Infrared astronomy

Spacecraft and astronomical observations of infrare

Modelling Epsilon Aurigae without solid particles

Three components can be expected to contribute to the emission of epsilon Aurigae. There is a primary F star. There is an opaque disk which occults it, and there is a gas stream which is observed to produce absorption lines. Evidence that the disk is not responsible for the gas stream lines comes both from the radial velocities, which are too small, and from the IR energy distribution out of eclipse, which shows free-free emission that would produce inadequate optical depth in electron scattering. The color temperature of the IR excess can give misleading indications of low temperature material. Free-free emission at 10,000 K between 10 and 20 microns has a color temperature of 350 K. Attempts to mold the system are discussed

Data mining using Matlab

Data mining is a relatively new field emerging in many disciplines. It is becoming more popular as technology advances, and the need for efficient data analysis is required. The aim of data mining itself is not to provide strict rules by analysing the full data set, data mining is used to predict with some certainty while only analysing a small portion of the data. This project seeks to compare the efficiency of a decision tree induction method with that of the neural network method. MATLAB has inbuilt data mining toolboxes. However the decision tree induction method is not as yet implemented. Decision tree induction has been implemented in several forms in the past. The greatest contribution to this method has been made by DR John Ross Quinlan, who has brought forward this method in the form of ID3, C4.5 and C5 algorithms. The methodologies used within ID3 and C4.5 are well documented and therefore provide a strong platform for the implementation of this method within a higher level language. The objectives of this study are to fully comprehend two methods of data mining, namely decision tree induction and neural networks. The decision tree induction method is to be implemented within the mathematical computer language MATLAB. The results found when analysing some suitable data will be compared with the results from the neural network toolbox already implemented in MATLAB. The data used to compare and contrast the two methods included voting records from the US House of Representatives, which consists of yes, no and undecided votes on sixteen separate issues. The voters are grouped into categories according to their political party. This can be either republican or democratic. The objective of using this data set is to predict what party a congressman is affiliated with by analysing their voting trends. The findings of this study reveal that the decision tree method can accurately predict outcomes if an ideal data set is used for building the tree. The neural network method has less accuracy in some situations however it is more robust towards unexpected data

No Nogo Now Where to Go?

AbstractNogo-A, a reticulon protein expressed by oligodendrocytes, contributes to the axonal growth inhibitory action of central myelin in growth cone collapse and neurite outgrowth in vitro assays, and antibody and inhibitor studies have implicated a role for Nogo in regeneration in the adult CNS in vivo. Three independent labs have now produced Nogo knockout mice with, quite unexpectedly, three different regeneration phenotypes

Pain TRPs

Transient receptor potential (TRP) ion channels are molecular gateways in sensory systems, an interface between the environment and the nervous system. Several TRPs transduce thermal, chemical, and mechanical stimuli into inward currents, an essential first step for eliciting thermal and pain sensations. Precise regulation of the expression, localization, and function of the TRP channels is crucial for their sensory role in nociceptor terminals, particularly after inflammation, when they contribute to pain hypersensitivity by undergoing changes in translation and trafficking as well as diverse posttranslational modifications

Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks

<p>Abstract</p> <p>Background</p> <p>Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.</p> <p>Results</p> <p>Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using <it>E. coli </it>expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for <it>E. coli </it>was integrated with 266 <it>E. coli </it>gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data.</p> <p>Conclusion</p> <p>POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.</p

Methods for Evaluating Respondent Attrition in Web-Based Surveys

Background: Electronic surveys are convenient, cost effective, and increasingly popular tools for collecting information. While the online platform allows researchers to recruit and enroll more participants, there is an increased risk of participant dropout in Web-based research. Often, these dropout trends are simply reported, adjusted for, or ignored altogether. Objective: To propose a conceptual framework that analyzes respondent attrition and demonstrates the utility of these methods with existing survey data. Methods: First, we suggest visualization of attrition trends using bar charts and survival curves. Next, we propose a generalized linear mixed model (GLMM) to detect or confirm significant attrition points. Finally, we suggest applications of existing statistical methods to investigate the effect of internal survey characteristics and patient characteristics on dropout. In order to apply this framework, we conducted a case study; a seventeen-item Informed Decision-Making (IDM) module addressing how and why patients make decisions about cancer screening. Results: Using the framework, we were able to find significant attrition points at Questions 4, 6, 7, and 9, and were also able to identify participant responses and characteristics associated with dropout at these points and overall. Conclusions: When these methods were applied to survey data, significant attrition trends were revealed, both visually and empirically, that can inspire researchers to investigate the factors associated with survey dropout, address whether survey completion is associated with health outcomes, and compare attrition patterns between groups. The framework can be used to extract information beyond simple responses, can be useful during survey development, and can help determine the external validity of survey results