Genetic and Epigenomic Modifiers of Diabetic Neuropathy

Diabetic neuropathy (DN), the most common chronic and progressive complication of diabetes mellitus (DM), strongly affects patients’ quality of life. DN could be present as peripheral, autonomous or, clinically also relevant, uremic neuropathy. The etiopathogenesis of DN is multifactorial, and genetic components play a role both in its occurrence and clinical course. A number of gene polymorphisms in candidate genes have been assessed as susceptibility factors for DN, and most of them are linked to mechanisms such as reactive oxygen species production, neurovascular impairments and modified protein glycosylation, as well as immunomodulation and inflammation. Different epigenomic mechanisms such as DNA methylation, histone modifications and non-coding RNA action have been studied in DN, which also underline the importance of “metabolic memory” in DN appearance and progression. In this review, we summarize most of the relevant data in the field of genetics and epigenomics of DN, hoping they will become significant for diagnosis, therapy and prevention of DN.     

An approach to efficient business intelligent system for financial prediction

A concept of business intelligent system for financial prediction is considered in this paper. It provides data needed for fast, precise and good business decision support to all levels of management. The aim of the project is the development of a new online analytical processing oriented on case-based reasoning (CBR) where a previous experience for every new problem is taken into account. Methodological aspects have been tested in practice as a part of the management information system development project of “Novi Sad Fair”. A case study of an improved application of CBR in prediction of future payments is discussed in the paper. This paper is originally presented at The International Conference on Hybrid Information Technology 2006, at the special session on “Intelligent Information Systems for Financial Engineering”, November 2006 in Cheju Island, Korea.     

Performance of ultra wide band system using nonlinear receiver in the presence of interference

The performance of a time hopping combined with pulse position modulation ultra wide band system with a nonlinear receiver using an adaptive filter and a soft-limiter in the presence of narrowband interference has been considered. This system brings a significant performance improvement compared with the system that uses an adaptive filter and a linear receiver, particularly in the case of high-interference power where its absence leads to the reception loss. Also, the proposed receiver has much better interference rejection performance compared with minimal mean-square error interference rejection, in case of high-interference power (interference to signal ratios from 30 to 65 dB).     

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.     

A propositional probabilistic logic with discrete linear time for reasoning about evidence

The aim of the paper is to present a sound, strongly complete and decidable probabilistic temporal logic that can model reasoning about evidence. The formal system developed here is actually a solution of a problem proposed by Halpern and Pucella (J Artif Intell Res 26:1–34, 2006).     

A case of using the Semantic Interoperability Framework for custom orthopedic implants manufacturing

The efficiency and effectiveness of the daily practice in orthopedic surgery depend on the availability, interoperability and unique access to a wide set of information, related to the patient’s medical record and diagnosis, domain knowledge and available resources and staff. The most important of the tangible resources, needed for the therapeutic or preventive actions are orthopedic implants. In some cases, the implants may be highly complex and customized products, which need to be manufactured (assembled) on basis of the above information in a shortest possible timeframe. In this paper, the case of the custom orthopedic implants manufacturing is described from the perspective of the collaborative enterprising, with special consideration of the interoperability issues of the involved enterprise collaboration. It is shown how the previously developed Semantic Interoperability Framework can be used to improve the efficiency of the manufacturing and other relevant processes.     

Ion‐exchange resins. II. Acrylamide crosslinked copolymers as precursors for some ion exchangers

Crosslinked copolymers of acrylamide (AA) and ethylacrylate and some ion exchangers derived from them containing either primary amine groups, obtained by the Hofmann degradation of the amide groups, or carboxylic groups, obtained by the alkaline hydrolysis of the ester groups, were studied. Divinylbenzene and N,N′‐methylenebisacrylamide were used as crosslinkers. The starting copolymers and the corresponding ion exchangers were characterized by IR spectroscopy, swelling behavior, and thermogravimetric analysis. The ion‐exchanger properties were correlated with the crosslinker nature and the chemical reactions performed on the AA copolymers. The average molecular weight between two crosslinks, determined from the swelling data in water, was compared with that calculated on the basis of the copolymerization stoichiometry only for the carboxylic cation exchangers. In this way, the preservation of the crosslink density after the hydrolysis was revealed. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2701–2707, 2003     

Effect of low‐temperature plasma and chitosan treatment on wool dyeing with Acid Red 27

This study examines in detail the influence of low‐temperature plasma and biopolymer chitosan treatments on wool dyeability. Wool knitted fabrics were treated and characterized by whiteness and shrink‐resistance measurements. Surface modification was assessed by contact‐angle measurements of human hair fibers, which were used as a model to study the wetting properties of the treated wool knitted fabrics. The dyeing behavior was assessed from the diffusion mechanism point of view. The dyeing kinetics were measured at two different pHs (4.2 and 6.5) and three different temperatures (60, 85, and 100°C) to gain information about the contribution of the surface modification treatment to the dyeing mechanism. The exhaustion and reflectance data were compared, and the apparent diffusion coefficients were calculated. On the basis of the obtained results, a model for the dyeing mechanism of the chitosan treated wool was proposed. When treated with chitosan, the polymer sheath spread on the surface of the fibers acted as a predominant dyeing site in very short dyeing times, thus interacting with the dye and in later stages imparting the dye to the wool fiber. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 2204–2214, 2005     

Ensemble-based noise detection: noise ranking and visual performance evaluation

Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of noise detection algorithms on data with randomly injected noise. A methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new noise detection algorithms into the proposed noise detection and performance evaluation workflows.