Characterizing RyR and SERCA function in the C57 and D2 mdx mouse models of Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is a male-affected muscle wasting disease caused by the complete loss of the sarcolemmal protein dystrophin. No cure exists and patients typically succumb to cardiorespiratory issues in the third or fourth decade of life. Dystrophin loss also leads to dysfunction in other pathways; including impaired sarcoplasmic reticulum (SR) calcium (Ca2+) handling, further perpetuating the disease. This thesis examined potential differences in SR Ca2+ handling in two mouse models of DMD. The D2.B10-Dmdmdx/J (D2 mdx) mouse has emerged as a more pathologically representative model of DMD than the C57BL/10ScSn-Dmdmdx/J (C57 mdx) mouse model, showing greater muscle weakness, wasting and earlier disease onset. However, SR Ca2+ has not yet been characterized in the D2 mdx mouse. Thus, the aim of this study was to compare SR Ca2+ handling in the D2 mdx and C57 mdx mice. Using age-matched (9-10 week-old) mice, we found that D2 mdx mice had less mass, smaller gastrocnemius muscles, and were less ambulant. The D2 mdx mice had significantly higher energy expenditure and respiratory exchange ratio compared with the D2 WT mice. Two separate SR Ca2+ uptake assays revealed that D2 mdx mice have less Ca2+ uptake and leak, and higher starting myoplasmic Ca2+. SERCA activity (ATP hydrolysis) was lower in D2 mdx mice while higher in C57 mdx mice. These dramatic impairments in SR Ca2+ handling were not attributed to differences in SERCA isoform content or changes in its regulator, sarcolipin. However, under reducing conditions, protein nitration and nitrosylation content were significantly higher in D2 mdx gastrocnemius muscles. Further, pre-treatment with dithiothreiotol (DTT) did not improve SR Ca2+ handling in these muscles, suggestive of irreversible reactive oxygen/nitrogen post-translational modifications. Finally, calpain proteolytic activity was examined to determine the consequence of the impaired SR Ca2+ handling in the D2 mdx mouse. While D2 WT mice already had higher levels of calpain activity, the D2 mdx mouse had significantly higher calpain activity vs the C57 mdx mouse. Altogether, the results from this thesis suggest that impaired SR Ca2+ handling may be partially responsible for more severe pathology found in the D2 mdx mice

Shedding light on a living lab: the CLEF NEWSREEL open recommendation platform

In the CLEF NEWSREEL lab, participants are invited to evaluate news recommendation techniques in real-time by providing news recommendations to actual users that visit commercial news portals to satisfy their information needs. A central role within this lab is the communication between participants and the users. This is enabled by The Open Recommendation Platform (ORP), a web-based platform which distributes users' impressions of news articles to the participants and returns their recommendations to the readers. In this demo, we illustrate the platform and show how requests are handled to provide relevant news articles in real-time

Comparative evaluation of the performance of online databases in answering toxicology queries

An evaluation of toxicology information resources is reported, comparing commercial online databases and a specialised in-house database. A mixed qualitative/quantitative approach, using ten test queries and detailed failure analysis was used. The main conclusions are: the in-house database is superior to any ‘general’ database in recall and precision; commercial databases are a useful complement, usually providing unique material; a range of databases should be used for good recall; for the commercial databases, complex search strategies are necessary, using the specific access points of each database; retrieval failures are due primarily to coverage, secondly to poor indexing of specific toxic effect. Detailed discussion is given of indexing policies and search strategies

Managing the Knowledge Creation Process of Large-Scale Evaluation Campaigns

Περιέχει το πλήρες κείμενοThis paper discusses the evolution of large-scale evaluation campaigns and the corresponding evaluation infrastructures needed to carry them out. We present the next challenges for these initiatives and show how digital library systems can play a relevant role in supporting the research conducted in these fora by acting as virtual research environments

Relevance similarity: an alternative means to monitor information retrieval systems

BACKGROUND: Relevance assessment is a major problem in the evaluation of information retrieval systems. The work presented here introduces a new parameter, "Relevance Similarity", for the measurement of the variation of relevance assessment. In a situation where individual assessment can be compared with a gold standard, this parameter is used to study the effect of such variation on the performance of a medical information retrieval system. In such a setting, Relevance Similarity is the ratio of assessors who rank a given document same as the gold standard over the total number of assessors in the group. METHODS: The study was carried out on a collection of Critically Appraised Topics (CATs). Twelve volunteers were divided into two groups of people according to their domain knowledge. They assessed the relevance of retrieved topics obtained by querying a meta-search engine with ten keywords related to medical science. Their assessments were compared to the gold standard assessment, and Relevance Similarities were calculated as the ratio of positive concordance with the gold standard for each topic. RESULTS: The similarity comparison among groups showed that a higher degree of agreements exists among evaluators with more subject knowledge. The performance of the retrieval system was not significantly different as a result of the variations in relevance assessment in this particular query set. CONCLUSION: In assessment situations where evaluators can be compared to a gold standard, Relevance Similarity provides an alternative evaluation technique to the commonly used kappa scores, which may give paradoxically low scores in highly biased situations such as document repositories containing large quantities of relevant data

Non-Sterilized Fermentative Production of Polymer-Grade L-Lactic Acid by a Newly Isolated Thermophilic Strain Bacillus sp. 2–6

-lactic acid is another limitation for PLA polymers to compete with conventional plastics.-lactic acid concentration of 182.0 g/liter was obtained from 30-liter fed-batch fermentation with an average productivity of 3.03 g/liter/h and product optical purity of 99.4%.-lactic acid production from renewable resources

PubMed related articles: a probabilistic topic-based model for content similarity

<p>Abstract</p> <p>Background</p> <p>We present a probabilistic topic-based model for content similarity called <it>pmra </it>that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance–but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH ^®in MEDLINE ^®.</p> <p>Results</p> <p>The <it>pmra </it>retrieval model was compared against <it>bm25</it>, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of <it>pmra </it>over <it>bm25 </it>in terms of precision.</p> <p>Conclusion</p> <p>Our experiments suggest that the <it>pmra </it>model provides an effective ranking algorithm for related article search.</p

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey