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Summary Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as “direct” and “indirect,” where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences. 

We propose an empirical Bayes formulation of the structure learning problem, where the prior specification assumes that all node variables have the same error variance, an assumption known to ensure the identifiability of the underlying causal directed acyclic graph. To facilitate efficient posterior computation, we approximate the posterior probability of each ordering by that of a best directed acyclic graph model, which naturally leads to an order-based Markov chain Monte Carlo algorithm. Strong selection consistency for our model in high-dimensional settings is proved under a condition that allows heterogeneous error variances, and the mixing behaviour of our sampler is theoretically investigated. Furthermore, we propose a new iterative top-down algorithm, which quickly yields an approximate solution to the structure learning problem and can be used to initialize the Markov chain Monte Carlo sampler. We demonstrate that our method outperforms other state-of-the-art algorithms under various simulation settings, and conclude the paper with a single-cell real-data study illustrating practical advantages of the proposed method. 

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility—a methodology we refer to asWiFi mobility models(WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC).WiMobcan construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally,WiMobenables us to designLCpolicies that close super-spreader locations on campus. By simulating disease spread with contact networks fromWiMob, we find thatLCmaintains the same reduction in cumulative infections asRIwhile showing greater reduction in peak infections and internal transmission. Moreover,LCreduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation.WiMobcan empower universities to conceive and assess a variety of closure policies to prevent future outbreaks. 

Abstract Lanthanide elements have been recently recognized as “new life metals” yet much remains unknown regarding lanthanide acquisition and homeostasis. InMethylorubrum extorquensAM1, the periplasmic lanthanide-dependent methanol dehydrogenase XoxF1 produces formaldehyde, which is lethal if allowed to accumulate.This property enabled a transposon mutagenesis study and growth studies to confirm novel gene products required for XoxF1 function. The identified genes encode an MxaD homolog,an ABC-type transporter, an aminopeptidase, a putative homospermidine synthase, and two genes of unknown function annotated asorf6andorf7. Lanthanide transport and trafficking genes were also identified. Growth and lanthanide uptake were measured using strains lacking individual lanthanide transport cluster genes, and transmission electron microscopy was used to visualize lanthanide localization. We corroborated previous reports that a TonB-ABC transport system is required for lanthanide incorporation to the cytoplasm. However, cells were able to acclimate over time and bypass the requirement for the TonB outer membrane transporter to allow expression ofxoxF1and growth. Transcriptional reporter fusions show that excess lanthanides repress the gene encoding the TonB-receptor. Using growth studies along with energy dispersive X-ray spectroscopy and transmission electron microscopy, we demonstrate that lanthanides are stored as cytoplasmic inclusions that resemble polyphosphate granules.