Collagen I Modifies Connexin-43 Hemichannel Activity via Integrin α2β1 Binding in TGFβ1-Evoked Renal Tubular Epithelial Cells

Chronic Kidney Disease (CKD) is associated with sustained inflammation and progressive fibrosis, changes that have been linked to altered connexin hemichannel-mediated release of adenosine triphosphate (ATP). Kidney fibrosis develops in response to increased deposition of extracellular matrix (ECM), and up-regulation of collagen I is an early marker of renal disease. With ECM remodeling known to promote a loss of epithelial stability, in the current study we used a clonal human kidney (HK2) model of proximal tubular epithelial cells to determine if collagen I modulates changes in cell function, via connexin-43 (Cx43) hemichannel ATP release. HK2 cells were cultured on collagen I and treated with the beta 1 isoform of the pro-fibrotic cytokine transforming growth factor (TGFβ1) ± the Cx43 mimetic Peptide 5 and/or an anti-integrin α2β1 neutralizing antibody. Phase microscopy and immunocytochemistry observed changes in cell morphology and cytoskeletal reorganization, whilst immunoblotting and ELISA identified changes in protein expression and secretion. Carboxyfluorescein dye uptake and biosensing measured hemichannel activity and ATP release. A Cytoselect extracellular matrix adhesion assay assessed changes in cell-substrate interactions. Collagen I and TGFβ1 synergistically evoked increased hemichannel activity and ATP release. This was paralleled by changes to markers of tubular injury, partly mediated by integrin α2β1/integrin-like kinase signaling. The co-incubation of the hemichannel blocker Peptide 5, reduced collagen I/TGFβ1 induced alterations and inhibited a positive feedforward loop between Cx43/ATP release/collagen I. This study highlights a role for collagen I in regulating connexin-mediated hemichannel activity through integrin α2β1 signaling, ahead of establishing Peptide 5 as a potential intervention.     

Segregation Neutralised Steels: Microstructural Banding Elimination from Dual-Phase Steel Through Alloy Design Accounting for Inherent Segregation

Metallurgical and Materials Transactions A - Banding in commercial dual-phase steels, such as banded ferrite and pearlite or ferrite and martensite microstructures, is inherited from segregation...     

Exploring aspects of cell intelligence with artificial reaction networks

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence.     

Gelatin supports with immobilized laccase as sustainable biocatalysts for water treatment

Millimeter-size beads of gelatin are manufactured by dripping process to give enzyme supports qualified for micropollutants biodegradation in alternative wastewater treatment. The bead diameter is dependent on the tip diameter, the gelatin solution viscosity and the swelling of polymer chains in the collecting bath. Chemical crosslinking was performed with glutaraldehyde using optimal concentration to give mechanical and thermal properties suitable for application in stirred reactor in aqueous medium. Laccases from Trametes versicolor are grafted on the gelatin beads with glutaraldehyde. Sixty percentage of the initial enzymatic activity, evaluated by the oxidation of 2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)diammonium salt (ABTS) is maintained after 10 successive cycles of reaction. Thermal stability at 60°C of immobilized biocatalysts is improved when compared to free enzymes (45% vs 10% of relative activity after 6 h of incubation). The simplicity of the procedure to form gelatin beads and their properties make them promising bio-based and biodegradable support for enzyme immobilization.     

Towards high accuracy calibration of electron backscatter diffraction systems

For precise orientation and strain measurements, advanced Electron Backscatter Diffraction (EBSD) techniques require both accurate calibration and reproducible measurement of the system geometry. In many cases the pattern centre (PC) needs to be determined to sub-pixel accuracy. The mechanical insertion/retraction, through the Scanning Electron Microscope (SEM) chamber wall, of the electron sensitive part of modern EBSD detectors also causes alignment and positioning problems and requires frequent monitoring of the PC. Optical alignment and lens distortion issues within the scintillator, lens and charge-coupled device (CCD) camera combination of an EBSD detector need accurate measurement for each individual EBSD system.This paper highlights and quantifies these issues and demonstrates the determination of the pattern centre using a novel shadow-casting technique with a precision of ∼10 μm or ∼1/3 CCD pixel.     

Systems modelling for effective mine water management

Concerns about the difficulties in securing water have led the Australian coal mining industry to seek innovative ways to improve its water management and to adopt novel strategies that will lead to less water being used and more water being reused. Simulation tools are essential to assess current water management performance and to predict the efficiency of potential strategies. As water systems on coal mines are complex and consist of various inter-connected elements, a systems approach was selected, which views mine site water management as a system that obtains water from various sources (surface, groundwater), provides sufficient water of suitable quality to the mining tasks (coal beneficiation, dust suppression, underground operations) and maintains environmental performance. In this paper, the model is described and its calibration is illustrated. The results of applying the model for the comparison of the water balances of 7 coal mines in the northern Bowen Basin (Queensland, Australia) are presented. The model is used to assess the impact of applying specific water management strategies. Results show that a simple systems model is an appropriate tool for assessing site performance, for providing guidance to improve performance through strategic planning, and for guiding adoption of site objectives.     

Leveraging technological knowledge transfer by using fuzzy linear programming technique for multiattribute group decision making with fuzzy decision variables

Despite the importance of knowledge transfer for firms involved in foreign direct investment activities, this area has not received appropriate attention from the perspectives of both the knowledge transferor (i.e., MNC parent) and the knowledge recipient. To fill in the gap in the current literature we propose a model to understand the links between criteria complicating the transfer of knowledge and preferences that the company has to focus. This model is based on both the existing literature as well as views of company representatives and provides a useful methodology for identifying decision making problems on the transfer of knowledge. In this paper, we investigate the fuzzy linear programming technique (FLP) to analyze these links and for multiple attribute group decision making (MAGDM) problems with preference information on criteria. To reflect the decision maker’s subjective preference information and to determine the weight vector of attributes, the technique for order preference by similarity to ideal solution (TOPSIS) developed by Hwang and Yoon (1995) and the linear programming technique for multidimensional analysis of preference (LINMAP) developed by Sirinivasan and Shocker (Psychometrica 38:337–369, 1973) are used.     

GEO_BUG: a geographical modelling environment for assessing the likelihood of pest development

This paper describes software designed to explore pest phenology (development) over space and time. The framework presented links sequences of interpolated daily maximum and minimum temperatures with a variety of process-based phenology and accumulated temperature models. The flexibility offered by this approach is demonstrated using examples of gridded maps of pest phenology on target dates, graphs of the sequences of pest development at individual locations and assessments of error in the predicted dates over the course of a model run. Finally, the potential application of the software in support of agricultural management systems, policy development and integrated research is discussed.     

A Cognitive Bias Approach to Feature Selection and Weighting for Case-Based Learners

Research in psychology, psycholinguistics, and cognitive science has discovered and examined numerous psychological constraints on human information processing. Short term memory limitations, a focus of attention bias, and a preference for the use of temporally recent information are three examples. This paper shows that psychological constraints such as these can be used effectively as domain-independent sources of bias to guide feature set selection and weighting for case-based learning algorithms.We first show that cognitive biases can be automatically and explicitly encoded into the baseline instance representation: each bias modifies the representation by changing features, deleting features, or modifying feature weights. Next, we investigate the related problems of cognitive bias selection and cognitive bias interaction for the feature weighting approach. In particular, we compare two cross-validation algorithms for bias selection that make different assumptions about the independence of individual component biases. In evaluations on four natural language learning tasks, we show that the bias selection algorithms can determine which cognitive bias or biases are relevant for each learning task and that the accuracy of the case-based learning algorithm improves significantly when the selected bias(es) are incorporated into the baseline instance representation.