An in vitro comparison of the neurotrophic and angiogenic activity of human and canine adipose-derived mesenchymal stem cells (MSCs): translating MSC-based therapies for spinal cord injury.

The majority of research into the effects of mesenchymal stem cell (MSC) transplants on spinal cord injury (SCI) is performed in rodent models, which may help inform on mechanisms of action, but does not represent the scale and wound heterogeneity seen in human SCI. In contrast, SCI in dogs occurs naturally, is more akin to human SCI, and can be used to help address important aspects of the development of human MSC-based therapies. To enable translation to the clinic and a comparison across species, we have examined the paracrine, regenerative capacity of human and canine adipose-derived MSCs in vitro. MSCs were initially phenotyped according to tissue culture plastic adherence, CD immunoprofiling and tri-lineage differentiation potential. Conditioned medium (CM) from MSC cultures was then assessed for its neurotrophic and angiogenic activity using established cell-based assays. MSC CM significantly increased neuronal cell proliferation, neurite outgrowth, and βIII tubulin immunopositivity. In addition, MSC CM significantly increased endothelial cell migration, cell proliferation and the formation of tubule-like structures in Matrigel assays. There were no marked or significant differences in the capacity of human or canine MSC CM to stimulate neuronal cell or endothelial cell activity. Hence, this study supports the use of MSC transplants for canine SCI, furthermore it increases understanding of how this may subsequently provide useful information and translate to MSC transplants for human SCI

Characterizing uncertainty of the hydrologic impacts of climate change

The high climate sensitivity of hydrologic systems, the importance of those systems to society, and the imprecise nature of future climate projections all motivate interest in characterizing uncertainty in the hydrologic impacts of climate change. We discuss recent research that exposes important sources of uncertainty that are commonly neglected by the water management community, especially, uncertainties associated with internal climate system variability, and hydrologic modeling. We also discuss research exposing several issues with widely used climate downscaling methods. We propose that progress can be made following parallel paths: first, by explicitly characterizing the uncertainties throughout the modeling process (rather than using an ad hoc “ensemble of opportunity”) and second, by reducing uncertainties through developing criteria for excluding poor methods/models, as well as with targeted research to improve modeling capabilities. We argue that such research to reveal, reduce, and represent uncertainties is essential to establish a defensible range of quantitative hydrologic storylines of climate change impacts

Characterizing uncertainty of the hydrologic impacts of climate change

The high climate sensitivity of hydrologic systems, the importance of those systems to society, and the imprecise nature of future climate projections all motivate interest in characterizing uncertainty in the hydrologic impacts of climate change. We discuss recent research that exposes important sources of uncertainty that are commonly neglected by the water management community, especially, uncertainties associated with internal climate system variability, and hydrologic modeling. We also discuss research exposing several issues with widely used climate downscaling methods. We propose that progress can be made following parallel paths: first, by explicitly characterizing the uncertainties throughout the modeling process (rather than using an ad hoc “ensemble of opportunity”) and second, by reducing uncertainties through developing criteria for excluding poor methods/models, as well as with targeted research to improve modeling capabilities. We argue that such research to reveal, reduce, and represent uncertainties is essential to establish a defensible range of quantitative hydrologic storylines of climate change impacts

Probabilistic Programming with Densities in SlicStan: Efficient, Flexible, and Deterministic

Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantics of the language has been mainly given in terms of intuition about implementation, and has not been formalised. This paper provides a formal treatment of the Stan language, and introduces the probabilistic programming language SlicStan --- a compositional, self-optimising version of Stan. Our main contributions are: (1) the formalisation of a core subset of Stan through an operational density-based semantics; (2) the design and semantics of the Stan-like language SlicStan, which facilities better code reuse and abstraction through its compositional syntax, more flexible functions, and information-flow type system; and (3) a formal, semantic-preserving procedure for translating SlicStan to Stan

Ancient numerical daemons of conceptual hydrological modeling 2. Impact of time stepping schemes on model analysis and prediction

Despite the widespread use of conceptual hydrological models in environmental research and operations, they remain frequently implemented using numerically unreliable methods. This paper considers the impact of the time stepping scheme on model analysis (sensitivity analysis, parameter optimization, and Markov chain Monte Carlo-based uncertainty estimation) and prediction. It builds on the companion paper (Clark and Kavetski, 2010), which focused on numerical accuracy, fidelity, and computational efficiency. Empirical and theoretical analysis of eight distinct time stepping schemes for six different hydrological models in 13 diverse basins demonstrates several critical conclusions. (1) Unreliable time stepping schemes, in particular, fixed-step explicit methods, suffer from troublesome numerical artifacts that severely deform the objective function of the model. These deformations are not rare isolated instances but can arise in any model structure, in any catchment, and under common hydroclimatic conditions. (2) Sensitivity analysis can be severely contaminated by numerical errors, often to the extent that it becomes dominated by the sensitivity of truncation errors rather than the model equations. (3) Robust time stepping schemes generally produce "better behaved" objective functions, free of spurious local optima, and with sufficient numerical continuity to permit parameter optimization using efficient quasi Newton methods. When implemented within a multistart framework, modern Newton-type optimizers are robust even when started far from the optima and provide valuable diagnostic insights not directly available from evolutionary global optimizers. (4) Unreliable time stepping schemes lead to inconsistent and biased inferences of the model parameters and internal states. (5) Even when interactions between hydrological parameters and numerical errors provide "the right result for the wrong reason" and the calibrated model performance appears adequate, unreliable time stepping schemes make the model unnecessarily fragile in predictive mode, undermining validation assessments and operational use. Erroneous or misleading conclusions of model analysis and prediction arising from numerical artifacts in hydrological models are intolerable, especially given that robust numerics are accepted as mainstream in other areas of science and engineering. We hope that the vivid empirical findings will encourage the conceptual hydrological community to close its Pandora's box of numerical problems, paving the way for more meaningful model application and interpretation. Copyright 2010 by the American Geophysical Union.Dmitri Kavetski and Martyn P. Clar

‘Caution! The Bread is Poisoned’: The Hong Kong Mass Poisoning of January 1857

This article examines the Hong Kong mass poisoning of 15 January 1857, in which bread from a Chinese bakery that supplied the colonial community was adulterated with arsenic. Even though there is a wealth of printed and manuscript documentation available many vital aspects of the poisoning remain unclear. What kind of incident was it: an act of terrorism and attempted mass murder, a war crime, a criminal conspiracy, an act of commercial sabotage, an accident or even an imagined or imaginary event? Throughout, our focus remains firmly fixed on the central act of the poisoning itself and on what it reveals about the precarious nature of early colonial Hong Kong. Interpretations have swarmed over the available ‘facts'. Equally ironic is what happened to the afterlife of how the event was understood. This article seeks to rescue the Hong Kong poisoning from being a freakish and isolated footnote of only local interest. Accepting this historical verdict would be a mistake as it is of significance not only at a local level, but geopolitically in Britain and across the empire

Examining the effects of sodium ions on the binding of antagonists to dopamine D2 and D3 receptors

Many G protein-coupled receptors have been shown to be sensitive to the presence of sodium ions (Na+). Using radioligand competition binding assays, we have examined and compared the effects of sodium ions on the binding affinities of a number of structurally diverse ligands at human dopamine D2 and dopamine D3 receptor subtypes, which are important therapeutic targets for the treatment of psychotic disorders. At both receptors, the binding affinities of the antagonists/inverse agonists SB-277011-A, L,741,626, GR 103691 and U 99194 were higher in the presence of sodium ions compared to those measured in the presence of the organic cation, N-methyl-D-glucamine, used to control for ionic strength. Conversely, the affinities of spiperone and (+)-butaclamol were unaffected by the presence of sodium ions. Interestingly, the binding of the antagonist/inverse agonist clozapine was affected by changes in ionic strength of the buffer used rather than the presence of specific cations. Similar sensitivities to sodium ions were seen at both receptors, suggesting parallel effects of sodium ion interactions on receptor conformation. However, no clear correlation between ligand characteristics, such as subtype selectivity, and sodium ion sensitivity were observed. Therefore, the properties which determine this sensitivity remain unclear. However these findings do highlight the importance of careful consideration of assay buffer composition for in vitro assays and when comparing data from different studies, and may indicate a further level of control for ligand binding in vivo

A Unified Approach for Process-Based Hydrologic Modeling: 2. Model Implementation and Case Studies

This work advances a unified approach to process-based hydrologic modeling, which we term the “Structure for Unifying Multiple Modeling Alternatives (SUMMA).” The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty

Joint editorial: Invigorating hydrological research through journal publications

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union – EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the journals listed here

A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies

This work advances a unified approach to process-based hydrologic modeling, which we term the ‘‘Structure for Unifying Multiple Modeling Alternatives (SUMMA).’’ The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty.Keywords: unified model, scaling behavior, hydrometeorologyKeywords: unified model, scaling behavior, hydrometeorolog