1,352 research outputs found
Correction: The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction
Notice of republication:
This article was republished on April 23, 2015, to correct errors in the equations that were introduced during the typesetting process. The publisher apologizes for the errors. Please download this article again to view the correct version
The Epidemiological Framework for Biological Invasions (EFBI): An interdisciplinary foundation for the assessment of biosecurity threats
Emerging microparasite (e.g. viruses, bacteria, protozoa and fungi) epidemics and the introduction of non-native pests and weeds are major biosecurity threats worldwide. The likelihood of these threats is often estimated from probabilities of their entry, establishment, spread and ease of prevention. If ecosystems are considered equivalent to hosts, then compartment disease models should provide a useful framework for understanding the processes that underpin non-native species invasions. To enable greater cross-fertilisation between these two disciplines, the Epidemiological Framework for Biological Invasions (EFBI) is developed that classifies ecosystems in relation to their invasion status: Susceptible, Exposed, Infectious and Resistant. These states are linked by transitions relating to transmission, latency and recovery. This viewpoint differs markedly from the species-centric approaches often applied to non-native species. It allows generalisations from epidemiology, such as the force of infection, the basic reproductive ratio R0, super-spreaders, herd immunity, cordon sanitaire and ring vaccination, to be discussed in the novel context of non-native species and helps identify important gaps in the study of biological invasions. The EFBI approach highlights several limitations inherent in current approaches to the study of biological invasions including: (i) the variance in non-native abundance across ecosystems is rarely reported; (ii) field data rarely (if ever) distinguish source from sink ecosystems; (iii) estimates of the susceptibility of ecosystems to invasion seldom account for differences in exposure to non-native species; and (iv) assessments of ecosystem susceptibility often confuse the processes that underpin patterns of spread within -and between- ecosystems. Using the invasion of lakes as a model, the EFBI approach is shown to present a new biosecurity perspective that takes account of ecosystem status and complements demographic models to deliver clearer insights into the dynamics of biological invasions at the landscape scale. It will help to identify whether management of the susceptibility of ecosystems, of the number of vectors, or of the diversity of pathways (for movement between ecosystems) is the best way of limiting or reversing the population growth of a non-native species. The framework can be adapted to incorporate increasing levels of complexity and realism and to provide insights into how to monitor, map and manage biological invasions more effectively
Occupational choice, number of entrepreneurs and output: theory and empirical evidence with Spanish data
This paper extends the (Lucas, Bell J Econ 9:508–523,1978) model of occupational choices by individuals with different skills, beyond the simple options of self-employment or wage-employment, by including a second choice for the self-employed. That is, an option to hire employees and so become self-employed with employees (SEWEs), or to be self-employed without employees (SEWNEs). We solve for the market equilibrium and examine the sensitivity of relative sizes of occupational groups, and of the level of productivity, to changes in the exogenous parameters. The results show that the positive (negative) association between number of SEWEs (SEWNEs) and productivity, observed in the Spanish data, can be explained, under certain conditions, as the result of cross-region and time differences in average skills. These findings point to the importance of distinguishing between SEWEs and SEWNEs in drawing valid conclusions concerning any link between entrepreneurship and economic development
Recommended from our members
An assessment of the impact of herb-drug combinations used by cancer patients
Background
Herb/Dietary Supplements (HDS) are the most popular Complementary and Alternative Medicine (CAM) modality used by cancer patients and the only type which involves the ingestion of substances which may interfere with the efficacy and safety of conventional medicines. This study aimed to assess the level of use of HDS in cancer patients undergoing treatment in the UK, and their perceptions of their effects, using 127 case histories of patients who were taking HDS. Previous studies have evaluated the risks of interactions between HDS and conventional drugs on the basis on numbers of patient using HDSs, so our study aimed to further this exploration by examining the actual drug combinations taken by individual patients and their potential safety.
Method
Three hundred seventy-five cancer patients attending oncology departments and centres of palliative care at the Oxford University Hospitals Trust (OUH), Duchess of Kent House, Sobell House, and Nettlebed Hospice participated in a self-administered questionnaire survey about their HDS use with their prescribed medicines. The classification system of Stockley’s Herbal Medicine’s Interactions was adopted to assess the potential risk of herb-drug interactions for these patients.
Results
127/375 (34 %; 95 % CI 29, 39) consumed HDS, amounting to 101 different products. Most combinations were assessed as ‘no interaction’, 22 combinations were categorised as ‘doubt about outcomes of use’, 6 combinations as ‘Potentially hazardous outcome’, one combination as an interaction with ‘Significant hazard’, and one combination as an interaction of “Life-threatening outcome”. Most patients did not report any adverse events.
Conclusion
Most of the patients sampled were not exposed to any significant risk of harm from interactions with conventional medicines, but it is not possible as yet to conclude that risks in general are over-estimated. The incidence of HDS use was also less than anticipated, and significantly less than reported in other areas, illustrating the problems when extrapolating results from one region (the UK), in one setting (NHS oncology) in where patterns of supplement use may be very different to those elsewhere
A Machine Learning Trainable Model to Assess the Accuracy of Probabilistic Record Linkage
Record linkage (RL) is the process of identifying and linking data that relates to the same physical entity across multiple heterogeneous data sources. Deterministic linkage methods rely on the presence of common uniquely identifying attributes across all sources while probabilistic approaches use non-unique attributes and calculates similarity indexes for pair wise comparisons. A key component of record linkage is accuracy assessment — the process of manually verifying and validating matched pairs to further refine linkage parameters and increase its overall effectiveness. This process however is time-consuming and impractical when applied to large administrative data sources where millions of records must be linked. Additionally, it is potentially biased as the gold standard used is often the reviewer’s intuition. In this paper, we present an approach for assessing and refining the accuracy of probabilistic linkage based on different supervised machine learning methods (decision trees, naïve Bayes, logistic regression, random forest, linear support vector machines and gradient boosted trees). We used data sets extracted from huge Brazilian socioeconomic and public health care data sources. These models were evaluated using receiver operating characteristic plots, sensitivity, specificity and positive predictive values collected from a 10-fold cross-validation method. Results show that logistic regression outperforms other classifiers and enables the creation of a generalized, very accurate model to validate linkage results
Selected reactive oxygen species and antioxidant enzymes in common bean after Pseudomonas syringae pv. phaseolicola and Botrytis cinerea infection
Phaseolus vulgaris cv. Korona plants were
inoculated with the bacteria Pseudomonas syringae pv.
phaseolicola (Psp), necrotrophic fungus Botrytis cinerea
(Bc) or with both pathogens sequentially. The aim of the
experiment was to determine how plants cope with multiple
infection with pathogens having different attack strategy.
Possible suppression of the non-specific infection with
the necrotrophic fungus Bc by earlier Psp inoculation was
examined. Concentration of reactive oxygen species
(ROS), such as superoxide anion (O2
-) and H2O2 and
activities of antioxidant enzymes such as superoxide dismutase
(SOD), catalase (CAT) and peroxidase (POD) were
determined 6, 12, 24 and 48 h after inoculation. The
measurements were done for ROS cytosolic fraction and
enzymatic cytosolic or apoplastic fraction. Infection with
Psp caused significant increase in ROS levels since the
beginning of experiment. Activity of the apoplastic
enzymes also increased remarkably at the beginning of
experiment in contrast to the cytosolic ones. Cytosolic
SOD and guaiacol peroxidase (GPOD) activities achieved
the maximum values 48 h after treatment. Additional forms
of the examined enzymes after specific Psp infection were
identified; however, they were not present after single Bc
inoculation. Subsequent Bc infection resulted only in
changes of H2O2 and SOD that occurred to be especially
important during plant–pathogen interaction. Cultivar Korona
of common bean is considered to be resistant to Psp and mobilises its system upon infection with these bacteria.
We put forward a hypothesis that the extent of defence
reaction was so great that subsequent infection did not
trigger significant additional response
- …