Scott Sommerfeldt Elected ASA President

Glycine receptors (GlyRs) mediate inhibitory neurotransmission in the central nervous system. The GlyRα2 subtype contributes to critical neural circuitry in early neurodevelopment and is also found in adults. GlyRα2 dysfunctions are implicated in neurodevelopmental disorders, including autism, epilepsy, and cognitive delays. GlyRα2 functional properties and pharmacology are distinct from GlyRα1, but the structural basis for these differences remains poorly defined. Here, we report cryo-electron microscopy structures of full-length, human GlyRα2 reconstituted in peptidiscs captured in multiple conformational states. In addition to symmetric resting and desensitized states, we resolved an asymmetric open state, previously observed only in heteromeric GlyRs. This suggests that asymmetry is intrinsic to GlyRα2, independent of β-subunit incorporation. Furthermore, we identified distinct conformations of GlyRα2 with the pore-blocker picrotoxin, providing new insights into allosteric interactions. These findings uncover the structural basis of GlyRα2 function, providing a foundation for understanding its role in development and in GlyRα2-associated disorders.

Despite significant advances in artificial intelligence (AI) algorithms for prostate cancer detection from whole slide images, the clinical applicability of these models remains limited. Variability in inter- and intrapathological grading, low generalizability of training datasets, and insufficient annotation precision restrict the performance of downstream models. This article introduces a novel Bayesian framework that addresses these challenges by generating pixel-wise posterior distributions, thereby providing a probabilistic output that enables the simulation of a panel consensus and enabling seamless integration of new data and models as they become available. The framework is demonstrated by integrating a Bayesian prior with a trained AI model to produce a per-pixel distribution of Gleason patterns. It is shown that using this distribution of Gleason patterns rather than a ground-truth label can improve model applicability, mitigate errors, and highlight areas of interest for pathologists. Furthermore, we present a high-quality, hand-curated dataset of prostate histopathological images annotated at the gland level by trained premedical students and verified by an expert pathologist. We highlight the potential of this adaptive and uncertainty-aware framework for developing clinically deployable AI tools that can support pathologists in accurate prostate cancer grading, improve diagnostic accuracy, and create positive patient outcomes. This work is presented as an early-stage, proof-of-concept study; the framework has not been validated for clinical use and is not intended for diagnostic deployment in its current form.

The Extended Murphy Good (EMG) mathematical theory proposed by Richard G. Forbes for Large Area Field Emitters is applied to Patterned As-Grown Carbon Nanotube field emitters as well as existing research for comparison. Patterned As-Grown Carbon Nanotubes (developed by Brigham Young University in collaboration with Varex Imaging) were transferred to titanium substrates by means of a precise thermal process which creates a unique titanium carbide nanostructure as an adhesion mechanism for Patterned As-Grown Carbon Nanotubes. This deposition process is called the VanDyke Deposition Method. Field emission IV curve data was measured from 10 mA to 100 mA and was placed in the EMG calculator. As the EMG data more correctly estimates the vertical axis intercept the EMG results show that As-grown CNTs adhered to titanium can have Formal Emission Areas of 318.9u² & 684.5u² .

The shapes and densities of midsized and large trans-Neptunian objects (TNOs) are pivotal for understanding a variety of important aspects of planet formation. In this work, we present a Bayesian shape modeling method that combines constraints from rotational light curves and satellite orbits to construct three-dimensional shape models of TNOs. We use it to reanalyze three stellar occultations of the TNOs (229762) G!kún∣∣’hòmdímà (2007 UK126), (136108) Haumea, and (174567) Varda. By assuming that their satellites (or rings) orbit in their respective equatorial planes, we are able to derive unique shape models for both G!kún∣∣’hòmdímà and Haumea. Our derived shape for G!kún∣∣’hòmdímà is spheroidal with  km and  km, with a system density  kg m−3. For Haumea, we find  km,  km, and  km, providing  kg m−3. For Varda, after updating its mutual orbit with its satellite Ilmarë, we find that currently published data are unable to fully constrain its three-dimensional shape. Intriguingly, Varda’s elongated limb appears to point toward its satellite at the time of the occultation. With a ∼2% chance of such an alignment happening randomly, this may be suggestive of a frozen-in tidal and/or rotational bulge. Our work emphasizes the importance of how external constraints can improve occultation analyses. With continued observations of rotational light curves, stellar occultations, and satellite orbits, these—and other—TNOs can have their shapes and densities further refined.

On November 19, 2024, Space-X launched the sixth test flight of their Starship rocket. Measurements were made by Brigham Young University at 21 different locations around the launch pad, including two 3-meter-radius vector probes located roughly 2 km north and 2 km south of the pad. There was also an array of measurement stations near the coast ranging from 3 km south of the pad to 27 km north. Using a broadband time-correlation technique, the direction of the sound source can be determined from the vector probes. Having two vector probes potentially allows the measurement of the trajectory of the rocket as it lifts off. It was found that the small distance between the two probes limited the accuracy of the position determination when the rocket was downrange. On this launch, the super-heavy booster did not return to the launch site but was instead diverted to the Gulf of Mexico. There were two transient events that occurred after the launch which were associated with the booster-return and the hot-staging ring reentry. These sonic booms were poorly localized by the intensity probes, but well localized by the arrival times of the booms at the array of other stations.

We present You Only Stack Once (YOSO), an automated pipeline designed to detect faint, slow-moving solar system objects in wide-field astronomical surveys. The pipeline integrates a novel Gaussian motion filter (GMoF) that operates at the pixel level to enhance the signal-to-noise ratio for objects exhibiting a range of apparent rates of motion. Unlike conventional shift-and-stack methods, which rely on discrete velocity trials, GMoF amplifies trails while suppressing random noise and static background features. Applied to a subset of DEEP observations from the Dark Energy Camera, YOSO discovered 45 out of 73 previously detected objects, as well as 11 new trans-Neptunian objects. It also discovered 216 objects in the near solar system. Although alternative shift-and-stack methods are sensitive to objects about 0.88 mag fainter, YOSO’s false-positive rate is extremely low, since it detects only sources that exhibit a trail and are consistent with a point source when shifted at the right rate. We show how this method can be deployed on large surveys like LSST, and be adapted for other domains that require motion-based signal enhancement, including exoplanet imaging through angular differential imaging and near-Earth object (NEO) detection for missions like the NEO Surveyor. YOSO thus provides a versatile, scalable approach for extracting faint, motion-dependent signals in the era of data-intensive astronomy.