A gene, reaper (rpr), that appears to play a central control function for the initiation of programmed cell death (apoptosis) in Drosophila was identified. Virtually all programmed cell death that normally occurs during Drosophila embryogenesis was blocked in embryos homozygous for a small deletion that includes the reaper gene. Mutant embryos contained many extra cells and failed to hatch, but many other aspects of development appeared quite normal. Deletions that include reaper also protected embryos from apoptosis caused by x-irradiation and developmental defects. However, high doses of x-rays induced some apoptosis in mutant embryos, and the resulting corpses were phagocytosed by macrophages. These data suggest that the basic cell death program is intact although it was not activated in mutant embryos. The DNA encompassed by the deletion was cloned and the reaper gene was identified on the basis of the ability of cloned DNA to restore apoptosis to cell death defective embryos in germ line transformation experiments. The reaper gene appears to encode a small peptide that shows no homology to known proteins, and reaper messenger RNA is expressed in cells destined to undergo apoptosis.

A large and increasing number of patients use medicinal herbs or seek the advice of their physician regarding their use. More than one third of Americans use herbs for health purposes, yet patients (and physicians) often lack accurate information about the safety and efficacy of herbal remedies. Burgeoning interest in medicinal herbs has increased scientific scrutiny of their therapeutic potential and safety, thereby providing physicians with data to help patients make wise decisions about their use. This article provides a review of the data on 12 of the most commonly used herbs in the United States. In addition, we provide practical information and guidelines for the judicious use of medicinal herbs.

Although much work has been completed with modeling head-neck movements as well with studying the intricacies of vision and eye movements, relatively little research has been conducted involving how vision affects human upper-body posture. By leveraging direct human optimized posture prediction (D-HOPP), we are able to predict postures that incorporate one's tendency to actually look towards a workspace or see a target. D-HOPP is an optimization-based approach that functions in real time with Santos TM , a new kind of virtual human with a high number of degrees-of-freedom and a highly realistic appearance. With this approach, human performance measures provide objective functions in an optimization problem that is solved just once for a given posture or task. We have developed two new performance measures: visual acuity and visual displacement. Although the visual-acuity performance measure is based on well-accepted published concepts, we find that it has little effect on the predicted posture when a target point is outside one's field of view. Consequently, we have developed visual displacement, which corrects this problem. In general, we find that vision alone does not govern posture. However, using multi-objective optimization, we combine visual acuity and visual displacement with other performance measures, to yield realistic and validated predicted human postures that incorporate vision.

Proper assessment of human reach posture is one of the essential functions for workspace design and evaluation in a CAD system with a built-in human model. Most existing models have used heuristic methods, which provide only the range of feasible human reaching postures, which may or may not include naturalistic reach posture. We present a multi-objective-optimization (MOO)-based approach for predicting realistic reach postures of digital humans, one based on our belief that humans assume different reach postures depending on different cost functions, i.e., multi-objective functions. In this work, the number of degrees of freedom (DOF) associated with the model is unlimited, and ranges of motion of joints are considered. The problem is formulated as MOO and single-objective optimization (SOO) algorithms where one or all cost functions (joint displacement, energy, effort, etc.) are considered as objective functions that drive the model to a solution set. A real-time simulation of the digital human’s motion is applied in the IOWA digital-human virtual environment.