The performance of approximations of farm contiguity compared to contiguity defined using detailed geographical information in two sample areas in Scotland: implications for foot-and-mouth disease modelling

BACKGROUND: When modelling infectious diseases, accurately capturing the pattern of dissemination through space is key to providing optimal recommendations for control. Mathematical models of disease spread in livestock, such as for foot-and-mouth disease (FMD), have done this by incorporating a transmission kernel which describes the decay in transmission rate with increasing Euclidean distance from an infected premises (IP). However, this assumes a homogenous landscape, and is based on the distance between point locations of farms. Indeed, underlying the spatial pattern of spread are the contact networks involved in transmission. Accordingly, area-weighted tessellation around farm point locations has been used to approximate field-contiguity and simulate the effect of contiguous premises (CP) culling for FMD. Here, geographic data were used to determine contiguity based on distance between premises’ fields and presence of landscape features for two sample areas in Scotland. Sensitivity, positive predictive value, and the True Skill Statistic (TSS) were calculated to determine how point distance measures and area-weighted tessellation compared to the ‘gold standard’ of the map-based measures in identifying CPs. In addition, the mean degree and density of the different contact networks were calculated. RESULTS: Utilising point distances <1 km and <5 km as a measure for contiguity resulted in poor discrimination between map-based CPs/non-CPs (TSS 0.279-0.344 and 0.385-0.400, respectively). Point distance <1 km missed a high proportion of map-based CPs; <5 km point distance picked up a high proportion of map-based non-CPs as CPs. Area-weighted tessellation performed best, with reasonable discrimination between map-based CPs/non-CPs (TSS 0.617-0.737) and comparable mean degree and density. Landscape features altered network properties considerably when taken into account. CONCLUSION: The farming landscape is not homogeneous. Basing contiguity on geographic locations of field boundaries and including landscape features known to affect transmission into FMD models are likely to improve individual farm-level accuracy of spatial predictions in the event of future outbreaks. If a substantial proportion of FMD transmission events are by contiguous spread, and CPs should be assigned an elevated relative transmission rate, the shape of the kernel could be significantly altered since ability to discriminate between map-based CPs and non-CPs is different over different Euclidean distances

Being Tamil, being Hindu:Tamil migrants’ negotiations of the absence of Tamil Hindu spaces in the West Midlands and South West of England

This paper considers the religious practices of Tamil Hindus who have settled in the West Midlands and South West of England in order to explore how devotees of a specific ethno-regional Hindu tradition with a well-established UK infrastructure in the site of its adherents’ population density adapt their religious practices in settlement areas which lack this infrastructure. Unlike the majority of the UK Tamil population who live in the London area, the participants in this study did not have ready access to an ethno-religious infrastructure of Tamil-orientated temples and public rituals. The paper examines two means by which this absence was addressed as well as the intersections and negotiations of religion and ethnicity these entailed: firstly, Tamil Hindus’ attendance of temples in their local area which are orientated towards a broadly imagined Hindu constituency or which cater to a non-Tamil ethno-linguistic or sectarian community; and, secondly, through the ‘DIY’ performance of ethnicised Hindu ritual in non-institutional settings

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Global soil and peat branched GDGT compilation dataset

Accurate temperature records for the deep geological past are a vital component of paleoclimate research. Distributional changes of branched glycerol dialkyl glycerol tetraether (brGDGT) lipids in geological archives including paleosoils are a promising indicators to infer past continental air temperatures. However, the 'orphan' status of the brGDGTs, the potential effect of temperature-independent parameters on their relative distribution, and the uneven geographical distribution of the soils used for calibration contribute to the high uncertainty of brGDGT-based transfer functions (root mean squared error, RMSE: ± 5 °C). Here, we expand the soil dataset from the previous calibration(s) with new and published soil data. We use Bayesian statistics to calibrate the relationship of the 5-methyl brGDGTs (MBT'5Me) and mean annual air temperature (MAAT). The addition of soils from warm (>28 °C) environments from India substantially increases the upper limit of the Bayesian calibration (BayMBT) from 25 to 29 °C, aiding in the generation of temperature records for past greenhouse climates, such as the Eocene. The BayMBT model also effectively minimizes the structured MAAT residuals prevalent in previous calibrations, therefore giving the opportunity to explore confounding factors on the calibration. We formulate a set of alternative calibration models to test the effect of specific environmental parameters and show that soils at mid-latitudes with temperature seasonalities >20 °C are not well described by the BayMBT model. We find that the MBT'5Me index is best correlated to the average temperature of all months >0 °C, called the BayMBT0 model. This finding supports the hypothesis that brGDGT production ceases or slows down in the winter months. However, a persistent feature of the BayMBT model and previous calibrations is the significant scatter at mid-latitudes, which is speculatively linked with a possible increase in diversity of microbial brGDGT-producing communities in these locations

BayMBT: A Bayesian calibration model for branched glycerol dialkyl glycerol tetraethers in soils and peats

