Fully probabilistic deep models for forward and inverse problems in parametric PDEs

We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation leverages conventional PDE discretization techniques, deep neural networks, probabilistic modelling, and variational inference to assemble a fully probabilistic coherent framework. In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by deep neural networks. The PDE residual is assumed to be an observed random vector of value zero, hence we model it as a random vector with a zero mean and a user-prescribed covariance. The model is trained by maximizing the probability, that is the evidence or marginal likelihood, of observing a residual of zero by maximizing the evidence lower bound (ELBO). Consequently, the proposed methodology does not require any independent PDE solves and is physics-informed at training time, allowing the real-time solution of PDE forward and inverse problems after training. The proposed framework can be easily extended to seamlessly integrate observed data to solve inverse problems and to build generative models. We demonstrate the efficiency and robustness of our method on finite element discretized parametric PDE problems such as linear and nonlinear Poisson problems, elastic shells with complex 3D geometries, and time-dependent nonlinear and inhomogeneous PDEs using a physics-informed neural network (PINN) discretization. We achieve up to three orders of magnitude speed-up after training compared to traditional finite element method (FEM), while outputting coherent uncertainty estimates

Simulated leaf litter addition causes opposite priming effects on natural forest and plantation soils

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The conversion of natural forests to tree plantations alters the quality and decreases the quantity of litter inputs into the soil, but how the alteration of litter inputs affect soil organic matter (SOM) decomposition remain unclear. We examined SOM decomposition by adding 13C-labeled leaf-litter of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) to soils from a natural evergreen broad-leaved forest and an adjacent Chinese fir plantation converted from a natural evergreen broad-leaved forest 42 years ago. Over 195 days, we monitored CO2 efflux and its δ13C, microbial biomass, and the composition of microbial groups by phospholipid fatty acids (PLFAs). To distinguish priming mechanisms, partitioning of C sources in CO2 and microbial biomass was determined based on δ13C. Leaf-litter addition to natural forest increased microbial biomass and induced up to 14% faster SOM decomposition (positive priming) than that in soil without litter. In contrast, negative priming in soils under plantation indicated preferential use of added leaf-litter rather than recalcitrant SOM. This preferential use of leaf-litter was supported by an increased fungal to bacterial ratio and litter-derived (13C) microbial biomass, reflecting increased substrate recalcitrance, the respective changes in microbial substrate utilization and increased C use efficiency. The magnitude and direction of priming effects depend on microbial preferential utilization of new litter or SOM. Concluding, the impact of coniferous leaf-litter inputs on the SOM priming is divergent in natural evergreen broad-leaved forests and plantations, an important consideration in understanding long-term C dynamics and cycling in natural and plantation forest ecosystems

Streptococcus suis Meningitis, United States

Streptococcus suis Meningitis, United State

A Multi-Stage Process to Develop Quality Indicators for Community-Based Palliative Care Using interRAI Data

Background: Individuals receiving palliative care (PC) are generally thought to prefer to receive care and die in their homes, yet little research has assessed the quality of home- and community-based PC. This project developed a set of valid and reliable quality indicators (QIs) that can be generated using data that are already gathered with interRAI assessments-an internationally validated set of tools commonly used in North America for home care clients. The QIs can serve as decision-support measures to assist providers and decision makers in delivering optimal care to individuals and their families. Methods: The development efforts took part in multiple stages, between 2017-2021, including a workshop with clinicians and decision-makers working in PC, qualitative interviews with individuals receiving PC, families and decision makers and a modified Delphi panel, based on the RAND/ULCA appropriateness method. Results: Based on the workshop results, and qualitative interviews, a set of 27 candidate QIs were defined. They capture issues such as caregiver burden, pain, breathlessness, falls, constipation, nausea/vomiting and loneliness. These QIs were further evaluated by clinicians/decision makers working in PC, through the modified Delphi panel, and five were removed from further consideration, resulting in 22 QIs. Conclusions: Through in-depth and multiple-stakeholder consultations we developed a set of QIs generated with data already collected with interRAI assessments. These indicators provide a feasible basis for quality benchmarking and improvement systems for care providers aiming to optimize PC to individuals and their families

Assessing Temperate Forest Growth and Climate Sensitivity in Response to a Long-Term Whole-Watershed Acidification Experiment

