ciliaFA : a research tool for automated, high-throughput measurement of ciliary beat frequency using freely available software

Background: Analysis of ciliary function for assessment of patients suspected of primary ciliary dyskinesia (PCD) and for research studies of respiratory and ependymal cilia requires assessment of both ciliary beat pattern and beat frequency. While direct measurement of beat frequency from high-speed video recordings is the most accurate and reproducible technique it is extremely time consuming. The aim of this study was to develop a freely available automated method of ciliary beat frequency analysis from digital video (AVI) files that runs on open-source software (ImageJ) coupled to Microsoft Excel, and to validate this by comparison to the direct measuring high-speed video recordings of respiratory and ependymal cilia. These models allowed comparison to cilia beating between 3 and 52 Hz. Methods: Digital video files of motile ciliated ependymal (frequency range 34 to 52 Hz) and respiratory epithelial cells (frequency 3 to 18 Hz) were captured using a high-speed digital video recorder. To cover the range above between 18 and 37 Hz the frequency of ependymal cilia were slowed by the addition of the pneumococcal toxin pneumolysin. Measurements made directly by timing a given number of individual ciliary beat cycles were compared with those obtained using the automated ciliaFA system. Results: The overall mean difference (± SD) between the ciliaFA and direct measurement high-speed digital imaging methods was −0.05 ± 1.25 Hz, the correlation coefficient was shown to be 0.991 and the Bland-Altman limits of agreement were from −1.99 to 1.49 Hz for respiratory and from −2.55 to 3.25 Hz for ependymal cilia. Conclusions: A plugin for ImageJ was developed that extracts pixel intensities and performs fast Fourier transformation (FFT) using Microsoft Excel. The ciliaFA software allowed automated, high throughput measurement of respiratory and ependymal ciliary beat frequency (range 3 to 52 Hz) and avoids operator error due to selection bias. We have included free access to the ciliaFA plugin and installation instructions in Additional file 1 accompanying this manuscript that other researchers may use

Spectrum of mutational signatures in T-cell lymphoma reveals a key role for UV radiation in cutaneous T-cell lymphoma

T-cell non-Hodgkin's lymphomas develop following transformation of tissue resident T-cells. We performed a meta-analysis of whole exome sequencing data from 403 patients with eight subtypes of T-cell non-Hodgkin's lymphoma to identify mutational signatures and associated recurrent gene mutations. Signature 1, indicative of age-related deamination, was prevalent across all T-cell lymphomas, reflecting the derivation of these malignancies from memory T-cells. Adult T-cell leukemia-lymphoma was specifically associated with signature 17, which was found to correlate with the IRF4 K59R mutation that is exclusive to Adult T-cell leukemia-lymphoma. Signature 7, implicating UV exposure was uniquely identified in cutaneous T-cell lymphoma (CTCL), contributing 52% of the mutational burden in mycosis fungoides and 23% in Sezary syndrome. Importantly this UV signature was observed in CD4 + T-cells isolated from the blood of Sezary syndrome patients suggesting extensive re-circulation of these T-cells through skin and blood. Analysis of non-Hodgkin's T-cell lymphoma cases submitted to the national 100,000 WGS project confirmed that signature 7 was only identified in CTCL strongly implicating UV radiation in the pathogenesis of cutaneous T-cell lymphoma

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Winnow based identification of potent hERG inhibitors in silico: comparative assessment on different datasets

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Peer Reviewe

The Formation of Orthogonal Balanced Experiment Designs Based on Special Block Matrix Operations on the Example of the Mathematical Modeling of the Pneumatic Gravity Seed Separator

