Intrinsic water transport in moisture-capturing hydrogels

Moisture-capturing hydrogels have emerged as attractive sorbent materials capable of converting ambient humidity into liquid water. Recent works have demonstrated exceptional water capture capabilities of hydrogels, while simultaneously, exploring different strategies to accelerate water capture and release. However, on the material level, an understanding of the intrinsic transport properties of moisture-capturing hydrogels is currently missing, which hinders their rational design. In this work, we combine absorption and desorption experiments of macroscopic hydrogel samples in pure-vapor with models of water diffusion in the hydrogels to demonstrate the first measurements of the intrinsic water diffusion coefficient in hydrogel-salt composites. Based on these insights, we pattern hydrogels with micropores to significantly decrease the required absorption and desorption time by 19% and 72%, respectively, while reducing the total water capacity of the hydrogel by only 4%. Thereby, we provide an effective strategy towards hydrogel material optimization, with a particular significance in pure-vapor environments

Balancing macronutrient stoichiometry to alleviate eutrophication

Reactive nitrogen (N) and phosphorus (P) inputs to surface waters modify aquatic environments and affect public health and recreation. Until now, source control is the dominating measure of eutrophication management, and biological regulation of nutrients is largely neglected, although aquatic microbial organisms have huge potential to process nutrients. The stoichiometric ratio of organic carbon (OC) to N to P atoms should modulate heterotrophic pathways of aquatic nutrient processing, as high OC availability favours aquatic microbial processing. Such microbial processing removes N by denitrification and captures N and P as organically-complexed, less eutrophying forms. With a global data synthesis, we show that the atomic ratios of bioavailable dissolved OC to either N or P in rivers with urban and agricultural land use are often distant from a ‘microbial optimum’. This OC-deficiency relative to high availabilities of N and P likely overwhelms within-river heterotrophic processing and we propose that the capability of streams and rivers to retain N and P may be improved by active stoichiometric rebalancing. This rebalancing should be done by reconnecting appropriate OC sources such as wetlands and riparian forests, many of which have become disconnected from rivers concurrent to the progress of agriculture and urbanization. However, key knowledge gaps leave questions in the safe implementation of this approach in management: Mechanistic research is required to (i) evaluate system responses to catchment inputs of dissolved OC forms and amounts relative to internal-cycling controls of dissolved OC from aquatic production and particulate OC from aquatic and terrestrial sources and (ii) evaluate risk factors in anoxia-mediated P desorption with elevated OC scenarios. Still, we find this to be an approach with high potential for river management and we recommend to evaluate this stoichiometric approach for alleviating eutrophication, improving water quality and aquatic ecosystem health

Bimodality and alternative equilibria do not help explain long-term patterns in shallow lake chlorophyll-a

Since its inception, the theory of alternative equilibria in shallow lakes has evolved and been applied to an ever wider range of ecological and socioecological systems. The theory posits the existence of two alternative stable states or equilibria, which in shallow lakes are characterised by either clear water with abundant plants or turbid water where phytoplankton dominate. Here, we used data simulations and real-world data sets from Denmark and north-eastern USA (902 lakes in total) to examine the relationship between shallow lake phytoplankton biomass (chlorophyll-a) and nutrient concentrations across a range of timescales. The data simulations demonstrated that three diagnostic tests could reliably identify the presence or absence of alternative equilibria. The real-world data accorded with data simulations where alternative equilibria were absent. Crucially, it was only as the temporal scale of observation increased (>3 years) that a predictable linear relationship between nutrient concentration and chlorophyll-a was evident. Thus, when a longer term perspective is taken, the notion of alternative equilibria is not required to explain the response of chlorophyll-a to nutrient enrichment which questions the utility of the theory for explaining shallow lake response to, and recovery from, eutrophication.C.D.S. and T.A.D. would like to thank June and Derek Sayer for extraordinary support over many years. The authors of this work have been supported by a number of projects over the elephantine gestation period of this manuscript. These include support from the Poul Due Jensen Fonden, Danmarks Frie Forskningsfond Natur og Univers project GREENLAKES (No. 9040-00195B) and the UFM-funded project LTER_DK for Long Term Ecosystem Research in Denmark. In addition, support was provided by The European Union’s Horizon 2020 research and innovation programmes under grant agreement No 869296—The PONDERFUL Project”, TREICLAKE under grant agreement No 951963, and the AQUACOSM project and by the European Commission EU H2020- INFRAIA-project (No. 731065) and AQUACOSMplus (No. 871081). E.J. was also supported by the TÜBITAK outstanding researcher programme2232 (project 118C250) and AnaEE, Denmark. The work of D.G. was funded by the Fourth Period of Programme-oriented Funding, Helmholtz Association of German ResearchCentres, Research Field Earth and Environment.C.D.S. and T.A.D. would like to thank June and Derek Sayer for extraordinary support over many years. The authors of this work have been supported by a number of projects over the elephantine gestation period of this manuscript. These include support from the Poul Due Jensen Fonden, Danmarks Frie Forskningsfond Natur og Univers project GREENLAKES (No. 9040-00195B) and the UFM-funded project LTER_DK for Long Term Ecosystem Research in Denmark. In addition, support was provided by The European Union’s Horizon 2020 research and innovation programmes under grant agreement No 869296—The PONDERFUL Project”, TREICLAKE under grant agreement No 951963, and the AQUACOSM project and by the European Commission EU H2020- INFRAIA-project (No. 731065) and AQUACOSMplus (No. 871081). E.J. was also supported by the TÜBITAK outstanding researcher programme2232 (project 118C250) and AnaEE, Denmark. The work of D.G. was funded by the Fourth Period of Programme-oriented Funding, Helmholtz Association of German ResearchCentres, Research Field Earth and Environment

Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

The ability to obtain gene expression profiles from human disease specimens provides an opportunity to identify relevant gene pathways, but is limited by the absence of data sets spanning a broad range of conditions. Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order to gain insight into disease pathogenesis. Unsupervised hierarchical clustering separated samples by disease as well as common cellular and molecular pathways. Disease-specific signatures were leveraged to build a multi-disease classifier, which predicted the diagnosis of publicly and prospectively collected expression profiles with 93% accuracy. In one sample, the molecular classifier differed from the initial clinical diagnosis and correctly predicted the eventual diagnosis as the clinical presentation evolved. Finally, integration of IFN-regulated gene programs with the skin database revealed a significant inverse correlation between IFN-β and IFN-γ programs across all conditions. Our study provides an integrative approach to the study of gene signatures from multiple skin conditions, elucidating mechanisms of disease pathogenesis. In addition, these studies provide a framework for developing tools for personalized medicine toward the precise prediction, prevention, and treatment of disease on an individual level

Modeling Progressive Fibrosis with Pluripotent Stem Cells Identifies an Anti-fibrotic Small Molecule.

Progressive organ fibrosis accounts for one-third of all deaths worldwide, yet preclinical models that mimic the complex, progressive nature of the disease are lacking, and hence, there are no curative therapies. Progressive fibrosis across organs shares common cellular and molecular pathways involving chronic injury, inflammation, and aberrant repair resulting in deposition of extracellular matrix, organ remodeling, and ultimately organ failure. We describe the generation and characterization of an in vitro progressive fibrosis model that uses cell types derived from induced pluripotent stem cells. Our model produces endogenous activated transforming growth factor β (TGF-β) and contains activated fibroblastic aggregates that progressively increase in size and stiffness with activation of known fibrotic molecular and cellular changes. We used this model as a phenotypic drug discovery platform for modulators of fibrosis. We validated this platform by identifying a compound that promotes resolution of fibrosis in in vivo and ex vivo models of ocular and lung fibrosis

How brains make decisions

This chapter, dedicated to the memory of Mino Freund, summarizes the Quantum Decision Theory (QDT) that we have developed in a series of publications since 2008. We formulate a general mathematical scheme of how decisions are taken, using the point of view of psychological and cognitive sciences, without touching physiological aspects. The basic principles of how intelligence acts are discussed. The human brain processes involved in decisions are argued to be principally different from straightforward computer operations. The difference lies in the conscious-subconscious duality of the decision making process and the role of emotions that compete with utility optimization. The most general approach for characterizing the process of decision making, taking into account the conscious-subconscious duality, uses the framework of functional analysis in Hilbert spaces, similarly to that used in the quantum theory of measurements. This does not imply that the brain is a quantum system, but just allows for the simplest and most general extension of classical decision theory. The resulting theory of quantum decision making, based on the rules of quantum measurements, solves all paradoxes of classical decision making, allowing for quantitative predictions that are in excellent agreement with experiments. Finally, we provide a novel application by comparing the predictions of QDT with experiments on the prisoner dilemma game. The developed theory can serve as a guide for creating artificial intelligence acting by quantum rules.Comment: Latex file, 20 pages, 3 figure