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Copper perchlorophthalocyanine (CuPcCl16, CuC32N8Cl16, Pigment Green 7) is one of the commercially most important green pigments. The compound is a nanocrystalline fully insoluble powder. Its crystal structure was first addressed by electron diffraction in 1972 [Uyeda et al. (1972). J. Appl. Phys. 43, 5181–5189]. Despite the commercial importance of the compound, the crystal structure remained undetermined until now. Using a special vacuum sublimation technique, micron-sized crystals could be obtained. Three-dimensional electron diffraction (3D ED) data were collected in two ways: (i) in static geometry using a combined stage-tilt/beam-tilt collection scheme and (ii) in continuous rotation mode. Both types of data allowed the crystal structure to be solved by direct methods. The structure was refined kinematically with anisotropic displacement parameters for all atoms. Due to the pronounced crystal mosaicity, a dynamic refinement was not feasible. The unit-cell parameters were verified by Rietveld refinement from powder X-ray diffraction data. The crystal structure was validated by many-body dispersion density functional theory (DFT) calculations. CuPcCl16 crystallizes in the space group C2/m (Z = 2), with the molecules arranged in layers. The structure agrees with that proposed in 1972.
Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.
Simple Summary: Acute myeloid leukemia (AML) is a genetically heterogeneous disease. Clinical phenotypes of frequent mutations and their impact on patient outcome are well established. However, the role of rare mutations often remains elusive. We retrospectively analyzed 1529 newly diagnosed and intensively treated AML patients for mutations of BCOR and BCORL1. We report a distinct co-mutational pattern that suggests a role in disease progression rather than initiation, especially affecting mechanisms of DNA-methylation. Further, we found loss-of-function mutations of BCOR to be independent markers of poor outcomes in multivariable analysis. Therefore, loss-of-function mutations of BCOR need to be considered for AML management, as they may influence risk stratification and subsequent treatment allocation.
Abstract: Acute myeloid leukemia (AML) is characterized by recurrent genetic events. The BCL6 corepressor (BCOR) and its homolog, the BCL6 corepressor-like 1 (BCORL1), have been reported to be rare but recurrent mutations in AML. Previously, smaller studies have reported conflicting results regarding impacts on outcomes. Here, we retrospectively analyzed a large cohort of 1529 patients with newly diagnosed and intensively treated AML. BCOR and BCORL1 mutations were found in 71 (4.6%) and 53 patients (3.5%), respectively. Frequently co-mutated genes were DNTM3A, TET2 and RUNX1. Mutated BCORL1 and loss-of-function mutations of BCOR were significantly more common in the ELN2017 intermediate-risk group. Patients harboring loss-of-function mutations of BCOR had a significantly reduced median event-free survival (HR = 1.464 (95%-Confidence Interval (CI): 1.005–2.134), p = 0.047), relapse-free survival (HR = 1.904 (95%-CI: 1.163–3.117), p = 0.01), and trend for reduced overall survival (HR = 1.495 (95%-CI: 0.990–2.258), p = 0.056) in multivariable analysis. Our study establishes a novel role for loss-of-function mutations of BCOR regarding risk stratification in AML, which may influence treatment allocation.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants’ choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors.