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Mathematical Models for Microbial Kinetics in Solid-State Fermentation: A Review
Davood Mazaheri
Seyed Abbas Shojaosadati
Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, IR Iran
10.5812/ijb.9426
Context:In this review, we discuss empirical and stoichiometric models, which have been developed recently in SSF processes and the influence of environmental conditions on the variables of these models. Additionally, new studies on modeling of product formation are also mentioned. Evidence Acquisition:Solid-state fermentation (SSF) is recognized as a cheap process for producing many valuable products like industrial enzymes and bioethanol. To develop, optimize, and scale-up this process, mathematical models are required. In this review, we collected all the papers regarding microbial growth and product formation modeling in SSF. The pros and cons of each model and confirmation with experimental data were also discussed. We discussed here the simple empirical growth kinetics models and the effect of environmental conditions on these models parameters, stoichiometric models and product formation models. Results:Simple empirical models are used widely in the kinetic modeling of SSF processes due to their simplicity and ease of use. However, more studies should be done in this field to make them more accurate, especially; the effect of environmental conditions, like temperature and moisture, on key variables of the model must be considered. Robust modeling methods, like stoichiometric models, are in their early stages in SSF processes and require more studies. Developing models in which transport phenomena models are coupled with the growth kinetics models can help better SSF bioreactor designing. On the other hand, to use SSF for producing valuable products, product formation models, which are not developed well in SSF processes, are necessary. Conclusions:To use SSF for producing valuable metabolites in large scales, more attention is required for modeling the SSF processes, especially for product formation models and using modern methods like stoichiometric models.
Solid-State Fermentation
Growth Kinetics
Product Formation Model
Solid-state fermentation (SSF) is a kind of fermentation, in which microorganisms grow on solid material in the absence (or near absence) of free water; however, sufficient moisture should exist in the solid material to support the growth and metabolism of the microorganisms (1). In this process the solid material may act as carbon/energy source or as an inert support (2). The inert support may also provide enough surface for microbial growth (3). SSF has some advantages over submerged fermentation such as cheaper substrate (usually agricultural wastes), lower energy requirements and investment cost, better volumetric yield and less wastewater production (which makes the downstream processes easier) (2, 4). Fungi and other microorganisms exhibit different physiologies in SSF that has been called physiology of solid medium. As a result of these different physiologies, enzymes and secondary metabolites are often produced at higher yields in SSF (5). According to studies on lovastatin production in SSF, the specific environment of SSF may induce higher transcription of the specific transcription factor.
On the other hand, some drawbacks cause difficulties in application and scale-up of SSF processes. For example, due to low heat conductivity of solid particles and lack of free water, heat removal is difficult in SSF processes, especially in large scales (6, 7). Additionally, due to the solid nature of the substrate, mixing is not effective in SSF. Thus, significant water and temperature gradients may appear in the solid bed. As a result of this heterogeneous composition of solid substrate, monitoring and controlling of the process parameters like temperature, moisture, pH and biomass content is difficult in SSF processes (2, 4, 8).
The application of SSF in industrial processes was held back due to difficulties in monitoring and controlling different involved variables. To study the growth kinetics of the microorganisms and mathematically model the heat and mass transfer in SSF, measurement of biomass content, substrate and produced metabolite concentrations and other process parameters are required (8). Unfortunately, direct measurement of biomass content is difficult in SSF, because separating the microorganism from solid particles is difficult, especially for fermentations involving filamentous fungi, because the fungal hyphae penetrate into the substrate (9). Therefore, many researchers used indirect measurement methods such as production of primary metabolites (10), carbon dioxide and oxygen metabolism (11-15) and extracellular enzymes (16), protein content of biomass (17), variation in the electrical conductivity between biomass and solid substrate (18), changes in the color of the fermentation medium (19), measuring other compounds such as ergosterol, glocusamine (20), nucleic acids (21), quantification of antibody reactivity in the mycelium cell wall via enzyme-linked immunosorbent assays (22), using FT-NIR (Fourier transform near-infrared) spectroscopy, and support vector data description (23).
Like other processes, SSF requires mathematical models for optimization and scale-up. However, due to the above-mentioned reasons, modeling of the SSF processes is quite complicated, and many of the proposed models in SSF have been simple empirical models that are under developed (8).
As Mitchell et al. mentioned in their review article (8), SSF mathematical models consist of two sub-models: (1) models that use transport phenomena relations to describe the mass and heat transfer within and between various phases of the process and (2) models that describe the growth kinetic of the microorganisms. For evaluating the growth kinetic models, simple empirical equations or mechanistic models can be used. The latter is more difficult and is considered as the intraparticle phenomena that occur at the level of individual particles (8, 24). Since there were no recent developments in this field, we do not consider such models in this review, and instead, we review the kinetic sub-models, which have been developed recently.
In recent years, some new models were developed based on the interactions within cells. These models (Stoichiometric models) considered of the microbial metabolism, metabolic pathways and metabolism regulation (25). In these models, steady-state mass balances were written within the cell, as a result, extracellular phenomena like biomass formation rate, substrate uptake and product formation rates could be coupled with intracellular carbon and energy fluxes. This kind of modeling is rarely used in SSF processes and we have also considered these models in this review.
SSF was reported to be a favorable process for the production of enzymes. Using agricultural wastes as solid substrates makes SSF a cost-effective process for production of different metabolites. In addition to enzymes, there is an increasing interest for production of bioethanol using SSF as an economic process in recent years. To scale-up these researches to the industrial scale, we need product formation models. Like the biomass measurement, determination of the product formed in the SSF process is also complex. Consequently, many researchers have only reported the production of a specific metabolite in the SSF process and have not included product formation rate in their model. As a result, we can observe the lack of product formation models for the SSF processes. Here, we also mention some new product formation models.
The current work reviews both simple empirical microbial growth rate models that have been used in most bioreactor models and recently developed stoichiometric models. Additionally, some new models on product formation are also discussed.
2. Evidence Acquisition
2.1. Empirical Growth Kinetics Models
One of the most important models is growth kinetic models which are essential for controlling and modeling processes (8). To develop a suitable mathematical model for describing the SSF processes, a growth kinetic model should be developed first. In SSF processes, heterogeneous solid substrates and low heat conductivity of solid particles will cause significant gradients of temperature, moisture content, O2, and other nutrient concentrations (1, 8, 26). Of course, mathematical modeling of such systems is more complicated and requires partial differential equations. Although mechanistic models can provide us with more accurate results (if developed well), the difficulties involved in this approach lead many researchers to use simple empirical models.
Linear, exponential, logistic, and two-phase models are the most important empirical growth rate models in SSF processes.
2.1.1. Logistic Growth Kinetics Model
The logistic model was first used by Okazaki et al. in SSF (10). This empirical model is used more frequently than other empirical models for studying the growth kinetics of SSF. The logistic model represents the growth limits and does not require transport phenomena relations (27). The logistic model is based on the fact that available surface area is limited in the SSF process and the rate of biomass growth depends on maximal biomass, Xm (8). The other assumption of this model is that specific growth rate during the initial logarithmic growth phase, µm, is not dependent on substrate concentration. The differentiated and integrated forms of logistic model are as follows:
where X0 is the initial microbial biomass.
Growth kinetic models can be used to develop a bioreactor model in order to describe the environmental condition (like temperature, moisture, etc.) in the bioreactor as a function of time, and predict the changes in the behavior of microorganism in these conditions. Obviously, the two most important environmental variables in SSF bioreactor models are temperature and moisture content of the solid medium. Due to the heat generated from respiration of microorganisms, the temperature in the bioreactor bed increases. On the other hand, due to the lack of free water in the bioreactor bed and low heat conductivity of solid particles, this generated metabolic heat could not be removed from the bioreactor bed, and consequently, temperature gradients appear in the process. Mixing is rarely used in SSF for removing the gradients, because most of the fungi and solid particles cannot resist against the shear forces that result from mixing. Consequently, microorganisms' growth is a non-isothermal process in SSF bioreactors (28).
In addition to temperature, moisture gradients should also be considered in modeling SSF bioreactors. For removing the generated heat from the bed, forced aeration is usually used. However, low amounts of heat is removed from the bed by heat convection, and instead, evaporation has the main role in this regard (29). This phenomenon leads to large moisture losses, and thus, moisture gradients appear in the moist bed of SSF bioreactors. Therefore, the effect of moisture content and temperature on microbial growth should be considered in the models.
2.1.1.1. Effect of Environmental Conditions on Microbial Growth
The three parameters of the logistic model, x0, xm and µm may have dependency on temperature. The amount of inoculum at the beginning of the fermentation, x0, is not temperature-dependent, but the lag phase period is temperature-dependent. Many researchers have investigated these dependencies (30). Many equations were developed for describing the effect of temperature on µm, like Esener (31), Rotkowsky model (32) and Arrhenius equation (30, 33) (Table 1). For the effect of temperature on the maximum amount of biomass xm, polynomial equation (34) and an extended Ratkowsky model can be used (Table 1).
Another model for the kinetics of microbial growth that contains the influence of temperature variations in SSF was developed by Dalsenter et al. (38). In this model, they used the level of an essential component within the biomass, F, in the logistic equation. It was assumed that the level of this component controls microbial growth. This component is dimensionless and varies between zero and one. When F=1, there are normal levels of the component in healthy cells. It is assumed that this component is an auto-synthesized parameter and the rate of its auto-synthesis is a power-law version of the logistic equation. On the other hand, its thermal denaturation is assumed to be a first-order process (38). Consequently, the equation for the level of the component in the cell (F) is:
Where n is the exponent in the power-law version of the logistic equation; t time, kS and kD are the rate coefficients of the synthesis and denaturation reactions, respectively. The temperature-dependency of rate coefficients of the synthesis and denaturation reactions of this component (kS and kD) are stated in the Arrhenius equation (38).
Consequently, by adding this component to the equation, the logistic model for the growth of the microorganism becomes:
According to Eq. 3, by changing the temperature, the specific growth rate constant, µ, for a fully healthy cell does not change, instead, the level of essential component, F, can change. Therefore, temperature changes can affect the growth rate by affecting the level of essential component, F (8, 38). To prove the model by experimental results, Dalsenter et al. (38) compared their model results to the literature data for the growth of Rhizopus oligosporus. They observed that the model predictions have reasonable agreement with all the experimental results. This model by considering the effect of temperature, made the logistic model more accurate, but more attempts should be done for better understanding of the effect of temperature and moisture on the parameters.
In all of the models mentioned above, only the present values of environmental conditions were considered to affect growth. Subsequently, some researchers suggested that the growth conditions experienced in the past should also be considered in the models (8). For instance, Bovill et al. developed one such model, in which, the parameter Q, was added to the logistic model for modifying the equation (8, 39).
This parameter (Q) represents the physiological state of the cell. The past environmental conditions could affect parameter Q, in result; the past conditions could affect the current microbial rate through this parameter. The effect of environmental condition on the physiological parameter can be expressed by a differential equation. Although it is possible to assume that Q might be the intracellular enzymes, much more studies are required to accurately define the nature of this parameter. Predictions of this model reasonably agreed with experimental results obtained from the growth of L. monocytogenes in pasteurized milk and chicken liver pate and Salmonella in pasteurized milk and minced chicken. The deviations of predictions from measurements were mainly due to less accurate lag predictions than growth rate predictions, and inhibition by the natural flora (39).
In another study, Fanaei and Vaziri developed these two previously-mentioned ideas for the growth kinetics of A. niger on wheat bran (40). They took into account both the impact of past temperature and current temperature on growth rate. Their kinetic model equation is as follows:
where X, Xm and ϕ are the biomass concentration, the maximum biomass concentration and level of physiological factor, respectively. In fact, the level of physiological factor (ϕ) is a dimensionless value and is responsible for its own synthesis similar to the variable F in Dalsenter's model. Thermal denaturation and rate of auto-synthesis of this factor can be expressed in the same way as variable F:
where α is the exponent in the power-law version of the logistic equation, and γS and γD are the rate coefficients of the synthesis and denaturation reactions, respectively. These rate coefficients were stated as functions of temperature with the Arrhenius equation.
This empirical equation was used to express the effect of temperature on the specific growth rate:
Where s is the sensitivity of the specific growth rate to increases in temperature. Tmax and Topt are the maximum and optimum temperatures for growth, respectively, and µopt is the optimum specific growth rate constant. When ϕ =1, it means that there are normal levels of the physiological factor within a fully healthy cell. Surprisingly, even when there is no growth in the system, the physiological factor can increase. The physiological factor is denatured when temperature rises, but it takes time for this phenomenon to happen. On the other hand, it also takes time for the physiological factor to be re-established when the temperature falls from harmful values to optimum values. Therefore, the role of the factor ϕ is to postpone the effects of temperature changes (40). Since the physiological factor has an important role on determination of growth rate, and high temperatures can denature this factor, physiological factor could be possibly intracellular enzymes or ribosomes (8, 40). The predicted results of this model were compared with data from the literature for the growth of Aspergillus niger on wheat bran and agreed reasonably with experimental results (40). Although analyses have been done for a particular microorganism and substrate, this modeling approach can be used for other microorganisms and substrates if the appropriate parameters are available.
In addition to temperature, another important environmental condition which has not been extensively studied in SSF modeling is moisture content (28). In the literatures, the effects of temperature and moisture on microbial growth were reported separately (30-37, 41-43). Recently, Hamidi-Esfahani et al. (28) studied the simultaneous influence of temperature and moisture on the growth of A. niger on wheat bran . To achieve this aim, they used the logistic model for calculating the growth parameters at different temperatures and moistures. Xm and µm were calculated by fitting the logistic equation to the experimental data at different initial moisture contents and temperatures. According to their results, µm depends on both temperature and moisture. Meanwhile, Xm is not temperature-dependent and only depends on moisture content. They used the Ratkowsky equation for temperature dependency and polynomial equation for moisture content dependency. They proposed the following equation for the effect of temperature and moisture content on µm (28):
Where Y is the moisture content, a1 and a2 are constants of correlation of specific growth rate and temperature and b0, b1 and b2 are constants of correlation of specific growth rate and water content, respectively. Furthermore, for Xm, which only depends on moisture content, the following quadratic polynomial can express the influence of moisture content on Xm:
Their results had a good agreement with experimental results of A. niger growth on wheat bran (28). Since this model is one of the rare models, which considers both the temperature and moisture in the model, it could be a promising and useful model in practical studies and can be used in heat and mass transfer studies of SSF processes. However, this model is a modification of the logistic model and still suffers from the disadvantages of the logistic model.
2.1.2. Two-Phase Kinetic Models
Actually, the real growth profile in SSF consists of two periods: a short period of rapid acceleration followed by a long period of slow growth deceleration. However, the logistic model does not predict such behavior and it is symmetric around the inflection point (8, 28, 44). This means that in the logistic model the acceleration and deceleration rates are the same. Consequently, the logistic model cannot describe the entire growth profile accurately, especially during early stages of growth.
We can explain the sudden growth deceleration at the end of the exponential phase by considering the microscopic phenomena. When hyphae from different expanding colonies meet each other, their extending tips may not remain active. This sudden decrease in the number of active hyphae tips can lead to sudden growth deceleration at the end of the exponential phase. Mitchell et al. proposed that the accumulation of inhibitory metabolites, draining of utilizable nutrients and the beginning of oxygen limitation might be the reasons for the deceleration of fungal growth in SSF.
These disadvantages led to the development of models with greater accuracy. One such model was the two-phase model developed by Ikasari and Mitchell (45). In this model, the growth curve has two phases: (1) an exponential phase, (2) a deceleration phase, and each phase has its own kinetic equation. In the first phase, an exponential equation, Eq. 11, was used and for the deceleration phase, Equitation 12 was developed, assuming first-order decay in the number of tips. In Equitation 12, the specific growth rate during the deceleration phase appears in square brackets and decreases as a result of two factors. One of these two factors is parameter L, which represents the ratio of the specific growth rate at the start of the deceleration phase to the specific growth rate during the previous exponential phase. The other factor is parameter k, a first-order rate constant of the exponential term in Equitation 12. This exponential term describes an exponential decay in specific growth rate throughout the deceleration phase (45):
ta is the transition time between the two phases, when the exponential phase finishes and the deceleration phase begins (45).
However, this model still suffers from some weak points, where the exponential phase is short and biomass concentration is low in this period. Consequently, a few data points could be gathered during this phase, therefore, it makes the determination of the parameters of the exponential phase difficult to determine and generates large error. Additionally, due to the use of specific growth rate of the exponential phase (µ) in the analysis of deceleration phase, errors could also be found in the deceleration phase (8, 44). The model was used to describe the growth profile of Rhizopus oligosporus in membrane cultures and had a reasonably good agreement (45).
The two-phase growth model is very simple to use. However, the simplicity of the model does limit its applicability. To have a useful model describing bioreactor performance, the model parameters need to be expressed as functions of the key environmental variables, especially temperature.
To improve the two-phase model, Hamidi-Esfahani et al. (44) developed a new two-phase kinetic model. In this new two-phase kinetic model, like the Ikasari-Mitchell model, the first phase is the exponential phase, but in contrast, the second phase is a logistic model. Their model parameters were temperature-dependent and were determined from the oxygen consumption rate (OUR) of A. niger during cultivation on wheat bran. Measuring the OUR has two advantages: first, it has a fast response time and, second, it is directly linked to metabolism of the microorganism (44, 46). The equations for the first phase of this new two-phase model (exponential phase) is as follows:
where rx1, µ1 , x1 and x01 are the growth rate, specific growth rate, biomass concentration and initial biomass concentration in the exponential phase, respectively.
The oxygen consumption rate was expressed with a linear-growth model, which depends on maintenance activity of the fungal biomass:
Where rO1 is the OUR in the exponential model, YOX is the yield coefficient and m is the maintenance coefficient.
At the second phase, the logistic model is used for describing the growth of the microorganism:
Where rx2, µm2, x2 and xm are the growth rate, maximum specific growth rate, biomass concentration and maximum biomass concentration in the logistic model, respectively.
