Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals

Enot, D. P., Beckmann, M., Overy, D., Draper, J. (2006). Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals. Proceedings of the National Academy of Sciences of the USA, 103(40), 14865-14870. Sponsorship: BBSRC RAE2008

Patient-reported outcomes in meta-analyses - part 2: methods for improving interpretability for decision-makers

Systematic reviews and meta-analyses of randomized trials that include patient-reported outcomes (PROs) often provide crucial information for patients, clinicians and policy-makers facing challenging health care decisions. Based on emerging methods, guidance on improving the interpretability of meta-analysis of patient-reported outcomes, typically continuous in nature, is likely to enhance decision-making. The objective of this paper is to summarize approaches to enhancing the interpretability of pooled estimates of PROs in meta-analyses. When differences in PROs between groups are statistically significant, decision-makers must be able to interpret the magnitude of effect. This is challenging when, as is often the case, clinical trial investigators use different measurement instruments for the same construct within and between individual randomized trials. For such cases, in addition to pooling results as a standardized mean difference, we recommend that systematic review authors use other methods to present results such as relative (relative risk, odds ratio) or absolute (risk difference) dichotomized treatment effects, complimented by presentation in either: natural units (e.g. overall depression reduced by 2.4 points when measured on a 50-point Hamilton Rating Scale for Depression); minimal important difference units (e.g. where 1.0 unit represents the smallest difference in depression that patients, on average, perceive as important the depression score was 0.38 (95%CI 0.30 to 0.47) units less than the control group); or a ratio of means (e.g. where the mean in the treatment group is divided by the mean in the control group, the ratio of means is 1.27, representing a 27%relative reduction in the mean depression score).

A new model construction by making a detour via intuitionistic theories II: Interpretability lower bound of Feferman's explicit mathematics T0

We partially solve a long-standing problem in the proof theory of explicit mathematics or the proof theory in general. Namely, we give a lower bound of Feferman’s system T0 of explicit mathematics (but only when formulated on classical logic) with a concrete interpretat ion of the subsystem Σ12-AC+ (BI) of second order arithmetic inside T0. Whereas a lower bound proof in the sense of proof-theoretic reducibility or of ordinalanalysis was already given in 80s, the lower bound in the sense of interpretability we give here is new. We apply the new interpretation method developed by the author and Zumbrunnen (2015), which can be seen as the third kind of model construction method for classical theories, after Cohen’s forcing and Krivine’s classical realizability. It gives us an interpretation between classical theories, by composing interpretations between intuitionistic theories.

A new framework of measuring national nutrients balance for international and global comparison

Nutrients balance such as nitrogen and phosphorus balance are increasingly used as an indicator of the environmental performance of agricultural sector in international and global context. However there still is a lack of harmony in the use of methods for estimating the nutrients balance among countries. This is because of the disagreement regarding the accuracy and uncertainty of different accounting methods. The lack of harmony in the methods used in different countries further increases the uncertainty in the context of the international comparisons. This paper provides a new framework for nutrients balance calculation using the farm-gate accounting method. The calculation under this new framework takes advantage of availability of data from FAO and other reliable national and international sources. Due to this, the proposed framework is highly adaptable in many countries, making the global comparison feasible. The paper also proposes three criteria including adaptability, accuracy and interpretability to assess the appropriateness of nutrients accounting method. Based on these criteria, the paper provides a comprehensive comparison of the farm-gate and soil-surface methods in accounting country-level nutrients balance of agricultural production. The paper identifies some shortcomings of the soil-surface balance and shows that the farm-gate method has a greater potential of providing a more accurate and meaningful estimation of national nutrients balance.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

A comparison of classification algorithms within the Classifynder pollen imaging system

We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples. © 2013 AIP Publishing LLC.

Development of a log-quadratic model to describe microbial inactivation, illustrated by thermal inactivation of Clostridium botulinum

In the commercial food industry, demonstration of microbiological safety and thermal process equivalence often involves a mathematical framework that assumes log-linear inactivation kinetics and invokes concepts of decimal reduction time (DT), z values, and accumulated lethality. However, many microbes, particularly spores, exhibit inactivation kinetics that are not log linear. This has led to alternative modeling approaches, such as the biphasic and Weibull models, that relax strong log-linear assumptions. Using a statistical framework, we developed a novel log-quadratic model, which approximates the biphasic and Weibull models and provides additional physiological interpretability. As a statistical linear model, the log-quadratic model is relatively simple to fit and straightforwardly provides confidence intervals for its fitted values. It allows a DT-like value to be derived, even from data that exhibit obvious "tailing." We also showed how existing models of non-log-linear microbial inactivation, such as the Weibull model, can fit into a statistical linear model framework that dramatically simplifies their solution. We applied the log-quadratic model to thermal inactivation data for the spore-forming bacterium Clostridium botulinum and evaluated its merits compared with those of popular previously described approaches. The log-quadratic model was used as the basis of a secondary model that can capture the dependence of microbial inactivation kinetics on temperature. This model, in turn, was linked to models of spore inactivation of Sapru et al. and Rodriguez et al. that posit different physiological states for spores within a population. We believe that the log-quadratic model provides a useful framework in which to test vitalistic and mechanistic hypotheses of inactivation by thermal and other processes. Copyright © 2009, American Society for Microbiology. All Rights Reserved.