Accurate temperature records for the deep geological past are a vital component of paleoclimate research. Distributional changes of branched glycerol dialkyl glycerol tetraether (brGDGT) lipids in geological archives including paleosoils are a promising indicators to infer past continental air temperatures. However, the ‘orphan’ status of the brGDGTs, the potential effect of temperature-independent parameters on their relative distribution, and the uneven geographical distribution of the soils used for calibration contribute to the high uncertainty of brGDGT-based transfer functions (root mean squared error, RMSE: ±5 °C). Here, we expand the soil dataset from the previous calibration(s) with new and published soil data. We use Bayesian statistics to calibrate the relationship of the 5-methyl brGDGTs (MBT′5Me) and mean annual air temperature (MAAT). The addition of soils from warm (>28 °C) environments from India substantially increases the upper limit of the Bayesian calibration (BayMBT) from 25 to 29 °C, aiding in the generation of temperature records for past greenhouse climates, such as the Eocene. The BayMBT model also effectively minimizes the structured MAAT residuals prevalent in previous calibrations, therefore giving the opportunity to explore confounding factors on the calibration. We formulate a set of alternative calibration models to test the effect of specific environmental parameters and show that soils at mid-latitudes with temperature seasonalities >20 °C are not well described by the BayMBT model. We find that the MBT′5Me index is best correlated to the average temperature of all months >0 °C, called the BayMBT0 model. This finding supports the hypothesis that brGDGT production ceases or slows down in the winter months. However, a persistent feature of the BayMBT model and previous calibrations is the significant scatter at mid-latitudes, which is speculatively linked with a possible increase in diversity of microbial brGDGT-producing communities in these locations

BayMBT : A Bayesian calibration model for branched glycerol dialkyl glycerol tetraethers in soils and peats

Accurate temperature records for the deep geological past are a vital component of paleoclimate research. Distributional changes of branched glycerol dialkyl glycerol tetraether (brGDGT) lipids in geological archives including paleosoils are a promising indicators to infer past continental air temperatures. However, the ‘orphan’ status of the brGDGTs, the potential effect of temperature-independent parameters on their relative distribution, and the uneven geographical distribution of the soils used for calibration contribute to the high uncertainty of brGDGT-based transfer functions (root mean squared error, RMSE: ±5 °C). Here, we expand the soil dataset from the previous calibration(s) with new and published soil data. We use Bayesian statistics to calibrate the relationship of the 5-methyl brGDGTs (MBT′5Me) and mean annual air temperature (MAAT). The addition of soils from warm (>28 °C) environments from India substantially increases the upper limit of the Bayesian calibration (BayMBT) from 25 to 29 °C, aiding in the generation of temperature records for past greenhouse climates, such as the Eocene. The BayMBT model also effectively minimizes the structured MAAT residuals prevalent in previous calibrations, therefore giving the opportunity to explore confounding factors on the calibration. We formulate a set of alternative calibration models to test the effect of specific environmental parameters and show that soils at mid-latitudes with temperature seasonalities >20 °C are not well described by the BayMBT model. We find that the MBT′5Me index is best correlated to the average temperature of all months >0 °C, called the BayMBT0 model. This finding supports the hypothesis that brGDGT production ceases or slows down in the winter months. However, a persistent feature of the BayMBT model and previous calibrations is the significant scatter at mid-latitudes, which is speculatively linked with a possible increase in diversity of microbial brGDGT-producing communities in these locations

Global soil and peat branched GDGT compilation dataset

Accurate temperature records for the deep geological past are a vital component of paleoclimate research. Distributional changes of branched glycerol dialkyl glycerol tetraether (brGDGT) lipids in geological archives including paleosoils are a promising indicators to infer past continental air temperatures. However, the 'orphan' status of the brGDGTs, the potential effect of temperature-independent parameters on their relative distribution, and the uneven geographical distribution of the soils used for calibration contribute to the high uncertainty of brGDGT-based transfer functions (root mean squared error, RMSE: ± 5 °C). Here, we expand the soil dataset from the previous calibration(s) with new and published soil data. We use Bayesian statistics to calibrate the relationship of the 5-methyl brGDGTs (MBT'5Me) and mean annual air temperature (MAAT). The addition of soils from warm (>28 °C) environments from India substantially increases the upper limit of the Bayesian calibration (BayMBT) from 25 to 29 °C, aiding in the generation of temperature records for past greenhouse climates, such as the Eocene. The BayMBT model also effectively minimizes the structured MAAT residuals prevalent in previous calibrations, therefore giving the opportunity to explore confounding factors on the calibration. We formulate a set of alternative calibration models to test the effect of specific environmental parameters and show that soils at mid-latitudes with temperature seasonalities >20 °C are not well described by the BayMBT model. We find that the MBT'5Me index is best correlated to the average temperature of all months >0 °C, called the BayMBT0 model. This finding supports the hypothesis that brGDGT production ceases or slows down in the winter months. However, a persistent feature of the BayMBT model and previous calibrations is the significant scatter at mid-latitudes, which is speculatively linked with a possible increase in diversity of microbial brGDGT-producing communities in these locations