DataCat: The Research Data Catalogue (University of Liverpool)
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Food Safety & Animal Welfare in the Pork Value Chain of Nairobi
No full textPork production and consumption in Kenya is increasing rapidly, with a projected 203% increase in production between 2000-2030[1]. Nairobi, the capital city of Kenya, is an example of a rapidly changing urban environment where pork consumption is rising. Previous value-chain mapping undertaken through the ‘Urban Zoo’ project identified four major pork value chains in the vicinity of Nairobi. The pork supply to Nairobi is dominated (84%) by one large integrated company which sells the majority of its products to middle-high income consumers. The next largest pork supply to Nairobi is through a peri-urban abattoir slaughtering approximately 215 pigs/week, the vast majority (95%) of which are raised in urban and peri-urban farms in the surrounding area and which supplies retail and ‘pork joint’ outlets serving the middle-low income consumers in Nairobi.
Pork consumption has been associated with risk of exposure to Taenia solium , Salmonella spp., and Toxoplasma gondii amongst other pathogens[2]. The WHO Foodborne disease reference group (FERG) has indicated that T. solium is responsible for one of the greatest burdens of all foodborne diseases [3] and the burden of foodborne disease is felt most heavily in low & middle income countries such as Kenya [4].
A preliminary survey, utilising a commercial antigen ELISA, estimated an apparent Taenia spp. prevalence of 8.7% in pigs slaughtered at this key abattoir, indicating just one of the potential hazards present in pork consumed in Nairobi [5]. The ag-ELISA used, however, has demonstrated cross-reactivity with the non-zoonotic tapeworm T. hydategena and confirmatory tests are therefore required to determine the infection status of animals entering the pork value chain [6]. Other research groups have identified a high (13.8%) prevalence of Salmonella spp. in pork produced in Nairobi [7], but as yet we do not have any data on the presence and prevalence of other foodborne hazards including T. gondii and antimicrobial residues, which may be an important driver to antimicrobial resistance in consumers.
While undertaking food-safety hazard identification we are provided an opportunity to also undertake an assessment of animal welfare. Not only is it a moral imperative for us to ensure the welfare of the livestock that we rely on is protected, poor animal welfare can lead to economic losses (through trimming, condemnation or downgrading of carcasses) and increase the likelihood of carcasses being contaminated with potentially pathogenic bacteria, i.e. through broken skin such as tail biting lesions.
Five hundred and twenty-nine pigs were sampled between 5th January and 5th March 2021 at a large, non-integrated abbattoir in Nariobi, Kenya. Observational data and biological samples were collected. Meat samples were tested for ultimate pH, colour, drip-loss % and presence of antimicrobial residues using the Premi-test microinhibition test. Sera was tested using a commercial indirect multi-species ELISA from ID Vet Innovative Diagnostic, Montpellier, France to determine the presence of Toxoplasma specific IgG and partial carcass dissection (masseters, heart, tounge) undertaken to detect presence of T. solium cysticercosis
Machine Learning Prediction of Metal-Organic Framework Guest Accessibility from Linker and Metal Chemistry
No full textThis data set contains the Jupyter notebook and four .csv files for 1M1L3D data set described in our paper. There is a summary file for the one-metal-one-linker-3D (1M1L3D) dataset that contains metal and linker identities for 14,296 3D MOFs reported in Cambridge Structural Database (CSD) that have exactly one type metal and linker. There are also three files that contain features used to train the machine learning models. The notebook implements 3 sequential models to predict the pore limiting diameter (sometimes referred to as a pore window, or a pore aperture) of a metal-organic framework structure that is likely to be observed for a given metal-linker combination defined by the user
Supplementary Data for A Smart and Responsive Crystalline Porous Organic Cage Membrane with Switchable Pore Apertures for Graded Molecular Sieving
Get PDFSource data for A Smart and Responsive Crystalline Porous Organic Cage Membrane with Switchable Pore Apertures for Graded Molecular Sieving
Relatedness modulates density-dependent cannibalism rates in Drosophila
No full text1. Cannibalism is taxonomically widespread, and can have large impacts on individual fitness and population-level processes. As such, identifying how cannibalism rates vary in response to ecological cues is important for predicting species evolution and population dynamics.
2. In this study, we aimed to identify several eco-evolutionary factors that affect cannibalism rate and measure how they interacted with one another.
3. To do this, we conducted two experiments using complimentary methods to measure how cannibalism rates varied among larval Drosophila melanogaster and Drosophila simulans in response to changes in conspecific relatedness, social familiarity and density.
4. We found that larvae were more likely to cannibalise non-related larval victims in both species, and that this effect increased at high densities in D. simulans. We found no evidence that Drosophila larvae use social familiarity to assess relatedness. Finally, in D. melanogaster, cannibalistic larvae prefer to cannibalise larvae that are being attacked by a greater number of conspecifics, implying that cues linked to conspecific abundance encourage cooperative cannibalism.
5. By showing that cannibalism frequency in Drosophila spp. is sensitive to relatedness and several other factors, we reveal the complex relationship between cannibalism frequency and species ecology. Also, by showing that the effect of relatedness on cannibalism frequency is density-dependent, we advance the current understanding of how ecological variables interact to affect kin selection.
