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Background. Ovarian cancer (OC) is one of the most frequently seen and fatal gynecological malignancies, and oxidative stress (OS) plays a critical role in the development and chemoresistance of OC. Materials and Methods. OS-related genes (OSRGs) were obtained from the Molecular Signatures Database. Besides, gene expression profiles and clinical information from The Cancer Genome Atlas (TCGA) were selected to identify the prognostic OSRGs. Moreover, univariate Cox regression, LASSO, and multivariate Cox regression analyses were conducted sequentially to establish a prognostic signature, which was later validated in three independent Gene Expression Omnibus (GEO) datasets. Next, gene set enrichment analysis (GSEA) and tumor mutation burden (TMB) analysis were performed. Afterwards, immune checkpoint genes (ICGs) and the tumor immune dysfunction and exclusion (TIDE) algorithm, together with IMvigor210 and GSE78220 cohorts, were applied to comprehensively explore the role of OSRG signature in immunotherapy. Further, the CellMiner and Genomics of Drug Sensitivity in Cancer (GDSC) databases were also applied in investigating the significance of OSRG signature in chemotherapy. Results. Altogether, 34 prognostic OSRGs were identified, among which 14 were chosen to establish the most valuable prognostic signature. The Kaplan-Meier (KM) analysis suggested that patients with lower OS-related risk score had better prognosis. The area under the curve (AUC) values were 0.71, 0.76, and 0.85 in 3, 5, and 7 years separately, and the stability of this prognostic signature was confirmed in three GEO datasets. As revealed by GSEA and TMB analysis results, OC patients in low-risk group might have better immunotherapeutic response, which was consistent with ICG expression and TIDE analyses. Moreover, both IMvigor210 and GSE78220 cohorts demonstrated that patients with lower OS-related risk score were more likely to benefit from anti-PD-1/L1 immunotherapy. In addition, the association between prognostic signature and drug sensitivity was explored. Conclusion. According to our results in this work, OSRG signature can act as a powerful prognostic predictor for OC, which contributes to generating more individualized therapeutic strategies for OC patients.

Background. Abdominal aortic aneurysm (AAA) is a progressive cardiovascular disease, which is a permanent and localized dilatation of the abdominal aorta with potentially fatal consequence of aortic rupture. Dysregulation of circRNAs is correlated with the development of various pathological events in cardiovascular diseases. However, the function of circRNAs in abdominal aortic aneurysm (AAA) is unknown and remains to be explored. This study is aimed at determining the regulatory mechanisms of circRNAs in AAAs. This study was aimed at exploring the underlying molecular mechanisms of abdominal aortic aneurysms based on the competing endogenous RNA (ceRNA) regulatory hypothesis of circRNA, miRNA, and mRNA. Methods. The expression profiles of circRNAs (GSE144431), miRNAs (GSE62179), and mRNAs (GSE7084, GSE57691, and GSE47472) in human tissue sample from the aneurysm group and normal group were obtained from the Gene Expression Omnibus database, respectively. The circRNA-miRNA-mRNA network was constructed by using Cytoscape 3.7.2 software; then, the protein-protein interaction (PPI) network was constructed by using the STRING database, and the hub genes were identified by using the cytoHubba plug-in. The circRNA-miRNA-hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in AAAs. Results. The present study identified 40 differentially expressed circRNAs (DECs) in the GSE144431, 90 differentially expressed miRNAs (DEmiRs) in the GSE62179, and 168 differentially expressed mRNAs (DEGs) with the same direction regulation (130 downregulated and 38 upregulated) in the GSE7084, GSE57691, and GSE47472 datasets identified regarding AAAs. The miRNA response elements (MREs) of three DECs were then predicted. Four overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 17 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of 4 miRNAs with 168 DEGs. Furthermore, the circRNA-miRNA-mRNA network was constructed through 3 circRNAs, 4 miRNAs, and 17 mRNAs, and three hub genes (SOD2, CCR7, and PGRMC1) were identified. Simultaneously, functional enrichment and pathway analysis were performed within genes in the circRNA-miRNA-mRNA network. Three of them (SOD2, CCR7, and PGRMC1) were suggested to be crucial based on functional enrichment, protein-protein interaction, and ceRNA network analysis. Furthermore, the expression of SOD2 and CCR7 may be regulated by hsa_circ_0011449/hsa_circ_0081968/hsa-let-7f-5p; the expression of PGRMC1 may be regulated by hsa_circ_0011449/hsa_circ_0081968-hsa-let-7f-5p/hsa-let-7e-5p. Conclusion. In conclusion, the ceRNA interaction axis we identified may be an important target for the treatment of abdominal aortic aneurysms. This study provided further understanding of the potential pathogenesis from the perspective of the circRNA-related competitive endogenous RNA network in AAAs.

Pseudo threshold pressure gradient (PTPG) is one of the main factors affecting the oil recovery of heterogeneous low-permeability and tight reservoirs. When the pressure gradient between the supply boundary and the bottom hole is less than PTPG, the crude oil in the formation cannot flow. In order to accurately measure the relationship between PTPG and permeability, a novel experimental device has been designed and a series of core flooding experiments using brine and oil have been carried out. The new experimental device eliminates the influence of rubber sleeve in core holder and core end-effect on the results, and greatly improves the accuracy of pressure measurement. The experimental results show the PTPG has a power function relationship with permeability, and has no obvious relationship with the viscosity of the fluid. Based on the equation for PTPG and permeability of crude oil and brine fitted by the experimental data, a new heterogeneous reservoir mathematical model considering PTPG were established. The influence of the coefficient of permeability variation, average permeability, and the production rate on oil recovery factor have been studied by this model. The results clearly show that the coefficient of permeability variation has the greatest influence on oil recovery factor, which represents inhomogeneity in low-permeability and tight reservoirs. Also, when the coefficient of permeability variation is less than a certain value (about 0.2 when average permeability is 1mD, in our model), there is only a slight decline in oil recovery factor, and when coefficient of permeability variation is larger than this value, the oil recovery factor decreases rapidly. The finding is consistent with oil field production and helpful to determine whether to take EOR(enhanced oil recovery) measures for a low-permeability heterogeneous reservoirs.

Background FYN is a nonreceptor tyrosine kinase that regulates diverse pathological processes. The pro-cancer role of FYN in multiple malignancies has been elucidated. However, the mechanisms that FYN promotes gastric cancer(GC) progression remain largely unknown. Methods In vitro and in vivo assays were used to investigate the function of FYN. FYN, TOPK, p-TOPK expression in GC specimens were detected by immunohistochemistry. Phosphoproteomics assays identify TOPK downstream substrate molecules. The molecular mechanism was determined using COIP assays, pull-down assays, immunofluorescence co-localization assays, western blotting, 32p-labeled isotope radioautography assays, vitro kinase assays ,and TOPK knockout mice. Results FYN was found to be significantly upregulated in GC as well as in GC cells. Knockdown of FYN expression markedly attenuated the malignant phenotype of GC cells in vitro and in vivo. Mechanistically, we identified TOPK/PBK as a novel downstream substrate of FYN, FYN directly phosphorylates TOPK at Y272. One phosphospecific antibodies against Y272 was developed to validate the phosphorylation of TOPK by FYN. Moreover, the TOPK-272F mutation impaired the interaction between TOPK and FYN, leading to disappeared TOPK phosphorylation. Consistently, human GC tissues displayed increased p-TOPK(Y272), which correlated with poor survival. Phosphoproteomics results showed a significant downregulation of both HSPB1 and p-HSPB1(ser15) in TOPK-knockdown cells, which was confirmed by TOPK-konckout mice. Conclusions FYN directly binds to TOPK in GC cells and phosphorylates TOPK at the Y272, which leads to proliferation and metastasis of GC. FYN-TOPK axis facilitates GC progression by phosphorylating HSPB1 at S15. Collectively, our study elucidates the pivotal role of the FYN-TOPK-HSPB1 cascade in GC.

