Exploring pig trade patterns to inform the design of risk-based disease surveillance and control strategies

An understanding of the patterns of animal contact networks provides essential information for the design of risk-based animal disease surveillance and control strategies. This study characterises pig movements throughout England and Wales between 2009 and 2013 with a view to characterising spatial and temporal patterns, network topology and trade communities. Data were extracted from the Animal and Plant Health Agency (APHA)’s RADAR (Rapid Analysis and Detection of Animal-related Risks) database, and analysed using descriptive and network approaches. A total of 61,937,855 pigs were moved through 872,493 movements of batches in England and Wales during the 5-year study period. Results show that the network exhibited scale-free and small-world topologies, indicating the potential for diseases to quickly spread within the pig industry. The findings also provide suggestions for how risk-based surveillance strategies could be optimised in the country by taking account of highly connected holdings, geographical regions and time periods with the greatest number of movements and pigs moved, as these are likely to be at higher risk for disease introduction. This study is also the first attempt to identify trade communities in the country, information which could be used to facilitate the pig trade and maintain disease-free status across the country in the event of an outbreak

Streaming histogram sketching for rapid microbiome analytics

Background: The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time. Results: We apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed ‘histosketch’ that can efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using the pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we use a ‘real life’ example to show that histosketches can train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a random forest classifier that could accurately predict whether the neonate had received antibiotic treatment (97% accuracy, 96% precision) and could subsequently be used to classify microbiome data streams in less than 3 s. Conclusions: Our method offers a new approach to rapidly process microbiome data streams, allowing samples to be rapidly clustered, indexed and classified. We also provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2 GB microbiome in 50 s on a standard laptop using four cores, with the sketch occupying 3000 bytes of disk space

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics

Physical and emotional health outcomes after 12 months of public-sector antiretroviral treatment in the Free State Province of South Africa: a longitudinal study using structural equation modelling

<p>Abstract</p> <p>Background</p> <p>African and Asian cohort studies have demonstrated the clinical efficacy of antiretroviral treatment (ART) in resource-limited settings. However, reports of the long-term changes in the physical and emotional quality of life (QoL) of patients on ART in these settings are still scarce. In this study, we assessed the physical and emotional QoL after six and 12 months of ART of a sample of 268 patients enrolled in South Africa's public-sector ART programme. The study also tested the impact of the adverse effects of medication on patients' physical and emotional QoL.</p> <p>Methods</p> <p>A stratified random sample of 268 patients undergoing ART was interviewed at baseline (< 6 months ART) and follow-up (< 12 months ART). A model of the relationships between the duration of ART, the adverse effects of medication, and physical and emotional QoL (measured using EUROQOL-5D) was tested using structural equation modelling.</p> <p>Results</p> <p>The improved physical and emotional QoL shown at baseline was sustained over the 12-month study period, because treatment duration was not significantly associated with changes in the patients' QoL. Physical QoL significantly and positively influenced the patients' emotional QoL (subjective well-being [SWB]) (β = 0.33, <it>P </it>< 0.01). Longitudinal data showed that patients reported significantly fewer adverse effects at follow-up than at baseline (β = -0.38, <it>P </it>< 0.001) and that these adverse effects negatively influenced physical (β = -0.27, <it>P </it>< 0.01) and emotional QoL (β = -0.15, <it>P </it>< 0.05).</p> <p>Conclusion</p> <p>This study provides evidence that the South African public-sector ART programme is effective in delivering sustained improvement in patient well-being. However, the results should encourage clinicians and lay health workers to be vigilant regarding the adverse effects of treatment, because they can seriously affect physical and emotional QoL.</p

Association between hospitalization-related outcomes, dynapenia and body mass index: The Glisten Study

Objective To compare the prognostic value of dynapenia, as evaluated by handgrip, and body mass index (BMI) on length of stay (LOS), days of bed rest, and other hospitalization-related outcomes in a population of older adults admitted to 12 italian acute care divisions.Methods Data on age, weight, BMI, comorbidities, ADL, physical activity level, muscle strength, were recorded at hospital admission. LOS, days of bed rest, intrahospital falls, and discharge destination were also recorded during the hospitalization. Subjects with BMI &lt;18.5 kg/m(2) were classified as underweight, subjects with BMI 18.5-24.9 as normal weight, subjects with BMI &gt;= 25 as overweight-obese.Results A total of 634 patients, mean age 80.8 +/- 6.7 years and 49.4% women, were included in the analysis. Overall dynapenic subjects (D) showed a longer period of LOS and bed rest compared with non-dynapenic (ND). When the study population was divided according to BMI categories, underweight (UW), normal weight (NW), and overweight-obese (OW-OB), no significant differences were observed in hospital LOS and days of bed rest. When analysis of covariance was used to determine the difference of LOS across handgrip/BMI groups, D/OW-OB and D/UW subjects showed significantly longer LOS (11.32 and 10.96 days, both p 0.05) compared to ND/NW subjects (7.69 days), even when controlling for age, gender, baseline ADL, cause of hospitalization and comorbidity. After controlling for the same confounding factors, D/OW-OB, D/NW and D/UW subjects showed significantly longer bed rest (4.7, 4.56, and 4.05 days, respectively, all p 0.05, but D/OWOB p 0.01) compared to ND/NW subjects (1.59 days).Conclusion In our study population, LOS is longer in D/UW and D/OW-OB compared to ND/NW subjects and days of bed rest are mainly influenced by dynapenia, and not by BMI class