Kinetics of ethanol inhibition in alcohol fermentation

The inhibitory effect of ethanol on yeast growth and fermentation has been studied for the strain Saccharomyces cerevisiae ATCC No. 4126 under anaerobic batch conditions. The results obtained reveal that there is no striking difference between the response of growth and ethanol fermentation. Two kinetic models are also proposed to describe the kinetic pattern of ethanol inhibition on the specific rates of growth and ethanol fermentation: \documentclass{article}\pagestyle{empty}\begin{document}$$\begin{array}{*{20}c} {\frac{{\mu _i }}{{\mu _0 }} = 1{\rm } - {\rm }\left( {\frac{P}{{P_m }}} \right);\alpha } \hfill & {\left( {{\rm for}\ {\rm growth}} \right)} \hfill \\ {\frac{{\nu _i }}{{\nu _0 }} = 1{\rm } - {\rm }\left( {\frac{P}{{P'_m }}} \right);\beta } \hfill & {\left( {{\rm for}\ {\rm ethanol}\ {\rm production}} \right)} \hfill \\ \end{array}$$\end{document} The maximum allowable ethanol concentration above which cells do not grow was predicted to be 112 g/L. The ethanol-producing capability of the cells was completely inhibited at 115 g/L ethanol. The proposed models appear to accurately represent the experimental data obtained in this study and the literature data.     

Prognose des Stoffhaushaltes von Staugewässern mit Hilfe kontinuierlicher und semikontinuierlicher biologischer Modelle. II. Prüfung der Prognosegenauigkeit. Prediction of the Balance of Matter in Storage Reservoirs by Means of Continuous or Semicontinuous Biological Models. II. Reliability of the Prediction Method

The phosphate removal in small, completely mixed storage reservoirs (preimpoundment basins) mainly is a function of the production of biomass by the phytoplankton. The knowledge of the critical detention time of the water is the most important premise to the prediction. The critical detention time t̄ is computed from the equation: \documentclass{article}\pagestyle{empty}\begin{document}$ \overline t _c = \frac{1}{{\mu ^* - 0,1}} $\end{document} and the growth rate μ* at a given combination of the light intensity J, temperature T and phosphate concentration P is computed from: \documentclass{article}\pagestyle{empty}\begin{document}$ \mu ^* = \frac{{\mu T \cdot \mu J \cdot \mu P}}{{\mu \max ^2 }}\mu \max \cdot \frac{P}{{K_p + P}}\frac{J}{{K_j + J}}\frac{T}{{T_{opt} }}, $\end{document} (μmax = maximum possible growth rate of the dominant species; K_p, K_j and T_opt are constants computed from batch cultures). The quotient \documentclass{article}\pagestyle{empty}\begin{document}$ \frac{{\bar t_{act.} }}{{\bar t_c }}(\bar t_{act.} = {\rm actual detention time in the water body)} $\end{document} enables prediction of the phosphate removal. A comparison of the predicted results from semicontinuous cultures and from the preimpoundment basin of the Weida reservoir revealed a satisfactory degree of conformity.     

Generalization of monod kinetics for analysis of growth data with substrate inhibition

The inhibitory effect of butanol on yeast growth has been studied for the strain Candida utilis ATCC 8205 growing aerobically on butanol under batch conditions. A mathematical expression was then proposed to fit the kinetic pattern of butanol inhibition on the specific growth rate: \documentclass{article}\pagestyle{empty}\begin{document}$$ \mu = \frac{{\mu _m S}}{{K_s + S}}\left[{1 - \frac{S}{{S_m }}} \right];n $$\end{document}The maximum allowable butanol concentration above which cells do not grow was predicted to be 9.16g/L. The proposed model appears to accurately represent the experimental data obtained in this study and the literature data developed for a variety of batch culture systems at widely ranging substrate concentrations.     

Kinetics of anaerobic purification of industrial wastewater

As a part of the development of an integral mathematical model describing the up-flow anaerobic sludge blanket (UASB) reactor, the kinetics of the conversion of organic wastes has to be known. We compared the Monod model with the model proposed by Andrews et al. Together with the assumption that the substrate for the anaerobic bacteria is formed by nonionized, volatile fatty acids, the Andrews model is able to describe substrate inhibition and reactor failure due to pH changes.From four batch experiments, with different concentrations of microorganisms, it could be concluded with a reliability of over 95% that the monod model was inadequate and Andrews' model was adequate to describe the measurements. Standard statistical techniques like the X2- and the F-test were used for this purpose.From a parameter sensitivity analysis for the Andrews model it followed that the maximum specific growth rate mu(A) (max) of the bacteria and the inhibition constant K(1) are the parameters which influence the system most. Thus, these parameter were determined experimentally and most accurately. The results are: \documentclass{article}\pagestyle{empty}\begin{document}$$\mu;{A}_{\max} = 16*10;{-4}{\rm h};{-1}\pm 2\%\quad {\rm and}\quad K_l = 0.0158\,{\rm g}\,{\rm HAc/L}\pm 2.5\%$$\end{document} The other parameters were taken from literature. From calculation of the Thiele modulus for the particles it follows that transport limitation of the substrate in the flocus is not significant. The efficiency eta is 0.85 in the worst case.     

Modellierung der enzymatischen Cellobiosehydrolyse – Vergleich linearer und nichtlinearer Parameterbestimmung

Experimental kinetic data (initial rate and high conversion) on the hydrolysis of cellobiose by 1,4-β-glucosidace (Gliocladium sp.) have been analysed and a competitive inhibition by glucose has been proposed. The determination of kinetic parameters from integral data is based upon algorithms for non-linear optimization and numerical integration. The values of kinetic constants \documentclass{article}\pagestyle{empty}\begin{document}$(v_{\max } = 1.02\frac{{\mu {\rm M}_{{\rm glucose}} }}{{{\rm mg}_{{\rm protein}} \cdot \min }},K_M = 2.6{\rm mM/l, and }K_P = 1.2{\rm mM/l)}$\end{document} agree well with the initialrate results. An important distinction is the confidence limit of parameters. Linear regression analysis shows a virtual accuracy and can lead to wrong conclusions.     

Energetics of the growth of Methanobacterium thermoautotrophicum and Methanococcus thermolithotrophicus on ammonium chloride and dinitrogen

Methanobacterium thermoautotrophicum was grown in continuous culture in a fermenter gassed with H₂ and CO₂ as sole carbon and energy sources, and in a medium which contained either NH₄Cl or gaseous N₂ as nitrogen source. Growth was possible with N₂. Steady states were obtained at various gas flow rates with NH₄Cl and with and the maintenance coefficient varied with the gas input and with the nitrogen source. Growth of Methanococcus thermolithotrophicus in continuous culture in a fermenter gassed with H₂, CO₂ as nitrogen, carbon and energy sources was also examined.Abbreviations molecular growth yield (g dry weight of cells per mol of CH₄ evolved) - growth rate (h^-1) - D dilution rate (h^-1) - rate (h_-1); relation of Neijssel and Tempest and of Stouthamer and Bettenhaussen - energy     

An electrochemical method of measuring the oxidation rate of ferrous to ferric iron with oxygen in the presence of Thiobacillus ferrooxidans

The oxidation of Fe(2+) with oxygen in sulfate solutions was studied in the presence of T. ferrooxidans. To measure the chemical activity of bacteria, and the oxidation rate of iron, the redox potentials of solutions were continuously monitored during the experiments. The redox potentials were simultaneously monitored on the platinum and pyrite indicator electrodes. The redox potential versus time curves were further used to calculate the basic kinetic parameters, such as the reaction orders, the activation energy, and the frequency factor. It was found that under atmospheric conditions, and at Fe(2+) < 0.001M, T < 25 degrees C, and at pH above 2.2, the oxidation of iron is governed by the following rate expression: \documentclass{article}\pagestyle{empty}\begin{document}$$ - \frac{{d[{\rm Fe};{2 + }]}}{{dt}} = 1.62 \times 10;{11} C_{{\rm bact}} [{\rm H}; + ][{\rm Fe};{2 + }]p{\rm O}_2 e;{ - (58.77/RT)} $$\end{document} Below pH = 2.2, the oxidation rate is independent of H(+) Concentration.     

