Glow discharge in low pressure plasma PVD: mathematical model and numerical simulations

In this paper we analyze the problem of glow discharge in low pressure plasma in industrial plant, for chambers of different shapes and various working parameters, like pressure and electric potential. The model described is based upon a static approximation of the AC configuration with two electrodes and a drift diffusion approximation for the current density of positive ions and electrons. A detailed discussion of the boundary conditions imposed is given, as well as the full description of the mathematical model. Numerical simulations were performed for a simple 1D model and two different 2D models, corresponding to two different settings of the industrial plant. The simpler case consists of a radially symmetric chamber, with one central electrode (cathode), based upon a DC generator. In this case, the steel chamber acts as the anode. The second model concerns a two dimensional horizontal cut of the most common plant configuration, with two electrodes connected to an AC generator. The case is treated in a "quasi-static" approximation. The three models show some common behaviours, particularly including the main expected features, such as dark spaces, glow regions and a wide "plasma region". Furthermore, the three shown models show some similarities with previously published results concerning 1D and simplified 2D models, as well as with some preliminary results of the full 3D case.Comment: 16 pages, 11 figures, in pres

Speeding up the Consensus Clustering methodology for microarray data analysis

<p>Abstract</p> <p>Background</p> <p>The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of <monospace>Consensus</monospace> (Consensus Clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, <monospace>Consensus</monospace> is a natural candidate for a speed-up.</p> <p>Results</p> <p>Since the time-precision performance of <monospace>Consensus</monospace> depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for <monospace>Consensus</monospace>. That is, the closely related algorithm <monospace>FC</monospace> (Fast Consensus) that would have the same precision as <monospace>Consensus</monospace> with a substantially better time performance. The performance of <monospace>FC</monospace> has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, <monospace>FC</monospace> turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of <monospace>Consensus</monospace>. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guaranteeing the same performance as the corresponding matrix produced by <monospace>Consensus</monospace>. We have also experimented with the use of <monospace>Consensus</monospace> and <monospace>FC</monospace> in conjunction with <monospace>NMF</monospace> (Nonnegative Matrix Factorization), in order to identify the correct number of clusters in a dataset. Although <monospace>NMF</monospace> is an increasingly popular technique for biological data mining, our results are somewhat disappointing and complement quite well the state of the art about <monospace>NMF</monospace>, shedding further light on its merits and limitations.</p> <p>Conclusions</p> <p>In summary, <monospace>FC</monospace> with a parameter setting that makes it robust with respect to small and medium-sized datasets, i.e, number of items to cluster in the hundreds and number of conditions up to a thousand, seems to be the internal validation measure of choice. Moreover, the technique we have developed here can be used in other contexts, in particular for the speed-up of stability-based validation measures.</p

bioNMF: a versatile tool for non-negative matrix factorization in biology

BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at

The Role of the Substantia Nigra Pars Compacta in Regulating Sleep Patterns in Rats

Background. As of late, dopaminergic neurotransmission has been recognized to be involved in the generation of sleep disturbances. Increasing evidence shows that sleep disturbances in Parkinson's disease (PD) patients are mostly related to the disease itself, rather than being a secondary phenomenon. Evidence contained in the literature lends support to the hypothesis that the dopaminergic nigrostriatal pathway is closely involved in the regulation of sleep patterns. Methodology/Principal Findings. To test this hypothesis we examined the electrophysiological activity along the sleep-wake cycle of rats submitted to a surgically induced lesion of the SNpc by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). We demonstrated that a 50% lesion of the substantia nigra pars compacta (SNpc) suffices to produce disruptions of several parameters in the sleep-wake pattern of rats. A robust and constant decrease in the latency to the onset of slow wave sleep (SWS) was detected throughout the five days of recording in both light [F((22.16)) = 72.46, p<0.0001] and dark [F((22.16)) = 75.0, p<0.0001] periods. Also found was a pronounced increase in the percentage of sleep efficiency during the first four days of recording [F((21.15)) = 21.48, p<0.0001], in comparison to the sham group. Additionally, the reduction in the SNpc dopaminergic neurons provoked an ablation in the percentage of rapid eye movement sleep (REM) during three days of the sleep-wake recording period with a strong correlation (r = 0.91; p<0.0001) between the number of dopaminergic neurons lost and the percentage decrease of REM sleep on the first day of recording. On day 4, the percentage of REM sleep during the light and dark periods was increased, [F((22.16)) = 2.46, p<0.0007], a phenomenon consistent with REM rebound. Conclusions/Significance. We propose that dopaminergic neurons present in the SNpc possess a fundamental function in the regulation of sleep processes, particularly in promoting REM sleep.AFIPCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Federal de São Paulo, Dept Psicobiol, São Paulo, BrazilUniv Fed Parana, Dept Farmacol, BR-80060000 Curitiba, Parana, BrazilUniversidade Federal de São Paulo, Dept Psicobiol, São Paulo, BrazilFAPESP: 98/14.303-3Web of Scienc

The Asp298 allele of endothelial nitric oxide synthase is a risk factor for myocardial infarction among patients with type 2 diabetes mellitus

Background: Endothelial dysfunction plays a central role in atherosclerotic progression and cardiovascular complications of type 2 diabetes mellitus (T2DM). Given the role of nitric oxide in the vascular system, we aimed to test hypotheses of synergy between the common endothelial nitric oxide synthase (eNOS) Asp(298) allele and T2DM in predisposing to acute myocardial infarction (AMI). Methods: In a population-based patient survey with 403 persons with T2DM and 799 healthy subjects from the population without diabetes or hypertension, we analysed the relation between T2DM, sex and the eNOS Asp(298) allele versus the risk for AMI. Results: In an overall analysis, T2DM was a significant independent risk factor for AMI. In patients with T2DM, homozygosity for the eNOS Asp(298) allele was a significant risk factor (HR 3.12 [1.49-6.56], p = 0.003), but not in subjects without diabetes or hypertension. Compared to wild-type non-diabetic subjects, all patients with T2DM had a significantly increased risk of AMI regardless of genotype. This risk was however markedly higher in patients with T2DM homozygous for the Asp(298) allele (HR 7.20 [3.01-17.20], p < 0.001), independent of sex, BMI, systolic blood pressure, serum triglycerides, HDL -cholesterol, current smoking, and leisure time physical activity. The pattern seemed stronger in women than in men. Conclusion: We show here a strong independent association between eNOS genotype and AMI in patients with T2DM. This suggests a synergistic effect of the eNOS Asp(298) allele and diabetes, and confirms the role of eNOS as an important pathological bottleneck for cardiovascular disease in patients with T2DM

Primary Raynaud's phenomenon in an infant: a case report and review of literature

Raynaud's phenomenon (RP) is an extremely unusual finding in early infancy. In the present report we describe a one-month-old previously healthy male infant who presented with unilateral acrocyanosis. Although infantile acrocyanosis is known to be a benign and self-resolving condition, it is generally bilateral and symmetric. The unilateral nature of the acrocyanosis was an atypical finding in this infant. Consequently, he was closely monitored to evaluate the progression of his acrocyanosis. Based on his benign clinical course and failure to demonstrate other etiologies contributing to his acrocyanosis, he was diagnosed to have primary RP. Due to the rarity of RP in children, we review the progress in understanding the pathophysiology, epidemiology and management of RP and additionally discuss the differential diagnosis of unilateral and bilateral acrocyanosis in infants

A non-canonical RNA silencing pathway promotes mRNA degradation in basal fungi

The increasing knowledge on the functional relevance of endogenous small RNAs (esRNAs) as riboregulators has stimulated the identification and characterization of these molecules in numerous eukaryotes. In the basal fungus Mucor circinelloides, an emerging opportunistic human pathogen, esRNAs that regulate the expression of many protein coding genes have been described. These esRNAs share common machinery for their biogenesis consisting of an RNase III endonuclease Dicer, a single Argonaute protein and two RNA-dependent RNA polymerases. We show in this study that, besides participating in this canonical dicer-dependent RNA interference (RNAi) pathway, the rdrp genes are involved in a novel dicer-independent degradation process of endogenous mRNAs. The analysis of esRNAs accumulated in wild type and silencing mutants demonstrates that this new rdrp-dependent dicer-independent regulatory pathway, which does not produce sRNA molecules of discrete sizes, controls the expression of target genes promoting the specific degradation of mRNAs by a previously unknown RNase. This pathway mainly regulates conserved genes involved in metabolism and cellular processes and signaling, such as those required for heme biosynthesis, and controls responses to specific environmental signals. Searching the Mucor genome for candidate RNases to participate in this pathway, and functional analysis of the corresponding knockout mutants, identified a new protein, R3B2. This RNase III-like protein presents unique domain architecture, it is specifically found in basal fungi and, besides its relevant role in the rdrp-dependent dicer-independent pathway, it is also involved in the canonical dicer-dependent RNAi pathway, highlighting its crucial role in the biogenesis and function of regulatory esRNAs. The involvement of RdRPs in RNA degradation could represent the first evolutionary step towards the development of an RNAi mechanism and constitutes a genetic link between mRNA degradation and post-transcriptional gene silencing

Clustering gene expression data with a penalized graph-based metric

<p>Abstract</p> <p>Background</p> <p>The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.</p> <p>Results</p> <p>In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric.</p> <p>Conclusions</p> <p>In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.</p

N-Acetyl Cysteine May Support Dopamine Neurons in Parkinson\u27s Disease: Preliminary Clinical and Cell Line Data.

BACKGOUND: The purpose of this study was to assess the biological and clinical effects of n-acetyl-cysteine (NAC) in Parkinson\u27s disease (PD). METHODS: The overarching goal of this pilot study was to generate additional data about potentially protective properties of NAC in PD, using an in vitro and in vivo approach. In preparation for the clinical study we performed a cell tissue culture study with human embryonic stem cell (hESC)-derived midbrain dopamine (mDA) neurons that were treated with rotenone as a model for PD. The primary outcome in the cell tissue cultures was the number of cells that survived the insult with the neurotoxin rotenone. In the clinical study, patients continued their standard of care and were randomized to receive either daily NAC or were a waitlist control. Patients were evaluated before and after 3 months of receiving the NAC with DaTscan to measure dopamine transporter (DAT) binding and the Unified Parkinson\u27s Disease Rating Scale (UPDRS) to measure clinical symptoms. RESULTS: The cell line study showed that NAC exposure resulted in significantly more mDA neurons surviving after exposure to rotenone compared to no NAC, consistent with the protective effects of NAC previously observed. The clinical study showed significantly increased DAT binding in the caudate and putamen (mean increase ranging from 4.4% to 7.8%; p CONCLUSIONS: The results of this preliminary study demonstrate for the first time a potential direct effect of NAC on the dopamine system in PD patients, and this observation may be associated with positive clinical effects. A large-scale clinical trial to test the therapeutic efficacy of NAC in this population and to better elucidate the mechanism of action is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT02445651