3D Phase Space Measurements in Plasmas

Therapeutic proteins face a critical pharmacokinetic challenge: rapid clearance from circulation limits their clinical efficacy. Albumin-binding domains (ABDs) offer an elegant solution by enabling therapeutic proteins to “hitchhike” on serum albumin’s favorable 19-day half-life through FcRn-mediated recycling. Clinical validation through approved therapeutics like ozoralizumab demonstrates the success of this approach, with preclinical studies showing fusion to an ABD extended half-life to 18 days. This review provides an analysis of ABD-fusion protein design, integrating structural biology, computational prediction, and rational engineering principles. We catalog the major classes of albumin-binding modalities, including bacterial three-helix bundle domains, engineered peptides, antibody-derived binders, and alternative scaffolds, comparing their binding properties, size contributions, cross-species reactivity, and production cost. Critical examination of linker architectures reveals that flexible glycine-serine linkers (particularly the widely successful (GGGGS)3 motif) provide optimal balance between domain independence and molecular economy, though linker choice profoundly influences not only spatial separation but also binding affinity, folding, stability, and pharmacokinetics. We evaluate the utility and limitations of the structure prediction tools for ABD-fusion design. We establish practical guidelines for integrating computational screening with experimental validation. This review provides protein engineers and synthetic biologists with a comprehensive framework for rational design of albumin-binding therapeutics, emphasizing the synergistic integration of structural insight, computational prediction, and systematic experimental validation to accelerate development of next-generation long-acting biotherapeutics.

 The detection and characterization of exoplanets in the habitable zone of stars is one of the aims of the Habitable Worlds Observatory mission concept. An internal coronagraph, which can extinguish light from the planets’ parent star to a contrast ratio of about 10⁻¹⁰ is key for characterizing Earth-sized exoplanets. Knowing the polarization behavior of optical elements as a function of angle-of-incidence (AOI) and wavelength is critical for this instrument, because polarization aberrations can impact the contrast. The diattenuation and retardance for freshly deposited and aged aluminum mirrors between 140 and 1600 nm with AOIs between 15 deg and 70 deg were measured using variable-angle spectroscopic ellipsometry. These mirrors are protected by thin LiF evaporated on a XeF₂-passivated Al thin film (Al + XeLiF). Long-term environmental testing showed that the retardance of samples increased while diattenuation was not significantly affected by increased temperature and humidity.

Analysis of the previously classified delta Scuti variable star MW Camelopardalis using data from the Transiting Exoplanet Survey Satellite sparked a deeper inquiry due to unexpected patterns within the target’s observed − calculated (O − C) diagram. The shape could be seen as either a saw-toothed pattern or a set of steps. The pattern was found to be replicated in the O − C diagrams of seven additional targets: TIC 17931346, TIC 44845403, TIC 123580083, TIC 173503902, TIC 302394816, TIC 194944219m and TIC 396465600. The Q value for the targets, their position in the δ Scuti star Leavitt Law, and their location in the instability strip show these objects to be low-mass, fundamental mode pulsators. Seven of the eight stars fill the full range between the blue and red edges of the fundamental-mode instability strip.

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depend on a relatively small number of parameter combinations. Therefore, we introduce an information-matching criterion based on the Fisher information matrix to select the most informative training data from a candidate pool. This method ensures that the selected data contain sufficient information to learn only those parameters that are needed to constrain downstream QoIs. It is formulated as a convex optimization problem, making it scalable to large models and datasets. We demonstrate the effectiveness of this approach across various modeling problems in diverse scientific fields, including power systems and underwater acoustics. Finally, we use information-matching as a query function within an active learning (AL) loop for materials science applications. In all these applications, we find that a relatively small set of optimal training data can provide the necessary information for achieving precise predictions. These results are encouraging for diverse future applications, particularly AL in large machine-learning models.

Reports of audible sonic booms along the south-central California coast during SpaceX Falcon 9 launch ascents prompted measurements in Ventura County during summer 2024. A total of 132 measurements were made over six launches, with 16–25 measurements per launch. The maximum overpressure measured was 1.90 psf (133 dB), but most measured booms had an overpressure below 0.5 psf and durations of several seconds. Two launches had appreciably lower overpressures and smaller terrestrial footprint, indicating that both meteorology and launch azimuth are important factors in terrestrial boom audibility. Agreement between this dataset and environmental assessment predictions was marginal.