NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 1

Figure 1 demonstrates a series of training experiments with the Naïve Bayes classifier using different neighborhoods for contextual features, different sizes of positive and negative training examples and evaluated the resulting classifiers with our annotated gold standard corpus. The data sets are the results of running NetiNeti on subset of 136 PubMedCentral tagged open access articles and with no stop list.A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms

Uncertain Systems Order Reduction by Aggregation Method

In the field of control engineering, approximating the higher-order system with its reduced model copes with more intricate problems. These complex problems are addressed due to the usage of computing technologies and advanced algorithms. Reduction techniques enable the system from higher-order to lower-order form retaining the properties of former even after reduction. This document renders a method for demotion of uncertain systems based on State Space Analysis. Numerical examples are illustrated to show the accuracy of the proposed method

Clinical, histopathological and immunofluorescent study of vesicobullous lesions of skin

Background: Vesiculobullous diseases have been the focus of intensive investigation in recent years. However, these disorders are still associated with substantial morbidity, considerable mortality and impaired quality of life. Accurate diagnosis of vesiculobullous lesions of skin entails evaluation of clinical, histopathologic and immunofluorescence findings.Methods: Hospital based prospective study for a period of 24 months from August 2014 to July 2016 in the Department of Pathology at Andhra Medical College, Visakhapatnam, India. Total of 50 patients aged 3-70 years with vesiculobullous lesions of both sexes attending the Department of Dermatology were selected and analysed clinically, histopathological examination and direct immunofluorescence (DIF).Results: In the present study, majority of patients presented between 51-60 yrs of age (32%) with male to female ratio of 1.08:1 and mean age of 46.02 years. Pemphigus vulgaris constituted the most common vesiculobullous disorder (32%) followed by bullous pemphigoid and pemphigus foliaceous, 18% each. Bullae were located intra epidermally in 68% and sub epidermally in 32% of the patients. DIF was positive in 80% of the cases. Overall clinicopathological correlation was established in 74%. Overall histopathological and direct immunofluorescence correlation was established in 78%. Out of 50 cases, 35 cases (70%) correlated clinically and histo-pathologically with direct immunofluorescence.Conclusions: In the present study, on histopathological examination alone pemphigus foliaceus and pemphigus vulgaris could be differentiated. Direct immunofluorescence was useful in differentiating epidermolysis bullosa acquisita from bullous pemphigoid which have similar histopathological picture. This study proves that direct immunofluorescence is confirmatory as well as diagnostic for vesiculobullous disorders

Application of a Catalytic Asymmetric Povarov Reaction using Chiral Ureas to the Synthesis of a Tetrahydroquinoline Library

A 2328-membered library of 2,3,4-trisubstituted tetrahydroquinolines was produced using a combination of solution- and solid-phase synthesis techniques. A tetrahydroquinoline (THQ) scaffold was prepared via an asymmetric Povarov reaction using cooperative catalysis to generate three contiguous stereogenic centers. A matrix of 4 stereoisomers of the THQ scaffold was prepared to enable the development of stereo/structure-activity relationships (SSAR) upon biological testing. A sparse matrix design strategy was employed to select library members to be synthesized with the goal of generating a diverse collection of tetrahydroquinolines with physicochemical properties suitable for downstream discovery.Chemistry and Chemical Biolog

CLUE-TIPS, clustering methods for pattern analysis of LC-MS data

Liquid Chromatography Mass Spectrometry (LC-MS) based proteomics is an important tool in detecting changes in peptide/protein abundances in samples potentially leading to the discovery of disease biomarker candidates. We present CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), an approach that compares complex proteomic samples for similarity/dissimilarity analysis. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value. We developed clustering and visualization methods for the intersample distance map to analyze various samples for differences at the sample level as well as the individual m/z level. An approach to query for specific m/z values that are associated with similarity/dissimilarity patterns in a set of samples was also briefly described. CLUE-TIPS can also be used as a tool in assessing the quality of LC-MS runs. The presented approach does not rely on tandem mass-spectrometry (MS/MS), isotopic labels or gels and also does not rely on feature extraction methods. CLUE-TIPS suite was applied to LC-MS data obtained from plasma samples collected at various time points and treatment conditions from immunosuppressed mice implanted with MCF-7 human breast cancer cells. The generated raw LC-MS data was used for pattern analysis and similarity/dissimilarity detection. CLUE-TIPS successfully detected the differences/similarities in samples at various time points taken during the progression of tumor, and also recognized differences/similarities in samples representing various treatment conditions.11 page(s