By assuming the same maintenance coefficient for both phases, OUR for the logistic phase can be written as:
For transition from exponential to logistic phase, the following assumption was used instead of ta:
The parameters x01, m, Yox and xmax are assumed to be temperature-independent and the parameters µ1, µm2 and x02 are temperature-dependent. The unknown parameters of the model were calculated from O2 consumption rates at various temperatures. A good correlation between the experimental results and model predictions for the new two-phase kinetic model was reported by Hamidi-Esfahani et al. in their original paper (44).
Comparing the results of the new two-phase model with the logistic model, Hamidi-Esfahani et al. observed that the logistic model does not fit the experimental results (the growth of A. niger on wheat bran) very well (44). The results predicted by the logistic model have a sharper peak than the experimental results. In addition, at the early stages of the growth curve, the results predicted by the logistic model for oxygen uptake are different from the experimental data. Meanwhile, the new two-phase model has better predictions in comparison to the logistic model predictions. Hamidi-Esfahani et al. (44) declared that by using x2 as the value of biomass during the second phase of the growth, they can overcome the limitations of the logistic model.
Hamidi-Esfahani et al. (44) also compared their new two-phase model with Ikasari-Mitchel two-phase model. They observed that the Ikasari-Mitchell predictions are as accurate as the new two-phase model predictions at the early stages of growth. However, after the first phase finishes, differences between the predictions of the two models can be observed at the second phase of growth. The Ikasari-Mitchell predictions had a positive deviation at the deceleration phase, especially at low temperatures. One of the reasons for the better predictions of the new two-phase model is the assumption made for the transition from the first phase to the second phase. In the new two-phase model, the fungal growth rate is used as a condition for transition from the exponential phase to the deceleration phase, instead of ta, and it seems to be a better and more accurate assumption. Furthermore, Hamidi-Esfahani et al. (44) considered the temperature dependencies of the parameters in their model and this makes their model more accurate than the Ikasari-Mitchell model. They believed that the new two-phase kinetic model combined with the mass and energy balances of the packed bed bioreactors model can predict microorganisms' growth and other parameters such as temperature and moisture content adequately. Furthermore, the new two-phase model also precisely describes the growth rate of Aspergillus oryzae on wheat.
2.2. Models Based on the Metabolic Pathways
As previously mentioned in the introduction, modeling microbial processes based on the metabolic pathways is one of the most advanced approaches. Such models focus on metabolic pathways and metabolic regulations. In this type of modeling, intracellular interactions and extracellular phenomena are considered (47). This robust approach has been used for studying phenomena that occur in submerged fermentations (48, 49), but there are rare reports about the application of this method for modeling SSF processes (25). This may be due to the heterogeneity of the SSF medium that makes it difficult to measure the required data for modeling.
Recently, Mazutti et al. (25) investigated the growth of Kluyveromyces marxianus in SSF within a packed-bed bioreactor. Their study is one of the first reports, which uses metabolic pathway models in SSF processes. They developed a mathematical model based on an artificial neural network (ANN) to predict the microbial rates as a function of fermentation time, initial total reducing sugar concentration, and inlet and outlet temperatures. The models responses were the cell mass, metabolic heat, CO2, metabolic water and ethanol production, and the total reducing sugar and oxygen consumptions.
They considered this general stoichiometry for aerobic microbial growth of Kluyveromyces marxianus with ethanol formation (25):
where α, β, λ, δ and σ are stoichiometric coefficients on the basis of the C-mol of biomass. The C, H, O, and N balances and the experimental measurements of CO2 and total reducing sugars could compute these coefficients. In this stoichiometry, the metabolism of yeast was considered aerobic and it was assumed that oxygen is not limited inside the bioreactor. Subsequently, by measuring the CO2 in the outlet air stream of the bioreactor, the oxygen concentration in the outlet air stream, the global metabolic water and ethanol production in the moist solid bed of the bioreactor could be calculated according to the above stoichiometry (25). Mazutti et al. used the following equation (previously developed by Brand et al. (50)) for calculating microbial growth expressed in terms of mass of cells considering oxygen uptake rate:
The model, which was developed in this way, was reported to show good performance during both training and validation steps of the ANN procedure. Mazutti et al. (25) expressed that this approach was capable of correlating complex metabolic rates involved in the fermentation of microorganisms in SSF processes. The results of the model can properly predict the growth of Kluyveromyces marxianus.
Mazutti et al. used artificial neural network (ANN) to develop their model. The artificial neural network can be used to model complex phenomena like microbial growth in SSF processes. The ANN method is capable of describing multivariable systems, especially highly non-linear dynamic systems like SSF processes. ANN shows the real capabilities of a real system: parallel processing, classification, learning and pattern recognition (51). Using these capabilities, ANNs can detect complicated relations between inputs and outputs, understand the patterns and re-create the behavior of the system after a training step with gathered data. This technique only uses knowledge obtained from experimental inputs and outputs of the system to predict performance without any background information about the details of phenomena happening during the process and solves complex equations ruling the system. In fact, one of the advantages of the ANN method is that no great deal of knowledge about the process under investigation is required. Consequently, the ANN methods are accepted as a modeling method for scientific and industrial applications (25). The capability of learning from experimental data and simplicity of performance are the other advantages of ANNs over other mathematical modeling methods. Additionally, developments in computer sciences and advanced computer programs with high ability to calculate mathematical procedures encourage researchers to use ANN for modeling systems. On the other hand, to obtain good results, neural network requires a large amount of data for training, and of course, it is difficult to obtain such a large quantity of data in some processes, like SSF processes. In ANN steps, the researcher has to choose the proper network parameters, and the selection of network parameters needs experience and knowledge about the process (52). These kinds of modeling, especially in SSF systems, are still at the beginning of their way to become a useful model, but according to their accurate basis and robust computer programs, a brilliant future can be predicted for them. However, using such models is much more difficult than empirical models.
2. 3. Product Formation Models
Growth of a microorganism is accompanied by the consumption of oxygen and nutrients and the production of metabolic heat, water, CO2, and various products. By using a low cost substrate, SSF has gained attention for the production of different products. It has been mentioned that SSF is a very good process for enzyme production (53, 54). Nowadays, many researchers use SSF for producing other valuable products like bioethanol due to its cheap and available substrate. Agricultural products like sweet sorghum (55), sugar beet pomaces (56, 57), mahula flowers (58), carob pods (3, 59), arrowroots (60), and many other low cost materials have been used for bioethanol production in SSF. For optimization and scale-up of such processes, mathematical models for product formation are essential. Because of the difficulties involved in separating the product from the fermentation medium, there are a few product formation models for SSF processes.
One of the common approaches for modeling the formation of products in biological processes is to assume growth-associated and non-growth associated components (8). The general equation for a product (P) is therefore:
Where YPX is the stoichiometric coefficient and mP is the maintenance coefficient. If the product is a secondary metabolite, the changes in both active biomass and limiting substrates should be described (61). In the case of modeling the production of secondary metabolites, the logistic model cannot be used, because as mentioned before, it cannot completely predict the microorganism's behavior during the death phase. In this section, we discuss some of the product formation models.
One of these few product formation models is the simple differential equation model developed by Gelmi et al., used to express the growth and production of a secondary metabolite in SSF under conditions of limited nitrogen (61). Applying mass balances in SSF, they developed a lumped parameter differential equation model, leading to eight differential equations (Eqs. 24-31). As an experimental case, they studied the growth of filamentous fungus Gibberella fujikuroi and the production of gibberellic acid (GA3). In this model, it was assumed that oxygen transfer resistance is negligible; the nitrogen source is the limiting substrate; the carbon source is not limiting and temperature, moisture and model parameters remain constant during cultivation (61).
The balance for total biomass (without lysis) leading to:
Where X is the active biomass. The change in active biomass was described by:
Where kd is the death rate coefficient. The consumption rate of the urea (the nitrogen source) is:
Where U is the concentration of urea, and k is the conversion rate from urea to available nitrogen for the microorganism (NI). NI can be directly converted into active biomass. The change in available nitrogen during fermentation is given by:
Where 0.47 represents the nitrogen content of the urea.
The consumption rate of the carbon source (soluble starch) can be written as:
The production rate of GA3 is proportional to the concentration of active biomass, because it is a secondary metabolite. To represent GA3 degradation, an additional term is required:
These two equations also represent CO2 production and O2 consumption:
After guessing the initial amounts of key parameters, a non-linear optimization routine was used to obtain the least square fit for the model. It was observed that this mathematical model could reproduce measured variables like biomass, urea, starch, CO2, O2 and GA3 (61).
Mass balances have also been used to develop the product formation models. In a recent study, Hashemi et al. used this approach to model different phases of bacterial growth curve and the production of α-amylase by Bacillus sp. in the SSF process (62). They assumed that the changes in total dry fermenting medium weight (W) corresponds to substrate consumption rate (dS/dt), biomass growth rate (dB/dt) and product formation rate (dP/dt).
