Mangrove plants are a productive ecosystem that provide several benefits for marine organisms and industry. They are considered to be a food source and habitat for many organisms. However, mangrove growth is limited by nutrient availability. According to some recent studies, the dwarfism of the mangrove plants is due to the limitation of nitrogen in the environment. Biological nitrogen fixation is the process by which atmospheric nitrogen is fixed into ammonium. Then, this fixed nitrogen can be uptaken by plants. Hence, biological nitrogen fixation increases the input of nitrogen in the mangrove ecosystem. In this project, we focus on measuring the rates of nitrogen fixation on Red Sea mangrove (Avicennia marina) located at Thuwal, Saudi Arabia. The nitrogen fixation rates are calculated by the acetylene reduction assay. The experimental setup will allow us to analyze the effect of crab bioturbation on nitrogen fixing rates. This study will help to better understand the nitrogen dynamics in mangrove ecosystems in Saudi Arabia. Furthermore, this study points out the importance of the sediment microbial community in mangrove trees development. Finally, the role of nitrogen fixing bacteria should be taken in account for future restoration activities.

Nitrogen is a critical nutrient for growth and reproduction in living organisms. Although the Earth’s atmosphere is composed of ~80% nitrogen gas (N2), it is inaccessible to most living organisms in that form. Biological nitrogen fixation, however, can be performed by microbes that harbor the enzyme nitrogenase. This enzyme converts N2 into bioavailable ammonia (NH3) and accounts for at least half of the “fixed”nitrogen on the planet. The other major contributor to ammonia production is the industrial Haber-Bosch process. While the Haber-Bosch process has made significant advances in sustaining the global food supply through the generation of fertilizer, it requires high temperature and pressure and fossil fuels. This makes nitrogenase an ideal system for study, as it is capable of performing this challenging chemistry under ambient conditions and without fossil fuels. Nitrogenase requires energy and electrons to convert N2into NH3. The work presented here examined how the enzyme receives electrons to perform the reaction. It was discovered that some microbes employ a novel mechanism that adjusts the energy state of the electrons so that nitrogenase can accept them. Further, the slowest step that takes place in nitrogenase once the electrons are taken up was identified. Finally, by capitalizing on fundamental knowledge, a biohybrid system was designed to grow nitrogen-fixing bacteria in association with electrodes for light-driven production of fixed nitrogen that has potential to be used as a fertilizer for plant growth. Gaining an in-depth understanding of nitrogenase provides insight into one of the most challenging biological reactions, and the newfound knowledge may be a catalyst in developing more efficient systems for sustainable ammonia production.

A greenhouse study was conducted involving interactive effects on bean yield of three levels of irrigation water salinity, three frequencies of irrigation water app li cation, two level s of nitrogen, and seven levels of phosphorus fertilization to determine if the effects of adverse saline conditions or water stress could be overcome by fertility and / or saline water management. Dry matter and bean yields were reduced with decreasing irrigation frequency (2 days to B days} and increasing salinity of irrigation water (0.5 mmho/cm to 8.0 mmho/cm). At low salinity levels (0 .5 mmho/cm) dry matter production and the number of pods per pot were increased with the addition of nitrogen in the irrigation water (at the rate of 112 kg N/ha). The application of banded fertilizer phosphorus helped beans overcome the effects of saline conditions and water stress and resulted in increased yield. The application of liquid 10-34-0 fertilizer produced a statistically significant increase in all yield parameters at every level of salinity, and every irrigation frequency studied. Nitrogen fixation was found to decrease as irrigation water salinity increased. The application of 0-50-40 as a fertilizer band treatment increased nitrogen fixation in t he presence of added nitrogen, due probably to the presence of potassium.

