Free living amoebae are unicellular protozoan that are ubiquitous in various environments. They mainly feed on bacteria through phagocytosis, and kill them in phagosome, which is a harsh acidic environment that contains different antimicrobial weapons. Amoebae grazing has been suggested to be one of the major forces that shaping bacterial abundance and diversity. However, some bacteria have developed strategies to survive phagocytosis by free-living amoebae and are able to exploit host cell resources. Below we try to summarize our current knowledge on the diverse mechanisms that are used by intracellular pathogens to overcome amoebae defenses.

The most obvious strategy is to escape from the phagosome so that intracellular pathogens can avoid amoebae killing. Because phagosome is generally viewed as a harsh environment where ingested bacteria are confronted with acidification, oxidative burst, nutrient deprivation, and various antimicrobial small molecules. For instance, some members of the genus Mycobacterium, such as Mycobacterium marinum and M. tuberculosis, have evolved the ability to escape from phagosome into the host cytosol. This process requires the mycobaterial type VII secretion system ESX-1. In addition, both M. marinum and M. tuberculosis can be ejected from the cell through an F-actin structure ejectosome to spread cell to cell [1,2]. In general, cytosol is considered as permissive for bacterial growth, as it provides nutrients and is protected from host immune killing [3]. Therefore it is an ideal place for bacteria to thrive after escaping from phagosome. However, some intracellular pathogens can invade more unusual intracellular niches such as the eukaryotic nucleus. This includes in the free living amoebae Naegleria clarki [4] and more recently in another amoeba strain Hartmannella sp. [5]. These so called intranuclear bacteria are relatively rare and current studies suggest an independent evolutionary origin of an intranuclear life style. Taken together, after escaping intracellular bacteria can live in either cytosol or nucleus.

The second strategy is to stay within the phagosomal vacuole, but subvert its antimicrobial mechanisms. These include preventing phagosome-lysosome fusion, modulating phagosomal pH, damaging phagosomal membranes, and/or quenching oxidative bursts [6]. Intracellular pathogens can utilize a combination of these approaches. For instance, Legionella pneumophila has evolved a complex system which allows the bacteria to hijack the phagocytic vacuole [7]. It can evade the endocytic pathway and the subsequent phagosome-lysosome fusion, delays its acidification and establishes a safe intracellular niche called Legionella containing vacuole (LCV), which allows intracellular replication [7,8]. Further studies suggest that L. pneumophila uses the Icm/Dot type IV secretion system (T4SS) and the Lsp type II secretion system (T2SS) to avoid killing and exploit host resources [7,9]. There are plenty of other bacteria using similar strategies [10]. However, a very special case is that some intracellular pathogens can exploit the complex cycle of the social amoeba. In the amoeba farming symbiosis, our lab group has found that some wild Dictyostelium discoideum clones stably associate with different bacterial partners and use them as food and weapons [11-14]. These clones are called farmers because they can seed and harvest their crops in new environments [14]. In addition, two clades of inedible Burkholderia bacteria have been found to induce farming, causing the amoeba host to carry them, along with edible crop bacteria [11]. Another recent case shows that Bordetella bronchiseptica can also exploit the complex life cycle of D. discoideum. Interestingly, B. bronchiseptica resides outside the D. discoideum spores, while the carried Burkholderia localize both inside and outside of spores, indicating these two bacteria have different exit strategies.

Overall, the majority of intracellular pathogens occupy phagosomal vacuole, while only some are able to escape the phagosome [6]. This is possibly due to the fact that specialized mechanisms are needed to escape from phagosome [3,6]. There is no clear relationship between the type of survival strategies and whether the microbe is an obligate or facultative intracellular pathogen [6].

References 1. Hagedorn M, Rohde KH, Russell DG, Soldati T (2009) Infection by Tubercular Mycobacteria Is Spread by Nonlytic Ejection from Their Amoeba Hosts. Science 323: 1729-1733.