On the other hand, substrate consumption rate can be explained by three equations: the equation for biomass growth ((dS/dt)g) and the yield coefficient for biomass (Yg), the equation for product formation ((dS/dt)p) and its yield coefficient (Yp) and equation for maintenance ((dS/dt)m):
For the kinetics of α-amylase production, Hashemi et al. used the Luedeking-Piret equation (63), in which the product formation rate depends on both biomass concentration (B) and growth rate (dB/dt):
Where α and β are empirical constants that may vary with fermentation conditions. Since each growth kinetic curve of microorganisms may be divided into three phases (exponential growth, stationary and death), Hashemi et al. presented the dry weight variation into three terms that indicate those three phases:
The final equations for each phase are provided in Table 2. Readers wishing to understand the details of the equations derivation should refer to the original paper (62). According to their results, it can be observed that bacterial growth and the production of α-amylase on wheat bran substrate could successfully be modeled based on variations in solid substrate weight. This model was validated by experimental data collected from a series of batch fermentations. The authors suggested their model for the development of growth, and α-amylase production in SSF processes (62).
Similar to growth kinetic models, stoichiometric models can also be used for product formation models. Mazutti et al. used the same procedure they had used before for modeling the growth kinetics to model inulinase production in SSF packed-bed bioreactor (64).
In general, product formation models in SSF process are not advanced enough to be used on practical and industrial scales, and to be able to use this cost-effective technology for producing different valuable products, more attempts should be done in this field.
Simple empirical models are widely used in SSF, and although some improvements (like two-phase models) were made in these models, there is still much to do in this field. Since there are many experimental difficulties in SSF processes, modeling of this process is improving slowly. Nevertheless, many researchers have attempted to develop advanced models for SSF bioreactors. One of the most important issues that should be studied more in the field of SSF processes modeling is the effect of environmental conditions, especially temperature and moisture, on key variables of the model.
It seems that stoichiometric models, focusing on microbial pathways, can predict the behavior of microorganisms well and can be a good substitution for simple empirical models in SSF processes. These models can be better coupled with heat and mass transfer models compared to empirical models. However, more studies must be done on these kinds of models in SSF.
Using very cheap substrates like agricultural wastes, SSF could be an economical process for producing many valuable metabolites such as industrial enzymes and bioethanol. For this purpose, more robust product formation models are required. So far, difficulties in separating the product from the solid medium prevent the development of product formation models. As a result, more studies should be done on the modeling of products to make the SSF process, an industrial and economical process for producing valuable compounds in the future.
To use SSF for producing valuable metabolites in large scales, more attention is required for modeling the SSF processes, especially for product formation models. Robust modeling methods like stoichiometric models and coupling the kinetic models with heat and mass transfer models should be considered more in SSF bioreactor models.
Developing more accurate models and combining them with mass and energy balance models for better controlling bioreactors, using modern methods like stoichiometric models, focusing on product formation models, and performing more studies on measuring the important parameters of SSF system during the process are the future challenges of SSF modeling.
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8. Mitchell DA, von Meien OF, Krieger N, Dalsenter FDH. A review of recent developments in modeling of microbial growth kinetics and intraparticle phenomena in solid-state fermentation. Biochemical Engineering Journal. 2004;17(1):15-26.
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11. Lareo C, Sposito AF, Bossio AL, Volpe DC. Characterization of growth and sporulation of Mucor bacilliformis in solid state fermentation on an inert support. Enzyme and Microbial Technology. 2006;38(3–4):391-9.
12. Carrizalez V, Rodríguez H, Sardiña I. Determination of the specific growth of molds on semi-solid cultures. Biotechnol Bioeng. 1981;23(2):321-33.
13. May BA, VanderGheynst JS, Rumsey T. The kinetics of Lagenidium giganteum growth in liquid and solid cultures. J Appl Microbiol. 2006;101(4):807-14.
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15. Koutinas AA, Wang R, Webb C. Estimation of fungal growth in complex, heterogeneous culture. Biochemical Engineering Journal. 2003;14(2):93-100.
16. Doelle HW, Mitchell DA, Rolz C. Solid substrate cultivation. Elsevier Applied Science; 1992.
17. S. A-A, G.S. H, M.A. H, M.I. AK, N. S. Indirect Method for Quantification of Cell Biomass During Solid-State Fermentation of Palm Kernel Cake Based on Protein Content. Asian Journal of Scientific Research. 2008;1:385-93.
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22. Dubey AK, Suresh C, Kumar SU, Karanth NG. An enzyme-linked immunosorbent assay for the estimation of fungal biomass during solid-state fermentation. Applied Microbiology and Biotechnology. 1998;50(3):299-302.
23. Jiang H, Liu G, Xiao X, Mei C, Ding Y, Yu S. Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description. Microchemical Journal. 2012;102(0):68-74.
24. Rahardjo YS, Tramper J, Rinzema A. Modeling conversion and transport phenomena in solid-state fermentation: a review and perspectives. Biotechnol Adv. 2006;24(2):161-79.
25. Mazutti MA, Zabot G, Boni G, Skovronski A, de Oliveira D, Di Luccio M, et al. Mathematical modeling of Kluyveromyces marxianus growth in solid-state fermentation using a packed-bed bioreactor. J Ind Microbiol Biotechnol. 2010;37(4):391-400.
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28. Hamidi-Esfahani Z, Shojaosadati SA, Rinzema A. Modelling of simultaneous effect of moisture and temperature on A. niger growth in solid-state fermentation. Biochemical Engineering Journal. 2004;21(3):265-72.
29. Sangsurasak P, Mitchell DA. Validation of a model describing two-dimensional heat transfer during solid-state fermentation in packed bed bioreactors. Biotechnol Bioeng. 1998;60(6):739-49.
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31. Esener AA, Roels JA, Kossen NWF. The influence of temperature on the maximum specific growth rate of Klebsiella pneumoniae. Biotechnol Bioeng. 1981;23(6):1401-5.
32. Ratkowsky DA, Lowry RK, McMeekin TA, Stokes AN, Chandler RE. Model for bacterial culture growth rate throughout the entire biokinetic temperature range. J Bacteriol. 1983;154(3):1222-6.
33. Ikasari L, Mitchell DA, Stuart DM. Response of Rhizopus oligosporus to temporal temperature profiles in a model solid-state fermentation system. Biotechnol Bioeng. 1999;64(6):722-8.
34. Hasan SM, Costa JV, Sanzo AL. Heat transfer simulation of solid state fermentation in a packed-bed bioreactor. Biotechnology Techniques. 1998;12(10):787-91.
35. Mitchell DA, von Meien OF. Mathematical modeling as a tool to investigate the design and operation of the zymotis packed-bed bioreactor for solid-state fermentation. Biotechnol Bioeng. 2000;68(2):127-35.
36. Rajagopalan S, Modak JM. Modeling of heat and mass transfer for solid state fermentation process in tray bioreactor. Bioprocess Engineering. 1995;13(3):161-9.
37. Weber FJ, Oostra J, Tramper J, Rinzema A. Validation of a model for process development and scale-up of packed-bed solid-state bioreactors. Biotechnol Bioeng. 2002;77(4):381-93.
38. Dalsenter FDH, Viccini G, Barga MC, Mitchell DA, Krieger N. A mathematical model describing the effect of temperature variations on the kinetics of microbial growth in solid-state culture. Process Biochemistry. 2005;40(2):801-7.
39. Bovill R, Bew J, Cook N, D'Agostino M, Wilkinson N, Baranyi J. Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature. International Journal of Food Microbiology. 2000;59(3):157-65.
40. Fanaei MA, Vaziri BM. Modeling of temperature gradients in packed-bed solid-state bioreactors. Chemical Engineering and Processing: Process Intensification. 2009;48(1):446-51.
41. Sangsurasak P, Mitchell DA. Incorporation of death kinetics into a 2-dimensional dynamic heat transfer model for solid state fermentation. Journal of Chemical Technology & Biotechnology. 1995;64(3):253-60.
42. Rajagopalan S, Modak JM. Heat and mass transfer simulation studies for solid-state fermentation processes. Chemical Engineering Science. 1994;49(13):2187-93.
43. Sargantanis J, Karim MN, Murphy VG, Ryoo D, Tengerdy RP. Effect of operating conditions on solid substrate fermentation. Biotechnol Bioeng. 1993;42(2):149-58.
44. Hamidi-Esfahani Z, Hejazi P, Shojaosadati SA, Hoogschagen M, Vasheghani-Farahani E, Rinzema A. A two-phase kinetic model for fungal growth in solid-state cultivation. Biochemical Engineering Journal. 2007;36(2):100-7.
45. Ikasari L, Mitchell DA. Two-phase model of the kinetics of growth of Rhizopus oligosporus in membrane culture. Biotechnol Bioeng. 2000;68(6):619-27.
46. Auria R, Morales M, Villegas E, Revah S. Influence of mold growth on the pressure drop in aerated solid state fermentors. Biotechnol Bioeng. 1993;41(11):1007-13.
47. Sainz J, Pizarro F, Perez-Correa JR, Agosin E. Modeling of yeast metabolism and process dynamics in batch fermentation. Biotechnol Bioeng. 2003;81(7):818-28.