Background & AimsHepatocyte growth and proliferation is dependent on the synthesis of membrane phospholipids. Lipid synthesis, in turn, requires short chain fatty acids (SCFA) generated by bacterial fermentation, delivered through the gut- liver axis. We therefore hypothesized that dysbiotic insults like antibiotics treatment not only negatively affect gut microbiota, but also impair hepatic lipid synthesis and liver regeneration.MethodsStable isotope labelling and 70% partial hepatectomy (PHx) was carried out in C57Bl/6J wildtype mice, in mice treated with broad-spectrum antibiotics, in germfree mice and gnotobiotic mice colonized with minimal microbiota. Microbiome was analysed by 16S rRNA gene sequencing and microbial culture. Gut content, liver and blood were tested by lipidomics mass spectrometry, qRT-PCR, immunoblot and immunohistochemistry for expression of proliferative and lipogenic markers. Matched biopsies from hyperplastic and hypoplastic liver tissue of human patients subjected to portal vein embolization were analysed by qRT-PCR for lipogenic enzymes and results were correlated with liver volumetry.ResultsThree days of antibiotics treatment induced persistent dysbiosis with significantly decreased beta-diversity and richness, but massive increase of Proteobacteria, accompanied by decreased colonic SCFA. After PHx, antibiotics- treated mice showed delayed liver regeneration, increased mortality, impaired hepatocyte proliferation and decreased hepatic phospholipid synthesis. Expression of the key lipogenic enzyme SCD1 was upregulated after PHx, but delayed by antibiotics-treatment. Germfree mice essentially recapitulated the phenotype of antibiotics-treatment. Importantly, phospholipid synthesis, hepatocyte proliferation, liver regeneration and survival were rescued in gnotobiotic mice colonized with a minimal SCFA-producing microbial community. SCD1 was required for human hepatoma cell proliferation, and its hepatic expression was associated with liver regeneration and hyperproliferation in human patients.ConclusionGut microbiota are pivotal for hepatic membrane phospholipid synthesis and liver regeneration.Lay SummaryGut microbiota affects the liver lipid metabolism through the gut-liver axis, and microbial metabolites promote liver regeneration. Perturbations of the microbiome, e.g., by antibiotics treatment, impair the production of bacterial metabolites, which serve as building blocks for new membrane lipids in liver cells. As a consequence, hepatocyte growth and proliferation, and ultimately, liver regeneration and survival after liver surgery is impaired.HighlightsPartial hepatectomy in mice pretreated with antibiotics is associated with impaired hepatocyte proliferation and phospholipid synthesis, delayed liver regeneration and increased mortalityThe delay in liver regeneration and impaired lipogenesis upon antibiotics treatment is preceded by dysbiosis of gut microbiota, increase of Proteobacteria and decreased short-chain fatty acids in the gutPartial hepatectomy in germfree mice essentially phenocopies the detrimental effects of antibiotic treatmentLiver regeneration and mortality, as well as phospholipid synthesis and hepatocyte proliferation in germfree mice are fully rescued upon colonisation with a minimal gut bacterial consortium capable of short-chain fatty acid productionIn human patients, the intrahepatic expression of lipid synthesis enzymes positively correlates with proliferation and liver regeneration in the clinicThus, liver regeneration is affected by composition of gut microbiotaClinically, pre-operative analysis of the gut microbiome may serve as biomarker to determine the extent of liver resections

This paper presents a minimally persistent graph generation and formation control strategy for multi-robot systems with sensing constraints. Specifically, each robot has a limited field of view (FOV) and range sensing capability. To tackle this problem, one needs to construct an appropriate interaction topology, namely assign neighbors to each robot such that all their sensing constraints are satisfied. In addition, as a stringent yet reasonable guarantee for the visual constraints, it is also required that the prescribed neighbors always stay within its visual field during the formation evolution. To this end, given a set of feasible initial positions, we first present a depth-first-search (DFS)-based algorithm to generate a minimally persistent graph, which encodes the sensing constraints via its directed edges. Then, based on the resultant graph, by invoking the gradient-based control technique and control barrier function (CBF), we propose a class of distributed formation control laws, rendering not only the convergence to the desired formation but also the satisfaction of sensing constraints. Simulation and experimental results are presented to verify the effectiveness of the proposed approach.

