Iron Age occupation evidence from Port Lobh, Colonsay (Scottish Inner Hebrides)

Evidence of a new Iron Age occupation site is presented from a site located at the southern edge of a former tidal estuary in western Colonsay. A radiocarbon date of between the 1st–2nd centuries BC is significant in a regional context, being the first of this period from the island. Recovered burnt occupation debris includes cattle bone, marine (limpet and periwinkle) shell and ceramics along with a terrestrial snail shell and carbonised macroplant assemblage. The site was identified from geophysical survey (magnetometry and resistivity) focused at an earlier 5th–4th millennia BC shell midden. The discovery highlights the value of alternative field techniques and looking beyond fortified sites to find more elusive settlement evidence

Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data.

There exists significant interest in developing statistical and computational tools for inferring 'who infected whom' in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains

FP6 CEDER Project Deliverable 3.2 "Benefits of a new reporting system"

Addressing the uncertainties in fishing activities, the CEDER project examines the use of observer reports, landings, e-logbooks, VMS and GPS tracks, and fishery-specific information. Such information was assessed in order to provide more accurate and timelier data on effort, catches, discards, and/or landings. This document contains CEDER¿s Project Implementation Plan for policy makers, as well as expected benefits for government, industry, and science. The CEDER consortium advocates the use of GPS data at 15 minute intervals for scientific purposes. Among these are improved spatial planning and a new fishing effort measure, the actual effort while fishing, which can be inferred from vessel behaviour. The correlation between catch and effort can be used as an indicator for inspectors, but one cannot reliably guess catches from effort. VMS and logbook data can be matched using rule-bases systems, leading to higher data quality and better use of quota. Furthermore, if fishing mortality were known in near real time, then the integration of current year fishing mortality into management plans would yield benefits for stock recovery. The full realisation of such benefits requires a re-appraisal of the 15% TAC revision rule. The CEDER consortium insists that any roll-out of the ERS e-logbook must be properly enforced, and that the e-logbook cannot by itself replace observer reports. Finally, estimating discards may be feasible in selected fisheries, but additional means such as gear sensors may be required in order to get more reliable data in the general case.JRC.G.4-Maritime affair

The Hornless Australian Burrowing Mayfly Ulmerophlebia (Ephemeroptera: Leptophlebiidae)

The hornless Australian burrowing mayfly genus Ulmerophlebia Demoulin (Leptophlebiidae) is revised based on comprehensive examinations of adult and larval material collected throughout Australia. Two new species [Ulmerophlebia deani n. sp. and U. minuta n. sp.] and three named species [U. annulata (Harker), U. mjobergi (Ulmer) and U. pipinna Suter] are included. The larva of U. deani can be distinguished by the moderately developed apicomedial expansion of gills and W-shaped markings on the abdominal terga. The male adult of U. minuta can be easily distinguished by the greatly reduced penes. Descriptions, diagnoses, line-drawings of key characters, material and distributional data, taxonomic remarks and adult and larval keys are provided

When are pathogen genome sequences informative of transmission events?

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of 'transmission divergence', defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data

epicontacts: Handling, visualisation and analysis of epidemiological contacts.

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub