Characterization of chemicals mediating ovipositional host-plant finding byAmyelois transitella females

Ovipositional host-finding in the navel orangeworm,Amyelois transitella (Walker), is brought about by an in-flight response to host odors. Wind-tunnel studies of the response of gravid females to almonds showed that this response is mediated primarily by long-chain fatty acids, particularly oleic acid and linoleic acid. Evidence for the behavioral activity of fatty acids is based on the fact that: (1) behavioral activity of almond oil was concentrated in a single liquid chromatographic fraction whose composition was predominantly long-chain fatty acids, (2) behavioral activity was lost when either almond oil or the active fraction of that oil was treated with diazomethane, (3) full activity was elicited by a selective extraction of free fatty acids from crude almond oil, and (4) upwind response by females was elicited by a blend of synthetic oleic and linoleic acids, albeit at a level less than that elicited by almond oil. Five fatty acids identified from the almond oil were: myristic acid (1%), palmitic acid (16%), stearic acid (3%), oleic acid (58%), and linoleic (22%). Attraction to various combinations of synthetic acids was observed only when oleic acid was present, and oleic acid elicited upwind flights to the source when presented alone; however, short-range responses were enhanced by the addition of linoleic acid, which elicited no long-range orientation by itself. Despite significant levels of attraction to synthetic blends, the percentage of females flying to the source was lower than that flying to acidulated almond oil, the best natural attractant tested. Thus, although longrange response may be mediated primarily by a blend of oleic and linoleic acids, additional and as yet unidentified components must also play an important role. Long-range chemically modulated host finding in this and other generalist plant feeders is discussed with respect to current models of the evolution of host finding, and it is argued that suggestions that long-range host finding should be correlated with narrowness of host utilization are logically flawed and are not supported by our current understanding of specific examples of host finding.     

Plasma and lipoprotein fatty acid composition in glycogen storage disease type I

Nocturnal intragastric feeding has been shown to be an effective means to improve clinical and biochemical features in glycogen storage disease type I (GSD-I). In this study, we investigated the fatty acid patterns in a whole plasma and in circulating lipoproteins in patients on this therapy. The results demonstrated massive concentration of total fatty acids coupled with higher levels of triglycerides, free cholesterol, cholesterol ester and phospholipids. This hyperlipidemia involved all fatty acids without distinction of carbon or bond numbers. However, the increase was more pronounced for saturated than polyunsaturated fatty acids, as was demonstrated by the ratios of both oleic acid to linoleic acid (1.91±0.40 vs 0.80±0.09 in controls) and of ω3+ω6 to ω9 fatty acid families (0.92±0.11 vs 1.66±0.08 in controls). The fatty acid patterns in very low (VLDL), low (LDL) and high (HDL) density lipoprotein showed substantial differences in composition, reflecting an association between an abnormal lipoprotein pattern and essential fatty acid deficiency. Furthermore, GSD-I patients exhibited a significant increase in VLDL (17±2 vs 47±7 mg/dl) and LDL cholesterol (124±7 vs 206±24 mg/dl), coupled with a decrease in HDL cholesterol (49±4 vs 28±3 mg/dl). These data documenting high LDL cholesterol and low HDL cholesterol associated with an increased concentration and proportion of saturated fatty acids suggest that GSD-I patients on nocturnal intragastric feeding are at high risk for atherosclerosis and its complications.     

ISM Interviews

Abstract

This issue's column on IS strategy presents the results of a recent survey on the state of strategic planning in U.S. companies.     

3D Motion recovery via affine Epipolar geometry

Algorithms to perform point-based motion estimation under orthographic and scaled orthographic projection abound in the literature. A key limitation of many existing algorithms is that they operate on the minimum amount of data required, often requiring the selection of a suitable minimal set from the available data to serve as a local coordinate frame. Such approaches are extremely sensitive to errors and noise in the minimal set, and forfeit the advantages of using the full data set. Furthermore, attention is seldom paid to the statistical performance of the algorithms.We present a new framework that allowsall available features to be used in the motion computations, without the need to select a frame explicitly. This theory is derived in the context of theaffine camera, which preserves parallelism and generalises the orthographic, scaled orthographic and para-perspective models. We define the affine epipolar geometry for two such cameras, giving the fundamental matrix in this case. The noise resistant computation of the epipolar geometry is discussed, and a statistical noise model constructed so that confidence in the results can be assessed.The rigid motion parameters are then determineddirectly from the epipolar geometry, using the novel rotation representation of Koenderink and van Doorn (1991). The two-view partial motion solution comprises the scale factor between views, the projection of the 3D axis of rotation and the cyclotorsion angle, while the addition of a third view allows the true 3D rotation axis to be computed (up to a Necker reversal). The computed uncertainties in these parameters permit optimal estimates to be obtained over time by means of a linear Kalman filter. Our theory extends work by Huang and Lee (1989), Harris (1990), and Koenderink and van Doorn (1991), and results are given on both simulated and real data.     

Progressive subretinal fibrosis and blindness in patients with multifocal granulomatous chorioretinitis

Since women with a first-degree relative with breast cancer are at increased risk for breast cancer, it is of special importance that they adhere to early detection programs. In this study, women with (389) and without (3295) a family history of breast cancer were compared with respect to risk perception, breast cancer anxiety, and early detection behavior. Special attention was paid to the role of knowing that family history is a breast cancer risk factor. It was found that 46% of "family history positives" did not know that their risk was increased by their family history. Still, family history positives had increased risk perception; our results suggest that this was partly caused by their knowing they belonged to a risk group and partly by their having experienced the disease at close range. Although family history positives had higher risk perceptions, no differences in early detection behavior were found. This could not be attributed to high anxiety levels. Implications for health education are discussed.     

Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study

This paper presents efficient and portable implementations of a powerful image enhancement process, the Symmetric Neighborhood Filter (SNF), and an image segmentation technique that makes use of the SNF and a variant of the conventional connected components algorithm which we call -Connected Components. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient connected components algorithm based on a novel approach for parallel merging. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results are consistent with the theoretical analysis (and provide the best known execution times for segmentation, even when compared with machine-specific implementations). Our test data include difficult images from the Landsat Thematic Mapper (TM) satellite data.Also affiliated with the Department of Electrical Engineering.Also affiliated with the Department of Computer Science and the Center for Automation Research.     

Obesity does not reduce maximum acceptable weights of lift

The maximum acceptable weights of lift (MAWL) of obese and non-obese participants were empirically investigated. Three obesity levels were considered: non-obese (18.5 kg/m(2)< or= body mass index (BMI)or= 40 kg/m(2)). Ten male and 10 female participants were recruited for each obesity level. The participants determined their MAWL for 18 different lifting task conditions (six lifting frequencies x three lifting heights). An analysis of variance (ANOVA) was conducted to determine the effects of obesity level, gender, lifting height, lifting frequency and their interactions on MAWL. Overall, the ANOVA results indicated that obesity does not reduce MAWL, and thus, suggested that the existing MAWL data can be used to accommodate both general and obese workers. However, further studies based on the biomechanical and physiological approaches are required to provide more complete understanding of obesity effects on lifting tolerance limits.     

Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework

Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.     

Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition

Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene/event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape/appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.