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Abstract We explore possible advantages of cyclic spectroscopy for observations of pulsars in instances where full cyclic deconvolution is not possible. We compute cyclic merits and full-deconvolution regime boundaries for pulsars observed by NANOGrav and discuss which sources stand to benefit the most from using cyclic spectroscopy when observed with the Green Bank Telescope and DSA-2000 in a given frequency range. We compare data products, namely the wavefield, in both full-deconvolution and partial-deconvolution regimes to demonstrate what can be accomplished with incomplete phase retrieval. Additionally, we show how some phase retrieval can still be achieved in the partial-deconvolution regime and how this allows for additional information in scintillation-based data products, like the dynamic wavefield power, compared to what can be found in traditional dynamic spectra. An examination of dynamic wavefield phase as a function of observing frequency reveals more complete phase retrieval is achieved the closer one gets to the full-deconvolution regime, agreeing with the expectations of cyclic merit. While we demonstrate that fragmentary recovery of the secondary wavefield can be accomplished in the partial-deconvolution regime, we advocate for a synergistic approach with phase retrieval methods like theθ−θtransform, although we also provide discussion about shortcomings of this strategy. Finally, we use the combination of modest cyclic merit and lack of discernible results for PSR J1903+0327 to motivate the creation of an updated “cyclic merit 2.0,” which relies on scintillation bandwidth instead of observing bandwidth. 

Anthropogenic NO_xemissions have observable impacts on naturally occurring chemical processes in remote areas hundreds of kilometers downwind. 

ABSTRACT Trichoptera (caddisflies) is one of the most species‐rich orders of aquatic insects. Species of caddisflies cover a broad ecological diversity as exemplified by various uses of underwater silk secretions. Diversity of silk use generally aligns with the evolution of major caddisfly lineages, specifically at the subordinal level: Annulipalpia (retreat makers) and Integripalpia (cocoon and tube‐case makers). However, silk use within suborders differs for a few exceptional species in these clades. In this study, we provide the first whole genome assemblies and annotations for two unusual Integripalpia species:Limnocentropus insolitus, whose hard tube‐case is anchored to boulders by a rigid, elongated silken stalk, andPhryganopsyche brunneawhich builds a “floppy” cylindrical case that lacks the typical robustness of tube‐cases. Its texture rather resembles that of the flexible retreats built by Annulipalpia. Using the two high‐quality genome assemblies, we identified and annotated the major silk gene,h‐fibroin, and compared its amino acid composition across various groups, including retreat, cocoon, and tube‐case makers. Our phylogenetic analysis confirmed the phylogenetic position of the two species in the tube‐case‐making clade. The major silk gene ofL. insolitusshows a similar amino acid composition to other tube‐case‐making species. In contrast, the amino acid composition ofP. brunnearesembles that of retreat‐making species, in particular with regard to the high content of proline. This is consistent with the hypothesis that proline could be linked to enhanced extensibility of silk fibers. Taken together, our results underscore the role of silk genes in shaping the evolutionary ecology of retreat‐ and tube‐case‐making in caddisflies. 

The Discrete Element Method is widely employed for simulating granular flows, but conventional integration techniques may produce unphysical results for simulations with static friction when particle size ratios exceed R ≈ 3. These inaccuracies arise under certain circumstances because some variables in the velocity-Verlet algorithm are calculated at the half-timestep, while others are computed at the full timestep. To correct this, we develop an improved velocity-Verlet integration algorithm to ensure physically accurate outcomes up to the largest size ratios examined (R = 100). The implementation of this improved synchronized_verlet integration method within the LAMMPS framework is detailed, and its effectiveness is validated through a simple three-particle test case and a more general example of granular flow in mixtures with large size-ratios, for which we provide general guidelines for selecting simulation parameters and accurately modeling inelasticity in large particle size-ratio simulations. 

This dataset includes measurements of the dissolved isotope radium-226 in the South Pacific and Southern Ocean. Samples were collected on the US GEOTRACES GP17-OCE cruise (Papeete, Tahiti to Punta Arenas, Chile) on R/V Roger Revelle from December 2022 to January 2023. Radium-223, radium-224, and radium-228 data will be made available in the future. 