The article describes solving the task of building a mathematical model of the pneumatic gravity seed separator by organizing an effective scheme for conducting (plan) experiments with a sufficiently large number of levels of factors and, simultaneously, a small number of experiments. The construction of a planning matrix, which has specified properties, provides a fairly high level of experimental research efficiency. For this purpose, proposed an algorithm for constructing a balanced orthogonal plan of the mn experiment, optimal by the criterion of D-efficiency. The point of the algorithm is to build a special balanced matrix by performing an operation of the Kronecker product in relation to some basis matrix and the subsequent implementation of block summation matrices. Based on the proposed algorithm, was constructed a balanced matrix of an experiment plan by 55 types, consisting of 25 experiments. On the grounds of the offered algorithm, implemented the experiment and constructed a mathematical model. Based on the mathematical model, was determined the optimal values of the parameters of the pneumatic gravity seed separator. The optimal values of the parameters found on the basis of a mathematical model are confirmed experimentally

Frequent and Persistent PLCG1 Mutations in Sezary Cells Directly Enhance PLC gamma 1 Activity and Stimulate NF kappa B, AP-1, and NFAT Signaling

Phospholipase C Gamma 1 (PLCG1) is frequently mutated in primary cutaneous T-cell lymphoma (CTCL). This study functionally interrogated nine PLCG1 mutations (p.R48W, p.S312L, p.D342N, p.S345F, p.S520F, p.R1158H, p.E1163K, p.D1165H, and the in-frame indel p.VYEEDM1161V) identified in Sézary Syndrome, the leukemic variant of CTCL. The mutations were demonstrated in diagnostic samples and persisted in multiple tumor compartments over time, except in patients who achieved a complete clinical remission. In basal conditions, the majority of the mutations confer PLCγ1 gain-of-function activity through increased inositol phosphate production and the downstream activation of NFκB, AP-1, and NFAT transcriptional activity. Phosphorylation of the p.Y783 residue is essential for the proximal activity of wild-type PLCγ1, but we provide evidence that activating mutations do not require p.Y783 phosphorylation to stimulate downstream NFκB, NFAT, and AP-1 transcriptional activity. Finally, the gain-of-function effects associated with the p.VYEEDM1161V indel suggest that the C2 domain may have a role in regulating PLCγ1 activity. These data provide compelling evidence to support the development of therapeutic strategies targeting mutant PLCγ1

Protocol for a human in vivo model of acute cigarette smoke inhalation challenge in smokers with COPD: monitoring the nasal and systemic immune response using a network biology approach

Introduction: Cigarette smoke contributes to a diverse range of diseases including chronic obstructive pulmonary disease (COPD), cardiovascular disorders and many cancers. There currently is a need for human challenge models, to assess the acute effects of a controlled cigarette smoke stimulus, followed by serial sampling of blood and respiratory tissue for advanced molecular profiling. We employ precision sampling of nasal mucosal lining fluid by absorption to permit soluble mediators measurement in eluates. Serial nasal curettage was used for transcriptomic analysis of mucosal tissue. Methods and analysis: Three groups of strictly defined patients will be studied: 12 smokers with COPD (GOLD Stage 2) with emphysema, 12 matched smokers with normal lung function and no evidence of emphysema, and 12 matched never smokers with normal spirometry. Patients in the smoking groups are current smokers, and will be given full support to stop smoking immediately after this study. In giving a controlled cigarette smoke stimulus, all patients will have abstained from smoking for 12 h, and will smoke two cigarettes with expiration through the nose in a ventilated chamber. Before and after inhalation of cigarette smoke, a series of samples will be taken from the blood, nasal mucosal lining fluid and nasal tissue by curettage. Analysis of plasma nicotine and metabolites in relation to levels of soluble inflammatory mediators in nasal lining fluid and blood, as well as assessing nasal transcriptomics, ex vivo blood platelet aggregation and leucocyte responses to toll-like receptor agonists will be undertaken. Implications: Development of acute cigarette smoke challenge models has promise for the study of molecular effects of smoking in a range of pathological processes.This study was funded by the Wellcome Trust and Dainippon Sumitomo Pharma Co Ltd, Osaka, Japan. Supported by: Dainippon Sumitomo Pharma Co Ltd, Osaka, Japan National Institute of Healthcare Research (Grant No: R3101002), NIHR Imperial Biomedical Research Centre (NIHR BMRC), Imperial Academic Health Science Centre (AHSC), Imperial Centre for Respiratory Infection (CRI, Grant No: 083567/Z/07/Z), Wellcome Trust (Grant No: 083429/Z/07/Z)

