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At metro stations with very large passenger volumes and severe congestion, random disturbances often occur, resulting in the original offline optimal timetable no longer applicable. Deep reinforcement learning has the advantages of self-learning and online learning, making it possible to solve the energy-aimed train timetable rescheduling (ETTR) problem under random disturbances. In this article, a deep reinforcement learning approach (DRLA) has been proposed and applied to the ETTR problem to reschedule the train timetable in order to achieve the online optimal timetable for the minimum energy usage under disturbances. The proposed DRLA has the advantages of a real-time performance, high learning efficiency, and better energy-saving effect compared with the traditional heuristic algorithms such as genetic algorithm (GA), deep learning methods such as the combination of improved GA and long short-term memory (IGSA-LSTM), or other reinforcement learning algorithms such as deep deterministic policy gradient (DDPG) algorithm. Various experiments are conducted to compare the performance of DRLA and other algorithms. The experimental results indicate that in the two-train metro system and the five-train metro system, DRLA can save an average of energy by 5.11% and 7.29% with an average reaction time of only 0.0013 and 0.0019 s against disturbances, respectively.

Matrix metalloproteinase-10 (MMP-10) is a zinc-dependent endopeptidase involved in regulating a wide range of biologic processes, such as apoptosis, cell proliferation, and tissue remodeling. However, the role of MMP-10 in the pathogenesis of acute kidney injury (AKI) is unknown. In this study, we show that MMP-10 was upregulated in the kidneys and predominantly localized in the tubular epithelium in various models of AKI induced by ischemia/reperfusion (IR) or cisplatin. Overexpression of exogenous MMP-10 ameliorated AKI, manifested by decreased serum creatinine, blood urea nitrogen, tubular injury and apoptosis, and increased tubular regeneration. Conversely, knockdown of endogenous MMP-10 expression aggravated kidney injury. Interestingly, alleviation of AKI by MMP-10 in vivo was associated with the activation of epidermal growth factor receptor (EGFR) and its downstream AKT and extracellular signal-regulated kinase-1 and 2 (ERK1/2) signaling. Blockade of EGFR signaling by erlotinib abolished the MMP-10-mediated renal protection after AKI. In vitro, MMP-10 potentiated EGFR activation and protected kidney tubular cells against apoptosis induced by hypoxia/reoxygenation or cisplatin. MMP-10 was colocalized with heparin-binding EGF-like growth factor (HB-EGF) in vivo and activated it by a process of proteolytical cleavage in vitro. These studies identify HB-EGF as a previously unrecognized substrate of MMP-10. Our findings also underscore that MMP-10 can protect against AKI by augmenting EGFR signaling, leading to promotion of tubular cell survival and proliferation after injury.

The study illustrates a crucial role of tissue microenvironment in mediating endothelial dropout and vascular rarefaction., Endothelial cell injury leading to microvascular rarefaction is a characteristic feature of chronic kidney disease (CKD). However, the mechanism underlying endothelial cell dropout is poorly defined. Here, we show a central role of the extracellular microenvironment in controlling endothelial cell survival and proliferation in CKD. When cultured on a decellularized kidney tissue scaffold (KTS) from fibrotic kidney, endothelial cells increased the expression of proapoptotic proteins. Proteomics profiling identified fibrillin-1 (FBN1) as a key component of the fibrotic KTS, which was up-regulated in animal models and patients with CKD. FBN1 induced apoptosis of endothelial cells and inhibited their proliferation in vitro. RNA sequencing uncovered activated integrin αvβ6/transforming growth factor–β signaling, and blocking this pathway abolished FBN1-triggered endothelial injury. In a mouse model of CKD, depletion of FBN1 ameliorated renal fibrotic lesions and mitigated vascular rarefaction. These studies illustrate that FBN1 plays a role in mediating vascular rarefaction by orchestrating a hostile microenvironment for endothelial cells.

Tenascin-C is an extracellular matrix glycoprotein that plays a critical role in kidney fibrosis by orchestrating a fibrogenic niche. Here, we demonstrate that tenascin-C is a biomarker and a mediator of kidney fibrogenesis by impairing tubular integrity. Tenascin-C was found to be increased in kidney biopsies from patients with chronic kidney disease (CKD). In a cohort of 225 patients with CKD, the urinary tenascin-C level was markedly elevated, compared to 39 healthy individuals. Moreover, the level of urinary tenascin-C in CKD was correlated with the severity of kidney dysfunction and fibrosis. In mouse model of acute kidney injury-to-CKD induced by ischemia/reperfusion, depletion of tenascin-C preserved tubular integrity and ameliorated renal fibrotic lesions. In vitro, tenascin-C impaired tubular cell integrity by inducing partial epithelial-mesenchymal transition. Using decellularized kidney tissue scaffolds, we found that tenascin-C-enriched scaffolds facilitated tubular epithelial-mesenchymal transition ex vivo. Mechanistically, tenascin-C specifically induced integrins αvβ6 in tubular cells and activated focal adhesion kinase (FAK). Blocking αvβ6 integrins or inhibition of FAK restored tubular integrity by repressing epithelial-mesenchymal transition and alleviated kidney fibrosis. Thus, our studies underscore that tenascin-C is a noninvasive biomarker of kidney fibrogenesis and a pathogenic mediator that impairs tubular integrity. Hence, blockade of the tenascin-C /αvβ6 integrin/FAK signal cascade may be a novel strategy for therapeutic intervention of kidney fibrosis.