Influence of anions on metal adsorption by Rhizopus arrhizus biomass

The presence of anions in solution was found to inhibit the uptake of La(3+), Cd(2+), Pb(2+), UO(2+) (2), and Ag(+) by Rhizopus arrhizus biomass. The effects ranged from total inhibition of Cd(2+) and Pb(2+) uptake at equimolar concentrations of EDTA to no change in uptake of La(3+) or UO(2+) (2) at 12-fold molar excesses of Cl(-) or CO(2-) (3). No anion was found to enhance metal uptake levels, and the degree of inhibition generally followed the series: \documentclass{article}\pagestyle{empty}\begin{document}$${\rm EDTA } \ge \ge {\rm SO}_{;{;{;{\rm 4} } } };{{\rm 2} - } \ge {\rm Cl}; - \ge {\rm PO}_{;{;{;{\rm 4} } } };{{\rm 3} - } \ge {\rm glutamate} \ge {\rm CO}_{;{;{\rm 3} } };{{\rm 2} - } $$\end{document} The chemical equilibrium model REDEQL2 was adapted to treat metal uptake by R. arrhizus biomass and used to predict the effects of anions in solution. Comparisons with the experimental results are made and discussed in light of the assumptions underlying the model.     

The basic flow equations of electrophysiology in the presence of chemical reactions: I. Development of the equations

Starting from the basic flux equation, it is possible to obtain an integral form relating the current componentsI _i at an arbitrary pointr ₂ to the distribution of mobilities and concentrationsc _i, potential forces\(\bar \mu \), and chemical productivityp _i without any restrictive assumptions such as constant mobilities, constant field, steady state, or electrical neutrality. The equation is

$$\begin{gathered} I_i (r_2 ) = G_i (r_2 )\left[ {\Delta \bar \mu _i - \int_{r_1 }^{r_2 } {z_i } FA\left( {p_i - dc_i /dt} \right)\left( {\frac{1}{{G_i (r)}}} \right)dr} \right]; \hfill \\ G_i (r) = 1/\int_{r_1 }^r {\frac{{dr}}{{z_i^2 F^2 c_i u_i }}.} \hfill \\ \end{gathered} $$     

Inhibierungserscheinungen bei der alkoholischen Gärung,I. Einfluß der Ethanolkonzentration auf die spezifische Ethanolbildungsgeschwindigkeit von Saccharomyces cerevisiae

Differential values of the specific ethanol production rate \documentclass{article}\pagestyle{empty}\begin{document}$$ v_{(t)} = \frac{1}{{x_{(t)} }} \cdot \frac{{dP}}{{dt}} $$ \end{document} can be calculated exactly from experimental batch fermentation process data by use of a nonlinear regression programme. The method used is based on the fact, that the function P = f(t) can be approximated by an exponential equation. The specific ethanol production rate is calculated then from the first differential derivation of this equation using the appropriated values of actual biomass concentration. For two strains of Saccharomyces cerevisiae a linear and nonlinear kinetic pattern, respectively, was found for product formation. This result can be explained by a simple mathematical relation according to ν=ν₀ ? a . P^b,in which the exponent becomes 1 in the case of linear kinetic pattern.     

Simultaneous nonlinear equations of growth

The simultaneous equations

$$\begin{gathered} \frac{{dx}}{{dt}} = \frac{{a_x }}{{k_x }}[k_x - x - f_x (y)] x \hfill \\ \frac{{dy}}{{dt}} = \frac{{a_y }}{{k_y }}[k_y - y - f_y (x)] y \hfill \\ \end{gathered}$$     

E-waste projection using life-span and population statistics

Purpose

E-waste is the most rapidly growing problem throughout the world, which has serious future concerns over its management and recycling. This article proposes a simple approach for future e-waste projection which can be obtained by using life-span data of various electronic items along with incorporation of population statistics.

Methods

For this purpose, 7-year sales data of electronic items were collected, which is then used to generate various mathematical equations. These mathematical relations are then modified by incorporating life-span and population data.

Results and discussion

By comparing sales data with their life-span (average) and population statistics, future e-waste can be quantified both in terms of specified area under investigation and proposed estimation area. The following equation is thus proposed: E - waste In terms of quantity = m Waste projection year ? Life - span ? Initial data collection year + C × Population of estimation area Population of study area $$ \begin{array}{c}\mathrm{E}-\mathrm{waste}\;\\ {}\left(\mathrm{In}\ \mathrm{terms}\ \mathrm{of}\ \mathrm{quantity}\right)=\left[m\left\{\mathrm{Waste}\;\mathrm{projection}\;\mathrm{year}-\mathrm{Life}-\mathrm{span}\right\}-\mathrm{Initial}\ \mathrm{data}\ \mathrm{collection}\ \mathrm{year}+C\right]\times \frac{\mathrm{Population}\ \mathrm{of}\ \mathrm{estimation}\ \mathrm{area}}{\mathrm{Population}\ \mathrm{of}\ \mathrm{study}\ \mathrm{area}\ }\end{array} $$ Where m and C can be obtained from plotting year-wise sales data over Excel sheet.

