This study features the fabrication of a planar-form, solid electrolyte modified, (PSEM) Ag/AgCl reference electrode using a screen-printing method. The PSEM Ag/AgCl reference electrode uses agar gel as the inner electrolyte and chloroprene rubber for the liquid junction and insulator. These common low-cost materials and the simple fabrication processes involved render the proposed reference electrode an ideal candidate for cost-efficient mass production. It is shown that the developed reference electrode is insensitive to most of the physiologically important ionic species, including Na+, K+, Li+, Ca2+, NH4+, and Cl-, under continuous measurement conditions. Moreover, as with conventional commercial reference electrodes, the proposed reference electrode exhibits a reversible response, which is maintained until the agar gel dries out. The PSEM Ag/AgCl reference electrode is integrated with an iridium oxide modified Pt-based pH indicator electrode to form a chip-type pH biosensor. The performance of this biosensor is consistent with that obtained from a pH meter based on a macroscopic commercial Ag/AgCl reference electrode. The experimental results confirm that the proposed biosensor is capable of providing precise pH measurements of various real samples. Accordingly, the PSEM Ag/AgCl reference electrode presented in this study provides a viable alternative to the macroscopic Ag/AgCl reference electrode used in many conventional chip-based pH sensors.

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Various nanoparticle (NP) properties such as shape and surface charge have been studied in an attempt to enhance the efficacy of NPs in biomedical applications. When trying to undermine the precise biodistribution of NPs within the target organs, the analytical method becomes the determining factor in measuring the precise quantity of distributed NPs. High performance liquid chromatography (HPLC) represents a more powerful tool in quantifying NP biodistribution compared to conventional analytical methods such as an in vivo imaging system (IVIS). This, in part, is due to better curve linearity offered by HPLC than IVIS. Furthermore, HPLC enables us to fully analyze each gram of NPs present in the organs without compromising the signals and the depth-related sensitivity as is the case in IVIS measurements. In addition, we found that changing physiological conditions improved large NP (200-500 nm) distribution in brain tissue. These results reveal the importance of selecting analytic tools and physiological environment when characterizing NP biodistribution for future nanoscale toxicology, therapeutics and diagnostics.

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A novel approach is proposed to group redundant time series in the frame of causality. It assumes that (i) the dynamics of the system can be described using just a small number of characteristic modes, and that (ii) a pairwise measure of redundancy is sufficient to elicit the presence of correlated degrees of freedom. We show the application of the proposed approach on fMRI data from a resting human brain and gene expression profiles from HeLa cell culture. [6]. Synchronization in dynamical networks is influenced by the topology of the network [7]. The inference of dynamical networks is related to the estimation, from data, of the flow of information between variables. Two major approaches are commonly used to estimate the information flow between variables, transfer entropy [8] and Granger causality [9].An important notion in information theory is the redundancy in a group of variables, formalized in [10] as a generalization of the mutual information. A formalism to recognize redundant and synergetic variables in neuronal ensembles has been proposed in [11] and generalized in [12]. Recently a quantitative definition to recognize redundancy and synergy in the frame of causality has been provided [13] and it has been shown that the maximization of the total causality, over all the possible partitions of variables, is connected to the detection of groups of redundant variables; the search over all the partitions is unfeasible but for small systems. We remark that the information theoretic treatments of groups of correlated degrees of freedom can reveal their functional roles in complex systems. The purpose of this work is to propose a simple approach to find groups of causally redundant variables (groups of variables sharing the same information about the future of the system), which can be applied also to large systems. The main assumption underlying our approach is that the essential features of the dynamics of the system under consideration are captured using just a small number of characteristic modes. Hence we use principal components analysis to obtain a compressed representation of the future state of the system. Then, we introduce a pairwise measure of the redundancy w.r.t. the prediction of the next configuration of the modes, thus obtaining a weighted graph. Finally, by maximizing the modularity [7], we find the natural modules of this weighted graph and identify them with the groups of redundant variables. In the following section we describe the method. In section II we describe the application of the method to fMRI data, and in section III to a gene expression data-set. Some conclusions are drawn in section IV.