Acid deposition is a major biogeochemical driver in forest ecosystems, but the impacts of long-term changes in deposition on forest productivity remain unclear. Using a combination of tree ring and forest inventory data, we examined tree growth and climate sensitivity in response to 26 years of whole-watershed ammonium sulfate ((NH4)2SO4) additions at the Fernow Experimental Forest (West Virginia, USA). Linear mixed effects models revealed species-specific responses to both treatment and hydroclimate variables. When controlling for environmental covariates, growth of northern red oak (Quercus rubra), red maple (Acer rubrum), and tulip poplar (Liriodendron tulipifera) was greater (40%, 52%, and 42%, respectively) in the control watershed compared to the treated watershed, but there was no difference in black cherry (Prunus serotina). Stem growth was generally positively associated with growing season water availability and spring temperature and negatively associated with vapor pressure deficit. Sensitivity of northern red oak, red maple, and tulip poplar growth to water availability was greater in the control watershed, suggesting that acidification treatment has altered tree response to climate. Results indicate that chronic acid deposition may reduce both forest growth and climate sensitivity, with potentially significant implications for forest carbon and water cycling in deposition-affected regions

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Distance travelled : Outcomes and evidence in flexible learning options

Flexible learning options (FLOs) provide individualised learning pathways for disengaged young people with strong emphasis on inclusivity and wellbeing support. Amidst a rapid expansion of Australia’s flexible learning sector, service providers are under increasing pressure to substantiate participant outcomes. This paper stems from a national study of the value of FLOs to young people and the broader Australian community. The study enumerates the outcomes valued by flexible learning practitioners, as well as the various evidence forms they cite to substantiate participant outcomes. Framing success as ‘distance travelled’ (i.e. an individual’s progress relative to his or her own starting point), practitioners demonstrate critical awareness of the social and structural mechanisms by which young people are marginalised from mainstream schooling. Holistic assessment practices also reveal practitioners’ efforts to expand the terms of reference by which educational outcomes may be validated in alternative education settings

Mechanical versus manual chest compression for out-of-hospital cardiac arrest (PARAMEDIC) : a pragmatic, cluster randomised controlled trial

BACKGROUND: Mechanical chest compression devices have the potential to help maintain high-quality cardiopulmonary resuscitation (CPR), but despite their increasing use, little evidence exists for their effectiveness. We aimed to study whether the introduction of LUCAS-2 mechanical CPR into front-line emergency response vehicles would improve survival from out-of-hospital cardiac arrest. METHODS: The pre-hospital randomised assessment of a mechanical compression device in cardiac arrest (PARAMEDIC) trial was a pragmatic, cluster-randomised open-label trial including adults with non-traumatic, out-of-hospital cardiac arrest from four UK Ambulance Services (West Midlands, North East England, Wales, South Central). 91 urban and semi-urban ambulance stations were selected for participation. Clusters were ambulance service vehicles, which were randomly assigned (1:2) to LUCAS-2 or manual CPR. Patients received LUCAS-2 mechanical chest compression or manual chest compressions according to the first trial vehicle to arrive on scene. The primary outcome was survival at 30 days following cardiac arrest and was analysed by intention to treat. Ambulance dispatch staff and those collecting the primary outcome were masked to treatment allocation. Masking of the ambulance staff who delivered the interventions and reported initial response to treatment was not possible. The study is registered with Current Controlled Trials, number ISRCTN08233942. FINDINGS: We enrolled 4471 eligible patients (1652 assigned to the LUCAS-2 group, 2819 assigned to the control group) between April 15, 2010 and June 10, 2013. 985 (60%) patients in the LUCAS-2 group received mechanical chest compression, and 11 (<1%) patients in the control group received LUCAS-2. In the intention-to-treat analysis, 30 day survival was similar in the LUCAS-2 group (104 [6%] of 1652 patients) and in the manual CPR group (193 [7%] of 2819 patients; adjusted odds ratio [OR] 0·86, 95% CI 0·64-1·15). No serious adverse events were noted. Seven clinical adverse events were reported in the LUCAS-2 group (three patients with chest bruising, two with chest lacerations, and two with blood in mouth). 15 device incidents occurred during operational use. No adverse or serious adverse events were reported in the manual group. INTERPRETATION: We noted no evidence of improvement in 30 day survival with LUCAS-2 compared with manual compressions. On the basis of ours and other recent randomised trials, widespread adoption of mechanical CPR devices for routine use does not improve survival