The Genotype Specific Competitive Ability Does Not Correlate with Infection in Natural Daphnia magna Populations

Different evolutionary hypotheses predict a correlation between the fitness of a genotype in the absence of infection and the likelihood to become infected. The cost of resistance hypothesis predicts that resistant genotypes pay a cost of being resistant and are less fit in the absence of parasites. The inbreeding-infection hypothesis predicts that the susceptible individuals are less fit due to inbreeding depression.Here we tested if a host's natural infection status was associated with its fitness. First, we experimentally confirmed that cured but formerly infected Daphnia magna are genetically more susceptible to reinfections with Octosporea bayeri than naturally uninfected D. magna. We then collected from each of 22 populations both uninfected and infected D. magna genotypes. All were treated against parasites and kept in their asexual phase. We estimated their relative fitness in an experiment against a tester genotype and in another experiment in direct competition. Consistently, we found no difference in competitive abilities between uninfected and cured but formerly infected genotypes. This was the case both in the presence as well as in the absence of sympatric parasites during the competition trials.Our data do not support the inbreeding-infection hypothesis. They also do not support a cost of resistance, however ignoring other parasite strains or parasite species. We suggest as a possible explanation for our results that resistance genes might segregate largely independently of other fitness associated genes in this system

Mortality and cancer incidence following occupational radiation exposure: third analysis of the National Registry for Radiation Workers

Mortality and cancer incidence were studied in the National Registry for Radiation Workers in, relative to earlier analyses, an enlarged cohort of 174 541 persons, with longer follow-up (to 2001) and, for the first time, cancer registration data. SMRs for all causes and all malignant neoplasms were 81 and 84 respectively, demonstrating a ‘healthy worker effect'. Within the cohort, mortality and incidence from both leukaemia excluding CLL and the grouping of all malignant neoplasms excluding leukaemia increased to a statistically significant extent with increasing radiation dose. Estimates of the trend in risk with dose were similar to those for the Japanese A-bomb survivors, with 90% confidence intervals that excluded both risks more than 2–3 times greater than the A-bomb values and no raised risk. Some evidence of an increasing trend with dose in mortality from all circulatory diseases may, at least partly, be due to confounding by smoking. This analysis provides the most precise estimates to date of mortality and cancer risks following occupational radiation exposure and strengthens the evidence for raised risks from these exposures. The cancer risk estimates are consistent with values used to set radiation protection standards

Correction: Internet Tool to Support Self-Assessment and Self-Swabbing of Sore Throat: Development and Feasibility Study.

[This corrects the article DOI: 10.2196/39791.]

Elucidating the aetiology of human Campylobacter coli infections

Peer reviewedPublisher PD

The Impact of Global Warming and Anoxia on Marine Benthic Community Dynamics: an Example from the Toarcian (Early Jurassic)

The Pliensbachian-Toarcian (Early Jurassic) fossil record is an archive of natural data of benthic community response to global warming and marine long-term hypoxia and anoxia. In the early Toarcian mean temperatures increased by the same order of magnitude as that predicted for the near future; laminated, organic-rich, black shales were deposited in many shallow water epicontinental basins; and a biotic crisis occurred in the marine realm, with the extinction of approximately 5% of families and 26% of genera. High-resolution quantitative abundance data of benthic invertebrates were collected from the Cleveland Basin (North Yorkshire, UK), and analysed with multivariate statistical methods to detect how the fauna responded to environmental changes during the early Toarcian. Twelve biofacies were identified. Their changes through time closely resemble the pattern of faunal degradation and recovery observed in modern habitats affected by anoxia. All four successional stages of community structure recorded in modern studies are recognised in the fossil data (i.e. Stage III: climax; II: transitional; I: pioneer; 0: highly disturbed). Two main faunal turnover events occurred: (i) at the onset of anoxia, with the extinction of most benthic species and the survival of a few adapted to thrive in low-oxygen conditions (Stages I to 0) and (ii) in the recovery, when newly evolved species colonized the re-oxygenated soft sediments and the path of recovery did not retrace of pattern of ecological degradation (Stages I to II). The ordination of samples coupled with sedimentological and palaeotemperature proxy data indicate that the onset of anoxia and the extinction horizon coincide with both a rise in temperature and sea level. Our study of how faunal associations co-vary with long and short term sea level and temperature changes has implications for predicting the long-term effects of “dead zones” in modern oceans

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Electron quantum metamaterials in van der Waals heterostructures

