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Alzheimer's disease (AD) represents a global health challenge, with an estimated 55 million people suffering from the non-curable disease across the world. While amyloid-β plaques and tau neurofibrillary tangles in the brain define AD proteinopathy, it has become evident that diverse coding and non-coding regions of the genome may significantly contribute to AD neurodegeneration. The diversity of factors associated with AD pathogenesis, coupled with age-associated damage, suggests that a series of triggering events may be required to initiate AD. Since somatic mutations accumulate with aging, and aging is a major risk factor for AD, there is a great potential for somatic mutational events to drive disease. Indeed, recent data from the Gozes team/laboratories as well as other leading laboratories correlated the accumulation of somatic brain mutations with the progression of tauopathy. In this review, we lay the current perspectives on the principal genetic factors associated with AD and the potential causes, highlighting the contribution of somatic mutations to the pathogenesis of late onset Alzheimer's disease. The roles that artificial intelligence and big data can play in accelerating the progress of causal somatic mutation markers/biomarkers identification, and the associated drug discovery/repurposing, have been highlighted for future AD and other neurodegenerations, with the aim to bring hope for the vulnerable aging population. [ABSTRACT FROM AUTHOR]

Highlights • Drivers of domestic users' price responsiveness are identified. • A neural network model is built to accurately estimate baseline consumption. • Variable selection is conducted by combining 3 advanced machine learning techniques. • Feasibility of selecting high potential user based on results is demonstrated. Abstract Time-based pricing for domestic electricity users has been effective in reducing peak demand and facilitating integration of renewable energies. However, high cost, price non-responsiveness and adverse selection present challenges. To tackle these challenges, it would be important to investigate which users exhibit a higher potential to respond to price change, such that time-based pricing can be introduced to such users. Few studies have examined which users are more price-responsive and what drives price responsiveness. This article aims to fill this gap by comprehensively identifying the drivers that determine electricity users' price responsiveness, in order to facilitate the selection of high potential users. We adopt a novel machine-learning approach to select the high potential users, using the Irish smart meter dataset (2009–10), which forms part of the national Time of Use trial. Our methodological novelties cover two aspects: First, using a feed-forward neural network model, we aim to determine precisely the price responsiveness of individual households, and address the nonlinearity of energy consumption and price response attributes. Second, we apply an integrated machine learning methodology to identify the drivers of price responsiveness. Our integrated approach outperforms the traditional variable selection methods by identifying drivers that are reliable. Our empirical results have shown that demographic and residential characteristics, psychological factors, historical electricity consumptionand appliance ownership are significant drivers. In particular, historical electricity consumption, income, household size, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are significant drivers of price responsiveness across the Irish electricity users. Our results have also verified that continual price increase within a moderate range will not drive additional peak demand reduction; there is an intention-behaviour gap, where stated intention does not lead to actual peak reduction behaviours. Based on such results, we have conducted a scenario analysis to demonstrate the feasibility of selecting high potential users of achieving significant peak electricity reduction. [ABSTRACT FROM AUTHOR]

Abstract Over the last three decades, rapid industrialization in China has generated an unprecedentedly high level of air pollution and associated health problems. Given that China accounts for one-fifth of the world population and suffers from severe air pollution, a comprehensive review of the indicators accounting for the health costs in relation to air pollution will benefit evidence-based and health-related environmental policy-making. This paper reviews the conventional static and the new dynamic approach adopted for air pollution-related health cost accounting in China and analyzes the difference between the two in estimating GDP loss. The advantages of adopting the dynamic approach for health cost accounting in China, with conditions guaranteeing its optimal performance are highlighted. Guidelines on how one can identify an appropriate approach for health cost accounting in China are put forward. Further, we outline and compare the globally-applicable and China-specific indicators adopted by different accounting methodologies, with their pros and cons being discussed. A comprehensive account of the available databases and methodologies for health cost accounting in China are outlined. Future directions to guide health cost accounting in China are provided. Our work provides valuable insights into future health cost accounting research in China. Our study has strengthen the view that the dynamic approach is comparatively more preferred than the static approach for health cost accounting in China, if more data is available to train the dynamic models and improve the robustness of the parameters employed. In addition, future dynamic model should address the socio-economic impacts, including benefits or losses of air pollution polices, to provide a more robust policy picture. Our work has laid the key principles and guidelines for selecting proper econometric approaches and parameters. We have also identified a proper estimation method for the Value of Life in China, and proposed the integration of engineering approaches, such as the use of deep learning and big data analysis for health cost accounting at the fine-grained level (city-district or sub-regional level). Our work has also identified the gap for more accurate health cost accounting at the fine-grained level in China, which will subsequently affect the quality of health-related air pollution policy decision-making at such levels, and the health-related quality of life of the citizens in China. Highlights • The dynamic health cost accounting a preferred approach in China • Future dynamic model should address parameter robustness and socio-economic impacts. • Guidelines for selecting proper econometric approaches and parameters stated • Estimation methods for Values of Life in China and integration of methods proposed • Accurate health cost accounting at fine spatial resolutions needed [ABSTRACT FROM AUTHOR]

The Fukushima Daiichi nuclear disaster has rekindled the world's attention to nuclear safety following the devastating nuclear accidents that occurred in the Three Mile Island and Chernobyl. As China continues to expand in nuclear power development, how it views and responds to nuclear safety carries significant implications on its nuclear safety and security in the future. This paper examines the Chinese authorities' response to nuclear safety pre- and post-Fukushima, based on (1) a longitudinal big-data discourse analysis of the Chinese newspaper articles published during the period 2008–2017, and (2) an in-depth comprehensive review of nuclear safety performance and safety governance based on credential and publicly available documents and websites, both locally and internationally. Our assessment reveals that (i) China's concerns over nuclear safety and accident surged immediately following the Fukushima crisis. Increasing attention towards nuclear emergency response has been observed since 2014, (ii) China has displayed strengths in reactor design and safety operation, and (iii) its safety governance has been constrained by institutional fragmentation, inadequate transparency, inadequate safety professionals, lack of a strong safety culture, amid its ongoing plans to increase nuclear capacity three-fold by 2050. To improve nuclear safety, China may further strengthen its safety standards, safety management and monitoring, improve institutional arrangements, increase the ratio of safety professionals, intensify its safety culture and transparency, develop process-based safety regulations, and champion international collaboration to keep itself abreast of the latest international best practices. • China's concerns over nuclear safety surged after the Fukushima crisis. • China displayed strengths in reactor technology design and safety operation. • China can benefit from better safety management culture, institutional integration and and and regulatory design, etc. [ABSTRACT FROM AUTHOR]