A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures

Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework

Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient λ, the shift factor b, and the rotation factor c. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations

Abstract P337: CD36 Is Required To Prime Cardiomyocytes For Proliferation

Many approaches have been explored to regenerate the heart muscle followingischemic injury, out of which is the induction of cardiomyocytes proliferation. Ourprevious work demonstrated that cell-cycle was successfully induced incardiomyocytes by viral expression of combination of four cell-cycle factors: cyclinB1, CDK1, cyclin D1 and CDK4; termed as 4 factors (4F). However, only 15-20 % ofthe cells expressing the four factors progress into cell-cycle, while the remainderare quiescent. A pertinent question in the field of cardiac regeneration is why allviral or in vivo transgenic approaches to induce adult cardiomyocyte proliferation,e.g., 4F, YAP, and CyclinA2, promote proliferation in only a subset ofcardiomyocytes. This general finding suggests that factors or conditions beyondcell-cycle induction influence the probability and perpetuation of cardiomyocytedivision.Here we aim to investigate why only a subpopulation of cardiomyocytes is able toprogress through cell-cycle.Temporal single cell RNA sequencing of 60 days mature hiPS-CMs 24, 48 and 72hours post infection with 4F or control virus revealed that a unique population ofcardiomyocytes in the LacZ control group [1026 cells out of 6761 cells (~15%)] thatdisappears after treatment with 4F; another unique cluster with similar cellnumbers appears 24 h after 4F transduction (Cluster 3), which suggests that theinitial population was primed to proliferate. One of the major characteristics of thisprimed subpopulation is the expression of CD36, a major fatty acids transporter incardiomyocytes, and that 4F induction of cell-cycle completely abolishes CD36expression. knocking down CD36 in hiPS-CMs for 48 hours followed by induction ofproliferation using 4F led to 50-70% reduction in proliferation capacity of thecardiomyocytes compared to the control cells. Furthermore, cardiomyocytesisolated at P7 from CD36 knockout mice showed 30-40 % reduction incardiomyocytes proliferation capacity at baseline and after 4F induction ofproliferation compared to cardiomyocytes isolated from WT controls.These findings suggest that CD36 is needed to prime the CMs to proliferate, andthis can be, at least partially, through provision of energy requirements throughβ-oxidation of fatty acids

MALAT1 promotes FOXA1 degradation by competitively binding to miR-216a-5p and enhancing neuroendocrine differentiation in prostate cancer

Objectives: Prostate cancer (PC) is a leading cause of cancer-related death in males worldwide. Neuroendocrine differentiation (NED) is a feature of PC that often goes undetected and is associated with poor patient outcomes. Long non-coding RNAs (lncRNAs), microRNAs (miRNAs/miRs), and messenger RNAs (mRNAs) play important roles in the development and progression of PC. Methods: In this study, we used transcriptome sequencing and bioinformatics analysis to identify key regulators of NED in PC. Specifically, we examined the expression of PC-related lncRNAs, miRNAs, and mRNAs in PC cells and correlated these findings with NED phenotypes. Results: Our data revealed that metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and zinc finger protein 91 (ZFP91) were upregulated in PC, while miR-216a-5p was down-regulated. Ectopic expression of MALAT1 induced NED and promoted malignant phenotypes of PC cells. Furthermore, we found that MALAT1 competitively bound to miR-216a-5p, upregulated ZFP91, and promoted the degradation of forkhead box A1 (FOXA1), a key gene involved in NED of PC. Conclusion: Taken together, these results suggest that MALAT1 plays an oncogenic role in NED and metastasis of PC via the miR-216a-5p/ZFP91/FOXA1 pathway. Our study highlights the potential of targeting this pathway as a novel therapeutic strategy for PC

FGF21/FGFR1-β-KL cascade in cardiomyocytes modulates angiogenesis and inflammation under metabolic stress

Diabetes is a metabolic disorder with an increased risk of developing heart failure. Inflammation and damaged vasculature are the cardinal features of diabetes-induced cardiac damage. Moreover, systemic metabolic stress triggers discordant intercellular communication, thus culminating in cardiac dysfunction. Fibroblast growth factor 21 (FGF21) is a pleiotropic hormone transducing cellular signals via fibroblast growth factor receptor 1 (FGFR1) and its co-receptor beta-klotho (β-KL). This study first demonstrated a decreased expression or activity of FGFR1 and β-KL in both human and mouse diabetic hearts. Reinforcing cardiac FGFR1 and β-KL expression can alleviate pro-inflammatory response and endothelial dysfunction upon diabetic stress. Using proteomics, novel cardiomyocyte-derived anti-inflammatory and proangiogenic factors regulated by FGFR1-β-KL signaling were identified. Although not exhaustive, this study provides a unique insight into the protective topology of the cardiac FGFR1-β-KL signaling-mediated intercellular reactions in the heart in response to metabolic stress

Restored autophagy is protective against PAK3-induced cardiac dysfunction

Summary: Despite the development of clinical treatments, heart failure remains the leading cause of mortality. We observed that p21-activated kinase 3 (PAK3) was augmented in failing human and mouse hearts. Furthermore, mice with cardiac-specific PAK3 overexpression exhibited exacerbated pathological remodeling and deteriorated cardiac function. Myocardium with PAK3 overexpression displayed hypertrophic growth, excessive fibrosis, and aggravated apoptosis following isoprenaline stimulation as early as two days. Mechanistically, using cultured cardiomyocytes and human-relevant samples under distinct stimulations, we, for the first time, demonstrated that PAK3 acts as a suppressor of autophagy through hyper-activation of the mechanistic target of rapamycin complex 1 (mTORC1). Defective autophagy in the myocardium contributes to the progression of heart failure. More importantly, PAK3-provoked cardiac dysfunction was mitigated by administering an autophagic inducer. Our study illustrates a unique role of PAK3 in autophagy regulation and the therapeutic potential of targeting this axis for heart failure

Paracrine signal emanating from stressed cardiomyocytes aggravates inflammatory microenvironment in diabetic cardiomyopathy

Myocardial inflammation contributes to cardiomyopathy in diabetic patients through incompletely defined underlying mechanisms. In both human and time-course experimental samples, diabetic hearts exhibited abnormal ER, with a maladaptive shift over time in rodents. Furthermore, as a cardiac ER dysfunction model, mice with cardiac-specific p21-activated kinase 2 (PAK2) deletion exhibited heightened myocardial inflammatory response in diabetes. Mechanistically, maladaptive ER stress-induced CCAAT/enhancer-binding protein homologous protein (CHOP) is a novel transcriptional regulator of cardiac high-mobility group box-1 (HMGB1). Cardiac stress-induced release of HMGB1 facilitates M1 macrophage polarization, aggravating myocardial inflammation. Therapeutically, sequestering the extracellular HMGB1 using glycyrrhizin conferred cardioprotection through its anti-inflammatory action. Our findings also indicated that an intact cardiac ER function and protective effects of the antidiabetic drug interdependently attenuated the cardiac inflammation-induced dysfunction. Collectively, we introduce an ER stress-mediated cardiomyocyte-macrophage link, altering the macrophage response, thereby providing insight into therapeutic prospects for diabetes-associated cardiac dysfunction