Modelling of digesta passage rates in grazing and browsing domestic and wild ruminant herbivores

Ruminant utilization of poor-quality feeds is governed by rates of digestion and of passage through the rumen. The passage rate of feed material determines the quantity of bypass nutrients and the efficiency of synthesis of microbial protein in the rumen, making modelling of passage rate important. Artificial neural networks were used to develop models of liquid and solid passage rates. Studies that reported fractional passage rates, along with class and body mass of ruminants, were included in the dataset. Factors that affect rates of passage in all the studies were identified, which included animal and feed factors. The dataset was composed of observations of domestic and wild ruminants of variable body mass (1.5 to 1238 kg) from 74 studies and 17 ruminant species from various climatic regions. Observations were randomly divided into two data subsets: 75% for training and 25% for validation. Developed models accounted for 66 and 82% of the variation in prediction of passage rates for solid and liquid, respectively. On validation with an independent dataset, these models attained 42 and 64% of precision in predicting passage rates for solid and liquid, respectively. Liquid and solid prediction passage rate models had no linear and mean bias in prediction. This study developed better prediction models for solid and liquid passage rates for ruminants fed on a variety of diets and/or feeds from different climatic regions.Keywords: Artificial neural networks, intake, mean retention time, prediction equation, rume

Novel optimal temperature profile for acidification process of Lactobacillus bulgaricus and Streptococcus thermophilus in yoghurt fermentation using artificial neural network and genetic algorithm

The acidification behavior of Lactobacillus bulgaricus and Streptococcus thermophilus for yoghurt production was investigated along temperature profiles within the optimal window of 38-44 degrees C. For the optimal acidification temperature profile search, an optimization engine module built on a modular artificial neural network (ANN) and genetic algorithm (GA) was used. Fourteen batches of yoghurt fermentations were evaluated using different temperature profiles in order to train and validate the ANN sub-module. The ANN captured the nonlinear relationship between temperature profiles and acidification patterns on training data after 150 epochs. This served as an evaluation function for the GA. The acidification slope of the temperature profile was the performance index. The GA sub-module iteratively evolved better temperature profiles across generations using GA operations. The stopping criterion was met after 11 generations. The optimal profile showed an acidification slope of 0.06117 compared to an initial value of 0.0127 and at a set point sequence of 43, 38, 44, 43, and 39 degrees C. Laboratory evaluation of three replicates of the GA suggested optimum profile of 43, 38, 44, 43, and 39 degrees C gave an average slope of 0.04132. The optimization engine used (to be published elsewhere) could effectively search for optimal profiles of different physico-chemical parameters of fermentation processes

Metagenomic assessment of nitrate-contaminated mine wastewaters and optimization of complete denitrification by indigenous enriched bacteria

Nitrate contamination in water remains to be on the rise globally due to continuous anthropogenic activities, such as mining and farming, which utilize high amounts of ammonium nitrate explosives and chemical-NPK-fertilizers, respectively. This study presents insights into the development of a bioremediation strategy to remove nitrates (NO3−) using consortia enriched from wastewater collected from a diamond mine in Lesotho and a platinum mine in South Africa. A biogeochemical analysis was conducted on the water samples which aided in comparing and elucidating their unique physicochemical parameters. The chemical analysis uncovered that both wastewater samples contained over 120 mg/L of NO3− and over 250 mg/L of sulfates (SO42-), which were both beyond the acceptable limit of the environmental surface water standards of South Africa. The samples were atypical of mine wastewaters as they had low concentrations of dissolved heavy metals and a pH of over 5. A metagenomic analysis applied to study microbial diversities revealed that both samples were dominated by the phyla Proteobacteria and Bacteroidetes, which accounted for over 40% and 15%, respectively. Three consortia were enriched to target denitrifying bacteria using selective media and then subjected to complete denitrification experiments. Denitrification dynamics and denitrifying capacities of the consortia were determined by monitoring dissolved and gaseous nitrogen species over time. Denitrification optimization was carried out by changing environmental conditions, including supplementing the cultures with metal enzyme co-factors (iron and copper) that were observed to promote different stages of denitrification. Copper supplemented at 50 mg/L was observed to be promoting complete denitrification of over 500 mg/L of NO3−, evidenced by the emission of nitrogen gas (N2) that was more than nitrous oxide gas (N2O) emitted as the terminal by-product. Modification and manipulation of growth conditions based on the microbial diversity enriched proved that it is possible to optimize a bioremediation system that can reduce high concentrations of NO3−, while emitting an environmentally-friendly N2 instead of N2O, that is, a greenhouse gas. Data collected and discussed in this research study can be used to model an upscale NO3− bioremediation system aimed to remove nitrogenous and other contaminants without secondary contamination

