Thursday, July 30, 2020

Strongly unified belief in the linear non-threshold model among panel members and their refusal to acknowledge that a low dose of radiation could exhibit a threshold, & an excessive degree of self-interest

The Muller-Neel dispute and the fate of cancer risk assessment. Edward J. Calabrese. Environmental Research, July 23 2020, 109961.

ABSTRACT: The National Academy of Sciences (NAS) Atomic Bomb Casualty Commission (ABCC) human genetic study (i.e., The Neel and Schull, 1956a report) showed an absence of genetic damage in offspring of atomic bomb survivors in support of a threshold model, but was not considered for evaluation by the NAS Biological Effects of Atomic Radiation (BEAR) I Genetics Panel. The study therefore could not impact the Panel's decision to recommend the linear non-threshold (LNT) dose-response model for risk assessment.1 Summaries and transcripts of the Panel meetings failed to reveal an evaluation of this study, despite its human relevance and ready availability, relying instead on data from Drosophila and mice. This paper explores correspondence among and between BEAR Genetics Panel members, including James Néel, the study director, and other contemporaries to assess why the Panel failed to use these data and how the decision to recommend the LNT model affected future cancer risk assessment policies and practices. This failure of the Genetics Panel was due to: (1) a strongly unified belief in the LNT model among panel members and their refusal to acknowledge that a low dose of radiation could exhibit a threshold, a conclusion that the Néel/Schull atomic bomb study could support, and (2) an excessive degree of self-interest among panel members who experimented with animal models, such as Hermann J. Muller, and feared that human genetic studies would expose the limitations of extrapolating from animal (especially Drosophila) to human responses and would strongly shift research investments/academic grants from animal to human studies. Thus, the failure to consider the Néel/Schull atomic bomb study served both the purposes of preserving the LNT policy goal and ensuring the continued dominance of Muller and his similarly research-oriented colleagues.

6. Conclusion

Human genetic data from over 25 years of the ABCC study (i.e., 1946–1972) demonstrated support for a threshold model for radiation-induced genetic damage in humans, but that information were both ignored and then rejected by the BEAR I and BEIR II Genetics Committees, respectively. The findings, now nearly 50 years later (Grant et al., 2015), have consistently continued to contradict a linear dose response, supporting a threshold response for a complex array of endpoints of genetic damage in humans. Furthermore, the decision to base the LNT recommendation on the male mouse data of Russell is now seen as flawed (Calabrese, 2017a,b), providing no support for the BEIR (1972) decision in favor of LNT.

The failure to assess the human genetic study of Neel and Schull (1956a) at this most crucial time in risk-assessment history represents a profound abrogation of responsibility by the NAS leadership and the BEAR Genetics Panels. This affirmative “failure of responsibility” appears to have been a goal of Muller as it would ensure the adoption of LNT and the continued professional dominance of Muller and his like-thinking and similar research-oriented colleagues. The adoption of LNT occurred during a “perfect storm” consisting of: heightened societal fear of nuclear confrontation; continuing nuclear fallout from atmospheric testing; ideologically based policy and scientific leadership of the Rockefeller Foundation and the US NAS; and a handpicked, highly LNT-biased Genetics Panel that was dominated by an even more-determined Hermann Muller to ensure adoption of the LNT. This history should represent a profound embarrassment to the US NAS, regulatory agencies worldwide, and especially the US EPA, and the risk-assessment community whose founding principles were so ideologically determined and accepted with little if any critical reflection.

Novel psychological construct characterised by high empathy and dark traits, the Dark Empath, is identified and described relative to personality, aggression, dark triad (DT) facets and wellbeing

The Dark Empath: Characterising dark traits in the presence of empathy. Nadja Heym et al. Personality and Individual Differences, July 29 2020, 110172.

• Latent profile analysis identifies 4 groups based on empathy and dark traits.
• Dark empath (DE, high empathy, dark traits) partly maintains an antagonistic core.
• DE and DT (low empathy, dark traits) are similar in vulnerable dark triad facets.
• DE and DT differ in extraversion, agreeableness, indirect aggression & wellbeing.
• Outside of the dark triad (empaths, typicals), empathy is unrelated to aggression.

Abstract: A novel psychological construct characterised by high empathy and dark traits: the Dark Empath (DE) is identified and described relative to personality, aggression, dark triad (DT) facets and wellbeing. Participants (n = 991) were assessed for narcissism, Machiavellianism, psychopathy, cognitive empathy and affective empathy. Sub-cohorts also completed measures of (i) personality (BIG5), indirect interpersonal aggression (n = 301); (ii) DT facets of vulnerable and grandiose narcissism, primary and secondary psychopathy and Machiavellianism (n = 285); and (iii) wellbeing (depression, anxiety, stress, anhedonia, self-compassion; n = 240). Latent profile analysis identified a four-class solution comprising the traditional DT (n = 128; high DT, low empathy), DE (n = 175; high DT, high empathy), Empaths (n = 357; low DT, high empathy) and Typicals (n = 331; low DT, average empathy). DT and DE were higher in aggression and DT facets, and lower in agreeableness than Typicals and Empaths. DE had higher extraversion and agreeableness, and lower aggression than DT. DE and DT did not differ in grandiose and vulnerable DT facets, but DT showed lower wellbeing. The DE is less aggressive and shows better wellbeing than DT, but partially maintains an antagonistic core, despite having high extraversion. The presence of empathy did not increase risk of vulnerability in the DE.

Music training is ineffective regardless of outcome measure (verbal, non-verbal, speed-related, etc.), participants’ age, & duration of training; & has no impact on people’s non-music cognitive skills or academic achievement

Cognitive and academic benefits of music training with children: A multilevel meta-analysis. Giovanni Sala & Fernand Gobet. Memory & Cognition, Jul 29 2020.

Abstract: Music training has repeatedly been claimed to positively impact children’s cognitive skills and academic achievement (literacy and mathematics). This claim relies on the assumption that engaging in intellectually demanding activities fosters particular domain-general cognitive skills, or even general intelligence. The present meta-analytic review (N = 6,984, k = 254, m = 54) shows that this belief is incorrect. Once the quality of study design is controlled for, the overall effect of music training programs is null (g¯ ≈ 0) and highly consistent across studies (τ2 ≈ 0). Results of Bayesian analyses employing distributional assumptions (informative priors) derived from previous research in cognitive training corroborate these conclusions. Small statistically significant overall effects are obtained only in those studies implementing no random allocation of participants and employing non-active controls (g¯ ≈ 0.200, p < .001). Interestingly, music training is ineffective regardless of the type of outcome measure (e.g., verbal, non-verbal, speed-related, etc.), participants’ age, and duration of training. Furthermore, we note that, beyond meta-analysis of experimental studies, a considerable amount of cross-sectional evidence indicates that engagement in music has no impact on people’s non-music cognitive skills or academic achievement. We conclude that researchers’ optimism about the benefits of music training is empirically unjustified and stems from misinterpretation of the empirical data and, possibly, confirmation bias.