ANALYSIS CODE IS WRITTEN IN R
Low thermal conductivity in a modular inorganic material with bonding anisotropy and mismatch
No full textRaw data to accompany the publication "Low thermal conductivity in a modular inorganic material with bonding anisotropy and
mismatch" in Scienc
Direct observation of hyperpolarization breaking through the spin diffusion barrier
No full textDynamic nuclear polarization (DNP) is a widely used tool for overcoming the low intrinsic sensitivity of nuclear magnetic resonance spectroscopy and imaging. Its practical applicability is typically bounded, however, by the so-called ‘spin diffusion barrier’, which relates to the poor efficiency of polarization transfer from highly polarized nuclei close to paramagnetic centers to bulk nuclei. A quantitative assessment of this barrier has been hindered so far by the lack of general methods for studying nuclear-polarization flow in the vicinity of paramagnetic centers. Here we fill this gap and introduce a general set of experiments based on microwave gating that are readily implemented. We demonstrate the versatility of our approach in experiments conducted between 1.2 – 4.2 K in static mode and at 100 K under magic angle spinning (MAS) — conditions typical for dissolution-DNP and MAS-DNP — and for the first time directly observe the dramatic dependence of polarization flow on temperature
Comparative evaluation of ten lateral flow immunoassays to detect SARS-CoV-2 antibodies
No full textBackground: Rapid mobilisation from industry and academia following the outbreak of the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), led to the development and availability of SARS-CoV-2 lateral flow immunoassays (LFAs). High-quality LFAs are urgently needed at the point of care to add to currently available diagnostic tools. In this study, we provide evaluation data for ten LFAs suitable for use at the point of care.
Methods: COVID-19 positive patients (N=45), confirmed by reverse transcription – quantitative polymerase chain reaction (RT-qPCR), were recruited through the International Severe Acute Respiratory and Emerging Infection Consortium - Coronavirus Clinical Characterisation Consortium (ISARIC4C) study. Sera collected from patients with influenza A (N=20), tuberculosis (N=5), individuals with previous flavivirus exposure (N=21), and healthy sera (N=4), collected pre-pandemic, were used as negative controls. Ten LFAs manufactured or distributed by ASBT Holdings Ltd, Cellex, Fortress Diagnostics, Nantong Egens Biotechnology, Mologic, NG Biotech, Nal von Minden, and Suzhou Herui BioMed Co. were evaluated.
Results: Compared to RT-qPCR, sensitivity of LFAs ranged from 87.0-95.7%. Specificity against pre-pandemic controls ranged between 92.0-100%. Compared to IgG ELISA, sensitivity and specificity ranged between 90.5-100% and 93.2-100%, respectively. Percentage agreement between LFAs and IgG ELISA ranged from 89.6-92.7%. Inter-test agreement between LFAs and IgG ELISA ranged between kappa=0.792-0.854.
Conclusions: LFAs may serve as a useful tool for rapid confirmation of ongoing or previous infection in conjunction with clinical suspicion of COVID-19 in patients attending hospital. Impartial validation prior to commercial sale provides users with data that can inform best use settings
Plasmid fitness costs are caused by specific genetic conflicts enabling resolution by compensatory mutation
No full textExperimental data associated with "Plasmid fitness costs are caused by specific genetic conflicts enabling resolution by compensatory mutation". See README file for more details. Full analysis scripts can be found at github.com/jpjh/COMPMUT. Raw RNAseq reads can be found at NCBI-GEO, accession number GSE151570
Local Authority Finance: Income - Planning and development services (FIN_07_44)
No full textSummary
This indicator describes the income generated from the provision of Planning and development services by every Local Authority in England since 2007. Planning and development services income is calculated from any sales, fees and charges, as well as other types of income that are associated with delivering such services.
Technical description
The indicator was compiled from annual revenue outturn estimates of Local Authority (LA) revenue expenditure and financing. The Planning and development services income is calculated from the sum of a) sales, fees and charges and b) other types of income generated by these services, and thus does not include central government funding, capital gains or council tax. Income values are expressed in thousands (£) and presented on the basis of financial years, i.e. from April 1st to March 31st. Since some services are provided in Upper Tier and others in Lower Tier LAs, individual income figures from Upper Tier LAs were distributed to Lower Tier LAs based on annual population ratios (indicator FIN_07_44L), and Lower Tier LA income was distributed to Upper Tier LAs by aggregating (indicator FIN_07_44U). Income values from historic LA geography have been referenced to the 2018 LA geography. This includes changes in name/codes, merges, or splits of old LAs to new LAs based on population ratios for that year. The services income generated is expressed as the total amount as well as per capita, for direct comparisons. However, annual figures were not adjusted for inflation
Local Authority Finance: Income - Police services (FIN_07_47)
No full textSummary
This indicator describes the income generated from the provision of Police services by every Local Authority in England since 2007. Police services income is calculated from any sales, fees and charges, as well as other types of income that are associated with delivering such services.
Technical description
The indicator was compiled from annual revenue outturn estimates of Local Authority (LA) revenue expenditure and financing. The Police services income is calculated from the sum of a) sales, fees and charges and b) other types of income generated by these services, and thus does not include central government funding, capital gains or council tax. Income values are expressed in thousands (£) and presented on the basis of financial years, i.e. from April 1st to March 31st. Since some services are provided in Upper Tier and others in Lower Tier LAs, individual income figures from Upper Tier LAs were distributed to Lower Tier LAs based on annual population ratios (indicator FIN_07_47L), and Lower Tier LA income was distributed to Upper Tier LAs by aggregating (indicator FIN_07_47U). Income values from historic LA geography have been referenced to the 2018 LA geography. This includes changes in name/codes, merges, or splits of old LAs to new LAs based on population ratios for that year. The services income generated is expressed as the total amount as well as per capita, for direct comparisons. However, annual figures were not adjusted for inflation
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