Conformational changes of a chemically modified HRP

Horseradish peroxidase is an all alpha-Helical enzyme, which widely used in biochemistry applications mainly because of its ability to enhance the weak signals of target molecules. This monomeric heme-containing plant peroxidase is also used as a reagent for the organic synthesis, biotransformation, chemiluminescent assays, immunoassays, bioremediation, and treatment of wastewaters as well. Accordingly, enhancing stability and catalytic activity of this protein for biotechnological uses has been one of the important issues in the field of biological investigations in recent years. In this study, pH-induced structural alterations of native (HRP), and modified (MHRP) forms of Horseradish peroxidase have been investigated. Based on the results, dramatic loss of the tertiary structure and also the enzymatic activity for both forms of enzymes recorded at pH values lower than 6 and higher than 8. Ellipticiy measurements, however, indicated very slight variations in the secondary structure for MHRP at pH 5. Spectroscopic analysis also indicated that melting of the tertiary structure of MHRP at pH 5 starts at around 45° C, which is associated to the pKa of His 42 that has a serious role in keeping of the heme prostethic group in its native position through natural hydrogen bond network in the enzyme structure. According to our data, a molten globule like structure of a chemically modified form of Horseradish peroxidase at pH 5 with initial steps of conformational transition in tertiary structure with almost no changes in the secondary structure has been detected. Despite of some conformational changes in the tertiary structure of MHRP at pH 5, this modified form still keeps its catalytic activity to some extent besides enhanced thermal stability. These findings also indicated that a molten globular state does not necessarily preclude efficient catalytic activity

Dynamics of learning motives and barriers in the context of changing human life roles

This paper promotes a theoretical discussion that focuses on the motives and barriers that make impact on adults learning as well as on their dynamics related to the change of social roles. The adult learning motives and barriers change and vary according to the prevailing social roles at different periods of one’s life. This dynamics of adult learning motives and barriers is mostly influenced by the importance and compatibility of acquired social roles, responsibility areas and spaces of a person and other factors. The qualitative data was gathered in March – April 2016 in Kaunas, Lithuania. The sample consisted of 30 narratives, written by informants, aged 35 to 65 years that were participating in professional training courses. There has been prepared 30 self-reflections that were analysed using content analysis. The analysis of empirical data shows that external learning motives and barriers prevail in the period when an individual is active in the labour market while the personal motives remain overshadowed. However, personal barriers prevail in the expression of learning barriers. This is influenced by the society’s attitude towards the performance of pupil and student roles and the value attitudes of surrounding people that partially control it

Dynamic social graphs: mining and modeling

In recent decades, we have faced an explosion of social data, in which unprecedented variety of personal information has become accessible to the public. Social data consists of data on individuals and on interactions among individuals. Once we integrate these two categories of social data, social graphs emerge. In this research, two main research challenges were addressed: how to turn social data into social graphs and how to analyze the evolving social graphs. In this thesis, effective data-driven approaches were proposed for turning heterogeneous social data into social graphs that successfully revealed new demographic patterns in real historical data corpora

Research positioning & trend identification: a data-analytics toolbox

AIDA was an initiative of TU Delft scientific staff in cooperation with TU Delft Library and Leiden University’s Centre for Science and Technology Studies (CWTS). The aim of the AIDA project was to provide TU Delft researchers and faculties with easy-to-use tools for research positioning and trend identifi cation.This booklet introduces a toolbox that will help a wide range of end-users – such as PhD candidates, researchers, group leaders, and university policymakers – to position their research and identify important patterns and trends within their domains of interest. The information provided and the case studies presented were developed based on the questions received from end-users at TU Delft. In compiling this information, extensive use was made of the experience of Leiden University’s Centre for Science and Technology Studies (CWTS) and the Research Support of TU Delft library. The presentation style is meant to be visual and easy to use, and to provide practical benefits.In this booklet, we first introduce the researcher’s toolbox, which consists of data collection, analysis, and communication tools. We then provide a list of frequent questions that we have received from our end-users and for each question we indicate the pages in which relevant case studies can be found. Each case study shows some of the methods that can be used to position research and identify trends. We provide a sample illustration and its description for each case study. We explain WHAT the case study is about, WHY it is important, WHO can benefi t from it, and HOW you can replicate it or make similar ones based on the data at hand.Transport Engineering and Logistic

A Hybrid Disambiguation Measure for Inaccurate Cultural Heritage Data

info:eu-repo/semantics/publishe

A Hybrid Disambiguation Measure for Inaccurate Cultural Heritage Data

info:eu-repo/semantics/publishe

Co-Expression and Microrna Regulatory Network Integration in Metastatic Prostate Tumor Using Microarray Expression Data Analysis

Background and objectives: Prostate cancer is one of the most prevalent cancer in men which shows high heterogeneity in genetic and pathological features. Elucidating the regulatory relationships between microRNAs and mRNAs can help to identification of gene regulatory patterns in metastatic prostate cancer.   Methods: In the present study prostate cancer patient’s expression data (gene and microRNA) was used to study gene interactions in metastatic prostate tumor. Weighted gene co-expression network have been implemented for gene clustering and finding gene modules correlation with biochemical recurrence free survival variable. Differentially expressed microRNA gene targets were obtained from experimentally validated and prediction based databases and alongside with gene and microRNA expression, Pearson correlations measurements were implemented to constructing microRNA regulatory network.   Results: Two significant modules were detected in co-expressed modules significant analysis for biochemical recurrence free variable. One of them involved in cell cycle regulation and cell proliferation pathways which are hallmark pathways of progressed cancers. Integration of gene co-expression network with microRNA regulation network shows, first: Micrornas target pathways which involved in the progression of prostate cancer to metastatic stage, and second: introduce several key factors including microRNA and genes.   Conclusion: Present study revealed more details on key functional components in prostate cancer development. These details may provide opportunities for the development of alternative therapeutic approaches. &nbsp

StaCo: Stackelberg-based Coverage Approach in Robotic Swarms

The Lloyd algorithm is a key concept in multi-robot Voronoi coverage applications. Its advantages are its simplicity of implementation and asymptotic convergence to the robots' optimal position. However, the speed of this convergence cannot be guaranteed and therefore reaching the optimal position may be very slow. Moreover, in order to ensure the convergence, the Hessian of the corresponding cost function has to be positive definite all the time. Validation of this condition is mostly impossible and, as a consequence, for some problems the standard approach fails and leads to a non-optimal positioning. In such situations more advanced optimization tools have to be adopted. This paper introduces Stackelberg games as such a tool. The key assumption is that at least one robot can predict short-term behavior of other robots. We introduce the Stackelberg games, apply them to the multi-robot coverage problem, and show both theoretically and by means of case studies how the Stackelberg-based coverage approach outperforms the standard Lloyd algorithm