Hyperprolactinemia in end‐stage renal disease and effects of frequent hemodialysis

Introduction: End‐stage renal disease is associated with elevations in circulating prolactin concentrations, but the association of prolactin concentrations with intermediate health outcomes and the effects of hemodialysis frequency on changes in serum prolactin have not been examined. Methods: The FHN Daily and Nocturnal Dialysis Trials compared the effects of conventional thrice weekly hemodialysis with in‐center daily hemodialysis (6 days/week) and nocturnal home hemodialysis (6 nights/week) over 12 months and obtained measures of health‐related quality of life, self‐reported physical function, mental health and cognition. Serum prolactin concentrations were measured at baseline and 12‐month follow‐up in 70% of the FHN Trial cohort to examine the associations among serum prolactin concentrations and physical, mental and cognitive function and the effects of hemodialysis frequency on serum prolactin. Findings: Among 177 Daily Trial and 60 Nocturnal Trial participants with baseline serum prolactin measurements, the median serum prolactin concentration was 65 ng/mL (25th–75th percentile 48–195 ng/mL) and 81% had serum prolactin concentrations >30 ng/mL. While serum prolactin was associated with sex (higher in women), we observed no association between baseline serum prolactin and age, dialysis vintage, and baseline measures of physical, mental and cognitive function. Furthermore, there was no significant effect of hemodialysis frequency on serum prolactin in either of the two trials. Discussion: Serum prolactin concentrations were elevated in the large majority of patients with ESRD, but were not associated with several measures of health status. Circulating prolactin levels also do not appear to decrease in response to more frequent hemodialysis over a one‐year period.     

Healing Effect of Controlled Anti‐Electromigration on Conventional and High‐Tc Superconducting Nanowires

The electromigration process has the potential capability to move atoms one by one when properly controlled. It is therefore an appealing tool to tune the cross section of monoatomic compounds with ultimate resolution or, in the case of polyatomic compounds, to change the stoichiometry with the same atomic precision. As demonstrated here, a combination of electromigration and anti‐electromigration can be used to reversibly displace atoms with a high degree of control. This enables a fine adjustment of the superconducting properties of Al weak links, whereas in Nb the diffusion of atoms leads to a more irreversible process. In a superconductor with a complex unit cell (La_2?x Ce_x CuO₄), the electromigration process acts selectively on the oxygen atoms with no apparent modification of the structure. This allows to adjust the doping of this compound and switch from a superconducting to an insulating state in a nearly reversible fashion. In addition, the conditions needed to replace feedback controlled electromigration by a simpler technique of electropulsing are discussed. These findings have a direct practical application as a method to explore the dependence of the characteristic parameters on the exact oxygen content and pave the way for a reversible control of local properties of nanowires.     

Uncertain supply chain network design considering carbon footprint and social factors using two-stage approach

Sustainable development has become one of the leading global issues over the period of time. Currently, implementation of sustainability in supply chain has been continuously in center of attention due to introducing stringent legislations regarding environmental pollution by various governments and increasing stakeholders’ concerns toward social injustice. Unfortunately, literature is still scarce on studies considering all three dimensions (economical, environmental and social) of sustainability for the supply chain. An effective supply chain network design (SCND) is very important to implement sustainability in supply chain. This study proposes an uncertain SCND model that minimizes the total supply chain-oriented cost and determines the opening of plants, warehouses and flow of materials across the supply chain network by considering various carbon emissions and social factors. In this study, a new AHP and fuzzy TOPSIS-based methodology is proposed to transform qualitative social factors into quantitative social index, which is subsequently used in chance-constrained SCND model with an aim at reducing negative social impact. Further, the carbon emission of supply chain is estimated by considering a composite emission that consists of raw material, production, transportation and handling emissions. In the model, a carbon emission cap is imposed on total supply chain to reduce the carbon footprint of supply chain. To solve the proposed model, a code is developed in AMPL software using a nonlinear solver SNOPT. The applicability of the proposed model is illustrated with a numerical example. The sensitivity analysis examines the effects of reducing carbon footprint cap, negative social impacts and varying probability on the total cost of the supply chain. It is observed that a stricter carbon cap over supply chain network leads to opening of more plants across the supply chain. In addition, carbon footprint of supply chain is found to be decreased in certain extent with the reduction in negative social impacts from suppliers. The carbon footprint of the supply chain is found to be reduced with increasing certainty of material supply from the suppliers. The total supply chain cost is observed to be augmented with increasing probability.     

Microfluidic continuous magnetophoretic protein separation using nanoparticle aggregates

We demonstrate a microfluidic continuous-flow protein separation process in which silica-coated superparamagnetic nanoparticles interact preferentially with hemoglobin in a mixture with bovine serum albumin, and the resulting hemoglobin-nanoparticle aggregates are recovered online using magnetophoresis. We present detailed modeling and analysis of this process yielding quantitative estimates of the recovery of both proteins, validated by experiments. While several previous studies utilize an average particle size in modeling magnetophoretic particle trajectories or process design, in this study we emphasize the importance of accounting for particle size distributions in calculating particle recovery, and therefore in estimating separation efficiency. We combine experimentally measured size distributions of protein-nanoparticle aggregates with simulations of particle trajectories and provide a simple analytical method to calculate the efficiency of separation at various flow speeds, which fully accounts for heterogeneity in particle sizes. Our method can potentially be used for affinity based biomolecular separations at both analytical and preparative scales by exploiting well-established techniques to functionalize nanoparticle surfaces with selective ligands. Further, the modeling methodology presented here may be applied to provide better estimates of particle recovery in a broad range of magnetophoretic separation processes involving heterogeneity in particle sizes.     

Visual quality assessment algorithms: what does the future hold?

Creating algorithms capable of predicting the perceived quality of a visual stimulus defines the field of objective visual quality assessment (QA). The field of objective QA has received tremendous attention in the recent past, with many successful algorithms being proposed for this purpose. Our concern here is not with the past however; in this paper we discuss our vision for the future of visual quality assessment research. We first introduce the area of quality assessment and state its relevance. We describe current standards for gauging algorithmic performance and define terms that we will use through this paper. We then journey through 2D image and video quality assessment. We summarize recent approaches to these problems and discuss in detail our vision for future research on the problems of full-reference and no-reference 2D image and video quality assessment. From there, we move on to the currently popular area of 3D QA. We discuss recent databases, algorithms and 3D quality of experience. This yet-nascent technology provides for tremendous scope in terms of research activities and we summarize each of them. We then move on to more esoteric topics such as algorithmic assessment of aesthetics in natural images and in art. We discuss current research and hypothesize about possible paths to tread. Towards the end of this article, we discuss some other areas of interest including high-definition (HD) quality assessment, immersive environments and so on before summarizing interesting avenues for future work in multimedia (i.e., audio-visual) quality assessment.     

Linking temporal first-order logic with Bayesian networks for the simulation of pervasive computing systems

The authors’ previous work discussed a scalable abstract knowledge representation and reasoning scheme for Pervasive Computing Systems, where both low-level and abstract knowledge is maintained in the form of temporal first-order logic (TFOL) predicates. Furthermore, we introduced a novel concept of a generalised event, an abstract event, which we define as a change in the truth value of an abstract TFOL predicate. Abstract events represent real-time knowledge about the system and they are defined with the help of well-formed TFOL expressions whose leaf nodes are concrete, low-level events using our AESL language.In this paper, we propose to simulate pervasive systems by providing estimated knowledge about its entities and situations that involve them. To achieve this goal, we enhance AESL with higher-order function predicates that denote approximate knowledge about the likelihood of a predicate instance having the value True with respect to a time reference. We define a mapping function between a TFOL predicate and a Bayesian network that calculates likelihood estimates for that predicate as well as a confidence level, i.e., a metric of how reliable the likelihood estimation is for that predicate.Higher-order likelihood predicates are implemented by a novel middleware component, the Likelihood Estimation Service (LES). LES implements the above mapping; first, for each abstract predicate, it learns a Bayesian network that corresponds to that predicate from the knowledge stored in the sensor-driven system. Once trained and validated, the Bayesian networks generate a likelihood estimate and a confidence level. This new knowledge is maintained in the middleware as approximate knowledge therefore providing a simulation of the pervasive system, in the absence of real-time data. Last but not least, we describe an experimental evaluation of our system using the Active BAT location system.     

ODTLES: A multi-scale model for 3D turbulent flow based on one-dimensional turbulence modeling

A novel multi-scale approach for extending the one-dimensional turbulence (ODT) model of [A.R. Kerstein. One-dimensional turbulence: model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows, J. Fluid Mech. 392 (1999) 277] to treat turbulent flow in three-dimensional (3D) domains is described. In this model, here called ODTLES, 3D aspects of the flow are captured by embedding three, mutually orthogonal, one-dimensional ODT domain arrays within a coarser 3D mesh. The ODTLES model is obtained by developing a consistent approach for dynamically coupling the different ODT line sets to each other and to the large scale processes that are resolved on the 3D mesh. The model is implemented computationally and its performance is tested by performing simulations of decaying isotropic turbulence at two different Reynolds numbers and comparing to the experimental data of [H. Kang, S. Chester, C. Meneveau. Decaying turbulence in an active-grid-generated flow and comparisons with large-eddy simulations, J. Fluid Mech. 480 (2003) 129; G. Comte-Bellot, S. Corrsin, Simple Eulerian correlation of full-and narrow band velocity signals in grid-generated ’isotropic’ turbulence, J. Fluid Mech. 48 (1971) 273].     

Strategy, networks and systems in the global translation services market

The globalisation of markets has led to an increased demand for language translation services that support and enable communication between economic partners. For example, technical documents, software systems, business documents and web sites all need to be translated into multiple languages for individual national markets, and the information that they contain changes periodically. This paper sets out a theoretical framework that describes the architectures of business processes within and between separate firms that are used to support the delivery and management of services. This is done by coordinating the fit between externally generated problem complexity, from customers, and the internally generated complexity of different potential network configuration solutions. The theoretical framework is an architecture of how complexity is generated and managed at the different structural levels and across the different processual stages of an industry. A case study of thebigword, a major international translation services company, illustrates how the framework is applied in practice. The case study analyses the implementation of an electronic market platform which enables the coordination of the different stakeholders involved in the translation services market. These stakeholders include translators, translation services companies and their clients in a global business network.