Distinctive gene expression profiles in Balb/3T3 cells exposed to low dose cobalt nanoparticles, microparticles and ions: potential nanotoxicological relevance.

Size-dependent characteristics of novel engineered nanomaterials might result in unforeseen biological responses and toxicity. To address this issue, we used cDNA microarray analysis (13443 genes) coupled with bioinformatics and functional gene annotation studies to investigate the transcriptional profiles of Balb/3T3 cells exposed to a low dose (1 μM) of cobalt nanoparticles (CoNP), microparticles (CoMP) and ions (Co2+). CoNP, CoMP and Co2+ affected 124, 91 and 80 genes, respectively. Hierarchical clustering revealed two main gene clusters, one up-regulated, mainly after Co2+, the other down-regulated, mainly after CoNP and CoMP. The significant Gene Ontology (GO) terms included oxygen binding and transport and hemoglobin binding for Co2+, while the GOs of CoMP and CoNP were related to nucleus and intracellular components. Pathway analysis highlighted: i) mitochondrial dysfunction for Co2+, ii) signaling, activation of innate immunity, and apoptosis for CoNP, and iii) cell metabolism, G1/S cell cycle checkpoint regulation and signaling for CoMP. Unlike ions, particles affected toxicologically-relevant pathways implicated in carcinogenesis and inflammation.

Efficacy of the Sander bite-jumping appliance in growing patients with mandibular retrusion: a randomized controlled trial.

OBJECTIVES: The efficacy of functional appliances remains highly debated. This randomized controlled trial investigated the skeletal and dentoalveolar effects determined by the Sander bite-jumping appliance (BJA). The null hypothesis to be tested was that the appliance would not induce supplementary mandibular growth compared to untreated controls. SETTING AND SAMPLE POPULATION: This study was carried out at the Section of Orthodontics, University of Naples Federico II, Italy. Forty-six patients receiving a clinical diagnosis of skeletal and dental class II due to mandibular retrusion were either allocated to a treatment (23 patients;15 boys, 8 girls; mean age ± SD: 10.9 ± 1.3 years) or to an untreated control group (23 patients;11 boys, 12 girls; mean age ± SD: 10.5 ± 1.2 years), by using a balanced block randomization. METHODS: Lateral cephalograms were taken before and after treatment and used for comparisons. Measurements were analyzed by descriptive statistics, univariate and multivariate statistical tests. RESULTS: Treated individuals had a significant increase in mandibular length (6.4 ± 2.3 vs. 3.5 ± 2.5 mm; p < 0.001), overjet reduction (-5.0 ± 2.9 vs. 0.3 ± 1.2 mm; p < 0.001) and molar relationship improvement (-5.3 ± 2.4 vs. 0.1 ± 1.1 mm; p < 0.001) compared to controls. The use of the appliance did not significantly affect jaw divergence. Proclination of lower incisors was slightly greater (3.0°, p = 0.023) in treated patients than in controls. The increase in mandibular length was not significantly influenced by cervical stage (p = 0.40). CONCLUSION: The BJA can effectively correct class II malocclusions by a combination of dentoalveolar and skeletal effects. The long-term stability of the correction needs to be evaluated.

Interactive data analysis and clustering of genomic data.

In this work a new clustering approach is used to explore a well- known dataset [Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., et al. (2002). Molecular biology of the cell: Vol. 13. Identification of genes periodically expressed in the human cell cycle and their expression in tumors (pp. 1977-2000)] of time dependent gene expression profiles in human cell cycle. The approach followed by us is realized with a multi-step procedure: after preprocessing, parameters are chosen by using data sub sampling and stability measures; for any used model, several different clustering solutions are obtained by random initialization and are selected basing on a similarity measure and a figure of merit; finally the selected solutions are tuned by evaluating a reliability measure. Three different models for clustering, K-means, Self-organizing Maps and Probabilistic Principal Surfaces are compared. Comparative analysis is carried out by considering: similarity between best solutions obtained through the three methods, absolute distortion value and validation through the use of Gene Ontology (GO) annotations. The GO annotations are used to give significance to the obtained clusters and to compare the results with those obtained in the work cited above.

A multi-step approach to time series analysis and gene expression clustering.

MOTIVATION: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. RESULTS: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. AVAILABILITY: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Complexity of time series associated to dynamical systems inferred from independent component analysis.

A not trivial problem for every experimental time series associated to a natural system is to individuate the significant variables to describe the dynamics, i.e., the effective degrees of freedom. The application of independent component analysis (ICA) has provided interesting results in this direction, e.g., in the seismological and atmospheric field. Since all natural phenomena can be represented by dynamical systems, our aim is to check the performance of ICA in this general context to avoid ambiguities when investigating an unknown experimental system. We show many examples, representing linear, nonlinear, and stochastic processes, in which ICA seems to be an efficacious preanalysis able to give information about the complexity of the dynamics.

A novel information geometric approach to variable selection in MLP networks.

In this paper, a novel information geometric-based variable selection criterion for multi-layer perceptron networks is described. It is based on projections of the Riemannian manifold defined by a multi-layer perceptron network on submanifolds defined by multi-layer perceptron networks with reduced input dimension. We show how the divergence between models can be used as a criterion for an efficient search in the space of networks with different inputs. Then, we show how the posterior probabilities of the models can be evaluated to rank the projected models. Finally, we test our algorithm on synthetic and real data, and compare its performances with other methods reported in literature.

Neural networks for blind-source separation of Stromboli explosion quakes.

Independent component analysis (ICA) is used to analyze the seismic signals produced by explosions of the Stromboli volcano. It has been experimentally proved that it is possible to extract the most significant components from seismometer recorders. In particular, the signal, eventually thought as generated by the source, is corresponding to the higher power spectrum, isolated by our analysis. Furthermore, the amplitude of the source signals has been found by using a simple trick and so overcoming, for this specific case, the classical problem of ICA regarding the amplitude loss of the separated signals.

Automated labeling for unsupervised neural networks: a hierarchical approach.

In this paper a hybrid system and a hierarchical neural-net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method consists in the application of nonneural clustering algorithms directly to the output of a neural net; the second one is based on a multilayer organization of neural units. Both methods are a substantial improvement with respect to the most important unsupervised neural algorithms existing in literature. Experimental results are shown to illustrate clustering performance of the systems.

Fuzzy neural networks for classification and detection of anomalies.

In this paper, a new learning algorithm for the Simpson's fuzzy min-max neural network is presented. It overcomes some undesired properties of the Simpson's model: specifically, in it there are neither thresholds that bound the dimension of the hyperboxes nor sensitivity parameters. Our new algorithm improves the network performance: in fact, the classification result does not depend on the presentation order of the patterns in the training set, and at each step, the classification error in the training set cannot increase. The new neural model is particularly useful in classification problems as it is shown by comparison with some fuzzy neural nets cited in literature (Simpson's min-max model, fuzzy ARTMAP proposed by Carpenter, Grossberg et al. in 1992, adaptive fuzzy systems as introduced by Wang in his book) and the classical multilayer perceptron neural network with backpropagation learning algorithm. The tests were executed on three different classification problems: the first one with two-dimensional synthetic data, the second one with realistic data generated by a simulator to find anomalies in the cooling system of a blast furnace, and the third one with real data for industrial diagnosis. The experiments were made following some recent evaluation criteria known in literature and by using Microsoft Visual C++ development environment on personal computers.

Inhibitory and excitatory responses to field stimulation in fetal and adult pig airway.

The innervation in airway tissues from young adult (15-26 wk) and fetal (95/115 d gestation) pigs was compared in isolated tracheal and bronchial preparations subjected to electrical field stimulation. End-organ responsiveness to carbachol, substance P, isoprenaline, and VIP was present by 95 d gestation. Electrical field stimulation (0.5-20 Hz, 70 V, 0.5 ms) resulted in a frequency-dependent contraction that was blocked by atropine (10(-6) M) and TTX (10(-6) M) at both ages. However, there was a 10-fold increase in threshold in the fetal airways because contractions were evoked at frequencies of approximately 5 Hz in the fetus compared with 0.5 Hz in the young adult airways. In the young adult airways, there were atropine-resistant contractions at longer pulse durations (1-5 ms, 20 Hz), but not usually in the fetus. The atropine-resistant contractions were not blocked by TTX. Capsaicin (10(-6) M) produced no contraction in the pig airway. In tissues contracted using the ED50 of carbachol, electrical stimulation (1-20 Hz, 70 V, 1 ms) caused marked relaxation, however, compared with those in the young adult, fetal responses were weak or absent. Propranolol (10(-6) M) partially reduced the relaxation of the young adult bronchus (approximately 25%), but it had little effect on responses in the other young adult and fetal preparations. Therefore, the inhibitory innervation of pig airways was predominantly nonadrenergic and the excitatory component was cholinergic. Neither of these components was fully developed in the fetus close to term.