48. B. O, Y. S-S, A. C, F.M. F. Intrinsic Activity of Inulinase from Kluyveromyces marxianus ATCC 16045 and Carbon and Nitrogen Balances. Food technol and biotechnol. 2006;44(4):479-84.
49. Ghaly AE, Kamal M, Correia LR. Kinetic modelling of continuous submerged fermentation of cheese whey for single cell protein production. Bioresour Technol. 2005;96(10):1143-52.
50. Brand D, Pandey A, Rodriguez-Leon JA, Roussos S, Brand I, Soccol CR. Packed bed column fermenter and kinetic modeling for upgrading the nutritional quality of coffee husk in solid-state fermentation. Biotechnol Prog. 2001;17(6):1065-70.
51. Ripley BD. Pattern Recognition and Neural Networks. Cambridge University Press; 1996.
52. Mazutti MA, Corazza ML, Maugeri Filho F, Rodrigues MI, Corazza FC, Treichel H. Inulinase production in a batch bioreactor using agroindustrial residues as the substrate: experimental data and modeling. Bioprocess Biosyst Eng. 2009;32(1):85-95.
53. Muller dos Santos M, Souza da Rosa A, Dal'Boit S, Mitchell DA, Krieger N. Thermal denaturation: is solid-state fermentation really a good technology for the production of enzymes? Bioresour Technol. 2004;93(3):261-8.
54. Viniegra-González G, Favela-Torres E, Aguilar CN, Rómero-Gomez SdJ, Dı́az-Godı́nez G, Augur C. Advantages of fungal enzyme production in solid state over liquid fermentation systems. Biochemical Engineering Journal. 2003;13(2–3):157-67.
55. Yu J, XuZhang, Tan T. Ethanol production by solid state fermentation of sweet sorghum using thermotolerant yeast strain. Fuel Processing Technology. 2008;89(11):1056-9.
56. Amin G. Conversion of sugar-beet particles to ethanol by the bacteriumZymomonas mobilis in solid-state fermentation. Biotechnology Letters. 1992;14(6):499-504.
57. Rodríguez LA, Toro ME, Vazquez F, Correa-Daneri ML, Gouiric SC, Vallejo MD. Bioethanol production from grape and sugar beet pomaces by solid-state fermentation. International Journal of Hydrogen Energy. 2010;35(11):5914-7.
58. Mohanty SK, Behera S, Swain MR, Ray RC. Bioethanol production from mahula (Madhuca latifolia L.) flowers by solid-state fermentation. Applied Energy. 2009;86(5):640-4.
59. Roukas T. Solid-state fermentation of carob pods for ethanol production. Applied Microbiology and Biotechnology. 1994;41(3):296-301.
60. Wu T-x, Wang F, Tang Q-l, Zhu Z-h. Arrowroot as a novel substrate for ethanol production by solid state simultaneous saccharification and fermentation. Biomass and Bioenergy. 2010;34(8):1159-64.
61. Gelmi C, Pérez-Correa R, Agosin E. Modelling Gibberella fujikuroi growth and GA3 production in solid-state fermentation. Process Biochemistry. 2002;37(9):1033-40.
62. Hashemi M, Mousavi SM, Razavi SH, Shojaosadati SA. Mathematical modeling of biomass and α-amylase production kinetics by Bacillus sp. in solid-state fermentation based on solid dry weight variation. Biochemical Engineering Journal. 2011;53(2):159-64.
63. Luedeking R, Piret EL. A kinetic study of the lactic acid fermentation. Batch process at controlled pH. Biotechnol Bioeng. 2000;67(6):636-44.
64. Mazutti MA, Zabot G, Boni G, Skovronski A, Oliveira Dd, Luccio MD, et al. Kinetics of inulinase production by solid-state fermentation in a packed-bed bioreactor. Food Chemistry. 2010;120(1):163-73.
Mazaheri, D., & Shojaosadati, S. A. (2013). Mathematical Models for Microbial Kinetics in Solid-State Fermentation: A Review. Iranian Journal of Biotechnology, 11(3), 156-167. doi: 10.5812/ijb.9426
Davood Mazaheri; Seyed Abbas Shojaosadati. "Mathematical Models for Microbial Kinetics in Solid-State Fermentation: A Review". Iranian Journal of Biotechnology, 11, 3, 2013, 156-167. doi: 10.5812/ijb.9426
Mazaheri, D., Shojaosadati, S. A. (2013). 'Mathematical Models for Microbial Kinetics in Solid-State Fermentation: A Review', Iranian Journal of Biotechnology, 11(3), pp. 156-167. doi: 10.5812/ijb.9426
Mazaheri, D., Shojaosadati, S. A. Mathematical Models for Microbial Kinetics in Solid-State Fermentation: A Review. Iranian Journal of Biotechnology, 2013; 11(3): 156-167. doi: 10.5812/ijb.9426


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'Guardians of the Galaxy' star offers reward for Trump-carved manatee
Get Healthy Orlando
Nonprofit seeks to end hunger with mobile app
By Kate Santich
Central Florida nonprofit, Feeding Children Everywhere, is hoping its new mobile app, "Fed 40" will become a more efficient, more cost-effective way to get meals to individuals in poverty.
From GrubHub to OpenTable and DailyBites, mobile apps help Americans find takeout, make dinner reservations and pursue their endless quest for pizza. But a Longwood nonprofit is hoping its new mobile app will do something more noble with food: end hunger.
Feeding Children Everywhere — the charity that has distributed more than 65 million meals to people living in poverty around the world — quietly launched its free "Fed 40" app in early November as a pilot program.
Available through the App Store and Google Play, it allows users living in designated low-income areas to request food for up to 40 meals by submitting a simple, four-question form.
Then, typically one business day later, a box of the nonprofit's rice-and-lentil casserole mix and a few suggested recipes are delivered to the person's doorstep, all at no cost. The casserole provides complete nutrition by itself, but recipients can add other ingredients to spice it up.
"We believe this is going to disrupt how we fight hunger in America," said Dave Green, CEO of Feeding Children Everywhere. "In the first week and a half alone, we delivered about 1,000 meals."
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So far, the program is limited to Parramore — considered a "food desert" for its lack of a major grocery store. There, Feeding Children Everywhere has contacted foundations, neighborhood organizations and churches to spread the word. Green said he wants to get feedback to fine-tune the program before he expands it to other neighborhoods and cities.
Research in 2015 found that half of low-income Americans own smartphones, a rate that was expected to rise, and they are more likely than wealthier Americans to rely on those phones as their sole means of accessing the internet. But for those without phones, Green said, the meals can be ordered online (at fed40.com) by using a free computer at a public library.
In Parramore, Pastor Glendy Hamilton of Harvest Baptist Church said many residents could use the additional food.
"They told me about this app, so I tried it, and in two days, the box was at my house," he said. "It's a good thing — especially in these times. I know people want mashed potatoes and meatloaf, but if you're hungry, you need to get this."
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Hamilton, a vegetarian, gave the delivery to his church's food pantry. But he was a little skeptical that a package intended to serve six would actually stretch that far.
"If they're big eaters, they're not going to get six meals," he said. "But my brother has [tried it], and he said it's good food."
Feeding Children Everywhere, started in the summer of 2010, is perhaps best known to Central Floridians for its massive "hunger projects" that recruit volunteers to mix, measure and package the four basic ingredients of the casserole: lentils, rice, dehydrated vegetables and mineral-rich pink Himalayan salt.
Initially, most of the meals were targeted for distribution in developing countries. But in 2012, as Americans continued to struggle through the Great Recession, the charity launched a domestic program. Now, 75 percent of its meals are distributed within the United States via food pantries, schools, churches and other nonprofits.
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Although the casserole is not typical American fare, supporters contend that it's versatile enough to appeal to a wide variety of palates by adding a few inexpensive extras — tomato sauce, cheese, tortillas or fresh vegetables, for instance. The suggested recipes include burrito bowls and jambalaya.
Fed 40 is especially helpful for shut-ins and people who don't own cars, Green said.
"When Dave was telling me about the app, I was actually surprised that nothing like that already existed," said Clayton Ferrara, executive director of IDEAS For Us, an Orlando-based nonprofit that looks for creative ways to solve societal ills. "I was elated that he has taken the [initiative] to do this himself."
Fed 40 is also more efficient, cost-effective and environmentally responsible than traditional methods of providing food, Green said, in part because the food makes fewer stops between growers and consumers. And it takes about 50 times as much water to get a gram of meat protein as it does for a gram of lentil protein, he said.
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"Our food is healthy, it's vegan and it tastes good," Green said. "Lentils are sort of the dream protein."
For now, his $3 million-a-year organization is covering the full cost of the meals and the shipping — about $7 per delivery —but Green hopes to enlist donors and a shipping company to come aboard as a project partner.
"So far, the demand is what we expected," he said. "But by the end of 2017, we plan to be doing this in at least 25 cities."
ksantich@orlandosentinel.com, @katesantich, 407-420-5503

redpajama_set_name: RedPajamaCommonCrawl


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Latest St. Louis News
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Leader of St. Louis black police union says racial bias real
ST. LOUIS (AP) — The president of a predominantly black St. Louis police union said Wednesday that the city prosecutor's lawsuit is correct in pointing out racial concerns in St. Louis, including biases within the police department itself. Ethical Society of Police President Heather Taylor stopped...
