Search results for "ciliated protozoa"

showing 3 items of 3 documents

Comparative study to predict toxic modes of action of phenols from molecular structures.

2013

Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. M…

Antiprotozoal AgentsQuantitative Structure-Activity RelationshipBioengineeringMachine learningcomputer.software_genreConstant false alarm ratePhenolsArtificial IntelligenceDrug DiscoveryTraining setModels StatisticalArtificial neural networkCiliated protozoanMolecular StructureChemistrybusiness.industryTetrahymena pyriformisGeneral MedicineLinear discriminant analysisSupport vector machineTest setTetrahymena pyriformisMolecular MedicineArtificial intelligenceNeural Networks ComputerBiological systembusinesscomputerSAR and QSAR in environmental research
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Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

2017

The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector…

Quantitative structure–activity relationshipAntiprotozoal AgentsQuantitative Structure-Activity RelationshipBioengineeringModes of toxic action010501 environmental sciencesMachine learningcomputer.software_genre01 natural sciencesMachine Learningchemistry.chemical_compoundPhenolsMolecular descriptorDrug DiscoveryPhenols0105 earth and related environmental sciencesCiliated protozoanArtificial neural networkbusiness.industryTetrahymena pyriformisGeneral Medicine0104 chemical sciencesSupport vector machine010404 medicinal & biomolecular chemistrychemistryTetrahymena pyriformisMolecular MedicineArtificial intelligenceNeural Networks ComputerbusinesscomputerSAR and QSAR in environmental research
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Structural Composition of Protozooplankton Communities in Relation to Environmental Factors in Shallow Lakes and Reservoirs of Rīga, Latvia

2015

Abstract Protozooplankton are dominant grazers of phytoplankton and an important component of the microbial food web, as a link between pico and nanoplankton to higher trophic levels. Their fast growing rate, relative abundance, biomass and diversity are used as indicators of organic and toxic pollution. The impact of urbanisation on ecosystems and their sustainability and biodiversity have recently been much studied. We studied the protozooplankton ciliate communities during the vegetation period from April to October in two small lakes (Bābelītis, Gaiļezers) and two reservoirs (Bolderāja, Saurieši). The largest peak of biomass (15.7 × 102 mg/l) was found in Gaiļezers Lake in August and of…

urban ecologyciliated protozoaMultidisciplinaryRelation (database)General interestEcologyScienceStructural compositionplanktonQshallow lakestructural compositionProceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.
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