Lift and drag forces on moving intruders in flowing granular materials are of fundamental interest but have not yet been fully characterized. Drag on an intruder in granular shear flow has been studied almost exclusively for the intruder moving across flow streamlines, and the few studies of the lift explore a relatively limited range of parameters. Here, we use discrete element method simulations to measure the lift force,$$F_{{L}}$$, and the drag force on a spherical intruder in a uniformly sheared bed of smaller spheres for a range of streamwise intruder slip velocities,$$u_{{s}}$$. The streamwise drag matches the previously characterized Stokes-like cross-flow drag. However,$$F_{{L}}$$in granular shear flow acts in the opposite direction to the Saffman lift in a sheared fluid at low$$u_{{s}}$$, reaches a maximum value and then decreases with increasing$$u_{{s}}$$, eventually reversing direction. This non-monotonic response holds over a range of flow conditions, and the$$F_{{L}}$$versus$$u_{{s}}$$data collapse when both quantities are scaled using the particle size, shear rate and overburden pressure. Analogous fluid simulations demonstrate that the flow around the intruder particle is similar in the granular and fluid cases. However, the shear stress on the granular intruder is notably less than that in a fluid shear flow. This difference, combined with a void behind the intruder in granular flow in which the stresses are zero, significantly changes the lift-force-inducing stresses acting on the intruder between the granular and fluid cases. 

Abstract Antifreeze proteins (AFPs) have enabled teleost fishes to repeatedly colonize polar seas. Four AFP types have convergently evolved in several fish lineages. AFPs inhibit ice crystal growth and lower tissue freezing point. In lineages with AFPs, species inhabiting colder environments may possess more AFP copies. Elucidating how differences in AFP copy number evolve is challenging due to the genes’ tandem array structure and consequently poor resolution of these repetitive regions. Here, we explore the evolution of type III AFPs (AFP III) in the globally distributed suborder Zoarcoidei, leveraging six new long-read genome assemblies. Zoarcoidei has fewer genomic resources relative to other polar fish clades while it is one of the few groups of fishes adapted to both the Arctic and Southern Oceans. Combining these new assemblies with additional long-read genomes available for Zoarcoidei, we conducted a comprehensive phylogenetic test of AFP III evolution and modeled the effects of thermal habitat and depth on AFP III gene family evolution. We confirm a single origin of AFP III via neofunctionalization of the enzyme sialic acid synthase B. We also show that AFP copy number increased under low temperature but decreased with depth, potentially because pressure lowers freezing point. Associations between the environment and AFP III copy number were driven by duplications of paralogs that were translocated out of the ancestral locus at which AFP III arose. Our results reveal novel environmental effects on AFP evolution and demonstrate the value of high-quality genomic resources for studying how structural genomic variation shapes convergent adaptation. 

Introduction: Coronavirus disease 2019 (COVID-19) has had a profound impact globally, causing the death of millions of people and deeply affecting socio-psychological, human health, and economic systems, with some nations bearing a disproportionate burden. Despite obesity having been established as one of the major risk factors of COVID-19 severity and other degenerative diseases, the effects that dietary pattern intake plays in COVID-19 outcomes remain poorly understood. The goal of this study is to look into the connection between eating habits, the number of non-obese and obese people, and COVID-19 outcomes in countries with populations exhibiting normal Body Mass Index (BMI), which is an indicator of obesity. Methods: The analysis includes data from 170 countries. From the 170 countries, we focused on 53 nations where the average, BMI falls within the normal range (18.5 to 24.9). A subset of 20 nations was selected for a more detailed examination, comprising 10 nations with the lowest BMI values within the normal range (18.5-19.8) and 10 nations with the highest BMI values within the normal range (23.5-24.9). We used Artificial Intelligence (AI) and Machine Learning (ML) applications to evaluate key metrics, including dietary patterns (sugar and vegetable intake), obesity prevalence, incidence rate, mortality rate, and Case Fatality Rate (CFR). Results: The results demonstrate a significant correlation between higher obesity prevalence and increased COVID-19 severity, evidenced by elevated incidence, mortality, and CFRs in countries like North Macedonia and Italy. In contrast, nations such as Iceland and New Zealand with well-established healthcare systems revealed low mortality rate and case fatality rate despite variations in dietary habits. The study also revealed that vegetable consumption appears to provide a slight to significant protective effects, suggesting that dietary patters alone do not consistently predict COVID-19 Outcomes. Conclusion: Data generated from this study showed the crucial role of healthcare infrastructure along with the testing capacity and data reporting in influencing the success of pandemic responses. It also highlights the need of integrating public health strategies, which focus on obesity management and improvement of healthcare preparedness. In addition, AI-driven predictive modeling offers valuable insights that may guide pandemic response efforts in the future, thereby enhancing global health crisis management and mitigating the impact of future health emergencies. Keywords: COVID-19; Dietary patterns; Obesity; Artificial intelligence; Machine learning; Public health; Health care systems