A novel two-stage approach for energy-efficient timetabling for an urban rail transit network

Urban rail transit (URT) is the backbone transport mode in metropolitan areas to accommodate large travel demands. The high energy consumption of URT becomes a hotspot problem due to the ever-increasing operation mileages and pressing agendas of carbon neutralization. The high model complexity and inconsistency in the objectives of minimizing passenger travel time and operational energy consumption are the main challenges for energy-efficient timetabling for a URT network with multiple interlinked lines. This study proposes a general model framework of timetabling and passenger path choice in a URT network to minimize energy consumption under passenger travel time constraints. To obtain satisfactory energy-efficient nonuniform timetables, we suggest a novel model reformulation as a tree knapsack problem to determine train running times by a pseudo-polynomial dynamic programming algorithm in the first stage. Furthermore, a heuristic sequencing method is developed to determine nonuniform headways and dwell times in the second stage. The suggested model framework and solution algorithm are tested using a real-world URT network, and the results show that energy consumption can be considerably reduced given certain travel time increments

Assessment of the tradeoff between energy efficiency and transfer opportunities in an urban rail transit network

Urban rail transit (URT) in metropolitan areas consumes huge energy. Energy-efficient timetabling (EET) of URT is an essential measurement of URT management and technologies toward carbon neutralization initiatives. However, the majority EET studies focus on single URT lines ignoring passenger transfer and path choice in the entire URT network. As passenger path choice and timetabling are interdependent in a URT network, the ignorance of passenger transfers potentially results in irrelevant energy efficiency of a URT network. This paper proposes a bi-objective EET model incorporating the minimization of passenger transfer times as an objective in addition to energy efficiency. The timetabling objectives and constraints are linearized, and the bi-objective is transformed into a single objective by a linear weighting method. Utilizing the passenger demand and speed profile data of URT in the City of Xi'an (China), a case study is performed to demonstrate the effectiveness of the proposed EET model. The numerical results show that an optimized timetable solution can reduce 25.1% energy consumption and save 3.3% passenger transfer time.</p

The Concurrent Initiation of Medications Is Associated with Discontinuation of Buprenorphine Treatment for Opioid Use Disorder

Introduction Retention in buprenorphine treatment for opioid use disorder (OUD) yields better opioid abstinence and reduces all-cause mortality for patients with OUD. Despite significant efforts have been made to expand the availability and use of buprenorphine in the United States, its retention rates remain on a low level. The current study examines discontinuation of buprenorphine with respect to concurrent initiation of other medications using real-world evidence. Methods Case-crossover study was conducted to examine discontinuation of buprenorphine using a large-scale longitudinal health dataset including 148,306 commercially-insured individuals initiated on medications for opioid use disorder (MOUD). Odds ratios and Bonferroni adjusted p-values were calculated for medications and therapeutic classes of medications. Results Clonidine was associated with increased discontinuation risk of buprenorphine both using the buprenorphine dataset alone (OR = 1.583 and adjusted p-value = 1.22 × 10−6) and using naltrexone as a comparison drug (OR = 2.706 and adjusted p-value = 4.11 × 10−5). Opioid medications (oxycodone, morphine and fentanyl) and methocarbamol were associated with increased discontinuation risk of buprenorphine using the buprenorphine dataset alone (adjusted p-value < 0.05), but not significant using naltrexone as a comparison drug. 6 drug therapeutic classes were associated with increased discontinuation risk of buprenorphine both using the buprenorphine dataset alone and using naltrexone as a comparison drug (adjusted p-value < 0.05). Conclusion Concurrent initiation of medications is associated with increased discontinuation risk of buprenorphine. Opioid medications are prescribed among patients on MOUD and associated with increased discontinuation risk of buprenorphine. Analgesics is associated with increased discontinuation risk of buprenorphine for patients without previous exposure of pain medications

Multimodal Single-Cell/Nucleus RNA Sequencing Data Analysis Uncovers Molecular Networks Between Disease-Associated Microglia and Astrocytes With Implications for Drug Repurposing in Alzheimer’s Disease

Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer\u27s disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metaboliteenzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P \u3c 1.0 × 10 - 8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P \u3c 1.0 × 10 - 8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning

Assimilation of GNSS-R Delay-Doppler Maps into Weather Models

Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that uses reflected satellite navigation signals from the Earth surface in a bistatic radar configuration. GNSS-R observations have been collected using receivers on stationary, airborne and spaceborne platforms. The delay-Doppler map (DDM) is the fundamental GNSS-R measurement from which ocean surface wind speed can be retrieved. GNSS-R observations can be assimilated into numerical weather prediction models to improve weather analyses and forecasts. The direct assimilation of DDM observations shows potential superiority over the assimilation of wind retrievals. This dissertation demonstrates the direct assimilation of GNSS-R DDMs using a two-dimensional variational analysis method (VAM). First, the observation forward model and its Jacobian are developed. Then, the observation\u27s bias correction, quality control, and error characterization are presented. The DDM assimilation was applied to a global and a regional case. In the global case, DDM observations from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission are assimilated into global ocean surface wind analyses using the European Centre for Medium-Range Weather Forecasts (ECMWF) 10-meter winds as the background. The wind analyses are improved as a result of the DDM assimilation. VAM can also be used to derive a new type of wind vector observation from DDMs (VAM-DDM). In the regional case, an observing system experiment (OSE) is used to quantify the impact of VAM-DDM wind vectors from CYGNSS on hurricane forecasts, in the case of Hurricane Michael (2018). It is found that the assimilation of VAM-DDM wind vectors at the early stage of the hurricane improves the forecasted track and intensity. The research of this dissertation implies potential benefits of DDM assimilation for future research and operational applications

An integrated model of energy-efficient timetabling of the urban rail transit system with multiple interconnected lines

Urban rail transit (URT) has been considered an effective means of addressing urban congestion problems in metropolises. The operations of a URT system involve high energy consumption and its trade-off with passenger travel times. Existing energy-efficient timetabling studies have predominately focused on single URT lines and thus are incapable of accurately modeling the energy consumption in a URT network with transfer opportunities and synchronization between the URT lines. To extend the energy-efficient timetabling from one single line to multiple interlinked lines, we propose a bi-level model incorporating the operator’s decision on a regular timetable and passengers’ path choice in a URT network. The objective of energy consumption and timetable constraints of the upper level are linearized and formulated as mixed-integer linear programming. The lower level captures the user equilibrium based path choice behavior responding to the timetable. We develop a heuristic algorithm for the bi-level model that produces near-optimal timetable solutions in a relaxation process. The proposed model and solution algorithm are validated in the URT network of Xi’an (China). It is found that the energy consumption is considerably reduced, compared with using the current timetable, at the expense of an acceptable increase in the average travel time