Natural climate solutions

Our thanks for inputs by L. Almond, A. Baccini, A. Bowman, S. CookPatton, J. Evans, K. Holl, R. Lalasz, A. Nassikas, M. Spalding, M. Wolosin, and expert elicitation respondents. Our thanks for datasets developed by the Hansen lab and the NESCent grasslands working group (C. Lehmann, D. Griffith, T. M. Anderson, D. J. Beerling, W. Bond, E. Denton, E. Edwards, E. Forrestel, D. Fox, W. Hoffmann, R. Hyde, T. Kluyver, L. Mucina, B. Passey, S. Pau, J. Ratnam, N. Salamin, B. Santini, K. Simpson, M. Smith, B. Spriggs, C. Still, C. Strömberg, and C. P. Osborne). This study was made possible by funding from the Doris Duke Charitable Foundation. Woodbury was supported in part by USDA-NIFA Project 2011-67003-30205 Data deposition: A global spatial dataset of reforestation opportunities has been deposited on Zenodo (https://zenodo.org/record/883444). This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1710465114/-/DCSupplemental.Peer reviewedPublisher PD

Artificial intelligence in mammographic phenotyping of breast cancer risk: A narrative review

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman\u27s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies

Natural climate solutions for the United States

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Science Advances 4 (2018): eaat1869, doi:10.1126/sciadv.aat1869.Limiting climate warming to <2°C requires increased mitigation efforts, including land stewardship, whose potential in the United States is poorly understood. We quantified the potential of natural climate solutions (NCS)—21 conservation, restoration, and improved land management interventions on natural and agricultural lands—to increase carbon storage and avoid greenhouse gas emissions in the United States. We found a maximum potential of 1.2 (0.9 to 1.6) Pg CO2e year−1, the equivalent of 21% of current net annual emissions of the United States. At current carbon market prices (USD 10 per Mg CO2e), 299 Tg CO2e year−1 could be achieved. NCS would also provide air and water filtration, flood control, soil health, wildlife habitat, and climate resilience benefits.This study was made possible by funding from the Doris Duke Charitable Foundation. C.A.W. and H.G. acknowledge financial support from NASA’s Carbon Monitoring System program (NNH14ZDA001N-CMS) under award NNX14AR39G. S.D.B. acknowledges support from the DOE’s Office of Biological and Environmental Research Program under the award DE-SC0014416. J.W.F. acknowledges financial support from the Florida Coastal Everglades Long-Term Ecological Research program under National Science Foundation grant no. DEB-1237517

The Task Force Initiative: Local Interventions at the Department Level—A Key Component of a Multi-tiered Approach to Promote Institutional Change

This NIH Transforming Academic Culture (NIH-TAC) trial is a multi-level coordinated intervention to enhance institutional culture, increase academic productivity, and improve job satisfaction for women faculty. This poster details a task force initiative in which each department and division reviewed their current practices and policies to recommend and implement change. This particular task force resulted in a list of interventions which were disseminated and implemented throughout the institution

External validation of a mammography-derived AI-based risk model in a U.S. breast cancer screening cohort of White and Black women

Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64-0.72) for all women, 0.67 (0.61-0.72) for White women, and 0.70 (0.65-0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all wome

Volumetric breast density estimation from three-dimensional reconstructed digital breast tomosynthesis images using deep learning

PURPOSE: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from two-dimensional (2D) images acquired using DBT and/or depend on the existence of raw DBT data, which is rarely archived by clinical centers because of storage constraints. METHODS: We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using previously validated software that uses 3D reconstructed slices and raw 2D DBT data. We developed a deep learning (DL) model that segments dense and fatty breast tissue from background. We then applied this model to estimate %VBD and absolute dense volume (ADV) in cm RESULTS: The DL model achieved unweighted and weighted Dice scores of 0.88 (standard deviation [SD] = 0.08) and 0.76 (SD = 0.15), respectively, on the held-out test set, demonstrating good agreement between the model and 3D reference segmentations. There was a significant association between the odds of breast cancer diagnosis and model-derived VBD (odds ratio [OR], 1.41 [95 % CI, 1.13 to 1.77]; CONCLUSION: DL-derived density measures derived from 3D reconstructed DBT images are associated with breast cancer diagnosis

Selection for Higher Gene Copy Number after Different Types of Plant Gene Duplications

The evolutionary origins of the multitude of duplicate genes in the plant genomes are still incompletely understood. To gain an appreciation of the potential selective forces acting on these duplicates, we phylogenetically inferred the set of metabolic gene families from 10 flowering plant (angiosperm) genomes. We then compared the metabolic fluxes for these families, predicted using the Arabidopsis thaliana and Sorghum bicolor metabolic networks, with the families' duplication propensities. For duplications produced by both small scale (small-scale duplications) and genome duplication (whole-genome duplications), there is a significant association between the flux and the tendency to duplicate. Following this global analysis, we made a more fine-scale study of the selective constraints observed on plant sodium and phosphate transporters. We find that the different duplication mechanisms give rise to differing selective constraints. However, the exact nature of this pattern varies between the gene families, and we argue that the duplication mechanism alone does not define a duplicated gene's subsequent evolutionary trajectory. Collectively, our results argue for the interplay of history, function, and selection in shaping the duplicate gene evolution in plants

Morphologic Blooming in Breast MRI as a Characterization of Margin for Discriminating Benign from Malignant Lesions

Develop a fully automated, objective method for evaluating morphology on breast MR and evaluate effectiveness of the new morphological method for detecting breast cancers