Summary of Results from the 2016 National Health Security Preparedness Index

The National Health Security Preparedness Index tracks the nation’s progress in preparing for, responding to, and recovering from disasters and other large-scale emergencies that pose risks to health and well-being in the United States. Because health security is a responsibility shared by many different stakeholders in government and society, the Index combines measures from multiple sources and perspectives to offer a broad view of the health protections in place for nation as a whole and for each U.S. state. The Index identifies strengths as well as gaps in the protections needed to keep people safe and healthy in the face of disasters, and it tracks how these protections vary across the U.S. and change over time. Results from the 2016 release of the Index, containing data from 2013 through 2015, reveal that preparedness is improving overall, but protections remain uneven across the U.S., and they are losing strength in some critical areas

Summary of Proposed Updates to the National Health Security Preparedness Index for 2015-2016

This report describes proposed updates in methodology and measures for the 2015-16 release of the National Health Security Preparedness Inde

Reproductive success of the wood warbler Phylloscopus sibilatrix varies across Europe

Differences in population trends across a species’ breeding range are ultimately linked to variation in demographic rates. In small songbirds, demographic rates related to fecundity typically have strong effects on population trends. Populations of a forest songbird, the wood warbler Phylloscopus sibilatrix, have been declining in many but not all regions of the European breeding range. We investigated if clutch size, hatching rate, nest survival and number of fledglings vary across Europe, and if nest survival is related to differences in the regionally dominant nest predator class (birds versus mammals). From 2009 to 2020, we monitored 1896 nests and used cameras at a subsample of 645 nests in six study regions: the United Kingdom (mid-Wales, Dartmoor, the New Forest), Germany (Hessen), Switzerland (Jura mountains) and Poland (Białowieża National Park). Number of fledglings was lowest in the New Forest (1.43 ± CI 0.23), intermediate in Jura (2.41 ± 0.31) and Białowieża (2.26 ± 0.24) and highest in mid-Wales (3.02 ± 0.48) and Dartmoor (2.92 ± 0.32). The reason for low reproductive success in the New Forest, Jura and Białowieża was low nest survival, and large clutch sizes in Białowieża did not compensate for high nest losses. High reproductive success in mid-Wales and Dartmoor was due to high nest survival and large clutch sizes. Overall predation rates were similar everywhere despite variation between the regions in the dominant nest predator class. Unsuccessful nests in mid-Wales were mainly predated by birds; in Dartmoor, the New Forest, Hessen and Jura similarly by birds and mammals; and in Białowieża exclusively by mammals. Regional reproductive success does not match the population trends recently reported for the wood warbler in the six study regions (i.e. high reproduction ≠ positive trend). Annual survival may be a decisive factor, but it is difficult to quantify for a nomadic species such as the wood warbler that rarely returns to the same breeding locations

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Simulations With the Marine Biogeochemistry Library (MARBL)

The Marine Biogeochemistry Library (MARBL) is a prognostic ocean biogeochemistry model that simulates marine ecosystem dynamics and the coupled cycles of carbon, nitrogen, phosphorus, iron, silicon, and oxygen. MARBL is a component of the Community Earth System Model (CESM); it supports flexible ecosystem configuration of multiple phytoplankton and zooplankton functional types; it is also portable, designed to interface with multiple ocean circulation models. Here, we present scientific documentation of MARBL, describe its configuration in CESM2 experiments included in the Coupled Model Intercomparison Project version 6 (CMIP6), and evaluate its performance against a number of observational data sets. The model simulates present-day air-sea CO2 flux and many aspects of the carbon cycle in good agreement with observations. However, the simulated integrated uptake of anthropogenic CO2 is weak, which we link to poor thermocline ventilation, a feature evident in simulated chlorofluorocarbon distributions. This also contributes to larger-than-observed oxygen minimum zones. Moreover, radiocarbon distributions show that the simulated circulation in the deep North Pacific is extremely sluggish, yielding extensive oxygen depletion and nutrient trapping at depth. Surface macronutrient biases are generally positive at low latitudes and negative at high latitudes. CESM2 simulates globally integrated net primary production (NPP) of 48 Pg C yr(-1) and particulate export flux at 100 m of 7.1 Pg C yr(-1). The impacts of climate change include an increase in globally integrated NPP, but substantial declines in the North Atlantic. Particulate export is projected to decline globally, attributable to decreasing export efficiency associated with changes in phytoplankton community composition. Plain Language Summary Numerical models of the ocean carbon cycle and biogeochemistry play a key role in understanding the fate of human carbon dioxide emissions and the magnitude of expected climate change over the next several decades to a century. Models are needed to quantify changes in the carbon reservoirs of the ocean and atmosphere and to explore interactions between climate change and carbon reservoirs that could amplify or damp future warming. This paper presents the Marine Biogeochemistry Library (MARBL), which is an ocean biogeochemistry model coupled to the Community Earth System Model (CESM). MARBL was designed to be compatible with multiple ocean models, a design motivated by an interest in building a diverse community of researchers around the development of MARBL. This paper presents a technical description of MARBL and an evaluation of the ocean biogeochemical simulation in CESM version 2. Overall, the model captures large-scale biogeochemical distributions, though several important biases are highlighted, including those dependent on the representation of circulation. MARBL provides a robust platform for researchers to address critical questions related to the impacts of climate variability and change on marine ecosystems