Hypocholesterolemic and hepatoprotective effects of flaxseed chutney: Evidence from animal studies

Rats fed with hypercholesterolemic diet showed a significant increase in serum total—cholesterol, liver homogenate total-cholesterol, HDL-cholesterol and changed LDL-cholesterol, and HDL/LDL ratio in comparison to control. Flaxseedchutney (FC) supplemented diet (15%, w/w) was found to be more effective in restoring lipid profile changes in rats fed with cholesterol, (1.0%). The activities of serum marker enzymes glutamate oxaloacetate transminase (GOT), glutamate pyruvate transaminase (GPT) and alkaline phosphatase (ALP) were elevated significantly in carbon tetrachloride induced rats. Administration of flaxseedchutney (15%, w/w) resulted in depletion of serum marker enzymes and exhibited recoupment thus showing significant hepatoprotective effect. It was observed that flaxseedchutney supplemented diet could lower the serum cholesterol and as a potential source of antioxidants it could exert protection against hepatotoxic damage induced by carbon tetrachloride (CCl₄) in rats.     

Unsupervised word sense disambiguation for Korean through the acyclic weighted digraph using corpus and dictionary

Word sense disambiguation (WSD) is meant to assign the most appropriate sense to a polysemous word according to its context. We present a method for automatic WSD using only two resources: a raw text corpus and a machine-readable dictionary (MRD). The system learns the similarity matrix between word pairs from the unlabeled corpus, and it uses the vector representations of sense definitions from MRD, which are derived based on the similarity matrix. In order to disambiguate all occurrences of polysemous words in a sentence, the system separately constructs the acyclic weighted digraph (AWD) for every occurrence of polysemous words in a sentence. The AWD is structured based on consideration of the senses of context words which occur with a target word in a sentence. After building the AWD per each polysemous word, we can search the optimal path of the AWD using the Viterbi algorithm. We assign the most appropriate sense to the target word in sentences with the sense on the optimal path in the AWD. By experiments, our system shows 76.4% accuracy for the semantically ambiguous Korean words.     

Optimization of some factors affecting the performance of query expansion

This paper examines the factors affecting the performance of global query expansion based on term co-occurrence data and suggests a way to maximize the retrieval effectiveness. Major parameters to be optimized through experiments are term similarity measure and the weighting scheme of additional terms. The evaluation of four similarity measures tested in query expansion reveal that mutual information and Yule's Y, which emphasize low frequency terms, achieve better performance than cosine and Jaccard coefficients that have the reverse tendency. In the evaluation of three weighting schemes, similarity weight performs well only with short queries, whereas fixed weights of approximately 0.5 and similarity rank weights were effective with queries of any length. Furthermore, the optimal similarity rank weight achieving the best overall performance seems to be the least affected by test collections and the number of additional terms. For the efficiency of retrieval, the number of additional terms needs not exceed 70 in our test collections, but the optimal number may vary according to the characteristics of the similarity measure employed.     

Troponin T and Heart Type Fatty Acid Binding Protein (h-Fabp) as Biomarkers in Patients Presenting with Chest Pain

Acute coronary syndrome (ACS) is a term for a range of clinical signs and symptoms suggestive of myocardial ischemia. It results in functional and structural changes and ultimately releasing protein from injured cardiomyocytes. These cardiac markers play a major role in diagnosis and prognosis of ACS. This study aims to assess the efficacy of heart type fatty acid binding protein (h-FABP) as a marker for ACS along with the routinely used hs-TropT. In our observational study, plasma h-FABP (cut-off 6.32 ng/ml) and routinely done hs-Trop T (cutoff 0.1 and 0.014 ng/ml) were estimated by immunometric laboratory assays in 88 patients with acute chest pain. Based on the clinical and laboratory test findings the patients were grouped into ACS (n = 41) and non-ACS (n = 47). The diagnostic sensitivity, specificity, NPV, PPV and ROC curve at 95 % CI were determined. Sensitivity of hs-TropT (0.1 ng/ml), hs-TropT (0.014 ng/ml) and h-FABP were 53, 86 and 78 % respectively and specificity for the same were 98, 73 and 70 % respectively. Sensitivity, specificity and NPV calculated for a cut-off combination of hs-TropT 0.014 ng/ml and h-FABP was 100, 51 and 100 % respectively. These results were substantiated by ROC analysis. Measurement of plasma h-FABP and hs-TropT together on admission appears to be more precise predictor of ACS rather than either hs-Trop T or h-FABP.     

Liquid–Liquid Extraction and Solid Phase Extraction for Urinary Organic Acids: A Comparative Study from a Resource Constraint Setting

Pre analytical process of extraction for accurate detection of organic acids is a crucial step in diagnosis of organic acidemias by GCMS analysis. This process is accomplished either by solid phase extraction (SPE) or by liquid–liquid extraction (LLE). Both extraction procedures are used in different metabolic laboratories all over the world. In this study we compared these two extraction procedures in respect of precision, accuracy, percent recovery of metabolites, number of metabolites isolated, time and cost in a resource constraint setup. We observed that the mean recovery from SPE was 84.1 % and by LLE it was 77.4 % (p value <0.05). Moreover, the average number of metabolites isolated by SPE and LLE was 161.8 ± 18.6 and 140.1 ± 20.4 respectively. The processing cost of LLE was economical. In a cost constraint setting using LLE may be the practical option if used for organic acid analysis.     

Neutralization theory and online software piracy: An empirical analysis

Accompanying the explosive growth of information technology is the increasing frequency of antisocial and criminal behavior on the Internet. Online software piracy is one such behavior, and this study approaches the phenomenon through the theoretical framework of neutralization theory. The suitability and applicability of nine techniques of neutralization in determining the act is tested via logistic regression analyses on cross-sectional data collected from a sample of university students in the United States. Generally speaking, neutralization was found to be weakly related to experience with online software piracy; other elements which appear more salient are suggested and discussed in conclusion.     

A new unsupervised method for document clustering by using WordNet lexical and conceptual relations

Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and speed.

Extending WHIRL with background knowledge for improved text classification

Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web.