Discriminating the contribution of unmodifiable random intrinsic DNA replication errors (‘bad luck’) to cancer development from those of other factors is critical for understanding cancer in humans and for directing public resources aimed at reducing the burden of cancer. Here, we review and highlight the evidence that demonstrates cancer causation is multifactorial, and provide several important examples where modification of risk factors has achieved cancer prevention. Furthermore, we stress the need and opportunities to advance understanding of cancer aetiology through integration of interaction effects between risk factors when estimating the contribution of individual and joint factors to cancer burden in a population. We posit that non-intrinsic factors drive most cancer risk, and stress the need for cancer prevention.

Climate change is a critical issue of our time, and its causes, pathways, and forecasts remain a topic of broader discussion. In this paper, we present a novel data driven pathway analysis framework to identify the key processes behind mean global temperature and sea level rise, and to forecast the magnitude of their increase from the present to 2100. Based on historical data and dynamic statistical modeling alone, we have established the causal pathways that connect increasing greenhouse gas emissions to increasing global mean temperature and sea level, with its intermediate links encompassing humidity, sea ice coverage, and glacier mass, but not for sunspot numbers. Our results indicate that if no action is taken to curb anthropogenic greenhouse gas emissions, the global average temperature would rise to an estimated 3.28 °C (2.46–4.10 °C) above its pre-industrial level while the global sea level would be an estimated 573 mm (474–671 mm) above its 2021 mean by 2100. However, if countries adhere to the greenhouse gas emission regulations outlined in the 2021 United Nations Conference on Climate Change (COP26), the rise in global temperature would lessen to an average increase of 1.88 °C (1.43–2.33 °C) above its pre-industrial level, albeit still higher than the targeted 1.5 °C, while the sea level increase would reduce to 449 mm (389–509 mm) above its 2021 mean by 2100.

Career planning consists of a series of decisions that will significantly impact one’s life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user preferences in career planning. Numerical results have demonstrated that our system is superior to other benchmarks in locating promising optimal career paths for users in long-term planning. Case studies have further revealed that our SSRL career path recommendation system would encourage people to gradually improve their career paths to maximize long-term benefits. Moreover, we have shown that the initial state (i.e., the first job) can have a significant impact, positively or negatively, on one’s career, while in the long-term view, a carefully planned career path following our recommendation system may mitigate the negative impact of a lackluster beginning in one’s career life.

It is broadly accepted that genetically engineered animal models do not always recapitulate human pathobiology. Therefore identifying best-fit mouse models of human cancers that truly reflect the corresponding human disease is of vital importance in elucidating molecular mechanisms of tumorigenesis and developing preventive and therapeutic approaches. A new hepatocellular carcinoma (HCC) mouse model lacking a novel putative tumor suppressor IQGAP2 has been generated by our laboratory. The aim of this study was to obtain the molecular signature of Iqgap2^−/− HCC tumors and establish the relevance of this model to human disease. Here we report a comprehensive transcriptome analysis of Iqgap2^−/− livers and a cross-species comparison of human and Iqgap2^−/− HCC tumors using Significance Analysis of Microarray (SAM) and unsupervised hierarchical clustering analysis. We identified the Wnt/β-catenin signaling pathway as the top canonical pathway dysregulated in Iqgap2^−/− livers. We also demonstrated that Iqgap2^−/− hepatic tumors shared genetic signatures with HCC tumors from patients with advanced disease as evidenced by a 78% mouse-to-human microarray data set concordance rate with 117 out of 151 identified ortholog genes having similar expression profiles across the two species. Collectively, these results indicate that the Iqgap2 knockout mouse model closely recapitulates human HCC at the molecular level and supports its further application for the study of this disease.