Modeling of the pyruvate production with<Emphasis Type="Italic"> Escherichia coli</Emphasis> in a fed-batch bioreactor

A family of 10 competing, unstructured models has been developed to model cell growth, substrate consumption, and product formation of the pyruvate producing strain Escherichia coli YYC202 ldhA::Kan strain used in fed-batch processes. The strain is completely blocked in its ability to convert pyruvate into acetyl-CoA or acetate (using glucose as the carbon source) resulting in an acetate auxotrophy during growth in glucose minimal medium. Parameter estimation was carried out using data from fed-batch fermentation performed at constant glucose feed rates of q_VG=10 mL h^–1. Acetate was fed according to the previously developed feeding strategy. While the model identification was realized by least-square fit, the model discrimination was based on the model selection criterion (MSC). The validation of model parameters was performed applying data from two different fed-batch experiments with glucose feed rate q_VG=20 and 30 mL h^–1, respectively. Consequently, the most suitable model was identified that reflected the pyruvate and biomass curves adequately by considering a pyruvate inhibited growth (Jerusalimsky approach) and pyruvate inhibited product formation (described by modified Luedeking–Piret/Levenspiel term).List of symbols c_A acetate concentration (g L^–1) - c_A,0 acetate concentration in the feed (g L^–1) - c_G glucose concentration (g L^–1) - c_G,0 glucose concentration in the feed (g L^–1) - c_P pyruvate concentration (g L^–1) - c_P,max critical pyruvate concentration above which reaction cannot proceed (g L^–1) - c_X biomass concentration (g L^–1) - K_I inhibition constant for pyruvate production (g L^–1) - K_I^A inhibition constant for biomass growth on acetate (g L^–1) - K_P saturation constant for pyruvate production (g L^–1) - K_P inhibition constant of Jerusalimsky (g L^–1) - K_S^A Monod growth constant for acetate (g L^–1) - K_S^G Monod growth constant for glucose (g L^–1) - m_A maintenance coefficient for growth on acetate (g g^–1 h^–1) - m_G maintenance coefficient for growth on glucose (g g^–1 h^–1) - n constant of extended Monod kinetics (Levenspiel) (–) - q_V volumetric flow rate (L h^–1) - q_VA volumetric flow rate of acetate (L h^–1) - q_VG volumetric flow rate of glucose (L h^–1) - r_A specific rate of acetate consumption (g g^–1 h^–1) - r_G specific rate of glucose consumption (g g^–1 h^–1) - r_P specific rate of pyruvate production (g g^–1 h^–1) - r_P,max maximum specific rate of pyruvate production (g g^–1 h^–1) - t time (h) - V reaction (broth) volume (L) - Y_P/G yield coefficient pyruvate from glucose (g g^–1) - Y_X/A yield coefficient biomass from acetate (g g^–1) - Y_X/A,max maximum yield coefficient biomass from acetate (g g^–1) - Y_X/G yield coefficient biomass from glucose (g g^–1) - Y_X/G,max maximum yield coefficient biomass from glucose (g g^–1) - growth associated product formation coefficient (g g^–1) - non-growth associated product formation coefficient (g g^–1 h^–1) - specific growth rate (h^–1) - _max maximum specific growth rate (h^–1)     

Improvement of a mammalian cell culture process by adaptive,model-based dialysis fed-batch cultivation and suppression of apoptosis

