Search for: All records

Photochromic radionuclide-based “claw machines” characterizedviaa combination of isothermal titration calorimetry and spectroscopic analysis unlock a pathway for on demand radionuclide capture and release. 

Abstract Tides are an important factor shaping the sea ice system in the Arctic Ocean by altering vertical heat fluxes and advection patterns. Unfortunately, observations are sparse, and the analysis of tides is complicated by the proximity of wind-driven inertial oscillations to the semidiurnal frequencies. Furthermore, computational costs typically prohibit the inclusion of tides in ocean models, leaving a significant gap in our understanding. Motivated by summer observations showing elevated downward surface heat fluxes in the presence of tides, we analyzed simulations carried out with an eddy-permitting coupled ice–ocean model to quantify the impact of tidal effects on Arctic sea ice. In line with previous studies, we find an overall decrease in sea ice volume when tides are included in the simulations, associated with increased vertical mixing and the upward flux of heat from deeper layers of the Arctic Ocean, but this sea ice volume decrease is less pronounced than previously thought. Surprisingly, our simulations suggest that in summer, Arctic sea ice area is larger, by up to 1.5%, when tides are included in the simulations. This effect is partly caused by an increased downward surface heat flux and a consequently lower sea surface temperature, delaying sea ice melting predominantly in the Siberian Seas, where tides are moderately strong and the warm Atlantic Water core is located relatively deep and does not encroach on the wide continental shelf. Here, tidally enhanced downward heat flux from the surface in summer can dominate over the increased upward heat flux from the warm Atlantic Water layer. Significance StatementThis study sheds light on the complex and understudied role of tides in Arctic sea ice dynamics. By utilizing advanced computer models, our research uncovers that, contrary to common expectations, tides contribute to a seasonal increase in sea ice area by up to 1.5% in summer. This effect is attributed to enhanced advection of sea ice into the Siberian Seas and a local increase in downward heat flux reducing sea surface temperatures, thereby delaying sea ice melting in this region. Our findings challenge prevailing notions about the negative impact of tides on sea ice and highlight the importance of incorporating tidal impacts in ocean models to improve predictions of Arctic sea ice changes, key for our understanding of both Arctic and global climate dynamics. 

The pathogenic mechanisms of many diseases are well understood at the molecular level, but there are prevalent syndromes associated with pathogenic signaling, such as diabetes and chronic inflammation, where our understanding is more limited. Here, we report that pathogenic signaling suppresses the mobility of a spectrum of proteins that play essential roles in cellular functions known to be dysregulated in these chronic diseases. The reduced protein mobility, which we call proteolethargy, was linked to cysteine residues in the affected proteins and signaling-related increases in excess reactive oxygen species. Diverse pathogenic stimuli, including hyperglycemia, dyslipidemia, and inflammation, produce similar reduced protein mobility phenotypes. We propose that proteolethargy is an overlooked cellular mechanism that may account for various pathogenic features of diverse chronic diseases. 

Authors of this proposal are members of an inter-institutional working group focused on the teaching and learning of transformations in college geometry courses taken by prospective secondary teachers. After exploring axioms and definitions for transformational geometry in our courses, we decided to shift to identifying not just what, but how students were learning about transformations in our courses. To explore this, we began a lesson study (Boyce et al., 2021). In this report, we discuss our engagement in the lesson study, its outcomes, and new directions. 

A rare-earth-containing compound, ytterbium aluminium antimonide, Yb 3 AlSb 3 (Ca 3 AlAs 3 -type structure), has been successfully synthesized within the Yb–Al–Sb system through flux methods. According to the Zintl formalism, this structure is nominally made up of (Yb 2+ ) 3 [(Al 1− )( 1b – Sb 2− ) 2 ( 2b – Sb 1− )], where 1b and 2b indicate 1-bonded and 2-bonded, respectively, and Al is treated as part of the covalent anionic network. The crystal structure features infinite corner-sharing AlSb 4 tetrahedra, [AlSb 2 Sb 2/2 ] 6− , with Yb 2+ cations residing between the tetrahedra to provide charge balance. Herein, the synthetic conditions, the crystal structure determined from single-crystal X-ray diffraction data, and electronic structure calculations are reported. 

Abstract. Use of an ocean parameter and state estimation framework – such as the Estimating the Circulation and Climate of the Ocean (ECCO) framework – could provide an opportunity to learn about the spatial distribution of the diapycnal diffusivity parameter (κρ) that observations alone cannot due to gaps in coverage. However, we show that the inclusion of misfits to observed physical variables – such as in situ temperature, salinity, and pressure – currently accounted for in ECCO is not sufficient, as κρ from ECCO does not agree closely with any observationally derived product. These observationally derived κρ products were inferred from microstructure measurements, derived from Argo and conductivity–temperature–depth (CTD) data using a strain-based parameterization of fine-scale hydrographic structure, or calculated from climatological and seafloor data using a parameterization of tidal mixing. The κρ products are in close agreement with one another but have both measurement and structural uncertainties, whereas tracers can have relatively small measurement uncertainties. With the ultimate goal being to jointly improve the ECCO state estimate and representation of κρ in ECCO, we investigate whether adjustments in κρ due to inclusion of misfits to a tracer – dissolved oxygen concentrations from an annual climatology – would be similar to those due to inclusion of misfits to observationally derived κρ products. We do this by performing sensitivity analyses with ECCO. We compare multiple adjoint sensitivity calculations: one configuration uses misfits to observationally derived κρ, and the other uses misfits to observed dissolved oxygen concentrations. We show that adjoint sensitivities of dissolved oxygen concentration misfits to the state estimate's control space typically direct κρ to improve relative to the observationally derived values. These results suggest that the inclusion of oxygen in ECCO's misfits will improve κρ in ECCO, particularly in (sub)tropical regions. 

Abstract The lack of continuous spatial and temporal sampling of hydrographic measurements in large parts of the Arctic Ocean remains a major obstacle for quantifying mean state and variability of the Arctic Ocean circulation. This shortcoming motivates an assessment of the utility of Argo-type floats, the challenges of deploying such floats due to the presence of sea ice, and the implications of extended times of no surfacing on hydrographic inferences. Within the framework of an Arctic coupled ocean–sea ice state estimate that is constrained to available satellite and in situ observations, we establish metrics for quantifying the usefulness of such floats. The likelihood of float surfacing strongly correlates with the annual sea ice minimum cover. Within the float lifetime of 4–5 years, surfacing frequency ranges from 10–100 days in seasonally sea ice–covered regions to 1–3 years in multiyear sea ice–covered regions. The longer the float drifts under ice without surfacing, the larger the uncertainty in its position, which translates into larger uncertainties in hydrographic measurements. Below the mixed layer, especially in the western Arctic, normalized errors remain below 1, suggesting that measurements along a path whose only known positions are the beginning and end points can help constrain numerical models and reduce hydrographic uncertainties. The error assessment presented is a first step in the development of quantitative methods for guiding the design of observing networks. These results can and should be used to inform a float network design with suggested locations of float deployment and associated expected hydrographic uncertainties.