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R2 retrotransposons reside exclusively within the 28S regions of 10–20% of all rDNA genes comprising the nucleolar organizer loci on the X and Y chromosomes of Drosophila melanogaster. These R2-inserted genes are normally silent and heterochromatic. When expressed, however, the R2 transcript is co-transcribed with the 28S rRNA. Self-cleavage releases a 3.6 kb mature R2 transcript that encodes a single protein with endonuclease and reverse transcriptase activities that facilitate R2 element transposition by target-primed reverse transcription. While we know the molecular details of R2 transposition, we know little about the genetic mechanisms that initiate R2 transcription. Here, we examine R2 expression in wild type versus mutant backgrounds. R2 expression in stage 1–4 wild type egg chambers was variable depending on the stock. R2 expression was silent in wild type stages 5–10 but was consistently active during nurse cell nuclear breakdown in stages 12–13 regardless of the genetic background. Massive R2 expression occurred in stages 5–10 upon loss of Udd, an RNA Pol I transcription factor. Similarly, loss of Nopp140, an early ribosome assembly factor, induced R2 expression more so in somatic tissues. Interestingly, over-expression of the Nopp140-RGG isoform but not the Nopp140-True isoform induced R2 expression in larval somatic tissues, suggesting Nopp140-RGG could potentially affect rDNA chromatin structure. Conversely, Minute mutations in genes encoding ribosomal proteins had minor positive effects on R2 expression. We conclude that R2 expression is largely controlled by factors regulating RNA Pol I transcription and early ribosome assembly. 

Abstract Tides are an important factor shaping the sea ice system in the Arctic Ocean by altering vertical heat fluxes and advection patterns. Unfortunately, observations are sparse, and the analysis of tides is complicated by the proximity of wind-driven inertial oscillations to the semidiurnal frequencies. Furthermore, computational costs typically prohibit the inclusion of tides in ocean models, leaving a significant gap in our understanding. Motivated by summer observations showing elevated downward surface heat fluxes in the presence of tides, we analyzed simulations carried out with an eddy-permitting coupled ice–ocean model to quantify the impact of tidal effects on Arctic sea ice. In line with previous studies, we find an overall decrease in sea ice volume when tides are included in the simulations, associated with increased vertical mixing and the upward flux of heat from deeper layers of the Arctic Ocean, but this sea ice volume decrease is less pronounced than previously thought. Surprisingly, our simulations suggest that in summer, Arctic sea ice area is larger, by up to 1.5%, when tides are included in the simulations. This effect is partly caused by an increased downward surface heat flux and a consequently lower sea surface temperature, delaying sea ice melting predominantly in the Siberian Seas, where tides are moderately strong and the warm Atlantic Water core is located relatively deep and does not encroach on the wide continental shelf. Here, tidally enhanced downward heat flux from the surface in summer can dominate over the increased upward heat flux from the warm Atlantic Water layer. Significance StatementThis study sheds light on the complex and understudied role of tides in Arctic sea ice dynamics. By utilizing advanced computer models, our research uncovers that, contrary to common expectations, tides contribute to a seasonal increase in sea ice area by up to 1.5% in summer. This effect is attributed to enhanced advection of sea ice into the Siberian Seas and a local increase in downward heat flux reducing sea surface temperatures, thereby delaying sea ice melting in this region. Our findings challenge prevailing notions about the negative impact of tides on sea ice and highlight the importance of incorporating tidal impacts in ocean models to improve predictions of Arctic sea ice changes, key for our understanding of both Arctic and global climate dynamics. 

Influence Maximization (IM), which seeks a small set of important nodes that spread the influence widely into the network, is a fundamental problem in social networks. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, finding near-optimal solutions for IM is difficult due to its NP-completeness. In this paper, we propose the first social quantum computing approaches for IM, aiming to retrieve near-optimal solutions. We propose a two-phase algorithm that 1) converts IM into a Max-Cover instance and 2) provides efficient quadratic unconstrained binary optimization formulations to solve the Max-Cover instance on quantum annealers. Our experiments on the state-of-the-art D-Wave annealer indicate better solution quality compared to classical simulated annealing, suggesting the potential of applying quantum annealing to find high-quality solutions for IM. 

A rare-earth-containing compound, ytterbium aluminium antimonide, Yb 3 AlSb 3 (Ca 3 AlAs 3 -type structure), has been successfully synthesized within the Yb–Al–Sb system through flux methods. According to the Zintl formalism, this structure is nominally made up of (Yb 2+ ) 3 [(Al 1− )( 1b – Sb 2− ) 2 ( 2b – Sb 1− )], where 1b and 2b indicate 1-bonded and 2-bonded, respectively, and Al is treated as part of the covalent anionic network. The crystal structure features infinite corner-sharing AlSb 4 tetrahedra, [AlSb 2 Sb 2/2 ] 6− , with Yb 2+ cations residing between the tetrahedra to provide charge balance. Herein, the synthetic conditions, the crystal structure determined from single-crystal X-ray diffraction data, and electronic structure calculations are reported. 

Abstract. Use of an ocean parameter and state estimation framework – such as the Estimating the Circulation and Climate of the Ocean (ECCO) framework – could provide an opportunity to learn about the spatial distribution of the diapycnal diffusivity parameter (κρ) that observations alone cannot due to gaps in coverage. However, we show that the inclusion of misfits to observed physical variables – such as in situ temperature, salinity, and pressure – currently accounted for in ECCO is not sufficient, as κρ from ECCO does not agree closely with any observationally derived product. These observationally derived κρ products were inferred from microstructure measurements, derived from Argo and conductivity–temperature–depth (CTD) data using a strain-based parameterization of fine-scale hydrographic structure, or calculated from climatological and seafloor data using a parameterization of tidal mixing. The κρ products are in close agreement with one another but have both measurement and structural uncertainties, whereas tracers can have relatively small measurement uncertainties. With the ultimate goal being to jointly improve the ECCO state estimate and representation of κρ in ECCO, we investigate whether adjustments in κρ due to inclusion of misfits to a tracer – dissolved oxygen concentrations from an annual climatology – would be similar to those due to inclusion of misfits to observationally derived κρ products. We do this by performing sensitivity analyses with ECCO. We compare multiple adjoint sensitivity calculations: one configuration uses misfits to observationally derived κρ, and the other uses misfits to observed dissolved oxygen concentrations. We show that adjoint sensitivities of dissolved oxygen concentration misfits to the state estimate's control space typically direct κρ to improve relative to the observationally derived values. These results suggest that the inclusion of oxygen in ECCO's misfits will improve κρ in ECCO, particularly in (sub)tropical regions. 

Enrollment in computing at the college level has skyrocketed, and many institutions have responded by enacting competitive enrollment processes. However, little is known about the effects of enrollment policies on students' experiences. To identify relationships between those policies and students' experiences, we linked survey data from 1245 first-year students in 80 CS departments to a dataset of department policies. We found that competitive enrollment negatively predicts first-year students' perception of the computing department as welcoming, their sense of belonging, and their self-efficacy in computing. Both belonging and self-efficacy are known predictors of student retention in CS. In addition, these relationships are stronger for students without pre-college computing experience. Our classification of institutions as competitive is conservative, and false positives are likely. This biases our results and suggests that the negative relationships we found are an underestimation of the effects of competitive enrollment.