Both conventional and genetic engineering techniques can significantly improve the performance of animal cell cultures for the large-scale production of pharmaceutical products. In this paper, the effect of such techniques on cell yield and antibody production of two NS0 cell lines is presented. On the one hand, the effect of fed-batch cultivation using dialysis is compared to cultivation without dialysis. Maximum cell density could be increased by a factor of ~5–7 by dialysis fed-batch cultivation. On the other hand, suppression of apoptosis in the NS0 cell line 6A1 bcl-2 resulted in a prolonged growth phase and a higher viability and maximum cell density in fed-batch cultivation in contrast to the control cell line 6A1 (100)3. These factors resulted in more product formation (by a factor ~2). Finally, the adaptive model-based OLFO controller, developed as a general tool for cell culture fed-batch processes, was able to control the fed-batch and dialysis fed-batch cultivations of both cell lines.Abbreviations A membrane area (dm²) - c _Glc,F glucose concentration in nutrient feed (mmol L^–1) - c _Glc,FD glucose concentration in dialysis feed (mmol L^–1) - c _Glc,i glucose concentration in inner reactor chamber (mmol L^–1) - c _Glc,o glucose concentration in outer reactor chamber (dialysis chamber) (mmol L^–1) - c _Lac,FD lactate concentration in dialysis feed (mmol L^–1) - c _Lac,i lactate concentration in inner reactor chamber (mmol L^–1) - c _Lac,o lactate concentration in outer reactor chamber (dialysis chamber) (mmol L^–1) - c _LS,FD limiting substrate concentration in dialysis feed (mmol L^–1) - c _LS,i limiting substrate concentration in inner reactor chamber (mmol L^–1) - c _LS,o limiting substrate concentration in outer reactor chamber (dialysis chamber) (mmol L^–1) - c _Mab monoclonal antibody concentration (mg L^–1) - F _D feed rate of dialysis feed (L h^–1) - F _Glc feed rate of nutrient concentrate feed (L h^–1) - K _d maximum death constant (h^–1) - k _d,LS death rate constant for limiting substrate (mmol L^–1) - k _Glc monod kinetic constant for glucose uptake (mmol L^–1) - k _Lac monod kinetic constant for lactate uptake (mmol L^–1) - k _LS monod kinetic constant for limiting substrate uptake (mmol L^–1) - K _Lys cell lysis constant (h^–1) - K _S,Glc monod kinetic constant for glucose (mmol L^–1) - K _S,LS monod kinetic constant for limiting substrate (mmol L^–1) - µ cell-specific growth rate (h^–1) - µ _d cell-specific death rate (h^–1) - µ _d,min minimum cell-specific death rate (h^–1) - µ _max maximum cell-specific growth rate (h^–1) - P _Glc membrane permeation coefficient for glucose (dm h^–1) - P _Lac membrane permeation coefficient for lactate (dm h^–1) - P _LS membrane permeation coefficient for limiting substrate (dm h^–1) - q _Glc cell-specific glucose uptake rate (mmol cell^–1 h^–1) - q _Glc,max maximum cell-specific glucose uptake rate (mmol cell^–1 h^–1) - q _Lac cell-specific lactate uptake/production rate (mmol cell^–1 h^–1) - q _Lac,max maximum cell-specific lactate uptake rate (mmol cell^–1 h^–1) - q _LS cell-specific limiting substrate uptake rate (mmol cell^–1 h^–1) - q _LS,max maximum cell-specific limiting substrate uptake rate (mmol cell ^–1 h^–1) - q _Mab cell-specific antibody production rate (mg cell^–1 h^–1) - q _MAb,max maximum cell-specific antibody production rate (mg cell^–1 h^–1) - t time (h) - V _i volume of inner reactor chamber (culture chamber) (L) - V _o volume of outer reactor chamber (dialysis chamber) (L) - X _t total cell concentration (cells L^–1) - X viable cell concentration (cells L^–1) - Y _Lac/Glc kinetic production constant (stoichiometric ratio of lactate production and glucose uptake) (–)     

Studies on the variables and kinetics of SCP production from nopal fruit juice

Summary Submerged batch cultivation under controlled environmental conditions of pH 3.8, temperature 30°C, and K_La200 h^–1 (above 180 mMO₂ l ^–1 h^–1 oxygen supply rate) produced a maximum (12.0 g·l ^–1) SCP (Candida utilis) yield on the deseeded nopal fruit juice medium containing C/N ratio of 7.0 (initial sugar concentration 25 g·l ^–1) with a yield coefficient of 0.52 g cells/g sugar. In continuous cultivation, 19.9 g·l ^–1 cell mass could be obtained at a dilution rate (D) of 0.36 h^–1 under identical environmental conditions, showing a productivity of 7.2 g·l ^–1·h^–1. This corresponded to a gain of 9.0 in productivity in continuous culture over batch culture. Starting with steady state values of state variables, cell mass (C_X–19.9 g·l ^–1), limiting nutrient concentration (C_ln–2.5 g·l ^–1) and sugar concentration (C_S–1.5 g·l ^–1) at control variable conditions of pH 3.8, 30°C, and K_La 200 h^–1 keeping D=0.36 h^–1 as reference, transient response studies by step changes of these control variables also showed that this pH, temperature and K_La conditions are most suitable for SCP cultivation on nopal fruit juice. Kinetic equations obtained from experimental data were analysed and kinetic parameters determined graphically. Results of SCP production from nopal fruit juice are described.Nomenclature C_ln concentration of ammonium sulfate (g·l ^–1) - C_S concentration of total sugar (g·l ^–1) - C_X cell concentration (g·l ^–1) - D dilution rate (h^–1) - K_ln Monod's constant (g·l ^–1) - m maintenance coefficient (g ammonium sulfate cell^–1 h^–1) - m_(S) maintenance coefficient (g sugar g cell^–1 h^–1) - t time, h - Y yield coefficient (g cells/g ammonium sulfate) - Y_m maximum of Y - Y_S yield coefficient based on sugar consumed (g cells · g sugar^–1) - Y_S(m) maximum value of Y_S - ^µm maximum specific growth rate constant (h^–1)     

Diazotrophic growth of Rhodopseudomonas acidophila and Rhodopseudomonas capsulata under microaerobic conditions in the dark

Diazotrophy of Rhodopseudomonas acidophila and Rhodopseudomonas capsulata was not obligatorily linked to photosynthesis. In the dark R. acidophila grew with dinitrogen as sole nitrogen source at a dissolved oxygen tension of 15 Torr (= 2.0 kPa); the doubling time was 8 h. Acetylene reduction by whole cells was more sensitive to oxygen in the light than in the dark. 16.5 mg N₂ were fixed per g lactic acid consumed. R. capsulata synthesized nitrogenase and fixed dinitrogen in the dark at a dissolved oxygen tension of less than one Torr (= 0.13 kPa). The doubling time of this bacterium was 16 h and 10.5 mg N₂ were fixed per g lactic acid consumed.Abbreviation kPa kilopascal     

Temperature and prey quality effects on growth of juvenile walleye pollock Theragra chalcogramma (Pallas): a spatially explicit bioenergetics approach

A bioenergetics model for juvenile age‐0 year walleye pollock Theragra chalcogramma was applied to a spatially distinct grid of samples in the western Gulf of Alaska to investigate the influence of temperature and prey quality on size‐specific growth. Daily growth estimates for 50, 70 and 90 mm standard length (L_S) walleye pollock during September 2000 were generated using the bioenergetics model with a fixed ration size. Similarities in independent estimates of prey consumption generated from the bioenergetics model and a gastric evacuation model corroborated the performance of the bioenergetics model, concordance correlation (r_c) = 0·945, lower 95% CL (transformed) (L₁) = 0·834, upper 95% CL (transformed) (L₂) = 0·982, P < 0·001. A mean squared error analysis (M_SE) was also used to partition the sources of error between both model estimates of consumption into a mean component (M_C), slope component (S_C), and random component (R_C). Differences between estimates of daily consumption were largely due to differences in the means of estimates (M_C= 0·45) and random sources (R_C= 0·49) of error, and not differences in slopes (S_C= 0·06). Similarly, daily growth estimates of 0·031–0·167 g day^?1 generated from the bioenergetics model was within the range of growth estimates of 0·026–0·190 g day^?1 obtained from otolith analysis of juvenile walleye pollock. Temperature and prey quality alone accounted for 66% of the observed variation between bioenergetics and otolith growth estimates across all sizes of juvenile walleye pollock. These results suggest that the bioenergetics model for juvenile walleye pollock is a useful tool for evaluating the influence of spatially variable habitat conditions on the growth potential of juvenile walleye pollock.     

Characterization of photosynthesis of Populus euphratica grown in the arid region

Net photosynthetic rate (P _N), stomatal conductance (g _s), intercellular CO₂ concentration (C _i), transpiration rate (E), water use efficiency (WUE), and stomatal limitation (L_s) of Populus euphratica grown at different groundwater depths in the arid region were measured. g _s of the trees with groundwater depth at 4.74 m (D₄) and 5.82 m (D₅) were lower and a little higher than that at 3.82 m (D₃), respectively. Compared with C _i and L_s of the D₃ trees, C _i decreased and L_s increased at 4.74 m, however, Ci increased and L_s decreased at D₅. Hence photosynthetic reduction of P. euphratica was attributed to either stomatal closure or non-stomatal factors depending on the groundwater depths in the plant locations. P _N of the D₃ trees was significantly higher than those at D₄ or D₅. The trees of D₄ and D₅ did not show a significant difference in their P _N, indicating that there are mechanisms of P. euphratica tolerance to mild and moderate drought stress.     

Suction volume and filtering efficiency of silver carp (Hypophthalmichthys molitrix Val.) and bighead carp (Aristichthys nobilis Rich.)

