Transposable Elements Are Co-opted as Oncogenic Regulatory Elements by Lineage-Specific Transcription Factors in Prostate Cancer

Transposable elements hold regulatory functions that impact cell fate determination by controlling gene expression. However, little is known about the transcriptional machinery engaged at transposable elements in pluripotent and mature versus oncogenic cell states. Through positional analysis over repetitive DNA sequences of H3K27ac chromatin immunoprecipitation sequencing data from 32 normal cell states, we report pluripotent/stem and mature cell state–specific “regulatory transposable elements.” Pluripotent/stem elements are binding sites for pluripotency factors (e.g., NANOG, SOX2, OCT4). Mature cell elements are docking sites for lineage-specific transcription factors, including AR and FOXA1 in prostate epithelium. Expanding the analysis to prostate tumors, we identify a subset of regulatory transposable elements shared with pluripotent/stem cells, including Tigger3a. Using chromatin editing technology, we show how such elements promote prostate cancer growth by regulating AR transcriptional activity. Collectively, our results suggest that oncogenesis arises from lineage-specific transcription factors hijacking pluripotent/stem cell regulatory transposable elements.</p

Transposable Elements Are Co-opted as Oncogenic Regulatory Elements by Lineage-Specific Transcription Factors in Prostate Cancer

Transposable elements hold regulatory functions that impact cell fate determination by controlling gene expression. However, little is known about the transcriptional machinery engaged at transposable elements in pluripotent and mature versus oncogenic cell states. Through positional analysis over repetitive DNA sequences of H3K27ac chromatin immunoprecipitation sequencing data from 32 normal cell states, we report pluripotent/stem and mature cell state–specific “regulatory transposable elements.” Pluripotent/stem elements are binding sites for pluripotency factors (e.g., NANOG, SOX2, OCT4). Mature cell elements are docking sites for lineage-specific transcription factors, including AR and FOXA1 in prostate epithelium. Expanding the analysis to prostate tumors, we identify a subset of regulatory transposable elements shared with pluripotent/stem cells, including Tigger3a. Using chromatin editing technology, we show how such elements promote prostate cancer growth by regulating AR transcriptional activity. Collectively, our results suggest that oncogenesis arises from lineage-specific transcription factors hijacking pluripotent/stem cell regulatory transposable elements.</p

Joint optimization of train scheduling and rolling stock circulation planning with passenger flow control on tidal overcrowded metro lines

This study proposes a joint optimization method for train scheduling and rolling stock circulation planning with the consideration of passenger flow control strategy on a tidal oversaturated metro line, in which different types of rolling stocks with various loading capacities are put into operations to satisfy the uneven passenger demand in different periods (e.g., peak hours and off-peak hours). To characterize the problem mathematically, a mixed-integer nonlinear programming model is formulated to minimize the passenger waiting time and operating costs of the metro system simultaneously. This model is further reformulated equivalently into a mixed-integer linear programming model via the linearization method. An effective heuristic algorithm based on the tabu search and CPLEX solver is designed to find high-quality solutions for the proposed problem. Finally, two sets of numerical examples, including a small-scale example and a large-scale example based on the Beijing metro Batong line, are conducted to validate the performance of the proposed methods. The experimental results demonstrate that scheduling multiple types of rolling stocks can effectively reduce the transportation costs and satisfy passenger demand in different periods

Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling

With the rapid increase in residents in megacities, the passenger demand of metro systems is rising sharply and steadily, bringing immense pressure to train operations. To improve the service quality, this paper discusses systematically investigating a joint optimization of the robust passenger flow control strategy and train timetable on a congested metro line. A deterministic model for train timetabling and passenger flow control at each station is first developed to make a trade-off between operation efficiency and service fairness. Then, the uncertain passenger demand is further considered at each station, and three integer linear programming models are formulated to derive the robust passenger flow control strategies. The first two models are related to the technique of Light Robustness, in which the uncertainty is handled by inserting expected protection levels at stations or on trains. In addition, with a stochastic scenario set that characterizes the uncertain passenger information, the last model aims to find a solution that is feasible for all involved scenarios, and thus, reduces the impact of the uncertainty in metro systems. To improve the computational efficiency of large-scale instances, a customized decomposition-based algorithm is developed. Finally, some real-world case studies based on the operation data of the Beijing metro Batong line are conducted to verify the performance and effectiveness of the proposed approaches

Pioneer of prostate cancer: past, present and the future of FOXA1

Abstract Prostate cancer is the most commonly diagnosed non-cutaneous cancers in North American men. While androgen deprivation has remained as the cornerstone of prostate cancer treatment, resistance ensues leading to lethal disease. Forkhead box A1 (FOXA1) encodes a pioneer factor that induces open chromatin conformation to allow the binding of other transcription factors. Through direct interactions with the Androgen Receptor (AR), FOXA1 helps to shape AR signaling that drives the growth and survival of normal prostate and prostate cancer cells. FOXA1 also possesses an AR-independent role of regulating epithelial-to-mesenchymal transition (EMT). In prostate cancer, mutations converge onto the coding sequence and cis-regulatory elements (CREs) of FOXA1, leading to functional alterations. In addition, FOXA1 activity in prostate cancer can be modulated post-translationally through various mechanisms such as LSD1-mediated protein demethylation. In this review, we describe the latest discoveries related to the function and regulation of FOXA1 in prostate cancer, pointing to their relevance to guide future clinical interventions

