The two figures above from the research article suggests a couple of interesting things. First, this kind of complex AI-driven analysis can be used to predict other kinds of diseases, cancers in the first figure, all kinds of diseases in the second. The point is that AI can comb through gigantic piles of biological data from tens of thousands of people and pick out meaningful bits of information among all the noise of normal, messy biology. I'm not sure humans alone can do that, but if they can, it is much slower going than what AI can do.
Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80 [exceeding 80% accuracy]. .... Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.
Mitigating climate change necessitates global cooperation, yet global data on individuals’ willingness to act remain scarce. In this study, we conducted a representative survey across 125 countries, interviewing nearly 130,000 individuals. Our findings reveal widespread support for climate action. Notably, 69% of the global population expresses a willingness to contribute 1% of their personal income, 86% endorse pro-climate social norms and 89% demand intensified political action. Countries facing heightened vulnerability to climate change show a particularly high willingness to contribute. Despite these encouraging statistics, we document that the world is in a state of pluralistic ignorance, wherein individuals around the globe systematically underestimate the willingness of their fellow citizens to act. This perception gap, combined with individuals showing conditionally cooperative behavior, poses challenges to further climate action. Therefore, raising awareness about the broad global support for climate action becomes critically important in promoting a unified response to climate change.
The world’s climate is a global common good and protecting it requires the cooperative effort of individuals across the globe. Consequently, the ‘human factor’ is critical and renders the behavioral science perspective on climate change indispensable for effective climate action. Despite its importance, limited knowledge exists regarding the willingness of the global population to cooperate and act against climate change. To fill this gap, we designed and conducted a globally representative survey in 125 countries, with the aim of examining the potential for successful global climate action. The central question we seek to answer is to what extent are individuals around the globe willing to contribute to the common good, and how do people perceive other people’s willingness to contribute (WTC)?
