关键词： Ischemia   reperfusion   PARP   Parthanatos   Necroptosis   Hypoxia   reoxygenation Oxidative stress  

摘要： Background: Poly(ADP-ribose) polymerase (PARP) is a DNA-repairing enzyme activated by extreme genomic stress, and therefore is potently activated in the remnant liver suffering from ischemia after surgical resection. However, the impact of PARP on post-ischemic liver injury has not been elucidated yet. Materials and methods: We investigated the impact of PARP on murine hepatocyte/liver injury induced by hypoxia/ischemia, respectively. Results: PJ34, a specific inhibitor of PARP, markedly protected against hypoxia/reoxygenation (H/R)-induced cell death, though z-VAD-fmk, a pan-caspase inhibitor similarly showed the protective effect. PJ34 did not affect H/R-induced caspase activity or caspase-mediated cell death. z-VAD-fmk also did not affect the production of PAR (i.e., PARP activity). Therefore, PARPand caspase-mediated cell death occurred in a mechanism independent of each other in H/R. H/R immediately induced activation of PARP and cell death afterwards, both of which were suppressed by PJ34 or Trolox, an antioxidant. This suggests that H/R-induced cell death occurred redox-dependently through PARP activation. H/R and OS induced nuclear translocation of apoptosis inducing factor (AIF, a marker of parthanatos) and RIP1-RIP3 interaction (a marker of necroptosis), both of which were suppressed by PJ34. H/R induced PARPmediated parthanatos and necroptosis redox-dependently. In mouse experiments, PJ34 significantly reduced serum levels of AST, ALT & LDH and areas of hepatic necrosis after liver ischemia/reperfusion, similar to z-VAD-fmk or Trolox. Conclusion: PARP, activated by ischemic damage and/or oxidative stress, may play a critical role in post-ischemic liver injury by inducing programmed necrosis (parthanatos and necroptosis). PARP inhibition may be one of the promising strategies against post-ischemic liver injury. (c) 2021 Elsevier Inc. All rights reserved.

关键词： pyrophosphate   PPi   atypical glycolysis   H+-pumping membrane-bound pyrophosphatase   glycogen cycling   Ppdk   acetate cycling   functional annotation   Clostridium thermocellum   Acetivibrio thermocellus  

摘要： The atypical glycolysis of Clostridium thermocellum is characterized by the use of pyrophosphate (PPi) as a phosphoryl donor for phosphofructokinase (Pfk) and pyruvate phosphate dikinase (Ppdk) reactions. Previously, biosynthetic PPi was calculated to be stoichiometrically insufficient to drive glycolysis. This study investigates the role of a H+-pumping membrane-bound pyrophosphatase, glycogen cycling, a predicted Ppdk-malate shunt cycle, and acetate cycling in generating PPi. Knockout studies and enzyme assays confirmed that clo1313_0823 encodes a membrane-bound pyrophosphatase. Additionally, clo1313_0717-0718 was confirmed to encode ADP-glucose synthase by knockouts, glycogen measurements in C. thermocellum, and heterologous expression in Escherichia coli. Unexpectedly, individually targeted gene deletions of the four putative PPi sources did not have a significant phenotypic effect. Although combinatorial deletion of all four putative PPi sources reduced the growth rate by 22% (0.30 +/- 0.01 h(-1)) and the biomass yield by 38% (0.18 +/- 0.00 g(biomass) g(substrate)-1), this change was much smaller than what would be expected for stoichiometrically essential PPi-supplying mechanisms. Growth-arrested cells of the quadruple knockout readily fermented cellobiose, indicating that the unknown PPi-supplying mechanisms are independent of biosynthesis. An alternative hypothesis that ATP-dependent Pfk activity circumvents a need for PPi altogether was falsified by enzyme assays, heterologous expression of candidate genes, and whole-genome sequencing. As a secondary outcome, enzymatic assays confirmed functional annotation of clo1313_1832 as ATP- and GTP-dependent fructokinase. These results indicate that the four investigated PPi sources individually and combined play no significant PPi-supplying role, and the true source(s) of PPi, or alternative phosphorylating mechanisms, that drive(s) glycolysis in C. thermocellum remain(s) elusive. IMPORTANCE Increased understanding

关键词： Task scheduling   Fast big data processing   Evolutionary algorithm   K-means algorithm  

摘要： Due to the rapid growth of production and dissemination of big data from various sources, the speed of data processing must inevitably increase. In distributed big data processing systems such as cloud computing, the task scheduler is responsible for mapping a large set of various tasks to a set of possibly heterogeneous computing nodes in a way to raise resource efficiency and data locality and reduce makespan. Scheduling strategies that try to achieve these goals in one pass have lower performance than multi-pass strategies. To achieve higher performance, we propose MOTS (a hierarchical multi-objective task scheduling scheme) by first clustering tasks using the K-means algorithm alongside a load balancing equation to increase resource efficiency and then optimizing clusters to reduce makespan using evolutionary algorithms. The latter is achieved by using the state of physical machines and sending related consecutive tasks to a physical machine to eliminate data transfer. We have simulated and tested our scheme in Cloudsim. Our experiments show reduction of approximately 10% makespan and 4% higher CPU efficiency compared to Mai's reinforcement learning approach and Bugerya's parallel implementation method. The cost of data transfer between consecutive tasks is also decreased by 10% compared to Bugerya's methods. With respect to the results and the fact that our proposed task scheduling scheme is inspired by the iHadoop method for parallel implementation, it is suitable for use in distributed big data processing systems. Information about previous executions of tasks and current status of computing nodes is highly influential in efficient mapping of tasks to computing nodes. Predictions of future resource needs of tasks and available capacities of computing nodes can complement the historical information in the way of finding a more near-to-optimal mapping, resulting in faster data processing. This issue and evaluation of our proposed scheme using real data will be

ISBN: (纸本)9787576309201

摘要： 线结构光3D智能相机因其成像速度快、精度高等优势在人工智能、工业检测、人脸支付、VR/AR等领域获得了广泛的应用.搭建了一种线结构光3D智能相机成像系统,开展了数据获取及处理的理论和实验研究,使用Python作为编程语言,使用Numpy、OpenCV、Open3D、Math等库,结合相机标定方法、光平面标定方法和线结构光三维重构理论,编写了三维重构算法,实现了三维重构.相对于已有的数据处理算法,该算法具有速度更快、精度更高的优势,体现了较大的应用价值.

关键词： Data fusion  

摘要： With the rapid expansion of network information and the emergence of a large number of electronic texts, how to organize and manage this massive information has become a major challenge. Automatic text categorization technology is to study how to let the machine classify unknown text through self-learning, thus solving the difficulties encountered in manual classification. Because granular computing can reduce the knowledge in solving complex problems, it is more convenient to summarize and acquire knowledge. It has become a hotspot in recent years, and it also provides new ideas for text classification research. The rough set model of granular computing can acquire knowledge by mining decision rules. The decision process is more transparent and easy to understand. It has been paid attention to and applied in text classification research. Based on the research of existing achievements, this paper makes a further study on the application of granular computing in text categorization. After analyzing the existing feature selection methods, the feature distribution is proposed based on the relationship between feature words and categories. By calculating the distribution distance between any two feature words, the feature words with similar distribution distances are aggregated, which effectively reduces the dimension of the feature space and also avoids the individual samples caused by the existing feature selection algorithm. A phenomenon that is discarded due to features. The experimental results show that the clustering method can obtain higher classification accuracy than other feature selection methods when using SVM as the classifier. SVM performs best, and the final text classification accuracy rate can reach 85.46%. According to the correlation principle of the rough set, feature selection is made for each information granularity, the selected feature is used as the condition attribute and the coordination matrix is constructed, and the most similar sample is h

关键词： Early diagnosis   Ovarian cancer   Raman spectroscopy   Machine learning   Classification models  

摘要： The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with

关键词： Artificial neural network    permeability   Well logs   Group method of data handling  

摘要： Permeability is the key variable for reservoir characterization used for estimating the flow patterns and volume of hydrocarbons. Modern computer advancement has highlighted the use of machine learning approaches such as group method of data handling (GMDH) in predicting permeability. However, the widely employed GMDH has intrinsic problems in its application. Therefore, the objective of this study is to present an enhanced GMDH based modified Levenberg-Marquardt (LM) as an improved alternative to conventional GMDH in predicting permeability from well logs. The study used natural gamma-ray, standard resolution formation density, limited effective porosity, shale volume of rock, and thermal neutron porosity well logs as input variables. Results show that an enhanced method has a reasonable reduction in processing time with high accuracy. Compared to conventional GMDH and backpropagation neural networks (BPNN), the GMDH-LM used 30% less computation time and performed excellently during training with the least error values of 0.092 and 0.018 for RMSE and MAE. Likewise, good results were observed during testing, obtaining the least error values of 0.679 and 0.056 for RMSE and MAE respectively. The modified generalization performance of GMDH-LM makes it an improved form of GMDH and can be adopted as an improved alternative in predicting permeability. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Wastewater   Prediction model   Machine learning   K-FOLD   Process parameters  

摘要： Wastewater treatment based on the activated sludge process is complex process, which is easily affected by influent quality, aeration time and other factors, leading to unstable effluent. Facing increasingly stringent sewage discharge standards in China, it is necessary to build a prediction model for early warning of effluent quality. In this study, nine machine learning algorithms were adopted to construct models for the prediction of effluent Chemical Oxygen Demand (COD). In order to improve the prediction accuracy of the models, model optimization was conducted by introducing the hysteresis condition [Hydraulic Retention Time (HRT) of 18 h], data processing method (K-FOLD) and process parameters [dissolved oxygen (DO), sludge return ratio (SRR) and mixed liquid suspended solids (MLSS)]. Results showed that both K-Nearest Neighbour (KNN) and Gradient Boosting Decision Tree (GBDT) displayed excellent prediction effects, the best results of MAPE, RMSE and R2 were 7.34%/1.29/0.92(COD, KNN). The optimized models were further applied to the prediction of effluent total phosphorus (TP), total nitrogen (TN) and pH. The MAPE/RMSE/R-2 were 7.43%/0.92/0.93(TN, GBDT), 17.81%/ 0.19/0.99(TP, KNN), 0.53%/0.16/0.99 (pH, KNN) respectively, indicating high prediction accuracy. The change and comparison of modeling conditions provide a new insight to wastewater prediction models. In addition, this study is close to the actual application scenario of WWTPs operation and management, also laying a foundation for the reverse regulation of energy saving and consumption reduction of wastewater treatment plants (WWTPs).

关键词： Mixtures   Liquids   Equilibrium   Liquid liquid equilibrium   Molecules  

摘要： The intention of this article is to clarify some concepts and relevant aspects of the experimental determination and treatment of liquid-liquid equilibrium data for ternary systems. Distinction is made between solubility curve and binodal curve, setting the stability limit which is defined by the spinodal curve. In addition, it is recommended to carry out the equilibrium data correlation using semiempirical models with a thermodynamic base, over the use of empirical equations. Moreover, the misuse of these empirical equations (Hand, Bachman, Othmer-Tobias, etc.) as proof of data quality or consistency is clarified.