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This study is the first detailed ecotoxicological study of the annulated sea snake, Hydrophis cyanocinctus. Concentrations of lead, cadmium, nickel and vanadium were evaluated in muscle, liver, kidney, skin and blood of the annulated sea snake (H. cyanocinctus) and in the whole bodies of its main prey species (Periophthalmus waltoni and Boleophthalmus dussumieri) in the Hara Protected Area, the Persian Gulf. The mean concentrations of lead and vanadium were highest in the kidney, which identified the kidney as a target organ for metals in sea snakes as it is in other reptilian groups. Mean concentrations of cadmium and nickel were highest in the liver and skin, respectively. Mean cadmium concentrations were significantly higher in the liver compared to prey species, which indicated that prey items may be a source of cadmium for the annulated sea snake in the study area. Data presented here may be considered as a baseline for further ecotoxicological studies in sea snakes.

In this study, the leaf area was estimated by different regression models and new methods of image processing by using artificial intelligence (AI) in bread (Triticum aestivum L.) and durum wheat (Triticum durum L.) and triticale (×Tritosecale Wittm. ex A. Camus) at seedling, booting, and milk development stages. Data on leaf traits in 1,000 plants of breed wheat, triticale, and durum wheat were studied. Among regression models using general data, LA = + √ , LA = + (), and LA = + (∕) models had a R 2 > 90% for all three cultivars (L and W represented length and width of leaves). For wheat bread, models + () and √ × () exhibited a good estimate of the leaf area at all stages of growth, whereas in triticale, the linear models that used the product of √ × and √ were more suitable to estimate leaf areas in booting and milk development. The multilayer perceptron (MLP) neural network modeling indicated that the 'trainlm,' 'trainlm,' and 'traincgb' algorithms with an optimal structure of 2-10-1, 2-3-1, and 2-10-1 had the least amount of the mean square error for bread wheat, triticale, and durum wheat, respectively, for each of the growth stages. Furthermore, based on the adaptive neuro-fuzzy inference system (ANFIS) method, a very accurate estimate of the leaf area was obtained for each of the growth stages. The comparison of different models in the leaf area estimation showed that unlike estimating leaf area by regression, AI-based methods did not depend on plant growth stages. In this study, we suggested the utilization of image processing and artificial intelligence in leaf area estimation. It will accelerate leaf area measurement in the field, by extending these methods and transferring it into smartphone applications.

Straight-chain alkanes (n-alkanes) have been proposed as biomarkers to assess petroleum pollution sources in marine environments. In this work, three sampling sites were selected along Iranian-protected mangrove forests in the Persian Gulf. Sample sites were chosen to represent different compositions and sources of n-alkanes in surface sediments (0-5 cm) from different locations in the Khamir Port, in the middle part of the Gulf, and in the Qeshm Island. Samples were analyzed by gas chromatography-mass spectrometry (GC-MS), and the obtained n-alkanes concentrations were analyzed by principal component analysis (PCA) and multivariate curve resolution alternating least squares (MCR-ALS) to deduce their possible sources and distribution patterns. Results revealed that n-alkanes have a dominant biogenic source in marginal Qeshm Island stations, which changed from biogenic to petrogenic sources in the Khamir Port areas. Sediment samples from the Khamir Port showed the significant presence of oil pollution due to transportation. These samples are being exposed to basic Persian Gulf currents entering from the Hormuz Strait causing oil pollution spread over the entire Gulf area. Sediment samples of the Middle Part stations located between two other sites show a combined condition of the two previous sections, with petrogenic and biogenic contributions. The present study demonstrates that the Hara Protected Area was already contaminated by background oil pollution as a result of continuous oil spills and war conflicts in the Persian Gulf.Additionally, the MCR-ALS method is shown to be a powerful chemometric tool for the investigation, resolution, identification, and description of pollution patterns distributed over a particular geographical area and environmental compartment. They can be used as well as parameters like unresolved to resolved ratio (U/R), pristane to phytane (Pr/Ph), n-C/Pr, n-C/Ph, and unresolved complex mixture (UCM) to assess petroleum pollution sources in the sediments.

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