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Abstract We conducted a mesocosm experiment to examine how ocean acidification (OA) affects communities of prokaryotes and eukaryotes growing on single‐use drinking bottles in subtropical eutrophic waters of the East China Sea. Based on 16S rDNA gene sequencing, simulated high CO 2 significantly altered the prokaryotic community, with the relative abundance of the phylum Planctomycetota increasing by 49%. Under high CO 2 , prokaryotes in the plastisphere had enhanced nitrogen dissimilation and ureolysis, raising the possibility that OA may modify nutrient cycling in subtropical eutrophic waters. The relative abundance of pathogenic and animal parasite bacteria also increased under simulated high CO 2 . Our results show that elevated CO 2 levels significantly affected several animal taxa based on 18S rDNA gene sequencing. For example, Mayorella amoebae were highly resistant, whereas Labyrinthula were sensitive to OA. Thus, OA may alter plastisphere food chains in subtropical eutrophic waters. 

Observations show that the teleconnection between the El Niño‐Southern Oscillation (ENSO) and the Asian summer monsoon (ASM) is non‐stationary. However, the underlying mechanisms are poorly understood due to inadequate availability of reliable, long‐term observations. This study uses two state‐of‐the‐art data assimilation‐based reconstructions of last millennium climate to examine changes in the ENSO–ASM teleconnection; we investigate how modes of (multi‐)decadal climate variability (namely, the Pacific Decadal Oscillation, PDO, and the Atlantic Multidecadal Oscillation, AMO) modulate the ENSO–ASM relationship. Our analyses reveal that the PDO exerts a more pronounced impact on ASM variability than the AMO. By comparing different linear regression models, we find that including the PDO in addition to ENSO cycles can improve prediction of the ASM, especially for the Indian summer monsoon. In particular, dry (wet) anomalies caused by El Niño (La Niña) over India become enhanced during the positive (negative) PDO phases due to a compounding effect. However, composite differences in the ENSO–ASM relationship between positive and negative phases of the PDO and AMO are not statistically significant. A significant influence of the PDO/AMO on the ENSO–ASM relationship occurred only over a limited period within the last millennium. By leveraging the long‐term paleoclimate reconstructions, we document and interrogate the non‐stationary nature of the PDO and AMO in modulating the ENSO–ASM relationship.

 

Centromeres are long, often repetitive regions of genomes that bind kinetochore proteins and ensure normal chromosome segregation. Engineering centromeres that function in vivo has proven to be difficult. Here we describe a tethering approach that activates functional maize centromeres at synthetic sequence arrays. A LexA-CENH3 fusion protein was used to recruit native Centromeric Histone H3 (CENH3) to long arrays of LexO repeats on a chromosome arm. Newly recruited CENH3 was sufficient to organize functional kinetochores that caused chromosome breakage, releasing chromosome fragments that were passed through meiosis and into progeny. Several fragments formed independent neochromosomes with centromeres localized over the LexO repeat arrays. The new centromeres were self-sustaining and transmitted neochromosomes to subsequent generations in the absence of the LexA-CENH3 activator. Our results demonstrate the feasibility of using synthetic centromeres for karyotype engineering applications. 

Abstract High-sensitivity interstellar scintillation and polarization observations of PSR B0656+14 made at three epochs over a year using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) show that the scattering is dominated by two different compact regions. We identify the one nearer to the pulsar with the shell of the Monogem Ring, thereby confirming the association. The other is probably associated with the Local Bubble. We find that the observed position angles of the pulsar spin axis and the spatial velocity are significantly different, with a separation of 19.°3 ± 0.°8, inconsistent with a previously published near-perfect alignment of 1° ± 2°. The two independent scattering regions are clearly defined in the secondary spectra, which show two strong forward parabolic arcs. The arc curvatures imply that the scattering screens corresponding to the outer and inner arcs are located approximately 28 pc from PSR B0656+14 and 185 pc from the Earth, respectively. Comparison of the observed Doppler profiles with electromagnetic simulations shows that both scattering regions are mildly anisotropic. For the outer arc, we estimate the anisotropy A R to be approximately 1.3, with the scattering irregularities aligned parallel to the pulsar velocity. For the outer arc, we compare the observed delay profiles with delay profiles computed from a theoretical strong-scattering model. Our results suggest that the spatial spectrum of the scattering irregularities in the Monogem Ring is flatter than Kolmogorov, but further observations are required to confirm this. 

By allowing people to manipulate digital content placed in the real world, Augmented Reality (AR) provides immersive and enriched experiences in a variety of domains. Despite its increasing popularity, providing a seamless AR experience under bandwidth fluctuations is still a challenge, since delivering these experiences at photorealistic quality with minimal latency requires high bandwidth. Streaming approaches have already been proposed to solve this problem, but they require accurate prediction of the Field-Of-View of the user to only stream those regions of scene that are most likely to be watched by the user. To solve this prediction problem, we study in this paper the watching behavior of users exploring different types of AR scenes via mobile devices. To this end, we introduce the ACE Dataset, the first dataset collecting movement data of 50 users exploring 5 different AR scenes. We also propose a four-feature taxonomy for AR scene design, which allows categorizing different types of AR scenes in a methodical way, and supporting further research in this domain. Motivated by the ACE dataset analysis results, we develop a novel user visual attention prediction algorithm that jointly utilizes information of users' historical movements and digital objects positions in the AR scene. The evaluation on the ACE Dataset show the proposed approach outperforms baseline approaches under prediction horizons of variable lengths, and can therefore be beneficial to the AR ecosystem in terms of bandwidth reduction and improved quality of users' experience. 

Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will bene t many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the rst large- scale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi- supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user- app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.