value of that action, predicated upon subjective, rather than objective, estimates of one's performance

Multi-layer scintillation detector for the MOON double beta decay experiment: Scintillation photon responses studied by a prototype detector MOON-1

An ensemble of multi-layer scintillators is discussed as an option of the high-sensitivity detector Mo Observatory Of Neutrinos (MOON) for spectroscopic measurements of neutrino-less double beta decays. A prototype detector MOON-1, which consists of 6 layer plastic-scintillator plates, was built to study the sensitivity of the MOON-type detector. The scintillation photon collection and the energy resolution, which are key elements for the high-sensitivity experiments, are found to be 1835+/-30 photo-electrons for 976 keV electrons and sigma = 2.9+/-0.1% (dE/E = 6.8+/-0.3 % in FWHM) at the Qbb ~ 3 MeV region, respectively. The multi-layer plastic-scintillator structure with good energy resolution as well as good background suppression of beta-gamma rays is crucial for the MOON-type detector to achieve the inverted hierarchy neutrino mass sensitivity.Comment: 8 pages, 16 figures, submitted to Nucl.Instrum.Met

Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy

Background: Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective: The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods: We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results: Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P.05). Conclusions: Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance

Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders

Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations

Stimulus-Dependent Adjustment of Reward Prediction Error in the Midbrain

Previous reports have described that neural activities in midbrain dopamine areas are sensitive to unexpected reward delivery and omission. These activities are correlated with reward prediction error in reinforcement learning models, the difference between predicted reward values and the obtained reward outcome. These findings suggest that the reward prediction error signal in the brain updates reward prediction through stimulus–reward experiences. It remains unknown, however, how sensory processing of reward-predicting stimuli contributes to the computation of reward prediction error. To elucidate this issue, we examined the relation between stimulus discriminability of the reward-predicting stimuli and the reward prediction error signal in the brain using functional magnetic resonance imaging (fMRI). Before main experiments, subjects learned an association between the orientation of a perceptually salient (high-contrast) Gabor patch and a juice reward. The subjects were then presented with lower-contrast Gabor patch stimuli to predict a reward. We calculated the correlation between fMRI signals and reward prediction error in two reinforcement learning models: a model including the modulation of reward prediction by stimulus discriminability and a model excluding this modulation. Results showed that fMRI signals in the midbrain are more highly correlated with reward prediction error in the model that includes stimulus discriminability than in the model that excludes stimulus discriminability. No regions showed higher correlation with the model that excludes stimulus discriminability. Moreover, results show that the difference in correlation between the two models was significant from the first session of the experiment, suggesting that the reward computation in the midbrain was modulated based on stimulus discriminability before learning a new contingency between perceptually ambiguous stimuli and a reward. These results suggest that the human reward system can incorporate the level of the stimulus discriminability flexibly into reward computations by modulating previously acquired reward values for a typical stimulus