Man sentenced to 5 years for Missouri post office heist
ST. LOUIS (AP) — A man has been sentenced to five years in prison for robbing a suburban St. Louis post office. Sixty-year-old Dywane Upchurch was sentenced Thursday for being a felon in possession of a firearm and assault while committing a robbery. He pleaded guilty in October. Authorities say Upchurch...
A St. Louis housing startup matches seniors with millennials
WENTZVILLE, Mo. (AP) — When her husband died in a car accident, Karen Krienke, 65, found herself unexpectedly alone. Her home in Wentzville — once crowded with children — felt big and empty. Moreover, a bad back was slowing her down. Her daughter, who lives in Washington, D.C. worried....
New Mets manager Beltrán out amid sign-stealing scandal
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Man sentenced for St. Louis carjacking with toddler in car
A 19-year-old St. Louis man who stole a woman's car with her 15-month-old son inside was sentenced Thursday to 11 years and two months in prison, the U.S. Attorney's Office said. Elijah Roberts, who was 17 at the time, threatened the woman with a gun and demanded her keys just after she had buckled her two...
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KINLOCH, Mo. (AP) — A St. Louis man has been charged will fatally shooting another man after getting mad at him about a missing lawn mower and leaf blower. Thirty-six-year-old Antonio Taylor was charged Thursday with first-degree murder and armed criminal action in the death of 40-year-old Gary Flemings,...
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Every now and again, the Japanese branches of McDonald's roll out what they call the Big America burgers. Each one is named after a place in the US and has a subtle nod to the region through its ingredients, such as the chili-soaked Texas burger.
The thing is, in the U.S. we don't call that a Texas burger, we call it a chili burger. Likewise, going into anywhere other than a Japanese McDonald's and asking for a Broadway or Idaho burger is going to leave the staff confused and you hungry.
It's always seemed a little strange that there's no California burger, since that's an established dish with a standard recipe, being avocado slices on a burger. Well, if McDonald's won't do it, Burger King Japan will, and even without a tip of the hat to the Golden State, their new lineup of burgers is packed with avocado.
Burger King has three special sandwiches on offer right now, all of which share the same theme.
▼ And no, it's not habanero chili peppers.
The cheapest of the bunch, at 290 yen (US$2.85), or 590 yen for a set with fries and a drink, is the Fresh Avocado Burger. It doubles its California connection by coming with Monterey Jack cheese, and also bacon, which has no particular connection to the state but is, presumably, delicious by its very nature of being bacon.
Moving up the price scale, we come to the contradictorily named Fresh Avocado Whopper Jr. Unlike the other two sandwiches, this one has been available at Japanese Burger King branches for a while now, but they're still being kind enough to knock 50 yen off the price, dropping it to 350 yen by itself of 690 for a set.
And finally, the flagship burger of the campaign is the Fresh Avocado Whopper Premium. 550 yen (910 yen for the set) gets you a full-sized version of Burger King's iconic burger, covered with an extra-large serving of avocado.
While it's not that cost-prohibitive, we realize 910 yen is a bit on the pricey side for a fast food meal, and we can almost hear the fiscally shrewd among you saying, "But is there any way I can eat 15 Fresh Avocado Whopper Premium sets for the price of 14?" Why yes you can, our oddly specific readers, by showing a screen capture of the 60-yen-off coupon at the end of this video.
Alternatively, you could show them this capture we've already taken for you, thereby saving yourself some cash and possibly introducing the clerk at Burger King to the website created by the Internet's nicest-smelling team of writers and editors.
None of these avocado burgers are available while the breakfast menu is being served, though, which means you'll have to stop by after 10:30 a.m. If there's no Burger King branch close enough to your office for a lunch run, we recommend going for dinner, since that'll give you more time to savor your burger, and also enjoy one of the whiskey cocktails the chain serves in Japan.


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December 10, 2002: Minutes
The December 10, 2002 Board of Supervisors' Meeting was called to order by Chairman Ruth Carlson at 7:30PM, at the Crane Lake Chapel Fellowship Hall with the Pledge of Allegiance recited by those in attendance including: Supervisors Ruth Carlson, Terrie Hoff and Mark Anderson, Treasurer Jacque Eggen, Clerk Jo Ann Pohlman, a reporter from the Cook News-Herald, and 11 members of the community.
There were no additions to the Agenda.
Minutes – Motion by Terrie, supported by Mark to approve the minutes of the November 11, 2002 Board of Supervisors' Meeting. Motion carried.
Treasurer's Report – Jacque reviewed the Cash Flow Statement and recommended the payment of bills as follows: MN Assn of Townships Agency – $400.90; MN Assn of Townships Agency – $179.00; Ruth L. Carlson – $35.00; Terrie Hoff – $324.82; Jacque Eggen – $89.75; Jo Ann Pohlman – $169.06 and Postmaster – $37.00. A motion was made by Mark, supported by Terrie to accept the Treasurer's Report and the payment of bills. Motion carried.
Correspondence – A motion was made by Terrie, supported by Mark to accept and place on file the correspondence as presented. Motion carried.
Fire Protection – Ruth reported that the signed Fire Protection Services Agreement had been forwarded to the Fire Chief for signature. The Fire Department has until December 31, 2002 to return the signed Agreement.
Welcome to Crane Lake Sign – Co-op with CLVTB – Ruth reported that she had called Kabetogama Township to see if they received grant money to refurbish their fish statue and surrounding area. They received $110,000 from the Federal Highway Department. The Town would not qualify for Federal Highway Funding as the Town has county roads. The Supervisors will continue to investigate funding options. Mark stated that the CLVTB is also working on the funding issue and that the Voyageur Statue has received a coat of primer paint and should be returned to Crane Lake in about one month.
Crane Lake Lodging Tax – Ordinance – Ruth said that the Crane Lake registered voters present at the Special Meeting held at 7PM voted unanimously in favor of the Town of Crane Lake collecting and administering the Crane Lake Lodging Tax. A Motion was made by Ruth, supported by Terrie to accept Ordinance No. 2002-01 – An Ordinance to Establish a Tax Imposed Upon Lodging to be collected and administered by the Town of Crane Lake. Motion carried.
Tobacco Licensing & Regulation – After checking with the county and the MAT attorney, it is suggest that the Town delay licensing tobacco until the year 2004. An ordinance must be written and adopted. The attorney felt this could not be accomplished by January 1, 2003 when the tobacco licenses must be issued. A motion was made by Ruth, supported by Terrie to delay issuing tobacco licenses until 2004, to send a letter to those establishments applying for tobacco sale licenses apprising them of the situation, and to call the county regarding the Town's delay of issuing tobacco licenses until 2004 with a letter to follow. Motion carried.
Web Site – Ruth stated that Deena Congdon, a manager of the Crane Lake Water & Sanitary District had offered to look into the expense of sharing the cost of their website with the Town of Crane Lake.
Minnesota Association of Townships Annual Conference 11/21-23 Report – Ruth explained that Terrie, Jacque, Jo Ann and Terrie went on a chartered bus to Alexandria for the very informative two-day conference.
Ruth said she learned how much more there was to learn. Ruth & Terrie attended the break-out sessions for supervisors while Jo Ann & Jacque attended workshops for clerks and treasurers.
Cell Phone Service – Ruth called the county about the tower that was recently built at the landfill. She spoke to Paul Kent, the head of communications at St. Louis County. They would be willing to rent to a cell phone provider. They cannot offer the service, but must be approached by the provider. Cell phone service was on the "want list" of the Community Action Plan. The Board is trying to follow those wishes and will continue to pursue this. Mark suggested that Cellular One be contacted as he feels they would be able to provide the best service in remote areas.
March Elections – Absentee Ballots: A letter was sent on December 9 to registered voters of Crane Lake explaining how to vote by absentee ballot for the March elections. A notice has been posted stating that applications for absentee voting are now available. Location for Absentee Voting: The Clerk's home on Saturday, March 8 from 10 AM until 12 Noon. Call for Election Judges: Two people have expressed interest. There will be 3 hours of training in March and 4 hours election duty on Election Day at $8 per hour. The Supervisors will appoint election judges at the February meeting. Char Bystrom said she would be interested in being an election judge.
Candidate Eligibility – All officer's terms will be up in March. Ruth explained the qualifications for candidacy and the procedure for filing for office. She also said that the MAT attorney defined Conflict of Interest as follows: Anyone can serve on the Town Board. If they are voting members of another board that deals financially with the Town Board, they may not discuss or vote on those issues. A point of interest was presented by Jacque – The Treasurer and the Clerk do not vote on the Town Board; therefore, they could serve on any other board and it would not be considered a conflict of interest.
Butch Eggen said he is excited about the possibility of cell phone service in the Crane Lake area.
Nancy Parvi said she was surprised at how much had been accomplished by the Board since the Town was established.