Optimization of Operational Parameters for Biohydrogen Production from Waste Sugarcane Leaves and Semi-Pilot Scale Process Assessment

This study modeled and optimized the operational parameters for biohydrogen production from waste sugarcane leaves and assessed hydrogen production on a semi-pilot scale. A Box-Behnken design with input variables of substrate concentration (8 to 24 g/L), inoculum concentration (10% to 50% v/v), and hydraulic retention time (HRT, 24 to 96 h) was used. A coefficient of determination (R2) of 0.90 and the predicted optimum operational set-points of 14.2 g/L substrate concentration, 32.7% inoculum concentration, and 62.8 h HRT were obtained. Experimental validation produced a biohydrogen yield of 12.8 mL H2/g fermentable sugar (FS). A semi-pilot scale process in a 13-L Infors reactor under optimized conditions gave a cumulative hydrogen volume and yield of 3740 mL and 321 mL H2 g-1 FS, respectively, with a peak hydrogen fraction of 37%. Microbial analysis from the process effluent conducted by Polymerase Chain Reaction cloning indicated the presence of hydrogen-producing bacteria belonging to Clostridium sp., Klebsiella sp., and Enterobacter sp. These findings highlight the feasibility of biohydrogen production from sugarcane waste and provide preliminary knowledge on process scale up

Antimicrobial resistance of bacterial strains isolated from orange juice products

Forty samples of twenty brands of sachet orange juice products were examined microbiologically. All the products were contaminated with bacteria and yeasts. The organisms encountered include Saccharomyces cerevisiae , Saccharomyces sp, Rhodotorula sp, Bacillus cereus , Bacillus subtilis , Escherichia coli , Staphylococcus aureus , Streptococcus pyogenes and Micrococcus sp. The resistances of thirty bacterial strains isolated from orange juice products to the commonly used antibiotics were studied. About 66.67% of the isolates were resistant to augmentin and amoxycillin; 63.33% to cotrimoxazole, 56% to cloxacillin, and 23.33% to tetracycline. Resistances of 10, 6.67, and 3.33% were obtained for gentamicin, erythromycin and chloramphenicol respectively. Among the eight antibiotics tested, seven patterns of drug resistance were obtained. Six out of these are multiple-drug resistance with number of antibiotics ranging between 2 to 8. While MIC of amoxycillin ranged between 10-25mg/ml for the strains of E. coli, MIC of 10-20mg/ml was obtained for the strains of S. aureus. The MIC for cloxacillin was 0.1-1.0mg/ml for E. coli strains, and 0.01-1.0mg/ml for S. aureus strains. In all, ten strains of the bacterial isolates had evidence for the production of b-lactamases

Biogenic synthesis of silver nanoparticles using a pod extract of Cola nitida: Antibacterial and antioxidant activities and application as a paint additive

This work reports the biogenic synthesis of silver nanoparticles (AgNPs) using the pod extract of Cola nitida, the evaluation of their antibacterial and antioxidant activities, and their application as an antimicrobial additive in paint. The AgNPs were characterized with UV–Vis spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, and transmission electron microscopy (TEM). The AgNP solution was dark brown with a maximum absorbance occurring at 431.5 nm. The FTIR spectrum showed strong peaks at 3336.85, 2073.48, and 1639.49 cm−1, indicating that proteins acted as the capping and stabilization agents in the synthesis of the AgNPs. The AgNPs were spherical, with sizes ranging from 12 to 80 nm. Energy dispersive X-ray (EDX) analysis showed that silver was the prominent metal present, while the selected area electron diffraction pattern conformed to the face-centred cubic phase and crystalline nature of AgNPs. At various concentrations between 50 and 150 μg/ml, the AgNPs showed strong inhibition of the growth of multidrug resistant strains of Klebsiella granulomatis, Pseudomonas aeruginosa, and Escherichia coli. In addition, at 5 μg/ml, the AgNPs completely inhibited the growth of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Aspergillus niger, A. flavus and A. fumigatus in a paint-AgNP admixture. The AgNPs exhibited a potent antioxidant activity with an IC50 of 43.98 μg/ml against 2,2-diphenyl-1-picrylhydrazyl and a ferric ion reduction of 13.62–49.96% at concentrations of 20–100 μg/ml. This study has demonstrated the biogenic synthesis of AgNPs that have potent antimicrobial and antioxidant activities and potential biomedical and industrial applications. To the best of our knowledge, this work is the first to use the pod extract of C. nitida for the green synthesis of nanoparticles