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Abstract The astronomical origins of the most energetic galactic cosmic rays and gamma rays are still uncertain. X-ray follow-up of candidate “PeVatrons”—systems producing cosmic rays with energies exceeding 1 PeV—can constrain their spatial origin, identify likely counterparts, and test particle emission models. Using ∼120 ks of XMM-Newton observations, we report the discovery of a candidate pulsar wind nebula, a possible counterpart for the LHAASO PeVatron J0343+5254u. This extended source has a power-law X-ray spectrum with spectral index Γ_X = 1.9—softer at greater distance from the center—and asymmetric spatial extension out to $\approx 2\prime$. We conduct leptonic modeling of the X-ray and gamma-ray radiation from this complex system, showing that a fully leptonic model with elevated IR photon fields can explain the multiwavelength emission from this source, similar to other very high-energy pulsar wind nebulas; excess gamma-ray emissivity not explained by a leptonic model may be due to hadronic interactions in nearby molecular cloud regions, which might also produce detectable astroparticle flux. 

Abstract We report a new CO observation survey of LHAASO J0341+5258, using the Nobeyama Radio Observatory 45-m telescope. LHAASO J0341+5258 is one of the unidentified ultra-high-energy (UHE;E> 100 TeV) gamma-ray sources detected by LHAASO. Our CO observations were conducted in 2024 February and March, with a total observation time of 36 hr, covering the LHAASO source (∼0 $\stackrel{\text{°}}{.}$3–0 $\stackrel{\text{°}}{.}$5 in radius) and its surrounding area (1° × 1 $\stackrel{\text{°}}{.}$5). Within the LHAASO source extent, we identified five compact (<2 pc) molecular clouds at nearby distances (<1–4 kpc). These clouds can serve as proton–proton collision targets, producing hadronic gamma rays via neutral pion decays. Based on the hydrogen densities (700–5000 cm⁻³) estimated from our CO observations and archived Hidata from the Dominion Radio Astrophysical Observatory survey, we derive the total proton energy ofW_p(E> 1 TeV) ∼ 10⁴⁵erg to account for the gamma-ray flux. One of the molecular clouds appears to be likely associated with an asymptotic giant branch (AGB) star with an extended CO tail, which may indicate some particle acceleration activities. However, the estimated maximum particle energy below 100 TeV makes the AGB-like star unlikely to be a PeVatron site. We conclude that the UHE emission observed in LHAASO J0341+5258 could be due to hadronic interactions between the newly discovered molecular clouds and TeV–PeV protons originating from a distant SNR or due to leptonic emission from a pulsar wind nebula candidate, which is reported in our companion X-ray observation paper. 

Magnetic microrobots are attractive tools for operation in confined spaces due to their small size and untethered wireless operation, particularly in biomedical and environmental applications. Over years of development, many microrobot fabrication methods have been developed; however, they typically require costly specialized physical vapor deposition (PVD) vacuum instrumentation and present homogeneity and conformality coating problems (especially in complex 3D structures). Herein, a solution‐based polydopamine (PDA)‐assisted electroless deposition method is developed to deposit a superparamagnetic nickel thin film on microrobots. The multilayered functional film design comprises PDA as an adhesive primer and reducing agent, silver nanoclusters as catalysts, and a nickel magnetic top film, all deposited in a batch solution‐based process on glass and 3D‐printed polymer substrates. This multilayer magnetic coating is implemented and demonstrated in three magnetic microrobot archetypes, including arbitrarily‐shaped active particles, microrollers, and helical swimming microrobots, each using distinct actuation working mechanisms. Due to the material‐independent interfacial adhesive properties of PDA, this multilayer functionalization strategy can open up new magnetic microrobot fabrication schemes with a broad compatibility with materials and structures (including complex 3D‐printed polymer microstructures) and without the need for and limitations of PVD coating approaches. 

Large-scale graph analytics has become increasingly common in areas like social networks, physical sciences, transportation networks, and recommendation systems. Since many such practical graphs do not fit in main memory, graph analytics performance depends on efficiently utilizing underlying storage devices. These out-of-core graph processing systems employ sharding and sub-graph partitioning to optimize for storage while relying on efficient sequential access of traditional hard disks. However, today's storage is increasingly based on solid-state drives (SSDs) that exhibit high internal parallelism and efficient random accesses. Yet, state-of-the-art graph processing systems do not explicitly exploit those properties, resulting in subpar performance. In this paper, we develop CAVE, the first graph processing engine that optimally exploits underlying SSD-based storage by harnessing the available storage device parallelism via carefully selecting which I/Os to graph data can be issued concurrently. Thus, CAVE traverses multiple paths and processes multiple nodes and edges concurrently, achieving parallelization at a granular level. We identify two key ways to parallelize graph traversal algorithms based on the graph structure and algorithm: intra-subgraph and inter-subgraph parallelization. The first identifies subgraphs that contain vertices that can be accessed in parallel, while the latter identifies subgraphs that can be processed in their entirety in parallel. To showcase the benefit of our approach, we build within CAVE parallelized versions of five popular graph algorithms (Breadth-First Search, Depth-First Search, Weakly Connected Components, PageRank, Random Walk) that exploit the full bandwidth of the underlying device. CAVE uses a blocked file format based on adjacency lists and employs a concurrent cache pool that is essential to the parallelization of graph algorithms. By experimenting with different types of graphs on three SSD devices, we demonstrate that CAVE utilizes the available parallelism, and scales to diverse real-world graph datasets. CAVE achieves up to one order of magnitude speedup compared to the popular out-of-core systems Mosaic and GridGraph, and up to three orders of magnitude speedup in runtime compared to GraphChi. 

High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performedk-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies. 

Abstract Foraminifera are unicellular organisms that inhabit the oceans. They play an important role in the global carbon cycle and record valuable paleoclimate information through the uptake of trace elements such as strontium into their calcitic shells. Understanding how foraminifera control their internal fluid composition to make calcite is important for predicting their response to ocean acidification and for reliably interpreting the chemical and isotopic compositions of their shells. Here, we model foraminiferal calcification and strontium partitioning in the benthic foraminiferaCibicides wuellerstorfiandCibicidoides mundulusbased on insights from inorganic calcite experiments. The numerical model reconciles inter-ocean and taxonomic differences in benthic foraminifer strontium partitioning relationships and enables us to reconstruct the composition of the calcifying fluid. We find that strontium partitioning and mineral growth rates of foraminiferal calcite are not strongly affected by changes in external seawater pH (within 7.8–8.1) and dissolved inorganic carbon (DIC, within 2100–2300 μmol/kg) due to a regulated calcite saturation state at the site of shell formation. 

Abstract The time-variable emission from the accretion flow of Sgr A*, the supermassive black hole at the Galactic center, has long been examined in the radio-to-millimeter, near-infrared (NIR), and X-ray regimes of the electromagnetic spectrum. However, until now, sensitivity and angular resolution have been insufficient in the crucial mid-infrared (MIR) regime. The MIRI instrument on JWST has changed that, and we report the first MIR detection of Sgr A*. The detection was during a flare that lasted about 40 minutes, a duration similar to NIR and X-ray flares, and the source's spectral index steepened as the flare ended. The steepening suggests that synchrotron cooling is an important process for Sgr A*'s variability and implies magnetic fields strengths ~ 40–70 G in the emission zone. Observations at 1.3 mm with the Submillimeter Array revealed a counterpart flare lagging the MIR flare by ≈10 minutes. The observations can be self-consistently explained as synchrotron radiation from a single population of gradually cooling high-energy electrons accelerated through (a combination of) magnetic reconnection and/or magnetized turbulence.