Historical knowledge in a knowledge economy – what types of knowledge matter?

This article examines the potential of history as a subject to contribute to a ‘knowledge economy’. Global trends in curricula reforms have often emphasised generic competences and development of students’ critical thinking to benefit the future economic position of citizens and nations. However, viewing knowledge in these terms presents a reductive view, particularly given that there is no clear definition of the nature of the knowledge which could or should be universally deployed in the pursuit of a ‘knowledge economy’. This paper presents an argument that a focus on ‘powerful’ disciplinary knowledge and ‘valuable’ frameworks of knowledge, in areas such as history education rather than generic competences and skills, would better serve a knowledge economy. Drawing on two empirical studies from England and New Zealand, which present different policy contexts, the paper explores the extent to which the potential of history education is being realised to develop such powerful and valuable knowledge. The data reveal similar patterns in both contexts; despite the history teachers in both countries sharing a disciplinary understanding of the subject this is not comprehensively reflected in the curricula they construct, and there are few attempts to create coherent frameworks of knowledge. This suggests that the opportunities for history education to support the development of a knowledge economy have not been fully realised and exploited

Comparative genomics of the emerging human pathogen Photorhabdus asymbiotica with the insect pathogen Photorhabdus luminescens

<p>Abstract</p> <p>Background</p> <p>The Gram-negative bacterium <it>Photorhabdus asymbiotica </it>(Pa) has been recovered from human infections in both North America and Australia. Recently, Pa has been shown to have a nematode vector that can also infect insects, like its sister species the insect pathogen <it>P. luminescens </it>(Pl). To understand the relationship between pathogenicity to insects and humans in <it>Photorhabdus </it>we have sequenced the complete genome of Pa strain ATCC43949 from North America. This strain (formerly referred to as <it>Xenorhabdus luminescens </it>strain 2) was isolated in 1977 from the blood of an 80 year old female patient with endocarditis, in Maryland, USA. Here we compare the complete genome of Pa ATCC43949 with that of the previously sequenced insect pathogen <it>P. luminescens </it>strain TT01 which was isolated from its entomopathogenic nematode vector collected from soil in Trinidad and Tobago.</p> <p>Results</p> <p>We found that the human pathogen Pa had a smaller genome (5,064,808 bp) than that of the insect pathogen Pl (5,688,987 bp) but that each pathogen carries approximately one megabase of DNA that is unique to each strain. The reduced size of the Pa genome is associated with a smaller diversity in insecticidal genes such as those encoding the Toxin complexes (Tc's), Makes caterpillars floppy (Mcf) toxins and the <it>Photorhabdus </it>Virulence Cassettes (PVCs). The Pa genome, however, also shows the addition of a plasmid related to pMT1 from <it>Yersinia pestis </it>and several novel pathogenicity islands including a novel Type Three Secretion System (TTSS) encoding island. Together these data suggest that Pa may show virulence against man via the acquisition of the <it>pMT1</it>-like plasmid and specific effectors, such as SopB, that promote its persistence inside human macrophages. Interestingly the loss of insecticidal genes in Pa is not reflected by a loss of pathogenicity towards insects.</p> <p>Conclusion</p> <p>Our results suggest that North American isolates of Pa have acquired virulence against man via the acquisition of a plasmid and specific virulence factors with similarity to those shown to play roles in pathogenicity against humans in other bacteria.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

LAYER DRYING - A MANAGEMENT MODEL FOR LOW-TEMPERATURE CORN DRYING SYSTEMS

A comprehensive layer drying model was developed to provide the operators of natural air or low-temperature corn drying systems with the technical information needed to make sound management decisions. The model as designed for use on AGNET, an interactive computer system developed in Nebraska, so that recommendations can be calculated for individual drying system setups. Development of the layer drying model emphasized the formulation of a procedure for determining maximum allowable layer filling rates. It is necessary to define the drying system setup and to project harvest conditions and bin drying rates in order to calculate allowable layer filling rates. Several individual simulation models are used to make these performance projections. The layer drying model is structured around a low-temperature drying model which is also used to predict drying performance once bin filling has been completed. Support subroutines include airflow and field dry down models. The maximum fill rate procedure uses minimum airflow rate requirements and equivalency ratios (which relate the corn in the bin to an equivalent amount of corn at the field moisture content) to determine allowable load sizes. Simulation results indicate that layer filling can be completed in a 2-3 week period for typical Lincoln, Nebraska conditions. Harvest can normally be started when field moisture contents reach 26% w.b. with the last corn loaded into the bin at 20-22%. Fan energy utilization values of 700 kJ/kg of water evaporated are not uncommon. For single fill applications, the bin can be filled in a one or two day period once field moisture contents reach 20-21%. Drying times are generally longer with single filled systems and thus energy requirements are also slightly higher. Field experiments which were performed to evaluate the loading procedure indicated disparities between projected and measured field and bin drying rates. Some of the difference was due to the fact that drying rate projections were based upon average year weather conditions. Nonuniformity of airflow within the bin was also identified as a potential problem. Sensitivity analyses were performed with the layer drying model to determine the relative importance of airflow rate, field dry down rate, minimum allowable daily loading amount, and year-to-year weather conditions upon bin filling recommendations. Field dry down rates were shown to have a considerable effect upon allowable layer filling rates