Small nucleolar RNA host gene 1 (SNHG1) is critical in the progression of cancers. However, the mechanism by which SNHG1 regulates the progression of colorectal cancer (CRC) remains unclear. Expressions of SNHG1 and miR‐137 in CRC tissues and cell lines were evaluated by quantitative real‐time polymerase chain reaction. A luciferase reporter gene assay was conducted to investigate miR‐137 target. Additionally, RNA pull‐down assay was performed to explore the physical association between miR‐137, SNHG1, and RNA induced silencing complex (RISC). Cell cycling and invasion were examined by flow cytometry (FCM) and transwell assays. The in vivo carcinogenic activity of SNHG1 was examined using murine xenograft models. Expression of RICTOR, serine/threonine kinase 1 (AKT), serum and glucocorticoid‐inducible kinase 1 (SGK1), p70S6K1, and LC3II/LC3I ratio was examined by Western blot analysis. SNHG1 upregulation was observed in CRC tissues and cell lines, which was associated with the lymph node metastasis, advanced TNM stage and poorer prognosis. SNHG1 increased RICTOR level in CRC via sponging miR‐137. In addition, SNHG1 silencing inhibited CRC cell proliferation and migration in vitro and in vivo. SNHG1 regulated RICTOR expression by sponging miR‐137 and promoted tumorgenesis in CRC.

The per- and polyfluoroalkyl substances (PFASs) are substantially produced and applied in industrial and domestic products, which have recently aroused great public concern for their potential toxicity to humans. In the present study, raw milk (n = 107) and cow feed samples (n = 70) were collected across nine Chinese provinces, in order to investigate the occurrence of PFASs in milk and feed, and the human exposure risk to milk. The concentrations of PFASs are in the range ofmethod detection limit -9.82 ng/g dw (average: 1.03 ng/g dw) for milk and 0.99-144 ng/g dw (7.68 ng/g dw) for feed. Perfluorobutanoic acid (34.0%) dominates in feed, while perfluorooctanesulfonic acid (67.5%) dominates in milk. No significant positive correlations of PFASs are observed between paired feed and milk (p 0.05). However, feeds collected around fluorination production area show relatively higher PFAS levels than those from other areas, which also increase PFAS levels in milk. Risk assessment of PFASs through milk consumption is carried out according to evolving reference doses (RfDs). The hazard quotient is more than one for both adults and children when the strictest RfDs are applied. The Monte Carlo Simulation shows that children face higher PFAS exposure risk than adults.

The reduction and dissolution mechanism of Fe3O4 in NaCl-CaCl2 melts system are analyzed by thermodynamics. The electrochemical methods of Fe3O4 in NaCl-CaCl2 melts are studied by cyclic voltammetry, square wave voltammetry and open-circuit chronopotentiometry. The effects of temperature on reduction potential and peak current are investigated. The diffusion coefficients of Fe3+ and Fe2+ at different temperatures are calculated, and the activation energy E is obtained. Electrolytic products are obtained after 10 min of potentiostatic electrolysis in the melts of NaCl-CaCl2-Fe3O4 at 1073 K and 1.9 V. The results of XRD show that no other compounds are formed in the melts, indicating that Fe3O4 do not react chemically in the NaCl-CaCl2 melts system at 973 K, which is consistent with the theoretical analysis. Fe3O4 is reduced by two-step quasi-reversible reaction in the melts of NaCl-CaCl2 at 973 K to obtain iron: Fe3+ → Fe2+ → Fe. The two-step reaction is controlled by diffusion. The diffusion coefficients are 4.53 × 10–6 cm2·s–1 and 1.17 × 10–6 cm2·s–1, respectively. Fe3O4 is reduced in two steps. The potential shifts with temperature and decreases with the increase of the proportion of Fe3O4. The activation energies of the two-step reaction are 62.38 kJ·mol–1 and 77.52 kJ·mol–1. A deposited layer of uniform distribution, high coverage and dense iron is prepared.

