Atomic force microscopy imaging reveals the formation of ASIC/ENaC cross-clade ion channels

ASIC and ENaC are co-expressed in various cell types, and there is evidence for a close association between them. Here, we used atomic force microscopy (AFM) to determine whether ASIC1a and ENaC subunits are able to form cross-clade hybrid ion channels. ASIC1a and ENaC could be co-isolated from detergent extracts of tsA 201 cells co-expressing the two subunits. Isolated proteins were incubated with antibodies against ENaC and Fab fragments against ASIC1a. AFM imaging revealed proteins that were decorated by both an antibody and a Fab fragment with an angle of ∼120° between them, indicating the formation of ASIC1a/ENaC heterotrimers.J.M.E. and P.J. are supported by Kidney Research U.K. A.P.S. was a member of the University of Cambridge MB/PhD Programme, and was supported by the Jean Shanks Foundation and the James Baird Fund. A research stay of S.H. at Cambridge was supported by a PhD fellowship from the Bayerische Forschungsstiftung

Cartilage-like electrostatic stiffening of responsive cryogel scaffolds

Cartilage is a structural tissue with unique mechanical properties deriving from its electrically-charged porous structure. Traditional three-dimensional environments for the culture of cells fail to display the complex physical response displayed by the natural tissue. In this work, the reproduction of the charged environment found in cartilage is achieved using polyelectrolyte hydrogels based on polyvinyl alcohol and polyacrylic acid. The mechanical response and morphology of microporous physically-crosslinked cryogels are compared to those of heat-treated chemical gels made from the same polymers, as a result of pH-dependent swelling. In contrast to the heat-treated chemically-crosslinked gels, the elastic modulus of the physical cryogels was found to increase with charge activation and swelling, explained by the occurrence of electrostatic stiffening of the polymer chains at large charge densities. At the same time, the permeability of both materials to fluid flow was impaired by the presence of electric charges. This cartilage-like mechanical behavior displayed by responsive cryogels can be reproduced in other polyelectrolyte hydrogel systems to fabricate biomimetic cellular scaffolds for the repair of the tissue.G.S.O. and M.L.O. are grateful to the Nano Doctoral Training Centre (NanoDTC), University of Cambridge, and the EPSRC who supported this work through the EP/G037221/1 grant. I.M. and R.M.H. were supported by the Biotechnology and Biological Research Council, grant BB/J018236/1. P.J. was supported by Kidney Research UK. S.K.S. was supported by the European Research Council (ERC), grant EMATTER (#280078)

Cartilage-like electrostatic stiffening of responsive cryogel scaffolds

Cartilage is a structural tissue with unique mechanical properties deriving from its electrically-charged porous structure. Traditional three-dimensional environments for the culture of cells fail to display the complex physical response displayed by the natural tissue. In this work, the reproduction of the charged environment found in cartilage is achieved using polyelectrolyte hydrogels based on polyvinyl alcohol and polyacrylic acid. The mechanical response and morphology of microporous physically-crosslinked cryogels are compared to those of heat-treated chemical gels made from the same polymers, as a result of pH-dependent swelling. In contrast to the heat-treated chemically-crosslinked gels, the elastic modulus of the physical cryogels was found to increase with charge activation and swelling, explained by the occurrence of electrostatic stiffening of the polymer chains at large charge densities. At the same time, the permeability of both materials to fluid flow was impaired by the presence of electric charges. This cartilage-like mechanical behavior displayed by responsive cryogels can be reproduced in other polyelectrolyte hydrogel systems to fabricate biomimetic cellular scaffolds for the repair of the tissue

Research data supporting "Cartilage-like electrostatic stiffening of responsive cryogel scaffolds

The following data files are provided: Summary of results: numerical values used to plot each figure in the manuscript, as well as values used to calculate each parameter when applicable. Raw macroindentation and microindentation data: the load vs. time curves obtained by spherical indentation on a universal testing machine and on an AFM. Confocal data: contains representative confocal datasets, presented as .lsm files, for each physical cryogel sample in the neutral and activated states. READ ME: A word document describing each file and its contents

Hydridotetrylene [Ar*EH] (E = Ge, Sn, Pb) coordination at tantalum, tungsten, and zirconium

In a reaction of tantalocene trihydride with the low valent aryl tin cation [Ar*Sn(C(6)H(6))][Al(OC{CF(3)}(3))(4)] (1a) the hydridostannylene complex [Cp(2)TaH(2)–Sn(H)Ar*][Al(OC{CF(3)}(3))(4)] (2) was synthesized. Hydride bridged adducts [Cp(2)WH(2)EAr*][Al(OC{CF(3)}(3))(4)] (E = Sn 3a, Pb 3b) were isolated as products of the reaction between Cp(2)WH(2) and cations [Ar*E(C(6)H(6))][Al(OC{CF(3)}(3))(4)] (E = Sn 1a, Pb 1b). The tin adduct 3a exhibits a proton migration to give the hydridostannylene complex [Cp(2)W(H)[double bond, length as m-dash]Sn(H)Ar*][Al(OC{CF(3)}(3))(4)] 4a. The cationic complex 4a is deprotonated at the tin atom in reaction with base (Me)NHC at 80 °C to give a hydrido-tungstenostannylene [Cp(2)W(H)SnAr*] 5a. Reprotonation of metallostannylene 5a with acid [H(Et(2)O)(2)][BAr(F)] provides an alternative route to hydridotetrylene coordination. Complex 4a adds hydride to give the dihydrostannyl complex [Cp(2)W(H)–SnH(2)Ar*] (7). With styrene 4a shows formation of a hydrostannylation product [Cp(2)W(H)[double bond, length as m-dash]Sn(CH(2)CH(2)Ph)Ar*][Al(OC{CF(3)}(3))(4)] (8). The lead adduct 3b was deprotonated with (Me)NHC to give plumbylene 5b [Cp(2)W(H)PbAr*]. Protonation of 5b with [H(Et(2)O)(2)][Al(OC{CF(3)}(3))(4)] at −40 °C followed by low temperature NMR spectroscopy indicates a hydridoplumbylene intermediate [Cp(2)W(H)[double bond, length as m-dash]Pb(H)Ar*](+) (4b). Hydrido-tungstenotetrylenes of elements Ge (5c), Sn (5a) and Pb (5b) were also synthesized reacting the salt [Cp(2)W(H)Li](4) with organotetrylene halides. The metallogermylene [Cp(2)W(H)GeAr*] (5c) shows an isomerization via 1,2-H-migration to give the hydridogermylene [Cp(2)W[double bond, length as m-dash]Ge(H)Ar*] (9), which is accelerated by addition of AIBN. 9 is at rt photochemically transferred back to 5c under light of a mercury vapor lamp. Zirconocene dihydride [Cp(2)ZrH(2)](2) reacts with tin cation 1a to give the trinuclear hydridostannylene adduct 10 [({Cp(2)Zr}(2){μ-H})(μ-H)(2)μ-Sn(H)Ar*][Al(OC{CF(3)}(3))(4)]. Deprotonation of 10 was carried out using benzyl potassium to give neutral [({Cp(2)Zr}(2){μ-H})(μ-H)μ-Sn(H)Ar*] (11). 11 was also obtained from the reaction of low valent tin hydride [Ar*SnH](2) with two equivalents of [Cp(2)ZrH(2)](2). The trihydride Ar*SnH(3) reacts with half of an equivalent of [Cp(2)ZrH(2)](2) under evolution of hydrogen and formation of a dihydrostannyl complex 13 [Cp(2)Zr(μ-H)SnH(2)Ar*](2) and with further equivalents of Ar*SnH(3) to give bis(hydridostannylene) complex [Cp(2)Zr{Sn(H)Ar*}(2)]

ClimateBench v1.0: A Benchmark for Data-Driven Climate Projections

Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench-the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection-Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge.VD

ClimateBench v1.0: a benchmark for data-driven climate projections

Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench—the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection-Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge