Refinement of a cell line based artificial diet for rearing the parasitoid wasp, Trichogramma pretiosum

An artificial diet incorporating insect cells originally developed for Trichogramma australicum Girault (Hymenoptera: Tricho-grammatidae) was successfully used to rear Trichogramm pretiosum Riley (Hymenoptera: Trichogrammatidae). To refine the diet, individual components were removed. Chicken egg yolk and the insect cells were identified as the most important components for T. pretiosum development. Their removal resulted in few pupae and no adults. Removal of Grace's insect medium, a common component of artificial diets, was found to markedly improve the development of T pretiosum, producing 60% larva to pupa transition and 19% pupa to adult transition. There was no significant difference in T pretiosum development on diets in which milk powder, malt powder or infant formula were interchanged, despite differences in nutrient composition. The use of yeast extract resulted in significantly higher survival to the adult stage when compared with yeast hydrolysate enzymatic and a combination of yeast extract and yeast hydrolysate enzymatic. Comparison of four antimicrobial agents showed the antibacterial agent Gentamycin and the antifungal agent Nystatin had the least detrimental effect on T pretiosum development. The use of insect cell line diets has the potential to simplify artificial diet production and significantly reduce T pretiosum production costs in Australia compared to diets using insect hemolymph or the use of natural or factitious hosts. (c) 2005 Elsevier Inc. All rights reserved.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD