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At least 1,261 records · Page 70Linked to original sources
PubMed2000

Robust changes of afferent-induced excitation in the rat spinal dorsal horn after conditioning high-frequency stimulation.

We investigated the neuronal plasticity in the spinal dorsal horn and its relationship with spinal inhibitory networks using an optical-imaging method that detects neuronal excitation. High-intensity single-pulse stimulation of the dorsal root activating both A and C fibers evoked an optical response in the lamina II (the substantia gelatinosa) of the dorsal horn in transverse slices of 12- to 25-day-old rat spinal cords stained with a voltage-sensitive dye, RH-482. The optical response, reflecting the net neuronal excitation along the slice-depth, was depressed by 28% for more than 1 h after a high-frequency conditioning stimulation of A fibers in the dorsal root (3 tetani of 100 Hz for 1 s with an interval of 10 s). The depression was not induced in a perfusion solution containing an NMDA antagonist, DL-2-amino-5-phosphonovaleric acid (AP5; 30 microM). In a solution containing the inhibitory amino acid antagonists bicuculline (1 microM) and strychnine (3 microM), and also in a low Cl(-) solution, the excitation evoked by the single-pulse stimulation was enhanced after the high-frequency stimulation by 31 and 18%, respectively. The enhanced response after conditioning was depotentiated by a low-frequency stimulation of A fibers (0.2-1 Hz for 10 min). Furthermore, once the low-frequency stimulation was applied, the high-frequency conditioning could not potentiate the excitation. Inhibitory transmissions thus regulate the mode of synaptic plasticity in the lamina II most likely at afferent terminals. The high-frequency conditioning elicits a long-term depression (LTD) of synaptic efficacy under a greater activity of inhibitory amino acids, but it results in a long-term potentiation (LTP) when inhibition is reduced. The low-frequency preconditioning inhibits the potentiation induction and maintenance by the high-frequency conditioning. These mechanisms might underlie robust changes of nociception, such as hypersensitivity after injury or inflammation and pain relief after electrical or cutaneous stimulation.

2-Amino-5-phosphonovalerate↗
PubMed2004

Robust classification of renal cell carcinoma based on gene expression data and predicted cytogenetic profiles.

Renal cell carcinoma (RCC) is a heterogeneous disease that includes several histologically distinct subtypes. The most common RCC subtypes are clear cell, papillary, and chromophobe, and recent gene expression profiling studies suggest that classification of RCC based on transcriptional signatures could be beneficial. Traditionally, however, patterns of chromosomal alterations have been used to assist in the molecular classification of RCC. The purpose of this study was to determine whether it was possible to develop a classification model for the three major RCC subtypes that utilizes gene expression profiles as the bases for both molecular genetic and cytogenetic classification. Gene expression profiles were first used to build an expression-based RCC classifier. The RCC gene expression profiles were then examined for the presence of regional gene expression biases. Regional expression biases are genetic intervals that contain a disproportionate number of genes that are coordinately up- or down-regulated. The presence of a regional gene expression bias often indicates the presence of a chromosomal abnormality. In this study, we demonstrate an expression-based classifier can distinguish between the three most common RCC subtypes in 99% of cases (n = 73). We also demonstrate that detection of regional expression biases accurately identifies cytogenetic features common to RCC. Additionally, the in silico-derived cytogenetic profiles could be used to classify 81% of cases. Taken together, these data demonstrate that it is possible to construct a robust classification model for RCC using both transcriptional and cytogenetic features derived from a gene expression profile.

Carcinoma, Renal Cell↗
PubMed2005

A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays.

We have developed a robust algorithm for copy number analysis of the human genome using high-density oligonucleotide microarrays containing 116,204 single-nucleotide polymorphisms. The advantages of this algorithm include the improvement of signal-to-noise (S/N) ratios and the use of an optimized reference. The raw S/N ratios were improved by accounting for the length and GC content of the PCR products using quadratic regressions. The use of constitutional DNA, when available, gives the lowest SD values (0.16 +/- 0.03) and also enables allele-based copy number detection in cancer genomes, which can unmask otherwise concealed allelic imbalances. In the absence of constitutional DNA, optimized selection of multiple normal references with the highest S/N ratios, in combination with the data regressions, dramatically improves SD values from 0.67 +/- 0.12 to 0.18 +/- 0.03. These improvements allow for highly reliable comparison of data across different experimental conditions, detection of allele-based copy number changes, and more accurate estimations of the range and magnitude of copy number aberrations. This algorithm has been implemented in a software package called Copy Number Analyzer for Affymetrix GeneChip Mapping 100K arrays (CNAG). Overall, these enhancements make CNAG a useful tool for high-resolution detection of copy number alterations which can help in the understanding of the pathogenesis of cancers and other diseases as well as in exploring the complexities of the human genome.