Considering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem. This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning. The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network. A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems. Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem. The experimental results show that in the two-train metro system, the three-train metro system, and the five-train metro system, the MGA-GRU method can save an average of energy by 4.45%, 6.16%, and 7.19%, while the average decision time is only 0.15 s, 0.27 s, and 0.33 s, respectively.

In metro systems, reducing traction energy consumption and increasing the use of regenerative braking energy (RBE) are two important methods of energy-saving optimization, which are closely related to the driving strategy and timetable of the trains. In order to minimize the net traction energy consumption (i.e., the difference between traction energy and feedback energy) of trains in a metro system, an energy-saving optimization strategy of multi-train metro timetable based on double decision variables is proposed. Considering the actual situation of the short distance between the stations of the Shanghai Metro Line One (SML1) pilot network, at the driving strategy level, two optimized driving strategies of acceleration-cruising-braking (ACRB) and acceleration-coasting-braking (ACOB) are considered respectively. At the timetable level, genetic algorithm (GA) is used to optimize the decision variables of the trains. The optimization of driving strategy and timetable balances the traction energy consumption and feedback energy to minimize the net traction energy consumption of the metro system. Finally, simulation experiments were conducted based on the pilot network of the SML1. The results show that the energy consumption of the proposed strategy can be reduced by 23.28%.

Matrix metalloproteinase-7 (MMP-7) is a secreted endopeptidase that degrades a broad range of substrates. Recent studies have identified MMP-7 as an early biomarker to predict severe acute kidney injury (AKI) and poor outcomes after cardiac surgery; however, the role of MMP-7 in the pathogenesis of AKI is unknown. In this study, we investigated the expression of MMP-7 and the impact of MMP-7 deficiency in several models of AKI. MMP-7 was induced in renal tubules following ischemia/ reperfusion injury or cisplatin administration, and in folic acid-induced AKI. MMP-7 knockout mice experienced higher mortality, elevated serum creatinine, and more severe histologic lesions after ischemic or toxic insults. Tubular apoptosis and interstitial inflammation were more prominent in MMP-7 knockout kidneys. These histologic changes were accompanied by increased expression of FasL and other components of the extrinsic apoptotic pathway, as well as increased expression of pro-inflammatory chemokines. In a rescue experiment, exogenous MMP-7 ameliorated kidney injury in MMP-7 knockout mice after ischemia/reperfusion. In vitro, MMP-7 protected tubular epithelial cells against apoptosis by directly degrading FasL. In isolated tubules ex vivo, MMP-7 promoted cell proliferation by degrading E-cadherin and thereby liberating β-catenin, priming renal tubules for regeneration. Taken together, these results suggest that induction of MMP-7 is protective in AKI by degrading FasL and mobilizing β-catenin, thereby priming kidney tubules for survival and regeneration.

The development of acute kidney injury (AKI) is a complex process involving tubular, inflammatory, and vascular components, but less is known about the role of the interstitial microenvironment. We have previously shown that the extracellular matrix glycoprotein tenascin-C (TNC) is induced in fibrotic kidneys. In mouse models of AKI induced by ischemia-reperfusion injury (IRI) or cisplatin, TNC was induced de novo in the injured sites and localized to the renal interstitium. The circulating level of TNC protein was also elevated in AKI patients after cardiac surgery. Knockdown of TNC by shRNA in vivo aggravated AKI after ischemic or toxic injury. This effect was associated with reduced renal β-catenin expression, suggesting an impact on Wnt signaling. In vitro, TNC protected tubular epithelial cells against apoptosis and augmented Wnt1-mediated β-catenin activation. Co-immunoprecipitation revealed that TNC physically interacts with Wnt ligands. Furthermore, a TNC-enriched kidney tissue scaffold prepared from IRI mice was able to recruit and concentrate Wnt ligands from the surrounding milieu ex vivo. The ability to recruit Wnt ligands in this ex vivo model diminished after TNC depletion. These studies indicate that TNC is specifically induced at sites of injury and recruits Wnt ligands, thereby creating a favorable microenvironment for tubular repair and regeneration after AKI.

This paper describes the design, synthesis, characterization and powder second harmonic generation (SHG) effect of some organo-titanium and -zirconium compounds of the type of Cp 2 M(Cl)-EAr (Cp = cyclopentadienyl, M = Ti or Zr, E = O, S).