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.     

Estimating recent growth in the cuttlefish Sepia officinalis: are nucleic acid-based indicators for growth and condition the method of choice?

A laboratory calibration study was undertaken with juvenile Sepia officinalis (80-85 g initial wet weight) to investigate the effects of different food rations and different starving intervals on RNA/dry weight (DW) ratios and RNA/DNA ratios in cephalopod mantle muscle at two different temperatures. The digestive gland index was also used as an additional indicator of recent growth. High food rations and low temperature went along with high RNA/DW ratios and high RNA/DNA ratios. Starving resulted in a linear decline in growth performance and a concomitant decrease in RNA/DW and RNA/DNA ratio, with RNA/DNA ratios representing the growth data better. RNA/DNA ratios decreased faster at higher temperatures. A fluorimetric assay for nucleic acid analysis was optimized for cephalopod mantle tissues and yielded reproducible RNA/DNA ratios with a relative variance below 10%. Thus, it may be possible to use this estimator of recently encountered feeding regime for the evaluation of mortality rates of early teuthid paralarvae to eventually support stock management. Also, log relative digestive gland weight showed a strong relationship with starving time, but, surprisingly, not with temperature. Data from the two temperatures analyzed could be combined to form a common regression line of relative digestive gland index with starving time. This indicator for recent growth might be especially suitable for large specimens with a well-developed digestive gland.     

Can zoosporic true fungi grow or survive in extreme or stressful environments?

Zoosporic true fungi are thought to be ubiquitous in many ecosystems, especially in cool, moist soils and freshwater habitats which are rich in organic matter. However, some of the habitats where these fungi are found may periodically experience extreme conditions, such as soils in extremely dry, hot and cold climates, acidic and alkaline soils, polluted rivers, anaerobic soil and water, saline soil and water, periglacial soils, oligotrophic soils, tree canopies and hydrothermal vents. It is clear that many ecotypes of zoosporic true fungi have indeed adapted to extreme or stressful environmental conditions. This conclusion is supported by studies in both the field and in the laboratory. Therefore, in our opinion, at least some true zoosporic fungi can be considered to be extremophiles.     

Time to Pregnancy: A Computational Method for Using the Duration of Non-Conception for Predicting Conception

An important problem in reproductive medicine is deciding when people who have failed to become pregnant without medical assistance should begin investigation and treatment. This study describes a computational approach to determining what can be deduced about a couple''s future chances of pregnancy from the number of menstrual cycles over which they have been trying to conceive. The starting point is that a couple''s fertility is inherently uncertain. This uncertainty is modelled as a probability distribution for the chance of conceiving in each menstrual cycle. We have developed a general numerical computational method, which uses Bayes'' theorem to generate a posterior distribution for a couple''s chance of conceiving in each cycle, conditional on the number of previous cycles of attempted conception. When various metrics of a couple''s expected chances of pregnancy were computed as a function of the number of cycles over which they had been trying to conceive, we found good fits to observed data on time to pregnancy for different populations. The commonly-used standard of 12 cycles of non-conception as an indicator of subfertility was found to be reasonably robust, though a larger or smaller number of cycles may be more appropriate depending on the population from which a couple is drawn and the precise subfertility metric which is most relevant, for example the probability of conception in the next cycle or the next 12 cycles. We have also applied our computational method to model the impact of female reproductive ageing. Results indicate that, for women over the age of 35, it may be appropriate to start investigation and treatment more quickly than for younger women. Ignoring reproductive decline during the period of attempted conception added up to two cycles to the computed number of cycles before reaching a metric of subfertility.