Estimating false positive and false negative error rates in cervical cell classification.

The performance of a cell recognition system on unknown data is often estimated in terms of its error rates on a test set. This paper investigates methods for producing estimates of error rates in cervical cell classification. Classification performance curves calculated using these methods are given for several classification schemes used to classify 1500 cervical cells.     

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications.     

Evolution of positive and negative density-dependent dispersal

Understanding the evolution of density-dependent dispersal strategies has been a major challenge for evolutionary ecologists. Some existing models suggest that selection should favour positive and others negative density-dependence in dispersal. Here, we develop a general model that shows how and why selection may shift from positive to negative density-dependence in response to key ecological factors, in particular the temporal stability of the environment. We find that in temporally stable environments, particularly with low dispersal costs and large group sizes, habitat heterogeneity selects for negative density-dependent dispersal, whereas in temporally variable environments, particularly with high dispersal costs and small group sizes, habitat heterogeneity selects for positive density-dependent dispersal. This shift reflects the changing balance between the greater competition for breeding opportunities in more productive patches, versus the greater long-term value of offspring that establish themselves there, the latter being very sensitive to the temporal stability of the environment. In general, dispersal of individuals out of low-density patches is much more sensitive to habitat heterogeneity than is dispersal out of high-density patches.     

Whole-tree methods for detecting differential diversification rates

Prolific cladogenesis, adaptive radiation, species selection, key innovations, and mass extinctions are a few examples of biological phenomena that lead to differential diversification among lineages. Central to the study of differential diversification rates is the ability to distinguish chance variation from that which requires deterministic explanation. To detect diversification rate variation among lineages, we propose a number of methods that incorporate information on the topological distribution of species diversity from all internal nodes of a phylogenetic tree. These whole-tree methods (M(Pi), M(Sigma), and M(R)) are explicitly connected to a null model of random diversification--the equal-rates Markov (ERM) random branching model--and an alternative model of differential diversification: M(Pi) is based on the product of individual nodal ERM probabilities; M(Sigma) is based on the sum of individual nodal ERM probabilities, and M(R) is based on a transformation of ERM probabilities that corresponds to a formalized system that orders trees by their relative symmetry. These methods have been implemented in a freely available computer program, SYMMETREE, to detect clades with variable diversification rates, thereby allowing the study of biological processes correlated with and possibly causal to shifts in diversification rate. Application of these methods to several published phylogenies demonstrates their ability to contend with relatively large, incompletely resolved trees. These topology-based methods do not require estimates of relative branch lengths, which should facilitate the analysis of phylogenies, such as supertrees, for which such data are unreliable or unavailable.     

The continuous positive and negative dielectrophoresis of microorganisms

The continuous dielectrophoresis of living cells is described. The technique uses stream-centered transport of suspended microorganisms through an especially shaped non-uniform electric field. The cells can be given a positive or negative displacement, i.e., can be pushed into or out of the region of higher field intensity, depending upon the frequency of the applied ac field, and upon the relative permittivities of the cells and the suspending medium. Yeast (Saccharomyces cerevisiae) and algal cells (Chlorella vulgaris) were found to provide spectra of dielectrophoretic responses varying with the applied frequency (10 to 600 kHz) and conductivity.     

Mining gene expression data for positive and negative co-regulated gene clusters

MOTIVATION: Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs. RESULTS: We experimented with the Yeast dataset and compared our resulting PNCGCs with the association rules generated by the Apriori mining algorithm. Our results show that our PNCGCs identify some missing co-regulations of association rules, and our algorithm greatly reduces the large number of rules involving uncorrelated genes generated by the Apriori scheme. AVAILABILITY: The software is available upon request.     

Antigen as a positive and negative regulator of proliferation in cytotoxic lymphocytes. A model for the differential regulation of proliferation and lytic activity

The goal of these experiments has been to capitalize on the functionally distinct responses of heteroclitic CTL to cross-reactive Ag to explore the role of Ag in regulating CTL proliferation and lytic function. The experiments here have examined one of these clones in detail but several such clones appear similar. The principal findings were: 1) proliferation and lysis may be optimally stimulated by quantitatively different interactions, 2) Ag can have negative as well as positive effects on the proliferative potential of the responding cell, and 3) both positive and negative effects on lysis can be mimicked by agents which are similar to the second messengers produced by stimulating the TCR.     

The positive aspects of correct negative reinforcement

Abstract

In the scheme of contemporary animal training, horse training is virtually unique because it relies on negative reinforcement (NR) rather than positive reinforcement (PR). Furthermore, horse trainers are largely unaware that they are using NR in training. Instead, they believe in the benevolent nature of the horse and see their task in training as one of improving the balance and gymnastic ability of the horse—outcomes that emerge when the rider is similarly properly balanced. Under these conditions, it is claimed the willing horse will perform its required maneuvers. These beliefs may be associated with several welfare issues and indicate areas requiring future research: 1. The absence of release of pressure, the release of pressure at the wrong times, the use of opposing pressures simultaneously and the absence of shaping procedures are central to the development of acute and chronic stress responses in horses.

2. Resultant conflict behaviors contribute to equine wastage statistics and include behaviors that are dangerous to horses and humans.

3. There is a need for research into the mechanics of NR because it is poorly researched compared to PR.

4. When NR responses are installed correctly, only mild pressures need to be used, and results are obtained in few trials.

5. Many qualified animal trainers misunderstand NR and confuse it with punishment. They believe that PR has positive welfare implications and thus NR being “negative,” has negative welfare implications. So there is a need for horse trainers to understand learning theory and the principles that surround NR.

6. Horse trainers are isolated from advances in animal training. Therefore they increasingly seek knowledge and solutions from the growing number of “horse whisperers” and unqualified “horse psychologists.” This is potentially detrimental for the welfare of the horse and the need is urgent for universities throughout the world to become the knowledge bases for equitation science.

     

DNA sequencing with positive and negative errors.

The problem addressed in this paper is concerned with DNA sequencing by hybridization. An algorithm is proposed that solves a computational phase of this approach in the presence of both positive and negative errors resulting from the hybridization experiment. No a priori knowledge of the nature and source of these errors is required. An extensive set of computational experiments showed that the algorithm behaves surprisingly well if only positive errors appear. The general case, where positive and negative errors occur, can be also solved satisfactorily for an error rate up to 10%.     

PIG-mediated cassava transformation using positive and negative selection

 In order to develop new selection systems for production of transgenic cassava (Manihot esculenta Crantz), two different selection regimes were assessed for their efficiency on regeneration of transgenic cassava plants: positive selection using mannose and negative selection using hygromycin. Explants from somatic cotyledons and embryogenic suspensions were used as target tissues in the transformation experiments and bombarded using the particle inflow gun. Different culture and selection strategies were assessed to optimise the selection protocols. For the first time transgenic plants could be obtained using positive, and in the case of embryogenic suspensions, hygromycin-based negative selection. The stably transformed nature of the regenerated cassava plant lines and the expression of the transgenes were verified with PCR, RT-PCR, Southern and northern analyses. A rooting test for transgenic plants on a medium supplemented with mannose was developed to further improve the efficacy of the positive selection system. Our results demonstrate that it is possible to obtain transgenic cassava plants using non-antibiotic positive selection. Received: 21 February 2000 / Revision received: 2 May 2000 / Accepted: 5 May 2000