Isolation and Characterization of Aeromonas jandaei from Nile Tilapia in Lake Volta, Ghana, and Its Response to Antibiotics and Herbal Extracts.

Production of Nile Tilapia Oreochromis niloticus contributes to economic growth in many countries. However, there has been a decline in its production over the years due to the influx of bacterial infections, with Aeromonas jandaei as an emerging threat. In this study, we identified and characterized A. jandaei from cage-cultured Nile Tilapia in Akosombo Stratum II of Lake Volta in Ghana and evaluated its response to commonly used antibiotics using the disc diffusion and agar well diffusion methods for herbal extracts at various concentrations (10, 30, 50, 70, and 100 mg/mL). The herbs considered included guava Psidium guajava leaf, bitter leaf Vernonia amygdalina, neem Azadirachta indica leaf, and their cocktail (GBNL in the ratio of 1:1:1). The bacterium was isolated from swab samples from the head kidneys of 27 moribund Nile Tilapia collected from nine fish farms. Samples were screened for A. jandaei by culturing and identification using morphological and molecular techniques. The bacterium isolate from fish in the study, identified as A. jandaei GH-AS II, had 92-93% identity to A. jandaei reference strains. Infection of healthy Nile Tilapia (n = 210) with the bacterium isolate showed that 1.0 × 105 CFU/mL was the lethal dose causing 50% mortality. Antibiotic susceptibility testing showed that A. jandaei GH-AS II was resistant to tetracycline and ampicillin. Herbal extracts at the various concentrations inhibited the growth of the bacterium isolate, with a significant increment in the zones of inhibition with increasing concentrations of leaf extracts. However, GBNL showed prominence compared to the other extracts only at 100 mg/mL. Management of A. jandaei GH-AS II by using herbal extracts at Nile Tilapia farms in Lake Volta may be recommended since the use of antibiotics, such as tetracycline and ampicillin, may not yield the needed result.

Tractor acquisition and agricultural performance among Nigerian farmers: Evidence from Co-integration Modeling technique.

Given the envisioned significance of the agricultural sector in Nigeria, which scholars have argued has the potential to promote pro-poor growth and poverty alleviation. However, questions have been raised about using and accessing modern agricultural machinery for farm cultivation. In this regard, this study uses a co-integration modelling technique to investigate the impact of tractor acquisition on agricultural performance. Two equations were established through the normalisation processes to proxy agricultural performance: the agricultural output and employment equations. Firstly, the analysis procedure requires a preliminary test to determine the statistical properties of the series used in this study; evidence from the outcome indicates that the series are not stationarity at level form and are integrated of order one. Secondly, based on the outcome of the preliminary result, the error correction model approach to co-integration was adopted, which helps to capture the short-term dynamics and long-term equilibrium of the effect of tractor acquisition on agricultural output and employment. The findings from the agricultural output equation show that credit to the agricultural sector, land cultivated for agricultural purposes, and tractor acquisition are statistically significant contributors to agricultural output. The agricultural employment equation shows that credit to agricultural purposes, land cultivated for agriculture and rainfall have a positive long-term relationship with employment. At the same time, tractor acquisition has a negative impact on agricultural employment in the long run. In the short run, exchange rate, rainfall, and tractor acquisition positively correlate with agricultural employment. In contrast, credit for agricultural purposes and land cultivated for agriculture negatively affects employment. Based on the findings, there is a need to strengthen policies that support access to credit, promote sustainable land use practices, and enhance water management. Also, policymakers should consider implementing measures that encourage the adoption of tractor technology while simultaneously addressing potential challenges related to job displacement.

TENDL-based evaluation and adjustment of p+111Cd between 1 and 100 MeV.

Proton induced reaction data are needed in the optimization of various radioisotope production routes, among others. In this work, the evaluation of proton-induced reactions on 111Cd between 1 and 100 MeV using the TALYS code system within an iterative Bayesian Monte Carlo (iBMC) framework, is presented. The method involves the simultaneous variation of a large number of nuclear reaction models included in the TALYS code system as well as their parameters. Each random TALYS calculation yields a vector of calculated values of cross section observables as well as the angular distributions, among others, which were compared with corresponding vectors of carefully selected differential experimental data for reaction channels where data were available. The random nuclear data file with the maximum likelihood function value obtained from combining the individual χ2s computed for the considered reaction channels was chosen as the parent vector and the starting point for the generation of a further set of random TALYS calculations. This was repeated multiple times until a targeted convergence of 5% was reached. The final evaluated file was compared with available experimental data from the EXFOR database as well as with the evaluations from the TENDL-2021 and JENDL5.0 libraries, and found to compare favorably.

Total Monte Carlo evaluation for dose calculations.

Total Monte Carlo (TMC) is a method to propagate nuclear data (ND) uncertainties in transport codes, by using a large set of ND files, which covers the ND uncertainty. The transport code is run multiple times, each time with a unique ND file, and the result is a distribution of the investigated parameter, e.g. dose, where the width of the distribution is interpreted as the uncertainty due to ND. Until recently, this was computer intensive, but with a new development, fast TMC, more applications are accessible. The aim of this work is to test the fast TMC methodology on a dosimetry application and to propagate the (56)Fe uncertainties on the predictions of the dose outside a proposed 14-MeV neutron facility. The uncertainty was found to be 4.2 %. This can be considered small; however, this cannot be generalised to all dosimetry applications and so ND uncertainties should routinely be included in most dosimetry modelling.