Structure of RapA, a Swi2/Snf2 Protein that Recycles RNA Polymerase During Transcription

SummaryRapA, as abundant as σ70 in the cell, is an RNA polymerase (RNAP)-associated Swi2/Snf2 protein with ATPase activity. It stimulates RNAP recycling during transcription. We report a structure of RapA that is also a full-length structure for the entire Swi2/Snf2 family. RapA contains seven domains, two of which exhibit novel protein folds. Our model of RapA in complex with ATP and double-stranded DNA (dsDNA) suggests that RapA may bind to and translocate on dsDNA. Our kinetic template-switching assay shows that RapA facilitates the release of sequestered RNAP from a posttranscrption/posttermination complex for transcription reinitiation. Our in vitro competition experiment indicates that RapA binds to core RNAP only but is readily displaceable by σ70. RapA is likely another general transcription factor, the structure of which provides a framework for future studies of this bacterial Swi2/Snf2 protein and its important roles in RNAP recycling during transcription

NF-κB Hyper-Activation by HTLV-1 Tax Induces Cellular Senescence, but Can Be Alleviated by the Viral Anti-Sense Protein HBZ

Activation of I-κB kinases (IKKs) and NF-κB by the human T lymphotropic virus type 1 (HTLV-1) trans-activator/oncoprotein, Tax, is thought to promote cell proliferation and transformation. Paradoxically, expression of Tax in most cells leads to drastic up-regulation of cyclin-dependent kinase inhibitors, p21CIP1/WAF1 and p27KIP1, which cause p53-/pRb-independent cellular senescence. Here we demonstrate that p21CIP1/WAF1-/p27KIP1-mediated senescence constitutes a checkpoint against IKK/NF-κB hyper-activation. Senescence induced by Tax in HeLa cells is attenuated by mutations in Tax that reduce IKK/NF-κB activation and prevented by blocking NF-κB using a degradation-resistant mutant of I-κBα despite constitutive IKK activation. Small hairpin RNA-mediated knockdown indicates that RelA induces this senescence program by acting upstream of the anaphase promoting complex and RelB to stabilize p27KIP1 protein and p21CIP1/WAF1 mRNA respectively. Finally, we show that down-regulation of NF-κB by the HTLV-1 anti-sense protein, HBZ, delay or prevent the onset of Tax-induced senescence. We propose that the balance between Tax and HBZ expression determines the outcome of HTLV-1 infection. Robust HTLV-1 replication and elevated Tax expression drive IKK/NF-κB hyper-activation and trigger senescence. HBZ, however, modulates Tax-mediated viral replication and NF-κB activation, thus allowing HTLV-1-infected cells to proliferate, persist, and evolve. Finally, inactivation of the senescence checkpoint can facilitate persistent NF-κB activation and leukemogenesis

Mobility pattern recognition based prediction for the subway station related bike-sharing trips

The free-floating bike-sharing (BS) system plays an important role in connection with the public transit system. However, few studies have addressed the impacts of the subway network on the BS system and integrated the features quantitatively into the BS trip prediction framework. Based on the observation of the close relationship between the BS and the urban rail transit, our study focuses on the trip forecasting of the BSs around the subway stations. Firstly, the subway station related sites are investigated based on the BS dataset in Beijing, China. Secondly, multiple categories of features are extracted, including the subway station related site categories by clustering, the BS site mobility patterns by tensor decomposition, as well as other features (e.g., temporal, POI, meteorological, and air quality information). Finally, a three-layer ensemble learning model based method (i.e., the SAP-SF method) under the stacking strategy is proposed with integrations of multiple features and the several selected machine learning algorithms. It is applied to the simultaneous prediction of the hourly return numbers for a large-scale BS system involving a total of 280 sites in Beijing. The output performance is also examined by comparing the results with those obtained from the benchmark models. It is indicated that the features of subway station related site categories and site mobility patterns jointly contribute to the improvement of BS trip prediction. The accuracy can be increased layer by layer and is superior to the single machine learning algorithm. The research finding can provide useful information for system administrators to perform service level checks and to rebalance BSs around subway stations

GPS Trajectory-Based Spatio-Temporal Variations of Traffic Accessibility under Public Health Emergency Consideration

Medical services are essential to public concerns and living qualities. Facing new public health events, the spatio-temporal variation of healthcare accessibility can be different, which is ignored in the previous accessibility studies. In this paper, we study the spatio-temporal variation of healthcare accessibility and residents’ accessibility to the designated hospitals under public health emergencies such as COVID-19. Metropolitan Beijing is chosen as the study area. Then, we analyze the spatial disparity and the temporal variation and measure the matching degree between healthcare accessibility and population density. From the perspective of epidemic prevention, we evaluate the medical capacity of the designated hospitals. The autocorrelation method is used to analyze the spatial correlation of residents’ accessibility to designated hospitals in the study area. A conclusion can be drawn that 74.14% grids in the study area have proportionate population density and healthcare accessibility. We find that the 5th Medical Center has sufficient medical resources, and Puren hospital is less affected by time. Moreover, the result of residents’ accessibility to the designated hospitals presents a pattern of high-value aggregation in the arterial road neighborhood. At the same time, the peripheral areas show a trend of low-value aggregation. The research in healthcare accessibility can provide a reference for policymakers in medical service development and public emergency management

A stochastic process traffic assignment model considering stochastic traffic demand

In real traffic network, both link capacity and traffic demand are subject to stochastic fluctuations. These random fluctuations are major sources of travel time uncertainty. All existing stochastic process traffic assignment model models considering the uncertainty of travel time are presented with fixed traffic demand. In this study, a stochastic process traffic assignment model is presented to consider stochastic traffic demand. The traffic demand is assumed to be comprised of two groups of travelers: commuters with fixed traffic demand and irregular travelers with discrete random demand. With mild conditions, it is proved that our stochastic process traffic assignment model is ergodic and has a unique stable distribution. An algorithm is given to describe the stochastic process model. By conducting numerical test, we analyze the effect of commuters' memory length, irregular travelers' demand and commuters' perception error on the stable distribution of our model.MOE (Min. of Education, S’pore)Accepted versio