2. Gerstenmaier L, Pilla R, Herrmann L, Herrmann H, Prado M, et al. (2015) The autophagic machinery ensures nonlytic transmission of mycobacteria. Proceedings of the National Academy of Sciences of the United States of America 112: E687-E692.

3. Ray K, Marteyn B, Sansonetti PJ, Tang CM (2009) Life on the inside: the intracellular lifestyle of cytosolic bacteria. Nature Reviews Microbiology 7: 333-340.

4. Schulz F, Horn M (2015) Intranuclear bacteria: inside the cellular control center of eukaryotes. Trends in Cell Biology 25: 339-346.

5. Schulz F, Lagkouvardos I, Wascher F, Aistleitner K, Kostanjsek R, et al. (2014) Life in an unusual intracellular niche: a bacterial symbiont infecting the nucleus of amoebae. ISME Journal 8: 1634-1644.

6. Casadevall A (2008) Evolution of Intracellular Pathogens. Annual Review of Microbiology 62: 19-33.

7. Hoffmann C, Harrison CF, Hilbi H (2014) The natural alternative: protozoa as cellular models for Legionella infection. Cellular Microbiology 16: 15-26.

8. Escoll P, Rolando M, Gomez-Valero L, Buchrieser C (2013) From amoeba to macrophages: exploring the molecular mechanisms of Legionella pneumophila infection in both hosts. Curr Top Microbiol Immunol 376: 1-34.

9. Hubber A, Kubori T, Nagai H (2014) Modulation of the Ubiquitination Machinery by Legionella. Molecular Mechanisms in Legionella Pathogenesis 376: 227-247.

10. Steinert M (2011) Pathogen-host interactions in Dictyostelium, Legionella, Mycobacterium and other pathogens. Seminars in Cell

## Genetic Algorithm Research Proposal

System Optimization The main concern is to find the optimum value for each design parameter for each prediction period for a total simulation time of 12 hours. The simulation is performed on the selected system based on the optimization timeframe with an acceptable accuracy and the optimization process is applied for a prediction period of one. The value of a single design parameter and internal loads are fixed during a prediction period and may vary from one prediction period to another.

Genetic Algorithm Modeling the liquid desiccant system with the CC/DV system is complex task with multi-variables involved, several equations are coupled and indirect relations between different parameters are present. Since several non-linear equations are solved, it is advised to use a revolutionary derivative free optimization tool that follows the direct search technique. The simplest optimization tool that could be used for the proposed case is the genetic algorithm optimization tool because it is derivative free, based on numerical analysis, and is somehow efficient if compared with other derivative based optimization schemes. Moreover, it fetches the global minimum of a specific function.

Our choice of using a derivative free algorithm to solve the optimization problem is implemented by the evolutionary genetic algorithm. Genetic algorithms are adaptive methods which may be used to solve search and optimization problems, and are based on the genetic process of biological organisms. Genetic algorithms are growing more and more popular and extending from simple design optimization to online process control. The power of the genetic algorithm arises from its robustness, being acceptably good in finding the near optimum solution and being relatively quick [1]. An efficient optimization technique uses two techniques to find the optimal solution, exploration and exploitation, and this is what genetic algorithm does.

The Genetic Algorithm terminology

The algorithm starts by seeding a set of trial combinations of the variables to be optimized and calculating the numerical value of the objective function for each combination selected. This set is called the “Initial Population”.

The set of numerical values calculated for the objective function from the first trial, is then evaluated according the “Fitness Criteria”. The fitness criteria can be defined as the condition for the objective function numerical value to be better convenient than its pears.

Based on their fitness, some combinations in the previously seeded set are chosen to be “Parents”. Parents then undergo either “Crossover” or “Mutation” procedure to produce “Children”. Most fitted parents simply jump to the next generated population without any change; such parents are referred as “Elite”.

The current population is replaced by children from the next population.

Elite children are the individuals in the current generation with the best fitness values. These individuals automatically survive to the next generation. Crossover children are created by combining the vectors of a pair of parents. Mutation children are created by introducing random changes, or mutations, to a single parent.

The algorithm stops when the “Tolerance” in the objective function values between two generations is less than a certain set error value, or when the maximum number of “Generations” is exceeded, or by any other defined “Stopping Criteria”.

For the optimized control strategy used for the chilled ceiling, displacement ventilation system the variables of the chilled ceiling and displacement ventilation are varied; this variation leads to a minimal optimal cost that results in the minimum cost that could be attained in the system.

Referring to the system figure and considering the optimal control strategy, the variables that may be used for cost optimization are:

The desiccant temperature at the inlet of membrane().

The supply air temperature().

The supply air mass flow rate().

Equation Chapter 6 Section 3Each variable in the optimization routine has a lower and an upper bound. These bounds define the interval where the genetic algorithm searches for the optimal cost and are based on physical considerations. The bounds for the different variables according to ASHRAE’s recommendations are:

The supply air temperature is considered to vary between 17 and 23 °C.

The supply air mass flow rate is considered to vary between 0.08 and 0.26 kg/s.

Optimization Constraints There are several non-linear constraints that are applicable to the system. These constraints are related to thermal comfort issues, condensation inside the room and physical constraints. The constraints may be redefined in the following list

The Percent People Dissatisfied inside the occupied zone is less than 10%. This condition is required for the human thermal comfort. The closer the PPD is to zero, it is assumed that the occupants inside the room would be more comfortable noting that the smallest percent people dissatisfaction is 5%.

The temperature gradient shall not be greater than 2.5 K/m or 2.5 °C/m. This condition is required so that there would not be any large gradients in the human body. Large gradients cause thermal discomfort for living beings.

The stratification height inside the room is greater than 1 m. This condition is required so that the stratified air does not mix with the breathing zone.

The relative humidity inside the occupied zone is greater than 56% and less than 76%.

The fitness function:

To be able to enhance the speed of the genetic algorithm, the electrical cost function and constraints are combined in a single cost function by using penalty functions, thus the fitness cost function may be written as:

The coefficients , , , , and in the above function are the weight factors for their related penalty costs. The weight factors values are set according to the system parameter. For the current system,‘s are set to unity.

electrical cost

The objective function that is to be optimized is the total operational cost of the system; this cost may be divided into:

The cost of running the chiller.

The cost of running the pump.

The cost of running the fan.

Note that in this work the cost is given in units of KW.

Chiller Cost

The chiller is the main energy consuming component in our system .The chiller cost is expressed in terms of the part load ratio. The part load ratio is defined as the ratio of the current load on the chiller divided by the design load that the chiller could handle. Mathematically, the part load ratio is found from the equation

The coefficient of performance of the chiller is correlated to the load equation by using the following correlation:

The cost of the chiller is calculated by using the following equation

Fan cost

The fan cost is directly related to the air mass flow rate by using the following equation:

Pump cost

The pump cost is related to the pump head, liquid desiccant mass flow rate, and the efficiency of the pump. The power of the pump is evaluated by multiplying the pressure difference by the volumetric flow rate and dividing the result by the pump efficiency; mathematically the pump cost equation may be written as

Note that the pump cost is not included in the cost function, since the desiccant mass flow rate is costant.

Therefore the total energy consumed can be expressed by the following equation:

The Constraints Cost Functions

The cost function for the constraints may be written such that they could be incorporated into the online cost function in a simple manner. These constraints are related to their respective threshold values such that when the constraints are violated, the fitness function would have a very large value.

For the predicted person dissatisfied, the cost function

The relative humidity cost function may be bounded from the upper side by using the relation

The stratification height cost is bounded to be larger than 1m, thus the stratification height cost is

The temperature gradient is to bounded to be less than 2.5 K/m, thus the temperature gradient cost function may be written as

The exponential term helps to penalize the cost function when-ever the thermal comfort level of occupants in the room TH decreases below the minimum set value THmin. This will increase the value of the cost function dramatically and the set of variables at hand is rejected. The integration of the constraint terms within the objective function expression and the use of the exponential form to control the constraints’ cost were implemented by Keblawi et al. [13] and Hammoud et al. [4].

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