Algorithms↗
PubMed2001

A robust identity-by-descent procedure using affected sib pairs: multipoint mapping for complex diseases.

Multipoint linkage analysis is a powerful tool to localize susceptibility genes for complex diseases. However, the conventional lod score method relies critically on the correct specification of mode of inheritance for accurate estimation of gene position. On the other hand, allele-sharing methods, as currently practiced, are designed to test the null hypothesis of no linkage rather than estimate the location of the susceptibility gene(s). In this paper, we propose an identity-by-descent (IBD)-based procedure to estimate the location of an unobserved susceptibility gene within a chromosomal region framed by multiple markers. Here we deal with the practical situation where some of the markers might not be fully informative. Rather the IBD statistic at an arbitrary within the region is imputed using the multipoint marker information. The method is robust in that no assumption about the genetic mechanism is required other than that the region contains no more than one susceptibility gene. In particular, this approach builds upon a simple representation for the expected IBD at any arbitrary locus within the region using data from affected sib pairs. With this representation, one can carry out a parametric inference procedure to locate an unobserved susceptibility gene. In addition, here we derive a sample size formula for the number of affected sib pairs needed to detect linkage with multiple markers. Throughout, the proposed method is illustrated through simulated data. We have implemented this method including exploratory and formal model-fitting procedures to locate susceptibility genes, plus sample size and power calculations in a program, GENEFINDER, which will be made available shortly.

Chromosome Mapping↗
PubMed2003

Methylenedioxymethamphetamine (MDMA, 'ecstasy') serves as a robust positive reinforcer in a rat runway procedure.

Although 'ecstasy' (3,4-methylenedioxymethamphetamine, MDMA) is, after marijuana, the second most prevalent illegal drug of abuse in European adolescents, animal experimental evidence of MDMA's reinforcing effect has remained scarce, particularly in the rodent model, raising questions about the robustness of MDMA's reinforcing effect under controlled laboratory conditions. In the present rat runway study, Sprague-Dawley and Long-Evans rats were given the opportunity to run for intravenous injections of saline or MDMA (1 mg/kg). MDMA significantly decreased runtimes in both rat strains. Thus, MDMA's positive reinforcing effect can be demonstrated not only across rat strains but also across operant conditioning paradigms. These findings should reassure the drug abuse research community that the investigation of MDMA's reinforcing effect in the inexpensive and widely used rodent model is indeed feasible.

Animals↗
PubMed2003

Robust indices of Hardy-Weinberg disequilibrium for QTL fine mapping.

Hardy-Weinberg disequilibrium (HWD) measures have been proposed using dense markers to fine map a quantitative trait locus (QTL) to regions < approximately 1 cM. Earlier HWD measures may introduce bias in the fine mapping because they are dependent on marker allele frequencies across loci. Hence, HWD indices that do not depend on marker allele frequencies are desired for fine mapping. Based on our earlier work, here we present four new HWD indices that do not depend on marker allele frequencies. Two are for use when marker allele frequencies in a study population are known, and two are for use when marker allele frequencies in a study population are not known and are only known in the extreme samples. The new measures are a function of the genetic distance between the marker locus and a QTL. Through simulations, we investigated and compared the fine mapping performance of the new HWD measures with that of the earlier ones. Our results show that when marker allele frequencies vary across loci, the new measures presented here are more robust and powerful.

Chromosome Mapping↗
PubMed1977

Sexual dimorphism of bone robusticity in different strains of rats.

A quantitative method was used to establish the sexual dimorphism of bone robusticity in 10 domesticated strains of rats and in wild rats. Sexual dimorphism had its highest frequency in the pelvis, followed by the ulna, the skull, and the femur and tibia. Humerus and tail showed the lowest frequency of such sexual dimorphism. This distribution in an unspecialized mammal such as the rat, in which selective pressures are at their minimum becauses of the factor of domestication, can serve as a baseline for comparison with taxa in which selective forces are more strongly active.

Animals↗
PubMed2003

Robust adenoviral and adeno-associated viral gene transfer to the in vivo murine heart: application to study of phospholamban physiology.

