ABSTRACT: BACKGROUND: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. RESULTS: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. CONCLUSIONS: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Gangliosides have been implicated in exerting multiple physiological functions, and it is important to understand how their distribution is regulated in the cell membrane. By using freeze-fracture immunolabeling electron microscopy, we showed that GM1 and GM3 make independent clusters that are significantly reduced by cholesterol depletion. In the present study, we examined the effects of actin depolymerization/polymerization and Src-family kinase inhibition on the GM1 and GM3 clusters. Both GM1 and GM3 clustering was reduced when the actin cytoskeleton was perturbed by latrunculin A or jasplakinolide, but the decrease was less significant than that induced by cholesterol depletion. On the other hand, inhibition of Src-family kinases decreased GM3 clustering more drastically than did cholesterol depletion, whereas its effect on GM1 clustering was less significant. GM1 and GM3 were segregated from each other in unperturbed cells, but co-clustering increased significantly after actin depolymerization. Our results indicate that the GM1 and GM3 clusters in the cell membrane are regulated in different ways and that segregation of the two gangliosides depends on the intact actin cytoskeleton.

A 79-year-old man was admitted to a previous hospital complaining of left precordial swelling. Chest CT scan showed destruction of left sternoclavicular joint and a mass of 5 cm in diameter. Needle biopsy was performed and the diagnosis of sternoclavicular joint tuberculosis was made on the basis of presence of M. tuberculosis in the specimen. The patient was treated with isoniazid, ethambutol, rifampicin, and pyrazinamid but he developed renal failure. Then, he was transferred to our hospital. All medications were suspended because of the possibility of the side effect of drugs. We performed renal biopsy and histopathological examination revealed interstitial nephritis and minimal-change glomerulonephritis. From the result of examination, we considered interstitial nephritis was due to rifamicin. The treatment with 50 mg/day of prednisolone and isoniazid, ethambutol, and levofloxacin was administrated and renal failure and precordial mass were improved. Tuberculous arthritis usually affect hip and knee joint and sternoclavicular joint involvement is very rare.

In a previous article we developed an in vitro 23 kHz magnetic field (MF) exposure system that generated an MF of 532 microT(rms). Using this system, the biological effects of 23 kHz MFs on cell functions have been reported. To further clarify the biological effect of intermediate-frequency (IF) MFs and investigate the dose-response relationship in cell lines, an exposure system that generates stronger MFs is required. To meet this requirement, we developed a 6.25 mT(rms) MF exposure system for in vitro study. This level is 1000 times the reference level for the general public in the ICNIRP guidelines. This system provides an MF of 6.25 mT(rms) at 23 kHz with a uniformity within +/-5%. To verify that in vitro experimental conditions are maintained, we examined the temperature, environmental MF, and MF leakage for a sham exposure system. In addition, we examined the harmonics, coil shape, and heat generated in the medium by the high-strength MF. As a result, it was confirmed that this system can be used to evaluate the biological effects of IF MFs. This article presents the design and successful construction of the in vitro exposure system. Bioelectromagnetics, 2009.(c) 2009 Wiley-Liss, Inc.

Transcription factor MafA is a key molecule in insulin secretion and the development of pancreatic islets. Previously, we demonstrated that some of the MafA-deficient mice develop overt diabetes mellitus, and the phenotype of these mice seems to be mild probably because of redundant functions of other Maf proteins. In this study, we generated hybrid transgenic mice that were MafA-deficient and also overexpressed MafK specifically in beta cells (MafA(-/-)MafK(+)). MafA(-/-)MafK(+) mice developed severe overt diabetes mellitus within 5 weeks old, and showed higher levels of proteinuria and serum creatinine. Histological analysis revealed that embryonic development of beta cells in the MafA(-/-)MafK(+) mice was significantly suppressed and the reduced number of beta cells was responsible for the early onset of diabetes. Furthermore, after uninephrectomy, these mice demonstrated three characteristics of human diabetic nephropathy: diffuse, nodular, and exudative lesions. MafA(-/-)MafK(+) mice might be a useful model for the analysis of human diabetic nephropathy.

