Alien aquatics in Europe: assessing the relative environmental and socio-economic impacts of invasive aquatic macroinvertebrates and other taxa

Invasive alien aquatic species, including marine and freshwater macroinvertebrates, have become increasingly important in terms of both environmental and socio-economic impacts. In order to assess their environmental and economic costs, we applied the Generic Impact Scoring System (GISS) and performed a comparison with other taxa of invaders in Europe. Impacts were scored into six environmental and six socio-economic categories, with each category containing five impact levels. Among 49 aquatic macroinvertebrates, the most impacting species were the Chinese mitten crab, Eriocheir sinensis (Milne-Edwards, 1853) and the zebra mussel, Dreissena polymorpha (Pallas, 1771). The highest impacts found per GISS impact category were, separately; on ecosystems, through predation, as competitors, and on animal production. Eleven species have an impact score > 10 (high impact) and seven reach impact level 5 in at least one impact category (EU blacklist candidates), the maximum score that can be given is 60 impact points. Comparisons were drawn between aquatic macroinvertebrates and vertebrate invaders such as fish, mammals and birds, as well as terrestrial arthropods, revealing invasive freshwater macroinvertebrates to be voracious predators of native prey and damaging to native ecosystems compared with other taxa. GISS can be used to compare these taxa and will aid policy making and targeting of invasive species for management by relevant agencies, or to assist in producing species blacklist candidates.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Factors influencing the output of rural cataract surgical facilities in China: the SHARP study

Purpose: To identify factors associated prospectively with increased cataract surgical rate (CSR) in rural Chinese hospitals.

Methods: Annual cataract surgical output was obtained at baseline and 24 months later from operating room records at 42 rural, county-level hospitals. Total local CSR (cases/million population/y), and proportion of CSR from hospital and local competitors were calculated from government records. Hospital administrators completed questionnaires providing demographic and professional information, and annual clinic and outreach screening volume. Independent cataract surgeons provided clinical information and videotapes of cases for grading by two masked experts using the Ophthalmology Surgical Competency Assessment Rubric (OSCAR). Uncorrected vision was recorded for 10 consecutive cataract cases at each facility, and 10 randomly-identified patients completed hospital satisfaction questionnaires. Total value of international nongovernmental development organization (INGDO) investment in the previous three years and demographic information on hospital catchment areas were obtained. Main outcome was 2-year percentage change in hospital CSR.

Results: Among the 42 hospitals (median catchment population 530,000, median hospital CSR 643), 78.6% (33/42) were receiving INGDO support. Median change in hospital CSR (interquartile range) was 33.3% (-6.25%, 72.3%). Predictors of greater increase in CSR included higher INGDO investment (P = 0.02, simple model), reducing patient dissatisfaction (P = 0.03, simple model), and more outreach patient screening (P = 0.002, simple and multiple model).

Conclusions: Outreach cataract screening was the strongest predictor of increased surgical output. Government and INGDO investment in screening may be most likely to enhance output of county hospitals, a major goal of China's Blindness Prevention Plan.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.