The AFBI Repository
Not a member yet
442 research outputs found
Sort by
Phosphorus Removal from Dirty Farmyard Water by Activated Anaerobic-Digestion-Derived Biochar
Get PDFPublication history: Accepted - 21 November 2022; Published online - 5 December 2022.The management of anaerobic digestate is important to realize the value of the waste and enhance the whole system
sustainability of anaerobic digestion. In this study, the phosphorus treatment of dirty irrigation water by biochar samples derived
from digestate of anaerobic digestion were investigated. The biochars were further activated by steam activation with different
duration time and KOH activation with different introducing ratios; the textural properties of biochars were optimized after
activation from the aspect of biochar characterization. Notably, AD-N2 demonstrates a remarkable adsorption effect of phosphorus,
with an adsorption efficiency of 8.99 mg g−1. Besides the effect of biochar dosage on phosphorus removal, adsorption kinetics and
thermodynamic isotherms are studied. According to the adsorption kinetics, the adsorption of phosphorus from dirty water fits the
Elovich equation (R2 = 0.95). Furthermore, the thermodynamic isotherm results illustrate the process of phosphorus removal by
biochar is endothermic (ΔH0 = 17.93 kJ mol−1) and spontaneous (ΔS = 96.24 J mol−1 K−1). Therefore, this work suggests a
promising solution to phosphorus-related environmental challenges in industry and agriculture.This project has received funding from the European Union’s
Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie grant agreement no. 823745. UK
Catalysis Hub is kindly thanked for the resources and support
provided via our membership of the UK Catalysis Hub
Consortium and funded by EPSRC grant: EP/R026939/1,
EP/R026815/1, EP/R026645/1, EP/R027129/1, or EP/
M013219/1 (biocatalysis)
The Antioxidant Effect of Natural Antimicrobials in Shrimp Primary Intestinal Cells Infected with Nematopsis messor
Get PDFPublication history: Accepted - 12 May 2022; Published - 15 May 2022.Nematopsis messor infections severely impact on shrimp’s health with devastating economic consequences on shrimp farming. In a shrimp primary intestinal cells (SGP) model of infection, a sub-inhibitory concentration (0.5%) of natural antimicrobials (Aq) was able to reduce the ability of N. messor to infect (p < 0.0001). To prevent N. messor infection of SGP cells, Aq inhibits host actin polymerization and restores tight junction integrity (TEER) and the expression of Zo-1 and occluding. The oxidative burst, caused by N. messor infection, is attenuated by Aq through the inhibition of NADPH-produced H2O2. Simultaneous to the reduction in H2O2 released, the activity of catalase (CAT) and superoxide dismutase (SOD) were also significantly increase (p < 0.0001). The antimicrobial mixture inactivates the ERK signal transduction pathway by tyrosine dephosphorylation and reduces the expression of DCR2, ALF-A, and ALF-C antimicrobial peptides. The observed in vitro results were also translated in vivo, whereby the use of a shrimp challenge test, we show that in N. messor infected shrimp the mortality rate was 68% compared to the Aq-treated group where the mortality rate was maintained at 14%. The significant increase in CAT and SOD activity in treated and infected shrimp suggested an in vivo antioxidant role for Aq. In conclusion, our study shows that Aq can efficiently reduce N. messor colonization of shrimp’s intestinal cells in vitro and in vivo and the oxidative induced cellular damage, repairs epithelial integrity, and enhances gut immunityThis study was supported by a grant awarded to NC by Environtech, Dublin, Ireland
Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle
Get PDFPublication history: Accepted - 9 February 2022; Published online - 26 March 2022Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling.This paper is the result of the concerted effort of all participants and support from the networks of COST Action FA1302 “METHAGENE: Large-scale methane measurements on individual ruminants for genetic evaluations.” The authors thank all individuals and groups who have directly or indirectly contributed to this work; special thanks are due to the technical and financial support from the COST Action FA1302 of the European Union. In addition, all financial and technical support from all participating countries and research centers involved in this work is greatly acknowledged
Biocides as Biomedicines against Foodborne Pathogenic Bacteria
Get PDFPublication history: Accepted - 31 January 2022; Published - 4 February 2022.Biocides are currently considered the first line of defense against foodborne pathogens in
