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Data for inactivation of free-living nematode Rhabditida from water environment using ultraviolet radiation.

ABSTRACT: Sometimes free-living nematodes in conventional water treatment processes are not eliminated and cause adverse health effects in water consumer. So, the efficiency of UV lamps (125 W) with irradiation intensity 1020 μW cm-2 on inactivation of free-living nematode Rhabditida released in water samples has been investigated along with the investigation of the effects of turbidity and change of temperature and exposure time in constant of pH 8 ± 0.2. The results showed that UV radiation could disabled the larval and adult nematodes after 12 and 15 min in the presence of turbidity 5 NTU, respectively. Also, increased turbidity up to 50 NTU decreased the inactivation efficiency of larval and adult nematodes from 100% to 73% and 64%, respectively. In addition, with increased temperature, the inactivation efficiency increased significantly in a short time. The results showed a significant relationship between increasing exposure time and temperature and turbidity reduction with UV radiation efficiency in the inactivation of the nematode (P < 0.00). It was also found that the efficiency of the lamp on nematode larvae was more than the adult nematode. Therefore, UV radiation can well inactivate larvae and then adult free-living nematode Rhabditida in water.

SUBMITTER: Biglari H

PROVIDER: S-EPMC5995771 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Data for inactivation of free-living nematode Rhabditida from water environment using ultraviolet radiation.

Biglari Hamed H Tatari Maryam M Narooie Mohammad Reza MR Ebrahimzadeh Gholamreza G Sharafi Hooshmand H

Data in brief 20180308

Sometimes free-living nematodes in conventional water treatment processes are not eliminated and cause adverse health effects in water consumer. So, the efficiency of UV lamps (125 W) with irradiation intensity 1020 μW cm-2 on inactivation of free-living nematode Rhabditida released in water samples has been investigated along with the investigation of the effects of turbidity and change of temperature and exposure time in constant of pH 8 ± 0.2. The results showed that ...[more]

PMID: 29896487

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Project description:BackgroundIdentifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903.MethodsA total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms.ResultsFor both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified.ConclusionThese results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.

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Data for inactivation of free-living nematode Rhabditida from water environment using ultraviolet radiation.

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Data for inactivation of free-living <i>nematode Rhabditida</i> from water environment using ultraviolet radiation.

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