Efficiency for Lives, Equality for Everything Else: How Allocation Preference Shifts Across Domains

The allocation of scarce public resources such as transplant organs and limited public funding involves a trade-off between equality—equal access and efficiency—maximizing total benefit. The current research explores how preferences shift when allocation decisions involve human lives versus when they do not. Fifteen experiments test this question using a variety of allocation scenarios including allocation of lifesaving medical aid, money, road construction, vaccines, and other resources. The results consistently show an increased preference for efficiency, when the allocation involves saving human lives, and equality, when the allocation involves outcomes with other consequences. We found no preference shift when stakes were manipulated in allocations where lives were not on the line, suggesting that the effect cannot be explained by lifesaving resources simply being higher stakes. These findings suggest a unique preference for efficiency for allocations involving life-and-death consequences that has implications for designing and conveying public resource allocation policies

Knowledge overconfidence is associated with anti-consensus views on controversial scientific issues

Public attitudes that are in opposition to scientific consensus can be disastrous and include rejection of vaccines and opposition to climate change mitigation policies. Five studies examine the interrelationships between opposition to expert consensus on controversial scientific issues, how much people actually know about these issues, and how much they think they know. Across seven critical issues that enjoy substantial scientific consensus, as well as attitudes toward COVID-19 vaccines and mitigation measures like mask wearing and social distancing, results indicate that those with the highest levels of opposition have the lowest levels of objective knowledge but the highest levels of subjective knowledge. Implications for scientists, policymakers, and science communicators are discussed

Genetic architecture of laterality defects revealed by whole exome sequencing

Aberrant left-right patterning in the developing human embryo can lead to a broad spectrum of congenital malformations. The causes of most laterality defects are not known, with variants in established genes accounting for <20% of cases. We sought to characterize the genetic spectrum of these conditions by performing whole-exome sequencing of 323 unrelated laterality cases. We investigated the role of rare, predicted-damaging variation in 1726 putative laterality candidate genes derived from model organisms, pathway analyses, and human phenotypes. We also evaluated the contribution of homo/hemizygous exon deletions and gene-based burden of rare variation. A total of 28 candidate variants (26 rare predicted-damaging variants and 2 hemizygous deletions) were identified, including variants in genes known to cause heterotaxy and primary ciliary dyskinesia (ACVR2B, NODAL, ZIC3, DNAI1, DNAH5, HYDIN, MMP21), and genes without a human phenotype association, but with prior evidence for a role in embryonic laterality or cardiac development. Sanger validation of the latter variants in probands and their parents revealed no de novo variants, but apparent transmitted heterozygous (ROCK2, ISL1, SMAD2), and hemizygous (RAI2, RIPPLY1) variant patterns. Collectively, these variants account for 7.1% of our study subjects. We also observe evidence for an excess burden of rare, predicted loss-of-function variation in PXDNL and BMS1- two genes relevant to the broader laterality phenotype. These findings highlight potential new genes in the development of laterality defects, and suggest extensive locus heterogeneity and complex genetic models in this class of birth defects

A Computational Approach to Analyze the Mechanism of Action of the Kinase Inhibitor Bafetinib

Prediction of drug action in human cells is a major challenge in biomedical research. Additionally, there is strong interest in finding new applications for approved drugs and identifying potential side effects. We present a computational strategy to predict mechanisms, risks and potential new domains of drug treatment on the basis of target profiles acquired through chemical proteomics. Functional protein-protein interaction networks that share one biological function are constructed and their crosstalk with the drug is scored regarding function disruption. We apply this procedure to the target profile of the second-generation BCR-ABL inhibitor bafetinib which is in development for the treatment of imatinib-resistant chronic myeloid leukemia. Beside the well known effect on apoptosis, we propose potential treatment of lung cancer and IGF1R expressing blast crisis

Causal models in prediction and diagnosis

Abstract not available

Sampling Assumptions and the Size Principle in Property Induction

The ‘size principle ’ emphasized in recent Bayesian models o

The dangers of categorical thinking

Human beings are categorization machines, taking in voluminous amounts of messy data and then simplifying and structuring it. That’s how we make sense of the world and communicate our ideas to others. But according to the authors, categorization comes so naturally to us that we often see categories where none exist. That warps our view of the world and harms our ability to make sound decisions—a phenomenon that should be of special concern to any business that relies on data collection and analysis for decision making. Categorical thinking, the authors argue, creates four dangerous consequences. When we categorize, we compress category members, treating them as more alike than they are; we amplify differences between members of different categories; we discriminate, favoring certain categories over others; and we fossilize, treating the categorical structure we’ve imposed as static. In the years ahead, companies will have to focus attention on how best to mitigate those consequence