hospitals or food processing facilities due to the versatility and efficiency of their chemical active
ingredients. Understanding the biological mechanisms responsible for their increased efficiency,
especially when used against foodborne pathogens on contaminated surfaces and materials, represents
an essential first step in the implementation of efficient strategies for disinfection as choosing
an unsuitable product can lead to antibiocide resistance or antibiotic–biocide cross-resistance. This
review describes these biological mechanisms for the most common foodborne pathogens and focuses
mainly on the antipathogen effect, highlighting the latest developments based on in vitro and in vivo
studies. We focus on biocides with inhibitory effects against foodborne bacteria (e.g., Escherichia
spp., Klebsiella spp., Staphylococcus spp., Listeria spp., Campylobacter spp.), aiming to understand their
biological mechanisms of action by looking at the most recent scientific evidence in the field.This research was funded by Environtech, grant number 49650
Assessment of Rapid MinION Nanopore DNA Virus Meta-Genomics Using Calves Experimentally Infected with Bovine Herpes Virus-1
Get PDFPublication history: Accepted - 20 August 2022; Published - 24 August 2022.Bovine respiratory disease (BRD), which is the leading cause of morbidity and mortality in
cattle, is caused by numerous known and unknown viruses and is responsible for the widespread use
of broad-spectrum antibiotics despite the use of polymicrobial BRD vaccines. Viral metagenomics
sequencing on the portable, inexpensive Oxford Nanopore Technologies MinION sequencer and
sequence analysis with its associated user-friendly point-and-click Epi2ME cloud-based pathogen
identification software has the potential for point-of-care/same-day/sample-to-result metagenomic
sequence diagnostics of known and unknown BRD pathogens to inform a rapid response and vaccine
design. We assessed this potential using in vitro viral cell cultures and nasal swabs taken from calves
that were experimentally challenged with a single known BRD-associated DNA virus, namely, bovine
herpes virus 1. Extensive optimisation of the standard Oxford Nanopore library preparation protocols,
particularly a reduction in the PCR bias of library amplification, was required before BoHV-1 could be
identified as the main virus in the in vitro cell cultures and nasal swab samples within approximately
7 h from sample to result. In addition, we observed incorrect assignment of the bovine sequence to
bacterial and viral taxa due to the presence of poor-quality bacterial and viral genome assemblies in
the RefSeq database used by the EpiME Fastq WIMP pathogen identification software
Farm Biosecurity Measures and Interventions with an Impact on Bacterial Biofilms
Get PDFPublication history: Accepted - 16 August 2022; Published - 18 August 2022.Farm biosecurity management includes a set of practical measures used to prevent and
limit the spread of infections to humans and animals. Infections, predominantly caused by zoonotic
agents, often occur due to a lack of safety standards monitoring on farms, but also because of the
use of inappropriate antimicrobial products leading to bacterial resistance, tolerance to biocides and
the emergence antimicrobial-resistant germs. To date, research was mainly focused on studying the
antimicrobial resistance in bacterial biofilms and the mechanisms involved in their occurrence. At
molecular level, the limited diffusion of biocides in the biofilm matrix, enzyme-mediated resistance,
genetic adaptation, efflux pumps, and levels of metabolic activity inside the biofilm are some of the
investigated biological mechanisms which can promote antimicrobial resistance in biofilms were also
investigated. Interventions, based on the identification of novel antimicrobial compounds, that would
exclude the occurrence of bacterial tolerance, including essential oils (oregano, cloves), organic acids
(tannic & oleic acid) and natural plant compounds (e.g. alkaloids, flavonoids, tannins and coumarins)
were also extensively studied and reviewed given their effectiveness against pathogen-produced
biofilms. The aim of this review was emphasize the importance of biosecurity and farm management
practices and to assess their impact on bacterial biofilm formation. Furthermore, we present the
recent intervention strategies aimed at reducing and combating the formation of bacterial biofilms in
livestock farms.Environtech, Dublin, Ireland - funding the PhD of Eugenia Butucel.
Grant number 49650
Ecological Footprint and Its Determinants in MENA Countries: A Spatial Econometric Approach
Get PDFPublication history: Accepted - 15 September 2022; Published online - 18 September 2022Countries in the Middle East and North Africa (MENA) have been facing serious environmental issues due to over-exploitation of natural resources. This paper analyzes the ecological footprint as a proxy of environmental degradation and determines its influencing factors in 18 MENA countries during 2000–2016. Despite the many studies on the relationship between the ecological footprint and its determinants in the region, the current study use spatial econometric models to take into account spatial dependence in the ecological footprint as well as its determinants. Using a spatial Durbin model, we revealed that neighbors’ behavior can significantly affect a country’s ecological footprint. Factors such as GDP per capita, trade openness, and financial development were found to increase environmental degradation, while the renewable energy consumption, urbanization, and quality of democracy effectively reduce the ecological footprint. These factors not only affect the ecological footprint in the host country, but also affect it in the adjacent countries in different ways. Due to the interdependence of the countries, we recommend development of a regional vision of the bio-economy such that the scope of the analysis goes beyond the country level to account for territorial effects. Furthermore, considering the great potential for renewable energy consumption in the region, we recommend MENA countries to develop use of renewable energy sources in order to reduce environmental degradation in the regio
The First Whole Genome Sequence and Characterisation of Avian Nephritis Virus Genotype 3
Get PDFPublication history: Accepted - 27 January 2021; Published - 3 February 2021.Avian nephritis virus (ANV) is classified in the Avastroviridae family with disease associations
with nephritis, uneven flock growth and runting stunting syndrome (RSS) in chicken and turkey
flocks, and other avian species. The whole genome of ANV genotype 3 (ANV-3) of 6959 nucleotides
including the untranslated 5’ and 3’ regions and polyadenylated tail was detected in a metagenomic
virome investigation of RSS-affected chicken broiler flocks. This report characterises the ANV-3
genome, identifying partially overlapping open reading frames (ORFs), ORF1a and ORF1b, and an
opposing secondary pseudoknot prior to a ribosomal frameshift stemloop structure, with a separate
ORF2, whilst observing conserved astrovirus motifs. Phylogenetic analysis of the Avastroviridae
whole genome and ORF2 capsid polyprotein classified the first complete whole genome of ANV-3
within Avastroviridae genogroup 2.This research was partially funded by the Department of Agriculture, Environment &
Rural Affairs—Code PG 13/02, and the Agri-Food and Biosciences Institute (AFBI)—Code 44955
Fine-Scale Heterogeneity of a Cold-Water Coral Reef and Its Influence on the Distribution of Associated Taxa
Get PDFPublication history: Accepted - 18 February 2021; Published online - 23 March 2021Benthic fauna form spatial patterns which are the result of both biotic and abiotic processes, which can be quantified with a range of landscape ecology descriptors. Fine- to medium-scale spatial patterns (200 m2 were created and all organisms were geotagged in order to illustrate their point pattern. The pair correlation function was used to establish whether organisms demonstrated a clustered pattern (CP) at various scales. We further applied a point pattern modelling approach to identify four potential point patterns: complete spatial randomness (CSR), an inhomogeneous pattern influenced by environmental drivers, random clustered point pattern indicating biologically driven clustering and an inhomogeneous clustered point pattern driven by a combination of environmental drivers and biological effects. Reef framework presence and structural complexity determined inhabitant distribution with most organisms showing a departure from CSR. These CPs are likely caused by an affinity to local environmental drivers, growth patterns and restricted dispersion reproductive strategies within the habitat across a range of fine to medium scales. These data provide novel and detailed insights into fine-scale habitat heterogeneity, showing that non-random distributions are apparent and detectable at these fine scales in deep-sea habitats.These data were collected during the Querci expedition, funded by the Marine Institute under the Ship Time Programme of the National Development Plan. DP was funded by the Natural Environmental Research Council (grant number NE/N012070/1) and University of Southampton GSNOCS European Exchange Programme. VH was funded by the NERC CLASS project (grant number NE/R015953/1). AL and VH were supported by the iAtlantic project of the EU H2020 Research and Innovation Programme (grant number 818123). CLI was supported by the H2020 MSC Action HABISS (GA 890815)
Assessing the impact of participatory extension programme membership on farm business performance in Northern Ireland.
Get PDFPublication history: Accepted - 26 September 2021; Published online - 30 September 2021.This paper evaluates the impact of membership of the Business Development Groups (BDG), a participatory extension programme in Northern Ireland on the economic performance of participating farmers for dairy and sheep enterprise groups. The study employs the conditional difference-in-differences approach which combines a non-parametric matching estimator with a difference-in-differences analytical technique to obtain a credible best-estimates of the causal effect of BDG membership on farmers’ economic performance assuming that BDG participation is as good as random after controlling for observable farm characteristics and that the parallel trends assumption holds between BDG participants and non-participants. The results of the analyses showed that membership in the BDG programme has a statistically significant impact on the economic performance of participating farmers. Specifically, the results showed that farmers who are members of the dairy and sheep BDGs increased their gross margin by £109.10 and £17.10 per head respectively compared to farmers that are non-members of the BDGs. The results of the study provide robust evidence to inform policy development around the area of participatory extension programmes. It also supports the design of efficient agricultural education and extension systems that incorporates the ideas of the farmers themselves through peer-to-peer learning thereby maximising the economic and social benefits accruable from such programmes.This research was undertaken as part of the Northern Ireland Department of Agriculture, Environment and Rural Affairs Evidence and Innovation Programme (DAERA E&I 18/1/01)—Impact of RDP: Evaluation of the Rural Development Programme 2014–2020, Farm Business Improvement
Scheme-Longitudinal Stud