Background and objective: The Lung Flute is a small self-powered audio device that generates sound waves, which vibrate in tracheobronchial secretions. This was a preliminary trial to evaluate the usefulness of the Lung Flute for sputum sampling in patients suspected of pulmonary tuberculosis (TB). Methods: Thirty-four patients who were not expectorating sputum, but for whom sputum examination was required for the differential diagnosis of TB or other diseases, were enrolled in the study. Patients were instructed to blow out fast and hard through the Lung Flute and to repeat this for a total 20 sets of two blows each. Results: Using the Lung Flute, sputum samples were collected within 10 or 20 min from 30 of 34 patients (88%). The device permitted a rapid diagnosis of TB in seven of 15 confirmed TB cases. In three patients acid-fast bacillus smears were positive. In four patients acid-fast bacillus smears were negative, but PCR tests for TB were positive. Hyperventilation-related symptoms occurred in three patients. Conclusions: The application of the Lung Flute may represent a promising technique for the rapid diagnosis of pulmonary TB.

DNA microarrays have become a powerful tool to describe gene expression profiles associated with different cellular states, various phenotypes and responses to drugs and other extra- or intra-cellular perturbations. In order to cluster co-expressed genes and/or to construct regulatory networks, definition of distance or similarity between measured gene expression data is usually required, the most common choices being Pearson's and Spearman's correlations. Here, we evaluate these two methods and also compare them with a third one, namely Hoeffding's D measure, which is used to infer nonlinear and non-monotonic associations, i.e. independence in a general sense. By comparing three different variable association approaches, namely Pearson's correlation, Spearman's correlation and Hoeffding's D measure, we aimed at assessing the most approppriate one for each purpose. Using simulations, we demonstrate that the Hoeffding's D measure outperforms Pearson's and Spearman's approaches in identifying nonlinear associations. Our results demonstrate that Hoeffding's D measure is less sensitive to outliers and is a more powerful tool to identify nonlinear and non-monotonic associations. We have also applied Hoeffding's D measure in order to identify new putative genes associated with tp53. Therefore, we propose the Hoeffding's D measure to identify nonlinear associations between gene expression profiles.

The lipid droplet (LD) is an organelle with a lipid ester core and a surface phospholipid monolayer. The mechanism of LD biogenesis is not well understood. The present study aimed to elucidate the LD growth process, for which we developed a new electron microscopic method that quantifies the proportion of existing and newly synthesized triglycerides in individual LDs. Our method takes advantage of the reactivity of unsaturated fatty acids and osmium tetroxide, which imparts LDs an electron density that reflects fatty acid composition. With this method, existing triglyceride-rich LDs in 3Y1 fibroblasts were observed to incorporate newly synthesized triglycerides at a highly uniform rate. This uniformity and its persistence even after microtubules were depolymerized suggest that triglycerides in fibroblasts are synthesized in the local vicinity of individual LDs and then incorporated. In contrast, LDs in 3T3-L1 adipocytes showed heterogeneity in the rate at which lipid esters were incorporated, indicating different mechanisms of LD growth in fibroblasts and adipocytes.

Multiple functionally independent pools of phosphatidylinositol 4,5-bisphosphate [PI(4,5)P(2)] have been postulated to occur in the cell membrane, but the existing techniques lack sufficient resolution to unequivocally confirm their presence. To analyze the distribution of PI(4,5)P(2) at the nanoscale, we developed an electron microscopic technique that probes the freeze-fractured membrane preparation by the pleckstrin homology domain of phospholipase C-delta1. This method does not require chemical fixation or expression of artificial probes, it is applicable to any cell in vivo and in vitro, and it can define the PI(4,5)P(2) distribution quantitatively. By using this method, we found that PI(4,5)P(2) is highly concentrated at the rim of caveolae both in cultured fibroblasts and mouse smooth muscle cells in vivo. PI(4,5)P(2) was also enriched in the coated pit, but only a low level of clustering was observed in the flat undifferentiated membrane. When cells were treated with angiotensin II, the PI(4,5)P(2) level in the undifferentiated membrane decreased to 37.9% within 10 sec and then returned to the initial level. Notably, the PI(4,5)P(2) level in caveolae showed a slower but more drastic change and decreased to 20.6% at 40 sec, whereas the PI(4,5)P(2) level in the coated pit was relatively constant and decreased only to 70.2% at 10 sec. These results show the presence of distinct PI(4,5)P(2) pools in the cell membrane and suggest a unique role for caveolae in phosphoinositide signaling.

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bi-manual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification.