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In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. A detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. A detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

Chris Sander and colleagues have extracted significant functional events from 12 tumor types. Tumors can be classified as being driven largely by either mutation or copy number changes, and, within this division, subclasses of cross-tissue patterns of events are discerned that suggest sets of combinatorial therapies. Cancer therapy is challenged by the diversity of molecular implementations of oncogenic processes and by the resulting variation in therapeutic responses. Projects such as The Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail. The interpretation of these maps remains a major challenge. Here we distilled thousands of genetic and epigenetic features altered in cancers to ∼500 selected functional events (SFEs). Using this simplified description, we derived a hierarchical classification of 3,299 TCGA tumors from 12 cancer types. The top classes are dominated by either mutations (M class) or copy number changes (C class). This distinction is clearest at the extremes of genomic instability, indicating the presence of different oncogenic processes. The full hierarchy shows functional event patterns characteristic of multiple cross-tissue groups of tumors, termed oncogenic signature classes. Targetable functional events in a tumor class are suggestive of class-specific combination therapy. These results may assist in the definition of clinical trials to match actionable oncogenic signatures with personalized therapies.

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO₂) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO₂ with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO₂ fluxes across Alaska during 2012–2014. We find that tundra ecosystems were a net source of CO₂ to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO₂ emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO₂ in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO₂ seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate.

Traumatic brain injury (TBI) is a global health priority, associated with substantial burden. Historically conceptualised as an injury event with finite recovery, TBI is now recognised as a chronic condition that can affect multiple domains of health and function, some of which might deteriorate over time. Many people who have had a TBI remain moderately to severely disabled at 5 years, are rehospitalised up to 10 years post-injury, and have a reduced lifespan relative to the general population. Understanding TBI as a chronic disease process can be highly informative for optimising care, which has traditionally focused on acute care. Chronic brain injury care models must be informed by a holistic understanding of long-term outcomes and the factors that can affect how care needs evolve over time. The United States Traumatic Brain Injury Model Systems of Care follows up individuals with moderate-to-severe TBI for over 30 years, allowing characterisation of the chronic (2–30 years or more post injury) functional, cognitive, behavioural, and social sequelae experienced by individuals who have had a moderate-to-severe TBI and the implications for their health and quality of life. Older age, social determinants of health, and lower acute functional status are associated with post-recovery deterioration, while younger age and greater functional independence are associated with risky health behaviours, including substance misuse and re-injury. Systematically collected data on long-term outcomes across multiple domains of health and function are needed worldwide to inform the development of models for chronic disease management, including the proactive surveillance of commonly experienced health and functional challenges.

Changes in climate and fire regimes are transforming the boreal forest, the world’s largest biome. Boreal North America recently experienced two years with large burned area: 2014 in the Northwest Territories and 2015 in Alaska. Here we use climate, lightning, fire and vegetation data sets to assess the mechanisms contributing to large fire years. We find that lightning ignitions have increased since 1975, and that the 2014 and 2015 events coincided with a record number of lightning ignitions and exceptionally high levels of burning near the northern treeline. Lightning ignition explained more than 55% of the interannual variability in burned area, and was correlated with temperature and precipitation, which are projected to increase by mid-century. The analysis shows that lightning drives interannual and long-term ignition and burned area dynamics in boreal North America, and implies future ignition increases may increase carbon loss while accelerating the northward expansion of boreal forest. The boreal forest is being transformed by changes in its climate–fire regime. Analysis now shows that lightning drives year-to-year and long-term ignition and burned area trends in boreal North America.

Diabetes-related lower limb amputation (LLA) is a leading cause of disability globally, impacting individuals' physical and mental health, and ultimately their quality of life. Physical activity can reduce risk of chronic disease and mortality while improving quality of life. However, people with LLA often have reduced balance and walking ability resulting in sedentary behaviour. We co-created a physical activity intervention, IMproving Physical Activity through Coaching and Technology following Lower Limb Loss (IMPACT-L3), to support physical activity behaviour change in people with dysvasular LLA. To date, no studies have assessed a peer-led physical activity behaviour change intervention for people with LLA. Prior to launching a large trial, a pilot study is required to assess feasibility and optimize design of a future trial. This pilot study is a parallel group randomized controlled trial (RCT) with an embedded qualitative component. The intervention group will have access to once-weekly virtual peer coaching sessions with a peer trained in brief action planning; web-based physical activity modules; and a wearable activity monitor for 8 weeks. The control group will continue usual care and be offered the intervention at the end of the follow-up period. Data on feasibility will be collected including assessment of process, resource, management and treatment indicators. The proposed primary outcomes will be measured at baseline, post-intervention and one month later: total physical activity counts per day measured by the ActiGraphTM activity monitor and self-efficacy measured by the Self-efficacy for Exercise scale. Secondary measures include patient reported outcome measures of physical activity, mobility, depression, social participation, balance confidence and quality of life. Semi-structured interviews will explore feasibility and acceptability of the intervention to participants and peers. This study will inform the design of a definitive RCT to determine the effectiveness of a peer-led physical activity intervention for people with dysvascular LLA.

Tissue segmentation of histology whole-slide images (WSI) remains a critical task in automated digital pathology workflows for both accurate disease diagnosis and deep phenotyping for research purposes. This is especially challenging when the tissue structure of biospecimens is relatively porous and heterogeneous, such as for atherosclerotic plaques. In this study, we developed a unique approach called ‘EntropyMasker’ based on image entropy to tackle the fore- and background segmentation (masking) task in histology WSI. We evaluated our method on 97 high-resolution WSI of human carotid atherosclerotic plaques in the Athero-Express Biobank Study, constituting hematoxylin and eosin and 8 other staining types. Using multiple benchmarking metrics, we compared our method with four widely used segmentation methods: Otsu’s method, Adaptive mean, Adaptive Gaussian and slideMask and observed that our method had the highest sensitivity and Jaccard similarity index. We envision EntropyMasker to fill an important gap in WSI preprocessing, machine learning image analysis pipelines, and enable disease phenotyping beyond the field of atherosclerosis.

Immune checkpoint inhibitors, which unleash a patient's own T cells to kill tumors, are revolutionizing cancer treatment. To unravel the genomic determinants of response to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two independent cohorts, higher nonsynonymous mutation burden in tumors was associated with improved objective response, durable clinical benefit, and progression-free survival. Efficacy also correlated with the molecular smoking signature, higher neoantigen burden, and DNA repair pathway mutations; each factor was also associated with mutation burden. In one responder, neoantigen-specific CD8+ T cell responses paralleled tumor regression, suggesting that anti–PD-1 therapy enhances neoantigen-specific T cell reactivity. Our results suggest that the genomic landscape of lung cancers shapes response to anti–PD-1 therapy.

Impact assessments for sonar operations typically use received sound levels to predict behavioural disturbance in marine mammals. However, there are indications that cetaceans may learn to associate exposures from distant sound sources with lower perceived risk. To investigate the roles of source distance and received level in an area without frequent sonar activity, we conducted multi-scale controlled exposure experiments ( n = 3) with 12 northern bottlenose whales near Jan Mayen, Norway. Animals were tagged with high-resolution archival tags ( n = 1 per experiment) or medium-resolution satellite tags ( n = 9 in total) and subsequently exposed to sonar. We also deployed bottom-moored recorders to acoustically monitor for whales in the exposed area. Tagged whales initiated avoidance of the sound source over a wide range of distances (0.8–28 km), with responses characteristic of beaked whales. Both onset and intensity of response were better predicted by received sound pressure level (SPL) than by source distance. Avoidance threshold SPLs estimated for each whale ranged from 117–126 dB re 1 µPa, comparable to those of other tagged beaked whales. In this pristine underwater acoustic environment, we found no indication that the source distances tested in our experiments modulated the behavioural effects of sonar, as has been suggested for locations where whales are frequently exposed to sonar.