Topological Crystalline Insulators in the SnTe Material Class

Topological crystalline insulators are new states of matter in which the topological nature of electronic structures arises from crystal symmetries. Here we predict the first material realization of topological crystalline insulator in the semiconductor SnTe, by identifying its nonzero topological index. We predict that as a manifestation of this nontrivial topology, SnTe has metallic surface states with an even number of Dirac cones on high-symmetry crystal surfaces such as {001}, {110} and {111}. These surface states form a new type of high-mobility chiral electron gas, which is robust against disorder and topologically protected by reflection symmetry of the crystal with respect to {110} mirror plane. Breaking this mirror symmetry via elastic strain engineering or applying an in-plane magnetic field can open up a continuously tunable band gap on the surface, which may lead to wide-ranging applications in thermoelectrics, infrared detection, and tunable electronics. Closely related semiconductors PbTe and PbSe also become topological crystalline insulators after band inversion by pressure, strain and alloying.Comment: submitted on Feb. 10, 2012; to appear in Nature Communications; 5 pages, 4 figure

Informing investment to reduce inequalities: a modelling approach

Background: Reducing health inequalities is an important policy objective but there is limited quantitative information about the impact of specific interventions. Objectives: To provide estimates of the impact of a range of interventions on health and health inequalities. Materials and methods: Literature reviews were conducted to identify the best evidence linking interventions to mortality and hospital admissions. We examined interventions across the determinants of health: a ‘living wage’; changes to benefits, taxation and employment; active travel; tobacco taxation; smoking cessation, alcohol brief interventions, and weight management services. A model was developed to estimate mortality and years of life lost (YLL) in intervention and comparison populations over a 20-year time period following interventions delivered only in the first year. We estimated changes in inequalities using the relative index of inequality (RII). Results: Introduction of a ‘living wage’ generated the largest beneficial health impact, with modest reductions in health inequalities. Benefits increases had modest positive impacts on health and health inequalities. Income tax increases had negative impacts on population health but reduced inequalities, while council tax increases worsened both health and health inequalities. Active travel increases had minimally positive effects on population health but widened health inequalities. Increases in employment reduced inequalities only when targeted to the most deprived groups. Tobacco taxation had modestly positive impacts on health but little impact on health inequalities. Alcohol brief interventions had modestly positive impacts on health and health inequalities only when strongly socially targeted, while smoking cessation and weight-reduction programmes had minimal impacts on health and health inequalities even when socially targeted. Conclusions: Interventions have markedly different effects on mortality, hospitalisations and inequalities. The most effective (and likely cost-effective) interventions for reducing inequalities were regulatory and tax options. Interventions focused on individual agency were much less likely to impact on inequalities, even when targeted at the most deprived communities

Redox linked flavin sites in extracellular decaheme proteins involved in microbe-mineral electron transfer

Extracellular microbe-mineral electron transfer is a major driving force for the oxidation of organic carbon in many subsurface environments. Extracellular multi-heme cytochromes of the Shewenella genus play a major role in this process but the mechanism of electron exchange at the interface between cytochrome and acceptor is widely debated. The 1.8 Å x-ray crystal structure of the decaheme MtrC revealed a highly conserved CX8C disulfide that, when substituted for AX8A, severely compromised the ability of S. oneidensis to grow under aerobic conditions. Reductive cleavage of the disulfide in the presence of flavin mononucleotide (FMN) resulted in the reversible formation of a stable flavocytochrome. Similar results were also observed with other decaheme cytochromes, OmcA, MtrF and UndA. The data suggest that these decaheme cytochromes can transition between highly reactive flavocytochromes or less reactive cytochromes, and that this transition is controlled by a redox active disulfide that responds to the presence of oxygen