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Intact tropical forests, free from substantial anthropogenic influence, store and sequester large amounts of atmospheric carbon but are currently neglected in international climate policy. We show that between 2000 and 2013, direct clearance of intact tropical forest areas accounted for 3.2% of gross carbon emissions from all deforestation across the pantropics. However, full carbon accounting requires the consideration of forgone carbon sequestration, selective logging, edge effects, and defaunation. When these factors were considered, the net carbon impact resulting from intact tropical forest loss between 2000 and 2013 increased by a factor of 6 (626%), from 0.34 (0.37 to 0.21) to 2.12 (2.85 to 1.00) petagrams of carbon (equivalent to approximately 2 years of global land use change emissions). The climate mitigation value of conserving the 549 million ha of tropical forest that remains intact is therefore significant but will soon dwindle if their rate of loss continues to accelerate.


The human brain undergoes significant functional and structural changes in the first decades of life, as the foundations for human cognition are laid down. However, non-invasive imaging techniques to investigate brain function throughout neurodevelopment are limited due to growth in head-size with age and substantial head movement in young participants. Experimental designs to probe brain function are also limited by the unnatural environment typical brain imaging systems impose. However, developments in quantum technology allowed fabrication of a new generation of wearable magnetoencephalography (MEG) technology with the potential to revolutionise electrophysiological measures of brain activity. Here we demonstrate a lifespan-compliant MEG system, showing recordings of high fidelity data in toddlers, young children, teenagers and adults. We show how this system can support new types of experimental paradigm involving naturalistic learning. This work reveals a new approach to functional imaging, providing a robust platform for investigation of neurodevelopment in health and disease.



To evaluate whether calorie labeling of menus in large restaurant chains was associated with a change in mean calories purchased per transaction.


Human listeners exhibit marked sensitivity to familiar music, perhaps most readily revealed by popular “name that tune” games, in which listeners often succeed in recognizing a familiar song based on extremely brief presentation. In this work, we used electroencephalography (EEG) and pupillometry to reveal the temporal signatures of the brain processes that allow differentiation between a familiar, well liked, and unfamiliar piece of music. In contrast to previous work, which has quantified gradual changes in pupil diameter (the so-called “pupil dilation response”), here we focus on the occurrence of pupil dilation events. This approach is substantially more sensitive in the temporal domain and allowed us to tap early activity with the putative salience network. Participants (N = 10) passively listened to snippets (750 ms) of a familiar, personally relevant and, an acoustically matched, unfamiliar song, presented in random order. A group of control participants (N = 12), who were unfamiliar with all of the songs, was also tested. We reveal a rapid differentiation between snippets from familiar and unfamiliar songs: Pupil responses showed greater dilation rate to familiar music from 100-300 ms post-stimulus-onset, consistent with a faster activation of the autonomic salience network. Brain responses measured with EEG showed a later differentiation between familiar and unfamiliar music from 350 ms post onset. Remarkably, the cluster pattern identified in the EEG response is very similar to that commonly found in the classic old/new memory retrieval paradigms, suggesting that the recognition of brief, randomly presented, music snippets, draws on similar processes.


CDC, the Food and Drug Administration, state and local health departments, and other public health and clinical stakeholders are investigating a national outbreak of electronic-cigarette (e-cigarette), or vaping, product use-associated lung injury (EVALI) (1). As of October 22, 2019, 49 states, the District of Columbia (DC), and the U.S. Virgin Islands have reported 1,604 cases of EVALI to CDC, including 34 (2.1%) EVALI-associated deaths in 24 states. Based on data collected as of October 15, 2019, this report updates data on patient characteristics and substances used in e-cigarette, or vaping, products (2) and describes characteristics of EVALI-associated deaths. The median age of EVALI patients who survived was 23 years, and the median age of EVALI patients who died was 45 years. Among 867 (54%) EVALI patients with available data on use of specific e-cigarette, or vaping, products in the 3 months preceding symptom onset, 86% reported any use of tetrahydrocannabinol (THC)-containing products, 64% reported any use of nicotine-containing products, and 52% reported use of both. Exclusive use of THC-containing products was reported by 34% of patients and exclusive use of nicotine-containing products by 11%, and for 2% of patients, no use of either THC- or nicotine-containing products was reported. Among 19 EVALI patients who died and for whom substance use data were available, 84% reported any use of THC-containing products, including 63% who reported exclusive use of THC-containing products; 37% reported any use of nicotine-containing products, including 16% who reported exclusive use of nicotine-containing products. To date, no single compound or ingredient used in e-cigarette, or vaping, products has emerged as the cause of EVALI, and there might be more than one cause. Because most patients reported using THC-containing products before symptom onset, CDC recommends that persons should not use e-cigarette, or vaping, products that contain THC. In addition, because the specific compound or ingredient causing lung injury is not yet known, and while the investigation continues, persons should consider refraining from the use of all e-cigarette, or vaping, products.


The main contributors to sea-level rise (oceans, glaciers, and ice sheets) respond to climate change on timescales ranging from decades to millennia. A focus on the 21st century thus fails to provide a complete picture of the consequences of anthropogenic greenhouse gas emissions on future sea-level rise and its long-term impacts. Here we identify the committed global mean sea-level rise until 2300 from historical emissions since 1750 and the currently pledged National Determined Contributions (NDC) under the Paris Agreement until 2030. Our results indicate that greenhouse gas emissions over this 280-y period result in about 1 m of committed global mean sea-level rise by 2300, with the NDC emissions from 2016 to 2030 corresponding to around 20 cm or 1/5 of that commitment. We also find that 26 cm (12 cm) of the projected sea-level-rise commitment in 2300 can be attributed to emissions from the top 5 emitting countries (China, United States of America, European Union, India, and Russia) over the 1991-2030 (2016-2030) period. Our findings demonstrate that global and individual country emissions over the first decades of the 21st century alone will cause substantial long-term sea-level rise.


Cardiorespiratory fitness is associated with risk of dementia, but whether temporal changes in cardiorespiratory fitness influence the risk of dementia incidence and mortality is still unknown. We aimed to study whether change in estimated cardiorespiratory fitness over time is associated with change in risk of incident dementia, dementia-related mortality, time of onset dementia, and longevity after diagnosis in healthy men and women at baseline.


To assess the validity of the WHO concept of intrinsic capacity in a longitudinal study of ageing; to identify whether this overall measure disaggregated into biologically plausible and clinically useful subdomains; and to assess whether total capacity predicted subsequent care dependence.


Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this ‘Eighty Five Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning.