Background Tomatoes (Solanum lycopersicon L.) are one of the main daily consumed vegetables in the human diet. Tomato has been classified as moderately sensitive to salinity at most stages of plant development, including seed germination, seedling (vegetative), and reproduction phases. In this study, we evaluated the performance and response of 39 tomato landraces from Jordan under salt stress conditions. Furthermore, the landraces were also genetically characterized using simple sequence repeat (SSR) markers. Results The studied morphological-related traits at the seedling stage were highly varied among landraces of which the landrace number 24 (Jo970) showed the best performance with the highest salt tolerance. The total number of amplification products produced by five primers (LEaat002, LEaat006, LEaat008, LEga003, LEta019) was 346 alleles. Primer LEta 019 produced the highest number of alleles (134) and generated the highest degree of polymorphism (100%) among landraces in addition to primers (LEaat002, LEaat006, LEaat008). The lowest dissimilarity among landraces ranged from 0.04 between accessions 25 (Jo969) and 26 (Jo981) and the highest dissimilarity (1.45) was found between accessions 39 (Jo980) and both 3 (Jo960) and 23 (Jo978). The dendrogram showed two main clusters and separated 30 landraces from the rest 9 landraces. High genetic diversity was detected (0.998) based on the average polymorphism information. Therefore, the used SSRs in the current study provide new insights to reveal the genetic variation among thirty-nine Jordanian tomato landraces. According to functional annotations of the gene-associated SSRs in tomatoes, a few of SSR markers gene-associated markers, for example, LEaat002 and LEaat008 markers are related to MEIS1 Transcription factors genes (Solyc07g007120 and Solyc07g007120.2). The LEaat006 is related to trypsin and protease inhibitor (Kunitz_legume) gene (Solyc03g020010). Also, the SSR LEga003 marker was related to the Carbonic anhydrase gene (Solyc09g010970). Conclusions The genetic variation of tomato landraces could be used for considering salt tolerance improvement in tomato breeding programs.

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Abstract-This paper is aimed at demonstrating a genetic algorithm method and applying it to predict the water quality of reservoir in Taiwan island using remote sensing data. Genetic algorithms will be combined with operation tree (GAOT) to find the relationships between input and output data. A fittest function type will be obtained automatically from this method. The advantages of GA are global optimization, nonlinearity, flexibility and parallelism. In the current case study, GA is used to construct the relationship between algae concentration and Landsat sensor data. The results show that the model has better performance than the traditional LN transform of linear regression method, and similar performance compared with back-propagation neural network (BPNN) method.Index Terms-Genetic algorithm, Landsat, LN transform linear regression, back-propagation neural network, operation tree.

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