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"Jeffrey, Benjamin"
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Toward a Post-Apocalyptic Rule of Law
This paper considers how science fiction, and the subgenres of speculative historicism and futurism in particular, might open legal discourse to hitherto unseen and potentially instructive perspectives. It begins with the proposition that recent historical events of global significance such as the election of Donald Trump in 2016, the outbreak of the Covid19 pandemic of 2020, and the extreme weather events of 2021, were widely predicted and foreseen in the media by way of political reporting as much as popular social and natural science reporting in the years and decades prior. The same tropes were also present in the plotlines of popular literature, television, and film during that period. The central argument of the paper is that before media pundits and policy-makers expressed their surprise at the fragility of the Rule of Law in the “unprecedented” ascent of Trump, the lethal capacity and transmissibility of a “novel” coronavirus, and the “sudden” arrival of climate change in the daily lives of North Americans and Europeans, the spectre of these menaces had already penetrated our collective conscious in a way that ought to have changed outcomes. Neil Postman’s conceptualization of the present epoch as “Technopoly” is a means of explaining how, despite ample warnings, we were not ready for much. Technopoly refers to the historical present as the historical moment in which the technocratic capacity of individuals, states, and markets to respond to existential problems is hindered by information overload, e.g., the threat to the Rule of Law presented by an outgoing American President who refuses to accept the verdict of the electorate; the threat to public health posed by persistent vaccine misinformation and inequitable global vaccine distribution; and, the threat posed to our collective habitat by extreme climate events. The paper concludes that fiction is a powerful potential antidote to the numbing effects of information overload in Technopoly if it is treated seriously as a source of normative authority rather than dismissed as pure diversion.
Journal Article
Integrating whole-genome sequencing within the National Antimicrobial Resistance Surveillance Program in the Philippines
National networks of laboratory-based surveillance of antimicrobial resistance (AMR) monitor resistance trends and disseminate these data to AMR stakeholders. Whole-genome sequencing (WGS) can support surveillance by pinpointing resistance mechanisms and uncovering transmission patterns. However, genomic surveillance is rare in low- and middle-income countries. Here, we implement WGS within the established Antimicrobial Resistance Surveillance Program of the Philippines via a binational collaboration. In parallel, we characterize bacterial populations of key bug-drug combinations via a retrospective sequencing survey. By linking the resistance phenotypes to genomic data, we reveal the interplay of genetic lineages (strains), AMR mechanisms, and AMR vehicles underlying the expansion of specific resistance phenotypes that coincide with the growing carbapenem resistance rates observed since 2010. Our results enhance our understanding of the drivers of carbapenem resistance in the Philippines, while also serving as the genetic background to contextualize ongoing local prospective surveillance.
Whole-genome sequencing (WGS) can support drug resistance surveillance, but is rare in low- and middle-income countries. Here, the authors integrate WGS within the national antimicrobial resistance surveillance program in the Philippines and conduct a retrospective sequencing survey to characterize bacterial populations and dissect resistance phenotypes.
Journal Article
Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK
Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility.
Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths.
Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister’s announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility.
Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.
Journal Article
Lean, mean, learning machines
This month’s Genome Watch examines how novel machine learning-enabled molecular diagnostic approaches can predict antibiotic resistance when genetic variation falls short.This month’s Genome Watch examines how novel machine learning-enabled molecular diagnostic approaches can predict antibiotic resistance when ge-netic variation falls short.
