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Abstract. ​​​​​​​Land–atmosphere coupling (LAC) has long been studied, focusing on land surface and atmospheric boundary layer processes. However, the influence of humidity in the lower troposphere (LT), especially that above the planetary boundary layer (PBL), on LAC remains largely unexplored. In this study, we use radiosonde observations from the US Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with the influence of background LT humidity and that without it. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local LAC indices such as the convective triggering potential and low-level humidity index (CTP / HILow), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting that the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and is largely controlled by moisture transport, in understanding land–atmospheric feedbacks under dry soil conditions, especially during droughts or dry spells over the SGP.

 

The National Science Foundation Research Initiation in Engineering Formation (RIEF) program aims to increase research capacity in the field by providing funding for technical engineering faculty to learn to conduct engineering education research through mentorship by an experienced social science researcher. We use collaborative autoethnography to study the tripartite RIEF mentoring relationship between Julie, an experienced engineering education researcher, and two novice education researchers who have backgrounds in biomedical engineering—Paul, a biomedical engineering faculty member and major professor to the second novice, Deepthi, a graduate student. We ground our work in the cognitive apprenticeship model and Eby and colleagues’ mentoring model.

Using data from written reflections and interviews, we explored the role of instrumental and psychosocial supports in our mentoring relationship. In particular, we noted how elements of cognitive apprenticeship such as scaffolding and gradual fading of instrumental supports helped Paul and Deepthi learn qualitative research skills that differed drastically from their biomedical engineering research expertise. We initially conceptualized our tripartite relationship as one where Julie mentored Paul and Paul subsequently mentored Deepthi. Ultimately, we realized that this model was unrealistic because Paul did not yet possess the social science research expertise to mentor another novice. As a result, we changed our model so that Julie mentored both Paul and Deepthi directly. While our mentoring relationship was overall very positive, it has included many moments of miscommunication and misunderstanding. We draw on Lent and Lopez’s idea of relation-inferred self-efficacy to explain some of these missed opportunities for communication and understanding.

This paper contributes to the literature on engineering education capacity building by studying mentoring as a mechanism to support technically trained researchers in learning to conduct engineering education research. Our initial mentoring model failed to take into account how challenging it is for mentees to make the paradigm shift from technical engineering to social science research and how that would affect Paul’s ability to mentor Deepthi. Our experiences have implications for expanding research capacity because they raise practical and conceptual issues for experienced and novice engineering education researchers to consider as they form mentoring relationships.

 

The Acyl-activating enzyme (AAE) 3 gene encodes an oxalyl-CoA synthetase that catalyzes the conversion of oxalate to oxalyl-CoA as the first step in the CoA-dependent pathway of oxalate catabolism. Although the role of this enzyme in oxalate catabolism has been established, its biological roles in plant growth and development are less understood. As a step toward gaining a better understanding of these biological roles, we report here a characterization of the Arabidopsis thaliana aae3 (Ataae3) seed mucilage phenotype. Ruthidium red (RR) staining of Ataae3 and wild type (WT) seeds suggested that the observed reduction in Ataae3 germination may be attributable, at least in part, to a decrease in seed mucilage accumulation. Quantitative RT-PCR analysis revealed that the expression of selected mucilage regulatory transcription factors, as well as of biosynthetic and extrusion genes, was significantly down-regulated in the Ataae3 seeds. Mucilage accumulation in seeds from an engineered oxalate-accumulating Arabidopsis and Atoxc mutant, blocked in the second step of the CoA-dependent pathway of oxalate catabolism, were found to be similar to WT. These findings suggest that elevated tissue oxalate concentrations and loss of the oxalate catabolism pathway downstream of AAE3 were not responsible for the reduced Ataae3 seed germination and mucilage phenotypes. Overall, our findings unveil the presence of regulatory interplay between AAE3 and transcriptional control of mucilage gene expression.

 

The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of “tropical cyclone worlds” to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly “turning on” mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill. 

Two multinary selenides, Ba8Hf2Se11(Se2) and Ba9Hf3Se14(Se2), with unprecedented structure types have been prepared using high-temperature synthesis techniques and represent the first known compounds in the Ba-Hf-Se system. Their structures were determined from single crystal X-ray diffraction (XRD) data. The Ba8Hf2Se11(Se2) compound crystallizes in the monoclinic C2/c space group with a = 12.3962(15) Å, b = 12.8928(15) Å, c = 18.1768(17) Å, and β = 90.685(4)°, while Ba9Hf3Se14(Se2) forms in the rhombohedral R space group with a = b = 19.4907(6) Å and c = 23.6407(11) Å. Both have pseudo-zero-dimensional structures with homoatomic Se–Se bonding in the form of (Se2)2− at distances of 2.400–2.402 Å. The structure of Ba8Hf2Se11(Se2) is comprised of [Hf2Se11]14−, Ba2+, and (Se2)2− dimers. Conversely, the Ba9Hf3Se14(Se2) structure contains a novel perovskite-type cluster constructed from eight octahedrally-coordinated Hf cations, i.e., [Hf8Se36]40−, and isolated [HfSe6]8− units which are separated by (Se2)2− dimers and Ba2+ cations. Polycrystalline Ba8Hf2Se11(Se2) is synthesized at 1073 K using a two-step solid-state synthesis method, with the co-formation of a small amount of a BaSe secondary phase. A direct bandgap of 2.2(2) eV is obtained for the polycrystalline sample of Ba8Hf2Se11(Se2), which is consistent with its yellow color. Density functional theory calculations reveal their bandgap transitions stem from predominantly filled Se-4p to empty Hf-5d at the edges of the valence bands (VB) and conduction bands (CB), respectively. The optical absorption coefficients are calculated to be relatively large, exceeding ∼105 cm−1 at about >2.0 eV with effective masses in the CB varying from ∼0.5 me (Γ → A) in Ba8Hf2Se11(Se2) to ∼1.0 me (Γ → L) in Ba9Hf3Se14(Se2). Thus, their optoelectronic properties are shown to be competitive with existing perovskite-type chalcogenides that have been a focus of recent research efforts. 

Percutaneous coronary interventions (PCIs) within left main coronary arteries are high-risk procedures that require optimization of interactions between stent(s) and diseased vessels. Optical Coherence Tomography (OCT) is a widely accepted tool that enhances physicians’ ability to assess proper stent appositions during clinical procedures. The primary aim of this study was to develop complementary post-procedure imaging methodologies to better assess and interpret outcomes of left main PCI procedures, utilizing both reanimated and perfusion-fixed human hearts.

PCIs were performed while obtaining OCT scans within the left main anatomies of six human hearts. Subsequently, each heart was scanned with a micro-CT scanner with optimized parameters to achieve resolutions up to 20 µm. Scans were reconstructed and imported into a DICOM segmentation software to generate computational models of implanted stents and associated coronary vessels. 2D images from OCT that were obtained during PCIs were compared to the 3D models generated from micro-CT reconstructions. In addition, the 3D models were utilized to create virtual reality scenes and enlarged 3D prints for development of “mixed reality” tools relative to bifurcation stenting within human left main coronary arteries.

We developed reproducible methodologies for post-implant analyses of coronary artery stenting procedures. In addition, we generated high-resolution 3D computational models, with ~ 20-micron resolutions, of PCIs performed within reanimated and perfusion-fixed heart specimens.

Generated computational models of left main PCIs performed in isolated human hearts can be used to obtain detailed measurements that provide further clinical insights on procedural outcomes. The 3D models from these procedures are useful for generating virtual reality scenes and 3D prints for physician training and education.

 

The vertical velocity distribution in the atmosphere is asymmetric with stronger upward than downward motion. This asymmetry is important for the distribution of precipitation and its extremes and for an effective static stability that has been used to represent the effects of latent heating on extratropical eddies. Idealized GCM simulations show that the asymmetry increases as the climate warms, but current moist dynamical theories based around small-amplitude modes greatly overestimate the increase in asymmetry with warming found in the simulations. Here, we first analyze the changes in asymmetry with warming using numerical inversions of a moist quasigeostrophic omega equation applied to output from the idealized GCM. The inversions show that increases in the asymmetry with warming in the GCM simulations are primarily related to decreases in moist static stability on the left-hand side of the moist omega equation, whereas the dynamical forcing on the right-hand side of the omega equation is unskewed and contributes little to the asymmetry of the vertical velocity distribution. By contrast, increases in asymmetry with warming for small-amplitude modes are related to changes in both moist static stability and dynamical forcing leading to enhanced asymmetry in warm climates. We distill these insights into a toy model of the moist omega equation that is solved for a given moist static stability and wavenumber of the dynamical forcing. In comparison to modal theory, the toy model better reproduces the slow increase of the asymmetry with climate warming in the idealized GCM simulations and over the seasonal cycle from winter to summer in reanalysis.

Upward velocities are stronger than downward velocities in the atmosphere, and this asymmetry is important for the distribution of precipitation because precipitation is linked to upward motion. An important and open question is what sets this asymmetry and how much it increases as the climate warms. Past work has shown that current theories greatly overestimate the increase in asymmetry with warming in idealized simulations. In this work, we develop a more complete theory and show that it is able to better reproduce the slow increase of the asymmetry with warming that is found over the seasonal cycle from winter to summer and in idealized simulations of warming climates.

 

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

 

Transiting Exoplanet Survey Satellite (TESS) photometry of the polars AM Herculis (AM Her) and AR Ursae Majoris (AR UMa) is presented, along with high-speed photometry. AM Her shows a variety of high states with frequent transitions between them. TESS photometry of AR UMa in the low state reveals no evidence of accretion, while the McDonald 2.1 m telescope caught AR UMa in its high accretion state. Roche-lobe overflow is shut off during low states of AR UMa, while accretion often still takes place during low states of AM Her. We derive inclinations of 50° and 70° for AM Her and AR UMa respectively. To model the high-state light curves of AM Her, we employ a self-organized map light-curve classification scheme to establish common accretion configurations. The cyclotron radiation properties then allow the production of emission region maps on the surface of the white dwarf. The accretion geometry of AM Her is most consistent with a multipolar field structure. The high-state photometry of AR UMa has stochastic accretion flaring, which we attribute to magnetically buffeted mass transfer through the inner Lagrangian point L1. To consider this possibility, we examine the magnetism of both stars and argue that the local magnetic field near L1 can initiate short-lived accretion events and affect transitions between high and low accretion states in both AM Her and AR UMa. In particular, AR UMa has the low state as its default, while AM Her and most other active polars are in the high state by default.

 

Quantum devices based on InSb nanowires (NWs) are a prime candidate system for realizing and exploring topologically-protected quantum states and for electrically-controlled spin-based qubits. The influence of disorder on achieving reliable quantum transport regimes has been studied theoretically, highlighting the importance of optimizing both growth and nanofabrication. In this work, we consider both aspects. We developed InSb NW with thin diameters, as well as a novel gating approach, involving few-layer graphene and atomic layer deposition-grown AlO_x. Low-temperature electronic transport measurements of these devices reveal conductance plateaus and Fabry–Pérot interference, evidencing phase-coherent transport in the regime of few quantum modes. The approaches developed in this work could help mitigate the role of material and fabrication-induced disorder in semiconductor-based quantum devices.