Joint optimization of train timetabling and rolling stock circulation planning: A novel flexible train composition mode

The tidal traffic phenomenon is one of the most prominent problems on some metro lines, where a large number of commuters during the peak hours might cause the non-equilibrium spatial–temporal distribution of passenger flow. In order to better match the passenger demand, this study proposes a mixed-integer linear programming (MILP) model to jointly optimize the train timetable and rolling stock circulation plan, in which the flexible train composition mode is particularly taken into account by allowing rolling stocks to change their compositions through uncoupling/coupling operations at the both ends of the focused metro line. To solve the model, a customized heuristic algorithm based on the variable neighborhood search (VNS) is developed to quickly generate high-quality solutions. Based on a small example and the real-world data from Beijing metro Batong line, two sets of numerical experiments are conducted to verify the effectiveness and applicability of the proposed methodology. The computation results show that in comparison to the fixed train composition mode, the proposed approaches can bring 17.1% reduction of operation costs in morning peak periods, with no increase of passenger waiting time

Flexible train capacity allocation for an overcrowded metro line: A new passenger flow control approach

Metro lines of some mega cities usually suffer from extreme congestions in peak hours, leading to serious operation risks. To relieve the extreme saturation for overcrowded metro lines, this study explores a novel train operation strategy, i.e., flexibly allocating the capacities through reserving carriages at different stations according to the time-variant passenger demand. With this strategy, the train capacities are reasonably distributed to each station, especially for stations with large passenger flows, so as to balance the passenger accumulation over the whole line. A nonlinear integer programming model is developed by considering the passenger dynamics, and an efficient variable neighborhood search (VNS) algorithm is developed to solve the problem of interest. Finally, a set of numerical experiments with real-world data from Beijing metro Batong line are conducted to verify the performance and effectiveness of the proposed model and algorithm. The experimental results show that our proposed approach can effectively obtain high-quality carriage reservation plans in a short computing time, and the generated plans can well balance the passenger accumulation at all involved stations

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019 (COVID-19) epidemic prevention and control have raised new requirements that necessitate operation-strategy innovation in urban rail transit. To alleviate increasingly serious congestion and further reduce the risk of cross-infection, a novel two-stage distributionally robust optimization (DRO) model is explicitly constructed, in which the probability distribution of stochastic scenarios is only partially known in advance. In the proposed model, the mean-conditional value-at-risk (CVaR) criterion is employed to obtain a tradeoff between the expected number of waiting passengers and the risk of congestion on an urban rail transit line. The relationship between the proposed DRO model and the traditional two-stage stochastic programming (SP) model is also depicted. Furthermore, to overcome the obstacle of model solvability resulting from imprecise probability distributions, a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form. A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming (MILP) solver is developed to improve the computational efficiency of large-scale instances. Finally, a series of numerical examples with real-world operation data are executed to validate the proposed approaches

Train timetabling with dynamic and random passenger demand: A stochastic optimization method

Considering the dynamics and randomness of passenger demand, this paper investigates a train timetabling problem in the stochastic environment for an urban rail transit system. With the scenario-based representation of passenger distribution, an integer nonlinear programming (INLP) model is first formulated to simultaneously optimize the total number of train services, headway settings and speed profile selection decision during the planning time horizon, in which the expected total service cost is treated as the objective function. Through an analysis of the features of the nonlinear constraints, a reformulation method is proposed to develop an equivalent integer linear programming (ILP) model that can be easily solved by commercial software. Moreover, a variable neighborhood search algorithm is developed to find the approximate optimal solutions for large-scale problems within the tolerable computing time. Finally, two sets of numerical experiments, with the operation environments of a simple urban rail transit line and Fuzhou Metro Line 1, are implemented to verify the solution quality and effectiveness of the proposed methods

A robust and energy-efficient train timetable for the subway system

In the subway system, passenger crowding in peak hours is likely to cause train delays that easily propagate to following trains, resulting in a lower efficiency of the system. Consequently, this paper focuses on determining a robust timetable for the trains on the one hand, i.e., finding a better timetable to avoid delay propagation as much as possible in case of a crowded subway system. On the other hand, this paper considers the energy efficiency, i.e., reducing the total energy consumption during operations by selecting appropriate speed profiles and maximizing the utilization of regenerative braking energy. A related mathematical optimization model is formulated with the objective of maximizing the robustness and minimizing the total energy consumption. In order to solve this model, an efficient algorithm, i.e., simulation-based variable neighborhood search algorithm, is presented to obtain a good timetable in reasonable amount of time. Finally, experiments are implemented to show the performance of the proposed algorithm