Conclusions

Local as well as global projection of future e-waste can be possible with the help of final equation.     

Kinetics of aflatoxin biosynthesis by Aspergillus parasiticus in the presence of N(alpha)-palmitoyl-L-lysyl-L-lysine-ethyl ester dihydrochloride or dichlorvos

N(alpha)-Palmitoyl-L-lysyl-L-lysine-ethyl ester dihydrochloride (PLL) has antimicrobial properties and may be useful as a food preservative. This study was conducted to see if PLL can inhibit growth and synthesis of aflatoxin by Aspergillus parasiticus. Growth of mold and accumulation of aflatoxins were monitored for up to 15 days. To compare these data with those of a known inhibitor of aflatoxin synthesis, dichlorvos was added to media, and mold growth and aflatoxin accumulation were monitored. The kinetic model of Brown and Vass that correlates growth and formation of secondary metabolites was applied to results of this study, and values for maturation time (t(m)) and aflatoxin accumulation rate constant (alpha) were calculated. Values of t(m) decreased when cultures contained PLL, whereas presence of dichlorvos resulted in a considerable increase. The lag phase of mold growth increased in the presence of PLL. The values of alpha increased with an increasing amount (up to 300 ppm) of PLL in media. Higher concentrations of PLL decreased the value of alpha. All levels of dichlorvos tested decreased the value of alpha. The aflatoxin accumulation rate constant (alpha) as a function of concentration of additive (C) followed the general equation: \documentclass{article}\pagestyle{empty}\begin{document}$$\alpha = \frac{{\alpha _m C\exp (- {C \mathord{\left/ {\vphantom {C {K_i }}} \right. \kern-\nulldelimiterspace} {K_i }})}}{{C + K_a }}$$\end{document} where alpha(m), K(a), and K(i) are constants.     

The density of protein precipitates and its effect on centrifugal sedimentation

Isoelectric soya-protein precipitate densities were measured for mean particle sizes ranging from 3.4-65 mum by gradient centrifugation, centrifugation in water-immiscible solvents, tracerdilution, gravity sedimentation of isolated particles. Coulter counter volume determination, and a comparison of Coulter counter and centrifugal sedimentation size distributions. The immiscible system and tracer dilution methods were both found to be unreliable due to experimental uncertainties. The Coulter counter volume measurement indicated the existence of a density-size relationship with the aggregate density decreasing as the size increased. Comparison with sedimentation measurements showed that the Coulter counter measures 80% of the total aggregate volume for 6-mum particles. The relation between aggregate density (rho(a), kg m (-3)) and size (d, mum) was measured for isoelectric soya protein and casein precipitated by ammonium sulfate, using a comparison of the Coulter counter size distribution and centrifugal sedimentation. The functions were described for soya by \documentclass{article}\pagestyle{empty}\begin{document}$$ \rho _a - 1004 = 246d;{ - 0.408} $$\end{document} and for casein by \documentclass{article}\pagestyle{empty}\begin{document}$$ \rho _a - 1136 = 31d;{ - 0.441} $$\end{document} The gradient centrifugation method measured the buoyant density of hydrated protein precipitate which was independent of size, and is consistent with an aggregate structure consisting of primary particles. However, the aggregate structure was not described for all sizes by the theoretical cubic packing of hard-sphere primary particles, nor by the successive random addition of primary particles. The density-size functions indicated up to a fivefold difference in Stokes settling velocities compared to those calculated assuming a constant density difference.     