In Active Traffic Management (ATM) system, realtime crash risk prediction is the basis for managing and improving traffic safety. The paper focuses upon the impact of training set on the cash risk prediction model and explores how to preprocess training set in order to improve the quality of the model in continuous data environment, based on the traffic data collected by loop detectors on 237 road segments of Shanghai Urban Expressway. The study operates full set of data, random undersampling data, and matched case-control data as the training set to build crash risk prediction models separately and compares the prediction effect of these models to determine a best sampling mechanism. In addition, we also apply the oversampling method SMOTE (Synthetic Minority Oversampling Technique) to the training set mentioned above to expand the crash data and then compared the prediction effects of models before and after expansion to explore if the oversampling method can improve the prediction accuracy of a real-time crash risk prediction model in continuous data environment. The commonly used logistic regression is used as the modeling algorithm, and the area under the ROC (Receiver Operating Characteristic) curve (AUC) value is employed as the evaluation index. The experimental results show that under the continuous data environment, the random undersampling data model has the best prediction effect with ratio of crash to non-crash 1:5, and the SMOTE method has little effect on the prediction accuracy improvement.

Liver sinusoidal endothelial cells (LSECs) control organ functions, metabolism, and development through the secretion of angiokines. LSECs express hepatocyte growth factor (Hgf), which is involved in prenatal development, metabolic homeostasis, and liver regeneration. This study aimed to elucidate the precise contribution of LSEC-derived Hgf in physiological homeostasis and liver regeneration. Stab2-iCretg/wt;Hgffl/fl (HgfΔLSEC) mice were generated to abrogate Hgf expression selectively in LSECs from early fetal development onwards, to study global development, metabolic and endothelial zonation, and organ functions as well as liver regeneration in response to 70% partial hepatectomy (PH). Although zonation and liver/body weight ratios were not altered, total body weight and total liver weight were reduced in HgfΔLSEC. Necrotic organ damage was more marked in HgfΔLSEC mice, and regeneration was delayed 72 hours after PH. This was associated with decreased hepatocyte proliferation at 48 hours after PH. Molecularly, HgfΔLSEC mice showed down-regulation of Hgf/c-Met signaling and decreased expression of Deptor in hepatocytes. In vitro knockdown of Deptor was associated with decreased proliferation. Therefore, angiocrine Hgf controls hepatocyte proliferation and susceptibility to necrosis after partial hepatectomy via the Hgf/c-Met axis involving Deptor to prevent excessive organ damage.

Nowadays, the judicial system has been hard to satisfy the growing judicial needs of the people. Therefore, the introduction of artificial intelligence into the judicial field is an inevitable trend. This paper incorporates deep learning into intelligent judicial sentencing and proposes a comprehensive network fusion model based on massive legal documents. The proposed method combines multiple networks, e.g., recurrent neural network and convolutional neural network, in the procedure of sentencing prediction. Specially, we use text classification and post-classification regression to predict the defendant’s conviction, articles of law related to the case and prison term. Moreover, we use the simulated gradient descent method to build a fusion model. Experimental results on legal documents datasets justify the effectiveness of the proposed method in sentencing prediction. The fused network model outperforms each individual model in terms of higher accuracy and stability when predicting the conviction, law article and prison term.

Autonomous driving has become a popular research project. How to control vehicle speed is a core problem in autonomous driving. Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to control the vehicle speed. However, the popular Q-learning algorithm is unstable in some games in the Atari 2600 domain. In this paper, a reinforcement learning approach called Double Q-learning is used to control a vehicle’s speed based on the environment constructed by naturalistic driving data. Depending on the concept of the direct perception approach, we propose a new method called integrated perception approach to construct the environment. The input of the model is made up of high dimensional data including road information processed from the video data and the low dimensional data processed from the sensors. During experiment, compared with deep Q-learning algorithm, double deep Q-learning has improvements both in terms of value accuracy and policy quality. Our model’s score is 271.73% times that of deep Q-learning.

Klebsiella pneumoniae is one of the most significant nosocomial pathogens in healthcare settings. Over the last decade, K. pneumoniae producing extended-spectrum β-lactamases (ESBLs) have been prevalent worldwide. ESBL-producing isolates are considered resistant to all penicillins, cephalosporins and aztreonam, and carbapenems are the only β-lactams that still remain consistently active.1 But owing to the overuse of carbapenems, resistant strains have become endemic in many countries as well. In addition, most carbapenem-resistant K. pneumoniae (CR-KP) are resistant to other antimicrobial classes, referred to as multidrug-resistant (MDR) and extensively drug-resistant strains.2 The lack of active antimicrobials against CR-KP continues to threaten public health.