Identification of informative features for predicting proinflammatory potentials of engine exhausts

Lin, Ying; Lin, Yen‐Chung; Jhang, Syu-Ruei; Tung, Chun Wei

doi:10.1186/s12938-017-0355-6

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…Also, the residual toxicity also exhibited an important reduction on human cells according with the TNFα factor levels expressed by MCPB exposed to supernatants after mycotreatment. It has been well documented that PAHs induce immunotoxicity, proinflammatory responses and carcinogenesis (Wang et al ., 2017). For this reason, we used TNFα as a bioindicator molecule of immunotoxicity after mycotreatments.…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic analysis of polyaromatic hydrocarbon degradation by the halophilic fungus Aspergillus sydowii at hypersaline conditions

Peidro-Guzmán

Pérez-Llano

González-Abradelo

et al. 2020

Environmental Microbiology

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Polycyclic aromatic hydrocarbons (PAHs) are among the most persistent xenobiotic compounds, with high toxicity effects. Mycoremediation with halophilic Aspergillus sydowii was used for their removal from a hypersaline medium (1 M NaCl). A. sydowii metabolized PAHs as sole carbon sources, resulting in the removal of up to 90% for both PAHs [benzo [a] pyrene (BaP) and phenanthrene (Phe)] after 10 days. Elimination of Phe and BaP was almost exclusively due to biotransformation and not adsorption by dead mycelium and did not correlate with the activity of lignin modifying enzymes (LME). Transcriptomes of A. sydowii grown on PAHs, or on glucose as control, both at hypersaline conditions, revealed 170 upregulated and 76 downregulated genes. Upregulated genes were related to starvation, cell wall remodelling, degradation and metabolism of xenobiotics, DNA/RNA metabolism, energy generation, signalling and general stress responses. Changes of LME expression levels were not detected, while the chloroperoxidase gene, possibly related to detoxification processes in fungi, was strongly upregulated. We propose that two parallel metabolic pathways (mitochondrial and cytosolic) are involved in degradation and detoxification of PAHs in A. sydowii resulting in intracellular oxidation of PAHs. To the best of our knowledge, this is the most comprehensive transcriptomic analysis on fungal degradation of PAHs.

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Section: Discussionmentioning

confidence: 99%

Transcriptomic analysis of polyaromatic hydrocarbon degradation by the halophilic fungus Aspergillus sydowii at hypersaline conditions

Peidro-Guzmán

Pérez-Llano

González-Abradelo

et al. 2020

Environmental Microbiology

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“…Based on the normalized feature vectors, a sequential forward selection algorithm [18] was applied to select informative features based on the performance of leave-oneout cross-validation (LOOCV). Sequential forward/backward selection algorithms are efficient solutions for the combinatorial optimization problems that are highly appreciated in the field of bioinformatics due to the high-dimensional feature space observed in numerous applications [19,20]. Briefly, the best features were sequentially selected and appended into the final feature set.…”

Section: Model Development and Feature Selectionmentioning

confidence: 99%

Ensemble learning for predicting ex vivo human placental barrier permeability

et al. 2022

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Background The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable. Methods A total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression, random forest, and ensemble models were applied to develop prediction models for ex vivo placental barrier permeability. Results Among the tested models, the ensemble model integrating the previous two methods performed best for predicting ex vivo human placental barrier permeability with correlation coefficients of 0.887 and 0.825 when considering the applicability domain. An additional test on seven newly curated chemicals from the literature showed a good correlation coefficient of 0.879 which was further improved to 0.921 by considering the variation of experiments. Conclusion In this study, the first valid predicting model for ex vivo human placental barrier permeability was developed following the OECD guideline. The model is expected to be useful for assessing the human placental barrier permeability and can be integrated with developmental toxicity prediction models for investigating the toxic effects of chemicals on the fetus.

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“…In relations ( 12) and ( 13) C v , x v and y v , and C F , x F and y F, respectively , refer to the constants to which values are pre-set. In relation (15), part dimensions L and D are expressed in millimetres.…”

Section: Case Studiesmentioning

confidence: 99%

“…The best compound features are used to construct fault prediction models. Outside the manufacturing domain, [15] proposes a computational method for identifying informative features for predicting pro-inflammatory potentials of engine exhausts and applies a principal component regression algorithm for developing prediction models.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Causal Modeling of the Manufacturing System

Frumusanu

Afteni

Epureanu

2021

TFAMENA

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In manufacturing system management, the decisions are currently made on the base of 'what if' analysis. Here, the suitability of the model structure based on which a model of the activity will be built is crucial and it refers to multiple conditionality imposed in practice. Starting from this, finding the most suitable model structure is critical and represents a notable challenge. The paper deals with the building of suitable structures for a manufacturing system model by data-driven causal modelling. For this purpose, the manufacturing system is described by nominal jobs that it could involve and is identified by an original algorithm for processing the dataset of previous instances. The proposed causal modelling is applied in two case studies, whereby the first case study uses a dataset of artificial instances and the second case study uses a dataset of industrial instances. The causal modelling results prove its good potential for implementation in the industrial environment, with a very wide range of possible applications, while the obtained performance has been found to be good.

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Identification of informative features for predicting proinflammatory potentials of engine exhausts

Cited by 6 publications

References 41 publications

Transcriptomic analysis of polyaromatic hydrocarbon degradation by the halophilic fungus Aspergillus sydowii at hypersaline conditions

Transcriptomic analysis of polyaromatic hydrocarbon degradation by the halophilic fungus Aspergillus sydowii at hypersaline conditions

Ensemble learning for predicting ex vivo human placental barrier permeability

Data-Driven Causal Modeling of the Manufacturing System

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