A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.     

Nuclear envelope‐associated dynein drives prophase centrosome separation and enables Eg5‐independent bipolar spindle formation

The microtubule motor protein kinesin‐5 (Eg5) provides an outward force on centrosomes, which drives bipolar spindle assembly. Acute inhibition of Eg5 blocks centrosome separation and causes mitotic arrest in human cells, making Eg5 an attractive target for anti‐cancer therapy. Using in vitro directed evolution, we show that human cells treated with Eg5 inhibitors can rapidly acquire the ability to divide in the complete absence of Eg5 activity. We have used these Eg5‐independent cells to study alternative mechanisms of centrosome separation. We uncovered a pathway involving nuclear envelope (NE)‐associated dynein that drives centrosome separation in prophase. This NE‐dynein pathway is essential for bipolar spindle assembly in the absence of Eg5, but also functions in the presence of full Eg5 activity, where it pulls individual centrosomes along the NE and acts in concert with Eg5‐dependent outward pushing forces to coordinate prophase centrosome separation. Together, these results reveal how the forces are produced to drive prophase centrosome separation and identify a novel mechanism of resistance to kinesin‐5 inhibitors.     

A physiological analogy of the niche for projecting the potential distribution of plants

Aim  To develop a physiologically based model of the plant niche for use in species distribution modelling. Location  Europe. Methods  We link the Thornley transport resistance (TTR) model with functions which describe how the TTR’s model parameters are influenced by abiotic environmental factors. The TTR model considers how carbon and nutrient uptake, and the allocation of these assimilates, influence growth. We use indirect statistical methods to estimate the model parameters from a high resolution data set on tree distribution for 22 European tree species. Results  We infer, from distribution data and abiotic forcing data, the physiological niche dimensions of 22 European tree species. We found that the model fits were reasonable (AUC: 0.79–0.964). The projected distributions were characterized by a false positive rate of 0.19 and a false negative rate 0.12. The fitted models are used to generate projections of the environmental factors that limit the range boundaries of the study species. Main conclusions  We show that physiological models can be used to derive physiological niche dimensions from species distribution data. Future work should focus on including prior information on physiological rates into the parameter estimation process. Application of the TTR model to species distribution modelling suggests new avenues for establishing explicit links between distribution and physiology, and for generating hypotheses about how ecophysiological processes influence the distribution of plants.     

Short hairpin RNAs against eotaxin or interleukin‐5 decrease airway eosinophilia and hyper‐responsiveness in a murine model of asthma

Background

Eosinophilia plays the major role in the pathogenesis of asthma and correlates with the up‐regulation of eotaxin, which, together with interleukin (IL)‐5, is important for differentiation, chemo‐attraction, degranulation, and survival of eosinophils in local tissue. In a previous study, we found that administration of lentivirus‐delivered short hairpin RNA (shRNA) to suppress the expression of IL‐5 inhibited airway inflammation. The present study aimed to investigate the role of eotaxin shRNA and the synergistic effect of eotaxin and IL‐5 shRNAs on airway inflammation in an ovalbumin (OVA)‐induced murine model of asthma.

Methods

Lentivirus‐delivered shRNAs were used to suppress the expression of eotaxin and/or IL‐5 in local tissue in an OVA‐induced murine asthma model.

Results

Intra‐tracheal administration of lentivirus containing eotaxin shRNA expressing cassette (eoSEC3.3) efficiently moderated the characteristics of asthma, including airway hyper‐responsiveness, cellular infiltration of lung tissues, and eotaxin and IL‐5 levels in bronchio‐alveolar lavage fluid. Administration of lentiviruses expressing IL‐5 or eotaxin shRNAs (IL5SEC4 + eoSEC3.3) also moderated the symptoms of asthma in a mouse model.

Conclusions

Local delivery of lentiviruses expressing IL‐5 and eotaxin shRNAs provides a potential tool in moderating airway inflammation and also has the potential for developing clinical therapy based on the application of shRNAs of chemokines and cytokines involved in T helper 2 cell inflammation and eosinophilia. Copyright © 2008 John Wiley & Sons, Ltd.     

How Can the Eco‐efficiency of a Region be Measured and Monitored?

The concept of eco-efficiency is commonly referred to as a business link to sustainable development. In this article, ecoefficiency is examined at a regional level as an approach to promoting the competitiveness of economic activities in the Finnish Kymenlaakso region and mitigating their harmful impacts on the environment. The aim is to develop appropriate indicators for monitoring changes in the eco-efficiency of the region. A starting point is to produce indicators for the environmental and economic dimensions of regional development and use them for measuring regional eco-efficiency. The environmental impact indicators are based on a life-cycle assessment method, producing different types of environmental impact indicators: pressure indicators (e.g., emissions of CO₂), impact category indicators (e.g., CO₂ equivalents in the case of climate change), and a total impact indicator (aggregating different impact category indicator results into a single value). Environmental impact indicators based on direct material input, total material input, and total material requirement of the Kymenlaakso region are also assessed. The economic indicators used are the gross domestic product, the value added, and the output of the main economic sectors of Kymenlaakso. In the eco-efficiency assessment, the economic and environmental impact indicators are monitored in the same graph. In a few cases eco-efficiency ratios can also be calculated (the economic indicators are divided by the environmental indicators). Output (= value added + intermediate consumption) is used as an economic indicator related to the environmental impact indicators, which also cover the upstream processes of the region's activities. In the article, we also discuss the strengths and weaknesses of using the different environmental impact indicators.