Analysis of factors contributing to success in a program of micromanipulation-assisted fertilization

OBJECTIVE: To determine factors important to clinical success in micromanipulation-assisted in vitro fertilization (IVF). DESIGN: Procedures invoked in two separate series of micromanipulation-assisted IVF cycles, one unsuccessful (series I) and the other successful (series II), were compared in an effort to identify changes that led to clinical success. SETTING: University-based IVF clinic. PATIENTS: In both IVF series involving micromanipulation, patients consisted of infertile couples who fit any of five categories of male-factor related infertility. The female patients underwent controlled hyperstimulation for oocyte retrieval and the oocytes were inseminated normally or were subjected either to partial zona dissection or subzonal sperm insertion to assist fertilization. Results in all groups were compared between the two patient series. RESULTS: In the successful series II, a noticeable improvement in fertilization rate and embryo quality was observed compared with series I. A significant increase in the percentage of patients reaching embryo transfer, the pregnancy rate per transfer, and the pregnancy rate per retrieval were noted in series II; a 25% ongoing pregnancy rate per retrieval was observed overall in this successful group, with "ongoing" defined as manifestation of at least a fetal sac on ultrasound with no detectable problems. Patients with a mixed transfer of embryos derived from manipulated and normally inseminated oocytes had a 75% rate of pregnancy per transfer in series II. Differences between the two series could not be attributed to patient selection or biases in selection of oocytes relegated to micromanipulation. However, oocyte handling, micromanipulation, and culture protocols differed significantly between the two series in that temperature and pH of oocytes was better controlled, and micromanipulation time was minimized in series II. CONCLUSION: Success in micromanipulation depends on maintenance of the oocyte in a stable and supportive environment throughout the micromanipulation procedure. It is also important to minimize trauma to the eggs by performing micromanipulation rapidly and with minimal distortion of the egg. Patients with a poor fertilization rate in standard IVF may experience a substantial increase in the likelihood of pregnancy when micromanipulation-assisted fertilization is performed on some eggs.     

Simultaneous nonlinear absorption and index effects in the propagation of intense TEA CO2 laser pulses through CDF3

Changes in the beam profile of the CO2 laser 10R(26) line, caused by transmission through, and absorption by, CDF3 were studied using an array of pyroelectric detectors. During the propagation of the laser beam through CDF3, nonlinear absorption and self-defocusing of the beam have both been determined from measurements of the effect on the exit beam of fluence, radiant energy, CDF3 pressure, transmission cell length, and distance from the exit of the cell to the detector array.     

Combined infra-red and X-ray studies of β-silicon nitride and β′-sialons

Previous investigations of the replacement of silicon by aluminium and nitrogen by oxygen in -silicon nitride have been based primarily on X-ray powder diffraction studies. In the present work this technique is coupled with parallel infra-red studies. X-ray analyses of sialons over a wide composition range confirm previous observations that increasing substitution of aluminium for silicon and nitrogen for oxygen in -silicon nitride is accompanied by an increase in cell size, with no evidence of any other structural modification. Parallel infra-red analyses show shifts in certain of the infra-red absorption bands to lower wavenumbers as the degree of substitution increases. Changes in the infrared spectra at the composition Si₂Al₄N₄O₄ indicate structural modifications which are not apparent from the X-ray investigations. It is suggested that these changes are a result of the ordering of the different atom types at this composition.     

Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

Cross-talk induces bifurcations in nonlinear models of synaptic plasticity

Linear models of synaptic plasticity provide a useful starting-point for examining the dynamics of neuronal development and learning, but their inherent problems are well known. Models of synaptic plasticity that embrace the demands of biological realism are therefore typically nonlinear. Viewed from a more abstract perspective, nonlinear models of synaptic plasticity are a subset of nonlinear dynamical systems. As such, they may therefore exhibit bifurcations under the variation of control parameters, including noise and errors in synaptic updates. One source of noise or error is the cross-talk that occurs during otherwise Hebbian plasticity. Under cross-talk, stimulation of a set of synapses can induce or modify plasticity in adjacent, unstimulated synapses. Here, we analyze two nonlinear models of developmental synaptic plasticity and a model of independent component analysis in the presence of a simple model of cross-talk. We show that cross-talk does indeed induce bifurcations in these models, entirely destroying their ability to acquire either developmentally or learning-related patterns of fixed points. Importantly, the critical level of cross-talk required to induce bifurcations in these models is very sensitive to the statistics of the afferents' activities and the number of afferents synapsing on a postsynaptic cell. In particular, the critical level can be made arbitrarily small. Because bifurcations are inevitable in nonlinear models, our results likely apply to many nonlinear models of synaptic plasticity, although the precise details vary by model. Hence, many nonlinear models of synaptic plasticity are potentially fatally compromised by the toxic influence of cross-talk and other sources of noise and errors more generally. We conclude by arguing that biologically realistic models of synaptic plasticity must be robust against noise-induced bifurcations and that biological systems may have evolved strategies to circumvent their possible dangers.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Accelerated hypothesis generation for multistructure data via preference analysis

Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points having arisen from the same model, and is obtained without recourse to domain knowledge (e.g., keypoint matching scores) typically used in previous sampling enhancement methods. More crucially, our approach encourages sampling within coherent structures and thus can very rapidly generate all-inlier minimal subsets that maximize the robust criterion. Sampling within coherent structures also affords a natural ability to handle multistructure data, a condition that is usually detrimental to other methods. The result is a sampling scheme that offers substantial speed-ups on common computer vision tasks such as homography and fundamental matrix estimation. We show on many computer vision data, especially those with multiple structures, that ours is the only method capable of retrieving satisfactory results within realistic time budgets.     

A criticism of the Flood-Grjotheim ionic treatment of slag-equilibria

The analysis by Flood and Grjotheim¹ of the equilibria between a metal and a fused slag utilizes the ionic behavior of the slag or salt, and it employs a reversible thermodynamic cycle. However, in the original analysis there appears to be an error in the use of free energy. A corrected treatment has been developed which involves a modest change and simplification in the original formulation. The revised analysis shows the importance of the thermodynamic behavior of ionic species in the slag, which may be nonideal. Data from the literature on slag-metal equilibria, for example: $$\left[ {Fe^{2 + } } \right] + \underline {Mn} = Fe\left( l \right) + \left[ {Mn^{2 + } } \right]$$ , are analyzed by the new formulation to obtain expressions for ratios of activity coefficients such as (γS^2?/γO^2?), (γMn²⁺/γFe²⁺) and [(γPO ₄ ^3? )²/(γO^2?)³] in terms of slag composition, the last two having been found to depend strongly on the oxygen-ion content of the slag.     

Development of the PenTile Matrix™ color AMLCD subpixel architecture and rendering algorithms

Abstract— Color subpixel rendering is enhanced by co‐optimizing the color subpixel architecture and algorithms with respect to human vision. This has resulted in the PenTile? display technology, which provides double the information content per subpixel when compared to a conventional RGB Stripe display. Output performance results from mathematical modeling, software simulations, and prototype AMLCDs displays demonstrate significant quality improvements to both text and full‐color images in comparison to images from RGB Stripe displays that have the same number of subpixels and column drivers.     

Fast image retrieval using color-spatial information

In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism. Received August 1, 1997 / Accepted December 9, 1997