Voluntary Running Exercise Alters Microbiota Composition and Increases n-Butyrate Concentration in the Rat Cecum

The effects of voluntary wheel-running exercise on cecal microbiota and short-chain fatty acid production were investigated in rats. The microbiota composition was notably different between the exercised and sedentary rats. Furthermore, the exercised rats showed a significantly higher n-butyrate concentration than the sedentary rats. This alteration of the cecal microbial environment may contribute to the beneficial effect of exercise on gastrointestinal disorders.     

Improved Microbial Production of Colominic Acid,a Homopolymer of N-Acetylneuraminic Acid

A culture medium has been devised for producing colominic acid in improved yields. Major improvements were obtained by using sorbitol as a source of carbon, by adding phosphate in high concentrations, and by supplementing a limited amount of yeast extract. E. coli O 16: Kl: HNM produced approximately 3000 µg/ml of colominic acid on cultivation at 37°C for 46 hr with a liquid medium consisting of sorbitol (2.0%), (NH₄)₂SO₄ (0.5%), K₂HPO₄ (1.4%), MgSO₄·7H₂O (0.05%), and yeast extract (0.05%).

Isolation and purification by deproteinization with ammonium sulfate, precipitation with ethanol, and by column chromatography on anion exchange resins resulted in a pure colominic acid preparation devoid of internal ester linkages.

In producing colominic acid, strains forming S-type colonies were more active than those forming R-type colonies.     

Application of Adaptive Cluster Sampling with a Data‐Driven Stopping Rule to Plant Disease Incidence

Plant pathologists need to manage plant diseases at low incidence levels. This needs to be performed efficiently in terms of precision, cost and time because most plant infections spread rapidly to other plants. Adaptive cluster sampling with a data‐driven stopping rule (ACS*) was proposed to control the final sample size and improve efficiency of the ordinary adaptive cluster sampling (ACS) when prior knowledge of population structure is not known. This study seeks to apply the ACS* design to plant diseases at various levels of clustering and incidences levels. Results from simulation study show that the ACS* is as efficient as the ordinary ACS design at low levels of disease incidence with highly clustered diseased plants and is an efficient design compared with simple random sampling (SRS) and ordinary ACS for some highly to less clustered diseased plants with moderate to higher levels of disease incidence.     

Suitability of cross-bred cows for organic farms based on cross-breeding effects on production and functional traits

Data from 113 Dutch organic farms were analysed to determine the effect of cross-breeding on production and functional traits. In total, data on 33 788 lactations between January 2003 and February 2009 from 15 015 cows were available. Holstein–Friesian pure-bred cows produced most kg of milk in 305 days, but with the lowest percentages of fat and protein of all pure-bred cows in the data set. Cross-breeding Holstein dairy cows with other breeds (Brown Swiss, Dutch Friesian, Groningen White Headed, Jersey, Meuse Rhine Yssel, Montbéliarde or Fleckvieh) decreased milk production, but improved fertility and udder health in most cross-bred animals. In most breeds, heterosis had a significant effect (P < 0.05) on milk (kg in 305 days), fat and protein-corrected milk production (kg in 305 days) and calving interval (CI) in the favourable direction (i.e. more milk, shorter CI), but unfavourably for somatic cell count (higher cell count). Recombination was unfavourable for the milk production traits, but favourable for the functional traits (fertility and udder health). Farm characteristics, like soil type or housing system, affected the regression coefficients on breed components significantly. The effect of the Holstein breed on milk yield was twice as large in cubicle housing as in other housing systems. Jerseys had a negative effect on fertility only on farms on sandy soils. Hence, breed effects differ across farming systems in the organic farming and farmers can use such information to dovetail their farming system with the type of cow they use.     

Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.     

Impacts of cage culture on physico-chemical and bacteriological water quality in Lake Volta,Ghana

The effects of cage fish farming on physico-chemical and bacteriological water quality in Lake Volta, Ghana, were investigated in 2013–2014. Farmed and unfarmed (control) areas of the lake were selected for monitoring. Nutrients, temperature, dissolved oxygen, conductivity, turbidity, pH, total coliforms, Pseudomonas and Vibrio spp. in the water were monitored monthly. Analyses of the water samples were carried out according to standard procedures. Physico-chemical quality of the water in both farm and control sites were within ranges typical of minimally impacted water and did not vary significantly between the two contrasting sites. The bacteriological analysis, however, revealed contamination of the lake water by fish farming. The bacterial counts at the farmed sites were significantly higher (p < 0.05) than those of the control sites, with figures at the farmed sites ranging from 132 to 1 708 cfu 100 ml?1 for total coliforms, 514 to 5 170 cfu 100 ml?1 Pseudomonas spp. and 14 to 516 cfu 100 ml?1 for Vibrio spp. The results suggested that cage fish farming has increased bacterial loads in the lake water, but has had minimal impact on its physico-chemical quality.     

Review: Milking robot utilization,a successful precision livestock farming evolution

Automatic milking systems (AMS), one of the earliest precision livestock farming developments, have revolutionized dairy farming around the world. While robots control the milking process, there have also been numerous changes to how the whole farm system is managed. Milking is no longer performed in defined sessions; rather, the cow can now choose when to be milked in AMS, allowing milking to be distributed throughout a 24 h period. Despite this ability, there has been little attention given to milking robot utilization across 24 h. In order to formulate relevant research questions and improve farm AMS management there is a need to determine the current knowledge gaps regarding the distribution of robot utilization. Feed, animal and management factors and their interplay on levels of milking robot utilization across 24 h for both indoor and pasture-based systems are here reviewed. The impact of the timing, type and quantity of feed offered and their interaction with the distance of feed from the parlour; herd social dynamics, climate and various other management factors on robot utilization through 24 h are provided. This novel review draws together both the opportunities and challenges that exist for farm management to use these factors to improved system efficiency and those that exist for further research.