In recent decades, scientists have developed the means to engineer synthetic periodic arrays with feature sizes below the wavelength of light. When such features are appropriately structured, electromagnetic radiation can be manipulated in unusual ways, resulting in optical metamaterials whose function is directly controlled through nanoscale structure. Nature, too, has adopted such techniques -- for example in the unique coloring of butterfly wings -- to manipulate photons as they propagate through nanoscale periodic assemblies. In this Perspective, we highlight the intriguing potential of designer sub-electron wavelength (as well as wavelength-scale) structuring of electronic matter, which affords a new range of synthetic quantum metamaterials with unconventional responses. Driven by experimental developments in stacking atomically layered heterostructures -- e.g., mechanical pick-up/transfer assembly -- atomic scale registrations and structures can be readily tuned over distances smaller than characteristic electronic length-scales (such as electron wavelength, screening length, and electron mean free path). Yet electronic metamaterials promise far richer categories of behavior than those found in conventional optical metamaterial technologies. This is because unlike photons that scarcely interact with each other, electrons in subwavelength structured metamaterials are charged, and strongly interact. As a result, an enormous variety of emergent phenomena can be expected, and radically new classes of interacting quantum metamaterials designed

Developing a digital intervention for cancer survivors: an evidence-, theory- and person-based approach

This paper illustrates a rigorous approach to developing digital interventions using an evidence-, theory- and person-based approach. Intervention planning included a rapid scoping review which identified cancer survivors’ needs, including barriers and facilitators to intervention success. Review evidence (N=49 papers) informed the intervention’s Guiding Principles, theory-based behavioural analysis and logic model. The intervention was optimised based on feedback on a prototype intervention through interviews (N=96) with cancer survivors and focus groups with NHS staff and cancer charity workers (N=31). Interviews with cancer survivors highlighted barriers to engagement, such as concerns about physical activity worsening fatigue. Focus groups highlighted concerns about support appointment length and how to support distressed participants. Feedback informed intervention modifications, to maximise acceptability, feasibility and likelihood of behaviour change. Our systematic method for understanding user views enabled us to anticipate and address important barriers to engagement. This methodology may be useful to others developing digital interventions

Extent and structure of health insurance expenditures for complementary and alternative medicine in Swiss primary care

BACKGROUND: The study is part of a nationwide evaluation of complementary and alternative medicine (CAM) in primary care in Switzerland. The goal was to evaluate the extent and structure of basic health insurance expenditures for complementary and alternative medicine in Swiss primary care. METHODS: The study was designed as a cross-sectional evaluation of Swiss primary care providers and included 262 certified CAM physicians, 151 noncertified CAM physicians and 172 conventional physicians. The study was based on data from a mailed questionnaire and on reimbursement information obtained from health insurers. It was therefore purely observational, without interference into diagnostic and therapeutic procedures applied or prescribed by physicians. Main outcome measures included average reimbursed costs per patient, structured into consultation- and medication-related costs, and referred costs. RESULTS: Total average reimbursed cost per patient did not differ between CAM physicians and conventional practitioners, but considerable differences were observed in cost structure. The proportions of reimbursed costs for consultation time were 56% for certified CAM, 41% for noncertified CAM physicians and 40% for conventional physicians; medication costs – including expenditures for prescriptions and directly dispensed drugs – respectively accounted for 35%, 18%, and 51% of costs. CONCLUSION: The results indicate no significant difference for overall treatment cost per patient between CAM and COM primary care in Switzerland. However, CAM physicians treat lower numbers of patients and a more cost-favourable patient population than conventional physicians. Differences in cost structure reflect more patient-centred and individualized treatment modalities of CAM physicians

MI-GWAS: a SAS platform for the analysis of inherited and maternal genetic effects in genome-wide association studies using log-linear models

<p>Abstract</p> <p>Background</p> <p>Several platforms for the analysis of genome-wide association data are available. However, these platforms focus on the evaluation of the genotype inherited by affected (i.e. case) individuals, whereas for some conditions (e.g. birth defects) the genotype of the mothers of affected individuals may also contribute to risk. For such conditions, it is critical to evaluate associations with both the maternal and the inherited (i.e. case) genotype. When genotype data are available for case-parent triads, a likelihood-based approach using log-linear modeling can be used to assess both the maternal and inherited genotypes. However, available software packages for log-linear analyses are not well suited to the analysis of typical genome-wide association data (e.g. including missing data).</p> <p>Results</p> <p>An integrated platform, Maternal and Inherited Analyses for Genome-wide Association Studies <b>(</b>MI-GWAS) for log-linear analyses of maternal and inherited genetic effects in large, genome-wide datasets, is described. MI-GWAS uses SAS and LEM software in combination to appropriately format data, perform the log-linear analyses and summarize the results. This platform was evaluated using existing genome-wide data and was shown to perform accurately and relatively efficiently.</p> <p>Conclusions</p> <p>The MI-GWAS platform provides a valuable tool for the analysis of association of a phenotype or condition with maternal and inherited genotypes using genome-wide data from case-parent triads. The source code for this platform is freely available at <url>http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htm</url>.</p