Gary Albertson referred to the conflict of interest issue. In his experience, an individual could not serve on two boards that tax the same people. An example would be the CLWSD and the Town Board.
Nancy Parvi said the Fire Department has been looking into cell phone service for rescue purposes. The Fire Department would support cell phone service in the area.
COMMENTS BY OFFICERS – Mark said he would not be able to attend the next meeting. Ruth said a letter should be sent to the Snowmobile Club thanking them for their financial help to the Township. She also thanked all for coming and wished all a Merry Christmas.
The next Board of Supervisors' Meeting will be on January 14, 2003 at 7:30PM at the Crane Lake Fellowship Hall.
There being no further business the meeting was adjourned by Chairman Ruth at 8:10PM.
Jo Ann Pohlman, Clerk Ruth Carlson, Chairman


text:
Weather will influence Iowa caucuses, Huckabee says
CNN Producer Gabriella Schwarz
(CNN) – If the weather is good on January 3, Mitt Romney will win the Iowa caucuses; if the weather is bad, a weaker turnout will result in a victorious Ron Paul, former Republican presidential candidate Mike Huckabee predicted Sunday.
Huckabee, who won the Iowa caucuses in 2008, said the devotion disparity between supporters of the two contenders could greatly impact the outcome in the early voting state.
- Follow the Ticker on Twitter: @PoliticalTicker
"Ron Paul has an exceptional organization there and it very well could be that he could end up winning because of the extraordinary devotion of his followers," the former Arkansas governor said on "Fox News Sunday." "He's got people that would walk over broken glass for him, and they break the glass to be able to say they did it."
"That's where Mitt is at a disadvantage, he doesn't have the devotion," Huckbee added.
He also said Rick Santorum is underestimated and suggested the former Pennsylvania senator might place in the top three or four.
Santorum has made social issues the pillars of his White House bid in the Hawkeye State, much like Huckabee, a former minister, did in 2008. Despite success among the state's religious voting bloc in the last presidential election, Huckabee's Iowa win failed to catapult him to the GOP nomination, which was ultimately captured by Arizona Sen. John McCain.
The latest Iowa poll from the American Research Group released Friday showed Paul, Romney and Newt Gingrich in a statistical tie - with 21%, 20% and 19% support respectively. All the remaining candidates received less than 10% each.
Huckabee said negative ads have hurt former House Speaker Gingrich, who surged to the top of national and state-wide polls a few weeks ago but has now seen his support start to recede. To Huckabee, the negative advertisements sow doubt in the minds of voters, particularly those who have yet to make a final decision.
"If you're soft on a candidate and every day you get pounded with about 20 messages … it's easier to switch at that point," Huckabee said. "That's what's happening to Newt."
But Huckabee cautioned against using state-wide polling as the key predictor for the caucuses. In the end, it is the committed and devoted supporters that make the difference in Iowa's system, he said.
"Polls, you pick up the phone and you say 'ya, I kinda like so and so,'" Huckabee said. "Caucus, you've got to drive somewhere and stand up and be counted for that candidate. It's a very different kind of atmosphere."
NOTE: While it's too early to forecast precisely what the weather will be in Iowa on January 3, long-range climatological models indicate a warmer and drier January across the Midwest, according to CNN Meteorologist Sean Morris. There is a strong La Nina in the eastern Pacific off the coast of South America, which typically causes winter storms to move across Canada instead of the lower 48 and keeps Arctic air bottle up North of the U.S.-Canadian border.
First lady helps track Santa on Christmas Eve
Write-ins prohibited in Virginia primaries
Candidates join families for Christmas greetings
Christmas greetings replace partisanship in weekly addresses
Filed under: 2012 • Iowa • Mike Huckabee • Mitt Romney • Ron Paul
SATAN-FOR-ROMNEY
Let's hope for good weather.
December 25, 2011 10:58 am at 10:58 am |
I disagree. With Ron Paul supporters, it does not matter it will be raining, snowing or sunshine. These supporters are not different from those who supported the President in 2008. It was then snowing like hell, very cold and still they came to support him. What matters here is determination!
God told him that? Then, if Mother Nature gives us snow, it must mean God votes for Ron Paul.
How idiotic.
Keith in Austin
I strongly support Ron Paul's conservative domestic policies re: spending overhauls and the major downsizing of Big Government but strongly disagree with his Isolationist views abroad. This is his greatest weakness at a time when America needs to reinsert the image of our strength to our enemies overseas that wish to harm us.
December 25, 2011 12:32 pm at 12:32 pm |
Fourteen year old boy
"This is his greatest weakness at a time when America needs to reinsert the image of our strength ...."
That's what she said.
December 25, 2011 04:02 pm at 4:02 pm |
Richard-Missouri
What??? What is the deal with Ron Paul potentially winning in Iowa? RIDICUOLOUS! Honestly, the guy stands about as much chance of becoming president as a snowball has of lasting in Hell! They guy is very smart and reasonable on many fiscal isssue, but a complete whack job on others – like foreign policy. Pauls supporters are complete whack jobs too, for even thinking Ron Paul could ever win! Let's get real folks! Backa REAL candidate that has a chance of winning, not some fringe nutjob who has just enough supporters to win straw polls and the like.
It dosen't matter which one wins, they are both losers.
Matheens
Ron Paul isn't an isolationist. He merely supports the idea that America shouldn't be the world's police force. Its not our job to send our military to other countries and tell them how to run their government. Thats the whole point of freedom, being able to make your own decisions however you want. Our country is built upon the idea of freedom. We shouldn't be invading other countries to try and force them to accept our form of government. Our military is meant to PROTECT our country from harm. I almost feel bad for Iraq, we invaded them on false pretenses and then threw their country into a living hell for nine years just so that we could take their oil and leave. if you ask me, Ron Paul's the only person unafraid to say what's right and stand up for his beliefs. Jon Huntsman seems like an intelligent guy too. The rest of the GOP candidates just pander to the special interest groups that make them rich. I must say, if its Romney vs Obama then I'm very afraid of the direction our country is headed.
Keith in Austin – You need to understand the difference between non-interventionism and isolationism. America has always been non-interventionist. It's the only affordable choice given our economy, and it is what Ron Paul advocates. HE DOES NOT ADVOCATE ISOLATIONISM.
You may not agree with Ron Paul's position now. But do some more research before you decide.
Joshua Steimle
What's not in doubt is that whether the weather is good or bad, if Paul wins the media will do their best to ignore it and prevent anyone from believing Paul is electable, because the dirty secret of the Romney campaign is that Romney isn't much different than Obama, and in the general election Romney stands a good chance of going the same way as McCain. Paul is the only candidate espousing policies that differ in any substantive way from those of Obama.
Strong national defense is what Ron Paul believes should be the militaries main priority and I agree. I personally think it is far smarter to have a strong national defense instead of endless, multi billion if not trillion dollar wars. I will be rooting for Ron Paul supporters who are in Iowa from here in New York. I'm happy we finally have a chance to vote someone who is not corrupt. Let me put it this way though, for anyone skeptical I encourage anyone to at least listen to one of Ron Paul's recent town halls and see what you think for yourself. As he rises in the polls I sm sure he will be asked more in the public's eye about his policies so everyone can better understand them snd make the right choice.
Ron Paul isn't about being a slick politician but to decide like with any candidate it's best to know their positions on the issues. Happy holidays.
Rain or shine, Romney has my vote. ROMNEY 2012!
No government equals the law of the jungle. Taliban Style.
markiejoe
Can I get in the CNN headlines with this prediction (which is 100% accurate):
Romney WILL BE the Republican Presidential nominee no matter what the weather in any state from now until the GOP nominating convention next summer. I've been posting this for months now, and it's still accurate. The Republicans have no other realistic choice but Romney.
That, of course, leaves the neocons and Christian right-wingers who can't stand Romney in a quandry: They can either (1) swallow their pride and their beliefs and vote for Romney anyway, (2) stay home in droves on election day next November, thus ensuring another Obama term, or (3) nominate their own ultra right-wing, evangelical as a third-party candidate, thus ensuring another Obama term.
That's it; that's the inevitable GOP scenario from now until November 2012. Put me in the CNN headlines.
The corporate-controlled media does not want to report positively on Paul.
I like Romney. He is for Romneycare on a national level. He is for gay marriage. He is for a women's right to choose. So far he is for all the things we have. That is the sign of a true conservative. Don't believe the RINO stuff.
once upon a horse
I thought it would be sort of anti-American to let the weather determine if you vote or not. Maybe it's a Republican thing.
You will be po d in 2012
Obama will win again in 2012.Look at all the buyers remorse of putttin repubs in power in such states as Wis,Fla,Ohio,And Michigan.Why is it that repub run states in the south need the most financial fedral help?If the south ever wakes up the right is finished because it does nt appear to be too many millionaires their so in what way do they fiot into republican financial plan?None,you have to be really confused to beleive cutting taxes for the rich and offset it by cutting your future ss is a good idea is absolutely cuckoo.SS is solvent until 2037 how come they are not that savvy about finding a way to pay for wars and tax cuts.
