Rhesus macaques build new social connections after a natural disaster

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record. Climate change is increasing the frequency and intensity of weather-related disasters such as hurricanes, wildfires, floods, and droughts. Understanding resilience and vulnerability to these intense stressors and their aftermath could reveal adaptations to extreme environmental change. In 2017, Puerto Rico suffered its worst natural disaster, Hurricane Maria, which left 3,000 dead and provoked a mental health crisis. Cayo Santiago island, home to a population of rhesus macaques (Macaca mulatta), was devastated by the same storm. We compared social networks of two groups of macaques before and after the hurricane and found an increase in affiliative social connections, driven largely by monkeys most socially isolated before Hurricane Maria. Further analysis revealed monkeys invested in building new relationships rather than strengthening existing ones. Social adaptations to environmental instability might predispose rhesus macaques to success in rapidly changing anthropogenic environments.National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Institutes of Health (NIH)National Science Foundation (NSF)The Royal SocietyNational Center for Research Resources (NCRR) and the Office of Research Infrastructure Programs (ORIP) of the National Institutes of HealthBruce McEwen Career Development Fellowship and the Animal Models for the Social Dimensions of Health and Aging Research Networ

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.Comment: Main text, supplemental inf

Crop wild relatives of the brinjal eggplant ( Solanum melongena ): Poorly represented in genebanks and many species at risk of extinction

This is the publisher's Early View pdf without final pagination .© 2016 Botanical Society of America. This is an open access article, available to all readers online, published under a creative commons license. The attached file is the published version of the article

Lack of association between venous hemodynamics, venous morphology and the postthrombotic syndrome after upper extremity deep venous thrombosis

Objectives To explore the association of the postthrombotic syndrome with venous hemodynamics and morphological abnormalities after upper extremity deep venous thrombosis. Methods Thirty-seven patients with a history of upper extremity deep venous thrombosis treated with anticoagulation alone underwent a single study visit (mean time after diagnosis: 44.4 ± 28.1 months). Presence and severity postthrombotic syndrome were classified according to the modified Villalta score. Venous volume and venous emptying were determined by strain-gauge plethysmography. The arm veins were assessed for postthrombotic abnormalities by ultrasonography. The relationship between postthrombotic syndrome and hemodynamic and morphological sequelae was evaluated using univariate significance tests and Spearman’s correlation analysis. Results Fifteen of 37 patients (40.5%) developed postthrombotic syndrome. Venous volume and venous emptying of the arm affected by upper extremity deep venous thrombosis did not correlate with the Villalta score (rho = 0.17 and 0.19; p = 0.31 and 0.25, respectively). Residual morphological abnormalities, as assessed by ultrasonography, did not differ significantly between patients with and without postthrombotic syndrome (77.3% vs. 86.7%, p = 0.68). Conclusions Postthrombotic syndrome after upper extremity deep venous thrombosis is not associated with venous hemodynamics or residual morphological abnormalities