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.     

Biobased thermosets from the free‐radical copolymerization of conjugated linseed oil

New polymeric thermosets were prepared through the bulk free‐radical copolymerization of 100% conjugated linseed oil, acrylonitrile, and divinylbenzene. Under the appropriate reaction conditions and with the appropriate curing sequence, 61–96 wt % of the oil was incorporated into the crosslinked thermosets. The resulting yellow, transparent thermosets varied from being soft and flexible to being hard and brittle. Dynamic mechanical analysis and thermogravimetric analysis showed that these thermosets had good mechanical properties and thermal stability. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 979–985, 2007     

Predictive functional control based on an adaptive fuzzy model of a hybrid semi-batch reactor

In this paper a new approach to the control of a nonlinear, time-varying process is proposed. It is based on a recursive version of the fuzzy identification method and predictive functional control. First, the recursive fuzzy identification method is derived, after which it is used in connection with fuzzy predictive functional control to construct an adaptive fuzzy predictive functional controller. The adaptive FPFC is then tested on a nonlinear, time-varying, semi-batch reactor process and compared with the standard FPFC, which uses non-adaptive fuzzy model. The simulation results are promising; they indicate that the control of time-varying, nonlinear processes with the FPFC can be improved with the use of an adaptive fuzzy model. An improvement in reference tracking and disturbance rejection can be observed, but the main advantage is the reduced number of switchings between hot and cold water. This is an important improvement in the case of real applications.     

Modelling of interstitials in the bcc phase

There are several widespread thermodynamic datasets which produce a spurious bcc interstitial solution at high temperature and high X content (X is an interstitally dissolved element). The reason for this is the standard model for bcc interstitial solutions (M(V a,X)₃), which requires careful selection of optimising parameters to minimise spurious appearances of the bcc phase. In this work the model M(V a,X)₁ is suggested as an alternative. This model is much easier to handle and its parameters can be directly compared with those of the fcc phase. The two models are compared for the Fe–C and Nb–N systems. In the Fe–C system almost identical results are achieved. In Nb–N there are some differences for high N content, but there is no experimental data to clearly support any model.     

The use of data-derived label hierarchies in multi-label classification

Instead of traditional (multi-class) learning approaches that assume label independency, multi-label learning approaches must deal with the existing label dependencies and relations. Many approaches try to model these dependencies in the process of learning and integrate them in the final predictive model, without making a clear difference between the learning process and the process of modeling the label dependencies. Also, the label relations incorporated in the learned model are not directly visible and can not be (re)used in conjunction with other learning approaches. In this paper, we investigate the use of label hierarchies in multi-label classification, constructed in a data-driven manner. We first consider flat label sets and construct label hierarchies from the label sets that appear in the annotations of the training data by using a hierarchical clustering approach. The obtained hierarchies are then used in conjunction with hierarchical multi-label classification (HMC) approaches (two local model approaches for HMC, based on SVMs and PCTs, and two global model approaches, based on PCTs for HMC and ensembles thereof). The experimental results reveal that the use of the data-derived label hierarchy can significantly improve the performance of single predictive models in multi-label classification as compared to the use of a flat label set, while this is not preserved for the ensemble models.     

A case study on the object-oriented framework for modeling product families with the dominant variation of the topology in the one-of-a-kind production

Traditional configuration-based product family modeling techniques do not yield favorable solutions for modeling the product families with the dominant product topology variations. This paper presents the case study for the modeling of variations of the product configuration, parameters, and topologies in mass production of custom windows and doors in a one-of-a-kind production (OKP) company. The case study is based on the object-oriented product family modeling framework we developed for OKP which is characterized by the dominant product variation by topology. The problem of variation in topology in our framework is solved by the manual model transformation in the computer-aided design module which creates a new metamodel of products with the desired topology and which is the basis for a further, automatic model transformation. Usage of our framework has a positive effect on the level of the product customizations and shortening of the leading time which helps OKP companies in obtaining a better position at the market. Our software solution for the designing and manufacturing of custom windows and doors based on our framework is implemented in more than 300 mostly small- and medium-sized enterprises. It proves our concept which can be used for the realization of the information systems for computer-aided one-of-a-kind production in the companies with a different production plan.     

A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images

Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of ‘mass’ that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover’s distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.     

Theoretical and experimental investigation of cold hobbing processes in cases of cone-like punch manufacturing

Optimal tool making is of great importance for reducing costs and increasing the performance of toolsets. The dies and punches, which are basic components of any metal forming toolset, are usually produced by machining, but this technology requires relatively long manufacturing times, characterized by significant material and energy waste. By replacing machining operations or combining them with forming ones, such as cold hobbing (indenting), significant cost and time savings, as well as improvement of tool performance could be achieved. Cold hobbing could be applied to making die cavities as well as shaping punch profiles. Investigations showed that a large number of variables influence the successful application of this technique. However, there is a scarcity of literature regarding the choice of relevant process parameters for the hobbing process. With this in mind, the aim of this paper is to yield further insight into the hobbing process. Special focus will be placed on the influence of the hob geometry on the deformation process and relevant process parameters. This paper presents theoretical, numerical, and experimental investigations of a cold hobbing process in which a cone-like punch is obtained. The upper-bound method was used for metal flow analysis and optimization of hob geometry. Stress–strain distribution, workpiece geometry prediction, and load estimation were obtained with the finite element method (FEM) using Simufact.Forming 8.1 software. The results were analyzed, compared, and discussed.     

Control of mineral wool thickness using predictive functional control

The production process of mineral wool is affected by several constantly changing factors. The ingredients for the mineral wool are melted in a furnace. The molten mineral charge exits the bottom of the furnace in a water-cooled trough and falls into a fiberization device (the centrifuge). The centrifuge forms the fibers. At this stage binders are injected to bind the fibers together. To ensure the quality of the end product (the consistent thickness) the flow of the bounded fibers must be as constant as possible. One way to ensure that is to control the speed of the conveyor belt that transports the bounded fibers from the centrifuge to the curing process. Predictive functional controller and PID controller are considered to replace an existing algorithm. Both can easily replace an existing one as they do not require any new sensor installation. All three algorithms are presented and tested on a developed plant model. The study showed that the predictive control gives better results than the existing and PID controller.