Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

This paper presents a solution to address the challenges of unexpected events in the operation of metro trains, which can lead to increased delays and safety risks. An improved Q-learning algorithm is proposed to reschedule train timetables via incorporating train detention and different section running times as actions. To enhance computational efficiency and convergence rate, a simulated annealing dynamic factor is introduced to improve action selection strategies. Additionally, importance sampling is employed to evaluate different policies effectively. A case study of Shenzhen Metro is conducted to demonstrate the effectiveness of the proposed method. The results show that the method achieves convergence, fast computation speed, and real-time adjustment capabilities. Compared to traditional methods such as no adjustment, manual adjustment, and FIFO (First-In-First-Out), the proposed method significantly reduces the average total train delay by 54% and leads to more uniform train headways. The proposed method utilizes a limited number of variables for practical state descriptions, making it well suited for real-world applications. It also exhibits good scalability and transferability to other metro systems

Machine learning and experimental validation identified autophagy signature in hepatic fibrosis

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell

Antiangiogenesis Efficacy of Ethanol Extract from Amomum tsaoko in Ovarian Cancer through Inducing ER Stress to Suppress p-STAT3/NF-kB/IL-6 and VEGF Loop

Natural plants are considered as a huge treasure for anticancer. Amomum tsaoko, a plant of Zingiberaceae, is used widely as a food and traditional medicine in East Asia. In previous studies, Amomum tsaoko has antitumor effect on liver cancer cells, but the mechanism is not clear. Here, we demonstrated that ethanol extract from Amomum tsaoko (At-EE) could inhibit ovarian cancer and decrease angiogenesis in vivo. At-EE did not influence vascular endothelial cells directly, but decreased IL-6 and VEGF secreted by ovarian cancer cells to inhibit angiogenesis through inhibition of p-STAT3 and NF-kB activation. In addition, we demonstrated that p-STAT3 and NF-kB could adjust each other and IL-6 and VEGF also mediate p-STAT3 and NF-kB too, which created a loop. In addition, At-EE interrupted p-STAT3/NF-kB/IL-6 and VEGF loop through induced ER stress. These results reveal that p-STAT3/NF-kB/IL-6 and VEGF is a cascade amplification loop in ovarian cancer for angiogenesis, and induced ER stress can interrupt it. Taken together, this work explored the anticancer activities of Amomum tsaoko, which could be a potential therapeutic candidate in the treatment of ovarian cancer

DataSheet_2_Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.docx

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.</p

DataSheet_1_Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.docx

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.</p

One-Step or Two-Step Acid/Alkaline Pretreatments to Improve Enzymatic Hydrolysis and Sugar Recovery from Arundo Donax L.

Energy crops are not easily converted by microorganisms because of their recalcitrance. This necessitates a pretreatment to improve their biodigestibility. The effects of different pretreatments, as well as their combination on the enzymatic digestibility of Arundo donax L. were systematically investigated to evaluate its potential for bioconversion. Dilute alkaline pretreatment (ALP) using 1.2% NaOH at 120 &deg;C for 30 min resulted in the highest reducing sugar yield in the enzymatic hydrolysis process because of its strong delignification and morphological modification, while ferric chloride pretreatment (FP) was effective in removing hemicellulose and recovering soluble sugars in the pretreatment stage. Furthermore, an efficient two-step ferric chloride-alkaline pretreatment (FALP) was successfully developed. In the first FP step, easily degradable cellulosic components, especially hemicellulose, were dissolved and then effectively recovered as soluble sugars. Subsequently, the FP sample was further treated in the second ALP step to remove lignin to enhance the enzymatic hydrolysis of the hardly degradable cellulose. As a result, the integrated two-step process obtained the highest total sugar yield of 420.4 mg/g raw stalk in the whole pretreatment and enzymatic hydrolysis process; hence, the process is a valuable candidate for biofuel production

Reproducibility of fluorescent expression from engineered biological constructs in E. coli

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices.Peer reviewe