Injectable solid hydrogel: mechanism of shear-thinning and immediate recovery of injectable β-hairpin peptide hydrogels

β-Hairpin peptide-based hydrogels are a class of injectable hydrogel solids with significant potential use in injectable therapies. β-hairpin peptide hydrogels can be injected as preformed solids, because the solid gel can shear-thin and consequently flow under a proper shear stress but immediately recover back into a solid on removal of the stress. In this work, hydrogel behavior during and after flow was studied in order to facilitate fundamental understanding of how the gels flow during shear-thinning and how they quickly recover mechanically and morphologically relative to their original, pre-flow properties. While all studied β-hairpin hydrogels shear-thin and recover, the duration of shear and the strain rate affected both the gel stiffness immediately recovered after flow and the ultimate stiffness obtained after complete rehealing of the gel. Results of structural analysis during flow were related to bulk rheological behavior and indicated gel network fracture into large (>200 nm) hydrogel domains during flow. After cessation of flow the large hydrogel domains are immediately percolated which immediately reforms the solid hydrogel. The underlying mechanisms of the gel shear-thinning and healing processes are discussed relative to other shear-responsive networks like colloidal gels and micellar solutions.     

Stochastic simulation and modelling of metabolic networks in a machine learning framework

Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in cells, their concentration, tissues elements and other biosamples. The careful study of all these elements has led to important insights in the functioning of metabolism. Recently, however, there is a growing interest towards an integrated approach to studying biological systems. This is the main goal in Systems Biology where a combined investigation of several components of a biological system is thought to produce a thorough understanding of such systems. Biological circuits are complex to model and simulate and many efforts are being made to develop models that can handle their intrinsic complexity. A significant part of biological networks still remains unknown even though recent technological developments allow simultaneous acquisition of many metabolite measurements. Metabolic networks are not only structurally complex but behave also in a stochastic fashion. Therefore, it is necessary to express structure and handle uncertainty to construct complete dynamics of these networks. In this paper we describe how stochastic modeling and simulation can be performed in a symbolic-statistical machine learning (ML) framework. We show that symbolic ML deal with structural and relational complexity while statistical ML provides principled approaches to uncertainty modeling. Learning is used to analyze traces of biochemical reactions and model the dynamicity through parameter learning, while inference is used to produce stochastic simulation of the network.     

Machine Learning for Intelligent Processing of Printed Documents

A paper document processing system is an information system component which transforms information on printed or handwritten documents into a computer-revisable form. In intelligent systems for paper document processing this information capture process is based on knowledge of the specific layout and logical structures of the documents. This article proposes the application of machine learning techniques to acquire the specific knowledge required by an intelligent document processing system, named WISDOM++, that manages printed documents, such as letters and journals. Knowledge is represented by means of decision trees and first-order rules automatically generated from a set of training documents. In particular, an incremental decision tree learning system is applied for the acquisition of decision trees used for the classification of segmented blocks, while a first-order learning system is applied for the induction of rules used for the layout-based classification and understanding of documents. Issues concerning the incremental induction of decision trees and the handling of both numeric and symbolic data in first-order rule learning are discussed, and the validity of the proposed solutions is empirically evaluated by processing a set of real printed documents.     

Development of calcium silicate–based catalytic filters for biomass fuel gas reforming

Calcium silicate–based particulate filters were catalytically activated by coating first with γ‐alumina and then nickel layers. Different coating techniques were compared, namely dry impregnation, wet impregnation, dry deposition‐precipitation, and wet deposition‐precipitation. All samples were characterized by ICP‐OES, XRD, and N₂ adsorption/desorption in order only to determine total surface area and loading but also to give insight into coating dispersion and coating‐substrate interaction. Regarding alumina layer, the best specific surface area was achieved when colloidal alumina sol was applied via dry impregnation method. Likewise, nickel loading onto alumina layer via dry impregnation was found to be feasible. All catalytic filters successfully gave cross‐sectional pressure drop values of below 25 mbar under flowing air, which was required for sustainable filtration. Catalytic activity tests performed under simulated H₂S‐free biomass gasification atmosphere gave reasonable methane and benzene conversion values between 77% and 88%. Tests performed under H₂S‐containing gas caused significant activity loss despite the addition of an alkali promoter to suppress sulfur‐catalyst interaction. However, the decrease of benzene conversion due to sulfur poisoning was not found to be as severe as that of methane. As a plausible explanation, a possible reaction of benzene with species like traces of CH_x and/or H₂S was claimed for the high benzene conversion.     

A New Call Admission Control Scheme Based on Mobile Position Estimation in DS-CDMA Systems

Call dropping is considered more annoying than call blocking in wireless cellular networks. The cost of the classical method of employing guard channels to decrease the call dropping rate is the increase in call blocking rate. Since subscriber mobility changes in time, the number of handoff attempts in each cell is subject to fluctuations, making static assignments (or periodical update) of a given number of guard channels inefficient. In this paper, we propose an adaptive scheme that employs reservations, instead of static assignments, to adaptively adjust the number of guard channels in each cell according to the current requirements. Thus, unnecessary allocation of guard channels is avoided resulting in a lower cost in terms of call dropping. The reservation requests are made according to the recent mobility pattern of the subscriber. A likelihood value is associated with each reservation request so that fewer channels are reserved by benefiting from the statistical accumulation of the requests. The channels are reserved by considering the interference that would be created once they are in use. The proposed scheme is evaluated against the classical guard channel scheme with a realistic mobility model.This author is currently associated with Georgia Institute of Technology-Regional Engineering Program (GTREP).Tuna Tugcu received his B.S. and Ph.D. degrees in computer engineering from Bogazici University in 1993 and 2001, respectively, M.S. degree from New Jersey Institute of Technology in 1994. He pursued post-doctorate study in Broadband and Wireless Networking Lab at Georgia Institute of Technology until July 2002. Currently, he is a visiting assistant professor at Georgia Institute of Technology-Regional Engineering Program. His research interests include real-time systems, communication networks, and wireless communications. Dr. Tugcu is a member of the IEEE.Cem Ersoy received his B.S. and M.S. degrees in electrical engineering from Bogazici University in 1984 and 1986, respectively. He worked as an R&D engineer in NETAS A.S. between 1984 and 1986. He received his PhD in electrical engineering from Polytechnic University in 1992. Currently, he is a professor in the Computer Engineering Department of Bogazici University. His research interests include performance evaluation and topological design of communication networks, wireless communications and mobile applications. Dr. Ersoy is a Senior Member of the IEEE.     

Electric and magnetic properties of ferromagnetic/piezoelectric bilayered composite

One of the most promising ways for the realization of multi-functional materials is the integration of oxides with different properties in artificial heterostructures. In this paper, a novel piezoelectric–ferromagnetic heterostructure consisting of 0.92Na_0.5Bi_0.5TiO₃–0.08BaTiO₃ (abbreviated as BNT–BT_0.08) and CoFe₂O₄ layers is fabricated on Si–Pt substrate, by sol–gel method coupled with spin-coating technique. The composite thin film shows only perovskite Bi_0.5Na_0.5TiO₃-like rhombohedral phase and CoFe₂O₄ cubic phase. The thickness of CoFe₂O₄ and BNT–BT_0.08 layers is ~?280 and?~?400 nm, respectively. BNT–BT_0.08/CoFe₂O₄ heterostructure thin film shows a saturation magnetization of 0.11 emu/g at 5 K and 0.07 emu/g at 295 K, dielectric constant of 235 at 1 kHz and tunability of 70% at 1 kHz and an electric field E?=?110 kV/cm. The results reveal that the investigated hybrid piezoelectric/ferromagnetic structure shows piezoelectric behavior, good ferroelectric and ferromagnetic properties. This bilayer composite can be used in miniature low-frequency magnetic sensor and piezoelectric sensor for biomedical domain.     

The hierarchical SMAA-PROMETHEE method applied to assess the sustainability of European cities

Measuring the level of sustainability taking into account many contributing aspects is a challenge. In this paper, we apply a multiple criteria decision aiding framework, namely, the hierarchical-SMAA-PROMETHEE method, to assess the environmental, social, and economic sustainability of 20 European cities in the period going from 2012 to 2015. The application of the method is innovative for the following reasons: (i) it permits to study the sustainability of the mentioned cities not only comprehensively but also considering separately particular macro-criteria, providing in this way more specific information on their weak and strong points; (ii) the use of PROMETHEE and, in particular, of PROMETHEE II, avoids the compensation between different and heterogeneous criteria, that is arbitrarily assumed in value function aggregation models; finally, (iii) thanks to the application of the Stochastic Multicriteria Acceptability Analysis, the method provides more robust recommendations than a method based on a single instance of the considered preference model compatible with few preference information items provided by the Decision Maker.

     

Elemental content and nutritional study of blood orange juice

BACKGROUND: The elemental content of Sicilian red orange juices and the bioaccessibility of many elements of particular nutritional interest was evaluated. RESULTS: The elemental content of fresh juices from Sicilian blood oranges was analysed by ICP–OES, GF AAS, HG–AAS and CV AAS. Among the macroelements (Na, Ca, K, Mg and P), potassium occurred in the greatest amount, with 3.64% of the requested RDA for 100 mL of juice daily intake. The values of RDA 100 mL^?1 of juice for Mg, P, Ca and Na resulted 1.61%, 1.26%, 0.59% and 0.09% respectively. For the trace elements Cr, Cu and Se, the RDA 100 mL^?1 of juice were 9.20%, 7.14% and 1.82%, respectively. In vitro tests simulating the digestive process showed high bioaccessibility only for boron and manganese while that of copper, zinc and iron were consistently lower. CONCLUSIONS: Sicilian blood orange juices gave different results from non‐pigmented orange juices in terms of a higher content of copper, boron and lower content of chromium and manganese. However, among the nutritionally important elements, only manganese showed high bioaccessibility. The low bioaccessibility found for iron, zinc and copper is likely due to the presence in the orange juice of ligands such as polyphenols and organic acids. Copyright © 2009 Society of Chemical Industry     

Synthesis and Microstructural Investigations of Organometallic Pd(II) Thiol-Gold Nanoparticles Hybrids

In this work the synthesis and characterization of gold nanoparticles functionalized by a novel thiol-organometallic complex containing Pd(II) centers is presented. Pd(II) thiol, trans, trans-[dithiolate-dibis(tributylphosphine)dipalladium(II)-4,4′-diethynylbiphenyl] was synthesized and linked to Au nanoparticles by the chemical reduction of a metal salt precursor. The new hybrid made of organometallic Pd(II) thiol-gold nanoparticles, shows through a single S bridge a direct link between Pd(II) and Au nanoparticles. The size-control of the Au nanoparticles (diameter range 2–10 nm) was achieved by choosing the suitable AuCl₄ ⁻/thiol molar ratio. The size, strain, shape, and crystalline structure of these functionalized nanoparticles were determined by a full-pattern X-ray powder diffraction analysis, high-resolution TEM, and X-ray photoelectron spectroscopy. Photoluminescence spectroscopy measurements of the hybrid system show emission peaks at 418 and 440 nm. The hybrid was exposed to gaseous NO _x with the aim to evaluate the suitability for applications in sensor devices; XPS measurements permitted to ascertain and investigate the hybrid –gas interaction.