Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Increased mobility of major histocompatibility complex I-peptide complexes decreases the sensitivity of antigen recognition.

CD8(+) cytotoxic T lymphocytes (CTL) can recognize and kill target cells expressing only a few cognate major histocompatibility complex (MHC) I-peptide complexes. This high sensitivity requires efficient scanning of a vast number of highly diverse MHC I-peptide complexes by the T cell receptor in the contact site of transient conjugates formed mainly by nonspecific interactions of ICAM-1 and LFA-1. Tracking of single H-2K(d) molecules loaded with fluorescent peptides on target cells and nascent conjugates with CTL showed dynamic transitions between states of free diffusion and immobility. The immobilizations were explained by association of MHC I-peptide complexes with ICAM-1 and strongly increased their local concentration in cell adhesion sites and hence their scanning by T cell receptor. In nascent immunological synapses cognate complexes became immobile, whereas noncognate ones diffused out again. Interfering with this mobility modulation-based concentration and sorting of MHC I-peptide complexes strongly impaired the sensitivity of antigen recognition by CTL, demonstrating that it constitutes a new basic aspect of antigen presentation by MHC I molecules.

Asymptotic robust inferences in the analysis of mean and covariance structures

Structural equation models are widely used in economic, socialand behavioral studies to analyze linear interrelationships amongvariables, some of which may be unobservable or subject to measurementerror. Alternative estimation methods that exploit different distributionalassumptions are now available. The present paper deals with issues ofasymptotic statistical inferences, such as the evaluation of standarderrors of estimates and chi--square goodness--of--fit statistics,in the general context of mean and covariance structures. The emphasisis on drawing correct statistical inferences regardless of thedistribution of the data and the method of estimation employed. A(distribution--free) consistent estimate of $\Gamma$, the matrix ofasymptotic variances of the vector of sample second--order moments,will be used to compute robust standard errors and a robust chi--squaregoodness--of--fit squares. Simple modifications of the usual estimateof $\Gamma$ will also permit correct inferences in the case of multi--stage complex samples. We will also discuss the conditions under which,regardless of the distribution of the data, one can rely on the usual(non--robust) inferential statistics. Finally, a multivariate regressionmodel with errors--in--variables will be used to illustrate, by meansof simulated data, various theoretical aspects of the paper.

A possible territorial or recognition behavior of Leptoglossus zonatus (Dallas) (Heteroptera, Coreidae)

The coreid Leptoglossus zonatus (Dallas, 1852) is commonly found in corn (Zea mays L.) fields in Brazil, and it has been observed flying and landing on objects or persons near these fields. During January, 1995, this behavior was studied in corn plantations. Results indicated that the bugs concentrated on objects (plastic cylinders traps) introduced into their habitat and that their number increased during the first 24 hs. However, as time passed (8 days), this possible territorial or recognition behavior gradually decreased, and tended to disappear.

Functional avidity of tumor antigen-specific CTL recognition directly correlates with the stability of MHC/peptide multimer binding to TCR.

Avidity of Ag recognition by tumor-specific T cells is one of the main parameters that determines the potency of a tumor rejection Ag. In this study we show that the relative efficiency of staining of tumor Ag-specific T lymphocytes with the corresponding fluorescent MHC class I/peptide multimeric complexes can considerably vary with staining conditions and does not necessarily correlate with avidity of Ag recognition. Instead, we found a clear correlation between avidity of Ag recognition and the stability of MHC class I/peptide multimeric complexes interaction with TCR as measured in dissociation kinetic experiments. These findings are relevant for both identification and isolation of tumor-reactive CTL.

Robust response transformations based on optimal prediction

Nonlinear regression problems can often be reduced to linearity by transforming the response variable (e.g., using the Box-Cox family of transformations). The classic estimates of the parameter defining the transformation as well as of the regression coefficients are based on the maximum likelihood criterion, assuming homoscedastic normal errors for the transformed response. These estimates are nonrobust in the presence of outliers and can be inconsistent when the errors are nonnormal or heteroscedastic. This article proposes new robust estimates that are consistent and asymptotically normal for any unimodal and homoscedastic error distribution. For this purpose, a robust version of conditional expectation is introduced for which the prediction mean squared error is replaced with an M scale. This concept is then used to develop a nonparametric criterion to estimate the transformation parameter as well as the regression coefficients. A finite sample estimate of this criterion based on a robust version of smearing is also proposed. Monte Carlo experiments show that the new estimates compare favorably with respect to the available competitors.