Zymographic assay of plant diamine oxidase on entrapped peroxidase polyacrylamide gel electrophoresis. A study of stability to proteolysis

A zymographic assay of diamine oxidase (DAO, histaminase, EC 1.4.3.6), based on a coupled peroxidase reaction, and its behavior at proteolysis in simulated gastric and intestinal conditions, are described. The DAO activity from a vegetal extract of Lathyrus sativus seedlings was directly determined on sodium dodecyl sulfate polyacrylamide electrophoretic gels containing entrapped horseradish peroxidase, with putrescine as substrate of histaminase and ortho-phenylenediamine as co-substrate of peroxidase. The accumulation of azo-aniline, as peroxidase-catalyzed oxidation product, led to well-defined yellow-brown bands on gels, with intensities corresponding to the enzymatic activity of DAO. After image analysis of gels, a linear dependency of DAO content (Coomassie-stained protein bands) and of its enzymatic activity (zymographic bands) with the concentration of the vegetal extract was obtained. In simulated gastric conditions (pH 1.2, 37 °C), the DAO from the vegetal extract lost its enzymatic activity before 15 min of incubation, either in the presence or absence of pepsin. The protein pattern (Coomassie-stained) revealed that the DAO content from the vegetal extract was kept almost constant in the simulated intestinal fluid (containing pancreatin or not), with a slight diminution in the presence of pancreatic proteases. After 10 h of incubation at 37 °C, the DAO enzymatic activity from the vegetal extract was 44.7% in media without pancreatin and 13.6% in the presence of pancreatin, whereas the purified DAO retained only 4.65% of its initial enzymatic activity in the presence of pancreatin

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

Historical Research Approaches to the Analysis of Internationalisation

Historical research methods and approaches can improve understanding of the most appropriate techniques to confront data and test theories in internationalisation research. A critical analysis of all “texts” (sources), time series analyses, comparative methods across time periods and space, counterfactual analysis and the examination of outliers are shown to have the potential to improve research practices. Examples and applications are shown in these key areas of research with special reference to internationalisation processes. Examination of these methods allows us to see internationalisation processes as a sequenced set of decisions in time and space, path dependent to some extent but subject to managerial discretion. Internationalisation process research can benefit from the use of historical research methods in analysis of sources, production of time-lines, using comparative evidence across time and space and in the examination of feasible alternative choices

Genetic-based approach for cue phrase selection in dialogue act recognition

Automatic cue phrase selection is a crucial step for designing a dialogue act recognition model using machine learning techniques. The approaches, currently used, are based on specific type of feature selection approaches, called ranking approaches. Despite their computational efficiency for high dimensional domains, they are not optimal with respect to relevance and redundancy. In this paper we propose a genetic-based approach for cue phrase selection which is, essentially, a variable length genetic algorithm developed to cope with the high dimensionality of the domain. We evaluate the performance of the proposed approach against several ranking approaches. Additionally, we assess its performance for the selection of cue phrases enriched by phrase’s type and phrase’s position. The results provide experimental evidences on the ability of the genetic-based approach to handle the drawbacks of the ranking approaches and to exploit cue’s type and cue’s position information to improve the selection. Furthermore, we validate the use of the genetic-based approach for machine learning applications. We use selected sets of cue phrases for building a dynamic Bayesian networks model for dialogue act recognition. The results show its usefulness for machine learning applications

Identification and Validation of Novel Cerebrospinal Fluid Biomarkers for Staging Early Alzheimer's Disease

Ideally, disease modifying therapies for Alzheimer disease (AD) will be applied during the 'preclinical' stage (pathology present with cognition intact) before severe neuronal damage occurs, or upon recognizing very mild cognitive impairment. Developing and judiciously administering such therapies will require biomarker panels to identify early AD pathology, classify disease stage, monitor pathological progression, and predict cognitive decline. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) proteome.CSF samples from individuals with mild AD (Clinical Dementia Rating [CDR] 1) (n = 24) and cognitively normal controls (CDR 0) (n = 24) were subjected to two-dimensional difference-in-gel electrophoresis. Within 119 differentially-abundant gel features, mass spectrometry (LC-MS/MS) identified 47 proteins. For validation, eleven proteins were re-evaluated by enzyme-linked immunosorbent assays (ELISA). Six of these assays (NrCAM, YKL-40, chromogranin A, carnosinase I, transthyretin, cystatin C) distinguished CDR 1 and CDR 0 groups and were subsequently applied (with tau, p-tau181 and Aβ42 ELISAs) to a larger independent cohort (n = 292) that included individuals with very mild dementia (CDR 0.5). Receiver-operating characteristic curve analyses using stepwise logistic regression yielded optimal biomarker combinations to distinguish CDR 0 from CDR>0 (tau, YKL-40, NrCAM) and CDR 1 from CDR<1 (tau, chromogranin A, carnosinase I) with areas under the curve of 0.90 (0.85-0.94 95% confidence interval [CI]) and 0.88 (0.81-0.94 CI), respectively.Four novel CSF biomarkers for AD (NrCAM, YKL-40, chromogranin A, carnosinase I) can improve the diagnostic accuracy of Aβ42 and tau. Together, these six markers describe six clinicopathological stages from cognitive normalcy to mild dementia, including stages defined by increased risk of cognitive decline. Such a panel might improve clinical trial efficiency by guiding subject enrollment and monitoring disease progression. Further studies will be required to validate this panel and evaluate its potential for distinguishing AD from other dementing conditions