BACKGROUND: Viral gene transfer to the whole heart in vivo has been achieved in several mammalian species but remained difficult to accomplish in murine hearts. We postulated that a key impediment derives from the use of proximal aortic occlusion during virus injection, because this eliminates coronary perfusion gradients in mice as aortic root and left ventricle pressures equalize. METHODS AND RESULTS: Pressure-volume analysis confirmed these mechanics. In contrast, descending aortic occlusion with whole-body cooling (20 degrees C) preserved transmyocardial perfusion gradients and allowed for sustained (>10-minute) dwell times in an upper-body perfusion circuit. This approach yielded robust cardiac transfection with adenovirus (AdV) and adeno-associated virus (AAV) injected into the left ventricle cavity or more simply via a central vein. Cardio-specific expression was achieved with a myocyte-specific promotor. Optimal AdV transfection required 9-minute aortic occlusion, versus 5-minute occlusion for AAV. Using this method, we examined the in vivo function of phospholamban (PLB) by stably transfecting PLB-null mice with AAV encoding PLB (AAV(PLB)). AAV(PLB) restored PLB protein to near control levels that colocalized with SERCA2A in cardiomyocytes. At baseline, PLB-null hearts exhibited enhanced systolic and diastolic function, but frequency-dependent reserve was blunted versus wild-type controls. These properties, particularly the frequency response, returned toward control 3 months after AAV(PLB) transfection. CONCLUSIONS: The new simplified approach for murine whole-heart viral transfection should assist molecular physiology studies.

Adenoviridae↗
PubMed2004

Combining insulin-like growth factor derivatives plus caffeinol produces robust neuroprotection after stroke in rats.

BACKGROUND AND PURPOSE: Insulin-like growth factor-1 (IGF-1) and caffeinol are both neuroprotective and probably have different mechanisms of action; therefore, they may be more effective in combination. METHODS: We tested the N-terminal tripeptide of IGF-1, Gly-Pro-Glu (GPE), and its analogue, G2MePE, alone and with caffeinol in a rat middle cerebral artery (MCA) suture occlusion model. We randomly assigned rats to 6 groups of 8 to 12 animals: (1) control; (2) GPE, 3 mg/kg per hour; (3) G2MePE, 0.3 mg/kg per hour; (4) caffeinol, a mixture of caffeine (10 mg/kg) with ethanol (0.32 g/kg); (5) GPE with caffeinol (combination of group 2 with 4); and (6) G2MePE with caffeinol (combination of group 3 with 4). Drugs were started 75 minutes after suture occlusion, at the start of reperfusion. Three days after MCA occlusion, neurological deficit (Neurological Deficit Score [NDS]) and lesion volume were measured. RESULTS: GPE and caffeinol improved NDS by 34% and 36%, respectively (P<0.01), and also decreased cortical but not striatal lesion volume compared with control (GPE cortex, 121 mm3; caffeinol cortex, 134 mm3; and control, 221 mm3; P<0.01). GPE plus caffeinol did not have more efficacy than either GPE or caffeinol alone. G2MePE slightly improved NDS (19.7%, P=0.05) but not lesion volume. However, G2MePE plus caffeinol very significantly improved NDS (64%) and lesion volume in both cortex (combination 95 mm3 versus control 221 mm3) and striatum (combination 74 mm3 versus control 110 mm3) (P<0.001), and was significantly more effective than either caffeinol or G2MePE alone. CONCLUSIONS: Both GPE and caffeinol significantly protect cortex after MCA occlusion. At the doses used in this study, the GPE analogue G2MePE by itself had minimal protective effects, but when combined with caffeinol, it demonstrated robust beneficial effects on cortical and subcortical lesion size and behavioral deficit. Further study of this combination appears justified.

Animals↗
PubMed2001

Analyzing holistic parsers: implications for robust parsing and systematicity.

Holistic parsers offer a viable alternative to traditional algorithmic parsers. They have good generalization performance and are robust inherently. In a holistic parser, parsing is achieved by mapping the connectionist representation of the input sentence to the connectionist representation of the target parse tree directly. Little prior knowledge of the underlying parsing mechanism thus needs to be assumed. However, it also makes holistic parsing difficult to understand. In this article, an analysis is presented for studying the operations of the confluent preorder parser (CPP). In the analysis, the CPP is viewed as a dynamical system, and holistic parsing is perceived as a sequence of state transitions through its state-space. The seemingly one-shot parsing mechanism can thus be elucidated as a step-by-step inference process, with the intermediate parsing decisions being reflected by the states visited during parsing. The study serves two purposes. First, it improves our understanding of how grammatical errors are corrected by the CPP. The occurrence of an error in a sentence will cause the CPP to deviate from the normal track that is followed when the original sentence is parsed. But as the remaining terminals are read, the two trajectories will gradually converge until finally the correct parse tree is produced. Second, it reveals that having systematic parse tree representations alone cannot guarantee good generalization performance in holistic parsing. More important, they need to be distributed in certain useful locations of the representational space. Sentences with similar trailing terminals should have their corresponding parse tree representations mapped to nearby locations in the representational space. The study provides concrete evidence that encoding the linearized parse trees as obtained via preorder traversal can satisfy such a requirement.

Algorithms↗
PubMed2001

Robust full Bayesian learning for radial basis networks.

We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters, regularization parameters, and noise parameters as unknown random variables. We develop a reversible-jump Markov chain Monte Carlo (MCMC) method to perform the Bayesian computation. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior specification. In addition, we propose a novel and computationally efficient reversible-jump MCMC simulated annealing algorithm to optimize neural networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima to a large extent. We show that by calibrating the full hierarchical Bayesian prior, we can obtain the classical Akaike information criterion, Bayesian information criterion, and minimum description length model selection criteria within a penalized likelihood framework. Finally, we present a geometric convergence theorem for the algorithm with homogeneous transition kernel and a convergence theorem for the reversible-jump MCMC simulated annealing method.

Journal Article↗
PubMed2002

Robust blind source separation by beta divergence.

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on the beta divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.

Journal Article↗
PubMed2004

Greedy learning of multiple objects in images using robust statistics and factorial learning.

We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.

Algorithms↗
PubMed2004

Trading variance reduction with unbiasedness: the regularized subspace information criterion for robust model selection in kernel regression.

A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results.

Bayes Theorem↗
PubMed2005

Data-robust tight lower bounds to the information carried by spike times of a neuronal population.

We develop new data-robust lower-bound methods to quantify the information carried by the timing of spikes emitted by neuronal populations. These methods have better sampling properties and are tighter than previous bounds based on neglecting correlation in the noise entropy. Our new lower bounds are precise also in the presence of strongly correlated firing. They are not precise only if correlations are strongly stimulus modulated over a long time range. Under conditions typical of many neurophysiological experiments, these techniques permit precise information estimates to be made even with data samples that are three orders of magnitude smaller than the size of the response space.

Action Potentials↗
PubMed1998

Online learning from finite training sets and robustness to input bias.

We analyze online gradient descent learning from finite training sets at noninfinitesimal learning rates eta. Exact results are obtained for the time-dependent generalization error of a simple model system: a linear network with a large number of weights N, trained on p = alphaN examples. This allows us to study in detail the effects of finite training set size alpha on, for example, the optimal choice of learning rate eta. We also compare online and offline learning, for respective optimal settings of eta at given final learning time. Online learning turns out to be much more robust to input bias and actually outperforms offline learning when such bias is present; for unbiased inputs, online and offline learning perform almost equally well.

Learning↗
PubMed2006

The human eye is an example of robust optical design.

In most eyes, in the fovea and at best focus, the resolution capabilities of the eye's optics and the retinal mosaic are remarkably well adapted. Although there is a large individual variability, the average magnitude of the high order aberrations is similar in groups of eyes with different refractive errors. This is surprising because these eyes are comparatively different in shape: Myopic eyes are longer whereas hyperopic eyes are shorter. In most young eyes, the amount of aberrations for the isolated cornea is larger than for the complete eye, indicating that the internal ocular optics (mainly the crystalline lens) play a significant role in compensating for the corneal aberrations, thereby producing an improved retinal image. In this paper, we show that this compensation is larger in the less optically centered eyes that mostly correspond to hyperopic eyes. This suggests a type of mechanism in the eye's design that is the most likely responsible for this compensation. Spherical aberration of the cornea is partially compensated by that of the lens in most eyes. Lateral coma is also compensated mainly in hyperopic eyes. We found that the distribution of aberrations between the cornea and lens appears to allow the optical properties of the eye to be relatively insensitive to variations arising from eye growth or exact centration and alignment of the eye's optics relative to the fovea. These results may suggest the presence of an auto-compensation mechanism that renders the eye's optics robust despite large variation in the ocular shape and geometry.

Adaptation, Physiological↗
PubMed2026

A stromal platform for robust expansion of functional IL-10-producing B cells for immune regulation.

IL-10-producing B cells exert immunosuppressive effects, yet their low abundance and poor in vitro viability have limited their therapeutic application. Here, we developed a stromal coculture system using MS5 cells engineered to express human CD40L, BAFF, and IFN-&#x3b2;1 (MS5-3F, for "3 factors"), which enables robust induction and greater than 1000-fold expansion of human IL-10-producing B cells. The expanded cells showed phenotypic and transcriptional profiles characteristic of unswitched (IgM+) plasmablasts and potently suppressed CD4+ T cell proliferation in an IL-10-dependent manner. MS5-3F-expanded B cells also increased the frequency of regulatory T cells in vitro, an effect that was not abrogated by IL-10/IL-10R blockade, suggesting contributions from additional mechanisms. IL-10 production originated predominantly from naive B cells, rather than memory B cells. Furthermore, B cells from patients with systemic lupus erythematosus, despite impaired IL-10 production under conventional conditions, were efficiently differentiated into IL-10-producing B cells using this system. The expanded cells showed minimal IgG-secreting output. Our platform offers a scalable strategy for generating human regulatory B cells, laying the foundation for B cell-based immunotherapies.

Humans↗