A greehhouse study was conducted to assess the interactive effects of three irrigation water salinity levels (1 . 0, 3. 0, and 9 . 0 mmho/cm) and three quantities of water app lied per irrigation (120 , 240, 360 ml) on plant growth and nitrogen fixation by alfalfa (Medicago sativa L. cv. Resistador). Harvest dates corresponded t o 10, 30, and 50 days after the initiation of salt and water treatments which were started after nodulation had been established in young plants. Alfalfa top growth was limited by both salt and water s tresses. Irrigation water s alinity had a grea t er effec t on top growt h than root grm;th while root distribution was unaffected by either the quant i ty of water app l ied or by water salinity. The effects of salinity on pl ant growth were reduced in the presence of l imi ting moisture. The specific nodul e act ivi t y (mmol c2H4/hr/g) of water s tressed alfalfa pl ants was enhanced by increasing the quantity of wat er applied a t each irriga t ion and was adversely effected by increased irrigation water salinity . In contrast, both nodulation and nodule growth were insensitive t o salt stress and sensi tive only to severe mo i s ture s tress . Alfalfa plants continued to exhibit acetylene reducing capacity at the third harvest even under severe moisture and salt stress. The species apparently continues to fix nitrogen even though environmental stress is quite substantial.

The investigations described in this paper are an attempt to further define and quantify the interrelationship of nitrogen fixation and iron nutritional physiology in these two species. Chapter II will present and compare data on nutritional ratios of field collected Trichodesmium colonies and laboratory Anabaena cultures, with the intent of examining possible correlations between observed iron levels and protein nitrogen and chlorophyll concentrations, as well as nitrogen fixation rates. Chapter Ill is an examination of nitrogen fixation and siderophore production in Anabaena with emphasis on the possible implications of hypothesized synergistic effects of these two physiological capabilities on cyanobacterial dominance and bloom formation. Chapter IV will deal with the possibility of gratuitous manganese repression of Anabaena siderophore production in the manner described by Hantke (1987) for regulation of siderophore production in E. coli. The Conclusions chapter (Chapter V) will present a discussion of the results of these experiments in the context of current problems in cyanobacterial physiology, ecology and evolution. It is hoped that a significant contribution can be made to our understanding of the related problems of cyanobacterial dominance in freshwater ecosysytems and the scarcity of cyanobacterial nitrogen fixation in marine ecosystems.

In the recent years, a huge success has been accomplished in prediction of human eye fixations. Several studies employed deep learning to achieve high accuracy of prediction of human eye fixations. These studies rely on pre-trained deep learning for object classification. They exploit deep learning either as a transfer-learning problem, or the weights of the pre-trained network as the initialization to learn a saliency model. The utilization of such pre-trained neural networks is due to the relatively small datasets of human fixations available to train a deep learning model. Another relatively less prioritized problem is amount of computation of such deep learning models requires expensive hardware. In this dissertation, two approaches are proposed to tackle abovementioned problems. The first approach, codenamed DeepFeat, incorporates the deep features of convolutional neural networks pre-trained for object and scene classifications. This approach is the first approach that uses deep features without further learning. Performance of the DeepFeat model is extensively evaluated over a variety of datasets using a variety of implementations. The second approach is a deep learning saliency model, codenamed ClassNet. Two main differences separate the ClassNet from other deep learning saliency models. The ClassNet model is the only deep learning saliency model that learns its weights from scratch. In addition, the ClassNet saliency model treats prediction of human fixation as a classification problem, while other deep learning saliency models treat the human fixation prediction as a regression problem or as a classification of a regression problem.

Switchgrass (Panicum virgatum L.) has become an important bioenergy crop. Warm, winter temperatures in the southeastern USA allow for fall establishment and winter growth of cool-season legumes that may provide nitrogen to the spring perenniating crop of switchgrass. Data indicates variation due to year and location, but hairy vetch plots provided a greater nitrogen percentage in the subsequent biomass production of switchgrass. In 2011, switchgrass fertilized with 56 kg ha-1 N was greater than the control and in 2012 it was greater than the 28 kg ha-1 N treatment. Variation around the means prevented clear separation among other treatments. The data also showed that hairy vetch had the greatest volunteer frequency and cover percentage throughout the year. Data from the Dairy Farm showed no differences in yields due to a lack of field management the previous years and only ball clover increased its coverage over time.

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