Experiments were conducted to measure the suction volume of silver carp and bighead carp of age 1 + with respiratory chamber, and to calculate the suction volume and the filtering efficiency with respect to changes in concentrations of food particles. Suction volume (B. ml/mouth) and filtering efficiency (E. %) were calculated using the following formula: C ₁=C₀(1-BE/v)ⁿ where C₀ and C₁ were the concentrations of specific food particles at the beginning and at the end of experiment, respectively, V was the volume (ml) of experimental water, and n was the total number of observation of suction made during the experimental period. The relationships between suction volume (ml/mouth) of age I+ silver carp (B_h) and bighead carp (B_a) and their standard lengths (L, cm) were: B _h=0.561L-8.94, B_a= 0.627L-7.48 while those of the fingerlings were: B _h= O.l70L-0.837, B_a= 0.157L-0.418. The suction volume of the fingerlings was mainly affected by fish size, the function of temperature between 15 and 25° C being negligible. However, temperature affected filtering rate (filtered volume per unit time) through its effect on filtering frequency. The filtering efficiency of the fishes for rotifers (Brachionus caliciflorus) was 100 per cent. The relationships between filtering efficiency and sizes of food particles smaller than or equal to that of a rotifer were: E _h=25.1 ln e.s.d. -13.6, E_a=22.2 In e.s.d. -33.1 where E_h and E_a were filtering efficiency of silver carp and bighead carp, respectively, and e.s.d. was the equivalent spherical diameter (μm) of food particles.     

Fed-batch production of baker's yeast using millet (Pennisetum typhoides) flour hydrolysate as the carbon source

A fermentation medium based on millet (Pennisetum typhoides) flour hydrolysate and a four-phase feeding strategy for fed-batch production of baker's yeast,Saccharomyces cerevisiae, are presented. Millet flour was prepared by dry-milling and sieving of whole grain. A 25% (w/v) flour mash was liquefied with a thermostable 1,4--d-glucanohydrolase (EC 3.2.1.1) in the presence of 100 ppm Ca²⁺, at 80°C, pH 6.1–6.3, for 1 h. The liquefied mash was saccharified with 1,4--d-glucan glucohydrolase (EC 3.2.1.3) at 55°C, pH 5.5, for 2 h. An average of 75% of the flour was hydrolysed and about 82% of the hydrolysate was glucose. The feeding profile, which was based on a model with desired specific growth rate range of 0.18–0.23 h^–1, biomass yield coefficient of 0.5 g g^–1 and feed substrate concentration of 200 g L^–1, was implemented manually using the millet flour hydrolysate in test experiments and glucose feed in control experiments. The fermentation off-gas was analyzed on-line by mass spectrometry for the calculation of carbon dioxide production rate, oxygen up-take rate and the respiratory quotient. Off-line determination of biomass, ethanol and glucose were done, respectively, by dry weight, gas chromatography and spectrophotometry. Cell mass concentrations of 49.9–51.9 g L^–1 were achieved in all experiments within 27 h of which the last 15 h were in the fedbatch mode. The average biomass yields for the millet flour and glucose media were 0.48 and 0.49 g g^–1, respectively. No significant differences were observed between the dough-leavening activities of the products of the test and the control media and a commercial preparation of instant active dry yeast. Millet flour hydrolysate was established to be a satisfactory low cost replacement for glucose in the production of baking quality yeast.Nomenclature C _ox Dissolved oxygen concentration (mg L^–1) - CPR Carbon dioxide production rate (mmol h^–1) - C _s0 Glucose concentration in the feed (g L^–1) - C _s Substrate concentration in the fermenter (g L^–1) - C _s.crit Critical substrate concentration (g L^–1) - E Ethanol concentration (g L^–1) - F _s Substrate flow rate (g h^–1) - i Sample number (–) - K _e Constant in Equation 6 (g L^–1) - K _o Constant in Equation 7 (mg L^–1) - K _s Constant in Equation 5 (g L^–1) - m Specific maintenance term (h^–1) - OUR Oxygen up-take rate (mmol h^–1) - q _ox Specific oxygen up-take rate (h^–1) - q _ox.max Maximum specific oxygen up-take rate (h^–1) - q _p Specific product formation rate (h^–1) - q _s Specific substrate up-take rate (g g^–1 h^–1) - q _s.max Maximum specific substrate up-take rate (g g^–1 h^–1) - RQ Respiratory quotient (–) - S Total substrate in the fermenter at timet (g) - S ₀ Substrate mass fraction in the feed (g g^–1) - t Fermentation time (h) - V Instantaneous volume of the broth in the fermenter (L) - V ₀ Starting volume in the fermenter (L) - V _si Volume of samplei (L) - x Biomass concentration in the fermenter (g L^–1) - X ₀ Total amount of initial biomass (g) - X _t Total amount of biomass at timet (g) - Y _p/s Product yield coefficient on substrate (–) - Y _x/e Biomass yield coefficient on ethanol (–) - Y _x/s Biomass yield coefficient on substrate (–) Greek letters Moles of carbon per mole of yeast (–) - Moles of hydrogen atom per mole of yeast (–) - Moles of oxygen atom per mole of yeast (–) - Moles of nitrogen atom per mole of yeast (–) - Specific growth rate (h^–1) - _crit Critical specific growth rate (h^–1) - _E Specific ethanol up-take rate (h^–1) - _max.E Maximum specific ethanol up-take rate (h^–1)     

Theoretical study on the aromaticity from d-AOs in cationic X 3 + (X = Sc,Y, La) clusters

The stable structures and aromatic characters for three cationic X₃⁺ (X = Sc, Y, and La) and three relevant neutral X₃Cl (X = Sc, Y, La) clusters are investigated at the DFT and post HF level of theory. The calculated results show that the X₃⁺ cations each has two stable structures: the regular trigon (D_3h) and the line (D_￥h {{\hbox{D}}_{\infty {\rm{h}}}} ) with the regular trigon (D_3h) being the ground state, while for three neutral X₃Cl clusters, Sc₃Cl has three stable isomers: the trigon-pyramidal (C_3v), bidentate (C_2v-1), and C_2v-2 structures, Y₃Cl and La₃Cl each has only two stable isomers: the trigon-pyramidal (C_3v) and bidentate (C_2v-1) structures. The ground states for three X₃Cl species are all the bidentate (C_2v-1) isomers. The calculations of the resonance energy (RE) and NICS show that trigonal X₃⁺ isomers exhibit higher degree of aromaticity. The detailed molecular orbital analyzes reveal that the isolated trigonal Sc₃⁺ and Y₃⁺ cations each has one delocalized π-type MO and shows single π-aromaticity, while the isolated trigonal La₃⁺ cation has one delocalized σ-type MO and shows single σ-aromaticity. The single π- or σ-aromaticity for X₃⁺ are attributed to the contributions mainly from the d AOs of the corresponding transition metal X atoms. However, when a singly negatively charged counterion Cl^- is added to Sc₃⁺, Y₃⁺, and La₃⁺ cations respectively, the aromatic type for the two Sc₃⁺, Y₃⁺ units in the corresponding neutral Sc₃Cl, Y₃Cl complexes are changed from π-aromaticity into σ-aromaticity, whereas the σ-aromaticity of the La₃⁺ units in the La₃Cl complex keeps unchanged in this process. Thus three Sc₃⁺, Y₃⁺, La₃⁺ units in the corresponding X₃Cl complexes all have only one σ-type MO and exhibit single σ-aromaticity.     

Significance of mesophyll conductance for photosynthetic capacity and water-use efficiency in response to alkaline stress in Populus cathayana seedlings

Cuttings of Populus cathayana were exposed to three different alkaline regimes (0, 75, and 150 mM Na₂CO₃) in a semicontrolled environment. The net photosynthesis rate (P _N), mesophyll conductance (g _m), the relative limitations posed by stomatal conductance (L _s) and by mesophyll conductance (L _m), photosynthetic nitrogen-use efficiency (PNUE), carbon isotope composition (δ¹³C), as well as specific leaf area (SLA) were measured. P _N decreased due to alkaline stress by an average of 25% and g _m decreased by an average of 57%. Alkaline stress caused an increase of L _m but not L _s, with average L _s of 26%, and L _m average of 38% under stress conditions. Our results suggested reduced assimilation rate under alkaline stress through decreased mesophyll conductance in P. cathayana. Moreover, alkaline stress increased significantly δ¹³C and it drew down CO₂ concentration from the substomatal cavities to the sites of carboxylation (C _i-C _c), but decreased PNUE. Furthermore, a relationship was found between PNUE and C _i-C _c. Meanwhile, no correlation was found between δ¹³C and C _i/C _a, but a strong correlation was proved between δ¹³C and C _c/C _a, indicating that mesophyll conductance was also influencing the ¹³C/¹²C ratio of leaf under alkaline stress.     

Construction of Equi-replicated Balanced Block Designs with Block Sizes Exceeding the Number of Treatments

In this note it is shown that the block design with incidence matrix Ñ = [NN… N], where N = c_1hN_h + c_oh (11′–N_h). c_oh and c_1h are any non-negative integers and N_h,h = 1, 2,…,p, are incidence matrices of balanced incomplete block designs with the same number of treatments t, is a balanced block design with the block sizes exceeding the number of treatments. In derivation the matrix M₀, introduced by CALIński (1971) is utilized.     

Preparation, spectroscopic properties, crystal and molecular structure of pentacarbonyl(3-methyl-1-(pyridin-2-yl)-1,2,4-triazole-N)tungsten(0)

The synthesis and molecular structure of pentacarbonyl(3-methyl-l-(pyridin-2-yl)-l,2,4-triazole-N⁴)tungsten(0) are described. Surprisingly the ligand is bound to the W(CO)₅ moiety via the triazole N⁴ (N24) atom, and the pyridine to triazole link is between the pyridine C² (Cl) atom and the N¹ (N21) atom of the triazole ring. The compound crystallises in the space group C2/c with a=24.559(18), b=9.693(16), c=13.817(12) Å, β=108.48(12)° and Z=8. A full matrix least-squares refinement resulted in a final R=0.052 (R_w=0.065) for 3688 unique reflections.     

Optimization of D-xylose to xylitol biotransformation by Candida guilliermondii cells permeabilized with Triton X-100

Abstract

The effect of NADP⁺ and glucose-6-phosphate (G6P) on the biotransformation of D-xylose to xylitol by cells of Candida guilliermondii permeabilized with surfactant Triton X-100 was evaluated. The experimental runs were performed with 12 g L^?1 of permeabilized cells and a reaction medium composed of Tris–HCl buffer (0.1 M pH 7), D-xylose (57 g L^?1), and MgCl₂.6H₂O (5 mM). The levels of NADP⁺ (from 0.0 to 1.7 mM) and G6P (from 0.00 to 0.17 M) were varied according a 2²-full factorial composed design. Under optimized conditions (NADP⁺ 0.5 mM and 0.05 M G6P), the xylitol volumetric productivity (Q_P) and yield factor (Y_P/S) predicted were 1.86 ± 0.03 g L^?1 h^{? 1} and 0.64 ± 0.03 g g^?1, respectively. These values were 94% and 19% higher than those obtained with unpermeabilized cells under fermentation conditions (0.97 g L^?1 h^?1 and 0.53 g g^?1, respectively). On the basis of the results, it can be concluded that xylitol production by biotransformation with cells of C. guilliermondii permeabilized with Triton X-100 is a promising alternative to the fermentative process.     

Observer design for differential-algebraic model of an aerobic culture of a recombinant yeast

The mathematical model of an aerobic culture of recombinant yeast presented in work by Zhang et al. (1997) is given by a differential-algebraic system. The classical nonlinear observer algorithms are generally based on ordinary differential equations. In this paper, first we extend the nonlinear observer synthesis to differential-algebraic dynamical systems. Next, we apply this observer theory to the mathematical model proposed in Zhang et al. (1997). More precisely, based on the total cell concentration and the recombinant protein concentration, the observer gives the online estimation of the glucose, the ethanol, the plasmid-bearing cell concentration and a parameter that represents the probability of plasmid loss of plasmid-bearing cells. Numerical simulations are given to show the good performances of the designed observer.Symbols C ₁ activity of pacing enzyme pool for glucose fermentation (dimensionless) - C ₂ activity of pacing enzyme pool for glucose oxidation (dimensionless) - C ₃ activity of pacing enzyme pool for ethanol oxidation (dimensionless) - E ethanol concentration (g/l) - G glucose concentration (g/l) - k _a regulation constant for (g glucose/g cell h^–1) - k _b regulation constant for (dimensionless) - k _c regulation constant for (g glucose/g cell h^–1) - k _d regulation constant for (dimensionless) - K _m1 saturation constant for glucose fermentation (g/l) - K _m2 saturation constant for glucose oxidation (g/l) - K _m3 saturation constant for ethanol oxidation (g/l) - L ( t) time lag function (dimensionless) - p probability of plasmid loss of plasmid-bearing cells (dimensionless) - P recombinant protein concentration (mg/g cell) - q _G total glucose flux culture time (g glucose/g cell h) - t culture time (h) - t _lag lag time (h) - X total cell concentration (g/l) - X ⁺ plasmid-bearing cell concentration (g/l) - Y ^F _{X / G} cell yield for glucose fermentation pathway (g cell/g glucose) - Y ^O _{X / G} cell yield for glucose oxidation pathway (g cell/g glucose) - Y _{X / E} cell yield for ethanol oxidation pathway (g cell/g ethanol) - Y _{E / X} ethanol yield for fermentation pathway based on cell mass (g ethanol·g cell) - ₂ glucoamylase yield for glucose oxidation (units/g cell) - ₃ glucoamylase yield for ethanol oxidation (units/g cell) - µ₁ specific growth rate for glucose fermentation (h^–1) - µ₂ specific growth rate for glucose oxidation (h^–1) - µ₃ specific growth rate for ethanol oxidation (h^–1) - µ_1max maximum specific growth rate for glucose fermentation (h^–1) - µ_2max maximum specific growth rate for glucose oxidation (h^–1) - µ_3max maximum specific growth rate for ethanol oxidation (h^–1)     

