Investigation of Structure – Biodegradability Relationships in Polychlorinated Biphenyls Using Self-Organising Maps

Cartwright, Hugh M.

doi:10.1007/s005210200013

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Prior research has shown that the ANN program can effectively predict the biodegradability of organic chemicals, particularly mono-and di-substituted benzenes, although predictions based on a more diverse set of organic chemicals may not be as accurate (Zitko 1991). Notably, predicting the biodegradability of dichloro-PCBs may pose greater challenges than that of other types of PCBs (Cartwright 2002). Additionally, soil organic matter has been demonstrated to predict biodegradability with a 25% margin of error for three-quarters of a set of 44 PCBs.…”

Section: Modeling Of Artificial Neural Network (Ann) In Pcb Biodegrad...mentioning

confidence: 99%

“…This quantitative relationship between PCBs, represented by the Molecular Distance-Edge Vector index and gas partition coefficient, is modeled using both multivariate linear regression (MLR) and ANN. Moreover, the self-organizing method, an unsupervised ANN, is utilized to rationalize and predict the degradation data for PCBs exposed to Aspergillus Niger (Cartwright, 2002). In fact, QSAR models are employed to establish the connection between the physiochemical properties of chemicals as molecular descriptors and biodegradability, which are instrumental in estimating remediation parameters (Luo et al 2015a).…”

Section: Introductionmentioning

confidence: 99%

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Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Singh,

Mukherjee

2024

Preprint

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The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian Network (BN), Quantitative Structure-Activity Relationship-Density Functional Theory (QSAR-DFT), Artificial Neural Network (ANN), Molecular docking (MD) and Molecular dynamics stimulation (MS) of PCBs biodegradation i.e. PCB-10, PCB-28, PCB-52, PCB-138, PCB-153, and PCB-180 in the soil system using fungi isolated from the transformer oil-contaminated sites. Results revealed that the efficacy of PCBs biodegradation best fits the first-order kinetics (R2 ≥ 0.93). The consortium treatment (29.44–74.49%) exhibited more efficient degradation of PCBs than those of Aspergillus tamarii (27.09–71.25%), Corynespora cassiicola sp. MN69 (23.76–57.37%), and Corynespora cassiicola sp. MN70 (23.09–54.98%). 3'-methoxy-2, 4, 4'-trichloro-biphenyl as an intermediate derivative was detected in the fungal consortium treatment. The BN analysis predicted that the biodegradation efficiency of PCBs ranged from 11.6–72.9%. The ANN approach showed the importance of quantum chemical descriptors (QCD) i.e., LUMO > Molecular weight > Ionization Potential > No. of Chlorine > HOMU in PCBs removal. Furthermore, the QSAR-DFT model between the QCD and rate constant (Log K) exhibited a high fit and good robustness of R2 = 99.12% in predicting ability. The MD and MS analyses showed the lowest binding energy through Normal Mode Analysis (NMA), implying stability in the interactions of the docked complexes. These findings provide crucial insights for devising strategies focused on natural attenuation, holding substantial potential for mitigating PCBs contamination within the environment.

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Section: Modeling Of Artificial Neural Network (Ann) In Pcb Biodegrad...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Singh,

Mukherjee

2024

Preprint

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“…They have been used in variety of academic disciplines to study a wide range of problems. Specifically, SOMs have been used in climate science research (Hewitson & Crane, 2002), medical research (Villmann, Hermann, & Geyer, 2000), the geographic information sciences (Agarwal & Skupin, 2008), image classification research (Richardson, Risien, & Shillington, 2003), the environmental sciences (Cartwright, 2002), engineering (Bossio, De Angelo, & Bossio, 2013), and physics (Camplani, Cannas, Fanni, Pautasso, & Sias, 2011).…”

Section: Introductionmentioning

confidence: 99%

Introducing Neural Computing in Governance Research: Applying Self‐Organizing Maps to Configurational Studies

Somers

Casal

2016

Corporate Governance

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Manuscript Type: EmpiricalResearch Issue/Question: Self-organizing maps (SOMs), a neural computing paradigm, were introduced as a methodology to enhance and extend configurational governance research.The capabilities of SOMs include assessment of nonlinear relationships among study variables and projection of firms and clusters in two-dimensional space based on their relative similarity.Research Findings/Insights: To demonstrate their application to governance research, SOMs were used to study patterns of immunity to institutional governance logics in the financial services industry. Firm sensemaking and governance logics were assessed by analyzing the language and meaning of corporate codes of conduct. Content analysis was guided by the DICTION software program. DICTION uses data dictionaries to analyze the meaning of text documents based on word usage. Our results supported a configurational model characterized by distinct groupings of firms with varying degrees of acceptance of prevailing institutional governance logics.Practitioner/Policy Implications: SOM analysis demonstrated that context influences firm governance logics. Specifically, different interpretations of environmental pressures led to different adaptive responses suggesting reconsideration of the notion of universal or best governance practices.

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“…The first 4'-OHPCB 33 occupied the DHEA binding site and amino acid residues around it were manually labeled (left), and the second 4'-OHPCB33 docked at the PAP binding site and amino acid residues around it were manually labeled (right). chlorine atoms on a biphenyl ring system ( Figure 1) (1)(2)(3). They were easily synthesized and were sold as mixtures (2,4).…”

mentioning

confidence: 99%

“…Inhibitory effects of OHPCBs on the sulfation of DHEA catalyzed by hSULT2A1. Radiochemical assays using 1µM3 [H]DHEA were carried out at pH 7.0 with variable concentrations of OHPCBs as described in Methods. (A) Those OHPCBs that were solely inhibitors and (B) Those OHPCBs that inhibited the sulfation of DHEA, but also served as alternate substrates.…”

mentioning

confidence: 99%

Structure-activity relationships for interactions of hydroxylated polychlorinated biphenyls with human hydroxysteroid sulfotransferase hSULT2A1

Ekuase¹

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Industrial chemicals known as polychlorinated biphenyls (PCBs) were widely used for decades until their production was banned worldwide due to their persistence and toxicities to humans and other animals. Upon oxidative metabolism by cytochrome P450, hydroxylated metabolites of PCBs (OHPCBs) are formed. OHPCBs have been shown to competitively displace thyroxine from transthyretin, block normal hormonal activity, and inhibit phenol or family 1 sulfotransferases (SULTs) which catalyze sulfation of thyroid hormones and estrogens. Recently, three OHPCBs were shown to also interact with hydroxysteroid or family 2 sulfotransferases that play a role in the homeostasis of steroid hormones such as dehydroepiandrosterone (DHEA). The objectives of the studies presented in this dissertation were to further examine the effects of selected OHPCBs on the activity of human hydroxysteroid sulfotransferase (hSULT2A1), to develop a three-dimensional quantitative structure activity relationship (3D-QSAR) model for OHPCBs as inhibitors of DHEA-sulfation catalyzed by this enzyme, and to investigate the mechanism of inhibition and binding of OHPCBs to hSULT2A1. All 15 OHPCBs examined inhibited the sulfation of 1 µM [ 3 H] DHEA, catalyzed by hSULT2A1 with IC 50 values ranging from 0.6 to 96 µM. The OHPCBs with a 3, 5dichloro-4-hydroxy substitution were the most potent inhibitors of DHEA sulfation, and they were also shown to be substrates for hSULT2A1. Eight OHPCBs were substrates for hSULT2A1, and seven were solely inhibitors (i.e. they inhibited the sulfation of DHEA, yet they were not themselves sulfuryl-acceptors in hSULT2A1-catalyzed reactions). A 3D-QSAR model was developed utilizing comparative molecular field analysis (CoMFA). The model fit the data well and also had good predictability. The kinetics of inhibition showed that these OHPCBs were noncompetitive inhibitors of hSULT2A1. Binding studies utilizing the displacement of a fluorescent iii ACKNOWLEDGMENTS I would like to express my immense gratitude to my advisor, Dr. Duffel. Thank you for your patience, mentorship and support over the years. I could not have achieved this milestone without your help. You were more than a mentor to me; you were like a friend. I came to you with all my problems, be it research related or personal, and you always gave me valuable advice on what to do. I say thank you, thank you and thank you again.

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Investigation of Structure – Biodegradability Relationships in Polychlorinated Biphenyls Using Self-Organising Maps

Cited by 6 publications

References 19 publications

Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Introducing Neural Computing in Governance Research: Applying Self‐Organizing Maps to Configurational Studies

Structure-activity relationships for interactions of hydroxylated polychlorinated biphenyls with human hydroxysteroid sulfotransferase hSULT2A1

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