NGC 206 in Andromeda Galaxy

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These “inverse folding” models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the p(structure|seq) Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as “BayesDesign”, that leverages Bayes’ Rule to maximize the p(structure|seq) objective instead of the p(seq|structure) objective common in inverse folding models. The efficacy of BayesDesign is evaluated in the context of two protein model systems, the NanoLuc enzyme and the WW structural motif. Both BayesDesign and the baseline ProteinMPNN algorithm increase the thermostability of NanoLuc and increase the conformational specificity of WW. The possible sources of error in the model are analyzed.

On 10 November 2022, measurements were made of the Atlas V JPSS-2 rocket launch from SLC-3E at Vandenberg Space Force Base, California. Measurements were made at 11 stations from distances of 200 m to 7 km from the launch pad. Measurement locations were arranged at various azimuthal angles relative to the rocket to investigate possible noise asymmetry. This paper discusses preliminary results from this measurement including overall levels, temporal and spectral characteristics, evidence of nonlinear propagation, and potential azimuthal asymmetry effects.

We report on magnetic orderings of nanospins in self-assemblies of Fe₃O₄ nanoparticles (NPs), occurring at various stages of the magnetization process throughout the superparamagnetic (SPM)-blocking transition. Essentially driven by magnetic dipole couplings and by Zeeman interaction with a magnetic field applied out-of-plane, these magnetic orderings include a mix of long-range parallel and antiparallel alignments of nanospins, with the antiparallel correlation being the strongest near the coercive point below the blocking temperature. The magnetic ordering is probed via x-ray resonant magnetic scattering (XRMS), with the x-ray energy tuned to the Fe−L₃ edge and using circular polarized light. By exploiting dichroic effects, a magnetic scattering signal is isolated from the charge scattering signal. We measured the nanospin ordering for two different sizes of NPs, 5 and 11 nm, with blocking temperatures TB of 28 and 170 K, respectively. At 300 K, while the magnetometry data essentially show SPM and absence of hysteresis for both particle sizes, the XRMS data reveal the presence of nonzero (up to 9%) antiparallel ordering when the applied field is released to zero for the 11 nm NPs. These antiparallel correlations are drastically amplified when the NPs are cooled down below TB and reach up to 12% for the 5 nm NPs and 48% for the 11 nm NPs, near the coercive point. The data suggest that the particle size affects the prevalence of the antiparallel correlations over the parallel correlations by a factor ∼1.6 to 3.8 higher when the NP size increases from 5 to 11 nm.

Understanding processing-structure-property (PSP) linkages of solid-phase microextraction (SPME) coating materials is crucial for the rational design and advancement of these new materials. As SPME is a diffusion-based extraction technique, analyzing the morphology of its coating materials is important for optimizing its performance. In this study, we assess the morphological evolution of micro/mesoporous amorphous silicon (a-Si) thin films sputtered at an oblique angle onto silicon, which serve as models for support materials in SPME devices. The contrast of scanning transmission electron microscopy (STEM) images is enhanced via ZnO infiltration by atomic layer deposition (ALD). Various metrics, including physical descriptors and two-point statistics methods, are employed to follow the films' evolution. Analysis of the two-point correlation function reveals a simple ellipse/spherical local pore geometry in contrast to the long-range irregular arrangement of pores identified by a range of traditional and novel metrics. Additionally, analyzing the internal structure of the pores through homology metrics aligns well with the theoretical understanding of morphological evolution in oblique sputtered films. These analyses show that the “average ratio of principal moment of inertia”, “Betti numbers”, and “two-point statistics” based metrics can capture valuable information during film growth.

The morphological analysis approach proposed in this study can be applied to analyze any nanoporous medium as a first step towards developing structure-property relationships that tie back to a given preparation method. Ultimately, a more extensive experimental and/or simulation-based study should confirm the correlations between these metrics and actual diffusion properties as the basis for process-structure-properties relations for improved design and optimization of this film.

Researchers have employed numerous array configurations and sampling densities for measuring sound radiations of live sources, including musical instruments and the human voice, with the sampling positions varying from well under a hundred to far more than a thousand. These inconsistencies highlight an underlying knowledge gap regarding the required numbers and positions of sampling positions for ideal directivity measurements. This work presents theoretical methods and practical metrics to clarify how to mitigate spatial aliasing effects for a given source. Initial results of theoretical and numerical models generalize to live source measurements. Further developments explore how source placements, acoustic source centering, and diffraction about the human body impact necessary sampling densities.

Applications of supervised machine learning to ocean acoustics is often limited by the lack of labeled measured data. To overcome this, synthetic data can be used for training. This paper explores the potential for unsupervised learning to provide labels for measured data. Specifically, a comparison is made between seabed classification from supervised learning and labels inferred from unsupervised learning. Both networks are trained on synthetic ship noise spectrograms. Six CNN-based supervised learning methods were trained using synthetic data labeled by seabed class. The trained networks were applied to 69 measured spectrograms from the Seabed Characterization Experiment 2017. The results show a distinct preference for seabeds with softer top layer (water-sediment sound speed ratios less than one). The unsupervised ML method, k-means clustering, is applied to same synthetic dataset, and the resulting clusters are evaluated based on the characteristics of the synthetic data samples placed into each cluster. The measured ship noise spectrograms are then passed through the trained clustering model, and the characteristics of the assigned clusters are evaluated. Of the 69 measured data samples, 70% are placed in clusters showing a distinct preference for seabed classes similar to those obtained from the CNN-based classifiers. Other measured data samples are placed in clusters that contain synthetic data samples from short ranges. This work illustrates the potential for using clustering to assign preliminary labels to unlabeled data.