Journal Article
Estimating Processing Times of Harvesters in Thinning Operations in Maine
Although harvester use in recent years has increased in Maine, in the past 25 years no productivity or cycle time information was made available for harvesters operating in Maine's softwood stands. In order to update regional production and cost models it was necessary to develop cycle time equations for harvesters. Time and motion studies of harvesters in thinning operations were conducted during the summer of 2012 at four harvest sites under a variety of stand and site conditions common to central Maine. Results show cycle time differences for harvesters based on stem size as well as hardwood and softwood species groupings. A linear mixed-effects model was developed to explain the influence of stem size and species on processing time. The combination of operator, machine, and site conditions was used as a random effect in this model, which explained 5 percent of data variance. The adjusted R2 for this model was 0.20, and the model was validated using two independent harvester time studies conducted in 2013. Validation results show that the developed model predicts total harvest time within 5 to 25 percent of the observed time. This model will allow for updated logging cost predictions by land managers and logging contractors, but it also clearly shows the effect of stem size on time consumption and subsequently on productivity.
Journal Article
Human-Centric Approaches to the Study of Forest Operations: A Review and Integration of Organizational Science Research Areas
Prior research has shed important light on a variety of factors associated with timber harvesting efficiency and costs. However, the understandings that have resulted from this work have mostly focused on nonoperator factors, such as stand and tract conditions, machine types and configurations, and other technology-based aspects. The intent of this article is to help expand research on forest operations to include more explicit focus on operator factors that influence harvesting efficiency and costs. To accomplish this goal, we review several research areas in the organizational sciences (knowledge-based perspectives, motivation, and team effectiveness), discuss their relevance to forest operations, and list a number of important future research questions.
Journal Article
Applying Innovation Theory to Maine's Logging Industry
Innovation is often identified as a critical aspect of continued growth and competitiveness of industries and businesses in general. Currently, very little literature exists on innovation in forest-based industries, and almost no literature exists on innovation in logging. Development, adoption, and assessment of innovation by contract logging services firms are poorly understood. Furthermore, very little is known about the innovation system--the interconnected groups and associated influences that is part of the innovation process of these firms. To better understand innovation in Maine's logging industry, a series of cases studies involving 10 innovative logging firms was performed. Results show that logging innovations are very capital intensive and carry high risk for the adopting firm. Logging innovation is typically focused on increasing profitability and production efficiency, but a given contractor's desire to conduct high-quality work can moderate these influences. Finally, the logging innovation system is strong with respect to the industry infrastructure, other logging firms, and market influences. Several weak connections exist with regard to policy and regulation and public research and education institutions. Increasing collaboration and idea transfer in the system could improve innovation development in the future.
Journal Article
Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modelling version 1; peer review: awaiting peer review
Background: Increasing antibiotic resistance in a location may be mitigated by changes in treatment policy, or interventions to limit transmission of resistant bacteria. Therefore, accurate forecasting of the distribution of antibiotic resistance could be advantageous. Two previously published studies addressed this, but neither study compared alternative forecasting algorithms or considered spatial patterns of resistance spread. Methods: We analysed data describing the annual prevalence of antibiotic resistance per country in Europe from 2012 – 2016, and the quarterly prevalence of antibiotic resistance per clinical commissioning group in England from 2015 – 2018. We combined these with data on rates of possible covariates of resistance. These data were used to compare the previously published forecasting models, with other commonly used forecasting models, including one geospatial model. Covariates were incorporated into the geospatial model to assess their relationship with antibiotic resistance. Results: For the European data, which was recorded on a coarse spatiotemporal scale, a naïve forecasting model was consistently the most accurate of any of the forecasting models tested. The geospatial model did not improve on this accuracy. However, it did provide some evidence that antibiotic consumption can partially explain the distribution of resistance. The English data were aggregated at a finer scale, and expected-trend-seasonal (ETS) forecasts were the most accurate. The geospatial model did not significantly improve upon the median accuracy of the ETS model, but it appeared to be less sensitive to noise in the data, and provided evidence that rates of antibiotic prescription and bacteraemia are correlated with resistance. Conclusion: Annual, national-level surveillance data appears to be insufficient for fitting accurate antibiotic resistance forecasting models, but there is evidence that data collected at a finer spatiotemporal scale could be used to improve forecast accuracy. Additionally, incorporating antibiotic prescription or consumption data into the model could improve the predictive accuracy.
Journal Article