Polarized fluorescence in an electric field. A model calculation with application to the geometry of the 2-hydroxy-4,4′-diamidinostilbene–DNA complex

Theoretical expressions are derived for the change in the polarized components of the fluorescence, resulting from the orientation of a rigid molecule bearing a chromophore with arbitrary angles for the absorption and transition moments \documentclass{article}\pagestyle{empty}\begin{document}$ \vec \mu _a $\end{document} and \documentclass{article}\pagestyle{empty}\begin{document}$ \vec \mu _e $\end{document} with respect to the molecular axis. The break in the symmetry relation H_V = V_H is related to the tilt angle between \documentclass{article}\pagestyle{empty}\begin{document}$ \vec \mu _a $\end{document} and \documentclass{article}\pagestyle{empty}\begin{document}$ \vec \mu _e $\end{document}. The theory is applied to a sonicated DNA–2-hydroxy-4,4′-diamidinostilbene complex, in the blue and red emission bands of this peculiar dye. Simultaneous measurements of linear dichroism and fluorescence lead to the determination of an angle of 47° between a fluorescent bound dye and the DNA axis, with no difference for the blue- and red-emitting species, but confirm the presence of nonfluorescent bound dye in a more perpendicular arrangement.     

On a new graphical method in the theory of multiple fixations and its application to the wurmser theory of isohemagglutination

The fundamental equation of the theory of multiple fixations without interaction (MFWI) is

$$\frac{1}{r} = \frac{1}{m} + \frac{1}{{mKA}}$$     

Catabolite repression of amylase synthesis in yeast

Amylase synthesis by the yeasts Saccharomycopsis fibuligera and Schwanniomyces castellii and alluvius is repressed by glucose. Steady state continuous culture data for amylase activity, E, biomass concentration, X, and reducing sugar concentration, S, were fitted to the three-parameter catabolite repression model \documentclass{article}\pagestyle{empty}\begin{document}$ \frac{E}{X} = \frac{{[1 + a(S/X)]}}{{[1 + b(S/X)]}}, $\end{document} and biomass productivity, DX, and amylase productivity, DE, were determined for S. castellii and S. alluvius.     

Regional Assessment of N Saturation using Foliar and Root \varvec {\delta}^{\bf 15}{\bf N}

N saturation induced by atmospheric N deposition can have serious consequences for forest health in many regions. In order to evaluate whether foliar may be a robust, regional-scale measure of the onset of N saturation in forest ecosystems, we assembled a large dataset on atmospheric N deposition, foliar and root and N concentration, soil C:N, mineralization and nitrification. The dataset included sites in northeastern North America, Colorado, Alaska, southern Chile and Europe. Local drivers of N cycling (net nitrification and mineralization, and forest floor and soil C:N) were more closely coupled with foliar than the regional driver of N deposition. Foliar increased non-linearly with nitrification:mineralization ratio and decreased with forest floor C:N. Foliar was more strongly related to nitrification rates than was foliar N concentration, but concentration was more strongly correlated with N deposition. Root was more tightly coupled to forest floor properties than was foliar . We observed a pattern of decreasing foliar values across the following species: American beech>yellow birch>sugar maple. Other factors that affected foliar included species composition and climate. Relationships between foliar and soil variables were stronger when analyzed on a species by species basis than when many species were lumped. European sites showed distinct patterns of lower foliar , due to the importance of ammonium deposition in this region. Our results suggest that examining values of foliage may improve understanding of how forests respond to the cascading effects of N deposition.     

Patterns of tooth size variability in the dentition of primates

Published data on tooth size in 48 species of non-human primates have been analyzed to determine patterns of variability in the primate dentition. Average coefficients of variation calculated for all species, with males and females combined, are greatest for teeth in the canine region. Incisors tend to be somewhat less variable, and cheek teeth are the least variable. Removing the effect of sexual dimorphism, by pooling coefficients of variation calculated for males and females separately, reduces canine variability but does not alter the basic pattern. Ontogenetic development and position in functional fields have been advanced to explain patterns of variability in the dentition, but neither of these appears to correlate well with patterns documented here. We tentatively suggest another explanation. Variability is inversely proportional to occlusal complexity of the teeth. This suggests that occlusal complexity places an important constraint on relative variability within the dentition. Even when the intensity of natural selection is equal at all tooth positions, teeth with complex occlusal patterns must still be less variable than those with simple occlusion in order to function equally well. Hence variability itself cannot be used to estimate the relative intensity of selection. Low variability of the central cheek teeth ( \documentclass{article}\pagestyle{empty}\begin{document}$ \mathop {\rm M}\frac{1}{1} $\end{document} and \documentclass{article}\pagestyle{empty}\begin{document}$ \mathop {\rm M}\frac{2}{2} $\end{document}) makes them uniquely important for estimating body size in small samples, and for distinguishing closely related species in the fossil record.