Growth and xanthan production of Xanthomonas campestris depending on the N-source concentration

Growth of X. campestris and production of xanthan were studied in several batch fermentations with different starting concentrations of N-source. The dependencies of growth, productivity and yields on initial N-source concentration were observed. The maximum yields in the course of cultivations were identified.List of Symbols Y _e economical yield coefficient - Y _P/sc product yield coefficient related to C-source - r _N specific growth rate related to cell number (h^–1) - Y _exp experimental yield coefficient - Y _T theoretical yield coefficient - c(Sn) concentration of N-source (mg/l) - c(Sc) concentration of C-source (g/l) - c(P) concentration of product (g/l) - N cell number (l^–1) - thermodynamic efficiency We are grateful to Mrs. B. Bhalová, PhD, for chemical analyses of the medium.     

Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches

Population census size (N _C) and effective population sizes (N _e) are two crucial parameters that influence population viability, wildlife management decisions, and conservation planning. Genetic estimators of both N _C and N _e are increasingly widely used because molecular markers are increasingly available, statistical methods are improving rapidly, and genetic estimators complement or improve upon traditional demographic estimators. We review the kinds and applications of estimators of both N _C and N _e, and the often undervalued and misunderstood ratio of effective-to-census size (N _e /N _C). We focus on recently improved and well evaluated methods that are most likely to facilitate conservation. Finally, we outline areas of future research to improve N _e and N _C estimation in wild populations.     

Growth,energy and nitrogen budgets and efficiencies of the growing larvae of Megachile pacifica (Panzer) (Hymenoptera: Megachilidae)

Summary The larvae of Megachile pacifica, the leafcutter bee, develop within a cell constructed of pieces of leaf by the adult female which also provides a plug of nectar and pollen, the sole source of nutrient for the larvae. In this study, eggs (1 per cell) hatched in 2–3 days and larvae were fully grown after a further 9 days at 28° C.The mean larval dry weight (dw) when fully grown (P _L ) was 18.89 mg. Larvae ate 45.99 mg (dw) (C) of their food leaving 0.90 mg (dw) unconsumed. The faeces (FU) weighed an average of 5.62 mg (dw) and their cocoon weighed 2.68 mg (dw) (P _E ). The components of their energy budget (C=(P _L +P _E )+R+FU) were C=1079.4 J, P _L =569.1 J, P _E =62.25 J, R (metabolic energy measured with a Gilson respirometer)=218.3 J and FU=134.4 J. R calculated by difference was 313.6 J.Full grown larvae contained 1.17 mg of nitrogen (N). They egested 0.19 mg N and incorporated 0.16 mg N in the cocoons. They ingested 1.51 mg N (measured) — the calculated consumption of nitrogen (by difference) was 1.52 mg.The ecological efficiencies showed that these larvae are among the most efficient invertebrate converters of energy and nitrogen yet recorded. The assimilation efficiencies (A·C ^-1 ) were 87.5% (energy), 87.2% (N), the net ecological efficiencies (P·A ^-1 ) were 66.8% (energy) and 100% (N) and the gross ecological efficiencies (P·C ^-1 ) were 58.5% (energy) and 87.6% (N). the production to respiration ratio (x100) was 201.5% using calculated R.     

The effect of micro-aerobic conditions on continuous ethanol production by Saccharomyces cerevisiae