Obviously the firmness of people's beliefs is checked better under some adverse conditions. If Mike's belief that most Romney's supporters are fair-weather friends is true, I wonder how much support would he have left as the President if it's time to make hard decisions - on protecting people's liberties, wars, responsible budgets, reducing legal burden, supporting business climate (at the cost of someone else, of course: it's always like that - there is no free cheese).
The president with devoted supporters who will stay put rain or shine while he or she keeps to his declared principles seems to be a preferable choice to the one with fair-weather supporters.
Val Rankov
My prediction: If the wather is bad Ron Paul wins. If the weather is good Ron Paul wins. If there is no weather Ron Paul wins.
I didn't know that Mike Huckabee was weather man on the side
Huckabee is a celebrity wannabe. He was a minister of a small baptist church in Pine Bluff Arkansas, got a taste of the limelight and loves it. He wants to be either a rock star or a politician.
GeorgeBos95
Huckabee's still grasping for attention?
Sirned
Romney is for unfettered trade and banks that have no regulation....Can you say Bush on steroids. ?
BeverlyNC
Republicans are so out of touch with the People, so destructive in their actions to gain power, so extremists in their views to attract so-called "Christians" who hate, judge, and live a life Jesus would not recognize anything close to His teachings, that they can not possibly credibly lead this nation of ALL people of different colors, backgrounds, beliefs, needs, and rights Republicans do not agree with. They want to legally end our rights, shove their sick social agenda down our throats, let the poor die, no one have healthcare, and on and on. Republicans are now almost a cult controlled by just a few wealthy men – they are not leaders, not for Democracy, and have no interest in following the Constitution. Get rid of every Republican in every local, state, and national seat. We must clean these morally bankrupt haters and liars out of our government and bring back our freedom, our democracy, and our nation that represents ALL people fairly. Stand with President Obama. Stand with the Democrats. They have never stopped working for ALL people, never stopped trying to fnd solutions for jobs, healthcare for all, saving SS and Medicare, and making sure America stays one valued for its values, humanity, and justice.


text:
Q: Creating shortest route using line-segment layer as network locations? I'm currently using Arc10.2.1 and new to the network analyst tool. I'm trying to create a truck route that begins at one location on a county street grid and ends up in the same place, following a feature class of line segments of guard rails throughout the whole county - we need to create the most efficient path possible.
I understand the concept of network analysis objects and network attributes, but need to use these line segments as such. However, the only line-based network analysis objects that can be defined as network locations are line and polygon barriers. I was successful in using my guardrail feature class to create the shortest route that avoids each guardrail, but not one that follows it. When I attempt to use the segments as "stop" input features it tells me that it is an incorrect input.
I thought of creating centroids from each segment to use as stops, but that provides little information for the truck drivers to know exactly where along the roadway the guardrails are located.
This seems like it would be a common problem, yet I haven't been able to find anything on it.

A: You cannot use lines as stop inputs for a network analysis. The only way you could possibly use the lines is to set them as barriers but give them a negative value (ie, rather than adding time, they take time away) such that they attract the route rather than avoid it. You still have to set stops though.
If centroids are not sufficient, it would be suggested to create points out of the start and ends of the guardrail sections. Unless the analysis is set using a curb approach restriction, you won't be able to define which point is the start vs end. If you do use a curb approach restriction, then you know based on direction of travel which will be the start and which the end. U-turn restrictions, etc, will also play a role. Your vehicle routing problem will need to allow reordering of stops for greatest efficiency.

redpajama_set_name: RedPajamaStackExchange


text:
"Macbeth" by Wayward Theatre Company at the James J. Hill House
Macbeth is one of Shakespeare's creepiest and darkest plays. Which makes it a perfect choice to produce during Halloween season, at the James J. Hill House which, while elegant during the day, is a bit creepy after hours. Wayward Theatre Company, experts at site-specific theater, is doing not only that, but also moving the audience around the house to eight different performance locations. For a three-hour play, the walking around adds interest and keeps the blood flowing. This intimate and immersive (but not interactive) show feels immediate and brings out all of the horror of this tale of unbridled greed.
Michael Kelley as Macbeth
(photo courtesy of Wayward)
The show begins in the room just off the gift shop (which was fortunately closed, otherwise I would have likely bought jewelry, hats, and books while waiting for the show to start). If you choose the 7pm start time, you'll be treated to a sort of pre-intermission break with music. The 730pm start time goes straight through and meets up with the first group. This allows the beginning scenes to be more intimate, as we traverse up the stairs to a few locations on the third and second floors, then back to the first floor concert hall. The group then moves as a whole (which takes a bit of time in transitions, but there's plenty to look at in this grand house), with just two locations in Act II. We're ushered by stern, efficient, and slightly terrifying guides, who also play roles in the scenes at times. The bloody (or in this case bloodless - it's a historic home so we don't want red fluids flying everywhere) murders happen in the hall in front of the staircase, which feels pretty brutal and horrifying. The Act II battle scenes take place in the basement, which is a bit more spacious and sparse and cold (literally, the upper floors can get warm so dress in layers).
Sarah Nargang as Lady Macbeth
What fun it must have been for director Tim McVean (Wayward co-founder) and assistant director Shannon Twohy to figure out where to stage the different parts of the play. The scenes that take place in the Macbeth's mansion feel particularly real as we are in a real mansion, and can hear sounds and scuffles happening in other parts of the house as the story calls for it. And I was surprised at the variety of lighting affects accomplished by lighting designer Jordon Noltner within the limitations of a non-traditional theater space, with rules about what can touch what (do not touch the wood!). The costuming of the piece (designed by Anna Hall) seems to set the story in the 1940s, the women in calf-length skirts with '40s styled hair, the men in military uniforms with a plaid sash to remind us we're in Scotland. It's a very smart and efficient use of the James J. Hill House, which has become quite the hot spot for theater.
It's fun to watch the huge and talented cast stay in character through all the transitions and locations, and observe their emotions up close. Wayward co-founders Michael Kelly and Sarah Nargang play the Lord and Lady Macbeth, and are both deliciously ambitious, evil, conflicted, and mad. Tina Frederickson, Megan Volkman-Wilson, and Alessandra Bongiardina are appropriately creepy as the three weird sisters, aka witches. Other highlights include Lucas Gerstner as the doomed Banquo, the adorable young Tristan Bodin as his son Fleance, and Kip Hathaway with the lone comic turn as the drunken Porter in this otherwise sober piece.
Macbeth continues at the James J. Hill House through November 17 on St. Paul's historic Summit Avenue, with performances almost nightly (except for Tuesdays).
Labels: Alessandra Bongiardina, Anna Hill, James J. Hill House, Jordon Noltner, Kip Hathaway, Lucas Gerstner, Macbeth, Megan Volkman-Wilson, Michael Kelley, Sarah Nargang, Tim McVean, Tina Frederickson, Wayward Theatre
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Anyone who dares to approach actor George Clooney's two Lake Como homes will be fined up to €500.
He's long been an honorary citizen of Laglio and now the town's mayor has moved to protect the star's privacy even more by making it illegal for anyone to approach Villa Oleandra and Villa Margherita.
Mayor Roberto Pozzi has introduced a fine of up to €500 for anyone who comes within 100 metres of the villas, either by water or by road.
A source at the town hall confirmed the move following a report in the British newspaper, The Telegraph, on Monday.
Clooney, who married barrister Amal Alamuddin last September, paid €7.5 million for the 18th century Villa Oleandra in 2001.
Needless to say, the home, previously owned by the Heinz family, has since been persistently besieged by fans and photogaphers.
Clooney later bought the adjoining Villa Margherita in a bid for more seclusion.
The ordinance follows one in 2013 banning people from gathering outside the homes.
Pozzi said at the time: "Clooney has the right to be left alone like any other tourist on holiday."
Still, Clooney's presence has boosted tourism to the area, as well as local property prices.
The actor once said the purchase of Villa Oleandra changed his life in a "very pleasant and unexpected way".
"I realized how beautiful life was in Italy and how it really helped calm me and not feel so pressured," he said.


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Q: Problemas en BackEnd con JavaScript al publicar imágenes y verlo en Postman Muestra del mensaje de error definido por mi en Postman y el código
El problema está en que, al momento de cambiar el condicional if de posición (abajo del 'success'), me da el mensaje success, pero aunque el if esté arriba, la imágen se sigue guardando en la dirección en la que la he dejado.
PEEERO, cuando quiero obtener la imagen, me da error aunque ponga el nombre de la imágen con su extensión
if (articleId) {
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                if (err || !articleUpdated) {
                    return res.status(200).send({
                        status: 'error',
                        message: 'Error al guardar la imagen de articulo!!!'
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text:
var current_user_id;

function sendMsg(e){
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}

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	//starting to send out the auth
	var url = mainserver + "/friendships.json";

	var client = Ti.Network.createHTTPClient({
	    onload : function(e) {
	    	Ti.API.info("Received text: " + this.responseText);
	    	console.log("enviado para o :" + current_user_id);
	    	alert('Friend Request Sent!');
	    },
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redpajama_set_name: RedPajamaGithub