Summary The effect of trace amounts of oxygen on the degree of ethanol inhibition in a continuous anaerobic culture of Saccharomyces cerevisiae was studied at the 100 gl ^–1 feed glucose concentration level. Results showed that the use of micro-aerobic conditions (0,5% of saturation) enhanced the utilisation of substrate by increasing the ethanol tolerance of the yeast without any significant decrease in the ethanol yield per unit substrate consumed. When the results were fitted to an equation of the form % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcLbyacaqG8o% GaaeypaiqabY7agaqcaiaab6cadaWcaaGcbaqcLbyacaqGdbWaaSba% aSqaaKqzagGaae4CaaWcbeaaaOqaaKqzagGaae4qamaaBaaaleaaju% gGbiaabohaaSqabaqcLbyacqGHRaWkcaqGlbWaaSbaaSqaaKqzagGa% ae4CaaWcbeaaaaqcLbyacaGGUaWaaSaaaOqaaKqzagGaae4samaaBa% aaleaajugGbiaabchaaSqabaaakeaajugGbiaabUeadaWgaaWcbaqc% LbyacaqGWbaaleqaaKqzagGaey4kaSIaaeywamaaBaaaleaajugGbi% aabchacaqGZbaaleqaaKqzagGaaiOlaiaacIcacaqGdbWaaSbaaSqa% aKqzagGaae4CaiaabAgaaSqabaqcLbyacqGHsislcaqGdbWaaSbaaS% qaaKqzagGaae4CaaWcbeaajugGbiaacMcaaaaaaa!6301!\[{\text{\mu = \hat \mu }}{\text{.}}\frac{{{\text{C}}_{\text{s}} }}{{{\text{C}}_{\text{s}} + {\text{K}}_{\text{s}} }}.\frac{{{\text{K}}_{\text{p}} }}{{{\text{K}}_{\text{p}} + {\text{Y}}_{{\text{ps}}} .({\text{C}}_{{\text{sf}}} - {\text{C}}_{\text{s}} )}}\]it was found that the values for % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGabeiVdyaaja% aaaa!373F!\[{\text{\hat \mu }}\], K_s and Y_ps were the same as for the non-aerobic case while the ethanol inhibition constant, K_p , had increased from 5,2 to 14,0 gl ^–1.Notation C_sf feed substrate concentration - gl ^–1 - C_s substrate concentration gl ^–1 - C_p product concentration - gl ^–1 - C_x cell concentration - gl ^–1 - D dilution rate - h^-1 - K_s substrate saturation constant - gl ^–1 - K_p product inhibition constant - gl ^–1 - m maintenance coefficient - h^–1 - Y_ps product yield coefficient - g EtOH/g glucose - Y_xs cell yield coefficient - g cells/g glucose - specific growth rate - h^–1 - % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGabeiVdyaaja% aaaa!373F!\[{\text{\hat \mu }}\] maximum specific growth rate - h^–1     

Fundamental aspects of waste sewage sludge treatment: Microbial solids biodegradation in an aerobic thermophilic semi-continuous system

The solubilization and biodegradation of whole microbial cells by an aerobic thermophilic microbial population was investigated over a 72 h period. Various parameters were followed including total suspended solids reduction, changes in the dissolved organic carbon, protein and carbohydrate concentrations, and carboxylic acid production and utilisation. From the rates of removal of the various fractions a simple model for the biodegradation processes is proposed and verified with respect to acetic acid production and utilization, and total suspended solids removal. The process is initiated by enzymic degradation of the substrate microbe cell walls followed by growth on the released soluble substrates at low dissolved oxygen concentration with concommitant carboxylic acid production. Subsequent utilization of the unbranched, lower molecular weight carboxylic acids allows additional energy supply following exhaustion of the easily utilisable soluble substrate from microbial cell hydrolysis.List of Symbols Y _Xp/Xs kg/kg yield process microbes on substrate yeast cells - Y _Xp/Ac kg/kg yield process microbes on acetate - Y _Ac/Ss kg/kg yield acetate produced by process microbes growing on substrate yeast cells - Y _Ss/Xs kg/kg yield soluble substrate from lysis of yeast cells - Y _Ss/Xp kg/kg yield soluble substrate from lysis of process microbes - Y _P/Xs kg/kg yield particulates from lysis of yeast cells - Y _P/Xp kg/kg yield particulates from lysis of process microbes - _{max (Ss)} h^–1 maximum specific growth rate constant for growth of process microbes on soluble substrate - _{max (Ac)} h^–1 maximum specific growth rate constant for growth of process microbes on acetate - Ks _Ss kg/m³ saturation coefficient for growth of process microbes on soluble substrate - Ks _Ac kg/m³ saturation coefficient for growth of process microbes on acetate - K _d h^–1 death/lysis rate constant for process microbes - K _i kg/m³ inhibition constant for growth of process microbes on acetate - K _L h^–1 lysis rate constant for whole yeast cells - K _h h^–1 hydrolysis rate constant for particulate biomass     

Structures and aromaticity of X2Y 2 − (X = C, Si, Ge and Y = N, P, As) anions

The equilibrium geometries, total energies, and vibrational frequencies of anions X₂Y₂ ⁻ (X = C, Si, Ge and Y = N, P, As) are theoretically investigated with density functional theory (DFT) method. Our calculation shows that for C₂N₂ ⁻ species, the D _2h isomer is the most stable four-membered structure, and for other species the C _2v isomer in which two X atoms are contrapuntal is the most stable structure at the B3LYP/6-311 +G_* level. Wiberg bond index (WBI) and negative nucleus-independent chemical shift (NICS) value indicate the existence of delocalization in stable X₂Y₂ ⁻ structures. A detailed molecular orbital (MO) analysis further reveals that stable isomers of these species have strongly aromatic character, which strengthens the structural stability and makes them closely connected with the concept of aromaticity.